Spotlight: Tech to Detect Birds | Wader Study 132(2)

Humans have long used technology to augment our senses when studying birds. Binoculars and telescopes allow us to see farther than our eyes alone can, cameras mounted on drones allow us to fly where the birds do, microphones allow us to capture vocalizations and machine learning lets us identify bird species from these tiny snippets of sound. This issue of Wader Study, contains two papers highlighting the use of technology to enhance human ability to detect birds. Tim Oortwijn and colleagues used thermal imaging to detect cryptic Red Knot Calidris canutus nests and chicks in Alaska and to monitor mist nets at night in Mauritania¹. Paul Lenrumé used acoustic monitoring to determine the distribution and numbers of breeding Pantanal Snipe Gallinago paraguaiae and Giant Snipe G. undulata in the coastal savannas of French Guiana ².

Shorebird nests are hard to find, and Red Knot nests are some of the most cryptic of all. The birds are camouflaged to blend in with the Arctic tundra and incubating birds stay perfectly still on the nest even when approached closely. A field researcher could pass right by a Red Knot nest and not notice a thing. When the chicks fledge, they are camouflaged too. The naked eye, and even eyes augmented by binoculars or scopes, cannot detect the birds against the tundra backdrop. However, birds give off heat that contrasts sharply with the cooler background, especially if the sun has not warmed the surrounding area. Tim Oortwijn and colleagues used hand-held thermal imaging devices see this heat.

The thermal imaging devices were useful for locating Red Knots on their nests in northwestern Alaska (C. c roselaari) because they could see the birds’ heat standing out from their surroundings. Conditions were optimal for this early in the morning or on cloudy days when there was no direct sunlight. This is because the stones and lichens of the tundra absorb solar radiation and can heat up to create a mosaic of varying temperatures making it hard to see the heat of the birds. Using hand-held thermal imaging devices, the researchers were able to find 16 nests in their 2024 field season (out of an estimated total of 20). They also found the thermal imaging devices useful for finding chicks after they had left the nest. When the researchers captured the adult on the nest, they glued a VHF radio transmitter to its back. They could follow the radio signal to the parent attending the chicks (usually the male in Red Knots). At that point, the thermal imaging technology could be used to quickly locate the chicks from a distance, minimizing disturbance.

Oortwijn and colleagues also used the thermal imaging binoculars to monitor mist nets when catching birds at night during the non-breeding season in Banc d’Arguin, Mauritania. Mist nets are nearly invisible, even in the daylight, and at night it is impossible to see them or whether any birds are caught in them. When used at night the thermal imaging binoculars provided ‘night vision’ making it possible to monitor the nets from afar and see when birds were caught. The birds’ warm bodies stood out against the cool, night background. This allowed the researchers to quickly respond when birds were caught, increasing safety for the birds.

Technology can also aid in detecting and identifying birds by their sound. Paul Lenrumé used acoustic monitoring to gather data about Pantanal Snipe and Giant Snipe in French Guiana. Both species produce loud calls on the ground and instrumental sounds (winnows) in aerial displays during the breeding season. In 2020 and 2021, Lenrumé and volunteers monitored these displays to obtain information about numbers of breeding snipe, their geographic distribution, the habitats they use, and variations in displays over time and due to season.

Lenrumé and volunteers used plain old ears for a total of 33 monitoring hours at 23 sites on 44 nights. However, using technology—a Zoom H4n Pro recorder— they were able to collect an impressive 540 hours of data on 45 complete nights (from dusk to dawn) at 25 sites. They then analysed this wealth of recordings with Audacity software.

The data revealed at least 33 different individual Pantanal Snipe at 35% of listening sites and 71 individual Giant Snipes at 80% of the listening sites. ‘Winnowing’ was by far the most common sound recorded for Pantanal Snipe, whereas for Giant Snipe their display ‘song’ (https://xeno-canto.org/664091) was the most commonly recorded alone or associated with the instrumental sound (https://xeno-canto.org/698814). The Giant Snipe was much more widespread than Pantanal Snipe. Giant Snipe displays were detected on about 95% of suitable wet savannas over a distance of at least 148 km. In contrast, Pantanal Snipe were detected over only about 38 km and only in low to medium grassy open wet savannas.

Lenrumé notes that the new information gathered through acoustic monitoring should be considered when reassessing the IUCN status of these species in French Guiana. Pantanal Snipe are currently categorized as Endangered but are not protected by law, whereas Giant Snipe are considered Critically Endangered and the species and its habitats are protected. Taken together, the data suggest that the Pantanal Snipe deserves the same legal protection status as the Giant Snipe in French Guiana.

The technology used in these studies is not revolutionary. Ornithologists have used applications of thermal technology since the 1960s and have studied everything from behaviour, to thermoregulation, to nest finding to illness monitoring³. Bird researchers have also listened to bird song and recorded vocalizations for decades, but now they can massively apply these older technologies with new machine learning algorithms and computing power. This has allowed truly revolutionary leaps in the amount of information that can be passively gathered and analysed over large geographical areas. For example, researchers were able to use BirdVoxDetect, a bird call detection system to record over 4,800 hours of vocalizations during migration in upstate New York and then apply machine learning algorithms to identify species and for further analyses. The researchers likened it to having an army of expert birders with superhuman hearing listening to the night sky during bird migration⁴. Or another team of researchers who deployed 1,600 microphones across approximately 6 million acres of Sierra Nevada Forest to record 700,000 hours of bird sounds. They then used BirdNET, a machine-learning algorithm, to identify different bird calls and analyse how bird distribution relates to various forest conditions⁵. These new tools will help us to study and protect forests and wildlife during a time of rapid environmental change. Sometimes it feels like we have too much technology, and so, it is heartening to see how it can be applied for the good of habitats, migratory routes and birds.

Deborah M. Buehler

Outreach Editor, Wader Study

 

¹ Oortwijn, T., J.A. Johnson, Z.M. Pohlen & J.A. van Gils. 2025. Handheld thermal imaging devices as important tools in wader fieldwork. Wader Study 132: 69–72.

² Lenrumé, P. 2025. Breeding distribution of the Pantanal Snipe Gallinago paraguaiae and Giant Snipe G. undulata in French Guiana based on acoustic monitoring. Wader Study 132: 36–44.

³ McCafferty, D.J. 2013. Applications of thermal imaging in avian science. Ibis 155: 4–15.

⁴ van Doren, B.M., V. Lostanlen, A. Cramer, J. Salamon, A. Dokter, S. Kelling, J.P. Bello & A. Farnsworth. (2023). Automated acoustic monitoring captures timing and intensity of bird migration. Journal of Applied Ecology 60: 433–444.

⁵ Brunk, K.M., J.F. Goldberg, C. Maxwell, M.Z. Peery, G.M. Jones, L.R. Gallagher, H.A. Kramer, A L. Westerling, J.J Keane, S. Kahl& C.M. Wood. (2025). Bioregional-scale acoustic monitoring can support fire-prone forest restoration planning. Frontiers in Ecology & the Environment. e2843.

 

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Featured image: Giant snipe (Gallinago undulata undulata). Xenocanto record by Paul Lenrumé.