Assessing Ecoregional-Scale Habitat Suitability Index Models for Priority Landbirds
Journal of Wildlife Management
Emerging methods in habitat and wildlife population modeling promise new horizons in conservation but only if these methods provide robust population–habitat linkages. We used Breeding Bird Survey (BBS) data to verify and validate newly developed habitat suitability index (HSI) models for 40 priority landbird species in the Central Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions. We considered a species' HSI model verified if there was a significant rank correlation between mean predicted HIS score and mean observed BBS abundance across the 88 ecological subsections within these Bird Conservation Regions. When we included all subsections, correlations verified 37 models. Models for 3 species were unverified. Rank correlations for an additional 5 species were not significant when analyses included only subsections with BBS abundance .0. To validate models, we developed generalized linear models with mean observed BBS abundance as the response variable and mean HSI score and Bird Conservation Region as predictor variables. We considered verified models validated if the overall model was an improvement over an intercept-only null model and the coefficient on the HIS variable in the model was .0. Validation provided a more rigorous assessment of model performance than verification, and models for 12 species that we verified failed validation. Species whose models failed validation were either poorly sampled by BBS protocols or associated with woodland and shrubland habitats embedded within predominantly open landscapes. We validated models for 25 species. Habitat specialists and species reaching their highest densities in predominantly forested landscapes were more likely to have validated models. In their current form, validated models are useful for conservation planning of priority landbirds and offer both insight into limiting factors at ecoregional scales and a framework for monitoring priority landbird populations from readily available national data sets.