Biogeographers take for granted that the footprint of past evolutionary and ecological processes is still evident in the extant distributions of species. Analysis of current species distributions can yield insights into prior events and point to possible future distributional and evolutionary pathways. However, given the ever increasing impact of the human ecological footprint – habitat loss and degradation, species extinctions and population extirpations, artificially introduced species, invasive species, bio-toxins, the impoundment and diversion of water, altered fire regimes, human-forced climate change – we must wonder how much longer it will be before the natural origins of plant and animal distributions become a muddy and indecipherable blur. In many bioregions, the characteristic flora and fauna are being homogenized as humans promote the distribution of certain species – by choice or accident – and remove natural barriers to some gene flows.
But, for now, biogeographers can still productively analyse species location data in cunning ways to reveal significant geographical patterns in biodiversity. A recent paper by Patten and Smith-Patten (2008) analysed bird species lists for forested sites in Meso-America to map significant Meso-American biogeographical boundaries. They measured the dissimilarity between the sites using a distance metric and then applied Monmonier’s algorithm to identify statistically significant boundaries. The results largely confirmed their hypothesis that ‘to the extent geographically isolated regions supported local diversification, this involved history should yield predictions for the placement of biogeographical boundaries’. The complex geological history of the Meso-American landscape was proposed as the primary isolating process (see also García-Moreno et al., 2006).
Monmonier’s algorithm was first published in the early 1970s (Monmonier, 1973). In the rush to promote novelty, we too often forget the good ideas buried in old papers. So, it is comforting to know that you can’t keep a good method down and some future researcher may yet rediscover the gems buried away in our own long-forgotten papers. I think Monmonier’s algorithm is still useful because it facilitates the explicit geographical analysis of the most common of biological data – species presence data – with a minimum of assumptions. Patten and Smith-Patten are correct to be wary of using numeric methods for biogeographical regionalization based on modelled estimates of species distributions. After all, the accuracy of modelled distributions tends to be correlated with the density of species location data. Monmonier’s algorithm allows a middle course between non-spatial analysis and overly confident spatial modelling.
In their article, Patten and Smith-Patten noted in passing the lack of investment in ongoing biological surveys to fill the geographical gaps in species location data. Adequate field sampling of natural distributions is a necessity if species modelling is to fulfil its potential as a key source of information for biogeographical studies. However, most regions in the tropical world remain poorly surveyed and largely unmonitored. Much will be lost before it is discovered or analysed to reveal the history of the land and its biota. Despite GPS and modern transport making modern field survey a breeze compared with the old days, it seems that field surveys are increasingly seen by institutional bean counters and research funders as an unaffordable luxury. Moreover, many researchers are unable to undertake the field work they know is necessary for additional reasons apart from lack of funds, including geopolitics and competing institutional demands on their time.
In any case, I agree with Patten and Smith-Patten’s concern that we should guard against the uncritical use of species distribution models as input to mapping biogeographical regions. Species modelling depends on finding empirical correlations between samples of a species’ current distribution and environmental variables that can be readily mapped on a landscape-wide basis (e.g. Austin, 2002). However, current distributions can reflect deep time rather than present conditions, e.g. the OCBIL (Old Climatically Buffered Infertile Landscape) theory proposed by Hopper & Gioia (2004) to explain the extraordinary levels of species richness, endemism and beta diversity in the south-west Australian floristic region.
I have argued elsewhere (Mackey et al., 2008) that numerical methods coupled to new data sources from GIS, remote sensing, environmental modelling and molecular techniques enable a more complete picture of biogeographical patterns to be discerned in ways that complement traditional approaches. However, there is no doubt that biogeographical studies will continue to be constrained by inadequate biological field data.
Patten and Smith-Patten do a good job of discussing the implications of their biogeographical boundaries, and the regions they delineate, for conservation planning. Systematic conservation planning (sensuMargules & Pressey, 2000) tends to make do with whatever data or maps are available – on the basis that the conservation imperative demands urgent action. However, erroneous output can result if due consideration is not given to the underlying geographical issues – especially those related to deep-time processes.
This is most evident in ‘gap analysis’ (Scott et al., 1993) undertaken to improve the representation in protected areas of regional patterns in biodiversity, especially higher-order patterns. If spurious biogeographical boundaries are drawn and regions delineated then little confidence can be placed in the results of gap analysis, however technically sophisticated. Whittaker et al. (2005) called for such shortcomings to be addressed by recognition of conservation biogeography as a key subfield of conservation biology. As noted by Patten and Smith-Patten, there is additional conservation value in understanding the patterns of species with related histories.
Looking to the future, and given the ongoing interference in natural distributions of species by humans, are there lessons to be learnt from understanding the role of deep-time processes, such as the geological histories revealed by Patten and Smith-Patten’s analyses, that will remain relevant in the coming century? One lesson is to remind us of just how ancient is the history of vertebrate animals. The most recent globally significant mass speciation event was perhaps the radiation of modern genera and species of song birds during the Pliocene (Norman et al., 2007) – geologically recent history but still some millions of years before the emergence of Homo sapiens. Given the massive global climatic oscillations revealed by the Vostok ice core over the last 420,000 years (Petit et al., 1999), the persistence of song birds to this day is surely a sign of hope for species and ecosystems in the face of the human-forced rapid global warming we are now experiencing.