Newman, Ken B., Buckland, Stephen T., Morgan, Byron J. T., King, Ruth, Borchers, David L., Cole, Diana J., Besbeas, Panagiotis, Gimenez, Olivier, Thomas, Len (2014) Modelling Population Dynamics: model formulation, fitting and assessment using state-space methods. Methods in Statistical Ecology . Springer, 215 pp. ISBN 978-1-4939-0976-6. E-ISBN 978-1-4939-0977-3. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:41252)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. |
Abstract
Provides unifying framework for estimating the abundance of open populations that are subject to births, deaths and movement in and out of the population
Going beyond the estimation of abundance, teaches ways of determining the reasons for variation in abundance over time and survival probabilities
Ecologists and wildlife managers will learn to model dynamics in annual cycles for populations of large vertebrates, including discrete time models
This book gives a unifying framework for estimating the abundance of open populations: populations subject to births, deaths and movement, given imperfect measurements or samples of the populations. The focus is primarily on populations of vertebrates for which dynamics are typically modelled within the framework of an annual cycle, and for which stochastic variability in the demographic processes is usually modest. Discrete-time models are developed in which animals can be assigned to discrete states such as age class, gender, maturity, population (within a metapopulation), or species (for multi-species models).
The book goes well beyond estimation of abundance, allowing inference on underlying population processes such as birth or recruitment, survival and movement. This requires the formulation and fitting of population dynamics models. The resulting fitted models yield both estimates of abundance and estimates of parameters characterizing the underlying processes.
Item Type: | Book |
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Subjects: |
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics Q Science > QH Natural history > QH541 Ecology |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Depositing User: | Byron Morgan |
Date Deposited: | 02 Jun 2014 11:27 UTC |
Last Modified: | 17 Aug 2022 10:57 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/41252 (The current URI for this page, for reference purposes) |
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