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A unified framework for modelling wildlife population dynamics

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Thomas_A unified framework for modelling wildlife population dynamics 2005preprint.pdf (224.2Kb)
sis_driver_A unified framework for modelling wildlife population dynamics.txt (2.621Kb)
sis_seal_A unified framework for modelling wildlife population dynamics.txt (48.48Kb)
util_A unified framework for modelling wildlife population dynamics.txt (2.135Kb)
Date
2005
Author
Thomas, Len
Buckland, Stephen T.
Newman, KB
Harwood, John
Keywords
auxiliary particle filter
ecology
Grey Seals
Halichoerus grypus
metapopulation
nonlinear stochastic matrix models
sequential importance sampling
state–space models
wildlife
conservation and management
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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.
Citation
Australian and New Zealand Journal of Statistics 47(1): 19-34 March 2005
ISSN
1369-1473
Type
Journal article
Rights
The definitive version is available at www.blackwell-synergy.com
Description
The pdf document contains the full article text; program code (in S-PLUS 6.1) for the example analysis is in the three text files; data is available from the Sea Mammal Research Unit (http://www.smru.st-and.ac.uk)
Collections
  • Centre for Research into Ecological & Environmental Modelling (CREEM) Research
  • Statistics Research
URI
http://dx.doi.org/10.1111/j.1467-842x.2005.00369.x
http://hdl.handle.net/10023/678

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