THE EFFECT OF MULTI-SCALE FOREST DISTURBANCE ON POOL BREEDING
AMPHIBIAN ECOLOGY
by
TIMOTHY EARL BALDWIN
A DISSERTATION
Submitted in partial fulfillment of the requirements
for the degree of Doctorate of Philosophy
in the Department of Biological and Environmental Sciences
in the School of Graduate Studies
Alabama A&M University
Normal, Alabama 35762
December 2013
Submitted by TIMOTHY EARL BALDWIN in partial fulfillment of the
requirements for the degree of DOCTOR OF PHILOSOPHY specializing in PLANT
AND SOIL SCIENCE.
Accepted on behalf of the Faculty of the Graduate School by the Dissertation
Committee:
_________________________________Major Advisor
_________________________________
_________________________________
_________________________________
_________________________________
_________________________________
_____________________________Dean of the Graduate School
Date
_____________________________
ii
Copyright by
TIMOTHY EARL BALDWIN
2013
iii
This dissertation is dedicated to my mother, father, and my brother.
iv
THE EFFECT OF MULTI-SCALE FOREST DISTURBANCE ON POOL BREEDING
AMPHIBIAN ECOLOGY
Baldwin, Timothy, Ph.D., Alabama A&M University, 2013. 353 pp.
Dissertation Advisor: Dr. Yong Wang, Ph.D.
The purpose of this project was to assess the influence that forest disturbance has on pool
breeding amphibian reproductive success. This assessment was split into two
components: a large scale landscape observational study and a smaller scale experimental
study. The landscape study was executed using twenty-four vernal pools in the James D
Martin Skyline Wildlife Management Area and the William B. Bankhead National
Forest. Each vernal pool belonged to one of three categories: closed, open, or partially.
Pools were surveyed biweekly from December through June in three breeding seasons
(2008-2011) to inventory amphibian adults, egg masses, larvae, and metamorphs. This
time period reflected adult and metamorph migration from natal ponds. The
experimental study was carried out within Burrows Cove in Grundy County, Tennessee.
A split-split design was used to examine the effect forest treatment (main factor),
distances to forest edge (sub-factor), and variation in amount of light reaching the pools
(split-split factor) on amphibian breeding parameters. The forest treatments included:
control with gaps (five replications), sheltwerwood (four replications), and an oak
shelterwood (five replications). Three artificial ponds were established at each of the
three distance cateogires (ten meters, fifty meters, and one hundred meter) in each of the
forest stands. Three pools at each distance were randomly assigned to allow thirty
v
percent, fifty percent, and eighty four percent of the light in relative to control stands to
reach the pools using light screens. Artificial pools were monitored over two breeding
seasons. Within the landscape study, amphibian diversity was different across vernal
classes, with amphibian diversity being highest within open vernal pools when compared
to closed pools Amphibian size did not differ across vernal pool classes with the
exception of the Quetelet’s Index which was higher in open pools when compared to
closed pools. In the field experiment larval amphibian abundance was higher within
shelterwood treatment and the 30 percent arrays. This study identifies how
environmental features influence larval amphibian abundance in response to forest
disturbance.
KEYWORDS: Amphibian, Migration, Reproduction, Vernal Pools, Disturbance, and
Forest Management
vi
TABLE OF CONTENTS
CERTIFICATE OF APPROVAL……………………………………………………...…ii
ABSTRACT………………………………………………………………………...…….v
TABLE OF CONTENTS………………………………………………………………..vii
LIST OF TABLES…………………………………………………………………....….ix
LIST OF FIGURES…………………………………………………………………..…xiii
ACKNOWLEDGEMENTS……………………………………………...…………....xxiii
CHAPTER 1.
INTRODUCTION (PREFACE)………..……...…………………………………….….1
CHAPTER 2.
LITERATURE REVIEW………………………………………………………….…….5
CHAPTER 3.
THE EFFECT OF THE INTERMEDIATE DISTURBANCE ON WILDLIFE………..14
Introduction……………………………………………………………………...15
Methods………………………………………………………………………….21
Results……………………………………………………………………...….....22
Discussion………………………………………………………………………..30
Conclusions……………………………………………………………………....40
CHAPTER 4.
ENVIRONMENTAL AND POOL MORPHOLOGY DIFFERENCES AMONG
VERNAL POOL CLASSES WITHIN NORTHERN ALABAMA………….………….41
Introduction……………………………………………………………..………..41
Methods………………………………………………………………….……….44
Results…………………………………………………………………….…...…52
vii
Discussion………………………………………………………………….…….81
CHAPTER 5.
RELATIONSHIP OF POOL BREEDING AMPHIBIAN DIVERSITY WITH LOCAL
AND LANDSCAPE FOREST COVER AROUND VERNAL POOLS IN NORTHERN
ALABAMA………………………………………………………………………...…....86
Introduction……………………………………………………………………....89
Methods…………………………………………………………………………..92
Results………………………………………………………….…………….......99
Discussion……………………………………………………….……………...159
CHAPTER 6.
THE RELATIONSHIP OF WETLAND CATEGORIES ON AMPHIBIAN SIZE AND
PERFORMANCE WITHIN NORTHERN ALABAMA………….…...……………....164
Introduction……………………………..………………….……………….…..164
Methods…………………………………………………………………….…...169
Results……………………………………………………….……...…….…….178
Discussion………………………………………………….…………………...198
CHAPTER 7.
THE INFLUENCE OF FOREST MANAGEMENT PRACTICES
ON BREEDING POOL ACCESSIBILITY, CHOICE, AND BREEDING
PERFORMANCEOF AMPHIBIANS IN GRUNDY COUNTY, TENNESSEE……...205
Introduction……………………………..……………………………………....205
Methods………………………………………………………………………....210
Results…………………………………………………………………………..227
Discussion……………………………………………………………………....286
CHAPTER 8.
CONCLUSIONS…………………………………………………………………….....302
REFERENCES……………………...………………………………………………….306
APPENDICES
APPENDIX 1.
LIST OF DEFINITIONS………………………………………..……………..328
APPENDIX 2.
BIBLIOGRAPHY……………………………………………………………...332
VITA……………………………………………………………………………………353
viii
LIST OF TABLES
Table
Page
4.1
Vernal pool names, wetland classes, size, depth, and global positioning
system coordinates…………………………………………………………..…...50
4.2
Factor loadings for rotated components using principal components
anaylsis based on (environmental variables of 24 vernal pools in
James D. Martin Skyline Wildlife Management Area and
William B. Bankhead National Forest in Alabama). These results were
based on varimax rotation with Kaiser Normalization………………………53-54
4.3
Factor loadings for rotated components using principal components
anaylsis based on (pool morphology variables of 24 vernal pools in
James D. Martin Skyline Wildlife Management Area and
William B. Bankhead National Forest in Alabama). These results were
based on varimax rotation with Kaiser Normalization…………………..………57
4.4
Mean and standard deviations of environmental and pool morphology
variables taken within closed, open, and partially open vernal pools in
James D. Martin Skyline Wildlife Management Area and William B.
Bankhead National Forest (2008-2009, 2009-2010, and 2010-2011).............59-63
5.1
Factor loadings for rotated components using principal components
analysis based on (aerial photography landscape variables of 24 vernal pools in
James D. Martin Skyline Wildlife Management Area and William B.
Bankhead National Forest in Alabama). These results were based on
varimax rotation with Kaiser Normalization………………………………...…101
5.2
Factor loadings for rotated components using principal components anaylsis
based on (normalized difference vegetation index variables of 24 vernal pools in
James D. Martin Skyline Wildlife Management Area and William B. Bankhead
National Forest in Alabama). These results were based on varimax rotation with
Kaiser Normalization……………………………………………….…….…….103
5.3
Mean and standard deviations of aerial photography landscape variables taken
within closed, open, and partially open vernal pools in James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest (Aerial
ix
Photography 2009 and 2011)……………………………..….…………..……..105
5.4
Mean and standard deviations of normalized difference vegetation
index (NDVI) landscape variables taken within closed, open, and partially
open vernal pools in James D. Martin Skyline Wildlife Management Area
and William B. Bankhead National Forest (Landsat TM 2009, 2010,
2011)…………………………………………………………………..…...107-110
5.5
Mean and standard deviations of ecological indices taken within closed,
open, and partially open vernal pools in James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest
(2008-2009, 2009-2010, and 2010-2011)……………………………….…118-119
5.6
Mean and standard deviations of amphibian species abundances taken
within closed, open, and partially open vernal pools in James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National
Forest (2008-2009, 2009-2010, and 2010-2011)……………………..……124-128
5.7
Multiple regression beta coefficients for larval amphibian abundance………...136
5.8
Multiple regression beta coefficients for larval amphibian species
observed (Sobs)………………………………………………………………….143
5.9
Multiple regression beta coefficients for alpha diversity for larval
amphibians……………………………………………………………….……..147
5.10
Multiple regression beta coefficients for the larval amphibian species richness
using the bootstrap method……………………………………………………..150
5.11
Multiple regression beta coefficients for the Shannon-Wiener index for larval
amphibians…………………………………………………...…………………153
5.13
Multiple regression beta coefficients for Simpson’s inverse diversity Index for
larval amphibians……………………………………………………………….155
6.1
Means (±Standard Deviations) of size metrics and body indices in forest
(closed), open, and partially open wetland classes within James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National
Forest in Alabama (2008-2011)…………………………………………....180-181
6.2
Multiple regression beta coefficients for larval amphibian snout to vent
length……………………………………………………………………..….….185
6.3
Multiple regression beta coefficients for larval amphibian total length….…….188
x
6.4
Multiple regression beta coefficients for larval amphibian weight…………….192
6.5
Multiple regression beta coefficients for scaled mass index for larval
amphibians……………………………………………………………………...194
6.6
Multiple regression beta coefficients for larval amphibian Quetelet’s Index….197
6.7
Multiple regression beta coefficients for larval amphibian Fulton’s index…….201
7.1
Factor loadings for rotated components using principal components analysis
based on (environmental variables of 123 artifical pools in Grundy County,
Tennessee for 2010). The results were based on varimax rotation with Kaiser
Normalization….…………………………………………………………….…229
7.2
Factor loadings for rotated components using principal components analysis
based on (environmental variables of 123 artifical pools in Grundy County,
Tennessee for 2011). The results were based on varimax rotation with Kaiser
Normalization….…………………………………………………………….…232
7.3
Means (±Standard Deviations) of environmental variables in shelterwood
harvest, oak shelterwood, and control with gaps treatments within Grundy
County, Tennessee (2010)…………………………………..………………….234
7.4
Means (±Standard Deviations) of environmental variables in shelterwood
harvest, oak shelterwood, and control with gaps treatments within Grundy
County, Tennessee (2011)…………………………………..………………….248
7.5
Means (±Standard Deviations) of environmental variables in ten meters, fifty
meters, and one hundred meters from edge subplots within Grundy County,
Tennessee (2010)…………………………………………………...…………..240
7.6
Means (±Standard Deviations) of environmental variables in ten meters, fifty
meters, and one hundred meters from edge subplots within Grundy County,
Tennessee (2011)…………………………………………………...…………..241
7.7
Means (±Standard Deviations) of amphibian captures in shelterwood harvest, oak
shelterwood, and control with gaps treatments within Grundy County, Tennessee
(2010 and 2011)………………………………………………………………...242
7.8
Means (±Standard Deviations) of amphibian captures in ten meters, fifty meters,
and one hundred meters from edge subplots within Grundy County, Tennessee
(2010 and 2011)……………………………………………………………..….243
xi
7.9
Means (±Standard Deviations) of environmental variables in thirty percent, fifty
percent, and eighty four percent canopy array subplots within Grundy County,
Tennessee (2011)…………………………….………………...….…….…252-254
7.10
Means (±Standard Deviations) of amphibian captures in thirty percent, fifty
percent, and eighty four percent canopy array subplots within Grundy County,
Tennessee (2011)………………………………………………….……….256-257
7.11
Multiple regression beta coefficients for larval amphibian abundance
(2010)…………………………………………………………………………...263
7.12
Multiple regression beta coefficients for Cope’s Gray Treefrog (Hyla
chrysoscelis) tadpole abundance (2010)………………………………………..268
7.13
Multiple regression beta coefficients for larval American Toad and
Fowler’s Toad (Anaxyrus americanus and Anaxyrus fowleri) tadpole
abundance (2010)……………………………………………………….......…..274
7.14
Multiple regression beta coefficients for Spring Peeper (Pseudacris crucifer)
tadpole abundance (2010)……………………………………………………....280
7.15
Multiple regression beta coefficients for larval amphibian abundance
(2011)…………………………………………………………………………...281
7.16
Multiple regression beta coefficients for Cope’s Gray Treefrog (Hyla
chrysoscelis) tadpole abundance (2011)………………………………………..288
7.17
Multiple regression beta coefficients for larval American Toad and
Fowler’s Toad (Anaxyrus americanus and Anaxyrus fowleri) tadpole
abundance (2011)……………………………………………………….......…..294
xii
LIST OF FIGURES
Figure
Page
2.1
The multi-scale mechanistic model of the amphibian breeding ecology study…..8
3.1
Conceptual model of intermediate disturbance hypothesis……………………...17
3.2
Total journal articles and dissertations that each wildlife group
represents when categorized by disturbance classification (n= 241 journal
articles and doctoral dissertations)……………………………..…………….…..23
3.3
Total journal articles and dissertations that each wildlife group represents when
categorized by the acceptance or rejection of the intermediate disturbance
hypothesis (n= 241 journal articles and doctoral dissertations)……………….....25
3.4
Total number of journal articles and dissertations that accepted or rejected
intermediate disturbance hypothesis when categorized by disturbance
classification (n= 241 journal articles and doctoral dissertations)………….……26
3.5
Total number of journal articles and dissertations that landscape or stand spatial
levels represent when categorized by disturbance type (n= 241 journal articles
and doctoral dissertations)……………………………………………...…..…....27
3.6
Total journal articles and dissertations that each landscape or stand spatial levels
represent when categorized by wildlife group (n= 241 journal articles and
doctoral dissertations)………………………......……………………………..…28
3.7
Total journal articles and dissertations that each temporal level (duration
of study) category represents when categorized by disturbance type
(n= 241 journal articles and doctoral dissertations)…………………………..…29
3.8
Percentage number of the total journal articles and dissertations that each
species level (species analyses) category represents when categorized by
acceptance of the intermediate disturbance hypothesis (n= 241 journal
articles and doctoral dissertations)…………………………………….…………31
xiii
3.9
Total number of journal articles and dissertations that each species
level (species analyses) category represents when categorized by
disturbance type (n= 241 journal articles and doctoral dissertations)…….…..…32
3.10
Percentage of total number of journal articles and dissertations that each
species level (species analyses) category represents when categorized by
wildlife group (n= 241 journal articles and doctoral dissertations)………….…..33
4.1
Vernal pool locations within James D. Martine Skyline Wildlife
Management Area, Alabama…………................................................................45
4.2
Vernal pool locations within William B. Bankhead National Forest,
Alabama…………………………………............................................................47
4.3
Boxplot with error bars showing difference in environmental principal
component one- canopy cover percentage and percent dissolved oxygen………64
4.4
Boxplot with error bars showing difference in environmental principal
component two- soil and water temperature……………………………………..67
4.5
Boxplot with error bars showing difference in environmental principal
component three- soil and water temperature maximum……………………...…65
4.6
Boxplot with error bars showing difference in environmental principal
component four- water pH maximum and range…………….………………..…68
4.7
Boxplot with error bars showing difference in environmental principal
component five- leaf litter depth………….…………………………...……...…69
4.8
Boxplot with error bars showing difference in environmental principal
component six- water pH average and minimum…………………….………….70
4.9
Boxplot with error bars showing difference in environmental principal
component seven- leaf litter depth minimum and canopy cover percentage…....71
4.10
Boxplot with error bars showing difference in forest cover percentage…...…....72
4.11
Boxplot with error bars showing difference in pool morphology principal
component one- pool area…………………………………………………….....73
4.12
Boxplot with error bars showing difference in pool morphology principal
component two- hydroperiod and maximum pool depth………………………..74
4.13
Linear regression between forest cover percentage and environmental
xiv
principal component one- canopy cover percentage and percent dissolved
oxygen………………………………………………………….…………......…76
4.14
Linear regression between forest cover percentage and environmental
principal component three- soil and water temperature maximum………...……77
4.15
Linear regression between forest cover percentage and environmental
principal component six- water pH average and minimum………………...……78
4.16
Linear regression between forest cover percentage and pool morphology
principal component one- pool area………………………………………...…...79
4.17
Linear regression between forest cover percentage and pool morphology
principal component – hydroperiod and maximum pool depth…………....……80
5.1
Boxplot with error bars showing difference in normalized difference vegetation
index principal component one- maximum and mean………..…………......…111
5.2
Boxplot with error bars showing difference in normalized difference vegetation
index principal component two- range………………………………………....112
5.3
Boxplot with error bars showing difference in normalized difference vegetation
index principal component three- mean at 427 meters………………………....113
5.4
Boxplot with error bars showing difference in normalized difference vegetation
index minimum at 427 meters…………………………………..……………....114
5.5
Boxplot with error bars showing difference in normalized difference vegetation
index minimum at 800 meters…………………………………………..……....115
5.6
Boxplot with error bars showing difference in normalized difference vegetation
index minimum at 1015 meters………………………………………………....116
5.7
Boxplot with error bars showing difference in normalized difference vegetation
index minimum at 1601 meters………………..………………………..……....117
5.8
Boxplot with error bars showing difference in alpha diversity for larval
amphibians……………………….……………………………………..……....120
5.9
Boxplot with error bars showing difference in larval amphibian species richness
using the bootstrap method……………………….…………………….……....121
5.10
Boxplot with error bars showing difference in alpha diversity for larval
amphibians……………………….……………………………………..……....122
xv
5.11
Boxplot with error bars showing difference in Marbled Salamander larval
abundance….…………………….……………………………………..……....129
5.12
Boxplot with error bars showing difference in Red Spotted Newt larval
abundance….…………………….……………………………………..……....130
5.13
Boxplot with error bars showing difference in Eastern Spadefoot tadpole
abundance….…………………….……………………………………..……....131
5.14
Canonical correspondence analysis bi-plot showing the relationship between
environmental variables (arrows) and larval amphibians (triangles) for the 20082009 breeding season in James D. Martin Skyline and William B. Bankhead
National Forest………………………………………………………………….132
5.15
Canonical correspondence analysis bi-plot showing the relationship between
environmental variables (arrows) and larval amphibians (triangles) for the 20092010 breeding season in James D. Martin Skyline and William B. Bankhead
National Forest………………………………………………………………….133
5.16
Canonical correspondence analysis bi-plot showing the relationship between
environmental and pool morphology variables (arrows) and larval amphibians
(triangles) for the 2010-2011 breeding season in James D. Martin Skyline and
William B. Bankhead National Forest……………………………………….…135
5.17
Linear regression between pool morphology principal component six- water pH
average and minimum and larval amphibian abundance………………..….….137
5.18
Linear regression between pool morphology principal component onepool area and larval amphibian abundance..……………………..…………..…138
5.19
Linear regression between pool morphology principal component two- soil and
water temperature and larval amphibian abundance……………………..….….139
5.20
Linear regression between pool morphology principal component five- soil and
water temperature and larval amphibian abundance……………………..…….140
5.21
Linear regression between normalized difference vegetation index principal
component three- mean at 427 meters and larval amphibian abundance……....141
5.22
Linear regression between forest cover percentage and larval amphibian species
observed………………………………………………………………………...144
xvi
5.23
Linear regression between environmental principal component four- water pH
average and minimum and larval amphibian abundance………………..….….145
5.24
Linear regression between normalized difference vegetation index principal
component three- mean at 427 meters and larval amphibian species
observed……………………………………………………………………......146
5.25
Linear regression between forest cover percentage and alpha diversity for
larval amphibians……………………………………….………………….…...148
5.26
Linear regression between normalized difference vegetation index principal
component three- mean at 427 meters and alpha diversity for larval
amphibians……………………………………………………………………...149
5.27
Linear regression between forest cover percentage and larval amphibian
species richness using bootstrap method……………………………………….151
5.28
Linear regression between normalized difference vegetation index principal
component three- mean at 427 meters and larval amphibian species richness
using bootstrap method…………………………………………………….…...152
5.29
Linear regression between aerial photography principal component three- main
road density and distance from main roads and the Shannon-Wiener index for
larval amphibians…………………………………………………………….…154
5.30
Linear regression between aerial photography principal component three- main
road density and distance from main roads and the Simpson’s inverse diversity
index for larval amphibians……………………………………………….....…156
5.31
Linear regression between pool morphology principal component one- pool area
and the Simpson’s inverse diversity index for larval amphibians…………...…157
5.32
Linear regression between environmental principal component one- canopy cover
percentage and percent dissolved oxygen and the Simpson’s inverse diversity
index for larval amphibians……………………………………………….....…158
6.1
Linear regression between pool morphology principal component one- pool area
and larval amphibian snout to vent length……………………………...………183
6.2
Linear regression between environmental principal component four- water pH
maximum and range and larval amphibian snout to vent length….…...…….…184
6.3
Linear regression between environmental principal component four- water pH
maximum and range and larval amphibian total length………………..….……186
xvii
6.4
Linear regression between environmental principal component six- water pH
average and minimum and larval amphibian total length………………..…..…187
6.5
Linear regression between environmental principal component six- water pH
average and minimum and larval amphibian weight……………………..….…189
6.6
Linear regression between pool morphology principal component twohydroperiod and maximum pool depth and larval amphibian total length…….190
6.7
Linear regression between environmental principal component four- water pH
maximum and range and larval amphibian total length…………………….…..191
6.8
Linear regression between environmental principal component six- water pH
average and minimum and scaled mass index for larval amphibians…………..193
6.9
Linear regression between pool morphology principal component twohydroperiod and maximum pool depth and Quetelet’s index for larval
amphibians……………………………………………………………………...195
6.10
Linear regression between pool morphology principal component one- pool area
and Quetelet’s index for larval amphibians…………..………………………...196
6.11
Linear regression between pool morphology principal component twohydroperiod and maximum pool depth and Fulton’s index for larval
amphibians……………………………………………………………………...199
6.12
Linear regression between pool morphology principal component one- pool area
and Fulton’s index for larval amphibians…………..………...………………...200
7.1
Canopy array loations at Burrows Cove in Grundy County, Tennessee
in 2010 and 2011……………………………………………………………….215
7.2
Diagram of shelterwood treatment with canopy array locations in Grundy
County, Tennessee in 2010 and 2011…………………………………………..216
7.3
Diagram of oak shelterwood treatment with canopy array locations in Grundy
County, Tennessee in 2010 and 2011…………………………………………..217
7.4
Diagram of control with gaps treatment with canopy array locations in Grundy
County, Tennessee in 2010 and 2011…………………………………………..218
7.5
Diagram of canopy array pools at Burrow Cove in Grundy County, Tennessee
in 2010 and 2011………………………………….……………………………219
xviii
7.6
Photograph of artificial pool dimensions and canopy array dimensions
without shade cloth……………………….…………………………………….220
7.7
Canopy array pools at canopy with gaps treatment (treatment thirty four) which
are ten meters from edge at Burrows Cove in Grundy County,
Tennessee in 2010 and 2011……………………………………………………221
7.8
Thirty percent canopy array pool at canopy with gaps treatment (treatment
thirty four) which is ten meters from edge at Burrows Cove in Grundy County,
Tennessee in 2010 and 2011…………………………………………….……...222
7.9
Fifty percent canopy array pool at canopy with gaps treatment (treatment thirty
four) which is ten meters from edge at Burrows Cove in Grundy County,
Tennessee in 2010 and
2011……………………………………………………………………………..223
7.10
Eighty four percent canopy array pool at canopy with gaps treatment (treatment
thirty four) which is ten meters from edge at Burrows Cove in Grundy County,
Tennessee in 2010 and 2011……………………………………………………224
7.11
Boxplot with error bars showing difference in environmental principal
component one- percent dissolved oxygen and water pH average and
maximum (2010)….…………………….……………..………………..……....236
7.12
Boxplot with error bars showing difference in environmental principal
component two- leaf litter depth minimum, soil and water temperature
maximum and range (2010)….………………………..………………..……....237
7.13
Boxplot with error bars showing difference in environmental principal
component three- water and soil temperature minimum and average (2010)….238
7.14
Boxplot with error bars showing difference in environmental principal
component five- water pH (2010)………………………………………………239
7.15
Boxplot with error bars showing difference in environmental principal
component one- percent dissolved oxygen and water pH average and
maximum (2010)………………………………………………………………..244
7.16
Boxplot with error bars showing difference in environmental principal
component three- soil temperature and maximum (2011)……………………...249
7.17
Boxplot with error bars showing difference in environmental principal
component four- water pH (2011)…………………...……………..…………..250
xix
7.18
Boxplot with error bars showing difference in environmental principal
component six- percent dissolved oxygen minimum (2011)…………………...251
7.19
Boxplot with error bars showing difference in larval amphibian abundance
(2010)…………………………………………………………………………...245
7.20
Boxplot with error bars showing difference in Spring Peeper (Pseudacris
crucifer) tadpole abundance (2010)………………………………………..…...246
7.21
Boxplot with error bars showing difference in Cope’s Gray Treefrog (Hyla
chrysoscelis) tadpole abundance (2011)………………………...……………...255
7.22
Boxplot with error bars showing difference larval amphibian abundance
(2011)…………………………………………………………………………...258
7.23
Canonical correspondence analysis bi-plot showing the relationship
between environment variables (arrows) and larval amphibians (triangles)
for the 2010 breeding season in Grundy County, Tennessee………………..….260
7.24
Canonical correspondence analysis bi-plot showing the relationship
between environment variables (arrows) and larval amphibians (triangles)
for the 2011 breeding season in Grundy County, Tennessee…………………...261
7.25
Linear regression between environmental principal component two- leaf litter
depth minimum and soil and water temperature maximum and range and larval
amphibian abundance (2010)…………………………………………………...264
7.26
Linear regression between environmental principal component one- percent
dissolved oxygen and water pH average and minimum and larval amphibian
abundance (2010)………………………….…………………………………....265
7.27
Linear regression between environmental principal component fivewater pH (2010)……..…………………….…………………………………....266
7.28
Linear regression between environmental principal component four- leaf litter
depth and Cope’s Gray Treefrog (Hyla chrysoscelis) tadpole abundance
(2010)…………………………………………..…………………….…………268
7.29
Linear regression between environmental principal component two- leaf litter
depth minimum and soil and water temperature maximum and range and
American and Fowler’s Toads (Anaxyrus americanus and Anaxyrus fowleri)
tadpole abundance (2010)……………………………………………...……….269
xx
7.30
Linear regression between environmental principal component three- water and
soil temperature minimum and average and American and Fowler’s Toads
(Anaxyrus americanus and Anaxyrus fowleri) tadpole abundance (2010)…......270
7.31
Linear regression between environmental principal component four- leaf litter
depth and American and Fowler’s Toads (Anaxyrus americanus and Anaxyrus
fowleri) tadpole abundance (2010)……………………………………………..271
7.32
Linear regression between environmental principal component one- percent
dissolved oxygen and water pH average and maximum and American and
Fowler’s Toads (Anaxyrus americanus and Anaxyrus fowleri) tadpole
abundance (2010)……………………………………………………………….272
7.33
Linear regression between environmental principal component two- leaf litter
depth minimum and soil and water temperature maximum and range and
Spring Peeper (Pseudacris crucifer) tadpole abundance (2010)………........….275
7.34
Linear regression between environmental principal component five- water pH
and maximum and Spring Peeper (Pseudacris crucifer) tadpole abundance
(2010)……………………………………………………………………..…….276
7.35
Linear regression between environmental principal component one- percent
dissolved oxygen and water pH average and maximum and Spring Peeper
(Pseudacris crucifer) tadpole abundance (2010)……………………………….277
7.36
Linear regression between environmental principal component three- water and
soil temperature minimum and average and Spring Peeper (Pseudacris crucifer)
tadpole abundance (2010)………………………………………………...…….278
7.37
Linear regression between environmental principal component three- soil
temperature and larval amphibian abundance (2011)……………………….….281
7.38
Linear regression between environmental principal component one- percent
dissolved oxygen, water and soil temperature minimum, and water
temperature range and larval amphibian abundance (2011)……………………282
7.39
Linear regression between environmental principal component five- water
temperature and larval amphibian abundance (2011)……………………….….283
7.40
Linear regression between environmental principal component seven- water
pH minimum and larval amphibian abundance (2011)……………………...….284
xxi
7.41
Linear regression between environmental principal component six- percent
dissolved oxygen minimum and larval amphibian abundance (2011)………….285
7.42
Linear regression between environmental principal component three- soil
temperature and Cope’s Gray Treefrog abundance (Hyla chrysoscelis)
(2011)…………………………………………………………………………...288
7.43
Linear regression between environmental principal component one- percent
dissolved oxygen, water and soil temperature minimum, and water
temperature range and Cope’s Gray Treefrog abundance (Hyla chrysoscelis)
(2011)…………………………………………………………………………...289
7.44
Linear regression between environmental principal component five- water
temperature and Cope’s Gray Treefrog abundance (Hyla chrysoscelis)
(2011)…………………………………………………………………………...290
7.45
Linear regression between environmental principal component seven- water
pH minimum and Cope’s Gray Treefrog abundance (Hyla chrysoscelis)
(2011)…………………………………………………………………………...291
7.46
Linear regression between environmental principal component six- percent
dissolved oxygen minimum and Cope’s Gray Treefrog abundance (Hyla
chrysoscelis) (2011)……………………………..……………………………...292
7.47
Linear regression between environmental principal component one- percent
dissolved oxygen, water and soil temperature minimum, and water
temperature range and American and Fowler’s Toads (Anaxyrus americanus
and Anaxyrus fowleri) tadpole abundance (2011)………………….…………..294
7.48
Linear regression environmental principal component three- soil temperature
and American and Fowler’s Toads (Anaxyrus americanus and Anaxyrus
fowleri) tadpole abundance(2011)………………….………….......…………...295
xxii
ACKNOWLEDGEMENTS
I would like to thank Drs. Wang, Ruark, Schweitzer, Tadesse, Cline and Lanham
for their guidance and help as I worked to complete my doctoraldegree. I want to thank
Dr. Yong Wang for his help, insight, and availability throughout this entire process. I
also want to thank Dr. Greg Ruark for his advice and help as I executed research,
prepared for conferences, and wrote my dissertation. I would also like to extend my
appreciation to Dr. Callie Schweitzer, Ryan Sisk, Nathan Brown, and Trey Petty for all of
their help with my field experiments in Grundy County, Tennessee. I want to extend my
grattitude to the technicians and interns who helped and supported me: Helen Czech,
Serica Zwack, Brandon Haslick, Kaysie Cox, Jon Elston, Jackie Schneider, Stacey Ng,
Jian Xu, Esther Morales-Vega, Deborah Ambrose, Sam Loscalzo, Kevin Creely, Colin
Barrett, Ashlee VanderHam, Leela Pahl, and Seth Kromis. Their diligence and hard
work were critical in the completion of this research project. Thanks also to my past and
present graduate student fellows for their help and support including Zach Felix, Bill
Sutton, Florence Chan, Chelsea Scott, Andrew Cantrell, Lisa Gardner, John Carpenter,
Heather Howell, Padraic Connor, and Rashidah Farid. I am thankful to the staff members
Kimi Sangalang, Allison Bohlman, Penny Stone, and Dawn Lemke for all of their help,
assistance, and words of encouragement during the pursuit of my degree.
xxiii
I am grateful to the entire Alabama A&M University’s Writing Center staff,
especially Mrs. Kiietti Walker-Parker, Mrs. Alfreda Handy-Sullivan, and Miss Fallon
Johnson. They spent countless hours looking over and assisting me with my dissertation.
I also want to thank Mrs. Pat McDonald for all of her support throughout my degree.
Finally, I want to thank my family for all of their support during this degree. I would not
have been able to do this without them.
xxiv
CHAPTER 1
INTRODUCTION (PREFACE)
Statement of Problem
Amphibian populations are declining globally (Collins and Storfer, 2003).
Researchers see habitat destruction, fragmentation, and alteration as a few of the leading
hypotheses (Collins and Storfer, 2003). In response to these landscape changes, the
research focus has shifted towards amphibian breeding ecology mechanisms for
explaining declines (Skelly et al., 2002, Buskirk, 2005). Canopy cover and pond
hydroperiod were predicted to have the greatest impact on larval amphibian fitness
(Lauck et al., 2005, Werner and Glennemeier, 1999). Skelly et al. (2002) found that
canopy cover influenced several different factors including light penetration, dissolved
oxygen concentration, water temperature, and algal production. From the information
found studying canopy cover (Buskirk, 2005), researchers validated earlier hypotheses
which stated that several factors are interacting to influence the habitat quality of larval
amphibians (Skelly, 2001; Skelly et al., 2005; Werner et al., 2007).
Halverson et al. (2003) found light penetration had differing effects on larval
amphibian development, with open canopy specialists developing faster in areas with
reduced canopy cover than areas with increased canopy cover. Artificial pools, small
1
scale enclosures (Lauck et al., 2005), and natural pools have been used to simultaneously
measure the impact of different mechanisms on larval amphibian development and
survival (Skelly et al., 2005).
Using Geographic Information Systems (GIS) and Remote Sensing techniques,
critical amphibian breeding pool characteristics have been determined. Landscape-level
variables such as road density, forest area, and wetland area have been found to be good
predictors of amphibian occurrence and species richness (Knutson et al., 1999). These
variables not only influence adult amphibian movements to breeding sites, but also adult
breeding site choice. Landscape-level analyses have found that critical habitat
surrounding breeding pools should be considered in conservation (Hermann et al., 2005).
Landscape studies have higlighted the importance of forested landscapes surrounding
ephemeral pools for increased reproductive fitness in amphibian species, including
ambystomatid salamanders (Rubbo and Kiesecker, 2005). Open canopy specialists such
as Pseudacris crucifer and Hyla chrysoscelis, were found to be more resilient to land-use
change and urban development (Gagne and Fahrig, 2007). Within landscape analyses,
distance gradients were studied to determine how differing habitat types influence adult
amphibian breeding site selection (Johnson and Semlitsch, 2003). Homan (2004), found
that closed canopy specialists were affected differently by habitat loss at different scales.
He recommended that multiple spatial level analyses give a more complete understanding
of how amphibians are affected within the landscape. Multiple-scale analyses can also
assist land managers in conservation recommendations.
2
Rationale
The purpose of this project was to determine how forest disturbance affects amphibian
reproductive fitness in Northern Alabama and Southern Tennessee within the
Cumberland Plateau.
Objectives
The overall objective of this research was to examine the effect of canopy
reduction disturbance on amphibian reproductive success in breeding pools at three
scales. These scales were breeding pool, forest stand, and landscape. The local scale
(breeding pool) objective was to investigate the effect of canopy reduction on pool
biophysical conditions and larval development. The stand scale objective was to examine
the effect of forest canopy reduction on adult amphibian accessibility and choice of egg
deposition sites. Finally, the landscape scale objective was to examine the effect of landuse on the accessibility of pools by breeding amphibians. The study utilized two
approaches: a large scale landscape observation and a smaller scale experiment. The
findings from different scales will be integrated to draw stronger inferences about scaledependent mechanisms related to canopy disturbances and amphibian breeding success
dynamics.
Hypotheses
The overall hypothesis was that canopy disturbance will affect amphibian
movements, species diversity, amphibian abundance, and body condition within the
3
breeding pools. The local scale hypothesis surmised that disturbance to forest canopy
directly above and immediately surrounding breeding pools will influence the
environmental conditions in the pool and affect amphibian choice of different pools for
breeding and development of juveniles. Secondly, the stand scale hypothesis stated that
canopy reduction will change the microclimate coniditons within the forest stands around
the vernal pool resulting in dryer and higher temperature conditions. These conditions
will decrease the potential for adult amphibians to access potential breeding pools. The
stand and landscape hypotheses predicted that reduction in forest canopy cover at the
landscape and forest stand level will serve as a filter and only those species that can
tolerate dryer and hotter conditions will be able to access the pools surrounded by these
condtions. Finally, the landscape scale hypothesis stated that the reduction of forest
canopy and fragmentation of forested habitat across the landscape will create movement
barriers for abult amphibians when accessing breeding pools.
4
CHAPTER 2
LITERATURE REVIEW
Amphibian populations are declining globally (Collins and Storfer, 2003).
Researchers see habitat destruction, fragmentation, and alteration as a few of the leading
hypotheses (Collins and Storfer, 2003). In response to these landscape changes, the
research focus has shifted towards amphibian breeding ecology mechanisms to explain
declines (Skelly et al., 2002, Buskirk, 2005). Canopy cover and pond hydroperiod have
been predicted to have the greatest impact on larval amphibian fitness (Lauck et al., 2005,
Werner and Glennemeier, 1999). Skelly et al. (2002) found that canopy cover influenced
several different factors including light penetration, dissolved oxygen concentration,
water temperature, and algal production. From the information found studying canopy
cover (Werner and Glennemeier, 1999), researchers validated earlier hypotheses that
several factors are interacting to influence the habitat quality of larval amphibians
(Skelly, 2001; Buskirk, 2005). Halverson et al. (2003) found light penetration had
differing effects on larval amphibian development, with open canopy specialists
developing faster in areas with reduced canopy cover.
5
Using Geographic Information Systems (GIS) and Remote Sensing techniques,
critical amphibian breeding pool characteristics has been determined. Landscape-level
variables such as road density, forest area, and wetland area have been found to be good
predictors of amphibian occurrence and species richness (Knutson et al., 1999). These
variables not only influence adult amphibian movements to breeding sites, but also adult
breeding site choice. Landscape-level analyses have found that critical habitat
surrounding breeding pools should be considered in conservation (Hermann et al., 2005).
Landscape studies have higlighted the importance of forested landscapes surrounding
ephemeral pools for increased reproductive fitness in amphibian species, including
ambystomatid salamanders (Rubbo and Kiesecker, 2005). Open canopy specialists,
Pseudacris crucifer and Hyla chrysoscelis, have been found to be more resilient to landuse change and urban development (Gagne and Fahrig, 2007). Within landscape
analyses, distance gradients are being studied to determine how differing habitat types
influence adult amphibian breeding site selection (Johnson and Semlitsch, 2003). Homan
(2004) found that closed canopy specialists were affected differently by habitat loss at
different scales. He recommended multiple spatial level analyses gives a more complete
understanding of how amphibians are affected within the landscape. Multiple-scale
analyses can also assist land managers in conservation recommendations.
Amphibians are important ecological components of many forest ecosystems in
North America (Petranka et al., 1993), and their biomass can rival that of birds and small
mammals in some ecosystems (Burton and Likens, 1975). Amphibians forage in forested
habitat and reproduce in temporary pools. A strong positive correlation has been shown
6
between the occurrence of certain forest ecosystems and amphibian species richness
(Guerry and Hunter, 2002; Herrmann et al., 2005). Larval amphibian foraging habits are
also beneficial to other species witin the vernal pool. Tadpoles and larval amphibian
consume large amounts of algae, and as a result they reduce the rate of natural
eutrophication which is an over-enrichment of water with nutrients, resulting in excessive
algal growth and oxygen depletion (Figure 2.1) (Collins and Crump, 2009; Calhoun and
DeMaynadier, 2008). In addition to their feeding habits, amphibians serve as food
sources for fish, reptiles, birds, and mammals (Collins and Crump, 2009). Amphibians
are important components of terrestrial and aquatic ecosystems, and they supply a large
amount of the energy and nutrients that are transferred between aquatic and terrestrial
habitats (Gibbons et al., 2006; Regester and Whiles, 2006).
While amphibians tend to be abundant within forests, they are susceptible to
habitat disturbances both at local and landscape levels during the aquatic and terrestrial
phases of their life cycles (Welsh and Droege, 2001). Amphibian populations have been
reported to be declining globally (Collins and Storfer, 2003; Stuart et al., 2004). Habitat
loss and fragmentation are thought to be the majority of causes for their decline. Some
amphibian species, such as many salamanders, spend the majority of their life in forested
habitats and move to aquatic habitats for breeding. Changes in forest composition and
spatial distribution of forest fragmentation across the landscape may introduce barriers
for breeding movements of adults and post-breeding dispersal of juveniles (Baldwin et
al., 2006; Todd and Rothermel, 2006), precluding the persistence of some species in
highly disturbed areas (Gibbs, 1998), and ultimately affecting the species composition
7
Figure 2.1. The multi-scale mechanistic model of the amphibian breeding ecology study.
8
locally at amphibian breeding pools (Homan et al., 2004; Skidds et al., 2007) and across
the landscape (Skelly, 2001).
Vernal pools, also known as “ephemeral ponds,” “autumnal pools,” or simply
“temporary pools,” are usually isolated wetlands characterized by a wet season in which
they are temporarily filled with water (Calhoun et al., 2008). These pools lack predatory
fish and contain unique herbaceous vegetation composition (Alpert et al., 1999; Colburn,
2004). Vernal pools are of ecological importance because they support diverse
populations of local and regional amphibian faunas (Semlitsch and Bodie, 1998). Vernal
pools are often used by amphibians for breeding. They support obligate species, which
depend on vernal pools during some stage in their reproduction. Many of these species
have adapted to the annual drying of these pools and therefore can not utilize more
permanent breeding habitats (Grant, 2005). They also support facultative species, which
do not depend on vernal pools, but prefer them for breeding habitat (Grant, 2005).
Vernal pools are important in the nutrient cycle of an ecosystem (Langlois, 2003). As
leaves collect in these pools, they provide nutrition for developing species and primary
consumers in the pools. This in turn supports the secondary consumers by providing
them with a healthy source of food. This occurs through the food chain, with
decomposers eventually inputting these nutrients back into the environment. Vernal
pools also place nutrients directly back into the ecosystems through biodegrading
materials which fall into the pools (Langlois, 2003).
Forest management practices may affect individual amphibian fitness by altering
the breeding success and survivorship and, the abundance and diversity at population and
9
community levels. Logging or stand manipulation for various reasons often result in the
reduction of canopy cover which increases light penetration in terrestrial habitats that
surround breeding pools, and directly affects the suitability of vernal pools for amphibian
breeding. Forest management activities that result in canopy removal can lead to lower
survival rates over two years post treatment (Rothermel and Luhring, 2005, Rothermel
and Semlitsch, 2006, Todd and Rothermel, 2006) and smaller body sizes of juvenile and
adult pool breeding amphibians (Todd and Rothermel, 2006). These demographic
changes may contribute to reduced abundance of pool breeding amphibians in clearcuts
(Renken et al., 2004, Patrick et al., 2006). Additionally, adult amphibians may avoid
clearcut areas due to increased mortality from dessication (Gibbs, 1998, ChanMcleod,
2003). A combination of fewer breeding adults and reduced suitable habitat within
clearcuts could result in fewer breeding events and egg masses at breeding pools.
The canopy reduction disturbance type and intensity determines whether it is
beneficial, detrimental, or has no effect on a species. It has been documented that the
amount of algae, a food resource for many larval amphibian species, is often affected by
the availability of light (Skelly et al., 2002). Increasing light intensity and duration
results in higher water temperature and algal production. It can then lead to increases in
dissolved oxygen and water pH (Werner and Glennemeier, 1999), factors which could
affect larval amphibian survival and development from embryo to metamorphosis
(Lauck, 2005). Logging and other practices that reduce forest canopy also alter the
habitat conditions within the forest stands and often result in hot and dry microclimate
conditions (Naughton et al., 2000). Because of their permeable skin, amphibians
10
typically have a low tolerance for dry and hot conditions (Naughton et al., 2000). Tree
disturbance, such as clearcuts may present movement difficulties for amphibians
traversing these areas (Chan-Mcleod, 2003). If amphibians are either unable or require
more time to move to suitable breeding pools to oviposit then the breeding success will
be lessened (Hocking and Semlitsch, 2007). Preference for open versus closed canopy
habitat conditions appears to be species-specific and based on factors such as food
availability or preference for high water temperature and dissolved oxygen levels (Skelly
and Golon, 2003, Werner and Glennemeier, 1999, Felix, 2007).
While salamanders are more specialized for habitats with cool moist conditions,
anurans usually have a greater dispersal capability and relatively higher tolerance for
warm and dry conditions (DeMaynadier and Hunter, 1995). Anurans have evolved
mechanisms to select oviposition sites where the survival of their offspring is maximized
(Reserartis and Wilbur, 1989, Reserartis and Wilbur, 1991), water depth (Crump, 1991),
pathogens (Kiesecker and Skelly, 2000), and canopy cover are known to influence
ovipostion site selection (Binckley and Reserarits, 2007). Pools with open canopy are
preferred over pools with closed canopy by ovipositiong Cope’s gray treefrogs (Hyla
chrysoscelis), squirrel treefrogs (Hyla squirrela), and gray treefrogs (Hyla versicolor)
(Binckley and Resetarits, 2007; Felix et al., 2007; Hocking and Semlitsch, 2007).
Disturbance to vegetation surrounding breeding pools can influence the number of eggs
deposited by influencing the number of adults that are able to access breeding pools,
influencing selection of pools by breeding adults. For example, gray treefrogs laid more
eggs in pools in clearcut stands than in ones located in close-canopy stands (Felix et al.,
11
2007; Hocking and Semlitsch, 2007). However, the increase in egg deposition in
clearcuts was observed 10 m into the clearcuts but not at 50 m, suggesting that large
clearcuts may limit the number of adults that reach pools to breed (Hocking and
Semlitsch, 2007). In other cases, the effects of disturbance at different scales are unclear
because they are confounded by the species being studied. Some amphibian species have
been observed to lay more eggs in clearcuts while other species laid more in undisturbed
control stands (Felix et al, in review). However, it is unclear how disturbances at the
pool and stand level acted to produce this pattern. Few studies have investigated the
mechanism of how forest canopy reduction affects amphibian breeding success, even
fewer studies have examined these mechanisms at multiple scales (Van Buskirk, 2005).
Because organismal response to disturbance is usually scale depenedent, we are limited
in our understanding of how forest amanagement actions affect most of our fauna
(Lindenmayer and Franklin, 2002).
To study the relationship between forest management practices and amphibian
breeding success, survivorship, and population dynamics, an ideal experiment would
involve randomly assigning treatment conditions to pools with relatively similar
conditions, while manipulating a breeding pool based on treatment prescription, and
monitoring amphibian breeding success and population dynamics to ascertain the link
between forest treatment and the occurrence or non-occurrence of distributional shifts
and amphibian productivities (Skelly, 2001). Because of logistic and financial
difficulties with implementing such studies, information of how habitat alteration affects
an amphibian’s accessibility to and choice of breeding pools (Hocking and Semlitsch,
12
2007) is limited. This proposed study combined the local experimental and landscape
observational approaches to examine the effect of forest management disturbance on
amphibian breeding ecology, and it will help to fill these knowledge gaps about the
mechanism of how the degree of tree disturbance influences breeding success by different
amphibian species.
13
CHAPTER 3
THE EFFECT OF THE INTERMEDIATE DISTURBANCE ON WILDLIFE
Abstract
Intermediate disturbance hypothesis (IDH) suggests that species diversity measures,
including species richness are highest in response to an intermediate level of disturbance
and species diversity is highest when there is a moderate frequency disturbance. To
assess the evidence that supports or rejects the intermediate disturbance hypothesis in
wildlife-related research, a literature review of 231 peer reviewed articles from 92
different journals and three doctoral dissertations from 1980-2011 was conducted.
Spatial and temporal components of the experimental design of these studies were
compared to determine differences among researchers when evaluating the intermediate
disturbance hypothesis. In addition to spatial and temporal scales, species level responses
were compared. Three disturbance types were identified for this assessment: urbanization
and agricultural development (94 articles), forest management (93), and natural
disturbance (52). Four wildlife groups were selected for this review: avian (82 articles),
herpetofaunal (56), invertebrate (48), and mammalian (53). Of the articles reviewed, 19
percent of them provided evidence to support predictions from the intermediate
14
disturbance hypothesis. The remaining 81 percent either rejected the hypothesis or did
not show conclusive evidence. Since these studies were different in disturbance intensity,
spatial extent, and temporally, it was difficult to evaluate the intermediate disturbance
hypothesis. Several studies did not actively test the intermediate disturbance hypothesis
in comparable ways as the spatial and temporal scales tested were dissimilar. Defining
disturbance intensity may be one of the reasons that there was low support of the
intermediate disturbance hypothesis.
Introduction
Disturbance is an important mechanism in ecological communities (Lake, 2000).
Disturbance is defined as a relatively abrupt change of biomass or structure from forces
within or outside the habitat which is characterized by its extent, frequency, intensity, and
severity (Walker, 2012). Disturbance frequency is the number of similar disturbance
events at a given location per unit time (Shea et al., 2004; Walker, 2012). Intensity is
measured by disturbance force, and it can be altered by past disturbances in the area as
well as by current conditions (Shea et al., 2004; Walker, 2012). Severity is the amount of
biomass lost or the degree of alteration of ecosystem structure or function caused by a
disturbance (Shea et al., 2004; Walker, 2012). Finally, extent is the area impacted by a
disturbance, and it varies from global to local scales (Shea et al., 2004; Walker, 2012).
Lake (2000) identified disturbance as a damaging force that comes into contact with an
organism or group of organisms’ habitat. In addition to the change of community
15
structure in natural communities, disturbance is frequently cited as a process that removes
or damages biomass (Grime, 1979; Hobbs and Huenneke, 1992). This mechanism is
theorized to change the landscape in ways that can support new species by creating a
mosaic of heterogeneous patches that support several species.
Intermediate disturbance hypothesis is one of the most important hypotheses in
ecology, and it is believed to help describe species richness patterns in terrestrial
ecosystems (Catford et al., 2011; Shea et al., 2004). The IDH inferred that species
richness is at its highest when disturbance levels are intermediate (Hobbs and Huenneke,
1992) (Figure 1). This hypothesis was first proposed by Grime (1973), and it was used to
help describe the effect of various environmental stresses on herbaceous vegetation.
Connell (1978) believed that species community composition did not stay constant and
the species composition need to change in order for the community to have high
diversity. At intermediate disturbance intensities and frequencies, there is a peak in
diversity because there is a balance between competitive exclusion and the loss of
dominant individuals from disturbance (Bruggisser et al., 2010; Mackey and Currie,
2001).
The basis for the IDH is that competition limits diversity (Fox, 1979). The
competitive exclusion principle is a cornerstone within the IDH. With infrequent
disturbance, competitive exclusion occurs and leads to low species diversity (Huston,
1979; Wilson, 1990). The competitive exclusion principle proposes that competition
among different species results in most species being eliminated from an area
16
Figure 3.1. Conceptual model of intermediate disturbance hypothesis (Grime, 1973;
Connell, 1978; Robbins et al., 2006).
17
unless some outside event continually delays this outcome (Connell, 1978; Huston, 1979;
Vandermeer, 2006).In a biological community, dominant species outcompete less fit species and
lead to an equilibrium in community structure when there is no disturbance. This idea is viewed
as competitive equilibrium, yet researchers believe that natural systems often do not reach this
point because disturbance events interrupt competitive equilibrium (Connell, 1978; Huston,
1979).
Under the IDH, diversity is the highest during intermediate sized disturbances when
compared to extreme disturbances (Connell, 1978). Intermediate can be defined in terms of the
proportion of individuals lost or the degree of structural displacement or resource alteration
(Hobbs and Huenneke, 1992). Too little disturbance leads to low diversity from competitive
exclusion and too much disturbance kills or displaces species that are not capable of rapid
recolonization (Wilkinson, 1999). As the disturbance frequency decreases, diversity should
increase because there is more time for additional species to colonize (Connell, 1978). Connell
(1978) recognized that moderate disturbance intensity interrupts competitive elimination but
does not restrict a species’ ability to recolonize habitat patches. Disturbance creates
heterogenous habitat patches which are different structurally and affect species diversity (Hobbs
and Huenneke, 1992).
Connell (1978) researched tropical forests and suggested that after a severe disturbance, a
few tree species colonized a particular habitat patch. Due to short colonization time, diversity
was low and only species that reproduced within the dispersal range colonized the area (Connell,
1978). Different species had different competitive capabilities. The recolonizing organisms that
competed most effectively were believed to be dominant. Even if the species were equal in
18
competitive ability, the one with most resillance to physical extremes or natural enemies
occupied a majority of the habitat space (Connell, 1978). This process resulted in only a few
individuals occupying the majority of the available habitat space unless environmental extremes
occurred to reset the process.
Intermediate Disturbance Hypothesis Criticisms
The IDH is perceived as acceptable because of its generality and simplicity (Fukami,
2001). It was originally applied to tropical forests and coral reefs (Connell, 1978). Since then, it
has been applied to invertebrates, avian, herpetofauna, and mammal species in various
ecosystems. IDH identifies intensity and frequency of disturbance as important predictors of
biodiversity with the relationship between biodiversity and disturbance hypothesized to be
hump-shaped (Robbins et al., 2006). The IDH suffered criticism because it was not viewed as
consistently accurate when systems and disturbances varied. For example, Mackey and Currie
(2001) noted that diversity-disturbance relationships did not consistently show the peaked pattern
predicted in IDH and were not as prevalent as commonly believed. In his studies of coral reefs,
Rogers (1993) found that diversity was explained via IDH but not species richness and evenness.
Critics believe that IDH’s oversimplification is problematic because it only includes one measure
of diversity and also does not account for invasive species impacts.
Even though the IDH infers that moderate disturbance will produce higher species
diversity, few species persist with frequent and severe disturbances, and only the longest lived,
best competitors survive in the absence of disturbance (Hobbs and Huenneke, 1992). Critics of
the hypothesis believe that IDH oversimplifies ecosystems (Walker, 2012). Disturbance extent,
19
frequency, severity, and intensity are factors needed to fully understand the effects of disturbance
on species. IDH is difficult to test because the intensity and disturbance rates must be monitored
accurately along with species response (Hill and Hill, 2001). Species life history also needs to be
considered. Different aspects of disturbance affect various species contrarily. Shea et al. (2004)
argued that disturbance frequency must be related to the generation time of the species within the
habitat patch. If disturbances occur too frequently such that even the shortest-lived species
cannot reproduce, then biodiversity will be limited to a few or no species (Shea et al., 2004; Leis
et al., 2005). When testing IDH, studies must include both disturbance characterization and the
affected species adaptation. Catford et al. (2011) asserted that disturbance can facilitate
biological invasion, resulting in increased threats to native diversity are increased. As invasive
species are known to be aggressive colonizers following disturbance, their establishment may
change the IDH predictive response curve primarily due to their outcompeting native species.
Objectives
The objectives of this review were to determine if the IDH is supported within wildlife
research and to evaluate the adequacy of the tests of the hypothesis conducted in those studies.
This review assessed many of the wildlife manuscripts published since IDH was officially
defined 30 years ago. Therefore this paper evaluates the usefulness of this hypothesis for
explaining wildlife diversity measures and wildlife response to a variety of disturbances.
Publications were categorized based on disturbance categories to determine if disturbance
affected the rate of acceptance when testing the IDH. Four wildlife groups were selected for this
20
analysis: invertebrate, herpetofauna, avian, herpetofauna, and mammal. Since extent, intensity,
frequency, and severity are seen as important factors affecting disturbance and species diversity,
both spatial and temporal scales were examined. Various study designs were compared to
determine if study design was related to the acceptance of the IDH. Finally, a comprehensive
assessment of the publications was taken to assess what was lacking in the research studies.
Methods
Peer-reviewed publications and doctoral dissertations written between 1980 and 2010 that
examined the effects of disturbance on wildlife and emphasized the IDH were reviewed using the
following databases: BIO ONE, JSTOR, Academic Search Premier, Biological Abstracts,
EBSCO A to Z, and Forestry Chronicle. Google Scholar provided additional journal articles and
doctoral dissertations. Two hundred thirty one journal articles from 92 peer-reviewed journals
and three doctoral dissertations were used. Articles were grouped into one of three disturbance
categories: urbanization and agricultural development, forest management, and natural
disturbance. The review was used to determine if there was a relationship between the IDH and
wildlife response. Within these different groupings and classifications, calculated percentages of
journal articles, dissertations, and compared differences among IDH studies were completed.
The number of articles that accepted or rejected the intermediate disturbance hypothesis were
also compared. IBM SPSS and Microsoft Excel were used to compare and contrast the data.
21
Results
The disturbance category was dominated by urbanization and agricultural development articles
while natural disturbance manuscripts represented less than a quarter of reviewed articles. Out
of the total reviewed articles, 82 were avian related, 56 herpetofaunal-related, 56 invertebraterelated, and 53 mammalian-related (Figure 4.3). Within the avian group, urbanization and
agricultural development accounted for 41 percent, forest management accounted for 38 percent,
and natural disturbance accounted for 21 percent (Figure 3.2). For the herpetofaunal articles the
disturbance categories were 33 percent urbanization and agricultural development, 50 percent
were forest management, and 17 percent were natural disturbance (Figure 3.2). Invertebrate
articles were evenly distributed among disturbance types at 33.3 percent for each. Articles
focusing on the mammalian categorical response had 45 percent examined the effect of
urbanization and agricultural development, 40 percent for forest management, and 15 percent for
natural disturbance (Figure 3.2). All of the 231 journal articles were categorized into 10 journal
genres with articles representing 92 peer-reviewed journal outlets. General biology had the
lowest representation at 1 percent, and ecology had the highest at 34 percent. To determine if the
22
Figure 3.2. Total journal articles and dissertations that each wildlife group represents when categorized by disturbance
classification (n= 241 journal articles and doctoral dissertations).
23
IDH was exhibited, each article was then classified based on the author’s reported acceptance or
rejection of the intermediate disturbance hypothesis. Three categories were created to quantify
the acceptance of the IDH in the publications. The categories included: accepted the IDH, did
not accept the IDH, or they did not find conclusive evidence to support or reject the IDH. Of the
total 234 research articles and dissertations, 81 perent concluded that the IDH was not accepted
or there was no evidence of its use to explain the results, while 19 percent concluded that the
IDH could be used to explain the results (Figure 3.3). This relationship was observed across all
four wildlife groups. Overall, the use of the IDH to explain these study results was not common
among the avian, herpetofaunal, mammalian, and invertebrate wildlife groups. Within each
disturbance category, less than a third of the papers accepted evidence of the IDH (Figure 3.4).
Spatial level analyses showed low levels of research articles and dissertations for both stand and
landscape scales that supported the IDH (Figures 3.5-3.6). The temporal component of these
studies also had a low number of articles that showed evidence of the IDH (Figure 3.7). This
relationship was noted across all the disturbance categories and wildlife groups (Figure 3.7).
Assessment by abundance (articles focused on single species) or by diversity indices (articles
focused on multiple species) resulted in low IDH acceptance or both (Figures 3.8-3.10).
Finally, wildlife groups were used to contrast spatial, temporal, and species scales. The
stand-level scale accounted for the majority of the articles for all wildlife groups except avian
studies. Within the stand-level scale, articles by wildlife group were 15 percent avian, 18 percent
herpetofauna, 20 percent invertebrate, and 16 percent mammalian (Figure 3.6). The landscape
scale had fewer articles; less than half of the total articles reviewed reported results at the
24
Figure 3.3. Total journal articles and dissertations that each wildlife group represents when categorized by the acceptance or
rejection of the intermediate disturbance hypothesis (n= 241 journal articles and doctoral dissertations published from 19802011).
25
Figure 3.4. Total number of journal articles and dissertations that accepted or rejected intermediate disturbance hypothesis
when categorized by disturbance classification (n= 241 journal articles and doctoral dissertations published from 1980-2011).
26
Figure 3.5. Total number of journal articles and dissertations that landscape or stand spatial levels represent when categorized
by disturbance type (n= 241 journal articles and doctoral dissertations published from 1980-2011).
27
Figure 3.6. Total number of journal articles and dissertations that landscape or stand
spatial levels represent when categorized by categorized by wildlife groups (n= 241
journal articles and doctoral dissertations published from 1980-2011).
28
Figure 3.7. Total number of journal articles and dissertations that each temporal level (duration of study) category represents when
categorized by disturbance type (n= 241 journal articles and doctoral dissertations published from 1980-2011).
29
landscape level. Within the landscape-level scale, 20 percent of all articles focused on
avian studies, 6 percent on herpetofaunal research, 2 percent on invertebrates, and 4
percent on mammal projects (Figure 3.6). Temporal classification analysis showed the
majority of the articles were executed between 1-3 years. Within the 1-3 years of
classification, avian research accounted for 21 percent, herpetofaunal research
represented 17 percent, invertebrate studies accounted for 20 percent, and mammal
studies represented 17 percent. Within the studies that were conducted for 4 or more
years, avian studies were 14 percent, herpetofauna research embodied 7 percent,
invertebrates were shown to be 2 percent, and mammal studies were 3 percent. Multiple
species analysis dominated over single species for the articles reviewed. The majority of
the multiple species articles focused on avian (23 percent) and invertebrates (17 percent),
while the single species articles were dominated by mammal studies (14 percent),
followed by avian and herpetofaunal (12 percent each) (Figure 3.9). When single species
analyses were assessed, avian studies were 12%, herpetofaunal studies accounted for 12
percent, invertebrates were 5 percent, and mammal studies embodied 14 percent (Figure
3.9).
Discussion
Avian Studies
In the 1980s IDH studies focused on species that moved little throughout their
lifetimes such as mussels and plants (Brawn et al., 2001). In the 1990s and early 2000s,
30
Figure 3.8. Total number of the journal articles and dissertations that each species level (species analyses) category represents when
categorized by acceptance of the intermediate disturbance hypothesis (n= 241 journal articles and doctoral dissertations published
from 1980-2011).
31
Figure 3.9. Total number of journal articles and dissertations that each species level (species analyses) category represents
when categorized by disturbance type (n= 241 journal articles and doctoral dissertations published from 1980-2011).
32
Figure 3.10. Total number of journal articles and dissertations that each species level
(species analyses) category represents when categorized by wildlife group (n= 241
journal articles and doctoral dissertations published from 1980-2011).
33
researchers considered species diversity at landscape and regional scales (Brawn et al.,
2001). To conduct research on larger scales, scientists targeted species whose mobility
enabled them to occupy habitat patches at various scales, and birds proved to be reliable
focal species, allowing ecologists to test this hypothesis across the landscape.
The majority of avian research that used IDH principles was executed within the
discipline of urbanization and agricultural development. These research studies were
able to assess IDH by using various land use categories and comparing the effects on bird
communities. These land use studies helped researchers assess the effects of disturbance
gradients on bird communities across different landscapes. MacLean et al. (2003)
noticed that their results supported the IDH. Lepczyk et al. (2008) noted that their
findings supported the IDH. Another gradient that resulted in moderate levels of
disturbance, and potential IDH applications, were found using neighborhoods as
disturbance. Native species richness showed a negative curvlinear relationship with
housing units (Lepczyk et al., 2008). In their study, disturbance intensity was discerned
by the number of housing units within the habitat patches. Markovchick-Nicholls et al.
(2008) saw that the IDH explained their results. Ecological disturbance is important to
bird conservation, and several bird species are associated with disturbance adapted
habitat and ecosystems (Brawn et al., 2001).
According to the time-scale and the habitat considered, species richness was
expected to change based on the disturbance intensity associated with forest management
practices (Devictor and Robert, 2009). Regeneration methods did show differing
34
responses in bird communities as a result of varying disturbance intensity (Brawn et al.,
2001).
For the articles reviewed, urbanization and agricultural development and natural
disturbance had the same number of studies that supported the IDH. Theoretical and
empirical evidence indicated that some communities reach maximum local and regional
diversity when subjected to disturbances of intermediate intensity or frequency (Connell,
1978; Marone, 1990). The dynamic nature of responses of tropical species to spatial and
temporal heterogeneity in the habitat mosaic of forest undergrowth emphasized the role
of the IDH in assemblage development (Karr and Freemark, 1983). The IDH assumed
that temporal changes in species composition and relative abundances were minimal and
that natural disturbances followed a return to equilibrium conditions for the area (Karr
and Freemark, 1983).
Community structure changes were cited more clearly between
years than between disturbed forest habitat (Tejeda-Cruz and Sutherland, 2005).
Changes in the bird community after disturbance affected newly created gaps and species
within the entire forest (Tejeda-Cruz and Sutherland, 2005). The disturbances affected
bird communities by changing the composition of the landscapes. Disturbances made the
landscape more heterogeneous, and as a result there were more bird species. Several bird
species were able to recolonize habitat patches after the disturbances.
Herpetofaunal Studies
Species richness increased with moderate disturbance, but decreased in severely
degraded wetlands, leading to the conclusion that moderately disturbed wetlands were
35
capable of supporting species that benefit from disturbance as well as disturbance
sensitive species (DeCatanzaro and Chow-Fraser, 2010). Terrestrial reptiles had similar
responses as the wetland turtles.
Disturbances created by silvicultural practices, create disturbances that vary in
spatial scale and intensity (Peltzer et al., 2000). McCleod and Gates (1998) found that
several amphibian species had significantly higher abundances in control treatments than
in harvested treatments, and that reptiles were significantly less abundant in control
versus harvested treatments. McCleod and Gates (1998) argued that heterogeneity
throughout the moisaic of habitat patches caused by disturbance should increase diversity
across the landscape even though reptile and amphibian diversity decreased within a
given patch. Amphibian and reptiles communities differed, with the amphibian reponse
not supporting IDH while the IDH was supported by the reptile response.
Loehle et al. (2005) found similar results while researching herpetofaunal
responses to forest management influences. Felix (2007) reached similar conclusions to
McLeod and Gates (1998) and Schurbon and Faith (2003) in regards to amphibian
species, but differed from McLeod and Gates (1998) on some of the reptile communities.
Felix (2007) found reptile diversity was highest at the most extreme levels of disturbance
such as clear cuts and did notice a relationship that supported the IDH with regards to his
lizard data. Sutton (2013) also looked at the effects of forest management on
herpetofaunal communities by investigating thinning and prescribed burns. He found that
the highest overall amphibian and reptile diversity was exhibited at the controls. This
distinction in herpetofauna illustrates the source of the basic controversy. That being
36
there are disagreements on what constitutes severe disturbance versus intermediate
disturbance. The severity of the disturbance needed to be taken into account when
determining if forest management studies involving herpetofaunal communities support
or reject the IDH.
Invertebrate Studies
In studies testing disturbance, invertebrates were seen as bioindicators in
urbanized landscapes, because of their sensitivity to environmental variation (Andersen
and Majer, 2004; Blair and Launer, 1997). Butterflies appeared to disappear as the
landscape became more urbanized, but showed a pattern of increased butterfly species
richness at intermediate urbanization levels (Blair and Launer, 1997). If disturbance is
too frequent or too severe, then only species capable of dispersing and quickly maturing
remain (Blair and Launer, 1997). If disturbance was too infrequent, then species that are
capable of competing dominate the community (Blair and Launer, 1997). Findings from
this study emphasized that human-disturbed systems respond in similar ways to naturallydisturbed systems (Blair and Launer, 1997). Since many arthropods occupied smaller
amounts of space than some vertebrate species, they were seen as ideal for analyzing the
possible impact of fragmentation in urbanized landscapes (Bolger et al., 2000). Bolger
(2000) predicted that declines in invertebrate communities that resulted from
fragmentation may have effects at higher trophic levels, affecting various members of the
ecosystem. Venn et al. (2003) determined that leaf litter decomposed at faster rates
within urbanized area compared to undisturbed sites. This restricted carbon and nitrogen
37
upon which carabid beetle communities depend. Reductions in the food sources replaced
ecosystems with ecological sinks that would lead to population declines throughout
habitat that had been altered (Venn et al., 2003).
Disturbance is often one of the dominant factors regulating diversity (Cleary,
2003). This researcher found that logging provided more butterfly habitat and increased
butterfly diversity, however, burning lowered butterfly diversity (Cleary, 2003). Moretti
et al. (2006) noticed that butterfly diversity increased in response to a single fire, but
declined in response to frequent fires. The frequency of disturbance appears to be a
factor in invertebrate diversity response to disturbance.
Freshwater macroinvertebrates were the taxonomic group that showed evidence
of IDH. Bouget (2005) found that mid-size forest canopy gaps resulted in a higher
abundance of floricolous beetles, but not other beetle guilds. It appeared that natural
disturbance effects were dependent on the guilds’ adaptations and habitat requirements.
When measuring predation pressure in pond microcosms, Thorp and Cothran (1984)
found that dragonfly species richness supported the intermediate disturbance hypothesis,
while species evenness did not. They surmized that the small range of species richness
values among treatments suggested predation was not the only factor contributing to the
regulation of the macroinvertebrate assemblage. Townsend et al. (1997) found low
invertebrate richness at high frequencies and intensities of disturbance reflected. They
documented that a greater disturbance frequency reduced richness by excluding taxa that
did not quickly recolonize in intervals between disturbances and a lesser frequency
permitted competitive exclusion of species that were capable colonists but poor
38
competitors (Townsend et al., 1997). This intermediate disturbance frequency allowed
wider range of species, by eliminating competitive exclusion among macroinvertebrates
(Townsend et al., 1997).
Whiles and Goldowitz (2001) observed that insect abundance and wetland
hydroperiod followed IDH patterns. Overall, less than a quarter of the invertebrate
research exhibited an IDH acceptance. The majority of the studies that accepted IDH
were executed in localized research. Urbanization, forest management, and natural
disturbance each had the same number of research studies that accepted IDH. The vast
majority of the studies executed were completed in less than 3 years. Localized studies
of macroinvertebrate communities that lasted 3 years or less characterized the majority of
studies that accepted the IDH for invertebrate studies. These commonalities seemed to be
trends that resulted in a higher acceptance of IDH within invertebrate studies.
Mammal Studies
Research on mammals had the lowest number of articles that accepted the IDH
among the wildlife groups. Of the 46 mammal-focused articles reviewed, only 1 article
accepted the IDH. Compared to the results reported on the other three wildlife groups,
mammal studies showed a more uniform response to disturbance. Ferreira and Van
Aarde (2000) found that rodent species richness on coastal dune forests were the highest
at intermediate disturbance levels. The one study that accepted IDH was a stand level
experiment that lasted less than 3 years and assessed multiple species. These
characteristics were similar to the invertebrate studies that accepted the IDH as well.
39
There seemed to be similarities among research articles that accepted IDH within various
wildlife groups.
Conclusions
The IDH predicts that species diversity is at its highest following an intermediate
level of disturbance (Connell, 1978). The wildlife research that accepted or showed
evidence of this hypothesis was less than 20 percent of the total articles. Multiple
confounding factors in the reviewed studies could affect this low acceptance rate in IDH
including: the species that were analyzed, knowledge of the species life histories, the type
of disturbance these species were responding to, the frequency of disturbance, the spatial
component of the disturbance and the study, the temporal scale of the study, information
regarding the physiographic province, and climate patterns of the locality
40
CHAPTER 4
ENVIRONMENTAL AND POOL MORPHOLOGY
DIFFERENCES AMONG VERNAL POOL CLASSES
WITHIN NORTHERN ALABAMA
Introduction
Wetlands are crucial for maintaining biodiversity (Semlitsch, 1998).
Due to the
ephemeral nature of some wetlands, these ecosystems have high biological productivity
and house biota that are only found in these ecosystems (Gibbs, 1998). Wetland
ecosystems depend on constant or recurrent-shallow inundation near the surface substrate
that gives rise to a niche for biota that adapt to flooding, particularly rooted plants
(National Research Council, 1995; Keddy, 2010). Common features in wetlands include
hydric soils and hydrophytic plants (National Research Council, 1995; Colburn, 2004).
Wetlands vary in their hydroperiods ranging from permanent to seasonal. Vernal pools
are seasonal wetlands that are flooded to capacity during winter and spring months, and
with hydroperiods varying from a few months each year to more semi-permanent cycles
when they may dry out completely every few years (Zedler et al., 2003; Brooks, 2004;
Calhoun and deMaynadier, 2008).
41
Vernal pools are a type of isolated wetland that often occur in or next to forests or
other wooded areas that lie in confined basins that lack a continuously flowing inlet or
outlet (Brooks and Hayashi, 2002; DiMauro and Hunter,2002; Brooks, 2004; Colburn,
2004). Vernal pools are usually less than a hectare in size but they still house a wide
range of species. This small size is characteristic of the ephemeral nature of these bodies
of water. The hydroperiod of these wetlands influence the amphibian species that
reproduce in these ecosystems (Werner et al., 2007).
Because of the temporary nature of surface water in vernal pools, fish are not able
to colonize. However, vernal pools provide an ideal habitat for many amphibian species
(Brooks, 2004 Colburn, 2004; Calhoun and deMaynadier, 2008), particularly those that
rely on temporary surface water for breeding (Brooks, 2004; Colburn, 2004). For
example, several caudate species, are obligate species that breed exclusively in isolated
wetlands (Calhoun and deMaynadier, 2008). There are also, a few anuran species that
are obligates though the majority are facultative species (Colburn, 2004). Some
amphibian species such as ranids can use a wide variety of breeding habitats, however
vernal pools are their preferred breeding locations (Skelly, 1999; Skelly et al., 2005;
Calhoun and deMaynadier, 2008)
Forest canopy cover influences biophysical conditions within inundated wetlands,
and the resulting conditions influence which amphibian species can successfully breed.
Closed canopy wetlands are associated with amphibian species that migrate and
reproduce in winter. These species need an extended period of time, in excess of two
months, to complete development from hatching through metamorphosis. Some
42
caudates, such as ambystomatids, are typically found within wetlands having extensive
canopy cover over breeding pools (deMaynadier and Hunter, 1999). Open canopy
wetlands are often associated with certain anuran species, including Spring Peeper
(Pseudacris crucifer) (Halverson et al., 2003) and Southern Leopard Frog (Lithobates
sphenocephalus utricularius) (Schiesari, 2006) which displays a higher growth rate with
decreased canopy cover (Skelly et al., 2002; Halverson, et al., 2003).
Amphibians use vernal pools and isolated wetlands for breeding, but vary in the
types of wetlands they use. For example, leopard frogs more often breed in open canopy
wetlands (Werner and Glennemeier, 1999) whereas spotted salamanders reproduce in
habitat with increased canopy cover (Homan et al., 2004; Werner et al., 2007). By
understanding the differences between wetland classes, the status of amphibian breeding
habitat across the landscape can be assessed.
The purpose of this study was to determine how canopy cover affects a pool’s
environmental conditions and morphology in various wetlands situated on the
Cumberland Plateau in northeastern and north central Alabama. In this study, three
wetland categories: (1) closed, (2) open, and (3) partially open wetlands were assessed.
These designations were based on the forest cover directly over the wetlands as measured
in full leaf out. Annual differences, among environmental and pool morphological
variables, were compared to determine if there was a difference between breeding
seasons.
It was hypothesized that environmental conditions and pool morphology would
vary among wetlands types: so that (1) open canopy allows more light penetration
43
resulting in vernal pools with higher algal photosynthetic rate and consequently, higher
dissolved oxygen, (2) wetlands with open canopy have lower canopy cover resulting in
higher water pH, as well as higher water and soil temperatures, and (3) open wetlands
will have less leaf litter and longer hydroperiod than closed canopy wetlands.
Methods
Study Sites
The two localities used for this study were James D. Martin Skyline Wildlife
Management Area (SWMA) and William B. Bankhead National Forest (BNF). The
SWMA, is located in Jackson County, Alabama and covers an area of 114 km2 (64.1 mi2)
(Lein, 2005) (Figure 4.1). Vegetation in the SWMA has been classified as mixed
mesophytic communities comprised of oak-hickory forests (Braun, 1950). Of the
timberland in Jackson County, 78.8% is a mix of oak and hickory species (Hartsell and
Brown, 2002). In June, 2012, ten dominant and co-dominant tree species whose crowns
surrounded or occurred within fifteen vernal pools and within the SWMA were identified
and selected randomly. The most dominant and codominant tree species in this area
include sweetgum (Liguidambar styraciflua), red maple (Acer rubrum), chinkapin oak
(Quercus muehlenbergii), white oak (Quercus alba), chestnut oak (Quercus prinus), post
oak (Quercus stellata), scarlet oak (Quercus coccinea), northern red oak (Quercus
rubra), blackgum (Nyssa sylvatica), sourwood (Oxydendrum arboreum), and tulip poplar
(Liriodendron tulipifera).
44
Figure 4.1. Vernal pool locations with James D. Martin Skyline Wildlife Management Area,
Alabama. Purple represent partially open (perimeter) vernal pools, blue are closed (forest)
vernal pools, and red are open vernal pools.
45
The BNF is 737 km2 (284 mi2) and located in Lawrence, Winston, and Cullman
counties in north central Alabama (Gaines and Creed, 2003) (Figure 4.2). Hartsell and
Brown (2002) classified the timberland in Lawrence County at 42% oak and hickory
species, Winston County at 47%, and Cullman County at 61% (Hartsell and Brown,
2002). In June 2012, the same sampling protocols as described for the SWMA were used.
The dominant tree species within and surrounding nine vernal pools within the BNF area
are sweetgum (L. styraciflua), loblolly pine (Pinus taeda), white oak (Q. alba), water oak
(Quercus nigra), and other red oak species, tulip poplar (L. tulipifera), blackgum (Nyssa
sylvatica), chestnut oak (Q. prinus), virginia pine (Pinus virginiana), winged elm (Ulmus
alata), red maple (A. rubrum), sourwood (O. arboreum), and water oak (Quercus nigra).
Fifteen vernal pools were located in the SWMA, while nine vernal pools were in
the BNF based on forest cover delineated from aerial photography of potential vernal
pool basins. Requirements for selection for vernal pools for this study included:
ephemeral or semi-permananent hydroperiods, dried out once a year or at least once
every 2-3 years, filled with rainwater, groundwater, or intermittent streams, and were
devoid of fish. The vernal pools were then grouped into different categories based on the
forest cover directly over the pool.
Vernal Pool Class Creation using Light Environments
Vernal pool canopy classes have been compared differently within pond
mesocosm experiments and vernal pool observational studies. Within a canopy cover
mesocosm experiment, shade cloth percentage has been used to simulate canopy cover
46
Figure 4.2. Vernal pool locations within William B. Bankhead National Forest, Alabama. Purple
represents partially open (perimeter) vernal pools, blue are closed (forest) vernal pools, and red
are open vernal pools.
47
that naturally overshadows vernal pools throughout the landscape (Skelly et al., 2002;
Skelly et al., 2005). Two treatments are noted: high and low canopy cover treatments
(Werner and Glennemeier, 1999). These treatments reflect canopy cover in open and
closed vernal pools. In contrast to the two class system of mescosm experiments, canopy
gradients have been identified within actual vernal pools (Halverson et al., 2003; Werner
et al., 2007). Mesocosm studies differ from the actual canopy cover differences found in
heterogenous forested systems. I sought to design wetland classes that incorporated
mesocosm experiment categories as well as observational studies’ canopy gradients. I
created vernal pool classes that reflected the canopy cover gradient found overshadowing
vernal pools as well as the high and low canopy conditions simumlated in pool mesocosm
studies.
Within my study, open canopy vernal pools were designated based on
experimental mesocosms (Werner and Glennemeier, 1999; Skelly et al., 2002; Skelly et
al., 2005; Earl et al., 2011; VanBuskirk et al., 2011). Pool mesocosms with low canopy
had 20-40 percent canopy cover within these experiments. Based on this range, I
designated open pools as having forest cover under 50 percent. Within pond
experiments, closed pools were categorized using shade cloth that ranged from 60 percent
to 81 percent (Skelly et al., 2002; Thurgate and Pechmann, 2007). Within my study, this
range resulted in two different classes: closed and partially open vernal pools. Closed
and partially open pools both had forest cover that was equal to or over 50 percent, but
the difference between these two classes was in the overstory. Closed canopy pools did
not have codominant or dominant tree crown size canopy gaps. Partially open vernal
48
pools had canopy gaps that were at least the size of codominant or dominant tree crown.
Essentially these classes depict a canopy cover gradient, while reflecting shadow cloth
percentages seen in pond mesocosm experiments (Skelly and Golon, 2003; Schiesari,
2006; Binckley and Resetarits, 2007).
The potential vernal pool was delineated using color leaf on and black and white
leaf off aerial photographs (Calhoun et al., 2003; Lathrop, et al., 2005; Oscarson and
Calhoun, 2007). The orthroimagery was National Agricultural Imagery Program Mosaic
from 2005 and 2006 (USDA Geospatial Data Gateway, 2013). These datasets
represented color leaf-on and black and white panchromatic aerial photography from
2005 and 2006 to determine forest cover over the maximum wetland area (Fox and
Hines, 2000). The spatial resolution was one meter ground sample distance (GSD). The
water within the wetlands was black compared to the grey and white of the surrounding
environment. Open wetlands had canopy closure representing less than 50 percent of the
maximum vernal pool basin (Table 4.1). Partially open wetlands had 51 to 99 percent of
the potential vernal pool area covered by the overstory (Table 4.1). Closed wetlands had
forest cover of 100 percent of the maximum wetland area (Table 4.1).
Of the twenty four vernal pools, eight were designated as open canopy, seven as
closed canopy vernal pools, and nine as partially open (Table 1). All the wetlands used
for this study were confirmed as amphibian breeding habitat based on on-site preliminary
sampling and records from previous studies (Chan, 2007; Felix, 2007; Scott, 2008;
Sutton, 2010).
49
Table 4.1. Vernal pool names, vernal pool classes, size, depth, and global positioning system coordinates (based on NAD1983
DATUM) of vernal pools used in this study at James D. Martin Skyline Wildlife Management Area and William B. Bankhead
National Forest, Alabama.
Locality
Pool Code
Wetland Class
Forest Cover
Percentage
SWMA
SWMA
SWMA
SWMA
BNF
BNF
BNF
BNF
BNF
BNF
BNF
BNF
BNF
SWMA
SWMA
SWMA
SWMA
SWMA
SWMA
SWMA
SWMA
SWMA
SWMA
SWMA
Alma3
Alma4
Alpa1
Alpa2
Bapo1
Bapo10
Bapo17
Bapo2
Bapo3
Bapo4
Bapo6
Bapo8
Bapo9
Hitr
Hotr1
Hotr2
Olli2
Poma1
Poma3
Posp1
Posp2
Sign
Taco
Wama
Open
Open
Partially Open
Closed
Partially Open
Closed
Partially Open
Open
Partially Open
Closed
Closed
Open
Partially Open
Partially Open
Partially Open
Closed
Open
Open
Open
Open
Closed
Partially Open
Partially Open
Closed
0.0
0.0
95.1
100.0
92.8
100.0
63.8
0.0
82.1
100.0
100.0
48.6
95.4
82.0
92.2
100.0
0.0
0.0
0.0
46.9
100.0
73.8
79.6
100.0
50
Pool Area
(square
meters)
601.7
325.7
1848.9
682.0
1646.9
979.8
3141.0
362.1
1622.8
1828.2
563.1
661.6
3432.4
3056.8
2756.0
1820.8
1957.2
2154.9
1405.9
2561.9
977.7
667.0
2808.4
912.4
Depth
(meters)
Northing
Easting
1.4
1.7
1.6
1.9
1.3
1.2
1.3
1.3
1.3
1.0
1.1
1.2
1.3
1.3
1.4
1.1
1.6
2.9
2.7
1.4
1.4
1.1
1.2
0.9
578198
578483
578206
578114
464882
460157
473723
468732
468422
469577
468671
462472
460132
583783
583152
583234
581337
578508
579245
579448
578793
584916
583719
580048
3856224
3856543
3856653
3856654
3803153
3803749
3794731
3796698
3800310
3802590
3796393
3803922
3803852
3870786
3869303
3869420
3858777
3861010
3860107
3858324
3861105
3871687
3866771
3858656
Environmental Data
Data on environmental conditions and morphology of vernal pools were collected
biweekly between June 2008 and June 2011. Sampling was executed from December
through June for each breeding season. This time encapsulated the larval development
and metamorphosis of the amphibian species found in these vernal pools. The
environmental variables measured were water and ambient temperatures, dissolved
oxygen percentage, water pH, canopy cover, soil temperature, pool size, maximum depth,
and leaf litter depth. Three different sampling points, at least five meters away from each
other, were selected randomly during each wetland visit. At each point, water
temperature, canopy cover, dissolved oxygen, and water pH were measured at one, two,
and three meters from the edge of the pool. Water temperature percent dissolved oxygen
was measured using a YSI 85 dissolved oxygen meter (YSI Inc, USA). The probe was
lowered within the pool, and the measurements were taken after fifteen minutes. Water
ph was measured using an Oakton ph Testr probe (Oakton Instruments, USA). A
spherical crown densitometer which was held at chest level was used to measure canopy
cover (Forestry Suppliers, USA). Four canopy cover measurements were taken at north,
south, east, and west. Understory was excluded from the canopy cover measurements.
Leaf litter depth and soil temperature were taken at the shoreline. Leaf litter depth was
calculated using a ruler. The depth from the soil to the top of the leaf litter was measured
to the nearest tenth of a centimeter. Soil temperature was measured using a digital pen
shape stem thermometer (H-B Intrument Company, USA). The thermometer’s probe was
inserted into the soil up to the hilt of the thermometer.
51
The area and perimeter of each
vernal pool were measured using Garmin Etrex Legend Vista Hcx Global Positioning
System Unit (Garmin, USA). To calculate the area and perimeter, the researcher walked
the perimeter of the vernal pool. The shape of the pool was reviewed in the GPS unit to
ensure that the area and perimeter had been correctly calculated. Global positioning
system (GPS) coordinates were also collected for each point.
Statistical Analysis
Since several variables were highly correlated, principal components analysis
(PCA) was used to reduce the number of variables. Two-way repeated measures analysis
of variance (ANOVA) was used to test if the principal compononets and variables that
were not represented by the components differed among vernal pool types, year, and if
the differences were consistent among the years. Any component or variables that were
found to have significant differences were then compared using Post-hoc tests (Tukey).
Results
Thirty environmental and pool morphology variables were collected during this
study. Two PCAs were executed: environmental PCA and pool morphology PCA. The
environmental PCA produced seven components that accounted for 84 percent of the
total variation among twenty four environmental variables (Table 4.2). The KaiserMeyer Olkin measure of sampling adequacy was 0.586, Bartlett’s test of of sphericity had
52
Table 4.2. Factor loadings for rotated components using principal components analysis based on (environmental variables of
24 vernal pools in James D. Martin Skyline Wildlife Management Area and William B. Bankhead National Forest in
Alabama). These results were based on varimax rotation with Kaiser Normalization.
Variables
Canopy Cover Percentage Average
Percent Dissolved Oxygen Average
Percent Dissolved Oxygen Maximum
Percent Dissolved Oxygen Range
Percent Dissolved Oxygen Minimum
Canopy Cover Percentage Maximum
Canopy Cover Percentage Minimum
Water Temperature Minimum
Soil Temperature Average
Soil Temperature Range
Water Temperature Range
Water Temperature Average
Soil Temperature Minimum
Water Temperature Maximum
Soil Temperature Maximum
Water pH Maximum
Water pH Range
Leaf Litter Depth Maximum
Leaf Litter Depth Range
Leaf Litter Depth Average
Water pH Minimum
PC1
-0.83
0.82
0.80
0.75
0.74
-0.72
-0.69
0.00
-0.19
0.19
0.184
-0.03
0.21
0.20
0.13
-0.07
0.03
0.04
0.03
-0.46
0.10
PC2
-0.01
0.42
0.42
0.47
-0.23
0.01
-0.14
-0.84
-0.81
0.79
0.78
-0.72
0.54
0.08
0.01
-0.09
0.22
-0.04
-0.03
-0.12
-0.11
53
Component
PC3
PC4
-0.35
-0.03
0.02
-0.02
0.07
0.01
0.08
0.00
-0.03
0.07
-0.07
-0.10
-0.46
0.08
-0.06
-0.14
0.38
-0.16
0.35
0.04
0.48
0.20
0.56
-0.15
0.07
-0.15
0.91
0.06
0.81
0.01
-0.03
-0.94
-0.03
0.92
0.05
-0.15
0.04
-0.17
-0.09
0.28
0.15
0.06
PC5
0.04
0.09
0.03
0.01
0.23
0.04
0.13
0.17
0.14
0.19
-0.16
-0.20
0.18
-0.05
0.26
0.15
-0.19
0.96
0.95
-0.48
0.01
PC6
-0.19
-0.03
-0.05
-0.04
-0.11
-0.33
-0.00
0.26
0.11
0.09
-0.11
0.07
0.32
0.03
0.23
-0.22
-0.09
-0.03
-0.03
-0.13
0.89
PC7
0.18
-0.26
-0.04
-0.04
0.01
-0.13
0.38
-0.10
0.10
-0.21
-0.07
-0.01
-0.13
-0.01
-0.07
0.04
-0.06
0.01
0.13
0.42
-0.05
Table 4.2. (Continued).
Variables
Water pH Average
Leaf Litter Depth Minimum
Canopy Cover Percentage Range
PC1
0.00
-0.07
0.28
PC2
-0.02
-0.06
0.17
Component
PC3
PC4
0.05
0.65
0.00
-0.14
0.46
-0.21
Eigenvalue
Variance Accounted (%)
Cumulative Variance (%)
7.01
29.19
29.19
4.01
16.72
45.92
3.07
12.79
58.72
54
2.05
8.52
67.24
PC5
-0.15
0.07
-0.07
PC6
0.68
-0.06
-0.23
PC7
0.07
0.85
-0.54
1.83
7.61
4.79
1.15
4.79
4.37
1.05
4.37
84.02
an approximate Chi-Square of 2634.5 (p < 0.001). The environmental variables were
comprised of canopy cover percentage average, percent dissolved oxygen average,
percent dissolved oxygen maximum, percent dissolved oxygen range, percent dissolved
oxygen minimum, canopy cover percentage maximum, canopy cover percentage
minimum, water temperature minimum, soil temperature average, soil temperature range,
water temperature range, water temperature average, soil temperature minimum, water
temperature maximum, soil temperature maximum, water pH maximum, water pH range,
leaf litter depth maximum, leaf litter depth range, leaf litter depth average, water pH
minimum, water pH average, leaf litter depth minimum, and canopy cover percentage
range (Table 2). The first component accounted for 29.2 percent of the total variation
PC1- percent dissolved oxygen and canopy cover percentage, Table 4.2), and was
comprised of environmental features including canopy cover percentage average, percent
dissolved oxygen average, percent dissolved oxygen maximum, percent dissolved oxygen
range, percent dissolved oxygen minimum, canopy cover percentage maximum, and
canopy cover percentage minimum (Table 4.2). Canopy cover percentage average,
canopy cover percentage maximum, and canopy cover percentage minimum had negative
factor loadings, while percent dissolved oxygen average, percent dissolved oxygen
maximum, percent dissolved oxygen range, and percent dissolved oxygen minimum all
had positive factor loadings. The second principal component (PC2- water and soil
temperature) represented 16.7 percent (Table 4.2). Water temperature minimum, soil
temperature average, soil temperature range, water temperature range, water temperature
average, and soil temperature minimum made up the second principal component. Water
55
temperature minimum, soil temperature average, and water temperature average had
negative factor loadings (Table 4.2). Soil temperature range, water temperature range,
soil temperature minimum had positive factor loadings (Table 4.2). Principal component
three included water and soil temperature maximum and it showed 12.8 percent of the
variation (Table 4.2). Both variables had positive factor loadings. The fourth principal
component accounted for 8.5 percent of the variation, and was made up of water pH
maximum and range. Water pH maximum had a negative factor loading, and water pH
range had a negative one (Table 4.2). The fifth principal component (PC 5- leaf litter
depth) defined 7.6 percent of the variation, and it was made up of leaf litter depth
maximum, range, and average (Tale 4.2). Leaf litter depth maximum and range had
positive factor loadings, and leaf litter depth average had a negative factor loading (Table
4.2). The sixth principal component made up 4.8 percent of the variation, and water pH
minimum and average were found within the component (Table 4.2). Both variables had
positive factor loadings. The seventh principal component had leaf litter depth minimum
and canopy cover percentage range, and showed 4.4 percent of the variation (Table 4.2).
Leaf litter depth minimum had a positive factor loading, and canopy cover percentage
range had a negative factor loading (Table 4.2).
The pool morphology PCA was composed of six variables, which included:
maximum pool area, maximum pool perimeter, average pool perimeter, average pool
area, maximum pool depth, and hydroperiod. This PCA produced two components and it
represented 85.4 percent of the variation (Table 4.3). The Kaiser-Meyer Olkin measure
of sampling adequacy was 0.747, Bartlett’s test of of sphericity had an approximate Chi-
56
Table 4.3. Factor loadings for rotated components using principal components analysis
based on (pool morphology variables of 24 vernal pools in James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest in Alabama).
These results were based on varimax rotation with Kaiser Normalization.
Component
Variables
Maximum Pool Area
Maximum Pool Perimeter
Average Pool Perimeter
Average Pool Area
Maximum Pool Depth
Hydroperiod
PC1
0.95
0.93
0.92
0.91
0.09
0.28
PC2
0.21
0.07
0.23
0.27
0.88
0.81
Eigenvalue
Variance Accounted (%)
Cumulative Variance (%)
3.97
66.18
66.18
1.16
19.25
85.43
57
Square of 421.5 (p < 0.001). The first principal component showed 66.2 percent of the
variation, and contained maximum pool area, maximum pool perimeter, average pool
perimeter (PC1- Pool Area), and average pool area (Table 4.3). All of the variables had
positive factor loadings. The second principal component analysis represented 19.3
percent of the variation, and it housed maximum pool depth and hydroperiod (Table 4.3).
Both variables had positive factor loadings.
Repeated Measures Factorial Analysis of Variance (RM ANOVA)
The seven principal components produced from the environmental PCA, the two
principal components from the pool morphology PCA, and the forest cover percentage
variable were each compared using a repeated measures factorial analysis of variance
across vernal pool classes, and they were compared among three successive breeding
seasons. Environmental principal component 1 (canopy cover percentage and percent
dissolved oxygen), environmental principal component 3 (soil and water temperature
maximum), forest cover percentage, pool morphology principal component 1 (pool area),
and pool morphology principal component 2 (hydroperiod and maximum pool depth)
were found to be significantly different across vernal pool classes (Table 4.4). Within the
tukey test, the environmental principal component 1 values for open and partially open
vernal pools were higher than closed canopy pools (Figure 4.3). The environmental
principal component 3 values for open and partially open pools were higher than closed
vernal pools (Figure 4.5). When the tukey test was compared for forest cover percentage,
58
Table 4.4. Mean and standard deviations of environmental and pool morphology variables taken within closed, open, and
partially open vernal pools in James D. Martin Skyline Wildlife Management Area and William B. Bankhead National Forest
(2008-2009, 2009-2010, and 2010-2011).
Variable
Environmental
Principal
Component 1
(Canopy Cover
Percentage and
Percent
Dissolved
Oxygen)
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
-0.37±0.54A
-0.46±0.38A
-0.39±0.48A
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
1.16±1.23B
1.01±1.06B
0.51±1.21B
Environmental
Principal
Component 2
(Soil and Water
Temperature)
-0.34±0.57A
-0.56±0.35A
-0.52±0.70A
Repeated Measures ANOVAa
Vernal
Breeding
Wetland
Pool
Season
Class x
Class
Breeding
Season
9.2**
2.7
1.5
2008-2009
-0.27±1.50
-0.49±1.32
-0.756±1.595
0.7
8.2**
0.1
2009-2010
-0.49±1.32
0.50±0.37
0.397±0.259
2010-2011
0.04±0.66
-0.07±0.68
-0.034±0.299
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
59
Table 4.4. (Continued).
Variable
Environmental
Principal
Component 3
(Soil and Water
Temperature
Maximum)
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
-0.17±1.06A
-0.81±0.48A
-1.22±0.67A
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
0.21±1.60B
0.66±0.91B
0.34±0.74B
Environmental
Principal
Component 4
(Water pH
Maximum and
Range)
0.54±0.82B
0.13±0.44B
-0.03±0.61B
Repeated Measures ANOVAa
Vernal
Breeding
Wetland
Pool
Season
Class x
Class
Breeding
Season
7.6**
2.4
1.5
2008-2009
-0.69±0.50
-0.31±0.89
-0.55±0.28
2.7
56.6***
0.8
2009-2010
-0.94±1.00
-0.59±0.32
-0.23±0.71
2010-2011
0.71±0.50
1.29±0.42
1.15±0.77
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
60
Table 4.4. (Continued).
Variable
Environmental
Principal
Component 5
(Leaf Litter
Depth)
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
-0.49±0.85
0.21±0.85
0.26±1.25
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
0.62±0.82
0.41±1.15
-0.75±1.07
Environmental
Principal
Component 6
(Water pH
Average and
Minimum)
-0.37±0.97
0.39±0.83
-0.25±0.68
Repeated Measures ANOVAa
Vernal
Breeding
Wetland
Pool
Season
Class x
Class
Breeding
Season
0.3
2.0
2.2
2008-2009
-0.58±0.79
-0.03±0.77
-0.59±1.01
2.3
12.4***
1.1
2009-2010
-0.03±0.77
0.86±0.91
-0.35±1.23
2010-2011
0.10±0.71
0.77±0.58
-0.00±0.97
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
61
Table 4.4. (Continued).
Variable
Environmental
Principal
Component 7
(Leaf Litter
Depth Minimum
and Canopy
Cover
Percentage
Range)
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
-0.54±0.67
0.72±1.58
0.85±1.12
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
-0.47±0.39
-0.04±0.59
-0.26±0.51
Forest Cover
Percentage
-0.13±1.54
-0.28±0.25
0.29±0.89
Repeated Measures ANOVAa
Vernal
Breeding
Wetland
Pool
Season
Class x
Class
Breeding
Season
2.1
3.4*
1.6
2008-2009
100.0±0.0B
11.9±22.1A
84.1±10.8B
82.8***
0.0
0.0
2009-2010
100.0±0.0B
11.9±22.1A
84.1±10.8B
2010-2011
100.0±0.0B
11.9±22.1A
84.1±10.8B
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
62
Table 4.4. (Continued).
Variable
Pool
Morphology
Principal
Component 1
(Pool Area)
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
-0.29±0.59AB
-0.26±0.62AB
-0.25±0.56AB
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
-0.55±1.11A
-0.50±1.14A
-0.45±1.162A
Pool
Morphology
Principal 2
(Hydroperiod
and Maximum
Pool Depth)
0.65±0.78B
0.59±0.86B
0.70±0.99B
Repeated Measures ANOVAa
Vernal
Breeding
Wetland
Pool
Season
Class x
Class
Breeding
Season
3.9*
0.7
0.2
2008-2009
-1.36±0.84A
0.82±0.45C
0.09±0.58B
27.7***
2.9
0.7
2009-2010
-0.87±0.72A
0.95±0.39C
0.19±0.52B
2010-2011
-1.25±0.96A
0.80±0.29C
0.13±0.59B
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different letters
were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
63
Figure 4.3. Boxplot with error bars showing difference in environmental principal
component 1 (canopy cover percentage and percent dissolved oxygen) observed during
2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest. The middle line
is the median, the box is 50% inter-quartile range, the whiskers are at maximum and
minimum values within 1.5 inter-quartile range, and the points outside the whisker are
outliers.
64
Figure 4.5. Boxplot with error bars showing difference in environmental principal
component 3 (soil and water temperature maximum) observed during 2008-2009, 20092010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest. The middle line is the median, the box
is 50% inter-quartile range, the whiskers are at maximum and minimum values within 1.5
inter-quartile range, and the points outside the whisker are outliers.
65
partially open and closed vernal pool forest cover percentages were higher than open
vernal pool values (Figure 4.10). When the pool morphology principal components were
compared, open pool morphology principal component 1 was higher than partially open
pools (Figure 4.11). Closed pools were not different from open or partially open vernal
pools (Figure 4.11). Finally, open pool had longer hydroperiods and deeper maximum
depths than partiall open and closed pools (Figure 4.12). Partially open pool
hydroperiods were longer and maximum pool depths were deeper than closed pools
(Figure 12). Environmental principal component 2 (soil and water temperature),
environmental principal component 4 (water pH maximum and range), environmental
principal component 6 (water pH average and minimum), environmental principal
component 7 (leaf litter depth minimum and canopy cover percentage range) were
different across breeding seasons (Figures 4.4 and 4.6-4.9) (Table 4.4). There were no
interaction between vernal pool classes and breeding season.
Multiple Linear Regression (MLR)
Multiple linear regression (MLR) was used to examine the influence of forest
cover percentage and breeding season on environmental principal components and pool
morphology principal components. Forward regression was used to determine if the
independent variables were predictors for environmental and pool morphology principal
components. Regression results showed that an overall model consisting of one
66
Figure 4.4. Boxplot with error bars showing difference in environmental principal
component 2 (soil and water temperature) observed during 2008-2009, 2009-2010, and
2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management Area and
William B. Bankhead National Forest. The middle line is the median, the box is 50%
inter-quartile range, the whiskers are at maximum and minimum values within 1.5 interquartile range, and the points outside the whisker are outliers.
67
Figure 4.6. Boxplot with error bars showing difference in environmental principal
component 4 (water pH maximum and range) observed during 2008-2009, 2009-2010,
and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management Area
and William B. Bankhead National Forest. The middle line is the median, the box is 50%
inter-quartile range, the whiskers are at maximum and minimum values within 1.5 interquartile range, and the points outside the whisker are outliers.
68
Figure 4.7. Boxplot with error bars showing difference in environmental principal
component 5 (leaf litter depth) observed during 2008-2009, 2009-2010, and 2010-2011
breeding seasons at James D. Martin Skyline Wildlife Management Area and William B.
Bankhead National Forest. The middle line is the median, the box is 50% inter-quartile
range, the whiskers are at maximum and minimum values within 1.5 inter-quartile range,
and the points outside the whisker are outliers.
69
Figure 4.8. Boxplot with error bars showing difference in environmental principal
component 6 (water pH average and minimum) observed during 2008-2009, 2009-2010,
and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management Area
and William B. Bankhead National Forest. The middle line is the median, the box is 50%
inter-quartile range, the whiskers are at maximum and minimum values within 1.5 interquartile range, and the points outside the whisker are outliers.
70
Figure 4.9. Boxplot with error bars showing difference in environmental principal
component 7 (leaf litter depth minimum and canopy cover percentage range) observed
during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National Forest. The
middle line is the median, the box is 50% inter-quartile range, the whiskers are at
maximum and minimum values within 1.5 inter-quartile range, and the points outside the
whisker are outliers.
71
Figure 4.10. Boxplot with error bars showing difference in forest cover percentage
observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D.
Martin Skyline Wildlife Management Area and William B. Bankhead National Forest.
The middle line is the median, the box is 50% inter-quartile range, the whiskers are at
maximum and minimum values within 1.5 inter-quartile range, and the points outside the
whisker are outliers.
72
Figure 4.11. Boxplot with error bars showing difference in pool morphology principal
component 1 (pool area) observed during 2008-2009, 2009-2010, and 2010-2011
breeding seasons at James D. Martin Skyline Wildlife Management Area and William B.
Bankhead National Forest. The middle line is the median, the box is 50% inter-quartile
range, the whiskers are at maximum and minimum values within 1.5 inter-quartile range,
and the points outside the whisker are outliers.
73
Figure 4.12. Boxplot with error bars showing difference in pool morphology principal
component 2 (hydroperiod and maximum pool depth) observed during 2008-2009, 20092010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest. The middle line is the median, the box
is 50% inter-quartile range, the whiskers are at maximum and minimum values within 1.5
inter-quartile range, and the points outside the whisker are outliers.
74
predictor (forest cover percentage) significantly predicted environmental principal component 1
(canopy cover percentage and percent dissolved oxygen) (Figure 13), R2 = 0.500, R2adj = 0.493,
F(1,70) = 69.9, p < 0.001. The model explained 50% of the variation. The overall model for the
environmental principal component 2 (soil and water temperature) had one significant predictor
variable (breeding season), R2 = 0.141, R2adj = 0.129, F(1,70) = 11.5, p < 0.001. Of the variance
in environmental principal component 3 (soil and water temperature maximum), the model was
able to explain 14.1% of the variation. The environmental principal component 3 (soil and water
temperature maximum) overall model had forest cover percentage as the significant independent
variables (Figure 4.14), R2 = 0.122, R2adj = 0.109, F(1,70) = 9.7, p < 0.001. The model was able
to explain 12.2 percent of the variation. Within environmental principal component 4 (water pH
maximum and range), the overall model had one signficant predictor (breeding season), R2 =
0.579, R2adj = 0.572, F(1,70) = 96.1, p < 0.001. The model described 57.9 percent of the
variation. The overall model for the environmental principal component 6 (water pH average
and minimum) had forest cover percentage as a significant predictor variable (Figure 4.15), R2 =
0.081, R2adj = 0.068, F(1,70) = 6.2, p < 0.001. The model defined 8.1 percent of the variation.
The overall model for the pool morphology principal component 1 (pool area) had a significant
independent variable (forest cover percentage) (Figure 4.16), R2 = 0.081, R2adj = 0.068, F (1,70)
= 6.2, p = 0.015. The model showed 8.1 percent of the variation (Table 4.10). Finally, the the
75
Figure 4.13. Linear regression between forest cover percentage and environmental
principal component 1 (canopy cover percentage and percent dissolved oxygen) observed
during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National Forest.
76
Figure 4.14. Linear regression between forest cover percentage and environmental
principal component 3 (soil and water temperature maximum) observed during 20082009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest.
77
Figure 4.15. Linear regression between forest cover percentage and environmental
principal component 6 (water pH average and minimum) observed during 2008-2009,
2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest.
78
Figure 4.16. Linear regression between forest cover percentage and pool morphology
principal component 1(pool area) observed during 2008-2009, 2009-2010, and 20102011 breeding seasons at James D. Martin Skyline Wildlife Management Area and
William B. Bankhead National Forest.
79
Figure 4.17. Linear regression between forest cover percentage and pool morphology
principal component 2 (hydroperiod and maximum pool depth) observed during 20082009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest.
80
overall model for the pool morphology principal component 2 had one significant
predictor (forest cover percentage) (Figure 4.17), R2 = 0.416, R2adj = 0.407, F(1,70) =
49.8, p < 0.001. The model was able to explain 41.6 percent of the variation.
Discussion
Canopy cover, dissolved oxygen, and water and soil temperature maximums were
different among vernal pool classes. Higher water and soil temperatures were associated
with open and partially open wetlands while closed wetlands had significantly lower
temperatures. Lower forest cover percentage allowed more solar radiation to warm the
wetland, which resulted in higher water temperature (Lauck et al., 2005). This is
important for amphibians as warmer water temperatures affect larval amphibian
development while soil temperatures can affect amphibian metamorphs’ ability to
emigrate from their natal ponds. Temperature has been shown to influence larval
amphibian growth with warmer temperatures facilitating shorter larval periods (Schiesari,
2006; Williams et al., 2008; Hawley, 2010).
The pool morphology components showed differences across vernal pool classes.
Pool morphology features such as pool area have been found to influence amphibian
community composition, and there is a positive relationship between larval amphibian
population abundance and available habitat within the inundated wetland (Burne and
Griffin, 2005). While area is important, there is the possibility of the largest pools
housing the highest amphibian predator diversity which decreases amphibian species
81
richness (Burne and Griffin, 2005). Burne and Griffin (2005) documented that breeding
pools exhibited a curvilinear relationship between area and species richnesswith the
largest pools housing the fewest species.
Open vernal pools had lower canopy cover and higher dissolved oxygen than that
of closed and partially open pools. Light environments of nonpermanent wetlands are
susceptible to the effects of vegetation, due to their small size, tree crowns and other
vegetation can overtop ephemeral wetlands (Skelly et al., 1999). Varying vegetation
structure over the pond basin creates a light gradient which is strongly associated with the
distribution and performance of amphibian species (Halverson et al., 2003). Lower
canopy cover allowed more photosynthetic light to penetrate the wetland resulting in a
higher algal photosynthetic rate and an increase in dissolved oxygen (Werner and
Glennemeier, 1999). This relationship was characterized by the canopy cover and
dissolved oxygen component (principal component 1). Canopy cover and dissolved
oxygen were different among the wetland categories. Dissolved oxygen was higher in
the open canopy wetlands while closed canopy was the lowest. Closed and partially open
wetlands did not show any differences. The negative relationship between canopy cover
and dissolved oxygen has been documented in shading experiments that simulated
canopy cover (Rittenhouse, 2011, Earl et al., 2011). Rittenhouse (2011) was able to show
an inverse relationship between canopy cover and dissolved oxygen. Higher canopy
cover results in lower percent dissolved oxygen while lower canopy cover has been
shown to result in higher dissolved oxygen due to more light penetration to the inundated
wetland (Lauck et al., 2005; Felix, 2007). Photosynthetic light results in higher algal
82
photosynthetic rates which results in more dissolved oxygen being produced in the
wetland (Rittenhouse, 2011). In addition to dissolved oxygen, soil temperature maximum
also showed an inverse relationship to canopy cover. This relationship also seems to be
related to light penetration, and a warming of the wetland. Lower canopy cover allows
for more sunlight which results in a higher soil temperature maximum (Williams et al.,
2008; Schiesari, 2006). Canopy cover not only influences biophysical conditions in a
vernal pool, but also pool breeding amphibians. Increased tree canopy cover has been
shown to be negatively correlated with amphibian richness (Burne and Griffin, 2005).
Canopy cover has been seen as a filter to amphibian occurrence in breeding pools (Burne
and Griffin, 2005; Williams et al., 2008). Even though species richness may be
negatively influenced by higher canopy cover, certain species have higher productivity in
closed canopy ponds such as ambystomatid salamanders (Egan and Paton, 2004; Earl et
al., 2011). Anurans, on the other hand, have been shown to have higher biomass in open
wetlands (Lauck et al., 2005; Van Buskirk, 2011), which may lead to a change biomass
which would move from mainly anurans in open canopy to a higher abundance of
salamanders in closed canopy vernal pools (Earl et al., 2011; Van Buskirk, 2011). While
lower canopy cover is important to anuran productivity, so is higher dissolved oxygen
which influences development rate and size (Liner et al., 2008).
Finally, Leaf litter principal component was not different among vernal pool
classes. Williams et al. (2008) found that pond litter affected tadpole growth and
developmental rates. In addition to litter type, leaf litter species were also found to affect
the growth of amphibians throughout larval development (Stoler and Reylea, 2011).
83
Rubbo and Kiesecker (2004) noted that the mixture of certain leaf litter species, such as
maple and oak leaf litter input negatively influenceed larval amphibian performance.
Forest cover influences the litter that is deposited within wetlands, which affect abiotic
conditions along with amphibian performance (Williams et al., 2008).
Water pH was also different among vernal pool classes. Open pools had higher
water pH than closed pools, but were not different from partially open vernal pools.
Acids enter vernal pools through soil and leaf litter input (Corburn, 2004). Since
increased leaf litter inputs decrease water pH, pools overshadowed with more canopy
cover have lower water ph than vernal pools with less canopy cover. Amphibian
breeding site choice can be influenced by water pH, with freshwater habitat having lower
pH housing fewer amphibians (Sayim et al., 2009). In addition to amphibian diversity,
reproductive fitness may also be affected. Rasanen et al. (2008) found that ranid clutch
size decreased in breeding habitat with lower pH, and acidity had a negative effect on
fecundity. The difference in water pH between vernal pool classes can influence the
species that breed within the vernal pools. Higher water pH, associated with open vernal
pools, could result in higher amphibian diversity than closed pools.
Hydroperiod was found to be different among open and closed vernal pools.
Partially open vernal pools were not different from open or closed pools. Hydroperiods
were different across vernal pool classes due to evapotranspiration. Closed and partially
open pools had more trees located in the pool basin than open pools, so hydrperiods are
shorter. Different hydroperiods influence amphibian productivity, because longer
hydroperiods allow for more species to use the habitat (Burne and Griffin, 2005). The
84
pool’s rate of drying has an effect on amphibian emergent size (DiMauro and Hunter,
2002). Pools with shorter hydroperiods force amphibians to accelerate development
which results in small metamorph size (Dimauro and Hunter, 2002. This difference in
hydroperiod among wetlands in different localities may affect the larval developmental
rate and size. The same species may exhibit different performances despite similarities in
the vernal pool.
In this study, environmental and pool morphology variables differed in response
to wetland classes. These categories exhibited varying values for features ranging from
dissolved oxygen to pool area. The variables in these classes may influence amphibian
development rate, size at metamorphosis, and the overall metamorphic amphibian
biomass that exits the natal ponds.
The forest cover differences among wetland classes seemed to influence the
environmental and pool morphology variables. Forest cover influences, not only the
amount of solar radiation that strikes a wetland, but the leaf litter that is deposited and
broken down within the wetland. Biophysical conditions, such as water pH, temperature,
and percent dissolved oxygen changed in response to the amount of forest cover and were
found to influence amphibian species diversity. Diversity changed in response to adult
site choice as well as larval amphibian diversity and performance. Taking into account
the water chemistry differences among various wetlands and understanding their
connection to pool breeding amphibian communities can help researchers and managers
gain a firm understanding of how changes in the amount of forest canopy affects
reproductive fitness.
85
CHAPTER 5.
RELATIONSHIP OF POOL BREEDING AMPHIBIAN DIVERSITY
WITH LOCAL AND LANDSCAPE FOREST COVER AROUND VERNAL
POOLS IN NORTHERN ALABAMA
Introduction
Amphibian species and populations are undergoing a global decline which was
first noted in the 1980s (Stuart et al., 2004; Alfords and Richards, 1999). Several
hypotheses have been identified, but habitat destruction has been noted as one of the
primary causes for the decline (Beebee and Griffiths, 2005) with habitat alteration and
fragmentation posing additional threats to amphibians (Collins and Storfer, 2003;
Hocking and Semlitsch, 2007). Habitat destruction can impose a barrier to migration,
emigration, and immigration of pool breeding amphibians. Fragmentation can restrict
adult amphibians from traversing the landscape to reproduce, and it can also affect
animals during foraging and searching for aestivation and brumation sites during periods
of high and low temperatures, respectively (Collins and Storfer, 2003; Storfer, 2003;
Beebee and Griffiths, 2005). Forest cover is one of the most important landscape scale
predictors for amphibian presence and abundance (Eigenbrod et al., 2008), so a
86
fragmenting of contiguous forest stands can have a negative effect on amphibian
populations. Since amphibians require moisture and cover for survival, large openings in
the forest overstory or forest fragmentation can result in an increase in air temperature
and a reduction in relative humidity. These conditions can lead to a loss of body weight
and potential desiccation. At a minimum, movement is restricted due to an increased
riskof desiccation across large open areas. In addition to foraging, reproductive
processes such as mass migrations and dispersal can be affected. Since local amphibian
populations require dispersal to persist, a breakdown in forest cover continuity can
negatively affect amphibian populations within a given area. Roads and forest edges can
also serve as barriers. These movement obstacles create smaller patches and increase
patch isolation which can break up local populations (Carr and Fahrig, 2002) and can
cause declines and local extinctions of obligate amphibian species (Harper et al., 2008).
Habitat alteration, such as conversion of forests into agricultural fields or open space, is
one of the most notable ways of introducing barriers within pool breeding amphibian
terrestrial habitats (James and Semlitsch, 2011). Defining a species habitat criterion is
critical due to amphibian declines and aquatic and terrestrial habitat alteration (Johnson
and Semltisch, 2003).
The surrounding landscape outside of the natal ponds influences juveniles’
capabilities to travel to the terrestrial habitat as well as their likelihood of returning to
natal ponds to reproduce when they reach reproductive maturity. Anthropogenic land use
of areas surrounding wetlands may affect larval and postmetamorphic amphibians by
influencing many ecological mechanisms that regulate the growth and mortality rates of
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individuals in aquatic and terrestrial environments (Gray and Smith, 2005). Most
amphibians undergo a biphasic life cycle that requires aquatic and terrestrial habitat.
Natural selection has maintained both an aquatic and a terrestrial stage to exploit the
benefits of these environments (Semlitsch, 2008). Many species require suitable
terrestrial habitats adjacent to the wetland for foraging, brumation, aestivation, and
shelter (Shulse et al., 2009). Terrestrial habitats are important for juvenile and adult life
stages so that they can grow and mature while aquatic habitats are needed for larval
growth and development (Harper and Semlitsch, 2007; Hocking and Semlitsch, 2007).
Movement between aquatic and terrestrial habitats or between breeding ponds is
important for population persistence (Birchfield and Deters, 2005; Homan et al., 2010).
Movements to and from breeding sites are essential for reproduction and survival of local
populations (Semlitsch, 2008). Any breakdown in the ability to travel between these
habitats or land use types can result in population decline and a decrease in gene flow
through the blockage of dispersal by way of emigration or immigration. Since
amphibians utilized aquatic and terrestrial habitat, they can be affected by changes that
occur within either environment (Johnson et al., 2008). In addition to dispersal,
reproductively mature adults may not be able to access breeding sites within their own
local population if there is any blockage to migration as a result of habitat alteration by
way of land use change. Being able to travel between breeding and foraging habitats is
critical, but the quality of each site is equally important for the persistence of local
populations. Therefore, both aquatic and terrestrial habitat quality determines
88
reproductive success and persistence of amphibian populations (James and Semlitsch,
2011).
Objectives
The objectives for this study were to (1) quantify the difference in amphibian
diversity within three vernal pool classes during successive years, (2) calculate amphibian
diversity and abundance across different vernal pool classes, and (3) assess the
relationship between amphibian diversity to local and landscape features.
Hypotheses
I hypothesize that (1) more amphibian species will be found in open vernal pools, (2)
anuran abundance will be greater in open vernal pools compared to partially open and
closed pools, (3) caudate abundance will be higher in closed wetlands when measured
against partially open and open wetlands, while (4) amphibian diversity will show a
negative relationship to features that are associated with migration barriers such as
disturbed land-use, presence of roads, and presence of edges.
Methods
Study Sites
The William B. Bankhead National Forest (BNF) is located in a strongly
dissected plateau subregion within the Southern Cumberland Plateau (Smalley, 1979).
The Southern Cumberland Plateau occupies 23,051 square kilometers. This strongly
89
dissected plateau is so dissected that it no longer resembles a plateau, and its topography
is complex with strongly sloping landscape within this section of the Cumberland Plateau
(Smalley, 1979).
The James D. Martin Skyline Wildlife Management Area (SWMA) is located
within the strongly dissected southern portion of the Mid Cumberland Plateau. This midplateau region covers about 11,525 square kilometers and has a temperate climate with
long, moderately hot summers and short, mild winters (Smalley, 1982).
The two localities used for this study were James D. Martin Skyline Wildlife
Management Area (SWMA) and William B. Bankhead National Forest (BNF). The
SWMA, is located in Jackson County, Alabama and covers an area of 114 km2 (64.1 mi2)
(Lein, 2005) (Figure 1). Vegetation in the SWMA has been classified as mixed
mesophytic communities comprised of oak-hickory forests (Braun, 1950). Of the
timberland in Jackson County, 78.8% is a mix of oak and hickory species (Hartsell and
Brown, 2002). In June, 2012, ten dominant and co-dominant tree species whose crowns
surrounded or occurred within fifteen vernal pools and within the SWMA were identified
and selected randomly. The most dominant and codominant tree species in this area
include sweetgum (Liguidambar styraciflua), red maple (Acer rubrum), chinkapin oak
(Quercus muehlenbergii), white oak (Quercus alba), chestnut oak (Quercus prinus), post
oak (Quercus stellata), scarlet oak (Quercus coccinea), northern red oak (Quercus
rubra), blackgum (Nyssa sylvatica), sourwood (Oxydendrum arboreum), and tulip poplar
(Liriodendron tulipifera).
90
The BNF is 737 km2 (284 mi2) and located in Lawrence, Winston, and Cullman
counties in north central Alabama (Gaines and Creed, 2003) (Figure 2). Hartsell and
Brown (2002) classified the timberland in Lawrence County at 42 percent oak and
hickory species, Winston County at 47 percent, and Cullman County at 61 percent
(Hartsell and Brown, 2002). In June 2012, the same sampling protocols as described for
the SWMA were used. The dominant tree species within and surrounding nine vernal
pools within the BNF area are sweetgum (Liquidambar styraciflua), loblolly pine (Pinus
taeda), white oak (Quercus alba), water oak (Q. nigra), other red oak species, tulip
poplar (Liriodendron tulipifera), blackgum (Nyssa sylvatica), chestnut oak (Q. prinus),
virginia pine (P. virginiana), winged elm (Ulmus alata), red maple (Acer rubrum),
sourwood (Oxydendrum arboreum), and water oak (Quercus nigra).
The study sites were located using forest cover delineated from aerial
photography of potential wetland basins. Fifteen vernal pools were selected in the
SWMA, while nine vernal pools were selected in the BNF. To be selected, vernal pools
needed to be ephemeral or have semi-permananent hydroperiods, dried out once a year or
at least once every 2-3 years, filled with rainwater, groundwater, or intermittent streams,
and were devoid of fish. The vernal pools were then grouped into different categories
based on the forest cover directly over the pool.
Vernal Pool Class Creation using Light Environments
Vernal pool canopy classes were designed based on the Canopy has been
compared differently within pond mesocosm experiments and vernal pool observational
91
studies. Within a canopy cover mesocosm experiment, shade cloth percentage has been
used to simulate canopy cover that naturally overshadows vernal pools throughout the
landscape (Skelly et al., 2002). Two treatments are noted: high and low canopy cover
treatments (Werner and Glennemeier, 1999). These treatments reflect canopy cover in
open and closed vernal pools. In contrast to the two class system of mescosm
experiments, canopy gradients have been identified within actual vernal pools (Halverson
et al., 2003; Werner et al., 2007). Mesocosm studies differ from the actual canopy cover
differences found in heterogenous forested systems. I sought to design wetland classes
that incorporated mesocosm experiment categories as well as observational studies’
canopy gradients. I created vernal pool classes that reflected the canopy cover gradient
found overshadowing vernal pools as well as the high and low canopy conditions
simumlated in pool mesocosm studies.
Within my study, open canopy vernal pools were designated based on
experimental mesocosms (Werner and Glennemeier, 1999; Skelly et al., 2002; Skelly et
al., 2005; Earl et al., 2011; VanBuskirk et al., 2011). Pool mesocosms with low canopy
had 20-40 percent canopy cover within these experiments. Based on this range, I
designated open pools as having forest cover to be under 50 percent. Within pond
experiments, closed pools were categorized using shade cloth that ranged from 60% to 81
percent (Skelly et al., 2002; Thurgate and Pechmann, 2007). Within my study, this range
resulted in two different classes: closed and partially open vernal pools. Closed and
partially open pools both had forest cover that was equal to or over 50 percent, but the
difference between these two classes was in the overstory. Closed canopy pools did not
92
have codominant or dominant tree crown size canopy gaps. Partially open vernal pools
had canopy gaps that were at least the size of codominant or dominant tree crown.
Essentially these classes reflect a canopy cover gradient, but reflect shadow cloth
percentages seen in pond mesocosm experiments (Skelly and Golon, 2003; Schiesari,
2006; Binckley and Resetarits, 2007).
The potential vernal pool was delineated using color leaf on and black and white
leaf off aerial photographs (Calhoun et al., 2003; Lathrop, et al., 2005; Oscarson and
Calhoun, 2007). The orthroimagery was National Agricultural Imagery Program Mosaic
from 2005 and 2006 (USDA Geospatial Data Gateway, 2013). These datasets
represented color leaf-on and black and white panchromatic aerial photography from
2005 and 2006 to determine forest cover over the maximum wetland area (Fox and
Hines, 2000). The spatial resolution was one meter ground sample distance (GSD). The
water within the wetlands was black compared to the grey and white of the surrounding
environment. Open wetlands had canopy closure representing less than 50 percent of the
maximum vernal pool basin (Table 1). Partially open wetlands had 51 to 99 percent of the
potential vernal pool area covered by the overstory (Table 1). Closed wetlands had forest
cover of 100 percent of the maximum wetland area (Table 1).
Of the twenty four vernal pools assessed for this study, eight were designated as
open canopy, seven vernal pools were classed as closed canopy wetlands, and nine
isolated wetlands were identified as partially open (Table 1). All the wetlands used for
this study were confirmed as amphibian breeding habitat based on on-site preliminary
93
sampling and records from previous studies (Chan, 2007; Felix, 2007; Scott, 2008;
Sutton, 2010).
Environmental Data
Data on environmental conditions and morphology of vernal pools were collected
biweekly between June 2008 and June 2011. Sampling was executed from December
through June for each breeding season. This time encapsulated the larval development
and metamorphosis of the amphibian species found in these vernal pools. The
environmental variables measured were water and ambient temperatures, dissolved
oxygen percentage, water pH, canopy cover, soil temperature, pool size, maximum depth,
and leaf litter depth. Pools were visited biweekly because it represented the shortest
amount of time between egg deposition and metamorphosis. Three different sampling
points, at least five meters away from each other, were selected randomly during each
wetland visit. At each point, water temperature, canopy cover, dissolved oxygen, and
water pH were measured at one, two, and three meters from the edge of the pool. Water
temperature percent dissolved oxygen was measured using a YSI 85 dissolved oxygen
meter (YSI Inc, USA). The probe was lowered within the pool, and the measurements
were taken after fifteen minutes. Water ph was measured using an Oakton ph Testr
probe (Oakton Instruments, USA). A spherical crown densitometer which was held at
chest level was used to measure canopy cover (Forestry Suppliers, USA). Four canopy
cover measurements were taken at north, south, east, and west. Understory was excluded
from the canopy cover measurements. Leaf litter depth and soil temperature were taken
94
at the shoreline. Leaf litter depth was calculated using a ruler. The depth from the soil to
the top of the leaf litter was measured to the nearest tenth of a centimeter. Soil
temperature was measured using a digital pen shape stem thermometer (H-B Intrument
Company, USA). The thermometer’s probe was inserted into the soil up to the hilt of the
thermometer.
The area and perimeter of each vernal pool were measured using Garmin
Etrex Legend Vista Hcx Global Positioning System Unit (Garmin, USA). To calculate
the area and perimeter, the researcher walked the perimeter of the vernal pool. The shape
of the pool was reviewed in the GPS unit to ensure that the area and perimeter had been
correctly calculated. Global positioning system (GPS) coordinates were also collected
for each point.
Animals were surveyed using minnow trapping (Dodd, 2009), and occurred at
each vernal pool biweekly as long as the vernal pools were inundated. Three minnow
traps were anchored and set at each wetland the day before sampling. The minnow traps
were anchored using twine, and they were anchored at three distances from the shoreline:
one, two, and three meters. Each minnow trap was set a minimum of five meters from
another to reduce disturbance to amphibians located in that area of the vernal pool. The
following day, each amphibian was identified by species and life stage. Three
measurements were taken of larval amphibians: snout to vent length, tail length, and
weight. Adult amphibians received one toe clip (Heyer, 1994) on the rear right index toe
to signify that the animal had been sampled before, and larval amphibian received a
tailfin clip (Heyer, 1994) on the upper dorsal fin near the tip. The following equation was
used to determine animals that would be measured and weighed: samples = log10[total
95
species captures for this minnow trap] * 10. This equation was used for each individual
species within each minnow trap. Within each minnow trap, the number of individuals
captured per species was recorded. This number was then placed in the equation and the
number of animals that were measured and processed was calculated. This was executed
for each species caught within each minnow trap. If there were fewer than 10 individuals
per species, then every individual was measured. Size metrics were compared for each
species across various life stages. The three main life stages used in this study were
larvae, juvenile, and reproductive mature adult. Life stage codes were used in a truncated
format based on the Gosner stages for anuran tadpoles and estimation for the caudate
larvae (Gosner, 1960).
Based on herpetofaunal guides (Mount, 1975; Trauth et al., 2004; Lannoo, 2005;
Jensen et al., 2008) and previous research (Felix, 2007; Chan, 2008; Scott, 2009; Sutton,
2010), I expected to capture 21 possible pool breeding amphibian species: 7 caudates and
14 anurans. The caudates included: Spotted Salamander (Ambystoma maculatum),
Marbled Salamander (A. opacum), Mole Salamander (A. talpoideum), Smallmouth
Salamander (A. texanum), Eastern Tiger Salamander (A. tigrinum), Red Spotted Newt
(Notophthalmus v. viridescens), and Four Toed Salamander (Hemidactylium scutatum).
The anurans included the following: Cope’s Gray Treefrog (Hyla chrysoscelis), Barking
Treefrog (H. gratiosa), Spring Peeper (Pseudacris c. crucifer), Mountain Chorus Frog (P.
brachyphona), Upland Chorus Frog (P. feriarum), Northern Cricket Frog (Acris c.
crepitans), American Toad (Anaxyrus americanus), Fowler’s Toad (A. fowleri), Eastern
Spadefoot Toad (Scaphiopus holbrookii), Bullfrog (Lithobates catesbeianus), Green Frog
96
(L. clamitans melanota), Pickerel Frog (L. palustris), Southern Leopard Frog (L.
sphenocephalus urticularius), and Eastern Narrowmouth Toad (Gastrohpyrne
carolinensis).
Landscape factors were separated based on four amphibian taxonomic groups
including: salamanders, newts, frogs, and toads. Maximum migration was used to
identify amphibian buffer distance. The maximum migration was calculated using the
publications that listed maximum species migration distances (Semlitsch and Bodie,
2003). The publications were then gathered based on taxonomic groups and the
publications with the largest maximums were used to represent the groups. Sample sizes
were also collected for both of these techniques but were not used in any calculations.
The buffer distances were calculated for each classifications and each taxonomic
group within each classification. Maximum migration was comprised of the following
buffer distances: 427 meters for salamanders (Kleeberger and Werner, 1983), 800 meters
for newts (Healy, 1975), 1,601 meters for frogs (Ingram and Raney, 1943), and 1015
meters for toads (Oldham, 1966). To determine the maximum migration buffer distances,
twenty eight publications were used which were comprised of 11 species and a sample
size of 1,252 animals.
Aerial photography was acquired using the USDA Geospatial Data Gateway
(United States Department of Agriculture, 2012). Using 2009 and 2011 national
agricultural imagery program (NAIP) aerial photography to calculate the following
landscape vairables at the amphibian migration buffer distances: distance to main roads,
distance to back roads, main road density, back road density, density to edge, pool
97
density, and distance to forest. Landsat thematic mapper (TM) was used to calculate
normalized difference vegetation index ( NDVI = Reflected radiant fluxnear infrared –
Reflected radiant fluxred/ Reflected radiant fluxnear infrared + Reflected radiatn fluxred)
(ERDAS ® 2013) for the amphibian migration buffer distances (Jensen, 20005). Landsat
TM scenes were downloaded from the USGS Global Visualization Viewer
(http://www.glovis.usgs.gov/, 2012). I used scenes that had less than 30% cloud cover
and these scenes were acquired between June and August for 2009, 2010, and 2011.
Statistical Analyses
Amphibian diversity indices were calculated using Estimate S (Colwell, 2013).
The diversity indices that compared were: larval amphibian abundance, alpha diversity,
species richness as calculated using the bootstrap method, species observed, ShannonWiener diversity Index, and Simpson’s inverse diversity index. Abundance for all of the
larval amphibians and individual species abundances were compared as well. Principles
Components Analysis (PCA) (IBM SPSS ® 19.0) was used to reduce the number of
variables at the landscape scale. Repeated measures analysis of variance (ANOVA)
(IBM SPSS ® 19.0) was used to compare amphibian diversity indices and species
abundances between different wetland class among various breeding seasons. Canonical
correspondence analysis (CCA) (Canoco ® 4.5) was applied to assess the relationship
between amphibian species and local (environmental and pool morphology) and
landscape principal component variables. Multiple linear regression was used to
determine the relationship between ecological indices and local (environmental and pool
98
morphology- Tables 4.2 and 4.3) and landscape variables surrounding the vernal pools.
Transformations were used to achieve a normal distribution for all of the ecological
indices. Evaluation of linearity led to log transformations for the larval amphibian
abundance, species observed, alpha diversity, larval amphibian species richness using the
bootstrap method.
Results
During this study, a total of 39, 182 individuals were captured including: 2,382
adults, 35,963 larvae, and 837 metamorphs. Of the 35.963 larvae sampled there were 173
recaptures. In addition to the animal captures, 6,954 egg masses and 467,245 embryos
were counted through the visual encounter surveys. Eighteen species were identified,
resulting in 5 caudate species and 13 anuran species. The following caudate species were
captured: Spotted Salamander, Marbled Salamander, Mole Salamander, Red Spotted
Newt, and Four Toed Salamander. The anurans captured included: Cope’s Gray
Treefrog, Sping Peeper, Mountain Chorus Frog, Upland Chorus Frog, Eastern Cricket
Frog, American Toad, Fowler’s Toad, Eastern Spadefoot Toad, Bullfrog, Green Frog,
Pickerel Frog, Southern Leopard Frog, and Eastern Narrowmouth Toad. To calculate
diversity indices for the amphibian community, the larval amphibian life stage was used.
Principal Components Analaysis (PCA)
99
Two principal compononets analyses (PCA) were executed for the landscape
scale variables: aerial photography and normalized difference vegetation index. The
aerial photography PCA used fifteen variables, and it produced four principal
components that accounted for 86.7 percent of the total variation (Table 5.1). The Kaiser
Meyer Olkin measure of sampling adequacy was 0.600, Bartlett’s test of sphericity had
an approximate Chi-Square of 160.1.2 (p-value < 0.001). Pool density at 427 meters,
pool density at 800 meters, pool density at 1015 meters, main road density at 427 meters,
pool density at 1601 meters, back road density at 800 meters, distance to back roads, back
road density at 1015 meters, back road density at 1601 meters, distance from forest, main
road density at 1601 meters, manin road density at 1015 meters, distance from main
roads, main road density at 800 meters, and distance from edge comprised the aerial
photography PCA (Table 5.1). The first component accounted for 40.1 percent of the
total variation (PC1- pool density and main road density at 427 meters), and it was
comprised of pool density at 427 meters, pool density at 800 meters, pool density at 1015
meters, main road density at 427 meters, and pool density at 1601 meters (Table 5.1). All
of these variables had positive factor loadings. The second principal component (PC2back road density and distance from forest) represented 30.1 percent of the total
variation, and it included the following variables: back road density at 800 meters,
distance from back roads, back road density at 1015 meters, back road density at 1601
meters, and the distance from forest (Table 5.1). All of the variables except for distance
from forest had positive factor loadings (Table 5.1). Distance from forest had a negative
factor loading. The third principal component (PC3- main road density and distance from
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Table 5.1. Factor loadings for rotated components using principal components analysis based on (aerial photography landscape
variables of 24 vernal pools in James D. Martin Skyline Wildlife Management Area and William B. Bankhead National Forest in
Alabama). These results were based on varimax rotation with Kaiser Normalization.
Component
Variables
Pool Density at 427 meters
Pool Density at 800 meters
Pool Density at 1015 meters
Main Road Density at 427 meters
Pool Density at 1601 meters
Back Road Density at 800 meters
Distance to Back Roads
Back Road Density at 1015 meters
Back Road Density at 1601 meters
Distance to Forest
Main Road Density at 1601 meters
Main Road Density at 1015 meters
Distance to Main Roads
Main Road Density at 800 meters
Distance to Edge
PC1
0.89
0.89
0.89
0.69
0.68
0.21
0.21
0.10
0.17
-0.49
0.16
0.33
-0.12
0.53
-0.16
PC2
0.30
0.27
0.25
-0.24
0.35
0.92
0.87
0.87
0.86
0.52
-0.24
-0.17
0.00
-0.19
-0.11
PC3
0.17
0.25
0.28
0.55
0.39
-0.07
-0.18
-0.22
-0.12
0.29
0.89
0.89
-0.86
0.79
0.11
PC4
0.00
0.06
0.08
0.16
0.25
0.06
-0.09
0.06
0.18
0.35
-0.13
0.09
0.06
-0.02
-0.94
Eigenvalue
Variance Accounted (%)
Cumulative Variance (%)
6.01
40.08
40.08
4.52
30.11
70.19
1.44
9.58
79.77
1.05
6.97
86.75
101
main roads) signified 9.6 percent of the total variation (Table 5.1). This component was
made up of main road density at 1601 meters, main road density at 1015 meters, distance
from main roads, main road density at 800 meters (Table 5.1). All of the variables except
for distance from main roads had positive factor loadings (Table 5.1). Distance from
main roads had a negative factor loading (Table 5.1). The fourth principal component
(PC4- distance from edge) characterized 6.9 percent of the total variation (Table 5.1).
Distance from edge was the only variable within the component, and it had a negative
factor loading (Table 5.1).
Using twelve variables, the normalized difference vegetation index PCA produced
three components that represented 81.4 percent of the total variation (Table 5.2). The
Kaiser Meyer Olkin measure of sampling adequacy was 0.696, Bartlett’s test of
sphericity had an approximate Chi-Square of 1081.3 (p-value < 0.001). Normalized
difference vegetation index maximum at 800 meters, normalized difference vegetation
index maxium at 1601 meters, normalized difference vegetation index mean at 800
meters, normalized difference vegetation index maximum at 427 meters, normalized
difference vegetation index mean at 1015 meters, normalized difference vegetation index
mean at 1601 meters, normalized difference vegetation index maximum at 1015 meters,
normalized difference vegetation index range at 800 meters, normalized difference
vegetation index range at 1015 meters, normalized difference vegetation index range at
427 meters, normalized difference vegetation index range at 1601 meters, and normalized
difference vegetation index mean at 427 meters comprised the normalized difference
vegetation index PCA (Table 5.2). The principal component accounted for 50.9 percent
102
Table 5.2. Factor loadings for rotated components using principal components analysis based on (normalized difference
vegetation index landscape variables of 24 vernal pools in James D. Martin Skyline Wildlife Management Area and William
B. Bankhead National Forest in Alabama). These results were based on varimax rotation with Kaiser Normalization.
Variables
Normalized Difference Vegetation Index Maximum at 800 meters
Normalized Difference Vegetation Index Maximum at 1601 meters
Normalized Difference Vegetation Index Mean at 800 meters
Normalized Difference Vegetation Index Maximum at 427 meters
Normalized Difference Vegetation Index Mean at 1015 meters
Normalized Difference Vegetation Index Mean at 1601 meters
Normalized Difference Vegetation Index Maximum at 1015 meters
Normalized Difference Vegetation Index Range at 800 meters
Normalized Difference Vegetation Index Range at 1015 meters
Normalized Difference Vegetation Index Range at 427 meters
Normalized Difference Vegetation Index Range at 1601 meters
Normalized Difference Vegetation Index Mean at 427 meters
PC1
0.86
0.86
0.80
0.79
0.79
0.78
0.78
-0.09
-0.10
-0.05
-0.19
-0.01
Component
PC2
-0.05
0.03
-0.52
-0.17
-0.54
-0.52
0.08
0.94
0.94
0.87
0.81
0.07
Eigenvalue
Variance Accounted (%)
Cumulative Variance (%)
6.11
50.95
50.95
2.63
21.88
72.83
103
PC3
-0.03
-0.23
-0.01
0.17
0.01
-0.08
0.06
-0.01
-0.01
0.10
0.02
0.97
1.03
8.59
81.43
of the total variation (PC1- normalized difference vegetation index maximum and mean), and it
was made up of normalized difference vegetation index maximum at 800 meters, normalized
difference vegetation index maxium at 1601 meters, normalized difference vegetation index
mean at 800 meters, normalized difference vegetation index maximum at 427 meters, normalized
difference vegetation index mean at 1015 meters, and normalized difference vegetation index
mean at 1601 meters (Table 5.2). These variables all had positive factor loadings (Table 5.2).
The second principal component (PC2- normalized difference vegetation index range)
represented 21.9 percent of the total variation, and it included normalized difference vegetation
index range at 800 meters, normalized difference vegetation index range at 1015 meters,
normalized difference vegetation index range at 427 meters, and normalized difference
vegetation index range at 1601 meters (Table 5.2). All of the variables had positive factor
loadings (Table 5.2). The third principal component (PC3- normalized difference vegetation
index mean at 427 meters) signified 8.6 percent of the total variation, and it contained
normalized difference vegetation index mean at 427 meters (Table 5.2). This variable had a
positive factor loading (Table 5.2).
Factorial Analysis of Variance (ANOVA)
A factorial analysis of variance was compared using aerial photography principal
components across vernal pool classes. None of the aerial photography principal components
were significantly different across vernal pool classes (Table 5.3).
104
Table 5.3. Mean and standard deviations of aerial photography landscape variables taken within closed, open, and partially
open vernal pools in James D. Martin Skyline Wildlife Management Area and William B. Bankhead National Forest (2009 and
2011).
Vernal Pool Classes
Variable
Closed
(n = 7)
Aerial Photography Principal Component 1 (Pool
Density and Main Road Density at 427 meters)
0.31±0.99
Aerial Photography Principal Component 2 (Back
Road Density and Distance to Forest)
-0.12±1.18
Aerial Photography Principal Component 3 (Main
Road Density and Distance to Main Roads)
-0.19±1.16
Aerial Photography Principal Component 4 (Distance
to Edge)
-0.16±0.38
a
F statistics from a factorial ANOVA with significance level
b
Means in the same row with different letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
105
Open
(n = 9)
Partially Open
(n = 8)
Repeated
Measures
ANOVAa
Vernal Pool
Class
-0.31±1.17
0.04±0.92
0.7
0.28±1.07
-0.16±0.89
0.4
0.23±1.21
-0.05±0.77
0.3
0.50±0.32
-0.32±1.54
1.6
Repeated Measures Analysis of Variance (RM ANOVA)
A repeated measures factorial analysis of variane (RM ANOVA) was used to
compare normalized difference vegetation index principal components from the
normalized difference vegetation index PCA, normalized difference vegetation index
minimum at 427 meters, normalized difference vegetation index minimum at 800 meters,
normalized difference vegetation index minimum at 1015 meters, normalized difference
vegetation index minimum at 1601 meters across vernal pool classes and compared
among successive breeding seasons (Table 5.4). None of the variables were different
across vernal pool classes. Normalized difference vegetation index principal component
one (maximum and mean), normalized difference vegetation index principal component
two (range), normalized difference vegetation index minimum at 427 meters, normalized
difference vegetation index minimum at 800 meters, normalized difference vegetation
index minimum at 1015 meters, normalized difference vegetation index minimum at
1601 meters were different among breeding seasons (Figures 5.1-5.7).
A repeated measures ANOVA was used to compare amphibian species abundance
and ecological indices differences across vernal pool classes and among successive
breeding seasons. Alpha diversity for larval amphibians, larval amphibian species
richness using the bootstrap method, and larval amphibian species observed were
different across vernal pool classes (Table 5.5). Alpha diversity was higher in open
vernal pools when compared to closed vernal pools (Figure 5.8). Alpha diversity within
partially open vernal pools was not different from closed or open vernal pools (Figure
5.8). Larval amphibian species richness using the bootstrap method and the larval
106
Table 5.4. Mean and standard deviations of normalized difference vegetation index landscape variables taken within closed,
open, and partially open vernal pools in James D. Martin Skyline Wildlife Management Area and William B. Bankhead
National Forest (2008-2009, 2009-2010, and 2010-2011).
Variable
Normalized
Difference
Vegetation Index
Principal
Component 1
(Maximum and
Mean)
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
0.21±0.33
-0.79±0.98
-0.07±0.99
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
0.13±0.26
-0.63±1.07
0.50±0.95
Normalized
Difference
Vegetation Index
Principal
Component 2
(Range)
0.69±0.44
-0.36±1.32
0.18±1.25
Repeated Measures ANOVAa
Vernal
Breeding Vernal Pool
Pool
Season
Class x
Class
Breeding
Season
0.8
8.4**
0.6
2008-2009
-0.99±0.68
-0.62±0.47
-0.17±0.73
1.7
37.5***
4.8*
2009-2010
0.29±0.66
1.37±1.09
0.34±1.13
2010-2011
-0.49±0.84
0.04±0.72
0.06±0.79
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
107
Table 5.4. (Continued).
Variable
Normalized
Difference
Vegetation Index
Principal
Component 3
(Mean at 427
meters)
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
-0.00±0.09
0.57±1.21
-0.04±0.08
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
-0.03±0.09
-0.59±2.70
-0.03±0.10
Normalized
Difference
Vegetation Index
Minimum at 427
meters
-0.05±0.06
0.24±0.80
-0.02±0.09
Repeated Measures ANOVAa
Vernal
Breeding Vernal Pool
Pool
Season
Class x
Class
Breeding
Season
0.9
0.1
0.9
2008-2009
0.47±0.12
0.42±0.09
0.38±0.16
0.9
27.7***
3.4*
2009-2010
0.22±0.23
-0.01±0.19
0.23±0.23
2010-2011
0.37±0.18
0.35±0.15
0.31±0.17
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
108
Table 5.4. (Continued).
Variable
Normalized
Difference
Vegetation Index
Minimum at 800
meters
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
0.46±0.17
0.11±0.16
0.29±0.23
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
0.39±0.14
-0.06±0.16
0.28±0.17
Normalized
Difference
Vegetation Index
Minimum at
1015 meters
0.33±0.16
0.16±0.25
0.24±0.16
Repeated Measures ANOVAa
Vernal
Breeding Vernal Pool
Pool
Season
Class x
Class
Breeding
Season
0.6
48.1***
4.3**
2008-2009
0.46±0.16
0.35±0.19
0.31±0.18
1.4
34.4***
2.3
2009-2010
0.06±0.13
-0.16±0.33
0.09±0.23
2010-2011
0.29±0.22
0.21±0.19
0.19±0.15
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
109
Table 5.4. (Continued).
Variable
Season
Closed
(n = 7)
Vernal Pool Classes
Open
Partially Open
(n = 9)
(n = 8)
Normalized
Difference
Vegetation Index
Minimum at
1601 meters
Repeated Measures ANOVAa
Vernal
Breeding Vernal Pool
Pool
Season
Class x
Class
Breeding
Season
2008-2009
0.41±0.18
0.27±0.18
0.24±0.19
0.7
21.5***
0.6
2009-2010
-0.09±0.42
-0.16±0.33
-0.04±0.35
2010-2011
0.29±0.23
0.16±0.21
0.15±0.12
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different letters
were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
110
Figure 5.1. Boxplot with error bars showing difference in normalized difference
vegetation index principal component 1 (maximum and mean) extracted from 2009,
2010, and 2011 landsat thematic Mapper (TM) Satellite Imagery for James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National Forest. The
middle line is the median, the box is 50% inter-quartile range, the whiskers are at
maximum and minimum values within 1.5 inter-quartile range, and the points outside the
whisker are outliers.
111
Figure 5.2. Boxplot with error bars showing difference in normalized difference
vegetation index principal component 2 (range) extracted from 2009, 2010, and 2011
landsat thematic Mapper (TM) Satellite Imagery for James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest. The middle line is the
median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum
values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
112
Figure 5.3. Boxplot with error bars showing difference in normalized difference
vegetation index principal component 3 (mean at 427 meters) extracted from 2009, 2010,
and 2011 landsat thematic Mapper (TM) Satellite Imagery for James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest. The middle line
is the median, the box is 50% inter-quartile range, the whiskers are at maximum and
minimum values within 1.5 inter-quartile range, and the points outside the whisker are
outliers.
113
Figure 5.4. Boxplot with error bars showing difference in normalized difference
vegetation index minimum at 427 meters extracted from 2009, 2010, and 2011 landsat
thematic Mapper (TM) Satellite Imagery for James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest. The middle line is the
median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum
values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
114
Figure 5.5. Boxplot with error bars showing difference in normalized difference
vegetation index minimum at 800 meters extracted from 2009, 2010, and 2011 landsat
thematic Mapper (TM) Satellite Imagery for James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest. The middle line is the
median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum
values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
115
Figure 5.6. Boxplot with error bars showing difference in normalized difference
vegetation index minimum at 1015 meters extracted from 2009, 2010, and 2011 landsat
thematic mapper (TM) Satellite Imagery for James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest. The middle line is the
median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum
values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
116
Figure 5.7. Boxplot with error bars showing difference in normalized difference
vegetation index minimum at 1601 meters extracted from 2009, 2010, and 2011 landsat
thematic Mapper (TM) Satellite Imagery for James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest. The middle line is the
median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum
values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
117
Table 5.5. Mean and standard deviations of environmental and pool morphology variables taken within closed, open, and
partially open vernal pools in James D. Martin Skyline Wildlife Management Area and William B. Bankhead National Forest
(2008-2009, 2009-2010, and 2010-2011).
Variable
Alpha Diversity
Bootstrap
Shannon-Wiener
Diversity Index
Closed
(n = 7)
Vernal Pool Classes
Open
(n = 8)
Partially Open
(n = 9)
2008-2009
2009-2010
2010-2011
2008-2009
2009-2010
2010-2011
3.22±1.40A
4.23±2.25A
5.16±3.86A
3.54±1.52A
4.15±2.10A
4.26±2.35A
5.58±1.91B
8.02±1.95B
5.04±2.03B
5.79±1.88B
7.53±1.82B
5.21±2.00B
4.91±2.07AB
6.62±4.75AB
3.30±1.35AB
5.09±1.92A
5.51±2.34A
3.56±1.30A
2008-2009
2009-2010
2010-2011
0.58±0.51
0.61±0.39
0.53±0.37
0.77±0.58
0.96±0.28
0.60±0.54
0.68±0.48
0.81±0.49
0.54±0.37
Season
Simpson Inverse
Index
Repeated Measures ANOVA
Vernal
Breeding Vernal Pool
Pool
Season
Class x
Class
Breeding
Season
4.2*
3.1
1.5
8.3**
3.0
1.1
0.9
1.8
0.2
2008-2009
1.65±1.05
1.94±1.13
1.72±0.89
0.6
1.9
0.4
2009-2010
1.49±0.76
2.16±0.61
2.06±0.79
2010-2011
1.39±0.70
1.43±1.24
1.52±0.76
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
118
Table 5.5. (Continued).
Variable
Season
Closed
(n = 7)
Vernal Pool Classes
Open
(n = 8)
Species Observed
(Sobs)
Partially Open
(n = 9)
Repeated Measures ANOVA
Vernal
Breeding
Vernal
Pool
Season
Pool
Class
Class x
Breeding
Season
2008-2009
3.00±1.29A
5.13±1.64B
4.44±1.59A
8.9**
2.8
2009-2010
3.57±1.81A
6.25±1.49B
4.33±1.73A
2010-2011
3.43±1.72A
4.50±1.77B
3.00±1.12A
Abundance
2008-2009
325.43±381.02
521.50±838.69
554.78±791.57
0.5
0.4
2009-2010
280.86±382.06
197.25±176.46
574.78±1176.49
2010-2011
336.57±492.58
196.00±271.50
511.56±1033.11
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
119
1.0
0.3
Figure 5.8. Boxplot with error bars showing difference in alpha diversity for larval amphibians
observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National Forest. The middle line
is the median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum
values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
120
Figure 5.9. Boxplot with error bars showing difference in larval amphibian species richness
using the bootstrap method observed during 2008-2009, 2009-2010, and 2010-2011 breeding
seasons at James D. Martin Skyline Wildlife Management Area and William B. Bankhead
National Forest. The middle line is the median, the box is 50% inter-quartile range, the whiskers
are at maximum and minimum values within 1.5 inter-quartile range, and the points outside the
whisker are outliers.
121
Figure 5.10. Boxplot with error bars showing difference in larval amphibian species observed
during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest. The middle line is the
median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum values
within 1.5 inter-quartile range, and the points outside the whisker are outliers.
122
amphibian species observed were higher in open vernal pools when compared to closed
and partially open vernal pools (Figure 5.9-5.10). Individual larval amphibian species
abundances were compared. Marbled salamander larval abundances were different
across vernal pool classes, with abundance being higher in closed vernal pools than open
vernal pools (Table 5.6 and Figure 5.11). Marbled salamander larval abundance in
partially open pools was not different from closed or open vernal pools (Figure 5.11).
Red Spotted Newt larval abundance and Eastern Spadefoot tadpole abundance were
different among breeding seasons (Figures 5.12-5.13).
Canonical Correspondence Analysis (CCA)
Canonical correspondence analyses (CCA) were executed for amphibian species
abundance during each breeding season. Each CCA assessed the relationship between
amphibian species abundance compared to local (environmental and pool morphology
principal component variables) and landscape (aerial photography and normalized
difference vegetation index principal component variables) variables. Canonical
correspondence analysis (CCA) for the amphibian species during the 2008-2009 breeding
season explained 82.1 percent (Axis 1: 28.3 percent, Axis 2: 22.2 percent, Axis 3: 21.2
percent, Axis 4: 10.4 percent) (Figure 5.14) of the variance associated with the species
data, and 84.7 percent of the species-evironment relationship variance (Axis 1: 29.2
percent, Axis 2: 22.9 percent, Axis 3: 21.9 percent, and Axis 4: 10.7 percent) (Figure
5.14). During the 2009-2010 breeding season, the CCA represented 67.6 percent (Axis 1:
19.5 percent, Axis 2: 18.9 percent, Axis 3: 17 percent, Axis 4: 12.2 percent) of the
123
Table 5.6. Mean and standard deviations of animal captures taken within closed, open, and partially open vernal pools in James
D. Martin Skyline Wildlife Management Area and William B. Bankhead National Forest (2008-2009, 2009-2010, and 20102011).
Variable
Northern Cricket
Frog
Season
Closed
(n = 7)
Vernal Pool Classes
Open
Partially Open
(n = 8)
(n = 9)
Repeated Measures ANOVA
Vernal
Breeding
Vernal
Pool
Season
Pool Class
Class
x Breeding
Season
2008-2009
2009-2010
2010-2011
0.00±0.00
0.00±0.00
0.00±0.00
0.00±0.00
62.50±175.97
0.00±0.00
0.00±0.00
0.00±0.00
0.00±0.00
1.0
0.9
1.0
2008-2009
2009-2010
2010-2011
11.57±9.66
33.86±39.93
22.29±20.87
16.37±43.52
31.13±56.38
10.13±12.54
12.22±14.23
27.89±24.99
25.89±29.28
0.1
2.8
0.5
Spotted Salamander
Marbled Salamander
2008-2009
156.29±94.12A
20.13±26.00B
55.11±58.94AB
9.7**
2.6
2009-2010
11.71±8.34A
3.00±3.96B
11.89±16.97AB
2010-2011
95.00±79.97A
41.38±35.75B
89.00±109.86AB
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
124
6.7
Table 5.6. (Continued).
Variable
Mole Salamander
American Toad
Fowler’s Toad
Season
2008-2009
2009-2010
2010-2011
2008-2009
2009-2010
2010-2011
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
5.57±6.35
17.29±23.04
15.43±21.64
0.00±0.00
0.00±0.00
122.14±323.16
0.00±0.00
0.00±0.00
0.00±0.00
Vernal Pool Classes
Open
Partially Open
(n = 8)
(n = 9)
11.50±22.03
4.00±5.50
10.63±13.39
2.88±3.98
37.38±62.92
1.13±3.18
0.00±0.00
0.00±0.00
0.75±2.12
12.89±20.24
9.11±12.26
12.44±13.53
0.00±0.00
400.67±1201.63
136.67±410.00
0.00±0.00
0.00±0.00
222.33±667.00
Repeated Measures ANOVA
Vernal
Breeding
Vernal
Pool
Season
Pool Class
Class
x Breeding
Season
0.5
1.6
0.2
0.5
0.8
0.8
0.8
0.8
0.8
Eastern
Narrowmouth Toad
2008-2009
1.57±4.16
0.00±0.00
1.00±3.00
0.6
2.1
2009-2010
0.00±0.00
0.00±0.00
0.00±0.00
2010-2011
0.00±0.00
0.00±0.00
0.00±0.00
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
125
0.6
Table 5.6. (Continued).
Variable
Four Toed
Salamander
Cope’s Gray
Treefrog
Season
Closed
(n = 7)
Vernal Pool Classes
Open
Partially Open
(n = 8)
(n = 9)
Repeated Measures ANOVA
Vernal
Breeding
Vernal
Pool
Season
Pool Class
Class
x Breeding
Season
2008-2009
2009-2010
2010-2011
0.00±0.00
0.00±0.00
0.00±0.00
0.00±0.00
0.00±0.00
0.00±0.00
0.00±0.00
0.22±0.67
0.00±0.00
0.8
0.7
0.8
2008-2009
2009-2010
2010-2011
0.71±1.49
0.00±0.00
1.14±1.95
84.38±210.37
7.75±17.75
78.00±210.69
10.00±29.63
0.11±0.33
1.33±3.64
1.2
1.2
0.9
Bullfrog
2008-2009
0.00±0.00
0.25±0.71
0.44±1.33
1.2
1.4
2009-2010
26.29±69.55
0.50±1.41
0.33±0.50
2010-2011
0.00±0.00
0.00±0.00
0.00±0.00
Green Frog
2008-2009
0.29±0.76
36.13±70.57
5.67±8.99
1.6
1.9
2009-2010
0.14±0.38
6.38±11.90
5.11±8.51
2010-2011
0.00±0.00
12.88±34.82
0.78±1.99
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
126
1.2
1.6
Table 5.6. (Continued).
Variable
Pickerel Frog
Southern Leopard
Frog
Season
Closed
(n = 7)
Vernal Pool Classes
Open
Partially Open
(n = 8)
(n = 9)
Repeated Measures ANOVA
Vernal
Breeding
Vernal
Pool
Season
Pool Class
Class
x Breeding
Season
1.5
1.1
1.1
2008-2009
2009-2010
2010-2011
0.00±0.00
0.00±0.00
0.00±0.00
0.00±0.00
1.88±3.72
9.50±23.71
0.00±0.00
0.33±2.25
0.11±0.33
2008-2009
2009-2010
2010-2011
1.71±3.30
17.86±31.06
5.00±12.36
6.50±6.82
19.50±21.82
10.75±15.97
5.33±10.38
88.67±222.67
0.56±0.88
0.5
1.8
0.8
2008-2009
2009-2010
2010-2011
0.00±0.00
0.00±0.00
0.00±0.00
3.38±6.32
3.25±4.30
0.00±0.00
1.11±1.17
1.56±1.81
0.00±0.00
2.2
4.4*
1.7
Red Spotted Newts
Mountain Chorus
Frog
2008-2009
0.00±0.00
0.00±0.00
0.00±0.00
0.7
2.0
2009-2010
0.00±0.00
0.00±0.00
0.00±0.00
2010-2011
0.20±0.53
0.00±0.00
0.11±0.33
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
127
0.7
Table 5.6. (Continued).
Variable
Spring Peeper
Season
2008-2009
2009-2010
2010-2011
Closed
(n = 7)
0.43±1.13
15.43±31.29
1.71±2.87
Vernal Pool Classes
Open
Partially Open
(n = 8)
(n = 9)
9.00±14.23
15.88±34.94
20.88±53.83
Eastern Spadefoot
Toad
6.89±13.51
5.78±10.01
2.44±4.90
Repeated Measures ANOVA
Vernal
Breeding
Vernal
Pool
Season
Pool Class
Class
x Breeding
Season
0.6
1.0
1.0
2008-2009
147.29±385.29
331.00±810.08
444.11±763.80
0.1
3.8*
0.8
2009-2010
158.29±413.50
4.13±4.19
23.33±51.79
2010-2011
73.86±195.41
0.00±0.00
29.04±110.23
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different letters
were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
128
Figure 5.11. Boxplot with error bars showing difference in Marbled Salamander larval
abundance observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D.
Martin Skyline Wildlife Management Area and William B. Bankhead National Forest. The
middle line is the median, the box is 50% inter-quartile range, the whiskers are at maximum and
minimum values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
129
Figure 5.12. Boxplot with error bars showing difference in Red Spotted Newt Larval abundance
observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National Forest. The middle line
is the median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum
values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
130
Figure 5.13. Boxplot with error bars showing difference in Eastern Spadefoot tadpole abundance
observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National Forest. The middle line
is the median, the box is 50% inter-quartile range, the whiskers are at maximum and minimum
values within 1.5 inter-quartile range, and the points outside the whisker are outliers.
131
Figure 5.14. Bi-plot based on canonical correspondence analysis, showing the
relationship between environmental variables (arrows) and larval amphibians (triangles)
for the 2008-2009 breeding season in at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest, Alabama. Amphibian species codes:
ACCR = Northern Cricket Frog, AMMA = Spotted Salamander, AMOP = Marbled
Salamander, AMTA = Mole Salamander, ANAM = American Toad, ANFO = Fowler’s
Toad, GACA = Eastern Narrowmouth Toad, HYCH = Cope’s Gray Treefrog, LICA =
Bullfrog, LICL = Green Frog, LIPA = Pickerel Frog, LISP = Southern Leopard Frog,
NOVI = Red Spotted Newt, PSCR = Spring Peeper, and SCHO = Eastern Spadefoot.
Environmental variable codes: EPC1 = environmental principal component 1 (canopy
cover percentage and percent dissolved oxygen), EPC2 = environmental principal
component 2 (soil and water temperature), EPC3 = environmental principal component 3
(soil and water temperature maxium), EPC4 = environmental principal component 4
(water pH maximum and range), EPC5 = environmental principal component 5 (leaf litter
depth), ECP6 = environmental principal component 6 (water pH average and minimum),
ECP7 = environmental principal component 7 (leaf litter depth minimum and canopy
cover percentage range), PMPC1 = pool morphology principal component 1 (pool area),
PMPC2 = pool morphology principal component 2 (hydroperiod and maximum and pool
depth), and FCP = forest cover percentage. Landscape variable codes: APPC1 = Aerial
photography principal component 1(pool density and main road density at 427 meters),
APPC2 = Aerial photography principal component 2 (back road density and distance
from forest), APPC3 = Aerial photography principal component 3 (main road density and
distance from main roads), APPC4 = Aerial photography principal component 4 (distance
from edge), NDVI_PC1 = Normalized difference vegetation index principal component 1
(maximum and mean), NDVI_PC2 = Normalized difference vegetation index principal
component 2 (range), NDVI_PC3 = Normalized difference vegetation index principal
component 3 (mean at 427 meters), NDVI_427 = Normalized difference vegetation index
minimum at 427 meters, NDVI_800 = Normalized difference vegetation index minimum
at 800 meters, NDVI_101 = Normalized difference vegetation index minimum at 1015
meters, and NDVI_160 = Normalized difference vegetation index minimum at 1601
meters.
132
Figure 5.15. Bi-plot based on canonical correspondence analysis, showing the
relationship between environmental variables (arrows) and larval amphibians (triangles)
for the 2009-2010 breeding season in at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest, Alabama. Amphibian species codes:
ACCR = Northern Cricket Frog, AMMA = Spotted Salamander, AMOP = Marbled
Salamander, AMTA = Mole Salamander, ANAM = American Toad, ANFO = Fowler’s
Toad, GACA = Eastern Narrowmouth Toad, HYCH = Cope’s Gray Treefrog, LICA =
Bullfrog, LICL = Green Frog, LIPA = Pickerel Frog, LISP = Southern Leopard Frog,
NOVI = Red Spotted Newt, PSCR = Spring Peeper, and SCHO = Eastern Spadefoot.
Environmental variable codes: EPC1 = environmental principal component 1 (canopy
cover percentage and percent dissolved oxygen), EPC2 = environmental principal
component 2 (soil and water temperature), EPC3 = environmental principal component 3
(soil and water temperature maxium), EPC4 = environmental principal component 4
(water pH maximum and range), EPC5 = environmental principal component 5 (leaf litter
depth), ECP6 = environmental principal component 6 (water pH average and minimum),
ECP7 = environmental principal component 7 (leaf litter depth minimum and canopy
cover percentage range), PMPC1 = pool morphology principal component 1 (pool area),
PMPC2 = pool morphology principal component 2 (hydroperiod and maximum and pool
depth), and FCP = forest cover percentage. Landscape variable codes: APPC1 = Aerial
photography principal component 1(pool density and main road density at 427 meters),
APPC2 = Aerial photography principal component 2 (back road density and distance
from forest), APPC3 = Aerial photography principal component 3 (main road density and
distance from main roads), APPC4 = Aerial photography principal component 4 (distance
from edge), NDVI_PC1 = Normalized difference vegetation index principal component 1
(maximum and mean), NDVI_PC2 = Normalized difference vegetation index principal
component 2 (range), NDVI_PC3 = Normalized difference vegetation index principal
component 3 (mean at 427 meters), NDVI_427 = Normalized difference vegetation index
minimum at 427 meters, NDVI_800 = Normalized difference vegetation index minimum
at 800 meters, NDVI_101 = Normalized difference vegetation index minimum at 1015
meters, and NDVI_160 = Normalized difference vegetation index minimum at 1601
meters.
133
variance related to the species data and 69.3 percent of the species-environment
relationship variance (Axis 1: 20.0 percent, Axis 2: 19.4 percent, Axis 3: 17.4 percent,
and Axis 4: 12.5 percent) (Figure 5.15). The 2010-2011 breeding CCA detailed 70.7
percent (Axis 1: 24.5 percent, Axis 2: 19.3 percent, Axis 3: 14.4 percent, and Axis 4: 12.5
percent) of the variance linked to the species data and 74.2 percent of the speciesenvironment relationship variance (Axis 1: 25.7 percent, Axis 2: 20.3 percent, Axis 3: 15
percent, and Axis 4: 13.2 percent) (Figure 5.16). Across all three breeding seasons, there
was a gradient between forest cover percentage when compared to soil and water
temperature maximum (Figures 5.14-5.16).
Multiple Linear Regression (MLR)
Forward multiple linear regression (MLR) was executed in order to assess the
relationship between local (environmental and pool morphology principal component
variables) and landscape (aerial photography and normalized difference vegetation index
principal component variables) variables. Regression results indicated that the overall
model of five predictors (environmental principal component sixe- water pH average and
minimum, pool morphology principal component one- pool area, environmental principal
component two- soil and water temperature, environmental principal component fiveleaf litter depth, and normalized difference vegetation index principal component threemean at 427 meters) that significantly predicted larval amphibian abundance (Figures
5.17-5.21), R2 = 0.347, R2adj = 0.297, F(5,66) = 7.0, p < 0.001 (Table 5.7). This model
represented 34.7 percent of the variance in larval amphibian abundance (Table 5.7).
134
Figure 5.16. Bi-plot based on canonical correspondence analysis, showing the
relationship between environmental variables (arrows) and larval amphibians (triangles)
for the 2010-2011 breeding season in at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest, Alabama. Amphibian species codes:
ACCR = Northern Cricket Frog, AMMA = Spotted Salamander, AMOP = Marbled
Salamander, AMTA = Mole Salamander, ANAM = American Toad, ANFO = Fowler’s
Toad, GACA = Eastern Narrowmouth Toad, HYCH = Cope’s Gray Treefrog, LICA =
Bullfrog, LICL = Green Frog, LIPA = Pickerel Frog, LISP = Southern Leopard Frog,
NOVI = Red Spotted Newt, PSCR = Spring Peeper, and SCHO = Eastern Spadefoot.
Environmental variable codes: EPC1 = environmental principal component 1 (canopy
cover percentage and percent dissolved oxygen), EPC2 = environmental principal
component 2 (soil and water temperature), EPC3 = environmental principal component 3
(soil and water temperature maxium), EPC4 = environmental principal component 4
(water pH maximum and range), EPC5 = environmental principal component 5 (leaf litter
depth), ECP6 = environmental principal component 6 (water pH average and minimum),
ECP7 = environmental principal component 7 (leaf litter depth minimum and canopy
cover percentage range) ), PMPC1 = pool morphology principal component 1 (pool area),
PMPC2 = pool morphology principal component 2 (hydroperiod and maximum and pool
depth), and FCP = forest cover percentage. Landscape variable codes: APPC1 = Aerial
photography principal component 1(pool density and main road density at 427 meters),
APPC2 = Aerial photography principal component 2 (back road density and distance
from forest), APPC3 = Aerial photography principal component 3 (main road density and
distance from main roads), APPC4 = Aerial photography principal component 4 (distance
from edge), NDVI_PC1 = Normalized difference vegetation index principal component 1
(maximum and mean), NDVI_PC2 = Normalized difference vegetation index principal
component 2 (range), NDVI_PC3 = Normalized difference vegetation index principal
component 3 (mean at 427 meters), NDVI_427 = Normalized difference vegetation index
minimum at 427 meters, NDVI_800 = Normalized difference vegetation index minimum
at 800 meters, NDVI_101 = Normalized difference vegetation index minimum at 1015
meters, and NDVI_160 = Normalized difference vegetation index minimum at 1601
meters.
135
Table 5.7. Multiple regression beta coefficients for larval amphibian abundance.
Model
Constant
Environmental Principal Component 6
(water pH average and minimum)
Pool Morphology Principal Component 1
(pool area)
Environmental Principal Component 2
(soil and water temperature)
Environmental Principal Component 5
(leaf litter depth)
Normalized difference vegetation index
principal component 3 (mean at 427 meters)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
2.23
0.05
Standardized Coefficients
Beta
t
42.64
P-value
<0.001
-0.18
0.05
-0.34
-3.41
0.001
-0.17
0.05
-0.32
-3.19
0.002
0.13
0.05
0.26
2.56
0.013
-0.12
0.05
-0.23
-2.25
0.028
-0.11
0.05
-0.21
-2.11
0.038
136
Figure 5.17. Linear regression between environmental principal component 6 (water pH
average and minimum) and larval amphibian abundance observed during 2008-2009,
2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest.
137
Figure 5.18. Linear regression between pool morphology principal component 1 (pool
area) and larval amphibian abundance observed during 2008-2009, 2009-2010, and 20102011 breeding seasons at James D. Martin Skyline Wildlife Management Area and
William B. Bankhead National Forest.
138
Figure 5.19. Linear regression between environmental principal component 2 (soil and
water temperature) and larval amphibian abundance observed during 2008-2009, 20092010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest.
139
Figure 5.20. Linear regression between environmental principal component 5 (leaf litter
depth) and larval amphibian abundance observed during 2008-2009, 2009-2010, and
2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management Area and
William B. Bankhead National Forest.
140
Figure 5.21. Linear regression between normalized difference vegetation index principal
component 3 (mean at 427 meters) and larval amphibian abundance observed during
2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest.
141
Larval amphibian species observed was predicted using three independent variables
(forest cover percentage, environmental principal component four- water ph maximum
and range, and normalized difference vegetation index principal component three- mean
at 427 meters) (Figures 5.22-5.24), R2 = 0.272, R2adj = 0.240, F(3,68) = 8.5, p < 0.001
(Table 5.8). The model was able to explain 27.2 percent of the variance (Table 5.8). The
model representing alpha diversity for larval amphibians found three predictor variables
(forest cover percentage, breeding season, normalized difference vegetation index
principal component three (mean at 427 meters) that significantly represented it (Figures
5.25-5.26), R2 = 0.224, R2adj = 0.189, F(3,68) = 6.5, p = 0.001 (Table 5.9). This model
showed 22.4 percent of the variance in alpha diversity for larval amphibians (Table 5.9).
The overall model found three predictor variables (forest cover percentage, breeding
season, and normalized difference vegetation index principal component three (mean at
427 meters) that could explain the larval amphibian species richness using the bootstrap
method (Figures 5.27-5.28), R2 = 0.244, R2adj = 0.211, F(3,68) = 7.3, p < 0.001 (Table
5.10). The model accounted for 24.4 percent of the variance (Table 5.10). The model
found one predictor (aerial photography principal component three- main road density
and distance from main roads) that significantly predicted the Shannon-wiener index
(Figure 5.29), R2 = 0.070, R2adj = 0.057, F(1,70) = 5.3, p = 0.024 (Table 5.11). The
model represented 7.0 percent of the variance (Table 5.11). The overall model predicted
three predictors (aerial photography principal component three- main road density and
distance from main roads, pool morphology principal component one- pool area,
environmental principal component one- canopy cover percentage and percent dissolved
142
Table 5.8. Multiple regression beta coefficients for larval amphibian species observed.
Model
Constant
Forest Cover Percentage
Environmental Principal Component 4
(water pH maximum and range)
Normalized difference vegetation index
principal component 3 (mean at 427 meters)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.72
0.04
0.00
0.00
Standardized Coefficients
Beta
-0.42
t
17.36
-4.03
P-value
<0.001
<0.001
0.06
0.02
-0.26
-2.49
0.015
-0.05
0.02
-0.24
-2.27
0.026
143
Figure 5.22. Linear regression between forest cover percentage and larval amphibian
species observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at
James D. Martin Skyline Wildlife Management Area and William B. Bankhead National
Forest.
144
Figure 5.23. Linear regression between environmental principal component 4 (water pH
maximum and range) and larval amphibian species observed during 2008-2009, 20092010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest.
145
Figure 5.24. Linear regression between normalized difference vegetation index principal
component 3 (mean at 427 meters) and larval amphibian species observed during 20082009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest.
146
Table 5.9. Multiple regression beta coefficients for alpha diversity for larval amphibians.
Model
Constant
Forest Cover Percentage
Breeding Season
Normalized difference vegetation index
principal component 3 (mean at 427 meters)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.72
0.06
0.00
0.00
0.15
0.06
-0.06
0.03
147
Standardized Coefficients
Beta
-0.31
0.27
t
13.21
-2.92
2.54
P-value
<0.001
0.005
0.013
-0.27
-2.03
0.046
Figure 5.25. Linear regression between forest cover percentage and alpha diversity for
larval amphibians observed during 2008-2009, 2009-2010, and 2010-2011 breeding
seasons at James D. Martin Skyline Wildlife Management Area and William B.
Bankhead National Forest.
148
Figure 5.26. Linear regression between normalized difference vegetation index principal
component 3 (mean at 427 meters) and alpha diversity for larval amphibians observed
during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin
Skyline Wildlife Management Area and William B. Bankhead National Forest.
149
Table 5.10. Multiple regression beta coefficients for larval amphibian species richness using the bootstrap method.
Model
Constant
Forest Cover Percentage
Breeding Season
Normalized difference vegetation index
principal component 3 (mean at 427 meters)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.74
0.05
0.00
0.00
0.11
0.05
-0.05
0.02
150
Standardized Coefficients
Beta
-0.36
0.23
t
16.02
-3.43
2.19
P-value
<0.001
0.001
0.032
-0.23
-2.17
0.034
Figure 5.27. Linear regression between forest cover percentage and larval amphibian
species richness using the bootstrap method observed during 2008-2009, 2009-2010, and
2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management Area and
William B. Bankhead National Forest.
151
Figure 5.28. Linear regression between normalized difference vegetation index principal
component 3 (mean at 427 meters) and larval amphibian species richness using the
bootstrap method observed during 2008-2009, 2009-2010, and 2010-2011 breeding
seasons at James D. Martin Skyline Wildlife Management Area and William B.
Bankhead National Forest.
152
Table 5.11. Multiple regression beta coefficients for the Shannon-Wiener index for larval amphibians.
Model
Constant
Aerial Photography Principal Component
3 (Main Road Density and Distance from
Main Roads)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.68
0.05
0.12
0.05
153
Standardized Coefficients
Beta
0.27
t
13.19
P-value
<0.001
2.30
0.024
Figure 5.29. Linear regression between aerial photography principal component 3 (main
road density and distance from main roads) and the Shannon-Wiener index for larval
amphibians observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at
James D. Martin Skyline Wildlife Management Area and William B. Bankhead National
Forest.
154
Table 5.12. Multiple regression beta coefficients for the Simpson’s inverse diversity index for larval amphibians.
Model
Constant
Aerial Photography Principal Component
3 (Main Road Density and Distance from
Main Roads)
Pool Morphology Principal Component 1
(Pool Area)
Environmental Principal Component 1
(Canopy Cover Percentage and Percent
Dissolved Oxygen)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
1.18
0.09
Standardized Coefficients
Beta
t
18.10
P-value
<0.001
0.22
0.10
0.25
2.22
0.030
0.29
0.10
0.33
2.93
0.005
0.26
0.11
0.29
2.41
0.019
155
Figure 5.30. Linear regression between aerial photography principal component 3 (main
road density and distance from main roads) and the Simpson’s inverse diversity index for
larval amphibians observed during 2008-2009, 2009-2010, and 2010-2011 breeding
seasons at James D. Martin Skyline Wildlife Management Area and William B.
Bankhead National Forest.
156
Figure 5.31. Linear regression between pool morphology principal component 1 (pool
area) and the Simpson’s inverse diversity index for larval amphibians observed during
2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest.
157
Figure 5.32. Linear regression between environmental principal component 1 (canopy
cover percentage and percent dissolved oxygen) and the Simpson’s inverse diversity
index for larval amphibians observed during 2008-2009, 2009-2010, and 2010-2011
breeding seasons at James D. Martin Skyline Wildlife Management Area and William B.
Bankhead National Forest.
158
oxygen) that significantly predicted the simpson’s inverse diversity index (Figure 5.305.32), R2 = 0.223, R2adj = 0.189, F(3,68) = 6.5, p = 0.001 (Table 5.12). The model
accounted for 22.3 percent of the variance for the simpson’s inverse diversity index
(Table 5.12).
Discussion
In response to the first hypothesis, the amphibian diversity was found to be higher
in open pools when compared to closed pools and partially open vernal pools. In reponse
to the second and third hypotheses, the only species that was different among wetland
classes was the Marbled Salamander, and it was found in higher abundances within
closed pools when compared to open vernal pools. In regards to the fourth hypothesis,
amphibian diversity indices were not correlated to disturbances or movement barriers.
Diversiy indices were related to local variables within the wetland or the pool
morphology variables, rather than the landscape variables.
Disturbance was measured using landscape variables such as main roads, back
roads, ad edges. These features serve as barriers to amphibian migration. There was not
a relationship found between intermediate disturbance and larval amphibian diversity.
Amphibian diversity increased in reponse to water and soil temperature, but decreased in
reponse to forest cover percentage.
The majority of anuran species executed a complex life cycle where they use both
aquatic and terrestrial habitats during the lives (Gagne and Fahrig, 2007). Burne and
159
Griffin (2005) suggested that habitat characteristtics within breeding pools including pool
area and tree canopy cover are more important in determining amphibian community
composition than features in the landscape. Within my study, there were no landscape
variables that explained the variance in the amphibian diversity indices. The majority of
the landscape features measured was barriers to migration or emigration by juvenile
amphibians. The negative influence that migration barriers present may be lessened by
the presence of forest corridors. While back roads, main roads, and forest edges were all
present within the migration buffer distances, the majority of the pools were near or
adjacent to forest stands. Vernal pools within continguous forest stands are important for
maintaining biological diversity (Hocking et al., 2008). These corridors may have
enabled various amphibian species to move through the landscape despite the presence of
barriers. Forest stands’ spatial extent may help explain anuran species richness and
abundance patterns throughout the landscape (Gagne and Fahrig, 2007). I did find a
weak positive relationship between roads and Shannon-wiener and Simpson’s inverse
diversity index, while Hartel et al. (2010) stated that landscape features such as distance
to forest and vernal pool presence differ in importance depending on the amphibian
species being studied, but roads did influence amphibian species richness. Eigenbrod et
al. (2008) found similar results, and he noted that traffic had a negative influence on
amphibian species richness. Gagne and Fahrig (2007) surmised that amphibian
communities may experience higher probability of local extinction due to mortality from
road traffic and other impervious surfaces (Gagne and Fahrig, 2007). Eigenbrod et al.
160
(2008) did condede that the importance of traffic density and forest cover varied by
species.
Amphibian breeding pool suitability can be affected by forest cover directly
overshadowing the vernal pool (Burne and Griffin, 2005). Gamble et al. (2009) stated
that it is important to consider breeding habitat and terrestrial habitat when monitoring
pool breeding amphibians. Forested habitat creates diverse habitats, through providing
shade, regulating temperature, and retaining moisture, which is paramount for poolbreeding amphibians (Hermann et al., 2005). Terrestrial amphibians risk desiccation as
they move across the landscape in search of new water resources, so any environmental
features that can this risk likely will increase survival (Batlet et al., 2004). Schiesari
(2006) saw that the degree of canopy cover had strong impacts on amphibian
performance, population dynamics, and community structure. Even though I did not see
direct relationships with canopy cover, I did see relationships between diversity and
forest cover and leaf litter depth. Litter is a key component in larval amphibian
performance (Cohen et al., 2012). Earl et al. (2011) found that ambystoma species
performed better in breeding habitat that had high leaf litter and high canopy cover when
compared to open breeding sites. Cohen et al. (2012) found similar results, and he
surmised that litter quantity would increase food availability to tadpoles, since some
larval amphibians are known to consume detritus material (Altig et al., 2007). Skelly et
al. (1999) found that variation in overhead forest canopy was correlated with amphibian
presence. Increased canopy cover is related to temperature, which is important to tadpole
development. Warmer temperatures are necessary for anuran species larval development
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(Glennemeier and Werner, 1999; Skelly et al., 2002; Hocking and Semltisch, 2007).
Open vernal pools are beneficial to anuran larvae because of higher temperatures that
influence growth and developmental rates (Hocking and Semlitsch, 2007). Tree canopy
closure over breeding pools may therefore help predict which pool breeding amphibian
occur within various vernal pools (Skelly et al., 2002; Burne and Griffin, 2005). While
open breeding sites are important to anuran species, deMaynadier and Hunter (1999)
found that habitat use during emigration and dispersal is not random for caudate species
and closed canopy specialists. Ambystoma species selected closed canopy forest
conditions and areas of dense vegetation (deMaynadier and Hunter, 1999). Preferential
use of closed-canopy habitat by emigrating amphibians has potentially important
conservation implications for management practices in the forested landscape around
vernal pools (deMaynadier and Hunter, 1999).
Ray et al. (2002) found that habitat alteration makes it important to find way to
assess the impact of changes on the distribution of amphibian populations. Rittenhouse
and Semlitsch (2007) found that determining how amphibians distribute themselves after
reproduction will help researchers determine the amount of habitat that is needed in order
to sustain viable populations. After breeding, both frogs and salamanders migrate in a
way where that highest abundances travel a moderate distance from breeding sites, and
abundance tends to decreas as amphibians travel further distances from the vernal pool
(Rittenhouse and Semlitsch, 2007). Habitat that is closer to the breeding pools may be
more critical that landscape that is located near the migration maximum of a particular
species. Since several anuran and caudate species depend extensively on upland habitat,
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it seems important to have stands located near breeding pools (Rubbo and Kiesecker,
2006).
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CHAPTER 6
THE RELATIONSHIP OF VERNAL POOL FEATURES
AND AMPHIBIAN SIZE AND REPRODUCTIVE PERFORMANCE
WITHIN NORTH ALABAMA
Introduction
Many amphibian species and populations are undergoing a global decline which
was first noted in the 1980s (Stuart et al., 2004; Alfords and Richards, 1999). Several
hypotheses have been identified, with habitat destruction being noted as one of the
primary causes (Beebee and Griffiths, 2005), and habitat alteration and fragmentation
posing additional threats to amphibians (Collins and Storfer, 2003; Hocking and
Semlitsch, 2007). Habitat destruction can impose a barrier to migration, emigration, and
immigration of pool breeding amphibians. Fragmentation can restrict adult amphibians
from traversing the landscape to reproduce, and it can also affect animals during foraging
and searching for aestivation and brumation sites during periods of high and low
temperatures, respectively (Collins and Storfer, 2003; Storfer, 2003; Beebee and
Griffiths, 2005). Forest cover is one of the most important landscape scale predictors for
amphibian presence and abundance (Eigenbrod et al., 2008), so a fragmenting of
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contiguous forest can have a negative effect on amphibian populations. Since amphibians
require moisture and refugia for survival, large openings in the forest overstory or forest
fragmentation can result in an increase in ambient temperature and a reduction in relative
humidity. These conditions can lead to a loss of body weight and potential desiccation.
At a minimum, movement is restricted due to an increased riskof desiccation across large
open areas. Since local amphibian populations require dispersal to persist, a breakdown
in forest cover continuity can negatively affect amphibian populations within a given
area. Roads and forest edges can also serve as barriers. These barriers create smaller
patches and increase patch isolation which can break up local populations (Carr and
Fahrig, 2002) and can cause declines and local extinctions of obligate amphibian species
(Harper et al., 2008). Habitat alteration, such as conversion of forests into agricultural
fields or open space, is one of the most notable ways of introducing barriers within pool
breeding amphibian terrestrial habitats (James and Semlitsch, 2011).
It is imperative to understand the phenotypic variation that these animals exhibit
within their larval and adult habitats, and how the environment influences these
morphological differences when defining stressors that cause amphibian decline. The
genotype is responsible for the various phenotypic possibilities an organism can
represent, while the environment influences the phenotype that is expressed (Denver,
1997; Miner et al., 2005). Phenotypic plasticity is a means of adaptation to changing
environments that is both prevalent among many taxa and occurs in various traits
(Kishida et al., 2006). Plasticity may be adaptive, allowing an organism to fit its
phenotype to the changeable environment (Newman, 1989; Nussey et al., 2007).
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Phenotypic plasticity predicts that populations and species that inhabit diverse
environments will express a wide range of plasticity (Buskirk, 2002). Biotic and abiotic
features of the environment create the situations in which fitness variation is produced
(Kaplan and Phillips, 2006). Many organisms that exploit variable environments display
phenotypic plasticity in life-history traits that are related to fitness (Gervasi and
Foufopulos, 2008). This can be seen most prominently in species with a complex life
cycle which is characterized by an abrupt morphological metamorphosis and usually
includes a change in habitat (Hensley, 1993). Species that occupy different environments
throughout their life cycle need to be able to metamorphose into an organism that can
thrive within a different habitat. The development during a larval stage can be affected by
the diminishing resources within the larval habitat, triggering the transition to a different
life cycle stage. In response to an increased risk of mortality, organisms alter their size at
metamorphosis by increasing their rate of development (Buskirk, 2002). The timing of
developmental events, such as metamorphosis and reproductive maturity, can influence
life cycles directly by determining the age at first reproduction or indirectly by affecting
age-specific fecundity (Hensley, 1993). In order to maximize growth and minimize
mortality, species must balance the trade off between a smaller size or older age at
metamorphosis (Gervasi and Foufopoulos, 2008). Variation in growth rate and timing of
metamorphosis generate variation in size at metamorphosis, which may affect fitness
(Newman, 1989). Larval developmental time plasticity results in metamorphic size
variation, and this size differential may affect survival in the terrestrial habitat (Denver et
al., 1998). Larger size at metamorphosis may confer greater fitness through increased
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terrestrial performance, higher juvenile survivorship, earlier maturity or larger size at
maturity (Denver, 1997).
Plasticity in growth and development rate influences various species larval
development time, size at metamorphosis, and age at metamorphosis (Gervasi and
Foufopoulos, 2008). Amphibians exhibit considerable variation, both between and within
species and in the duration of the larval period (Denver, 1997). Tadpoles of some species
are facultative species, while others are found within a more narrow range of habitats
(Buskirk, 2002). Duration of the ephemeral wetlands, in which amphibian larvae
develop, is an important environmental variable that dictates animals’ abilities to
metamorphose (Newman, 1989). Vernal pools are time-constrained habitat, and they
represent a temporally heterogeneous environment (Laurila et al., 2002; Lind and
Johnansson, 2007). In such unpredictable habitats, larval survivorship is heavily
influenced by desiccation, and species that breed in these pools have evolved traits lead
to successful development (Denver et al., 1998). Using extreme plasticity in
metamorphosis timing, several amphibian species are able to respond to pool drying by
accelerating metamorphosis (Denver et al., 1998). Early larval amphibian metamorphosis
results in desiccation avoidance, but these individuals may suffer from reduced juvenile
survivorship, physiological performance, and size at first reproduction (Denver et al.,
1998). If metamorphosis can be postponed, then the larvae have a higher chance of
survival within the terrestrial environment. Delayed metamorphosis is commonly
associated with a larger body size, which often leads to higher survival rates and
fecundity, although a large size often requires additional time for growth and suffers from
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increased larval habitat desiccation risk (Gervasi and Foufopoulos, 2008).
Metamorphosis timing may be determined by the relative opportunity for growth and
mortality risk in the aquatic and terrestrial environments (Newman, 1989). In the case of
anurans, juveniles that are larger at metamorphosis usually remain larger or have higher
growth rates than small conspecifics (Lind and Johnansson, 2007). While phenotypic
plasticity enables amphibians to survive in fluctuating aquatic habitat, there are trade-offs
when an individual favors development time over size at metamorphosis. Amphibian
developmental time and metamorphic size are phenotypic responses to mortality risks
associated with pond drying, but they also affect the likelihood of survival once juveniles
emigrate from the wetland to the surrounding landscape.
Objectives
The objectives for this study were to (1) quantify the difference in amphibian size
and body condition within three wetland classes during successive years, and (2) assess
the relationship between larval amphibian size and body coniditon local environmental
features.
Hypotheses
I hypothesize that (1) amphibian larvae will be larger in size and (2) have higher body
condition index values within open vernal pools when compared to closed and partially
open vernal pools, while (3) larval amphibian body condition indices will show a positive
correlation to features that are associated with lower canopy cover within the wetland.
168
Methods
Study Sites
The William B. Bankhead National Forest (BNF) is located in a strongly
dissected plateau subregion within the Southern Cumberland Plateau (Smalley, 1979).
The Southern Cumberland Plateau occupies 23,051 square kilometers. This strongly
dissected plateau is so dissected that it no longer resembles a plateau (Smalley, 1979). Its
topography is complex and its strongly sloping landscape predominates (Smalley, 1979)
this section of the Cumberland Plateau.
The James D. Martin Skyline Wildlife Management Area (SWMA) is located
within the strongly dissected southern portion of the Mid Cumberland Plateau. This midplateau region covers about 11,525 square kilometers and has a temperate climate
characterized by long, moderately hot summers and short, mild winters (Smalley, 1982).
The two localities used for this study were James D. Martin Skyline Wildlife
Management Area (SWMA) and William B. Bankhead National Forest (BNF). The
SWMA, is located in Jackson County, Alabama and covers an area of 114 km2 (64.1 mi2)
(Lein, 2005) (Figure 1). Vegetation in the SWMA has been classified as mixed
mesophytic communities comprised of oak-hickory forests (Braun, 1950). Of the
timberland in Jackson County, 78.8% is a mix of oak and hickory species (Hartsell and
Brown, 2002). In June, 2012, ten dominant and co-dominant tree species whose crowns
surrounded or occurred within fifteen vernal pools and within the SWMA were identified
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and selected randomly. The most dominant and codominant tree species in this area
include sweetgum (Liguidambar styraciflua), red maple (Acer rubrum), chinkapin oak
(Quercus muehlenbergii), white oak (Quercus alba), chestnut oak (Quercus prinus), post
oak (Quercus stellata), scarlet oak (Quercus coccinea), northern red oak (Quercus
rubra), blackgum (Nyssa sylvatica), sourwood (Oxydendrum arboreum), and tulip poplar
(Liriodendron tulipifera).
The BNF is 737 km2 (284 mi2) and located in Lawrence, Winston, and Cullman
counties in north central Alabama (Gaines and Creed, 2003) (Figure 2). Hartsell and
Brown (2002) classified the timberland in Lawrence County at 42 percent oak and
hickory species, Winston County at 47 percent, and Cullman County at 61 percent
(Hartsell and Brown, 2002). In June 2012, the same sampling protocols as described for
the SWMA were used. The dominant tree species within and surrounding nine vernal
pools within the BNF area are sweetgum (Liquidambar styraciflua), loblolly pine (Pinus
taeda), white oak (Quercus alba), water oak (Q. nigra), other red oak species, tulip
poplar (Liriodendron tulipifera), blackgum (Nyssa sylvatica), chestnut oak (Q. prinus),
virginia pine (P. virginiana), winged elm (Ulmus alata), red maple (Acer rubrum),
sourwood (Oxydendrum arboreum), and water oak (Quercus nigra).
The study sites were located using forest cover delineated from aerial
photography of potential wetland basins. Fifteen vernal pools were selected in the
SWMA, while nine vernal pools were selected in the BNF. To be selected, vernal pools
needed to be ephemeral or have semi-permananent hydroperiods, dried out once a year or
at least once every 2-3 years, filled with rainwater, groundwater, or intermittent streams,
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and were devoid of fish. The vernal pools were then grouped into different categories
based on the forest cover directly over the pool.
Vernal Pool Class Creation using Light Environments
Vernal pool canopy classes have been compared differently within pond
mesocosm experiments and vernal pool observational studies. Within a canopy cover
mesocosm experiment, shade cloth percentage has been used to simulate canopy cover
that naturally overshadows vernal pools throughout the landscape (Skelly et al., 2002;
Reylea et al., 2006). Two treatments are noted: high and low canopy cover treatments
(Werner and Glennemeier, 1999). These treatments reflect canopy cover in open and
closed vernal pools. In contrast to the two class system of mescosm experiments, canopy
gradients have been identified within actual vernal pools (Halverson et al., 2003; Werner
et al., 2007). Mesocosm studies differ from the actual canopy cover differences found in
heterogenous forested systems. I sought to design wetland classes that incorporated
mesocosm experiment categories as well as observational studies’ canopy gradients. I
created vernal pool classes that reflected the canopy cover gradient found overshadowing
vernal pools as well as the high and low canopy conditions simumlated in pool mesocosm
studies.
Within my study, open canopy vernal pools were designated based on
experimental mesocosms (Werner and Glennemeier, 1999; Skelly et al., 2002; Skelly et
al., 2005; Earl et al., 2011; VanBuskirk et al., 2011). Pool mesocosms with low canopy
had 20-40 percent canopy cover within these experiments. Based on this range, I
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designated open pools as having forest cover under 50 percent. Within pond
experiments, closed pools were categorized using shade cloth that ranged from 60 to 81
percent (Skelly et al., 2002; Thurgate and Pechmann, 2007). Within my study, this range
resulted in two different classes: closed and partially open vernal pools. Closed and
partially open pools both had forest cover that was equal to or over 50 percent, but the
difference between these two classes was in the overstory. Closed canopy pools did not
have codominant or dominant tree crown size canopy gaps. Partially open vernal pools
had canopy gaps that were at least the size of codominant or dominant tree crown.
Essentially these classes depict a canopy cover gradient, while reflecting shadow cloth
percentages seen in pond mesocosm experiments (Skelly and Golon, 2003; Schiesari,
2006; Binckley and Resetarits, 2007).
The potential vernal pool was delineated using color leaf on and black and white
leaf off aerial photographs (Calhoun et al., 2003; Lathrop, et al., 2005; Oscarson and
Calhoun, 2007). The orthroimagery was National Agricultural Imagery Program Mosaic
from 2005 and 2006 (USDA Geospatial Data Gateway, 2013). These datasets
represented color leaf-on and black and white panchromatic aerial photography from
2005 and 2006 to determine forest cover over the maximum wetland area (Fox and
Hines, 2000). The spatial resolution was one meter ground sample distance (GSD). The
water within the wetlands was black compared to the grey and white of the surrounding
environment. Open wetlands had canopy closure representing less than 50 percent of the
maximum vernal pool basin (Table 1). Partially open wetlands had 51 to 99 percent of the
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potential vernal pool area covered by the overstory (Table 1). Closed wetlands had forest
cover of 100 percent of the maximum wetland area (Table 1).
Of the twenty four vernal pools assessed for this study, eight were designated as
open canopy, seven vernal pools were classed as closed canopy wetlands, and nine
isolated wetlands were identified as partially open (Table 1). All the wetlands used for
this study were confirmed as amphibian breeding habitat based on on-site preliminary
sampling and records from previous studies (Chan, 2007; Felix, 2007; Scott, 2008;
Sutton, 2010).
Environmental Data
Data on environmental conditions and morphology of vernal pools were collected
biweekly between June 2008 and June 2011. Sampling was executed from December
through June for each breeding season. This time encapsulated the larval development
and metamorphosis of the amphibian species found in these vernal pools. The
environmental variables measured were water and ambient temperatures, dissolved
oxygen percentage, water pH, canopy cover, soil temperature, pool size, maximum depth,
and leaf litter depth. Pools were visited biweekly because it represented the shortest
amount of time between egg deposition and metamorphosis. Three different sampling
points, at least five meters away from each other, were selected randomly during each
wetland visit. At each point, water temperature, canopy cover, dissolved oxygen, and
water pH were measured at one, two, and three meters from the edge of the pool. Water
temperature percent dissolved oxygen was measured using a YSI 85 dissolved oxygen
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meter (YSI Inc, USA). The probe was lowered within the pool, and the measurements
were taken after fifteen minutes. Water ph was measured using an Oakton ph Testr
probe (Oakton Instruments, USA). A spherical crown densitometer (Forestry Suppliers,
USA) which was held at chest level was used to measure canopy cover. Four canopy
cover measurements were taken at north, south, east, and west. Understory was excluded
from the canopy cover measurements. Leaf litter depth and soil temperature were taken
at the shoreline. Leaf litter depth was calculated using a ruler. The depth from the soil to
the top of the leaf litter was measured to the nearest tenth of a centimeter. Soil
temperature was measured using a digital pen shape stem thermometer (H-B Intrument
Company, USA). The thermometer probe was inserted into the soil up to the hilt of the
thermometer.
The area and perimeter of each vernal pool were measured using Garmin
Etrex Legend Vista Hcx Global Positioning System Unit (Garmin, USA). To calculate
the area and perimeter, the researcher walked the perimeter of the vernal pool. Once, the
researcher had finished he or she reviewed the shape of the pool in the GPS unit to ensure
that the area and perimeter had been correctly calculated. Global positioning system
(GPS) coordinates were also collected for each point.
Animals were surveyed using minnow trapping (Dodd, 2009), and occurred at
each vernal pool biweekly as long as the vernal pools were inundated. Three minnow
traps were anchored and set at each wetland the day before sampling. The minnow traps
were anchored using twine, and they were anchored at three distances from the shoreline:
one, two, and three meters. Each minnow trap was set a minimum of five meters from
another to reduce disturbance to amphibians located in that area of the vernal pool. The
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following day, each amphibian was identified by species and life stage. Three
measurements were taken of larval amphibians: snout to vent length, tail length, and
weight. Adult amphibians received one toe clip (Heyer, 1994) on the rear right index toe
to signify that the animal had been sampled before, and larval amphibian received a
tailfin clip (Heyer, 1994) on the upper dorsal fin near the tip. The following equation was
used to determine animals that would be measured and weighed: samples = log10[total
species captures for this minnow trap] * 10. This equation was used for each individual
species within each minnow trap. Within each minnow trap, the total captures for each
species were counted. This number was put into the equation, and the number of animals
that would be sampled, measured, And processed were calculated. If there were fewer
than 10 individuals per species, then every individual was measured. Size metrics were
compared for each species across various life stages. The three main life stages used in
this study were larvae, juvenile, and reproductive mature adult. Life stage codes were
used in a truncated format based on the Gosner stages for anuran tadpoles and estimation
for the caudate larvae (Gosner, 1960).
Based on herpetofaunal guides (Mount, 1975; Trauth et al., 2004; Lannoo, 2005;
Jensen et al., 2008) and previous research (Felix, 2007; Chan, 2008; Scott, 2009; Sutton,
2010), I expected to capture 21 possible pool breeding amphibian species: 7 caudates and
14 anurans. The caudates included: Spotted Salamander (Ambystoma maculatum),
Marbled Salamander (A. opacum), Mole Salamander (A. talpoideum), Smallmouth
Salamander (A. texanum), Eastern Tiger Salamander (A. tigrinum), Red Spotted Newt
(Notophthalmus v. viridescens), and Four Toed Salamander (Hemidactylium scutatum).
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The anurans included the following: Cope’s Gray Treefrog (Hyla chrysoscelis), Barking
Treefrog (H. gratiosa), Spring Peeper (Pseudacris c. crucifer), Mountain Chorus Frog (P.
brachyphona), Upland Chorus Frog (P. feriarum), Northern Cricket Frog (Acris c.
crepitans), American Toad (Anaxyrus americanus), Fowler’s Toad (A. fowleri), Eastern
Spadefoot Toad (Scaphiopus holbrookii), Bullfrog (Lithobates catesbeianus), Green Frog
(L. clamitans melanota), Pickerel Frog (L. palustris), Southern Leopard Frog (L.
sphenocephalus urticularius), and Eastern Narrowmouth Toad (Gastrohpyrne
carolinensis).
Body Condition Indices
Three body condition indices were used during this study: scaled mass index, Quetelet’s
Index, and Fulton’s Index (Stevenson and Woods, 2006; Peig and Green, 2009; Peig and
Green, 2010). These three body condition indices have been used on herpetofauna (Peig
and Green, 2009; MacCracken and Stebbings, 2012). Scaled mass index is a condition
index that is based on the Thorpe-Lleonart model of scaling (Peig and Green, 2009):
Scaled Mass Index = Mi [Lo/Li]bsma.
Mi = Body mass of individual i when the linear body measure is standardized to Lo.
Li = Linear body measurement of individual i.
Lo = The mean of linear body measurement
bsma = Slope from ordinary least square regression/ Pearson correlation coefficient
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Quetelet’s Index is also known as the body mass index (Stevenson and Woods, 2006). It
is computed as the body mass divided by the square of length (Stevenson and Woods,
2006). The Quetelet’s Index is calculated using the following equation:
Quetelet’s Index = M/ L2.
The Fulton Index is computed as body mass divided by the cubed of body length
(Stevenson and Woods, 2006).
Fulton Index: = M/ L3.
A multiplier of 1000 is used when the mass is measured in grams and the length in
millimeters (Stevenson and Woods, 2006).
Statistical Analyses
Repeated measures analysis of variance (ANOVA) (IBM SPSS ® 19.0) was used
to compare amphibian morphometrics, weight, and body condition indices across
different wetland classes among various breeding seasons. Post hoc Tukey tests were
used to determine the differences in size, weight, and body condition, if the ANOVA
results were found to be significant. Multiple linear regression was used to determine the
177
relationship between ecological indices and the environmental principal components and
the pool morphology principal components.
Results
During this study, a total of 39,182 individuals were captured including: 2,382
adults, 35,963 larvae, and 837 metamorphs. Of the 35, 963 larvae sampled (173
recaptures), 10,709 larvae were processed, measured, and weighed. During this study the
following species were captured: Spotted Salamander, Marbled Salamander, Mole
Salamander, Red Spotted Newt, Four Toed Salamander, Cope’s Gray Treefrog, Sping
Peeper, Mountain Chorus Frog, Upland Chorus Frog, Northern Cricket Frog, American
Toad, Fowler’s Toad, Eastern Spadefoot Toad, Bullfrog, Green Frog, Pickerel Frog,
Southern Leopard Frog, and Eastern Narrowmouth Toad. When calculating body
condition indices, only the larvae were used. To compare weight, morphometric
measurements, and body condition indices, animals of a particular life stage needed to be
sampled at all twenty four vernal pools. Larvae were the only life stage that had captures
at all of the vernal pools. Mixed model analysis of variances was used initially, but the
adult and metamorph life stages were not captured at numerous pools. Consequently,
larvae were used to compare body condition and size metrics. Repeated measures
factorial analysis of variance (ANOVA) was used to compare across vernal pool classes,
since larvae were sampled at all of the study pools. Individual species were not
178
compared because no species were found at all of the vernal pools. The data were then
pooled across taxa to assess differences in size and body condition.
Repeated Measures Analysis of Variance (RM ANOVA)
A repeated masures analysis of variance was compared across vernal pool classes
from June 2008 through June 2011. Snout to vent length, total length, weight, Fulton’s
Index, and the scaled mass index did not exhibit differences across vernal pool classes,
but weight did show a difference across breeding seasons (Table 6.1). The larval
amphibians were heavier during the 2009-2010 breeding season when compared to the
2008-2009 and 2010-2011 breeding seasons. Quetelet’s Index indicated differences
across vernal pools classes and among breeding seasons (Table 6.1). Open vernal pools
had higher quetelet’s index values when compared to closed pools, but were not different
from partially open pools. Quetelet’s index values were higher during the 2009-2010
breeding season when compared to the 2008-2009 and 2010-2011 breeding seasons.
Finally, there was no siginificant relationship in the interaction between vernal pools
classes and breeding seasons (Table 6.1).
Multiple Regresssion
A forward multiple linear regression was executed to examine the influence of
environmental and pool morphology variables on larval amphibian morophometry,
weight, and body condition indices throughout the vernal pools studied. Linearity
evaluation led to the log transformations of snout to vent length, total length,
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Table 6.1. Means (±Standard Deviations) of size metrics and body condition indices for larval amphibians in forest (closed),
open, and partially open wetland classes within James D. Martin Skyline Wildlife Management Area and William B. Bankhead
National Forest in Alabama (2008-2011).
Variables
Snout to Vent Length (mm)
Breeding
Seasons
Forest
(Closed)
n=7
Wetland Classes
Open
Partially Open
n=8
n=9
2008-2009
2009-2010
2010-2011
2008-2009
2009-2010
2010-2011
2008-2009
2009-2010
2010-2011
RM ANOVAa
Wetland
Breeding
Wetland
Class
Season
Class x
Breeding
Season
0.1
0.4
1.1
20.0±2.2
17.8±4.6
18.8±3.1
19.7±4.2
18.5±6.1
19.5±3.6
18.2±1.8
19.7±3.1
17.6±3.7
Total Length (mm)
37.3±4.6
38.8±9.3
39.1±7.9
0.2
1.6
41.3±13.2
39.3±10.5
41.2±9.0
35.7±2.6
41.5±9.0
34.1±6.7
Weight (grams)
0.5±0.2
0.9±0.60
0.8±0.4
1.5
8.1**
1.2±1.2
1.3±0.5
1.3±0.6
0.5±0.1
1.1±0.9
0.5±0.2
-3
-3
1.2 x 10 ±
2.3 x 10 ±
1.9 x 10-3±
Quetelet’s Index
2008-2009
2.8 x10-4Ab
6.4 x 10-4B
8.9 x 10-4AB
3.8*
5.9**
-3
-3
2.1 x 10 ±
2.8 x 10 ±
2.5 x 10-3±
2009-2010
1.4 x 10-3A
8.9 x 10-4B
6.9 x 10-4AB
-3
-3
1.4 x 10 ±
2.2 x 10 ±
1.7 x 10-3±
-4
-3
2010-2011
5.5 x 10 A
1.2 x 10 B
9.0 x 10-4AB
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
180
1.3
0.8
0.203
Table 6.1. (Continued).
Variables
Breeding
Seasons
Forest
(Closed)
n=7
Wetland Classes
Open
Partially Open
n=8
n=9
Fulton’s Index
Wetland
Class
Breeding
Season
Wetland
Class x
Breeding
Season
0.9
2008-2009
0.1±0.1
0.2±0.1
0.1±0.1
1.3
0.9
2009-2010
0.1±0.1
0.3±0.5
0.2±0.1
2010-2011
0.1±0.1
0.1±0.1
0.2±0.3
Scaled Mass Index
2008-2009
0.5±0.2
0.7±0.4
0.6±0.3
2.8
4.0*
1.6
2009-2010
1.1±1.1
1.2±0.9
1.0±0.5
2010-2011
0.7±0.3
1.9±1.6
0.9±0.5
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different letters
were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
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weight, scaled mass index, Quetelet’s Index, and Fulton’s Index. Forward regression was
carried out to determine which variables were predictors for larval amphibian snout to
vent length. Regression results showed an overall model of two predictors (pool
morphology principal component 1- pool area and environmental principal component 4water pH maximum and range) that significantly predicted larval amphibian snout to vent
lengh (Figures 6.1-6.2), R2 = 0.133, R2adj = 0.108, F(2,69) = 5.3, p = 0.007 (Table 6.4).
This model represented 13.3 percent of the variance in larval amphibian snout to vent
length (Table 6.4). In regards to larval amphibian total length, forward regression was
used to explain which variables could predict this dependent variable. Two variables
(environmental component 4- water pH maximum and range and environmental principal
component 6- water pH average and minimum) predicted larval amphibian total length
(Figures 6.3-6.4), R2 = 0.141, R2adj = 0.116, F(2,69) = 5.6, p < 0.005 (Table 6.5). Of the
variance in larval amphibian total length, the model was able to explain 14.1 percent of
the variation (Table 6.5). Amphibian weight was predicted using three predictor
variables (environmental principal component 6- water pH average and minimum, pool
morphology principal component 2- hydroperiod and maximum pool depth, and
environmental principal component 4- water pH maximum and range) (Figures 6.5-6.7),
R2 = 0.352, R2adj = 0.313, F(4, 67) = 9.1, p < 0.001 (Table 6.6). The model was able to
interpret 20.2 percent of variance in larval amphibian weight (Table 6.6). The model
scaled mass index found one variable (environmental principal component 6- water ph
average and minimum) that significantly represented scaed mass index for larval
amphibians (Figure 6.8), R2 = 0.215, R2adj = 0.204, F(1,70) = 19.2, p < 0.001 (Table 6.7).
182
Figure 6.1. Linear regression between pool morphology principal component 1 (pool
area) and larval amphibian snout to vent length observed during 2008-2009, 2009-2010,
and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management Area
and William B. Bankhead National Forest.
183
Figure 6.2. Linear regression environmental principal component 4 (water pH maximum
and range) and larval amphibian snout to vent length observed during 2008-2009, 20092010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest.
184
Table 6.2. Multiple regression beta coefficients for larval amphibian snout to vent length.
Model
Constant
Pool morphology principal
component 1 (pool area)
Environmental principal
component 4 (water pH
maximum and range)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
1.266
0.010
Standardized Coefficients
Beta
t
126.343
P-value
<0.001
0.025
0.010
0.277
2.471
0.016
-0.023
0.010
-0.252
-2.247
0.028
185
Figure 6.3. Linear regression between environmental principal component 4 (water pH
maximum and range) and larval amphibian total length observed during 2008-2009,
2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest.
186
Figure 6.4. Linear regression between environmental principal component 6 (water pH
average and minimum) and larval amphibian total length observed during 2008-2009,
2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife
Management Area and William B. Bankhead National Forest.
187
Table 6.3. Multiple regression beta coefficients for larval amphibian total length.
Model
Constant
Environmental Principal Component 4
(Water pH Maximum and Range)
Environmental Principal Component 6
(Water pH Average and Minimum)
Coefficients
Unstandardized Coefficients
Beta
Standard
Error
1.58
0.01
Standardized Coefficients
Beta
t
P-value
153.53
<0.001
-0.03
0.01
-0.29
-2.56
0.013
0.02
0.01
0.24
2.18
0.033
188
Figure 6.5. Linear regression between environmental principal component 6 (water pH
average and minimum) and larval amphibian weight observed during 2008-2009, 20092010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest.
189
Figure 6.6. Linear regression between pool morphology principal component 2
(hydroperiod and maximum pool depth) and larval amphibian weight observed during
2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest.
190
Figure 6.7. Linear regression between environmental principal component 4 (water pH
maximum and range) and larval amphibian weight observed during 2008-2009, 20092010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest.
191
Table 6.4. Multiple regression beta coefficients for larval amphibian weight.
Model
Constant
Breeding Season 2
Environmental Principal Component 6
(water pH average and minimum)
Pool Morphology Principal Component 2
(hydroperiod and maximum pool depth)
Environmental Principal Component 4
(water pH maximum and range)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
-0.17
0.03
0.13
0.06
Standardized Coefficients
Beta
0.23
t
-4.97
2.09
P-value
<0.001
0.040
0.07
0.03
0.25
2.51
0.014
0.08
0.03
0.29
2.83
0.006
-0.08
0.03
-0.28
-2.49
0.015
192
Figure 6.8. Linear regression between environmental principal component 6 (water pH
average and minimum) and the scaled mass index for larval ampbians observed during
2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D. Martin Skyline
Wildlife Management Area and William B. Bankhead National Forest.
193
Table 6.5. Multiple regression beta coefficients for the scaled mass index for larval amphibians.
Model
Constant
Environmental Principal Component 6
(Water pH Average and Minimum)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
-0.12
0.03
0.14
0.03
194
Standardized Coefficients
Beta
0.46
t
P-value
-3.87
<0.001
4.38
<0.001
Figure 6.9. Linear regression between pool morphology principal component 2
(hydroperiod and maximum pool depth) and the Quetelet’s Index for larval amphibians
observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D.
Martin Skyline Wildlife Management Area and William B. Bankhead National Forest.
195
Figure 6.10. Linear regression between pool morphology principal component 1 (pool
area) and the Quetelet’s Index for larval amphibians observed during 2008-2009, 20092010, and 2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management
Area and William B. Bankhead National Forest.
196
Table 6.6. Multiple regression beta coefficients for the Quetelet’s Index’s for larval amphibians.
Coefficients
Unstandardized Coefficients
Standardized Coefficients
Model
Constant
Pool Morphology Principal
Component 2 (Hydroperiod
and Maximum Pool Depth)
Breeding Season
Pool Morphology Principal
Component 1 (Pool Area)
Beta
Standard Error
-2.78
0.02
0.08
0.13
0.02
0.04
-0.04
0.02
197
Beta
t
P-value
-115.19
<0.001
0.38
0.31
3.76
3.10
<0.001
0.003
-0.22
-2.16
0.035
This model revealed that 21.5 percent of variance in scaled mass index for larval
amphibian (Table 6.7). The model found three predictor variables (pool morphology
principal component 2- hydroperiod and maximum pool depth, breeding season, pool
morphology principal component 1- pool area) that could explain Quetelet’s Index for
larval amphibians (Figures 6.9-6.10), R2 = 0.314, R2adj = 0.284, F(3,68) = 10.4, p < 0.001
(Table 6.8). The model accounted for 31.4 percent of the variation for the Quetelet’s
Index for larval amphibians (Table 6.8). Finally, the overall model found two predictors
(pool morphology principal component 2- hydroperiod and maximum pool depth and
pool morphology principal component 1- pool area) that significantly predicted Fulton’s
Index for larval amphibians (Figures 6.11-6.12), R2 = 0.166, R2adj = 0.141, F(2,69) = 6.8,
p = 0.002 (Table 6.9).
The model represented 16.6 percent variation for the Fulton’s
Index for larval amphibians (Table 6.9).
Discussion
In this study, I assessed the differences between amphibian morphometrics,
weight, and body condition indices. With respect to my three hypotheses, there were not
enough amphibian larvae captured within each pool to compare by individual amphibian
species. All of the amphibian species were pooled to look for overall differences across
vernal pool classes. There were no differences found, with the exception of the
Quetelet’s Index. This could be the result of variation across body condition of the 18
different amphibian species sampled. The Quetelet’s Index showed that the amphibians
198
Figure 6.11. Linear regression between pool morphology principal component 2
(hydroperiod and maximum pool depth) and the Fulton’s Index for larval amphibians
observed during 2008-2009, 2009-2010, and 2010-2011 breeding seasons at James D.
Martin Skyline Wildlife Management Area and William B. Bankhead National Forest.
199
Figure 6.12. Linear regression between pool morphology principal component (pool area)
and Fulton’s Index for larval amphibians observed during 2008-2009, 2009-2010, and
2010-2011 breeding seasons at James D. Martin Skyline Wildlife Management Area and
William B. Bankhead National Forest.
200
Table 6.7. Multiple regression beta coefficients for the Fulton’s Index for larval amphiobians.
Coefficients
Unstandardized Coefficients
Model
Constant
Pool Morphology Principal Component 2
(Hydroperiod and Maximum Pool Depth)
Pool Morphology Principal Component 1
(Pool Area)
Beta
-0.91
Standard Error
0.03
0.09
0.03
-0.08
0.03
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Standardized
Coefficients
Beta
t
-27.25
P-value
<0.001
0.32
2.89
0.005
-2.54
-2.31
0.024
with the higest body condition values were found within open pools, and the larval
amphibians with the lowest body condition values were found within the closed pools.
Partially open pools were not different from open or closed pools, so these vernal pools
appeared to serve as intermediate class between open and closed ponds. When these
variables were compared using a multiple regression, the predictors differed among size
metrics, but there were some similarities, such as water pH, pool area, and hydroperiod
and maximum pool depth.
The water quality within the vernal pools influences larval development through
rate of metamorphosis and size (Hocking and Semlitsch, 2008; Harper and Semlitsch,
2007. Pool biophysical conditions have been noted in amphibian abundances. James and
Semlitsch (2011) noted that reductions in amphibian abundance are attributed to aquatic
breeding sites characteristics such as water quality (James and Semlitsch, 2011). I did
note relationships between environmental variables within the vernal pools and larval
amphibian body condition, but the variance explained was never higher than 35 percent.
Water pH seemed to have a weak positive relationship among larval amphibian snout to
vent length, total length, weight, and the scaled mass index. While the relationship was
significant, the environmental variables within vernal pools were weak predictors.
Hecnar and M’Closkey (1996) found a similar relationship with water chemistry
variables when attempting to predict amphibian species richness. While their relationship
was significant, the model only explained 19 percent of the variance. Larval amphibians
have been shown to be sensitive to low pH, and acidity have affected larval growth
(Cummins, 1989; Alford and Richards, 1999). Dale and Freedman (1985) found that
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hatching success decreased in pool breeding amphibians at water pH decreased.
Mazerolle (2005) found a similar relationship when examining greenfrog egg masses and
tadpoles.
Pool area hydroperiod also had weak positive relationships with larval amphibian
snout to vent length, weight, the Quetelet’s Index, and the Fulton’s Index. Dissolved
oxygen is important for larval amphibian development in anuran tadpoles. Algae that
bloom within vernal pools give off oxygen when they conduct photosynthesis in reponse
to sunlight that penetrates the pool’s surface. This relationship may have been weak,
because not all amphibian larvae feed on algae to develop and metamorphose (Altig et
al., 2007). For example caudate species, such as amsytomatids feed on detritus material
within vernal pools (Altig et al., 2007).
Increased growth and development benefits the larvae by reducing the time they
are vulnerable to predators and decreasing the risk of mortality due to pond drying
(Hocking and Semlitsch, 2007). This relationship was shown in the Quetelet’s Index.
The majority of the species captured were anuran species, which may explain why body
condition was higher in open vernal pools when compared to closed ones. Hocking and
Semlitsch (2008) noted that Gray treefrog tadpoles emerged earlier in open pools, but
they tended to be smaller in size at metamorphosis than tadpoles in closed habitat.
Smaller size at metamorphosis can reduce fitness through lower energy stores, delayed
reproductive maturity, reduced fecundity of females, and lower survival (Hocking and
Semlitsch, 2008). Hocking and Semlitsch (2008) also claimed that a smaller body size
would increase the surface area to volume ratio and make small individuals more
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susceptible to desiccation. Amphibian reproductive success and persistence can be
determined by aquatic habitat quality (James and Semlitsch, 2011).
There was a relationship between environmental variables and amphibian size,
but the variance explained was low and the relationship was weak. The differences
across the wetland classes may be seen more clearly across species, but it is difficult to
compare these size differences when species occurrence was limited throughout the
vernal pool study. While a difference in body condition was seen in one index, it may
have been more pronounced if I had been able to compare across different species.
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CHAPTER 7
THE INFLUENCE OF FOREST MANAGEMENT PRACTICES
ON BREEDING POOL ACCESSIBILITY, BREEDING-SITE SELECTION, AND
BREEDING PERFORMANCE OF AMPHIBIANS IN GRUNDY COUNTY,
TENNESSEE
Introduction
In Eastern North American forests, amphibians are one of the most abundant and
diverse vertebrate taxa (Russell et al., 2004). They are often inconspicuous, due to their
small size, nocturnal, and in many cases fossorial behavior (Pough et al., 1987;
deMaynadier and Hunter, 1995; Homyack and Haas, 2009). Of the 450 native
herpetofauna species in North America, half are found in the Southeast, with 20 percent
being endemic (Russell et al., 2004). Amphibians are important components of many
ecosystems because their abundance and biomass affect ecosystem function through
complex trophic interactions (Dodd et al., 2007). Amphibian species not only vary in
their habitat and geographic range, but also in their life cycle.
Pool-breeding amphibians transfer energy from aquatic to terrestrial habitats.
These animals undergo a biphasic life cycle that begins with a larval stage within aquatic
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habitat, and then metamorphose into a terrestrial form which can emigrate from natal
ponds and disperse to upland forests within surrounding landscapes (Corburn, 2004;
Calhoun and deMaynadier, 2008). While several amphibian species have biphasic life
cycles which require aquatic and terrestrial habitats, juveniles and adults spend the
majority of their lives foraging within the terrestrial environments (Semlitsch et al.,
2009). Of these taxa, many salamander species are migratory and move along forest
corridors or between habitat patches (Davic and Welsh, 2004). Within the terrestrial
habitat, anurans and caudates occupy small home ranges and are limited in their dispersal
abilities (Semlitsch et al., 2009). Several species of salamanders also migrate to vernal
pools in order to reproduce but return to upland forest to forage, aestivate, and brumate
(Russell et al., 2004). Most pool-breeding salamanders depend on upland forests for
foraging, aestivation, and brumation (Brodman, 2010). Once emergent amphibians
disperse from vernal pools into the surrounding uplands, they form an energy pathway
across the aquatic-terrestrial gradient (deMaynadier and Hunter, 1995). In addition to
their role in energy pathways, amphibians can potentially indicate environmental changes
such as increased surface temperature, soil erosion, and other environmental disturbances
(deMaynadier and Hunter, 1995).
Amphibians have evolved the ability to maximize their foraging and breeding
activity within cool and moist conditions (Dupuis et al., 1995). Their life history
characteristics have resulted in an ability to use moist forest leaf litter as refuge as well as
make use of underground retreats (Davic and Welsh, 2004). Amphibians have semipermeable, moist skin which serves as a respiratory function, but this adaptation also
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increases their susceptibility to desiccation when exposed to hot and dry conditions
(Dupuis et al., 1995; Greenberg and Waldrop, 2008). Most amphibians exhibit a
complex life cycle. Therefore, the loss or alteration of either aquatic or terrestrial habitats
by fragmentation can have an adverse effect on amphibian survivorship (Semlitsch et al.,
2009; Brodman, 2010).
Several authors have reported that amphibians are in decline worldwide, and
various hypotheses have been suggested to explain the causes (Alfords and Richards,
1999; Stuart et al., 2004; Beebee and Griffiths, 2005; Loehle et al., 2005). As seen with
worldwide losses in biodiversity, the loss, alteration, and degradation of habitat are
among the most important factors threatening populations of wildlife species in the
United States (deMaynadier and Hunter, 1995; Homyack and Haas, 2009). Despite
recognition of all of these factors as major contributors to amphibian declines, it is habitat
loss that is thought to be the major threat to forest dwelling species (Marsh and Beckham,
2004; Semlitsch et al., 2009). Anthropogenic alteration and degradation of habitat can
also produce differing landscape patterns which can lead to local amphibian decline or
population extinction (Hansen et al., 1991; Mazerolle, 2001; Homyack and Haas, 2009).
To effectively manage a forested landscape for amhpibians, it is necessary to understand
the potential effects tmanagement techniques have on these organisms (Knapp et al.,
2003).
While natural resource extraction is strongly associated with the loss and
modification of forests and ensuing affects on wildlife populations, forest management
studies have shown that amphibian populations are affected differently (Felix, 2007;
207
Peterman and Semlitsch, 2009; Semlitsch et al., 2009; Sutton, 2010; Cantrell et al., 2013).
Forest management practices that result in extensive loss of canopy cover and disturbance
to ground cover have modified the microclimate of the management unit and surrounding
forests, as well as been associated with reductions in amphibian relative abundance
(Herbeck and Larsen, 1999; Rothermel and Luhring, 2005). While forest management
practices have the potential to negatively influence amphibian populations, studies have
shown that amphibians were unchanged by shelterwood and thinning, and in some cases
an increased in American Toad abundance was observed (Greenberg and Waldrop, 2008;
Cantrell et al., 2013; Sutton et al., 2013) .
Shelterwood and thinning can be used to promote oak regeneration and these can
produce a wide range of amphibian habitat changes (Raybuck et al., 2012). Shade
intolerant species, such as oaks and hickories, have been regenerated using even-aged
silviculture (Herbeck and Larsen, 1999). While shelterwood harvests reduce canopy
cover, they also also promote vegetation repsrouting, and can potentially increase coarse
woody debris (Raybuck et al., 2012). The shelterwood method of regeneration involves
the removal of overstory trees in at least two separate harvests (Bartman et al., 2001).
Microhabitat responses to regeneration cuttings have been known to alter salamander
microhabitats (Herbeck and Larsen, 1999), but Felix (2007) showed that while
microhabitat features were initially different after regeneration treatments with control
treatments having higer leaf litter depth, eventually all of the treatments became similar.
Cantrell et al. (2013) also found a difference between leaf litter depth among shelterwood
treatments, with control treatments having higher leaf litter depth than shelterwood
208
treatments. While leaf litter decreased in these studies, coarse woody debris did increase
within shelterwood treatments, thereby providing refugia for amphibians (Felix, 2007;
Cantrell et al., 2013). The response of vertebrate communities to anthropogenic
disturbance has been known to be affected by local physical and biotic changes as well as
changes in the landscape patterns within the managed forest stands (Hansen et al., 1991;
Lowe and Bolger, 2002).
Amphibian response to forest management has been relatively well researched in
the eastern United States, and timber harvesting can often affect these animals’ habitat
(Herbeck and Larsen, 1999; Knapp et al., 2003). Studies investigating the impacts of
timber management activities on salamanders have typically examined the response to
various cutting practices (Duguay and Wood, 2002). Openings in the forest canopy result
in more variable temperature (Strojny and Hunter, 2010). These changes in the canopy
can result in a change in microclimate that could lead to amphibian desiccation
(Greenberg, 2001; Greenberg and Lathrop, 2008). Amphibians can potentially be
affected by soil wan water temperature changes that result from increased levels of solar
radiation following canopy loss (Hutchens e al., 2004). Forest management can remove
forest canopy, which opens the forest canopy and exposes the forest floor to increased
light penetration, which can increase forest floor temperatures through accelerated
evaporative water loss from the soil and understory (Herbeck and Larsen, 1999; Bartman
et al., 2001; Brooks and Kyker-Snowman, 2008; Crawford and Semlitsch, 2008; Raybuck
et al., 2012). High ambient temperature and wind can cause amphibian dehydration
(Dupuis et al., 1995). If relative humidity is held constant, dehydration rates will increase
209
as temperature increases (Rothermel and Luhring, 2005). Essential surface activities,
such as foraging and mating, could be restricted by hot, dry conditions (Strojny and
Hunter, 2010).
The objectives of this research were to (1) determine the influence of forest
management practices on pool breeding amphibian diversity within artificial pools (2) to
assess the adult amphibian accessibility to the breeding site after the canopy was
removed, (3) assess adult choice of breeding sites with different canopy cover conditions,
and (4) compare amphibian breeding performance among pools with different conditions.
I hypothesized that (1) reduction of forest canopy and fragmentation of forested
habitat will lower adult and larval amphibian abundance during breeding season. I
predicted that (2) canopy reductions will change the microclimate conditions within the
forest stands around the vernal pool resulting in dryer and higher temperature conditions.
Finally, (3) reduction in forest canopy cover at the forest stand level would serve as a
“filter”, and it will result in higher abundances of larval and adult anurans.
Methods
Site Description
The Mid Cumberland Plateau region covers 11,525 square kilometers in all or
part of 18 counties in Tennessee (Smalley, 1982). The study site was located in Grundy
County, Tennessee at an elevation between 390-550 meters above sea level (Cantrell et
al., 2013). The forest stands are located on strongly dissected margins and sides of the
210
Cumberland Plateau escarpment, which is located along the eastern escarpment of
Burrow’s Cove (Greenberg et al., 2008; Cantrell et al., 2013). The site is composed of a
mixed mesophytic forest, where mixed oak and oak-hickory communities were dominant
(Braun, 1950; Smalley, 1982). Within the uplands, the dominant overstory trees at the
site include yellow poplar (Liriodendron tulipifera), sugar maple (Acer saccharum),
white oak (Quercus alba), pignut hickory (Carya glabra), and northern red Oak (Quercus
rubra) (Cantrell et al., 2013). The stands have an average basal area of 22.5 meters/
hectare and 164 stems per hectare (Cantrell et al., 2013).
Study Design
Regional Oak Study Treatments
The Upland Hardwood Ecology and Management Research Work Unit of The
Southern Research Station of the United States Department of Agriculture Forest Service
(USDA FS) implemented an oak regeneration study (Regional Oak Study- ROS) at forest
stands within Grundy County. The study implemented three treatments (shelterwood,
oak shelterwood, and control) to test the effectiveness of silvicultural methods currently
predicted to regenerate oak across moisture gradients of site qualities in upland oak
systems.
The experimental design was a completely randomized design (CRD) (Greenberg,
2008; Cantrell, 2011). The following criteria were met for all the treatment units which
included: (1) each treatment unit had mature closed canopy stands with trees that were
over 70 years old, and (2) the stands had not been affected by anthropogenic or natural
211
disturbances within the last 15-20 years (Cantrell, 2011). A total of twenty treatment
units (three treatments plus one control, five replicates) were used with each being five
hectares in size (Greenberg et al., 2008). Treatments were randomly assigned to each
unit, resulting in a completely randomized design (CRD) (Greenberg, 2008). Most stands
were adjacent to one another, separated by buffers that were at least twenty meters in size
(Cantrell et al., 2013). Treatment units contain fully stocked, closed-canopied stands in
which oak comprises at least 10% of the overstory, and they had not encountered major
anthropogenic or natural disturbances within the last fifteen to twenty years (Greenberg et
al., 2008; Cantrell et al., 2013). Basal area contains approximately two meters2/ hectare
of basal area beneath the main canopy, and at least 1000 oak seedlings/ hectare
(Greenberg et al., 2008). Treatment stands were similar in elevation, slope, aspect, and
forest composition (Cantrell et al., 2013).
The prescription for the shelterwood treatment followed the guidelines outlined in
Brose et al. (1999) (Greenberg et al., 2008). The treatments removed sixty to seventy
percent of the basal area using chainsaw felling and grapple skidding along predesignated trails (Cantrell et al., 2013). Residual trees were based on species, diameter,
and quality (Greenberg et al., 2008; Cantrell et al., 2013). Many dominant and codominant oak species were left on site (Greenberg et al., 2008; Cantrell et al., 2013).
Trees that were harvested had their crown, limbs, and branches removed on site leaving
the majority of slash within the unit (Cantrell et al., 2013). Harvest was conducting in
fall-winter of 2008-2009 and summer-fall 2010 (Cantrell, 2011).
212
The prescription for the oak shelterwood treatment followed the guidelines
presented in Loftis (1990) (Greenberg et al., 2008). The initial step was to remove the
complete midstory (trees >5.0cm and <25.0 cm dbh) using the herbicide Garlon 3A by
way of the hack and squirt method (Greenberg et al., 2008). Approximately one ml of
diluted (1:1) Garlon 3A solution was sprayed into 1 waist height incision per every 7.5
cm of bole diameter of each midstory tree marked for removal (Greenberg et al., 2008).
Application of the herbicide was made prior to leaf fall in late 2008, but the initial 2008
treatment did not kill the targeted midstory trees, so the herbicide was successfully
reapplied in the fall/ winter of 2009 (Greenberg et al., 2008; Cantrell et al., 2013).
Amphibian Field Experiment
A three-factor split-split plot design was created with the main factor being the
disturbance treatment, distance to the forest edge as subplot, and shading condition as
split-split plot. I used shelterwood and oak-shelterwood treatment stands of the ROS
study. In addition control stands ere established solely for the field experiment (Figure
7.1). The aps in the control stands were created by killing a codominant tree at ten, fifty,
and one hundred meters from road edge using the same herbicide as used in the oak
shelterwood treatment. These gaps wer roughly five meters in diameter and created
variation in light condition with control stands.
Two treatments had five replications (control and oak shelterwood), and
shelterwood treatment had four replications. Within each treatment, there were three
pool arrays with each array consisting of three pools, at ten meters, fifty meters, and one
213
hundred meters from the treatment’s edge (Figures 7.2-7.4). Each pool array consisted of
a plastic pool (91 cm X 61 cm X 46 cm) which was buried flush with the ground and
received one of three shading treatments that manipulated canopy cover (Figures 7.5-7.6).
The three shading treatments were thirty percent, fity percent, and eighty four percent of
the light levels within each treatment stand (Pentair ltd., USA) (Figures 7.7-7.10). Each
array was made of 2.54 cm diameter pvc pipe that was 2.1 meters by 2.4 meters with
shade cloth zipped tied to the sides and corners. Each canopy array was 0.38 meters tall,
and was buried 0.15 meters below ground.
This study was conducted from March 2010 through September 2011. These
pools were sampled monthly from March through June and November during the 2010
field season. The artificial ponds were not sampled from July through October, 2010 due
to canopy array construction and placement over the artificial ponds. During the 2011
field season, these sites were sampled monthly from January through September
excluding August due to canopy array repairs.
Amphibian Sampling
Two types of amphibian sampling were used in this project: visual encounter
surveys for amphibian eggs and net sampling for amphibian larvae and juveniles. A one
minute visual encounter survey was conducted at each artificial pond. During this
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Figure 7.1. Canopy array locations at Burrows Cove in Grundy County, Tennessee in
2010 and 2011. Different colored polygons symbolize various treatment conditions.
215
Figure 7.2. Diagram of Shelterwood treatment with canopy array locations within the
treatment at Burrows Cove in Grundy County, Tennessee in 2010 and 2011.
216
Figure 7.3. Diagram of Oak Shelterwood treatment with canopy array locations within
the treatment at Burrows Cove in Grundy County, Tennessee in 2010 and 2011.
217
Figure 7.4. Diagram of Control with Gaps treatment with canopy array locations within
the treatment at Burrows Cove in Grundy County, Tennessee in 2010 and 2011.
218
Figure 7.5. Diagram of Canopy Array pools at Burrows Cove in Grundy County, Tennessee in 2010 and 2011.
219
Figure 7.6. Photograph of artificial pool dimensions and canopy array dimensions without shade cloth.
220
Figure 7.7. Canopy Array pools at Canopy with Gaps Treatment (Treatment thirty four) which are ten meters from edge at Burrows
Cove in Grundy County, Tennessee in 2010 and 2011.
221
Figure 7.8. Thirty percent canopy array pool at Canopy with Gaps Treatment (Treatment thirty four) which is ten meters from edge at
Burrows Cove in Grundy County, Tennessee in 2010 and 2011.
222
Figure 7.9. Fifty percent canopy array pool at Canopy with Gaps Treatment (Treatment thirty four) which is ten meters from edge at
Burrows Cove in Grundy County, Tennessee in 2010 and 2011.
223
Figure 7.10. Eighty four percent canopy array pool at Canopy with Gaps Treatment (Treatment thirty four) which is ten meters from
edge at Burrows Cove in Grundy County, Tennessee in 2010 and 2011.
224
survey, the egg masses were counted and identified to species. The number of eggs
within each mass was counted as well. Visual-encounter-egg-mass surveys were
executed using a headlamp and a tally counter. After the egg surveys were completed,
larval and juvenile amphibians were sampled within the artificial pools. A 7.6 cm x 7.6
cm aquarium net was used to sample for animals. Twenty lengthwise swipes within each
pond were used to calculate the amphibian total captures during each sample event. A
subsample of animals was processed for size metrics such as snout to vent length, tail
length, and weight. To calculate the animals that would be sampled and then measured,
the total captures were placed into an equation. The equation used was Log10 [Total
Captures per species] * 10 = animal samples per species. The equation was used for each
species. Once all of the animals had been captured within each artificial pool, the total
number of captures per species was determined. This capture number was then put into
the equation, and the number of animals that would be sampled, processed, and measured
was calculated. Three measurements were taken (plastic dial caliper and a digital scale)
from each larval amphibian including: snout to vent length (svl), tail length (tl), and
weight (wt). The number of egg masses and larval amphibians were tallied to provide a
measure of reproductive output and survival of larval amphibians within each pool
(Halverson et al., 2003; Heyer et al., 1994). The larval amphibians were tagged using a
dorsal fin clip as part of a mark-recapture study (Heyer et al., 1994; Ferner, 2007).
Survivorship was documented as the larval amphibians developed.
Microclimate and Microhabitat
225
A suite of environmental variables was measured at each pool including water and
soil temperature, pH, and dissolved oxygen. Leaf litter depth was also taken at each pool
during each survey. A ruler measured the leaf litter depth from the top of the leaf litter to
the mineral soil the nearest millimeter (Felix, 2007; Cantrell, 2011). The measurement
was taken adjacent to each pool.
H8 Hobo Data Loggers © (Onset Computer Coporation Bourne, MA) were used
to collect ambient temperature and relative humidity at three locations within each
treatment. Three data loggers were placed in each treatment. One data logger was placed
ten meters from treatment edge, fifty meters from treatment edge, and one hundred
meters from treatment edge. One data logger was placed adjacent to each set of three
canopy arrays to measure microclimate variables along the distance gradient within each
treatment. Data loggers recorded data during four times of day which included: 0000,
0600, 1200, and 1800h (Central Standard Time). That data were not used in this analysis,
due to problems with the data loggers.
Statistical Analysis
Split-split-plot experiment data waere tested Factoral analysis of variance
(ANOVA) and multiple comparisons (Tukey Test) were used based on the results of the
ANOVA (SAS 9.2). Since the canopy arrays were being constructed and implemented
between 2009 and 2011, a split-plot design was used for the 2010 breeding season.
Treatment was the main factor and distance was the subfactor. During the 2011 breeding
season a split-split plot design was used with treatment being the main factor, distance
226
being a subfactor, and canopy array being the sub-subfactor. Since sampling was
executed at different interval between the 2010 and 2011 field seasons, ANOVAs were
run separately for each year. A principal components analysis (PCA) was used to discern
associations of the environmental variables during the 2011 field season. Canonical
correspondence analysis (CCA) was applied to assess the relationship between amphibian
species and local environmental variables. Measurements were not compared between
breeding seasons due to unequal sampling. Multiple linear regression was used to
compare the environmental variables against amphibian abundance which was used as
the dependent variables. Transformations were executed to achieve a normal distribution
for all of the amphibian abundances. Evaluation of linearity led to log transformations
for the amphibian, Cope’s Gray Treefrog tadpole , American and Fowler’s Toad tadpole,
and Spring Peeper tadpole abundances abundances.
Results
During this study, 4,324 larval amphibians were counted and 146 (5,776 embryos)
amphibian egg masses were tallied. During the 2010 breeding season 1,540 tadpoles
were captured (297 tadpoles measured and weighed), and 2,784 tadpoles were sampled
during the 2011 breeding season (1,390 tadpoles measured and weighed). Eight larval
amphibian species were captured. These species included: Hyla chrysoscelis (Cope’s
Gray Treefrog), Pseudacris c. crucifer (Sping Peeper), Acris c. crepitans (Eastern Cricket
Frog), Anaxyrus americanus (American Toad), Anaxyrus fowleri (Fowler’s Toad),
227
Scaphiopus holbrookii (Eastern Spadefoot Toad), Lithobates palustris (Pickerel Frog),
and Lithobates sphenocephalus urticularius (Southern Leopard Frog). All eight species
were pooled for the total abundance measurements, while Cope’s Gray Treefrog, Spring
Peeper, and American and Fowler’s Toads tadpole abundances were analyzed.
Few amphibian egg masses were found during this study. One Spring Peeper egg
mass was counted during the 2010 breeding season, and 145 Cope’s Gray Treefrog
Fowler’s Toad egg masses were encountered during 2011. Egg mass were only found in
20 artificial ponds, so they were excluded from the analysis.
Ptincipal Components Analysis (PCA)
Principal components analysis (PCA) was executed on the environmental
variables for the 2010, and a second PCA was carried out on the environmental variables
for the 2011 breeding season. Using twenty variables, the 2010 PCA produced five
components that accounted for 85 percent of the total variation (Table 7.1). The Kaiser
Meyer Olkin measure of sampling adequacy was 0.582, Bartlett’s test of sphericity had
an approximate Chi-Square of 4644.4 (p-value < 0.001). Percent dissolved oxygen
average, percent dissolved oxygen maximum, water pH average, water pH maximum,
percent dissolved oxygen range, percent dissolved oxygen minimum, soil temperature
maximum, soil temperature range, water temperature maximum, water temperature
range, leaf litter depth minimum, water temperature minimum, soil temperature
minimum, water temperature average, soil temperature average, leaf litter depth
maximum, leaf litter depth average, leaf litter depth range, water pH minimum, and water
228
Table 7.1. Factor loadings for rotated components using principal components analysis based on (environmental variables of 123
artificial pools in Grundy County, Tennessee from 2010). The results were based on varimax rotation with Kaiser Normalization.
Variables
Percent Dissolved Oxygen Average
Percent Dissolved Oxygen Maximum
Water pH Average
Water pH Maximum
Percent Dissolved Oxygen Range
Percent Dissolved Oxygen Minimum
Soil Temperature Maximum
Soil Temperature Range
Water Temperature Maximum
Water Temperature Range
Leaf Litter Depth Minimum
Water Temperature Minimum
Soil Temperature Minimum
Water Temperature Average
Soil Temperature Average
Leaf Litter Depth Maximum
Leaf Litter Depth Average
Leaf Litter Depth Range
Water pH Minimum
Water pH Range
PC1
0.92
0.89
0.86
0.84
0.75
0.59
0.23
0.12
0.14
-0.06
-0.26
0.11
0.10
0.17
0.27
0.02
-0.20
0.14
0.25
0.41
PC2
0.06
0.16
0.00
0.21
0.41
-0.18
0.87
0.86
0.86
0.84
-0.57
-0.29
-0.29
0.38
0.31
0.07
-0.34
0.35
-0.14
0.24
Eigenvalue
Variance Accounted (%)
Cumulative Variance (%)
6.06
30.31
30.31
4.96
24.78
55.09
229
Component
PC3
0.24
0.19
0.00
-0.08
0.09
0.34
0.26
-0.37
0.35
-0.39
0.30
0.89
0.88
0.81
0.74
-0.14
-0.04
-0.29
0.02
-0.06
PC4
-0.02
0.02
-0.13
0.03
0.03
-0.14
0.00
0.01
0.05
0.07
0.24
-0.04
-0.05
-0.26
-0.34
0.96
0.86
0.75
-0.13
0.11
PC5
0.01
0.08
-0.33
0.21
0.16
-0.29
0.14
0.10
0.08
0.11
-0.18
0.01
0.03
-0.09
-0.12
0.14
-0.05
0.21
-0.92
0.83
2.58
12.88
67.96
2.12
10.59
78.55
1.29
6.47
85.02
pH range comprised the 2010 PCA (Table 7.1). The first component accounted for 30.3
percent of the total variation (PC1- Percent Dissolved Oxygen, water pH average and
maximum), and it was comprised of percent dissolved oxygen average, percent dissolved
oxygen maximum, water pH average, water pH maximum, percent dissolved oxygen
range, and percent dissolved oxygen minimum (Table 7.1). All of these variables had
positive factor loadings. The second principal component (PC2- leaf litter depth
minimum, soil and water temperature maximum and range) represented 24.8 percent of
the total variation, and it included the following variables: soil temperature maximum,
soil temperature range, water temperature maximum, water temperature range, and leaf
litter depth minimum (Table 7.1). All of the variables had positive factor loadings,
except leaf litter depth which had a negative facto loading. The third principal
component (PC3- water and soil temperature minimum and average) signified 12.9
percent of the total variation (Table 7.1). This component was made up of water
temperature minimum, soil temperature minimum, water temperature average, soil
temperature average, and all of the variables had positive factor loadings (Table 7.1).
The fourth principal component (PC4- leaf litter depth) characterized 10.6 percent of the
total variation (Table 7.1). The following features made up the component: leaf litter
depth maximum, leaf litter depth average, and leaf litter depth range (Table 7.1). All of
the variables had positive factor loadings (Table 7.1). Finally, the fifth principal
component (PC5- water pH minmum and range) exhibited 6.5 percent of the variation,
and the following variables made up this component: water pH minimum and water pH
230
range (Table 7.1). Water pH minimum had a negative factor loading and water pH range
had a positive factor loading (Table 7.1).
Using twenty variables, the 2011 PCA produced seven components that
represented 87.3 percent of the total variation (Table 7.2). The Kaiser Meyer Olkin
measure if sampling adequacy was 0.626, Bartlett’s test of sphericity had an approximate
Chi-Square of 4220.9 (p-value < 0.001). Water temperature minimum, soil temperature
minimum, water temperature range, percent dissolved oxygen range, percent dissolved
oxygen maximum, percent dissolved oxygen average, leaf litter depth average, leaf litter
depth maximum, leaf litter depth range, leaf litter depth minimum, water temperature
minimum, soil temperature minimum, water temperature average, soil temperature
average, leaf litter depth maximum, leaf litter depth average, leaf litter depth range, water
pH minimum, and water pH range comprised the 2011 PCA (Table 7.2). The first
principal component accounted for 33.9 percent of the total variation (PC1- percent
dissolved oxygen, water and soil temperature minimum, and water temperature range),
and it was made up of water temperature minimum, soil temperature minimum, water
temperature range, percent dissolved oxygen range, percent dissolved oxygen maximum,
and percent dissolved oxygen average (Table 7.2). Water and soil temperature
minimums had negative factor loadings, while water temperature range, percent dissolved
oxygen range, percent dissolved oxygen maximum, and percent dissolved oxygen
averages had positive factor loadings (Table 7.2). The second principal component (PC2leaf litter depth) represented 14.8 percent included leaf litter depth average, maximum,
231
Table 7.2. Factor loadings for rotated components using principal components analysis based on (environmental variables of
123 artificial pools in Grundy County, Tennessee from 2011). The results were based on varimax rotation with Kaiser
Normalization.
Variables
Water Temperature Minimum
Soil Temperature Minimum
Water Temperature Range
Percent Dissolved Oxygen Range
Percent Dissolved Oxygen Maximum
Percent Dissolved Oxygen Average
Leaf Litter Depth Average
Leaf Litter Depth Maximum
Leaf Litter Depth Range
Leaf Litter Depth Minimum
Soil Temperature Average
Soil Temperature Maximum
Soil Temperature Range
Water pH Maximum
Water pH Range
Water pH Average
Water Temperature Average
Water Temperature Maximum
Percent Dissolved Oxygen Minimum
Water pH Minimum
PC1
-0.88
-0.86
0.84
0.78
0.73
0.65
0.19
0.20
0.21
-0.07
-0.08
0.38
0.49
0.15
0.17
0.07
-0.17
0.48
-0.11
-0.10
PC2
-0.30
-0.22
0.25
0.02
-0.03
-0.06
0.94
0.93
0.89
0.56
-0.05
0.15
0.19
0.07
0.07
0.03
-0.16
0.08
-0.07
-0.04
PC3
-0.17
-0.01
0.12
0.23
0.25
0.22
0.09
0.14
0.17
-0.32
0.93
0.88
0.80
0.09
0.10
-0.03
0.17
0.09
0.01
-0.06
Component
PC4
-0.04
0.03
0.10
0.27
0.25
0.27
0.01
0.08
0.08
0.00
0.06
0.07
0.6
0.90
0.77
0.73
0.02
0.68
-0.11
-0.03
Eigenvalue
Variance Accounted (%)
Cumulative Variance (%)
6.77
33.86
33.86
2.97
14.85
48.71
1.99
9.93
58.64
1.73
8.65
67.29
232
PC5
0.02
-0.02
0.35
-0.04
-0.02
-0.06
0.00
0.00
0.01
-0.13
0.21
0.06
0.02
-0.04
-0.01
0.19
0.92
0.84
0.07
-0.04
PC6
0.13
0.23
-0.12
0.31
0.47
0.62
-0.07
-0.09
-0.11
0.16
0.18
0.01
-0.05
-0.04
-0.04
0.04
0.09
-0.02
0.78
0.02
PC7
0.06
-0.02
-0.06
-0.13
-0.13
-0.04
-0.05
-0.02
-0.02
0.01
-0.05
-0.04
-0.04
-0.12
-0.58
0.41
-0.01
-0.01
0.06
0.94
1.57
7.86
75.15
1.35
6.73
81.88
1.09
5.45
87.33
range, and minimum (Table 7.2). All of these variables had positive factor loadings
(Table 7.2). The third principal component (PC3- soil temperature) signified 9.9 percent
of the total variation, and it contained soil temperature average, maximum, and range
(Table 7.2). All of these variables had positive factor loadings. The fourth principal
component (PC4- water pH) represented 8.7 percent of the variation (Table 7.2). This
principal component was made up of water pH maximum, range, and average (Table
7.2). These variables had positive factor loadings. The fifth principal component (PC5water temperature) exhibited 7.9 percent of the variation, and it housed water temperature
average and maximum (Table 7.2). Both variables had positive factor loadings. The
sixth principal component (PC6- percent dissolved oxygen minimum) accounted for 6.7
percent of the variation, and percent dissolved oxygen minimum was the only variable
within this component (Table 7.2). It had a positive factor loading. The seventh
principal component (PC7- water pH minimum) showed 5.4 percent of the variation, and
water pH minimum was the variable that was represented by this component (Table 7.2).
It had a positive factor loading.
Factorial Analysis of Variance (ANOVA)
A factorial analysis of variance (ANOVA) was compared across treatments,
distance from edge categories, and their categories for the 2010 breeding season.
Environmental principal component one (percent dissolved oxygen, water pH average
and minimum), environmental principal component two (leaf litter depth minimum, soil
and water temperature maximum and range), environmental principal component three
233
Table 7.3. Means (±Standard Deviations) of environmental variables in shelterwood harvest, oak shelterwood, and control with
gaps treatments within Grundy County, Tennessee (2010).
Treatments
Variables
Year
Canopy with Gaps
(n=5)
Oak Shelterwood
(n=5)
Shelterwood
(n=4)
Factorial
ANOVAa
Treatment
Environmental Principal Component 1
(Percent Dissolved Oxygen, water pH
Average and Maximum)
2010
0.59±0.73C
-0.93±0.41A
0.18±1.04B
45.7***
Environmental Principal Component 2
(Leaf Litter Depth Minimum, Soil, and
Water Temperature Maximum and
Range)
2010
-0.41±1.02A
0.11±0.68B
0.34±1.08B
7.0**
Environmental Principal Component 3
(Water and Soil Temperature Minimum
and Average)
2010
-0.28±1.22A
-0.19±0.64A
0.47±0.83B
7.9**
Environmental Principal Component 4
(Leaf Litter Depth)
2010
0.31±0.83
-0.15±0.91
-0.19±1.17
3.5
Environmental Principal Component 5
(Water pH)
2010
-0.43±0.89A
0.08±0.59B
0.39±1.21B
8.2***
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
234
(water and soil temperature minimum and average), and environmental principal
component five (water pH) were significantly different among treatments (Table 7.3).
Environmental principal component one was higher in the canopy with gaps treatments
when compared to shelterwood and oak shelterwood treatments (Figure 7.11). The
environmental principal component one was higher in shelterwood treatments when
compared to oak shelterwood treatments (Figure 7.11). Environmental principal
components two and five were higher in shelterwood and oak shelterwood treatments
when compared to control with gaps treatments (Figures 7.12 and 7.14). The third
environmental principal component was highest in shelterwood treatments when
compared to control with gaps and oak shelterwood treatments (Figure 7.13).
Environmental principal component one was significantly different among distance from
edge subplots (Table 7.5). Larval amphibian abundance was also found to be different
across treatments (Table 7.7). Shelterwood treatments had higher abundances than oak
shelterwood and canopy with gaps treatments (Figure 7.19). In addition to overall larval
amphibian abundance, Spring Peeper tadpole abundance was difference across treatments
with (Table 7.7). Spring Peeper tadpole abundance was higher in shelterwood treatments
when compared to control with gap and oak shelterwood treamtents (Figure 7.20).
Environmental principal component one was different across distance to edge subplots
(Table 7.6). Environmental principal component one was higher at the ten and one
hundred meter subplots, when compared to the fifty meter subplots (Figure 7.15). There
was no difference in larval amphibian abundance across distance from edge treatment
subplots (Table 7.8).
235
Figure 7.11. Boxplot with error bars showing difference in environmental principal
component 1 (percent dissolved oxygen and water pH average and maximum) across
control with gaps treatment, oak shelterwood treatment, and shelterwood treatment
observed during the 2010 breeding season at Burrows Cove in Grundy County,
Tennessee. The middle line is the median, the box is 50% inter-quartile range, the
whiskers are at maximum and minimum values within 1.5 inter-quartile range, and the
points outside the whisker are outliers.
236
Figure 7.12. Boxplot with error bars showing difference in environmental principal
component 2 (leaf litter depth minimum, soil and water temperature maximum and range)
across control with gaps treatment, oak shelterwood treatment, and shelterwood treatment
observed during the 2010 breeding season at Burrows Cove in Grundy County,
Tennessee. The middle line is the median, the box is 50% inter-quartile range, the
whiskers are at maximum and minimum values within 1.5 inter-quartile range, and the
points outside the whisker are outliers.
237
Figure 7.13. Boxplot with error bars showing difference in environmental principal
component 3 (water and soil temperature minimum and average) across control with gaps
treatment, oak shelterwood treatment, and shelterwood treatment observed during the
2010 breeding season at Burrows Cove in Grundy County, Tennessee. The middle line is
the median, the box is 50% inter-quartile range, the whiskers are at maximum and
minimum values within 1.5 inter-quartile range, and the points outside the whisker are
outliers.
238
Figure 7.14. Boxplot with error bars showing difference in environmental principal
component 5 (water pH) across control with gaps treatment, oak shelterwood treatment,
and shelterwood treatment observed during the 2010 breeding season at Burrows Cove in
Grundy County, Tennessee. The middle line is the median, the box is 50% inter-quartile
range, the whiskers are at maximum and minimum values within 1.5 inter-quartile range,
and the points outside the whisker are outliers.
239
Table 7.5. Means (±Standard Deviations) of environmental variables in ten meters, fifty meters, and one hundred meters from
edge subplots within Grundy County, Tennessee (2010).
Variables
Year
10 meters
(n=14)
Distance from Edge
50 meters
100 meters
(n=14)
(n=13)
Factorial ANOVAa
Distance Distance x
Treatment
Environmental Principal Component 1
(Percent Dissolved Oxygen, water pH
Average and Maximum)
2010
0.37±1.06Bb
-0.38±0.75A
0.02±1.04B
10.5***
0.8
Environmental Principal Component 2
(Leaf Litter Depth Minimum, Soil, and
Water Temperature Maximum and
Range)
2010
0.15±0.83
0.01±0.71
-0.16±1.35
0.8
1.7
Environmental Principal Component 3
(Water and Soil Temperature
Minimum and Average)
2010
0.04±0.94
-0.22±0.91
0.19±1.12
2.2
0.4
Environmental Principal Component 4
(Leaf Litter Depth)
2010
-0.14±1.06
0.02±0.82
0.12±1.11
0.8
0.5
Environmental Principal Component 5
(Water pH)
2010
0.09±1.06
0.02±1.11
-0.12±0.78
0.4
1.2
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among distance from edge subplots based on a Tukey test using year averages. Means in the same row
with different letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
240
Table 7.6. Means (±Standard Deviations) of environmental variables in ten meters, fifty meters, and one hundred meters from
edge subplots within Grundy County, Tennessee (2011).
Variables
Year
10 meters
(n=14)
Distance from Edge
50 meters (n=14)
100 meters
(n=13)
Factorial ANOVA
Distance Distance x
Treatment
Environmental Principal
Component 1 (Percent Dissolved
Oxygen, Water and Soil
Temperature Minimum, and Water
2011
0.14±0.82
-0.11±1.02
-0.02±1.14
0.6
0.6
Temperature Range)
Environmental Principal
Component 2 (Leaf Litter Depth)
2011
-0.13±0.66
0.23±1.44
-0.11±0.64
1.8
0.5
Environmental Principal
Component 3 (Soil Temperature)
2011
0.06±0.99
0.06±1.00
-0.12±1.02
0.4
1.8
Environmental Principal
Component 4 (Water pH)
2011
0.12±1.19
-0.05±0.87
-0.06±0.92
0.3
0.8
Environmental Principal
Component 5 (Water Temperature) 2011 0.32±1.41A
-0.08±0.89AB
-0.23±0.39B
2.3
2.1
Environmental Principal
Component 6 (Percent Dissolved
Oxygen Minimum)
2011
0.00±1.12
-0.04±0.85
0.04±1.05
0.1
1.9
Environmental Principal
Component 7 (Water pH Minimum) 2011
0.12±0.84
0.05±0.61
-0.17±1.39
0.7
1.3
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among distance from edge subplots based on a Tukey test using year averages. Means in the same row
with different letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
***p < 0.001
241
Table 7.7. Means (±Standard Deviations) of amphibian captures in shelterwood harvest, oak shelterwood, and control with
gaps treatments within Grundy County, Tennessee (2010 and 2011).
Treatments
Species
Amphibians
Cope’s Gray
Treefrog (Hyla
chrysoscelis)
American and
Fowler’s Toads
(Anaxyrus
americanus and
fowleri)
Year
Recaptures
2010
2011
Captures
(excluding
recaptures)
1539
3160
2010
2011
2010
2011
Factorial
ANOVAa
0
2
Canopy with
Gaps
(n=5)
13.2±23.1Ab
274.2±251.3
Oak
Shelterwood
(n=5)
1.6±3.1A
101.0±148.7
Shelterwood
(n=4)
366.3±315.6B
321.0±303.8
Treatment
5.1*
2.9
133
2195
0
2
7.6±10.8
271.0±247.1B
0.2±0.4
37.2±28.9A
23.5±45.7
163.8±175.1AB
2.5
4.8**
1
936
0
0
0.0±0.0
3.0±6.2
0.0±0.0
61.8±138.2
0.3±0.3
153.0±163.9
Spring Peepers
(Pseudacris
crucifer)
1.3
2.8
2010
1357
0
5.6±12.5A
1.4±3.6A
330.5±320.0B
4.4*
2011
4
0
0.4±0.9
0.0±0.0
0.5±0.6
0.9
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
242
Table 7.8. Means (±Standard Deviations) of amphibian captures in ten meters, fifty meters, and one hundred meters from edge
subplots within Grundy County, Tennessee (2010 and 2011).
Species
Year
Amphibians
Cope’s Gray
Treefrog (Hyla
chrysoscelis)
American and
Fowler’s Toads
(Anaxyrus
americanus and
fowleri)
Distance from Edge
10 meters
50 meters
100 meters
(n=14)
(n=14)
(n=13)
Factorial ANOVAa
Distance
Distance x
Treatment
Recaptures
2010
2011
Captures
(excluding
recaptures)
1539
3160
0
0
69.6±201.9
94.2±104.8
36.7±91.4
55.4±94.9
3.9±13.9
82.0±128.0
1.4
0.9
1.2
2.0
2010
2011
133
2195
0
2
2.1±6.4
48.7±59.3
7.1±23.4
34.3±44.8
0.4±1.1
79.5±127.6
1.1
1.7
1.2
2.2
2010
2011
1
936
0
0
0.1±0.3
43.9±92.7
0.0±0.0
20.8±62.4
0.0±0.0
2.4±6.4
1.4
2.2
1.3
0.9
Spring Peepers
(Pseudacris
crucifer)
2010
1357
0
67.3±202.1
29.6±70.1
0.0±0.0
1.6
1.5
2011
4
0
0.3±0.6
0.0±0.0
0.0±0.0
3.2
0.9
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among canopy array subplots based on a Tukey test using year averages. Means in the same row with
different letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
243
Figure 7.15. Boxplot with error bars showing difference in environmental principal
component 1 (percent dissolved oxygen and water pH average and maximum) across ten
meters from edge, fifty meters from edge, and one hundred meters from edge observed
during the 2010 breeding season at Burrows Cove in Grundy County, Tennessee. The
middle line is the median, the box is 50% inter-quartile range, the whiskers are at
maximum and minimum values within 1.5 inter-quartile range, and the points outside the
whisker are outliers.
244
Figure 7.19. Boxplot with error bars showing difference in larval amphibian abundance
across control with gaps treatment, oak shelterwood treatment, and shelterwood treatment
observed during the 2010 breeding season at Burrows Cove in Grundy County,
Tennessee. The middle line is the median, the box is 50% inter-quartile range, the
whiskers are at maximum and minimum values within 1.5 inter-quartile range, and the
points outside the whisker are outliers.
245
Figure 7.20. Boxplot with error bars showing difference in Spring Peeper tadpole
abundance across control with gaps treatment, oak shelterwood treatment, and
shelterwood treatment observed during the 2010 breeding season at Burrows Cove in
Grundy County, Tennessee. The middle line is the median, the box is 50% inter-quartile
range, the whiskers are at maximum and minimum values within 1.5 inter-quartile range,
and the points outside the whisker are outliers.
246
A second factorial ANOVA was compared across treatments, distance from edge
subplots, canopy array sublplots, and their interactions for the 2011 breeding season.
Environmental principal component three (soil temperature), environmental principal
component four (water pH), and environmental principal component six (percent
dissolved oxygen minimum) were signficiantly different across treatments (Table 7.4).
Environmental principal component three, four, and six were higher in the canopy with
gaps treatment than the oak shelterwod treatment (Figure 7.16-7.18). Environmental
principal component three, four, and size within the shelterwood treatment were not
different from the control with gap or the oak shelterwood treatment (Figure 7.16-7.18).
There was no difference across the distance from edge or canopy array subplots subplots
(Table 7.6 and 7.9). Cope’s Gray treefrog tadpole abundance was different across
treatments (Table 7.7). The canopy with gaps treatment had higher Cope’s Gray
Treefrog tadpole abundance than the oak shelterwood treatment (Figure 7.21). The
Cope’s Gray Treefrog tadpole abundance within the shelterwood treatment was not
different from the canopy with gaps treatment or the oak shelterwood treatment (Figure
7.21). There was a difference in larval amphibian abundance across canopy array
subplots (Table 7.10). Larval amphibian abundance was higher in thirty percent canopy
arrays than eighty four percent canopy arrays (Figure 7.22). Fifty percent canopy arrays
were not different from thirty or eighty four percent canopy arrays (Figure 7.22).
Canonical Correspondence Analysis (CCA)
247
Table 7.4. Means (±Standard Deviations) of environmental variables in shelterwood harvest, oak shelterwood, and control with
gaps treatments within Grundy County, Tennessee (2011).
Treatments
Variables
Year
Canopy with Gaps
(n=5)
Oak Shelterwood
(n=5)
Shelterwood
(n=4)
Factorial
ANOVAa
Treatment
Environmental Principal Component 1
(Percent Dissolved Oxygen, Water and
Soil Temperature Minimum, and Water
Temperature Range)
2011
0.18±0.62
-0.32±0.83
0.09±1.35
2.5
Environmental Principal Component 2
(Leaf Litter Depth)
2011
0.01±0.53
0.27±1.55
-0.24±0.69
2.4
Environmental Principal Component 3
(Soil Temperature)
2011
0.35±1.26Bb
-0.27±0.81A
-0.13±0.72AB
4.3*
Environmental Principal Component 4
(Water pH)
2011
0.32±1.01B
-0.23±1.03A
-0.13±0.89AB
3.0*
Environmental Principal Component 5
(Water Temperature)
2011
0.21±1.54
-0.09±0.32
-0.14±0.57
1.6
Environmental Principal Component 6
(Percent Dissolved Oxygen Minimum)
2011
0.37±0.89B
-0.46±0.79A
0.01±1.12AB
8.1**
Environmental Principal Component 7
(Water pH Minimum)
2011
0.01±1.46
-0.18±0.49
0.14±0.68
1.0
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among treatments based on a Tukey test using year averages. Means in the same row with different
letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
248
Figure 7.16. Boxplot with error bars showing difference in environmental principal
component 3 (soil temperature) across control with gaps treatment, oak shelterwood
treatment, and shelterwood treatment observed during the 2011 breeding season at
Burrows Cove in Grundy County, Tennessee. The middle line is the median, the box is
50% inter-quartile range, the whiskers are at maximum and minimum values within 1.5
inter-quartile range, and the points outside the whisker are outliers.
249
Figure 7.17. Boxplot with error bars showing difference in environmental principal
component 4 (water pH) across control with gaps treatment, oak shelterwood treatment,
and shelterwood treatment observed during the 2011 breeding season at Burrows Cove in
Grundy County, Tennessee. The middle line is the median, the box is 50% inter-quartile
range, the whiskers are at maximum and minimum values within 1.5 inter-quartile range,
and the points outside the whisker are outliers.
250
Figure 7.18. Boxplot with error bars showing difference in environmental principal
component 6 (percent dissolved oxygen minimum) across control with gaps treatment,
oak shelterwood treatment, and shelterwood treatment observed during the 2011 breeding
season at Burrows Cove in Grundy County, Tennessee. The middle line is the median,
the box is 50% inter-quartile range, the whiskers are at maximum and minimum values
within 1.5 inter-quartile range, and the points outside the whisker are outliers.
251
Table 7.9. Means (±Standard Deviations) of environmental variables in thirty percent, fifty percent, and eighty four percent
canopy array subplots within Grundy County, Tennessee (2011).
Variables
Year
30 percent
(n=42)
Canopy Arrays
50 percent
(n=40)
84 percent
(n=41)
Canopy
Array
Factorial ANOVAa
Canopy
Canopy
Array x
Array x
Distance
Treatment
Canopy
Array x
Distance x
Treatment
Environmental
Principal
Component 1
(Percent Dissolved
Oxygen, Water and
Soil Temperature
Minimum, and
Water Temperature
Range)
2011
0.01±0.96
0.03±1.02
-0.03±1.04
0.1
0.2
0.1
0.3
Environmental
Principal
Component 2 (Leaf
Litter Depth)
2011
-0.04±0.69
0.00±0.70
0.04±1.44
1.8
0.2
0.4
0.9
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among canopy array subplots based on a Tukey test using year averages. Means in the same row with
different letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
252
Table 7.9. (Continued).
Variables
Year
30 percent
(n=42)
Canopy Arrays
50 percent
(n=40)
84 percent
(n=41)
Canopy
Array
Factorial ANOVAa
Canopy
Canopy
Array x
Array x
Distance
Treatment
Canopy
Array x
Distance x
Treatment
Environmental
Principal
Component 3 (Soil
Temperature)
2011
0.08±1.03
-0.09±0.91
0.02±1.07
0.2
0.2
0.5
0.2
Environmental
Principal
Component 4
(Water pH)
2011
0.04±0.86
0.05±1.21
-0.09±0.91
0.3
1.1
0.5
1.2
Environmental
Principal
Component 5
(Water
Temperature)
2011
0.03±0.87
0.11±1.17
-0.14±0.94
2.3
0.6
0.7
0.3
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among canopy array subplots based on a Tukey test using year averages. Means in the same row with
different letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
253
Table 7.9. (Continued).
Variables
Year
30 percent
(n=42)
Canopy Arrays
50 percent
(n=40)
84 percent
(n=41)
Canopy
Array
Factorial ANOVAa
Canopy
Canopy
Array x
Array x
Distance
Treatment
Canopy
Array x
Distance x
Treatment
Environmental
Principal
Component 6
(Percent Dissolved
Oxygen Minimum)
2011
0.16±0.98
0.11±1.04
-0.28±0.95
2.5
0.1
0.6
2.3*
Environmental
Principal
Component 7
(Water pH
Minimum)
2011
-0.19±1.32
0.12±0.89
0.07±0.68
0.9
1.1
0.8
0.7
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among canopy array subplots based on a Tukey test using year averages. Means in the same row with
different letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
254
Figure 7.21. Boxplot with error bars showing difference in Cope’s Gray Treefrog tadpole
abundance across control with gaps treatment, oak shelterwood treatment, and
shelterwood treatment observed during the 2011 breeding season at Burrows Cove in
Grundy County, Tennessee. The middle line is the median, the box is 50% inter-quartile
range, the whiskers are at maximum and minimum values within 1.5 inter-quartile range,
and the points outside the whisker are outliers.
255
Table 7.10. Means (±Standard Deviations) of amphibian captures in thirty percent, fifty percent, and eighty four percent
canopy array subplots within Grundy County, Tennessee (2010 and 2011).
Species
Year
Captures
(excluding
recaptures)
3160
Recaptures
30% Array
(n=42)
Canopy Arrays
50% Array
(n=40)
84% Array
(n=41)
Amphibians
2011
0
43.4±84.1Bb 21.9±33.6AB
11.2±19.9A
Cope’s Gray
Treefrog (Hyla
chrysoscelis)
2011
2195
2
29.7±63.6
14.1±24.9
9.3±17.4
American and
Fowler’s Toads
(Anaxyrus
americanus and
fowleri)
2011
936
0
13.4±55.9
7.8±23.8
1.5±9.1
Spring Peepers
(Pseudacris
crucifer)
2011
4
0
0.1±0.2
0.0±0.0
0.1±0.3
a
F statistics from a factorial ANOVA with significance level
b
Multiple comparisons among canopy array subplots based on a Tukey test using year averages. Means in the same row with
different letters were different (p<0.05).
*p < 0.05
**p < 0.01
***p < 0.001
256
Table 7.10. (Continued).
Species
Year
Canopy Array
Factorial ANOVAa
Canopy Array x Canopy Array x
Distance
Treatment
Amphibians
Cope’s Gray Treefrog
(Hyla chrysoscelis)
American and Fowler’s
Toads (Anaxyrus
americanus and fowleri)
Spring Peepers
(Pseudacris crucifer)
2011
3.3*
0.1
0.3
Canopy Array x
Distance x
Treatment
0.9
2011
2.5
0.6
1.3
0.3
2011
1.2
0.3
0.9
1.6
2011
1.4
1.4
0.6
0.6
257
Figure 7.22. Boxplot with error bars showing difference in larval amphibian abundance
across thirty percent, fifty percent, and eighty four percent canopy arrays observed during
the 2011 breeding season at Burrows Cove in Grundy County, Tennessee. The middle
line is the median, the box is 50% inter-quartile range, the whiskers are at maximum and
minimum values within 1.5 inter-quartile range, and the points outside the whisker are
outliers.
258
Canonical correspondence analysis (CCA) for the abundance of pool-breeding
amphibian larvae during the 2010 breeding season explained 100 percent (Axis 1: 55.9
percent, Axis 2: 0.2 percent, and Axis 3: 43.9 percent) of the variance associated with the
species data and 100 percent of the species-environment relationship variance (Axis 1:
99.7 percent and Axis 2: 2.3 percent) (Figure 7.23). During the 2011 breeding season,
the CCA represented 100 percent (Axis 1: 31.6 percent and Axis 2: 68.4 percent) of the
variance related to the species data and 100 percent of the species-environment
relationship variance (Axis 1: 100.0 percent) (Figure 7.24). The 2010 CCA showed a
gradient between percent dissolved oxygen, water pH, soil and water temperature, and
leaf litter depth. Within the 2010 CCA, the first axis was positively correlated to leaf
litter depth and water pH, but it was negatively correlated to percent dissolved oxygen,
soil and water temperatue, water pH average and maximum, and leaf litter depth
minimum (Figure 7.23). American and Fowler’s Toad tadpole abundance was positively
correlated with leaf litter depth minimum and soil and water temperature maximum and
range (Figure 7.23). In the 2011 CCA, there was a gradient between percent dissolved
oxygen, leaf litter depth, soil and water temperature, and water pH (Figure 7.24). Axis
one showed a gradient from higher water pH, water temperature, and percent dissolved
oxygen minimum to higher percent dissolved oxygen, leaf litter depth, and soil
temperature (Figure 7.24). Cope’s Gray Treefrog and American and Fowler’s Toad
tadpoles were weakly related to the variables (Figure 7.24). These species behaved as
generalists.
259
Figure 7.23. Bi-plot based on canonical correspondence analysis, showing the
relationship between environmental variables (arrows) and larval amphibians (triangles)
for the 2010 breeding season in at Burrows Cove in Grundy County, Tennessee.
Amphibian species codes: ANAM_ANF = American and Fowler’s Toads, HYCH =
Cope’s Gray Treefrog, and PSCR = Spring Peeper. Environmental variable codes: EPC1
= environmental principal component 1 (percent dissolved oxygen and water pH average
and maximum, EPC2 = environmental principal component 2 (leaf litter depth minimum
and soil and water temperature maximum and range), EPC3 = environmental principal
component 3 (water and soil temperature minimum and average), EPC4 = environmental
principal component 4 (leaf litter depth), and EPC5 = environmental principal component
5 (water pH).
260
Figure 7.24. Bi-plot based on canonical correspondence analysis, showing the
relationship between environmental variables (arrows) and larval amphibians (triangles)
for the 2011 breeding season at Burrows Cove in Grundy County, Tennessee.
Amphibian species codes: ANAM_ANF = American and Fowler’s Toads and HYCH =
Cope’s Gray Treefrog. Environmental variable codes: EPC1 = environmental principal
component 1 (percent dissolved oxygen, water and soil temperature minimum, and water
temperature range), EPC2 = environmental principal component 2 (leaf litter depth),
EPC3 = environmental principal component 3 (soil temperature), EPC4 = environmental
principal component 4 (water pH), EPC5 = environmental principal component 5 (water
temperature), EPC6 = environmental principal component 6 (percent dissolved oxygen
minimum), and EPC7 = environmental principal component 7 (water pH minimum).
261
Multiple Linear Regression (MLR)
Forward multiple linear regression was executed to determine the influence of
environmental variables on larval amphibian abundance in artificial pools. Multiple
regression was executed for larval abundances during the 2010 and 2011 breding seasons.
Regression results indicated that the overall model for larval amphibian abundance
(2010) found three predictor variables (environmental principal component two- leaf
litter depth minimum and soil and water temperature maximum and range, environmental
principal component one- percent dissolved oxygen and water pH average and maximum,
and environmental principal component five- water pH) significantly predicted total
larval amphibian abundance (Figures 7.25-7.27), R2 = 0.211, R2adj = 0.190, F(3,117), p <
0.001 (Table 7.11). This model represented 21.1 percent of the variance in larval
amphibian abundance (Table 7.11). The regression results showed that an overall model
of one predictor (environmental principal component four- leaf litter depth) (Figure 7.28)
significantly predicted Cope’s Gray Treefrog tadpole abundance (2010), R2 = 0.040, R2adj
= 0.032, F(1,119) = 4.9, p = 0.028 (Table 7.12). The model showed 4 percent variance in
Cope’s Gray Treefrog tadpole abundance (Table 7.12). The regression results indicated
that the overall model made up of four predictors (environmental principal component
two- leaf litter depth minimum and soil and water temperature maximum and range,
environmental principal component three- water and soil temperature minimum and
average, environmental principal component four- leaf litter depth, and environmental
principal component pone- percent dissolved oxygen and water pH average and
maximum) significantly predicted American and Fowler’s Toad tadpole abundance
(2010) (Figures 7.29-7.32), R2 = 0.154, R2adj = 0.124, F(4,116) = 5.3, p = 0.001 (Table
262
Table 7.11. Multiple regression beta coefficients for larval amphibian abundance at Burrows Cove in Grundy County,
Tennessee (2010).
Model
Constant
Environmental Principal Component 2
(Leaf Litter Depth Minimum and Soil and
Water Temperature Maximum and Range)
Environmental Principal Component 1
(Percent Dissolved Oxygen and Water pH
Average and Maximum)
Environmental Principal Component 5
(Water pH)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.26
0.05
Standardized Coefficients
Beta
t
5.35
P-value
<0.001
0.21
0.05
0.36
4.38
<0.001
0.13
0.05
0.23
2.75
0.007
0.10
0.05
0.17
2.11
0.037
263
Figure 7.25. Linear regression between environmental principal component 2 (leaf litter depth
minimum, soil and water temperature maximum and range) and larval amphibian abundance at
Burrows Cove in Grundy County, Tennessee (2010).
264
Figure 7.26. Linear regression between environmental principal component 1 (percent dissolved
oxygen and water pH average and maximum) and larval amphibian abundance at Burrows Cove
in Grundy County, Tennessee (2010).
265
Figure 7.27. Linear regression between environmental principal component 5 (water pH)
and larval amphibian abundance at Burrows Cove in Grundy County, Tennessee (2010).
266
Table 7.12. Multiple regression beta coefficients for Cope’s Gray Treefrog tadpole abundance at Burrows Cove in Grundy County,
Tennessee (2010).
Model
Constant
Environmental Principal Component 4
(Leaf Litter Depth)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.07
0.02
0.05
0.02
267
Standardized Coefficients
Beta
0.20
t
3.19
P-value
0.002
2.23
0.028
Figure 7.28. Linear regression between environmental principal component 4 (leaf litter depth)
and Cope’s Gray Treefrog tadpole abundance at Burrows Cove in Grundy County, Tennessee
(2010).
268
Figure 7.29. Linear regression between environmental principal component 2 (leaf litter depth
minimum and soil and water temperature maximum and range) and American and Fowler’s Toad
tadpole abundance at Burrows Cove in Grundy County, Tennessee (2010).
269
Figure 7.30. Linear regression between environmental principal component 3 (water and soil
temperature minimum and average) and American and Fowler’s Toad tadpole abundance at
Burrows Cove in Grundy County, Tennessee (2010).
270
Figure 7.31. Linear regression between environmental principal component 4 (leaf litter depth)
and American and Fowler’s Toad tadpole abundance at Burrows Cove in Grundy County,
Tennessee (2010).
271
Figure 7.32. Linear regression between environmental principal component 1 (percent
dissolved oxygen and water pH average and maximum) and American and Fowler’s Toad
tadpole abundance at Burrows Cove in Grundy County, Tennessee (2010).
272
Table 7.13. Multiple regression beta coefficients for American and Fowler’s Toad tadpole abundance at Burrows Cove in Grundy
County, Tennessee (2010).
Model
Constant
Environmental Principal Component 2
(Leaf Litter Depth Minimum and Soil and
Water Temperature Maximum and Range)
Environmental Principal Component 3
(Water and Soil Temperature Minimum
and Average)
Environmental Principal Component 4
(Leaf Litter Depth)
Environmental Principal Component 1
(Percent Dissolved Oxygen and Water pH
Average and Maximum)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.00
0.00
Standardized Coefficients
Beta
t
1.07
P-value
0.287
0.01
0.00
0.23
2.65
0.009
0.01
0.00
0.19
2.29
0.024
-0.01
0.00
-0.19
-2.21
0.029
0.01
0.00
0.17
1.99
0.049
273
7.13). The model showed 15.4 percent of the variance in American and Fowler’s toad
tadpole abundance (2010) (Table 7.13). The regression results showed that an overall
model of four predictors (environmental principal component two- leaf litter depth
minimum and soil and water temperature maximum and range, environmental principal
component five- water pH, environmental principal component one- percent dissolved
oxygen and water pH average and maximum, environmental principal component threewater and soil temperature minimum and average) significantly predicted Spring Peeper
tadpole abundance (2010) (Figures 7.33-7.36), R2 = 0.202, R2adj = 0.175, F(4,116) = 7.4,
p < 0.001 (Table 7.14). This model accounted for 20.2 percent of the variance in Spring
Peeper tadpole abundance (Table 7.14).
Forward multiple regression was also carried out for the 2011 breeding season.
The regression results found that an overall model of five predictors (environmental
principal component three- soil temperature, environmental principal component onepercent dissolved oxygen, water and soil temperature minimum, and water temperature
range, environmental principal component five- water temperature, environmental
principal component seven- water pH minimum, and environmental principal component
six- percent dissolved oxygen minimum) significantly predicted larval amphibian
abundance (2011) (Figures 7.37-7.41), R2 = 0.478, R2adj = 0.455, F(5,114) = 20.8, p <
0.001 (Table 7.15). This model represented 47.8 percent of the variance in larval
amphibian abundance (Table 7.15). The regression results displayed an overall model of
five predictors (environmental principal component three- soil temperature,
environmental principal component one- percent dissolved oxygen, water and soil
274
Figure 7.33. Linear regression between environmental principal component 2 (leaf litter depth
minimum and soil and water temperature maximum and range) and Spring Peeper tadpole
abundance at Burrows Cove in Grundy County, Tennessee (2010).
275
Figure 7.34 Linear regression between environmental principal component 5 (water pH) and
Spring Peeper tadpole abundance at Burrows Cove in Grundy County, Tennessee (2010).
276
Figure 7.35. Linear regression between environmental principal component 1 (percent dissolved
oxygen and water pH average and maximum) and Spring Peeper tadpole abundance at Burrows
Cove in Grundy County, Tennessee (2010).
277
Figure 7.36. Linear regression between environmental principal component 3 (water and
soil temperature minimum and average) and Spring Peeper tadpole abundance at Burrows
Cove in Grundy County, Tennessee (2010).
278
Table 7.14. Multiple regression beta coefficients for Spring Peeper tadpole abundance at Burrows Cove in Grundy County,
Tennessee (2010).
Model
Constant
Environmental Principal Component 2
(Leaf Litter Depth Minimum and Soil and
Water Temperature Maximum and Range)
Environmental Principal Component 5
(water pH)
Environmental Principal Component 1
(Percent Dissolved Oxygen and Water pH
Average and Maximum)
Environmental Principal Component 3
(Water and Soil Temperature Minimum
and Average)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.19
0.04
Standardized Coefficients
Beta
t
4.24
P-value
<0.001
0.17
0.05
0.32
3.77
<0.001
0.11
0.05
0.21
2.51
0.014
0.09
0.05
0.18
2.13
0.035
0.09
0.05
0.17
2.10
0.038
279
Table 7.15. Multiple regression beta coefficients for larval amphibian abundance at Burrows Cove in Grundy County,
Tennessee (2011).
Model
Constant
Environmental Principal Component 3
(Soil Temperature)
Environmental Principal Component 1
(Percent Dissolved Oxygen, Water and
Soil Temperature Minimum, and Water
Temperature Range)
Environmental Principal Component 5
(Water Temperature)
Environmental Principal Component 7
(Water pH Minimum)
Environmental Principal Component 6
(Percent Dissolved Oxygen Minimum)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.81
0.05
Standardized Coefficients
Beta
t
15.85
P-value
<0.001
0.34
0.05
0.44
6.51
<0.001
0.33
0.05
0.43
6.33
<0.001
0.16
0.05
0.21
3.12
0.002
-0.13
0.05
-0.17
-2.51
0.013
0.12
0.05
0.16
2.39
0.018
280
Figure 7.37. Linear regression between environmental principal component 3 at Burrows Cove in
Grundy County, Tennessee (soil temperature) and larval amphibian abundance (2011).
281
Figure 7.38. Linear regression between environmental principal component 1 (percent dissolved
oxygen, water and soil temperature minimum, and water temperature range) and larval
amphibian abundance at Burrows Cove in Grundy County, Tennessee (2011).
282
Figure 7.39. Linear regression between environmental principal component 5 (water
temperature) and larval amphibian abundance at Burrows Cove in Grundy County, Tennessee
(2011).
283
Figure 7.40. Linear regression between environmental principal component 7 (water pH
minimum) and larval amphibian abundance at Burrows Cove in Grundy County, Tennessee
(2011).
284
Figure 7.41. Linear regression between environmental principal component 6 (percent
dissolved minimum) and larval amphibian abundance at Burrows Cove in Grundy
County, Tennessee (2011).
285
temperature minimum, and water temperature range, environmental principal component
five- water temperature, environmental principal component seven- water pH minimum,
and environmental principal component six- percent dissolved oxygen minimum)
significantly projected Cope’s Gray Treefrog tadpole abundance (2011) (Figures 7.427.46), R2 = 0.461, R2adj = 0.437, F(5,114) = 19.5, p < 0.001 (Table 7.16). The model
showed 46.1 percent of the variance in Cope’s Gray Treefrog tadpole abundance (Table
7.16). The regression results found two predictors (environmental principal component
one- percent dissolved oxygen, water and soil temperature minimum, and water
temperature range and environmental principal component three- soil temperature)
significantly anticipated American and Fowler’s Toad tadpole abundance (2011) (Figures
7.47-7.48), R2 = 0.095, R2adj = 0.079, F(2,117) = 6.1, p = 0.003 (Table 7.17). The model
exhibited 9.5 percent of the variance in American and Fowler’s Toad tadpole abundance
(Table 7.17).
Discussion
Despite capturing
eight amphibian species, species richness and diversity indexes were not calculated due
to the fact that 87.6 percent of the total amphibian captures came from four species. The
286
Table 7.16. Multiple regression beta coefficients for Cope’s Gray Treefrog tadpole abundance at Burrows Cove in Grundy
County, Tennessee (2011).
Model
Constant
Environmental Principal Component 3
(Soil Temperature)
Environmental Principal Component 1
(Percent Dissolved Oxygen, Water and
Soil Temperature Minimum, and Water
Temperature Range)
Environmental Principal Component 5
(Water Temperature)
Environmental Principal Component 7
(Water pH Minimum)
Environmental Principal Component 6
(Percent Dissolved Oxygen Minimum)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.73
0.05
Standardized Coefficients
Beta
t
15.29
P-value
<0.001
0.28
0.05
0.40
5.87
<0.001
0.28
0.05
0.39
5.72
<0.001
0.18
0.05
0.26
3.79
<0.001
-0.14
0.05
-0.21
-2.99
0.003
0.13
0.05
0.18
2.63
0.010
287
Figure 7.42. Linear regression between environmental principal component 3 (soil temperature)
and Cope’s Gray Treefrog tadpole abundance at Burrows Cove in Grundy County, Tennessee
(2011).
288
Figure 7.43. Linear regression between environmental principal component 1 (percent dissolved
oxygen, water and soil temperature minimum, and water temperature range) and Cope’s Gray
Treefrog tadpole abundance at Burrows Cove in Grundy County, Tennessee (2011).
289
Figure 7.44. Linear regression between environmental principal component 5 (water
temperature) and Cope’s Gray Treefrog tadpole abundance at Burrows Cove in Grundy County,
Tennessee (2011).
290
Figure 7.45. Linear regression between environmental principal component 5 (water pH
minimum) and Cope’s Gray Treefrog tadpole abundance at Burrows Cove in Grundy County,
Tennessee (2011).
291
Figure 7.46. Linear regression between environmental principal component 6 (percent dissolved
oxygen minimum) and Cope’s Gray Treefrog tadpole abundance at Burrows Cove in Grundy
County, Tennessee (2011).
292
Table 7.17. Multiple regression beta coefficients for American and Fowler’s Toad tadpole abundance at Burrows Cove in
Grundy County, Tennessee (2011).
Model
Constant
Environmental Principal Component 1
(Percent Dissolved Oxygen, Water and
Soil Temperature Minimum, and Water
Temperature Range)
Environmental Principal Component 3
(Soil Temperature)
Coefficients
Unstandardized Coefficients
Beta
Standard Error
0.17
0.05
Standardized Coefficients
Beta
t
3.73
P-value
<0.001
0.13
0.05
0.25
2.83
0.005
0.09
0.05
0.18
2.06
0.041
293
Figure 7.47. Linear regression between environmental principal component 1 (percent
dissolved oxygen, water and soil temperature minimum, and water temperature range)
and American and Fowler’s Toad tadpole abundance at Burrows Cove in Grundy County,
Tennessee (2011).
294
Figure 7.48. Linear regression between environmental principal component 3 (soil
temperature) and American and Fowler’s Toad tadpole abundance at Burrows Cove in
Grundy County, Tennessee (2011).
295
vast majority of the artificial pools only held one or no species. The four dominant
species included: Cope’s Gray treefrog tadpoles, Spring Peeper tadpoles, American Toad
tadpoles, and Fowler’s Toad tadpoles. Finally, amphibian performance through
sizecomparisons was not assessed because a large number of the artificial ponds housed
fewer than five individuals (89.4 percent during the 2010 breeding season and 49.5
percent during the 2011 breeding season). The artificial pools housed few larval
amphibians. Most of the artificial pools were placed near the center of the treatment
(along the distance from edge gradient). Within the shelterwood treatment, the pools
were located near the skid trails, which are some of the most severely disturbed area
within the treatment. The locations of the pools may have influence the amphibians’
abilities to colonize and reproduce within these artificial pools. The fact that amphibians
were not present in these pools affects the statistical ability to compare body condition
between treatments, distance to edge subplots, and canopy array subplots.
Intermediate disturbance hypothesis was assessed using the larval amphibian
abundance. The intermediate disturbance hypothesis was not supported in this study.
Larval amphibian abundance was highest in shelterwood treatments in 2010, and there
was no difference during the 2011 breeding season. Felix (2007) did accept the
intermediate disturbance hypothesis when he assessed amphibian diversity across years.
Sutton et al. (2013) also did not accept the intermediate disturbance hypothesis.
Amphibian species diversity did not differ among treatments. Sutton (2010) noted that
partial harvesting procedures result in no influence on woodland amphibians. Felix
(2007) assessed more severe disturbance through clearcuts, but did not examine the effect
296
of the distance gradient. The reason that my results did not support intermediate
disturbance hypothesis is probably because the treatment stands were too small.
In this project, I hypothesized that canopy reductions would change the
environemtnal conditions within the forest stands around the vernal pool resulting in
higher temperature conditions. Water and soil temperature showed differences across
treatments, showing the highest values in the shelterwood treatment with some overlap
with the control with gaps treatment. The opening of the canopy seemed to expose the
understory to direct sunlight, which increased forest floor temperatures as well as
artificial pool water temperatures (Brooks and Kyker-Snowman, 2008; Felix et al., 2010).
Breeding pools overshadowed with less canopy closure have been been noted to have
high water temperatures than pools with higher canopy cover (Skelly et al., 2002; Felix et
al., 2010). A similar effect seemed to occur for dissolved oxygen, with the openings in
the canopy resulting in higher dissolved oxygen (Halverson et al., 2003). Both
temperature and dissolved oxygen are critical to anuran tadpole development with higher
temperatures causing anuran tadpoles develop and reach metamorphosis faster than the
same species that development in closed canopy vernal pools (Werner and Glennemeier,
1999; Skelly et al., 1999). Environmental conditions did differ among treatments with
shelterwood and canopy with gap treatments having higher water and soil temperatures
than oak shelterwood treatment did. The control with gaps’ similarity shelterwood
treatments in environmental conditions seems to be the result of the overstory openings
directly over the artificial ponds within the control with gaps treatments. One of the
environmental conditions that did not differ was leaf litter depth. Cantrell et al. (2013)
297
found differences between controls and shelterwood treatments, while I did not. This
difference could have been the result of the method by which leaf litter was collected.
Cantrell et al. (2013) used transect to determine leaf litter within the treatments, while I
measure leaf litter depth adjacent to the canopy arrays. The control with gaps treatment
was not different from the shelterwood treatments. This lack of a difference could be the
result of limited sampling as compared to Cantrell et al. (2013) or the microhabitat
conditions that was created directly beneath the the gap where the artificial pools were
implemented. The opening in the overstory may have been similar to the microhabitat
conditions of the shelterwood artificial pools.
For the distance gradient subplot, the percent dissolved oxygen wand water pH
showed a significant difference among subplots, with pools that were closest to edge had
the highest levels of dissolved oxygen and pools located at the fifty meter distance from
the edge have the lowest values. Pools at the one hundred meter have values that were
similar to or slightly lower than the pools at the ten meter distance. There appeared to be
a distance gradient that was seen during the 2010 breeding with percent dissolved
oxygen, but not during the 2011 breeding season. The canopy arrays showed no
environmental differences. This could be the result of the small size of the artificial pools
and the close proximity of each pool. The microclimate may be similar between the
pools despite the difference in artificial canopy cover.
I also hypothesized that a reduction in forest canopy would create movement
barriers for adult amphibians when accessing breeding pools. Larval amphibian
abundance showed differences across treatment with the majority of tadpoles being
298
sampled within the shelterwood treatments during the 2010 breeding season, even though
there was no difference during the 2011 breeding season. This could be the result of
bufonid species colonizing the breeding pools. During the 2010 breeding season, there
were few American and Fowler’s Toads that bred in the pools. During the 2011 breeding
season, these tadpoles represented 33 percent of the total abundance. American and
Fowler’s Toad tadpole abundance did not show a difference across treatments, and this
trend was noted by Cantrell (2013). American Toads are known to be habitat generalists,
so their ability to forage in a wide range of habitats could be the reason that a difference
was not seen across treatments during the breeding seasons (Forester et al., 2006;
Rittenhouse et al., 2008). The disturbance that was produced during the implementation
of the artificial was believed to be minimal and not affect the pool-breeding amphibians.
Cope’s Gray Treefrogs did show a difference among treatments during the 2011
breeding season even though there was not a difference during the 2010 breeding season.
More tadpoles were found in the control with gaps treatment than the oak shelterwood
treatment, and the shelterwood treatment was not different from the control with gaps
treatment or the oak shelterwood. Treefrog tadpoles were found most frequently in the
canopy with gaps and shelterwood treatments. During larval development, this species
was found in areas with higher temperatures (Hocking and Semlitsch, 2007). The
regression of 2011 data showed weak but positive relationships between treefrog tadpole
abundance and soil and water temperature, percent dissolved oxygen, and water pH.
Felix (2010) found that warmer temperatures, higher percent dissolved oxygen, and water
pH were optimal for Cope’s Gray Treefrog egg mass survival. Felix (2007) found that
299
these variables were related to tadpole food sources which are periphyton. Felix et al.
(2010) found that canopy cover affected the pool-breeding amphibian ovipositioning
sites. Hocking and Semlitsch (2008) noted similar effects in response to Cope’s Gray
Treefrog performance, with tadpoles having higher survival rates in sites with lower
canopy.
There was no relationship observed between amphibian larvae and distance to
forest edge. Felix et al. (2010) did not find a relationship between distance and
amphibian egg deposition. Spatial extent may explain why there was no difference
between breeding site accessibility and distance from the edge. The treatments were four
to five hectares in size, American and Fowler’s Toads can migrate distances between 231016 meters (Olham, 1966; Forester et al., 2006). Cope’s Gray Treefrog migration
distances range from 5.6 to 369.5 meters (Johnson et al., 2007). These distances exceed
the distance gradient and the width of each treatment. Hocking and Semlitsch (2007)
also found that Cope’s Gray Treefrogs were not impeded by a distance gradient of fifty
meters. While my gradient was longer, it did not serve as a barrier to egg ovipisiton and
larval development. To more adequately measure this gradient, distance from edge
subplots would need to reflect the migration capabilities of pool breeding amphibians
(Semlitsch and Bodie, 2003). Another consideration is that amphibians do not begin their
mirgations from the road edge. The distance gradient would need to take the migration
starting point into consideration.
Overall larval amphibian abundance showed a difference across pools with
different artificial canopy cover, with the highest abundance being in the pools with the
300
lowest amount of artificial canopy cover. Even though differences were not seen across
different amphibian species, there was preference tfor artificial pools with lower artificial
canopy over when all species were combined. Because hylid and bufonid species
accounted for 99.9 percent of the captures, the data suggested that artificial pools with
moderate to low (fifty percent or less) shading seemed to prefer by these species over the
pools with the highest artificial canopy cover. The anuran species that used the pools
develop faster in warmer temperatures and they also feed on algae that needs direct
sunlight to bloom within vernal pools (Earl et al., 2011; Skelly et al., 2005). These
adaptations allow anuran species to breed in and migrate from habitat that would cause
caudate species to increase their risk of desiccation (Veysey et al., 2009).
301
CHAPTER 8.
CONCLUSIONS
This study examined the effect of forest disturbance on pool breeding amphibian
reproductive fitness. Using two components, I was able to gain insight into the the
factors that influence pool breeding, amphibian diversity, size, and abundance.
Responses from the field experiment showed that anuran abundance within shelterwood
treatments was similar to control with gap stands. The first breeding season showed a
treatment effect, where larval amphibian abundance was higher in shelterwood treatments
than oak shelterwood or control treatments. The reasoning behind this difference
possibly stems from the fact that the amphibians occured were mostly anuran species:
hylids and bufonids, during the first field season. Anurans are more tolerant to warmer
conditions than caudate species. There was a positive relationship between overall larval
abundance and water temperature maximums and soil temperature. The ability to resist
desiccation of anuran species’ possibly plays a role in their ability to survive and breed
within artificial pools in shelterwood treatments. Anuran tadpoles also require warmer
302
temperatures to develop and reach metamorphosis. Despite this difference, there was not
an overall abundance difference in the second season. Even though there were no
differences among the overall abundance during the seond field season, there were
differences in abundance for Cope’s Gray Treefrogs. This species was found at higher
abundances in the control and the shelterwood treatments when compared to the oak
shelterwood treatments. This could possibly be associated with the warmer water
temperatures that this species needs to develop (Hocking and Semlitsch, 2007).
Intermediate disturbances such as shelterwoods may increase the abundances of anuran
species throughout hardwood landscapes.
Within my landscape study, anuran species represented the majority of the species
sampled, with the dominant species being Eastern Spadefoot Toads and Cope’s Gray
Treefrog. There were no differences in abundances across wetland classes, but ranges in
water temperature showed a positive relationship when predicting overall larval
amphibian abundance. This type of range is indicative of habitats that have a more open
canopy and allow more light penetration to vernal pool surface water. Warmer water
temperatures accelerate larval anuran development, so providing gaps in the overstory
overshadowing vernal pools may be critical for anuran colonization and breeding success.
Ficetola and Bernardi (2004) found that the breeding ponds with the highest amphibian
species richness had high sun exposure and were near other wetlands with numerous
species.
In addition to the openness of the pools, hydroperiod needs of amphibians must be
taken into consideration. Amphibian species that breed in vernal pools are either obligate
303
or facultative. These species only breed in these ecosystems or they prefer to breed in
these ecosystems. While surveying vernal pools, I noticed that several pools in the BNF
and SWMA were either altered or stocked with fish. Pools that were altered had to be
deepened or expanded so that they could become permanent. These pools rarely held any
salamander species, except for mole salamander paedomorphs, and even the anuran
species richness was lower in these wetlands that included vernal pools which were part
of my study. Introducing invasive predators into these vernal pools runs the risk of
negatively influencing amphibian populations to the landscape. Fish presence was an
important limiting factor for amphibian species (Ficetola and Bernardi, 2004). Vernal
pools are chosen over permanent wetlands by amphibian species, because they can not
support fish populations (Ficetola and Bernardi, 2004; Calhoun and deMaynadier, 2008).
The majority of the amphibian species that I sampled either bred exclusively in vernal
pools or prefered vernal pools as their breeding sites. Since alteration is seen as the
primary cause of amphibian declines, habitat maintenance is required once aquatic and
terrestrial habitats have been restored (Semlitsch, 2002). If habitat degradation is a
function of ongoing forces or threats such as erosion or succession, then active
maintenance is required (Semlitsch, 2002). Management of amphibian populations to
reverse recent declines will require defining high quality habitat for individual species or
groups of species, followed by efforts to retain or restore these habitats on the landscape
(Knutson et al., 1999).
While salamanders prefer forests with cool, shaded, and moist forest floors,
anuran species can benefit from the side effects associated with timber harvesting such as
304
the opening of the forest canopy which exposes vernal pools to direct sunlight (Brooks
and Kyker-Snowman, 2008). Within my study, the open pools were the habitat with the
highest diversity. Additionally, warmer soil temperatures had a positive influence on the
overall Cope’s Gray Treefrog abundance within my field experiment. The removal of
canopy trees and the resultant exposure could increase forest floor temperature (Brooks
and Kyker-Snowman, 2008). While opening the canopy can be beneficial to hylids,
having adjacent forest stands that work as corridors can reduce desiccation risks for
closed canopy species that may breed within open vernal pools (Brooks and KykerSnowman, 2008).
While several amphibians were sampled during this study, adult and juvenile
amphibian captures were lower than larval amphibian captures. To gain a more
complete idea of amphibian reproductive fitness, higher abundances of adult amphibians
that access and use vernal pools for reproduction are needed. Future research should
also look at metamorphic and juvenile amphibians that emigrate from vernal pools and
disperse within the landscape. Studies that assess reproductive fitness are critical. Drift
fences have been used in the James D. Martin Skyline Wildlife Management Area, but
this study monitored six pools and only ambystomatid salamander adults were
inventoried (Scott, 2008). Drift fences should be used to assess adults and juveniles
amphibians that use these wetlands. In addition to the drift fences, egg mass visual
encounter surveys would be helpful as well in allowing researchers a more complete
picture of reproductive fitness. While this type of research is ideal, it is difficult to
constantly monitor vernal pools throughout the landscape.
305
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APPENDIX 1.
LIST OF DEFINITIONS
Aestivation - A state of torpor characterized by reduced activity and metabolism
associated with summer or periods of high temperatures or drought.
Brumation - A condition of torpor during extended periods of low temperature (winter
dormancy), intended to distinguish such states of inactivity of amphibians and reptiles
from the term hibernation that is used commonly in reference to birds and mammals.
Canopy closure - the ground area covered by the crowns of trees or woody vegetation as
delimited by the vertical projection of crown perimeters and commonly expressed as a
percent of total ground cover. It measures the extent to which the crowns of trees are
nearing general contact with each other
Canopy cover - The proportion of ground or water covered by a vertical projection of the
outermost perimeter of the natural spread of foliage or plants, including small openings
within the canopy
Codominant (crown class) - a tree whose crown helps to form the general level of the
main canopy in even-aged stands, or in uneven aged stands, above the crown of the tree’s
immediate neighbors and receiving full light from above and partial light from the sides
Crown class - A category of tree based on its crown position relative to those of adjacent
trees
Desiccate - Reference to total drying
Dispersal- Movements of individuals away from a specific location to another, related to
various factors such as territoriality, departure from natal or denning sites, resources,
weather, or seasonal phenomena
Dominant (crown class)- a tree whose crown extends above the general level of the main
canopy of even-aged stands or, in uneven aged stands, above the crowns of the tree’s
immediate neighbors and receiving full
328
Edge- The more or less well-defined boundary between two or more elements of the
environment
Emigration- Reference to individuals leaving a population and traveling to another
location
Facultative- Not obligatory. Possessing flexibility to function or live under more than
one set of environmental conditions, or to express a characteristic capable of change
according to environmental demands. A behavior that may or may not occur, depending
on circumstances.
Fecundity- The quality of producing offspring, often used in reference to the numbers of
offspring. Potential fertility or reproductive capacity, specifically the quantity of
gametes, generally eggs, produced per individual over a specified period of time
Fitness- The ability of an organism to contribute its genes to the next generation, roughly
the number of offspring contributed to the next generation weighted by the probability of
surviving to reproduce
Forest cover- All trees and other plants occupying the ground in a forest, including any
ground cover.
Forest matrix- The most extensive and connected landscape element that plays the
dominant role in the landscape functioning. A landscape element surrounding a patch.
Gene flow- The exchange of genes between different populations attributable to
emigration and immigration of individuals
Genotype- The genetic make-up of an individual, which, along with other factors such as
environment, determines the organism’s phenotype.
Immigration- The process of individuals moving to, or entering, a population or region
from another location
Life cycle- Reference to the series of development stages characteristics of organisms
from fertilization through reproduction and death
Life history- The history of an individual organism, from the fertilization of the egg to its
death.
Migration- The directed movement of individuals, hence gene flow, between different
regions or populations.
329
Morphometry- Reference to the quantitative prameters of structures. A quantitative
measurement of a form or structure. The scientific measurement or analysis of the shape
or form of organisms. Quantitative analysis of variation in shape.
Obligate- Essential or necessary. Restricted or limited to a specific condition of life or
environment. Not flexible with respect condition of environment.
Overstory- That portion of the trees, in a forest of more than one story, forming the upper
or uppermost canopy layer.
Oviposition- The act of laying or depositing eggs
Performance- Key taks or functions such as feeding, digesting, moving, growing,
regulating water, that are crucial for survival and reproduction
Plasticity- Generally, the capacity for change in response to an external influence
Phenology- Reference to natural phenomena that recur periodically such as migration and
reproduction
Phenotype- The physical characteristics of an organism, which comprise the
manifestation of genetic expression. Any property of an organism that can be measured
(Lillywhite, 2008)
Phenotypic plasticity- Phenotypic variation due to environmental influence on the
phenotype associated with a particular genotype, representing genotype-environment
interaction
Philopatry- Reference to organisms that tend to remain in the location where they were
born
Recruitment- The influx of new members into a population by reproduction or
immigration
Reproductive success- The number of offspring from a given individual that survive to
reproduce
Sympatry- Living in the same geographic locationwith reference usually to the overlap in
geographic range of two closely related species, which remain otherwise distinct.
Torpor- A period or state of inactivity that is normally induced by cold or other adverse
climatic condition.
Understory- All forest vegetation growing under the midstory and overstory
330
Vagility- The ability to move from place to place
331
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Vita
Timothy Earl Baldwin was born on September 13, 1982 in Charlotte, North
Carolina to Tom and Phyllis Baldwin. He attended North Carolina State University in
Raleigh, North Carolina, and he received Bachelor of Science Degree in Zoology and
History in 2005. Tim then attended Marshall University in Huntington, West Virginia;
he received his Masters of Science Degree in Biological Sciences in 2007. He was
accepted into the doctoral program at Alabama Agricultural and Mechanical University.
Tim received his Ph.D in December 2013.
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