Issue 102:2 of the Journal will be online very soon. The Editor’s Choice paper from this issue is “Probabilistic and spatially variable niches inferred from demography” by Diez et al.
Editor’s Choice 102:2
Why do we find a species in some sites but not in others? Niche theory hypothesizes that a species’ distribution is governed by habitat suitability, species interactions like competitive exclusion, dispersal limitation and source-sink dynamics. Spatial population dynamics form the core of the mechanics of these processes: are suitable sites reached by dispersing seeds? Can those seeds once germinated, grow and flower, and can they establish a viable population given the local biotic and abiotic conditions? Still, population models based on demographic field data have rarely been used to test niche hypotheses. This paper by Diez and co-authors (2014) presents a conceptual framework that can be used to integrate population dynamics and niche theory.
Diez et al. (2014) illustrate their framework by applying it to their demographic data of Rattlesnake Plantain (i.e. the orchid Goodyera pubescens) which they studied in six populations for six years. To the basic demographic data of survival, growth and reproduction they fitted Bayesian regression models. In these regressions the effects of light availability and soil moisture were included in a spatially hierarchical fashion. Together these regression models form an Integral Projection Model (IPM) which was used to see how much the effects of light and moisture on each of the vital rates affected the projected population growth rates. At this point the Bayesian statistics came in handy as they can integrate the uncertainty in the regression parameters to arrive at a distribution of population growth rates. This allowed the authors to calculate, for each of their 2x2m plots, the probability that a low-density population would at least be stable, given the local environmental conditions. Interestingly, these probabilities were well correlated with abundance at the population level, but not with occurrence or abundance at the 4m2 scale. Chance events like limited dispersal and demographic stochasticity are suspect to cause this local mismatch in model habitat suitability and local distributions of individuals.
Species can display a wide range of life histories between populations and across their entire distribution (see e.g. Jongejans et al. 2010). In the case of the Rattlesnake Plantains it was statistically not necessary to include population differences in the responses of individuals to light and moisture. However, it will be interesting to find out whether other species or larger spatial scales will require modelling differential plastic responses between populations, for example based on genetic differences. These important developments of demographically driven species distribution models promise a more mechanistic understanding of landscape-wide responses of species and communities to changing climatic conditions (see also Vanderwel et al. 2013; Merow et al. 2014).
Associate Editor, Journal of Ecology
Diez, J. M., Giladi, I., Warren, R., & Pulliam, H. R. (2014). Probabilistic and spatially variable niches inferred from demography. Journal of Ecology, n/a–n/a. doi:10.1111/1365-2745.12215
Jongejans, E., Jorritsma-Wienk, L. D., Becker, U., Dostál, P., Mildén, M., & de Kroon, H. (2010). Region versus site variation in the population dynamics of three short-lived perennials. Journal of Ecology, 98, 279–89. doi:10.1111/j.1365-2745.2009.01612.x
Merow, C., Latimer, A. M., Wilson, A. M., Rebelo, A. G., & Silander, J. A. (2014). On using integral projection models to build demographically driven species distribution models. Ecography, in press
Vanderwel, M. C., Lyutsarev, V. S., & Purves, D. W. (2013). Climate-related variation in mortality and recruitment determine regional forest-type distributions. Global Ecology and Biogeography, 22, 1192–1203. doi:10.1111/geb.12081