Correspondence site: http://www.respond2articles.com/MEE/
Using dispersal information to model the species–environment relationship of spreading non-native species
Article first published online: 29 MAY 2012
© 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society
Methods in Ecology and Evolution
Volume 3, Issue 5, pages 870–879, October 2012
How to Cite
Sullivan, M. J. P., Davies, R. G., Reino, L. and Franco, A. M. A. (2012), Using dispersal information to model the species–environment relationship of spreading non-native species. Methods in Ecology and Evolution, 3: 870–879. doi: 10.1111/j.2041-210X.2012.00219.x
- Issue published online: 5 OCT 2012
- Article first published online: 29 MAY 2012
- Received 7 February 2012; accepted 16 April 2012 Handling Editor: Robert Freckleton
- common waxbill;
- dispersal limitation;
- range expansion;
- spatial autocorrelation;
- species distribution modelling
1. Non-native species can be major drivers of biodiversity loss and cause economic damage. Predicting the potential distribution of a non-native species, and understanding the environmental factors that limit this distribution, is useful for informing their potential management. This is often carried out using species distribution models (SDMs) that attempt to classify grid cells as suitable or unsuitable for a species based on a set of environmental covariates.
2. A key assumption of SDMs is that a species is in equilibrium with its environment. Spreading non-native species often violate this assumption due to dispersal limitation.
3. We present a simple method for dealing with this problem: dispersal weighting (DW). This uses the probability that a species can disperse to a grid cell to weight a SDM. We use simulations to compare the ability of DW and unweighted models at parameterising the true species–environment relationship (SER) of a simulated species, and to test their ability at predicting the future distribution of this species. We investigate how varying the degree of spatial autocorrelation in explanatory variables affects the performance of the methods.
4. Dispersal weighting models outperformed unweighted models at parameterising the SER, and at predicting the future distribution of the species when dispersal probabilities were incorporated into the model predictions. Unweighted models had a stronger tendency than DW models to overestimate the magnitude of relationships with spatially autocorrelated explanatory variables, but underestimate the magnitude of relationships with randomly distributed variables.
5. We then applied our method to a real case study, using it to model the distribution of the non-native common waxbill Estrilda astrild in the Iberian Peninsula as a function of climate and land-use variables. The relative performance of DW and unweighted models reflected the results of the simulation.
6. We conclude that DW models perform better than unweighted models at modelling the true SER of non-native species, and recommend using DW whenever enough data exist to create a dispersal model.