## 4 Discussion  
This study modeled the spatial distribution of small-scale Acacia _decurrens_ plantation forests, utilizing high-resolution satellite data and environmental variables by employing six different algorithms available in the SDM package. The assessment of multicollinearity was a crucial phase aimed at identifying and addressing strong correlations among multiple predictor variables used in the identification process. The implementation of the VIF method, recognized for discerning collinearity among predictor variables [78], resulted in the exclusion of almost three-fourths of the total variables due to collinearity issues, potentially decreasing the efficiency of prediction and increasing the uncertainty of the SDM [96]. When VIF exceeds 10, it serves as an indicator of collinearity issues within the model [97]. Furthermore, collinearity represents a significant concern that can potentially result in the incorrect identification of relevant predictor variables [98]. The high correlation among many of the input predictor variables can be attributed to the fact that observations were made within a relatively local scale and due to the similarity in spectral vegetation indices. It is worth noting that all the reduced variables were vegetation indices and raw bands, implying a relatively higher similarity between the generated vegetation indices. This result aligns with the findings of [99], possibly due to the limited spectral resolution of the PS image with only four bands. The use of a combination of SDM algorithms, such as GLM, MARS, BRT, RF, SVM, and CART, in a complementary manner, along with the incorporation of accuracy estimators based on presence/absence data, enables a more effective representation of the spatial distribution of species at a local scale. The overall accuracy of the algorithms is relatively good, with the values exceeding 0.8 for AUC and 0.6 for TSS (Table 2). Predictive accuracy pertains to the ability of the algorithms to gauge the disparity between observed and predicted values [100]. RF exhibits an AUC above 0.9 and a TSS above 0.8, signifying near-perfect agreement. BRT and SVM demonstrate AUC values above 0.8 and TSS values above 0.7, indicating substantial agreement. The result aligns with the findings of [PERSON] et al. [101], who found that machine learning outperformed regression algorithms for species identification. RF attains higher values than other algorithms for both evaluation metrics. This aligns with previous studies that have shown RF's superior performance in species identification [102; 103; 104; 105; 28], remote sensing image classification [106; 107], and data mining [108] compared to other algorithms. This is because the RF algorithm generates predictions by creating thousands of trees and aggregating their results through averaging [86]. This approach allows the algorithm to prevent overfitting, thereby enhancing predictive  
Figure 7: Environmental variable response curve for Acacia _decurrens_ presence: blue-black curves show the mean response and gray shades are \(\pm\)SD calculated through 10 replicate runs in the ensemble model. performance and reducing variance [109]. Thus, RF proves to be a robust technique for modeling species distribution prediction, as supported by previous studies [110; 111; 112; 113]. In other studies, it has been noted that generative methods such as RF tend to yield improved results with small sample sizes, possibly due to their faster convergence toward their higher asymptotic error when compared to discriminative methods [114]. The performance of all the applied algorithms was effective, as indicated by the mentioned measures, enabling their inclusion in the ensemble modeling. The spatial extent of Acacia decurrens plantation in the study area was 22.44% during 2022. [PERSON] et al. [63] reported that 17.97% of the district was covered by the plantations during 2017. The proportion of plantations was described as 33.9% in one of the watersheds in the district during 2017 [17].