Evaluating Habitat Suitability Index (HSI) Models for Landbird Conservation Planning: Challenges & Opportunities Todd Jones-Farrand 1, John Tirpak 2, Charles Baxter 2, Jane Fitzgerald 3, Frank Thompson 4, Dan Twedt 5, and Bill Uihlein 2 1 USGS Missouri Cooperative Fish and Wildlife Research Unit, University of Missouri, Columbia, Missouri, USA; 2 Lower Mississippi Valley Joint Venture, US Fish and Wildlife Service, Vicksburg, Mississippi, USA; 3 Central Hardwoods Joint Venture, American Bird Conservancy, St. Louis, Missouri, USA; 4 USDA Forest Service, North Central Research Station, Columbia, Missouri, USA; 5 USGS Patuxent Wildlife Research Center, Vicksburg, Mississippi, USA Introduction Validation Methods: Study Area Results:Challenges Acknowledgments Purpose: Link habitat conditions to priority bird numbers to assist conservation partners translate population goals into on-the-ground objectives. Objective: Assess performance of multi-scale Habitat Suitability Index (HSI) models for 40 species of forest and shrubland birds. Approach: Compare HSI predictions to existing population monitoring data sets to (1)verify model performance and (2) determine proper scale of model application. We focused our research in the Central Hardwoods (CH) and the West Gulf Coastal Plain (WGCP) Bird Conservations Regions (BCR) (Figures 1 & 2). Data Sources HSI models were built from 6 national datasets: Bailey’s Ecoregions, Forest Inventory and Analysis (FIA) data, the National Land Cover Dataset (NLCD), the National Elevation Dataset (NED), U.S. General Soil Map (STATSGO) data, and the National Hydrography Dataset (NHD). Avian population data for the evaluation came from the Breeding Bird Survey (BBS) and the U.S. Forest Service Region 8 Bird Monitoring Protocol (R8Bird). BBS data included smoothed maps (21,475 km 2 grid cells) of average counts per route ( ), as well as route-level average counts ( ; Figure 2). R8Bird data included point count data from on 4 National Forests and Land Between the Lakes (Figure 1). R8Bird also provided spatially exact habitat data for most points, which we substituted for FIA in calculating HSI values. Conclusions Opportunities BBS Methods: No detection adjustment for counts. BBS grids assume abundance declines with distance from route. BBS grids are model results not intended for rigorous analyses. FIA uncertainty in route-level analysis (scale mismatch). R8Bird Method: Limited geographic coverage in CH BCR Limited number of species with sufficient detections Vegetation measurements difficult to equate with FIA measurements Frequency of vegetation measurements inconsistent across sites Fairly consistent habitat conditions across points BBS Methods: Able to assess models for most species Good geographic coverage in both BCRs Sampled a wider range of landscape types (e.g., agricultural) R8Bird Method: Detection-corrected densities Site-specific forest structure information Both evaluation data sets present us with challenges in implementation and interpretation. Each method provided some indication of the usefulness the HSI models have for conservation planning. Neither dataset is capable of fully testing the underlying relationships & assumptions (i.e., hypotheses) in the models. Validation of habitat models is best accomplished with surveys specifically designed for that purpose Thanks to M. Nelson & M. Hatfield for assistance using FIA data, F. La Sorte for assistance with Program Distance and the R8Bird database, M. Trani for assistance with R8Bird point locations, and W. Thogmartin & S. Sheriff for statistical advice. BBS: Compare average HSI values to average BBS counts at 2 scales using log-linear regression in SAS. AIC model selection used to chose appropriate link function for regression (Poisson, Negative Binomial, or Zero-inflated Poisson). Ecological subsections (n=88). BBS = area-weighted average count per route ( ) for subsection from smoothed BBS grid. HSI = subsection average (Zonal Statistics tool in ArcGIS). Predicted count = e a +  1*HSI +  2*BCR. Assess model AIC compared to Null model. Assess sign of coefficient on HSI parameter. BBS routes (n=147) BBS = average count per route ( ). HSI = average within 3 km of route (Zonal Statistics). Predicted count = e a +  1*HSI +  2*BCR. Assess model AIC compared to Null model. Assess sign of coefficient on HSI parameter. R8Bird: Compare HSI value to bird density at individual survey points. using repeated measures log-linear regression in SAS. AIC model selection used to chose appropriate distribution (Poisson or Negative Binomial). R8Bird monitoring points (n=species-specific, range ). HSI calculated from locally collected data and landscape statistics (NLCD, NHD, DEM). Some site-level model variables approximated with R8 data, some unavailable. Density calculated with Distance 5.0 based on 3 distance bands (25, 50, infinity). Data stratified by Site*Year, Site, or Year depending on sample size and best model fit. Predicted density = e a +  1*HSI +  2*BCR. Assess model AIC compared to Null model. Assess sign of coefficient on HSI parameter. Model PerformanceParameter Estimates SpeciesAnalysisDist. a Nr b AIC Null  AIC c Gen. R 2 d a 11 22 Acadian Flycatcher BBS SubsectionNB BBS RouteNB R8BirdNB Black-and- white Warbler BBS SubsectionNB BBS RouteNB R8BirdNB Blue-gray Gnatcatcher BBS SubsectionNB BBS RouteNB R8BirdNB Prairie Warbler BBS SubsectionP BBS RouteNB R8Birdn/a Table 1 shows model selection and parameter estimate results for 4 species that represent a range of habitat associations (forest-interior, early-successional, bottomland hardwoods, and generalist). Three of the 4 species could be evaluated by each method; the prairie warbler model could not be evaluated using the R8Bird data because information was lacking. BBS analyses could be performed for 38 of 40 HSI models. R8 Bird analyses could only be performed for 20 HSI models due to either a lack of detections for calculating density or insufficient habitat information. We consider these HSI models validated because they outperform a null model and showed a positive relationship between HSI value and the population measure in each analysis. Originally, we expected our models to have better predictive power in the R8Bird analysis due to the spatial exactness of the site-level habitat data. This was true of some models (e.g., Acadian Flycatcher) but not others. This is due to inefficiencies in translating habitat data collection between FIA and R8Bird (e.g. continuous canopy cover versus 4 classes). Figure 1. Study area Bird Conservation Regions (BCRs) and location of public lands in the R8Bird database. Verification Methods Compare average HSI values to average BBS counts across ecological subsections using Spearman’s Rank Correlation. Assess model outputs to ensure high HSI values for subsections with high counts and low values for areas with low counts. Revised models as necessary. a The distribution chosen to model the data based on AIC score and goodness-of-fit as measured by Pearson’s chi square / degrees of freedom. NB=negative binomial, P=Poisson. b Spearman’s rank correlation coefficient for model show were all significant at P < c The difference between the AIC value for the model of interest and the null (intercept-only) model. d The Generalized R2 from Allison (1999). It provides a measure of predictive ability ranging from 0-1 based on the likelihood ratio chi square. Table 1. Validation results for selected species using 3 evaluation data sets. Figure 2. Study area Bird Conservation Regions (BCRs) and location of Breeding Bird Survey (BBS) routes used in the analyses.