AN ABSTRACT OF THE DISSERTATION OF
Zhiaiang Yanq for the degree of Doctor of Philosophy in Forest Science
presented on January 12, 2004.
Title: Early Forest Succession Following Clearcuts in Western Oreqon:
Patterns and Abiotic Contrs
Abstract approved:
Signature redacted for privacy.
Mark E. Harmon
Warren B. Cohen
Historical aerial photos were used to examine the early phase of
forest succession after stand replacement disturbance, covering the Coast
Ranges Province (CRP) and the western Cascades Province (WCP) of
western Oregon. The study consisted of two components: characterizing
the pattern of forest succession in western Oregon; analyzing the influence
of climatic and physiographic factors on forest succession.
Succession has many dimensions.
In
this study,
I
examined
succession after stand replacement disturbance in terms of canopy cover
change of different life forms: shrubs, hardwood trees, and conifer trees.
Canopy cover changes from 1959 to 1997 for selected sample stands were
obtained by photo interpretation. Canopy cover growth curves for each
sample stand were developed based on a Chapman-Richards growth
function. Seven successional pathways were defined to characterize forest
successional processes based on projected canopy cover at age 50 and
the process of canopy composition change over this 50 years period. In
addition, seven parameters from the canopy cover growth curves were
derived for the purpose of comparison of successional patterns in the CRP
and WCP. These include time to reach 5% and 70% of canopy cover,
weighted mean absolute growth rate, weighted mean relative growth rate,
maximum absolute growth rate, time to reach maximum absolute growth
rate, and active growth period. Results indicated that a wide range of
variation in forest succession exists in western Oregon. Successional
patterns for the CRP and WCP were different in terms of conifer
development.
Possible abiotic controls of two successional parameters were
examined by stepwise regression analysis. Only 23%, 37%, and 29% of the
total variation in the delay, and 29%, 25% and 57% of the total variation in
rate parameters can be explained by abiotic factors for the WCP, CRP, and
across provinces, respectively. Unexplained variation was likely due to
stochastic, biotic, and management factors, as well as experimental error
due to photo interpretation and derivation of successional paramters. The
most important influential climatic factor was temperature, and the most
important physiographic factor was elevation. Depending on location,
interactions among climatic and physiographic factors also influenced
successional delay and rate.
©Copyright by Zhiqiang Yang
January 12, 2004
All Rights Reserved
Early Forest Succession Following Clearcuts in Western Oregon:
Patterns and Abiotic Controls
by
Zhiqiang Yang
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented January 12, 2004
Commencement June 2004
Doctor of Philosorhy dissertation of Zhiaianq Yanq presented on January
12, 2004
APPROVED:
Signature redacted for privacy.
Co-Major Professor, representing Forest Science
Signature redacted for privacy.
C6-Major Professor, representing Forest Science
Signature redacted for privacy.
Head of the Department of Fores.t-Sicnce
Signature redacted for privacy.
Dean of tj'Gráduate School
I understand that my dissertation will become part of the permanent
collection of Oregon State University libraries. My signature below
authorizes release of my dissertation to any reader upon request.
Signature redacted for privacy.
(
" Zhiqiang Yang, Author
ACKNOWLEDGEMENTS
I
wish to acknowledge the many people and organizations who
supported me through this endeavor.
To my major professors, Dr. Warren Cohen and Dr. Mark Harmon,
for their continued support, advice, encouragement, and inspiration along
the way.
To my committee members Dr. Steven Radosevich, Dr. Thomas
Dietterich, and Dr. Eric Hansen for their support, advice, and valuable
contribution to my knowledge, as well as for the enrichment to my life
experience.
To those who provided financial support or data, including NASA, the
Department of Forest Science at Oregon State Univeristy, Siuslaw National
Forest, Willamette National Forest, U.S. Forest Service Pacific Northwest
Research Station, and University of Oregon Library.
And to all who assisted by reviewing part of this dissertation, by
providing valuable suggestions, including Sean Healey, Robert Kennedy,
Tom Maiersperger, Patrick Cunningham, Tom Spies, Fred Swanson, Hua
Chen, Doug Oetter, Michael Lefsky, Karin Fassnacht, and many others.
Lastly, to my family, to my parents for their unconditional support and
continuous trust in me; to my wife, for her companionship, encouragement
and love; to my daughter, for the happiness she brings to me.
TABLE OF CONTENTS
PaQe
Chapter 1 Introduction
1
Chapter 2 Patterns of Early Forest Succession in Western Oregon
4
2.1 ABSTRACT
5
2.2 INTRODUCTION
7
2.3 STUDY AREA
9
2.3.1 Physical Environment
2.3.2 Vegetation
2.4 METHODS
2.4.1 Data Acquisition
2.4.2 Fitting Vegetation Cover Growth Curves
2.4.3 Successional Pathways
2.4.4 Successional Trajectories
2.4.5 Classification of Successional Trajectories
2.5 RESULTS
2.5.1 General Successional Patterns
2.5.2 Successional Pathways
2.5.3 Successional Trajectories
2.5.4 Successional Trajectory Classification
2.6 DISCUSSION
2.6.1 Multiple Successional Pathways and Rates
2.6.2 Ecological and Management Implications
10
10
11
11
12
14
17
19
20
20
20
24
27
29
29
32
2.7 CONCLUSIONS
34
2.8 REFERENCES
35
TABLE OF CONTENTS (Continued)
Chapter 3 The Effect of Abiotic Factors on Forest Succession
in Western Oregon
39
3.1 ABSTRACT
40
3.2 INTRODUCTION
43
3.3 METHODS
46
3.3.1 Study Area
3.3.2 Successional Descriptors
3.3.3 Controlling Factors
3.3.4 Data Analysis
3.4 RESULTS
3.4.1 General Correlation Patterns
3.4.2 Regression Models
3.5 DISCUSSION
3.5.1 Influence of Climate
3.5.2 Influence of Physiography
3.5.3 Degree of Variations Explained
46
48
49
52
53
53
63
64
67
70
72
3.6 CONCLUSIONS
76
3.7 REFERENCES
78
Chapter 4 Conclusions
86
Bibliography
92
LIST OF FIGURES
FiQure
2.1 Digital elevation model of the study area
Pane
9
2.2 Boundary conditions for successional pathway categories
17
2.3 Scatter plots of conifer cover over time
21
2.4 Modeled conifer cover trajectories
21
2.5 Examples of successional pathways
22
2.6 Successional Pathway Distribution for CRP and WCP
23
2.7 Distribution of successional trajectory parameters
25
3.1 Digital elevation model of the study area
47
3.2 Scatter plots of DELAY and temperature parameters
56
3.3 Scatter plots of RATE and temperature parameters
57
3.4 Scatter plots of DELAY and precipitation parameters
58
3.5 Scatter plots of RATE and precipitation parameters
59
3.6 Scatter plots of DELAY and radiation parameters
60
3.7 Scatter plots of RATE and radiation parameters
60
3.8 Scatter plots of DELAY and physiographic parameters
61
3.9 Scatter plots of RATE and physiographic parameters
62
4.1 Simulated total carbon store dynamics after stand replacement
disturbance
88
LIST OF TABLES
Table
PaQe
2.1 Historical aerial photos used for photo interpretation
12
2.2 Definitions of successional pathway stages
15
2.3 Successional trajectory parameters
19
2.4 Comparison of successional trajectory characteristics
24
2.5 Correlation matrix for successional trajectory characteristics
26
2.6 Successional trajectory classification criteria
27
2.7 Successional trajectory classification across and within
provinces
28
3.1 Successional descriptor variables
49
3.2 Codes and definitions of climatic and physiographic controlling
factors considered
50
3.3 Correlation coefficients for the relationships of DELAY and
RATE with potential controlling factors
54
3.4 Results of stepwise regress for DELAY
65
3.5 Results of stepwise regress for RATE
66
LIST OF ACRONYMS
AGP
active growth period
GB
closed broadleaf stage
CC
closed conifer stage
CM
closed mixed stage
CONTPRE
CRP
mean percentage of annual precipitation in JuneAugust
Coast Ranges Province
DELAY
time to reach 5% cover
DEM
digital elevation model
EASTNESS
sin(aspect)
ELEV
elevation
FROSTDAY
mean number of days with minimum temperature
below 0°C in one year
maximum absolute growth rate
MaxRate
OP
mean number of days without precipitation in one
year
open stage
PNW
Pacific Northwest
PRCP
mean dafly precipitation
RSP
relative slope position
SH
shrub stage
SC
semi-closed stage
SLOPE
slope
SMRPRCP
mean daily precipitation in May - September
SMRTMP
mean daily temperature in May - September
SMRTSMRP
moisture stress during the growing season
SOUTHNESS
cos(aspect + 180)
SRAD
mean daily potential solar radiation
RATE
weighted mean absolute growth rate
NORAIN
LIST OF ACRONYMS (Continued)
T70
time to reach 70% cover
TDAY
mean daily precipitation
TMAX
maximum daily maximum temperature
TmaxRate
time to reach maximum absolute growth rate
TMIN
minimum daily minimum temperature
TRANSASP
cos(45-aspect)+ 1
TRMI
topographic relative moisture index
VPD
mean daily water vapor pressure
wcP
Western Cascades Province
wmAGR
weighted mean absolute growth rate
wmRGR
weighted mean relative growth rate
WTRPRCP
mean daily precipitation in November - March
WTRTMP
mean daily temperature in November - March
Early Forest Succession Following Clearcuts in Western Oregon:
Patterns and Controls
Chapter 1 Introduction
Understanding forest succession is critical for forest ecosystem
management and biodiversity conservation planning (Knight and Wallace,
1989).
In
particular, successional pathway and rate have important
implications for biogeochemical cycling of nitrogen (Pastor and Post, 1986)
and carbon (Botkin and Running, 1984; Harmon, 2001a, b).
After stand replacement disturbance in the Pacific Northwest (PNW)
region of the United States, forest succession generally leads to a closed
canopy conifer condition (Franklin and Dyrness, 1988). However, the rate
and density of conifer recovery can vary significantly across the region
(Franklin and Hemstrom, 1981; Nesje, 1996; Tappeineretal., 1997; Poage,
2001). Many studies in PNW have investigated the problem of conifer
regeneration failure and
it
has been widely recognized that numerous
factors contribute to successional variation. However, most of these studies
were based on a field survey of sample plots over a small spatial extend,
and results among these studies are inconsistent.
The purpose of this study was to add to our understanding of early
forest succession
in
western
Oregon by examining forest canopy
development. To that purpose, I try to answer the following questions about
forest succession after stand replacement disturbance in western Oregon:
How variable is secondary succession in western Oregon?
What are the controlling abiotic factors on the variation in forest
succession?
Succession is a term used to describe many aspects of vegetation
change over different scales of space and time. In this study, succession
was studied in western Oregon by focusing on two components: 1) the
temporal directional change of forest stand composition and vegetation
physiognomy, and 2) the rate of conifer cover accumulation.
My examination of forest succession in western Oregon consists of
two major components, reported
in
the two main chapters of this
dissertation. While ground level works provide details for one location, the
use of historical aerial photographs and a geographic information system
makes the study of forest succession patterns over western Oregon
feasible. Through examination of forest canopy change over time, I was
able to characterize forest successional patterns in western Oregon.
Moreover, by exploring various abiotic factors that might control forest
succession, we can achieve a better understanding of forest successional
processes in western Oregon.
In chapter 2, I examined patterns of succession in western Oregon
using historical aerial photos. To characterize succession, I defined a set of
successional pathways based on projected canopy cover and the canopy
composition change over time, which enable me to compare forest
succession among sample stands distributed over landscape. From photo
interpretation, vegetation change for each site was recorded as proportions
of vegetation life forms. Classes of successional trajectory were developed
which encompass rates, temporal patterns, and directions of change. From
these data, I was able to establish the frequency distribution of slow, normal
and fast rates toward canopy closure.
Chapter 3 reports the results of my analysis of abiotic factors
controlling forest sucession processes in western Oregon, focusing on two
successional parameters defined in Chapter 2: DELAY and RATE. The
explanatory variables examined were mainly climatic and phygiographic
factors. These variables were examined using stepwise regression to
determine which variables were important in explaining the patterns
observed in Chapter 2.
4
Chapter 2
Patterns of Early Forest Succession Following Clearcuts in
Western Oregon
Zhiqiang Yang, Warren B. Cohen, and Mark E. Harmon
Forest Science Department
Oregon State Univeristy
Corvallis, Oregon
January 12, 2004
5
2.1 ABSTRACT
In the Pacific Northwest (PNW), the pattern of conifer development
after stand-replacement disturbance has important implications for many
forest processes (eg. carbon storage, nutrient cycling, biodiversity). This
chapter examines conifer development in the Coast Ranges Province
(CRP) and the Western Cascades Province (WCP) of Oregon using historic
aerial photographs. Ninety-four stands from the WCP and 59 stands from
CRP were photo-interpreted from 1959 to 1997 in roughly 5 year intervals.
A Chapman-Richards growth function was used to model conifer cover
development for all sample stands. Based on the photo data and Chapman-
Richards function, these stands were classified into one of seven early
forest successional pathways. The interaction of successional pathway and
rate, i.e. rate of conifer cover accumulation, was used to classify stands into
9 different successional trajectories. Successional trajectories in the CRP
and WCP were compared using the set of seven parameters derived from
the Chapman-Richards growth function. Our results echo previous studies
in that rates and densities of conifer regeneration varied markedly among
sites. Results also indicate that early forest succession patterns are
different for the two study regions in terms of both pathway and trajectories.
Conifer regeneration in the WCP tends to have longer delay and lower rate
of development compared to the CRP. We conclude by briefly examining
6
climatic, biotic, and management factors that might influence successional
trajectories in western Oregon.
7
2.2 INTRODUCTION
There is a growing demand for landscape-scale information on forest
successional
trends
and
factors
that
contribute
to
divergence
of
successional trajectories. Increasingly, spatial models are being used for
regional planning and research efforts. Model output, for example, might
include current and future estimates of biodiversity or biogeochemical
fluxes. Models requiring future status for individual forest stands use data
on the projected rate and pathway of succession for parameterization
and/or validation (Hall, 1991). For example, the overall carbon budget for a
coniferous
stand
is
dependent on the time since disturbance for
decomposition of detritus and for the amount of live biomass accumulated.
In such a system, if rates of conifer establishment are variable, carbon
dynamics will also be variable (Harmon et aL, 1990). As very little work has
been done to characterize successional variability across a landscape or
region, models commonly assume an average rate of forest succession and
growth (Cohen etal., 1996).
Succession is a term used to describe many aspects for vegetation
change over different scales of space and time. Here we study early conifer
forest succession in western Oregon focusing on two components: 1) the
temporal directional change of forest stand composition and vegetation
physiognomy, and 2) the rate of conifer cover accumulation. The causes of
8
these changes are related to natural as well as human influenced
processes.
In the Pacific Northwest (PNW) region of the United States, the order
of potential vegetation transitions at a site is generally predictable, with
larger growth forms replacing smaller ones (e.g. shrubs replacing grass and
trees replacing shrubs). However, the rates and specific pathways of
succession can vary considerably among sites. For example, after wildfire
in this coniferous region, some stands may go directly from recolonizing
shrubs to hardwood trees for an extended period before eventually
returning to conifer condition. Other stands might go rapidly and directly
from shrub condition to conifer condition (Franklin and Dyrness, 1988,
Halpern, 1988). In commercially harvested stands, there are often more
extremes of pattern with examples of highly accelerated and severely
stagnated stands not uncommon (Perry etal., 1989).
In this paper, we use historical aerial photos to examine secondary
forest succession after stand replacement disturbance in terms of canopy
cover change of different life forms: shrubs, hardwood trees, and conifer
trees. The main objectives were to: (1) verify the existence of divergent
trajectories of early succession among harvested stands in western
Oregon, (2) develop a method for quantifying successional pathway and
rate, and (3) compare early forest successional trajectories between the
9
Coast Ranges Province (CRP) and Western Cascades Province (WCP)
(Figure 2.1).
2.3 STUDY AREA
The study area is the portions of the CRP and WCP of western
Oregon contained in Landsat TM path/row 46/29 (Figure 2.1). Since the
availability of historical aerial photos varies, but is most extensive over
National Forest lands, stands from the Siuslaw National Forest in CRP and
Willamette National Forest in WCP were used.
Oregon
\,.
A
S,u.I
National Forest
Wliaorett National Orest
Figure 2.1 Digital elevation model of study area. Portion of the Siuslaw
National Forest and Willamette National Forest contained within Landsat
TM, Aug. 19, 1995 Path 46, Row 29
10
2.3.1 Physical Environment
The Pacific Northwest region has a climate of warm, dry summers
and mild, wet winters. These climate conditions favor growth of evergreen
life forms. The climate exhibits a strong gradient with change of latitude,
longitude, and elevation.
The CRP is characterized by mild temperature with prolonged cloudy
periods. The average temperature ranges from 5 C in January to 16 °C in
July. Annual precipitation in the CRP is about 3000 mm. Depending on
location, summers in the CRP range from cool to warm. Elevations
generally vary in the range of 450-750 meters in the CRP. The climate of
WCP is also maritime with mild, wet winters and warm, dry summers.
Average temperature ranges from 5 00 in January to 23 °C in July. Annual
precipitation in the CRP is about 2300mm. Elevations in the WCP vary in
the range of 450-3100 meters.
Geologic conditions are different for CRP and WCP. Sedimentary
rock types are typical of the CRP, while the volcanic rocks dominate most of
the WCP. Forest soils vary in the two regions, reflecting the variation in
parent materials and topography (Franklin and Dyrness, 1988).
2.3.2 Vegetation
In contrast to other moist and mesic regions in the world where
hardwood typically dominates, PNW forests have a ratio of coniferous to
hardwood of 1000:1 (Kuchler, 1964). Moreover, forests in PNW are
11
characterized as having among the greatest biomass accumulations and
some of the highest productivity level forests on earth (Waring and Franklin,
1979). Two major forest vegetation zones exist in the study area (Franklin
and Dyrness, 1988). The CRP consists of the Sitka Spruce Zone and the
Western Hemlock Zone, and WCP consists of the Western Hemlock Zone.
Dominant trees are typically conifers, except in riparian areas where
hardwood trees often dominate. Besides conifer and hardwood trees, many
different shrub and herbaceous species exist in the study area.
2.4 METHODS
2.4.1 Data Acquisition
Previous studies indicate that elevation and aspect interact to affect
conifer development in the H.J. Andrews Experimental Forest (Nesje 1996),
the most studied location in the region. Therefore to collect data for this
study, we classified the study area into 8 aspect classes (45 degree
intervals) and 6 elevation classes (300 meter intervals) using 30 meter DEM
data, for a total of 48 aspect-elevation classes. Topographic facets with less
than 36 DEM cells (2.25 hectares) were removed from the potential sample
area. This ensured that the final samples had at least a 25m buffer around
them. Stratified random sampling from the 48 classes with an average of 5
samples per class yielded a potential sample size of 240 1-ha stands. Of
these, 94 were selected from WCP and 59 were selected from CRP based
12
on the availability of aerial photographs. Most of the aerial photos used
were color having various scales (Table 2.1).
Table 2.1 Historical aerial photos used for photo interpretation.
WCP
b1 967**
1972**
1979*
b1g88***
1990*
1997*
CRP
1961/1962*
1968/1 969**
1972**
1979*
1984*
1989*
1993/1995*
b: black and white photo; otherwise color photo
* 1:12000; ** 1:15840; *** 1:40000
By photo interpretation, percent cover of conifer trees, hardwood
trees, shrub/grass, and open condition was determined for each 1-ha
sample stand (Avery and Berlin, 1985). For WCP samples, stand origin
dates were obtained from the VEGIS database (Willamette National Forest
2001). For CRP samples, origin dates were obtained from the VEGE arccoverage (Siuslaw National Forest, 1992).
2.4.2 Fitting Vegetation Cover Growth Curves
The stand origin and duration of photo-interpreted period varied
among sample stands in the study area, thus, it was hard to make direct
comparison among stands without normalization. To normalize the photo-
interpreted data, cover proportions were modeled as a function of time
since stand origin using a mathematical growth function. Historically,
various growth functions have been used, including monomolecular
13
(Gregory, 1928), logistic (Reed & Holland, 1919, Robertson, 1923), and
Chapman-Richards (Richards, 1969; Causton et al., 1978). See Richards
(1969) for an extensive review of various growth functions. We choose the
Chapman-Richards function as it could accommodate the wide range of
shapes existing in our data. A Chapman-Richards function (Richards 1959,
Hunt 1982, Ratkowsky 1990) has the form of
f(t) = A x (1 - e'
)h
(1)
where f(t) is the canopy cover at time t, A is the asymptotic maximum
value (the theoretical maximum size), b describes the shape of the fitted
curve, c positions the curve in relation to the time axis, and k is a rateconstant whose interpretation depends on the value of b. Based on the
assumption that all stands will eventually reach 100 percent canopy cover
asymptotically and the initial percentage cover was 0 percent, A and c were
assumed to be 100 and 0 respectively. Therefore, a simplified function was
used to model canopy cover change for each sample stand:
.f(t)=!OOx(1_e_kt)h
(2)
Stands defining the trajectory envelope for each province were
identified to highlight the variability of conifer accumulation in each
province. To characterize cross-sample variability, percentage cover as a
function of time since stand origin (up to 50 years) was modeled separately
for each sample stand using SAS (SAS, 1999). The parameters of each
14
"trajectory" regression were defined for both percent conifer cover and for
percentage tree cover (conifer and hardwood combined).
2.4.3 Successional Pathways
Successional pathway after stand
replacement disturbance is
here as the shift over time
vegetation from ephemeral
defined
in
herbaceous life-forms, to taller perennial herbs and shrubs to either
hardwood trees, conifer trees, or a mixture of the two (Franklin and Dyrness
1988, Perry et al. 1989). The process of vegetation shifts are influenced by
both natural forest regeneration and human activities like planting and stand
management. For this study we classified successional pathways into these
stages as described in Table 2.2. The open stage (OP) occurs immediately
after stand replacement disturbance, at which time grasses and herbaceous
plants quickly occupy a site. The OP stage transitions to the shrub stage
(SH) until tree cover reaches between 30 and 70%, at which time it
is
defined as in a semi-closed stage (SC). During the SC stage, the dominant
life form can switch between conifer, broadleaf, and a mixture of the two. At
the time of canopy closure (70% or greater tree cover), a stand will be
dominated by conifer trees (CC), broadleaf trees (CB), or in a mixed
condition (CM).
If a closed conifer stand was mixed or dominated by
broadleaf trees when in the SC stage, it is designated as CC II, otherwise it
is designated CC I. Likewise, if a CB stands was mixed or dominated by
conifer during the SC stage, it is designated as pathway CB II, otherwise it
15
is designated CB I. Stands reaching the CM stage may have been in mixed
or pure tree condition during the SC stage, but were in a mixed condition at
age 50.
Table 2.2 Definitions of successional pathway stages.
% Tree % Conifer or
Cover % Hardwood
N/A
0
Abbreviation
Name
OP
Open
SH
Shrub & <30
Herb
30-70
Semiclosed
>70
Closed
Conifer
SC
CC (I, II)
Variable
Variable
Conifer >70%
Definition
Usually the
after
stage
replacement
disturbance.
initial
stand
Tree cover
is less
than 30%.
is
Tree
cover
between 30-70%.
Total tree cover is
greater than 70%
with more than 70%
of tree cover being
conifer.
or
I
II
indicates a stand
different
in
condition
during the SC stage
CB (I, II)
Closed
Broadleaf
>70
Broadleaf
> 70%
(see text).
Total tree cover is
greater than 70%
with more than 70%
of tree cover being
broadleaf.
I
or
II
indicates a stand in
different
condition
during the SC stage
CM
Closed
Mixed
>70
30%<Conifer
or
Broadleaf<70
%
(see text).
Total tree cover is
greater than 70%.
Both broadleaf and
conifer tree are within
the range of 30 to
70% of
cover.
total
tree
16
Figure 2.2 shows the boundary conditions defining seven basic
successional pathway categories ((OP is not considered as a pathway) in
Table 2.2. Stands following successional pathway SH do not reach 30%
tree cover within 50 years after disturbance (lower curve, Figure 2.2a).
Similarly, stands in the SC category have total tree cover between 30 and
70% at age 50 (upper curve, Figure 2.2a). In both Figure 2.2b and 2.2c, the
top lines represent total tree canopy cover. The lower line in Figure 2.2b
represents 30% of the top line and the lower line in Figure 2.2c represents
70% of the top line. In both Figures 2.2b and 2.2c, the areas under the
lower line represent the proportion of broadleaf cover. A stand belongs to
CC I if the proportion of conifer cover is always above 30% of total tree
cover. Otherwise, it belongs to CC II (Figure 2.2b). A stand belongs to CB I
if the proportion of broadleaf tree cover is always greater than 30% of total
tree cover. Otherwise, it belongs to CB II. In Figure 2.2d, the lower line and
the middle line are 30% and 70% of total tree cover respectively. A stand
belongs to CM if both proportions of conifer and broadleaf tree cover are
within the 30% and 70% of the total tree cover.
To precisely describe the successional pathways for the sampled
stands, the actual photo-interpretation data were used together with the
modeled stage at 50 years since disturbance. That is, successional stage
before age 50 for each stand was determined from photo interpretation as
17
this is the most accurate information available. As not all samples stands
had reached 50 years since disturbance, the modeled successional stages
at age 50 were used as the final reference stage for cross stand
comparison.
100
-'
Shrub I Semi-Closed
100
-.
70
0
0
0
C.)
C.)
Closed Conifer (I, II)
70
Con fe r
Broad'eaf
130
V
S-
30
Conifer
Broadleaf
0
Time (years)
50
Time (years)
a
100
Closed Broadleaf (I, II)
100
Closed Mixed
Conifer
Con ifor
70
70
0
0
0
0
C)
Mixture
C)
V
30
30
Broadleaf
Time (years)
Time (years)
53
d
Figure 2.2 Boundary conditions for successional pathway categories
2.4.4 Successional Trajectories
In this study, successional rate represents how fast conifer cover
increased over time. Together with successional pathway, succession rate
determines the successional trajectory. As such, successional trajectories
for stands following the same pathway may be different due to differences
18
in rate. Since parameter k and b of the Chapman-Richard function do not
have direct biological significance of their own and they have to be
interpreted together, it is not useful to directly compare the differences
between k and
b
values from
Equation
2.
Thus,
to
enable the
characterization of variation in successional trajectories and comparison of
trajectories between WCP and CRP, several parameters were derived from
the Chapman-Richards function (Table 2.3) (Richard 1959, Causton and
Venus 1981). DELAY is defined as the time to reach 5% canopy cover,
which can be used to indicate whether growth stagnation has occurred after
stand replacement disturbance. Weighted mean relative growth rate
(wmRGR) and weighted mean absolute growth rate (wmAGR) were used to
characterize the overall canopy development rate over the entire growth
period. The maximum absolute growth rate (MaxRate) represents the
potential maximum canopy development rate. More detailed discussion
about the biological significance of these parameters was presented by
Richards (1959). In addition to the above three direct measures of rate, we
added three time-associated parameters which indirectly characterize
succession rate. As canopy development rate eventually decreases with
time, TmaxRate represents how quickly a stand will reaches its maximum
absolute growth rate. T70 represents the time to reach closed canopy forest
(70% cover), and AGP is the active growth period, the period in which
conifer cover is continuously increasing.
19
Since conifer development was the main interest in this study, the
parameters listed above were evaluated only for conifer cover of each
sample stand. To compare the successional trajectories for CRP and WCP,
the density distribution of the parameters from Table 2.3 were derived using
S-plus (Insightful Corporation, 2001). The two regions were also compared
in term of these parameters using a t-test.
Table 2.3 Successional trajectory parameters.
Parameter
DELAY
Formula
ln(1
0.05'")
Detail
Time to reach 5% cover
k
wmRGR
Weighted mean
growth rate
k
b+1
wmAGR
MaxRate
TmaxRate
Weighted mean absolute
Axk
2 x (b + 2)
growth rate
Axkx(1_)/)"b
Maximum absolute growth
!n(b)
Time to reach maximum
In(1
rate
absolute growth rate
k
T70
relative
O.7'")
Time to reach 70% cover
k
AGP
T70 - DELAY
Active growth period
2.4.5 Classification of Successional Trajectories
Correlation analyses were conducted for the parameters listed in
Table 2.3. To get the most independent parameters, the least correlated
parameters were selected as classification criteria of successional
trajectory. Based on the selected parameters, all sample stands were
classified into three groups using the cross-province mean and standard
20
deviation of the parameter under consideration. If the value of the selected
parameter is less than the mean minus one standard deviation of the
parameter population, it belongs to group 1. If the selected parameter value
is within one standard deviation of the mean, it belongs to group 2. If the
selected parameter value
is
larger than the mean plus one standard
deviation, it belongs to group 3. The classifications from each individual
selected parameter were combined for the final classification.
2.5 RESULTS
2.5.1 General Successional Patterns
Scatter plots of conifer cover over time (Figure 2.3) indicate that
there was a wide range of conifer cover accumulation over time among
stands. Figure 2.4 shows the individual conifer accumulation models based
on a Chapman-Richards growth function. This confirms that conifer
accumulation over time is highly variable among stands, and showed that
conifer cover accumulation for stands in CRP was generally faster than that
in the WCP. In addition, Figure 2.4 also reveals that stands also varied in
the time when conifer cover starts to accumulate more rapidly.
2.5.2 Successional Pathways
Photo interpretation revealed that in both the CRP and WCP,
herbaceous vegetation quickly occupied a site immediately after stand
replacement disturbance, although much of the herb cover was eventually
replaced by trees. Examples of actual successional pathways based on
21
0
ISO
000
o
00 0
70
0
0
4O
30
0
20
00
0
0
0
5o
0
0
0
0
0
00
00
0
0*
000 0
00000
0
10
0
0
00000
000
0
00 000000
0
0
20
0
80
0
0
°3o
0
o
90
0
00
0
00000
o
0000
0
0
8
0
40
000
0
88 900
0
,50
000000
100
0
0
o88 888
00
0
0
0
0
O8O88
0
0
OOg
0
8 000
00
0 00 0 0
0000 00
70
00
000000 0000
880
08
88
80
o
o
0
90
10
0
0
0
5
00
II
20
100. y..r
25
.oy..)
30
30
20
40
30
Tb,,. 94I bI0 .,cb,)
Figure 2.3 Scatter plots of conifer cover over time from the CRP (left) and
WCP (right). The lines indicate the upper and lower envelope of the data
cloud for the two sample areas by linking the extreme stand data.
100
00
80
60
0
U
40
C
0
U
20
20
20
30
Tim,, (9slr IInC origin)
20
30
50
10100. (y.ar bl.. .IgIn)
Figure 2.4 Modeled conifer cover trajectories for the CRP (left) and WCP
(right). Each line represents a single sample stand over time.
photo interpretation data were shown in Figure 2.5. Pathway models
revealed that by age 50, the majority of sampled stands returned to CC
stage in both provinces (CC I and CC II in Figure 2.6). However, in the
WCP over 25% of stands had not returned to closed tree canopy condition,
whereas in the CRP, all sampled stands returned to closed condition. In the
CRP, nearly 7% of the sampled stands returned to a closed canopy
broadleaf condition (CB I and CB II), and about 9% returned to a mixed
22
SN
100
70
Conifur
SHb
30
3.
00
CM
100
70
-9-90
19
-9-
20
10
30
I
40
II
I
I00
all
70
7.
21
3*
3.
3.
45
/1
ci.
U
J
30
II
1
Tkn
3.
.9
S
0
II
25
30
00
10
70
30
10
Tone (y9909)
ccl
cci
100
10
30
/
0
1e
TI
IS
35
1
I
9-9--.
3,
40
Figure 2.5 Examples of successional pathways. Data used were from photo
interpretation of selected sample stands.
23
condition (CM). In the WCP, there were no sample stands that became
closed broadleaf stands, but about 3% returned to a mixed forest condition.
These results indicate that disturbed forest stands return more
quickly to closed tree condition in the CRP than in the WCP. However
hardwood trees were more likely to occur during early succession in the
CRP than in the WCP. The results also indicate the existence of stands in
WCP that may be returning to closed conditions very slowly (SH and SC in
Figure 2.6).
90
79.7
80
69.1
70
60
50
40
30
22,3
20
10
8.5
3.4
3.2
3.4
3.2
0
SH
Sc
CBI
CBII
CM
CCI
CCII
Figure 2.6 Successional Pathway Distribution for CRP and WCP
24
2.5.3 Successional Trajectories
Based on the modeled growth curves (Figure 2.4), the distribution of
successional trajectories varied both within province and across provinces.
This was confirmed
in
Figure 2.7, which shows the distribution of
successional trajectory parameters defined in Table 2.3, and Table 2.4
shows a summary of these distributions with time needed for the WCP was
longer than the CRP.
Table 2.4 Comparison of Successional Trajectory Characteristics.
DELAY (years)
wmRGR (1/year)
wmAGR (%/year)
MaxRate (%/year)
TmaxRate (years)
T70 (years)
AGP (years)
WCP
mean
8.80
0.12
2.26
3.36
18.20
42.30
33.50
Each of parameters
std
4.45
0.06
1.19
1.74
7.72
28.24
27.67
in
CRP
mean
6.60
0.22
4.82
7.12
12.4
22.9
16.2
p-value oft-test
std
3.98
0.08
2.11
3.09
8.88
23.32
20.94
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
Table 2.3 had a significantly different
distribution in each province (Figure 2.7) (Table 2.4). The variability of each
parameter was of the same magnitude in both provinces (Table 2.4).
Sample stands in the WCP have longer DELAY than the CRP. Once
a stand had reached 5% canopy cover, it generally showed a continuous
increase in canopy cover (Figure 2.4). However, on average, stands were
delayed about 7 years in the CRP, while they were delayed about 9 years in
WCP (Table 2.4).
25
DELAY
Coast Ranges Province (CRP)
Western Cascades Province (WCP)
wmAP
1muRat
170
MxRa%e
AGP
Figure 2.7 Distribution of successional trajectory parameters
26
Weighted mean relative growth rate (wmRGR), weighted mean
absolute growth rate (wmAGR), and maximum absolute growth rate
(MaxRate) were all highly correlated (r's = 0.90-0.99) (Table 2.5). These
three parameters have similar distribution both across provinces and within
province (Figure 2.7). Student's t-tests indicated that all three parameters
for conifer accumulation were significantly different (Table 2.4) between the
WCP and CRP. For the CRP, wmAGR was more than twice that for stands
in the WCP (2.26). Similar relationships exist between wmRGR and
MaxRate for the WCP and CRP.
Table 2.5 Correlation Matrix for Successional Trajectory Characteristics*
DELAY wmRGR wmAGR MaxRate TmaxRate T70
DELAY
wmRGR
wmAGR
MaxRate
TmaxRate
T70
AGP
AGP
1
-0.385
-0.169
-0.178
0.880
0.391
0.247
1
0.904
0.912
-0.624
-0.589
-0.556
1
0.999
-0.441
-0.640
-0.646
1
-0.450
0.592
0.478
1
-0.640
-0.645
1
0.988
1
*Units for the parameters are: DELAY (years), wmRGR (1/year), wmAGR
(%/year), MaxRate (%/year), TmaxRate (years), T70 (years), AGP (years)
Time to reach maximum absolute growth rate (TmaxRate), time to
reach 70% conifer cover (T70), and active growth period (AGP) showed
similar pattern among sample stands both across and within provinces
(Figure 2.7).
Of the succession parameters examined, the correlation of DELAY
with succession rate (wmAGR) is the lowest (r = -0.17). 170 and AGP were
27
highly correlated and both were moderately correlated with TmaxRate.
However, TmaxRate was closely related to DELAY. Based on these results,
DELAY and wmAGR (hereafter refered to as RATE) were selected as
successional trajectory descriptors and used to classify successional
trajectories.
2.5.4 Successional Trajectory Classification
Using the group designation of the selected descriptors for all
samples (i.e. across provinces), DELAY was clustered into three categories
(short, medium, and long) (Table 2.6). Likewise, RATE was also clustered
into three categories (slow, moderate, and fast).
Table 2.6 Successional trajectory classification criteria. Mean value of
DELAY was 8.0 years with standard deviation of 4.4 years. Mean value of
wmAGR was 3.2 with standard deviation of 2.0 years.
DELAY(years)
RATE(%/year)
Group 1
Group 2
Group 3
<3.6
3.6to 12.4
>12.4
Short
medium
long
<1.2
1.2to5.2
>5.2
Slow
moderate
fast
Of the nine possible trajectory classes, across provinces about 52%
belonged to medium delay, normal rate category (Table 2.7). Twenty four
percent were medium delay but in the slow (9%) and fast (15%) rate
category. Similarly, 17% were medium rate, but had either short or long
delay. Very few samples occupied the extreme categories.
28
Tab(e 2.7 Successional trajectory classification across and within provinces.
Numbers are percent of samples.
Across Provinces
Short
Slow
Moderate
Fast
Medium
Long
Sum
2.9
8.7
13.0
69.6
1.4
17.4
0.7
9.4
8.7
1.4
52.2
14.6
10.8
76.2
13.0
100
Short
Medium
Long
Sum
1.1
13.8
60.9
3.5
13.8
18.4
2.3
0
2.3
5.7
77.0
17.3
100
Short
Medium
Long
Sum
Slow
Moderate
Fast
0
2.0
3.9
2.0
37.2
35.3
3.9
4.0
52.9
43.1
Sum
19.6
74.5
5.9
100
Sum
wcP
Slow
Moderate
Fast
Sum
4.6
0
79.3
CRP
15.7
0
The two provinces behaved differently in terms of distribution of the 9
successional trajectory classes. In the WCP about 61% of the sampled
stands belonged to the medium delay, normal rate category, compared to
only about 37% in the CRP. About 17% of the stands in the WCP had a
long delay versus 6% in the CRP. In contrast, the CRP had about 20% in
the short delay category versus 6% in the WCP. The most striking
29
difference was the occurrence of 43% fast and 4% slow rate in the CRP
versus the occurrence of 2% fast and 18% slow rate in the WCP.
26 DISCUSSION
2.6.1 Multiple Successional Pathways and Rates
Patterns of vegetation change after stand replacement disturbance
are affected by the intensity of disturbance, local environmental conditions,
and
stochastic processes. Therefore, following disturbance,
multiple
successional pathways are commonly observed (Noble and Slatyer 1980,
Abrams, 1985; McCune and Allen 1985) with different rates of recovery
highly variable (Myster and Pickett, 1994). Halpern (1988) demonstrated
this for six forest communities along a disturbance severity gradient, using
ordination techniques on understory communities from two watersheds of
the H. J. Andrews Experimental Forest within our study area.
Our results support the theory of multiple successional pathways.
Our results also indicate that the patterns of succession were different for
the two provinces. We defined succession as a process of changing life
forms over time, and we used canopy cover data to represent successional
pathways. For simplicity, we described successional pathways using the
successional stage at age 50 (Table 2.2), modified by transition states at
younger ages. We classified successional pathways into seven different
categories (Figure 2.2).
30
The underlying common trends for both the CRP and WCP were
rapid occupation of herbaceous life forms followed by a gradual return to
closed canopy recovery (predominantly conifer) on most of the stands. The
process of returning to a conifer life form was not the same for all the
stands, with a multiplicity of successional pathways being manifested in
both the sequence of succession stage change and the rate of change.
Both the photo-interpreted data and modeled data showed that in
the CRP a significant proportion of disturbed stands experienced a
prolonged period of broadleaf and mixed tree cover, whereas this was
uncommon in the WCP. This observation agrees with existing knowledge
for these two provinces (Franklin and Dyrness, 1988). The CRP contains
both the Sitka Spruce Zone and Western Hemlock Zone, whereas the WCP
mainly contains the Western Hemlock Zone only. In the Sitka Spruce Zone,
two major kinds of seral forest stands are known to commonly exist:
coniferous and hardwood (Franklin and Dyrness, 1988). Broadleaf trees
can rapidly occupy disturbed stands (Zavitkovski and Stevens, 1972) and
the replacement of broadleaf trees by conifer trees can be a slow process,
due to the dense understory in broadleaf stands (Meurisse and Youngberg,
1971). Broadleaf stands are not common in forests of the Western Hemlock
Zone even in the CRP, except on very recently disturbed sites or in riparian
zones (Franklin and Dyrness, 1988). However, broadleaf Iifeforms are
known to occur in the riparian zone of both the CRP and WCP. Close
31
examination of those samples belonging to the CB and CM categories
indicated that most occurred within the Sitka Spruce Zone. In the WCP, we
also observed that about 3 percent of the sampled stands remained in the
SH category and about 22% in the SC category, whereas no sample stands
in CRP belongs to these categories.
It has been known for some time that rates of forest succession are
variable, yet there have been few remotely sensed studies to document this
phenomenon. Successional rate has been interpreted as either the time for
recovery to a terminal stage (Major 1974a, Burrows 1990) or as rate of
change in vegetation composition (Major 1974b, Prach 1993). For change
rates, species turnover and turnover rates have been evaluated by similarity
indices (Bornkamm 1981, Donnegan and Rebertus 1999).
We combined both interpretations of successional rate
in our
analyses. Because we were interested in early forest succession, the
terminal stage for our purposes was 70% conifer cover. The derived
parameter T70 from the Chapman-Richard's function represents one
interpretation of successional rate. However, instead of using similarities
measure for species turnover, we used conifer life-form and canopy cover
change rates derived from Chapman-Richards function to describe
succession rate (wmRGR, wmAGR, maxRate). In other studies, where
succession rates were based on species composition and turnover, species
richness and diversity were more strongly emphasized. The use of a
32
similarity index was more often limited by the available data. Our approach
using life-form and canopy cover is more closely related to the biomass
aspect of succession and can be easily related to other large scale studies
such as carbon modeling. Characterization of succession in this study was
based on curve fitting for lifeform and canopy cover data obtained from
aerial photo interpretation, which makes it possible to compare data sets for
stands of different origin.
Succession, as we defined it, in the CRP was generally faster than in
the WCP. The average time required to reach 70% conifer cover for CRP
samples was 23 years, which is much shorter than the 42 years for the
WCP. Also the rate measurements (wmAGR, wmRGR, maxRate) indicated
that the average rate for the CRP was about double that of the WCP
(Figure 2.7).
26.2 Ecological and Management Implications
Many factors can affect forest succession. Succession rate has been
shown to be highly correlated with moisture and/or soil fertility (Olson 1958,
Shugart and Heet 1973, Gleeson and Tilman 1990, Prach 1993, Donnegan
and Rebertus 1999). In our study area, there is more precipitation in the
CRP than in the WCP, and fog and low clouds in the CRP help to alleviate
moisture stress for the drier summer period. Soil in the CRP is relative
deep, rich, and fine textured compared to soil in the WCP (Franklin and
Dyrness 1988). In a subsequent study, we will quantify the relationship
33
between environmental factors and succession. However, we anticipate
finding that variations of successional patterns cannot be explained solely
by regional environmental factors such as precipitation and temperature.
In addition to the extreme environmental conditions, biological and
management factors are likely also to be important in shaping successional
patterns in western Oregon. Besides vegetative competition, below ground
processes could be important mechanisms controlling conifer seedling
survival and conifer growth. It has been indicated that ectomycorrhizae and
subsequent effects were important reforestation aid in western Oregon
(Perry et al., 1987). Forest management practices after stand replacement
disturbance could affect succession in many different ways including direct
reduction of vegetation competition, improved soil nutrition, and more
importantly,
planting
of
certain
species.
However,
biological
and
management related factors are more difficult to measure.
We demonstrated
that
succession
after
stand
replacement
disturbance was highly variable across western Oregon. In our study area,
early succession differed in terms of both successional pathway and
trajectory. The classification of successional pathway and trajectory, as was
developed in this study, should be useful for modeling changes in regional
carbon stores over time (Harmon et al., 1996; O'Connell, in prep.).
34
2.7 CONCLUSIONS
In this study, we evaluated early forest succession by characterizing
changes of life forms (shrub/herb, broadleaf tree, conifer tree) and cover
using air photos. We defined seven categories of successional pathway,
and a set of parameters to describe the successional patterns. Our analysis
indicated that successional patterns for the CRP and WCP are different in
terms of pathway, rate, and delay factors for conifer cover development.
The method used
in
this paper allowed
for comparison
of
heterogeneous data sets. The effects of short term fluctuations during
succession and a lack of replication were alleviated by treating each sample
separately, by means of fitting a trajectory to model the sample data.
Quantifying the importance of controlling factors on the succession patterns
is a critical next step.
35
2.8 REFERENCES
Abrams, M. D., D. G. Sprugel, and D.
Dickmann. 1985. Multiple
successional pathways on recently disturbed jackpine sites in Michigan,
I.
Forest Ecology and Management 10:31-48.
Amaranthus, M. P. and 0. A. Perry. 1987. Effect of soil transfer on
ectomycorrhiza formation and the survival and growth of conifer seedlings
on old, nonreforested clear-cuts. Can. J. For. Res. 17:944-950.
Avery, 1. E. and G. L. Berlin. 1985. Fundamentals of Remote Sensing and
Airphoto Interpretation, Fifth Edition. Prentice Hall, New Jersey.
Bornkamm, R. 1981. Rates of change in vegettion during secondary
succession. Vegetatio 47:213-220.
Burrows, C. J. 1990. Process of vegetation change. Unwin Hyman, London.
Causton, D. R., C. 0. Elias, and P. Hardley. 1978. Biometrical studies of
plant growth. 1. The Richards function and its application in analyzing the
effects of temperature on leaf growth. Plant Cell Eviron. 1:163-184.
Causton, D. R. and J. C. Venus. 1981. The Biometry of Plant Growth.
Edward Arnold, London.
Cohen, W. B., M. E. Harmon, D. 0. Wallin, and M. Fiorella. 1996. Two
decades of carbon flux from forests of the Pacific Northwest. BioScience
46:836-844.
Donnegan, J. A., and A. J. Rebertus. 1999. Rates and mechanisms of
subalpine forest succession along an environmental gradient. Ecology
80:1370-1 384.
Franklin, J. F. and C. T. Dyrness. 1988. Natural Vegetation of Oregon and
Washington. Oregon State Univeristy Press.
Gleeson, S. K., D. Tilman. 1990. Allocation and the transient dynamics of
succession on poor soil. Ecology 71: 1144-1155.
Gregory, F. F. 1928. Studies in the energy relations of plants. II. Ann. Bot.
42:469-507.
Hall, F. G. 1991. Large-scale patterns of forest succession as determined
by remote sensing. Ecology 72:628-640.
36
Halpern, C. B. 1988. Early successional pathways and the resistance and
resilience of forest communities. Ecology 69: 1703-1715.
Harmon, M. E., W. K. Ferrell, and J. F. Franklin. 1990. Effects on carbon
storage of conversion of old-growth forests to young forests. Science
247:699-702.
Harmon, M. E., J. M. Harmon, W. K. Ferrell, and D. Brooks. 1996. Modeling
carbon stores in Oregon and Washington forest products: 1900-1992.
Climatic Change 33:521-550.
Hunt, R. 1982. Plant Growth Curves. University Park Press, Baltimore.
Kuchler, A. W. 1964. Manual to accompany the map of potential vegetation
of the conterminous United States. Special Publication No. 36. New York:
American Geographical Society. 77 p.
Insightful Corporation. 2001. S-Plus 6 for Windows, Guide to Statistics,
Volume I, Corporate Author: Seattle, WA.
Major, J. 1974a. Differences in duration of successional seres. In: Knapp R.
(ed.): Vegetation dynamics, Handbook of Vegetation Science 8:138-160.
Major. J. 1974b. Kinds and rates of changes in vegetation and chronofunctions. In: Knapp R. (ed.): Vegetation dynamics, Handbook of Vegetation
Science 8:7-18.
McCune, B. and T. F. H. Allen. 1985. Will smilar forest develop on similar
sites? Canadian J. of Botany 63: 367-376.
Meurisse, R. 1. and C. T. Youngberg. 1971. Soil-vegetation survey and site
classification report for Tillamook and Munson Falls tree farms, Publishers
Paper Company lands, Tillamook County, Oregon. Corvallis, OR: Oregon
State University, Department of Soils. 116 p.
Myster, R. W. and S. T. A,
Pickett.
1994. A comparison or rate of
succession over 18 yr in 10 contrasting old fields, Ecology 75:387-392.
Nesje, A. M. 1996. Spatial patterns of early forest succession in Lookout
Creek basin. M.S. Research Paper. Oregon State University, Corvallis, OR.
Noble, I. R. and R. 0. Slatyer. 1980. The use of vital attributes to predict
successional changes in plant
disturbances. Vegetatio 43:5-21.
communities
subject
to
recurrent
37
Olson, J. S. 1958. Rates of succession and soil changes on southern Lake
Michigan sand dunes. Bot. Gaz. 119: 125-1 30.
O'Connell, K. E. B., S. A. Acker, H. J. Bruner, C. B. Halpern, and M. E.
Harmon. 2003. Carbon dynamics of Douglas-fir forests with different
successional rates after timer harvest. In prep.
Perry, D. H., R. Meurisse, B. Thomas, R. Miller, J. Boyle, J. Means, C. R,
Perry, and R. F. Powers. 1989. Maintaining the Long-Term Productivity of
Pacific Northwest Forest Ecosystems. Timber Press, Portland, Oregon.
Perry, D. A., R. Molina, and M. P. Amaranthus. 1987. Mycorrhizae,
mychorrhizospheres, and reforestation: current knowledge and research
needs. Can. J. of For. Res. 17: 929-940.
Prach, K., P. Pysek, and P. Smilauer. 1993. On the rate of succession.
Oikos 66: 343-346.
Ratkowsky, D. A. 1990. Handbook of nonlinear regression models. Marcel
Dekker, New York.
Reed, H. S. and R.
H.
Holland. 1919. Growth of sunflower seeds.
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1992.
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GIS
Siuslaw
National
Forest.
http://www.fs .fed. us/r6/siuslaw/maps/g is/d ictionary/vege .shtml.
coverage,
Waring, R. H. and J. F. Franklin. 1979. Evergreen coniferous forests of the
Pacific Northwest. Science 204:1380-1 386.
38
Willamette National Forest. 2001. VEGIS database, USDA Forest Service,
Willamette National Forest, Eugene.
Zavitkovski, J., R. D. Stevens. 1972. Primary productivity of red alder
ecosystems. Ecology 53: 235-242.
39
Chapter 3 The Effect of Abiotic Factors on Forest Succession
in Western Oregon
Zhiqiang Yang, Warren B. Cohen, and Mark E. Harmon
Forest Science Department
Oregon State Univeristy
Corvallis, Oregon
January 12, 2004
40
3.1 ABSTRACT
In this study, we examined the effect of climatic and physiographic
factors on the early phase of forest succession following clearcuts in
western Oregon. Succession was defined as the directional change with
time of life-form composition of a stand, manifested through changes in
canopy cover of different life-forms. The study area consisted of the
portions of Coast Ranges Province (CRP) and Western Cascades Province
(WCP) that were contained within a Landsat TM scene (path 46, row 29). In
total 116 sample stands were examined.
Aerial photos from 1959 to 1997, in roughly 5-year intervals, were
interpreted for proportion of conifer cover. Conifer cover change over time
was fitted with a Chapman-Richards growth function for each sampled
stand, and from these growth curves, parameters of interest related to early
forest succession were extracted. These parameters included, for each
sample stand, (1) the number of years to reach 5% conifer cover (DELAY),
and (2) weighted mean absolute growth rate of conifer cover over the whole
growth period (RATE). Both DELAY and RATE showed a wide range of
variation over the study area. Many factors could contribute to the variation
of
DELAY
and
RATE,
including
biological,
environmental,
and
management. However, as only environmental factors (climatic and
physiographic) were readily available, these were used in correlation and
41
regression analysis to explore how much of the variation in DELAY and
RATE could be explained.
Correlation analysis indicated that among the climatic variables
examined, temperature was one of the most important factors influencing
both DELAY and RATE both within and across provinces. Precipitation and
radiation were less important than temperature. Of all the physiographic
variables considered, elevation was the most important one, probably due
to its influence of temperature.
Regression analysis explained 23%, 37%, and 29% of the variation
in DELAY,
and 24%, 25%, and 57% of the variation in RATE, for the WCP,
the CRP, and across provinces, respectively. No significant interactions
among climatic and physiographic factors were identified for DELAY in the
WCP, or for RATE in the CRP. For DELAY in the CRP, the interaction of
relative slope position (RSP) and solar radiation (SRAD) were important.
Across provinces, the interaction of vapor pressure (VPD) and summer
temperature were important for DELAY. For RATE, the interaction of
elevation (ELEV) and RSP was important in the WCP, while the interaction
of ELEV and SRAD was important in the across provinces analysis.
The variability in succession explained by this study was relatively
low. Unexplained variability is likely due to stochastic, biological, and
management-related factors, as well as experimental errors associated with
characterizing climate variables and successional parameters. Inclusion of
42
these factors will be important in future research on early forest succession
in western Oregon.
43
32 INTRODUCTION
Understanding forest succession is critical for forest ecosystem
management and biodiversity conservation planning (Knight and Wallace,
1989). In
particular, successional pathway and rate have important
imp'ications for biogeochemical cycling of nitrogen (Pastor and Post, 1986),
and carbon (Botkin and Running, 1984; Harmon, 2001a). Consequently,
estimates of successional pathway and rate are important inputs for many
ecological models (Hall etal., 1988; Hall, 1991; Harmon etal., 1990; Cohen
etal., 1992; O'Connell etal., in prep.).
After stand replacement disturbance in the Pacific Northwest (PNW)
region of the United States, forest succession generally leads to a closed
canopy conifer condition (Franklin and Dyrness, 1988). However, the rate
and density of conifer recovery can vary significantly across the region
(Franklin and Hemstrom, 1981; Nesje, 1996; Tappeiner etal., 1997; Poage,
2001). Here, we study early forest succession in western Oregon, focusing
on the relationships of two major successional descriptors with various
potential controlling abiotic factors.
Succession has many dimensions and has been studied extensively
from numerous perspectives (Connell and Slatyer, 1977; Finegan, 1984;
Pickett etal., 1987; Halpern, 1988; Glenn-Lewin etal., 1992; Peet, 1992). It
is widely recognized that many factors are important contributors to
44
successional variation, including biotic, abiotic, and management related
factors (Turner etal., 1998; Halpern, 2001).
Biotic factors often contribute to forest succession through resource
competition (Peterson etal., 1988; Newton and Preest, 1988; Nearty etal.,
1990; Farnden, 1994; Stein, 1995; Strothmann and Roy, 1995; Gray and
Spies, 1997) and allelopathy (del Moral and Cates, 1971; Mallik, 1987), or
indirectly by modifying the environment in a way that is beneficial or
detrimental to seedling survival and growth (Coates et al., 1991). Perry et
al. (1987, 1989) and Ponge et al. (1998) studied the effects of mycorrhizal
fungi on forest regeneration and suggested that below-ground biological
mechanisms could strongly affect forest regeneration.
Abiotic factors have also been examined, especially with respect to
forest regeneration stocking level. In a study of regeneration within mixedconifer clearcuts in the Cascades Range, Seidel (1979) found that greater
stocking was generally associated with more northerly aspects. Similar
patterns were also reported by other researchers (Strothmann, 1979;
Minore et al., 1982, and Ferguson et al., 1994). In western Oregon, Nesje
(1996) studied the spatial pattern of early forest succession in Lookout
Creek
Basin, classifying
succession into slow,
expected,
and
fast
categories. In her analysis, interactions of elevation and aspect, as well as
aspect and a topographic convergence index were significant for slow
45
stands. Interaction
of elevation and aspect, and elevation and the
topographic convergence index were significant for fast stands.
Management
factors
include
site
preparation,
planting,
and
silvicultural practices. Broadcast burning has been a commonly used site
preparation technique. This can cause increased moisture stress by
exposing seedlings directly to environmental extremes (Minore etal., 1982;
Ferguson
et aL,
1994).
Broadcast burning can also influence the
persistence of mycorrhizal fungi (Parke et al., 1984; and Amaranthus and
Perry, 1989). Axelrood etal. (1998) reported that planting of seedlings with
poor vigor is more likely to lead to regeneration failures.
Most of the above-mentioned studies of forest succession were
based on choronosequences or relatively short periods of observations
involving small areas, and results among these studies were inconsistent.
In a previous study of early successional patterns in western Oregon, we
used historical aerial photos for samples stands from a portion of Siuslaw
National Forest and Willamette National Forest contained within a single
Landsat Thematic Mapper (TM) image. The succession process studied
here includes both natural and human influenced succession process.
Although the majority of disturbed stands that we studied returned to closed
conifer condition within 50 years, a wide range of successional pathways
and rates were observed. Thus, we defined succession as the temporal
directional change of life-form composition, as manifested through changes
46
in
canopy cover of different life-forms. Successional pathways were
classified into 7 categories that could be characterized by two descriptors:
DELAY (time to reach 5% conifer cover) and RATE (weighted mean
absolute growth rate), defined as the mean rate of conifer cover
accumulation (Yang et al., in prep.). In the current study, we examined
potential controlling factors on these two descriptors.
In this study, it was not practical to quantify biotic influences over the
large area studied. Additionally detailed information about management
treatments was not available for many of the stands studied. Thus, we
examined how much of the variation observed in forest succession could be
accounted for by readily available spatial databases containing abiotic
factor information. The objective was therefore to examine effects of abiotic
factors and their interactions on DELAY and RATE. The factors studied
were primarily climatic and physiographic variables.
3.3 METHODS
3.3.1 Study Area
The area studied was the portions of the Coast Ranges Province
(CRP) and the Western Cascades Province (WCP) of western Oregon
contained in Landsat TM path/row 46/29 (Figure 3.1). Because the
availability of historical aerial photos varies, but is most extensive over
National Forest lands, stands from the Siuslaw National Forest in CRP and
Willamette National Forest in WCP were studied.
47
Oregon
A
StuIaw Nitlonal orst
WIIam,tt National orest
Figure 3.1 Digital elevation model of study area. Portion of the Siuslaw
National Forest and Willamette National Forest contained within Landsat
TM, Aug. 19, 1995 Path 46, Row 29
In general, the PNW region has a maritime climate of warm, dry
summers and mild, wet winters, which exhibits a strong gradient with
changes in latitude, longitude, and elevation. These climatic conditions
favor growth of evergreen life-forms, except in riparian areas where
broadleaf trees may dominate. Upsiope forests have a ratio of coniferous to
broadleaf trees of 1000:1 in this region (Kuchler, 1964), and have among
48
the greatest biomass accumulations and highest productivity levels of any
ecosystem on earth (Waring and Franklin, 1979). Besides conifer and
broadleaf trees, many shrub and herbaceous species exist in the study area
(Frankin and Dyrness, 1988).
The CRP is characterized by mild temperature with prolonged cloudy
periods. Average temperatures range from 5 °C in January to 16 00 in July,
and the mean annual precipitation is 3000mm. Depending on location,
summers range from cool to warm and elevations range from 450 to 750m.
Sedimentary rock types are typical in the CRP. Two primary forest zones
include the Sitka Spruce Zone and the Western Hemlock Zone (Franklin
and Dyrness, 1988).
The climate of the WCP is also maritime with mild, wet winters and
warm, dry summers. Average temperatures range from
00 in January to
23 °C in July and annual precipitation averages about 2300mm. Elevations
range from 450 to 3100m, with volcanic rocks dominating most of the area.
The province consists primarily of the Western Hemlock Zone (Franklin and
Dyrness, 1988).
3.3.2 Successional Descriptors
In Yang et al. (see Chapter 2), changes in percent cover of different
life-forms were observed from historical aerial photos for 138 randomly
selected 1-ha plots in the CRP and the WCP. With these data, a Chapman-
Richards growth function was used to model observed cover changes.
49
Based on this growth function, two derived parameters: DELAY and RATE
(Table 3.1), were used to characterize successional trajectories for each
plot. Of the 138 sample plots, 22 were removed from the current analysis
due to an insufficient number of temporal data points to adequately fit the
Chapman-Richards growth function. This yielded a total of 116 sample
)h
f(t)
plots. For the Chapman-Richards growth function, f(t) = 100 x (1 - ekt
is the canopy cover at time t, b describes the shape of the fitted curve, and
k is a rate constant.
Table 3.1 Successional descriptor variables.
Parameter
DELAY
Detail
Formula
Jn(1
0.05'")
Time to reach 5% cover
k
Axk
2x(b+2)
RATE
Weighted mean absolute
growth rate
3.3.3 Controlling factors
Precipitation, temperature, radiation, and vapor pressure data for
sample plots were extracted from the Daymet U.S. database, an 18 year
(1980-1997) 1-km resolution daily data set (Thornton et al., 1997, 1999,
2000). From the daily data, mean (TDAY), maximum (TMAX), and minimum
(TMIN)
daily
temperature,
mean number of frost days per year
(FROSTDAY), mean daily precipitation (PROP), mean number of no-rain
days per year (NORAIN), mean daily potential solar radiation (SRAD), and
50
Table 3.2 Codes and definitions of climatic and physiographic controlling
factors considered.
Code
Description
Units
Climate Factors
Temperature
TDAY
TMAX
°C
TM IN
OC
FROSTDAY
days
Mean daily temperature
Mean daily maximum temperature
Mean daily minimum temperature
Mean number of days with
minimum temperature below 0°C
in one year
SMRTMP
OC
Mean daily temperature in May -
WTRTMP
°C
September
daily
temperature
Mean
November - March
Precipitation
PRCP
NORAIN
in
cm
days
Mean daily precipitation
SMRPRCP
cm
Mean daily precipitation in May -
WTRPRCP
cm
SMRTSMRP
°C/cm
CONTPRE
%
Radiation
SRAD
VPD
Mean number of days without any
precipitation in one year
September
in
precipitation
Mean
daily
November - March
Moisture stress during the growing
season,
SMRTMP/SMRPRE
of annual
percentage
Mean
precipitation falling in June-August
W/m2
Pa
Mean daily potential solar radiation
Mean daily water vapor pressure
m
%
Elevation
Slope
cos(45-aspect)+ I
cos(aspect 180)
sin(aspect)
Relative slope position
Topographic
relative
index
Physiographic Factors
ELEV
SLOPE
TRANSASP
SOUTH NESS
EASTNESS
RSP
TRMI
moisture
51
mean daily vapor pressure (VPD) were derived. Ohrnann and Spies (1998)
demonstrated the importance of regional climate conditions during the
growing season and the cool season for woody plant community
distribution. Thus, we also derived mean summer (SMRTMP) and winter
(WTRTMP) daily temperature, mean summer (SMRPRCP) and winter
(WTRPRCP) daily precipitation, summer moisture stress (SMRTSMRP) and
summer precipitation concentration (CONTPRE) from the daily Daymet
data.
All physiographic data (Table 3.2) were derived from the USGS 10-rn
digital elevation model (DEM) dataset and represent estimates of the mean
values for each 1-ha plot. Several physiographic variables were derived.
Elevation (ELEV) and slope (SLOPE) were derived as simple averages.
Aspect (compass direction of slope) was transformed by trigonometric
functions
(Beers
et
al.,
1966;
Roberts,
1986)
into
TRANSASP,
SOUTHNESS, and EASTNESS. These transformations convert the more
standard measure of aspect into measurements of exposure. Relative slope
position (RSP) was calculated as an index of a plot's position relative to the
top and bottom of the slope. RSP affects both the general thermal and
hydrologic regime of a site. Topographic relative moisture index (TRMI)
(Parker, 1982) values were computed from slope steepness, topographic
position, and slope configuration (convex to concave). TRMI theoretically
increases with soil moisture by assuming that on steep slopes or ridgetops,
52
the soil is shallow and therefore limited in moisture capacity. As slope
decreases and topographic position moves down the slope, accumulated
drainage area and soil moisture increase.
3.3.4 Data Analysis
Preliminary analysis revealed non-linearity in the relationships of
RATE and DELAY with the potential controlling factors. Thus, RATE and
DELAY were log-transformed to linearize the relationships. Pearson
correlation coefficients were used to explore the relationships of DELAY
and RATE with individual potential controlling factors. This analysis was
conducted both within the WCP and CRP, and across provinces to explore
if there were provincial-level effects controlling succession. For plotting
purposes, simple least squares regression models were derived for each
variables and its relationship with both DELAY and RATE.
We also evaluated the interactions of controlling variables. Given
that some of the variables in Table 3.2 were highly correlated, not all of the
variables listed were significant in the presence of other variables.
Therefore, using the correlation matrix of the potential controlling factors on
RATE and DELAY, we identified and used only the most uncorrelated
variables
that
represent
temperature,
precipitation,
radiation,
and
physiography. These included TMIN, SMRTMP, SMRPRCP, CONTPRE,
SRAD, VPD, ELEV, SLOPE, RSP, SOUTHNESS, EASTNESS, and TRMI.
All selected variables were standardized by subtracting the mean value to
53
reduce multicollinearity (Aiken and West, 1991), before being subjected to a
stepwise regression. Even though a log transformation was conducted to
linearize the relationships, exploratory analysis revealed that quadratic
relationships were more appropriate for DELAY with TMIN and ELEV.
Therefore, quadratic terms for TMIN and ELEV were also included. The
significance levels for entering and leaving the model were 0.10 and 0.05,
respectively. However, when a variable was used as part of an interaction
term, it was always included as a main effect regardless of the p-value
(Aiken and West, 1991). All statistical analyses were done in SAS (SAS
Institute, 1999). Statistical results were judged as not significant (p > 0.05),
significant (0.01 <p
0.05), or highly significant (p
0.01).
3.4 RESULTS
3.4.1 General Correlation Patterns
The correlations of DELAY and RATE with the potential controlling
variables were generally low, with only about 25% of the correlations larger
than 0.35 (Table 3.3). For both DELAY and RATE in the CRP, most of the
correlations considered were not significant, and the highest correlations
were 0.35 and -039, respectively. About one-half of the correlations for
both DELAY and RATE in the WCP were significant. The highest
correlations were -0.41 for both successional descriptors. Results across
provinces were similar to those of the WCP for DELAY. However, for RATE,
several correlation values were greater than 0.50.
Table 3.3 Correlation coefficients for the relationships of DELAY and RATE with potential controlling factors
CRP
Ln(DELAY) Ln(RATE)
Climate
Temperature
TDAY
TMAX
TMIN
FROSTDAY
SMRTMP
WTRTMP
Precipitation
PROP
NORAIN
SMRPRCP
WTRPRCP
SMRTSMRP
CONTPRE
Radiation
SRAD
VPD
0 22
0 27
0 04
-0 03
0.13
0.15
0.06
-0 37**
-0 35**
0 01
0 35*
0 19
0.04
0 39**
34**
0.04
** P
0.01; * P
0.05
-0.41 **
0 38**
0 38**
0 37**
-0 39**
0 36**
0.40**
Across Provinces
Ln(DELAY)
Ln(RATE)
0 37**
-0 34**
-0 39**
0 39**
0 71 **
-0 71 **
-0 21 *
0 28**
O.70**
0.01
0.09
0.27**
0.48**
0 26*
0.31**
0.06
0 23*
0 23*
032**
-0 31 **
-003
036**
0.29**
0.58**
0.34**
-0.01
001
002
035**
0.15
-0.23
0.10
-0.19
0.34**
-0.13
0.29
0.16
0.25**
0.19*
-0.16
-0.28
0.15
0.03
-0.31 **
-0.03
0.33**
0.14
0.00
-0.13
0.33**
-0.18
-0.41 **
0.37**
0.09
-0.11
-0.15
0.09
-0.12
-0.13
0.03
-0.09
-0.09
0.25*
0.11
0.31
-0.02
-0 08
-0.02
0.19
0.13
-0.13
0.06
0.14
0.10
0.35*
-0.21
-0.07
-0.31 *
-0.17
0.39*
-0.16
-0.17
0.10
-0.15
0.29
0 64**
0 56**
039**
O.39**
-0.01
-0.09
-0 08
Physiography
ELEV
SLOPE
RSP
SOUTHNESS
EASTNESS
TRANSASP
TRMI
WCP
Ln(DELAY)
Ln(RATE)
0.11
0.01
0.37**
-0.15
0.72**
0.11
0.04
-0.12
-0.05
0.04
0.14
55
DELAY decreased and RATE increased with an increase in all the
temperature variables except FROSTDAY both in the WCP and across
provinces analysis (signs in Table 3.3). No obvious patterns existed for
precipitation and radiation variables. ELEV was the most important
physiographic variable considered (Table 3.3).
Compared to the CRP, there generally was considerable more
variation in the controlling factors within the WCP (Figure 3.2-3.9). As such,
across province patterns were mainly influenced by patterns in the WCP.
Furthermore, in some cases (e.g. DELAY with TDAY and TMAX),
relationships were opposite for the two provinces. However, in these cases
and others, across province relationships were a function of the difference
between the provinces.
In general, for the WCP and across provinces, both DELAY and
RATE showed similar relationships with the potential controlling variables
studied. The notable exceptions were precipitation (PRCP, NORAIN,
WTRPRCP, and CONTPRE) and radiation (VPD) variables, where
relationship for the WCP and across provinces were opposite for both
DELAY and RATE.
The WCP and CRP responded to the controlling factors differently
(Table 3.3). With regard to temperature, both DELAY and RATE showed
stronger correlation with temperature variables (jrjO.34-O.41) in WCP than
CRP. Only SMRTMP (r=O.35) was significantly correlated with DELAY and
56
4
4
0
0
3
0
0
U
0
U
o
00
0
0
o
0
00
0
0
3
00
U
0
0
0
0
U
-' O.
%
U
0
0
7
5
Ii
10
9
12
13
l2
10
14
is
73
is
IMAX cc)
TDAY cC)
b
4
0
00
0
c0O
a
_l
0
0
0
0
0
0
0
0
0
4
3
0
50
TMIN rc
150
100
200
FRO8TDAY day.)
4
0
0
0
0 o0 01°0
0
U
0
0
0
00
U
a
-J
U
0
0
14
15
16
17
SURTUP rci
is
0
2
6
8
WTRTMP (C)
Figure 3.2 Scatter plots of DELAY and temperature variables. Dotted and
solid lines indicate least squares fittings for across and within provinces,
respectively. See Table 3.3 for correlation coefficients and significance
levels. Circles and squares represent samples from WCP and CRP,
respectively.
70
57
3
U
2
o
oc:2
.00 0
jl
o
..0
000 0 8
0
0
0
000
5
00
0
0
.
0
E
0
0
-2
00
0
0
d2o
000
0
0
0
0
-2
6
10
11
13
12
10
14
II
12
13
15
14
TDAY rcj
17
16
10
TMAX (C)
I
b
U
_p.
I
2
J1
o
O
C)
0
A
0
0
-1
0
0
0
0
0
-2
ISO
50
2
3
4
0
5
7
150
200
PNO$TDAY dayi
TMINrCI
d
C
3
2-
2
0
o0
11
.e
-
!
0
0
-1
0
0
0
0
5
0
0
0
0
-1
0
0
-2
-2
2
*4
15
19
18
17
SMATMP
19
10
4
WTRTMP
C)
C)
f
Figure 3.3 Scatter plots of RATE and temperature variables. Dotted and
solid lines indicate least squares fittings for across and within provinces,
respectively. See Table 3.3 for correlation coefficients and significance
levels. Circles and squares represent samples from WCP and CRP,
respectively.
58
4
4
3
0
I
U
I
0
0
0
43D
oO
0
00
0
0
0
a
0
03
0.4
0.5
0.$
0.7
0.6
200
205
210
215
PRCP (c.i)
230
225
220
235
NORAIN (dsyi
b
4-
4
0
o
0
0
0
ID
0
0
a
0r-
0
00
0
as
0
0.15
020
U
025
030
05
I'.-
SI
U
U
.0
0
0
SMRPRCP (cn)
U,
0
0
U
U
II
0.8
WTRPNCP
1.4
)
d
4
0
a
6
0
°0o
0
c*c01
0
I c;.o
oOD a
o
00
0
0
U
C
C
0
oI0o
.
-
a
SO
0
a
a
0
0
o&0
0
0
5
45
65
75
85
95
¶05
115
CONTP#E (%
5MRT5URP rcom
e
f
Figure 3.4 Scatter plots of DELAY and precipitation variables. Dotted and
solid lines indicate least squares fittings for across and within provinces,
respectively. See Table 3.3 for correlation coefficients and significance
levels. Circles and squares represent samples from WCP and CRP,
respectively.
59
:'
2
ji
%:
JI
0
-
0
00
0
-1
0
0
?
08o
0 0%
0
-1
J.
-. _i_
'-p
-U
-000
0
0
ffi
2
-
0
-2
-2
0.1
03
0.5
0.?
0.0
0.5
200
205
215
210
220
225
230
235
NORAIM day,)
PRCP (am)
b
a
3.
2-
00 00
0
Oo
-
0
0c
-.
0
0
0
0
-2
015
020
0.25
05
0.30
14
08
WTRPRCP (oo,)
SMOPRCP (om)
3
2
.l
00
0
.1
0
00
'U
o000
0
0
0
0
-1
0
0
-2
-2
45
55
65
75
05
95
5MRT8M!P rClam)
105
115
125
S
6
7
CONTPRE 1%)
Figure 3.5 Scatter plots of RATE and precipitation variables. Dotted and
solid lines indicate least squares fittings for across and within provinces,
respectively. See Table 3.3 for correlation coefficients and significance
levels. Circles and squares represent samples from WCP and CRP,
respectively.
4
0
3
0
I
0
2
0
ID3
0
U
4 #.;'
U'
8
0
0
0
270
280
290
310
320
500
550
0
.000
k
0
U
300
0
0
0
0
280
0
0
0
600
0
850
8AO (WIm')
700
150
600
850
900
VPD (Pm)
Figure 3.6 Scatter plots of DELAY and radiation variables. Dotted and solid
lines indicate least squares fittings for across and within provinces,
respectively. See Table 3.3 for correlation coefficients and significance
levels. Circles and squares represent samples from WCP and CRP,
respectively.
.
2
U
0
0
0000
0
O
260
0
00
-
0
270
290
290
0 00
0
0
3RAD )WIm')
o0
0
0
0
300
0
0
0
C'
0
0
0
320
500
550
800
650
700
750
800
850
902
VPD (Pm)
Figure 3.7 Scatter plots of RATE and radiation variables. Dotted and solid
lines indicate least squares fittings for across and within provinces,
respectively. See Table 3.3 for correlation coefficients and significance
levels. Circles and squares represent samples from WCP and CRP,
respectively.
61
00
0o
00
0
', 00
I
0
0
00
000
..
0
,.2. "405Y
03
S
S
I
1
0
1
(0
S
:
C
IS
25
10
25
10
30
40
SLOPE (MI
0
0
0
20
5
.I..,rn
0
0
0
3
00
.3
0
--
10
S
00
C
C
0
c
0
0
IS
10
40
(0
RIP
U.0
S
0
00
00
OS
50UT4*ISS0
0
0.00
0
0
0
0
10
A
0
I
.05
.10
00
(0
05
EASThISI
I
05
0
15
20
TPANSASP
0
00
(0
00
.
15
20
25
10
15
40
1RII
Figure 3.8 Scatter plots of DELAY and physiographic variables. Dotted and
solid lines indicate least squares fittings for across and within provinces,
respectively. See Table 3.3 for correlation coefficients and significance
levels. Circles and squares represent samples from WCP and CRP,
respectively.
62
U
0
1°
0
00
0
00
0
00
CC
0
0
0
0
0
B.EY
e
5
TO
TI
20
3
50
30
¶0
SLOPE ('00
)
%
00
0
00
o0o
0
C)
0
0
00
00
50
50
50
00
I
.5
2
00
.00
05
5.0
00
00
TO
1.5
rn-isa,
£500500000
U
o00 0
0
10
-
00
00
0
0
o
C)
0
0
IS
50
'5
50
¶0
Figure 3.9 Scatter plots of RATE and physiographic variables. Dotted and
solid lines indicate least squares fittings for across and within provinces,
respectively. See Table 3.3 for correlation coefficients and significance
levels. Circles and squares represent samples from WCP and CRP,
respectively.
63
all other correlations were either not significant or very weak (r
0.27) in
the CRP. In addition, DELAY in the WCP was negatively correlated with
SMRTMP, while in CRP these were positively correlated.
With regard to precipitation, DELAY generally increased and RATE
decreased with an increase in precipitation in WCP. However, DELAY and
RATE did not respond significantly to precipitation in the CRP.
For radiation, DELAY decreased with an increase in VPD in the
WCP (r=-O.31), while it increased (r=0.31) with increasing VPD in the CRP.
In contrast, RATE increased in an increase in VPD in the WCP (r0.33), but
VPD did not have much influence on RATE in the CRP (r0.15).
Most of the physiographic variables studied were not important
except ELEV for the WCP. However, RATE in CRP was negatively
correlated with slope (SLOPE, r= -0.35) and aspect (SOUTHNESS, r=-0.31;
EASTNESS, r=-0.39). No physiographic factors were important for DELAY
in CRP (In <0.20).
3.4.2 Regression Models
The models for both DELAY and RATE in WCP and CRP were all
weak (r2 = 0.23-0.37) and no precipitation factors were included in the final
models (Table 3.4).
With respect to DELAY, only temperature (TMIN) and physiographic
variables (SOUTHNESS and TRMI) were included in the final model for
WCP (Table 3.4B). No interactions were important for the WCP. For the
64
CRP, in addition to temperature (SMRTMP) and radiation (SRAD), the
interaction of SRAD and RSP were also significant (Table 3.4A).
With respect to RATE, for the WCP, only physiographic variables
(ELEV and RSP) were included in the model (Table 35B). The interaction
of ELEV and RSP was also significant. No climatic variables were in the
final model. For the CRP, only physiographic variables (SLOPE and
SOUTHNESS) were included, but no interactions were important and no
climatic variables were included (Table 3.5A).
The across province model for DELAY was also weak (r20.29), but
the model for RATE was much stronger (2 = 0.57) (Table 3.4c and Table
3.5c). No precipitation factors were included in the models for DELAY and
RATE. For DELAY, only temperature (TMIN and SMRTMP) and radiation
(VPD) were included. Interaction of SMRTMP and VPD was also significant,
and a quadratic term for TMIN was included. For RATE, all of the main
effects included were physiographic variables (ELEV and TRMI). The
interaction of ELEV and radiation (SRAD) was important, but SRAD was not
significant in the model as a main effect.
3.5 DISCUSSION
We studied the influence of various climatic and physiographic
factors on delay of conifer recovery (DELAY) and rate of conifer canopy
accumulation
(RATE)
after
stand
replacement disturbance.
Results
indicated that only a small proportion of variability in DELAY and RATE can
65
Table 3.4 Results of stepwise regression for DELAY
A. CRP (R2 = 037)
Analysis of Variance
Source
DF
Sum of Squares
Model
4
3.0686
Error
5.2596
35
8.3283
C. Total
39
Parameter Estimates
Estimate
Term
1.8129
Intercept
SMRTMP
0.3971
SRAD
-0.0247
RSP
0.0016
RSP*SRAD
-0.0027
Model
Error
C. Total
5
110
115
Parameter Estimates
Term
Estimate
Intercept
1.7007
TMIN
-0.4644
SMRTMP
1.0459
VPD
-0.0117
TM I N*TM IN
0.0815
VPD*SMRTMP
-0.0009
Prob> F
t Ratio
29.34
3.65
-2.12
0.29
-2.98
Prob> itt
<.0001
0.0009
0. 0409
0.7772
0.0052
Mean Square
1.3857
F Ratio
7.0713
0. 1960
Prob> F
0.0003
t Ratio
41.02
-3.74
2.26
2.25
Prob> it
<.0001
Mean Square
1.6971
F Ratio
9.1642
0.1852
Prob>F
Std Error
0.0508
0.0457
0.1135
0.0102
C. Across provinces (R2 = 0.29)
Analysis of Variance
Source
Sum of Squares
DF
F Ratio
5.1050
0.0024
Std Error
0.0618
0.1089
0.0116
0.0057
0.0009
B. WCP (R2 = 0.23)
Analysis of Variance
Source
DF
Sum of Squares
4.1573
Model
3
14.1098
Error
72
18.267 1
75
C. Total
Parameter Estimates
Estimate
Term
Intercept
2.0831
TMIN
-0.1708
SOUTHNESS
0.2563
TRMI
0.0231
Mean Square
0.7672
0.1503
8.4855
20.3707
28.8562
Std Error
0.1166
0.1277
0.3717
0.0047
0.0234
0.0004
0.0004
0.0269
0.0274
<.0001
t Ratio
14.58
-3.64
2.81
-2.50
3.48
-2.08
Prob>Iti
<.0001
0.0004
0.0058
0.0141
0.0007
0.0395
66
Table 3.5 Results of stepwise regression for RATE.
A. CRP (R2=O.25)
Analysis of Variance
Source
DF
Sum of Squares
1.4927
Model
2
Error
4.4222
37
5.9150
C. Total
39
Parameter Estimates
Estimate
Term
1.5582
Intercept
SLOPE
-0.0216
SOUTHNESS
-0.2439
0. 7464
F Ratio
6.2447
0.1195
Prob> F
Mean Square
0.0046
Std Error
0.0547
0.0078
0.0966
t Ratio
28.51
-2.76
-2.53
Prob> ti
<.0001
0.0090
0.0159
B. WCP(R=O.24)
Analysis of Variance
Source
Model
Error
C. Total
DF
3
72
75
Sum of Squares
5.6039
17.2987
22.9026
Parameter Estimates
Term
Estimate
Intercept
0.6219
-0.0010
ELEV
RSP
-0.0073
ELEV*RSP
0.00003
Mean Square
1.8680
0.2402
F Ratio
7.7747
Prob> F
0.0001
t Ratio
10.88
-4.17
-1.85
2.06
Std Error
0.0569
0.0002
0.0039
0.00002
Prob> jti
<.0001
<.0001
0.0683
0.0430
C. Across provinces (R2O. 57)
Analysis of Variance
Source
DF
Sum of Squares
Model
30.0521
4
Error
22.7642
111
C.Total
115
52.8163
Parameter Estimates
Term
Estimate
Intercept
0.86611
ELEV
-0.001177
TRMI
0.0189563
SRAD
-0.000618
ELEV*SRAD
0.0000255
Mean Square
7.5130
F Ratio
36.6341
0.2051
Prob> F
Std Error
0.050095
0.000129
0.006572
0.004662
0.000011
<.0001
t Ratio
17.29
-9.12
2.88
-0.13
2.39
Prob> Iti
<.0001
<.0001
0.0047
0.8947
0.01 85
67
be accounted for by the climatic and physiographic factors both within and
across provinces (r2=O.23-O.37). One notable exception was for RATE
across provinces (r2=O.57).
Patterns across provinces were mainly dominated by patterns in the
WCP, with a few exceptions. For most of the factors studied, the WCP and
CRP had different ranges with the WCP expressing more variation.
Therefore, in most cases, patterns across provinces were dominated by
patterns within the WCP. For those exceptions, for example Figure 3.5a
and 3.7b, both the WCP and CRP showed the same trend along
environmental gradients, but across provinces there were an opposite
trend. In general, the controlling factors had less influence in CRP than in
wcP.
3.5.1 Influence of Climate
The literature suggested that temperature is probably the most
important environmental factor influencing seedling survival and growth in
PNW. Many biochemical and physiological processes are directly
influenced by temperature (Raven and Curtis, 1970; Lavender, 1990;
Sutton 1991). Temperature regime, especially extreme temperatures, had
great influence on seedling survival and growth (Cui and Smith, 1991;
Farnden, 1994). Water deficiency has been reported to occur with low soil
temperature (Goldstein et al., 1985; Carter et al., 1988), even when soil
moisture was not limiting (Farnden, 1994). In addition to reducing water flow
68
through the soil-plant-air continuum, low temperature, especially early in the
growing season, can also limit root and shoot growth and mineral and water
uptake (Lavender and Overton, 1972; Halverson and Emmingham, 1982;
Farnden, 1994). This in turn reduces tree growth during the whole growing
season. Frost is one of the most critical factors affecting seedling survival
and early growth (Stathers, 1989; Butt, 1990; Steen et al., 1990; Farnden,
1994). While extreme low temperature has the potential to cause mortality,
high summer temperature can also cause problems for seedling survival
and growth (Cochran, 1969; Heninger and White, 1974; Stathers, 1989),
especially on south aspects.
The temperature variables examined in this study could be classified
into three general groups representing average (TDAY), low (TMIN,
WTRTMP, and FROSTDAY), and high (TMAX and SMRTMP) temperature
for a stands. Our results indicated that temperature was the most
influencing
climatic factor for development
of conifer cover.
Low
temperature variables were more closely associated with DELAY in WCP,
while high temperature variables had more influential effects on DELAY in
CRP. The main reason for this phenomenon is that the temperature
regimes for WCP and CRP are different. Generally, CRP has higher
temperature than WCP (Figure 3.2), resulting
in
different extreme
temperature conditions for WCP and CRP. Through those possible
mechanisms described above, effects of low temperature were more
69
evident in WCP, while effects of high temperature were more evident in
CRP. This suggests that to reduce the DELAY length and to enhance
seedling survival and growth, different planting seasons may be necessary
for WCP and CRP. Fall planting would be more appropriate for CRP, while
spring planting would be more appropriate for WCP.
Temperature did not exhibit the same effects on RATE as on
DELAY. For both the WCP and CRP, RATE increased with increasing
temperature (Table 3.3, Figure 3.3). This was not unexpected. In this study,
RATE was represented by weighted mean absolute growth rate (Table 3.1).
Since less weight was given to growth rate during the early period, the
growth rate especially during the delay period has less influence on RATE.
Therefore, the influence of extreme temperature was minimized in RATE.
This also suggested that extreme temperatures were less likely to exert
influence on established seedlings. After the delay period, established
seedlings were able to deal with extreme temperature conditions more
successfully, and increased temperature generally results in more active
physiological processes, hence increased growth rate.
Generally, even though precipitation has an important influence on
vegetation distribution, it is not a limiting factor for DELAY and RATE in the
studied region. The effects of radiation are exerted mainly through
controlling other important factors for establishment and growth of trees
such as air and soil temperature, precipitation, and soil moisture.
70
Correlation analysis revealed that there were trends of increasing
DELAY and decreasing RATE with increasing precipitation (Figure 3.4 and
3.5). The exact reason for this pattern is unclear, but we think this might be
due to several factors. First, precipitation was not a limiting factor for
DELAY and RATE. Second, the correlation results may have been
confounded by other more important factors. For example, increased
precipitation would increase soil water content, which in turn affects soil
warming due to the high volumetric heat capacity of water (Stather and
Spittlehouse, 1990), resulting in cold soil.
Weather conditions during planting and immediately after planting
may also be important contributors to successional variation, especially for
DELAY. It has been suggested that most mortality occurred during the first
growing season, and much of this
is
probably caused by extreme
environmental conditions. However, in this study, due to lack of detailed
information about planting season for the sample stands, these more time
specific variables could not be used in this study.
35.2 Influence of Physiography
It is known that forest regeneration is slower for high elevation
stands than low elevation stands. In the PNW, forest regeneration is
generally less successful as elevation increases (Strothmann, 1979; Minore
and Dubrasich, 1981) with the exception of southern and warmer sites
(Halverson and Emmingham, 1982). The main effect of elevation
is
71
manifested through the decreasing gradient of temperature associated with
increasing elevation. The cooler temperatures and shorter growing seasons
associated with higher elevation were reflected in forest growth, with slower
growth at higher elevation. In addition to the effects on plant physiological
processes through the temperature regime, the decreasing daily average
temperature with increased elevation may also result in spring-time delay of
plant development (Ogilvie, 1969), which in turn affects the overall growth
rate. Even though there were no strong patterns for DELAY and RATE in
the CRP, we did observe that DELAY increased and RATE decreased with
increasing elevation for WCP and across provinces. This agrees with the
observation that effects of elevations are manifested through effects on
temperature regime.
Slope and aspect interacted with each other to affect stand radiation
and temperature. On south aspects, steeper slopes would increase the
radiation interception, while
on
north facing
slopes,
interception
is
dramatically decreased. The direct effects of aspect and slopes have
implications not only for temperature, but indirect effect on ecosystem
processes such as photosynthesis and moisture regimes. Since south
aspects have more incoming solar radiation, sites with southern aspects will
have earlier reduction
in
soil moisture contents resulting in improved
conditions for soil warming. However, extreme high surface temperature is
more likely on south-facing slopes, especially for low elevation stands. It
72
has been reported that conifer establishment following harvest is often more
difficult to ensure on south- and west-facing slopes than on north- and east-
facing slopes (Seidel 1979; Strothmann 1979; Graham et al. 1982; Minore
etal. 1982; Ferguson etal. 1994).
In this study, neither slope nor aspect was significantly correlated
with succession except for RATE in CRP (Table 3.3). Conifer cover
accumulation was slower on south facing slopes than north facing slopes in
CRP. Even though aspect was not a significant contributor in WCP, the
regression model for DELAY indicated that together with TMIN and TRMI,
aspect (SOUTHNESS) was also significant.
Soil moisture is the key to tree growth and development, as virtually
every aspect of plant morphology and physiology is highly correlated with
soil water stress (Zahner, 1968; Kramer and Boyer, 1995). In this study,
TRMI was used as a surrogate for soil moisture content and results
indicated that RATE increased with TRMI in the WCP and CRP, but not for
across provinces.
35.3 Degree of Variations Explained
The regression models developed in this study explained: 37% of the
variability in DELAY and 25% of the variability in RATE for CRP; 23% of the
variability in DELAY and 24% of the variability in RATE for WCP; 29% of
the variability in DELAY and 57% of the variability in RATE across
provinces.
73
The large proportion of unexplained variability could be caused by
numerous factors, including stochastic factors, and lack of explanatory
information.
Several factors related to successional descriptors and
potential controlling factors may also have resulted in variability that could
not be explained. These include errors from photo interpretation, imperfect
fitting of the Chapman-Richards growth function, errors in the Daymet
database, and errors in the DEM data.
A large amount of variability in vegetation recovery is caused by
stochastic factors (Franklin et al., 1985; Vitousek and Walker, 1987;
Halpern and Franklin, 1990; del Moral and Bliss, 1993). These include
temperature variation right after planting, natural seed regeneration from
the surrounding forest, etc. All these factors could influence forest
succession in the study area.
Perhaps equally, or more important are variations caused by biotic
and management related factors, which were not included in this study.
Biotic interference or enhancement, site preparation, planting techniques,
planting season, and condition of planted seedlings are all important factors
influencing conifer development. These factors constitute unaccounted
source of errors in our analysis.
Biotic factors
Competition from associated vegetation has a significant influence
on seedling survival and growth (e.g., Newton and Preest 1988; Coates et
74
al.
1991; Farnden 1994; Whitehead and Harper 1998). Above-ground,
competition can occur for growing space and light, while below-ground,
competition can occur for soil moisture and nutrients (Haeussler and
Coates 1986). Severe competition can lead to death or suppressed growth
for unsuccessful competitors. However, associated vegetation does not
always exert negative effects on seedling survival and growth. The
vegetation of a site can also facilitate seedling survival by protecting
seedlings from either frost or excessive solar radiation, by adding soil
organic matter and nutrients, or by stabilizing soil on steep slopes,
especially in extreme environments (Farnden 1994).
Mycorrhizal fungi play an important role in the establishment and
survival of tree seedlings (Amaranthus and Perry, 1987). Mycorrhizal fungi
enhance nutrient and water uptake while increasing disease resistance for
the host plants. Absence of mycorrhizal fungi from where they used to exist
(for example, caused by death after forest clearcut) may lead to depletion in
nutrients, organic matter, water holding capacity and physical structure
(Perry et al. 1989). Detailed information on the effects of mycorrhiza fungi
can be found in Wright and Tarrant (1958), Parke (1982), Rose (1983),
Parke etal. (1984), Amaranthus etal. (1989), Read (1991), Mallik (2003).
Interaction with animals can also be important in seedling survival.
Grazing from herbivores can offset tree growth, but can also cause seedling
mortality (Stein 1995). Gashwiler (1971) found that insect damage can
75
cause significant seedling mortality in the H. J. Andrews Experimental
Forest in western Oregon.
It
is unlikely that the biotic factors described above would have
caused the same effects on DELAY and RATE. We hypothesize that the
effects of mycorrhizal fungi would be more evident on DELAY and effects of
vegetative competition would be more evident on RATE. The negative
effects of animal damage will have more effect on DELAY than on RATE,
while damage from insects could affect both DELAY and RATE.
Management factors
Site preparation, planting and stand management practices can have
significant effects on seedling survival and growth. Historically, in the PNW,
broadcast burning is usually applied to logging slash to reduce fire hazard,
to facilitate planting, and to reduce competition from shrub species.
However, reduced fuel loading reduces future organic matter and nitrogen
input to the burned stand, resulting in greater soil instability. In addition,
intensive burning also exposes newly planted seedlings directly to
environmental extremes, which may result in seedling mortality due to
increased moisture stress (Ferguson et al. 1994). Broadcast burning can
also influence the persistence of mycorrhizal fungi on disturbed stands.
Wright and Tarrant (1958), Parke et al. (1984), and Amaranthus et al.
(1989) suggested that mycorrhizal fungi
in
burned clearcuts
(and
76
particularly in severely burned sites) can be greatly reduced relative to
undisturbed or unburned soils.
The physiological condition of planted seedlings directly affects their
survival. Sites planted with poor seedlings are more likely to have
regeneration failures (Axeirood etal. 1998). In addition, planting time during
the year can also have negative or positive effects on seedling survival
(Strothmann, 1979).
For some planted stands, thinning and fertilization may be applied to
improve tree growth. Fertilization can improve site quality by adding a new
source of nutrients to the soil. Thinning is a common silvicultural practice
which removes some trees within a stand to provide more growing space
for the remaining trees. All these management related factors would
contribute to the variation in DELAY and RATE.
In general, we expect most of these management-related factors
have more influence on DELAY than on RATE, except thinning and
fertilization. Factors such as site preparation, planting time etc. have
influence on seedling survival, hence DELAY.
3.6 CONCLUSIONS
No simple regression model can fully explain the variation in forest
succession in western Oregon. In this study, only a small proportion of
variation in DELAY (time to reach 5% canopy cover) and RATE (weighed
mean absolute growth rate over the enitry growing period) of early
77
secondary
forest succession
could
be
explained
by
climatic
and
physiographic factors. We hypothesize that the unexplained variation was
mainly due to stochastic, biotic, and management factors. To investigate
controlling
factors for early forest succession,
including
biotic and
management factors would help to understand succession processes.
Research on the effects of controlling factors through a more mechanistic
model could give more insight on early forest successional process.
The length of delay tends to be longer in severe environmental
conditions, especially extreme temperature. The controlling effects of
climatic and physiographic factors were different for WCP and CRP. In part,
this was caused by the fact that the WCP and CRP occupy different regions
along an environmental gradient. DELAY in the WCP was influenced more
by low temperature than by high temperature, while DELAY in the CRP was
influenced more by high temperature than by low temperature. This has
some important silviculture implications. For example, to reduce the DELAY
for forest succession, spring planting is more appropriate for WCP, while fall
planting is more appropriate for CRP.
78
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86
Chapter 4 Conclusions
The
research
reported
in
this
dissertation
forest
examined
succession from the perspective of changing life forms after stand
In this research,
replacement disturbance.
I
sought to accomplish two
tasks: (1) characterize the patterns of forest succession in western Oregon;
and
(2)
examine
the
relative importance
of various
climatic
and
physiographic factors on the observed patterns of forest succession.
I
studied forest succession from the perspective of changing life-
forms instead of the traditional species composition change. For the
purpose of characterizing successional
patterns,
successional pathways were defined in this study.
approach
to
characterize
successional
patterns
seven early forest
I
developed a new
based
on
photo-
interpretation and modeling with a Chapman-Richards growth function. This
new approach makes it possible to compare data sets for stands of different
origin and to analyze data more robustly when there are no replicates.
Results from photo-interpretation and canopy cover modeling
indicated that forest succession after stand replacement disturbance varied
greatly across the region studied.
I compared successional patterns in the WCP with those in the CRP
using derived parameters from the Chapman-Richards growth function. The
CRP and WCP not only differed in succesional pathways, but also in term
of successional rates. With respect to successional pathways, it is more
87
likely for a stand to return to closed broadleaf forest conditions in the CRP
than in the WCP, whereas shrub and semi-closed pathways were more
common in the WCP than in the CRP. Conifer regeneration in the WCP
generally had a longer delay and lower rate of development, compared to
the CRP.
These findings regarding the successional pathways and trajectories
have important implications for other ecological research. An average rate
of forest succession is commonly assumed in landscape modeling. From
the perspective of carbon dynamics, a faster succession rate is more
beneficial, as it can quickly offset losses of carbon from decomposition.
However, I have demonstrated that there exists a wide range of rates and
pathways over a region. Harmon (2001b) has demonstrated that during
secondary succession total carbon stores usually decrease for some time
until increases in the live pool can offset the losses from the detritus and
soils pools. Figure 4.1 shows ranges in total carbon store dynamics using 5
hypothetical scenarios for the CC pathway with different rates of conifer
accumulation. Given this range, regional carbon dynamics could be directly
influenced by patterns of live biomass change after stand replacement
disturbance. Successional pathways developed in this study can be used to
estimate the potential amount of biomass, while successional rate can be
used to estimate the rate of live biomass changes. The results from this
88
study can be linked with a regional carbon store modeling framework, such
as STANDCARB (Harmon et al., 1996).
Total Carbon Store Dynamics After Stand Replacement Disturbance
500
450
400
C.,
300
250
200
150
100
50
0
0
50
100
200
150
300
250
Time (years)
Figure 4.1 Simulated total carbon store dynamics after stand replacement
disturbance. Each line represents one of 5 hypothetical forests with a
different successional rate. Modified from Carbon Cycling Simulation Model
(Harmon, 2001).
The implications of the findings
in
this study for biodiversity
conservation are different from carbon dynamics. Large variation
in
successional pathways and rates is more likely to enhance biodiversity than
a homogeneous succession pattern. Different successional pathways and
rates
after stand
replacement disturbance
would
create
a
more
heterogeneous landscape, which could theoretically maintain more species.
89
To understand controls on early forest succession processes,
I
examined the relationships of climatic and physiographic factors with the
successional descriptors: DELAY and RATE. Correlation analysis indicated
that temperature was one of the most important factors for both DELAY and
RATE. In general, conifer cover accumulation was faster with higher
temperature. However, temperature effects in the CRP and WCP were
different for DELAY. DELAY in the CRP increases with temperature,
especially at high temperature, whereas in the WCP, DELAY decreases
with an increase in temperature, especially at low temperature. Precipitation
and radiation were not as important explanatory factors as temperature.
Of all the physiographic factors considered, elevation was the most
important. All other physiographic factors did not have a major influence on
forest succession, in terms of DELAY and RATE. The influences of
physiographic factors were probably through their influence on temperature,
and possibly through effects on soil moisture.
The variability explained by climatic and physiographic factors was
relatively low. Regression analysis explained 23%, 37%, and 29% of the
variation in DELAY, and 24%, 25%, and 57% of the variation in RATE, for
the WCP, the CRP, and across provinces analysis, respectively. No
interactions among climatic and physiographic factors were found for
DELAY in the WCP, or RATE in the CRP. For DELAY, relative slope
position (RSP) and solar radiation (SRAD) interacted in the CRP, vapor
90
pressure (VPD) and summer temperature interacted in across provinces.
For RATE, elevation (ELEV) and RSP interacted in the WCP, while ELEV
and SRAD interacted across provinces. The unexplained variability could
be caused by data quality in photo-interpretation and statistical derivation of
successional descriptors. Stochastic, biological, and management related
factors are also likely important for the unexplained variation.
There are several logical extensions of this study. The relations
established in this study
for DELAY and RATE with climate and
physiography were not very strong except for RATE across provinces. The
mode(s developed in this study may have limited application in regional and
landscape modeling directly, but from this study we confirmed that forest
succession after stand replacement disturbance is a variable and complex
process. No simple model can adequately explain variations
in
forest
succession. The controlling effects appeared to be more localized over the
landscape. Future study on forest succession should focus more on the
biotic and management-related factors.
The models developed in this study, while they indicate reasonable
controls, may not be very useful for prediction purposes. To provide useful
information for regional and landscape modeling, it may be better to make
observations directly. Aerial photos are valuable source of information for
observing forest succession, but these photos are not always available over
the entire landscape and have infrequent intervals. In addition, photo
91
interpretation is a very time consuming process.
In
this study, forest
succession was interpreted as a change in life-forms manifested through
canopy cover changes, which have also been monitored successfully by
satellite remote sensing technology (Cohen et al., 1998; Woodcock et al.,
2001). Hence, we suggest that successional processes could be more
efficiently observed over the landscape with satellite-based remote sensing.
For example, examining spectral trajectories from historical MSS, Landsat
TM, and ETM+ data could be used to help monitoring early forest
succession and provide knowledge about forest succession over the
landscape.
The findings from this study are important for understanding early
forest succession in western Oregon. More importantly, they suggest some
opportunities for further research. The suggestions listed are only a few that
I will pursue in the future.
92
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