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ORIGINAL RESEARCH article
Front. Ecol. Evol.
Sec. Conservation and Restoration Ecology
Volume 13 - 2025 | doi: 10.3389/fevo.2025.1527976
This article is part of the Research TopicDiagnostic Tools and Research Applications to Combat Wildlife Trade IssuesView all 6 articles
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Market-driven poaching and unsustainable wildlife harvest has emerged as a significant driver of population decline for many species of plants and animals with Asian and African elephants, rhinos, parrots and pangolins among the higher profile species now threatened with extinction. Here we explore the emerging role of conservation technology in combating illegal and unsustainable wildlife trade, showcasing how innovative tools are revolutionizing the detection and disruption of wildlife trade with a focus on those that are available to frontline staff working to prevent poaching and trafficking from source sites. We consider the diverse array of technologies being deployed, from open-source software platforms, AI and mobile apps to cutting-edge hardware including camera traps, acoustic sensors, and remote sensing tools. These tools empower rangers, park staff, wildlife and fisheries inspectors, customs officials, police, and conservation practitioners with unprecedented capabilities to monitor threatened wildlife, detect illegal and unsustainable harvest activities, gather evidence, and support law enforcement interventions.
Keywords: Conservation technology, poaching and unsustainable wildlife harvest, AI, Open Source Software, mobile apps. camera-traps, remote sensing, Drones, Acoustic sensors
Received: 14 Nov 2024; Accepted: 07 Apr 2025.
Copyright: © 2025 Lynam, Cronin, Wich, Steward, Howe, Markovina, Torrico, Reyes, Sophalrachana, Stevens, Schmidt and Cox. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Antony John Lynam, Allen Institute for Artificial Intelligence, Seattle, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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