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mmrefpoints: Projecting long-term marine mammal abundance with bycatch |
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8 March 2022 |
paper.bib |
Fisheries bycatch is one of the top threats to marine mammals worldwide. Data on bycatch and marine mammal abundance are required to make management decisions, yet many marine mammal populations exist in locations where these data are sparse. Population models offer a way to explore the long-term impacts of different bycatch scenarios even when data are sparse or absent. Our modeling tool, mmrefpoints, uses very basic information about marine mammal life history, estimates of abundance and bycatch, and population models to project long-term (e.g., 20-100 yr) outcomes of different bycatch rates. These long-term outcomes are the basis of several reference points used for marine mammal management including the Potential Biological Removal (PBR) approach [@wade_calculating_1998]. The goal is to make complex population models accessible to managers and stakeholders who need them, and to students who are learning about risk-based approaches to managing marine mammal populations.
This tool provides a way for managers and other stakeholders to explore bycatch scenarios, based on simple information about marine mammal life history and rough estimates of abundance and bycatch. The tool consists of an R package [@r2021] and a Shiny application [@shiny2021]. The primary machinery in the package is an age-structured population dynamics model, which is used to model future population size based on the current population size, life history traits, and bycatch rates. The package also contains tools for calculating performance metrics, as well as the U.S. reference point for bycatch (Potential Biological Removal or PBR) and a solver that estimates the maximum bycatch rate that will meet management objectives. For users who would prefer to see outputs in an interactive user interface, there is a Shiny app called the Marine Mammal Bycatch Impacts Exploration Tool (MMBIET) that shows projections, explains and calculates reference points, and creates a report summarizing inputs and outputs. The app is hosted publicly on the web via the shinyapps.io server, or it can be run locally by installing the package and running the run_app() function in the R or RStudio console:
remotes::install_github("mcsiple/mmrefpoints")
library(mmrefpoints)
run_app()
The population model is a simplified version of the model described by @breiwick_population_1984 and @punt_a._e._annex_1999. The model is a single-sex, age-structured population model. The number of calves or pups born each year is density-dependent, based on the number of mature adults, the pregnancy rate at pre-exploitation equilibrium, maximum theoretical fecundity rate, degree of compensation, and the total abundance relative to carrying capacity K. User inputs determine the parameters for calf survival, adult survival, age at maturity, and plus group age. Default parameters are based on those used by @punt_conserving_2018, with additional default parameters for other species drawn from literature values [@arso_civil_variations_2019; @olafsdottir_growth_2003; @speakman_mark-recapture_2010]. Because of the variation in pinniped life history parameters, parameter estimates for several pinniped species are provided within the Shiny app. These life history parameters are taken from @butterworth_effects_1995, @delong_age-_2017, @dillingham_improved_2016, @hastings_sex-_2012, and @moore_unpublished_2019.
The full model description including equations is contained in the “Model description” vignette and the “About the Model” tab of the app.
- Exploring outcomes for bycatch rates in populations with little or no information
- Calculating reference points like PBR to estimate maximum allowable bycatch for marine mammal populations
- Permitting specific management actions regarding bycatch. We refer users to @hammond_estimating_2021, @moore_estimating_2021, and @wade_best_2021 for guidance on developing a management program for marine mammal bycatch including monitoring.
- Calculating PBR for marine mammal stocks that already have a stock assessment. If reference points have already been calculated for the stock, those should be used.
- Fitting population models to data (we direct readers to other tools like rSPAMM for this type of need)
The authors would like to thank several pilot testers for reviewing a beta version of the MMBIET Shiny app, and Christine Stawitz and Jay Barlow for reviewing an earlier version of the mmrefpoints R package and the app.
At the time of this submission, three papers have cited mmrefpoints and/or MMBIET:
Hammond, P. S., Francis, T. B., Heinemann, D., Long, K. J., Moore, J. E., Punt, A. E., Reeves, R. R., Sepúlveda, M., Sigurðsson, G. M., Siple, M. C., Víkingsson, G., Wade, P. R., Williams, R., & Zerbini, A. N. (2021). Estimating the Abundance of Marine Mammal Populations. Frontiers in Marine Science, 8, 1316. https://doi.org/10.3389/fmars.2021.735770
Moore, J. E., Heinemann, D., Francis, T. B., Hammond, P. S., Long, K. J., Punt, A. E., Reeves, R. R., Sepúlveda, M., Sigurðsson, G. M., Siple, M. C., Víkingsson, G. A., Wade, P. R., Williams, R., & Zerbini, A. N. (2021). Estimating Bycatch Mortality for Marine Mammals: Concepts and Best Practices. Frontiers in Marine Science, 8, 1793. https://doi.org/10.3389/fmars.2021.752356
Wade, P. R., Long, K. J., Francis, T. B., Punt, A. E., Hammond, P. S., Heinemann, D., Moore, J. E., Reeves, R. R., Sepúlveda, M., Sullaway, G., Sigurðsson, G. M., Siple, M. C., Víkingsson, G. A., Williams, R., & Zerbini, A. N. (2021). Best Practices for Assessing and Managing Bycatch of Marine Mammals. Frontiers in Marine Science, 8, 1566. https://doi.org/10.3389/fmars.2021.757330
Support for this project is provided by the Ocean Modeling Forum and the Lenfest Ocean Program. M. Siple was supported by the Ocean Modeling Forum and a James S. McDonnell Postdoctoral Fellowship.