BIRD DIVERSITY AND ABUNDANCE ON RECLAIMED SURFACE
MINES IN ALABAMA: TEMPORAL AND HABITAT RELATED
VARIATIONS
by
RICHARD ROBERT BORTHWICK
A THESIS
Submitted in partial fulfillment of the requirements
for the degree of Master of Science
in the Department of Biological and Environmental Sciences
in the School of Graduate Studies
Alabama A&M University
Normal, Alabama 35762
May 2015
CERTIFICATE OF APPROVAL
Submitted by RICHARD BORTHWICK in partial fulfillment of the requirements
for the degree of MASTER OF SCIENCE specializing in BIOLOGY
Accepted on behalf of the Faculty of the Graduate School by the Thesis
Committee:
___________________________Dr. Yong Wang, Major Advisor
___________________________Dr. Luben Dimov
___________________________Dr. Dawn Lemke
___________________________Dr. Callie Schwetizer
_______________________________ Dean of the Graduate School
_______________________________ Date
ii
Copyright by
Richard Robert Borthwick
2015
iii
BIRD DIVERSITY AND ABUNDANCE ON RECLAIMED SURFACE COAL MINES
IN ALABAMA: TEMPORAL AND HABITAT RELATED VARIATIONS
Borthwick, Richard R., M.Sc., Alabama A&M University, 2015. 110 pp.
Thesis Advisor: Dr. Yong Wang
Surface mining transforms landscapes and ecosystems through the removal of vegetation
and soil. In the Shale Hills Region (SHR) of northern Alabama, approximately 30,500 ha
of land were permitted for surface mining between 1980 and 2005. Losses of vegetation
correlate with declines and displacement of songbird communities, while mines and mine
reclamation influence songbird communities. I studied avian response to reclaimed
surface mines by evaluating the following: what are avian species diversity, richness, and
abundance at the reclaimed mines? How do avian composition and abundance vary by
vegetation type and by chronosequence? How do avian communities relate to vegetation
succession? And, how do environmental conditions affect the avifauna? Point counts
were carried out at 200 plots on mined and surrounding non-mined areas. Mine categories
were classified by habitat and time since closure. The least diverse (1.75 ± 0.07) and
species-rich (5.22 ± 0.23) habitat type was conifer forests less than 15 years since
closure, while the most diverse (2.23 ± 0.18) and species-rich (6.42 ± 0.45) was
grasslands less than 15 years since closure. Interactions between time since closure and
habitat type affected 16 bird species, while habitat type affected 3 species and time
affected 2 species. Habitat type had a stronger link to bird community dynamics than
chronosequence. General Linear Models were linked to landscape-level management and
ordination techniques were used to help refine smaller scale habitat relationships with
iv
individual species. The presented findings demonstrate the importance of considering
cumulative effects, outline prospective timelines for ‘macroscopically successful’ mine
reclamation, and provide a scientific foundation for selecting management and restoration
objectives on mine sites, as related to avifauna biodiversity.
KEY WORDS: reclamation, diversity, occupancy, abundance, habitat, succession.
v
TABLE OF CONTENTS
CERTIFICATE OF APPROVAL
II
TABLE OF CONTENTS
VI
LIST OF TABLES
VIII
LIST OF FIGURES
IX
LIST OF ABBREVIATIONS
XII
ACKNOWLEDGEMENTS
XIV
CHAPTER 1 – INTRODUCTION AND LITERATURE REVIEW
0
Statement of the Problem
6
Purpose and Objectives of the Study
7
Hypotheses
9
Assumptions
10
Literature Review
History of Mining in Alabama
Mining Types
Landscape and Vegetation
Reclamation
Avifauna Response to Mining and Reclamation
Study Site
12
12
14
15
16
19
23
CHAPTER 2 - METHODS AND MATERIALS
25
Study Site Description
25
Mine Selection
Land Access
28
28
vi
Sampling Plot Selection
29
Avifauna Surveys
30
Vegetation Sampling
32
Statistical Analysis
34
CHAPTER 3 – RESULTS
41
Assessment of Between Year Variation in Bird Community
41
Assessment of Independence of Sampling Plots
44
Habitat Related Variation
45
Overall Diversity, Richness, and Densities of Birds
46
Species Specific Responses
51
Bird and Habitat Relationships
60
CHAPTER 4 – DISCUSSION AND MANAGEMENT IMPLICATIONS
63
Assumption Justifications
63
Results Exploration
66
Management Implications
71
APPENDICES
76
A 1 – Land Use Permission
77
A 2 – Detailed Location Maps
85
A 3 – Observed Bird Species
88
A 4 – Between-mine and Within-mine Comparisons
92
A.5 – Species Detection Curves
96
LITERATURE CITED
100
vii
LIST OF TABLES
Table 2-1. Treatment summary information. Area, Year Closed, and Years Since are an
average across the three mines in each treatment, and the number of sites is a total.
30
Table 3-1. Results of a rotated Principal Component Analysis of habitat variables.
44
Table 3-2. Results of General Linear Model test of vegetation variables compared across
temporal and habitat variations. NS or S denote significance at α = 0.05.
46
Table 3-3: Species that showed significant contrast values from comparing control means
to treatment means.
55
Table 3-4: Proportional Index of Community Similarities by treatments.
55
Table 3-5. Results of General Linear Models test of bird densities (per site, ~4.9 ha)
compared across temporal and habitat variations.
viii
57
LIST OF FIGURES
Figure 1-1: The Shale Hills Region of Alabama is demarcated by the grey polygon,
colored polygons are an indication of current activity in these areas. The area of focus for
this study is identified by a red square and was targeted as a result of long-term mine
activity.
5
Figure 1-2: Overview map of project-mine locations throughout the Shale Hills subRegion of Alabama as related to Birmingham. Highlighted polygons are mapped in more
detail in Appendix 2 – Detailed Location Maps. Translucent images are the wildlife
management areas targeted for non-mined sites.
11
Figure 1-3: Alabama with surface coal mines marked as black triangles. The area of
densest coal mine locations corresponds with the study area observed in figures 1-1 and
1-2 (EIA, 2014).
6
Figure 1-4: Mapped physiographic lines for the Cumberland Plateau (left) and the Coastal
Plains (right). These two region meet in Tuscaloosa County (red) within the project
footprint, and are transitional through Bibb County as well (University of Alabama,
2015).
24
ix
Figure 2-1. Percent cover guidelines used to estimate cloud cover (B.C. Ministry of
Forests, 1997).
32
Figure 3-1: Comparison ofadjusted songbird abundance at each sampling plot between
2013 (n=23) and 2014 (n=23). Eror bars are standard error. No significant differences
were observed (paired t-test, t(α=0.025,22) = 1.79, df = 22, p = 0.087).
42
Figure 3-2: Comparison of adjusted abundance per plot of Pileated Woodpecker
(Dryocopus pileatus), Downy Woodpecker (Picoides pubescens), and Blue Grosbeak
(Passerina caerulea) at each sampling plot between 2013 (n=23) and 2014 (n = 23).
Error bars are standard error.
43
Figure 3-3: Shannon diversity across 9 treatments of reclaimed surface mines in
northwestern Alabama with standard error and Tukey test results. Two-letter label
categories correspond with TIME by HABITAT treatments respectively. Time is either
young (Y), medium (M), or old (O). Habitat is either grassland (G), conifer (C), or mixed
(M). So, YC is ‘young conifer’, while MM is ‘medium mixed’.
47
Figure 3-4: Rarefied species richness across 9 treatments of reclaimed surface mines in
northwestern Alabama with standard error and Tukey test results. Two-letter label
categories correspond with TIME by HABITAT treatments respectively. Time is either
young (Y), medium (M), or old (O). Habitat is either grassland (G), conifer (C), or mixed
(M). So, YC is ‘young conifer’, while MM is ‘medium mixed’.
x
48
Figure 3-5: Average bird densities per hectare across 9 treatments of reclaimed surface
mines in northwestern Alabama with standard error and Tukey test results. Two-letter
label categories correspond with TIME by HABITAT treatments respectively. Time is
either young (Y), medium (M), or old (O). Habitat is either grassland (G), conifer (C), or
mixed (M). So, YC is ‘young conifer’, while MM is ‘medium mixed’.
48
Figure 3.6: Species accumulation curves across treatments. Asymptotes indicate the
number of sites required per treatment to ensure full species detections. Shading
represents variance.
49
Figure 3-7a: Detection function of American Crow (Corvus brachyrhynchos), indicating
likelihood of detection of the species at a given distance.
50
Figure 3-7b: Detection function of Blue-Gray Gnatcatcher (Polioptila caerulea),
indicating likelihood of detection of the species at a given distance.
50
Table 3-6: Tests of nine canonical dimensions, calculated within the R environment
(Afifi et al., 2004). The first three dimensions were significant, and only dimensions 1
and 2 were graphically displayed.
60
Figure 3-8: Canonical Correspondence Analysis of 41 bird species located across 27
reclaimed surface mines. Appendix 3 – Observed Bird Species provides a breakdown of
four-letter codes.
62
xi
LIST OF ABBREVIATIONS
AIC – Akeike’s Information Criteria
ANOVA – Analysis of Variance
AOC – Approximate Original Contour
ARRI – Appalachian Reclamation and Reforestation Initiative
ASMC – Alabama Surface Mining Commission
BTU – British Thermal Unit
CCA – Canonical Correspondence Analysis
DBH – Diameter at Breast Height
EDR – Effective Detection Radius
EIA – Energy Information Administration
FRA – Forest Reclamation Approach
GLM – General Linear Model
GRTS – General Random Tessellation Stratified
MANOVA – Multivariate Analysis of Variance
MC – Medium Conifer Forest, a conifer dominated forest established 15 – 20 years ago
MG – Medium Grassland, a grassland dominated mine reclaimed 15 – 20 years ago
MM – Medium Mixed Forest, a deciduous dominated forest established 15 -20 years ago
OC – Old Conifer Forest, a conifer dominated forest established > 20 years ago
xii
OG – Old Grassland, a grassland dominated mine reclaimed > 20 years ago
OM – Old Mixed Forest, a deciduous dominated forest established > 20 years ago
OSM – Office of Surface Mining
PCA – Principal Component Analysis
SHR – Shale-Hills Sub-Region
SMCRA – Surface Mining Control and Reclamation Act
USDA – United States Department of Agriculture
WMA – Wildlife Management Area
YC – Young Conifer Forest, a conifer dominated forest established < 15 years ago
YG – Young Grassland, a grassland dominated mine reclaimed < 15 years ago
YM – Young Mixed Forest, a deciduous dominated forest established < 15 years ago
xiii
ACKNOWLEDGEMENTS
I sincerely thank my major advisor, Dr. Yong Wang, who has sacrificed a good
deal of his time on this project, and my advisory committee: Dr. Luben Dimov, Dr. Dawn
Lemke, and Dr. Callie Schweitzer, for their expertise and support.
I am thankful to the faculty, staff, and students of Alabama A&M University who
have kindly contributed their assistance, advice, and personal time to my research.
Special thanks to Kimi Sangalang, Penny Stone, Andrew Cantrell, my interns Jeremy
Conant and Sam Polfer, and Dr. Wang’s graduate students: Padraic Conner, Eric
Margenau, Kevin Messenger, Brandie Stringer, and Emily Summers.
I am indebted to Chas Moore and the Alabama Department of Conservation and
Natural Resource, the US Forest Service, Alawest, and the Westervelt Company for
helping me with land access.
Funding for this project was provided by the Alabama Ornithological Society,
Birmingham Audubon Society, NSF CREST program, and Alabama A&M University.
Finally, my most sincere thanks and gratitude is for my loving family, Danica,
Daya, and Eli. None of this would have been possible without all of your love,
encouragement, and support.
xiv
CHAPTER 1 – INTRODUCTION AND LITERATURE REVIEW
Natural resource extraction can transform landscapes and ecosystems, and
different types of disturbance yield varying impacts on the environment. Surface mining
disturbed over 2.6 million ha of terrestrial habitat in the USA between 1930 and 2005
(Amos, 2009; Energy Information Administration (EIA), 2015; Zeleznik & Skousen,
1996), resulting in large landscape changes as soils and vegetation were removed.
Historically mining has resulted in large-scale degradation of natural environments
including compacted soils, chemical and nutrient deficiencies, and spread of invasive
plant species (Burger, Zipper, Angel, Evans, & Eggerud, 2011). With the enactment of the
Surface Mine Control and Reclamation Act (SMCRA) in 1977 some impacts associated
with mining were mitigated (Buehler and Percy 2012). Major issues such as site
stabilization, restoration of the original contours of a landscape, and reestablishment of
vegetation are directly addressed through SMCRA (SMCRA, 2006).
Coal mining is managed federally through SMCRA, but Alabama state legislation
also exists to protect mined lands by ensuring they are restored to pre-development or
better than pre-development characteristics (Alabama Surface Mining Commission
(ASMC) Administrative Code, 2013), guaranteed through bonds. However, criteria for
0
assessing reclamation success are often focused around soil erosion and vegetation
coverage and fail to account for other biota (Mummey, Stahl, & Buyer, 2002a; Mummey,
Stahl, & Buyer, 2002b), and even in this specific focus, reclamation may not be working
to meet long-term objectives.
Holl (2002) reviewed the long-term (5-25 year time frame) impact of vegetation
restoration at reclaimed surface coal-mine sites and found that the restoration
requirements related to vegetation are often met for the short term (~5 yrs), but rarely
account for any time period that would constitute natural succession in eastern USA
hardwood forests. Short-term vegetation targets can be directed towards “Undecided”
future land use during the permitting process, and often are focused on grassland
development or woody shrub establishment (Burger, 2011) which can prolong seedling
establishment and growth as native plants are obstructed by thick, turf-like non-native
grasses (Vogel, 1973; Rudgers & Orr, 2009). Delayed re-establishment can be relevant in
hardwood forests of the southeast where natural re-growth can already take greater than
35 years to reflect reference sites (Holl, 2002). Monitoring community ecology can help
shape an understanding of the successes of mine reclamation (Buehler & Percy, 2012). To
understand the full implications of wildlife response, more research is needed to examine
community dynamics post-reclamation (Buehler & Percy, 2012). To this end, I studied
bird communities across reclaimed surface coal-mines in northwestern Alabama.
Birds are a practical and often used wildlife study group as they are conspicuous,
relatively easily detected, and respond quickly to changes to the environment (Julliard,
Clavel, Devictor, Jiguet, & Couvet, 2006). Songbird communities are often displaced
1
with the removal of vegetation or a decrease in habitat suitability (James & Wamer, 1982;
Loss, Ruiz, & Brawn, 2009), and many bird groups, particularly grassland birds, are on
the decline (Sauer & Link, 2011). Reclamation techniques that provide early successional
and grassland habitats contribute to population increases in many grassland songbirds
(i.e. Henslow’s Sparrow (Ammodramus henslowii), Bajema & Lima, 2001), and other
benefiting wildlife (i.e., white-tailed deer) (Buehler & Percy, 2012), but may further
prolong reclamation for forest interior species—many of which are species of concern.
Reclamation methods may influence the biodiversity of a system and both the rate and
way avian species composition are restored. On reclaimed mine lands which were
originally forested, avian communities shift from forest interior bird communities to
communities associated with early successional habitats (Bajema & Lima, 2001; DeVault,
Scott, Bajema, & Lima, 2002), which may cause a relevant ecological shift in bird
communities particularly when considering fragmentation (Bolger, Alberts, & Soule,
1991). For the purposes of this study, habitats were described based on the structure and
composition of vegetation, and were classified into the following categories: grassland,
conifer forest, and mixed forest. Habitat was viewed at three chrono-sequential
categories: young (<15 years since closure), medium aged (15-20 years since closure),
and old (>20 years since closure). Time categories were considered to have started at
mine closure then, within the above time frames, a five-year monitoring period was
completed and was reclamation approved by the Alabama Surface Mining Commission
(ASMC). These time categories were chosen, to divide temporal categories into five year
windows to correspond with this monitoring time frame (ASMC, 2010).
2
Although many studies have examined the impacts of forest management
practices on avifauna (Sallabanks, Arnett, & Marzluff, 2000 for a review), few studies
evaluating forest bird species responses to mine reclamation have been completed.
Sallabanks et al. (2000) found that many of these forest management studies—associated
with avifauna—lacked replication and did not properly identify causal mechanisms to be
translated into meaningful management
recommendations.
Furthermore, forest
regeneration prescriptions for timber management differ from post-mining forest
activities in terms of the nature and timing of regeneration that occurs (Bulluck &
Buehler, 2006). Mine site reclamation takes more time and the vegetation responds more
slowly (Holl, 2002). This study contributes to a better understanding of the time frame
associated with bird community restoration on small-scale surface mines in northwestern
Alabama.
The Shale Hills sub-Region (SHR) of the south Cumberland Plateau in Alabama
(Figure 1-1) had 30,498 ha of permitted mine land between 1980 and 2005 (publicly
available information through the ASMC office in Jasper), and development continues.
Surface coal mines in this region alone grossed ~$626 million for the state in 2011
(Young, Kendell, & Raghuveer, 2012). The study area is relatively important for coal
mines in the state (Figure 1-2). Coal extraction continues to be an economic driver in
Alabama, is likely to persist in the near future, and continues to shape the landscape
(Young et al., 2012). The habitat changes due to mining practices may have conservation
implications for birds in Alabama, particularly those of conservation concern, such as the
Cerulean Warbler (Setophaga cerulean) (Buehler et al., 2008; Buehler, Welton, &
3
Beachy,
2006),
Red-cockaded
Woodpeckers
(Picoides
borealis),
Red-headed
Woodpeckers (Melanerpes erythrocephalus), and some Nearctic-Neotropical migrants.
Mining affects forest songbirds in adjacent forested areas because of the creation of edge
effects (Hardt & Forman, 1989; Winter, Johnson, & Faaborg, 2000), grassland
proliferation (Bajema & Lima, 2001; Galligan, DeVault, & Lima, 2006), and forest
fragmentation (Bolger et al., 1991; Herzog et al., 2001; Wickham, Riitters, Wade, Coan,
& Homer, 2007). Assessing the influences of various reclamation types on bird
communities and the temporal changes in avian communities on reclaimed mine sites will
contribute to the knowledge and tools for resource managers considering avian
biodiversity and the general success of reclamation practices.
4
.
5
Figure 1-1: The Shale Hills Region of Alabama is demarcated by the grey polygon, colored polygons are an
indication of current activity in these areas. The area of focus for this study is identified by a red square and
was targeted as a result of long-term mine activity.
Figure 1-2: Alabama with surface coal mines marked as black triangles. The area of
densest coal mine locations corresponds with the study area observed in figures 1-1 and
1-2 (EIA, 2014).
6
Statement of the Problem
Terrestrial and aquatic ecosystems are impacted through coal extraction and the
effects can endure for decades (Botello-Samson, 2006). Prior to federal regulation under
SMCRA, severe environmental degradation to these ecosystems was common (Buehler
& Percy, 2012). The SMCRA requirements reduced impacts on wildlife resources and
some wildlife populations responded favorably (Bajema & Lima, 2001). However,
Buehler and Percy (2012) found that several issues related to wildlife impacts still
remain: a need for addressing the landscape-level effects of mining on wildlife
populations, assessing the cumulative impacts of mining from multiple sites at the
landscape scale, and developing reclamation practices that promote ecological restoration
of native plant and animal communities while protecting soil and water resources. This
lack of information can lead to poorly informed decisions at the management level for
mine reclamation and hinder the effectiveness of biodiversity conservation efforts.
Purpose and Objectives of the Study
Birds have often been used as indicators of ecosystem function and health because
many avian species are habitat obligates (Julliard et al. 2006), and are sensitive to
changes in the vegetation at the micro-site and landscape-levels (Bolger et al. 1991;
Devictor, Julliard, & Jiguet, 2008; Gillies & St. Clair, 2008). With coal mining being an
integral part of Alabama economics, how mine reclamation impacts bird communities is
an important question, especially now with growing pressure on industry to complete
Forest Reclamation Approaches (FRA) explored by the Office of Surface Mining (OSM)
7
through the Appalachian Regional Reforestation Initiative (ARRI) (Angel, Davis, Burger,
Graves, & Zipper, 2005). Understanding avian temporal response to reclamation type will
contribute to the growing body of knowledge of cumulative effects with regards to mine
reclamation.
Because of the long history of mines in the SHR, a unique opportunity exists to
look at the chronosequence of bird communities following mine closures. I studied avian
response to reclaimed surface mines using sites from an invasive plant modeling study in
the area as a foundation (Lemke, Schweitzer, Tazisong, Wang, & Brown, 2012).
Specific questions that were explored include the following:
1) What are the avian species diversity, richness, and abundance at the
reclaimed mines?
2) How do avian composition and abundance vary spatially?
3) How do avian composition and abundance change temporally and how
does it relate to vegetation succession?
4) How are habitat types and environmental conditions related to avian
community dynamics?
I used ordination techniques in combination with general linear models to
examine the relationships among bird species and the contributions of habitat type. The
results from this project will help to lend insight into the issues related to wildlife that
still revolve around mining practices. It may also, locally, provide information on the
timelines and reclamation techniques that provide the most species richness, species
diversity, or abundance or target species.
8
Hypotheses
Avian species diversity, richness, and abundance at the reclaimed mines are
expected to fluctuate across treatment. Because many bird species are disturbance
dependent and require early successional habitat for various life-history stages, it is
anticipated that diversity and richness will be highest where edges and mixed seral stages
are most diverse. This is expected to be highest in mixed forests that were mined 15-20
years ago and lower in conifer forests mined greater than 15 years ago (Holl, 2002).
Richness and abundance will fluctuate on a by-site and by-species basis, but in general
mixed forests and moderate-aged reclaimed mined sites are expected to have the highest
richness and abundance.
Avian composition and abundance are not expected to vary spatially. However,
temporal changes based on three classifications of habitat type should vary across the
landscape, and avian communities should show strong associations with vegetation
community succession (Graves, Rodewald, & Hull, 2010; Julliard et al. 2006).
Habitat characteristics and environmental conditions will show correlations to
avian community dynamics. Percent of midstory cover, canopy closure, and basal area
are anticipated important habitat variables, and temperature and cloud cover at the time of
surveys are anticipated to be important environmental variables influencing songbird
activity and detection (James & Wamer, 1982).
9
Assumptions
Several assumptions were made for the purposes of this study. First, it was
assumed that the approach used for older grassland-reclaimed mines are similar to those
used for newly reclaimed grassland mines. This assumption seems reasonable in that
most techniques have not been changed dramatically since the implementation of
SMCRA although equipment has improved.
A second assumption was that permitted mine areas were actually mined. This
was identified by visually surveying for tree stumps at each plot location. If stumps were
present, it was not considered mined, and the locations were excluded from the study.
However, some sites outside of the mined area lacked stumps, were still retained for the
study, but may not have reflected the specific reclamation works done on mined land.
Lastly, it was assumed that non-mined landscapes were reflective of a landscape
that was comparable to mined sites with the only exception being that the sites had either
been mined or not at some point in history. To address this, many of the non-mined sites
were established across three state operated wildlife management areas (WMA) to ensure
that disturbances were relatively minimal (Figure 1-3).
10
Figure 1-3: Overview map of project-mine locations throughout the Shale Hills subRegion of Alabama as related to Birmingham. Highlighted polygons are mapped in more
detail in Appendix 2 – Detailed Location Maps. Translucent images are the wildlife
management areas targeted for non-mined sites.
11
Literature Review
History of Mining in Alabama
The surface coal mining industry in Alabama has resulted in more than 30,000 ha
of land being permitted for excavation between 1980 and 2005. This study focused on the
SHR, and considered mines in Walker, Tuscaloosa, Bibb and Shelby counties (Figure 12). These four counties provided more than 50% (24 of 43) of all new surface mines
developed and 5,226 of the 7,432 metric tonnes (70%) produced in the state in 2011
(EIA, 2012a). It is a high production area for Alabama grossing ~$626M in 2011
($98.62/metric tonne). Alabama as a whole produced 1.7% of the USA total coal volume
in 2011, and provided more than 4,750 jobs (Young et al. 2012).
The large economic benefits realized through mining in Alabama encourage its
persistence. Young et al. (2012) indicated that employment trends and production are on
the rise. Hendryx and Ahern (2009) summarized the Energy Information Administration
(EIA) data on mines from 1997 to 2005, and found overall increases in economic
contributions in coal mining counties in the Appalachians. Additionally, Apergis, Loomis,
and Payne (2010) found that coal-based energy consumption is likely to persist in the
long-term; that changes in energy conservation policies and coal-use reduction policies
will only have short-term effects.
In the year 2010, 719 trillion BTUs of coal energy were consumed in Alabama.
That comprised 31% of the energy consumed state-wide that year, and the highest
consumption of an individual energy resource. Alabama produced 493 trillion BTUs,
12
falling short of the consumption rate (EIAb, 2012). Coal demand will likely remain high
to meet the energy needs of the state.
Coal mining and consumption for energy have been scrutinized for their
environmental impacts for decades (Finkelman, 1999; Pope et al., 2009; Stracher &
Taylor, 2004; Terry, 1978; Vietor, 1980 as examples). In the United States, surface mining
in general was causing concern regarding the environment prior to the early 1970s. In
1973, the Subcommittee on Environment, and the Subcommittee on Mines and Mining
presented cases in Washington DC (Regulation of Surface Mining, 1973) helping shape
the reclamation options at the time (Washington, 1974). This lead to the Nation’s
Renewable Resources Assessment in 1975 (USDA Forest Service, 1975), and ultimately
contributed to the development of SMCRA in 1977. Traditionally, mines were managed
at the state level, and this legislation sought to focus coal management under a federal
umbrella to ensure uniformity in reclamation and mining techniques (Botello-Samson,
2006). Federally, surface mines are regulated through the Office of Surface Mining
(OSM), who has primary jurisdiction over coal mining. However, many states have
approved mining regulations that serve for mining operations. In Alabama, the Alabama
Surface Mining Commission (ASMC) manages the permits, best-management practices,
guidelines, and general compliance monitoring in conjunction with the OSM.
With SMCRA in place, many environmental impacts associated with surface
mining were identified; research continues to try and mitigate the impacts associated with
mining as the industry expands (Buehler & Percy, 2012).
13
Mining Types
Broadly characterized, there are two types of mining: surface and sub-surface.
Within each of these broad classifications there are several specific types of mining. Subsurface mine types are not discussed further, as they do not pertain to the proposed study.
Surface mining includes the following categories:

Open-pit mining: an activity whereby minerals or materials are recovered
from an open pit in the ground;

Quarrying: similar to open-pit mining, but focuses on gathering building
materials from a large excavated pit and sorting them by grade for
commercial use and sale;

Strip mining: stripping surface layers off to reveal ore/seams underneath,
generally targeting shallower seams; and,

Mountaintop removal mining: commonly associated with coal mining,
involves taking the top of a mountain off to reach ore deposits at depth.
Strip mining was exclusively employed for the mines considered in this study
using area, contour, and auger methods. Area mines are common in order to collect
shallow mineral deposits over large areas of flat topography. Contour mines relate to
pulling back overburden from slopes in hilly terrain. Auger mining often takes place after
contour mining and consists of drilling into the residual high-wall to collect deposits.
14
Landscape and Vegetation
Vegetation is directly impacted through surface mining practices as top soil and
vegetation are removed during mine construction. Research on songbird habitat and
influences of timber harvest have been compiled for years (Rice, Ohmart, & Anderson,
1983; Van Horne & Wiens, 1991; Sallabanks, Riggs, & Cobb, 2002; Brotons, Thuiller,
Araujo, & Hirzel, 2004). However, this dataset predominantly revolves around harvest
for forest management (Sallabanks et al., 2000), which is not directly comparable to the
impacts of timber removal for mine construction (Bulluck & Buehler, 2006). Bulluck and
Buehler (2006) found that early successional habitat in mined landscapes was different
than in clear cut forests. Specifically differences existed in the saplings, forbs, and grass
layers; shrubs were similar in composition and cover regardless of disturbance type.
Habitat data are frequently broken into forest cover type based on dominant tree species
and relative maturity (James & Wamer, 1982). Bulluck and Buehler (2006), although not
focused on long-term reclamation, suggested that re-establishment of ecosystem services
and habitat availability differs across disturbance type specifically at the grass and forbs
level. These conditions, set early by reclamation technique, may influence restoration of
the landscape.
James and Wamer (1982) identified nine principal vegetation variables potentially
influencing songbird habitat use: tree density, basal area, tree species, percent
composition, percent ground cover, percent canopy cover, canopy height, percent
coniferous, the difference between maximum and minimum canopy height, and the
density of trees in each size class. When considering cover, three independent layers were
15
defined to address multiple habitat preferences (i.e. understory, midstory, overstory), and
the percent cover of each layer remained independent during assessment (Bulluck &
Buehler, 2006). By following James and Wamer’s (1982) guidelines, my proposed
hypotheses were tested on reclaimed mines. Additionally, using broad vegetation
classification groups with the data I collected, bird communities were linked to habitat
type and individual species were categorized into guilds based on appropriate habitat
associations (Verner, 1984; O’Connell, Jackson, & Brooks, 2000).
Reclamation
Since the introduction of SMCRA, restoration is a requirement at each new mine
site upon closure of the permit (SMCRA, 2006). Reclamation goals relate to the land use
prior to mining, and focus on restoring them. To meet the objectives outlined in SMCRA
as cost-effectively as possible, grassland restoration has typically been the most common
reclamation form (Burger, 2011). Forest harvest is a common land use in the region, and
the primary management platform in the area is the establishment of loblolly plantations.
A growing awareness regarding ecosystem-level management of contiguous forest
systems is on the rise, resulting in increased attention on environmental health, and
landscape-level restoration of forest communities. The Appalachian Regional
Reforestation Initiative (ARRI), started in 2004 by the OSM, has been pursuing targeted
restoration initiatives (Zipper et al., 2011). Traditionally, the SMCRA objectives were
effectively met through stabilizing the site (i.e. returning to the approximate original
contour (AOC)) and minimizing soil loss and impacts to water quality (Brenner, 2007). In
16
most cases grassland reclamation was utilized for fast establishment of vegetation. Using
low cost seed resulted in contamination with invasive species, and this subsequently lead
to a spread in invasive plants (Burger, 2011), a rising demand for invasive plant
management, and demand for native plant reestablishment (Beckerle, 2004; Boyce, 2002;
Buckley & Franklin, 2008; Lemke et al. 2012).
Burger (2011) identified four periods of mined land “reforestation”: the prefederal-law tree planting period, the grassland period, the woody shrub period, and the
native forest restoration period. Rehabilitation of mined areas prior to 1977 consisted of
tree-planting, which Rodrigue, Burger, and Oderwald (2002) found produced
merchantable timber at higher productivity than reference non-mined sites, but was
inconsistent and unregulated. Once SMCRA was implemented, high-wall backfilling and
landscape reshaping to AOC were addressed, toxic materials were isolated to prevent
seepage and oxidation, sedimentation and air quality provisions were instituted
(Schuman, 2002) and the graded mined sites were re-vegetated using agricultural grasses
and legumes (Burger, 2011). This grassland reclamation approach focused on sediment
stability (Burger, 2011). Tree planting became less common as the graded sites were
heavily compacted and thick turf-like grasses served to block young seedling
establishment and growth (Holl, 2002; Groninger, Fillmore, & Rathfon, 2006).
As environmental awareness grew, coal companies were more highly scrutinized
for their reclamation efforts, including the poor condition of the ‘rehabilitated’ land. This
heightened awareness prompted management schemes that aimed to increase plant
diversity onsite, restore ecosystem services, and manage for multiple land uses
17
(Schuman, 2002). Shrub species and small pioneer tree species were added to the
vegetation-restoration assemblage, though invasive species proved the most successful
(Burger, 2011).
Since 2006, federal and state agencies have encouraged mine managers to use
different grading and contouring techniques to minimize compaction on reclaimed mine
sites; this has resulted in increased success in native forest establishment (Zipper et al.,
2011). With these shifts in intention, and the agencies seeking to manage for the most
sustainable reclamation approach, the fourth stage in reclamation history commenced: the
Forest Reclamation Approach (FRA). This approach is currently being researched and
refined through the OSM and their ARRI affiliates, but is poorly implemented in
Alabama.
As the tools and techniques for administering and measuring successful
reclamation change, the scope of reclamation objectives evolves. No accepted definition
of successful mine reclamation has been established. Burger’s aforementioned categories
focus solely on vegetation. However, Mummey et al. (2002) determined that reclamation
efforts did not successfully return bacterial, fungal, and total biomass biomarkers back to
pre-disturbance conditions in the short-term, but did show a trend towards undisturbed
levels. This suggested that assessing soil health through biomarkers is necessary to
monitor reclamation success at a microscopic level. Soil remains a critical component of
reclamation success, as reported in the work by Pichtel, Dick, and Sutton (1994) who
described options for management of toxic soils on abandoned mine lands, and Mendez
and Maier (2008), who described emerging technologies to remediate tailings facilities.
18
However, without proper regulation and monitoring, these techniques can lead to
additional leachate entering the ecosystem (Stehouwer, Day, & Macneal, 2006).
Reclamation is broadening to try and address soil health, vegetation success, wildlife
return rates, and the return of ecosystem services for subsequent land-use management.
Unfortunately, evaluating all of these parameters in this study was not logistically
feasible, so the focus was on a wildlife group that is frequently used as an indicator of
ecosystem services at the macroscopic level: avifauna.
Avifauna Response to Mining and Reclamation
Buehler and Percy (2012) conducted a review of the effect of coal mining on
wildlife in the eastern United States, and found that coal mining has affected wildlife
populations and their habitat. With the loss of habitat, ensuring that reclamation practices
meet the target needs of the bird communities is critical for maintaining avian diversity.
In their meta-analysis of the literature, Buehler and Percy (2012) targeted the
following components of reclamation: reforestation, vegetation reestablishment, and the
impacts on flora and fauna. Their literature review returned several hundred articles with
bird studies and reclamation studies being the most common.
Birds are a common focus for wildlife-targeted studies as they are relatively easy
to monitor through various count-based surveys (Buehler & Percy, 2012), and they are
often habitat specialists (Rice, Ohmart, & Anderson, 1983). There is currently little
research for any wildlife in pre-mining and post-mining dynamics on a mine site, due to
the obvious temporal constraints (Buehler & Percy, 2012; Burger, 2011; Holl, 2002).
19
Many post reclamation studies have focused mainly on grassland reclamation techniques
(Burger, 2011).
Grassland birds have been documented, relatively recently, as the most
abundance-depleted bird group in North America (Butchart et al., 2010; Ribic, Guzy, &
Sample, 2009; Sauer et al., 2007; Vickery & Heckery, 2001). As a result, the impacts of
grassland mine reclamation on grassland birds of conservation concern have been
researched extensively in the mid-western and eastern United States (Ammer, 2003;
Bajema, DeVault, Scott, & Lima, 2001; Bajema & Lima, 2001; DeVault, Scott, Bajema,
& Lima, 2002; Galligan, DeVault, & Lima, 2006; Mattice, Brauning, & Diefenbach,
2005; Monroe & Ritchison, 2005; Scott, DeVault, Bajema, & Lima, 2002; Stauffer, 2008;
Stauffer, Diefenbach, Marshall, & Brauning, 2011). Grassland reclamation has
successfully helped reestablish habitat for many species (Buehler & Percy, 2012),
providing more widely spread habitat for birds that have low rates-of-return to nesting
sites (Ingold, Dooley, & Cavender, 2009). However, because of the proliferation of
grassland and, more recently, scrub-shrub reclamation (Bajema & Lima, 2001), little
published research could be found examining the impacts of mine reclamation on forest
bird species.
Although few publications exist describing forest bird response to mine
reclamation, there is a large body of knowledge describing forest bird response to forest
harvest techniques. Sallabanks, Arnett, and Marzluff (2000) reviewed research on the
effect of timber harvest on bird populations, and found that previous research has lacked
replication, long-term study, and parameters related to avian fitness and population
20
viability. The same issues that plagued research reviewed by Buehler and Percy (2012).
Though the studies referenced by Sallabanks et al. (2000) were often limited in their
scope, and related to forest management, a different disturbance type than mining
(Bulluck & Buehler, 2006), they are the largest available body of published research
relating to avifauna responses to large-scale forest disturbance and forest restoration. In
this context these studies may provide a scope with which one can develop a prediction
of forest bird species reaction to large-scale habitat removal.
In contrast to grassland birds, which have low return rates to nesting sites
(Ammer, 2003; Ingold et al., 2009), one study based in British Columbia, Canada,
showed cavity nesting birds—generally habitat specialists due to nesting tree
requirements (Welsh & Capen, 1992)—reusing nests at rates of 48% on actively managed
landscapes (Aitken, Wiebe, & Martin, 2002). Stable return rates can provide the
necessary population structure to maintain natal populations, and declines in return rates
can lead to local population collapses (Roth & Johnson, 1993). Fragmentation and
deforestation, even proximal to the nesting area, not necessarily directly in it, can cause
drops in return rates (Bélisle, Desrochers, & Fortin, 2001), which can alter population
structure and dynamics.
In many cases, individual bird species are targeted for research (Bajema & Lima
2001; Bajema et al., 2001; Ammer, 2003). Although it is necessary to explore individual
species ecology, the functionality of applying an observation from one species across a
community is not always accurate. Verner (1984) explored the concept of guilds as a
research and management tool, and it has been subsequently used in many peer-reviewed
21
studies across the globe (Whitmore, 1979; Daily & Ehrlich, 1994; Marone, 1991,
Kissling et al., 2011 for examples). Guilds can be identified by primary feeding and
nesting zones, by specific nesting or feeding types, or by migration or residency status
(Verner, 1984). By using guilds, inferences can be drawn about groups of birds based on
similar life-history requirements as opposed to single species foci which merely qualify
single species reactions. Habitat components are an important life-history facet that may
provide the basis for guild development (Verner, 1984).
Habitat type has a direct impact on foraging and nesting locations for birds, and
the following environmental parameters have been found to play a role in bird behaviour:
temperature, wind speed, cloud cover, and precipitation (Anderson & Ohmart, 1977;
Dawson, 1981). Temperature can play a role in brain gland functions in sparrows
(Binkley, Kluth, & Menaker, 1971), influencing circadian rhythms and activity. Increased
wind speeds result in an increase in the metabolic rates of birds managing convective heat
loss and increased complexity in flight (Torre-Bueno & Larochelle, 1978). Precipitation
has also been shown to reduce flying speed (Gordo, 2007; Richardson, 1978; Richardson,
1990). Ringelman and Flake (1980) found that wind-speeds even as mild as 7 km/hour
can depress bird detectability, that day-time temperatures exceeding 23ºC resulted in a
decrease in observations, but, in contrast to Anderson and Ohmart (1977), that cloud
cover was not found to be a factor.
The combination of environmental parameters and habitat requirements all help
contribute to our understanding of bird community dynamics and observer detections.
22
Study Site
The unique mined history in the SHR provides an opportunity to explore some of
the outstanding issues with mine reclamation and disturbance as it relates to birds
communities. By exploring 27 mines throughout this region, there is an opportunity to
explore the temporal and habitat type influences on avifauna community in a context that
can help address the outstanding issues identified by Buehler and Percy (2012). This
region lies at the merger point of the Coastal Plains physiographic region and the
Cumberland Plateau (Figure 1-4), and is characterized by low rolling hills, with a
heterogeneous vegetation composition, mild climate (1-32ºC), and a large subterranean
shale play.
Mines in this region tend to be small in size (less than 250 ha), and relatively
shallow. Mines were area or contour mines, not generally large deep excavations.
For each of the suitable mines, land access had to be gained. Most of the mines
were located in WMA and were accessible at least to walking traffic. However, seven
mines used in this study were on private land and land use agreements (Appendix 1 –
Land Use Permission) were required. Landowners were found through Alabama GIS
(Flagship GIS, 2014), a free public-access website. This service provided contact
information for many individual landowners as well as corporations. The Westervelt
Corporation and Altawest provided access for seven mines, collectively. Additional
landowners were contacted for access, but were either unavailable, required greater than
one million dollars’ worth of liability insurance, or did not want anyone accessing their
23
Figure 1-4: Mapped physiographic lines for the Cumberland Plateau (left) and the Coastal
Plains (right). These two region meet in Tuscaloosa County (red) within the project
footprint, and are transitional through Bibb County as well (University of Alabama,
2015).
24
CHAPTER 2 - METHODS AND MATERIALS
Study Site Description
The Shale Hills sub-region of the Cumberland Plateau (Figure -1) is a primarily
forested landscape (~41-80% (Iverson, Cook, & Graham, 1994)) that has a temperate
climate characterized by hot summers (maximum-mean 32ºC), mild winters (minimummean 1ºC), and approximately 1400 mm of precipitation per year (Smalley, 1979). This
region has been subjected to forest management and mining interests for decades and is
comprised of second and third growth forests (Iverson, Cook, & Graham, 1994). It is the
southern-most foothills of the Cumberland Plateau with topography characterized by a
series of rolling hills as opposed to the higher elevation ridgelines or plateaus observed to
the north (Lemke et al. 2012). Reclaimed mines considered in this study were subjected
to three different management techniques: A) reforestation using loblolly pine (Pinus
taeda L.) planted at a rate of 1235-1730 pines per hectare (Lemke et al. 2012), with 1112
stems per hectare considered successfully reclaimed, such that bonds would be released
(ASMC, 2012); B) mines were rehabilitated using predominantly grassland cover; C) past
developments were reclaimed with a mixture of deciduous and coniferous trees.
For the purposes of this study, and as a consequence of edge habitat
characteristics, grasslands, normally considered sites with no tree cover, are considered to
be any habitat grouping with canopy closures lower than 30%. Because plot locations
25
were independent and no treatments had truly 0 % canopy cover, the grassland category
is, vegetatively, more reflective of a combination of grassland and woodland
characteristics, hereafter, ‘grassland’ is used to refer to these site conditions to imply the
lowest mature-tree density sites with low canopy closures.
Non-mined landscapes were also considered; though they were actively managed
(i.e. forestry, agriculture) they were not mined. These sites are hereafter referred to as
control sites, though they should in no way be assumed to be reflective of undisturbed
habitats; they are non-mined landscapes, and are compared to mined landscapes
specifically in that context. Non-mined sites are distributed across three wildlife
management areas (WMAs) within the project area. Subterranean to this project area is a
large shale play (Figure 1-1) that has resulted in four counties of Alabama providing over
50% of the new mines in 2012, and 70% of the coal extracted within the state: Bibb,
Shelby, Tuscaloosa, and Walker (EIAa, 2012).
Permits issued from 1977 through 2005, for mines closed between 1982 and 2008,
provided by the Alabama Surface Mining Commission (ASMC; 2012), were used to
identify current and proposed reclaimed future land use. Future land use is the proposed
use of the land after mine reclamation as designated on a mine application; it influences
which re-vegetation techniques may be approved during a mine-permit application.
Future land use for this region, as recorded on the mine permit applications, included
timber harvesting, mineral resource extraction, natural gas extraction, and wildlife.
Several of the areas studied are state managed wildlife areas that also cater to the above
management activities.
26
Sites were divided by three habitat types: mixed forest composition (< 60%
conifer in canopy composition), conifer forests (> 60% conifer in canopy composition),
and grassland/savannas (<30% canopy closure) (Friedl et al., 2002). Grassland/savannas
were considered habitat types that met two of the three following parameters on average
across plots within the mine:

Less than 25% canopy closure, and/or

Tree Density of less than 0.1 stems / 1 m2, and/or

Average Basal Area less than 10 m2 / ha.
At times grasslands had relatively high densities and basal areas, linked primarily
to the small size-class measurements taken (2.5 cm and up), and the density of shrubs
along mine edges. This has led to the combination grassland/savanna definition, though
for reader ease in tables only ‘grassland’ is used throughout this document. Differences
were compared between time categories, habitat types, and the interaction of these
elements.
A two-way factorial design was used with three time categories: < 15 years since
mine closure (young), 15 – 20 years since closure (medium), and > 20 years since closure
(old); and, three habitat types: grassland, conifer forest, and mixed forest, as defined
above. These treatments were combined to create nine (9) treatments across the
landscape: young grasslands (YG), young conifer forests (YC), young mixed forests
(YM), medium grasslands (MG), medium conifer forests (MC), medium mixed forests
(MM), old grasslands (OG), old conifer forests (OC), and old mixed forests (OM).
27
Mine Selection
Mines within the Shale Hills Region of the south Cumberland Plateau that were
permitted after 1977 and closed prior to 2008 were targeted for this project. This ensured
that all study mines were permitted after the implementation of SMCRA (1977) and that
all mines were closed 5 years prior to assessment to allow for reclamation. Mine closure
is considered to be when raw minerals or ore are no longer being removed from the site,
at which point reclamation may commence. Reclamation is monitored for five years
before success is evaluated and bonds released (ASMC, 2012). Mines ranged in size and
shape, but as mines were initially intended to serve as the sample unit, it was imperative
to maximize mine numbers. Consequently, there were no size constraints applied to
mines, but mine areas were covered as comprehensively as possible.
Land Access
Twenty-seven mines (Table 2-1) were selected based on available access. Mines
were grouped by time since closure as follows: <15 years (1), 15-20 years (2), and >20
years (3) as these were each approximately five year windows matching the required
monitoring time frame for mine reclamation and assumes that this time frame marks a
suitable measurement interval, hence its current application in the legislation (ASMC,
2012).
28
Sampling Plot Selection
Sampling plots were established throughout the study area for surveying the bird
community and habitat data collection. Plots were distributed across mines, and also
across the non-mined landscape, focused primarily around state lands (specifically the
Wolf Creek, Cahaba River, and Mulberry Forks Wildlife Management Areas; Figure 1-2).
The non-mined plots comprise a control for comparison between mined and non-mined
landscapes. They were placed in areas that had never been permitted for mining prior to
1977, according to digital records from the ASMC (2012).
Plots were placed using the stratified, spatially balanced, sampling design,
Generalised Random Tessellation Stratified (GRTS), allowing flexibility in sampling.
This spatially balances sampling plots, to maximize plot distribution and ensure that if a
plot is inaccessible–due to land access issues or health and safety concerns—the next plot
in the sample-list can be selected while maintaining spatial balance (Stevens & Olsen,
2004). Plots from Lemke et al.’s (2012) survey were used for this project in addition to
plots generated through ArcGIS 10 (ESRI, 2011) using the same technique. Plots were
greater than 250 m apart (Ralph, Droege, & Sauer, 1995), and placed to maximize
locations within each temporal grouping of mines. Plots surveyed by Lemke et al. (2012)
were used opportunistically to minimize vegetation data collection, and randomly
generated plots within 250 m of these original sites were not used. Non-mined plots were
randomly created using GRTS within polygons created on state wildlife management
areas within the project area and compared with digital records for pre-1977 mine
footprints. Wildlife management areas were chosen for non-mined plots as a result of
29
access limitations. Figures in Appendix 2 – Detailed Location Maps show where nonmined plots were located as well as how plots were distributed across mines.
Table 2-1. Treatment summary information. Area, Year Closed, and Years Since are an
average across the three mines in each treatment, and the number of sites is a total.
Area (ha) Year Closed Years Since
68.7
2002
12
55.1
2005
9
60.3
2001
13
185.9
1990
24
56.5
1991
23
81.3
1991
23
67.9
1995
19
28.8
1998
16
128.1
1997
17
30000
No. Sites/
Treatment
Sites 100 ha
Young Mixed
23
33.5
Young Grassland
7
12.1
Young Conifer
21
34.8
Old Mixed
21
11.3
Old Grassland
11
19.5
Old Conifer
25
30.7
Medium Mixed
14
20.6
Medium Grassland
9
31.3
Medium Conifer
24
18.7
Control
30
0.1
Avifauna Surveys
Avian communities were sampled during the breeding season (May-June) using
point-counts with distance estimates (Buckland, Anderson, Burnham, & Laake, 1993;
Ralph et al. 1995) in 2013 and 2014. Point-counts were chosen because they are better
suited to multiple species investigations in patchy terrain than transects (Buckland et al.,
1993). Plots were accessed on foot and flagged for subsequent identification (i.e.,
vegetation and year two surveys). Radial distances for observations were estimated from
the plot center and grouped into one of the following four categories (minimum
recommended by Rosenstock, Anderson, Giesen, Leukering, & Carter, 2002): <20 m, 2050 m, 50-100 m, and >100 m in an unlimited detection radius. The investigator waited for
two minutes before beginning the survey to allow avifauna activities to settle after the
30
investigator’s initial arrival (Rosenstock et al. 2002). These two minutes were used to fill
in the following environmental conditions: wind velocity, using a Lacrosse, McCormick,
USA anemometer; precipitation, based on subjective none and light categories, moderate
and heavy rains resulted in survey cancellation; air temperature from the anemometer;
and cloud cover, estimated to the nearest 5% (Figure 2-3).
Each bird survey was composed of point counts of three five-minute intervals (15
minutes total) to ensure the completeness of detections (Lynch, 1995) and to allow for the
calculation of detection probabilities (Alldredge, Simons, & Pollock, 2007). Bird species
identified while travelling between points were recorded for presence (+) as well, to
further solidify presence data (Hutto, Pletschet, & Hendricks, 1986), however each
species observed between plots was also heard during a survey. As a result, between-site
observations and species with less than ten detections are removed from many analyses
(specified below), but species composition is considered comprehensive. With each
observation the following was recorded: species, distance from plot, activity, sex,
intervals of detection, and time of first detection. A list of observed species is presented in
Appendix 3 – Observed Bird Species.
Surveys commenced within 15 minutes of sunrise (U.S. Naval Observatory’s
Astronomical Applications Department sunrise and set for Birmingham, AL,
http://aa.usno.navy.mil/data/docs/RS_OneYear.php), and persisted for a minimum of four
hours after sunrise (Lynch, 1995), ending at 10:30.
31
Figure 2-1. Percent cover guidelines used to estimate cloud cover (B.C. Ministry of
Forests, 1997).
To ensure that data were comparable across years, 12% of the plots surveyed in
2013 were randomly selected for secondary surveys in 2014 to assess the annual variation
in bird community.
Vegetation Sampling
Vegetation structure has long been linked to bird community structure and
population parameters (Anderson & Ohmart, 1977b). At each completed survey point,
vegetation data were collected following the process used by Lemke et al. (2012). Fixed
radii plots (11.4 m) were used for vegetation surveys, as James and Shugart (1970) found
32
that by using five or more 11.4 m (1/10th acre) circular plots habitat information for areas
up of at least 3.2 hectares (8 acres) can be obtained with maximum efficiency and
minimum effort. At plot center, fixed radii plots were established using Distance
Measuring Equipment (DME), where trees were measured from their center to determine
inclusion within the plot radius.
The following vegetation data were collected: tree species; canopy closure
determined with a hand-held spherical densiometer; forest type: pine, mixed, or
grassland; regeneration type: natural or planted; diameter at breast height (DBH) of trees;
overstory, midstory, and understory cover and composition were also recorded. These
data are consistent with that collected by Lemke and others (2012). Tree species and
DBH were measured by starting at 0o bearing or at unique landscape features (i.e. a snag,
an extraordinarily large tree, a previously flagged tree) and working clockwise to ensure
all qualified trees (> 2.5 cm DBH) were accounted for. Percent cover of three vertical
strata, overstory, midstory, and understory, were independent, such that cover across these
vegetation types could sum to >100% (Bulluck & Buehler, 2006). Overstory was
considered above 5 m, midstory was considered 1-5 m, and both were collected using a
spherical densiometer. Understory was ground cover less than 1 m in height and was
collected as a percentage using a 1 m2 plastic square. Understory assessment did not
include species identification, but a tally of number of species present.
Because of the time-sensitive nature of bird breeding, vegetation sampling took
place after breeding bird surveys were no longer seasonally affective. This was mid-July
and through August each year. Vegetation data were only collected once for each site
33
even if point counts were repeated as it was assumed that vegetation characteristics
would not vary significantly.
Statistical Analysis
Throughout the study area, 27 mines were identified and inventoried for bird and
habitat compositions (Table 3-1); mines were various shapes and sizes, but were all
developed using area or contour mining.
In addition to species-specific assessments, I classified bird species based on
nesting, foraging, and habitat guilds (Verner, 1984) according to the following categories:

Nesting Guilds: Ground Nesting, Shrub Nesting, Tree Nesting,
Building/Cliff Nesting, and Cavity Nesting.

Foraging-Behaviour Guilds: Bark Gleaner, Foliage Gleaner, Aerial
Forager, Ground Foragers, and Carnivores.

Habitat Guilds: Open Woodland, Forest, Grassland, Scrub/Shrub, and
Other.

Migratory guilds were not considered because this study did not look at
migration timing, nutrition, body condition, or overwintering conditions,
so any differences between migratory bird species or guilds and resident
species could be linked to a variety of variables that I could not address.
Species that may be suitable for additional guild categories (i.e. wetland or marsh
specialists, or the burrow-nesting Belted Kingfisher) were observed in such low numbers
that they were not considered for guild related analyses with one exception. The Red34
winged Blackbird (Agelaius phoeniceus) was considered for species-specific analyses in
the following: nesting guild (shrub) analyses, foraging guild (ground forager) analyses,
and habitat guild (other).
Detection adjusted abundance values (from Distance (Miller, Burt, Rexstad, &
Thomas, 2013)) were used in a t-test to compare total bird numbers, and a paired t-test
was used to look at species specific variation between years. Distance is a program and
package within the R environment that calculates a decay function for the likelihood of
bird-species’ detections based on distance from the observer. These calculations were
linked to the distance estimates taken for each observed bird, and an assumption was
made that within 15 minutes, all birds less than 20 m from the plot center were detected.
Adjusted density estimates were used to compare annual differences in avifauna
detections.
To explore the relationship among habitat variables, I first conducted correlation
analysis among the habitat variables, and then applied a Principal Component Analysis
(PCA) with correlation matrix. To facilitate the interpretation, the components from the
initial PCA were rotated with varimax rotation (Holland, 2008). This was used in
determining which habitat variables to explore to confirm plot-independence.
Each plot was treated as an independent sample unit although multiple plots were
sampled from each of the mines in this study. One major concern of this approach is that
the plots from the same mine could be pseudo-replications (Hurlbert, 1984). I tried to
avoid this problem by 1) maintaining a distance of at least 250 m between plots (Ralph,
Sauer & Droege, 1995), and 2) testing if the habitat variables from within a mine were
35
more similar than the plots across mines for each treatment type. Within-mine and
between-mine variation comparisons were explored using percent conifer, number of
forbs, midstory cover, and canopy closure. Percent conifer was selected as it showed tight
relationships with many avian species, and was better linked to describing variation than
“live canopy”, a correlated variable. “Number of forbs” was selected as it showed similar
species links to ground cover, but was more independent of canopy closure. Midstory
appeared to be a key habitat component for many avian species. Canopy closure was
selected as it showed tight relationships with ground cover (inverse) and average basal
area, but described more variation. Using these habitat variables in an ANOVA, it was
determined that the bird and vegetation sampling points were independent from each
other at the scale of the study, even those that were from the same mines.
Bird species observed less than ten times throughout the study were excluded
from modeling and species-specific calculations due to poor representation. They were,
however, included for richness and diversity. Bird diversity (Shannon & Weaver, 1948),
richness (number of species), and density were calculated for each plot to assess
community composition. To examine the effect of time-frame, habitat type, and their
interaction on avian community a multivariate analysis of variance (MANVOA) was
performed using IBM SPSS v20.0 (IBM Corp., 2011).
The definition of species diversity changes depending on the scale of the
questions being asked, and is a poor parameter to measure biological success or
community composition of a site (Hurlbert, 1971). However, by considering a
combination of abundance, species richness, and species evenness as originally explored
36
by Shannon and Weaver (1948), an index of diversity across treatments can contribute to
an assessment of community assemblage. Unfortunately, diversity estimates completely
ignore the relative contributions of a given species to the community (Shannon &
Weaver, 1948).
Diversity was calculated in the R environment using package Vegan (Oksanen et
al., 2007) which employs the following diversity formula:
(1)
Where pi is the proportion of species i, S is the number of species, and b is the
base of the logarithm (natural logarithms were used). Because diversity is an index, this
was calculated with all 78 observed species (Appendix 3) as this would provide the
widest disparity in diversity to illustrate any observed differences. Values were then
averaged across mines.
Species richness, the number of species within an experimental unit, is a valuable
ecological consideration. However, because richness can be influenced by the size and
shape of sample units and effort, rarefaction was completed on richness to ensure that
values could be appropriately compared across mines (Hurlbert, 1971; Oksanen, 2013).
Expected number of species in a rarefied community from N to n is (Hurlbert, 1971):
Where:
(2)
37
Where
is the count of species i and
is the binomial coefficient, and
gives
the probabilities that species i does not occur in a sample of size n. This analysis was
completed using all observed species, as richness focuses on species observed and not
numbers of individuals. Species accumulation curves were used with rarefied richness
values to examine whether or not surveys resulted in reasonably comprehensive species
detections.
Abundance is the number of individuals of a species or group of species, and is
often displayed as density where it is linked with some unit area. Abundance can be
measured directly through a census or indirectly through estimation parameters
(Buckland et al., 2001). To determine a density estimate of some robustness, bird
perceptibility (q) and availability (p, Nichols, Thomas, & Conn, 2009) were determined
through double observer counts (Nichols, 2000), removal models (Alldredge, Pollock,
Simons, Collazo, & Shriner, 2007), and distance models (Buckland et al., 2001).
Densities displayed as birds / ha are displayed for all subsequent analyses except annual
comparisons where adjusted average detections per plot were used.
Densities were calculated with only audio observations and for species with
greater than 10 observations. Species with fewer observations do not provide a suitable
subsample for the resultant analyses.
Removal counts are influenced by the rate of singing. If a singing rate is constant
within a sampling event, the time to first singing event will follow an exponential
38
distribution, and the cumulative distribution of times to first detection is given by
(Alldredge, Pollock, et al., 2007; Barker & Sauer, 1995; Solymos, et al., 2014):
Where p(tj) is the probability that an individual sings during cumulative time tj,
given presence and
is the singing rate (Poisson parameter—a non-normal, count-based
distribution).
Distance estimates classified observations in four distance categories from plot
center (<20 m, 20-50 m, 50-100 m, and >100 m; Rosenstock et al., 2002). The maximum
detection radius for this project was considered to be 125 m; a distance at which as many
species are detected beyond this distance as within it (Buckland et al. 2001). As distance
increases, detections decrease to some point where observations are no longer recorded,
and the probability that an individual located within a maximum detectable radius is
detected is expressed as (Solymos et al., 2014):
Where r is the detection distance, t is the effective detection radius (EDR), and
is a normalizing constant.
39
Abundances were estimated using the Distance and Detect packages within the R
environment (Solymos et al., 2014; Miller et al., 2014). Hazard-rate models within the
“Distance” package were compared against removal and distance estimates within the
“detect” package, and consistently provided the lowest AIC values (Miller et al., 2014).
To examine the difference of habitat features and avian community among
treatments, I first tested if the control was different from the other 9 treatments by
performing a contrast between the dangling control and other 9 treatments (Tabachnick &
Fidel, 2007). I then conducted a two-way ANOVA to test the effect of time, habitat type,
and their interaction on various habitat and bird variables. These analyses were
performed with R and SPSS. All statistical tests were declared significant if P < 0.05, and
means are reported with standard errors, unless otherwise indicated.
40
CHAPTER 3 – RESULTS
Assessment of Between Year Variation in Bird Community
In 2014, 12% of the plots surveyed in 2013 were randomly selected for secondary
surveys to ensure that annual variation in the bird community would not influence the
general findings. Annual comparisons included the following:

Average bird density of all birds at each plot between years – No
significant differences were observed across years (P = 0.12; Figure 3-1)

Average bird density of each species at each plot between years – Three
species showed significant differences: Pileated Woodpecker (Dryocopus
pileatus, P=0.011), Blue Grosbeak (Passerina caerulea, P = 0.0024), and
Downy Woodpecker (Picoides pubescens, P=0.022) (Figure 3-2 a-c).
Yearly variation in bird community was ignored for the following analysis
because there was no observed annual difference for total birds detected and only a small
portion of species showed species-specific annual differences. Additionally, I adjusted
bird abundance for each species using species-specific detection probabilities.
41
Adjusted Total Abundance
16
14
12
10
8
2013
6
2014
4
2
0
Year
Figure 3-1: Comparison ofadjusted songbird abundance at each sampling plot between
2013 (n=23) and 2014 (n=23). Eror bars are standard error. No significant differences
were observed (paired t-test, t(α=0.025,22) = 1.79, df = 22, p = 0.087).
42
0.5
Average Observations
Average Observations
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
2013
2013
2014
2014
Year
Year
b) Blue Grosbeak annual variation (P = 0.0024)
a) Pileated Woodpecker annual variation (P =
0.011)
Average Observations
0.5
0.4
0.3
0.2
0.1
0.0
2013
2014
Year
c) Downy Woodpecker annual variation (P =
0.022)
Figure 3-2: Comparison of adjusted abundance per plot of Pileated Woodpecker
(Dryocopus pileatus), Downy Woodpecker (Picoides pubescens), and Blue Grosbeak
(Passerina caerulea) at each sampling plot between 2013 (n=23) and 2014 (n = 23).
Error bars are standard error.
43
Assessment of Independence of Sampling Plots
Four habitat variables were compared to assess differences in between-mine and
within-mine variance: midstory cover, canopy closure, percent conifer, and forbs. These
variables were selected from the original 9 habitat variables using a PCA (Table 3-1).
Between-mine and within-mine variation were considered similar enough that, at the
scale of this project, individual sites could be combined and independence maintained.
There were, however, 4 significant differences in variation observed (Appendix 4 –
Between-mine and Within-mine Comparisons). Variation in midstory cover differed in
old mixed forests and old grassland mines, variation in canopy closure differed in young
mixed mines, and variation in percent conifer differed in young grassland mines.
Justification for tolerance of these differences is explored in the subsequent text (Chapter
4 – Discussion and Management Implications).
Table 3-1. Results of a rotated Principal Component Analysis of habitat variables.
Variable
Total Trees
Percent Conifer
Ground Cover
Live Canopy
Forbes
Average Basal Area
Midstory Cover
Canopy Closure
Tree Density
Component Importance
Standard Deviation
Proportion of Variation
Cumulative Proportion
PC1
0.42
0.16
-0.40
-0.14
-0.31
0.40
0.25
0.34
0.42
PC2
0.37
-0.49
0.10
-0.21
0.22
-0.32
0.38
-0.36
0.37
PC3
0.37
0.51
0.37
0.49
0.18
-0.07
-0.19
-0.13
0.37
PC4
0.13
0.13
0.17
-0.68
-0.22
-0.01
-0.60
-0.21
0.14
PC5
-0.05
0.14
-0.14
0.20
-0.76
-0.36
0.17
-0.43
-0.05
1.77
0.35
0.35
1.30
0.19
0.54
1.13
0.14
0.68
1.01
0.11
0.79
0.87
0.08
0.88
44
Habitat Related Variation
Habitat type and reclamation age had a significant interaction for ground cover,
midstory cover, and canopy closure. Percent ground cover was significantly higher in
young grasslands (mean = 78.1 % ± 4.3) than old grasslands (25.9 % ± 8.7) and conifer
forests of all age classes: young (35.9 % ± 8.5), medium (48.1 % ± 6.5), and old (39.2 %
± 5.8). Midstory percent cover was significantly higher in old grasslands (48.5 % ± 10.3)
than all other treatments with the exception of old conifer forests (26.4 % ± 6.2). Canopy
closure was significantly higher in medium conifer forests (57.9 % ± 5.8) and old conifer
forests (62.5 % ± 4.9) than young grasslands (15.3 % ± 5.9) and medium grasslands (20.9
% ± 8.9). Additionally, medium mixed forests (57.2 % ± 4.2) had significantly higher
canopy closure than young grasslands.
Habitat type differences were observed in total trees (per plot, ~ 405 m2), percent
conifers, number of forbs species, and basal area. Conifer forests more trees per plot
(80.5 ± 18.2) than mixed forests (38.0 ± 7.8), but grasslands were statistically similar to
both largely due to wide standard error. Percent canopy closure was lowest in young
grasslands, and significantly lower in grasslands (48.7 % ± 11.7) than conifer forests
(77.5 % ± 6.2). There were significantly more forbs species in grasslands (4.4 ± 0.8) and
mixed forests (3.7 ± 0.64) than conifer forests (3.2 ± 0.45). Average basal area was
significantly lower in grasslands (8.9 m2/ha ± 2.1) than conifer forests (15.0 m2/ha ± 1.7)
and mixed forests (12.5 m2/ha ± 2.1).
45
Contrast comparisons had no significant differences between non-mined and
mined sites. Other management activities can influence vegetation similarly to reclaimed
surface mines.
Table 3-2. Results of General Linear Model test of vegetation variables compared across
temporal and habitat variations. NS or S denote significance at α = 0.05.
Habitat
Variable
Total Trees
(per 405 m2 plot)
Percent Conifer
(based on stem
densities)
Ground Cover
(%)
Live Canopy (%)
Forbs (number
based on 1 m2)
Average Basal
Area (m2/ha)
Midstory Cover
(%)
Canopy Closure
(%)
Time Cat
Young
Medium
Old
Young
Medium
Old
Young
Medium
Old
Young
Medium
Old
Young
Medium
Old
Young
Medium
Old
Young
Medium
Old
Young
Medium
Old
Mix Mean
(±SE)
42.5 ± 7.4ab
35.23 ± 7.6b
36.14 ± 8.3b
62.1± 7.8abc
70.7 ± 9.2abc
46.3 ± 8.8bc
51.9 ± 7.2ab
46.1 ± 9.4ab
52.6 ± 7.7ab
48.1 ± 5.8
38.5 ± 6.2
45.4 ± 7.0
3.7 ± 0.55ab
3.5 ± 0.76 ab
4.2 ± 0.61ab
11.8 ± 2.0ab
14.9 ± 2.3 abc
10.9 ± 1.9 abc
19.7 ± 5.0b
12.0 ± 6.2 b
12.8 ± 3.8b
43.8 ± 6.9abc
57.2 ± 4.2 ab
38.5 ± 6.1 abc
Con Mean
(±SE)
105.5 ± 22.4 a
62.13 ± 19.5 ab
73.76 ± 12.8 ab
78.8 ± 6.2 a
81.5 ± 6.4 a
72.1 ± 6.0 ab
35.9 ± 8.5 b
48.1 ± 6.5 b
39.2 ± 5.8 b
46.4 ± 6.4
47.5 ± 5.2
48.7 ± 5.5
4.4 ± 0.60 ab
2.25 ± 0.30 b
2.8 ± 0.46 ab
12.0 ± 1.5 ab
15.8 ± 2.2 a
17.1 ± 1.4 a
17.9 ± 6.2 b
5.8 ± 2.1 b
26.4 ± 6.2 ab
41.7 ± 5.7 abc
57.9 ± 5.8 a
62.5 ± 4.9 a
Grass Mean
(±SE)
52.2 ± 28.8 ab
31.0 ± 15.0 b
59.67 ± 10.6 ab
39.1 ± 10.0c
62.3 ± 15.7 abc
44.9 ± 9.5 abc
78.1 ± 4.3 a
60.6 ± 7.1 ab
25.9 ± 8.7b
27.5 ± 7.9
40.6 ± 12.3
35.8 ± 8.6
4.7 ± 0.50 a
5.33 ± 1.3 a
3.33 ± 0.64 ab
6.4 ± 1.7 c
6.7 ± 2.5 bc
13.5 ± 2.0 ab
12.0 ± 4.0 b
13.7 ± 6.4 b
48.5 ± 10.3 a
15.3 ± 5.9 c
20.9 ± 8.9 bc
42.4 ± 6.3 abc
Time
(P)
NS
0.306
Habitat
(P)
S
0.007
Time X
Habitat (P)
NS
0.759
NS
0.077
S
0.000
NS
0.604
S
0.023
NS
0.075
S
0.002
NS
0.891
NS
0.099
NS
0.698
NS
0.186
S
0.039
NS*
0.053
*NS
0.051
S
0.002
NS
0.140
S
0.001
NS
0.134
S
0.004
S
0.011
S
0.000
S
0.030
*These values were close to significant so post-hoc results for interactions were calculated
Overall Diversity, Richness, and Densities of Birds
Across two years, I observed 78 species of birds (Appendix 3 – Observed Bird
Species). Shannon diversity and rarefied richness had interaction effects across time and
habitat categories (Figure 3-3). Significant differences (P = 0.001) were observed
between young conifer sites, with the lowest diversity (mean = 1.75 ± 0.07), and medium
conifer (2.19 ± 0.07), older conifer (2.18 ± 0.06), and young grassland (2.23 ± 0.18) sites
46
with the highest diversities. Diversity on non-mined sites was relatively low (1.91 ±
0.07). Rarefied species richness showed similar trends (P = 0.003) (Figure 3-4). Average
species richness was 5.9 ± 0.03 species per visit per treatment.
Average bird densities across sites showed that older mines had generally higher
densities and midstory cover (Figure 3-5). Densities in medium conifer forests (0.28
birds/ha ± 0.02), old conifer forests (0.24 birds/ha ± 0.02), and young grasslands (0.24
birds/ha ± 0.02) were higher than young conifer forests (0.15 ± 0.01). Only medium
conifer forests and old conifer forests had significantly higher densities than young
conifer forests (Figure 3-5). Species-specific interactions are explored below.
2.5
A
A
A
AB
Diversity Index
2
AB
AB
YM
MG
B
AB
AB
1.5
1
0.5
0
YG
YC
MC
MM
OG
OC
OM
Figure 3-3: Shannon diversity across 9 treatments of reclaimed surface mines in
northwestern Alabama with standard error and Tukey test results. Two-letter label
categories correspond with TIME by HABITAT treatments respectively. Time is either
young (Y), medium (M), or old (O). Habitat is either grassland (G), conifer (C), or mixed
(M). So, YC is ‘young conifer’, while MM is ‘medium mixed’.
47
7
A
AB
6
AB
AB
YM
MG
Rarefied Richness
B
AB
A
AB
AB
5
4
3
2
1
0
YG
YC
MC
MM
OG
OC
OM
Figure 3-4: Rarefied species richness across 9 mine reclamation treatments in
northwestern Alabama with standard error and Tukey test results. Two-letter label
categories correspond with TIME by HABITAT treatments respectively. Time is either
young (Y), medium (M), or old (O). Habitat is either grassland (G), conifer (C), or mixed
(M). So, YC is ‘young conifer’, while MM is ‘medium mixed’.
Average Densities in Birds/ha
0.35
A
0.3
0.25
ABC
ABC
ABC
0.2
AB
ABC
ABC
BC
C
0.15
0.1
0.05
0
YG
YC
YM
MG
MC
MM
OG
OC
OM
Figure 3-5: Average bird densities per hectare across 9 mine reclamation treatments
mines in northwestern Alabama with standard error and Tukey test results. Two-letter
label categories correspond with TIME by HABITAT treatments respectively. Time is
either young (Y), medium (M), or old (O). Habitat is either grassland (G), conifer (C), or
mixed (M). So, YC is ‘young conifer’, while MM is ‘medium mixed’.
48
Medium Conifer
5
10
15
20
0 10
0 10
0
30
exact
50
50
Old Conifer
30
exact
30
0 10
exact
50
Young Conifer
0
5
Sites
10
15
20
0
5
Sites
15
20
Sites
Medium Grassland
Old Grassland
0
5
10
15
20
30
0
0 5
10
20
exact
15
exact
25
10 20 30 40
0
exact
40
Young Grassland
10
0
5
Sites
10
15
20
0
5
Sites
Medium Mixed
20
15
20
Old Mixed
50
0 10
0
0
5
10
Sites
15
20
30
exact
20
10
exact
30
0 10
exact
30
50
15
Sites
40
Young Mixed
10
0
5
10
Sites
15
20
0
5
10
Sites
Figure 3.6: Species accumulation curves across treatments. Asymptotes indicate the
number of sites required per treatment to ensure full species detections. Shading
represents variance.
Species accumulation curves were used to show trends towards asymptotic
species numbers. Because species richness values are dependent on sample effort, species
49
accumulation curves can be used as an indicator to demonstrate how many sites were
required to detect the maximum number of species (Figure 3-6). The closer the curves are
to their asymptotes, the more likely it is that all species have been detected.
Hazard-rate models provided the best fit for calculating detection probabilities.
Example detection functions are shown below for the American Crow (Corvus
brachyrhynchos), a species with a low detection decay-rate function with increasing
distance from plot center (Figure 3-7a), and Blue-gray Gnatcatchers (Polioptila
caerulea), a species with a very high detection decay-rate function (Figure 3-7b).
Additional species detection curves for all species are available in Appendix 5 – Species
Detection Curves.
Blue-Gray Gnatcatcher Detection Function
0.8
0.4
0.6
Detection probability
1.0
0.8
0.0
0.0
0.2
0.2
0.4
0.6
Detection probability
1.0
American Crow Detection Function
0
20
40
60
80
100
0
Distance
20
40
60
80
100
Distance
Figure 3-7a: Detection function of
American Crow (Corvus brachyrhynchos),
indicating likelihood of detection of the
species at a given distance.
Figure 3-7b: Detection function of BlueGray Gnatcatcher (Polioptila caerulea),
indicating likelihood of detection of the
species at a given distance.
50
Species Specific Responses
The average density of 16 bird species had significant time and habitat type
interactions, while 3 species showed habitat type effect, and 2 species showed time
responses (Table 3-5). The Pine Warbler (Setophaga pinus) showed both a time and
habitat type response, but not an interaction response, and is tallied twice above.
Bank Swallows (Riparia riparia) showed an interaction effect, and were
significantly higher in young grasslands (0.6 ± 0.18 individuals per plot, ~4.9 ha) than
any other treatment type (P < 0.001). The only two other treatments that had observations
were old mixed forests and medium conifer forests.
Blue-Gray Gnatcatchers showed an interaction effect, and were observed in their
highest densities in medium aged conifer forests (7.45 ± 1.89) and old mixed forests
(5.37 ± 1.96); young mixed forests (3.58 ± 0.53), young conifer forests (3.58 ± 0.53),
medium grasslands (1.51 ± 2.52), and . This corresponded most with understory cover
(Figure 3-7).
Blue Grosbeaks had significant interactions and were more abundant in young
grasslands (1.31 ± 0.21) than medium (0 ± 0.31) and old (0 ± 0.26) grasslands, young (0
± 0.20) and old (0.24 ± 0.19) conifer forests, and young mixed (0.17 ± 0.19) forests.
Blue Jays (Cyanocitta cristata) showed an interaction effect and were higher in
medium conifer forests (0.54 ± 0.10) than young conifer forests (0.10 ± 0.10).
Carolina Chickadees (Poecile carolinensis) showed a habitat type treatment
effect, and were denser in conifer forests than other habitat types. At a treatment level,
51
they were significantly higher in old conifer (5.0 ± 0.79) forests than young (1.36 ± 0.82)
and medium (0.80 ± 1.09) mixed forests and medium grasslands (0.58 ± 1.31). Carolina
Chickadees were also significantly more common on non-mined (2.60 ± 0.29) sites and
mined (2.43 ± 0.08) sites (P = 0.025)
Carolina Wren (Thryothorus ludovicianus) showed an interaction effect where
young grasslands (2.09 ± 0.34) and medium conifer forests (1.63 ± 0.31) had
significantly higher concentrations than young conifer forests (0.14 ± 0.34), medium
grasslands (0 ± 0.51), and old grasslands (0.43 ± 0.44).
Chipping Sparrows (Spizella passerina) had an interaction effect, and old
grasslands (2.22 ± 0.47) had significantly more individuals than old mixed forests (0.89 ±
0.35).
Common Yellowthroats (Geothlypis trichas) were observed significantly more in
medium grasslands (3.70 ± 0.94) than in medium conifer (0.20 ± 0.58) or mixed (0 ±
0.78) forests, or young conifer (0.23 ± 0.62) forests; an interaction effect. The COYE
code in Figure 3-5 is, similar to the Barn Swallows and Red-winged Blackbird, inversely
related to percent conifer, and loosely linked to open canopies.
Downy Woodpeckers had a habitat type response and were highest in old mixed
(0.24 ± 0.06) forests, and were significantly lower in old conifer (0 ± 0.06) forests.
Percent conifer likely played a role in their distribution (Figure 3-7).
Eastern Phoebes (Sayornis phoebe) had an interaction effect, and were highest in
young grasslands (2.63 ± 0.62). They were significantly lower in old grasslands (0 ±
0.28), conifer forests (0.47 ± 0.19) and mixed forests (0.11 ± 0.21) as well as medium
52
grasslands (0.26 ± 0.32), young conifer forests (0.11 ± 0.21), and young mixed forests (0
± 0.20). Additionally, medium mixed forests (1.27 ± 0.26) were significantly higher than
old conifer forests, medium grasslands, old mixed forests, old grasslands, young conifer
forests, and young mixed forests. Lastly, young mixed forests were also significantly
lower than medium conifer forests (0.79 ± 0.19).
Field sparrows (Spizella pusilla) were one of the few species where non-mined
sites had a significantly different number of individuals than mined sites (Table 3-3).
Field sparrows were more common in sites than had been mined once than sites that had
not. They also showed a treatment interaction effect and were more common in medium
conifer forests (2.54 ± 0.0.36) than medium mixed forests (0.31 ± 0.48), old grasslands
(0.68 ± 0.50), and young conifer forests (0.87 ± 0.38).
Mourning Doves (Zenaida macroura) showed a treatment interaction effect and
were significantly higher in young grasslands (0.65 ± 0.12) than young conifer forests
(0.10 ± 0.12) and young mixed forests (0.17 ± 0.11).
Pine Warblers showed effects in all categories. They were significantly lower in
young grasslands (0.78 ± 0.54) than young (4.08 ± 0.52), medium (4.36 ± 0.49), and old
(4.19 ± 0.48) conifer forests, and old (2.96 ± 0.52) and medium (3.88 ± 0.66) mixed
forests.
Red-eyed Vireo (Vireo olivaceus) numbers were significantly higher in old
grasslands (4.71 ± 0.81) than old (1.24 ± 0.66) and young (1.51 ± 0.63) mixed forests.
Red-headed Woodpeckers (Melanerpes erthythrocephalus) had an interaction
effect across treatments, and were highest in medium grasslands (0.22 ± 0.06) and
53
significantly lower in young (0 ± 0.04), medium (0 ± 0.03) and old (0 ± 0.03) conifer
forests and in young grasslands (0 ± 0.04), and young mixed forests (0.04 ± 0.03).
Red-winged Blackbirds (Agelaius phoeniceus) had an interaction effect with
young grasslands (1.00 ± 0.17) being significantly higher than all treatments except
young mixed forests (0.52 ± 0.16) and medium conifer forests (0.56 ± 0.16). They were
likely linked more to water than measured habitat variables as seen in figure 3-5 the way
they and Common Yellowthroats are grouped separate from the measured habitat
components.
Summer tanagers (Piranga rubra) showed a marginally significant difference
between young grasslands (1.13 ± 0.29) and old grasslands (0 ± 0.37). This was an
interaction effect.
Wild Turkeys (Meleagris gallopavo) had an interaction effect with the following
treatment effects: medium mixed forests (0.31 ± 0.06) were significantly higher than all
treatments except the next highest treatment, old grasslands (0.083 ± 0.07).
Worm-eating Warblers (Helmitheros vermivorum), though poorly represented on
the landscape, showed an interaction effect with presence linked to old grassland mines
(4.61 ± 0.79). Conversely, Figure 3-5 helps link habitat variables to species presence, and
we can see Worm-eating Warblers are linked to mature forest related habitat variables:
midstory cover, basal area, and canopy closure. Worm-eating warbler densities on old
grasslands were significantly higher than all other treatments.
Yellow-breasted Chats (Icteria virens) showed a time effect. They were highest in
medium grasslands (2.80 ± 0.59), medium conifer forests (2.63 ± 0.36), and young
54
grasslands (2.10 ± 0.40). Young mixed forests (0.82 ± 0.37) were significantly lower than
medium grasslands and medium conifer forests.
Contrasts between mined and non-mined sites should significant differences in six
species: Carolina Chickadee, Chipping Sparrow, Field Sparrow, Hooded Warbler, Pine
Warbler, and Prairie Warbler. The Carolina Chickadee was negatively impacted by
mining having occurred on the landscape while the remaining five species showed
positive responses to mining on a landscape (Table 3-3).
Table 3-3: Species that showed significant contrast values from comparing control means
to treatment means.
Species
Carolina Chickadee
Chipping Sparrow
Field Sparrow
Hooded Warbler
Pine Warbler
Prairie Warbler
Control mean
2.60
1.50
1.26
1.05
3.17
1.52
Treatments’ mean
2.43
1.58
1.33
1.14
3.31
4.18
SE
0.08
0.05
0.03
0.05
0.06
0.09
P
0.025
0.015
0.016
0.002
0.005
0.006
Table 3-4: Proportional Index of Community Similarities by treatments.
Cont
Cont 1.00
0.69
MC
0.53
MG
0.67
MM
0.78
OC
0.77
OG
0.68
OM
0.72
YC
0.65
YG
0.70
YM
MC
0.69
1.00
0.61
0.70
0.76
0.65
0.73
0.71
0.75
0.70
MG MM OC OG OM YC YG YM
0.53 0.67 0.78 0.77 0.68 0.72 0.65 0.70
0.61 0.70 0.76 0.65 0.73 0.71 0.75 0.70
1.00 0.66 0.67 0.55 0.74 0.69 0.59 0.69
0.66 1.00 0.77 0.65 0.71 0.74 0.77 0.70
0.67 0.77 1.00 0.72 0.79 0.85 0.75 0.78
0.55 0.65 0.72 1.00 0.66 0.68 0.61 0.68
0.74 0.71 0.79 0.66 1.00 0.74 0.69 0.77
0.69 0.74 0.85 0.68 0.74 1.00 0.69 0.72
0.59 0.77 0.75 0.61 0.69 0.69 1.00 0.74
0.69 0.70 0.78 0.68 0.77 0.72 0.74 1.00
55
Proportional community similarities were compared to further identify areas of
difference between non-mined control sites and reclaimed surface mines (Table 3-4).
Controls were more similar to older sites on average, and they were least similar to
medium-aged sites. The highest similarities were between old conifer forests and young
conifer forests (85%). The lowest similarities were between mixed grasslands and
controls (53%). Grasslands were the least similar across chronosequential categories.
56
Table 3-5. Results of General Linear Models test of bird densities (per site, ~4.9 ha)
compared across temporal and habitat variations.
Species
Time
Cat
Acadian Flycatcher
(Empidonax
virescens)
American Crow
(Corvus brachyrhynchos)
Bank Swallow
(Riparia riparia)
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Blue-Gray
Gnatcatcher
(Polioptila caerulea)
Blue Grosbeak
(Passerina caerulea)
Blue Jay (Cyanocitta
cristata)
Brown-Headed
Cowbird (Molothrus
ater)
Brown Thrasher
(Toxostoma rufum)
Carolina Chickadee
(Poecile carolinensis)
Carolina Wren
(Thryothorus
ludovicianus)
Chipping Sparrow
(Spizella passerina)
Common
Yellowthroat
(Geothlypis trichas)
Downy Woodpecker
(Picoides pubescens)
Eastern Bluebird
(Sialia sialis)
Eastern Kingbird
(Tyrannus tyrannus)
Mix Mean
0.65 ± 0.28
0.00 ± 0.22
0.18 ± 0.21
0.69 ± 0.16
0.71 ± 0.13
0.74 ± 0.12
0.00 ± 0.08b
0.05 ± 0.07 b
0.00 ± 0.06 b
3.58 ± 0.53 ab
5.37 ± 1.96 b
3.58 ± 0.53 ab
0.31 ± 0.26abc
0.38 ± 0.20 abc
0.17 ± 0.19 bc
0.15 ± 0.13ab
0.24 ± 0.10 ab
0.35 ± 0.10 ab
0.14 ± 0.14
0.09 ± 0.11
0.16 ± 0.10
0.00 ± 1.43
0.57 ± 1.13
2.10 ± 1.08
0.80 ± 1.09b
2.73 ± 0.86 ab
1.36 ± 0.82 b
0.60 ± 0.43ab
0.87 ± 0.34ab
0.79 ± 0.32ab
2.25 ± 0.45ab
0.89 ± 0.35b
1.27 ± 0.34ab
0.00 ± 0.78b
0.68 ± 0.62ab
2.69 ± 0.59ab
0.23 ± 0.08ab
0.24 ± 0.06a
0.09 ± 0.06ab
0.76 ± 0.38
0.24 ± 0.30
0.00 ± 0.29
1.27 ± 0.26
0.11 ± 0.21
0.00 ± 0.20
Con Mean
0.35± 0.20
0.00 ± 0.20
0.00 ± 0.22
1.00 ± 0.12
0.84 ± 0.12
0.71 ± 0.13
0.04 ± 0.06b
0.00 ± 0.06b
0.00 ± 0.07b
7.45 ± 1.9a
3.58 ± 0.53ab
3.58 ± 0.53ab
1.01 ± 0.19ab
0.24 ± 0.19bc
0.00 ± 0.20c
0.54 ± 0.10a
0.24 ± 0.10ab
0.10 ± 0.10b
0.15 ± 0.10
0.07 ± 0.10
0.09 ± 0.11
0.00 ± 1.05
1.45 ± 1.03
1.15 ± 1.13
3.26 ± 0.80ab
5.00 ± 0.79a
4.20 ± 0.86ab
1.63 ± 0.31a
0.94 ± 0.31ab
0.14 ± 0.34b
1.11 ± 0.33ab
2.02 ± 0.32ab
1.39 ± 0.35ab
0.20 ± 0.58b
1.71 ± 0.57ab
0.23 ± 0.62b
0.13 ± 0.06ab
0.00 ± 0.06b
0.05 ± 0.06ab
0.83 ± 0.28
0.00 ± 0.27b
0.47 ± 0.30
0.79 ± 0.19
0.47 ± 0.19
0.11 ± 0.21
57
Grass Mean
Time
(P)
Habitat
(P)
0.00 ± 0.33
0.00 ± 0.29
0.63 ± 0.22
0.89 ± 0.20
1.00 ± 0.17
0.85 ± 0.13
0.00 ± 0.10b
0.00 ± 0.09b
0.55 ± 0.07a
1.51 ± 2.48b
4.77 ± 1.39ab
3.58 ± 0.53ab
0.00 ± 0.31c
0.00 ± 0.27c
1.31 ± 0.21a
0.22 ± 0.16ab
0.42 ± 0.14ab
0.15 ± 0.11ab
0.00 ± 0.16
0.15 ± 0.14
0.36 ± 0.11
0.00 ± 1.72
4.02 ± 1.49
3.02 ± 1.15
0.58 ± 1.31b
2.61 ± 1.14ab
1.30 ± 0.88ab
0.00 ± 0.51b
0.43 ± 0.44b
2.09 ± 0.34a
1.48 ± 0.54ab
2.22 ± 0.47a
1.60 ± 0.36ab
3.70 ± 0.94a
0.40 ± 0.82ab
0.48 ± 0.63ab
0.00 ± 0.09ab
0.00 ± 0.08ab
0.05 ± 0.06ab
0.55 ± 0.46
0.41 ± 0.40
0.74 ± 0.31
0.26 ± 0.32
0.00 ± 0.28
1.53 ± 0.21
NS
0.216
NS
0.676
Time X
Habitat
(P)
NS
0.242
NS
0.656
NS
0.245
NS
0.725
S
0.004
S
0.015
S
0.000
NS
0.204
NS
0.103
S
0.021
NS
0.236
NS
0.689
S
0.000
NS
0.410
NS
0.865
S
0.027
NS
0.444
NS
0.793
NS
0.527
NS
0.101
NS
0.320
NS
0.720
NS
0.066
S
0.000
NS
0.998
NS
0.581
NS
0.868
S
0.000
NS
0.619
NS
0.663
S
0.036
NS
0.822
NS
0.360
S
0.000
NS
0.603
S
0.009
NS
0.321
NS
0.230
NS
0.723
NS
0.569
NS
0.566
NS
0.185
NS
0.203
Species
Eastern Phoebe
(Sayornis phoebe)
Time
Cat
Med
Old
Eastern Towhee
(Pipilo erythrophthalmus)
Field Sparrow
(Spizella pusilla)
Gray Catbird
(Dumetella
carolinensis)
Hooded Warbler
(Setophaga citrina)
Indigo Bunting
(Passerina cyanea)
Northern Cardinal
(Cardinalis
cardinalis)
Northern Flicker
(Colaptes auratus)
Northern
Mockingbird (Mimus
polyglottos)
Mourning Dove
(Zenaida macroura)
Pileated Woodpecker
(Dryocopus pileatus)
Pine Warbler
(Setophaga pinus)
Prairie Warbler
(Setophaga discolor)
Red-Bellied
Woodpecker
(Melanerpes
carolinus)
Red-Eyed Vireo
(Vireo olivaceus)
Red-Headed
Woodpecker
(Melanerpes
erthythro-cephalus)
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Mix Mean
Con Mean
0.11 ± 0.21cd
0.00 ± 0.20d
0.00 ± 0.43
0.73 ± 0.34
1.33 ± 0.32
0.31 ± 0.48b
1.45 ± 0.38ab
1.68 ± 0.36ab
0.44 ± 0.63
1.91 ± 0.50
1.49 ± 0.48
0.00 ± 0.47
1.14 ± 0.37
1.22 ± 0.36
5.30 ± 1.02
4.92 ± 0.80
3.56 ± 0.77
2.27 ± 0.60
1.97 ± 0.47
1.54 ± 0.45
0.08 ± 0.09
0.10 ± 0.07
0.22 ± 0.06
0.22 ± 0.32
0.69 ± 0.25
0.63 ± 0.24
0.38 ± 0.15ab
0.43 ± 0.12ab
0.17 ± 0.11b
0.23 ± 0.13
0.19 ± 0.10
0.30 ± 0.10
3.88 ± 0.66a
2.96 ± 0.52a
2.53 ± 0.50ab
5.46 ± 0.79
4.78 ± 0.63
3.82 ± 0.60
0.08 ± 0.11
0.33 ± 0.09
0.04 ± 0.08
0.79 ±
0.19abc
0.47 ±
0.19bcd
0.11 ± 0.21cd
0.00 ± 0.32
0.41 ± 0.31
0.24 ± 0.34
2.54 ± 0.36a
1.30 ± 0.35ab
0.87 ± 0.38b
0.72 ± 0.47
0.92 ± 0.46
0.56 ± 0.50
1.17 ± 0.35
0.80 ± 0.34
0.57 ± 0.37
5.02 ± 0.75
4.65 ± 0.74
2.66 ± 0.80
3.08 ± 0.44
2.72 ± 0.43
1.41 ± 0.47
0.00 ± 0.06
0.08 ± 0.06
0.05 ± 0.07
0.24 ± 0.24
0.35 ± 0.23
0.14 ± 0.25
0.38 ± 0.11ab
0.52 ± 0.11ab
0.10 ± 0.12b
0.25 ± 0.10
0.36 ± 0.09
0.19 ± 0.10
4.36 ± 0.49a
4.19 ± 0.48a
4.08 ± 0.52a
4.18 ± 0.58
4.18 ± 0.57
3.18 ± 0.63
0.13 ± 0.08
0.24 ± 0.08
0.05 ± 0.09
3.34 ± 0.84ab
1.24 ± 0.66b
1.51 ± 0.63b
0.00 ± 0.05b
0.05 ± 0.04ab
0.04 ± 0.03b
2.53 ± 0.62ab
3.13 ± 0.60ab
1.85 ± 0.66ab
0.00 ± 0.03b
0.00 ± 0.03b
0.00 ± 0.04b
1.27 ± 0.26ab
58
Grass Mean
Time
(P)
Habitat
(P)
S
NS
Time X
Habitat
(P)
S
0.014
0.737
0.000
NS
0.891
NS
0.164
NS
0.052
NS
0.537
NS
0.397
S
0.003
NS
0.252
NS
0.230
NS
0.724
NS
0.740
NS
0.523
NS
0.193
NS
0.078
NS
0.726
NS
0.357
NS
0.119
NS
0.416
NS
0.407
NS
0.814
NS
0.073
NS
0.596
NS
0.193
NS
0.328
NS
0.693
NS
0.661
NS
0.678
S
0.011
NS
0.281
NS
0.517
NS
0.302
S
0.001
S
0.002
NS
0.052
NS
0.166
NS
0.193
NS
0.242
NS
0.083
NS
0.969
NS
0.124
NS
0.065
NS
0.662
S
0.018
NS
0.207
S
0.012
S
0.040
0.26 ± 0.32bcd
0.00 ± 0.28cd
1.53 ± 0.21a
0.57 ± 0.52
0.00 ± 0.45
0.00 ± 0.35
1.81 ± 0.58ab
0.68 ± 0.50b
1.42 ± 0.39ab
1.27 ± 0.76
1.91 ± 0.66
1.14 ± 0.51
0.44 ± 0.57
0.33 ± 0.49
0.60 ± 0.38
5.74 ± 1.23
3.23 ± 1.06
4.73 ± 0.82
1.97 ± 0.72
2.96 ± 0.63
2.36 ± 0.49
0.22 ± 0.10
0.17 ± 0.09
0.15 ± 0.07
0.32 ± 0.39
0.96 ± 0.33
0.29 ± 0.26
0.44 ± 0.18ab
0.17 ± 0.16ab
0.65 ± 0.12a
0.11 ± 0.16
0.50 ± 0.13
0.45 ± 0.10
4.74 ± 0.80a
2.26 ± 0.69ab
0.78 ± 0.54b
4.64 ± 0.96
2.44 ± 0.83
4.18 ± 0.64
0.00 ± 0.13
0.17 ± 0.11
0.30 ± 0.09
0.97 ± 1.01ab
4.71 ± 0.87a
1.74 ± 0.68ab
0.22 ± 0.06a
0.08 ± 0.05ab
0.00 ± 0.04b
Species
Time
Cat
Mix Mean
Red-Winged
Blackbird (Agelaius
phoeniceus)
Scarlet Tanager
(Piranga olivacea)
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
Med
Old
Young
0.00 ± 0.22b
0.00 ± 0.17b
0.52 ± 0.16ab
0.24 ± 0.12
0.07 ± 0.10
0.07 ± 0.09
0.50 ± 0.36ab
0.62 ± 0.28ab
0.70 ± 0.27ab
0.59 ± 0.25
0.59 ± 0.20
0.27 ± 0.19
0.00 ± 0.13
0.17 ± 0.10
0.16 ± 0.10
0.31 ± 0.06a
0.05 ± 0.05b
0.04 ± 0.05b
0.00 ± 0.09
0.00 ± 0.07
0.05 ± 0.06
0.61 ± 0.75b
0.00 ± 0.59b
1.37 ± 0.57b
1.78 ± 0.49ab
1.20 ± 0.39ab
0.82 ± 0.37b
Summer Tanager
(Piranga rubra)
Tufted Titmouse
(Baeolophus bicolor)
White-Breasted
Nuthatch (Sitta
carolinensis)
Wild Turkey
(Meleagris
gallopavo)
Wood Thrush
(Hylocichla
mustelina)
Worm-Eating
Warbler (Helmitheros
vermivorum)
Yellow-Breasted
Chat (Icteria virens)
Con Mean
0.56 ± 0.16ab
0.11 ± 0.16b
0.06 ± 0.17b
0.13 ± 0.09
0.13 ± 0.09
0.00 ± 0.10
0.81 ± 0.26ab
0.39 ± 0.26ab
0.15 ± 0.28ab
0.52 ± 0.19
0.99 ± 0.18
0.29 ± 0.20
0.15 ± 0.10
0.00 ± 0.10
0.26 ± 0.10
0.04 ± 0.05b
0.08 ± 0.05b
0.00 ± 0.05b
0.00 ± 0.06
0.14 ± 0.06
0.00 ± 0.07
0.00 ± 0.56b
0.63 ± 0.54b
0.38 ± 0.59b
2.63 ± 0.36a
1.77 ± 0.36ab
1.30 ± 0.39ab
Grass Mean
Time
(P)
Habitat
(P)
0.00 ± 0.26b
0.00 ± 0.22b
1.00 ± 0.17a
0.00 ± 0.15
0.13 ± 0.13
0.24 ± 0.10
0.00 ± 0.43ab
0.00 ± 0.37b
1.13 ± 0.29a
0.00 ± 0.30
0.52 ± 0.26
0.70 ± 0.20
0.00 ± 0.16
0.15 ± 0.14
0.00 ± 0.11
0.00 ± 0.08b
0.08 ± 0.07ab
0.00 ± 0.05b
0.13 ± 0.10
0.10 ± 0.09
0.24 ± 0.07
0.00 ± 0.91b
4.61 ± 0.79a
0.39 ± 0.61b
2.80 ± 0.59a
1.40 ± 0.51ab
2.10 ± 0.40ab
S
0.003
NS
0.632
Time X
Habitat
(P)
S
0.001
NS
0.968
NS
0.850
NS
0.387
NS
0.377
NS
0.681
S
0.049
NS
0.155
NS
0.547
NS
0.111
NS
0.657
NS
0.673
NS
0.317
NS
0.086
S
0.043
S
0.034
NS
0.700
NS
0.095
NS
0.350
S
0.021
NS
0.057
S
0.000
S
0.014
NS
0.056
NS
0.656
*S is a significant result and NS is non-significant, both categories are followed by the P value, and
significant differences are followed by the control means and the alternate treatment means.
59
Bird and Habitat Relationships
As a visual representation of the link behind bird species and habitat variables, a
CCA was completed (Figure 3-8). Nine canonical dimensions were returned in this
model, with the first three being statistically significant (Afifi, Clark, & May, 2004;Table
3-6). Dimension 1 had a canonical correlation of 0.75 between variables and dimension 2
had a canonical correlation of 0.72; they are compared visually below (Figure 3-8). For
the bird-species variables, the first dimension was most strongly influenced by Red-eyed
Vireo presence (0.36) and for the second dimension: Blue Jay (-0.32), Eastern Phoebe
(0.35), Field Sparrow (-0.38), Red-headed Woodpecker (0.33), Wild Turkey (-0.33), and
Worm-eating Warbler (0.31). For the habitat variables, dimension 1 was most strongly
influenced by total trees (0.25) and tree density (0.25), while dimension 2 was most
associated with percent conifer (0.28) and canopy closure (0.28).
Table 3-6: Tests of nine canonical dimensions, calculated within the R environment
(Afifi et al., 2004). The first three dimensions were significant, and only dimensions 1
and 2 were graphically displayed.
Dimension Canonical Corr. Wilks’ Lambda
1
0.75
0.03
2
0.72
0.08
3
0.67
0.16
4
0.55
0.28
5
0.46
0.40
6
0.44
0.51
7
0.40
0.64
8
0.38
0.76
9
0.33
0.89
60
F
1.75
1.49
1.22
0.98
0.85
0.80
0.73
0.68
0.59
df1
360
312
266
222
180
140
102
66
32
df2
1333
1195
1054
910
764
616
465
312
157
p
0.00
0.00
0.02
0.58
0.91
0.95
0.97
0.97
0.96
Disturbance dependent and open woodland or grassland species like the Field
Sparrow, Mourning Dove, and Yellow-breasted Chat are better linked to low canopy
closure, an increase in forbs species, and increase in ground cover. Conversely, species
associated with forest interiors, like the Worm-eating Warbler, Carolina Chickadee,
White-breasted Nuthatch, and American Crow are better linked to higher canopy closure,
thicker midstory, and increased basal area. These factors are segregated well along the
dimensional axes, such that canopy closure and tree density are along the horizontal axis,
with higher values to the right and lower values to the left. Percent conifer and live
canopy were better oriented along the vertical axes, with higher values being closer to the
top of the image, and lower values being towards the bottom. Treatments were not tightly
related to the dimensions, but, in general, older and medium aged mines were more
tightly clustered, while younger mines were more unique (Figure 3-8). Each habitat type
classification of mine segregated reasonably well (Figure 3-8) suggesting that habitat
type is the first influential parameter on bird community composition, followed
secondarily by time since reclamation.
61
1.0
PIWA
COYE
Live Canopy
0.5
RHWO
Percent Conifer
PRAW
FISP
YBCH
BLJA
NOMO
CHSP
DOWO
GRCA
WITUNOCA
RBWO
ACFL
0.0
Dimension 2
INBU
EAKI
ForbsNOFL
EABL
BGGN
GroundMODO
Cover
Canopy
Basal Area
Total Trees
WBNU
REVI
WEWA
Midstory
AMCR
CACH
WOTH
-0.5
HOWA
TUTI
EATO
CARW
SCTA
-1.0
EAPH BRTH
RWBB
BLGR
BANS
SUTA
BHCO
PIWO
-1.0
-0.5
0.0
0.5
1.0
Dimension 1
Figure 3-8: Canonical Correspondence Analysis of 41 bird species located across 27
reclaimed surface mines. Appendix 3 – Observed Bird Species provides a breakdown of
four-letter codes.
62
CHAPTER 4 – DISCUSSION AND MANAGEMENT IMPLICATIONS
Assumption Justifications
Avifauna communities can serve as macroscopic indicators of ecosystem services
(Gregory et al., 2003). In the case of mine reclamation, birds have been well documented
in grassland reclaimed surface mines (Bajema & Lima, 2001; Devault et al., 2002), but
little attention has gone towards species in formerly forested landscapes, assessing the
cumulative impacts of mining from multiple sites at the landscape scale, and identifying
management-related merits of various treatments and their related chronosequential
successional status while protecting soil and water resources (Buehler & Percy, 2012). To
address these issues, I have evaluated effects of mining on bird communities through a
relatively long time-frame (~26 years) over a large area of northwestern Alabama. This
addressed one aspect of cumulative impacts of mining by measuring the temporal scope
of impact that small-scale surface mining in highly productive ecosystems has on
avifauna. We can also help support habitat associations for various well-represented
species. Lastly, we can highlight which practices, currently in use, serve to promote
ecological restoration of certain groups of avifauna. By highlighting the merits of these
reclamation practices, resource managers may be better prepared to approve or amend
proposed reclamation plans, depending on the local conservation need.
63
To assess the influence mining had on bird communities, we explored 200 plots
on 27 reclaimed surface mines and surrounding non-mined areas across the northwestern
portion of Alabama. Mines were broken into three temporal categories: young (< 15
years), medium (15-20 years), and old (>20 years), based on time since closure, and three
habitat type classifications: grassland/savannas, conifer forest, and mixed forest. Most
studies of this nature are limited in sample size, and this project, though still limited, did
allow for the use of individual plots as sample units without causing pseudo-replication
issues by ensuring that within-mine and between-mine variances were similar
(Srivastava, 1999). There were exceptions to the variance being similar, but they were, at
most, related to one habitat variable and were considered tolerable. Each case is further
explored below.
Percent conifer in young grasslands had higher variances between mines than
within mines (Appendix 4 – Between-mine and Within-mine Comparisons). For
grasslands the bird communities are more linked to forbs, canopy closure (inversely), and
mid-story cover, not the forest composition (Thompson, 2013). The significant difference
in the composition of the few trees on young grasslands was not considered an issue for
the purposes of this study.
Midstory differences in older mixed forests were linked primarily to only having
one site on mine P3520 due its small size. At 5.3 ha in area, one audio point-count survey
covered almost the entire area of the mine (~4.9 ha), and did not allow for the placement
of a second independent sample location (Ralph, 1995). However, the random location
had the densest midstory of all sites within this treatment and no variation could be
64
calculated. Because this site only showed one unique habitat variable, and because this
mine as a whole was being served by this individual point regardless, it was considered
appropriate to consider plot locations as independent.
Young mixed forests showed different between-mine and within-mine variation in
canopy closure, which was likely linked to the definition of canopy (> 5 m above ground)
used for this study. A canopy level of > 2 m, as used by Holl (2002) may have been more
appropriate as at 15 years, the number of individual trees that contribute to the canopy,
based on random plots, may be linked to topographical features or environmentally
different micro-sites not explored in this data set. This may simply be the difference in
number of trees that have grown tall enough quickly enough. Because percent conifer
composition, midstory cover, and understory composition were similar, it was considered
acceptable to consider plots independent.
Lastly, old grasslands had significantly more variation in midstory cover across
mines than within. This was likely linked to the distance to edge habitat (Warner, 1994)
and specific timing of mine closure (Holl, 2002). If sites were close to the forested edge
of a grassland-reclaimed mine site, midstory cover was more dense, whereas it was nonexistent in the center of many of the grassland mines. Smaller mines tended to have more
sites near the edge of the permitted area to maximize point-count locations. As a result,
smaller mines tended to have more trees and shrubs. Due to the limitations of ensuring
sample independence, and the difficulty in finding mines that met the required
parameters, this last assumption regarding independence of plots was tolerated for data
exploration purposes.
65
The study took place over two years, and there were no significant differences in
overall detections from year to year, so all sites were combined. Three species of birds
were not observed in 2013, but were observed in 2014 on the subset of plots analyzed in
both years: Blue Grosbeak, Pileated Woodpecker, and Downy Woodpecker. Because
more than 94% of species observed on comparison sites were considered similar in
numbers across years, it was considered appropriate to combine annual data.
Results Exploration
An interaction effect was observed for ground cover, midstory cover, and canopy
closure when examining habitat related variation. There were no other variables that were
significantly linked to time, but four of the remaining variables: percent conifer, total
trees, number of forbs species, and average basal area showed habitat type variation.
Forest canopy composition was not expected to drastically change over 26 years nor were
the species of forbs (though their specific densities did, expressed through ground cover),
and these are supported by the findings. Although the total number of trees in the canopy
may not have been significantly different at various chronosequence stages, it was higher
at later time categories, which was expected. Average basal area should have been linked
to time, as it increases with individual tree growth through time, and Biging and
Dobbertin (1992) found these increases could be substantial for conifers. In this particular
case, it is possible that sites on younger treatments were located in close proximity to
edges which may have included large trees along the periphery of the reclaimed site.
66
This, coupled with the small minimum stem requirement (2.5 cm) may have led to higher
than expected basal areas in younger mines.
Diversity indices were similar; however, young conifer forests were significantly
lower than young grasslands, old conifer forests, and medium conifer forests. Young
conifer sites were expected to be low as they are generally limited in species diversity
and midstory growth (Hedman, Grace, & King, 2000). These early stands tend to be
planted in combination with non-native grasses, like Lespedeza sp., that can slow the
establishment of other pioneer and understory vegetation (Burger, 2011; Holl, 2002).
These young stands may also be heavily managed to ensure high survivorship of planted
stock, which may impact midstory regeneration.
Diversity was also low (non-significant) in medium grasslands, young mixed
forests, and non-mined sites. Diversity seemed to be linked to midstory cover, and
complexity of vegetation both in number of forbs species (grassland/savanna) and
vertical structure (older forested sites). Non-mined locations were expected to have high
diversity and richness values, but this was not the case. Richness followed the same
general trends as diversity.
Densities showed species-specific responses, but the general trends were that bird
densities were higher in medium aged forests. Specifically, medium aged conifer forests,
older conifer forests, young mixed forests, and young grasslands had the highest densities
while young conifer forests and older and medium aged grasslands had the lowest.
Densities for many species behaved as expected: grassland linked species were
higher in grasslands (i.e. Prairie Warblers and Mourning Doves), and mature forest linked
67
species were higher in mature forests (i.e. White-Breasted Nuthatch). However, the Field
Sparrow and Worm-eating Warbler were observed in their highest densities in sites
within unexpected treatments.
Field Sparrows are generally linked with open grasslands (Best, 1978). They are
ground nesters and need low-lying forage and cover for nesting (Best, 1978). They were
expected to be observed in their highest densities in grassland habitat types, and likely in
young or medium aged time classes. However, medium conifer forests had significantly
more Field Sparrows than most other habitat classifications. CCA showed that Field
Sparrows were appropriately linked to ground cover and inversely linked to forest cover.
This finding identifies the issues associated with a coarse-scale, landscape-level
assessment of bird communities, and highlights the importance of supporting surveys
with habitat data onsite and ordination techniques. Field Sparrows were still linked with
the anticipated habitat variables there was just an exceptional situation where those
habitat variables existed on an un-likely treatment.
Worm-eating Warblers are secretive birds nesting near the base of mature trees in
older mature forests, generally on large slopes (Murray & Stauffer, 1995). The fact that
they were commonly observed on grassland mines was therefore surprising. By
evaluating habitat and bird relationships through ordination techniques, we can see that
they are indeed linked to the anticipated closed canopies and higher basal areas. These
habitat variables were available within the mapped permit area of grassland mines. The
sites within which the warblers were located were off the edge of the mine footprint, and
68
were in mature trees. This resulted in an odd linkage to treatment type, but was caught
through ordination techniques.
These discrepancies are likely linked to the diversity of reclamation that can occur
across a large mine-site. Though point-counts were selected over transect investigations
to capture the mosaic of habitats across a given mine treatment, this study does
demonstrate the need for high coverage of sampling points to ensure that habitat
variations may be accounted for through ordination techniques.
Other density estimates from this study are reasonably well linked to the findings
of other studies for similar species within the Alabama northwest (Wang, Lesak, Felix, &
Schweitzer, 2006). Wang et al. (2006) were looking at silviculture responses of
songbirds, and their density estimates are based on a landscape influenced by forest
management in an upland oak-hickory system as opposed to mining. As a result, we
observed larger densities of Indigo Buntings, possibly linked to the larger scale of
disturbance or to proximity to edge (Suarez, Pfennig, & Robinson, 2003), and higher
densities of Blue-gray Gnatcatchers.
Red-eyed vireo were observed to be linked with midstory density, but inversely
related to percent conifer for our study as previously observed (Wood, Birger, Bowman,
& Hardy, 2004). Densities were also representative of Wang et al.’s findings.
Blue-gray gnatcatchers had the highest calculated densities, and they were
substantially higher (7.45 birds/site, or 1.52 birds / ha) on these sites than were observed
by Wang et al. (0.83 birds / ha). These differences were possibly linked to higher percent
conifer, and more open habitat types (Root, 1967) in my study sites providing more
69
suitable habitat. Small aggregations of Blue-gray Gnatcatchers were observed audibly
and visually at close range on several sites, and few were observed at distances greater
than 50 m; it was estimated that we were unable to observe 93% of the potential
individuals within a site. These estimates should be relatively accurate regardless of the
difficulty in detecting individuals at farther distances (Buckland et al., 1993), but
detectability of the species may have differed across the studies for reasons not addressed
in either case.
Wood Thrush and White-breasted Nuthatches are both mature forest birds that were
observed on several sites, but it should be noted that these mature forest species were
observed in very low numbers, and were likely only just returning to these disturbed
landscapes after more than 20 years. Densities of the Wood Thrush were higher in control
sites (0.20 birds / ha) than averages across sites (0.03 birds / ha), and these densities were
much lower than should have been observed. Boecklen (1984) observed densities ranging
from 18 territories / 10 ha (1.8 birds / ha) to 218 territories / 10 ha (~22 birds / ha) of
Wood Thrush from all across the eastern United States and Canada.
White-breasted nuthatches were observed in densities of 0.27 birds / ha in control
sites and 0.19 birds / ha across treatments. These densities were slightly higher than those
found in Colorado by McEllin (1979). If forest-bird species are of concern within an area,
then disturbances should be spaced at greater than 25 year intervals. Further study is
required to specify that time-frame more.
Most species were similar across the non-mined and mined landscapes, but of the
species that showed significant responses, only Carolina Chickadees showed negative
70
responses to mining and restoring a landscape. The remaining five species performed
better on landscapes that had once been mined. Disturbance is an important component of
southeastern habitats; this should be considered carefully by agencies that are evaluating
proposed developments.
Vegetation was fairly diverse across broad treatment types, and ordination
techniques were imperative to support causal linkages for avian species presence.
Grassland/savannas showed relatively high tree densities in some cases, linked to the
small size of measured stems (2.5 cm); many shrubs were included. They were however
significantly lower than other treatments in basal area. Also, canopy closures were high
in old grassland/savannah treatments (40%). This was a function of grassland/savannas
only needing to meet two of the three specified parameters and subsequently being
grouped by age. If all grassland mines were pooled, canopy closure was less than 30%.
Conifer forests provided a suitable habitat type for many avian species, and should not be
considered as a viable contribution to a mosaic of landscape-level management efforts.
Management Implications
Diversity was lowest in young conifer forests but amongst the highest in medium and
old conifer forests showing the importance and limitations of conifer dominant forests in
the mosaic of landscape management required for the region. The monoculture
plantations with minimal secondary vegetation produce a limited number of habitat
niches available for songbirds. It is not until approximately 15 years after a mine is
closed, that more natural succession begins to take over and a more diverse blend of
71
vegetation results in higher diversity levels. When time was considered without a habitat
element, old mines had the highest diversity, suggesting that without careful
consideration of desired bird species, and the habitat type most applicable to their
presence, it can take greater than 20 years, even in highly productive systems with smallscale mines, to restore or manage for some species. Interestingly, control sites were low
in diversity, which suggests that disturbance is necessary for many of the avifauna
species present in northern Alabama.
The time frame observed for this study is a poor representation of other ecosystems,
and it is likely time of reclamation success increases exponentially with larger mines at
higher latitudes or elevations where growing seasons are shorter and conditions are
harsher. These findings should be considered appropriate only within the study area. For
this study area, disturbance cycles of approximately 15 to 20 years appear to maximize
diversity of songbirds within the timeframe evaluated. However, this study did not
evaluate any of the bio-markers, micro-biotic, soil or aquatic parameters that should be
kept in mind when considering cumulative impacts and the total influence of a minedevelopment on the landscape (Mummey et al., 2002a; Mummey et al, 2002b). Nor does
maximizing diversity support the mature-forest species (i.e. Wood Thrush, Whitebreasted Nuthatch) which were observed in very low densities even in 26 year old mines.
This stresses the importance of prolonging subsequent disturbance beyond the 15-20 year
time frame, at least at times, to ensure that the mature-forest bird community is also
appropriately managed.
72
Many of the bird species were found in higher numbers in mixed forests. For most of
the species that did not show statistical differences across habitat type, they showed
anecdotal increases in abundance with mixed forests at various temporal stages. This
aligns with the objectives of the ARRI (Zipper et al., 2011).
Wild Turkeys were most dense in mixed medium forests. This suggests that diversity
of tree species and less common disturbances, possibly with frequent openings, provide
the ideal circumstance for game species. This may help contribute to the restoration of
the northern bobwhite quail (Colinus virginianus) as well. The northern bobwhite is a
species that was observed in low numbers during the study, but is of management interest
in the region.
Abundances were linked, generally, to the characteristics focused on with the FRA.
This study aligns with those findings from Angel et al. (2009) that suggest that FRA may
be a beneficial reclamation tool in the state when related to songbird community
structures.
It is recommended that mine reclamation plans focus on a blend of openings and
vegetation diversity with a secondary growth of commercially viable cash-crop tree
species (Angel et al., 2009). Borrow-pits and depressions that will form wetlands should
be left onsite as these provide unique and important habitat features for many species that
were otherwise absent from reclaimed surface mines. In this study these water-dependent
species included Bank Swallows, Belted Kingfishers, Common Yellowthroats, and RedWinged Blackbirds. This information should be considered when reviewing development
proposals to ensure reclamation is best suited to meet local objectives.
73
These findings should be considered contributing tools for local resource managers
to help best meet their priority targets and needs. Reclamation on mined sites can
appropriately be completed using any combination of vegetation-reestablishment effort.
No one approach should be considered comprehensive. This paper helps identify the
potential outcomes of some treatments and the time scale that results in changes in avian
community assemblages.
Grassland bird species benefitted from short-term grassland reclamation and
disturbances around 15 years. Be wary of invasive plant spread through this method. The
linkage for bird distribution and invasive plant spread in these areas is an area that
requires further exploration.
Diversity can be maximized by employing a 15-20 year disturbance cycle with
diverse vegetation composition, but this does nothing for mature forest species, some of
which are the most at risk species within the area.
Mature forest species should be included in management objectives by maintaining
stable areas for greater than 25 years. Known habitat associations for Red-cockaded
Woodpecker (Garabedian, Moorman, Peterson, & Kilgo, 2014) and Cerulean Warbler
(Carpenter, Wang, Schweitzer, & Hamel, 2011), both of which are at risk species that can
be found near the project study area, are linked to habitat types observed in the older
mined sites, and are not linked to maximal diversity values.
These study findings are limited to their geographic region and to small-scale
surface mines, and should not be considered appropriate reclamation timelines or bird
74
communities for larger mines, mines at higher elevations, latitudes, longitudes, or in
different ecotypes.
.
75
APPENDICES
76
A 1 – Land Use Permission
Attached below are the land-use agreements that were used for permission to
access private land containing mines. The first, from The Westervelt Corporation, is a
multipage agreement. The second, from Alawest, is simply an email correspondence.
77
78
79
80
81
82
83
Alawest-AL, LLC
3 messages
Charlie Hagan <hagan@warriortractor.com>
To: richborthwick@gmail.com
Mon, Mar 24, 2014 at 10:33 AM
Richard,
There will not be a problem with you using ALAWEST-AL, LLC property. I will
need to contact the hunting clubs to let them know, also to get you a key to the gates.
Thanks,
Charlie Hagan
84
A 2 – Detailed Location Maps
The following figures display mines at a large enough scale to allow for visual
interpretation of plot locations and mine boundaries. These are not navigation-level maps,
and should be considered informative only.
Western Sites – Map 2
Northwestern Sites – Map 3
85
Central Sites – Map 4a: West
Central Sites – Map 4b: East
Northwestern Sites – Map 5
Southeastern Sites – Map 6
86
Southwestern Site – Only the northernmost site was accessible.
87
A 3 – Observed Bird Species
Species observed throughout the project study area across both years.
Nesting
Guild
Foraging
* Guild
Habitat
**
Guild
Species
Latin Name
Alpha
Code
Acadian
Flycatcher
Empidonax virescens
ACFL
Tree
AF
F
American Crow
Corvus
brachyrhynchos
AMCR
Canopy
GF
OW
Spinus tristis
AMGO
Shrub
FG
OW
Turdus migratorius
AMRO
GF
T
Bank Swallow
Riparia riparia
BANS
AF
Marshº
Barn Swallow
Hirundo rustica
BARS
Tree
Building
/Cliff
Building
/Cliff
AF
T
Belted
Kingfisher
Megaceryle alcyon
BEKI
Burrow
C
Marshº
Black Vulture
Coragyps atratus
BLVU
Building
/Cliff
C
OW
Mniotilta varia
BAWW
Ground
BF
F
Setophaga virens
BTNW
Tree
FG
F
Passerina caerulea
Cyanocitta cristata
BLGR
BLJA
Shrub
Tree
GF
GF
OW
F
Polioptila caerulea
BGGN
Tree
FG
F
Vireo solitarius
BHVI
Tree
FG
F
Vermivora cyanoptera
BWWA
Ground
FG
OW
Buteo platypterus
BWHA
Canopy
C
F
American
Goldfinch
American Robin
Black-and-White
Warbler
Black-throated
Green Warbler
Blue Grosbeak
Blue Jay
Blue-gray
Gnatcatcher
Blue-headed
Vireo
Blue-winged
Warbler
Broad-winged
Hawk
88
Species
Latin Name
Alpha
Code
Nesting
Guild
Foraging
* Guild
Brown Thrasher
Brown-headed
Cowbird
Canada Goose
Carolina
Chickadee
Toxostoma rufum
BRTH
Shrub
GF
Habitat
**
Guild
S
Molothrus ater
BHCO
Tree*
GF
G
Branta canadensis
CANG
Ground
GF
Marshº
Poecile carolinensis
CACH
Cavity
FG
F
Thryothorus
ludovicianus
CARW
Cavity
GF
OW
Spizella passerina
CHSP
Shrub
GF
OW
Antrostomus
carolinensis
CWWI
Ground
AF
OW
Quiscalus quiscula
COGR
Tree
GF
T
Geothlypis trichas
COYE
Shrub
FG
S
Spiza americana
DICK
Shrub
GF
G
Picoides pubescens
DOWO
Cavity
BF
F
Sialia sialis
EABL
Cavity
GF
G
Tyrannus tyrannus
EAKI
Tree
AF
G
Eastern Phoebe
Sayornis phoebe
EAPH
Building
/Cliff
AF
OW
Eastern Towhee
Pipilo
erythrophthalmus
EATO
Ground
GF
S
Contopus virens
EWPE
Tree
AF
F
Spizella pusilla
Corvus ossifragus
Dumetella
carolinensis
FISP
FICR
Ground
Tree
GF
GF
S
Marshº
GRCA
Shrub
GF
OW
Myiarchus crinitus
GCFL
Cavity
AF
OW
Picoides villosus
HAWO
Cavity
BF
F
Setophaga citrina
Haemorhous
mexicanus
Passerina cyanea
HOWA
Shrub
FG
F
HOFI
Tree
GF
T
INBU
Shrub
FG
OW
Carolina Wren
Chipping
Sparrow
Chuck-Will's
Widow
Common
Grackle
Common
Yellowthroat
Dickcissel
Downy
Woodecker
Eastern Bluebird
Eastern
Kingbird
Eastern WoodPewee
Field Sparrow
Fish Crow
Gray Catbird
Great Crested
Flycatcher
Hairy
Woodpecker
Hooded Warbler
House Finch
Indigo Bunting
89
Species
Kentucky
Warbler
Killdeer
Mourning Dove
Northern
Bobwhite
Northern
Cardinal
Northern Flicker
Northern
Mockingbird
Northern Parula
Orchard Oriole
Ovenbird
Pileated
Woodpecker
Pine Warbler
Prairie Warbler
Prothonotary
Warbler
Red-bellied
Woodpecker
Red-breasted
Nuthatch
Red-eyed Vireo
Red-Headed
Woodpecker
Red-shouldered
Hawk
Red-tailed Hawk
Red-winged
Blackbird
Rock Pigeon
Ruby-throated
Hummingbird
Scarlet Tanager
Summer
Tanager
Tufted Titmouse
Latin Name
Alpha
Code
Nesting
Guild
Foraging
* Guild
Habitat
**
Guild
Geothlypis formosa`
KEWA
Ground
GF
F
Charadrius vociferus
Zenaida macroura
KILL
MODO
Ground
Tree
GF
GF
G
OW
Colinus virginianus
NOBO
Ground
GF
G
Cardinalis cardinalis
NOCA
Shrub
GF
OW
Colaptes auratus
NOFL
Cavity
GF
OW
Mimus polyglottos
NOMO
Shrub
GF
T
Setophaga americana
Icterus spurius
Seiurus aurocapilla
NOPA
OROR
OVEN
Tree
Tree
Ground
FG
FG
GF
F
OW
F
Dryocopus pileatus
PIWO
Cavity
BF
F
Setophaga pinus
Setophaga discolor
PIWA
PRAW
Tree
Shrub
BG
FG
F
OW
Protonotaria citrea
PROW
Cavity
BF
F
Melanerpes carolinus
RBWO
Cavity
BF
F
Sitta canadensis
RBNU
Cavity
BG
F
Vireo olivaceus
Melanerpes
erythrocephalus
REVI
Tree
FG
F
RHWO
Cavity
AF
OW
Buteo lineatus
RSHA
Canopy
C
F
Buteo jamaicensis
RTHA
Canopy
C
G
Agelaius phoeniceus
RWBB
Shrub
GF
Marshº
Columba livia
ROPI
Building
/Cliff
GF
T
Archilochus colubris
RTHU
Tree
AF*
OW
Piranga olivacea
SCTA
Tree
FG
F
Piranga rubra
SUTA
Tree
FG
OW
Cavity
FG
F
Baeolophus bicolor
TUTI
90
Species
Latin Name
Alpha
Code
Turkey Vulture
Cathartes aura
TUVU
Nesting
Guild
Building
/Cliff
Tree
Ground
Foraging
* Guild
Habitat
**
Guild
C
OW
Vireo gilvus
WAVI
FG
OW
Warbling Vireo
AF
OW
Whip-Poor-Will Antrostomus vociferus WPWI
White-breasted
Sitta carolinensis
WBNU Cavity
BF
F
Nuthatch
White-eyed
Vireo griseus
WEVI
Shrub
FG
S
Vireo
Meleagris gallopavo
WITU
Ground GF
OW
Wild Turkey
Hylocichla mustelina
WOTH Tree
GF
F
Wood Thrush
Helmitheros
Worm-eating
WEWA Ground FG
F
vermivoru m
Warbler
YEWA
Shrub
FG
OW
Yellow Warbler Setophaga petechia
Yellow-billed
Coccyzus americanus YBCU
Tree
FG
OW
Cuckoo
Yellow-breasted
Icteria virens
YBCH
Shrub
FG
S
Chat
Yellow-throated
Setophaga dominica
YTWA
Tree
BF
F
Warbler
*Foraging Guilds: AF - Aerial Forager; BF – Bark Forager; C – Carnivore; FG – Foliage
Gleaner; GF – Ground Forager.
**Habitat Guilds: F – Forest; OW – Open Woodland; S – Shrub/Scrub; T – Tree.
ºSpecies from this guild were not included in habitat analyses, due to poor representation.
91
A 4 – Between-mine and Within-mine Comparisons
The following tables outline the comparison of within and between group
variance for treatments.
< 15 Year Old Conifer Forests
Habitat
Variable
Percent Conifer
Sum of
Squares
Midstory Cover
Canopy Closure
Mean Square
0.26
2
0.13
Within Groups
1.37
18
0.08
Total
Forbs
df
Between Groups
1.63
20
12.26
2
6.13
Within Groups
138.88
18
7.72
Total
151.14
20
Between Groups
Between Groups
1311.33
2
655.66
Within Groups
14968.69
18
831.59
Total
16280.01
20
Between Groups
1327.17
2
663.58
Within Groups
12085.82
18
671.43
Total
13412.99
20
F
Sig.
1.74
0.21
0.80
0.47
0.79
0.47
0.99
0.39
< 15 Year Old Grasslands
Habitat
Variable
Percent Conifer
Sum of
Squares
Midstory Cover
Canopy Closure
Mean Square
2.35
2
1.18
Within Groups
1.44
17
0.09
Total
Forbs
df
Between Groups
3.80
19
Between Groups
10.48
2
5.24
Within Groups
83.72
17
4.93
Total
94.20
19
Between Groups
656.35
2
328.18
Within Groups
5545.16
17
326.19
Total
6201.51
19
Between Groups
760.45
2
380.22
Within Groups
12318.03
17
724.59
Total
13078.48
19
92
F
Sig.
13.86
0.00*
1.06
0.37
1.01
0.39
0.53
0.60
< 15 Year Old Mixed Forests
Habitat
Variable
Percent Conifer
Sum of
Squares
Midstory Cover
Canopy Closure
Mean Square
0.35
2
0.17
Within Groups
2.70
20
0.14
Total
Forbs
df
Between Groups
3.04
22
11.73
2
5.87
Within Groups
142.71
20
7.14
Total
154.44
22
Between Groups
Between Groups
309.98
2
154.99
Within Groups
12494.49
20
624.72
Total
12804.47
22
Between Groups
8927.29
2
4463.64
Within Groups
15191.36
20
759.57
Total
24118.65
22
F
Sig.
1.28
0.30
0.82
0.45
0.25
0.78
5.88
0.01*
15 – 20 Year Old Conifer Forests
Habitat
Variable
Percent Conifer
Forbs
Sum of
Squares
2
.05
Within Groups
2.15
21
.10
Total
2.25
23
Between Groups
4.73
2
2.37
45.77
21
2.18
Total
Canopy Closure
Mean Square
0.10
Within Groups
Midstory Cover
df
Between Groups
50.50
23
583.27
2
291.63
Within Groups
1926.52
21
91.74
Total
2509.79
23
Between Groups
Between Groups
129.60
2
64.80
Within Groups
18239.46
21
868.55
Total
18369.05
23
F
Sig.
0.46
0.64
1.09
0.36
3.18
0.06
0.08
0.93
15 – 20 Year Old Grasslands
Habitat
Variable
Percent Conifer
Forbs
Sum of
Squares
Between Groups
Canopy Closure
Mean Square
2
0.26
Within Groups
1.25
6
0.21
Total
1.78
8
Between Groups
50.00
2
25.00
Within Groups
80.00
6
13.33
Total
Midstory Cover
df
.53
130.00
8
Between Groups
1645.01
2
822.51
Within Groups
1336.32
6
222.72
Total
2981.33
8
Between Groups
3197.30
2
1598.65
Within Groups
2560.44
6
426.74
Total
5757.73
8
93
F
Sig.
1.26
0.35
1.88
0.23
3.69
0.09
3.75
0.09
15 – 20 Year Old Mixed Forests
Habitat
Variable
Percent Conifer
Sum of
Squares
Midstory Cover
Canopy Closure
Mean Square
0.57
2
0.28
Within Groups
1.21
11
0.11
Total
Forbs
df
Between Groups
1.78
13
Between Groups
11.13
2
5.57
Within Groups
80.08
11
7.28
Total
91.21
13
Between Groups
1657.23
2
828.61
Within Groups
4421.38
11
401.94
Total
6078.61
13
Between Groups
1847.35
2
923.67
Within Groups
3944.16
11
358.56
Total
5791.51
13
F
Sig.
2.58
0.12
0.76
0.49
2.06
0.17
2.58
0.12
> 20 Year Old Conifer
Habitat
Variable
Percent Conifer
Forbs
Sum of
Squares
2
0.09
Within Groups
1.96
22
0.09
Total
2.13
24
Between Groups
1.00
2
.50
127.00
22
5.77
Total
Canopy Closure
Mean Square
0.17
Within Groups
Midstory Cover
df
Between Groups
128.00
24
1882.44
2
941.22
Within Groups
21151.31
22
961.42
Total
23033.75
24
Between Groups
Between Groups
2222.54
2
1111.27
Within Groups
12024.97
22
546.59
Total
14247.51
24
F
Sig.
0.95
0.40
0.09
0.92
0.98
0.39
2.03
0.16
> 20 Year Old Grassland
Habitat
Variable
Percent Conifer
Forbs
Sum of
Squares
Between Groups
0.48
Within Groups
Total
0.24
0.71
9
0.08
1.19
11
Between Groups
16.29
2
8.14
Within Groups
38.38
9
4.27
Between Groups
Within Groups
Total
Canopy Closure
Mean Square
2
Total
Midstory Cover
df
Between Groups
54.67
11
11463.70
2
5731.85
2604.33
9
289.37
14068.03
11
367.34
2
183.67
536.36
Within Groups
4827.24
9
Total
5194.58
11
94
F
Sig.
3.05
0.10
1.91
0.20
19.81
0.00*
0.34
0.72
> 20 Year Old Mixed Forest
Habitat
Variable
Percent Conifer
Sum of
Squares
Midstory Cover
Canopy Closure
Mean Square
0.21
2
.11
Within Groups
3.06
18
.17
Total
Forbs
df
Between Groups
3.27
20
13.26
2
6.63
Within Groups
142.55
18
7.92
Total
155.81
20
Between Groups
Between Groups
2392.50
2
1196.25
Within Groups
3777.87
18
209.88
Total
6170.37
20
Between Groups
428.60
2
214.30
Within Groups
15287.06
18
849.28
Total
15715.67
20
F
Sig.
0.62
0.55
0.84
0.45
5.70
0.01*
0.25
0.78
Control
Habitat
Variable
Percent Conifer
Sum of
Squares
2
0.13
Within Groups
1.54
27
0.06
1.80
29
Between Groups
Within Groups
Total
Midstory Cover
Canopy Closure
Mean Square
0.26
Total
Forbs
df
Between Groups
34.93
2
17.47
177.74
27
6.58
212.67
29
3720.35
2
1860.17
Within Groups
24246.35
27
898.01
Total
27966.69
29
Between Groups
Between Groups
3290.19
2
1645.10
Within Groups
18950.80
27
701.88
Total
22240.99
29
F
Sig.
2.30
0.12
2.65
0.09
2.07
0.15
2.34
0.12
*Denotes a significant difference. All values with an ‘*’ are explored in more detail in the text below.
95
A.5 – Species Detection Curves
96
97
98
99
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VITA
Richard R.Borthwick, son of Robert Borthwick and Elizabeth (Cox) Borthwick, was born
April 23, 1984, Salmon Arm, British Columbia, Canada. In September 2003, he entered
University of Northern British Columbia, Prince George, British Columbia, and received
the degree of Bachelor of Science with a double major in Natural Resource Management
and Wildlife and Fisheries in May 2007. He entered graduate school at Alabama A&M
University, Normal, Alabama, in August 2012 after 8 years an ecologist and consultant at
various locations across North America. He married the love of his life, Danica Johnston
Ortiz on March 28, 2014. After obtaining his M.S. in Biology with a concentration in
Ecology at Alabama A&M University in May 2015, he continued researching and
compiling publications with the University.