Avian point-counts from Rhode Island and Connecticut used to test species distribution models

Spatial-biases are a common feature of presence-absence data from citizen scientists. Spatial thinning can mitigate errors in species distribution models (SDMs) that use these data. When detections or non-detections are rare, however, SDMs may suffer from class imbalance or low sample size of the minority (i.e. rarer) class. Poor predictions can result, the severity of which may vary by modeling technique. To explore the consequences of spatial bias and class imbalance in presence-absence data, we used eBird citizen science data for 102 bird species from the northeastern USA to compare spatial thinning, class balancing, and majority-only thinning (i.e., retaining all samples of the minority class). We created SDMs using two parametric or semi-parametric techniques (generalized linear models and generalized additive models) and two machine-learning techniques (random forest and boosted regression trees). We tested the predictive abilities of these SDMs using an independent and systematic...