Perspectives in machine learning for wildlife conservation
By Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, Mackenzie W. Mathis, Frank van Langevelde, T. Burghardt and 8 more. Nature Communications, 2021
659 citations recorded by the source corpus.
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. Animal ecologists are increasingly limited by constraints in data processing. Here, Tuia and colleagues discuss how collaboration between ecologists and data scientists can harness machine learning to capitalize on the data generated from technological advances and lead to novel modeling approaches.
Subfields: Developmental Biology, Ecological Modeling, Ecology
View Paper: https://www.semanticscholar.org/paper/d9b34c6b616f75485856794478bfbeab1ea93b81
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Citations in the corpus
- A high-resolution canopy height model of the Earth (2022, neutral): “, 2021) and has great potential to advance environmental monitoring (Reichstein et al., 2019; Tuia et al., 2022), but its application to global remote sensing is technically challenging due to the large data volume (Reichstein et al.”
- Use of object detection in camera trap image identification: assessing a method to rapidly and accurately classify human and animal detections for research and application in recreation ecology (2022, background): “Though this option provides the ability to have on demand access to high-performance computing, costs can quickly increase and exceed those of purchasing physical hardware (Tuia et al., 2021).”
- Machine learning and deep learning—A review for ecologists (2022, methodology): “…), ecosystem service management (Dietterich et al., 2012; Scowen et al., 2021 ), wildlife management (Humphries et al., 2018 ) and conservation (see Tuia et al., 2022 ), assessing the risk of invasive species (Barbet-Massin et al., 2018 ; Jensen et al., 2020 ), and biodiversity assessments…”