Fearer1
Todd M.
& Dean F. Stauffer, Dept. of
Fisheries and Wildlife
Stephen P. Prisley, Dept. of Forestry
Evaluating Population-Habitat Relationships of Forest Breeding
Birds at Multiple Scales Using Forest Inventory and Analysis Data
Introduction
Methods
• Long-term declines in abundance evident in several forest
bird species
• Others are stable or increasing
• These trends are occurring over broad geographic extents
but do show some regional variation
• Limited understanding of habitat use at these broad scales
• Most studies are snapshots…
– Temporally, short timespan
– Geographically, small area
– Spatial scale, only one considered
Ignores multiscale habitat selection (Fig. 1)
• These shortcomings limit applicability of existing studies
for addressing these broad scale bird-habitat relationships.
The data required for such studies are difficult to obtain
using traditional field study methods and render larger
projects logistically and financially prohibitive.
Study Area
Five physiographic provinces in 14 states
Landscape (the city or town)
Regional (the neighborhood)
Microhabitat (the house)
Figure 1 Birds select habitat based on characteristics present at different scales,
just like we select a house based on its design, the neighborhood, and the
surrounding town or city. Different characteristics are important at different scales
and are considered simultaneously when a bird selects habitat. Therefore these
characteristics need to be modeled simultaneously.
Possible solution to these challenges:
Integrate independent long-term datasets
U.S. Geological Survey Breeding
Bird Survey
+
(www.pwrc.usgs.gov/bbs/)
BBS Data
• Included 26 bird species delineated into 4 guilds based on
their nesting characteristics
– Mature forest ground/shrub
– Mature forest canopy
– Young forest (early successional)
– Cavity nesters
• For each bird species, calculated presence-absence and
average abundance per BBS route across a 4 year window
– This window minimized the impact of spurious years
– End year of the window based on when state completed
its 2000 FIA inventory
– 227 BBS routes included in study
Forest Variables from FIA Data
• Based on data collected during the 2000 FIA Inventory
cycle
• Developed 43 variables describing habitat characteristics
– Focused on structure of habitat and forest stand
• Calculated variables at the FIA plot level and extrapolated
to larger scales (e.g. county) as necessary
Landscape Variables
• Incorporated National Land Cover Data (NLCD) data from 1992 to characterize landscape patterns
• Used Fragstats spatial statistical software to develop landscape metrics describing patch shape, size, edge, and other
landscape characteristics
Multiscale Habitat Selection
• Buffered BBS routes at 3 spatial scales: 100 m, 1 km, and 10 km (distance is buffer radius)
• Intersected the buffer layer with the county boundary layer (Fig. 2)
• For each route, calculated forest and landscape variables within each buffer size
• Value of forest variable within a buffer was the weighted average of the county values, with the weights calculated as the
proportional area a given county comprised within the buffer
Objectives
1. Determine the extent to which the FIA and BBS databases
are spatially and temporally compatible.
2. Determine if FIA data can explain spatial variations in
forest songbird population indices as measured by the
BBS.
3. Develop predictive models that identify conditions
favorable to forest breeding songbirds.
210B Cheatham Hall
Blacksburg, VA 24061-0321
540-231-2530; tfearer@vt.edu
Summary of Model Performance
• Classification trees (modeled bird presence-absence)
– Average variability in presence-absence explained by
models = 56.5% (ranged from 25.0% to 81.1%)
– Among the best models, least variability explained =
43.2%
• Among the 3 buffers and the multiscale model for each species,
the ‘best’ model was the one that explained the most variability
with the fewest predictor variables.
– 53% of the best models were the multiscale models
• Regression trees (modeled bird abundance)
– Average variability in abundance explained by models =
52.7% (ranged from 15.9% to 77.6%)
– Among the best models, least variability explained =
33.2%
– 46% of the best models were multiscale models
Model Example: Cerulean Warbler (Dendroica cerulea)
• Regression tree model explained 77.6% of variability in
abundance (multiscale model; Fig. 3)
• 100 m: Species diversity of dominant trees (DSW_N)
• 1 km: % landscape in deciduous forest cover (PLAND)
• 10 km: Average
diameter of dominant trees (DDIAMAVG)
CERULEAN
Mean=1.935
SD=2.089
N=62
V2PLAND<70.857
Mean=1.055
SD=0.983
N=41
Mean=3.655
SD=2.584
N=21
V3DDIAMAVG<11.768
Mean=0.814
SD=0.533
N=35
Split
1
2
3
4
Forest Inventory & Analysis (FIA)
• Collects data on the status and
trends of U.S. forests
• National program initiated ~1930
• Inventories occur at 10 year cycles,
but shifting to pseudo-annual
• One inventory plot per 2428 ha
(6000 ac)
• Public and private land sampled
• Data available online
(www.fia.fs.fed.us/)
Funding provided by the National
Council for Air and Stream
Improvement, Inc. (NCASI) and
the U.S. Forest Service.
Results
U.S. Forest Service Forest
Inventory and Analysis Program
Breeding Bird Survey (BBS)
• Provides index of breeding bird
population trends in the U.S.
• Annual survey initiated in 1966
• Survey routes are 39.4 km (24.5
miles) located on secondary roads
• 50 stops at 800 m (0.5 mile)
intervals
• Surveyed late May, early June
• Data available online
1Postdoctoral Associate
Variable
V2PLAND
V3DDIAMAVG
V1DSW_N
V3DDIAMAVG
Male cerulean warbler
V3DDIAMAVG<11.751
Mean=2.458
SD=1.742
N=6
PRE Improvement
0.353
0.353
0.605
0.252
0.724
0.120
0.776
0.052
Mean=2.656
SD=2.020
N=16
Mean=6.850
SD=1.154
N=5
V1DSW_N<1.465
Mean=1.705
SD=1.177
N=11
Mean=4.750
SD=1.969
N=5
Figure 3 Regression tree of cerulean warbler abundance (n = 62 BBS routes) relative
to forest and landscape variables calculated within 100 m (V1), 1 km (V2), and 10 km
(V3) buffers around each BBS route. Models are read like a dichotomous key from top
down. The first split explains the greatest variability and subsequent splits account for
further variations in the data. Each node (square) is labeled with the mean of the
response variable for that group (bird abundance in this case), a measure of variability
around that measure, and the number of samples in that group. Labels along the
branches identify the factor creating the split for the underlying nodes.
Conclusions
1. Initial overlay of BBS route and county boundaries
2. Buffer established around BBS route
3. Clip county boundaries based on buffer boundary
Figure 2 Illustration of steps used to intersect the county boundary layer to the buffers created around each BBS route and thereby assign forest metric values to
the BBS routes.
Model Development
• Used Pearson’s correlations to initially assess relationships among BBS, forest, and landscape variables to identify potential
predictor variables for each bird species.
• Developed bird-habitat models for each of the 3 buffer sizes first. Pooled the variables selected for each buffer into a ‘new’
suite of potential predictor variables that were used to develop a multiscale model for each bird species.
• Here we report results using classification and regression trees (CART) as the modeling technique. Developed models
relating spatial variability in bird presence-absence and abundance on BBS routes to the forest and landscape metrics.
• Using measures of bird presence-absence and abundance
developed from the BBS, forest variables developed from
the FIA, and landscape variables developed from NLCD
imagery, we were able to produce a suite of models
describing bird-habitat relationships at multiple spatial
scales.
• Able to model fine resolution variables (small-scale habitat
components) across broad geographic regions.
• This methodology provides a baseline framework for
integrating other independent large-scale databases for
evaluating wildlife-habitat relationships at multiple scales
over broad geographic regions.