Ecological niche models and the methods to create them continue to evolve. These techniques provide a tidy way to relate the distributions of taxa to environmental variables from the present, past, or future. Oh, and they are pretty too:
Those pretty maps of niche models assign some measure of suitability to each pixel, which indicates how close the conditions at that point are to a species’ most-optimum environmental conditions. One particular juicy prediction that results from niche modelling is that there may be a positive relationship between the “probability of occurrence” and the abundance of a species. Having this sort of forecasting power would provide some helpful inference for population demography and associated genetic diversity, right?
However, combining genetic data with ecological niche models can be a tricky business. Some of the concerns and caveats are summarized nicely in a review by Alvarado-Serrano and Knowles last year in Molecular Ecology Resources. One empirical example for these considerations appears in the same journal from Diniz-Filho and colleagues. They used 14 general methods for niche modelling with four climate data models and tested how these variations affect inferences of genetic diversity (heterozygosity measured by microsatellites) of Dipteryx alata, a widely distributed tree species in Brazil.
The correlation between He and environmental suitability would then reflect the effects of variable population size in geographical space that, under distinct environmental conditions, leads to a well-known pattern in which larger populations are able to maintain more genetic diversity.
Modelling method was the most influential factor in predicting genetic diversity, with our old friend Maxent having the highest mean correlation across the different climate models (Pearson correlation = 0.438, P = 0.037). However, the overall message is that the type of modelling methodology can have drastic effects on the correlations between predicted occurrence and genetic diversity.
As an alternative to stacking an ensemble of methods for modelling, Diniz-Filho and colleagues provide a R-script that creates distributions of correlations with random combinations of modelling method and climate data, providing the ability to simply visualize if variation in modelling method affects the ability to detect a pattern of interest (genetic diversity here, but could be something else!).
Alvarado‐Serrano, D. F., & Knowles, L. L. (2014). Ecological niche models in phylogeographic studies: applications, advances and precautions. Molecular Ecology Resources, 14(2), 233-248.
Diniz‐Filho, J. A. F., Rodrigues, H., Telles, M. P. D. C., Oliveira, G. D., Terribile, L. C., Soares, T. N., & Nabout, J. C. (2015). Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account. Molecular Ecology Resources, 15(5), 1059-1066.