Bayesian Analysis of Spatially Structured Population Dynamics [electronic resource] / by Qing Zhao.

Por: Zhao, Qing [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoSeries Ecological Studies, Analysis and Synthesis ; 253Editor: Cham : Springer International Publishing : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XVI, 386 p. 58 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031645181Tema(s): Ecology  | Biometry | Biotic communities | Population biology | Ecology -- Methodology | Landscape ecology | Zoology | Ecology | Biostatistics | Community and Population Ecology | Ecological Modelling | Landscape Ecology | ZoologyFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 577 Clasificación LoC:QH540-549.5Recursos en línea: Libro electrónicoTexto
Contenidos:
Chapter 1: Background -- Chapter 2: Occupancy Models -- Chapter 3: N-mixture models -- Chapter 4: Integrated population models (IPMs) -- Chapter 5: Spatial capture-recapture (SCR) models -- Chapter 6: Summary and outlook.
En: Springer Nature eBookResumen: 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 detection/non-detection data, count data, combined count and capture-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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Chapter 1: Background -- Chapter 2: Occupancy Models -- Chapter 3: N-mixture models -- Chapter 4: Integrated population models (IPMs) -- Chapter 5: Spatial capture-recapture (SCR) models -- Chapter 6: Summary and outlook.

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 detection/non-detection data, count data, combined count and capture-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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