Applying machine learning to predict reproductive condition in fish - Random Forest classification study

Aug 5, 2026

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Knowledge of reproductive traits in exploited marine populations is essential for management and conservation. Fish maturity is commonly assigned using macroscopic observation and histology, but macroscopy is highly error-prone and histology is costly and often unavailable. This study applies a Random Forest machine-learning classifier to predict fish reproductive condition using Chilean hake data (Merluccius gayi gayi), combining environmental and biological covariates. The model achieves high accuracy and strong agreement with histology, with key predictors including GSI and total length, supporting broader, more reliable maturity staging.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Importance of reproductive condition assessment

Limits of macroscopic staging

Value and constraints of histology

Need for alternative methods

Methods (from abstract)

Random Forest classification setup

Features and training classes

Results (from abstract)

Prediction accuracy and agreement

Key variables for reproductive condition

Ecological Informatics 80 (2024) 102481 Contents lists available at ScienceDirect Ecological Informatics journal [homepage:](homepage: www.elsevier.com/locate/ecolinf)[ www.elsevier.com/locate/ecolinf](homepage: www.elsevier.com/locate/ecolinf) | Applying machine learning to predict reproductive condition in fish Andr´es Flores a, *, Rodrigo Wiffb, c, Carl R. Donovan d, e, Patricio G´alvez f
a Independent Researcher, Vi˜na del Mar, Chile
b Center of Applied Ecology and Sustainability (CAPES), Pontificia Universidad Cat´olica de Chile, Santiago, Chile c Instituto Milenio en Socio-Ecología Costera (SECOS), Chile
d DMP Statistical Solutions UK Ltd, St. Andrews, Scotland, United Kingdom
e School of Mathematics and Statistics, University of St Andrews, Scotland, United Kingdom f Divisi´on de Investigaci´on Pesquera, Instituto de Fomento Pesquero, Valparaíso, Chile | | | | | --- | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Histology Gonadosomatic index Maturity
Random forest Merluccius gayi gayi | | Knowledge of reproductive traits in exploited marine populations is crucial for their management and conservation. The maturity status in fish is usually assigned by traditional methods such as macroscopy and histology. Macroscopic analysis is the assessing of maturity stages by naked eye and usually introduces large amount of error. In contrast, histology is the most accurate method for maturity staging but is expensive and unavailable for many stocks worldwide. Here, we use the Random Forest (RF) machine learning method for classification of reproductive condition in fish, using the extensive data from Chilean hake (Merluccius gayi gayi). Gonads randomly collected from commercial industrial and acoustic surveys were classified as immature, mature-active and mature-inactive. A classifier for these three maturity classes was fitted using RFs, with the continuous covariates total length (TL), gonadosomatic index (GSI), condition factor (Krel), latitude, longitude, and depth, along with month as a factor variable. The RF model showed high accuracy (>82%) and high proportion of agreement (>71%) compared to histology, with an OOB error rate lower than 15%. GSI and TL were the most important variables for predicting the reproductive condition in Chilean hake, and to lesser extent, depth when using survey data. The application of the RF shows a promising tool for assigning maturity stages in fishes when covariates are available, and also to improve the accuracy of maturity classification when only macroscopic staging is available. | | 1. Introduction Assessing the reproductive condition in any marine harvested population is key to their management and sustainable exploitation. In fishes, reproductive attributes may change across time as a result of many endogenous and exogenous factors. Environmental factors, such as temperature, dissolved oxygen and food availability can markedly affect the timing and length of spawning as well as fecundity in marine fishes (Genner et al., 2009; Pankhurst and Munday, 2011; Sancho et al., 2000). Beyond natural environmental conditions, harvested fish populations may be affected by selective fishing gears that promote juvenility and early maturation (Law, 2000). Evaluating changes in reproductive condition and their causes has profound effects on our understanding of the population biology of harvested fish species. Studying the population dynamics of a species is essential to understand its role in the marine ecosystem. Population dynamics are based on replenishment from new individuals, so maturity is one of the most important life history traits in marine populations. Maturation is a gradual and continuous process across time, part of which sees stored resources channelled into the production and growth of gametes (Bernardo, 1993). Reproduction is determined by a complex combination of growth and environmental drivers and, particularly

FAQs

Which variables are most influential for predicting reproductive condition in Chilean hake?

Gonadosomatic index (GSI) and total length (TL) are the most important predictors, with depth contributing to a lesser extent when using survey data.

Aug 5, 2026

Upload by Eliana