Pearson, Richard G., Terence P. Dawson, and Canran Liu. “Modelling species distributions in Britain: a hierarchical integration of climate and land‐cover data.” Ecography 27.3 (2004): 285-298.
DOI: 10.1111/j.0906-7590.2004.03740.x
Pearson et. al, makes an argument that using a hierarchical framework approach for modeling species distribution benefits the understanding of unique roles and combined effects that climate change and landscape disturbance have on the determination of species distribution. The authors address the interaction between climate and land use change as determinants of species distribution by integrating both at fine scale (land cover data) with coarser scale climate data. Incorporating climate and land cover data at different spatial scales identifies the possibility that different environmental factors have a different impact on species depending on the scale. METHOD: They used presence-absence data of four plant species in Britain (which represent a range of habitat associations, life-forms, and distribution characteristics). The fine scale suitability surface was generated using the bioclimatic model SPECIES, which uses an Artificial Neural Network to first identify suitability at the European extent (continental scale – climate driven), then trained at the regional-scale (Great Britain) at 10 km then 1 km resolutions (climate and land cover driven). It is believed that at these scales these environemental factors are most apparent. Climate suitability was ultimately refined based on correlations between land cover type and observed distributions at 1 km and 10 km resolution. In order to match resolution from continent to regional scales, it was necessary to artificially aggregate suitability of cells. The hierarchical methodology was tested against a non-hierarchical method (see text) and performance of the models were evaluated using K statistic and AUC. Three threshold values were chosen (which will ultimately depend on the management situation for the species of interests). Incorporating land cover data improved model performance for some species, suggesting that the importance of different environmental variables on species distribution depends on the species requirements. Hierarchical vs. non-hierachichal methods (and across finer spatial scale (10 km vs 1 km)) did not perform better than the other when modeling current distribution of species. Ultimately, for predicting future species distributions, it is important to initially determine whether the decline of the species is driven from land cover of climatic variables. Theoretically, integrating hierarchical data seems like the ideal way to model species distributions, but of course there are data limitations which makes this method less feasible. It would be very interesting to apply this approach to a vertebrate/invertebrate species and compare conclusions.