Leveraging Novel Technologies and AI for Biodiversity Monitoring and Conservation

Graduate / Professional

With ever-growing threats to species worldwide, decision-relevant information is needed to empower local communities, businesses, governments, and conservation practitioners to deliver biodiversity outcomes. Rapidly advancing technologies such as visual and acoustic sensors, eDNA, UAV-based survey approaches, community science, GPS-tracking, computer vision and AI-informed organismal detections and identifications, are hailed as central to this goal and as avenues to accelerate our understanding of biodiversity across scales. This year’s EEB 713 course will survey and critically assess novel technological avenues for local wildlife monitoring and species assessment. We will explore their potential to support global biodiversity monitoring at large and to advance ecological concepts. Each week, we will evaluate a different technology arena and discuss its scalability and potential impact in applied and basic ecological contexts. We will focus specifically on the use of these methods in currently understudied regions, such as the tropics, and assess the emerging opportunities, as well as limitations and risks, around overcoming existing information and knowledge biases and inequities. Finally, we will identify the opportunities for integrating different technologies combined with remote sensing to support a more robust monitoring of planetary biodiversity change. The course will consist of faculty, guest, and participant presentations addressing specific themes, followed by group discussions. Course participants will be expected to read literature around both the application and potential broader scientific relevance of new technologies. The course will draw on some of our own engagement in the recent XPrize Rainforest Final and the upcoming CBD COP 16 and will include select guest presentations from these arenas. Target meeting time is W 3pm at 310 Prospect. Meeting time may be changed by consensus. W 3pm-5pm