Predicting road-crossing passability for river connectivity analysis

Data, code, and figures supporting the manuscript "Predicting road-crossing passability for river connectivity analysis" (Goodrum et al, 2025)
/Data - Raw data used in analysis
/Analysis - Code for processing data and generating results and figures
/Figures - PDF files of manuscript figures

Abstract:
Road-crossing structures limit organism movement but their passabilities are rarely measured because they are numerous and time-consuming to survey. Instead, road crossing passability could be treated in one of four ways: assuming equal passability at all locations (uniform method), assigning random passability values sample from barrier surveys (random sample method), or using remote sensing data to infer presence (presence/absence method) or rate passability (rating category method). Each prediction method produces different passability estimates for individual barriers, but how these differences affect river connectivity estimates has not been systematically evaluated. We compared river connectivity estimates from these four road-crossing passability prediction methods in the Bear River Basin, USA. We parameterized barrier passability methods with Bonneville Cutthroat Trout Oncorhynchus clarkii utah passage survey data at 140 road crossings. Road crossings blocked fish passage at 37% of survey locations. Those road-crossing barriers that obstructed fish movement also decreased the proportion of connected reaches in the river network from 12% (with dams and all road crossings assumed to be passable), to just 3%. All passability prediction methods produced similar results and had considerable uncertainty predicting passability for individual barriers. Our findings suggest that more simple methods, like uniform or random sample road-crossing passability predictions are sufficient to characterize river connectivity. Our work highlights the importance of identifying road crossings that act as barriers to organism passage and identifies critical limitations to predicting barrier status for connectivity analysis and conservation planning.