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Population viability analysis for several populations using multivariate state-space models

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  • Hinrichsen, Richard A.

Abstract

The International Union for the Conservation of Nature and Natural Resources (IUCN), the world's largest and most important global conservation network, has listed approximately 16,000 species worldwide as threatened. The most important tool for recognizing and listing species as threatened is population viability analysis (PVA), which estimates the probability of extinction of a population or species over a specified time horizon. The most common PVA approach is to apply it to single time series of population abundance. This approach to population viability analysis ignores covariability of local populations. Covariability can be important because high synchrony of local populations reduces the effective number of local populations and leads to greater extinction risk. Needed is a way of extending PVA to model correlation structure among multiple local populations. Multivariate state-space modeling is applied to this problem and alternative estimation methods are compared. The multivariate state-space technique is applied to endangered populations of pacific salmon, USA. Simulations demonstrated that the correlation structure can strongly influence population viability and is best estimated using restricted maximum likelihood instead of maximum likelihood.

Suggested Citation

  • Hinrichsen, Richard A., 2009. "Population viability analysis for several populations using multivariate state-space models," Ecological Modelling, Elsevier, vol. 220(9), pages 1197-1202.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:9:p:1197-1202
    DOI: 10.1016/j.ecolmodel.2009.02.014
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    Cited by:

    1. Tian, Yu & Wu, Jianguo & Smith, Andrew T. & Wang, Tianming & Kou, Xiaojun & Ge, Jianping, 2011. "Population viability of the Siberian Tiger in a changing landscape: Going, going and gone?," Ecological Modelling, Elsevier, vol. 222(17), pages 3166-3180.
    2. Imron, Muhammad Ali & Gergs, Andre & Berger, Uta, 2012. "Structure and sensitivity analysis of individual-based predator–prey models," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 71-81.
    3. Gantchoff, Mariela G. & Conlee, Laura & Boudreau, Melanie R. & Iglay, Raymond B. & Anderson, Charles & Belant, Jerrold L., 2022. "Spatially-explicit population modeling to predict large carnivore recovery and expansion," Ecological Modelling, Elsevier, vol. 470(C).

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