Change modeling in LCM is empirically based (the empirical method chosen for this study is the multi-layer perceptron neural network), using areas of known transition from land cover maps along with user-specified explanatory variable maps that express the state of driving forces or the inherent suitability of land to undergo a specific transition. In this illustration, a total of 26 variables were used (Figure 1). The result is a transition potential map for each modeled land cover transition -- an expression of the readiness of land to undergo a transition in the next time step. These transition potential maps then serve as primary inputs to the prediction process, along with predicted quantities of change derived by Markov Chain Analysis. The specific allocation of change is then achieved by means of a multi-objective land competition process with intermediate recalculation of dynamic variables (such as proximity to disturbance) and associated transition potential maps.
In our case, forest change was modeled based on land cover maps from 1992 to 2001 (Figure 2). To assess the quality of the model, a prediction was made to 2004 for comparison with a known validation map. The resulting model attained a Pierce Skill Score of .61. Two types of maps were produced: a hard prediction map that expresses one change scenario (Figure 3) and a soft prediction map (Figure 4) which provides a comprehensive assessment of change potential, i.e., a map of vulnerability to change for the selected set of transitions. The soft prediction result therefore shows the potential that any given area may change at some future date and is very important for identifying areas of high risk: an important planning concern.
Once the model was calibrated, additional future scenarios were then produced. One such scenario was a prediction of the Bolivian Lowlands to the year 2015 (Figure 3) and the assessment of this scenario on endemic species habitat. Using collections of species range polygons, initially supplied by NatureServe and subsequently recalibrated using LCM, a species richness map was developed for all birds, mammals, and amphibians. The Bolivian Lowlands is home to 73 endemic species, 36 amphibians, 16 mammals, and 21 birds. This map was combined with the 2015 soft prediction map to derive a risk of biodiversity loss (Figure 5). We then applied the same process to all the endemic vertebrate species to derive a risk of endemic loss (Figure 6).
Assuming business as usual, the potential loss could be devastating to biodiversity in Bolivia. For the period we do know, from 2001 to 2004, 4% of current mammal habitat was lost (Figure 7). Tools such as these provide an important means to assist those engaged in important conservation practices and to prioritize threatened environments.