Conservation groups test AI in the field as environmental costs remain unclear
Editorial Team
••📖5 min read
Onboard fisheries AI and faster satellite fire alerts show how conservation groups are shortening response times. Human oversight and deployment choices matter, while project costs and environmental footprints remain unquantified.
Onboard AI can shorten fisheries video review, while satellite warnings give forest patrols more time to respond.
Both systems depend on people to validate information or act on it.
The project accounts report operational gains without comparable operating costs or complete environmental footprints.
Conservation groups are changing where and how they process information to make monitoring faster. The Nature Conservancy, a global organization protecting lands and waters, has released an open-source fisheries AI system that analyzes video aboard vessels. In Cambodia, Conservation International, a conservation organization working with communities around Tonle Sap lake, connects satellite fire detection to local patrols. Their experience shows why the deployment system matters as much as the detection technology.
The environmental trade-off is prompting different responses. A November 7 report on environmental organizations’ AI use describes the World Resources Institute, an environmental research group, limiting image and video generation and caching query results to avoid repeat processing. Greenpeace International says it uses some AI embedded in existing software but would prefer to avoid the technology because of its environmental impacts. Adoption does not imply agreement about how much infrastructure should be built.
Members of the Ou Taprok community patrol team put out a fire. Photo: Conservation International-Cambodia. Source: Conservation International.
Fire warnings depend on community response
Conservation International’s June 22 account of its Cambodia program describes a problem that predates the new warning system. The flooded forests around Tonle Sap, Southeast Asia’s largest freshwater lake, provide fish nursery habitat and shelter floating homes from storms. Millions of people depend on the lake’s fisheries. Receding dry-season water leaves vegetation vulnerable to fire; the organization says most fires are caused by people, including accidental ignitions and deliberate burning.
The organization cites research finding that more than 30 percent of Tonle Sap’s flooded forests were lost to fire and agricultural clearing between 1993 and 2017. It also describes longer, hotter dry periods as making the forest more vulnerable.
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The organization previously used an open-access monitoring system whose alerts could take hours. In 2022 it partnered with OroraTech, a wildfire-detection company, to obtain faster satellite warnings. Staff receive alerts within minutes of detection and relay GPS coordinates to 17 participating community groups. Conservation International says it has trained and equipped 70 community members to respond.
The organization reports that the program stopped more than 50 wildfires over three years and shielded 64,000 hectares of forest. That is its account of the combined warning-and-response program, not an independently measured estimate of forest that would otherwise have burned. The account does not isolate an AI contribution to those results.
For two years, staff validated every alert to build community trust. The organization reports accuracy of 80 to 90 percent, but does not define the measure or its denominator. That limits comparisons with other detection systems.
The earlier monitoring service was already a technological alternative. The change described here is faster information delivered to people with coordinates, equipment and training. Detection is useful only if a patrol can reach a fire while it is still containable.
The Nature Conservancy’s April 27 announcement builds on electronic monitoring, which already uses cameras, GPS and sensors to document fishing activity. The organization says self-reported catch is largely unverifiable without independent monitoring. Its stated problem with existing video systems is the cost and logistics of reviewing large volumes of footage. Hard drives are often shipped to human review centers, with analysis arriving months after a vessel returns and seafood enters the supply chain.
The organization says its new system follows three years of prototyping and stress-testing in the Eastern Tropical Pacific. Computer vision models detect, track and classify catch aboard longline fishing vessels. Predictions are compared daily with captains’ electronic logbooks to flag potential noncompliance before vessels return to port.
This is edge computing: processing data near where it is collected. It does not require uploading all the video for centralized analysis. Starlink satellite-internet terminals transmit daily summaries, including predictions and associated footage for expert validation. Human reviewers remain part of the system.
Tryolabs, the AI development partner that built the software stack, says onboard processing cut video review from months to minutes and achieved a 6 percent catch-count error compared with expert reviewers. Those claims appear in the conservation organization’s announcement. The counting figure does not establish species-identification accuracy, performance across other fleets or a reduction in illegal fishing; the announcement does not supply the test sample or error formula.
Open-source software gives governments, scientists and industry a foundation they can reuse. Equipment, connectivity and expert review still have to be provided. The announcement offers no comparable per-vessel operating costs for the new system and the previous approach.
The prototype was one of 15 projects selected for the Bezos Earth Fund’s AI for Climate and Nature Grand Challenge. Its $2 million award supports expansion into the Western and Central Pacific, including Palau, where it will enter the country’s electronic monitoring program. The announcement also identifies the Patrick J. McGovern Foundation as a collaborator. This is funding for expansion, rather than evidence that fleets can sustain deployment without philanthropic support.
Expansion must establish costs and outcomes
The Nature Conservancy says processing aboard vessels reduces energy use and data-transfer requirements, according to the November report. That is a claim about its deployment approach, not a published measurement of the project’s full environmental footprint. Neither project account supplies such an assessment or establishes a net environmental benefit.
The next decisions concern expansion: Conservation International wants to extend its warning system across Tonle Sap and along the Mekong River; the fisheries project’s award supports deployment in another ocean region. Both need to show that their reported gains hold in those settings.
For the fire program, that means separating alert performance from the contribution of patrol capacity and measuring conservation outcomes. For fisheries, it means testing catch verification across fleets, showing whether faster review changes enforcement or management, and disclosing ongoing costs. Computing, equipment and connectivity footprints would then allow a more credible assessment of environmental costs alongside those outcomes.
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