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Importance of correlations among matrix entries in stochastic models in relation to number of transition matrices
Södertörn University, School of Life Sciences.
Södertörn University, School of Life Sciences.ORCID iD: 0000-0002-0260-3978
2005 (English)In: Oikos, ISSN 0030-1299, E-ISSN 1600-0706, Vol. 111, no 1, 9-18 p.Article in journal (Refereed) Published
Abstract [en]

Stochastic matrix models are used to predict population viability and the risk of extinction. Different stochastic methods require different amounts of estimation effort and may lead to divergent estimates. We used 16 transition matrices collected from ten populations of the perennial herb Primula veris to compare population estimates produced by different stochastic methods, such as selection of matrices, selection of vital rates, selection of matrix elements, and Tuljapurkar's approximation. Specifically, we tested the reliability of the methods using different numbers of transition matrices, and examined the importance of correlations among matrix entries. When correlations among matrix entries were included in the models, selection of vital rates produced the lowest and Tuljapurkar's approximation produced the highest estimates of mean population growth rates. Selection of matrices and matrix elements often produced nearly similar population estimates. Simulations based on incompletely estimated correlations among matrix entries considerably differed from those based on all correlations estimated, particularly when correlations were strong. The magnitude of correlations among matrix entries depended on the number of matrices, which made it difficult to generalize correlations within a species. Given that selection of vital rates or matrix elements is used, correlations among matrix entries should usually be included in the model, and they should preferably be estimated from the present data rather than according to other information of the species.

Place, publisher, year, edition, pages
2005. Vol. 111, no 1, 9-18 p.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:sh:diva-6058DOI: 10.1111/j.0030-1299.2005.13940.xISI: 000231864900002Scopus ID: 2-s2.0-26444600781OAI: oai:DiVA.org:sh-6058DiVA: diva2:395511
Available from: 2011-02-07 Created: 2011-02-07 Last updated: 2016-12-22Bibliographically approved
In thesis
1. Population viability analysis for plants: practical recommendations and applications
Open this publication in new window or tab >>Population viability analysis for plants: practical recommendations and applications
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Population viability analysis (PVA) is commonly used in conservation biology to predict population viability in terms of population growth rate and risk of extinction. However, large data requirements limit the use of PVA for many rare and threatened species. This thesis examines the possibility of conducting a matrix model-based PVA for plants with limited data and provides some practical recommendations for reducing the amount of work required. Moreover, the thesis applies different forms of matrix population models to species with different life histories. Matrix manipulations on 37 plant species revealed that the amount of demographic data required can often be reduced using a smaller matrix dimensionality. Given that an individual’s fitness is affected by plant density, linear matrix models are unlikely to predict population dynamics correctly. Estimates of population size of the herb Melampyrum sylvaticum were sensitive to the strength of density dependence operating at different life stages, suggesting that in addition to identifying density-dependent life stages, it is important to estimate the strength of density dependence precisely. When a small number of matrices are available for stochastic matrix population models, the precision of population estimates may depend on the stochastic method used. To optimize the precision of population estimates and the amount of calculation effort in stochastic matrix models, selection of matrices and Tuljapurkar’s approximation are preferable methods to assess population viability. Overall, these results emphasize that in a matrix model-based PVA, the selection of a stage classification and a model is essential because both factors significantly affect the amount of data required as well as the precision of population estimates. By integrating population dynamics into different environmental and genetic factors, matrix population models may be used more effectively in conservation biology and ecology in the future.

Place, publisher, year, edition, pages
Stockholm: Botaniska institutionen, 2006. 15 p.
Keyword
demography, matrix population models, population viability analysis, population growth rate, stochastic models
National Category
Biological Sciences
Identifiers
urn:nbn:se:sh:diva-31511 (URN)91-7155-192-1 (ISBN)
Public defence
2006-04-01, MA 231, Alfred Nobels allé 7, Huddinge, 13:00
Opponent
Supervisors
Available from: 2016-12-22 Created: 2016-12-22 Last updated: 2016-12-22Bibliographically approved

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CiteExportLink to record
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Citation style
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