Evidence map›Paper›PMID 40933365›Full record

ArticlePNAS nexus2025

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

Isla Duporge, Zijing Wu, Zeyu Xu, Peng Gong, Daniel Rubenstein, David W Macdonald, Anthony R E Sinclair, Simon Levin, Stephen J Lee, Tiejun Wang

Erratum issuedAbstract read
In one paragraph

Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Isla DuporgeDepartment of Ecology and Evolutionary Biology, Princeton University, 11 Ivy Lane, Princeton, NJ 08540, USA.ORCID https://orcid.org/0000-0001-8463-2459
Zijing WuDepartment of Geography, The University of Hong Kong, Hong Kong, SAR 999077, China.ORCID https://orcid.org/0000-0002-2582-9646
Zeyu XuITC, Faculty Geo-Information Science and Earth Observation, Hallenweg 8, 7522 NH Enschede, The Netherlands.ORCID https://orcid.org/0000-0002-7695-3356
Peng GongDepartment of Geography, The University of Hong Kong, Hong Kong, SAR 999077, China.ORCID https://orcid.org/0000-0003-1513-3765
Daniel RubensteinDepartment of Ecology and Evolutionary Biology, Princeton University, 11 Ivy Lane, Princeton, NJ 08540, USA.ORCID https://orcid.org/0000-0001-9049-5219
David W MacdonaldWildlife Conservation Research Unit, Department of Biology, University of Oxford, Abingdon Road, Oxfordshire OX13 5QL, United Kingdomc.ORCID https://orcid.org/0000-0003-0607-9373
Anthony R E SinclairBiodiversity Research Centre, University of British Columbia, 2212 Main Mall, Vancouver, BC, Canada V6T 1Z4.
Simon LevinDepartment of Ecology and Evolutionary Biology, Princeton University, 11 Ivy Lane, Princeton, NJ 08540, USA.ORCID https://orcid.org/0000-0002-8216-5639
Stephen J LeeDEVCOM Army Research Laboratory, 800 Park Offices Dr, Durham, NC 27703, USA.
Tiejun WangITC, Faculty Geo-Information Science and Earth Observation, Hallenweg 8, 7522 NH Enschede, The Netherlands.ORCID https://orcid.org/0000-0002-1138-8464

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID40933365
PMCPMC12418379

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.