Fine-scale drivers of plateau pika burrow distribution: Integrating UAV deep learning and GAMM to reveal...

The plateau pika (Ochotona curzoniae) profoundly alters alpine meadow ecosystems on the Qinghai-Tibet Plateau (QTP) through its bioturbation activities. A major challenge for precise monitoring and management lies in reconciling this impact. To address this, we present an advanced fine-scale analytical method that combines unmanned aerial vehicle (UAV) imagery, a deep learning (DL) model, and Generalized Additive Mixed Models (GAMMs) to unravel the distribution of pika burrows at a fine scale. The U-net semantic segmentation model accurately extracts burrows at a 30-m flight altitude (Precision = 84.00%, counting R² = 0.86), outperforming coarser resolutions (100 m) in detecting small ecological features. GAMM analysis revealed that pika colonization is constrained by a combination of vegetation structure (NDVI) and micro-topography (aspect). Specifically, burrow density showed a negative linear response to NDVI, with a notable non-linear interaction identifying a "high-risk niche" where pikas aggregate on shady, northern slopes with sparse vegetation (NDVI <0.4) to balance thermoregulation and predator avoidance. Conversely, dense vegetation (NDVI >0.6) constitutes a strong ecological threshold, universally suppressing burrowing regardless of topography. These findings clarify the fine-scale microhabitat selection mechanisms of alpine fossorial mammals, providing a new paradigm for monitoring and managing grassland herbivores. The proposed framework supports precision ecological regulation through threshold-based approaches, positioning vegetation restoration as a central management strategy.