NOAA Fisheries Integrated Toolbox
Tool: Rceattle (Rceattle)
Authors
Grant Adams
Contributors
Cole Monnahan, Sophia Wasserman, Kirstin Holsman, Andre Punt, Raquel Ruiz Diaz, Jim Ianelli
Description
Rceattle is an R package for fitting climate-linked, single- and multi-species age-structured stock assessment models and testing via diagnostics, simulation, and management strategy evaluation. Rceattle implements models in R using Template Model Builder. The package has been used for operational stock assessments, ESPs/ESRs, MSE, and research. Capabilities include:
- Single-species (msmMode = 0) and multispecies (msmMode > 0) configurations, with one- or two-sex population dynamics
- One or multiple single-species stock assessment models can be fit jointly
- Multiple fisheries and surveys with flexible catchability and selectivity parameterizations (logistic, double-logistic, non-parametric, time-varying, etc.)
- Stock–recruitment options (Beverton–Holt, Ricker, mean-recruitment, environmentally-driven
- Estimable growth (von Bertalanffy / Richards / empirical weight-at-age)
- Environmental linkages and priors on recruitment, natural mortality, and growth parameters
- Bioenergetics-based predation with input or temperature-specific consumption (multispecies mode)
- Forward projections under alternative harvest control rules and climate scenarios
- Closed-loop MSE, retrospective, jitter, and simulation testing
- Tidy outputs via S3 methods (as.data.frame, coef, logLik, vcov, residuals, plot)
References
- Adams, G. D., Holsman, K. K., Barbeaux, S. J., Dorn, M. W., Ianelli, J. N., Spies, I., Stewart, I. J., & Punt, A. E. (2022). An ensemble approach to understand predation mortality for groundfish in the Gulf of Alaska. Fisheries Research, 251, 106303. https://doi.org/10.1016/j.fishres.2022.106303
- Adams, G. D., Holsman, K., Rovellini, A., Stewart, I. J., Privitera-Johnson, K., Wassermann, S. N., & Punt, A. E. (2025). Implications of predator–prey dynamics for single species management. Canadian Journal of Fisheries and Aquatic Sciences, 82, 1–19. https://doi.org/10.1139/cjfas-2024-0225
- Holsman, K. K., Ianelli, J., Aydin, K., Punt, A. E., & Moffitt, E. A. (2016). A comparison of fisheries biological reference points estimated from temperature-specific multi-species and single-species climate-enhanced stock assessment models. Deep Sea Research Part II: Topical Studies in Oceanography, 134, 360–378. https://doi.org/10.1016/j.dsr2.2015.08.001
- Kapur, M. S., Adams, G., Lapeyrolerie, M., & Thorson, J. T. (2026). AI for Fisheries Science: Neural Network Tools for Forecasting, Spatial Standardization, and Policy Optimization. https://doi.org/10.64898/2026.03.13.711664
- Wassermann, S. N., Adams, G. D., Haltuch, M. A., Kaplan, I. C., Marshall, K. N., & Punt, A. E. (2024). Even low levels of cannibalism can bias population estimates for Pacific hake. ICES Journal of Marine Science, 82(1). https://doi.org/10.1093/icesjms/fsae064
Installation Instructions
# Rceattle (pulls CRAN dependencies automatically)install.packages("remotes")remotes::install_github("grantdadams/Rceattle")
Citation
Adams, G. D., Holsman, K. K., Barbeaux, S. J., Dorn, M. W., Ianelli, J. N., Spies, I., Stewart, I. J., & Punt, A. E. (2022). An ensemble approach to understand predation mortality for groundfish in the Gulf of Alaska. Fisheries Research, 251, 106303. https://doi.org/10.1016/j.fishres.2022.106303
Keywords
- Age Structured Model
- C++
- Decision Support Tool
- Multispecies Model
- R Package