3. Results

In total, for each of the random and background weighting schemes we fitted 128 preliminary models based on the 10 cross-validated folds of the training occurrence points. Since the GLM is inherently a simple algorithm, for this method only one set of parameters was trained. Although for both traning and test dataset the AUC and TSS of this model in BkWt scheme decreased in comparison to random scheme (Table 1 & 2), it successfully classified all the test data, i.e., sensitivity = 1, by using the BkWt scheme. Totally, GLM obtained AUC of 0.92 and 0.89 and TSS of 0.66 and 0.65 for random and BkWt schemes, respectively (Table 1). From the multiple combination of the GBM parameters, for the random background selection a model characterized by shrinkage = 0.01, interaction depth = 5, and ntrees = 1800 showed highest ROC value (ROC = 1, sensitivity = 1, and specificity = 0.85). For BkWt, the fine-tuned GBM model (ROC = 1, sensitivity = 1, and specificity = 0.64) had shrinkage = 0.01, interaction depth = 5, and ntrees = 2000. Although for both training and test datasets the AUC and TSS of this model were almost equal (AUC = 0.976 and 0.971 and TSS = 0.59 and 0.58 for random and BkWt schemes, respectively), it lost the ability to truly predict presence points, i.e., sensitivity, compared to othe models (Table 2). For the RF model, the fine-tuned model of both random and BkWt schemes was charachterized by ntrees = 1000, and nodesize = 1. However, the mtry was 2 and 3 for random and Bkwt schemes, respectively. Similar to the GBM, RF obtained almost equal AUC and TSS scores for both training and test datasets (AUC = 0.97 and 0.96 and TSS = 0.45 and 0.58 for random and BkWt schemes, respectively), but the sensitivity of this model was low (Table 2). For the MaxEnt model the best-fitted model with the highest AICw of the ENMeval analysis obtained rm 0.5 and 1.5, and fc LQ and LQHP for random and BkWt schemes, respectively. In the final habitat suitability maps of the MaxEnt model, the test data had AUC 0.93 and 0.95 and TSS 0.66 and 0.80 for random and BkWt schemes, respectively (Table 2). Overall, the highest TSS score of the test data was obtained in the MaxEnt model that was fitted based on the BkWt background selection scheme (Table 2).
The predicted suitability maps are shown in Fig. 2. We found a good consistency between the patterns of occurrence points and suitable habitats. Comparing the spatial pattern of suitable habitats in random and background weighting methods showed that all models represented different results except for MaxEnt model in which comparable results were obtained (Fig. 2). Accordingly, we calculated the correlation coefficient between the two background selection schemes of the four SDM methods revealing that the highest correlation was obtained for MaxEnt model (r = 0.85), followed by GLM (r = 0.61), GBM (r = 0.45), and RF (r = 0.42). These findings were also confirmed by sensitivity and specificity graphs (Fig. 3). We found that while the capacity of GBM and RF to predict the training and test background points (i.e. models’ specificity) was maintained excellent even at higher thresholds, their capability to predict presence data (i.e. models’ sensitivity) were reduced at lower thresholds. On the contrary, GLM and MaxEnt models showed good performances to predict presence data, but lost their capacity to classify background data at lower thresholds especially in the BkWt scheme (Fig. 3). The comparison of the response curves of the variables between the two bakhground selection schemes indicated identical pattern, however, for GBM and RF the response curves of the BkWt scheme were more rugged compared to the smoother variation in the random background selection scheme (Fig. 4).