Wrong, but useful: regional species distribution models may not be improved by range‐wide data under biased sampling - Main figures

<b>Figure 1:</b> The distribution of <i>Asellia tridens </i>at spatial (a) and environmental (b) space. The map a shows the species‐specific global extent of this species, with dots representing the spatial distribution at global (blue) and regional (black) scales. Panel b shows a scatterplot of the first two PCA axes of all available environmental covariates within the entire study area. The first two axes account for 94.2% of the environmental variation. Blue and black dots are presence locations of the species outside and inside Egypt, respectively; light gray points are pixels without any sightings at global scale; dark gray points represent the available environmental space in Egypt. Figure S1 shows equivalent plot for all study species together.<br><br><b>Figure 2:</b> Mean permutation importance of environmental variables used at global (left) and regional (right) models (from Maxent). Dots and error bars represent the overall mean and standard deviation of the average permutation importance of the seventeen study species, respectively. Blue dots/bars represent environment‐only models; red dots/bars represent comparable models with accessibility bias variables incorporated as predictors. When included, bias predictors have a high contribution (particularly distance to main cities at both scales, and distance to roads in Egypt), compared to many environmental variables. For more details on the environmental variables used, see Table S2.<br><br><b>Figure 3:</b> Boxplots for the geographical congruence (Schoener's D) between mean predictions of global and regional models for Egypt (with no priors). Schoener's D ranges from zero to one, representing situations of no to full congruence, respectively. “Env‐only” are models calibrated only with environmental variables. “Bias‐predictor” models add accessibility bias variables as predictors to the model. “Bias‐corrected” models also use bias variables to set bias to zero during prediction (i.e., bias factored-out).<br><br><b>Figure 4: </b>Boxplots for the mean AUC values (on cross‐validation) calculated for different options of modeling algorithms, bias manipulations, and priors. (a) A comparison between mean AUC values of no‐prior regional models and equivalent models that use different options of priors (without regional bias incorporated as predictors). (b) Same as a, with regional bias variables included as predictors.<br><br><b>Figure 5:</b> Geographical congruence between the predictions of regional SDMs calibrated without priors and the three versions of regional models that used a prior variable. Bias variables were not incorporated as predictors in the regional SDMs. There were three options of prior options: “Env‐only” are predictions of global SDMs without incorporating sampling bias; “Bias‐predictor” priors incorporate global accessibility bias variables as predictors in the model; and “Bias‐corrected” priors incorporate bias‐corrected (set to zero) predictions from global models for Egypt.<br>