Wildlife Habitat Occurrence Models for
Project and Landscape Evaluations in the Lake Tahoe Basin
Final Report to the U.S. Department of the Interior, Bureau of Land Management
Angela M. White and Patricia N. Manley
USDA Forest Service, Pacific Southwest Research Station
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Table of Contents
MANAGEMENT SUMMARY ..................................................................................................................................................... 3
1.0 INTRODUCTION AND BACKGROUND ................................................................................................................................ 4
2.0 GOALS AND OBJECTIVES ................................................................................................................................................... 5
3.0 METHODS .......................................................................................................................................................................... 5
3.1 STUDY SITES .................................................................................................................................................................. 5
3.2 HABITAT AND ENVIRONMENTAL COVARIATES ............................................................................................................. 6
3.3 WILDLIFE SAMPLING ..................................................................................................................................................... 8
3.3.1 BIRDS ...................................................................................................................................................................... 8
3.3.2 SMALL MAMMALS ................................................................................................................................................. 9
3.4 DATA ANALYSIS ............................................................................................................................................................... 10
4.0 RESULTS........................................................................................................................................................................... 12
4.1 BIRDS ........................................................................................................................................................................... 12
4.1.1 GIS-based models................................................................................................................................................. 12
4.1.2 Field-based models .............................................................................................................................................. 17
4.2. SMALL MAMMALS ..................................................................................................................................................... 19
4.2.1 GIS-based models................................................................................................................................................. 20
4.2.2 Field-based models .............................................................................................................................................. 22
5.0 DISCUSSION ..................................................................................................................................................................... 24
5.1 PROJECT EVALUATIONS .............................................................................................................................................. 24
5.2 LANDSCAPE EVALUATIONS ......................................................................................................................................... 25
6.0 APPENDICES .................................................................................................................................................................... 32
6.1 APPENDIX A: Parameter Estimates – Avian species (GIS-based covariates)............................................................... 33
6.2 APPENDIX B: Parameter Estimates – Avian species (field-based covariates) ............................................................. 43
6.3 APPENDIX C: Parameter Estimates – Small mammal species (GIS-based covariates) ................................................ 52
6.4 APPENDIX D: Parameter Estimates – Small mammal species (field-based covariates) .............................................. 54
6.5 APPENDIX E: Species Conservation Status .................................................................................................................. 56
6.6 APPENDIX F: Species Distribution Maps ..................................................................................................................... 59
6.7 APPENDIX G: SDM Evaluations.................................................................................................................................. 130
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MANAGEMENT SUMMARY
BACKGROUND
A myriad of sources of environmental change and associated land management challenges exist in the Lake
Tahoe basin, as is the case for wildland forest ecosystems across the country. Sources of change are diverse,
including land development, recreational uses, wildfire, insects, disease, forest management, and climate
change. The complexity of the challenge is four-parted: 1) the broad suite and multifarious character of
influences affecting forest ecosystem conditions; 2) the limited information on historical conditions, particularly
in regard to temporal and spatial heterogeneity; 3) the prospect of rapid rates of climate change with uncharted
ecological responses; and 4) the uncertainty of how best to achieve targeted conditions through land
management. To help address these needs, the Lake Tahoe basin needs a basin-specific evaluation tool that can
provide reliable estimations of species occurrence across landscapes and over time that can be responsive to a
variety of demands for evaluating current and potential future environmental conditions.
METHOLOGY AND PRINCIPAL FINDINGS
We use a set of multi-species occurrence model to estimate habitat associations for 66 avian species and 16
species of small mammals in the Lake Tahoe Basin. Our approach which accounts for variations in detectability
of species during sampling, estimates occurrence probabilities of all species in a community by linking species
occurrence models into one hierarchical community model, thus improving inferences on all species, especially
those that are rare of observed infrequently. The results of these models can be used by managers in the Lake
Tahoe Basin to better understand how variation in different abiotic and biotic variables can influence the suite
of species that currently occur in the area.
IMPLICATIONS FOR MANAGEMENT
Biodiversity is integral to ecosystem functioning (Ehrlich and Ehrlich, 1981; Folke et al., 1996; Tilman, 1999)
and services that are essential for human well-being (Daily, 1997; Naeem et al., 2009). Although the importance
of biodiversity conservation is recognized, it remains one of the key challenges of land stewardship (Mac Nally
et al., 2002; Fischer et al., 2004; Noon et al., 2009) due to several ecological and practical limitations (Margules
and Pressey, 2000). This synthesis of data on bird and small mammal populations in the Lake Tahoe Basin is
intended to improve the capacity and confidence of stakeholders tasked with making decisions that could
impact biodiversity. The interests of stakeholders can be influenced to varying degrees by social, economic,
ecological and political factors and we suggest how the data presented can be used to evaluate the potential
impact of management decisions at the project and landscape scale on individual species and the ecosystem.
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1.0 INTRODUCTION AND BACKGROUND
Lake Tahoe is a treasured place in the Sierra Nevada, and it attracts hundreds of thousands of visitors each year.
In addition to the growing resident population, many residents of California and elsewhere have summer homes
in Lake Tahoe. The competing pressures to keep Tahoe blue, wild, and safe from wildfire make land
management extremely complex. The difficulties are reflected in multiple on-going planning efforts in the
basin. For example, a large-scale implementation of fuels reduction treatments is planned for the basin, as
outlined in the Lake Tahoe Multi-jurisdictional Fuel Reduction and Wildfire Prevention Strategy (Marlow,
2007). Perceptions of fire danger are driving forest management activities in the Lake Tahoe basin at the present
time, as they are on public lands throughout the west. The CWHR database (CDFG, 2008) determines habitat
suitability based on proxies for seral stage, namely average tree diameter and canopy cover. Although forest
management to reduce fuels is designed to significantly alter forest structure, it does not typically change the
seral stage classification of a stand. Fuels reduction treatments target the removal of smaller diameter trees, and
as such average diameter either stays the same or increases. Although more refined habitat models exist for a
few high interest species (e.g., American marten, California spotted owl), the basin lacks the ability to address
many other species of interest (e.g., Northern flying squirrel, Trowbridge’s shrew, Pileated Woodpecker) or
biological diversity as a whole in the assessment of the effects of management at stand or landscape scales.
Climate change further complicates land management assessments in that its future effects are unknown but are
likely to be ecologically significant, yet it is unclear how the effects of present-day management will interface
with future changes in environmental conditions imposed by changes in temperature and precipitation. Research
directed at understanding historical vegetation (Taylor, 2004; North, 2012) will generate models of landscape
conditions and dynamics that can be interpreted in terms of how plants and fire may respond to current and
future conditions, how to design landscapes to be well adapted to potential future climate conditions, and how
wildlife may respond to various future scenarios.
The Lake Tahoe basin needs a basin-specific evaluation tool that can provide reliable estimations of species
occurrence across landscapes and over time that can be responsive to a variety of demands for evaluating
current and potential future environmental conditions. The development of predictive tools for multiple species
of wildlife can be accomplished in a variety of ways. Most commonly, quantitative models are developed based
on qualitative data, and they are posed as working hypotheses for habitat suitability and quality. Habitat
Suitability Index is a fairly common example of this type of model (Tirpak et al., 2009). They are satisfying in
that they attempt to address specific habitat needs, such as nest site substrates, other breeding habitat needs, and
foraging and resting needs in the quantitative evaluation of site specific habitat suitability. In application,
however, they are difficult to validate, and therefore their utility is primarily limited to large-scale relative
outcomes. The alternative is to build habitat models based on empirical data, which is likely to limit the scope
of model, but greatly improve its reliability and credibility for effectiveness and desired condition monitoring
and evaluations. Models that predict abundance are highly dependent upon the techniques, effort, and year of
sampling, thus is it is difficult to develop a generic predictive model for measures of abundance that others can
reliably apply in a variety of contexts. Models that predict probability of occupancy, however, are more
temporally robust, and they can account for variation in sampling effort and techniques. Fortunately, empirical
wildlife occurrence data exist across the basin, which we can employ to build reliable and robust habitat
occupancy models.
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2.0 GOALS AND OBJECTIVES
A myriad of sources of environmental change and associated land management challenges exist in the Lake
Tahoe basin, as is the case for wildland forest ecosystems across the country. Sources of change are diverse,
including land development, recreational uses, wildfire, insects, disease, forest management, and climate
change. The complexity of the challenge is four-parted: 1) the broad suite and multifarious character of
influences affecting forest ecosystem conditions; 2) the limited information on historical conditions, particularly
in regard to temporal and spatial heterogeneity; 3) the prospect of rapid rates of climate change with uncharted
ecological responses; and 4) the uncertainty of how best to achieve targeted conditions through land
management.
Tools exist to evaluate the effects of various sources of change on wildlife, particularly forest management, but
they fall short of management’s information needs in a number of important ways. At the present time, the
primary multispecies assessment tool, California Wildlife Habitat Relationships (CWHR) Database System
(CDFG, 2008), is inadequate to the task because it is insensitive to most present-day changes in forest
composition, structure, and configuration. For example, Manley et al. (unpublished data) and Murphy et al.
(unpublished data) found in two different landscapes that standard fuels reduction treatments did not affect a
change in forest habitat classification as per CWHR, despite substantial changes in forest structure and in bird
and small mammal composition and abundance following treatment. Thus, the CWHR database was not
effective in predicting or reflecting changes in habitat associated with forest fuels reduction treatments. The
Urban Biodiversity study found that the amount of development in the landscape and human disturbance had
significant effects on the occurrence and/or abundance of many bird species (Schlesinger et al., 2008). Changes
in forest conditions resulting from climate change are likely to be even more subtle, at least initially, and
therefore it is unlikely that CWHR will be effective in evaluating climate change effects. The Lake Tahoe basin
needs an assessment tool that can be applied to a variety of wildlife and biodiversity evaluation needs to help
inform land management planning and implementation in light of multiple interacting change agents acting
across the landscape and over time. A rich array of empirical data is available and can be tapped to provide this
important tool, and thereby increase the value of past and current agency investments in research and
monitoring.
The goal of this project is to use existing empirical field data that were collected in a systematic manner in the
Lake Tahoe basin to develop species distribution maps and habitat occupancy models for forest associated
vertebrate species in the Lake Tahoe basin. These models will facilitate site and landscape-scale evaluations of
management treatments, climate change, and other change agents that affect forest structure and composition
today and in the future. This project proposes to develop habitat occupancy models (estimates of probability of
occurrence have a range between 0 and 1) based on site-specific environmental conditions. These will be first
generation models, and as such, they target simple objectives that can be readily met with existing data.
3.0 METHODS
3.1 STUDY SITES
The Lake Tahoe Basin is located on the eastern crest of the Sierra Nevada straddling the states of California and
Nevada. Elevation and precipitation vary markedly in the basin and both environmental gradients are known to
have large effects on productivity (Schluter and Ricklefs, 1993). Elevation ranges from 1900 m at the surface of
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Lake Tahoe to 3400 m at the highest mountain peak. Mean annual precipitation is 150 cm, varying greatly with
elevation and latitude, with the west shore experiencing 50% higher precipitation than the east shore (Kittel,
1998). The majority of precipitation occurs as snow and falls between the months of December and March.
Approximately 67% of Basin’s forests were clear-cut during the last third of the 19th century with less intensive
harvesting continuing into the 20th century (Lindström, 2000). Common tree species today include Jeffrey pine
(Pinus jeffreyi), white fir (Abies concolor), red fir (A. magnifica), incense-cedar (Calocedrus decurrens),
lodgepole pine (P. contorta), and sugar pine (P. lambertiana).
The project utilizes data from two primary datasets: 1) the Multi-Species Inventory and Monitoring (MSIM)
data, collected at 100 sites on NFS lands throughout the basin from 2002-2005; 2) the Lake Tahoe Urban
Biodiversity Study (LTUB) data collected at 100 sites across multiple land ownerships at lower elevations
(<7500 ft) in the basin from 2003-2005. Sample locations were selected using a combination of systematic/grid
sampling and stratified random sampling. Four points were randomly selected from within a hexagonal grid laid
across the Lake Tahoe Basin using spacing parameters of the Forest Inventory and Analysis program.
Additional locations were randomly selected across a range of urban development classes. At each of these
primary sampling locations, a cluster of additional sampling points was conducted 200 m from each primary
point count station. Sampling locations for each year of the study were selected randomly. There was a
minimum distance of 200 m between all sampling points. Together they represent the full spectrum of the
primary environmental conditions occurring in the basin, meaning locations around the lake and sites from lake
level to near the crest. At each site, bird, small mammal, and vegetation data were collected (Figure 1).
3.2 HABITAT AND ENVIRONMENTAL COVARIATES
For GIS-based models we characterized habitat using several explanatory variables with Geographic
Information Systems (GIS) for the area within a 150-m radius of the survey locations. We selected a 150-m
radius to define our habitat variables as birds and small mammals on the edge of the sampling radius are likely
responding to surrounding forest conditions. Habitat parameters were derived from a GIS vegetation layer (30m X 30-m raster cell) based on Dobrowski et al. (2006). Forest habitat parameters at our sample points included
mean tree size (diameter at breast height: DBH), mean and standard deviation in percent canopy cover, mean
and standard deviation in an index of tree size class diversity (McWethy et al., 2009), mean shrub cover, and
mean herbaceous cover (Table 1). These parameters were selected to represent forest structure because they are
components of the forest known to impact biodiversity (Erdelen, 1984; Matlock and Edwards, 2006; Verschuyl
et al., 2008) and these variables had low pairwise correlations (r ≤ 0.5) in our dataset. In addition, we extracted
three environmental variables from remotely sensed databases that we hypothesized also would affect the
probability of species occurrence: urban development, elevation and easting. The percentage of the area in
urban land development was extracted from impervious surface data collected in 2003 (Manley et al., 2009).
For field based models we characterized habitat using common field sampling techniques. Our analysis of
habitat variables was limited to those variables in which both studies used a similar sampling protocol (Table 1).
In both studies, the number of trees ≥12.5 cm and snags ≥28 cm in DBH were recorded within a 17.6-m (0.1-ha)
circular plot. Trees were classified as small (≥12-28 cm DBH), medium (>28-61 cm DBH), and large (>61cm
DBH) and tree size diversity was quantified using the Shannon-Weiner diversity index. Each snag was
characterized by decay class (Thomas et al., 1979). The percent cover of shrubs was estimated visually to the
nearest percent within a 30-m radius of plot center (Jennings et al., 1999). The volume of coarse woody debris
was quantified using line-intercept transect methods. Along each transect the length, diameter at each end and
decay class (Maser et al., 1979) of all logs that intersected the transect and were ≥ 10 cm diameter at either end
were recorded. Log density was calculated for each log and coarse woody debris volume was then calculated
(m3/HA) per site (Waddell, 2002).
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a)
b)
c)
d)
Figure 1. Location of the sampling points in the Lake Tahoe Basin Management Unit a) used for the GIS-based avian model, b) field-based avian
model, c) small mammal GIS-based model, and d) small mammal field-based model. MSIM sample points (blue) and LTUB sample points
(yellow) are indicated with green shading symbolizing increasing levels of canopy cover.
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3.3 WILDLIFE SAMPLING
3.3.1 BIRDS
We complied bird-count survey data collected at 179 sample sites between MSIM and LTUB projects (Figure
1a) during the course of the breeding season (mid-May to early July) of 2002 to 2005 (Ralph et al., 1993; Siegel
et al., 2010). Each sample site had 5-7 point count stations set approximately 200 m apart (Figure 2), and the
majority of sites (n = 1091) were sampled three times during the course of a breeding season, with the
remaining sites limited to two sampling occasions due to logistical constraints. Although the number of point
count stations per site was different between projects, they were both clustered in a circular or oblong formation
at 200-m spacing. All stations within a given sample site were sampled on the same day and surveys were not
conducted in inclement weather. Both studies used similar point count protocols in which all birds detected
(seen or heard) in a 10-minute period within 100 m from the sample location were recorded. Point counts were
conducted during the breeding season between mid-May and mid-August between 0530 and 1000 hrs. Visits to
Table 1. Description of the covariates used in GIS-based and field-based occurrence models.
Parameter
GIS-based:
Development (%)
Elevation (m)
Easting (m)
DBH (cm)
Canopy cover (%)
Variance in canopy
cover
Tree size diversity
Mean
SD
Min.
Max.
13.5
2153.7
757306.0
55.0
34.4
15.5
269.8
8866.5
10.4
16.7
0.0
1879.5
739938.5
0.0
0.0
85.8
3121.5
769518.5
92.5
73.9
8.3
6.3
0.0
29.6
0.7
0.3
0.0
1.1
0.1
0.1
0.0
0.5
Description
Proportion of area occupied by urban development
Digital elevation model
Digital elevation model
Mean diameter at breast height of trees
Mean proportion of area occupied by 5 canopy cover
classes
Standard deviation in the proportion of area occupied
by 5 canopy cover classes
Mean Shannon-Weiner diversity index for four tree size
classes
Standard deviation in tree size diversity
Variance in tree size
diversity
Shrub cover (%)
Herbaceous cover (%)
Field-based:
Trees per hectare
Tree size diversity
32.0
12.2
13.0
8.4
0.8
0.0
81.8
100.0
Proportion of area covered by shrubs
Proportion of area covered by herbs
262.8
0.6
237.5
0.3
0.0
0.0
1456.4
1.1
Shrub cover (%)
Soft snags per hectare
19.4
7.7
20.3
14.2
0.0
0.0
90.0
190.0
Hard snags per hectare
12.6
25.9
0.0
220.0
Soft coarse woody debris
(m3/HA)
Hard coarse woody
debris (m3/HA)
Large trees per hectare
89.1
217.9
0.0
2169.3
21.2
89.1
0.0
1803.5
23.6
22.0
0.0
184.9
Number of trees (>12.5 cm) within 17.6-m radius plot
Shannon-Weiner diversity index for three tree size
classes (small:12-28 cm, medium: >28-61 cm; large:
>61 cm)
Percent cover of shrubs within 30 m
Number of standing dead trees (>28 cm) within 17.6-m
with decay class 3-5
Number of standing dead trees (>28 cm) within 17.6-m
with decay class 1-2
Sum of log volume for each log >10 cm in diameter at
large end (decay class 3-5) measured along transects
Sum of log volume for each log >10 cm in diameter at
large end (decay class 1-2) measured along transects
Number of trees (>61 cm) within 17.6 m plot
8
the same location were separated by approximately one week. Within a season, stations were visited by multiple
observers (2 to 3 each year) to limit observer bias across study sites. Although locations were visited repeatedly
within a season, each station was only visited in a single year. Point counts were located on federal, state and
private lands. For field based models the sample size was reduced to 778 survey locations as vegetation was not
sampled via field methods at all points (Figure 1b).
a)
b)
Figure 2. Configuration of point count station from a) Multi-species Inventory and Monitoring and b) Lake
Tahoe Urban Biodiversity study protocols. Dots represent each point count station and circles represent a 100-m
radius circle from each point. Figure adapted from Manley and McIntyre (2006).
3.3.2 SMALL MAMMALS
Small mammals were surveyed at 173 sites with Sherman live-traps as part of the MSIM and LTUB projects
(Figure 1c). Sampling protocols were similar across the two projects, with trap size (extra long [4” x 4.5” x 15”]
for MSIM, extra long and large [3” x 3.5”x 9”] for LTUB), number (64-104), spacing (15 m), bait (oats and
seeds), trap duration (3 days and nights), and trap check frequency (twice per day) all being consistent. The grid
configuration of traps did vary among projects (Figure 3). In the LTUB project, 64 traps were arrayed in a
square 8 x 8 grid (~ 1.1 ha; 2.8 ac). For MSIM, 104 traps were arrayed in a more open transect pattern
occupying an area approximately 10 ha. Point counts were located on federal, state and private lands. For field
based models the sample size was reduced to 152 survey locations as vegetation was not sampled via field
methods at all points (Figure 1d).
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a)
b)
Figure 3. Configuration of point count station from a) Multi-species Inventory and Monitoring and b) Lake
Tahoe Urban Biodiversity study protocols. Dotted lines represent linear trap transects. Figure adapted from
Manley and McIntyre (2006).
3.4 DATA ANALYSIS
We analyzed the data using a hierarchical multi-species modeling approach developed in Dorazio and Royle
(2005) and Dorazio et al. (2006) to estimate species-specific occupancy probabilities relative to the abiotic and
biotic (e.g., forest structure) variables. To do this, we combined individual species occurrence models in a
single model by assuming that species covariate effects come from a common distribution, allowing for more
precise estimates of occupancy (Zipkin et al., 2009; Kery, 2010). For species-level models, we assumed that the
occurrence of species i, zi, is a Bernoulli process where the probability that species i is present at location j (zi,j =
1) is ψi,j (MacKenzie and Kendall, 2002). For GIS-based models, we then modeled the occurrence probability
for each species i at location j using the logit link function and the relevant covariates such that:
logit (ψi,j) = α0i + α1i * developmentj + α2i * elevationj + α3i * elevation2j + α4i * eastingj
+ α5i * DBHj + α6i * canopy coverj + α7i * canopy cover2j + α8i * canopy cover SDj
+ α9i * tree size diversityj + α10i * tree size diversity SDj + α11i * shrub coverj
+ α12i * shrub cover2 + α13i * herbaceous cover
where α0i is the intercept and α1i - α13i are the effects of the habitat covariates on species i. Parameters α1i - α4i
are the abiotic covariates that measure the effect of urban development (linear term), elevation (linear and
squared terms), and easting (linear term) on the occurrence probability of species i. The parameters α5i - α13i
are effects related to the forest structure at location j (i.e., the forest within a 150-m radius of the point where the
survey was conducted): α5i is the effects of the average diameter at breast height (DBH) of trees (linear term);
α6i - α8i are the effects of percent forest canopy cover (linear and squared terms) and the standard deviation of
forest canopy cover; α9i - α10i are the effects of tree size class diversity (linear term) and the standard deviation
in tree size class diversity; α11i - α13i are the effects of percent forest shrub cover (linear and squared terms)
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and percent herbaceous cover (linear term). We chose to include squared terms for some covariates, but not all,
based on our hypotheses of the relationships of covariates to species’ occurrences. All covariates were
standardized to have a mean of zero and a standard deviation of one.
Similarly for field-based models, we modeled the occurrence probability for each species i at location j using
the logit link function and the relevant covariates such that:
logit (ψi,j) = α0i + α1i * developmentj + α2i * elevationj + α3i * elevation2j + α4i * eastingj
+ α5i * stemj + α6i * stem2j + α7i * tree size diversityj + α8i * shrub coverj
+ α9i * soft snagj + α10i * hard snagj + α11i * soft cwdj + α12i * hard cwdj
+ α13i * large tree
where α0i is the intercept and α1i - α13i are the effects of the habitat covariates on species i. Parameters α1i - α4i
are the abiotic covariates that measure the effect of urban development (linear term), elevation (linear and
squared terms), and easting (linear term) on the occurrence probability of species i. These parameters are
identical to the previous model. The parameters α5i - α13i are effects related to the forest structure at location j:
α5i - α6i are the effects of the density of trees (linear and squared terms); α7i is the effects of tree size class
diversity (linear term); α8i is the effect of percent shrub cover (linear term); α9i - α10i are the effects of density
of soft (decay class 3-5) and hard (decay class 1-2) snags; α11i - α12i are the effects of the volume of soft
(decay class 3-5) and hard (decay class 1-2) coarse woody debris and α13i is the effect of large (>61 cm DBH)
tree density. All covariates were standardized to have a mean of zero and a standard deviation of one.
Because species are detected imperfectly during sampling [69], we assumed that true occurrence, zi,j is a latent
process that is only partially observable. If an observer detected species i at location j during sampling occasion
k, denoted xi,j,k = 1, then it can be determined that zi,j = 1. However, if a species is not detected it could be that
the species was absent or that the species was missed during sampling. To account for detection biases, we used
a repeated sampling protocol, assuming that the species pool was closed and that xi,j,k ~ Bern(pi,j,k * zi,j) where
pi,j,k is the detection probability for species i at location j during sampling occasion k given that the species was
present. We similarly modeled species detection probabilities using the logit link function:
logit(pi,j,k,t) = b0i + b1i * Datej,k + b2i * Date2j,k + b3i * year2003i + b4i * year2005j
+ b5i * year2005j
where b0i is the intercept, b1i – b2i are the linear and squared effects of sampling day (Julian day 145-217,
standardized to have mean zero and standard deviation of one) and b3i – b5i are year effects on detection as
measured relative to a baseline year of 2002. Thus, our assumption is that the species pool was closed over the
four-year sampling period (2002-2005) and similarly, that “occurrence” in this case is defined as species use of
a location on at least one occasion during this time frame. We believe our occurrence estimates are robust to this
assumption because each location was sampled in only one of the survey years and within a period of a few
weeks. For the community-level component, we assumed that each of the species-specific parameter values
from the occurrence (α0i - α13i) and detection (b0i – b5i) models were drawn from parameter-specific
community-level distributions (Dorazio and Royle, 2005; Dorazio et al., 2006). Thus we assumed that each of
the covariate estimates (e.g., all the αi estimates) came from a normal distribution with a common mean and
variance across all i species (e.g., α ~ N(μ αi,, σ αi)).
11
Because the small mammal trapping protocols for the two datasets differed in both the number of traps and the
size of the area sampled, we incorporated an additional variable in the GIS-based hierarchical model for small
mammals. The incorporation of this variable allowed us test whether the sampling design influenced the
probability of occurrence for each species. In particular, the MSIM trapping protocol surveyed a larger area and
we predicted that this would increase the probability of occurrence within the area sampled. Similarly, the
difference in the number of traps between the two sampling designs could result in differences in the probability
of detection therefore this variable was also incorporated in the detection model. However, the incorporation of
this variable is not feasible when using the model to make predictions and is only presented to ascertain the
degree to which this variable influences model outcomes.
Parameters were estimated using a Bayesian approach with Markov chain Monte Carlo (MCMC) implemented
in the programs R and WinBUGS with flat priors for each of the community-level parameters. We ran three
chains of the model for 15000 iterations after a burn-in of 10000 iterations and saved every fifth estimate
(resulting in 1000 values for each parameter). We assessed that the model had convergence using the R-hat
statistic (Gelman and Hill, 2007) with max R-hat values less than 1.10 for all parameters. We did not perform a
formal assessment of model fit to our data. Model assessment and selection is complex in hierarchical models in
general, and in multi-species models in particular. As such, there is no well-established method to determine
model fitness for community models (Zipkin et al., 2012). However, because of the low correlations between
our covariates and the number of nonzero parameter estimates (e.g., posterior intervals that did not overlap
zero), we believe that our model is adequate in describing our data in that it balances the inclusion of relevant
factors while maintaining parsimony relative to the amount of available data.
4.0 RESULTS
4.1 BIRDS
We recorded 66 species of birds for which point count surveys are typically used to monitor their populations.
Birds were detected during 2937 visits to 1091 point count stations. Eight avian species considered very rare,
Hammond’s Flycatcher Empidonax hammondii, House Finch Haemorhous mexicanus, Lesser Goldfinch Spinus
psaltria, Lazuli Bunting Passerina amoena, Pacific-slope Flycatcher Empidonax difficilis, Purple Finch
Carpodacus purpureus, Ruby-crowned Kinglet Regulus calendula, and Savannah Sparrow Passerculus
sandwichensis were observed at fewer than 20 sites (Table 2), and they were included in our hierarchical model
but not in our presentation of covariate estimates for individual species because their covariate estimates could
be misleading. The mean and standard deviation of the occurrence and detection probabilities for all species
included in the model are presented in Table 2. Mean covariate estimates for the occurrence model are
presented for all species (except the eight very rare species) in Appendix A and Appendix B.
4.1.1 GIS-based models
Mean occurrence probabilities varied substantially across species from <1% to 98% (Table 2) when all abiotic
and biotic covariates were held at their mean values. Urban development had the strongest, most consistent
effect on the probability of species occurrence (Table 3). The mean covariate estimate on urban development
(α1) was negative for 56 species (indicating a decline in occupancy with higher levels of development for 85%
of species) with the posterior intervals of 35 species not overlapping zero (Figure 4). Of these species, the
Dusky Flycatcher Empidonax oberholseri, Hermit Thrush Catharus guttatus, Hermit Warbler Dendroica
12
occidentalis, and Townsend’s Solitaire Myadestes townsendi had the largest mean parameter estimates
indicating these species may be the most sensitive to development (Appendix A). Of the species that responded
positively to development the mean estimates for the Brewer's Blackbird Euphagus cyanocephalus, Brownheaded Cowbird Molothrus ater, Band-tailed Pigeon Patagioenas fasciata, Pygmy Nuthatch Sitta pygmaea, and
Table 2. The number of sites and detections of each modeled avian species. Mean and standard deviation in
detection and occurrence probabilities are reported.
Species Name
American Robin (Turdus migratorius)
Band-tailed Pigeon (Patagioenas fasciata)
Black-backed Woodpecker (Picoides arcticus)
Black-headed Grosbeak (Pheucticus melanocephalus)
Brewer's Blackbird (Euphagus cyanocephalus )
Brown Creeper (Certhia americana)
Brown-headed Cowbird (Molothrus ater)
Calliope Hummingbird (Stellula calliope)
Cassin's Finch (Carpodacus cassinii)
Cassin's Vireo (Vireo cassinii)
Chipping Sparrow (Spizella passerina)
Clark's Nutcracker (Nucifraga columbiana)
Common Raven (Corvus corax)
Dark-eyed Junco (Junco hyemalis)
Downy Woodpecker (Picoides pubescens)
Dusky Flycatcher (Empidonax oberholseri)
Evening Grosbeak (Coccothraustes vespertinus)
Fox Sparrow (Passerella iliaca)
Golden-crowned Kinglet (Regulus satrapa)
Green-tailed Towhee (Pipilo chlorurus)
Hairy Woodpecker (Picoides villosus)
Hammond's Flycatcher (Empidonax hammondii)*
Hermit Thrush (Catharus guttatus)
Hermit Warbler (Dendroica occidentalis)
House Finch (Haemorhous mexicanus)*
House Wren (Troglodytes aedon)
Lazuli Bunting (Passerina amoena)*
Lesser Goldfinch (Spinus psaltria)*
Lincoln's Sparrow (Melospiza lincolnii)
Macgillivray's Warbler (Oporornis tolmiei)
Mountain Chickadee (Poecile gambeli)
Mountain Quail (Oreortyx pictus)
Mourning Dove (Zenaida macroura)
Nashville Warbler (Oreothlypis ruficapilla)
Northern Flicker (Colaptes auratus)
Olive-sided Flycatcher (Contopus cooperi)
Code
AMRO
BTPI
BBWO
BHGR
BRBL
BRCR
BHCO
CAHU
CAFI
CAVI
CHSP
CLNU
CORA
DEJU
DOWO
DUFL
EVGR
FOSP
GCKI
GTTO
HAWO
HAFL
HETH
HEWA
HOFI
HOWR
LAZB
LEGO
LISP
MGWA
MOCH
MOQU
MODO
NAWA
NOFL
OSFL
Detections
1842
375
28
244
505
1091
1337
33
519
279
148
802
272
2260
124
979
996
1526
517
210
648
10
509
131
4
129
13
17
44
426
2522
338
787
516
1142
745
Sites
Detected
864
168
21
107
186
544
556
29
322
162
94
440
124
1004
65
524
448
698
308
141
321
10
307
94
2
86
13
7
38
256
1068
225
330
302
555
416
Detection
Probability
0.578 ± 0.018
0.476 ± 0.041
0.149 ± 0.053
0.409 ± 0.051
0.699 ± 0.039
0.448 ± 0.022
0.672 ± 0.022
0.238 ± 0.072
0.306 ± 0.023
0.302 ± 0.035
0.284 ± 0.040
0.474 ± 0.025
0.437 ± 0.046
0.743 ± 0.015
0.361 ± 0.056
0.528 ± 0.022
0.455 ± 0.027
0.754 ± 0.017
0.443 ± 0.027
0.346 ± 0.038
0.407 ± 0.029
0.332 ± 0.104
0.450 ± 0.031
0.343 ± 0.044
0.464 ± 0.155
0.202 ± 0.031
0.265 ± 0.094
0.574 ± 0.128
0.199 ± 0.045
0.389 ± 0.031
0.761 ± 0.015
0.365 ± 0.031
0.562 ± 0.032
0.448 ± 0.029
0.416 ± 0.022
0.440 ± 0.025
Occurrence
Probability
0.816 ± 0.025
0.162 ± 0.027
0.059 ± 0.026
0.073 ± 0.017
0.007 ± 0.003
0.633 ± 0.035
0.411 ± 0.039
0.046 ± 0.017
0.371 ± 0.048
0.110 ± 0.021
0.098 ± 0.021
0.464 ± 0.040
0.068 ± 0.014
0.976 ± 0.009
0.077 ± 0.019
0.562 ± 0.037
0.316 ± 0.038
0.787 ± 0.025
0.359 ± 0.038
0.140 ± 0.025
0.299 ± 0.033
0.009 ± 0.005
0.316 ± 0.040
0.067 ± 0.017
0.003 ± 0.002
0.100 ± 0.021
0.012 ± 0.012
0.003 ± 0.002
0.043 ± 0.015
0.323 ± 0.038
0.982 ± 0.007
0.317 ± 0.041
0.196 ± 0.027
0.362 ± 0.041
0.544 ± 0.033
0.596 ± 0.038
13
Species Name
Orange-crowned Warbler (Oreothlypis celata )
Pacific-slope Flycatcher (Empidonax difficilis)*
Pileated Woodpecker (Dryocopus pileatus)
Pine Grosbeak (Pinicola enucleator)
Pine Siskin (Spinus pinus)
Purple Finch (Carpodacus purpureus)*
Pygmy Nuthatch (Sitta pygmaea)
Red Crossbill (Loxia curvirostra)
Red-breasted Nuthatch (Sitta canadensis)
Red-breasted Sapsucker (Sphyrapicus ruber)
Rock Wren (Salpinctes obsoletus )
Ruby-crowned Kinglet (Regulus calendula)*
Rufous Hummingbird (Selasphorus rufus)
Savannah Sparrow (Passerculus sandwichensis )*
Song Sparrow (Melospiza melodia )
Sooty Grouse (Dendragapus fuliginosus )
Spotted Towhee (Pipilo maculatus)
Steller's Jay (Cyanocitta stelleri)
Townsend's Solitaire (Myadestes townsendi)
Warbling Vireo (Vireo gilvus)
Western Tanager (Piranga ludoviciana)
Western Wood-pewee (Contopus sordidulus)
White-breasted Nuthatch (Sitta carolinensis)
White-crowned Sparrow (Zonotrichia leucophrys )
White-headed Woodpecker (Picoides albolarvatus)
Williamson's Sapsucker (Sphyrapicus thyroideus)
Wilson's Warbler (Wilsonia pusilla)
Winter Wren (Troglodytes hiemalis )
Yellow Warbler (Dendroica petechia)*
Yellow-rumped Warbler (Dendroica coronata)
Code
OCWA
PSFL
PIWO
PIGR
PISI
PUFI
PYNU
RECR
RBNU
RBSA
ROWR
RCKI
RUHU
SAVS
SOSP
SOGR
SPTO
STJA
TOSO
WAVI
WETA
WEWP
WBNU
WCSP
WHWO
WISA
WIWA
WIWR
YWAR
YRWA
Detections
24
6
60
92
667
21
895
126
1691
152
37
10
110
6
138
82
201
2326
586
565
1527
1089
807
54
598
237
254
30
24
1836
Sites
Detected
23
6
30
76
385
9
353
80
799
85
29
10
87
4
69
64
81
999
369
304
723
500
442
35
275
180
162
25
17
882
Detection
Probability
0.150 ± 0.052
0.302 ± 0.111
0.386 ± 0.075
0.185 ± 0.037
0.400 ± 0.026
0.370 ± 0.123
0.578 ± 0.033
0.181 ± 0.033
0.558 ± 0.019
0.305 ± 0.045
0.341 ± 0.079
0.281 ± 0.109
0.113 ± 0.026
0.381 ± 0.142
0.429 ± 0.054
0.246 ± 0.047
0.542 ± 0.056
0.714 ± 0.016
0.357 ± 0.025
0.526 ± 0.029
0.607 ± 0.020
0.606 ± 0.024
0.331 ± 0.021
0.398 ± 0.081
0.409 ± 0.031
0.191 ± 0.024
0.376 ± 0.036
0.366 ± 0.073
0.402 ± 0.091
0.646 ± 0.017
Occurrence
Probability
0.028 ± 0.013
0.010 ± 0.006
0.023 ± 0.009
0.127 ± 0.031
0.433 ± 0.038
0.007 ± 0.004
0.227 ± 0.030
0.140 ± 0.033
0.859 ± 0.025
0.147 ± 0.028
0.004 ± 0.002
0.008 ± 0.005
0.087 ± 0.026
0.002 ± 0.002
0.050 ± 0.014
0.089 ± 0.021
0.039 ± 0.011
0.958 ± 0.011
0.558 ± 0.044
0.402 ± 0.035
0.798 ± 0.027
0.503 ± 0.036
0.567 ± 0.042
0.009 ± 0.004
0.220 ± 0.030
0.404 ± 0.055
0.221 ± 0.034
0.020 ± 0.008
0.027 ± 0.010
0.927 ± 0.015
*These species were included in the hierarchical model, but were not presented in the manuscript as they were
detected too infrequently (<20 sites) to produce precise parameter estimates
White-headed Woodpecker Picoides albolarvatus all had posterior intervals that did not overlap zero. As
predicted, avian species were also significantly (posterior interval did not overlap zero) influenced by elevation
(linear and squared combined: 43 species) and easting (28 species) with similar numbers of species responding
positively and negatively to these parameters. The Brewer’s Blackbird, Brown-headed Cowbird, Mourning
Dove Zenaida macroura, Spotted Towhee Pipilo maculatus, and Western Wood-pewee Contopus sordidulus
were all constrained to lower elevations while Williamson's Sapsucker Sphyrapicus thyroideus, Pine Grosbeak
Pinicola enucleator, White-crowned Sparrow Zonotrichia leucophrys, and Rock Wren Salpinctes obsoletus
occurred more commonly at higher elevations. The Black-headed Grosbeak Pheucticus melanocephalus,
Brown-headed Cowbird, and Pygmy Nuthatch were positively associated with the wetter areas of the west.
14
Table 3. Number of avian species in the GIS-based model in which the species-specific parameter estimate was
negative or positive. Values in parenthesis indicate the subset of species in which the posterior intervals do not
overlap zero.
Parameter
Negative
Positive
Development
56(35)
10(5)
Elevation
36(18)
30(19)
2
Elevation
49(23)
17(6)
Easting
32(16)
34(12)
DBH
47(12)
19
Canopy cover
31(11)
35(15)
2
Canopy cover
39(11)
27(2)
Canopy SD
31(2)
35(8)
Tree size diversity
46(9)
20
Tree size diversity SD
47(3)
19(1)
Shrub cover
27(5)
39(17)
2
Shrub cover
31
35(1)
Herbaceous cover
18(3)
48(16)
In general, mean parameter estimates for any single structural aspect of the forest were small relative to the
abiotic variables suggesting that species within the basin may be more restricted by these factors than by
variability in forest structure (Appendix A). Of the modeled habitat covariates, percent canopy cover
significantly influenced the occurrence probability of 35 species (linear and squared combined), DBH for 12
species, percent shrub cover for 22 species (linear and squared combined), and percent herbaceous cover for 19
species (Figure 4, Table 3). Increases in the standard deviation in canopy cover were associated with an increase
in the probability of occurrence for eight species and a decrease in occurrence probability for two species. Tree
size class diversity and variance in tree size class diversity influenced nine and four species respectively. The
Dusky Flycatcher, Fox Sparrow Passerella iliaca, Macgillivray's Warbler Oporornis tolmiei, Pine Siskin
(Spinus pinus) Warbling Vireo Vireo gilvus, and Wilson’s Warbler Cardellina pusilla were influenced by the
greatest number of structural components (≥ four of the seven structural components modeled) suggesting that
these species may be most sensitive to changes in overall forest structure. Parameter estimates for each species
are available in Appendix A.Variation in the response across species for each environmental variable
underscores both the consistent effect of development and the importance of heterogeneous habitat for
maintaining species diversity.
Estimates derived from each species-specific model were used to generate a Basin-wide spatial distribution map
with the probability of occurrence for each species predicted at a 30-m pixel resolution. These maps are
presented in Appendix F and are electronically available in raster format. Two methods are presented to
evaluate the validity and performance of each species-specific model: 1) the ability of the model to distinguish
between occupied and unoccupied sites, and 2) the ability of the model to quantify habitat suitability
(calibration, Vaughan and Ormerod 2005). These are presented in the Appendix G.
Figure 4. Mean parameter estimates and posterior intervals for the effect of a) percent development, b)
elevation, c) easting, d) mean DBH, e) tree size diversity, f) standard deviation in tree size diversity, g) percent
canopy cover, h) standard deviation in canopy cover, i) shrub cover, and j) herbaceous cover for each avian
species included in our GIS-based analysis. Values indicate the change in occurrence predicted as a function of
the change in one standard deviation of change in each response variable.
15
16
4.1.2 Field-based models
Of the modeled habitat covariates collected via field sampling methods, percent shrub cover influenced the
largest number of species, with the occurrence of nine species negatively and eight species positively influenced
by increasing levels of shrub cover. In particular, the probability of occurrence of the Dusky Flycatcher, Fox
Sparrow, Spotted Towhee, and Nashville Warbler Oreothlypis ruficapilla were positively associated with
increasing levels of shrub cover. In contrast, the probability of occurrence of Downy Woodpecker Picoides
pubescens, Pine Siskin and Song Sparrow Melospiza melodia decreased with increasing levels of shrub cover.
A similar number of species were estimated to be positively affected by increasing stem density as were
predicted to be negatively affected, although these relationships were rarely significant (Table 4, Figure 5). The
Red-breasted Nuthatch Sitta canadensis and Hermit Thrush were significantly associated with increasing stem
density, while the Green-tailed Towhee Pipilo chlorurus and Cassin's Finch Carpodacus cassinii were
negatively associated. Of the other metrics associated with live woody plants, tree size class diversity
influenced the probability of occurrence of three species (Hairy Woodpecker Picoides villosus, Western
Tanager Piranga ludoviciana, Golden-crowned Kinglet Regulus satrapa) and the density of large (>61 cm)
trees influenced five species (positively: Brown creeper Certhia americana, Western tanager, Golden-crowned
Kinglet; negatively: Fox sparrow, Mountain Quail Oreortyx pictus). The density of snags was associated with
significant changes in the probability of occurrence for three (soft snags) and one (hard snags) species. The
probability of occurrence of Hairy Woodpeckers increased at increased densities of hard snags, whereas the
Red-breasted Nuthatch was associated with soft sang density. Hard CWD influenced the probability of
occurrence for six species including Northern Flicker Colaptes auratus, Wilson’s Warbler, and Pileated
Woodpecker Dryocopus pileatus.
Table 4. Number of avian species in the field-based model in which the species-specific parameter estimate was
negative or positive. Values in parenthesis indicate the subset of species in which the posterior intervals do not
overlap zero.
Parameter
Negative
Positive
Development
58(38)
8(4)
Elevation
30(22)
36(21)
2
Elevation
48(17)
18(3)
Easting
31(15)
35(11)
Trees/HA
42(2)
24(2)
2
(Trees/HA)
41(1)
25(1)
Tree size diversity
33(1)
33(2)
Shrub cover
40(9)
26(8)
Soft snags/HA
36(2)
30(1)
Hard snags/HA
47
19(1)
3
Soft CWD (m /HA)
47
19
Hard CWD (m3/HA)
17
49(6)
Large trees/HA
40(2)
26(3)
17
Figure 5. Mean parameter estimates and posterior intervals for the effect of a) stem density, b) tree size
diversity, c) large tree density, d) shrub cover, e) soft snag density, f) hard snag density, g) volume of soft
CWD, and h) volume of soft CWD for each avian species included in our field-based analysis. Values indicate
the change in occurrence predicted as a function of the change in one standard deviation of change in each
response variable.
18
4.2. SMALL MAMMALS
We captured 16 species of small mammals during 525 days of trapping at 175 sites. Four small mammal species
considered very rare, montane vole Microtus montanus, brush mouse Peromyscus boylii, western gray squirrel
Sciurus griseus, and western jumping mouse Zapus princeps were observed at fewer than 10 sites (Table 5), and
they were included in our hierarchical model but not in our presentation of covariate estimates for individual
species because their covariate estimates could be misleading. The mean and standard deviation of the
occurrence and detection probabilities for all species included in the model are presented in Table 5. Mean
covariate estimates for the occurrence model are presented for all species (except the four very rare species) in
Appendix C and Appendix D.
Table 5. The number of sites and detections of each modeled small mammal species. Mean and standard deviation
in detection and occurrence probabilities are reported.
Species Name
Allen's chipmunk (Tamias senex)
Brush mouse (Peromyscus boylii)*
Bushy-tailed woodrat (Neotoma cinera)
California ground squirrel (Spermophilus beecheyi)
Deer mouse (Peromyscus maniculatus)
Douglas' squirrel (Tamiasciurus douglasii)
Golden-mantled ground squirrel (Spermophilus lateralis)
Lodgepole chipmunk (Tamias speciosus)
Long-eared chipmunk (Tamias quadrimaculatus)
Long-tailed vole (Microtus longicaudus)
Montane vole (Microtus montanus)*
Northern flying squirrel (Glaucomys sabrinus)
Trowbridge's shrew (Sorex trowbridgii)
Western gray squirrel (Sciurus griseus)*
Western jumping mouse (Zapus princeps)*
Yellow-pine chipmunk (Tamias amoenus)
Code
Detections
TASE
203
PEBO
9
NECI
26
SPBE
187
PEMA
399
TADO
141
SPLA
287
TASP
211
TAQU
354
MILO
83
MIMO
16
GLSA
31
SOTR
15
SCGR
7
ZAPR
6
TAAM
318
Sites
Detected
82
6
14
83
144
78
115
80
137
43
9
26
13
6
5
116
Detection
Probability
0.791 ± 0.038
0.359 ± 0.149
0.527 ± 0.117
0.767 ± 0.042
0.916 ± 0.018
0.554 ± 0.062
0.813 ± 0.031
0.872 ± 0.030
0.838 ± 0.029
0.633 ± 0.064
0.477 ± 0.129
0.153 ± 0.054
0.104 ± 0.052
0.102 ± 0.100
0.188 ± 0.125
0.906 ± 0.021
Occurrence
Probability
0.731 ± 0.073
0.120 ± 0.095
0.147 ± 0.064
0.434 ± 0.085
0.975 ± 0.018
0.374 ± 0.090
0.872 ± 0.047
0.795 ± 0.073
0.838 ± 0.051
0.347 ± 0.084
0.066 ± 0.042
0.551 ± 0.198
0.382 ± 0.241
0.251 ± 0.258
0.187 ± 0.173
0.736 ± 0.071
The differences between the estimated species-specific detection and occurrence probabilities between the
predictive model and the model incorporating the project (MSIM/LTUB) as a covariate were similar. Because
trapping density was higher at LTUB sites, the mean standardized group-level estimate (all species combined)
on detection (0.062) resulting in a higher detection prediction for sites that had used an LTUB protocol;
however, the largest species-specific difference in the probability of detection was 0.08% for the northern flying
squirrel (0.55% versus 0.47%). The larger trapping area at MSIM sites resulted in the mean standardized grouplevel estimate on occurrence (-1.168) resulting in a higher occurrence predictions for sites that had used an
MSIM protocol; however, the largest species-specific difference in the probability of occurrence was 0.03% for
the montane vole Microtus montanus.
19
4.2.1 GIS-based models
Mean occurrence probabilities varied substantially across species from <1% to 98% (Table 5) when all abiotic
and biotic covariates were held at their mean values. As predicted, small mammal species were significantly
influenced by elevation with the probability of occurrence for the majority of species being highest at low to
mid-elevations (Table 5). The probability of occurrence for lodgepole chipmunk and deer mouse Peromyscus
maniculatus increased with elevation. Golden-mantled ground squirrels, bushy-tailed woodrats Neotoma cinera, and
yellow-pine chipmunks Tamias amoenus were predicted to more commonly occur in the east, whereas Allen's chipmunks
Tamias senex were predicted to occur more commonly in the west. The mean covariate estimate on urban
development (α1) was negative for 11 species (indicating a decline in occupancy with higher levels of
development for 70% of species) with the posterior intervals of two species (golden-mantled ground squirrel
Spermophilus lateralis, lodgepole chipmunk Tamias speciosus) not overlapping zero (Table 5, Figure 6).
In general, mean parameter estimates for any single structural aspect of the forest were small relative to the
abiotic variables with the exception of shrub cover and herbaceous cover (Appendix C). Of the modeled habitat
covariates, percent canopy cover significantly influenced the occurrence probability of the golden-mantled
ground squirrel, percent shrub cover significantly influenced the occurrence probability of the golden-mantled
ground squirrel (negatively) and the California ground squirrel Spermophilus beecheyi (positively), and percent
herbaceous cover was positively associated with the occurrence of the long-tailed vole Microtus longicaudus
(Table 6). Increases in the standard deviation in canopy cover were associated with an increase in the
probability of occurrence of yellow-pine chipmunks.
Table 6. Number of small mammal species in the GIS-based model in which the species-specific parameter
estimate was negative or positive. Values in parenthesis indicate the subset of species in which the posterior
intervals do not overlap zero.
Parameter
Negative
Positive
Development
11(2)
5(1)
Elevation
10(2)
6(2)
2
Elevation
14(6)
2
Easting
7(1)
9(3)
DBH
13
3
Canopy cover
10(1)
6
Canopy cover2
10
6
Canopy SD
5
11(1)
Tree size diversity
13
3
Tree size diversity SD
2
14
Shrub cover
9(1)
7(1)
5
11
Shrub cover2
6
10(2)
Herbaceous cover
Estimates derived from each species-specific model were used to generate a Basin-wide spatial distribution map
with the probability of occurrence for each species predicted at a 30-m pixel resolution. These maps are
presented in Appendix F and are electronically available in raster format. Two methods are presented to
evaluate the validity and performance of each species-specific model: 1) the ability of the model to distinguish
between occupied and unoccupied sites, and 2) the ability of the model to quantify habitat suitability
(calibration, Vaughan and Ormerod 2005). These are presented in Appendix G.
20
21
Figure 6. Mean parameter estimates and posterior intervals for the effect of a) percent development, b)
elevation, c) easting, d) mean DBH, e) tree size diversity, f) standard deviation in tree size diversity, g) percent
canopy cover, h) standard deviation in canopy cover, i) shrub cover, and j) herbaceous cover for each small
mammal species included in our GIS-based analysis. Values indicate the change in occurrence predicted as a
function of the change in one standard deviation of change in each response variable.
4.2.2 Field-based models
Of the modeled habitat covariates collected via field sampling methods, the small mammal community seemed
to be negatively associated with stem and large tree density, but positively associated with diversity in tree size.
The probability of occurrence or the golden-mantled ground squirrel was the only species however to be
significantly influenced by tree size diversity. Similarly, the occurrence of lodgepole pine chipmunks was the
sole species whose parameter estimate was significant for the density of trees and the yellow-pine chipmunk
was the only species significantly associated with the density of large trees (Table 7, Figure 7). Percent shrub
cover was significantly associated with a decrease in the golden-mantled ground squirrel and the yellow-pine
chipmunk.
Table 7. Number of small mammal species in the field-based model in which the species-specific parameter
estimate was negative or positive. Values in parenthesis indicate the subset of species in which the posterior
intervals do not overlap zero.
Parameter
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree size diversity
Shrub
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
Negative
10(2)
8(2)
14(4)
7(1)
13(1)
6
0
10(2)
12(1)
10
11
9
14(1)
Positive
6(1)
8(4)
2
9(1)
3
10(3)
16(1)
6
4
6
5
7
2
22
Figure 7. Mean parameter estimates and posterior intervals for the effect of a) stem density, b) tree size
diversity, c) large tree density, d) shrub cover, e) soft snag density, f) hard snag density, g) volume of soft
CWD, and h) volume of soft CWD for each small mammal species included in our field-based analysis. Values
indicate the change in occurrence predicted as a function of the change in one standard deviation of change in
each response variable.
23
5.0 DISCUSSION
Biodiversity is integral to ecosystem functioning (Ehrlich and Ehrlich, 1981; Folke et al., 1996; Tilman, 1999)
and services that are essential for human well-being (Daily, 1997; Naeem et al., 2009). Although the importance
of biodiversity conservation is recognized, it remains one of the key challenges of land stewardship (Mac Nally
et al., 2002; Fischer et al., 2004; Noon et al., 2009) due to several ecological and practical limitations (Margules
and Pressey, 2000). First, the distribution, abundance and habitat requirements of all species in a particular area
are rarely known, rendering optimal management solutions (if they exist) nearly impossible to discern. Second,
management interventions that improve habitat conditions for one species can decrease the quality of habitat for
others (Pulliam, 2000). Lastly, how a species responds to a particular set of habitat conditions may vary
spatially and temporally based on site-specific biotic and abiotic factors. We use a set of multi-species
occurrence model to estimate habitat associations for 66 avian species and 16 species of small mammals in the
Lake Tahoe Basin. Our approach which accounts for variations in detectability of species during sampling,
estimates occurrence probabilities of all species in a community by linking species occurrence models into one
hierarchical community model, thus improving inferences on all species, especially those that are rare of
observed infrequently. The results of these models can be used by managers in the Lake Tahoe Basin to better
understand how variation in different abiotic and biotic variables can influence the suite of species that currently
occur in the area.
This synthesis of data on bird and small mammal populations in the Lake Tahoe Basin is intended to improve
the capacity and confidence of stakeholders tasked with making decisions that could impact biodiversity. The
interests of stakeholders can be influenced to varying degrees by social, economic, ecological and political
factors and below we suggest how the data presented can be used to evaluate the potential impact of
management decisions at the project and landscape scale on individual species and the ecosystem.
5.1 PROJECT EVALUATIONS
The ecological impact of a proposed project is evaluated to assess the potential effects of the project on
individual species. Typically this includes the impact of the project on federally-listed species (Biological
Assessment) and Forest Service and State-listed species (Biological Evaluation). However the majority of
wildlife species that occur in the Basin are not currently listed, although several of the species in our analyses
have been identified by the LTBMU (Pathway 2007), the US Fish and Wildlife Service and the Department of
Fish and Game as species of concern (Appendix E). Additionally, the majority of species considered in our
models are uncommon to rare and ensuring their persistence is important from a biodiversity as well as an
individual species perspective. Therefore, when addressing the ecological impact of a project a stakeholder may
consider the following questions:
• What species are likely to be impacted given the location of the project?
• Are any of these species considered species of special concern or limited in the Tahoe basin?
• How does forest structure change as a consequence of the proposed activity?
• How are these changes predicted to impact the occurrence of these species?
• Is the location of the project in an ecologically significant area for species of concern?
• Is the location of the project in an ecologically significant area for biodiversity within the basin?
• Are there alterations to the project that could mitigate the effects?
24
For example, the use of fuel reduction treatments to address concerns regarding the high fuel loads and altered
conditions of many of the dry forests of the western U.S. is ubiquitous (Brown et al., 2004). Recently, more
emphasis has been placed on not only reducing fire hazard through forest thinning, but designing treatments to
simulate a heterogeneous stand structure more characteristic of the past (Carey, 2003; North et al., 2009;
Verschuyl et al., 2011). Thinning of trees in fire-suppressed forests could increase the probability of occurrence
of many species as gaps in the tree canopy have many beneficial ecological effects, including increasing plant
diversity and shrub cover (North et al., 2005) and can provide a variety of microhabitat for species dependent
upon shrubs for reproductive or foraging purposes (Purcell and Stephens, 2005). Species whose probability of
occurrence increased by >20% when canopy cover was reduced from 65% to 40% included: Western Woodpewee, White-breasted Nuthatch (Pathway 2007), Olive-sided Flycatcher (DFG 2011, USFWS 2011, Pathway
2007), Townsend’s Solitaire (USFWS 2011), Williamson’s Sapsucker (USFWS 2011) and Northern Flicker. In
contrast, the probability of occurrence of the Evening Grosbeak, Macgillivray’s Warber (USFWS 2011,
Pathway 2007), Wilson’s Warbler (Pathway 2007), Nashville Warbler (Pathway 2007), Golden-crowned
Kinglet (Pathway 2007) and Hermit Thrush (Pathway 2007) was predicted to decrease by >20%. However, as
the majority of fuel reduction treatments are targeted in the more developed areas at lower elevations, the
treatments may be more likely in those species that are also associated with lower elevation sites and are
typically more tolerant of development. Although the Nashville Warbler has a higher probability of occurrence
at lower elevations and sites with higher canopy cover, the occurrence of this species is also associated with
higher shrub cover. Although fuel reduction projects using mechanical removal of trees can open the canopy
quickly, shrub growth is predicted to occur in response and may help mitigate the effect of this species some
number of years following treatment.
For project-level evaluations, managers can use the Tahoe Wildlife Evaluation Tool (TWILD) database to
understand how the occurrence of species or concern is predicted to change in response to components of forest
structure at a specific elevation and easting coordinate. Appendix E provides a list of species that have been
identified as a species of concern by various public and private stakeholders, and the status (rare, common, etc.)
of all species that were included in our models. The species distribution maps (Appendix F) serve as a visual
guide of potential restrictions to the distribution of species that may make them more or less likely to be
impacted by the project given its spatial location.
In addition, changes in climatic conditions in the central Sierra Nevada are predicted to alter the distribution and
abundance of many species. Future climates are predicted to be warmer due to a greater concentration of
greenhouse gases resulting in a shift from precipitation falling as snow to rain (Morelli et al., 2011). Species
that are currently restricted by current climatic conditions are expected to change. Of particular concern are
those species that exist at high elevation: Williamson's Sapsucker Sphyrapicus thyroideus, Pine Grosbeak Pinicola
enucleator, White-crowned Sparrow Zonotrichia leucophrys, and Rock Wren Salpinctes obsoletus. Gardali et al (2012)
list several avian species as high priority based on their vulnerability to climate change (Appendix E)
5.2 LANDSCAPE EVALUATIONS
Management actions that are driven by one or a few focal species are not likely to maintaining biodiversity if
they result in decreased variability in habitat conditions. An integrated approach that emphasizes conserving a
diversity of habitats across environmental gradients and minimizing the extent of urbanization impacts is likely
to more effectively conserve and restore biodiversity and enhance ecosystem functioning than a single-species
focus. The use of multi-species approaches to inform land management can also enhance biodiversity
conservation by identifying habitat conditions that support unique suites of species. Management approaches
that consider the extent and distribution of habitat conditions across landscapes have the greatest likelihood of
conserving and restoring biodiversity and ecosystem functions.
25
The GIS-based and field-based models are not directly comparable as they are based on different sample sizes
(particularly for avian models), suite of habitat variables, and scale. The GIS-based models represent a coarsefilter approach, while the field-based models provide habitat variables at a much finer scale. For birds, there was
more confidence in field-based parameter estimates (i.e. parameter estimates in which confidence intervals did
not overlap zero) than with the GIS-based model. Although this may suggest that a coarser-filter approach may
sufficiently explain avian-habitat relationships, this could also be attributed to the larger sample size of the GISbased model. Small mammal GIS-based and field-based models had similar sample sizes and predictive power.
Parameter estimates from the GIS-based avian model underscore the negative impact of development on
biodiversity. The majority of avian species also had a negative parameter estimate associated with mean tree
size (DBH) suggesting that more of the avian community used habitat with on average smaller trees. However
this measure includes areas (30-m X 30-m pixel) lacking trees and could be interpreted as the avian community
preferring forested areas interspersed with more open areas. This later interpretation is supported in the
generally positive response to increasing tree size diversity. Unlike average DBH, this measure only takes into
account forested areas and a net positive response suggests that most avian species prefer forested areas with
mixed-age stands. Further supporting this explanation is both the number of species responding positively to
lower levels of canopy cover, and higher levels of shrub and herbaceous cover. Whereas parameter estimates for
birds in GIS-based models indicates that most avian species would respond positively to shrub cover, the fieldbased model suggest that many species of birds would respond negatively to increasing levels of shrub cover
within the stand.
Parameter estimates from the small mammal models also indicated that the majority of these species responded
negatively to development and average DBH. Unlike the avian community, more species of small mammal
tended to respond negatively to tree size class diversity, but had positive parameter estimates associated with
variance in tree size diversity. While tree size diversity measures differences in each 30-m pixel, variance in
tree size diversity accounts for differences across pixels. A net positive response to variance in tree size
diversity can be interpreted as small mammals preferring a more “heterogeneous” forest structure with stronger
differences between stands of trees (i.e. juxtaposition of forests in different successional stages).
Finer-scale, field-based models indicated that the majority of birds and small mammals would benefit from
stand thinning as both groups tended to have negative parameter estimates associated with the number of
trees/HA. In contrast to GIS-based models, at the majority of small mammal species tended to respond
positively to increasing levels of tree size class diversity at the stand scale. However, although this measure
(and shrub cover) may appear equivalent across model types, the field-based estimate included smaller diameter
trees than the GIS-based measure allows. Combined with a general negative response to the density of large
trees, this may indicate that the majority of small mammals prefer early to mid-successional habitats.
Field-based models incorporated several variables that are considered important habitat feature for wildlife:
large trees, snags and coarse woody debris. Model parameter estimates did not suggest that wildlife responded
strongly to the current variation in the Lake Tahoe Basin in these covariates. Indeed, with the exception of the
effect of hard coarse woody debris on the probability of occurrence for members of the avian community, there
tended to be a net negative response for increases in many of these forest elements. This incongruity in the
predicted response may be different in areas with lower levels of snags and coarse woody debris and may
suggest that current levels in the basin may be saturated. Under a more active fire regime we would expect to
see a shift in the range of these variables with less soft wood and possibly larger snags and logs and large trees.
Parameters estimated through our multi-species model emphasize the importance of within and between standlevel heterogeneity in meeting biodiversity objectives. At the stand level, some species responded positively to
26
higher variance in tree size and canopy cover; thus, increasing forest heterogeneity in forest stands would
improve habitat suitability for these species (see North et al., 2009; North, 2012). Species responses to the suite
of abiotic and biotic variables were variable with a similar number of species in our models responding
positively and negatively to the most influential abiotic and biotic variables, suggesting the importance of
heterogeneity between forest stands. The results of our model indicate that practices and management
approaches that lead to increased homogenization of the forest will have negative impacts on diversity.
Management approaches, such as fuel reduction treatments, or the use of prescribed or managed wildland fire,
may be designed to restore at least some of the variability within and among stands that existed during an active
fire regime, thereby enhancing habitat conditions for conserving biodiversity. Structurally complex landscapes
also increase resiliency and the capacity to recover from a disturbance (Lindenmayer et al., 2008).
Therefore, when addressing the ecological resiliency of the landscape a stakeholder may consider the following
questions:
• What are the threats to the landscape?
• How are these threats predicted to change in future climates?
• Which species are most likely to be impacted by these threats?
• If species are lost from the system are there other functionally similar species that would remain?
• What sites with high ecological value may be impacted?
Conservation of biological diversity is one of the goals of multi-objective forest management. Maintaining the
biological diversity of our forests is important as loss of native biodiversity can negatively affect ecosystem
properties and the impacts of species loss and compositional change of communities on ecosystem functioning
have been the focus of much research (reviewed in Hooper et al., 2005). Because it is difficult to predict how
the loss of a particular species would impact ecosystem functioning, many biodiversity targets focus on
retaining or restoring the greatest number of species. Ecosystem functioning may not be affected by loss in
species richness per se, if loss of a species is ameliorated by the presence of a functionally similar species
(Walker, 1992; Naeem, 2002) therefore, maintaining redundancy in groups of species that fill similar ecological
roles (i.e. guilds, functional groups) may improve ecosystem resiliency.
27
Acknowledgements
Thanks first and foremost to the Tahoe Science Consortium and the Bureau of Land Management for granting
funds for this project through the Sierra Nevada Public Lands Management Act. Many agencies and individuals
contributed to funding and implementing the projects that contributed data to this project, including the USFS
Lake Tahoe Basin Management Unit, Tahoe Regional Planning Agency, and California Tahoe Consevancy. .
Special thanks to Matthew Schlesinger, Susan Merideth, Kristian McIntyre, Julie Roth, and Ted Thayer for their
tireless efforts designing and leading the field efforts for these studies. Marcel Holyoak and Dennis Murphy
contributed to the overall design and implementation of the LTUB study. We thank Gina Tarbill, Tray Biasiolli,
Matthew Strusis-Timmer and Claire Gallagher for their vast knowledge of birds. We thank Ross Gerrard for his
help with our geographic data extraction and for his mapping expertise. Discussions and comments from John
Keane and Malcolm North were important in the development of this report. Thanks to all the agencies and land
owners in the Lake Tahoe basin that allowed us access to sample sites.
28
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31
6.0 APPENDICES
32
6.1 APPENDIX A: Parameter Estimates – Avian species (GIS-based covariates)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
AMRO
1.496 ± 0.168
(1.182, 1.844)
0.175 ± 0.129
(-0.068, 0.448)
-0.301 ± 0.160
(-0.605, 0.022)
0.073 ± 0.087
(-0.094, 0.250)
0.153 ± 0.109
(-0.056, 0.360)
-0.148 ± 0.085
(-0.317, 0.019)
0.170 ± 0.117
(-0.067, 0.396)
-0.052 ± 0.089
(-0.230, 0.123)
0.220 ± 0.100
(0.022, 0.418)
0.024 ± 0.101
(-0.168, 0.227)
-0.133 ± 0.088
(-0.305, 0.041)
0.134 ± 0.106
(-0.080, 0.340)
0.019 ± 0.058
(-0.093, 0.136)
0.424 ± 0.162
(0.138, 0.759)
BBWO
-2.857 ± 0.452
(-3.719, -1.920)
-0.762 ± 0.393
(-1.583, -0.053)
0.722 ± 0.368
(0.025, 1.516)
-0.537 ± 0.227
(-1.031, -0.145)
0.209 ± 0.232
(-0.250, 0.693)
0.066 ± 0.173
(-0.245, 0.431)
-0.192 ± 0.256
(-0.689, 0.328)
-0.179 ± 0.165
(-0.509, 0.129)
-0.198 ± 0.166
(-0.529, 0.123)
-0.072 ± 0.167
(-0.412, 0.260)
-0.150 ± 0.136
(-0.423, 0.102)
-0.424 ± 0.229
(-0.878, 0.023)
-0.004 ± 0.096
(-0.198, 0.181)
-0.026 ± 0.229
(-0.503, 0.397)
BHCO
-0.364 ± 0.163
(-0.677, -0.046)
0.710 ± 0.163
(0.407, 1.041)
-1.653 ± 0.168
(-1.978, -1.313)
0.188 ± 0.114
BHGR
-2.563 ± 0.260
(-3.104, -2.087)
0.059 ± 0.101
(-0.139, 0.262)
-0.617 ± 0.203
(-1.027, -0.246)
0.035 ± 0.149
(-0.048, 0.406)
0.643 ± 0.102
(0.435, 0.840)
-0.045 ± 0.104
(-0.240, 0.172)
0.064 ± 0.136
(-0.200, 0.327)
-0.057 ± 0.086
(-0.227, 0.111)
-0.123 ± 0.092
(-0.305, 0.063)
-0.053 ± 0.110
(-0.265, 0.164)
-0.049 ± 0.091
(-0.232, 0.130)
0.287 ± 0.117
(0.054, 0.516)
0.035 ± 0.058
(-0.079, 0.149)
0.593 ± 0.129
(0.348, 0.847)
(-0.262, 0.312)
0.612 ± 0.166
(0.301, 0.949)
-0.138 ± 0.142
(-0.412, 0.142)
0.210 ± 0.186
(-0.149, 0.566)
-0.302 ± 0.131
(-0.573, -0.057)
0.135 ± 0.108
(-0.072, 0.342)
0.023 ± 0.148
(-0.268, 0.313)
0.001 ± 0.096
(-0.186, 0.185)
-0.221 ± 0.174
(-0.565, 0.107)
0.013 ± 0.088
(-0.168, 0.181)
0.338 ± 0.129
(0.101, 0.613)
BLGR
-2.354 ± 0.258
(-2.864, -1.842)
-0.863 ± 0.357
(-1.607, -0.210)
0.949 ± 0.243
(0.504, 1.435)
-0.363 ± 0.154
(-0.679, -0.080)
-0.393 ± 0.156
(-0.692, -0.074)
-0.055 ± 0.130
(-0.316, 0.207)
-0.040 ± 0.186
(-0.406, 0.317)
-0.078 ± 0.135
(-0.326, 0.192)
-0.016 ± 0.127
(-0.275, 0.235)
0.021 ± 0.137
(-0.240, 0.284)
-0.015 ± 0.118
(-0.246, 0.221)
0.300 ± 0.149
(0.021, 0.603)
0.064 ± 0.072
(-0.072, 0.201)
0.253 ± 0.146
(-0.045, 0.534)
BRBL
-5.008 ± 0.473
(-6.063, -4.200)
0.534 ± 0.112
(0.323, 0.755)
-2.806 ± 0.418
(-3.730, -2.062)
0.594 ± 0.197
(0.180, 0.946)
0.356 ± 0.184
(0.004, 0.715)
-0.300 ± 0.150
(-0.611, -0.019)
-0.431 ± 0.215
(-0.864, -0.013)
0.027 ± 0.137
(-0.236, 0.293)
-0.069 ± 0.109
(-0.272, 0.155)
-0.040 ± 0.158
(-0.351, 0.269)
0.243 ± 0.112
(0.020, 0.463)
-0.254 ± 0.214
(-0.671, 0.157)
-0.040 ± 0.097
(-0.241, 0.147)
0.229 ± 0.132
(-0.033, 0.487)
BRCR
0.548 ± 0.153
(0.249, 0.858)
-0.449 ± 0.087
(-0.622, -0.283)
-0.236 ± 0.130
(-0.484, 0.018)
-0.179 ± 0.091
(-0.366, -0.004)
0.022 ± 0.103
(-0.181, 0.222)
-0.349 ± 0.092
(-0.540, -0.175)
0.783 ± 0.123
(0.548, 1.029)
-0.199 ± 0.090
(-0.379, -0.021)
-0.115 ± 0.083
(-0.283, 0.046)
-0.057 ± 0.104
(-0.260, 0.144)
-0.033 ± 0.082
(-0.191, 0.130)
-0.282 ± 0.107
(-0.503, -0.077)
-0.038 ± 0.060
(-0.159, 0.079)
0.115 ± 0.098
(-0.070, 0.321)
33
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
BTPI
-1.655 ± 0.199
(-2.043, -1.261)
0.556 ± 0.102
(0.352, 0.765)
-0.127 ± 0.189
(-0.496, 0.238)
-0.196 ± 0.133
(-0.469, 0.049)
0.059 ± 0.127
(-0.192, 0.309)
0.012 ± 0.112
(-0.202, 0.237)
0.180 ± 0.145
(-0.098, 0.468)
-0.222 ± 0.103
(-0.428, -0.027)
-0.098 ± 0.095
(-0.281, 0.085)
-0.283 ± 0.124
(-0.533, -0.050)
-0.080 ± 0.088
(-0.247, 0.091)
-0.103 ± 0.128
(-0.357, 0.153)
0.052 ± 0.065
(-0.073, 0.177)
-0.318 ± 0.154
(-0.637, -0.046)
CAFI
-0.533 ± 0.208
(-0.917, -0.115)
-0.395 ± 0.107
(-0.610, -0.203)
0.795 ± 0.191
(0.436, 1.198)
0.359 ± 0.165
CAHU
-3.096 ± 0.383
(-3.814, -2.348)
-0.678 ± 0.384
(-1.459, 0.018)
0.616 ± 0.331
(-0.004, 1.289)
-0.585 ± 0.223
(0.059, 0.695)
-0.111 ± 0.113
(-0.326, 0.112)
-0.022 ± 0.164
(-0.334, 0.307)
-0.696 ± 0.171
(-1.044, -0.372)
0.075 ± 0.102
(-0.126, 0.274)
-0.065 ± 0.092
(-0.237, 0.117)
0.092 ± 0.139
(-0.173, 0.382)
0.008 ± 0.096
(-0.173, 0.198)
-0.346 ± 0.146
(-0.633, -0.071)
0.086 ± 0.076
(-0.059, 0.239)
0.022 ± 0.128
(-0.223, 0.277)
(-1.022, -0.167)
0.000 ± 0.211
(-0.412, 0.414)
0.121 ± 0.154
(-0.177, 0.427)
-0.508 ± 0.248
(-1.016, -0.063)
0.042 ± 0.155
(-0.258, 0.345)
0.024 ± 0.152
(-0.279, 0.307)
-0.107 ± 0.156
(-0.419, 0.194)
0.032 ± 0.125
(-0.218, 0.274)
0.135 ± 0.185
(-0.222, 0.506)
-0.030 ± 0.078
(-0.188, 0.121)
0.298 ± 0.159
(-0.012, 0.616)
CAVI
-2.105 ± 0.213
(-2.514, -1.699)
-0.997 ± 0.176
(-1.349, -0.661)
-0.688 ± 0.182
(-1.034, -0.332)
-0.125 ± 0.146
(-0.440, 0.139)
-0.045 ± 0.112
(-0.264, 0.175)
-0.131 ± 0.118
(-0.363, 0.106)
0.119 ± 0.153
(-0.181, 0.413)
0.188 ± 0.097
(-0.003, 0.377)
0.219 ± 0.097
(0.025, 0.414)
0.125 ± 0.126
(-0.115, 0.373)
-0.153 ± 0.105
(-0.362, 0.049)
0.056 ± 0.140
(-0.212, 0.322)
0.070 ± 0.067
(-0.062, 0.197)
-0.087 ± 0.126
(-0.337, 0.151)
CHSP
-2.237 ± 0.239
(-2.702, -1.770)
-0.413 ± 0.145
(-0.702, -0.131)
-0.255 ± 0.194
(-0.640, 0.124)
0.099 ± 0.124
(-0.146, 0.344)
-0.062 ± 0.148
(-0.349, 0.232)
-0.318 ± 0.128
(-0.572, -0.069)
-0.270 ± 0.184
(-0.627, 0.083)
0.039 ± 0.122
(-0.199, 0.268)
0.144 ± 0.111
(-0.082, 0.361)
0.031 ± 0.140
(-0.237, 0.306)
-0.157 ± 0.106
(-0.369, 0.052)
0.483 ± 0.162
(0.174, 0.805)
-0.088 ± 0.076
(-0.242, 0.056)
0.590 ± 0.143
(0.333, 0.881)
CLNU
-0.147 ± 0.163
(-0.479, 0.163)
-0.122 ± 0.096
(-0.312, 0.062)
1.121 ± 0.158
(0.816, 1.437)
0.178 ± 0.135
(-0.064, 0.462)
0.505 ± 0.105
(0.301, 0.711)
0.040 ± 0.102
(-0.168, 0.231)
-0.304 ± 0.123
(-0.546, -0.065)
-0.009 ± 0.089
(-0.191, 0.163)
-0.141 ± 0.086
(-0.311, 0.032)
-0.108 ± 0.108
(-0.318, 0.103)
-0.074 ± 0.087
(-0.242, 0.097)
0.261 ± 0.114
(0.032, 0.476)
0.115 ± 0.071
(-0.020, 0.257)
-0.109 ± 0.097
(-0.300, 0.072)
CORA
-2.633 ± 0.226
(-3.080, -2.183)
-0.008 ± 0.097
(-0.197, 0.175)
-0.909 ± 0.186
(-1.271, -0.547)
0.267 ± 0.107
(0.050, 0.470)
0.209 ± 0.141
(-0.063, 0.489)
-0.036 ± 0.112
(-0.250, 0.192)
-0.188 ± 0.153
(-0.489, 0.105)
0.124 ± 0.103
(-0.080, 0.317)
-0.051 ± 0.101
(-0.252, 0.150)
-0.188 ± 0.131
(-0.443, 0.076)
0.018 ± 0.092
(-0.158, 0.194)
0.103 ± 0.147
(-0.179, 0.389)
-0.101 ± 0.075
(-0.258, 0.035)
0.026 ± 0.105
(-0.181, 0.229)
34
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
DEJU
3.800 ± 0.421
(3.087, 4.735)
-0.337 ± 0.115
(-0.565, -0.112)
0.686 ± 0.276
(0.163, 1.262)
-0.458 ± 0.129
(-0.733, -0.207)
-0.193 ± 0.195
(-0.588, 0.175)
0.027 ± 0.130
(-0.228, 0.294)
0.435 ± 0.200
(0.056, 0.823)
-0.029 ± 0.137
(-0.301, 0.233)
0.054 ± 0.122
(-0.182, 0.304)
-0.105 ± 0.147
(-0.388, 0.187)
-0.170 ± 0.098
(-0.368, 0.013)
0.087 ± 0.185
(-0.273, 0.449)
0.099 ± 0.095
(-0.074, 0.296)
0.223 ± 0.141
(-0.033, 0.521)
DOWO
-2.515 ± 0.271
(-3.072, -2.006)
-0.134 ± 0.136
(-0.404, 0.129)
-0.495 ± 0.264
(-1.026, 0.004)
-0.333 ± 0.209
(-0.777, 0.070)
0.058 ± 0.157
(-0.240, 0.372)
-0.292 ± 0.141
(-0.569, -0.011)
0.087 ± 0.199
(-0.297, 0.488)
-0.160 ± 0.135
(-0.434, 0.105)
0.093 ± 0.112
(-0.128, 0.316)
0.136 ± 0.149
(-0.154, 0.447)
-0.111 ± 0.112
(-0.332, 0.104)
-0.061 ± 0.190
(-0.425, 0.308)
-0.019 ± 0.090
(-0.199, 0.151)
0.116 ± 0.128
(-0.141, 0.359)
DUFL
0.250 ± 0.153
(-0.048, 0.558)
-1.228 ± 0.181
(-1.600, -0.896)
0.414 ± 0.142
(0.130, 0.690)
-0.117 ± 0.085
(-0.285, 0.045)
-0.201 ± 0.106
(-0.411, 0.005)
-0.313 ± 0.108
(-0.537, -0.112)
0.334 ± 0.119
(0.104, 0.553)
0.037 ± 0.086
(-0.133, 0.202)
-0.056 ± 0.086
(-0.220, 0.118)
-0.299 ± 0.114
(-0.531, -0.085)
-0.140 ± 0.093
(-0.325, 0.041)
1.075 ± 0.129
(0.836, 1.342)
-0.029 ± 0.075
(-0.170, 0.122)
0.182 ± 0.100
(-0.012, 0.376)
EVGR
-0.776 ± 0.175
(-1.118, -0.442)
0.029 ± 0.085
(-0.133, 0.197)
-0.692 ± 0.146
(-0.978, -0.407)
0.297 ± 0.105
FOSP
1.311 ± 0.147
(1.024, 1.600)
-0.219 ± 0.084
(-0.380, -0.060)
-0.172 ± 0.133
(-0.427, 0.080)
-0.436 ± 0.093
GCKI
-0.586 ± 0.167
(-0.910, -0.258)
-0.435 ± 0.132
(-0.699, -0.183)
0.338 ± 0.157
(0.026, 0.630)
-0.614 ± 0.133
GTTO
-1.829 ± 0.204
(-2.233, -1.413)
-0.238 ± 0.177
(-0.592, 0.080)
0.680 ± 0.192
(0.306, 1.061)
-0.260 ± 0.115
(0.102, 0.510)
-0.899 ± 0.113
(-1.121, -0.690)
0.126 ± 0.105
(-0.068, 0.338)
0.657 ± 0.124
(0.426, 0.907)
0.056 ± 0.088
(-0.121, 0.226)
0.047 ± 0.089
(-0.124, 0.222)
-0.024 ± 0.111
(-0.246, 0.192)
-0.053 ± 0.082
(-0.208, 0.108)
0.086 ± 0.114
(-0.139, 0.305)
0.017 ± 0.062
(-0.098, 0.142)
0.052 ± 0.098
(-0.141, 0.240)
(-0.613, -0.259)
-0.430 ± 0.105
(-0.635, -0.234)
-0.192 ± 0.089
(-0.372, -0.025)
-0.058 ± 0.112
(-0.276, 0.157)
-0.021 ± 0.076
(-0.173, 0.132)
-0.040 ± 0.079
(-0.193, 0.114)
-0.303 ± 0.104
(-0.500, -0.102)
-0.155 ± 0.075
(-0.298, -0.010)
0.910 ± 0.111
(0.697, 1.135)
-0.029 ± 0.069
(-0.161, 0.110)
-0.260 ± 0.086
(-0.434, -0.099)
(-0.872, -0.354)
-0.436 ± 0.104
(-0.640, -0.233)
-0.320 ± 0.097
(-0.518, -0.133)
1.019 ± 0.139
(0.747, 1.287)
0.005 ± 0.098
(-0.191, 0.193)
0.138 ± 0.089
(-0.034, 0.312)
-0.082 ± 0.109
(-0.295, 0.141)
-0.020 ± 0.094
(-0.206, 0.160)
-0.129 ± 0.115
(-0.356, 0.096)
0.060 ± 0.060
(-0.058, 0.178)
0.036 ± 0.118
(-0.210, 0.261)
(-0.505, -0.051)
0.110 ± 0.129
(-0.137, 0.367)
-0.012 ± 0.105
(-0.212, 0.202)
-0.207 ± 0.149
(-0.509, 0.083)
0.219 ± 0.113
(-0.014, 0.447)
-0.039 ± 0.107
(-0.252, 0.181)
0.001 ± 0.118
(-0.226, 0.236)
-0.056 ± 0.100
(-0.258, 0.138)
0.865 ± 0.144
(0.595, 1.163)
-0.020 ± 0.076
(-0.167, 0.132)
0.070 ± 0.117
(-0.162, 0.305)
35
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
HAWO
-0.855 ± 0.157
(-1.155, -0.530)
-0.541 ± 0.094
(-0.731, -0.364)
-0.657 ± 0.137
(-0.924, -0.387)
0.246 ± 0.082
HETH
-0.778 ± 0.187
(-1.135, -0.417)
-1.253 ± 0.268
(-1.816, -0.771)
0.812 ± 0.171
(0.476, 1.146)
-0.478 ± 0.111
HEWA
-2.665 ± 0.270
(-3.203, -2.151)
-1.410 ± 0.343
(-2.130, -0.814)
-0.196 ± 0.254
(-0.703, 0.295)
-0.369 ± 0.192
(0.082, 0.406)
0.133 ± 0.099
(-0.061, 0.332)
-0.090 ± 0.093
(-0.279, 0.094)
0.174 ± 0.121
(-0.046, 0.410)
-0.135 ± 0.087
(-0.307, 0.032)
-0.026 ± 0.083
(-0.185, 0.139)
-0.213 ± 0.100
(-0.408, -0.010)
-0.109 ± 0.082
(-0.278, 0.051)
-0.007 ± 0.109
(-0.223, 0.199)
0.003 ± 0.057
(-0.108, 0.114)
0.036 ± 0.099
(-0.149, 0.234)
(-0.704, -0.258)
-0.351 ± 0.103
(-0.560, -0.156)
-0.032 ± 0.099
(-0.232, 0.158)
0.665 ± 0.126
(0.424, 0.915)
0.169 ± 0.091
(-0.007, 0.347)
0.059 ± 0.096
(-0.135, 0.247)
-0.174 ± 0.105
(-0.379, 0.025)
-0.049 ± 0.091
(-0.227, 0.129)
-0.031 ± 0.110
(-0.249, 0.188)
0.002 ± 0.059
(-0.118, 0.114)
0.108 ± 0.100
(-0.085, 0.305)
(-0.753, -0.008)
-0.282 ± 0.131
(-0.538, -0.031)
-0.059 ± 0.140
(-0.328, 0.221)
0.448 ± 0.182
(0.113, 0.815)
0.164 ± 0.114
(-0.060, 0.386)
0.266 ± 0.116
(0.043, 0.483)
-0.110 ± 0.136
(-0.378, 0.154)
-0.028 ± 0.111
(-0.241, 0.195)
-0.196 ± 0.169
(-0.529, 0.122)
0.001 ± 0.080
(-0.161, 0.157)
-0.178 ± 0.184
(-0.566, 0.157)
HOWR
-2.217 ± 0.236
(-2.686, -1.759)
-0.279 ± 0.143
(-0.568, -0.007)
-0.292 ± 0.193
(-0.670, 0.106)
-0.197 ± 0.151
(-0.505, 0.093)
0.151 ± 0.149
(-0.137, 0.439)
0.038 ± 0.126
(-0.205, 0.286)
-0.204 ± 0.174
(-0.529, 0.161)
0.098 ± 0.119
(-0.138, 0.334)
0.098 ± 0.114
(-0.123, 0.324)
-0.108 ± 0.133
(-0.377, 0.144)
0.007 ± 0.104
(-0.200, 0.208)
0.346 ± 0.154
(0.045, 0.647)
-0.016 ± 0.071
(-0.158, 0.120)
0.359 ± 0.133
(0.101, 0.634)
LISP
-3.169 ± 0.358
(-3.893, -2.487)
-0.912 ± 0.373
(-1.710, -0.250)
0.848 ± 0.286
(0.308, 1.424)
-0.319 ± 0.174
MGWA
-0.746 ± 0.173
(-1.090, -0.399)
-0.390 ± 0.115
(-0.623, -0.171)
0.208 ± 0.153
(-0.082, 0.507)
-0.389 ± 0.123
(-0.657, -0.009)
-0.438 ± 0.190
(-0.823, -0.065)
-0.177 ± 0.141
(-0.457, 0.105)
0.009 ± 0.243
(-0.460, 0.498)
-0.067 ± 0.150
(-0.360, 0.217)
0.088 ± 0.145
(-0.189, 0.365)
0.183 ± 0.158
(-0.112, 0.504)
-0.002 ± 0.121
(-0.241, 0.243)
-0.075 ± 0.181
(-0.442, 0.276)
0.031 ± 0.087
(-0.138, 0.204)
0.670 ± 0.137
(0.402, 0.937)
(-0.635, -0.152)
-0.015 ± 0.101
(-0.210, 0.184)
-0.148 ± 0.115
(-0.379, 0.073)
0.471 ± 0.138
(0.197, 0.742)
0.162 ± 0.091
(-0.013, 0.339)
0.370 ± 0.092
(0.191, 0.550)
-0.046 ± 0.112
(-0.262, 0.169)
0.092 ± 0.092
(-0.081, 0.274)
0.601 ± 0.125
(0.359, 0.852)
-0.003 ± 0.064
(-0.126, 0.119)
0.245 ± 0.107
(0.040, 0.462)
MOCH
4.054 ± 0.384
(3.373, 4.859)
-0.084 ± 0.254
(-0.566, 0.436)
-0.556 ± 0.365
(-1.280, 0.149)
0.070 ± 0.167
(-0.261, 0.395)
0.036 ± 0.232
(-0.442, 0.481)
-0.225 ± 0.156
(-0.553, 0.064)
0.432 ± 0.264
(-0.054, 0.957)
-0.099 ± 0.154
(-0.405, 0.196)
0.012 ± 0.158
(-0.299, 0.316)
-0.050 ± 0.155
(-0.355, 0.262)
-0.062 ± 0.129
(-0.318, 0.195)
-0.065 ± 0.185
(-0.433, 0.276)
-0.001 ± 0.080
(-0.154, 0.156)
0.031 ± 0.185
(-0.292, 0.416)
36
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
MODO
-1.420 ± 0.174
(-1.755, -1.079)
-0.026 ± 0.083
(-0.191, 0.138)
-1.460 ± 0.158
(-1.764, -1.154)
0.246 ± 0.112
MOQU
-0.775 ± 0.189
(-1.147, -0.400)
-0.710 ± 0.194
(-1.120, -0.362)
0.274 ± 0.178
(-0.062, 0.627)
-0.482 ± 0.135
NAWA
-0.573 ± 0.178
(-0.914, -0.218)
-0.576 ± 0.136
(-0.848, -0.309)
-0.295 ± 0.169
(-0.612, 0.025)
-0.646 ± 0.146
(0.013, 0.447)
0.553 ± 0.115
(0.331, 0.787)
0.068 ± 0.118
(-0.165, 0.306)
-0.062 ± 0.136
(-0.327, 0.214)
-0.204 ± 0.093
(-0.383, -0.018)
-0.128 ± 0.085
(-0.302, 0.036)
-0.134 ± 0.116
(-0.366, 0.091)
0.036 ± 0.085
(-0.131, 0.203)
-0.010 ± 0.126
(-0.259, 0.241)
-0.024 ± 0.065
(-0.154, 0.105)
-0.142 ± 0.103
(-0.337, 0.064)
(-0.770, -0.243)
-0.744 ± 0.118
(-0.990, -0.518)
-0.190 ± 0.106
(-0.410, 0.010)
-0.033 ± 0.129
(-0.279, 0.218)
0.052 ± 0.093
(-0.123, 0.234)
-0.016 ± 0.099
(-0.214, 0.173)
-0.228 ± 0.116
(-0.458, -0.006)
-0.061 ± 0.102
(-0.272, 0.136)
0.401 ± 0.126
(0.160, 0.653)
-0.089 ± 0.065
(-0.210, 0.038)
-0.074 ± 0.129
(-0.328, 0.178)
(-0.932, -0.356)
-0.586 ± 0.109
(-0.804, -0.381)
-0.109 ± 0.117
(-0.343, 0.108)
0.381 ± 0.139
(0.117, 0.659)
0.245 ± 0.094
(0.065, 0.432)
0.075 ± 0.095
(-0.110, 0.263)
-0.235 ± 0.116
(-0.468, 0.003)
0.081 ± 0.095
(-0.098, 0.261)
0.616 ± 0.126
(0.373, 0.875)
-0.032 ± 0.067
(-0.157, 0.102)
0.294 ± 0.106
(0.102, 0.515)
NOFL
0.177 ± 0.133
(-0.087, 0.434)
-0.034 ± 0.083
(-0.198, 0.132)
-0.619 ± 0.127
(-0.873, -0.383)
0.135 ± 0.076
(-0.019, 0.284)
0.023 ± 0.090
(-0.153, 0.201)
-0.063 ± 0.078
(-0.210, 0.094)
-0.091 ± 0.104
(-0.291, 0.114)
-0.209 ± 0.078
(-0.365, -0.056)
-0.166 ± 0.077
(-0.320, -0.015)
-0.056 ± 0.090
(-0.239, 0.116)
-0.110 ± 0.075
(-0.258, 0.035)
0.161 ± 0.096
(-0.024, 0.360)
0.025 ± 0.052
(-0.076, 0.125)
0.262 ± 0.117
(0.049, 0.501)
OCWA
-3.653 ± 0.456
(-4.538, -2.687)
-0.930 ± 0.405
(-1.781, -0.211)
-0.120 ± 0.358
(-0.809, 0.578)
-0.311 ± 0.237
(-0.784, 0.142)
-0.403 ± 0.243
(-0.908, 0.061)
0.070 ± 0.168
(-0.246, 0.416)
-0.593 ± 0.274
(-1.150, -0.078)
0.055 ± 0.159
(-0.268, 0.367)
-0.035 ± 0.162
(-0.360, 0.284)
-0.099 ± 0.164
(-0.405, 0.222)
-0.073 ± 0.132
(-0.336, 0.187)
0.323 ± 0.207
(-0.085, 0.738)
0.054 ± 0.082
(-0.103, 0.214)
0.187 ± 0.184
(-0.186, 0.545)
OSFL
0.393 ± 0.160
(0.073, 0.700)
-0.420 ± 0.100
(-0.615, -0.221)
-0.025 ± 0.130
(-0.265, 0.236)
-0.492 ± 0.100
PIGR
-1.957 ± 0.284
(-2.522, -1.399)
-0.668 ± 0.356
(-1.401, -0.032)
1.437 ± 0.293
(0.905, 2.034)
-0.538 ± 0.168
(-0.681, -0.291)
-0.209 ± 0.096
(-0.406, -0.022)
0.037 ± 0.100
(-0.162, 0.228)
-0.250 ± 0.112
(-0.481, -0.039)
-0.174 ± 0.086
(-0.346, -0.012)
-0.144 ± 0.084
(-0.306, 0.025)
-0.229 ± 0.103
(-0.437, -0.038)
-0.122 ± 0.087
(-0.288, 0.051)
0.202 ± 0.101
(0.008, 0.400)
0.020 ± 0.059
(-0.092, 0.137)
0.016 ± 0.091
(-0.161, 0.199)
(-0.880, -0.222)
-0.183 ± 0.160
(-0.505, 0.128)
0.122 ± 0.133
(-0.136, 0.395)
0.151 ± 0.173
(-0.186, 0.483)
0.034 ± 0.122
(-0.201, 0.272)
0.154 ± 0.128
(-0.100, 0.409)
-0.210 ± 0.138
(-0.482, 0.057)
-0.066 ± 0.115
(-0.295, 0.161)
-0.043 ± 0.152
(-0.334, 0.267)
-0.042 ± 0.076
(-0.194, 0.093)
0.176 ± 0.165
(-0.144, 0.496)
37
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
MODO
-1.420 ± 0.174
(-1.755, -1.079)
-0.026 ± 0.083
(-0.191, 0.138)
-1.460 ± 0.158
(-1.764, -1.154)
0.246 ± 0.112
MOQU
-0.775 ± 0.189
(-1.147, -0.400)
-0.710 ± 0.194
(-1.120, -0.362)
0.274 ± 0.178
(-0.062, 0.627)
-0.482 ± 0.135
NAWA
-0.573 ± 0.178
(-0.914, -0.218)
-0.576 ± 0.136
(-0.848, -0.309)
-0.295 ± 0.169
(-0.612, 0.025)
-0.646 ± 0.146
(0.013, 0.447)
0.553 ± 0.115
(0.331, 0.787)
0.068 ± 0.118
(-0.165, 0.306)
-0.062 ± 0.136
(-0.327, 0.214)
-0.204 ± 0.093
(-0.383, -0.018)
-0.128 ± 0.085
(-0.302, 0.036)
-0.134 ± 0.116
(-0.366, 0.091)
0.036 ± 0.085
(-0.131, 0.203)
-0.010 ± 0.126
(-0.259, 0.241)
-0.024 ± 0.065
(-0.154, 0.105)
-0.142 ± 0.103
(-0.337, 0.064)
(-0.770, -0.243)
-0.744 ± 0.118
(-0.990, -0.518)
-0.190 ± 0.106
(-0.410, 0.010)
-0.033 ± 0.129
(-0.279, 0.218)
0.052 ± 0.093
(-0.123, 0.234)
-0.016 ± 0.099
(-0.214, 0.173)
-0.228 ± 0.116
(-0.458, -0.006)
-0.061 ± 0.102
(-0.272, 0.136)
0.401 ± 0.126
(0.160, 0.653)
-0.089 ± 0.065
(-0.210, 0.038)
-0.074 ± 0.129
(-0.328, 0.178)
(-0.932, -0.356)
-0.586 ± 0.109
(-0.804, -0.381)
-0.109 ± 0.117
(-0.343, 0.108)
0.381 ± 0.139
(0.117, 0.659)
0.245 ± 0.094
(0.065, 0.432)
0.075 ± 0.095
(-0.110, 0.263)
-0.235 ± 0.116
(-0.468, 0.003)
0.081 ± 0.095
(-0.098, 0.261)
0.616 ± 0.126
(0.373, 0.875)
-0.032 ± 0.067
(-0.157, 0.102)
0.294 ± 0.106
(0.102, 0.515)
NOFL
0.177 ± 0.133
(-0.087, 0.434)
-0.034 ± 0.083
(-0.198, 0.132)
-0.619 ± 0.127
(-0.873, -0.383)
0.135 ± 0.076
(-0.019, 0.284)
0.023 ± 0.090
(-0.153, 0.201)
-0.063 ± 0.078
(-0.210, 0.094)
-0.091 ± 0.104
(-0.291, 0.114)
-0.209 ± 0.078
(-0.365, -0.056)
-0.166 ± 0.077
(-0.320, -0.015)
-0.056 ± 0.090
(-0.239, 0.116)
-0.110 ± 0.075
(-0.258, 0.035)
0.161 ± 0.096
(-0.024, 0.360)
0.025 ± 0.052
(-0.076, 0.125)
0.262 ± 0.117
(0.049, 0.501)
OCWA
-3.653 ± 0.456
(-4.538, -2.687)
-0.930 ± 0.405
(-1.781, -0.211)
-0.120 ± 0.358
(-0.809, 0.578)
-0.311 ± 0.237
(-0.784, 0.142)
-0.403 ± 0.243
(-0.908, 0.061)
0.070 ± 0.168
(-0.246, 0.416)
-0.593 ± 0.274
(-1.150, -0.078)
0.055 ± 0.159
(-0.268, 0.367)
-0.035 ± 0.162
(-0.360, 0.284)
-0.099 ± 0.164
(-0.405, 0.222)
-0.073 ± 0.132
(-0.336, 0.187)
0.323 ± 0.207
(-0.085, 0.738)
0.054 ± 0.082
(-0.103, 0.214)
0.187 ± 0.184
(-0.186, 0.545)
OSFL
0.393 ± 0.160
(0.073, 0.700)
-0.420 ± 0.100
(-0.615, -0.221)
-0.025 ± 0.130
(-0.265, 0.236)
-0.492 ± 0.100
PIGR
-1.957 ± 0.284
(-2.522, -1.399)
-0.668 ± 0.356
(-1.401, -0.032)
1.437 ± 0.293
(0.905, 2.034)
-0.538 ± 0.168
(-0.681, -0.291)
-0.209 ± 0.096
(-0.406, -0.022)
0.037 ± 0.100
(-0.162, 0.228)
-0.250 ± 0.112
(-0.481, -0.039)
-0.174 ± 0.086
(-0.346, -0.012)
-0.144 ± 0.084
(-0.306, 0.025)
-0.229 ± 0.103
(-0.437, -0.038)
-0.122 ± 0.087
(-0.288, 0.051)
0.202 ± 0.101
(0.008, 0.400)
0.020 ± 0.059
(-0.092, 0.137)
0.016 ± 0.091
(-0.161, 0.199)
(-0.880, -0.222)
-0.183 ± 0.160
(-0.505, 0.128)
0.122 ± 0.133
(-0.136, 0.395)
0.151 ± 0.173
(-0.186, 0.483)
0.034 ± 0.122
(-0.201, 0.272)
0.154 ± 0.128
(-0.100, 0.409)
-0.210 ± 0.138
(-0.482, 0.057)
-0.066 ± 0.115
(-0.295, 0.161)
-0.043 ± 0.152
(-0.334, 0.267)
-0.042 ± 0.076
(-0.194, 0.093)
0.176 ± 0.165
(-0.144, 0.496)
38
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
PISI
-0.271 ± 0.156
(-0.564, 0.043)
-0.081 ± 0.086
(-0.254, 0.088)
0.358 ± 0.138
(0.103, 0.644)
-0.016 ± 0.103
(-0.208, 0.207)
0.045 ± 0.093
(-0.140, 0.222)
0.048 ± 0.099
(-0.152, 0.234)
-0.025 ± 0.118
(-0.247, 0.212)
0.066 ± 0.082
(-0.092, 0.223)
0.105 ± 0.078
(-0.048, 0.259)
-0.226 ± 0.099
(-0.417, -0.029)
-0.129 ± 0.082
(-0.292, 0.028)
-0.295 ± 0.104
(-0.503, -0.096)
-0.019 ± 0.055
(-0.132, 0.088)
0.200 ± 0.087
(0.044, 0.381)
PIWO
-3.837 ± 0.389
(-4.638, -3.103)
-0.825 ± 0.302
(-1.464, -0.299)
-1.077 ± 0.387
(-1.863, -0.343)
-0.223 ± 0.264
(-0.749, 0.260)
-0.079 ± 0.198
(-0.470, 0.316)
0.023 ± 0.172
(-0.296, 0.367)
0.083 ± 0.253
(-0.406, 0.592)
-0.048 ± 0.148
(-0.352, 0.234)
-0.077 ± 0.144
(-0.362, 0.198)
-0.074 ± 0.159
(-0.391, 0.250)
0.008 ± 0.131
(-0.250, 0.263)
0.062 ± 0.224
(-0.373, 0.512)
0.016 ± 0.095
(-0.173, 0.205)
-0.120 ± 0.211
(-0.566, 0.259)
PYNU
-1.234 ± 0.170
(-1.560, -0.910)
0.267 ± 0.096
(0.087, 0.458)
-1.350 ± 0.156
(-1.660, -1.043)
0.153 ± 0.104
(-0.055, 0.348)
0.716 ± 0.124
(0.474, 0.969)
-0.163 ± 0.105
(-0.370, 0.037)
-0.283 ± 0.140
(-0.554, -0.008)
-0.320 ± 0.100
(-0.522, -0.127)
-0.094 ± 0.086
(-0.263, 0.074)
-0.037 ± 0.120
(-0.267, 0.198)
-0.020 ± 0.089
(-0.191, 0.156)
-0.254 ± 0.126
(-0.493, -0.008)
0.001 ± 0.069
(-0.135, 0.134)
-0.082 ± 0.096
(-0.269, 0.116)
RBNU
1.821 ± 0.209
(1.438, 2.248)
-0.392 ± 0.096
(-0.581, -0.204)
0.224 ± 0.171
(-0.100, 0.575)
-0.091 ± 0.090
(-0.277, 0.080)
-0.454 ± 0.147
(-0.746, -0.174)
-0.078 ± 0.089
(-0.258, 0.093)
1.224 ± 0.160
(0.922, 1.550)
-0.124 ± 0.115
(-0.351, 0.096)
-0.056 ± 0.097
(-0.252, 0.128)
-0.380 ± 0.120
(-0.620, -0.151)
-0.253 ± 0.085
(-0.420, -0.089)
0.107 ± 0.109
(-0.104, 0.328)
0.103 ± 0.062
(-0.019, 0.228)
-0.082 ± 0.091
(-0.261, 0.092)
RBSA
-1.774 ± 0.224
(-2.226, -1.338)
-0.088 ± 0.138
(-0.369, 0.174)
0.331 ± 0.197
(-0.041, 0.729)
-0.246 ± 0.147
(-0.549, 0.031)
-0.117 ± 0.134
(-0.383, 0.144)
-0.118 ± 0.135
(-0.382, 0.156)
0.256 ± 0.185
(-0.100, 0.618)
-0.212 ± 0.124
(-0.465, 0.030)
0.145 ± 0.111
(-0.073, 0.360)
0.092 ± 0.133
(-0.166, 0.364)
-0.033 ± 0.108
(-0.255, 0.178)
0.160 ± 0.147
(-0.118, 0.450)
0.021 ± 0.072
(-0.121, 0.162)
0.132 ± 0.138
(-0.154, 0.388)
RECR
-1.845 ± 0.275
(-2.364, -1.294)
-0.265 ± 0.148
(-0.569, 0.011)
-0.119 ± 0.232
(-0.562, 0.319)
-0.227 ± 0.166
(-0.569, 0.082)
-0.366 ± 0.149
(-0.677, -0.086)
0.174 ± 0.143
(-0.091, 0.470)
-0.456 ± 0.178
(-0.817, -0.109)
-0.074 ± 0.125
(-0.323, 0.169)
0.110 ± 0.120
(-0.132, 0.339)
0.052 ± 0.139
(-0.222, 0.32)
-0.032 ± 0.109
(-0.253, 0.181)
-0.195 ± 0.152
(-0.496, 0.107)
-0.028 ± 0.080
(-0.191, 0.131)
0.070 ± 0.134
(-0.192, 0.340)
ROWR
-5.658 ± 0.574
(-6.879, -4.637)
-0.552 ± 0.479
(-1.525, 0.326)
1.783 ± 0.504
(0.854, 2.936)
-0.301 ± 0.169
(-0.653, 0.037)
-0.294 ± 0.255
(-0.796, 0.180)
-0.022 ± 0.115
(-0.240, 0.209)
-0.872 ± 0.334
(-1.577, -0.275)
0.309 ± 0.183
(-0.033, 0.678)
-0.036 ± 0.172
(-0.377, 0.308)
-0.147 ± 0.148
(-0.447, 0.138)
-0.025 ± 0.125
(-0.271, 0.222)
0.172 ± 0.171
(-0.162, 0.515)
0.068 ± 0.075
(-0.079, 0.220)
-0.254 ± 0.196
(-0.664, 0.104)
39
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
RUHU
-2.389 ± 0.321
(-3.020, -1.733)
-0.924 ± 0.393
(-1.791, -0.225)
1.160 ± 0.289
(0.610, 1.706)
-0.319 ± 0.140
(-0.603, -0.063)
-0.396 ± 0.174
(-0.740, -0.060)
0.090 ± 0.127
(-0.146, 0.346)
-0.511 ± 0.208
(-0.921, -0.122)
0.052 ± 0.144
(-0.237, 0.325)
0.138 ± 0.140
(-0.126, 0.413)
-0.164 ± 0.139
(-0.455, 0.106)
0.043 ± 0.120
(-0.186, 0.279)
0.328 ± 0.159
(0.037, 0.658)
0.004 ± 0.085
(-0.155, 0.181)
0.324 ± 0.152
(0.022, 0.631)
SOSP
-2.975 ± 0.285
(-3.539, -2.418)
-0.395 ± 0.154
(-0.707, -0.103)
-0.540 ± 0.222
(-0.972, -0.089)
-0.010 ± 0.175
(-0.363, 0.324)
0.379 ± 0.172
(0.057, 0.729)
-0.243 ± 0.145
(-0.532, 0.040)
0.202 ± 0.229
(-0.238, 0.632)
-0.155 ± 0.143
(-0.453, 0.112)
0.254 ± 0.117
(0.027, 0.489)
0.026 ± 0.155
(-0.267, 0.335)
0.004 ± 0.11
(-0.214, 0.222)
0.297 ± 0.201
(-0.098, 0.701)
-0.019 ± 0.086
(-0.193, 0.146)
0.914 ± 0.154
(0.619, 1.229)
SPTO
-3.243 ± 0.303
(-3.855, -2.668)
-0.072 ± 0.120
(-0.304, 0.165)
-1.424 ± 0.287
(-2.006, -0.913)
-0.196 ± 0.223
(-0.658, 0.231)
0.515 ± 0.170
(0.189, 0.855)
-0.197 ± 0.143
(-0.478, 0.069)
-0.114 ± 0.202
(-0.509, 0.277)
-0.002 ± 0.127
(-0.259, 0.247)
-0.088 ± 0.109
(-0.308, 0.122)
0.041 ± 0.153
(-0.252, 0.352)
-0.160 ± 0.107
(-0.375, 0.050)
0.590 ± 0.196
(0.217, 0.964)
-0.104 ± 0.085
(-0.280, 0.049)
-0.036 ± 0.138
(-0.309, 0.219)
STJA
3.155 ± 0.267
(2.646, 3.711)
0.366 ± 0.334
(-0.213, 1.082)
-1.233 ± 0.251
(-1.745, -0.742)
0.066 ± 0.115
(-0.161, 0.289)
0.361 ± 0.158
(0.057, 0.676)
-0.266 ± 0.112
(-0.494, -0.056)
-0.170 ± 0.169
(-0.509, 0.163)
-0.209 ± 0.116
(-0.435, 0.021)
-0.053 ± 0.126
(-0.303, 0.191)
-0.092 ± 0.122
(-0.333, 0.152)
-0.145 ± 0.109
(-0.351, 0.067)
0.007 ± 0.134
(-0.260, 0.270)
0.074 ± 0.071
(-0.063, 0.219)
-0.387 ± 0.105
(-0.589, -0.178)
TOSO
0.235 ± 0.179
(-0.108, 0.596)
-1.022 ± 0.154
(-1.332, -0.741)
0.153 ± 0.150
(-0.142, 0.441)
-0.441 ± 0.100
WAVI
-0.399 ± 0.148
(-0.685, -0.107)
-0.700 ± 0.117
(-0.934, -0.468)
-0.191 ± 0.139
(-0.468, 0.079)
-0.611 ± 0.127
(-0.643, -0.252)
0.280 ± 0.105
(0.077, 0.482)
-0.023 ± 0.105
(-0.230, 0.183)
-0.207 ± 0.135
(-0.480, 0.047)
-0.187 ± 0.091
(-0.368, -0.014)
-0.094 ± 0.090
(-0.268, 0.077)
-0.281 ± 0.115
(-0.510, -0.065)
-0.041 ± 0.091
(-0.216, 0.143)
-0.239 ± 0.129
(-0.507, 0.012)
0.077 ± 0.062
(-0.039, 0.199)
-0.217 ± 0.112
(-0.432, 0.002)
(-0.861, -0.356)
0.167 ± 0.099
(-0.019, 0.358)
-0.255 ± 0.107
(-0.456, -0.040)
0.433 ± 0.127
(0.190, 0.678)
-0.104 ± 0.090
(-0.283, 0.068)
0.263 ± 0.086
(0.094, 0.428)
0.136 ± 0.111
(-0.076, 0.358)
0.023 ± 0.087
(-0.143, 0.192)
0.087 ± 0.114
(-0.128, 0.315)
0.016 ± 0.060
(-0.096, 0.137)
0.516 ± 0.121
(0.299, 0.766)
WBNU
0.270 ± 0.173
(-0.051, 0.609)
-0.519 ± 0.095
(-0.710, -0.335)
0.012 ± 0.145
(-0.270, 0.297)
0.044 ± 0.095
(-0.136, 0.234)
0.322 ± 0.102
(0.129, 0.520)
-0.172 ± 0.099
(-0.368, 0.024)
0.057 ± 0.119
(-0.174, 0.290)
-0.330 ± 0.086
(-0.497, -0.161)
-0.130 ± 0.084
(-0.292, 0.034)
-0.033 ± 0.111
(-0.246, 0.171)
-0.188 ± 0.086
(-0.362, -0.029)
-0.059 ± 0.106
(-0.270, 0.156)
0.108 ± 0.058
(0.002, 0.225)
0.041 ± 0.096
(-0.142, 0.241)
40
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
WCSP
-4.740 ± 0.434
(-5.624, -3.939)
-0.355 ± 0.435
(-1.272, 0.436)
1.672 ± 0.396
(0.920, 2.484)
-0.124 ± 0.162
(-0.456, 0.182)
-0.656 ± 0.235
(-1.137, -0.203)
0.076 ± 0.130
(-0.177, 0.336)
-0.192 ± 0.278
(-0.750, 0.346)
0.202 ± 0.161
(-0.111, 0.517)
0.027 ± 0.156
(-0.279, 0.338)
0.081 ± 0.149
(-0.198, 0.386)
-0.041 ± 0.125
(-0.285, 0.210)
0.460 ± 0.186
(0.099, 0.826)
-0.160 ± 0.086
(-0.338, 0.002)
0.661 ± 0.149
(0.374, 0.961)
WETA
1.384 ± 0.170
(1.055, 1.722)
-0.335 ± 0.089
(-0.511, -0.162)
-0.303 ± 0.143
(-0.583, -0.016)
-0.316 ± 0.102
(-0.521, -0.120)
0.124 ± 0.109
(-0.092, 0.337)
-0.278 ± 0.083
(-0.445, -0.112)
0.936 ± 0.130
(0.682, 1.192)
-0.123 ± 0.105
(-0.327, 0.079)
-0.184 ± 0.089
(-0.361, -0.015)
-0.072 ± 0.101
(-0.266, 0.130)
-0.121 ± 0.082
(-0.279, 0.034)
-0.007 ± 0.104
(-0.215, 0.190)
0.010 ± 0.054
(-0.092, 0.115)
0.147 ± 0.092
(-0.027, 0.341)
WEWP
0.010 ± 0.144
(-0.266, 0.291)
-0.776 ± 0.093
(-0.965, -0.599)
-1.524 ± 0.150
(-1.829, -1.229)
-0.091 ± 0.113
(-0.324, 0.121)
0.330 ± 0.091
(0.152, 0.510)
-0.015 ± 0.094
(-0.197, 0.179)
-0.408 ± 0.116
(-0.625, -0.173)
-0.218 ± 0.081
(-0.371, -0.062)
0.201 ± 0.080
(0.049, 0.354)
-0.083 ± 0.099
(-0.272, 0.109)
-0.019 ± 0.080
(-0.175, 0.138)
-0.074 ± 0.097
(-0.268, 0.121)
0.052 ± 0.053
(-0.048, 0.155)
0.547 ± 0.118
(0.320, 0.788)
WHWO
-1.277 ± 0.173
(-1.598, -0.916)
0.217 ± 0.084
(0.059, 0.391)
-0.439 ± 0.144
(-0.717, -0.160)
0.059 ± 0.113
(-0.178, 0.271)
-0.037 ± 0.104
(-0.246, 0.162)
0.110 ± 0.118
(-0.115, 0.353)
0.369 ± 0.135
(0.120, 0.632)
-0.050 ± 0.086
(-0.214, 0.120)
0.070 ± 0.083
(-0.088, 0.230)
0.006 ± 0.110
(-0.200, 0.228)
-0.070 ± 0.083
(-0.233, 0.093)
0.215 ± 0.119
(-0.013, 0.454)
0.087 ± 0.058
(-0.027, 0.201)
0.096 ± 0.099
(-0.109, 0.295)
WISA
-0.394 ± 0.231
(-0.819, 0.083)
-0.640 ± 0.200
(-1.063, -0.276)
1.270 ± 0.221
(0.880, 1.753)
-0.235 ± 0.130
(-0.466, 0.041)
-0.352 ± 0.137
(-0.629, -0.089)
-0.377 ± 0.118
(-0.620, -0.165)
-0.041 ± 0.160
(-0.354, 0.272)
-0.315 ± 0.119
(-0.552, -0.084)
-0.144 ± 0.112
(-0.365, 0.077)
0.117 ± 0.127
(-0.137, 0.370)
-0.179 ± 0.111
(-0.397, 0.034)
-0.082 ± 0.139
(-0.354, 0.183)
-0.096 ± 0.072
(-0.238, 0.045)
0.215 ± 0.144
(-0.075, 0.500)
WIWA
-1.273 ± 0.199
(-1.662, -0.885)
-0.574 ± 0.170
(-0.915, -0.252)
0.329 ± 0.186
(-0.022, 0.694)
-0.784 ± 0.168
(-1.133, -0.475)
0.056 ± 0.113
(-0.163, 0.275)
-0.337 ± 0.128
(-0.590, -0.093)
0.406 ± 0.151
(0.117, 0.709)
0.224 ± 0.103
(0.021, 0.428)
0.520 ± 0.108
(0.317, 0.737)
-0.053 ± 0.119
(-0.286, 0.192)
0.031 ± 0.099
(-0.159, 0.226)
0.340 ± 0.134
(0.087, 0.608)
-0.024 ± 0.065
(-0.147, 0.097)
0.151 ± 0.112
(-0.069, 0.376)
WIWR
-3.962 ± 0.385
(-4.701, -3.227)
-1.056 ± 0.411
(-1.924, -0.308)
-0.237 ± 0.375
(-1.008, 0.491)
-0.369 ± 0.248
(-0.876, 0.095)
-0.338 ± 0.206
(-0.742, 0.076)
0.086 ± 0.170
(-0.217, 0.426)
0.268 ± 0.240
(-0.182, 0.751)
0.001 ± 0.144
(-0.281, 0.287)
0.139 ± 0.155
(-0.149, 0.450)
-0.187 ± 0.164
(-0.520, 0.116)
0.047 ± 0.132
(-0.202, 0.318)
-0.404 ± 0.233
(-0.876, 0.058)
0.013 ± 0.098
(-0.185, 0.200)
-0.150 ± 0.224
(-0.624, 0.256)
41
APPENDIX A (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
YRWA
2.556 ± 0.226
(2.115, 3.023)
-0.329 ± 0.096
(-0.516, -0.137)
0.576 ± 0.179
(0.240, 0.938)
-0.132 ± 0.102
(-0.318, 0.079)
-0.594 ± 0.154
(-0.895, -0.302)
-0.013 ± 0.115
(-0.244, 0.207)
0.434 ± 0.159
(0.132, 0.747)
-0.186 ± 0.106
(-0.386, 0.029)
0.030 ± 0.104
(-0.165, 0.233)
0.041 ± 0.128
(-0.213, 0.291)
-0.015 ± 0.089
(-0.194, 0.155)
-0.306 ± 0.141
(-0.58, -0.038)
-0.071 ± 0.060
(-0.187, 0.049)
0.078 ± 0.109
(-0.118, 0.303)
42
6.2 APPENDIX B: Parameter Estimates – Avian species (field-based covariates)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
AMRO
1.612 ± 0.150
(1.332, 1.920)
0.049 ± 0.168
(-0.275, 0.392)
-0.722 ± 0.194
(-1.120, -0.341)
0.141 ± 0.090
(-0.031, 0.322)
0.231 ± 0.101
(0.036, 0.431)
-0.090 ± 0.125
(-0.323, 0.157)
-0.049 ± 0.051
(-0.148, 0.047)
0.062 ± 0.098
(-0.131, 0.251)
-0.198 ± 0.095
(-0.385, -0.015)
0.058 ± 0.098
(-0.126, 0.263)
-0.104 ± 0.083
(-0.270, 0.061)
-0.040 ± 0.080
(-0.196, 0.113)
-0.005 ± 0.086
(-0.161, 0.171)
0.156 ± 0.104
(-0.042, 0.370)
BBWO
-3.810 ± 0.459
(-4.752, -2.928)
-1.058 ± 0.557
(-2.267, -0.083)
1.056 ± 0.431
(0.257, 1.955)
-0.342 ± 0.194
(-0.743, 0.016)
0.018 ± 0.210
(-0.370, 0.449)
-0.022 ± 0.185
(-0.384, 0.342)
-0.038 ± 0.087
(-0.220, 0.117)
0.007 ± 0.143
(-0.277, 0.285)
-0.200 ± 0.198
(-0.613, 0.182)
0.007 ± 0.130
(-0.260, 0.245)
0.011 ± 0.127
(-0.249, 0.248)
-0.007 ± 0.119
(-0.254, 0.216)
0.007 ± 0.130
(-0.254, 0.250)
0.036 ± 0.149
(-0.263, 0.333)
BHCO
0.545 ± 0.172
(0.212, 0.894)
0.732 ± 0.214
(0.333, 1.158)
-1.734 ± 0.201
(-2.126, -1.342)
0.157 ± 0.117
(-0.080, 0.369)
0.538 ± 0.100
(0.338, 0.733)
0.054 ± 0.123
(-0.188, 0.304)
-0.092 ± 0.054
(-0.200, 0.010)
-0.050 ± 0.097
(-0.243, 0.129)
0.030 ± 0.094
(-0.151, 0.212)
-0.012 ± 0.082
(-0.172, 0.152)
-0.130 ± 0.079
(-0.286, 0.025)
-0.011 ± 0.080
(-0.173, 0.142)
-0.002 ± 0.089
(-0.170, 0.176)
-0.039 ± 0.096
(-0.229, 0.142)
BHGR
-2.641 ± 0.227
(-3.083, -2.203)
-0.007 ± 0.126
(-0.257, 0.241)
-1.166 ± 0.241
(-1.647, -0.695)
0.156 ± 0.148
(-0.160, 0.413)
0.470 ± 0.152
(0.170, 0.768)
-0.123 ± 0.137
(-0.401, 0.141)
-0.025 ± 0.068
(-0.171, 0.099)
-0.097 ± 0.104
(-0.303, 0.111)
-0.131 ± 0.121
(-0.373, 0.102)
0.005 ± 0.104
(-0.217, 0.198)
-0.052 ± 0.102
(-0.256, 0.144)
-0.025 ± 0.110
(-0.250, 0.183)
-0.017 ± 0.094
(-0.211, 0.153)
0.062 ± 0.098
(-0.128, 0.249)
BLGR
-3.082 ± 0.349
(-3.832, -2.473)
-1.221 ± 0.496
(-2.268, -0.359)
1.139 ± 0.329
(0.499, 1.810)
-0.369 ± 0.152
(-0.681, -0.074)
-0.343 ± 0.152
(-0.662, -0.056)
-0.244 ± 0.167
(-0.600, 0.073)
-0.057 ± 0.086
(-0.227, 0.107)
-0.078 ± 0.128
(-0.324, 0.178)
0.024 ± 0.153
(-0.286, 0.319)
-0.080 ± 0.131
(-0.340, 0.168)
-0.080 ± 0.120
(-0.330, 0.143)
-0.084 ± 0.109
(-0.313, 0.116)
0.007 ± 0.117
(-0.230, 0.224)
-0.055 ± 0.134
(-0.320, 0.204)
BRBL
-3.971 ± 0.436
(-4.926, -3.174)
0.741 ± 0.133
(0.487, 1.008)
-3.615 ± 0.568
(-4.757, -2.556)
0.240 ± 0.268
(-0.312, 0.751)
0.329 ± 0.157
(0.020, 0.631)
-0.180 ± 0.136
(-0.450, 0.080)
0.028 ± 0.065
(-0.103, 0.155)
-0.177 ± 0.108
(-0.390, 0.031)
-0.260 ± 0.121
(-0.502, -0.030)
-0.180 ± 0.121
(-0.438, 0.040)
-0.078 ± 0.104
(-0.292, 0.120)
-0.091 ± 0.116
(-0.332, 0.140)
0.057 ± 0.085
(-0.103, 0.239)
0.006 ± 0.101
(-0.183, 0.209)
BRCR
0.257 ± 0.139
(-0.014, 0.536)
-0.790 ± 0.118
(-1.019, -0.551)
-0.667 ± 0.165
(-0.987, -0.346)
-0.093 ± 0.099
(-0.300, 0.093)
0.088 ± 0.089
(-0.088, 0.259)
0.105 ± 0.110
(-0.110, 0.319)
0.035 ± 0.053
(-0.068, 0.143)
0.114 ± 0.087
(-0.056, 0.292)
-0.013 ± 0.085
(-0.174, 0.158)
0.166 ± 0.100
(-0.013, 0.373)
0.039 ± 0.079
(-0.112, 0.199)
0.119 ± 0.084
(-0.039, 0.293)
0.097 ± 0.080
(-0.055, 0.261)
0.330 ± 0.091
(0.160, 0.516)
43
APPENDIX B (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
BTPI
-1.435 ± 0.174
(-1.774, -1.110)
0.575 ± 0.123
(0.337, 0.821)
-0.084 ± 0.200
(-0.467, 0.307)
-0.188 ± 0.138
(-0.474, 0.074)
-0.148 ± 0.108
(-0.363, 0.071)
-0.105 ± 0.125
(-0.349, 0.136)
-0.074 ± 0.069
(-0.219, 0.054)
0.047 ± 0.095
(-0.145, 0.234)
-0.148 ± 0.103
(-0.352, 0.046)
-0.082 ± 0.098
(-0.277, 0.107)
-0.056 ± 0.094
(-0.244, 0.124)
-0.058 ± 0.093
(-0.251, 0.118)
0.165 ± 0.085
(0.008, 0.335)
0.011 ± 0.088
(-0.164, 0.175)
CAFI
-1.257 ± 0.165
(-1.574, -0.922)
-0.403 ± 0.137
(-0.683, -0.132)
0.387 ± 0.193
(0.024, 0.775)
0.422 ± 0.141
(0.151, 0.714)
-0.003 ± 0.101
(-0.198, 0.197)
-0.250 ± 0.126
(-0.508, -0.007)
0.125 ± 0.055
(0.020, 0.231)
-0.045 ± 0.097
(-0.236, 0.146)
-0.208 ± 0.096
(-0.400, -0.015)
0.006 ± 0.088
(-0.174, 0.176)
-0.091 ± 0.088
(-0.267, 0.074)
-0.118 ± 0.088
(-0.294, 0.057)
0.084 ± 0.079
(-0.067, 0.246)
-0.135 ± 0.097
(-0.330, 0.042)
CAHU
-4.139 ± 0.418
(-4.985, -3.373)
-1.107 ± 0.532
(-2.265, -0.179)
0.780 ± 0.407
(0.035, 1.614)
-0.298 ± 0.185
(-0.667, 0.043)
0.032 ± 0.208
(-0.377, 0.442)
-0.323 ± 0.205
(-0.767, 0.049)
-0.029 ± 0.093
(-0.221, 0.149)
-0.104 ± 0.142
(-0.385, 0.163)
-0.024 ± 0.189
(-0.402, 0.339)
-0.026 ± 0.137
(-0.306, 0.233)
-0.023 ± 0.129
(-0.291, 0.223)
-0.097 ± 0.127
(-0.352, 0.139)
0.008 ± 0.130
(-0.272, 0.256)
-0.084 ± 0.151
(-0.392, 0.207)
CAVI
-1.891 ± 0.199
(-2.301, -1.523)
-1.459 ± 0.236
(-1.963, -1.025)
-0.649 ± 0.195
(-1.021, -0.268)
-0.146 ± 0.141
(-0.438, 0.114)
-0.008 ± 0.106
(-0.219, 0.198)
0.139 ± 0.132
(-0.110, 0.403)
-0.014 ± 0.055
(-0.127, 0.091)
0.072 ± 0.103
(-0.126, 0.269)
-0.144 ± 0.101
(-0.349, 0.044)
0.055 ± 0.082
(-0.111, 0.215)
-0.149 ± 0.085
(-0.321, 0.015)
0.018 ± 0.089
(-0.163, 0.195)
0.035 ± 0.092
(-0.163, 0.204)
0.002 ± 0.099
(-0.198, 0.201)
CHSP
-2.602 ± 0.215
(-3.044, -2.209)
-0.771 ± 0.202
(-1.181, -0.380)
-0.538 ± 0.235
(-1.007, -0.091)
0.187 ± 0.113
(-0.039, 0.409)
-0.024 ± 0.131
(-0.280, 0.227)
-0.174 ± 0.146
(-0.476, 0.101)
0.034 ± 0.066
(-0.100, 0.157)
-0.125 ± 0.114
(-0.349, 0.089)
-0.012 ± 0.118
(-0.242, 0.212)
-0.075 ± 0.110
(-0.294, 0.130)
-0.140 ± 0.111
(-0.370, 0.064)
-0.168 ± 0.117
(-0.422, 0.051)
0.047 ± 0.095
(-0.144, 0.231)
-0.028 ± 0.117
(-0.263, 0.196)
CLNU
-0.567 ± 0.144
(-0.854, -0.296)
-0.140 ± 0.123
(-0.389, 0.091)
1.305 ± 0.179
(0.955, 1.667)
0.044 ± 0.127
(-0.191, 0.300)
0.436 ± 0.097
(0.243, 0.629)
0.003 ± 0.118
(-0.232, 0.241)
0.039 ± 0.049
(-0.060, 0.135)
-0.046 ± 0.093
(-0.230, 0.141)
0.272 ± 0.087
(0.102, 0.445)
-0.246 ± 0.106
(-0.456, -0.047)
0.060 ± 0.079
(-0.090, 0.218)
-0.090 ± 0.084
(-0.254, 0.077)
0.004 ± 0.086
(-0.174, 0.163)
-0.163 ± 0.094
(-0.351, 0.016)
CORA
-2.256 ± 0.210
(-2.688, -1.858)
-0.056 ± 0.125
(-0.310, 0.175)
-1.014 ± 0.224
(-1.463, -0.582)
0.039 ± 0.157
(-0.285, 0.328)
0.151 ± 0.128
(-0.108, 0.404)
-0.253 ± 0.142
(-0.538, 0.015)
0.009 ± 0.068
(-0.123, 0.140)
0.070 ± 0.104
(-0.137, 0.276)
-0.082 ± 0.109
(-0.301, 0.132)
0.021 ± 0.099
(-0.181, 0.200)
-0.104 ± 0.102
(-0.316, 0.088)
-0.189 ± 0.115
(-0.430, 0.026)
-0.068 ± 0.101
(-0.289, 0.107)
-0.042 ± 0.098
(-0.237, 0.143)
44
APPENDIX B (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
DEJU
3.144 ± 0.317
(2.607, 3.822)
-0.566 ± 0.142
(-0.858, -0.292)
0.797 ± 0.297
(0.243, 1.384)
-0.387 ± 0.122
(-0.638, -0.165)
-0.280 ± 0.163
(-0.609, 0.034)
0.044 ± 0.143
(-0.242, 0.326)
0.017 ± 0.071
(-0.116, 0.166)
0.114 ± 0.110
(-0.093, 0.336)
-0.009 ± 0.133
(-0.269, 0.261)
0.020 ± 0.117
(-0.193, 0.251)
0.027 ± 0.111
(-0.186, 0.249)
-0.052 ± 0.115
(-0.273, 0.188)
0.033 ± 0.093
(-0.133, 0.229)
0.120 ± 0.110
(-0.088, 0.337)
DOWO
-2.975 ± 0.265
(-3.501, -2.475)
-0.169 ± 0.178
(-0.536, 0.168)
-0.964 ± 0.307
(-1.582, -0.389)
-0.210 ± 0.211
(-0.635, 0.175)
0.296 ± 0.177
(-0.035, 0.653)
-0.024 ± 0.156
(-0.332, 0.286)
-0.064 ± 0.078
(-0.231, 0.074)
0.058 ± 0.120
(-0.181, 0.296)
-0.436 ± 0.164
(-0.764, -0.132)
0.172 ± 0.095
(-0.023, 0.360)
0.037 ± 0.103
(-0.170, 0.235)
0.015 ± 0.112
(-0.208, 0.231)
-0.011 ± 0.103
(-0.232, 0.173)
-0.152 ± 0.127
(-0.415, 0.081)
DUFL
-0.715 ± 0.171
(-1.054, -0.378)
-1.925 ± 0.259
(-2.445, -1.445)
0.464 ± 0.168
(0.128, 0.795)
-0.131 ± 0.079
(-0.288, 0.020)
-0.134 ± 0.091
(-0.314, 0.042)
-0.046 ± 0.120
(-0.275, 0.190)
0.034 ± 0.051
(-0.066, 0.135)
0.047 ± 0.097
(-0.137, 0.238)
0.507 ± 0.095
(0.326, 0.699)
-0.014 ± 0.085
(-0.183, 0.156)
0.023 ± 0.077
(-0.124, 0.171)
0.013 ± 0.077
(-0.132, 0.173)
-0.012 ± 0.097
(-0.214, 0.165)
-0.164 ± 0.091
(-0.343, 0.013)
EVGR
-0.213 ± 0.155
(-0.512, 0.105)
-0.138 ± 0.114
(-0.361, 0.093)
-0.931 ± 0.176
(-1.271, -0.593)
0.158 ± 0.105
(-0.063, 0.355)
-0.933 ± 0.107
(-1.150, -0.733)
0.144 ± 0.111
(-0.069, 0.367)
-0.009 ± 0.051
(-0.103, 0.093)
-0.029 ± 0.090
(-0.212, 0.150)
0.062 ± 0.091
(-0.116, 0.240)
0.030 ± 0.094
(-0.149, 0.222)
0.050 ± 0.080
(-0.101, 0.210)
-0.030 ± 0.087
(-0.202, 0.138)
0.010 ± 0.081
(-0.144, 0.176)
0.130 ± 0.083
(-0.031, 0.295)
FOSP
1.103 ± 0.139
(0.827, 1.379)
-0.283 ± 0.110
(-0.502, -0.068)
0.471 ± 0.161
(0.166, 0.789)
-0.453 ± 0.098
GCKI
-0.802 ± 0.165
(-1.135, -0.478)
-0.881 ± 0.183
(-1.255, -0.524)
0.112 ± 0.186
(-0.253, 0.476)
-0.470 ± 0.131
GTTO
-2.267 ± 0.204
(-2.674, -1.880)
-0.562 ± 0.250
(-1.070, -0.082)
0.801 ± 0.232
(0.351, 1.251)
-0.203 ± 0.100
(-0.644, -0.267)
-0.300 ± 0.091
(-0.484, -0.129)
-0.069 ± 0.109
(-0.284, 0.145)
-0.075 ± 0.049
(-0.169, 0.022)
0.015 ± 0.087
(-0.160, 0.180)
0.559 ± 0.100
(0.366, 0.768)
0.013 ± 0.077
(-0.134, 0.168)
0.005 ± 0.081
(-0.150, 0.171)
0.004 ± 0.077
(-0.142, 0.155)
0.158 ± 0.092
(-0.012, 0.348)
-0.163 ± 0.081
(-0.321, -0.008)
(-0.728, -0.219)
-0.500 ± 0.099
(-0.696, -0.308)
0.227 ± 0.124
(-0.003, 0.480)
-0.049 ± 0.054
(-0.155, 0.058)
0.086 ± 0.098
(-0.104, 0.278)
-0.241 ± 0.096
(-0.425, -0.053)
0.117 ± 0.081
(-0.036, 0.278)
-0.137 ± 0.080
(-0.301, 0.011)
0.129 ± 0.086
(-0.033, 0.305)
0.050 ± 0.077
(-0.109, 0.197)
0.295 ± 0.090
(0.129, 0.474)
(-0.400, -0.016)
0.031 ± 0.123
(-0.226, 0.273)
-0.337 ± 0.160
(-0.671, -0.031)
-0.017 ± 0.079
(-0.176, 0.129)
-0.038 ± 0.112
(-0.259, 0.180)
0.022 ± 0.122
(-0.217, 0.256)
-0.038 ± 0.116
(-0.267, 0.176)
-0.117 ± 0.113
(-0.350, 0.093)
-0.060 ± 0.099
(-0.264, 0.132)
0.052 ± 0.098
(-0.154, 0.235)
-0.210 ± 0.129
(-0.470, 0.040)
45
APPENDIX B (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
HAWO
-0.870 ± 0.134
(-1.127, -0.607)
-0.627 ± 0.120
(-0.862, -0.395)
-0.688 ± 0.160
(-1.007, -0.370)
0.157 ± 0.080
HETH
-1.598 ± 0.277
(-2.193, -1.078)
-1.967 ± 0.420
(-2.875, -1.214)
0.956 ± 0.211
(0.559, 1.377)
-0.380 ± 0.110
(0.002, 0.311)
0.025 ± 0.091
(-0.155, 0.200)
0.011 ± 0.112
(-0.209, 0.228)
-0.010 ± 0.051
(-0.110, 0.089)
-0.172 ± 0.090
(-0.355, -0.002)
0.077 ± 0.081
(-0.084, 0.231)
0.103 ± 0.081
(-0.050, 0.270)
0.192 ± 0.076
(0.048, 0.350)
-0.095 ± 0.084
(-0.266, 0.064)
-0.019 ± 0.079
(-0.179, 0.130)
-0.061 ± 0.083
(-0.226, 0.101)
(-0.605, -0.181)
-0.413 ± 0.100
(-0.613, -0.220)
0.447 ± 0.145
(0.176, 0.741)
-0.053 ± 0.057
(-0.165, 0.058)
0.148 ± 0.104
(-0.049, 0.358)
0.128 ± 0.098
(-0.061, 0.319)
0.001 ± 0.088
(-0.181, 0.165)
0.053 ± 0.081
(-0.103, 0.217)
-0.037 ± 0.083
(-0.203, 0.122)
0.099 ± 0.095
(-0.092, 0.272)
0.099 ± 0.099
(-0.095, 0.295)
HEWA
-3.153 ± 0.363
(-3.899, -2.489)
-1.990 ± 0.507
(-3.043, -1.093)
-0.022 ± 0.273
(-0.538, 0.520)
-0.329 ± 0.181
(-0.700, 0.008)
-0.317 ± 0.132
(-0.578, -0.061)
0.114 ± 0.149
(-0.175, 0.409)
0.101 ± 0.058
(-0.010, 0.212)
0.064 ± 0.124
(-0.175, 0.311)
-0.113 ± 0.123
(-0.357, 0.121)
0.028 ± 0.100
(-0.180, 0.224)
-0.041 ± 0.097
(-0.242, 0.139)
0.029 ± 0.097
(-0.168, 0.219)
-0.058 ± 0.122
(-0.308, 0.169)
0.085 ± 0.122
(-0.166, 0.319)
HOWR
-2.520 ± 0.225
(-2.977, -2.092)
-0.410 ± 0.205
(-0.803, -0.010)
0.133 ± 0.244
(-0.355, 0.616)
-0.121 ± 0.131
(-0.387, 0.118)
0.123 ± 0.146
(-0.161, 0.413)
-0.278 ± 0.155
(-0.593, 0.016)
0.031 ± 0.074
(-0.126, 0.168)
-0.146 ± 0.115
(-0.377, 0.071)
0.013 ± 0.131
(-0.247, 0.267)
0.029 ± 0.110
(-0.191, 0.238)
-0.139 ± 0.115
(-0.376, 0.076)
-0.121 ± 0.116
(-0.367, 0.096)
0.050 ± 0.096
(-0.143, 0.233)
-0.087 ± 0.128
(-0.349, 0.146)
LISP
-3.999 ± 0.452
(-4.957, -3.183)
-1.172 ± 0.572
(-2.400, -0.178)
1.074 ± 0.404
(0.333, 1.875)
-0.241 ± 0.165
(-0.567, 0.081)
-0.377 ± 0.195
(-0.761, -0.013)
-0.111 ± 0.178
(-0.452, 0.228)
0.001 ± 0.083
(-0.171, 0.158)
-0.090 ± 0.136
(-0.358, 0.157)
-0.162 ± 0.186
(-0.537, 0.192)
-0.097 ± 0.137
(-0.383, 0.152)
-0.067 ± 0.130
(-0.336, 0.179)
0.054 ± 0.112
(-0.177, 0.272)
-0.022 ± 0.127
(-0.287, 0.214)
-0.004 ± 0.146
(-0.293, 0.277)
MGWA
-0.770 ± 0.158
(-1.086, -0.457)
-0.587 ± 0.154
(-0.907, -0.288)
0.334 ± 0.177
(-0.020, 0.670)
-0.339 ± 0.116
(-0.569, -0.117)
-0.039 ± 0.095
(-0.223, 0.150)
0.019 ± 0.117
(-0.205, 0.254)
-0.054 ± 0.055
(-0.168, 0.052)
-0.099 ± 0.093
(-0.285, 0.083)
-0.011 ± 0.087
(-0.186, 0.159)
0.026 ± 0.078
(-0.130, 0.179)
-0.031 ± 0.078
(-0.192, 0.114)
-0.007 ± 0.085
(-0.169, 0.153)
0.085 ± 0.078
(-0.070, 0.237)
0.077 ± 0.090
(-0.100, 0.251)
MOCH
3.899 ± 0.320
(3.291, 4.529)
-0.111 ± 0.335
(-0.737, 0.552)
-0.801 ± 0.370
(-1.547, -0.095)
0.061 ± 0.130
(-0.183, 0.326)
0.010 ± 0.209
(-0.392, 0.417)
-0.001 ± 0.182
(-0.361, 0.357)
0.018 ± 0.085
(-0.140, 0.196)
0.119 ± 0.143
(-0.165, 0.410)
-0.042 ± 0.186
(-0.407, 0.337)
0.047 ± 0.138
(-0.215, 0.319)
-0.023 ± 0.128
(-0.261, 0.239)
0.016 ± 0.119
(-0.205, 0.253)
0.081 ± 0.127
(-0.145, 0.354)
0.080 ± 0.148
(-0.199, 0.377)
46
APPENDIX B (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
MODO
-1.130 ± 0.155
(-1.450, -0.829)
-0.042 ± 0.109
(-0.264, 0.179)
-1.544 ± 0.181
(-1.906, -1.196)
0.213 ± 0.112
(-0.025, 0.421)
0.421 ± 0.102
(0.226, 0.620)
-0.192 ± 0.114
(-0.416, 0.033)
0.064 ± 0.050
(-0.033, 0.163)
0.121 ± 0.090
(-0.055, 0.293)
0.006 ± 0.087
(-0.169, 0.173)
-0.062 ± 0.084
(-0.241, 0.093)
-0.034 ± 0.077
(-0.184, 0.114)
-0.046 ± 0.090
(-0.224, 0.122)
-0.023 ± 0.075
(-0.177, 0.123)
-0.117 ± 0.080
(-0.273, 0.041)
MOQU
-1.516 ± 0.205
(-1.921, -1.114)
-0.978 ± 0.251
(-1.489, -0.498)
0.567 ± 0.216
(0.160, 1.004)
-0.403 ± 0.133
NAWA
-0.853 ± 0.177
(-1.192, -0.513)
-0.860 ± 0.175
(-1.211, -0.545)
0.226 ± 0.186
(-0.144, 0.587)
-0.459 ± 0.143
(-0.677, -0.158)
-0.682 ± 0.110
(-0.900, -0.470)
-0.121 ± 0.132
(-0.381, 0.141)
0.023 ± 0.057
(-0.086, 0.129)
0.056 ± 0.105
(-0.150, 0.261)
0.212 ± 0.097
(0.025, 0.401)
-0.077 ± 0.093
(-0.263, 0.098)
-0.060 ± 0.086
(-0.225, 0.111)
-0.016 ± 0.088
(-0.193, 0.162)
-0.064 ± 0.108
(-0.284, 0.125)
-0.217 ± 0.114
(-0.450, -0.005)
(-0.736, -0.189)
-0.569 ± 0.104
(-0.774, -0.358)
-0.032 ± 0.123
(-0.276, 0.200)
0.049 ± 0.051
(-0.051, 0.149)
0.032 ± 0.097
(-0.162, 0.220)
0.357 ± 0.090
(0.182, 0.534)
0.043 ± 0.083
(-0.119, 0.215)
-0.040 ± 0.080
(-0.196, 0.115)
-0.073 ± 0.087
(-0.236, 0.102)
-0.079 ± 0.095
(-0.280, 0.093)
-0.069 ± 0.098
(-0.264, 0.116)
NOFL
0.252 ± 0.120
(0.020, 0.483)
-0.049 ± 0.109
(-0.257, 0.166)
-0.523 ± 0.152
(-0.818, -0.224)
0.098 ± 0.074
(-0.050, 0.241)
0.057 ± 0.083
(-0.102, 0.223)
-0.068 ± 0.101
(-0.259, 0.141)
-0.026 ± 0.047
(-0.117, 0.067)
0.104 ± 0.084
(-0.057, 0.272)
0.129 ± 0.084
(-0.040, 0.295)
0.004 ± 0.078
(-0.147, 0.156)
-0.118 ± 0.071
(-0.259, 0.020)
-0.061 ± 0.076
(-0.205, 0.092)
0.185 ± 0.095
(0.011, 0.387)
-0.076 ± 0.076
(-0.226, 0.066)
OCWA
-4.477 ± 0.516
(-5.572, -3.549)
-1.110 ± 0.555
(-2.334, -0.129)
0.576 ± 0.444
(-0.326, 1.441)
-0.291 ± 0.213
(-0.723, 0.095)
-0.287 ± 0.250
(-0.776, 0.206)
-0.261 ± 0.205
(-0.706, 0.115)
-0.010 ± 0.095
(-0.208, 0.165)
-0.132 ± 0.150
(-0.436, 0.150)
0.277 ± 0.186
(-0.090, 0.645)
-0.051 ± 0.140
(-0.329, 0.219)
-0.102 ± 0.138
(-0.397, 0.162)
-0.056 ± 0.128
(-0.312, 0.187)
-0.004 ± 0.132
(-0.275, 0.242)
-0.152 ± 0.166
(-0.496, 0.164)
OSFL
-0.218 ± 0.143
(-0.501, 0.069)
-0.552 ± 0.139
(-0.822, -0.289)
0.584 ± 0.165
(0.269, 0.924)
-0.383 ± 0.098
PIGR
-2.849 ± 0.351
(-3.597, -2.207)
-0.946 ± 0.494
(-2.029, -0.045)
1.600 ± 0.351
(0.943, 2.297)
-0.400 ± 0.145
(-0.584, -0.197)
-0.210 ± 0.089
(-0.383, -0.038)
-0.133 ± 0.113
(-0.346, 0.088)
0.005 ± 0.050
(-0.095, 0.105)
-0.027 ± 0.086
(-0.197, 0.138)
0.335 ± 0.086
(0.166, 0.506)
0.069 ± 0.079
(-0.090, 0.226)
-0.133 ± 0.076
(-0.280, 0.016)
-0.069 ± 0.080
(-0.231, 0.084)
0.143 ± 0.079
(0.001, 0.309)
-0.054 ± 0.088
(-0.227, 0.117)
(-0.695, -0.131)
-0.171 ± 0.154
(-0.480, 0.122)
-0.103 ± 0.165
(-0.433, 0.207)
-0.103 ± 0.091
(-0.293, 0.064)
0.054 ± 0.123
(-0.191, 0.288)
-0.122 ± 0.159
(-0.441, 0.189)
-0.118 ± 0.135
(-0.396, 0.130)
0.051 ± 0.114
(-0.176, 0.269)
-0.027 ± 0.106
(-0.242, 0.176)
0.008 ± 0.121
(-0.237, 0.236)
0.033 ± 0.128
(-0.220, 0.280)
47
APPENDIX B (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
PISI
-0.478 ± 0.128
(-0.731, -0.235)
-0.141 ± 0.115
(-0.365, 0.086)
0.271 ± 0.158
(-0.040, 0.574)
-0.014 ± 0.084
(-0.170, 0.161)
-0.215 ± 0.087
(-0.387, -0.049)
0.023 ± 0.110
(-0.185, 0.243)
0.016 ± 0.050
(-0.086, 0.111)
0.003 ± 0.085
(-0.166, 0.172)
-0.292 ± 0.086
(-0.457, -0.123)
-0.032 ± 0.078
(-0.187, 0.118)
-0.069 ± 0.077
(-0.222, 0.078)
-0.034 ± 0.076
(-0.178, 0.112)
0.066 ± 0.075
(-0.074, 0.212)
-0.045 ± 0.079
(-0.207, 0.115)
PIWO
-3.885 ± 0.375
(-4.643, -3.173)
-1.416 ± 0.449
(-2.371, -0.606)
-0.645 ± 0.382
(-1.361, 0.090)
-0.338 ± 0.241
(-0.830, 0.108)
0.087 ± 0.204
(-0.309, 0.481)
-0.106 ± 0.177
(-0.466, 0.218)
-0.086 ± 0.089
(-0.266, 0.077)
0.031 ± 0.138
(-0.236, 0.296)
0.192 ± 0.154
(-0.097, 0.499)
-0.096 ± 0.130
(-0.361, 0.141)
0.126 ± 0.107
(-0.094, 0.330)
0.023 ± 0.117
(-0.214, 0.252)
0.249 ± 0.097
(0.062, 0.434)
-0.094 ± 0.148
(-0.403, 0.187)
PYNU
-0.709 ± 0.163
(-1.038, -0.395)
0.377 ± 0.132
(0.118, 0.634)
-1.460 ± 0.189
(-1.843, -1.11)
0.207 ± 0.106
(-0.007, 0.411)
0.736 ± 0.109
(0.528, 0.948)
0.043 ± 0.120
(-0.204, 0.278)
-0.120 ± 0.059
(-0.239, -0.008)
0.039 ± 0.096
(-0.147, 0.220)
0.076 ± 0.091
(-0.105, 0.256)
0.027 ± 0.081
(-0.132, 0.185)
-0.037 ± 0.078
(-0.188, 0.117)
-0.056 ± 0.088
(-0.233, 0.110)
-0.094 ± 0.083
(-0.270, 0.057)
-0.033 ± 0.089
(-0.208, 0.143)
RBNU
1.616 ± 0.179
(1.276, 1.977)
-0.630 ± 0.122
(-0.872, -0.402)
0.080 ± 0.182
(-0.270, 0.454)
-0.191 ± 0.090
(-0.378, -0.025)
-0.502 ± 0.126
(-0.759, -0.262)
0.284 ± 0.126
(0.053, 0.547)
-0.022 ± 0.064
(-0.141, 0.114)
0.184 ± 0.096
(0.000, 0.378)
0.232 ± 0.104
(0.034, 0.441)
0.251 ± 0.133
(0.015, 0.532)
0.178 ± 0.113
(-0.027, 0.413)
0.110 ± 0.104
(-0.074, 0.324)
0.167 ± 0.108
(-0.018, 0.399)
0.128 ± 0.093
(-0.047, 0.313)
RBSA
-1.980 ± 0.209
(-2.380, -1.568)
-0.231 ± 0.184
(-0.609, 0.130)
0.204 ± 0.238
(-0.269, 0.660)
-0.176 ± 0.139
(-0.461, 0.087)
0.034 ± 0.128
(-0.217, 0.291)
0.019 ± 0.144
(-0.269, 0.297)
-0.087 ± 0.075
(-0.239, 0.051)
-0.008 ± 0.115
(-0.227, 0.219)
-0.095 ± 0.121
(-0.338, 0.137)
-0.081 ± 0.109
(-0.313, 0.123)
0.004 ± 0.097
(-0.190, 0.196)
0.083 ± 0.097
(-0.108, 0.274)
0.127 ± 0.080
(-0.030, 0.285)
0.022 ± 0.108
(-0.195, 0.231)
RECR
-2.213 ± 0.225
(-2.663, -1.782)
-0.162 ± 0.189
(-0.530, 0.205)
0.211 ± 0.263
(-0.311, 0.722)
-0.209 ± 0.153
(-0.521, 0.075)
-0.375 ± 0.142
(-0.653, -0.103)
-0.137 ± 0.148
(-0.429, 0.149)
-0.031 ± 0.075
(-0.189, 0.106)
-0.053 ± 0.118
(-0.289, 0.174)
-0.025 ± 0.129
(-0.277, 0.230)
-0.108 ± 0.118
(-0.353, 0.115)
-0.119 ± 0.110
(-0.343, 0.087)
-0.145 ± 0.117
(-0.386, 0.074)
-0.102 ± 0.116
(-0.352, 0.099)
-0.049 ± 0.122
(-0.287, 0.195)
ROWR
-5.683 ± 0.618
(-6.991, -4.542)
-0.711 ± 0.630
(-2.020, 0.506)
1.913 ± 0.525
(0.961, 3.017)
-0.150 ± 0.158
(-0.474, 0.147)
-0.368 ± 0.239
(-0.846, 0.094)
-0.304 ± 0.211
(-0.725, 0.072)
0.001 ± 0.099
(-0.193, 0.200)
-0.238 ± 0.153
(-0.569, 0.051)
0.046 ± 0.211
(-0.370, 0.451)
-0.099 ± 0.149
(-0.407, 0.173)
-0.070 ± 0.141
(-0.352, 0.200)
-0.089 ± 0.132
(-0.366, 0.156)
0.004 ± 0.128
(-0.262, 0.241)
-0.187 ± 0.173
(-0.550, 0.129)
48
APPENDIX B (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
RUHU
-3.570 ± 0.461
(-4.593, -2.788)
-1.321 ± 0.621
(-2.750, -0.261)
1.765 ± 0.355
(1.082, 2.469)
-0.324 ± 0.124
(-0.563, -0.078)
-0.400 ± 0.165
(-0.730, -0.080)
-0.153 ± 0.175
(-0.525, 0.181)
-0.081 ± 0.091
(-0.266, 0.086)
-0.011 ± 0.130
(-0.261, 0.240)
0.038 ± 0.162
(-0.277, 0.352)
-0.126 ± 0.133
(-0.399, 0.120)
-0.116 ± 0.131
(-0.389, 0.128)
-0.026 ± 0.106
(-0.231, 0.185)
-0.019 ± 0.135
(-0.306, 0.225)
-0.138 ± 0.142
(-0.422, 0.142)
SOSP
-2.820 ± 0.227
(-3.278, -2.388)
-0.637 ± 0.190
(-1.022, -0.259)
-0.741 ± 0.246
(-1.231, -0.275)
0.031 ± 0.139
(-0.260, 0.298)
0.311 ± 0.158
(0.003, 0.617)
-0.023 ± 0.150
(-0.321, 0.270)
-0.051 ± 0.072
(-0.200, 0.084)
-0.205 ± 0.121
(-0.442, 0.026)
-0.292 ± 0.142
(-0.581, -0.030)
-0.127 ± 0.122
(-0.377, 0.090)
-0.054 ± 0.109
(-0.285, 0.151)
-0.088 ± 0.113
(-0.320, 0.130)
0.004 ± 0.102
(-0.213, 0.179)
0.054 ± 0.113
(-0.170, 0.265)
SPTO
-2.665 ± 0.256
(-3.177, -2.158)
-0.149 ± 0.149
(-0.457, 0.128)
-1.130 ± 0.318
(-1.771, -0.527)
-0.305 ± 0.222
(-0.777, 0.098)
0.678 ± 0.165
(0.366, 1.006)
0.146 ± 0.148
(-0.148, 0.440)
-0.123 ± 0.079
(-0.289, 0.027)
0.055 ± 0.116
(-0.170, 0.287)
0.451 ± 0.115
(0.228, 0.678)
-0.104 ± 0.123
(-0.361, 0.119)
-0.143 ± 0.106
(-0.368, 0.052)
0.056 ± 0.111
(-0.164, 0.266)
0.041 ± 0.093
(-0.155, 0.208)
-0.085 ± 0.106
(-0.293, 0.114)
STJA
3.473 ± 0.322
(2.903, 4.177)
0.506 ± 0.445
(-0.288, 1.425)
-1.387 ± 0.339
(-2.073, -0.763)
0.231 ± 0.118
TOSO
-0.527 ± 0.166
(-0.855, -0.209)
-1.214 ± 0.197
(-1.627, -0.855)
0.648 ± 0.1740
(0.311, 0.977)
-0.439 ± 0.104
WAVI
-0.772 ± 0.160
(-1.089, -0.449)
-0.957 ± 0.160
(-1.291, -0.656)
-0.221 ± 0.168
(-0.559, 0.117)
-0.428 ± 0.133
(0.009, 0.478)
0.191 ± 0.159
(-0.121, 0.510)
0.038 ± 0.164
(-0.287, 0.368)
-0.009 ± 0.073
(-0.150, 0.142)
-0.028 ± 0.123
(-0.270, 0.215)
-0.002 ± 0.167
(-0.308, 0.339)
-0.086 ± 0.121
(-0.318, 0.164)
0.054 ± 0.127
(-0.194, 0.313)
-0.033 ± 0.104
(-0.229, 0.194)
0.035 ± 0.122
(-0.191, 0.291)
0.035 ± 0.130
(-0.213, 0.297)
(-0.654, -0.241)
0.074 ± 0.094
(-0.106, 0.258)
-0.042 ± 0.115
(-0.266, 0.178)
0.047 ± 0.050
(-0.049, 0.148)
-0.042 ± 0.095
(-0.231, 0.135)
-0.087 ± 0.090
(-0.264, 0.084)
-0.145 ± 0.095
(-0.329, 0.035)
-0.083 ± 0.079
(-0.242, 0.063)
-0.044 ± 0.082
(-0.200, 0.115)
0.055 ± 0.087
(-0.122, 0.213)
-0.024 ± 0.092
(-0.203, 0.158)
(-0.698, -0.191)
0.140 ± 0.093
(-0.037, 0.324)
0.099 ± 0.114
(-0.130, 0.321)
-0.031 ± 0.051
(-0.130, 0.067)
0.014 ± 0.094
(-0.165, 0.191)
-0.210 ± 0.087
(-0.378, -0.042)
-0.095 ± 0.086
(-0.272, 0.067)
0.035 ± 0.074
(-0.105, 0.182)
0.004 ± 0.086
(-0.163, 0.167)
0.012 ± 0.082
(-0.155, 0.165)
-0.061 ± 0.090
(-0.242, 0.111)
WBNU
-0.179 ± 0.144
(-0.459, 0.107)
-0.637 ± 0.122
(-0.877, -0.398)
-0.001 ± 0.166
(-0.335, 0.319)
0.054 ± 0.094
(-0.119, 0.255)
0.394 ± 0.095
(0.211, 0.592)
0.073 ± 0.112
(-0.146, 0.297)
-0.049 ± 0.052
(-0.149, 0.055)
0.129 ± 0.089
(-0.045, 0.302)
0.120 ± 0.083
(-0.042, 0.280)
0.044 ± 0.081
(-0.114, 0.203)
-0.072 ± 0.075
(-0.219, 0.070)
0.101 ± 0.085
(-0.058, 0.276)
0.004 ± 0.076
(-0.158, 0.147)
-0.013 ± 0.081
(-0.168, 0.151)
49
APPENDIX B (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
WCSP
-5.162 ± 0.570
(-6.391, -4.187)
-0.516 ± 0.560
(-1.697, 0.496)
1.755 ± 0.477
(0.810, 2.699)
-0.164 ± 0.142
(-0.424, 0.125)
-0.356 ± 0.223
(-0.808, 0.083)
-0.226 ± 0.195
(-0.620, 0.134)
-0.025 ± 0.096
(-0.219, 0.160)
-0.089 ± 0.142
(-0.379, 0.179)
-0.075 ± 0.210
(-0.491, 0.321)
-0.039 ± 0.147
(-0.322, 0.254)
-0.059 ± 0.142
(-0.344, 0.214)
-0.102 ± 0.126
(-0.351, 0.137)
0.003 ± 0.130
(-0.277, 0.236)
-0.131 ± 0.159
(-0.458, 0.171)
WETA
1.191 ± 0.159
(0.894, 1.506)
-0.708 ± 0.120
(-0.931, -0.477)
-0.532 ± 0.173
(-0.873, -0.194)
-0.232 ± 0.108
(-0.445, -0.030)
0.138 ± 0.099
(-0.054, 0.330)
0.107 ± 0.118
(-0.117, 0.334)
0.032 ± 0.062
(-0.087, 0.158)
0.256 ± 0.094
(0.081, 0.448)
0.070 ± 0.091
(-0.109, 0.242)
0.101 ± 0.105
(-0.095, 0.317)
0.139 ± 0.104
(-0.053, 0.349)
0.114 ± 0.091
(-0.052, 0.303)
0.046 ± 0.081
(-0.104, 0.219)
0.227 ± 0.093
(0.052, 0.415)
WEWP
-0.063 ± 0.127
(-0.316, 0.192)
-0.794 ± 0.117
(-1.027, -0.564)
-1.032 ± 0.162
(-1.336, -0.711)
-0.015 ± 0.099
(-0.211, 0.174)
0.214 ± 0.086
(0.050, 0.388)
-0.039 ± 0.109
(-0.256, 0.168)
-0.050 ± 0.048
(-0.145, 0.044)
-0.087 ± 0.085
(-0.248, 0.080)
-0.070 ± 0.079
(-0.223, 0.089)
-0.187 ± 0.082
(-0.358, -0.032)
-0.059 ± 0.072
(-0.203, 0.082)
-0.140 ± 0.081
(-0.306, 0.012)
0.060 ± 0.074
(-0.088, 0.216)
0.039 ± 0.080
(-0.111, 0.201)
WHWO
-1.024 ± 0.148
(-1.304, -0.738)
0.082 ± 0.107
(-0.128, 0.299)
-0.658 ± 0.171
(-1.003, -0.307)
0.104 ± 0.103
(-0.110, 0.287)
-0.137 ± 0.096
(-0.327, 0.055)
0.001 ± 0.113
(-0.226, 0.222)
0.000 ± 0.052
(-0.100, 0.104)
0.089 ± 0.088
(-0.086, 0.268)
0.130 ± 0.082
(-0.029, 0.289)
-0.019 ± 0.079
(-0.175, 0.136)
-0.004 ± 0.074
(-0.154, 0.140)
-0.066 ± 0.088
(-0.238, 0.105)
0.072 ± 0.076
(-0.074, 0.221)
0.006 ± 0.079
(-0.153, 0.159)
WISA
-1.577 ± 0.229
(-2.025, -1.124)
-0.907 ± 0.289
(-1.485, -0.364)
1.052 ± 0.243
(0.578, 1.528)
-0.119 ± 0.148
(-0.362, 0.224)
-0.164 ± 0.128
(-0.421, 0.070)
-0.065 ± 0.142
(-0.350, 0.214)
-0.076 ± 0.070
(-0.221, 0.055)
-0.006 ± 0.115
(-0.234, 0.212)
-0.156 ± 0.121
(-0.387, 0.073)
0.120 ± 0.089
(-0.054, 0.300)
-0.055 ± 0.094
(-0.249, 0.125)
0.088 ± 0.092
(-0.095, 0.264)
0.031 ± 0.098
(-0.170, 0.213)
0.008 ± 0.115
(-0.219, 0.242)
WIWA
-1.558 ± 0.209
(-1.957, -1.125)
-0.800 ± 0.248
(-1.307, -0.318)
0.488 ± 0.232
(0.062, 0.971)
-0.683 ± 0.171
(-1.030, -0.365)
-0.070 ± 0.110
(-0.283, 0.148)
0.037 ± 0.140
(-0.223, 0.320)
0.018 ± 0.055
(-0.094, 0.124)
-0.041 ± 0.107
(-0.251, 0.160)
-0.213 ± 0.112
(-0.436, -0.002)
0.030 ± 0.085
(-0.140, 0.191)
0.032 ± 0.085
(-0.135, 0.200)
0.042 ± 0.086
(-0.130, 0.209)
0.195 ± 0.082
(0.045, 0.361)
0.090 ± 0.103
(-0.113, 0.288)
WIWR
-4.318 ± 0.437
(-5.188, -3.498)
-1.426 ± 0.550
(-2.627, -0.476)
0.083 ± 0.404
(-0.708, 0.872)
-0.325 ± 0.222
(-0.783, 0.092)
-0.390 ± 0.213
(-0.804, 0.029)
-0.072 ± 0.183
(-0.429, 0.277)
0.023 ± 0.076
(-0.129, 0.165)
0.034 ± 0.149
(-0.252, 0.319)
-0.278 ± 0.195
(-0.682, 0.077)
-0.024 ± 0.127
(-0.284, 0.206)
-0.112 ± 0.132
(-0.396, 0.128)
0.003 ± 0.120
(-0.240, 0.226)
0.047 ± 0.123
(-0.204, 0.270)
0.083 ± 0.152
(-0.213, 0.370)
50
APPENDIX B (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
YRWA
1.786 ± 0.188
(1.421, 2.160)
-0.453 ± 0.118
(-0.685, -0.229)
0.300 ± 0.205
(-0.106, 0.712)
-0.042 ± 0.111
(-0.242, 0.191)
-0.497 ± 0.136
(-0.778, -0.246)
0.197 ± 0.133
(-0.053, 0.465)
0.001 ± 0.070
(-0.122, 0.154)
0.077 ± 0.097
(-0.114, 0.266)
-0.146 ± 0.105
(-0.348, 0.062)
-0.021 ± 0.099
(-0.206, 0.184)
0.008 ± 0.092
(-0.167, 0.195)
-0.056 ± 0.091
(-0.223, 0.135)
0.063 ± 0.095
(-0.107, 0.267)
0.091 ± 0.093
(-0.091, 0.285)
51
6.3 APPENDIX C: Parameter Estimates – Small mammal species (GIS-based covariates)
Mean, standard error, and confidence intervals in parameter estimates for small mammal species included in our analysis. Values indicate the change
in occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
GLSA
-0.105 ± 0.873
(-1.607, 1.869)
-0.091 ± 0.344
(-0.771, 0.542)
-0.275 ± 0.580
(-1.453, 0.851)
-0.793 ± 0.448
(-1.751, -0.059)
-0.121 ± 0.397
(-0.931, 0.668)
-0.098 ± 0.316
(-0.755, 0.532)
0.658 ± 0.480
(-0.138, 1.771)
0.058 ± 0.239
(-0.385, 0.530)
0.194 ± 0.265
(-0.299, 0.740)
-0.028 ± 0.283
(-0.563, 0.578)
0.074 ± 0.298
(-0.542, 0.658)
-0.155 ± 0.395
(-0.928, 0.633)
-0.074 ± 0.242
(-0.575, 0.401)
-0.396 ± 0.609
(-1.706, 0.699)
MILO
-0.750 ± 0.382
(-1.534, -0.015)
0.519 ± 0.259
(0.018, 1.027)
0.274 ± 0.354
(-0.411, 0.969)
-0.395 ± 0.232
(-0.887, 0.031)
-0.038 ± 0.245
(-0.503, 0.448)
-0.315 ± 0.222
(-0.775, 0.084)
-0.015 ± 0.244
(-0.500, 0.479)
-0.028 ± 0.182
(-0.378, 0.334)
0.129 ± 0.182
(-0.230, 0.478)
-0.161 ± 0.226
(-0.597, 0.276)
0.061 ± 0.219
(-0.387, 0.494)
-0.191 ± 0.249
(-0.705, 0.289)
0.185 ± 0.158
(-0.088, 0.532)
1.002 ± 0.367
(0.362, 1.792)
NECI
-1.889 ± 0.527
(-2.910, -0.842)
-0.188 ± 0.375
(-0.928, 0.497)
-0.055 ± 0.525
(-1.124, 0.937)
-1.170 ± 0.532
(-2.396, -0.336)
0.681 ± 0.357
(0.038, 1.443)
0.287 ± 0.305
(-0.237, 0.973)
-0.033 ± 0.314
(-0.641, 0.595)
-0.041 ± 0.218
(-0.497, 0.364)
0.170 ± 0.244
(-0.292, 0.661)
-0.436 ± 0.296
(-1.074, 0.093)
0.118 ± 0.268
(-0.414, 0.665)
-0.016 ± 0.305
(-0.628, 0.586)
0.051 ± 0.158
(-0.264, 0.371)
-0.587 ± 0.536
(-1.759, 0.282)
PEMA
2.963 ± 0.562
(1.939, 4.128)
-0.086 ± 0.225
(-0.515, 0.385)
1.730 ± 0.595
(0.703, 3.058)
-0.125 ± 0.377
(-0.783, 0.706)
0.023 ± 0.282
(-0.552, 0.577)
-0.214 ± 0.292
(-0.813, 0.321)
0.039 ± 0.308
(-0.573, 0.681)
0.177 ± 0.213
(-0.238, 0.615)
0.083 ± 0.203
(-0.313, 0.493)
-0.084 ± 0.267
(-0.601, 0.456)
0.270 ± 0.272
(-0.220, 0.849)
0.118 ± 0.339
(-0.533, 0.798)
0.164 ± 0.227
(-0.234, 0.659)
0.679 ± 0.471
(-0.183, 1.661)
SOTR
-0.694 ± 1.229
(-2.726, 2.541)
-0.266 ± 0.452
(-1.268, 0.536)
-0.028 ± 0.749
(-1.515, 1.549)
-0.614 ± 0.493
(-1.586, 0.335)
-0.547 ± 0.518
(-1.698, 0.365)
0.017 ± 0.349
(-0.658, 0.775)
0.173 ± 0.381
(-0.520, 0.995)
0.176 ± 0.258
(-0.299, 0.762)
-0.014 ± 0.310
(-0.673, 0.552)
-0.170 ± 0.318
(-0.826, 0.450)
0.107 ± 0.314
(-0.503, 0.755)
0.402 ± 0.438
(-0.411, 1.339)
0.306 ± 0.314
(-0.199, 1.054)
0.642 ± 0.653
(-0.566, 2.014)
SPBE
-0.292 ± 0.366
(-1.056, 0.406)
0.381 ± 0.301
(-0.142, 1.029)
-1.502 ± 0.355
(-2.212, -0.837)
0.166 ± 0.231
(-0.290, 0.611)
0.319 ± 0.225
(-0.108, 0.782)
-0.010 ± 0.212
(-0.423, 0.421)
-0.232 ± 0.282
(-0.814, 0.318)
-0.156 ± 0.188
(-0.539, 0.198)
-0.051 ± 0.192
(-0.437, 0.309)
-0.158 ± 0.226
(-0.627, 0.278)
0.172 ± 0.221
(-0.253, 0.605)
0.752 ± 0.319
(0.155, 1.383)
-0.239 ± 0.147
(-0.534, 0.029)
-0.423 ± 0.308
(-1.080, 0.124)
SPLA
1.509 ± 0.360
(0.841, 2.212)
-0.562 ± 0.222
(-1.025, -0.155)
0.211 ± 0.307
(-0.388, 0.815)
-0.471 ± 0.199
(-0.850, -0.058)
0.430 ± 0.222
(0.016, 0.885)
-0.284 ± 0.223
(-0.726, 0.136)
-0.520 ± 0.280
(-1.11, -0.012)
-0.023 ± 0.167
(-0.353, 0.318)
-0.009 ± 0.177
(-0.342, 0.339)
-0.165 ± 0.227
(-0.618, 0.272)
0.061 ± 0.211
(-0.359, 0.49)
-0.584 ± 0.283
(-1.196, -0.088)
0.061 ± 0.135
(-0.199, 0.331)
-0.144 ± 0.289
(-0.709, 0.436)
52
APPENDIX C (cont.)
Mean, standard error, and confidence intervals in parameter estimates for small mammal species included in our analysis. Values indicate the change
in occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
DBH
Canopy cover
Canopy cover2
Canopy SD
Tree diversity
Tree diversity SD
Shrub cover
Shrub cover2
Herb cover
TAAM
0.869 ± 0.340
(0.204, 1.525)
-0.271 ± 0.211
(-0.696, 0.144)
-0.099 ± 0.312
(-0.715, 0.522)
0.013 ± 0.218
(-0.368, 0.490)
0.698 ± 0.230
(0.265, 1.172)
-0.287 ± 0.213
(-0.740, 0.103)
-0.180 ± 0.235
(-0.657, 0.287)
0.120 ± 0.164
(-0.195, 0.460)
0.489 ± 0.200
(0.124, 0.911)
0.029 ± 0.219
(-0.390, 0.472)
-0.086 ± 0.230
(-0.562, 0.348)
0.314 ± 0.242
(-0.149, 0.791)
0.015 ± 0.143
(-0.255, 0.301)
0.423 ± 0.350
(-0.218, 1.139)
TADO
-0.480 ± 0.381
(-1.222, 0.321)
0.369 ± 0.310
(-0.157, 1.065)
-1.385 ± 0.392
(-2.195, -0.666)
-0.073 ± 0.288
(-0.652, 0.453)
0.058 ± 0.247
(-0.452, 0.552)
0.012 ± 0.252
(-0.489, 0.516)
-0.073 ± 0.280
(-0.639, 0.495)
-0.083 ± 0.191
(-0.469, 0.291)
-0.097 ± 0.209
(-0.538, 0.288)
-0.042 ± 0.253
(-0.542, 0.466)
-0.055 ± 0.239
(-0.537, 0.388)
-0.238 ± 0.275
(-0.787, 0.322)
0.098 ± 0.152
(-0.186, 0.401)
-0.430 ± 0.393
(-1.232, 0.280)
TAQU
1.872 ± 0.391
(1.158, 2.686)
0.012 ± 0.255
(-0.456, 0.533)
-0.104 ± 0.334
(-0.794, 0.550)
-0.383 ± 0.209
(-0.807, 0.021)
-0.050 ± 0.243
(-0.545, 0.421)
-0.041 ± 0.205
(-0.457, 0.362)
0.193 ± 0.257
(-0.298, 0.719)
0.021 ± 0.182
(-0.332, 0.403)
0.139 ± 0.191
(-0.229, 0.516)
0.146 ± 0.234
(-0.293, 0.608)
0.246 ± 0.227
(-0.179, 0.705)
0.457 ± 0.259
(-0.036, 0.970)
-0.094 ± 0.141
(-0.363, 0.183)
-0.488 ± 0.271
(-1.054, 0.025)
TASE
0.728 ± 0.362
(0.024, 1.472)
-0.381 ± 0.256
(-0.915, 0.092)
0.267 ± 0.314
(-0.370, 0.895)
-0.535 ± 0.22
(-0.993, -0.137)
-0.995 ± 0.267
(-1.555, -0.495)
-0.165 ± 0.215
(-0.610, 0.234)
0.043 ± 0.253
(-0.435, 0.547)
-0.024 ± 0.172
(-0.360, 0.321)
0.042 ± 0.186
(-0.330, 0.390)
-0.267 ± 0.233
(-0.734, 0.179)
0.275 ± 0.222
(-0.139, 0.738)
-0.175 ± 0.245
(-0.673, 0.297)
-0.068 ± 0.134
(-0.341, 0.192)
0.214 ± 0.286
(-0.302, 0.798)
TASP
1.339 ± 0.445
(0.511, 2.236)
-0.646 ± 0.374
(-1.475, -0.020)
2.634 ± 0.603
(1.556, 3.908)
-0.297 ± 0.377
(-0.958, 0.536)
0.322 ± 0.257
(-0.181, 0.836)
-0.100 ± 0.251
(-0.608, 0.394)
-0.171 ± 0.308
(-0.816, 0.416)
-0.101 ± 0.194
(-0.491, 0.280)
0.084 ± 0.198
(-0.304, 0.489)
-0.117 ± 0.261
(-0.633, 0.410)
0.243 ± 0.250
(-0.229, 0.779)
-0.194 ± 0.329
(-0.869, 0.437)
0.165 ± 0.193
(-0.179, 0.588)
0.532 ± 0.429
(-0.25, 1.398)
53
6.4 APPENDIX D: Parameter Estimates – Small mammal species (field-based covariates)
Mean, standard error, and confidence intervals in parameter estimates for small mammal species included in our analysis. Values indicate the change
in occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
GLSA
0.631 ± 1.051
(-0.897, 3.123)
0.054 ± 0.446
(-0.739, 1.063)
-0.494 ± 0.794
(-2.266, 0.897)
-0.868 ± 0.512
(-1.931, 0.031)
0.088 ± 0.376
(-0.655, 0.825)
0.055 ± 0.388
(-0.645, 0.873)
-0.005 ± 0.247
(-0.476, 0.501)
0.203 ± 0.282
(-0.335, 0.808)
-0.321 ± 0.353
(-1.053, 0.353)
-0.231 ± 0.320
(-0.871, 0.430)
-0.027 ± 0.305
(-0.595, 0.590)
-0.089 ± 0.268
(-0.601, 0.447)
-0.040 ± 0.271
(-0.548, 0.523)
0.033 ± 0.270
(-0.465, 0.591)
MILO
-0.846 ± 0.318
(-1.490, -0.232)
0.693 ± 0.260
(0.223, 1.224)
0.744 ± 0.358
(0.047, 1.459)
-0.315 ± 0.213
(-0.741, 0.095)
-0.200 ± 0.206
(-0.599, 0.204)
-0.147 ± 0.238
(-0.637, 0.326)
0.072 ± 0.131
(-0.190, 0.337)
0.007 ± 0.208
(-0.399, 0.400)
0.083 ± 0.210
(-0.342, 0.489)
-0.029 ± 0.211
(-0.446, 0.372)
0.103 ± 0.205
(-0.302, 0.497)
-0.003 ± 0.171
(-0.352, 0.323)
-0.212 ± 0.204
(-0.666, 0.146)
0.051 ± 0.193
(-0.321, 0.439)
NECI
-2.360 ± 0.528
(-3.430, -1.384)
-0.561 ± 0.571
(-1.869, 0.362)
0.649 ± 0.592
(-0.455, 1.885)
-1.067 ± 0.518
(-2.220, -0.227)
0.350 ± 0.297
(-0.191, 0.979)
-0.161 ± 0.303
(-0.757, 0.427)
0.251 ± 0.157
(-0.034, 0.575)
0.059 ± 0.253
(-0.469, 0.542)
0.057 ± 0.270
(-0.470, 0.584)
-0.079 ± 0.290
(-0.659, 0.502)
0.225 ± 0.233
(-0.227, 0.706)
-0.043 ± 0.216
(-0.493, 0.371)
0.084 ± 0.212
(-0.328, 0.516)
-0.392 ± 0.266
(-0.963, 0.074)
PEMA
2.932 ± 0.575
(1.918, 4.166)
-0.237 ± 0.234
(-0.705, 0.226)
1.969 ± 0.655
(0.886, 3.424)
-0.079 ± 0.420
(-0.726, 0.928)
-0.223 ± 0.311
(-0.847, 0.362)
-0.064 ± 0.292
(-0.657, 0.519)
0.257 ± 0.188
(-0.068, 0.664)
0.340 ± 0.240
(-0.105, 0.832)
-0.181 ± 0.222
(-0.619, 0.258)
-0.365 ± 0.215
(-0.831, 0.019)
-0.088 ± 0.186
(-0.445, 0.280)
0.046 ± 0.223
(-0.355, 0.536)
-0.014 ± 0.215
(-0.423, 0.430)
-0.126 ± 0.211
(-0.537, 0.292)
SOTR
-0.060 ± 1.370
(-2.108, 3.687)
-0.345 ± 0.558
(-1.556, 0.738)
-0.092 ± 0.779
(-1.693, 1.411)
-0.227 ± 0.394
(-1.023, 0.481)
-0.590 ± 0.529
(-1.817, 0.349)
-0.145 ± 0.358
(-0.840, 0.611)
0.127 ± 0.256
(-0.367, 0.666)
0.047 ± 0.288
(-0.568, 0.580)
0.258 ± 0.388
(-0.421, 1.085)
0.052 ± 0.311
(-0.539, 0.688)
-0.231 ± 0.354
(-1.005, 0.419)
0.023 ± 0.275
(-0.508, 0.580)
-0.122 ± 0.312
(-0.775, 0.472)
-0.222 ± 0.299
(-0.832, 0.350)
SPBE
-0.292 ± 0.333
(-0.918, 0.421)
0.557 ± 0.401
(-0.086, 1.445)
-1.437 ± 0.361
(-2.160, -0.769)
0.203 ± 0.216
(-0.229, 0.602)
0.163 ± 0.207
(-0.255, 0.553)
-0.165 ± 0.247
(-0.633, 0.340)
0.062 ± 0.137
(-0.211, 0.329)
0.113 ± 0.204
(-0.295, 0.506)
0.113 ± 0.207
(-0.286, 0.543)
-0.256 ± 0.195
(-0.660, 0.107)
-0.120 ± 0.196
(-0.482, 0.290)
-0.118 ± 0.165
(-0.462, 0.191)
-0.113 ± 0.186
(-0.482, 0.243)
-0.268 ± 0.188
(-0.653, 0.098)
SPLA
1.406 ± 0.330
(0.783, 2.043)
-0.497 ± 0.239
(-0.991, -0.063)
0.392 ± 0.344
(-0.258, 1.070)
-0.397 ± 0.192
(-0.781, -0.026)
0.344 ± 0.215
(-0.081, 0.760)
-0.440 ± 0.263
(-0.971, 0.048)
-0.036 ± 0.132
(-0.294, 0.218)
0.408 ± 0.219
(0.003, 0.871)
-0.523 ± 0.203
(-0.931, -0.137)
-0.224 ± 0.203
(-0.644, 0.152)
-0.214 ± 0.201
(-0.641, 0.141)
0.120 ± 0.203
(-0.233, 0.574)
-0.111 ± 0.172
(-0.442, 0.214)
-0.208 ± 0.183
(-0.554, 0.158)
54
APPENDIX D (cont.)
Mean, standard error, and confidence intervals in parameter estimates for avian species included in our analysis. Values indicate the change in
occurrence predicted for each change in one standard deviation in the response variable. Bold indicates that the posterior interval did not overlap
zero.
Parameter
Intercept
Development
Elevation
Elevation2
Easting
Trees/HA
(Trees/HA)2
Tree diversity
Shrub cover
Soft snags/HA
Hard snags/HA
Soft CWD (m3/HA)
Hard CWD (m3/HA)
Large trees/HA
TAAM
0.575 ± 0.294
(0.008, 1.155)
-0.318 ± 0.226
(-0.768, 0.113)
-0.468 ± 0.325
(-1.095, 0.146)
0.141 ± 0.210
(-0.241, 0.608)
0.539 ± 0.206
(0.143, 0.944)
-0.224 ± 0.243
(-0.696, 0.256)
0.304 ± 0.163
(0.005, 0.638)
0.295 ± 0.203
(-0.103, 0.697)
-0.366 ± 0.189
(-0.742, -0.002)
-0.476 ± 0.235
(-1.000, -0.058)
0.083 ± 0.191
(-0.275, 0.482)
0.003 ± 0.162
(-0.318, 0.337)
0.195 ± 0.178
(-0.125, 0.577)
-0.349 ± 0.178
(-0.706, -0.006)
TADO
-0.121 ± 0.370
(-0.831, 0.622)
0.499 ± 0.366
(-0.063, 1.365)
-1.084 ± 0.374
(-1.841, -0.371)
-0.037 ± 0.269
(-0.572, 0.455)
0.073 ± 0.221
(-0.350, 0.501)
-0.195 ± 0.258
(-0.710, 0.316)
-0.056 ± 0.151
(-0.349, 0.247)
0.297 ± 0.224
(-0.138, 0.746)
-0.137 ± 0.220
(-0.567, 0.286)
0.105 ± 0.292
(-0.351, 0.800)
0.192 ± 0.219
(-0.188, 0.677)
0.126 ± 0.176
(-0.190, 0.497)
0.090 ± 0.203
(-0.283, 0.531)
-0.111 ± 0.191
(-0.483, 0.263)
TAQU
1.831 ± 0.336
(1.204, 2.505)
0.064 ± 0.252
(-0.397, 0.585)
-0.015 ± 0.331
(-0.642, 0.635)
-0.333 ± 0.197
(-0.751, 0.032)
0.055 ± 0.222
(-0.388, 0.501)
-0.210 ± 0.241
(-0.677, 0.252)
-0.027 ± 0.118
(-0.262, 0.208)
0.163 ± 0.206
(-0.243, 0.571)
0.167 ± 0.234
(-0.259, 0.652)
0.123 ± 0.238
(-0.286, 0.644)
0.100 ± 0.237
(-0.330, 0.608)
-0.176 ± 0.169
(-0.505, 0.152)
0.131 ± 0.206
(-0.218, 0.570)
-0.123 ± 0.185
(-0.477, 0.251)
TASE
0.165 ± 0.306
(-0.445, 0.758)
-0.447 ± 0.278
(-1.029, 0.055)
0.613 ± 0.308
(0.016, 1.223)
-0.486 ± 0.212
(-0.933, -0.096)
-0.893 ± 0.245
(-1.408, -0.443)
-0.210 ± 0.245
(-0.699, 0.244)
0.315 ± 0.141
(0.059, 0.610)
0.373 ± 0.200
(-0.008, 0.806)
-0.087 ± 0.194
(-0.468, 0.283)
0.134 ± 0.206
(-0.240, 0.558)
0.013 ± 0.188
(-0.363, 0.384)
-0.023 ± 0.175
(-0.354, 0.339)
0.002 ± 0.195
(-0.352, 0.426)
-0.173 ± 0.181
(-0.536, 0.183)
TASP
0.577 ± 0.414
(-0.199, 1.400)
-0.650 ± 0.386
(-1.548, -0.016)
2.388 ± 0.548
(1.397, 3.545)
-0.076 ± 0.408
(-0.769, 0.817)
0.219 ± 0.242
(-0.225, 0.702)
-0.563 ± 0.297
(-1.175, -0.019)
0.072 ± 0.144
(-0.210, 0.350)
0.194 ± 0.223
(-0.240, 0.640)
-0.416 ± 0.229
(-0.867, 0.028)
-0.224 ± 0.266
(-0.796, 0.270)
-0.409 ± 0.314
(-1.166, 0.109)
-0.007 ± 0.167
(-0.330, 0.323)
-0.015 ± 0.201
(-0.403, 0.409)
-0.358 ± 0.213
(-0.796, 0.047)
55
6.5 APPENDIX E: Species Conservation Status
Status designation of birds and small mammals surveyed in the Lake Tahoe Basin between 2002-2005 and listing
by various State and Federal agencies as species of conservation concern. Species were classified within the basin
as ubiquitous (occurrence probability ≥85%), common (≥50% and <85%), uncommon (≥25% and <50%), rare
(≥10 and <25%) and very rare (<10%).
Species Name
Birds:
American Robin (Turdus migratorius)
Band-tailed Pigeon (Patagioenas fasciata)
Black-backed Woodpecker (Picoides arcticus)
Black-headed Grosbeak (Pheucticus melanocephalus)
Brewer's Blackbird (Euphagus cyanocephalus )
Brown Creeper (Certhia americana)
Brown-headed Cowbird (Molothrus ater)
Calliope Hummingbird (Stellula calliope)
Cassin's Finch (Carpodacus cassinii)
Cassin's Vireo (Vireo cassinii)
Chipping Sparrow (Spizella passerina)
Clark's Nutcracker (Nucifraga columbiana)
Common Raven (Corvus corax)
Dark-eyed Junco (Junco hyemalis)
Downy Woodpecker (Picoides pubescens)
Dusky Flycatcher (Empidonax oberholseri)
Evening Grosbeak (Coccothraustes vespertinus)
Fox Sparrow (Passerella iliaca)
Golden-crowned Kinglet (Regulus satrapa)
Green-tailed Towhee (Pipilo chlorurus)
Hairy Woodpecker (Picoides villosus)
Hammond's Flycatcher (Empidonax hammondii)*
Hermit Thrush (Catharus guttatus)
Hermit Warbler (Dendroica occidentalis)
House Finch (Haemorhous mexicanus)*
House Wren (Troglodytes aedon)
Lazuli Bunting (Passerina amoena)*
Lesser Goldfinch (Spinus psaltria)*
Lincoln's Sparrow (Melospiza lincolnii)
Macgillivray's Warbler (Oporornis tolmiei)
Mountain Chickadee (Poecile gambeli)
Mountain Quail (Oreortyx pictus)
Mourning Dove (Zenaida macroura)
Nashville Warbler (Oreothlypis ruficapilla)
Northern Flicker (Colaptes auratus)
Olive-sided Flycatcher (Contopus cooperi)
Status
Common
Rare
Very rare
Very rare
Very rare
Common
Uncommon
Very rare
Uncommon
Rare
Very rare
Uncommon
Very rare
Ubiquitous
Very rare
Common
Uncommon
Common
Uncommon
Rare
Uncommon
Very rare
Uncommon
Very rare
Very rare
Rare
Very rare
Very rare
Very rare
Uncommon
Ubiquitous
Uncommon
Rare
Uncommon
Common
Common
Pathway
2007
DFG
2011a
USFWS
BCC
2011b
X
Climate
2012c
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
56
Species Name
Orange-crowned Warbler (Oreothlypis celata )
Pacific-slope Flycatcher (Empidonax difficilis)*
Pileated Woodpecker (Dryocopus pileatus)
Pine Grosbeak (Pinicola enucleator)
Pine Siskin (Spinus pinus)
Purple Finch (Carpodacus purpureus)*
Pygmy Nuthatch (Sitta pygmaea)
Red Crossbill (Loxia curvirostra)
Red-breasted Nuthatch (Sitta canadensis)
Red-breasted Sapsucker (Sphyrapicus ruber)
Rock Wren (Salpinctes obsoletus )
Ruby-crowned Kinglet (Regulus calendula)*
Rufous Hummingbird (Selasphorus rufus)
Savannah Sparrow (Passerculus sandwichensis )*
Song Sparrow (Melospiza melodia )
Sooty Grouse (Dendragapus fuliginosus )
Spotted Towhee (Pipilo maculatus)
Steller's Jay (Cyanocitta stelleri)
Townsend's Solitaire (Myadestes townsendi)
Warbling Vireo (Vireo gilvus)
Western Tanager (Piranga ludoviciana)
Western Wood-pewee (Contopus sordidulus)
White-breasted Nuthatch (Sitta carolinensis)
White-crowned Sparrow (Zonotrichia leucophrys )
White-headed Woodpecker (Picoides albolarvatus)
Williamson's Sapsucker (Sphyrapicus thyroideus)
Wilson's Warbler (Wilsonia pusilla)
Winter Wren (Troglodytes hiemalis )
Yellow Warbler (Dendroica petechia)*
Yellow-rumped Warbler (Dendroica coronata)
Small mammals:
Allen's chipmunk (Tamias senex)
Brush mouse (Peromyscus boylii)*
Bushy-tailed woodrat (Neotoma cinera)
California ground squirrel (Spermophilus beecheyi)
Deer mouse (Peromyscus maniculatus)
Douglas' squirrel (Tamiasciurus douglasii)
Golden-mantled ground squirrel (Spermophilus lateralis)
Lodgepole chipmunk (Tamias speciosus)
Long-eared chipmunk (Tamias quadrimaculatus)
Long-tailed vole (Microtus longicaudus)
Montane vole (Microtus montanus)*
Northern flying squirrel (Glaucomys sabrinus)
Status
Very rare
Very rare
Very rare
Rare
Uncommon
Very rare
Rare
Rare
Ubiquitous
Rare
Very rare
Very rare
Very rare
Very rare
Very rare
Very rare
Very rare
Ubiquitous
Common
Uncommon
Common
Common
Common
Very rare
Rare
Uncommon
Rare
Very rare
Very rare
Ubiquitous
Common
Rare
Rare
Uncommon
Ubiquitous
Uncommon
Ubiquitous
Common
Ubiquitous
Uncommon
Very rare
Common
Pathway
2007
DFG
2011a
USFWS
BCC
2011b
Climate
2012c
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
57
Species Name
Trowbridge's shrew (Sorex trowbridgii)
Western gray squirrel (Sciurus griseus)*
Western jumping mouse (Zapus princeps)*
Yellow-pine chipmunk (Tamias amoenus)
Status
Uncommon
Uncommon
Uncommon
Common
Pathway
2007
X
DFG
2011a
USFWS
BCC
2011b
Climate
2012c
X
a
California Department of Fish and Game. Animals of Conservation Concern, b US Fish and Wildlife,
Birds of Conservation Concern, c Gardeli et al. 2012
58
6.6 APPENDIX F: Species Distribution Maps
The following maps predict the probability of occurrence for each species within the Lake Tahoe Basin at a 30m X
30m resolution. The value of each cell is a function of the effect of abiotic and biotic covariates of the probability
of species’ occurrence. Abiotic covariates include the effect of percent development, elevation and easting. Biotic
covariates related to forest structure include the average diameter at breast height (DBH) of trees, percent canopy
cover, standard deviation in canopy cover, tree size class diversity, standard deviation in tree size class diversity,
percent shrub cover, and percent herbaceous cover. Forest structural variables were calculated within a 150-m
radius of the point where the survey was conducted from the Tahoe Existing Vegetation Layer (Eveg 4.1). Species
are depicted as detected in they were recorded during any visit to the survey location.
Avian Species
American Robin (Turdus migratorius)
Band-tailed Pigeon (Patagioenas fasciata)
Black-backed Woodpecker (Picoides arcticus)
Black-headed Grosbeak (Pheucticus melanocephalus)
Brewer's Blackbird (Euphagus cyanocephalus )
Brown Creeper (Certhia americana)
Brown-headed Cowbird (Molothrus ater)
Calliope Hummingbird (Stellula calliope)
Cassin's Finch (Carpodacus cassinii)
Cassin's Vireo (Vireo cassinii)
Chipping Sparrow (Spizella passerina)
Clark's Nutcracker (Nucifraga columbiana)
Common Raven (Corvus corax)
Dark-eyed Junco (Junco hyemalis)
Downy Woodpecker (Picoides pubescens)
Dusky Flycatcher (Empidonax oberholseri)
Evening Grosbeak (Coccothraustes vespertinus)
Fox Sparrow (Passerella iliaca)
Golden-crowned Kinglet (Regulus satrapa)
Green-tailed Towhee (Pipilo chlorurus)
Hairy Woodpecker (Picoides villosus)
Hermit Thrush (Catharus guttatus)
Hermit Warbler (Dendroica occidentalis)
House Wren (Troglodytes aedon)
Lincoln's Sparrow (Melospiza lincolnii)
Macgillivray's Warbler (Oporornis tolmiei)
Mountain Chickadee (Poecile gambeli)
Mountain Quail (Oreortyx pictus)
Mourning Dove (Zenaida macroura)
Nashville Warbler (Oreothlypis ruficapilla)
Northern Flicker (Colaptes auratus)
Olive-sided Flycatcher (Contopus cooperi)
Orange-crowned Warbler (Oreothlypis celata )
Pileated Woodpecker (Dryocopus pileatus)
61
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
59
Pine Grosbeak (Pinicola enucleator)
Pine Siskin (Spinus pinus)
Pygmy Nuthatch (Sitta pygmaea)
Red Crossbill (Loxia curvirostra)
Red-breasted Nuthatch (Sitta canadensis)
Red-breasted Sapsucker (Sphyrapicus ruber)
Rock Wren (Salpinctes obsoletus )
Rufous Hummingbird (Selasphorus rufus)
Song Sparrow (Melospiza melodia )
Sooty Grouse (Dendragapus fuliginosus )
Spotted Towhee (Pipilo maculatus)
Steller's Jay (Cyanocitta stelleri)
Townsend's Solitaire (Myadestes townsendi)
Warbling Vireo (Vireo gilvus)
Western Tanager (Piranga ludoviciana)
Western Wood-pewee (Contopus sordidulus)
White-breasted Nuthatch (Sitta carolinensis)
White-crowned Sparrow (Zonotrichia leucophrys )
White-headed Woodpecker (Picoides albolarvatus)
Williamson's Sapsucker (Sphyrapicus thyroideus)
Wilson's Warbler (Wilsonia pusilla)
Winter Wren (Troglodytes hiemalis )
Yellow-rumped Warbler (Dendroica coronata)
Small mammal species
Allen's chipmunk (Tamias senex)
Bushy-tailed woodrat (Neotoma cinera)
California ground squirrel (Spermophilus beecheyi)
Deer mouse (Peromyscus maniculatus)
Douglas' squirrel (Tamiasciurus douglasii)
Golden-mantled ground squirrel (Spermophilus lateralis)
Lodgepole chipmunk (Tamias speciosus)
Long-eared chipmunk (Tamias quadrimaculatus)
Long-tailed vole (Microtus longicaudus)
Northern flying squirrel (Glaucomys sabrinus)
Trowbridge's shrew (Sorex trowbridgii)
Yellow-pine chipmunk (Tamias amoenus)
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
118
119
120
121
122
123
124
125
126
127
128
129
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
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129
6.7 APPENDIX G: SDM Evaluations
For each species the panel of the left is a bean plot displaying the distributions of detections and non-detections
during surveys in relation the predicted probability of occurrence. Each horizontal grey line depicts a survey
observation, regions outlined in each column show density traces for detection and non-detection data points. Bold
black lines depict mean observation values.
Panels on the right are calibration plots that depict model performance. Observed prevalence for each species is
depicted along the x-axis with the corresponding predicting prevalence of the y-axis. These plots indicated whether
a prediction of the probability of occurrence corresponds to a observed occurrence of 60% and whether these
prediction are twice as likely as prediction of 30% (Vaughan & Ormerod 2005). Circles around each point indicate
the proportion of data that contributes to each observation.
Example: American Robin. The mean predicted occurrence for American Robin within the LTBMU is 82%. The
distribution of survey location where this species was detected corresponded to a peak in detections in locations
predicted to have a 90% chance of the species occurring. Alternatively, locations where this species went
undetected during surveys still had a 75% chance of being occupied. This suggests that either the model is poor at
predicting occurrence, or that the species often fails to be detected during surveys. Based on the calibration plot
and a mean probability of detection of 58%, the model appears to have stronger predictive power for locations in
which the detection probability was greater than 70%.
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133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153