Modeling the Current and Future Spatial Distribution of Pericopsis elata (Afrormosia) under Climate Change Scenarios in the Democratic Republic of the Congo
DOI:
https://doi.org/10.59228/rcst.026.v5.i1.240Keywords:
Species distribution modeling, Pericopsis elata, Climate change scenarios, MaxEnt algorithm, Democratic Republic of CongoAbstract
Pericopsis elata (Afrormosia), a valuable and threatened species, is experiencing a sharp decline in suitable habitats in the Democratic Republic of the Congo due to the combined effects of climate change and increasing human pressures. This study modeled its current and future spatial distribution using the MaxEnt algorithm, based on 72 occurrence points from national forest inventories conducted by the DIAF, filtered to reduce sampling bias. The model showed excellent performance, with AUC values of 0.95 (training) and 0.952 (testing). Out of 19 initial environmental variables, seven were retained after correlation analysis, notably the precipitation of the driest month (Bio_14), isothermality (Bio_3), and altitude as the main explanatory factors. Results reveal that only 2.8% of the national territory (66,039 km²), mainly located in the provinces of Mongala, Équateur, and Tshopo, currently provide highly suitable habitats, while 94.9% are less suitable. The distribution strongly depends on water availability during periods of hydric stress and moderate climatic conditions.Under two climate scenarios (SSP2-4.5, optimistic, and SSP5-8.5, pessimistic), suitable habitats are expected to shrink significantly by 2070. SSP2-4.5 predicts a slight increase to 4.25% of suitable areas in 2050 before declining to 1.94% in 2070. Under SSP5-8.5, 99.6% of the territory will become unsuitable, leaving only 0.11% of highly suitable habitat. These findings underscore the urgent need to develop conservation strategies integrating key climate variables. Considering multiple climate models is recommended to reduce uncertainty. This study informs sustainable management and protection of P. elata in the face of climate change.
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