AI-based satellite survey offers independent assessment of migratory wildebeest numbers in the Serengeti.

IF 3.8 Q2 MULTIDISCIPLINARY SCIENCES
PNAS nexus Pub Date : 2025-09-09 eCollection Date: 2025-09-01 DOI:10.1093/pnasnexus/pgaf264
Isla Duporge, Zijing Wu, Zeyu Xu, Peng Gong, Daniel Rubenstein, David W Macdonald, Anthony R E Sinclair, Simon Levin, Stephen J Lee, Tiejun Wang
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引用次数: 0

Abstract

The Great Wildebeest Migrationin the Serengeti-Mara ecosystem is a globally iconic wildlife phenomenon that supports the health and biodiversity of the region by supporting predator populations, regulating herbivore densities, and driving nutrient cycling. This study presents the first AI-powered satellite survey, using two deep learning-based models (U-Net and YOLOv8) to detect and count wildebeest over more than 4,000 km² across two consecutive years in August 2022 and 2023 with F1 scores reaching 0.830 (Precision: 0.832, Recall: 0.838). The satellite-based results show fewer than 600,000 individuals-approximately half the widely cited estimate of 1.3 million wildebeest, which has remained largely unchanged since the 1970s. While some variation may arise from differences in spatial and temporal coverage between survey methods, the satellite approach employs rigorously validated AI models with demonstrated accuracy. Rather than undermining previous methods, this discrepancy underscores the importance of using independent and complementary monitoring tools to refine population estimates and improve our understanding of wildebeest movement dynamics.

基于人工智能的卫星调查提供了对塞伦盖蒂迁徙角马数量的独立评估。
塞伦盖蒂-马拉生态系统中的角马大迁徙是一种全球标志性的野生动物现象,它通过支持食肉动物种群、调节食草动物密度和推动营养循环来支持该地区的健康和生物多样性。本研究提出了第一个人工智能卫星调查,使用两个基于深度学习的模型(U-Net和YOLOv8)在2022年8月和2023年8月连续两年检测和计数超过4000平方公里的牛羚,F1得分达到0.830(精度:0.832,召回率:0.838)。基于卫星的结果显示,只有不到60万头——大约是被广泛引用的130万头角马估计的一半,这个数字自20世纪70年代以来基本保持不变。虽然调查方法之间的时空覆盖差异可能会产生一些差异,但卫星方法采用了经过严格验证的人工智能模型,具有一定的准确性。这种差异并没有破坏以前的方法,而是强调了使用独立和互补的监测工具来改进种群估计和提高我们对角马运动动态的理解的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.80
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