Climate Change and Indigenous Arabica Coffee Figure 2. Predicted and actual distribution of indigenous Arabica. Figure 2A. Green dots show recorded data-points. Coloured areas (yellow to red) show predicted distribution based on MaxEnt modelling (see internal legend). Highest predicted area (dark red) indicates ‘core region’. Figure 2B. The same map with thresholds of bioclimatic suitability applied at 68% (optimal), 95% (intermediate) and 100% (marginal) to the localities. Prediction values for each locality are represented by colour and size (see internal legend) with values for low predictions labelled in red, superimposed on predicted surface (space) according to the area analysis. Localities with the smallest (dark green) circles represent ‘core localities’; highest (optimal) predicted area (green) indicates the ‘core region’. See main text for further details. doi:10.1371/journal.pone.0047981.g002 environment for the species [56,57] (S. Demissew, pers. comm.; I. Friis, pers. comm.;T. Gole, pers. observ; .). (4) Vegetation is not included in the modelling; assumptions based on the predictions assume that the vegetation is intact and will remain so until 2080. This does not represent the current situation and future for remaining natural vegetation in the study area. The forest types holding indigenous Arabica are highly fragmented, and zero land- use change from now until 2080 is totally unrealistic. These assumptions were implemented because remaining (actual) vege- tation maps for Ethiopia and South Sudan are not available. Climate change scenarios (2020–2080) — a locality analysis The locality analysis uses actual localities, recorded from 1941 to 2006. As explained above, we used 713 records, representing 349 unique localities (i.e. no duplication of localities for identical geo-referenced points; referred to as ‘localities’ in the main part of this contribution). The localities were used to produce the MaxEnt distribution (reduced to 197 samples for the modelling) for both the locality and area analyses, but in the former the localities were directly analysed against the MaxEnt models through time. For each emission scenario (A1B, B2A, and A2A) a model of future distribution was produced, and then the localities were queried against each scenario and classified into the same thresholds (68%, 95%, 100% and outside 100%) as used for the present day (i.e. the year 2000) predictive models (Figures 3 and 4). For interpretation of the results generated by the locality analysis we need to consider the assumptions used in the downscaling methods when applied to the HadCM3 model [58], which are: (1) changes in climates vary only over large distances, i.e. as large as Global Circulation Model cell size; (2) relationships between variables in the baseline (‘current climates’) are likely to be maintained towards the future. That is, any generalisations or problems in the original BIOCLIM data will be maintained. To calculate the viability of the localities across all scenarios and dates (including 2000) and as a means of visualizing the ‘core localities’, the totals from the models were calculated for each locality. Where the models return a consistently high predictive value (i.e. where there are high predictions across all scenarios and dates), localities exist in spaces that are predicted to have a relatively constant optimal bioclimatic environment. This provides a simple but powerful means of visualising the data for conservation planning and management. This could be extended by viewing the variability for each locality (to show those localities with highly changeable environments), and providing standard deviations for each locality across all scenarios and dates (data available on request). Climate change scenarios (2020–2080) — an area analysis For this analysis each scenario and date map was reclassified using the three thresholds calculated from the original year 2000 threshold percentages (68%, 95% and 100%) to give area-only predicted distribution (suitable bioclimatic space) (Figures 5 and 6). If an area was totally unsuitable in the year 2000, but became suitable in later scenarios, these areas were excluded in the re- classification. Thus, the prediction for 2000 provides a control for future migrations in our modelling (see Discussion – Modelling overview). This assumption was made because significant natural range extension, which requires effective dispersal and coloniza- tion, is highly improbable (see Discussion – Implications for wild populations of Arabica coffee). PLOS ONE | www.plosone.org 5 November 2012 | Volume 7 | Issue 11 | e47981