An expert-based global assessment of threats and conservation measures for subterranean ecosystems

Subterranean ecosystems host unique biodiversity and deliver important services to humans. Yet, available data for subterranean ecosystems are limited in space and/or taxonomic scope and global monitoring programs are absent, preventing practitioners to develop effective conservation and management strategies. Expert opinion may help overcome some of these knowledge gaps. We surveyed experts on a wide variety of subterranean ecosystems and taxa around the world, aiming to quantify the importance of anthropogenic threats and conservation measures for subterranean ecosystems and to provide a roadmap on how to preserve these fundamental habitats and associated species and ecosystem services. We obtained 279 responses from 155 experts in different subterranean habitats, taxa, and regions. Experts perceived surface habitat change, direct habitat destruction (e. g., pollution, damming, mining), and climate change as the most relevant threats impacting subterranean ecosystems. Legislation, land protection, and education were scored as the most effective conservation measures, whereas species-level conservation was deemed less relevant. Whenever lacking hard data, expert opinion may be an effective, largely available, yet often overlooked source of information to implement timely conservation interventions for subterranean ecosystems.

Automated summary

Subterranean ecosystems are important for biodiversity and human well-being, but there is limited data and global monitoring programs for these ecosystems, hindering effective conservation strategies. We surveyed experts on subterranean ecosystems worldwide to determine the threats and conservation measures. Experts identified habitat change, direct destruction, and climate change as the most relevant threats, and legislation, land protection, and education as the most effective conservation measures. Expert opinion can provide valuable information for conservation interventions when data is lacking.