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taxa. Cardillo et al. (2006) termed this “latent risk” and proposed 20 regions with largely intact, but intrinsically susceptible, mammalian faunas. These include the Nearctic boreal forests and the island arc of Southeast Asia, and are mostly not exceptionally high in numbers of total, endemic, or threatened species. Many have much less than 10% of their land within reserves, and some (especially in Southeast Asia) face very rapid human population growth. As such, latent-risk hotspots might represent cost-effective options for long-term conservation. However, these analyses do not yet consider realistic scenarios of future driver patterns; rather, they implicitly assume that places with low intensity will experience an increase to more typical levels (Cardillo et al., 2006). The next section discusses how more policy-relevant predictions could be obtained by projecting future driver patterns based on explicit scenarios.


Predicting future declines is more complex than explaining present declines, because the future is not just a linear extrapolation from the past and present. Past extinctions were largely caused by invasive species and overexploitation; habitat alteration is now a more important driver (Baillie et al., 2004). Changes in land use have been mapped historically ( and are tracked in the present day (, but analogous spatial data for other main drivers are more problematic. Wild species might be most vulnerable to overexploitation where people live at high density and have few other protein sources, suggesting that predictive models can be developed at regional scales (Fa et al., 2003; Ling and Milner-Gulland, 2006). The patterns and driving processes behind invasive species have varied over time (Mack and Lonsdale, 2001), and, although there are clear associations with global movements from human migration and trade, identifying clear predictive methods for the intensity of invasives is a work in progress (Hastings et al., 2005). The same is true for disease (Pedersen et al., 2007).

Given the difficulties in obtaining spatial data on the present intensity of direct drivers, let alone future projections, an alternative is to work with information on indirect drivers—in particular, human population density and growth and patterns of land conversion. Projections of these drivers are available under a range of socioeconomic scenarios (Millennium Ecosystem Assessment, 2005b). Intensity data alone are not enough, however; the response curves linking intensity to decline are also needed, and responses will depend on how bulletproof the biota is. Thus, declines need to be modeled as a function of both driver intensity and relevant biological attributes. A first step (Cardillo et al., 2004) considered a single driver (human population density) under a single growth scenario, cou-

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