Climate Change and Indigenous Arabica Coffee Figure 3. Locality analysis overview I. Predicted climate change outcomes for indigenous Arabica localities for the year interval 2000, 2020, 2050 and 2080. Stacked bar-charts based on Table 1. Green = optimal [bioclimatic] localities (68%); yellow = intermediate (suboptimal) [bioclimatic] localities (95%); red = marginal (extreme) [bioclimatic] localities (100%); grey = unsuitable bioclimatic localities. doi:10.1371/journal.pone.0047981.g003 Results Model of historical and present-day distribution The potential present-day distribution of indigenous Arabica, as derived from MaxEnt [42–44], is shown in Figures 1 and 2, which include the locality records (1941 to 2006), representing the 713 data records, giving the 349 unique localities. For modelling purposes 197 data points were used, as only one data point was retained where points were clustered in a single cell (see Methods). AUC (area under the curve) is often used to evaluate species distribution models [50,59]. An AUC of 1 represents a perfect prediction and generally values of 0.5 or lower are no better than random, although there are fundamental problems when using AUC for validation [50]. We retrieved an AUC value of 0.99 for the predicted distribution across the whole of Africa, using all 197 sample points; removing spatial sorting bias (i.e. clustering), for the same region gave an AUC of 0.988. Finally, testing of the models after the removal of the spatial sorting bias, and using only the core region (as in Figure 5), gave an AUC value of 0.78. This was performed in order to test the models to the extreme, but not for the actual models used in the rest of the analyses (see Methods). Based on the outcome of the cross-validation tests [50] and expert visual assessment of the predicted distribution we believe that our MaxEnt models were very robust. The distribution includes south- western Ethiopia, south-eastern South Sudan (Boma Plateau) and northern Kenya (Mt. Marsabit). Within Ethiopia, the highest density of recorded presence is in the western-most part of the distribution, and there is a clear division between this area and the eastern side of the Great Rift Valley in the Bale Mountains, although two sites have been recorded between these two main areas of distribution (Figures 1 and 2). Very obvious outlying populations occur on the south-eastern part of Boma Plateau, near Towot (c. 6u69N 34u269E), and on Mt. Marsabit (2u169N 37u589E). Three localities have very low predictive scores, i.e. Mt. Marsabit (0.11) and two others (0.10 and 0.040; Figure 2B). The latter two localities are physically very close (9.8 and 6.7 km, respectively) to areas with higher predictions. The anomaly could be due to model sensitivity, or data inaccuracies, for example small areas of suitable bioclimatic space that are not picked up by the climate data modelling or the scale of the analyses, such as gallery forest (a forest growing along a watercourse in a region otherwise devoid of trees (or another vegetation type); in many cases gallery forest is less than 1 km wide). Given that these low scoring localities represent a small proportion of the total number of localities, and that the model fit is very strong, they hold little significance in terms of the overall modelling. In addition, even though the Mt. Marsabit locality is only marginally suitable for the occurrence of indigenous Arabica as a whole, there are smaller areas on the mountain that receive a slightly higher prediction than for the entire mountain area (data not shown). The following BIOCLIM layers give the highest contributions to the model of predicted distribution: bio 4 (Temperature Seasonality); bio 10 (Mean Temperature of Warmest Quarter); bio 14 (Precipitation of Driest Month); and bio 8 (Mean Temperature of Wettest Quarter). Therefore, temperature, precipitation, and their relationship with seasonality, are the biggest drivers for the models, although it should be reiterated (see Methods) that these are not necessarily the actual environmental drivers for the occurrence of the species. The threshold values (modelled scores given in parentheses) produced for the present day predictive models were 68% of localities (.0.564; ‘optimal’), 95% (.0.392; ‘intermediate’), and 100% (.0.0398; ‘marginal’), as shown in Figure 2B. The optimal localities, i.e. those with 68% or higher, show an excellent visual fit with the actual recorded distribution (the 349 localities), and Figure 4. Locality analysis overview II. Predicted climate change outcomes for indigenous Arabica localities (349 in total) for the year intervals 2000, 2020, 2050 and 2080. Histograms for actual predicted values, under each scenario. Dashed line and red text indicate thresholds (68%, 95%, 100%, of the 2000 models). This figure provides finer-scale detail than Figure 3, including the subtle shifts around the thresholds that are evident in the locality analysis. For example in scenario B2A, in 2000 there are a high proportion of localities in optimum bioclimatic space (0.6 and 0.65), but by 2080 most of the localities are outside of all suitable bioclimatic space, with a small number of localities (‘core localites’) still occupying optimal bioclimatic space. doi:10.1371/journal.pone.0047981.g004 PLOS ONE | www.plosone.org 6 November 2012 | Volume 7 | Issue 11 | e47981