Finite mixtures in capture–recapture surveys for modeling residency patterns in marine wildlife populations

Author:

Caruso Gianmarco12ORCID,Alaimo Di Loro Pierfrancesco3ORCID,Mingione Marco4ORCID,Tardella Luca2ORCID,Pace Daniela Silvia56ORCID,Jona Lasinio Giovanna2ORCID

Affiliation:

1. MRC Biostatistics Unit University of Cambridge Cambridge UK

2. Department of Statistical Sciences Sapienza University of Rome Rome Italy

3. Department GEPLI Libera Univerità Maria Ss. Assunta (LUMSA) Rome Lazio Italy

4. Department of Political Sciences University of Roma Tre Rome Italy

5. Department of of Environmental Biology Sapienza University of Rome Rome Italy

6. Institute for the Study of Anthropogenic Impacts and Sustainability in the Marine Environment CNR Trapani Italy

Abstract

AbstractThis work aims to show how prior knowledge about the structure of a heterogeneous animal population can be leveraged to improve the abundance estimation from capture–recapture survey data. We combine the Open Jolly‐Seber model with finite mixtures and propose a parsimonious specification tailored to the residency patterns of the common bottlenose dolphin. We employ a Bayesian framework for our inference, discussing the appropriate choice of priors to mitigate label‐switching and nonidentifiability issues, commonly associated with finite mixture models. We conduct a series of simulation experiments to illustrate the competitive advantage of our proposal over less specific alternatives. The proposed approach is applied to data collected on the common bottlenose dolphin population inhabiting the Tiber River estuary (Mediterranean Sea). Our results provide novel insights into this population's size and structure, shedding light on some of the ecological processes governing its dynamics.

Funder

Medical Research Council

Sapienza Università di Roma

NIHR Cambridge Biomedical Research Centre

Publisher

Wiley

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A semi-parametric maximum-likelihood analysis of measurement error in population size estimation;Journal of the Royal Statistical Society Series C: Applied Statistics;2024-08-14

2. Occupancy models with autocorrelated detection heterogeneity;Environmental and Ecological Statistics;2024-06-09

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