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 an eastward, large-river sampling bias that was evident within the
unfiltered presence dataset. In particular, our novel riverscape filter
provided a repeatable means of obtaining a relatively even coverage of
presence data among watersheds and streams of varying sizes. The greatest
differences in estimated distributions were observed among models
constructed with default versus AICC -selected parameterization. Although
all models had similarly high performance and evaluation metrics, the AICC
-selected models were more inclusive of westward-situated and smaller,
headwater streams. Overall, our results solidified the importance of
accounting for model complexity and spatial sampling bias in SDMs
constructed within stream networks and provided a roadmap for future
paddlefish restoration efforts in the study area.