Population viability analysis for several populations using multivariate state-space models
Richard A. Hinrichsen
Ecological Modelling, 2009, vol. 220, issue 9, 1197-1202
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.
Keywords: IUCN Red List; Population viability analysis (PVA); Stochastic growth rate; Multivariate; State-space models; Restricted maximum likelihood; Measurement error; Salmon; Covariance (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304380009001227
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:220:y:2009:i:9:p:1197-1202
DOI: 10.1016/j.ecolmodel.2009.02.014
Access Statistics for this article
Ecological Modelling is currently edited by Brian D. Fath
More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().