A recent paper published in Applied Geography explored ways to improve the determination of biogeographic regions using clustering techniques. Biogeographic regions are geographical areas that are defined based on the species found in them, which provides invaluable information to ecologists and natural resources managers for understanding large scale processes that affect species and ecosystems. Famously, Alfred Russell Wallace’s delineation of the first biogeographic regions idenitified similar species living oceans away from each other, provided inspiration for the discovery of continental drift. Historically, these regions were defined based on the occurance of similar species or the same species using clusering techniques that do not consideration of physical geographic boundaries or other environmental variables in their sample site. These researchers studied the spatial distribution of four mammal species in Angola and performed a spatial (REDCAP) and a non-spatial (Ward’s Clustering) clustering analyses on records of observations of those species to see which analysis defined biogeographic regions that more closely correlated with different climatic conditions, existing ecoregions, and groupings of species within those regions. Ecoregions are similar to biogeographic regions, but instead of defining regions based on species occurance, they define space based on environmental variables like rainfall, temperature, and humidity. Different types of ecosystems occur in different ecoregions. A comparison of both is shown below, with the ecoregions in Angola defined by the World Wildlife Fund below for reference (Olson et al, 2014).
We know that species evolve to fill different “niches” in the environment, so if these biogeographic regions cover the geographic extent of different niches based on known climate and ecological information, it is more likely that they are reflective of where that species is occuring. So, if the clusters correlated better with existing climate and species grouping data, it is probably the better biogeographic model. The model that used a spatial clustering analysis more closely reflected other known data, and provided more meaningful results for ecologists and managers. To make biogeographic regions more useful, spatial clustering methods should be used instead of traditional non-spatial techniques.
Gao, P., & Kupfer, J. A. (2018). Capitalizing on a wealth of spatial information: Improving biogeographic regionalization through the use of spatial clustering. Applied Geography, 99, 98-108. doi:10.1016/j.apgeog.2018.08.002
Olsen, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V., Underwood, E. C., . . . Kasseem, K. R. (2001). Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience, 51(11), 933-938.
Article by H. Conrad