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 (www.mnp.nl/hyde) and are tracked in the present day (http://glcf. umiacs.umd.edu/data), 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 pling explanatory models of carnivore extinction risk from comparative analyses with human population projections to identify species whose conservation status was likely to worsen.
Here, we enlarge this approach in a preliminary analysis of two drivers and all mammals. Across ecoregions, the proportion of species with risk status higher than LC was modeled (as a binomial denominator) as a function of two drivers and two summaries of biological vulnerability by using generalized additive models (Wood, 2006) to avoid forcing any particular form on the relationship. A smooth relationship was fitted to link risk level to mean human population density (Center for International Earth Science Information Network and Centro Internacional de Agricultura Tropical, 2005) and the proportion of land converted to urban or cropland (European Commission, Joint Research Centre, 2003; Center for International Earth Science Information Network et al., 2004). A second smooth relationship was fitted to control for two biotic variables [proportion of species weighing >3 kg, the size at which ecology and life history begin to influence risk strongly (Cardillo et al., 2005), and the proportion of species in the lowest quartile of global range size (K.E.J., J.B., A.P., C.D.L.O., Susanne A. Fritz, Christina Connolly, Amber Teacher, J.L.G., R.G., Elizabeth Boakes, Michael Habib, Janna Rist, Chris Carbone, Christopher A. Plaster, O.R.P.B.-E., Janine K. Foster, Elisabeth A. Rigby, Michael J. Cutts, Samantha A. Price, Wes Sechrest, Justin O'Dell, Kamran Safi, M.C., and G.M.M., unpublished data)]. Fig. 14.4 shows the marginal effect of the drivers on extinction risk. The two drivers are strongly correlated across ecoregions and interact strongly. As expected, risk is low when both drivers are at the very lowest levels. However, risk rises rapidly as either driver increases. Medium levels of land conversion and low density are associated with high levels of risk, but risk falls as land conversion rises further. This suggests that land conversion is an extinction filter (Balmford, 1996), removing one set of species sufficiently thoroughly that highly converted regions can again have low levels of risk, with only the more bulletproof taxa remaining. Scenario analysis will obviously need to count projected extinctions as well as declines and may need to consider historical as well as present driver patterns. As human density reaches high levels, risk levels become uniformly higher.
A more refined model, perhaps incorporating other drivers, could be combined with projected future driver intensity to predict where a high proportion of species will decline. Such an approach uses the spatial heterogeneity in present driver intensity as a surrogate time series, with high-intensity ecoregions suggesting what will happen elsewhere as conditions deteriorate. However, incorporating climate change into this modeling approach presents major challenges. Because it has not been a major driver of present risk patterns, we have not yet seen how intensity
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FIGURE 14.4 The surface relating the prevalence of extinction risk to urban and agricultural land use and human population density (HPD), controlling for two indices of biological susceptibility, among ecoregions. White regions in the top left and bottom right corners contain no ecoregions. Light = low risk; dark = high
!og(humart population density)
FIGURE 14.4 The surface relating the prevalence of extinction risk to urban and agricultural land use and human population density (HPD), controlling for two indices of biological susceptibility, among ecoregions. White regions in the top left and bottom right corners contain no ecoregions. Light = low risk; dark = high will relate to impact and cannot use spatial heterogeneity as a guide. Bio-climatic envelope models suggest that climate change is already affecting many species including mammals (Parmesan and Yohe, 2003) and may soon be the dominant driver, possibly exacerbated by interactions with invasive species and other threats (Thuiller, 2007). Climate change is likely to particularly impact species that face geographical or biological barriers to dispersal or that depend on environmental cues that may break down as climate changes (Bradshaw and Holzapfel, 2006).
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