ADVANCES IN PLANT DEMOGRAPHY USING MATRIX MODELS
Matrix projection models meet variation in the real world
Article first published online: 25 JAN 2010
© 2010 The Authors. Journal compilation © 2010 British Ecological Society
Journal of Ecology
Volume 98, Issue 2, pages 250–254, March 2010
How to Cite
Salguero-Gómez, R. and De Kroon, H. (2010), Matrix projection models meet variation in the real world. Journal of Ecology, 98: 250–254. doi: 10.1111/j.1365-2745.2009.01635.x
- Issue published online: 25 JAN 2010
- Article first published online: 25 JAN 2010
- Received 3 December 2009; accepted 9 December 2009 Handling Editor: Michael Hutchings
- demographic buffering;
- integral projection model;
- plant demography;
- population dynamics;
- projection matrix;
- stochastic elasticity;
- stochastic life table response experiment (SLTRE);
- transient dynamics
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.