These 5 Research Projects Show How AI Is Revolutionizing Bird Conservation

Conservation researchers will go to great lengths to survey birds—getting up before dawn, driving hours on back roads, undertaking grueling hikes—all for only a brief snapshot of which species or how many individuals are in an area. Such effort is worth it to collect data critical to monitoring and protecting avian populations in important habitats across the world, says Matthew Weldy, Audubon’s senior manager of quantitative science. Strategically placed cameras and audio recorders can extend experts’ reach, but the sheer volume of information these devices produce quickly becomes unmanageable. 

Artificial intelligence is revolutionizing these surveys—rapidly. Scientists are turning to the technology to draw insights from months of sound clips in a forest, for example, or weeks of images from a remote island. “It’s not just sifting through data; it’s getting to answers pretty quickly,” says Tom Denton, a software engineer turned research scientist who developed Perch, one of a number of predictive AI models that researchers use to analyze avian sounds. 

Today wildlife biologists are increasingly employing AI to address conservation challenges across vast areas and ask novel research questions. “It’s a transformative thing for the field,” says Weldy. In a time of swift biodiversity loss, habitat degradation, and climate change, answers can’t come quickly enough.

Empowering Communities  

The sprawling mangroves of Panama’s Parita Bay shelter tens of thousands of migratory birds, forming a key stopover on the Pacific Flyway. To bolster a case for protecting the habitat, conservationists wanted to gauge the relationship between avian populations and the ecosystem’s health, especially the amount of carbon in the soil and trees. But surveying birds living among the mangroves’ mucky earth and gnarled roots would be especially challenging, says Jorge Velásquez, Audubon’s science director for Latin America and the Caribbean. 

To determine which birds were present, researchers turned to acoustic monitors to eavesdrop on songs and calls. Then they used an AI model, previously trained on thousands of hours of recordings to recognize nuanced patterns in avian sounds, to analyze the data. The team found 27 species previously unknown in Parita Bay. What’s more, they used their findings to show that protecting mangroves with abundant birds also preserves carbon-rich areas that help mitigate climate change.

The project demonstrated to Velásquez how AI tools can accelerate efforts to safeguard biodiversity havens. Now fueled by a $2 million grant from the Bezos Earth Fund, Audubon is scaling up their use with Escucha Aves, a collaborative project with a range of technology and conservation partners. Starting in nine areas of Colombia, with a planned expansion in Peru and Bolivia, Velásquez and collaborators are training community members and organizations to deploy acoustic monitors. Locals serve as co-investigators in the endeavor; they help decide where to place devices, retrieve the data, run the AI model, and validate the findings, he says. Experts then check the data, providing a list of detected species, and will also map the results.

In Colombia’s rugged Eastern Antioquia region, the work has already yielded promising findings in San Carlos. Staff with the local organization Fundación Darién, which works with landowners, placed recorders in areas where they hoped to expand an existing conservation reserve network. The AI analysis turned up an unexpected bird—the Tawny-faced Quail—more than 60 miles away from its known range. Later, they set up a camera trap to confirm the quail’s presence, and now local groups aim to use the discovery to attract more birders to visit. Cultivating such economic opportunities tied to conservation can build community support for reserves and help make them financially sustainable. “We are building this platform to empower communities,” Velásquez says, and so far, “the commitment has been incredible. At a most basic level, they want to know what they have so they can protect it.”

Managing Ecosystems    

Ecologist Connor Wood began his acoustics research career in 2017 when he sought to track dwindling numbers of Spotted Owls across California’s Sierra Nevada, an area the size of Vermont. To keep tabs on the nocturnal bird, which hoots and whistles at night, he and University of Wisconsin–Madison ecologist Zach Peery, along with a field team, started placing what eventually grew to a network of 1,600 recorders across the landscape. At a colleague’s suggestion, Wood programmed the units to run through morning—perhaps the sounds they captured could be useful.

The decision paid off, says Wood, now on the research faculty at the Cornell K. Lisa Yang Center for Conservation Bioacoustics. In 2020 the audio data provided the first successful large-scale test of BirdNet, an open-source AI model managed at Cornell University that’s now used by researchers worldwide to identify birds by snippets of their songs. Later, the program detected more than 90 species in 1 million hours of Wood’s recordings.
 

Buoyed by their success detecting an array of birds, Wood and Peery expanded their work in the Sierra Nevada from monitoring a single species to a more ambitious endeavor to help public land agencies manage the ecosystem. With a grant from NASA, they combined their avian acoustic data with satellite imagery—which reveals metrics like canopy cover and tree height—to build a decision support tool for the U.S. Forest Service in California. The software predicts how a prescribed fire, for example, will affect various bird populations of interest, from warblers to woodpeckers. This can enable land managers to make smarter and more timely decisions that take a range of biodiversity into account. “The goal is to do badly needed forest restoration at a much faster scale,” says Wood. “In today’s world, faster is necessary.”

Today the freely available BirdNet can identify 6,500 avian species. Moving forward it could help scientists detect more wildlife. While initially designed for birds, the program translates all sound into a long string of numbers that effectively acts as a barcode, which Wood has used to track gray wolves, frogs, and toads in the Sierra Nevada. Future custom versions of the program, he anticipates, will be fine-tuned to individual biomes to incorporate a range of vocal animals.

An early test of that, BirdNet Pantanal, is in development. Working with the Wildlife Conservation Society, Wood aims to use AI to monitor all species that make sound—from singing birds and croaking frogs to roaring jaguars—in the sprawling South American wetland, which faces varied threats. Such advances mean that “ecologists can dream big,” Wood says. They are empowered with a more nuanced view of an ecosystem’s health than he once thought possible.

Decoding Songs 

Teacher, teacher, teacher. As the weather warms, birders will recognize the distinctive, often piercing tune of the Ovenbird, a gold-topped warbler that breeds in the northeastern United States. But is the Ovenbird you hear singing one morning the same one chanting the next day? Even most experts can’t tell—and until recently, neither could software, says Sam Lapp, a Ph.D. student at the University of Pittsburgh. Identifying an individual bird by its song “seemed far-fetched or mythical,” he says, “until we really started looking closer.”

Existing AI models that pinpoint a bird species usually aim to remove vocal variation among individuals, which helps differentiate an Ovenbird from, say, a Tufted Titmouse, whose song sounds like peter, peter, peter. To pick out a single Ovenbird from all others, Lapp and his collaborators analyzed spectrograms: visual representations of the pitch and volume of its sounds. One teacher contains four to six notes, and he learned that each bird had a distinct acoustic signature in those notes—something that most humans struggle to make out, but that the warblers themselves can likely discern. “The information has been there the whole time, but we were not looking at it the right way,” he says.

Using four years of recordings at 126 locations in Pennsylvania’s state game lands and state parks, Lapp deployed an AI model to automatically recognize different Ovenbirds. In total, he distinguished 405 unique individuals and detected many coming back to the same site each year; 72 did so all four years. Knowing how many Ovenbirds return to a particular place, he says,  adds another way for land managers to measure habitat quality and gauge the success of forest restoration techniques they’d been testing at these sites.  

Such research is at the bleeding edge of AI-informed conservation. The ability to track specific birds over time is highly valuable, as it helps scientists to better understand avian movements, behaviors, and survival. Yet acquiring this data is traditionally tricky and time-consuming, usually involving tagging or banding techniques that require the repeated capture of birds. Though audio-based ID methods may not work well for individuals of all avian species, Lapp thinks the approach has huge potential. Plenty of birds and other wildlife may use vocalizations to recognize one another. “The missing link,” he says, “is for us to be able to teach machines to pick up on those differences.”

Detecting Threats

On rugged, remote islands around the world, the nonprofit Island Conservation often spends years eradicating invasive rats, cats, or other intruders that harm native wildlife such as petrels, albatrosses, and other ground-nesting birds. One of the biggest challenges is knowing when the job is done, says David Will, senior director of impact and innovation. Even a few missed critters can render their costly, painstaking efforts moot. 

Since 2010 Island Conservation has utilized motion-triggered camera traps to keep watch for lingering animals. Typically, someone must visit each device to download the images for later analysis, and that is no small feat. Take Robinson Crusoe Island in Chile. With trekking across the landscape to recover files, waiting for a flight to the mainland, and then processing the data, getting results could take up to six months from start to finish, says Will. By then, any rogue rats have had ample time to reproduce. 

In 2023 on Floreana Island in the Galápagos, the group launched a pilot project to speed up the process. Partnering with the nonprofits Conservation X Labs and Fundación Jocotoco, they deployed a wireless device called Sentinel to turn their cameras into a real-time alarm system. The gear’s onboard AI program automatically scrutinized images and sent alerts when species of interest crossed a camera’s path. In some cases, rangers on the ground could set out in minutes to trap an invader. Yet the experiment hit a snag when a satellite network shut down and cut connectivity. Now a team is working with technology partners to implement an improved system: a low-power network that can transmit data over a long range, which may more reliably deliver alerts from remote areas and help surveil a wide range of wildlife on Floreana. 

Island Conservation isn’t the only group adopting real-time AI technology to address threats to birds. In the Maya Biosphere Reserve, home to nationally endangered Scarlet Macaws, the Wildlife Conservation Society’s Guatemala program is using discreet audio units, BirdNet, and a monitoring platform called Earth Ranger to keep an ear out for sounds of shotguns, chainsaws, and other signs of illegal logging or poaching, says Rony García-Anleu, the program’s biological research director. And in Australia, the local transportation department recently trialed an AI system to help Southern Cassowaries cross a treacherous road. When cameras and sensors detected the large birds nearby, warning signs flashed—and vehicles took heed.

Surveying Seabirds

For the past five decades, the annual Texas Waterbird Survey has deployed teams of people to carefully count terns, pelicans, egrets and other birds on more than 175 nesting islands along the state’s coast. Typically, the most precise counts come when surveyors gingerly walk through the colonies while dodging rambunctious chicks. But the approach causes disturbance, so to reduce impacts to wildlife or when an island is inaccessible, they often conduct tallies from a boat.

In 2018, Houston Audubon decided to check the accuracy of the offshore counts by flying a drone above one island in Galveston Bay that was  especially hard to survey. Conservation biologist Anna Vallery, now at Audubon Washington, spent 80 hours manually tallying birds in the aerial images. She was stunned by how many the boat surveyors had overlooked. “We were missing half of our Brown Pelicans and 90 percent of Laughing Gulls,” says Vallery. “Those are big birds.”

To more accurately count wildlife and access difficult terrain, nesting-colony managers wanted to launch drones more often, but spending weeks poring over a set of images wasn’t practical, says Audubon Texas director of conservation Richard Gibbons. They needed to automate. Existing bird-detection programs, however, worked best with ground-level images, and thus weren’t helpful. He and Vallery decided to build their own. 

They gathered high-definition aerial images of more than 20,000 individual birds and partnered with Rice University’s Data to Knowledge Lab to develop an AI model that could classify them. The program, called SeeBird, automatically detects birds in drone images and then groups and tallies them in 16 visual categories, such as “Brown Pelican chicks” or “white wading birds.” In other words, it swiftly turns hundreds of nondescript dots into robust population data. 

Currently, SeeBird is available for waterbirds of the Texas Gulf region, and in 2027, the team aims to release a general waterbird detector that can be used anywhere. “We were just trying to build a tool to make our lives easier,” says Vallery. “We’ve since realized there is a serious need.” 

Gibbons hopes that SeeBird can also help researchers use drones to gauge breeding success, track changes in erosion, or spot dead birds that may signal an avian influenza outbreak. Using technology effectively is critical, he says, to getting the most out of limited conservation funding. That’s especially true today, given the urgency of climate change: Artificial intelligence, he hopes, can speed up the pace of adaptation.

This story originally ran in the Summer 2026 issue as “AI in the Wild.” To receive our print magazine, become a member by making a donation today.