Articolo in rivista, 2022, ENG, 10.1038/s41467-022-27980-y

Perspectives in machine learning for wildlife conservation

D. Tuia, B. Kellenberger, S. Beery, B.R. Costelloe, S. Zuffi, B. Risse, A. Mathis, M.W. Mathis, F. van Langevelde, T. Burghardt, R. Kays, H. Klinck, M. Wikelski, I.D. Couzin, G. van Horn, M. Crofoot, C.V. Stewart, and T. Berger-Wolf

School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Computing and Mathematical Sciences, California Institute of Technology (Caltech), Pasadena, CA, United States; Max Planck Institute of Animal Behavior, Radolfzell, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany; Department of Biology, University of Konstanz, Konstanz, Germany; Institute for Applied Mathematics and Information Technologies, IMATI-CNR, Pavia, Italy; Computer Science Department, University of Münster, Münster, Germany; School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Environmental Sciences Group, Wageningen University, Wageningen, Netherlands; Computer Science Department, University of Bristol, Bristol, United Kingdom; Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, United States; North Carolina Museum of Natural Sciences, Raleigh, NC, United States; Cornell Lab of Ornithology, Cornell University, Ithaca, NY, United States; Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, United States, Translational Data Analytics Institute, The Ohio State University, Columbus, OH, United States; Departments of Computer Science and Engineering; Electrical and Computer Engineering; Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, United States

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.

Nature communications 13 (1), pp. 792–?

Keywords

N/A

CNR authors

Zuffi Silvia

CNR institutes

IMATI – Istituto di matematica applicata e tecnologie informatiche "Enrico Magenes"