Human conservation efforts have pros and cons for endangered animals. Metagenomic analysis of the gut microbiome provides valuable insights and tools for wildlife conservation. However, most studies only focus on changes in microbial species and potential functional genes, leaving out the assembly mechanisms and metabolic activities of wildlife gut microbiome. Herein, we investigated the gut microbiome of golden snub-nosed monkeys under varied conservation strategies (wild, captive, and food provision) using metagenome and metabolome analysis. We found that the conserved monkeys had more non-redundant genes, distinct community diversities, and less stable gut microbiota. Source-tracking analysis showed captive group has fewer wild-sourced microbes than food provision group. The captive group had more habitat specialists with narrower niche breadth compared to the provision/wild counterparts. Besides, captive group’s microbiota governed by deterministic processes more than provisioned/wild groups. Procrustes analysis revealed changes in antibiotic resistance genes and virulence factors linked to mobile genetic elements in conserved monkeys. Notably, gut microbiota and metabolomic dataset had significant co-variation and clear separation among the wild, captive and food provision groups. Weighted correlation network analysis identified co-varying modules of microbiota and metabolites with opposite variation trends between the wild and conserved animals, specifically linked to co-metabolism pathways associated with essential amino acids. This study provides new insights into the structural features, assembly mechanisms, community functions, and microbiota-metabolome associations of wildlife gut microbiome under different conservation strategies and have significant implications for advancing sustainable conservation practices.

Global climate change poses major challenges for current biodiversity conservation efforts. Assessing species’ vulnerability to climate change is a prerequisite for developing effective strategies to reduce emerging climate-related threats. We used the maximum entropy algorithm (MaxEnt model) to assess potential changes in snow leopard (Panthera uncia) suitable habitat in Qinghai Province, China under a mild climate change scenario. Our results showed that the area of snow leopard suitable habitat in Qinghai Province was 302,821 km2 under current conditions and 228,997 km2 under 2050’s climatic scenario, and that its mean elevation would shift upward 90 m. At present, nature reserves protect 38.78% of the currently suitable habitat and will protect 42.56% of future suitable habitat. Current areas climate refugia amounted to 212,341 km2, mainly distributed in Sanjiangyuan, Qilian mountains and surrounding areas. Our results provide valuable information for formulating strategies to meet future conservation challenges brought on by climate stress. We suggest that conservation efforts in Qinghai Province should focus on protecting areas of climate refugia and on maintaining or building corridors when planning for future species management.