Can Automated Monitoring Enable State-Dependent Conservation?

In contrast to traditional conservation management which relies on generalized practices or historical data, potentially leading to inefficient use of resources, the concept of state-dependent conservation promises an adaptive management strategy that uses (near) real-time ecosystem or species presence data to make targeted decisions about implementing conservation measures. However, given that the monitoring requirements for state-dependent conservation are much higher, the concept has largely been dismissed as being unfeasible. With the rise of automated monitoring technology and increasing capabilities of AI assisted species classification, overcoming this barrier might have become possible. Against this backdrop, we set out to revisit the concept of state-dependent conservation.

 

This study integrates Passive Acoustic Monitoring (PAM) technologies, AI assisted species classification and cost-effectiveness analysis in agri-environment schemes (AES). We present a proof-of-concept case study where PAM is deployed across agricultural plots to detect meadow-nesting birds during their breeding season that will be conducted in the Spreewald Biosphere Reserve in spring 2025. We assume varying probabilities of bird presence and analyze the costs of monitoring and compensating farmers for state-dependent and conventional conservation measures. We also demonstrate early results for a custom AI classifier trained not to detect all species, but rather specific bird calls which indicate bird breeding behaviour (and which can thus inform conservation actions). This state-dependent approach, facilitated by novel monitoring technologies, theoretically allows more targeted conservation actions only when necessary, potentially reducing overall expenditure or freeing up resources for other conservation targets.

 

Comparative analysis with conventional AES approaches will investigate trade-offs between fixed conservation payments and dynamic, monitoring-informed actions. Our analysis aims to identify conditions under which state-dependent AES can outperform conventional approaches in maximizing conservation outcomes relative to cost.

 

This approach may have implications for policy-makers and conservation managers seeking economically sustainable and ecologically effective AES solutions. By leveraging advancements in monitoring technology, state-dependent conservation schemes could potentially improve biodiversity outcomes in managed agricultural landscapes.