Titlebook: Bayesian Analysis of Spatially Structured Population Dynamics; Qing Zhao Textbook 2024 The Editor(s) (if applicable) and The Author(s), un

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Manuel Ferrer,Ana Beloqui,Peter N. Golyshind Abadi 2011; Schaub and Kéry 2022; Frost et al. 2023) with a focus on their spatial versions (Borysiewicz et al. 2009; Péron et al. 2010; McCrea et al. 2010; Zhao 2020; Schaub and Kéry 2022; Chapter 20). The idea of data integration in IPMs is to leverage abundance and demographic information by jo

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The Human Genome, Microbiomes, and Disease,ir dynamic extensions (Gardner et al. 2010; Ergon and Gardner 2014; Chandler and Clark 2014). These are truly individual-based models in the sense that they consider the uniqueness of each individual, such as their spatial locations (and thus environment) and characteristics. Furthermore, dynamic SC

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0070-8356 detailed R code for applying and further developing these m.The book introduces a series of state-of-art Bayesian models that can be used to understand and predict spatially structured population dynamics in our changing world. Several chapters are devoted to introducing models that utilize detecti

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N-Mixture Models,mixture models also provide some demographic information, as the change in true abundance is exactly due to the BIDE processes. However, we will see that dynamic N-mixture models’ ability to provide demographic information is still limited, due to stringent model assumptions.

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0070-8356 de of Metropolis-Hastings algorithms that allow efficient computing of these complex models. The book is aimed at graduate students and researchers who are interested in using and further developing these models..978-3-031-64518-1Series ISSN 0070-8356 Series E-ISSN 2196-971X

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Textbook 2024e-recapture data, and spatial capture-recapture data, respectively. The book provides R code of Metropolis-Hastings algorithms that allow efficient computing of these complex models. The book is aimed at graduate students and researchers who are interested in using and further developing these models..

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https://doi.org/10.1007/978-1-0716-2795-2mixture models also provide some demographic information, as the change in true abundance is exactly due to the BIDE processes. However, we will see that dynamic N-mixture models’ ability to provide demographic information is still limited, due to stringent model assumptions.