- What I mean with bioregionalisation and what it is good for.
- Comparison of two different quantitative approaches to defining bioregions, clustering and network analysis.
- Practical how-to guide to inferring bioregions with clustering in the software Biodiverse.
- Practical how-to guide to inferring bioregions with network analysis in R.
- Beyond species presence and absence, i.e. using phylogenies for bioregionalisation.
What do I mean with bioregionalisation?
The idea is to divide a study region - perhaps a country, a continent or the whole world - into natural regions. There are obviously lots of different ways of doing so. A well-known one is climatic, where we would have arctic, temperate, subtropical, and tropical regions. Closer to what I am talking about are vegetation zones; in this case the general appearance of the natural vegetation and the life form of its constituent species are used to define zones such as tundra, boreal forest, mallee, or savanna.
But that still is not what this is going to be about. The bioregions I am going to discuss are defined by the taxa that occur in them. A very high-level classification is shown, for example, in the following map from earthonlinemedia.com:
As we can see there are no 'tropics', but instead the American tropics are separated from the African and South Asian ones. Why might that be the case? As a botanist I can immediately think of two important plant families that are very characteristic of the Neotropics but are (with the exception of one rather odd, small genus) entirely missing from the Paleotropics: the cactus family Cactaceae and the pineapple family Bromeliaceae.
This, then, is what bioregions as I will subsequently discuss them are: they are regions defined by the presence of (plant, animal, ...) taxa they do not share with other regions. Another way of putting it is that bioregionalisation aims to maximise the endemism of its regions. And this immediately suggests the possibility of quantitative, objective analyses as long as we can somehow quantify endemism.
But these approaches are for other posts. More importantly now:
Why do we care? What are these bioregions good for?
I can think of at least two use cases. The first is quite simply that we like to classify things, and climate and vegetation form do not capture all there is to natural regions. Specifically, the presence e.g. of bromeliads, leaf cutter ants and hummingbirds in the New World and their absence in the Old World is an accident of history that is orthogonal to the shared climate and to the fact that 'tropical rainforest' kind of looks the same from a distance in all continents. But it still matters because these groups of organisms have evolved unique characteristics, like the hummingbirds' high metabolic rate, that have an ecological impact. A neotropical cloud forest 'works' a bit differently than a southeast Asian one.
The second use case is that of finding objectively defensible regions for biogeographic analysis, a problem that still does not have a single widely accepted solution. For example, we may be interested in conducting an inference of ancestral areas and biogeographic processes using the R package BioGeoBears, because we want to know if our study group started evolving in the temperate part of our continent and then spread into the tropics or vice versa. For this analysis we need (a) a time-calibrated phylogeny and (b) a data table of taxa-by-regions showing for each region what taxa are naturally occurring in them.
Taking one step back, it is obvious then that we first need to define regions. This may be easy if we can simply use the islands of an island group, but taking a big blob of land like Australia as an example, how do we cut that up? States? Clearly political units are kind of iffy for biogeography, because they are human inventions. Climate or vegetation zones are more natural, but are they meaningful for our specific study group? How meaningful would a region be for my purposes that happens to have one of my study taxa scored as present because it comes in from the side into 5% of that region's extent?
To me at least it seems as if the solution is bioregionalisation by taxon content: take small units like 100 x 100 km cells or similar and use an objective bioregionalisation approach to group them into meaningful larger regions. As mentioned above this maximises endemism, which is precisely what I would want for the inference of ancestral areas and biogeographic history.