Climate-Informed Conservation and Restoration Priorities for High-Andean Polylepis Forests Under Climate Change

The high-Andean forests formed by Polylepis rugulosa in southern Peru are key ecosystems for biodiversity conservation and the provision of ecosystem services; however, they are facing fragmentation and degradation processes associated with human activities and climate change. In this context, the present study modeled the near-term and future potential distribution of P. rugulosa under the SSP1-2.6, SSP3-7.0, and SSP5-8.5 shared socioeconomic pathways, considering three analysis periods: 2011–2040, 2041–2070, and 2071–2100. To achieve this, occurrence records, bioclimatic variables, and topographic variables were integrated in BIOMOD2 and complemented with spatial analyses in QGIS. After controlling for multicollinearity using the Variance Inflation Factor (VIF), seven non-redundant environmental predictors were retained and incorporated into an ensemble model built from the four algorithms with the highest predictive performance: Random Forest (RF), Gradient Boosting Machine (GBM), Flexible Discriminant Analysis (FDA) and Artificial Neural Network (ANN). The final ensemble model achieved high predictive accuracy, with True Skill Statistic (TSS) values close to 0.94 and ROC/AUC values ranging from 0.997 to 0.998. Continuous habitat suitability projections were subsequently reclassified into four habitat suitability categories: most unsuitable, marginally unsuitable, marginally suitable, and most suitable. Under the SSP3-7.0 scenario, the most suitable areas decreased from 30,965 ha during 2011–2040 to 374 ha during 2071–2100, indicating a progressive contraction of favorable habitat. A spatiotemporal analysis was then conducted, in which areas of habitat loss, stability, and gain were further disaggregated into specific projected habitat categories: immediate loss, long-term loss, stable niche, expansion and establishment, and delayed expansion. The probability density curves of key environmental variables revealed that stable habitat was mainly associated with mean diurnal temperature ranges of 10.5–11.5 °C, precipitation during the driest quarter of 35–45 mm, slopes of 30–40°, and altitudes of 4000–4500 m a.s.l. Finally, based on the projected habitat trajectories of P. rugulosa, three strategic management typologies were defined: conservation in stable areas, restoration in areas experiencing habitat loss, and preventive conservation and monitoring in areas with future establishment potential.