GIANMARCO CARUSO

Dottore di ricerca

ciclo: XXXV



Titolo della tesi: Bayesian finite mixture models to account for latent heterogeneity in Capture-Recapture analysis

Gaining knowledge about the size of an animal population in a given area is of particular interest for wildlife management and conservation. Indeed, over the last decades, thousands of species worldwide have been experiencing either an outsize expansion or, more often, a dramatic shrinkage in their abundances: in the worst cases, the latter trend has even led to their extinction. Since carrying out a complete count of animal populations is generally a challenging task, Capture-Recapture models have arisen as valuable tool to estimate the population abundance in the chosen study area, along with some other demographically meaningful parameters. A further ecological key issue for wildlife managers involves the identification of distinct groups of individuals that share similar biological patterns. In this spirit, we bring to light how finite mixtures can be easily embed into Capture-Recapture models in order to carry out jointly the estimation and the classification task. We adopt a Bayesian modelling perspective and this requires ad-hoc solutions in this specific context. Indeed, the literature about Bayesian finite mixture Capture-Recapture models is scarce in addressing some issues that arise in the implementation of the model, such as the common label-switching problem that affect finite mixtures and the specification of suitable prior distributions on component-specific parameters. Notably, we deal with these two issues by proposing two novel flexible classes of joint priors for parameters bounded in the [0,1] set. The idea is to specify joint priors that both retain the flexibility to induce the desired marginal behaviour on the component-specific parameters and help the correct identification of their posterior distributions. The proposals are enhanced by the derivation of some theoretical results. Moreover, we propose a class of parsimonious cross-classified mixture models which can be successfully used to identify different residency patterns in wildlife populations. Notably, when the existence of such patterns is known in advance, finite mixtures can be leveraged to model the structure of the population under study. For each proposed methodology, a simulation study is carried out to investigate its inferential benefits and pitfalls. The application of the outlined models and methods is illustrated on wildlife datasets, revealing their merits and validity in real-world examples and giving insights that may be useful to practitioners.

Produzione scientifica

11573/1699823 - 2024 - Finite mixtures in capture–recapture surveys for modeling residency patterns in marine wildlife populations
Caruso, Gianmarco; Alaimo Di Loro, Pierfrancesco; Mingione, Marco; Tardella, Luca; Pace, Daniela Silvia; Jona Lasinio, Giovanna - 01a Articolo in rivista
rivista: BIOMETRICAL JOURNAL (Weinheim: Wiley-VCH, 1977-[2020] Berlin: Akad.-Verl., anfangs) pp. 1-24 - issn: 0323-3847 - wos: WOS:001142543000001 (2) - scopus: (0)

11573/1656316 - 2022 - Specification of informative priors for capture-recapture finite mixture models
Alaimo Di Loro, Pierfrancesco; Caruso, Gianmarco; Mingione, Marco; Jona Lasinio, Giovanna; Tardella, Luca - 04b Atto di convegno in volume
congresso: 51st Scientific Meeting of the Italian Statistical Society (Caserta; Italy)
libro: Book of the short papers SIS 2022 - (9788891932310)

11573/1565751 - 2021 - Mixtures of regressions for size estimation of heterogeneous populations
Caruso, Gianmarco - 04b Atto di convegno in volume
congresso: 50th edition of the Scientific Meeting of the Italian Statistical Society (Pisa)
libro: Book of short papers SIS 2021 - (9788891927361)

11573/1565763 - 2021 - Model-based clustering for estimating cetaceans site-fidelity and abundance
Caruso, Gianmarco; Panunzi, Greta; Mingione, Marco; Alaimo Di Loro, Pierfrancesco; Moro, Stefano; Bompiani, Edoardo; Lanfredi, Caterina; Pace, Daniela Silvia; Tardella, Luca; Jona Lasinio, Giovanna - 04b Atto di convegno in volume
congresso: 13th scientific meeting of the classification and data analysis group, Firenze, September 9-11, 2021 (Firenze)
libro: CLADAG 2021 book of abstracts and short papers - (978-88-5518-340-6)

11573/1644910 - 2021 - Model-based clustering for monitoring cetaceans population dynamics
Panunzi, G.; Caruso, G.; Mingione, M.; Alaimo Di Loro, P.; Moro, S.; Bompiani, E.; Lanfredi, C.; Pace, D. S.; Tardella, L.; Jona Lasinio, G. - 04f Poster
congresso: GRASPA 2021 (Rome; Italy)
libro: Graspa 2021 - (979-12-200-8496-3)

11573/1466511 - 2020 - ABC model choice via mixture weights estimation
Caruso, Gianmarco; Tardella, Luca; Robert, Christian P. - 04b Atto di convegno in volume
congresso: 50th Meeting of the Italian Statistical Society (Pisa)
libro: Book of short papers SIS 2020 - (9788891910776)

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