Improving coarse species distribution data for conservation planning in biodiversity‐rich, data‐poor, regions: no easy shortcuts

In situ conservation – the protection of species in their natural habitats – is the most powerful biodiversity conservation strategy, but protected areas cannot be expected to conserve what is not represented in them in the first place. Historically, most protected areas were created opportunistically on an individual basis (Pressey & Tully, 1994), but the networks obtained from this piecemeal approach are often incomplete in their representation of biodiversity (e.g. Scott et al., 2001; Rodrigues et al., 2004b), and inefficient in their use of land available to conservation (e.g., Pressey, 1994; Fuller et al., 2010). Systematic conservation planning emerged from the recognition that, given limited conservation resources, protected areas need to be selected not just based on their individual characteristics but as coherent networks of complementary sites (Pressey et al., 1993; Margules & Pressey, 2000). Yet this approach is extremely data-hungry, requiring data on the spatial distribution of all biodiversity features of interest, across all candidate sites (Margules & Pressey, 2000), which seriously limits its applicability to the regions of the world that need it the most: biodiversity-rich, data-poor regions, often with the least developed protected area networks (Pimm, 2000). Species distribution data in such regions are typically available in one of two forms – extent of occurrence (EOO) data, or point locality records (Gaston, 1994) – that suffer from contrasting limitations in their value to conservation planning (Rondinini et al., 2006). EOO data are generalized polygons of plausible range, often obtained through interpolation from point records, and may include relatively extensive areas from which the species is absent (e.g. freshwater species mapped as continuous EOO polygons covering both freshwater terrestrial habitats). EOO data typically overestimate (sometimes vastly so) the area occupied by each species (Jetz, Sekercioglu &Watson, 2008), resulting in high levels of commission errors (false presences) whereby species are assumed to be protected in sites where in fact they do not occur (Rondinini et al., 2006). Point locality data are obtained from recent records of confirmed species presence, often very incomplete and biased representations of species’ true area of occupancy. When applied to conservation planning, they result in high levels of omission errors (false absences), whereby species are assumed absent from places they actually occur (Rondinini et al., 2006). For conservation purposes, it is more important to minimise commission errors than omission errors (Rodrigues et al., 2004b), because assuming a species to be conserved when it is not may ultimately result in its loss. However, omission errors affect the efficiency of systematic conservation planning, by reducing the spatial options available to the planer (Rondinini et al., 2006). In this issue, Beresford and colleagues propose a method for obtaining a compromise between these two types of data: they refined EOO data of 157 globally threatened bird species (BirdLife International, 2008) into maps of extent of suitable habitat (ESH). Whereas much more sophisticated models of species distribution can be obtained using other methods (e.g. Segurado & Araujo, 2004), this approach is appealing for practical conservation planning because it combines data – coarse EOO polygons, descriptive habitat information and satellite land cover data – that are readily available for many species in data-poor regions (Bartholomé & Belward, 2005; IUCN, 2010). Beresford et al. (2011) were conservative, including all habitat types associated with each species, yet they found species’ range was reduced in average to 28% of its original EOO. The most interesting result in Beresford et al. (2011) comes, in my opinion, from their use of an independent dataset to ground-truth the results of EOO and ESH species maps: records of threatened species in important bird areas (IBAs) of Africa (Fishpool & Evans, 2001). IBAs are a network of key sites for the conservation of birds, selected based on the presence of globally threatened, rare and