Modeling distribution and abundance of multiple species: Different pooling strategies produce similar results

Quantifications of spatial distribution and abundance of animals are essential to identifying key landscape characteristics and targeting locations for conservation action. Since conservation decisions often focus on multiple species aggregated in groups, e.g., guild‐level, rather than individual species, predictions of species group abundance are of central importance. However, areas chosen for conservation action may differ if results from various modeling strategies also differ. Therefore, we compared three different strategies for modeling species group distribution and abundance: predict first, assemble later (PA); assemble first, predict later (AP); and the combined assemble, predict, then assemble (APA). All strategies were performed using Boosted Regression Trees (BRTs), which were fit to individual species data and then grouped after modeling, or fit to datasets that were grouped before modeling. Modeling strategies produced very similar results in terms of statistical performance assessed through four evaluation metrics and in spatial patterns in predicted abundance. To further assess potential functional implications of any numeric differences to conservation planning, we examined the relative proportion of the predicted population within existing Canadian protected areas. This metric further confirmed similarity in predictions from the three modeling strategies. Our results suggest that locations targeted for conservation action would be highly consistent among modeling strategies. Slight differences we observed in spatial predictions may be due to data coverage across species ranges, data quality, and the flexibility of the BRTs.