BMC Ecology and Evolution is calling for submissions to our Collection on Automated and remote biodiversity monitoring. Preventing, halting, and reversing biodiversity loss is now being prioritized globally. In December 2022, world leaders adopted the Kunming-Montreal Global Biodiversity Framework. The Framework outlines goals for 2050 to be achieved via quantitative targets actioned by 2030 to protect and restore nature. Technological advances that allow automated or remote biodiversity monitoring are rapidly evolving and promise to help meet these global targets by providing scientific evidence to inform and evaluate ecosystem management and policy decisions.
In support of the United Nations’ Sustainable Development Goals (SDGs) 6: Clean water and sanitation, 13: Climate action, 14: Life below water, and 15: Life on land, BMC Ecology and Evolution welcomes research harnessing the power of remote sensing, unmanned aerial vehicles (UAVs), remotely operated vehicles (ROVs), camera traps, distributed and connected sensor systems, lidar technology, environmental DNA (eDNA) analysis, mobile applications, and bioacoustics for automated and remote biodiversity monitoring. We also encourage the submission of research that uses machine learning and artificial intelligence to tackle the challenge of analyzing the abundance of data generated by such technologies for biodiversity monitoring.
Jenni Raitoharju is currently an Assistant Professor of Signal Processing at University of Jyväskylä, Finland and a part-time Senior Research Scientist at the Finnish Environment Institute. She received her PhD in Information Technology from Tampere University of Technology in 2017. Her research interests include machine learning and pattern recognition methods along with their application in environmental monitoring and autonomous systems. Raitoharju has been involved in several projects related to automated and remote environmental monitoring. Currently, she is the PI of an ongoing Academy of Finland project "TIMED: Taxa Identification with Machine Learning Enhanced by DNA Metabarcoding" and a task leader in a large Finnish Biodiversity Information Facility FIRI project. She has co-authored 78 papers in international peer-reviewed journals and conferences.
Luiz G. M. Silva, ETH-Zürich, Institute of Environmental Engineering, Switzerland
Dr Luiz G. M. Silva is an ecohydraulics scientist with a background in fish ecology and engineering. He has over 17 years of experience studying the effects of water infrastructure on freshwater fish and is highly active in discussions about measures to halt freshwater biodiversity loss. Thus, he has grown his research interest in freshwater monitoring tools and using artificial intelligence to enhance our capacity to collect and process data. He is also a member of the Freshwater Biodiversity Observation Network.
The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.