Spatial Point Processes and Brown Creepers (Certhia americana):
Estimating Habitat Use of a Silvicultural Experiment
Maria Mayrhofer
A thesis submitted in partial fulfillment of the
requirements for the degree of
Master of Science
University of Washington
2006
Program Authorized to Offer Degree:
College of Forest Resources
University of Washington
Graduate School
This is to certify that I have examined this copy of a master’s thesis by
Maria Mayrhofer
and have found that it is complete and satisfactory in all respects,
and that any and all revisions required by the final
examining committee have been made.
Committee Members:
David A. Manuwal
John M. Marzluff
Mark S. Handcock
Date:________________________________________
In presenting this thesis in partial fulfillment of the requirements for a master’s
degree at the University of Washington, I agree that the library shall make its copies
freely available for inspection. I further agree that extensive copying of this thesis is
allowable only for scholarly purposes, consistent with “fair use” as prescribed in the
U.S. Copyright Law. Any other reproduction for any purposes or by any means shall
not be allowed without my written permission.
Signature________________________________________
Date________________________________________
TABLE OF CONTENTS
Page
List of Figures...............................................................................................................iii
List of Tables................................................................................................................iv
Introduction ...................................................................................................................1
Methods ......................................................................................................................... 4
Study Area and Design............................................................................................ 4
Field Methods.......................................................................................................... 5
Brown Creeper Monitoring ............................................................................... 5
Nest Monitoring................................................................................................. 7
Quantifying DEMO Resources................................................................................ 7
Brown Creeper Territories................................................................................. 7
Landcover .......................................................................................................... 7
Contrast-weighted Edge .................................................................................... 8
Distance to Nest................................................................................................. 8
Linking Bird Locations to Stand-level Resources................................................... 9
Spatial Point Process Model.............................................................................. 9
Describing Stand-level Resource Use .............................................................12
Results ......................................................................................................................... 14
Use of Stand-level Resource Attributes ................................................................ 14
Nest Placement and Success.................................................................................. 16
Discussion.................................................................................................................... 17
Implications for Use of Harvested Forest Patches ................................................ 17
Implications for Nesting at Edges .........................................................................19
Evaluation of the Spatial Point Process Model ..................................................... 21
Conclusions ........................................................................................................... 22
i
Page
Bibliography ................................................................................................................40
Appendix A: Sample Code for RUF Computation in Program R ............................... 53
Appendix B: Fitting the Spatial Point Process Model................................................. 54
Appendix C: Bark Arthropods as Food Abundance.................................................... 59
ii
LIST OF FIGURES
Figure Number
Page
1. DEMO variable forest retention design................................................................... 24
2. Brown Creeper with color-taped retrices ............................................................... 25
3. Brown Creeper territories ........................................................................................ 26
4. Nesting success on DEMO treatments and surrounding forest ...............................27
5. Nesting success at 85m edge definition................................................................... 28
6. Nesting success at 100m edge definition................................................................. 29
iii
LIST OF TABLES
Table Number
Page
1. Land cover class descriptions.................................................................................. 30
2. Definition of contrast-weighted edge ...................................................................... 30
3. Average estimated RUF for Brown Creepers at DEMO study area........................ 31
4 a-d. Standardized RUF coefficients..........................................................................33
5. RUF of birds nesting on and around the 40% aggregated treatments .....................36
6 a-c. Comparing RUF coefficients and nesting success ............................................ 37
7. Nesting success and territory productivity .............................................................. 39
iv
ACKNOWLEDGEMENTS
I would like to thank my source of funding, the USDA Forest Service, PNW Research
Station, Forestry Sciences Laboratory, Olympia; especially Keith Aubry and Charlie
Peterson for their continued support and faith in my study until completion.
There were many people whose invaluable contributions aided in the success of my
thesis project: The graduate students of the wildlife sciences lab, especially Dr. Robert
Gitzen for his friendly, untiring, and invaluable analytical input; also Scott Horton, Nick
Palazzotto, and Sara Gregory. Dr. Charlie Halpern, Shelly Evans, and Rick Abbott for
support in various aspects of the DEMO study; my field research assistants Bobby J.
Cummins, Michelle Wilcox, Renee Ripley, Zed Rulen, Abram Burkholder, Laura
Palasz, Raphael Demers, and Brianna Gary for their aid in the collection of the field
research data; Taro Narahashi for GIS support; Sally Lee for extensive assistance in
high speed data entry and Korean tea.
Special thanks to Dr. Juraj Halaj who conducted the entomological component of this
study by looking at food availability for Brown Creepers in form of bark-arthropods.
Deepest gratitude to my family in Germany for their continuous loving assistance
during the long years of my education abroad; and to Sameh Farah for his faith in me,
his understanding and boundless support.
Last, I would like to acknowledge wholeheartedly my graduate committee David
Manuwal, John Marzluff, and Mark Handcock, whose guidance and support enabled
this valuable learning experience. I am indebted to my graduate advisor Dr. Manuwal,
who provided untiring assistance for the many aspects and problems of this study and
always lend a kind ear in times of troubles. Dr. Handcock, who spent countless hours
with the development of the RUF computation code which is the analytical core of this
study, his invaluable addition of statistical details to the methods section, and for
technical support 24/7. Dr. Marzluff for his insightful guidance, constructive criticism
and a revolutionary way of looking at wildlife sciences and research.
v
1
Introduction
Forest managers are challenged to develop and apply new silvicultural
approaches to sustain biodiversity and productivity of forest ecosystems (Hansen 1995,
Kohm and Franklin 1997, Lindenmayer and Franklin 2002). Ecologically sustainable
management must integrate multiple and often conflicting objectives to meet the needs
of all forest organisms and maintain ecological processes, many of which are not well
understood (Kohm and Franklin 1997). Silvicultural techniques, such as retention of
old trees, snags and logs, can help to introduce or maintain the structural complexity in
managed forests that is inherent to natural forests.
Variable density retention methods are increasingly being adopted by ecological
silviculturists (Kohm and Franklin 1997). This type of retention can make substantial
contributions to biodiversity conservation by the preservation of characteristics of latesuccessional forests (Aubry et al. 1999, Halpern et al. 1999). Effective implementation
of this goal requires a thorough understanding of how changes in forest structure affect
ecological processes and community development. The U.S. Forest Service-sponsored
Demonstration of Ecosystem Management Options (DEMO) study is an attempt to
provide the needed empirical evidence to evaluate consequences of green-tree retention
in the Douglas-fir region of the Pacific Northwest United States (Aubry et al. 1999,
Halpern et al. 1999). From an ecological standpoint the DEMO design provides a
unique platform to study species and habitat interactions at scales relevant to forest
management.
Previous studies on experimental silvicultural techniques in Oregon (Hagar et
al. 1996, Chambers et al. 1999) and Canada (Beese and Bryant 1999, Lance and
Phinney 2000, Schieck et al. 2000, Tittler et al. 2001) have reported positive effects on
bird community composition and abundance with increasing retention of forest legacy
components. However, recent reviews of forest fragmentation and thinning point out
that most studies merely recorded changes in bird abundance but failed to investigate
2
underlying ecological mechanisms (Sallabanks et al. 2000, McGarigal and Cushman
2002). The understanding of biological processes that determine avian population
viability can provide insight on the effects of various silvicultural treatments and help
to make inferences on forest management strategies (Marzluff et al. 2000).
The Brown Creeper (Certhia americana) is a small, forest-dwelling songbird
that obtains its diet of bark arthropods by foraging primarily on the boles of mature
coniferous trees in the western United States (Mariani and Manuwal 1990, Weikel and
Hayes 1999, Hejl et al. 2002). The Brown Creeper occurs most commonly in mature
and old-growth forests with a continuous canopy cover and trees of large size classes
(Carey et al. 1991, Gilbert and Allwine 1991, Lundquist and Mariani 1991, Hansen et
al. 1995). They are sensitive to habitat fragmentation, forest thinning, and the creation
of forest edges (Rosenber and Raphael 1986, Hobson and Bayne 2000a, b, Hutto 2000,
Tittler et al. 2001, Anderson and Crompton 2002). A close relative to the Brown
Creeper, the Eurasian Treecreeper (Certhia familiaris) in Finland, is considered an
umbrella species for old-growth forest ecosystems and an indicator species for the
effects of forest management activities (Kuitunen and Mäkinen 1993, Virkkala et al.
1994, Huhta et al. 2004, Suorsa et al. 2005).
The availability of adequate nest sites in the form of snags might constitute a
limiting factor to the abundance of a cavity nesting bird such as the Brown Creeper
(Cline et al. 1980, Mannan et al. 1980, Raphael and White 1984). The snags used by
cavity-nesting birds are often located in the center of dense tree clumps, surrounded by
large diameter trees and snags (Saab and Dudley 1988). Creepers generally create their
nest cavity under slabs of peeling or sloughing bark in large diameter (>50cm) snags
and trees showing advanced decay or heart rot (Carey et al. 1991, Hejl et al. 2002).
Trees with these characteristics occur in older forests. Forest management may affect
creepers directly by reducing tree density and removing snags (Mannan et al. 1980), or
indirectly by creating edge and reducing interior forest conditions (Hobson and Bayne
2000a, Brand and George 2000, 2001). Food availability, such as bark arthropods,
3
might change due to the effects of forest harvest activities on microclimatic regimes
(Huhta 1971, Hoekstra et al. 1995, Moore et al. 2002). Furthermore, songbird nests
located close to forest edges are more likely subject to nest predation in some settings
(Sandström 1991, Marzluff and Restani 1999, Brand and George 2000). Bird nests in
forests are under predation pressure from a large and diverse community of predators
(Walters and Miller 2001, Bradley and Marzluff 2003).
The causal ecological mechanisms that underlie the Brown Creeper’s habitat
needs have not been well-studied (Hejl et al. 2002). Various habitat factors can
influence bird abundance such as suitable nest sites, presence of predators, food
abundance and the quality of foraging sites (Marzluff et al. 2000). The experimental
green-tree retention treatments of the DEMO study can affect these factors to different
degrees (Lehmkuhl et al. 1999). Specific habitat resources can be variously important
to the animal and can constitute an ultimate threshold that determines the presence or
absence of a bird species (Kernohan et al. 2001, Marzluff et al. 2001). A DEMO postharvest assessment of forest bird communities indicated a significant decline of Brown
Creeper densities with increased forest harvest compared to the control units (Leu et al.,
in preparation). Consequently, the Brown Creeper is a good indicator species for
evaluating the response to various forest harvest treatments of the DEMO study.
Analyzing the variable use of resources that a bird utilizes in its home range can
provide insights into the functional weight of specific habitat attributes.
This study is part of a larger investigation of the trophic interactions between
bark-gleaning birds, arthropods and vegetation conditions within the context of the
DEMO research. A separate study investigates the effects of green-tree retention on the
abundance and guild composition of corticolous arthropods as potential food resource
for bark-gleaning birds (Halaj, in preparation). My study concerns the effects of
variable retention harvest on avian resource utilization of the retained green-trees and
snags within the DEMO experimental research units. Specifically, the objectives of my
study are to: (1) explain the application of a new computational device to analyze
4
spatial point patterns in the light of animal resource use, and (2) relate this new method
to the use of stand-level resources by the Brown Creeper.
Methods
Study Area and Design
The Demonstration of Ecosystem Management Options (DEMO) study
consisted of a randomized block design with six forest harvest treatments that were
replicated in six geographic locations (blocks), four in southwestern Washington and
two in southwestern Oregon. My study utilized the experimental blocks Watson Falls
and Dog Prairie located in the Umpqua National Forest in the Cascade Range of
southwestern Oregon. Watson Falls lies within the western hemlock (Tsuga
heterophylla) forest zone, Dog Prairie within the white fir (Abis concolor) zone
(Franklin and Dyrness 1988). The Douglas-fir (Pseudotsuga menziesii) dominated
forest units were all beyond the stem exclusion phase, ranging in age from 110-165
years with a disturbance history of thinning at Dog Prairie and salvage logging at
Watson Falls (Halpern et al. 1999). The study blocks were situated in a landscape
matrix of mature and old-growth upland forest interspersed with variable sized patches
of harvested forest of diverse management history.
Each of my two study areas had six experimental harvest retention treatments
which differed in the level and spatial dispersion of retained trees (Aubry et al. 1999,
Halpern et al. 1999): 100% retention, 75% aggregated retention, 40% and 15%
dispersed retention, and 40% and 15% aggregated retention units (Figure 1). The 40%
and 15% aggregated retention treatments contained one hectare-sized, circular forest
retention patches (five and two aggregated patches, respectively). The 75% retention
treatment was not utilized in this study. In all harvested areas 6.5 snags per ha were
created by girdling existing dominant or co-dominant green trees. Each treatment unit
was 13 ha in size and had a permanent grid installed with 40 m spacing (for more
5
detailed information on the DEMO experiment please see the special issue of
Northwest Science, Volume 73, 1999).
Field Methods
Brown Creeper Monitoring. – During the breeding season of 2003 and 2004
(May through July), I captured and color-banded 42 male Brown Creepers that
established a territory within or in the vicinity of the five DEMO treatment units at the
Watson Falls and Dog Prairie blocks. Male Brown Creepers were caught with mist-nets
by attracting them with territorial call-backs and bird decoys. Each bird was banded
with a US Fish and Wildlife Service aluminum leg band and with two colored leg
bands. To improve individual bird identification in the field the creeper’s two central
retrices were also color-marked using duct tape in the same color as the leg band
combination (Figure 2). Determination of age, sex, weight, body fat and plumage
condition followed Pyle (1997).
Throughout the breeding season all Brown Creepers were spot-mapped using
the existing grid system to determine the location and size of their territories at the
DEMO treatment units (Bibby et al. 1992, Ralph et al. 1993). In addition, I also used
GPS units (Garmin Etrex Venture) to map Brown Creeper movements beyond the
DEMO experimental units. This method ensured that the entire Brown Creeper territory
was mapped. The GPS locations were accurate within 5-12 meters, points whose
recorded precision was >12 meters were deemed inaccurate and not recorded. Each of
the treatment units was visited every 2-4 days for a total of about 23 visits per season
starting shortly before sunrise until 12.00 – 14.00 h PST. Special care was taken that all
birds were sampled equally in terms of early and late morning sessions throughout the
season to prevent a sampling bias of relocation points for specific times of the day
(Garton et al. 2001). I did not collect data in the afternoon or evening when bird activity
was minimal. To avoid detection bias in treatments with the greatest visibility (little
forest retention, aggregate edges, nest trees, and favored perches), I placed special
6
emphasis on sampling areas more frequently that were away from edges and had higher
tree density. I specifically covered the forest that surrounded the treatment units to
make sure I recorded any habitat use outside the treatment units.
The Brown Creeper is a highly mobile species that can visit any part of its
relatively small territory (2.3-6.4 ha, Hejl et al. 2002, and 12 ha, Suorsa et al. 2005)
within a matter of seconds. Its foraging behavior is systematic (Davis 1978, Weikel and
Hayes 1999, Hejl et al. 2002): the bird lands at the lower section of the tree bole and
works its way upward foraging on bark arthropods. Once the tree crown area is reached
the creeper flies off and starts foraging at the bottom of another tree trunk located often
within only a few meters of the previous tree. Given these biological traits, I found that
the birds would be most adequately sampled by recording every observed location of
the marked males and their mates regardless of the time between relocations (Kernohan
et al. 2001). Obtaining a representative sample for an accurate record of animal
resource use is more important than to worry about statistical independence of
relocation data (Otis and White 1999, Millspaugh and Marzluff 2001). However, care
was taken that a tree location was only recorded once per sampling visit as Brown
Creepers sometimes utilized the same trees repeatedly for foraging, especially trees in
the vicinity of the nest. I never recorded any activity on the actual nest tree. To
represent resource selection of a single or paired Brown Creeper during the breeding
season, I recorded the following observations: foraging, singing, food collection for
feeding the mate or nestlings, and the gathering of nesting material. Since females were
unbanded and the Brown Creeper is not sexually dimorphic, I only recorded data when
the identity of the bird was assured by other clues (presence of marked male, use of
nest tree etc.). Field data were collected up to two weeks post-fledging as the family
groups stay within the territory for early fledgling care. The territories only break down
late in the fledgling period (Davis 1978).
Nest Monitoring. – I conducted nest searches within the DEMO treatment units
and the surrounding forest for Brown Creeper nests. The nesting progress was checked
7
every 4-7 days. To determine the breeding status and reproductive success, I calculated
the Vickery Index for every territory that was established in the study area, including
territories without nests and unpaired males (n=55; Vickery et al. 1992). All Brown
Creeper nest locations were recorded using GPS units in the field. The nearest distance
of the nest to edge was measured using ArcView GIS 3.2a software. Edge was defined
as the boundary between different types of forest cover i.e. between the surrounding
forest and a harvested treatment unit, or between the retained circular forest patches and
the adjacent cleared areas within the aggregated retention units. Roads were not
considered as edge-creating habitat and their presence was ignored (Ortega and Capen
2002). The roads leading to the treatment units were small and unimproved and usually
ran parallel to a treatment boundary.
Quantifying DEMO Resources
Brown Creeper Territories. – All relocations were merged for each bird
territory and geo-referenced using ArcView (Figure 3). I only constructed territories for
Brown Creepers that made at least one nesting attempt (n=42); birds that never nested
or their nest was not found were not considered (n=12). At least 30, but preferably
more than 50 observations are recommended for an accurate construction of fixed
kernel home range estimates (Seaman et al. 1999, Kernohan et al. 2001). A minimum
of 30 locations explained >75% of space use for Steller’s jays home ranges during the
breeding season on the Olympic Peninsula, Washington (Marzluff et al. 2004). I
created all Brown Creeper home ranges using at least 50 observations, the average
sample size was 118 ± 41 points.
Landcover. – I quantified forest vegetation of the DEMO study area by creating
a classified land cover grid in ArcView. I used a base map of the DEMO harvest units
obtained from the US Forest Service, Diamond Lake Ranger District. The cover classes
represent the different harvest levels within the DEMO treatments and the 100% cover
of the forest surrounding the experimental units (see Table 1 for a description of the
8
cover classes). I chose a land cover grid resolution of 8 meters as a compromise
between correctly addressing the grain of resources selection (Wiens 1989) by the
Brown Creeper without adding excessive computing burden by constructing a more
detailed grid. As a preparation for the computation in program R (R Development Core
Team 2005) I generated sampling points with cover class values attached using the
ArcView extension FOCAL PATCH (Marzluff et al. 2004).
Contrast weighted Edge. – The edge contrast between adjacent land cover
classes can differ depending on the forest classes of these neighboring patches. The
degree of the edge contrast can influence various effects on organisms and the
environment (McGarigal and Marks 1995). I specified a contrast-weighted edge to
reflect a potential sensitivity of the Brown Creeper to increased forest harvest among
the DEMO treatments (Table 2). The amount of edge contrast between adjacent forest
patches of the DEMO study area can be assigned by defining weights between 0 (no
contrast) and 1 (maximum contrast). Utilizing the moving window function of the
program FRAGSTATS 3.3 I attached contrast-weighted edge values to my 8 m land
cover grid (McGarigal et al. 2002). The sampling points for the edge grid were
generated once more with extension FOCAL PATCH in ArcView. The contrastweighted edge file was further processed in program R where the covariate was
normalized to a maximum value of 1.0 for each bird.
Distance to Nest. – I included the covariate distance to nest to examine the
potential influence of nest location on the habitat use of Brown Creepers within their
territory. The covariate was created in program R as the Euclidian distance from the
nest in meters for each breeding pair.
9
Linking Bird Locations to Stand-level Resources
Spatial Point Process Model. – A new method relating habitat resources to
animal space use, the Resource Utilization Function (RUF), uses the animal’s
relocation points and a fixed kernel home range estimator to relate a probabilistic
measure of use to habitat resources (Marzluff et al. 2004). The coefficients of the
regression function estimate the degree of resource use by the animal within its home
range. In this thesis I employ the same conceptual framework, but use an alternative
statistical measure of the RUF. To link my observed bird locations to the various
resources at the DEMO study area, I modeled them as a spatial point process (Diggle
2003). These models are fitted using the spatial point process package SPATSTAT that
operates in the statistical computation program R (Baddeley and Turner 2005). The
models that I computed for Brown Creeper relocation data incorporate spatial trends,
spatial resource covariates and the interaction between the location points (see
Appendix A for the computation code used in R and Appendix B for a graphic output
of the fitted model). The process describes both spatial inhomogeneity (‘trend’) and the
stochastic dependence between points such as spatial clustering (‘interaction’;
Baddeley and Turner 2000). The argument trend can specify the dependence on spatial
covariates such as landcover classes or contrast-weighted edge. The conditional
intensity is a function of both, the spatial trend (and/or spatial covariate effects) and the
dependence between points (Baddeley and Turner 2005).
Explicitly I model the n observed locations x = ( x1 , …, xn ) as a realization
from a utilization distribution (UD). The UD is modeled as a spatial point process with
probability density fUD ( x;θ ) with respect to Poisson process of rate 1. A Poisson
process of rate 1 represents uniform utilization at the rate of 1 utilization per square
meter. The parameter θ = ( β , γ ) represents the coefficients of the covariates ( β ) and
the interaction parameters ( γ ). The model for fUD ( x;θ ) is expressed through the
(Papangelou) conditional intensity of utilization at location u :
10
λUD (u; x, θ ) =
fUD ( x1 , …, xn , u;θ )
fUD ( x;θ )
which is interpreted as the conditional probability of utilization at u given the rest of
the bird’s utilization. For homogeneous and inhomogeneous Poisson processes the
conditional intensity and the intensity function coincide. In this paper I model the
conditional intensity by:
λUD (u; x,θ ) = e β
T
Z (u )
γ S (u;x )
where Z (u ) is the q-vector of covariates for the location u and S (u; x ) is the spatial
interaction function. The parameter coefficient ( β k ) of the k th covariate is interpreted
in a way similar to logistic regression. Consider the probabilities of utilization at a
location u under two different resource utilization functions fUD ( x;θ ) . The two
functions are identical, except the second function has the value of the k th covariate one
unit higher than in the first at location u . Then the ratio of the probabilities of
utilization at location u is e βk . This holds the utilization at points other than u fixed. In
simple sense, e βk is the effect of a unit change in the covariate holding the other
covariates and utilization fixed.
The package SPATSTAT utilizes a maximum pseudolikelihood method
(MPLE) for model fitting instead of the maximum likelihood estimator, which allows
for a rapid fitting of a large variety of models (see Geyer 1999 in defense of MPLE, and
Baddeley and Turner, 2000, 2005). It is possible to compute maximum likelihood
estimates using a Markov Chain Monte Carlo (MCMC) algorithm, but this was not
regarded as being worth the additional computation burden. MCMC methods do not
allow the fast and flexible fitting of a wide range of models such as the MPLE can
achieve.
The spatial point interaction is modeled as a Geyer saturation process. Under
this model the total contribution to the potential from each point (from its pairwise
11
interaction with all other points) is limited to a user-defined maximum value s (the
saturation threshold) within a spatial radius r of the point (Geyer 1999). Explicitly,
n
S (u; x ) = ∑ min(s, #{i :| u − xi |< r})
i =1
The threshold and radius parameters define the shape of the point interaction in
the same way the kernel function defines the shape of the interaction of the model in
Marzluff et al. (2004). They are specified by the user. Specifically, for the models in
this thesis I use a range of 35m and saturation threshold of 5. With both radius and
threshold fixed the Geyer process is part of the exponential family of distributions. In
contrast to linear and nonlinear regression models, models of the exponential family
have the advantage that they do not need to satisfy the assumption of the response
variable following the normal distribution (Myers et al. 2002). The limiting effect of
the saturation threshold in the clustering region of the spatial point pattern caps the
probability density of the model (Geyer 1999). Areas of intense use will exhibit strong
clustering of points which can in part be due to autocorrelated data. Hence, the
conservative approach of Geyer’s saturation threshold in areas of high point density
ensures model robustness to spatial autocorrelation.
The user-defined interaction radius r acts like a moving window that examines the
animal’s point locations in correlation to the underlying habitat resources (covariates)
and in relationship to all other points of the bird’s point pattern. In rapid succession this
window will make each bird’s location the center cell of its focal point and examine the
pairwise point interactions between the focal and the nearest neighbor points within the
radius of the window. Thus the Geyer model relates the probability density of used
space to the associated resource covariates in a spatially explicit way. This interaction
model serves the same purpose as the spatial covariance model used by Marzluff et al.
(2004). The interaction model has a further parameter γ which reflects the strength of
the interaction. It serves the same role as the bandwidth in the kernel method. If γ = 1
the model is a Poisson spatial process with the number of observed locations in each
12
spatial area following a Poisson distribution, and independence of resource utilization
between disjoint spatial areas. If γ > 1 the resource utilization is correlated between
spatial areas that are close, leading to a clustering of resource utilization above and
beyond that implied by the covariates. If γ < 1 the resource utilization is also correlated
between spatial areas that are close, but in this case the resource utilization follows a
more regular pattern. This spatial interaction parameter is estimated jointly with the
coefficients of the covariates. The advantage of the spatial point process approach is
that it jointly estimates the coefficients of the covariates and the spatial interaction of
the relocations, and a separate model fit is not required.
The model output of the Geyer saturation process is the estimated intensity
function that depicts resource use similar to the concept of the RUF (Marzluff et al.
2004). The RUF quantifies differential resource use within the animal’s home range by
relating a probabilistic measure of use, the utilization distribution, to habitat resources.
The home range is defined by a 99% fixed kernel estimator. The Geyer saturation
process does not utilize the kernel method but estimates the home range with the fitted
Geyer intensity function of the bird’s relocation points. Note that this range will contain
100% of the observed locations, and hence 100% of the observed resource utilization
and homerange. Hypothetically, further or more intense observation of the bird would
have lead to further locations and a probable extension of the observed homerange. The
model adapts to this by estimating the theoretical home range. The moving window
analysis of the spatial point process is confined within the boundaries of this estimated
home range. This is analogous to Manly and McDonald’s Type III design (Manly et al.
2002) where the use of resources is measured separately for each animal within its
home range and therefore defines the individual bird as the sampling unit.
Describing Stand-level Resource Use. – The degree of dependence on spatial
covariates is indicated by the coefficients of the fitted RUF. The magnitude of the
coefficient indicates the importance of the covariate for resource utilization. The
13
positive and negative prefix of the coefficient depicts if use will increase or decrease
with a change in the quantity of the resource attribute. Since each bird was regarded as
an independent sampling unit I was able to generate an average RUF for all nesting
pairs of Brown Creepers at the DEMO study area. I also created average RUFs for
subgroups of birds that had a comparable choice of resource attributes in their home
∧
ranges (Table 3). I pooled and averaged the unstandardized coefficients ( β *) and
estimated the variance for each individual bird’s resource use following the methods
outlined in Marzluff et al. (2004).
Bootstrapping simulations computed the standard deviation for each covariate
(SDcovariate), the standard error (SEcovariate), and the overall RUF standard deviation
(SDRUF) for each bird’s RUF estimate. Using SDcovariate and SDRUF I standardized the
estimated resource coefficients for a more equal comparison of relative importance of
resource attributes. I tested the null hypotheses that the average standardized
∧
coefficients ( β ) per bird group is not different from 0 using t-tests at the 0.05 alpha
level including the sample variance of the mean (Zar 1999, Marzluff et al. 2004). The
∧
significance of the β –value and the proportion of Brown Creepers whose use was
either positively or negatively correlated with the resource coefficients indicate the
consistency in resource selection at the population level (Table 4a-e).
I constructed two average RUFs for Brown Creepers that nested on the 40%
aggregated treatment units (n=3) vs. birds that chose to nest in the forest surrounding
these treatments (n=9; Table 5). I only created average RUFs comparing nest locations
between treatment and surrounding forest for the bird group utilizing the area of the
40% aggregated treatments, because overall only few birds chose to nest on the DEMO
treatment units (n=6).
I tested if Brown Creepers showed a preference for nest location either on a
DEMO treatment unit or in the forest surrounding the treatment (birds nesting on the
control units were excluded). I used the Fisher’s Exact test (one-tailed; Zar 1999) for
14
count data to assess the null hypothesis that nests placed in the forest surrounding any
treatment unit were more successful (Figure 4). I was also interested if edge was a
factor in nest placement, where edge was characterized as the border area within a
forest patch with continuous canopy cover (can be an aggregated forest patch or
surrounding forest) adjacent to an area with reduced cover due to harvest treatments.
The edge effect on nest placement was defined at two distances based on a priori
knowledge: <85 m (Brand and George 2001) and <100m (Kuitunen and Mäkinen
1993). Using a 1-sample proportions test with continuity correction at the 0.05 alpha
level (Zar 1999) I tested the null hypothesis if the proportion of nest site selection is
equal for nesting (a) on vs. off treatment units and (b) at edge vs. away from edge. In
addition, I used the Fisher’s Exact test (one-tailed) to assess the null hypothesis that
nests placed away from edge had a higher proportion of fledging success (Figure 5
and 6).
I standardized RUF coefficients for groups of nesting Brown Creepers that
either placed their nest within <85m of edge vs. nested >85m away from edge (Brand
and George 2001). The birds are further grouped by successful and unsuccessful nesters
and their mean coefficients tested for difference using a 2-sample t-test at α=0.05
(Table 6a-c; Zar 1999). I did not create average RUFs for birds nesting >100 from edge
(Kuitunen and Mäkinen 1993), because the sample size would have been too small
(n=2 for successful and n=1 for unsuccessful nesters).
Results
Use of Stand-level Resource Attributes
A combined total of 54 male Brown Creepers set up their territories in the
DEMO study area in the breeding seasons of 2003 (n=25) and 2004 (n=29). For this
paper I restricted the spatial point process analysis to territories of paired Brown
Creepers who attempted to nest at least once per season (n=42). The average estimated
15
RUF coefficients show that all birds avoided the use of the clearcut area compared to
the use of an equal area in the surrounding forest; this is true at the 40% as well as at
∧
∧
the 15% aggregated treatments ( β *= -2.42 and β *= -0.85, respectively; Table 3). Ttests of the estimated standardized RUF coefficients confirm a highly significantly
negative association with clearcut area (p<<0.001 for all groups; Table 4a-c). All
∧
Brown Creeper are positively associated with the use of edge habitat ( β *= 0.28), but
not significantly so (p=0.24). However, use of edge is significant for birds that placed
their territories around the 40% aggregated retention treatments (p=0.004). The average
RUF for all Brown Creepers shows that 40% and 15% retained forest patches are used
∧
less then an equal area in the surrounding forest far away from edge ( β *= -0.74).
Conversely, the standardized coefficient for birds at the 40% aggregated unit indicate
significantly positive use of the 40% forest patches (p=0.004). Use of 15% forest
patches is negative though (p=0.91). A similar pattern of habitat use is indicated by the
unstandardized vs. standardized coefficients of the thinned DEMO treatments. The
average unstandardized coefficient for all birds shows less use of thinned DEMO
treatments than the use of an equal area in the surrounding forest far away from edge
∧
( β *= -0.25). Specifically, the standardized coefficient indicates a negative association
with the 15% dispersed retention treatments, but not significantly so (p=0.10). Then
again, use of the 40% dispersed retained forest is positive, but also not significantly
(p=0.49).
The DEMO control units were located adjacent to other retention treatments and
thus there was some edge habitat present at parts of the unharvested units. However,
every Brown Creeper who nested at the control units was neither positively nor
∧
negatively influenced in habitat use by this resource attribute ( β *= 0). All groups of
birds show a weak clustering in the distribution of their spatial point pattern
∧
(Interaction: β *>0), indicating that resource utilization is correlated between spatial
areas that are close. The distance to nest covariate is significantly important for all
Brown Creepers (p<<0.001). With increasing distance from the nest the use of a
resource in that area declines.
16
The Brown Creepers nesting in the forest surrounding the 40% aggregated units
∧
use the clearcut area less ( β *= -2.82) than the creepers nesting on the treatment (β*= 1.24; Table 5). Forest-nesting birds also use the aggregated patches less compared to an
∧
equal area of forest interior habitat ( β *= -0.94), whereas this relationship is reversed
∧
for birds nesting on the treatment ( β *= 0.86). In addition, treatment-nesting creepers
∧
∧
use edge habitat more ( β *= 1.26) than forest nesting birds ( β *= 0.86).
There was no significant difference between any of the pooled standardized
RUF coefficients when comparing the two groups of all Brown Creepers that placed
their nests either within <85m or >85m away from edge (Table 6c). Neither were there
any significant differences between the coefficients for the four subgroups of successful
vs. unsuccessful nesting birds (Table 6a-b). Although the comparison was not
significant, birds that nested successfully within <85m from edge were negatively
∧
associated with the use of aggregated forest patches ( β = -0.04) and used edge habitat
∧
less ( β =0.01) vs. unsuccessful nesters showed a positive association with patchy
∧
∧
habitat ( β =0.12) and used edge more ( β =0.08). A similar pattern can be observed
when comparing the two groups of birds nesting >85m from edge: the successful
∧
∧
nesting creepers did not use patchy habitat ( β =0) and used edge less ( β =0.07), but the
∧
unsuccessful birds had a positive coefficient for use of forest patches ( β =0.17) and
∧
edges ( β =0.17).
Nest Placement and Success
I located a total of 48 Brown Creeper nests in the DEMO study area for the
combined breeding seasons of 2003 and 2004. This total also includes second nesting
attempts of 6 Brown Creeper pairs. The average Vickery rank and nesting success for
all nests and territories was much higher in the 2003 season than in 2004 (Table 7). The
Vickery rank for all territories at my DEMO study area was 3.9 ± 1.4, the average
nesting success was 54%. Six out of 48 nests were located at a treatment unit (4 at 40%
Aggregated, 1 at 40% Dispersed and 1 and 15% Aggregated); all other nests were
17
placed in the forest surrounding the treatments. Brown Creepers chose to nest outside
of treatments with high significance (p<<0.001, 1-sample proportions test). Nests
placed in the surrounding forest were not more successful than nests on treatment units
(p=0.36, Fisher’s Exact test; Figure 4). When edge was defined within <85m of the
forest patch boundary 33 nests out of 48 were placed near the edge area (of which 18
fledged successfully, 15 unsuccessfully), and 15 nests were located away from edge (of
which 8 were successful, 7 unsuccessful; Figure 5). When edge was defined within
<100m then 43 out of 48 nests were found at edge (21 successful, 22 unsuccessful), and
only 5 nests were found in forest away from edge (5 successful, 0 unsuccessful; Figure
6). At <85m edge definition significantly more Brown Creepers chose to nest in the
edge area than in the forest (p=0.01, 1-sample proportions test). At <100m edge
definition this result is highly significant (p<<0.001). The Fisher’s Exact test indicates
that at an edge definition of <85m, nests placed away from edge did not have a
significantly higher nesting success than nests placed at edges (p=0.7). However, at the
<100m edge definition nests away from edge were significantly more successful
(p=0.04).
Discussion
Implications for Use of Harvested Forest Patches
The fitted Geyer intensity function estimates the use of stand-level resources by
individual birds. All Brown Creepers showed a higher use of the 40% aggregated forest
patches and the edge habitat created by the various forest retention harvests, than the
use of the surrounding forest away from edge. This observation is contrary to the
classification of the Brown Creeper as a forest interior specialist that avoids edge and is
sensitive to habitat fragmentation (Hobson and Bayne 2000a, b, Hutto 2000, Tittler et
al. 2001, Anderson and Crompton 2002). However, literature review shows inconsistent
responses of Brown Creepers to forest fragmentation in North America (Hejl et al.
18
2002), and also for the Eurasian Treecreeper in Northern Europe (Kuitunen and Helle
1988). Observations of edge avoidance might not be due to a sensitivity to edge rather
than a response to differences in habitat types, as edge often consists of different
vegetation than forest interior habitat.
The preferential use of small forest fragments and edges at the DEMO sites
might be due to a higher abundance of bark arthropods as potential food source.
Changes in food abundance can affect bird productivity and survival (Hutto 1990,
Smith and Rotenberry 1990, Pettersson et al. 1995, Suorsa et al. 2004, Virkkala 2004).
A concurrent study at the DEMO sites estimated that there were significantly more bark
arthropods at the forest harvest treatments than at the control units, with significantly
higher abundance at the 15% retention units than the 40% retention (Halaj, in
preparation; also see Appendix C). Forest harvest creates changes in the microclimate
and understory vegetation that is often favorable to arthropod abundance (Shure and
Phillips 1990, Chen et al. 1993, Bedford and Asher 1994, Peltonen et al. 1997,
Heithecker and Halpern, in press). Thus habitat fragmentation due to small scale forest
retention harvests, such as the 40% aggregated DEMO treatments, might provide
attractive habitat to Brown Creepers due to increased food availability. However, the
retention patches must consist primarily of mature, medium to large diameter trees, as
these are the preferred foraging substrate of Brown Creepers, and a high density of
large snags as nesting substrate (Mariani and Manuwal 1990, Carey et al. 1991, Weikel
and Hayes 1999, Hejl et al. 2002). Old-growth and mature trees offer a larger amount
of arthropods than smaller trees.
In contrast to the positive association with the 40% aggregated and dispersed
DEMO resources, breeding pairs of Brown Creepers are negatively associated with the
15% aggregated and dispersed treatments. In addition, all Brown Creepers significantly
avoid the clearcut areas of the 40% and 15% aggregated retention units. The near
absence of creepers in the clearcut areas is not a surprise as the bark-foraging birds are
tree-obligates. The 15% retention units might constitute a threshold at which thinned
19
and fragmented habitat begins to loose its attractiveness to the Brown Creepers despite
elevated levels of food abundance. A stand with low tree density can increase traveltime between food patches (tree boles) and thus negatively affect foraging energy
expenditures (Perrins and Birkhead 1984, Franzreb 1985, Stephens and Krebs 1986).
Brown Creepers and Treecreepers have been found to be either absent or in modest
densities at low forest retentions and clearcuts (Hansen et al. 1995, Hutto and Young
1999, Tittler et al. 2001, Anderson and Crompton 2002, Suorsa 2005). In Finland,
Treecreepers were found not to be much affected by changes in habitat configuration
(more patchiness, more edge), but rather by habitat loss itself (loss of old-growth
forests, loss of large diameter trees; Suorsa 2005). This observation might explain the
mixed response of Brown Creepers in various fragmentation studies where the effects
of habitat fragmentation and habitat loss might have been confounded.
Implications for Nesting at Edges
The Brown Creepers at the DEMO study area relied heavily on the forest
surrounding the treatment units for nest placement (42 out of 48 nests). Although the
retention treatments might be attractive for foraging, they are poorly suited for nesting.
For all birds, the use of DEMO resource attributes declined sharply with distance from
nest. The activity of breeding Brown Creepers appears to concentrate in the area close
to the nest location (Davis 1978, Kuitunen and Mäkinen 1993, Hejl et al. 2002). To
optimize foraging energetics, central place foraging theory suggests the use of all trees
in minimum possible distance in a circular area around the nest (Stephens and Krebs
1986, Kuitunen and Mäkinen 1993). As Brown Creepers prefer mature trees for
foraging and large diameter snags for nesting, naturally they preferentially nested in the
continuous, mature forests surrounding the DEMO treatment units. However, a
significant number of the birds chose to place their nests in the surrounding forest area
close to the treatment edges within <85m and <100m, even though there were portions
of the bird’s territory far away from edge (Figure 4). This is contrary to an estimated
edge sensitivity of 85m for the relative density of Brown Creepers in coast redwood
20
forest patches in Northern California (Brand and George 2001). Experiments with nest
boxes determined a distance of a 100m from edge as the first nest site choice by
Treecreepers in boreal forest patches of southern Finland (Kuitunen and Mäkinen
1993). The increased food abundance in form of bark arthropods in edge habitat and
thus better foraging energetics might explain this preference of nest locations close to
forest edges (Shure and Phillips 1990, Kuitunen and Mäkinen 1993, Bedford and Asher
1994, Peltonen et al. 1997; Halaj, in preparation).
Bird nests in forests are under predation pressure from a diverse community of
predators and nests close to edge are more likely subject to predation (Sandström 1991,
Brand and George 2000, Walters and Miller 2001, Bradley and Marzluff 2003).
However, creeper nests I observed that were placed at the forest edge were not less
successful than nests >85m from the edge. Nests placed >100m from the edge were
significantly more successful, although the sample size was small (n=5). The overall
nesting success for both field seasons was 54% (63% in 2003 and 48% in 2004). The
lower rate of nesting success in 2004 was probably due to unusually cold and wet
weather conditions in the spring compared to the previous year. Brown Creepers nest
early in the season and are susceptible to cold and rainy weather conditions for
survivorship and breeding success (Hejl et al. 2002, Huhta et al. 2004).
The abundance and community composition of potential nest predators depends
on the type and quality of the habitat and the influence of the surrounding landscape
(Andren 1992, Tewksbury et al. 1998, Marzluff and Restani 1999, DeSanto and
Willson 2001, Rodewald 2002, Huhta 2004). The degree of nest predation at habitat
edges situated in a forested landscape, such as the DEMO study area, can be less than
predation rates at edges in proximity to agriculture and urban settlements. Settings in
the wildland-urban interface can provide habitat for a large and abundant predator
community and thus contribute to higher predation pressure. In contrast, the average
nesting success and territory productivity at my study area was very close to Brown
Creepers nesting in the suburban landscape around Seattle, Washington (54% and 59%,
21
respectively, Table 7; Blewett and Marzluff 2005). In this fragmented habitat, Brown
Creepers were found to breed successfully and had similar densities to populations in
managed mature and old-growth forests situated in the wildlands of the Olympic
Peninsula. However, the birds were most abundant at the suburban study sites that
contained the highest percent of forest cover and largest patch sizes. Brown Creepers
were not present at built portions of the sites which indicates their dependency on
forested areas. Large areas of remnant continuous forest around the urban region might
serve as a population source for the Brown Creepers in the fragmented, suburban
landscape.
Brown Creepers can breed successfully in the moderately fragmented landscape
such as the DEMO study area. A matrix of continuous, mature forest with large trees
and snags is important for nesting and foraging, as the harvest treatments are not
suitable for nesting. The 40% aggregated retention treatment appears to be most
attractive for foraging Brown Creepers and for the placement of their nests in the
nearby forest around the harvested units.
Evaluation of the Spatial Point Process Model
The spatial point process model estimates resource use by fitting an intensity
function directly to the animal’s relocation points. This model is an advancement of the
Marzluff et al. (2004) approach as it can fit the RUF without separately deriving the
animal’s utilization distribution. Another advantage is that the Geyer process
automatically caps the probability density of the model in areas of highly clustered
observations and thus exhibits robustness to spatial autocorrelation. As the Geyer
process model is a member of the exponential family, the fitted data do not need to
meet the normality assumption.
The model performs well in estimating resource use within small territories
such as the Brown Creeper’s. It is a good tool for describing the variable response of
22
individual birds to the micro-fragmentation induced by the DEMO harvest treatments.
The statistical computation program R and the spatial point process package
SPATSTAT are readily available and free of charge (Baddely and Turner 2005). The
SPATSTAT package allows a fast and flexible way of fitting a variety of models for
spatial data analysis. Thus spatial point processes are a valuable and economical aid for
the evaluation and management of habitat resources.
Simulations of the fitted Geyer intensity function indicate a good model fit (see
Appendix B for a graphic example of simulations). However, there is a need for more
vigorous tests that assess the performance and proper fit of the various spatial point
process models. The construction of RUFs for Brown Creepers in the DEMO study
area requires the incorporation of several resource covariates into the model. An
extensive computing knowledge of program R is necessary to create a code that can
handle the construction of complex animal-resource models utilizing the package
SPATSTAT and other additional applications in R. The creation of such a code can be
demanding on personnel resources. The availability of sample code applied to resource
utilization studies can be of great help (see Appendix A for the code used in this study).
Baddely and Turner (2005) provide an extensive manual about the application of the
SPATSTAT package. A great advancement to the applicability of this spatial point
process approach would be the development of a user-friendly GUI surface that allows
a quick plug in of spatial data and the construction of resource covariates without the
necessity of extensive computer programming. A good example is the program
FRAGSTATS for the analysis of landscape metrics (McGarigal et al. 2002).
Conclusions
(1) Spatial point processes, specifically the Geyer saturation point process
model, are an effective tool for the analysis of animal resource use in a spatially explicit
way and are proficient in detecting differential use in small home ranges such as Brown
Creeper territories.
23
(2) The 40% aggregated forest retention is attractive habitat for foraging Brown
Creepers, although the birds depend on a landscape setting of continuous, mature forest
to provide suitable nest sites.
24
100 %
75 %
40 %
15 %
Aggregated
Dispersed
Figure 1: Design of the six variable forest retention treatments at the DEMO study area.
The percent values indicate the level, and the black color the pattern of retained forest
cover (adapted from Leu et al., in preparation).
25
Figure 2: Banded Brown Creeper with color-taped retrices (indicated by the arrow).
26
Figure 3: A total of three birds established territories at the 40% aggregated retention
treatment at the Dog Prairie block during the season of 2003. Each color represents a
different bird, the dots are the relocations, and the yellow square is the location of the
nest. The green surface correspond to areas of 100% forest cover, the brown color
indicates harvested sections. The five green circles represent the 40% retained,
aggregated forest patches; each patch is about 100m in diameter. The three brown
circles represent the harvested forest patches of the adjacent 75% retention treatment.
27
35
30
16
25
20
Unsuccessful
Successful
15
10
5
0
17
4
2
Treatment
Forest
Figure 4: Successful and unsuccessful nests of birds that nested either on any of the
DEMO treatment units (n=6) or nested in the forest surrounding the treatment (n=33).
I tested the hypothesis that nesting success would be higher for nests placed in the
surrounding forest (Fisher’s Exact test, p=0.36).
28
35
30
25
15
20
Unsuccessful
Successful
15
10
18
5
7
8
0
Edge
No Edge
Figure 5: Successful and unsuccessful nests of birds that nested close to the edge
(<85m) around the DEMO treatment units (n=33) and that nested in the forest interior,
away from edge (>85m, n=15). I tested the hypothesis that nesting success would be
higher for nests placed >85m away from edge (Fisher’s Exact test, p=0.56).
29
45
40
35
30
25
22
Unsuccessful
Successful
20
15
10
5
21
0
5
Edge
No Edge
0
Figure 6: Successful and unsuccessful nests of birds that nested close to the edge
(<100m) around the DEMO treatment units (n=43) and that nested in the forest interior,
away from edge (>100m, n=5). I tested the hypothesis that nesting success would be
higher for nests placed >100m away from edge (Fisher’s Exact test, p=0.04).
30
Table 1. The land cover classes represent the different harvest levels within the DEMO
treatments and the 100% cover of the forest surrounding the experimental units. The
control units were considered as the same 100% cover class as the surrounding forest.
Land Cover Classes
100% forest cover surrounding each treatment
40% forest cover of dispersed retention
15% forest cover of dispersed retention
Five circular forest patches within the 40% aggregated retention unit
Two circular forest patches within the 15% aggregated retention unit
Clearcut area within 40% aggregated retention unit
Clearcut area within 15% aggregated retention unit
Table 2. Definition of contrast weights per interface of two different land cover classes.
The range of weights is between 0 (no contrast) and 1 (maximum contrast; for
calculations and definitions see McGarigal et al. 2002). The contrast-weighted edge
was specified to reflect a potential sensitivity of the Brown Creeper to increased forest
harvest among the DEMO treatments.
Landcover Interface
Contrast
Weights
Surrounding Forest to Clearcut Area at 15% Aggregated Retention
1.0
Surrounding Forest to Clearcut Area at 40% Aggregated Retention
0.8
Surrounding Forest to 15% Dispersed Retention
0.6
Surrounding Forest to 40% Dispersed Retention
0.4
40% Dispersed Retention to Clearcut Area at 15% Aggregated Retention
0.3
15% Dispersed Retention to Clearcut Area at 15% Aggregated Retention
0.2
Table 3. Average RUF for all nesting pairs of Brown Creepers and for subgroups of birds with comparable choices of forest
∧
resource attributes in their territory during the breeding season in the DEMO study area. The estimates of unstandardized RUF( β *)
coefficients are listed with their standard error (SE) in parenthesis. The SE indicates uncertainty in the estimated resource use by
each individual Brown Creeper. A positive coefficient indicates that use increases with increasing quantity of the resource.
Group
Estimates of RUF coefficients (SE)
∧
All Brown
Creepers (n=42)
∧
∧
β * CWED5
β * Distance Nest
β * Interaction6
-6.02 (0.01)
-1.75 (0.40)
-0.74 (0.31)
-0.25 (0.15)
0.28 (0.02)
-1.11 (0.001)
0.1 (0.001)
β * Forest (Intercept)
β *Clearcut 40%A
β * Patches 40%A
n/a
β * CWED
β * Distance Nest
β * Interaction
-5.94 (0.04)
-2.42 (0.93)
-0.94 (0.14)
-
0.96 (0.05)
-1.19 (0.01)
0.06 (0.001)
β * Patches 15%A
n/a
β * CWED
-1.08 (1.43)
-
-0.15 (0.03)
β * Forest (Intercept)
-6.59 (0.03)
∧
∧
β *Clearcut 15%A
-0.85 (0.54)
∧
∧
∧
∧
∧
∧
∧
∧
β * Distance Nest
-0.70 (0.003)
∧
∧
∧
β * Interaction
0.15 (0.001)
∧
β * Forest (Intercept)
n/a
n/a
β * Thinned 40%D
β * CWED
β * Distance Nest
β * Interaction
-6.35 (0.04)
-
-
0.05 (0.01)
0.12 (0.04)
-1.08 (0.005)
0.16 (0.001)
β * Forest (Intercept)
n/a
n/a
β * Thinned 15%D
β * CWED
β * Distance Nest
β * Interaction
-5.81(0.04)
-
-
-0.47 (0.43)
0.12 (0.15)
-1.17 (0.01)
0.07 (0.001)
∧
∧
∧
∧
∧
25
31
15% Dispersed
(n=11)
∧
β * Thinned4
∧
40% Dispersed
(n=8)
∧
β * Patches3
∧
15% Aggregated
(n=9)
∧
β * Clearcut2
∧
40% Aggregated
(n=12)
∧
β * Forest (Intercept)1
Table 3 (continued)
∧
100% Control
(n=6)
∧
∧
∧
β * Forest (Intercept)
n/a
n/a
n/a
β * CWED
β * Distance Nest
β * Interaction
-5.14 (0.06)
-
-
-
0 (0)
-1.55 (0.01)
0.09 (0.06)
1
Forest = represents the forested area surrounding the treatment unit
Clearcut = clearcut area at the 15% and 40% aggregated harvest treatments
3
Patches = retained forest patches at the 15% and 40% aggregated harvest treatments
4
Thinned = evenly retained trees at the 15% and 40% dispersed harvest treatments
5
CWED = Contrast-weighted edge
6
Interaction = indicates if the spatial point pattern displays clustering in its distribution
2
32
33
∧
Table 4a-e. Estimates of standardized RUF coefficients ( β ) for all nesting pairs of
Brown Creepers and for four subgroups of birds with comparable choices of forest
resource attributes in their territories. The null hypothesis tests that the average β=0 per
group of birds at α=0.05% (p-values in bold font indicate significance). The magnitude
of the β-value shows the relative importance of the resource. Consistency in resource
selection at population level is indicated by significance of β and the proportion of
birds whose use was either positively or negatively correlated with the resource.
(a) All Nesting Brown Creepers at the DEMO Study Area.
All Resources
at the DEMO Study
Area
Surrounding Forest
1
1
Mean
Standardized
∧
β
95%
Confidence
Interval
P-value
Ho: β =0
Number of birds
associated with
resource (n=10)
+
-
-2.77
(-3.28) - (-2.40)
<<0.001
0
10
Clearcut Area2
-0.40
(-0.52) - (-0.29)
<<0.001
0
20
Retained Patches3
0.04
(-0.09) - (0.17)
0.53
12
7
Thinning4
-0.10
(-0.28) - (0.07)
0.23
7
12
CWED5
0.07
(-0.05) - (0.19)
0.24
23
16
Distance to Nest
-0.78
(-0.95) - (-0.64)
<<0.001
2
37
Surrounding Forest = represents the forested area surrounding the treatment unit
Clearcut Area = clearcut area at the 15% and 40% aggregated harvest treatments
3
Retained Patches = retained forest patches at the 15% and 40% aggregated harvest treatments
4
Thinning = evenly retained trees at the 15% and 40% dispersed harvest treatments
5
CWED = Contrast-weighted edge
2
34
Table 4 (continued)
(b) Brown Creeper Group at the 40% Aggregated Retention Treatment.
DEMO Resources
at Treatment
40% Aggregated
Mean
Standardized
∧
β
95%
Confidence
Interval
Number of birds
P-value associated with
Ho: β =0 resource (n=10)
+
-
Surrounding Forest
-2.24
(-2.81) - (-1.68) <<0.001
0
12
Clearcut Area
-0.64
(-0.65) - (-0.27)
<<0.001
1
11
40% A. Patch
0.08
(0.24) - (0.42)
<<0.001
8
4
CWED
0.22
(0.09) - (0.36)
0.004
10
2
Distance to Nest
-0.63
(-0.91) - (-0.35)
<<0.001
1
11
(c) Brown Creeper Group at the 15% Aggregated Retention Treatment.
DEMO Resources
at Treatment
15% Aggregated
Mean
Standardized
∧
β
95%
Confidence
Interval
Number of birds
P-value associated with
Ho: β =0 resource (n=8)
+
-
Surrounding Forest
-3.14
(-3.93) – (-2.34) <<0.001
0
9
Clearcut Area
-0.33
(-0.46) – (-0.19)
<<0.001
1
8
15% A. Patch
-0.01
(-0.26) – (0.24)
0.91
6
3
CWED
-0.01
(-0.20) – (0.18)
0.90
4
5
Distance to Nest
-0.50
(-0.72) – (-0.29)
<<0.001
1
8
35
Table 4 (continued)
(d) Brown Creeper Group at the 40% Dispersed Retention Treatment.
Mean
Standardized
Number of birds
P-value associated with
Ho: β =0 resource (n=8)
+
-
β
95%
Confidence
Interval
Surrounding Forest
-2.58
(-3.76) – (-1.40)
0.001
0
8
40% D. Retention
0.07
(-0.17) – (0.31)
0.49
5
3
CWED
0.05
(-0.11) – (0.21)
0.45
4
4
Distance to Nest
-0.68
(-1.00) – (-0.36)
0.002
1
7
DEMO Resources
at Treatment
40% Dispersed
∧
(e) Brown Creeper Group at the 15% Dispersed Retention Treatment.
Mean
DEMO Resources
Standardized
at Treatment
∧
15% Dispersed
β
95%
Confidence
Interval
P-value
Ho: β =0
Number of birds
associated with
resource (n=9)
+
-
Surrounding Forest
-3.18
(-4.39) – (-1.96)
<<0.001
0
11
15% D. Retention
-0.18
(-0.44) – (0.05)
0.10
2
9
CWED
0.05
(-0.14) – (0.24)
0.57
6
5
Distance to Nest
-0.74
(-0.87) – (-0.60)
<<0.001
0
11
Table 5. Average estimated RUF for breeding pairs of Brown Creeper that nested in the area of 40% aggregated treatment
(n=12). Nine birds nested in the forest surrounding the treatment and three birds nested on the treatment. For information on
interpretation of coefficients and SE please see caption of Table 1.
Birds with comparable
resource choice
Estimates of RUF coefficients (SE)
∧
β *Forest (Intercept)
∧
β *Clearcut 40%A
∧
∧
∧
∧
β *Patches 40%A
β *CWED
β *Distance Nest
β *Interaction
-1.19 (0.01)
0.06 (0.001)
All birds nesting in area
of 40% Aggregated (n=12)
-5.94 (0.04)
-2.42 (0.93)
-0.94 (0.14)
0.96 (0.05)
Nesting in Forest (n=9)
-5.75 (0.06)
-2.82 (1.65)
-0.94 (0.25)
0.86 (0.09)
Nesting on Treatment (n=3)
-6.49 (0.14)
-1.24 (0.05)
0.86 (0.06)
1.26 (0.03)
-1.26 (0.01)
-0.99 (0.02)
0.06 (0.002)
0.05 (0.003)
36
37
∧
Table 6a-c. Estimates of standardized RUF coefficients ( β ) for groups of nesting
Brown Creepers that either placed their nest within <85m or >85m away from edge.
The birds are further grouped by successful and unsuccessful nesters and their mean
coefficients tested for difference using a 2-sample t-test at α=0.05. For a definition of
the coefficients see Table 4a-c.
(a) Brown Creepers nesting within <85m from edge.
∧
Mean Standardized coefficients ( β )
∧
∧
∧
∧
β Clearcut
β Patches
β Thinned
β CWED
All birds nesting within
<85m from edge
(n=30)
-0.44
0.03
-0.12
0.04
Successful Nests
(n=17)
-0.47
-0.04
-0.21
0.01
Unsuccessful Nests
(n=13)
-0.40
0.12
-0.01
0.08
0.59
0.25
0.32
0.60
∧
∧
P-value (Ho: β S = β U)
(b) Brown Creeper nesting >85m away from edge.
∧
Mean Standardized coefficients ( β )
∧
∧
∧
∧
β Clearcut
β Patches
β Thinned
β CWED
All birds nesting >85m
away from edge
(n=12)
-0.18
0.11
-0.05
0.12
Successful Nests
(n=7)
-0.46
0
0.06
0.07
Unsuccessful Nests
(n=5)
-0.05
0.17
-0.16
0.17
sample size
too small
sample size
too small
0.82
0.50
∧
∧
P-value (Ho: β S = β U)
38
Table 6 (continued)
(c) Comparing all Brown Creepers nesting within <85m or >85m from edge.
∧
Mean Standardized coefficients ( β )
∧
All birds nesting within
<85m from edge
(n=30)
All birds nesting >85m
away from edge
(n=12)
∧
∧
P-value (Ho: β S = β U)
∧
∧
∧
β Clearcut
β Patches
β Thinned
β CWED
-0.44
0.03
-0.12
0.04
-0.18
0.11
-0.05
0.12
0.21
0.73
0.85
0.47
39
Table 7. Average nesting success and territory productivity (Vickery Index ± SE; see
Vickery et al. 1992 for a definition of the index) for all Brown Creepers in the DEMO
study area during the breeding seasons 2003 and 2004. Productivity and nesting success
is compared to a population of Brown Creepers in the suburban area of Seattle,
Washington (Blewett and Marzluff 2005).
This Study
(2003)
This Study
(2004)
This Study
(2003 and 2004)
Suburban
Area
Average Vickery Rank
(no. of territories)
4.4 ± 1.4
(25)
3.5 ± 1.3
(29)
3.9 ± 1.4
(55)
3.7 ± 0.2
(61)
Percent Nesting
Success
(no. of nests)
63 (27)
48 (21)
54 (48)
59 (27)
40
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53
Appendix A: Sample Code for RUF Computation in Program R
This sample code will fit a Geyer saturation process function to the relocation points of
a Brown Creeper located at the DEMO study area for the creation of a RUF, with the
covariates for landcover “vergrd8m.csv”, contrast-weighted edge “area_cwed.csv”,
and distance to nest “nestloc”. This code was developed by Mark S. Handcock.
For the code package please visit the following web site:
http://csde.washington.edu/~handcock/ruf/
54
Appendix B: Fitting the Spatial Point Process Model
Section 1: Relocations of the Brown Creeper “wo03” (small circles) and land cover
covariates at the 40% aggregated treatment unit. The white area represents the
surrounding forest, the dark grey circles the 40% aggregated patches, and the light grey
area the clearcut area.
Section 2: Use of the land covers by the bird “wo03”.
55
Section 3: Contrast-weighted edge covariate and the bird “wo03”. Shades of dark grey
to lighter grey represent a decrease in the edge contrast from high to low, respectively.
Section 4: Use of the contrast-weighted edge resource by the bird “wo03”.
56
Section 5: Distance to nest covariate and the bird “wo03”. Shades of light grey to
darker grey represent the increasing distance away from the nest.
Section 6: Use of resources in relation to distance to nest by the bird “wo03”.
57
Section 7: Fitted Geyer process intensity function for the bird “wo03”. The intensity of
resource use is characterized by the shades from dark grey (highest use) to white (low
or no use).
58
Section 8: Simulation of fitted Geyer process intensity function for the bird “wo03”.
Section 9: Another simulation of the fitted intensity function for the bird “wo03”.
59
Appendix C: Bark Arthropods as Food Abundance
I collected fecal samples during the banding process of Brown Creepers that set up
territories at my study sites (n=9 in 2003 and n=4 in 2004). Fecal analysis can assist in
the understanding of diet preferences of my focal bird species (Calver and Wooller
1982, Ralph et al. 1985, Van Horne and Bader 1990, Burger et al. 1999). Although this
technique has its limitations (Rosenberg and Cooper 1990), it can be used successfully
as a complementary method to observations of bird foraging behavior and prey
availability assessment to gain a better understanding of avian foraging ecology (Halaj,
in preparation). There was a 92% similarity between bark arthropod samples and the
food for nestlings of the Eurasian Treecreeper (Certhia familiaris; Kuitunen 1989).
The fecal sample analysis was performed by Cascadian, Inc. and Pacific Analytics,
L.L.C (for more details see Brenner 2005, and Halaj, in preparation). The samples
contained mainly fragments of small beetles (Coleoptera), of which some were
identified to be in the family of Staphylinidae, Elateridae, and Curculionidae (Section 1
and 2). There were also some fragments of spiders (Arachnidae), one fly and fly egg
(Diptera), and parts of Lepidoptera larvae (probably Tortricidae). All beetles were
assumed to be less than 10 mm in size. The main size class of diet items by biomass for
nestling Treecreepers in southern Finland was about 5-10mm (Kuitunen and Törmälä
1983).
Coleoptera also comprised the largest portion of Brown Creeper stomach content in
California (Otvos and Stark 1985). Spiders contributed most in biomass and energy to
the diet of adult and nestling Treecreepers in Finland (Kuitunen and Törmälä 1983,
Suhonen and Kuitunen 1991), and also were most abundant in the diets of Brown
Creepers in North America, Washington (Mariani and Manuwal 1990). Fecal analysis
might not reflect adequately the amount of soft bodied prey such as arachnids or
lepidoptera species, due to the destructive effects of digestion.
60
Section 1: Estimated number of prey items in each fecal sample.
Fecal
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
Coleoptera
3
2
1
3
5
2
2
4
2
1
4
1
0
Lepidoptera
Larvae
0
0
0
0
0
0
0
0
1
0
1
0
0
Diptera Fly
Diptera Egg
Spider
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
1
1
0
0
1
0
0
1
Section 2: Proportions of arthropod and arachnid prey items in the fecal samples of
captured Brown Creepers at my study sites (n=13).
Spider
17%
Diptera Egg
2%
Diptera Fly
2%
Lepidoptera
5%
Coleoptera
Lepidoptera
Coleoptera
74%
Diptera Fly
Diptera Egg
Spider