This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1111/faf.12874. This is version 3 of this Preprint.

The benefits of hierarchical ecosystem models: demonstration using a new state-space mass-balance model EcoState
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Abstract
Ecosystem models predict changes in productivity and status for multiple species, and are important for incorporating climate-linked dynamics in ecosystem-based fisheries management. However, fishery regulations are primarily informed by single-species stock assessment models, which estimate unexplained variation in dynamics (e.g., recruitment, survival, fishery selectivity, etc) using random effects. We review the general benefits of estimating random effects in ecosystem models: (1) better representing biomass cycles and trends for focal species; (2) conditioning interactions upon observed biomass for predators and prey; (3) easier replication of model results using formal estimation rather than informal model “tuning;” (4) attributing process errors via comparison among different models. We then demonstrate these by introducing a new state-space model EcoState (and associated R-package) that extends mass-balance dynamics from Ecopath with Ecosim. This model estimates mass-balance (Ecopath) and time-dynamics (Ecosim) parameters dynamics directly via their fit to time-series data (biomass indices and fisheries catches) while also estimating the magnitude of process errors using RTMB. A real-world application involving Alaska pollock (Gadus chalcogrammus) in the eastern Bering Sea suggests that fluctuations in krill consumption are associated with cycles of increased and decreased pollock production. A self-test simulation experiment confirms that estimating process errors can improve estimates of productivity (growth and mortality) rates. Overall, we show that state-space mass-balance models can be fitted to time-series data (similar to surplus production stock assessment models), and can attribute time-varying productivity to both bottom-up and top-down drivers including the contribution of individual predator and prey interactions.
DOI
https://doi.org/10.32942/X2QK81
Subjects
Life Sciences
Keywords
Ecopath with Ecosim, state-space model, process errors, eastern Bering Sea, Alaska pollock, mass-balance model
Dates
Published: 2024-07-30 07:53
Last Updated: 2025-03-18 02:43
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License
CC-By Attribution-NonCommercial-NoDerivatives 4.0 International
Additional Metadata
Conflict of interest statement:
None.
Data and Code Availability Statement:
All data and code are included in R-package EcoState release 0.1.0 (https://github.com/James-Thorson-NOAA/EcoState), which is available as a public GitHub repository during review, and intended for submission to CRAN upon acceptance. EcoState release 0.1.0 includes three vignettes: (1) “simulation” shows how to fit the simulated 6-species ecosystem using EcoState, and contrasts it with package Rpath; (2) “surplus production” shows how to fit single-species data simulated using a Fox production function as a state-space biomass-dynamics model using EcoState, and contrasts fit with JABBA (Winker et al. 2024) and SPiCT (Pedersen and Berg 2017); (3) “eastern Bering Sea” shows how to fit the eastern Bering Sea case study involving 10 functional groups and 1 detritus pool.
Language:
English
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