Studying Species Demography and Distribution in Natural Conditions: Hidden Markov Models
| Autor: | Gimenez, Olivier, Louvrier, Julie, Lauret, Valentin, Santostasi, Nina Luisa |
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| Přispěvatelé: | Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, 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)-Université de Montpellier (UM), Leibniz Institute for Zoo and Wildlife Research (IZW), Leibniz Association, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Nathalie Peyrard, Olivier Gimenez, ANR-16-CE02-0007,DEMOCOM,Effets de la gestion et du climat sur la dynamique des communautés - Développement d'une démographie multi-espèce.(2016) |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Statistical Approaches for Hidden Variables in Ecology Nathalie Peyrard; Olivier Gimenez. Statistical Approaches for Hidden Variables in Ecology, Wiley, pp.45-60, 2022, 9781789450477. ⟨10.1002/9781119902799.ch3⟩ |
| Popis: | International audience; This chapter shows how hidden Markov models (HMMs) can be used to develop capture–recapture and occupancy models, traditionally used to study the dynamics of populations and the distribution of species in a context of imperfect detection. It shows how the HMM formulation permits the estimation of hidden variables in two different case studies. These case studies include: estimating the prevalence of dog–wolf hybrids with uncertain individual identification; and estimating the distribution of a wolf population with species identification errors and heterogeneous detection. The hidden variables encountered in the study of animal populations are living/dead; developmental states, which are generally discrete, such as sexual maturity; epidemiological states; or social states. HMM will be used to model species distribution in a case featuring identification errors and heterogeneous detection. The main advantage of the HMM approach lies in the ability to infer the ecological states of individuals and species which are partially observable: these are hidden variables. |
| Databáze: | OpenAIRE |
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