Predicting potential habitat suitability of Quercus suber L . in Algeria under climate change scenarios

Using ensemble modeling to predict the current distribution of Pistacia atlantica Desf. in Algeria

To understand the distribution of Atlas pistachio (Pistacia atlantica Desf.) in Algeria, we analyzed the environmental factors influencing its habitat. This study employs an ensemble modelling (EM) approach, a robust predictive technique in ecological niche modelling that enables us to identify critical environmental drivers affecting plant distributions across different ecosystems' focal species. The EM incorporated four prediction algorithms (generalized linear model, boosted regression trees, random forest, and maximum entropy algorithms); we modelled Atlas pistachio's niche with 2810 occurrence points and 32 environmental variables, including climatic, edaphic, topographic, and anthropogenic factors. The model demonstrated high accuracy, with an AUC of 0.97 and TSS of 0.88. Key factors influencing distribution were precipitation in the driest month (Bio14), soil bulk density (BD), cation exchange capacity (CEC), human modification, and average diurnal amplitude (Bio2), with a relative importance of 20.1%, 12.7%, 6.7%, 4.9%, and 3.1%, respectively. These findings underscore the utility of ensemble modelling to pinpoint specific environmental variables critical to the species' presence and ecological adaptability, which has broader implications for other plant species in arid landscapes. Notably, the probability of Atlas pistachio occurrence increased with BD and decreased with CEC and human influence. Our results emphasize the EM approach as a versatile tool in ecological modelling, facilitating species-specific analyses that contribute to broader ecological restoration efforts, especially in degraded arid and semi-arid regions. This study advances our understanding of Atlas pistachio's environmental requirements and highlights the importance of EM in developing targeted programs to restore degraded ecosystems.

Predicting suitable habitats of the major forest trees in the Saïda region (Algeria): A reliable reforestation tool

Modeling potential habitat for plant species is an appropriate approach to maintain biodiversity, developing proper reforestation campaigns, and rehabilitating ecosystems. In this study, we investigated the potential distributions of four forest species, namely, Quercus faginea Lam.; Q. ilex L.; Tetraclinis articulata (Vahl) Mast.; and Pistacia atlantica Desf. In the north-western Algeria at Saïda region. The MAXENT method was used to model the habitats of these species using topographic data as predictive variables at a resolution of 100 m. Moreover, the model evaluation process was achieved using the area under the operating characteristic curve of the receiver (AUC) and Jackknife test.

The generated models were found to be accurate. AUC results are ranging between 0.98 and 0.91 for the training set and 0.87 and 0.97 for the testing set. The results of the distribution probability of this study provide a useful tool for the local decision-makers of reforestation campaigns.