Predicting geographic distributions of fishes in remote stream networks using maximum entropy modeling and landscape characterizations

Abstract

The Bureau of Land Management (BLM) manages the National Petroleum Reserve - Alaska on the remote North Slope but has limited data on fish distributions on which to base leasing and management decisions. To address this, we used environmental DNA, traditional sampling, watershed landscape characterizations, and maximum entropy modeling to develop species distribution models (SDMs) for 19 fish species. The difficulty of characterizing up stream environments for every stream-reach has limited the development of SDMs for riverine taxa to using either only local conditions or a small subset of potential watersheds. We apply a new technique (StreamCat) to characterize the background variation in watershed conditions. We also assessed how including temporal variation in addition to spatial variation and how adjusting the parameters that controlled model parsimony would affect model performance. The best models (mean TSS = 0.87 across all 19 taxa) used only static data, regularization parameters between 1.0 (default) and 2.0 (slightly more parsimonious), and watershed background data. Important predictors in these models included temperature, slope, and land cover. Approaches like this have great potential for providing critically needed data in rapidly developing but data poor regions like the North Slope of Alaska.