Modeling Forest Biodiversity under Climate Change: A MaxEnt Case Study of Cedrus Deodara in Khyber Pakhtunkhwa, Pakistan
Keywords:
GBIF, MaxEnt, Species Distribution, Habitat Conservation, Biodiversity, Climate ChangeAbstract
This study models the current and future distribution of Cedrus Deodara, a native conifer of Khyber Pakhtunkhwa (KP), Pakistan, at a spatial resolution of 1 km. Presence-only occurrence data were obtained from the Global Biodiversity Information Facility (GBIF) and combined with bioclimatic and topographic variables. To reduce redundancy, a multicollinearity test was performed on 22 candidate predictors, and highly correlated variables (r ≥ 0.9) were excluded. The Maximum Entropy (MaxEnt) model was applied to predict species distribution under present conditions and two future climate scenarios (SSP2-4.5 and SSP5-8.5) for 2090. Model performance was evaluated using the AUC, TSS, and overall accuracy. The obtained results show a good performance of the model for present and future models as well, gaining the ROC-AUC value of 0.72 and 0.77, respectively. Based on the 10th percentile training presence-threshold dependent, the overall accuracy of the model and TSS are 0.75 and 0.74, respectively. Jackknife analysis revealed precipitation variables as the most influential contributors to Cedrus Deodara distribution. Results suggest that the species will experience substantial habitat contraction under SSP2-4.5, whereas distributional shifts under SSP5-8.5 are comparatively minor. These findings emphasize the vulnerability of Cedrus Deodara to climate change and demonstrate the value of MaxEnt modeling for informing conservation and management strategies in biodiversity-rich regions.
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