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Bayesian analysis of spatially structured population dynamics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian analysis of spatially structured population dynamicsby Qing Zhao.
Author:	Zhao, Qing.
Published:	Cham :Springer International Publishing :2024.
Description:	xvi, 386 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	PopulationStatistical methods.
Online resource:	https://doi.org/10.1007/978-3-031-64518-1
ISBN:	9783031645181$q(electronic bk.)

Bayesian analysis of spatially structured population dynamics
Zhao, Qing.

Bayesian analysis of spatially structured population dynamics[electronic resource] /by Qing Zhao. - Cham :Springer International Publishing :2024. - xvi, 386 p. :ill. (chiefly color), digital ;24 cm. - Ecological studies, analysis and synthesis,v. 2532196-971X ;. - Ecological studies, analysis and synthesis ;219..

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.

ISBN: 9783031645181$q(electronic bk.)

Standard No.: 10.1007/978-3-031-64518-1doiSubjects--Topical Terms:

183103
Population
--Statistical methods.

LC Class. No.: HA155

Dewey Class. No.: 304.60727

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520 $a 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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