Finite mixtures in capture-recapture surveys for modelling residency patterns in marine wildlife populations

TL;DR

This paper introduces a Bayesian finite mixture model tailored for capture-recapture data to estimate marine wildlife populations, demonstrating its effectiveness through simulations and real dolphin population analysis.

Contribution

It presents a novel Bayesian finite mixture modeling approach that incorporates prior knowledge to improve population estimates and address label-switching issues.

Findings

Accurate population size estimation for bottlenose dolphins.

Model outperforms less specific alternatives in simulations.

Provides ecological insights into dolphin population dynamics.

Abstract

In this work, the goal is to estimate the abundance of an animal population using data coming from capture-recapture surveys. We leverage the prior knowledge about the population's structure to specify a parsimonious finite mixture model tailored to its behavioral pattern. Inference is carried out under the Bayesian framework, where we discuss suitable priors' specification that could alleviate label-switching and non-identifiability issues affecting finite mixtures. We conduct simulation experiments to show the competitive advantage of our proposal over less specific alternatives. Finally, the proposed model is used to estimate the common bottlenose dolphins' population size at the Tiber River estuary (Mediterranean Sea), using data collected via photo-identification from 2018 to 2020. Results provide novel insights on the population's size and structure, and shed light on some of the ecological processes governing the population dynamics.