The GIS was then used to extract the physical covariates values at each of the 400 points. These spatial variables were imported into SPSS v.11 statistical software package (SPSS Inc., Chicago, IL) and transformed to prevent outliers from having a disproportionate influence on the analysis. Next, a Spearman’s rank correlation was conducted to test for collinearity between the four spatial

covariates. Non-independence was identified between slope and elevation, so a data reduction technique (PCA) was performed. This produced two components SB431542 cell line (with eigenvalues of 0.3532 and 0.0511, respectively) that were then used in subsequent analyses, instead of the original covariates. Logistic regression analyses were performed to determine which covariates, individually and in combination, best explained deforestation across the study area. Models were compared on the basis of the Akaike Information Criterion (AIC) and Akaike weights (w i ) (Burnham and Anderson 2002). Models that were within two AIC units (∆AIC) of the top ranked model with the smallest AIC were considered as p38 MAPK inhibitor plausible candidate models and their results discussed. The performance of a final regression model was then evaluated by calculating

the area under the curve of receiver operating characteristics (ROC) plots. The presence of spatial autocorrelation Go6983 cost in the model was then tested by calculating Moran’s I statistic (Cliff and Ord 1981) using the Crime-Stat v1.1 software package (N Levine and Associates, Annadale, VA). Next, a spatially explicit forest risk model was constructed within the GIS, using the significant spatial covariates and their beta coefficient values within the final logistic regression equation. A Mann–Whitney U test was performed to investigate the accuracy of of the deforestation risk model. For this,

the mean predicted risk values were extracted for 100 randomly selected points that were cleared between 2002 and 2004 and compared with 100 randomly selected points that had not been cleared during the same period. Modeling conservation intervention scenarios Based on the amount of remaining forest cover in 2002, the 1985–2002 deforestation rate was recalculated as the area of forest predicted to be cleared in the following year (i.e. 2003). Next, to predict and map deforestation patterns across the study area, a three stage iterative process was performed. First, the most threatened forest patches (1 ha pixels) equivalent to the calculated area of forest loss were identified and removed from the forest risk model. Second, this forest loss was then incorporated within an updated distance to forest edge covariate which, along with the other spatial covariates, formed a revised spatial dataset.