Zimmermann, N. E., Edwards, T. C., Graham, C. H., Pearman, P. B. and Svenning, J.-C. (2010), New trends in species distribution modelling. Ecography, 33: 985–989. doi: 10.1111/j.1600-0587.2010.06953.x
*Keep in mind this was written in 2010*
From 2000 to 2010, SDMs underwent rapid development; taking advantage of new computational resources. Major areas of improvement include:
- implementation of new statistical models
- the evaluation of sampling design on performance
- sample size and and prevalence impact on accuracy
- removal of spatial autocorrelation from model fitting
- comparison of a range of statistical methods
- model evaluation
More recent studies have shifted focus to clarification of the niche concept, model parameterization schemes, model selection, model evaluation and variable selection methods.
The papers focus on five active areas of research involving SDMs, including: 1) historical legacies; 2) niche stability and evolution; 3) biotic interactions; 4) the importance of sample designs; and 5) species invasions. We believe these papers set the stage for future SDM research questions, and represent several next logical steps in SDM research and application.
1) Legacy of history: The effect of history on range size and distribution patterns is generally not considered, in other words, the assumption of range equilibrium. However, violating this assumption can lead to incorrect conclusions.
2) Niche stability and evolution: Niche stability can be thought of as a measure of phylogenetic conservation. Stable niches of closely related species will have similar environmental constraints, while differences can be attributed to local adaptation. SDMs can be used in this area by examining niche response to environmental drivers at a sub-species level.
3) Biotic interactions: Commonly, biotic interactions are ignored when modeling species ranges in large spatial scales. However, inclusion of biotic factors have increase model performance given an environmental disturbance.
4)Design for sampling: Available datasets are highly biased due to the haphazard sampling nature. Exploring the impact of different biased sampling in silico or controlled surveys, will guide future SDM sampling bias corrections.
5) Species invasion: SDM are often used to assess invasion risk. However, the equilibrium assumption in the native range or novel favorable habitat in the foreign range may lead to an underestimation. Developing methods to overcome these limitations will greatly improve SDM accuracy with respect to invasions.