CERULEAN

Mean=1.935

SD=2.089

N=62

Mean=1.055

SD=0.983

N=41

V2PLAND<70.857

Mean=3.655

SD=2.584

N=21

Mean=2.656

SD=2.020

N=16

V3DDIAMAVG<11.751

Mean=6.850

SD=1.154

N=5

Mean=1.705

SD=1.177

N=11

Mean=4.750

SD=1.969

N=5

V1DSW_N<1.465

Mean=0.814

SD=0.533

N=35

V3DDIAMAVG<11.768

Mean=2.458

SD=1.742

N=6

Introduction

Evaluating Population-Habitat Relationships of Forest Breeding Birds at Multiple Scales Using Forest Inventory and Analysis Data

Todd M. Fearer1 & Dean F. Stauffer, Dept. of Fisheries and Wildlife

Stephen P. Prisley, Dept. of Forestry

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.

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

(www.pwrc.usgs.gov/bbs/)

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.

• 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.

Objectives

Possible solution to these challenges: Integrate independent long-term datasets

U.S. Geological Survey Breeding Bird Survey

U.S. Forest Service Forest Inventory and Analysis Program

+

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.

Microhabitat (the house)Regional (the neighborhood)Landscape (the city or town)

Methods

Study AreaFive physiographic provinces in 14 states

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

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.

ResultsSummary 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)

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.

Split Variable PRE Improvement 1 V2PLAND 0.353 0.353 2 V3DDIAMAVG 0.605 0.252 3 V1DSW_N 0.724 0.120 4 V3DDIAMAVG 0.776 0.052

Conclusions• 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.

Male cerulean warbler

1Postdoctoral Associate210B Cheatham HallBlacksburg, VA 24061-0321540-231-2530; tfearer@vt.edu