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

By: Contributor(s): Material type: TextSeries: Ecological Studies, Analysis and Synthesis ; 253Publisher: Cham : Springer International Publishing : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XVI, 386 p. 58 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031645181
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleLOC classification:
  • QH540-549.5
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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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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