A novel adaptive similarity-based ecological niche model for the endangered Yunnan snub-nosed monkey (Rhinopithecus bieti) using UAV LiDAR data

Abstract

Ecological niche models (ENMs) are crucial for identifying habitat distribution patterns, understanding habitat preferences, and formulating effective conservation policies. However, accurately quantifying the three-dimensional (3D) structure of habitats, a fundamental component, presents challenges. These estimations heavily depend on the quality of original samples (presence/absence), yet reliable absence data requires prolonged and repeated observations, limiting both efficiency and accuracy. In the study, we focused on the endangered Yunnan snub-nosed monkey (Rhinopithecus bieti), listed on the International Union for Conservation of Nature (IUCN) Red List. We developed an adaptive similarity-based model that introduced a “similarity” pseudo-absence sampling approach for ecological niche modeling using fine-scale (20 m) 3D environmental variables from UAV LiDAR data. This approach integrated geographic similarity with an adaptive kernel density estimation (AKDE) method to prioritize pseudo-absence data sampling and then employed three typical machine learning models (SVM, BRT, and RF) for prediction, verifying the feasibility of this approach and offering direct insights into habitat distribution and preferences. The results indicated that the AKDE method provided the best fit in measuring similarity features. Through the model, the performance of estimations exhibited improved (AUC = 0.89–0.94, TSS = 0.71–0.82, and COR = 0.69–0.79), with average increases of 7 %, 14 %, and 12 %, respectively. The RF model produced more coherent suitable habitats, identifying regions at higher elevations (3100 m–3300 m) with preferences for low understory vegetation density (2–5 m, <10 %), moderate canopy relief ratio (> 0.35), lower tree height (10 m–25 m), and sunny slopes (0.60–1). Our findings demonstrate that integrating UAV LiDAR data with ecological niche modeling, along with the improved pseudo-absence sampling approach, enhances habitat assessment and offers significant potential for advancing conservation strategies.