Testing novel methods for short-term forecasting of European glass eel recruitment

• Published in: Fisheries Research
• Main Authors: Bénézech, Mathilde, Drouineau, Hilaire, Acou, Anthony, Bardonnet, Agnes, Boulenger, Clarisse, Lambert, Patrick, Prévost, Etienne, Beaulaton, Laurent
• Other Authors: Ecosystèmes aquatiques et changements globaux (UR EABX), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Pôle OFB-INRAE-Institut Agro-UPPA pour la gestion des migrateurs amphihalins dans leur environnement (MIAME), Université de Pau et des Pays de l'Adour (UPPA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Office français de la biodiversité (OFB)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Patrimoine naturel (PatriNat), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Direction surveillance, évaluation,données (OFB - DSUED), Office français de la biodiversité (OFB)-Office français de la biodiversité (OFB), Ecologie comportementale (EC), École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Rennes (UR)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université de Bourgogne (UB)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM), Unité Expérimentale d'Ecologie et d'Ecotoxicologie Aquatique - U3E (Rennes, France) (U3E ), Ecologie Comportementale et Biologie des Populations de Poissons (ECOBIOP), Université de Pau et des Pays de l'Adour (UPPA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Service conservation et gestion durable des espèces exploitées (OFB SEEX), Direction de la recherche et de l’appui scientifique (OFB - DRAS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2024
• Subjects: European eel; Recruitment forecasting; Bayesian stock assessment; Random walk; Multicriteria; [SDV.SA.STP]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of fishery; [STAT.AP]Statistics [stat]/Applications [stat.AP]; Anguilla anguilla
• Online Access: https://hal.science/hal-04402206; https://hal.science/hal-04402206/document; https://hal.science/hal-04402206/file/benezech.pdf; https://doi.org/10.1016/j.fishres.2023.106915

International audience Recruitment forecasting constitutes a major issue in population dynamics, especially in stock assessments. In many cases, high recruitment stochasticity thwarts the determination of a stock-recruitment (SR) relationship. When no SR relationship is available, recruitments can be projected from past trends using methods such as the geometric means of the past recruitments. This approach implicitly assumes stability in upcoming years and might not be appropriate in the case of regime shifts. Recruitment forecasting is particularly critical when predicting fishing opportunities for recruitment fisheries. The fishery of glass European eels (Anguilla anguilla) is an example of a recruitment fishery. The French glass eel fishery, which is currently guided by two models to forecast upcoming recruitments and derive fishing opportunities, has failed to achieve management targets. This study develops new models that forecast glass eel recruitments without assumptions about the SR relationship, and with flexible assumptions about future recruitment evolutions. Tests based on multiple criteria found the best performance in a random slope model, which provides flexibility to track recruitment dynamics while balancing the interests of fisheries, management and conservation. This model has wide potential for trend projections in natural resource management, economics and other fields.