Species Distribution Models and Abundance Estimates Enhance Breeding Bird Atlas Data.

Breeding bird atlases play a crucial role in understanding bird species distribution and abundance during the breeding season. This information is essential for creating accurate species distribution maps, which are fundamental for understanding the geographic range of species and identifying areas of high conservation value. Our primary goal was to develop species distribution models (SDMs) for as many Minnesota breeding bird species as possible using data from the Minnesota Breeding Bird Atlas (MNBBA; 2009-2013). The MNBBA combined volunteer atlas observations with systematic point-count surveys, resulting in datasets that varied in structure and quality across species. Recognizing this variability, we applied multiple modeling approaches tailored to the available data, which also led to differing ecological interpretations. To maximize species coverage given heterogeneous data characteristics, we used three modeling strategies to maximize the number of species we modeled: (1) bootstrapped Poisson generalized linear models with a detectability offset to predict species' density and population size, (2) bootstrapped Poisson generalized linear models to predict a species' point count index of abundance, and (3) Maxent models to predict a species' index of environmental suitability. We applied the first strategy to 73 species, the second to 30, and the third to 33 species each (136 species in total). We also produced statewide population estimates for the 73 species using the first strategy. Our framework demonstrates that linking model choice to data structure significantly increases the number of species that can be modeled compared to a single-model approach. While these results serve as a foundation for broad-scale distribution and abundance hypotheses, we suggest this adaptable methodology be tested in other regions to maximize the utility of diverse atlas datasets.