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Among-Population Variation in the Ability of Fitness Components to Predict Population Viability in an Annual Herb
Södertörn University, School of Life Sciences.
(English)Manuscript (preprint) (Other academic)
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:sh:diva-31512OAI: oai:DiVA.org:sh-31512DiVA: diva2:1059532
Note

Som manuskript i avhandling. As manuscript in dissertation.

Available from: 2016-12-22 Created: 2016-12-22 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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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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  • Other locale
More languages
Output format
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