Cooperative Fish and Wildlife Research Units Program: Pennsylvania
Education, Research and Technical Assistance for Managing Our Natural Resources

Pennsylvania Project

Using North American Breeding Bird Survey Data to Evaluate Portential Consequences of Energy Development and Other Land Use Changes on Bird Populations in the Northeast Appalachian Region

September 2012 - September 2014


Participating Agencies

  • Patuxent Wildlife Research Center

Existing data from the BBS, linked to USGS land-use data and other spatially-explicit databases, will be used to develop spatially-explicit models of bird species abundance and distribution. Hierarchical modeling procedures used for the analysis of BBS data (Sauer and Link 2011) will be used, modified to incorporate spatial associations defined from grid cells or other spatial structuring (e.g., Thogmartin et al. 2004). These models can be used to evaluate covariates that influence species abundance at the route or grid-cell scale. These models for bird species will be used to predict the consequences of changes in land use due to urban development, climate change, and drilling in Marcellus shale on bird species abundance and distribution for specified time frames. Models will be developed for species with a variety of life histories and habitat requirements, and hypotheses will be developed regarding (1) covariates likely to influence abundance for the species and (2) the effects of habitat changes associated with alternative scenarios of energy development for the species. The modeling and prediction will permit evaluation of these hypotheses. We will also consider the possible use of stop-specific data from the BBS in the spatial models, as these data may provide a more local-scale evaluation of habitat influences on bird abundance. In addition to BBS data, Pennsylvania recently completed a statewide breeding bird atlas that has finer-scale bird data based on methods that attempted to account for imperfect detection of birds. This dataset could be used to compare results based on BBS data and serve as a tool to evaluate the robustness of model predictions.