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Fitting stochastic predator-prey models using both population density and kill rate data

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBARRAQUAND, Frédéric
hal.structure.identifierCentre d’Ecologie Fonctionnelle et Evolutive [CEFE]
dc.contributor.authorGIMENEZ, Olivier
dc.date.accessioned2024-04-04T02:32:38Z
dc.date.available2024-04-04T02:32:38Z
dc.date.issued2021-04
dc.identifier.issn0040-5809
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190401
dc.description.abstractEnMost mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data -- including in small amounts -- can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed.
dc.description.sponsorshipEffets de la gestion et du climat sur la dynamique des communautés - Développement d'une démographie multi-espèce. - ANR-16-CE02-0007
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.title.enFitting stochastic predator-prey models using both population density and kill rate data
dc.typeArticle de revue
dc.identifier.doi10.1016/j.tpb.2021.01.003
dc.subject.halSciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
dc.subject.halSciences de l'environnement/Biodiversité et Ecologie
dc.subject.halSciences du Vivant [q-bio]/Biodiversité/Evolution [q-bio.PE]
dc.subject.halStatistiques [stat]
dc.identifier.arxiv1904.02145
bordeaux.journalTheoretical Population Biology
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-02362303
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02362303v1
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