Spatial sampling bias and model complexity in stream-based species distribution models: a case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, U.S.A.

Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity.  Despite elevated rates of biodiversity imperilment in stream systems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in non-raster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream segment-level within the Arkansas River basin, U.S.A, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove ...