The SA-FARI dataset and its accompanying publication were made possible only through the dedication of countless researchers, engineers, and conservationists, all of whom we recognize and thank in the acknowledgments that follow:
Osa Conservation acknowledges field support from staff members, volunteers and the Osa Camera Trap Network to collect and annotate their video data, and funding from Michael Simons and Sabrina Karklins, Biome Conservation, the Bobolink Foundation, the BAND Foundation, the Krystyna and Dan Houser Foundation, Troper Wojcicki Philanthropies, the KHR McNeely Family Fund, and the Mazar Family Charitable Foundation Trust.
We thank Haitham Khedr and Ho Kei Cheng for their help with model evaluations, and Tengyu Ma for help with annotations.
We thank the Pan African Programme: ‘The Cultured Chimpanzee’ team and its collaborators for allowing the use of their data for this paper. We thank Amelie Pettrich, Antonio Buzharevski, Eva Martinez Garcia, Ivana Kirchmair, Sebastian Schütte, Linda Gerlach and Fabina Haas. We also thank management and support staff across all sites; specifically Yasmin Moebius, Geoffrey Muhanguzi, Martha Robbins, Henk Eshuis, Sergio Marrocoli and John Hart. Thanks to the team at https://www.chimpandsee.org, particularly Briana Harder, Anja Lands-mann, Laura K. Lynn, Zuzana Macháčková, Heidi Pfund, Kristeena Siglerand, Jane Widness. The work that allowed for the collection of the dataset was funded by the Max Planck Society, Max Planck Society Innovation Fund, and Heinz L. Krekeler. In this respect we would like to thank: Ministère des Eaux et Forêts, Ministère de l’Enseignement supérieur et de la Recherche scientifique in Côte d’Ivoire; Institut Congolais pour la Conservation de la Nature, Ministère de la Recherche Scientifique in Democratic Republic of Congo; Forestry Development Authority in Liberia; Direction Des Eaux Et Forêts, Chasses Et Conservation Des Sols in Senegal; Makerere University Biological Field Station, Uganda National Council for Science and Technology, Uganda Wildlife Authority, National Forestry Authority in Uganda; National Institute for Forestry Development and Protected Area Management, Ministry of Agriculture and Forests, Ministry of Fisheries and Environment in Equatorial Guinea. The authors would like to thank the Animal Biometrics group within the Machine Learning and Computer Vision (MaVi) research group at the University of Bristol for their valuable support. The work carried out by the group was partly supported by the UKRI Centre for Doctoral Training in Interactive Artificial Intelligence (CDT in Interactive AI) under grant EP/S022937/1.
We thank the Biodiversa+ project “Big_Picture” (ref. Proyecto PCI2024-153504 convocatoria europea Biodiversa+, funded by MICIU/AEI) and Plan Nacional ref. PID2022-142919OB-100 for sharing their data for this paper.We extend our sincere gratitude to Conservación Amazónica–ACCA for access to the Los Amigos Conservation Concession and for ongoing logistical support. We also thank the promotores (field rangers) of Los Amigos, whose dedication to maintaining the concession and whose assistance with camera trap deployment and monitoring made this work possible.
