Quantifying uncertainty and dynamical changes in multi-species fishing mortality rates, catches and biomass by combining state-space and mechanistic multi-species models

TL;DR

This paper introduces a state-space approach to fit mechanistic multi-species models to data, quantifying uncertainty and improving long-term predictions for marine fish stocks, addressing limitations of traditional single-species models.

Contribution

It demonstrates a novel method to directly fit complex multi-species models with quantifiable uncertainty, reducing reliance on assumptions and propagating errors from single-species models.

Findings

Successfully fitted multi-species model parameters to Celtic Sea data

Reduced error propagation from single-species to multi-species models

Enhanced model credibility and potential for management applications

Abstract

In marine management, fish stocks are often managed on a stock-by-stock basis using single-species models. Many of these models are based upon statistical techniques and are good at assessing the current state and making short-term predictions; however, as they do not model interactions between stocks, they lack predictive power on longer timescales. Additionally, there are mechanistic multi-species models that represent key biological processes and consider interactions between stocks such as predation and competition for resources. Due to the complexity of these models, they are difficult to fit to data, and so many mechanistic multi-species models depend upon single-species models where they exist, or ad hoc assumptions when they don't, for parameters such as annual fishing mortality. In this paper we demonstrate that by taking a state-space approach, many of the uncertain parameters can be treated dynamically, allowing us to fit, with quantifiable uncertainty, mechanistic multi-species models directly to data. We demonstrate this by fitting uncertain parameters, including annual fishing mortality, of a size-based multi-species model of the Celtic Sea, for species with and without single-species stock-assessments. Consequently, errors in the single-species models no longer propagate through the multi-species model and underlying assumptions are more transparent. Building mechanistic multi-species models that are internally consistent, with quantifiable uncertainty, will improve their credibility and utility for management. This may lead to their uptake by being either used to corroborate single-species models; directly in the advice process to make predictions into the future; or used to provide a new way of managing data-limited stocks.