Abstract
The world’s ecosystems are undergoing rapid changes driven by climate change and human development, leading to accelerated habitat loss. These combined processes have already resulted in extinction and decline in many species population, threatening the sustainability of multiple ecosystems, and, ultimately, the survival of all life on earth, including human life. Facing this biodiversity crisis, scientists and nature conservation organizations lack important data regarding the state of the populations of most species on the planet, with only a limited fraction systematically monitored. Current population estimation methods are too slow to match the rapid extinction rates. This paper outlines initial stages of our work, which ultimately aims to ecological monitoring by integrating machine learning with crowdsourced citizen science. Specifically, here we report on our work-in-progress testing two approaches for classifying camera-trap data: engaging school students through Zooniverse and using a gamified platform with a broader audience. Our goal with this work is to determine the better approach to approximate expert classifications, in terms of reliability, scalability, and speed. Eventually this research aims to enhance ecologists’ ability to cover more species, and create timely reports about the state of nature, to inform the creation of interventions and policies for a sustainable future.
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Notes
- 1.
Juvenile males, also referred to as yearlings, tend to leave their original herd and form small groups, which last for a short while before they become adult, and separate. Whereas juvenile females normally remain with their herd.
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Acknowledgments
We thank Dan Malkinson, Ilan Shimshoni and Yuval Nov for advice. We acknowledge Ayala Granat, Bar Lavi and Peter Mason for technical assistance with coding and with the Zooniverse platform; Roi Talbi and Ariel Shamir for expert media classification, and Erik Duhaime and Gunnar Epping at Centaur Labs for technical advice and support. We thank school students and other volunteers who contributed classifications. Special thanks to Achiad Davidson for sharing his expertise, advice, and support, and for his work with school students and volunteers. Partial funding was received from the DSRC at the University of Haifa.
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Nagar, Y., Shaheen, W., Arazy, O. (2025). Comparing Competing Approaches to Crowdsourced Classifications of Biological Species. In: Themistocleous, M., Bakas, N., Kokosalakis, G., Papadaki, M. (eds) Information Systems. EMCIS 2024. Lecture Notes in Business Information Processing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-031-81325-2_18
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