{"id":3532,"date":"2026-06-04T11:22:09","date_gmt":"2026-06-04T11:22:09","guid":{"rendered":"https:\/\/scienceblog.com\/horizon\/?p=3532"},"modified":"2026-06-04T11:22:09","modified_gmt":"2026-06-04T11:22:09","slug":"ai-listens-in-to-help-protect-wildlife","status":"publish","type":"post","link":"https:\/\/scienceblog.com\/horizon\/3532\/ai-listens-in-to-help-protect-wildlife\/","title":{"rendered":"AI listens in to help protect wildlife"},"content":{"rendered":"

AI systems are learning to listen to and decode the sounds of nature, helping scientists to track species and spot ecosystem changes much faster than traditional field surveys.<\/p>\n

By Michael Allen<\/p>\n

Strolling through a forest,\u00a0you may notice\u00a0that the air is filled with sound. Birds sing, insects and small mammals rustle through the undergrowth, and at dusk bats squeak as they communicate with each other.<\/p>\n

These soundscapes contain a wealth of information about which animals are present, how many there are and how healthy an ecosystem may be. But analysing all that audio is a huge challenge.<\/p>\n

Scientists can now collect enormous quantities of recordings using small autonomous devices placed in forests, wetlands and urban areas. The problem is no longer gathering data, but making sense of it quickly enough to be useful.<\/p>\n

Professor\u00a0Dan Stowell\u00a0from\u00a0the Naturalis Biodiversity Centre in Leiden, the Netherlands, is one of the leading researchers in the emerging field of computational bioacoustics that uses AI to analyse wildlife sounds and environmental recordings.<\/p>\n

\u201cWe now have so many ways to record animal sounds and soundscapes,\u201d said Stowell. \u201cBut the scale of the data is absolutely overwhelming.\u201d<\/p>\n

His research focuses on developing AI systems that can help scientists monitor biodiversity more efficiently and at much larger scale than traditional field methods allow.<\/p>\n

Cutting through the noise<\/strong><\/p>\n

Naturalis, the Dutch national centre for biodiversity research, has joined forces with researchers from across Europe and the UK in a four\u2011year EU\u2011funded research effort called BioacAI. The team, led by Stowell, is working to bridge the gap between the\u00a0massive acoustic data\u00a0collected and our ability to make sense of it.<\/p>\n

The initiative, which ends in 2027, is developing new AI tools\u00a0capable of automatically identifying species from sound recordings. The research team believes this could reshape how scientists monitor biodiversity across Europe and beyond.<\/p>\n

\u201cWe don\u2019t want to replace experts,\u201d said Stowell. \u201cBut we want to be able to take all that valuable information you can hear whenever you\u2019re in a forest, or even an urban environment, and turn it into useful information about animals and biodiversity.\u201d<\/p>\n

The research collaboration is also responding to a skills crisis in the field. No existing training programme currently produces researchers with expertise spanning acoustics, AI, zoology and ecology.<\/p>\n

BioacAI\u2019s doctoral network is designed to fill that gap, training a new generation of professionals with what Stowell calls \u201cfull stack\u201d bioacoustic AI skills.<\/p>\n

Tracking biodiversity in decline<\/strong><\/p>\n

Biodiversity loss is accelerating globally. Among the species most at risk are birds and insects, whose declining populations could trigger knock-on effects across ecosystems and agriculture, and ultimately human wellbeing. To respond effectively, scientists need reliable large-scale data on the status of different species.<\/p>\n

Traditional wildlife surveys \u2013 where scientists walk set routes recording what they see and hear \u2013 are labour-intensive, costly and limited in scope.<\/p>\n

One increasingly popular approach is passive acoustic monitoring, in which small recording devices are left in the field to capture everything they hear. These recordings can provide a detailed picture of what is happening in an environment over long periods of time.<\/p>\n

The BioacAI team is collaborating with specialist European bioacoustics companies to develop a new generation of smarter recording devices. These are able to run recognition algorithms directly\u00a0on the device and\u00a0synchronise\u00a0between multiple units to locate calling animals.<\/p>\n

Alongside improving monitoring methods, the aim is to reduce power demands and lower the environmental footprint of large-scale monitoring deployments.<\/p>\n

But there is a catch. These devices can generate hundreds of gigabytes of data within weeks. Multiply that across a national monitoring scheme and the volume quickly becomes unmanageable.<\/p>\n

Making sense of bat calls<\/strong><\/p>\n

Dr Lia Gilmour, research manager at the UK\u2019s Bat Conservation Trust, a partner in the BioacAI consortium, is all too familiar with this problem.<\/p>\n

\u201cOur projects generate hundreds of terabytes of data a year, which would take 20 to 30 years of human input to get through,\u201d she said.<\/p>\n

Because of their elusive nature and nocturnal activity, bats are difficult to study, making passive acoustic monitoring particularly important. \u201cWe need to record them to be able to understand their population trends and behaviour,\u201d Gilmour said.<\/p>\n

At night, bats use ultrasound \u2013 emitting high-frequency pulses and listening for the echoes\u00a0\u2013\u00a0to navigate and detect prey. Different species often call at different frequencies, but they also adapt their echolocation sounds to their surroundings. This makes distinguishing between closely related species difficult, even for experts.<\/p>\n

Researchers are therefore exploring whether bat social calls might provide a better way to identify species. These chirps, sometimes audible to humans, tend to be more species-specific than hunting or navigation sounds.<\/p>\n

Although bats make these communication calls less frequently, classifying them could help AI systems identify species that currently challenge acoustic monitoring.<\/p>\n

And it could dramatically reduce the data backlog.<\/p>\n

\u201cWithout these classifiers and this system, we would be looking at decades of manual work,\u201d Gilmour said.<\/p>\n

From data overload to discovery<\/strong><\/p>\n

The AI tools being developed through BioacAI learn to recognise the distinctive acoustic signatures of different species, operating at a speed and scale no human could match.<\/p>\n

Apps such as\u00a0Merlin Bird ID, which allows birdwatchers to identify species using a smartphone microphone, have already shown what is possible for common, well-documented birds. But the BioacAI researchers are trying to go further by identifying species for which relatively little data exists.<\/p>\n

To tackle this, the team is using an AI technique known as deep embeddings that places animal sounds within a spatial map so that acoustically similar sounds cluster together. As well as suggesting which known species unfamiliar sounds may resemble, the technique can also flag unusual or previously unclassified sounds for further investigation.<\/p>\n

\u201cWe have masses of uncurated data,\u201d Stowell said. \u201cIf we can identify the sounds or locations where a little bit of investigation is needed, this would be fantastic.\u201d<\/p>\n

This is where human expertise remains essential.<\/p>\n

Soundscapes from around the world could eventually be analysed using networks of acoustic sensors, research teams and citizen scientists. This will give researchers and policymakers a much clearer picture of how ecosystems are changing.<\/p>\n

Such work could reveal previously unidentified species, new habitats for known animals or emerging biodiversity hotspots. It could also support the EU\u2019s Biodiversity Strategy for 2030 by improving how ecosystems and species are monitored across Europe.<\/p>\n

\u201cWe have huge power to really detect the drivers of change in populations, to study these populations at large scale and gather datasets we simply couldn\u2019t access before,\u201d Gilmour said.<\/p>\n

This article was originally published\u202fin\u00a0<\/em>Horizon<\/em><\/a>\u00a0the EU Research and Innovation Magazine.<\/em><\/p>\n

Research in this article was funded by the EU\u2019s Horizon Programme. <\/em><\/p>\n

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