Assessing between-individual variability in bioenergetics modelling: Opportunities, challenges, and potential applications

Miquel Palmer, Irene Moro-Martínez, Joaquim Tomàs-Ferrer, Amalia Grau, María Dolores López-Belluga, Marine Herlin, Orestis Stavrakidis-Zachou and Andrea Campos-Candela

Ecological Modelling, 2024, vol. 498, issue C

Abstract: Population dynamics is influenced by between-individual variability. Dynamic Energy Budget (DEB) theory is an appealing framework for assessing such a variability, yet DEB parameters have rarely been estimated at the individual level. Bayesian hierarchical models show promise for inferring individual variability in DEB parameters, thought computational challenges have limited their use due to the need to solve differential equations. Timely, Stan has emerged as a general-purpose statistical tool for fitting dynamic models. This paper introduces an analytical strategy using Bayesian parametric inference and hierarchical modelling to estimate individual-specific DEB parameters. Two biologically relevant DEB parameters were successfully estimated for 69 Gilt-head breams (Sparus aurata) with up to 11 measures of length and wet weight each. The estimated between-individual variability in these two DEB parameters explained well the observed patterns in length and weight at between- and within-individual levels. Moreover, data-simulation experiments highlighted the potential and limitations of our approach, suggesting that improved data collection could enable to increase precision and the number of DEB parameters that can be estimated at the individual level. This strategy can better represent between-individual variability in DEB parameters, which ultimately may improve forecasting of population dynamics after integrating DEB into population models.

Keywords: Between-individual differences; Dynamic Energy Budget; Bayesian (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:498:y:2024:i:c:s0304380024002369

DOI: 10.1016/j.ecolmodel.2024.110848

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