Elly Knight has been attempting to comprehend a bird that makes every effort to avoid being understood in the boreal forest of northeastern Alberta. The common nighthawk is a nocturnal, highly mobile, and well-camouflaged bird.
The scientific literature on its behavior in the boreal forest has been sparse for many years. Although there was traditional ecological knowledge, formal data was hard to come by. The nighthawks, along with everything else moving and calling in the forest at night, were captured in years’ worth of audio by Canadian wildlife agencies, who then started deploying autonomous recording units throughout the landscape.
Once the recordings were available, the issue became clear. There was no practical way for any human expert to locate the nighthawk calls in that ecosystem’s nearly complete sonic archive, which Knight had access to. She told Yale Environment 360, “Realistically, we are only able to do that expert analysis for 1 percent of recordings.” “So there’s this other 99 percent that’s sitting there.”
| Category | Details |
|---|---|
| Subject | AI applications in wildlife conservation and biodiversity monitoring |
| Key Institution — Research | University of Alberta; MIT (Sara Beery, conservation AI specialist) |
| Key Institution — Applications | WWF (World Wildlife Fund), Conservation International, iNaturalist |
| Key Field Application — Acoustic | University of Alberta’s Elly Knight — AI analysis of common nighthawk recordings in Alberta’s boreal forest |
| Key Field Application — Camera Traps | TrailGuard AI in Madhya Pradesh’s Kanha-Pench Corridor — 300+ tigers, 600,000 nearby residents |
| Key Field Application — Species Discovery | iNaturalist — half a billion images; ~1 new species discovered per month; used in 6,000+ scientific papers |
| Key Field Application — Population Management | Idaho Fish and Game — 18 million images/year processed in weeks instead of years by 4 people using AI |
| New Yellowstone Project | Colossal Biosciences + Yellowstone Forever — AI acoustic fingerprinting of individual wolf howls, pack tracking, gunshot detection |
| Key Limitation | AI relies on data from wealthy countries; risk of excluding Indigenous/traditional knowledge; energy/water consumption |
| Academic Horizon Scan | 27 experts identified 21 AI applications likely to benefit conservation (Trends in Ecology & Evolution, 2025) |
| Key Critic | Hamish van der Ven, University of British Columbia — concerns about AI reducing direct field engagement |
| Reference Website | WWF — Artificial Intelligence and Conservation |
When she used AI to search through the archive, the results altered the research’s conclusions about where the birds lived, when they were present, and how their nesting and foraging habitats varied from one microhabitat to the next. A glimpse into a world that was essentially invisible but technically captured.
That is the most accurate description of what AI is currently accomplishing for conservation. It’s not magic. It is a tool for bridging the gap that has grown significantly in the fields of ecology and wildlife biology as sensor technology has surpassed human analytical capability.
“The bottleneck has really shifted from being hard-to-collect data to making sense of the enormous amount of data at our fingertips,” stated Ali Swanson, Conservation International’s director of innovation, technology, and nature. “We’re drowning in data.” In order to combat that drowning, conservation teams are now implementing AI systems that process images from camera traps, acoustic recordings, satellite imagery, and citizen science photos.
That capacity has evolved from an academic resource to something more immediate in the forested Kanha-Pench Corridor of Madhya Pradesh. Approximately 600,000 people reside within or close to the protected area, and the area is home to more than 300 tigers, the largest population in central India. Local communities occasionally take revenge when a tiger kills livestock.
These days, TrailGuard AI camera traps capture images of animals moving through the corridor, instantly identify the species, and send the data to forest rangers. Rangers can promptly notify local livestock operators if a tiger or other predator is moving in a specific direction so they can relocate their animals to safety before a confrontation arises. In addition to protecting tigers, the technology offers local communities something more useful than hope.
Sometimes it’s difficult to comprehend the scope of what’s now feasible. Half a billion photos have been collected by iNaturalist, a smartphone app that allows users to take pictures of wildlife and get instant AI species identification. Over 6,000 scientific studies have used its data. Users of the app have found about one new species every month, including a recently discovered praying mantis in Australia that was named Inimia nat in honor of the app.
They have also rediscovered species that have been absent for decades. Because it took so long to analyze two million camera trap photos annually, Idaho Fish and Game was basing hunting quotas on data that was five years old. Four workers can now process 18 million photos in a few weeks thanks to AI, which aligns management choices with the year the data was gathered. This is crucial, as MIT’s Sara Beery pointed out, “given how quickly everything is changing.”
Colossal Biosciences and Yellowstone Forever announced a new project at Yellowstone this spring that aims to do something more subdued: create acoustic fingerprints of individual wolves, differentiating growls from barking and chorus howls from solo howls, in order to noninvasively track packs, their movements, and their behavior without upsetting them. The same system can detect gunshots, allowing for quick action in the event of an unlawful killing. AI that manages the surveillance so that researchers can concentrate on the interpretation is how the technology appears at its most cautious.
However, it’s difficult to ignore the fact that not everyone finds the direction to be consistently encouraging. Concerned that AI is putting a technological barrier between biologists and the organisms they are meant to understand, Hamish van der Ven of the University of British Columbia has stated unequivocally that if he could un-invent it, he would.
The underlying concern isn’t trivial, but that’s probably too strong. AI colonialism is a real risk, according to a horizon scan conducted by 27 conservation scientists and AI specialists and published in Trends in Ecology and Evolution in 2025. Because these systems train mostly on data from wealthy nations, the insights they produce are biased toward ecosystems and methodologies that are already well-documented, potentially perpetuating knowledge gaps about biodiversity in the areas where it is most at risk and least studied. Additionally, the technology’s water and energy usage conflicts with its environmental goals.
That does not imply that the direction is incorrect. It implies that the direction must be closely monitored and developed with the same rigor as the ecosystems it is intended to safeguard.
