deletions | additions       diff --git a/materials_and_methods.tex b/materials_and_methods.tex  index 95209f0..3358bf9 100644  --- a/materials_and_methods.tex  +++ b/materials_and_methods.tex 
...
Our main goal is to extact as much ecological information as possible from both the data and the model to reach a better understanding of Neanderthals ecology. To do so we analyzed the influence of the environmental factors over the habitat suitability from a continental and a local scale.  We applied Random Forest \cite{Breiman20015} to analyze the influence of the environmental factors over Neanderthals habitat suitability at the continental scale. scale, and to assess the drivers of uncertainty (standard deviation) withinn the ensemble.  The ability of Random Forest to deal with non-linearity makes it perfect to analyse our ensemble, since non-linearity may arise when averaging the results of multiple GLMs. Also, Random Forest is regarded as a robust method to assess variable importance \cite{Cutler20072783}. We used both measures of variable importance available in the randomForest R function (Liaw and Wiener 2012): mean decrease in accuracy and total decrease in node impurities (node impurity: heterogeneity of target categories within a node). To analyze the influence of environmental factors at the local scale, we firstly defined \textit{local scale} as the average home range of Neanderthals. According to \cite{Daujeard201232}, and based on the transportation of raw lithic materials, the regional mobility range of Neanderthals during the Middle Palaeolithic was around 50 kilometers. Other measures of mobility given by Roebreks et al. 1998 and (Feblot-Augustins 1993) are around 100 and 300 km, but we considered them to bee too large to be considered local. We divided the habitat suitability model and the predictors into 50 km cells, and fitted a linear model (lm function of the R software) separately for each predictor at each cell. We assigned to each of the 50 km cells the adjusted R squared, as a measure of local importance, the coefficient to measure the direction of the relationship, and the p-value to assess the statistical significance of the predictor's local importance. We mapped both the adjusted R squared and the coefficient values by hiding cells with non-significant relationship (p-value < 0.05) and less than 30 5 km cells. We also mapped variable importance and habitat suitability together by using the whithening method explained above, using color to code habitat suitability and whithening to code the local importance of the variables. Finally, we composed a categorical map showing the variable with the higher importance at the local scale at each cell to enhance the visual analysis.To analyze the local effect of temperature, water and topography, we repeated the previous process, but using the combinations bio6 + bio5, bio12 + bio18 and slope + topographic diversity to fit the local linear models.  The materials and R scripts used to perform our analysis are available HERE.