1. Matrix projection models meet variation in the real world
- Author
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Salguero-Gomez, Roberto and De Kroon, Hans
- Subjects
Plant populations -- Models ,Plant populations -- Analysis ,Plant population genetics -- Models ,Plant population genetics -- Analysis ,Ecology -- Models ,Ecology -- Analysis ,Ecosystems -- Models ,Ecosystems -- Analysis ,Biological sciences ,Environmental issues - Abstract
To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1365-2745.2009.01635.x Byline: Roberto Salguero-Gomez (1), Hans de Kroon (2) Keywords: demographic buffering; integral projection model; plant demography; population dynamics; projection matrix; stochastic elasticity; stochastic life table response experiment (SLTRE); transient dynamics Abstract: Summary 1. Projection matrices have become the dominant modelling approach in plant demography because they (i) are relatively easy to formulate, (ii) compile complex data in a structured and analytically tractable manner, (iii) provide numerous parameters with direct biological meaning, (iv) allow the investigator to address broad or specific, experimental and/or theoretical, ecological and evolutionary questions, and (v) produce uniform outputs, enabling direct comparisons between the results of different studies. 2. The last decade has witnessed major advancements in this field that have brought demographic models much closer to the real world, in particular in the analysis of effects of spatial and temporal environmental variation on populations. The present Special Feature contributes to that progress with novel methodologies and applications on Integral Projection Models, stochastic Life Table Response Experiment analyses, stochastic elasticities, transient dynamics and phylogenetic analyses. 3.Synthesis. Environmental stochasticity is an integral part of ecosystems, and plant populations exhibit a tremendous array of demographic strategies to deal with its effects. The analytical challenge of understanding how populations avoid, tolerate or depend on stochasticity is finally overcome with the new matrix approaches. The tools are now available to interpret the effects of changes in temporal and spatial variation on plant populations. Author Affiliation: (1)Department of Biology, University of Pennsylvania, 215 South University Avenue, 321 Leidy Laboratories, Philadelphia, PA 19104, USA (2)Department of Experimental Plant Ecology, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands Article History: Received 3 December 2009; accepted 9 December 2009 Handling Editor: Michael Hutchings Article note: (*) Correspondence author. E-mail: salguero@sas.upenn.edu
- Published
- 2010