ABSTRACT:

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.

ABSTRACT:

Methods that accurately identify suitable aquatic habitat with minimal complexity are need to inform resource management. Habitat suitability models intersect environmental variables to predict habitat quality, but previous approaches are spatially and ecologically limited, and are rarely validated. This study estimated aquatic habitat at large spatial scales with publicly-available national datasets. We evaluated 15 habitat suitability models using unique combinations of percent mean annual discharge (MAD), velocity, gradient, and stream temperature to predict monthly habitat suitability for Bonneville Cutthroat Trout and Bluehead Sucker in Utah. Environmental variables were validated with observed instream conditions and species presence observations verified habitat suitability estimates. Results indicated that simple models using few environmental variables best predict habitat suitability. Stream temperature best predicted Bonneville Cutthroat Trout presence, and gradient and percent MAD best predicted Bluehead Sucker presence. Additional environmental variables improved habitat suitability accuracy in specific months, but reduced overall accuracy.

ABSTRACT:

Globally changing temperature and precipitation patterns are causing rapid changes stream temperatures, which in turn drive changes in the life histories and distributions of aquatic biota. However, large-scale stream temperature datasets have not been developed, and observational data remains limited. In order to better understand how ongoing thermal regime changes impact aquatic species, managers and researchers need better methods of quantifying stream temperatures at large spatial scales. Here, a linear regression model is used to develop a relationship between air and stream temperature, then is used to predict stream temperatures across the state of Utah in the month of August. Model validity was assessed by examining goodness of fit to observation data using R², Nash-Sutcliffe Efficiency index, and root mean square error-observations standard deviation ratio (RSR). Impact of outliers were assessed by examining mean absolute error (MAE), root mean square error (RMSE), and residuals. The approach presented here contributes to the well-described linear air/stream temperature model by providing a study of its performance at large spatial scales.

ABSTRACT:

This resource presents an example method for taking raw GAMUT sensor data and calculating daily averages across a given time period using the Python programming language. The example code here uses turbidity as an example, but other variables and timescales can use a similar method. This resource was created as part of the coursework associated with CEE6110 at Utah State University.