Acoustic Monitoring Enables Multi-Taxa Conservation Assessment and Prioritisation Over Large Scales and for Rare and Cryptic Species
Abstract
Aim: To combat the global biodiversity crisis, robust and scalable data are needed to target, monitor and evaluate conservation efforts, particularly in data-poor regions and for cryptic taxa. Passive acoustic monitoring (PAM) has the potential to provide solutions, but real-world examples are still rare. We demonstrate how PAM data can be used to rapidly and effectively map distributions of multiple taxa over large scales, in data-poor regions. We show how these data can be used to assess the importance of existing protected areas and prioritise future conservation efforts, including for rare and cryptic species often neglected in such assessments. Using machine learning and manual verification, we identified bats, nocturnally active birds, small mammals and bush crickets from over 34,000 monitoring hours at 506 sites in the Polesia region of Belarus and Ukraine. Using multi-species generalised mixed models in a Bayesian framework, we then predicted occupancy and acoustic activity for these species and their associations with protected areas, over a 151,000 km2 project area. We identified areas of high conservation priority as measured by species richness and/or importance for globally or regionally threatened species. Our approach provides a roadmap for collecting and processing large-scale, multi-taxa biodiversity data using passive acoustic monitoring. In our case study region, we show that although existing protected areas contain a relatively large proportion of high conservation priority areas, there are significant gaps in the protected area network. We also show low surrogacy of areas of high conservation priority between taxa at fine scales, but did at larger scales, showing the importance of multi-taxa monitoring to prioritise protected areas that conserve a wide variety of species.
