1 Disclaimer

The following work is preliminary and subject to ongoing investigation.

These analyses do not necessarily reflect the point of view of ICCAT or the FAO and in no way anticipate ICCAT or FAO future policy.

2 Objective

Develop a flexible and powerful indicator of stock status for tunas, billfish and sharks that can be tested theoretically and empirically.

3 Project Details

Title: ‘Simulation Testing Ecosystem Indicators: Support to ICCAT’s Ecosystem Approach to Fisheries Management (EAFM)’

Term Dec 2023 - June 2026
Funding body International Commission for the Conservation of Atlantic Tunas (ICCAT)
Funding stream FAO Global Environmental Facility (GEF) Common Oceans ABNJ Tuna Project Fund
Call No. # 11903 / 2023
Project Partners Blue Matter Science Ltd.
Blue Matter Team Tom Carruthers, Adrian Hordyk, Quang Huynh
ICCAT Collaborators Nathan Taylor

4 Introduction

4.1 Problem Statement

To meet the requirements of the precautionary approach and the ecosystem approach to fisheries management (EBFM), indicators of stock status are needed for secondary species, defined here as those lack sufficient data or capacity to conduct routine stock assessments (see Carruthers et al. 2024 for a detailed problem statement). Any such indicator be sound theoretically and should be validated empirically.

A well-documented, defensible and transparent framework is needed to support tactical decision making to move beyond the single species assessment paradigm and make progress towards the essential goals of EBFM.

4.2 Approach

The EcoTest project aims to use simulation modelling to identify data types and algorithms that can inform stock status of secondary species and then validate these indicators empirically in cases where there have been defensible stock assessments.

4.3 Indicator 1

Previous EcoTest research consolidated the dynamics of six stock assessments for species caught in the North Atlantic longline fisheries (swordfish, bigeye tuna, albacore tuna, shortfin mako shark, white marlin and blue marlin) into a multi-species, multi-fleet operating model (Huynh et al. 2022). Those models were used to simulate various future scenarios for fishing and stock dynamics to develop Indicator 1: an indicator specific to the longline case study that could predict stock status for those 6 species without undergoing a full stock assessment (Carruthers et al 2024). The performance of Indicator 1 was generally very good, but it was applicable only to the 6 species of the operating model.

A key advance in developing Indicator 1 was the use of deep learning to train neural network indicators. The large number of simulated fisheries provided both true, simulated stock status and also a wide range of possible input data types (features). This training dataset was essentially a large table, including more than 20 thousand fishery simulations. Each line representing a simulation. The first column contained the true, simulated stock status, the remaining 450+ columns were ‘features’ - potential data inputs that could be available in a real fishery setting for estimating stock status.

4.4 Uses

In the EcoTest framework, the training dataset serves as a central resource for testing indicators. Those could include proposed data types for use in an indicator or those available for a given case study.

If for a real stock 15 data inputs (features) are available, the training dataset can be subsetted and neural networks trained for that set of inputs to see whether in theory those data could provide sufficient information about stock status. Hence EcoTest has a theoretical test of a proposed indicator built-in.

The flexibility of neural networks alleviates the need to specify and code bespoke fishery dynamics models for every possible combination of data inputs. For example, a neural network indicator can be trained on (and use for prediction) only catches and mean lengths or alternatively, recent CPUE indices and recent catches. The focus is on the information content of the data and the estimation performance of the indicator.

4.5 Indicator 2

The ETest package is built around Indicator 2: an evolution of the original approach, that includes a testing dataset from a much more general set of simulations encompassing population and fishing dynamics for a wide range of pelagic highly migratory species (sharks, billfish and tunas) Carruthers & Taylor 2025.

5 The Idea Behind EcoTest

5.1 A Theoretical Model of Stock and Fishery Dynamics

EcoTest uses a conventional age-structured models to generate simulations of fisheries over a wide range of life-history and exploitation dynamics. Those age-structured equations are the theoretical basis of the stock assessments models used in the provision of managment advice for data-rich tuna, billfish and shark species. Ie in its current form EcoTest assumes the same underlying theory used in management advice. In principle, other theoretical such as Ecosim-with-Ecopath (Figure below) or Atlantis could be used for simulating fisheries.

5.2 The Training Dataset

Over a very large number of simulations a single year is selected and true, simulated stock status is taken from the model along with simulated data, and observable aspects of life history (e.g. growth and longevity) and fishing dynamics (e.g. selectivity). The outputs are processed into a large training dataset (many thousands of simulations) where each row is a simulation containing a number of columns. The first is the target variable of interest (stock status) and each subsequent column is a potential data input that could be used to estimate the target variable (e.g., slope in catches, current mean length relative to mean, somatic growth etc. etc. ).

The data inputs relate to trajectories, levels and variability of data such as catches, CPUE indices and mean length in catches and are processed and formatted in such a way that they can be calculated for any fishery (e.g. mean length in catches is phrased as a percentage of asymptotic length, slopes in data are slopes in log data etc).