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A unified framework for modelling wildlife population dynamics

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A unified framework for modelling wildlife population dynamics. / Thomas, Len; Buckland, Stephen Terrence; Newman, Kenneth Brian; Harwood, John.

In: Australian and New Zealand Journal of Statistics, Vol. 47, No. 1, 03.2005, p. 19-34.

Research output: Contribution to journalArticle

Harvard

Thomas, L, Buckland, ST, Newman, KB & Harwood, J 2005, 'A unified framework for modelling wildlife population dynamics' Australian and New Zealand Journal of Statistics, vol. 47, no. 1, pp. 19-34. https://doi.org/10.1111/j.1467-842x.2005.00369.x

APA

Thomas, L., Buckland, S. T., Newman, K. B., & Harwood, J. (2005). A unified framework for modelling wildlife population dynamics. Australian and New Zealand Journal of Statistics, 47(1), 19-34. https://doi.org/10.1111/j.1467-842x.2005.00369.x

Vancouver

Thomas L, Buckland ST, Newman KB, Harwood J. A unified framework for modelling wildlife population dynamics. Australian and New Zealand Journal of Statistics. 2005 Mar;47(1):19-34. https://doi.org/10.1111/j.1467-842x.2005.00369.x

Author

Thomas, Len ; Buckland, Stephen Terrence ; Newman, Kenneth Brian ; Harwood, John. / A unified framework for modelling wildlife population dynamics. In: Australian and New Zealand Journal of Statistics. 2005 ; Vol. 47, No. 1. pp. 19-34.

Bibtex - Download

@article{3462b3860dc64235bbc9dc1f07b37644,
title = "A unified framework for modelling wildlife population dynamics",
abstract = "This paper proposes a unified framework for defining and fitting stochastic, discrete-time, discrete-stage population dynamics models. The biological system is described by a state–space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources of uncertainty in the inputs, including uncertainty about model specification, are readily incorporated. The paper shows how the state process can be represented as a generalization of the standard Leslie or Lefkovitch matrix. By dividing the state process into subprocesses, complex models can be constructed from manageable building blocks. The paper illustrates the approach with a model of the British Grey Seal metapopulation, using sequential importance sampling with kernel smoothing to fit the model.",
keywords = "auxiliary particle filter, ecology, Grey Seals, Halichoerus grypus, metapopulation, nonlinear stochastic matrix models, sequential importance sampling, wildlife, conservation and management, grey seals, state-space models, wildlife conservation and management",
author = "Len Thomas and Buckland, {Stephen Terrence} and Newman, {Kenneth Brian} and John Harwood",
year = "2005",
month = "3",
doi = "10.1111/j.1467-842x.2005.00369.x",
language = "English",
volume = "47",
pages = "19--34",
journal = "Australian and New Zealand Journal of Statistics",
issn = "1369-1473",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - A unified framework for modelling wildlife population dynamics

AU - Thomas, Len

AU - Buckland, Stephen Terrence

AU - Newman, Kenneth Brian

AU - Harwood, John

PY - 2005/3

Y1 - 2005/3

N2 - This paper proposes a unified framework for defining and fitting stochastic, discrete-time, discrete-stage population dynamics models. The biological system is described by a state–space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources of uncertainty in the inputs, including uncertainty about model specification, are readily incorporated. The paper shows how the state process can be represented as a generalization of the standard Leslie or Lefkovitch matrix. By dividing the state process into subprocesses, complex models can be constructed from manageable building blocks. The paper illustrates the approach with a model of the British Grey Seal metapopulation, using sequential importance sampling with kernel smoothing to fit the model.

AB - This paper proposes a unified framework for defining and fitting stochastic, discrete-time, discrete-stage population dynamics models. The biological system is described by a state–space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources of uncertainty in the inputs, including uncertainty about model specification, are readily incorporated. The paper shows how the state process can be represented as a generalization of the standard Leslie or Lefkovitch matrix. By dividing the state process into subprocesses, complex models can be constructed from manageable building blocks. The paper illustrates the approach with a model of the British Grey Seal metapopulation, using sequential importance sampling with kernel smoothing to fit the model.

KW - auxiliary particle filter

KW - ecology

KW - Grey Seals

KW - Halichoerus grypus

KW - metapopulation

KW - nonlinear stochastic matrix models

KW - sequential importance sampling

KW - wildlife

KW - conservation and management

KW - grey seals

KW - state-space models

KW - wildlife conservation and management

UR - http://hdl.handle.net/10023/678

U2 - 10.1111/j.1467-842x.2005.00369.x

DO - 10.1111/j.1467-842x.2005.00369.x

M3 - Article

VL - 47

SP - 19

EP - 34

JO - Australian and New Zealand Journal of Statistics

T2 - Australian and New Zealand Journal of Statistics

JF - Australian and New Zealand Journal of Statistics

SN - 1369-1473

IS - 1

ER -

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ID: 246388