Data and Tables for Evaluating Alternative Methods for Modeling Trap Efficiencies of Outmigrating Juvenile Salmonids
March 12, 2026
These datasets consist of files used in simulation modeling and the case study, as well as original and formatted model output files, described in "Evaluating alternative methods for modeling trap efficiencies of outmigrating juvenile salmonids" (Walden and Som 2026). Files are named and formatted to be used directly by or as created by the R scripts and code in the accompanying U.S. Geological Survey Software Release (https://doi.org/10.5066/P1RZ7YER).
References:
Walden, M. A., Som, N. A. (2026). Evaluating alternative methods for modeling trap efficiencies of out-migrating juvenile salmonids North American Journal of Fisheries Management, 2026, vqag005. https://doi.org/10.1093/najfmt/vqag005
Citation Information
| Publication Year | 2026 |
|---|---|
| Title | Data and Tables for Evaluating Alternative Methods for Modeling Trap Efficiencies of Outmigrating Juvenile Salmonids |
| DOI | 10.5066/P1PN4IHO |
| Authors | Margarete A Walden, Nicholas A Som |
| Product Type | Data Release |
| Record Source | USGS Asset Identifier Service (AIS) |
| USGS Organization | Cooperative Research Units Program |
| Rights | This work is marked with CC0 1.0 Universal |
Related
Evaluating alternative methods for modeling trap efficiencies of out-migrating juvenile salmonids Evaluating alternative methods for modeling trap efficiencies of out-migrating juvenile salmonids
Objective We aimed to compare two machine learning approaches—boosted beta regression (BBR) and beta mixed model forest (BMF)—to a Bayesian mixed-effects beta regression (BME) for the prediction of rotary screw trap (RST) efficiency for out-migrating juvenile salmonids from environmental covariates.Methods We identified two machine learning approaches that shared the ability to model...
Authors
M. A. Walden, Nicholas A Som
Related
Evaluating alternative methods for modeling trap efficiencies of out-migrating juvenile salmonids Evaluating alternative methods for modeling trap efficiencies of out-migrating juvenile salmonids
Objective We aimed to compare two machine learning approaches—boosted beta regression (BBR) and beta mixed model forest (BMF)—to a Bayesian mixed-effects beta regression (BME) for the prediction of rotary screw trap (RST) efficiency for out-migrating juvenile salmonids from environmental covariates.Methods We identified two machine learning approaches that shared the ability to model...
Authors
M. A. Walden, Nicholas A Som