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USGS2021

Environmental conditions associated with occurrences of the threatened Yaqui Catfish in the Yaqui River Basin, Mexico

The Yaqui Catfish Ictalurus pricei is an understudied species, with limited information available on its ecology, distribution, and local habitat use. Native to the southwestern United States and northwestern Mexico, Yaqui Catfish populations are declining, which has prompted listing of the species as threatened in the United States and as a species of concern in Mexico. Water overallocation, habitat degradation, invasive species introductions, and hybridization with nonnative Channel Catfish I . punctatus have caused the populations in Mexico to decline. The United States population collapsed after years of low recruitment. To better focus conservation efforts as well as define habitat associated with Yaqui Catfish occurrences, we assessed the distribution in the Yaqui River basin of Mexico by using historical data at a landscape scale. Yaqui Catfish were historically found across the watershed among a diversity of environments but were most frequently associated with small, intermittent streams. Basin land cover was dominated by forest, shrubland, and grassland, and Yaqui Catfish generally occurred in stream segments at similar proportions. However, a small number of Yaqui Catfish occurrences were associated with urban and cropland land cover types in proportions greater than the availability of those categories on the landscape. With the species facing declines in the region, this work will help to inform future conservation efforts aimed at securing the Yaqui Catfish, protecting suitable habitat, and better defining its current status in Mexico.

Yaqui River Basin↗

USGS2019

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

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 ecosystems, 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 nonraster 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, USA, 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 AIC C -selected parameterization. Although all models had similarly high performance and evaluation metrics, the AIC C -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.

Arkansas, Colorado, Kansas, Missouri, Nebraska, Ne↗