Dynamic structural equation models for ecosystem-based fisheries management: case studies and a practical guide
- 1University of Delaware
Abstract
Once central challenge in adopting ecosystem-based fisheries management (EBFM) is understanding and predicting the interconnected processes, feedbacks, and temporal dependencies that characterize ecosystem dynamics. Structural equation models and structural causal models (SEM and SCM) quantify interconnected relationships and evaluate causality from observational data but cannot readily accommodate the incomplete and irregularly sampled data typical of complex systems. Dynamic structural equation models (DSEM) estimate SEMs but accommodate lagged effects, missing data, and non-linear and non-stationary effects. However, there is little guidance regarding how DSEM can be used to support EBFM, particularly within governance and decision-making contexts. Here, we present three case studies that highlight distinct inferential goals for understanding ecosystem processes in the context of EBFM. First, we quantify ecosystem-level indicators to support monitoring of climate-driven ecosystem changes. Second, we examine whether hypothesized direct and indirect ecosystem linkages improve predictions of recruitment. Finally, we compare support for hypotheses of causal drivers of fish productivity. To provide practical advice, we synthesize lessons learned into a workflow to for others navigating DSEM. We suggest that the first step is identifying the inferential objective, which establishes the foundation for the remainder of the workflow, guiding the development of graphical models, model specification, and the selection of statistical and scientific validation steps (e.g., testing conditional independencies) needed to evaluate hypothesized causal relationships. By integrating observational data, expert knowledge, and mechanistic hypotheses within a unified framework, DSEM is a powerful tool for advancing EBFM, and more broadly, understanding the processes that govern complex ecosystems.
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BibTeX
@misc{bigman2026dynamic,
title = {{Dynamic structural equation models for ecosystem-based fisheries management: case studies and a practical guide}},
author = {J. S. Bigman and J. T. Thorson and S. I. Large and S. K. Shotwell and C. C. Monnahan and S. Gonzalez and S. Wise},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.05.749621},
url = {https://www.biorxiv.org/content/10.64898/2026.09.05.749621v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Dynamic structural equation models for ecosystem-based fisheries management: case studies and a practical guide
AU - J. S. Bigman
AU - J. T. Thorson
AU - S. I. Large
AU - S. K. Shotwell
AU - C. C. Monnahan
AU - S. Gonzalez
AU - S. Wise
PY - 2026
DA - 2026/09/05
PB - bioRxiv
DO - 10.64898/2026.09.05.749621
UR - https://www.biorxiv.org/content/10.64898/2026.09.05.749621v1
AB - Once central challenge in adopting ecosystem-based fisheries management (EBFM) is understanding and predicting the interconnected processes, feedbacks, and temporal dependencies that characterize ecosystem dynamics. Structural equation models and structural causal models (SEM and SCM) quantify interconnected relationships and evaluate causality from observational data but cannot readily accommodate the incomplete and irregularly sampled data typical of complex systems. Dynamic structural equation models (DSEM) estimate SEMs but accommodate lagged effects, missing data, and non-linear and non-stationary effects. However, there is little guidance regarding how DSEM can be used to support EBFM, particularly within governance and decision-making contexts. Here, we present three case studies that highlight distinct inferential goals for understanding ecosystem processes in the context of EBFM. First, we quantify ecosystem-level indicators to support monitoring of climate-driven ecosystem changes. Second, we examine whether hypothesized direct and indirect ecosystem linkages improve predictions of recruitment. Finally, we compare support for hypotheses of causal drivers of fish productivity. To provide practical advice, we synthesize lessons learned into a workflow to for others navigating DSEM. We suggest that the first step is identifying the inferential objective, which establishes the foundation for the remainder of the workflow, guiding the development of graphical models, model specification, and the selection of statistical and scientific validation steps (e.g., testing conditional independencies) needed to evaluate hypothesized causal relationships. By integrating observational data, expert knowledge, and mechanistic hypotheses within a unified framework, DSEM is a powerful tool for advancing EBFM, and more broadly, understanding the processes that govern complex ecosystems.
ER -