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Matrix dimensionality in demographic analyses of plants: when to use smaller 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 3, 563-573 p.Article in journal (Refereed) Published
Abstract [en]

Large data requirements may restrict the use of matrix population models for analysis of population dynamics. Less data are required for a small population matrix than for a large matrix because the smaller matrix contains fewer vital rates that need to be estimated. Smaller matrices, however, tend to have a lower precision. Based on 37 plant species, we studied the effects of matrix dimensionality on the long-term population growth rate (lambda) and the elasticity of lambda in herbaceous and woody species. We found that when matrix dimensionality was reduced, changes in lambda were significantly larger for herbaceous than for woody species. In many cases, lambda of woody species remained virtually the same after a substantial decrease in matrix dimensionality, suggesting that woody species are less susceptible to matrix dimensionality. We demonstrated that when adjacent stages of a transition matrix are combined, the magnitude of a change in lambda depends on the distance of the population structure from a stable stage distribution, and the difference in the combined vital rates weighted by their reproductive values. Elasticity of lambda to survival and fecundity usually increased, whereas elasticity to growth decreased both in herbaceous and in woody species with reduced matrix dimensionality. Changes in elasticity values tended to be larger for herbaceous than for woody species. Our results show that by reducing matrix dimensionality, the amount of demographic data can be decreased to save time, money, and field effort. We recommend the use of a small matrix dimensionality especially when a limited amount of data is available, and for slow-growing species having a simple matrix structure that mainly consists of stasis and growth to the next stage.

Place, publisher, year, edition, pages
2005. Vol. 111, no 3, 563-573 p.
National Category
Natural Sciences
URN: urn:nbn:se:sh:diva-6046DOI: 10.1111/j.0030-1299.2005.13808.xISI: 000233306800016ScopusID: 2-s2.0-29144529096OAI: diva2:395512
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.
demography, matrix population models, population viability analysis, population growth rate, stochastic models
National Category
Biological Sciences
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
Available from: 2016-12-22 Created: 2016-12-22 Last updated: 2016-12-22Bibliographically approved

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