The green woodpecker (Picus viridis) can be heard particularly frequently in the forest in the mornings from mid-March to April. Credit: David Singer
Everyone knows that if you want to enjoy the full experience of the dawn chorus in the forests of Central Europe, or carry out scientific research on bird species, you have to get up very early in the morning. Until now, however, detailed data about daily and seasonal patterns in birdsong has only been available for a few species, as the observations required are time-consuming.
A research team from the University of Göttingen and the Northwest German Forest Research Institute has now, for the first time, analyzed the song and calls of 53 European forest bird species during a breeding season with the help of artificial intelligence. They were able to show that singing behavior recorded with automatic audio recorders differs from previous expert knowledge. The findings were published in the Journal of Ornithology.
The researchers collected data at 256 forest locations in Lower Saxony in Germany. They recorded bird calls with small audio recorders around the clock for 30 seconds every 10 minutes from March to May. They then used AI to identify the bird species based on their calls and songs. The researchers checked the AI’s suggestions to ensure that only reliable identifications of species were included in the analysis.
High-resolution analysis of a total of 6.4 million recorded sounds revealed that forest bird species have individual patterns of activity.
“Our data show that there are far more activity types among forest bird species than just ‘larks’ and ‘owls,'” explains David Singer, lead author of the study and Ph.D. student in Forest Nature Conservation at the University of Göttingen.
In addition to a large group of species who are active during the hours of daylight, some species—such as blackbirds and woodcocks—were active around dawn and twilight and could in fact be heard twice a day.
Blackbirds were also heard significantly more often in the evening than in the morning, a result that had not shown up in previous bird counts. There were also subgroups within the group of species active in the day.
While many species of tit and the black woodpecker were most active in early spring and were heard significantly less from the end of April onward, species such as the dunnock and the wren became active in April. Nocturnal species were in their own group, as were migratory birds, which do not arrive in Central Europe until May.
The researchers then went on to compare their results with previous recommendations for methods to carry out surveys of breeding birds.
They were able to demonstrate that the recommended times to carry out surveys for many species often did not even overlap with their phases of peak song activity. For example, it was previously assumed that the great spotted woodpecker could be easily detected throughout the morning.
However, according to the latest data, this species is significantly less likely to be heard around two hours after sunrise than shortly after sunrise. For other typical forest bird species such as the blue tit and the chiffchaff, song activity remained high beyond the recommended recording period in the morning, meaning that these species can still be reliably counted later in the day.
Small audio recording devices were used to record bird songs in the forest around the clock. Credit: David Singer
“By combining traditional methods of counting birds with the new AI-supported methods of analyzing birdsong, we can significantly improve our knowledge and understanding of bird behavior,” says Göttingen University forest ecologist Professor Andreas Schuldt, who jointly supervised the work.
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“In particular, measurements of species with short peaks of activity are improved by this new method.” For example, gray-headed and lesser spotted woodpeckers, which can only be heard during a relatively short time window, can be recorded particularly well with the new method, whereas ornithologists would be very lucky indeed to observe these species.
Working together with the Dachverband Deutscher Avifaunisten (DDA), the results of the study have already been incorporated into the latest edition of the book “Methodenstandards zur Erfassung der Brutvögel Deutschlands” (Methodological Standards for Recording Breeding Birds in Germany), so that strategies for carrying out research on birds can be better planned in future.
A comparable evaluation of bird species in agricultural landscapes will be possible in the future thanks to ongoing research projects.
The study is part of the biodiversity monitoring program of the Nordwestdeutsche Forstliche Versuchsanstalt together with the University of Göttingen’s Forest Nature Conservation, and Conservation Biology research groups.
More information:
David Singer et al, Diel and seasonal vocal activity patterns revealed by passive acoustic monitoring suggest expert recommendations for breeding bird surveys need adjustment, Journal of Ornithology (2025). DOI: 10.1007/s10336-025-02307-y
Citation:
Researchers use AI to discover when different forest birds sing (2025, July 21)
retrieved 21 July 2025
from https://phys.org/news/2025-07-ai-forest-birds.html
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The Artificial Intelligence Institute for Next Generation Food Systems at the University of California, Davis, was one of the seven original Artificial Intelligence institutes announced in August 2020.
The Artificial Intelligence Institute for Next Generation Food Systems is funded by the U.S. Department of Agriculture’s National Institute for Food and Agriculture.
At the same time, the National Science Foundation leads the overall Artificial Intelligence Institutes program.
Recently, UC Davis announced that the National Science Foundation has awarded the institution $5 million over five years to run the Artificial Intelligence Institutes Virtual Organization, a community hub for AI institutes established by the federal government.
The Artificial Intelligence Institutes Virtual Organization is part of a $100 million public-private investment in AI recently announced by the National Science Foundation.
In December 2024, the Artificial Intelligence Institutes Virtual Organization received $1.75 million from Google.org to support AI education, including AI curriculum for K-16 and workforce training, AI-assisted learning, and summer programs in AI for high school teachers and students.
As of July 29, 2025, the Artificial Intelligence Institutes Virtual Organization was a virtual organization with support from the National Science Foundation, run by staff from the Artificial Intelligence Institute for Next Generation Food Systems (AIFS) at UC Davis. With the new investment, it will become a National Science Foundation-branded community hub.
The Artificial Intelligence Institutes Virtual Organization began as an effort to coordinate activities among the original federal AI institutes, including the Artificial Intelligence Institute for Next Generation Food Systems (AIFS) at the University of California, Davis, and then to share knowledge with new institutes as they were established.
It has expanded into a virtual hub that supports all the institutes, including organizing an annual summit for the leadership of AI institutes.
Under the new contract, the Artificial Intelligence Institutes’ Virtual Organization will provide events and venues that bring the AI Institutes’ personnel and other stakeholders together, creating mechanisms for cross-institute connection.
It will also foster the development of new public-private partnerships and promote a positive interest in university-based AI research, as well as the development and use of AI for the greater good.
Less than a year ago, Illinois state legislator Bob Morgan heard from a group of social workers. They asked him to look into artificial intelligence therapy bots.
Morgan said he heard “story after story of new apps and new examples of AI therapy bots that are really providing bad advice — and sometimes dangerous advice” to individuals dealing with substance abuse, psychosis, suicidal ideation, and other life-or-death issues.
In one particular example from a therapist, Morgan said a chatbot told a person with an addiction to take more drugs “because it felt good in the moment.”
So, the state representative got to work drafting a bill that bans therapists from using AI other than for administrative purposes, like notetaking or scheduling. The law also says chatbots cannot diagnose or treat mental illness — or market themselves as if they do.
“We’re stepping in and saying, if you’re an AI bot pretending to be a therapist, that is inappropriate, and we’re going to shut that down,” Morgan said.
Illinois is not the first state to pass legislation regarding the use of AI in psychotherapy. Utah and Nevada have passed laws this year to rein in the use of chatbots and other tools in mental health treatment.
This “patchwork approach” by the states is likely to continue, according to Vaile Wright, senior director of health care innovation at the American Psychological Association.
Wright said a federal approach is preferred because “then you would have some uniformity and greater specificity across the different states that could have better outcomes.”
But such regulation is unlikely, given efforts this summer by the U.S. House of Representatives to ban states from regulating artificial intelligence for a decade as part of President Donald Trump’s One Big Beautiful Bill Act. The U.S. Senate eventually voted to strike that provision from the bill.
Federal laws aside, Wright said AI therapy bans like Illinois’ don’t address one of the biggest issues plaguing her field: people going to generative AI platforms like ChatGPT and Character AI for mental health support.
“They call themselves companions; they say they’ll help with your loneliness. But when you read the fine print, they very clearly say, ‘We are not a therapeutic aid,’” Wright said.
She added that the business model for these platforms is to keep visitors on by being validating and reinforcing.
“Basically, they’re telling you exactly what you want to hear. This is the antithesis of therapy,” she said.
However, Wright does see a future where mental health chatbots are “rigorously tested, rooted in psychological science, co-created with experts, and they’ll have humans monitoring the interactions,” she said.
Such tools — if federally regulated — couldhelp fill a void in the U.S.’s growing mental health crisis, Wright added.
Until then, licensed psychologists like Michelle Kalnasy Powell say they will watch and wait.
“I am skeptical about AI,” she said, adding that Illinois’ ban is a good starting point, but it may not go far enough.
Kalnasy Powell currently uses AI for billing, but not for taking notes during sessions with patients. Some of her peers use dictation software that complies with patient confidentiality laws.
“Even then, I question when I read the terms of service,” she said. “It’s kind of like sending a session off into the ether. What are you doing with that content? Is it really deleted?”
She added that her work is very personal and vulnerable.
“It is a privilege and an honor to be able to hear people’s stories, both the joy, the happiness and the sorrow,” she said. “[If] that would somehow wind up out there in a way that none of us could predict, but wind up harming the client — I’m just not willing to risk it.”
In the rapidly evolving world of artificial intelligence, Chinese startup DeepSeek is emerging as a formidable player, prioritizing cutting-edge research over immediate commercial gains. Founded in 2023, the company has quickly gained attention for its innovative approaches to large language models, challenging the dominance of Silicon Valley giants. Unlike many U.S.-based firms that chase profitability through aggressive monetization, DeepSeek’s strategy emphasizes foundational advancements in AI architecture, drawing praise from industry observers for its long-term vision.
This focus on research has allowed DeepSeek to develop models that excel in efficiency and performance, particularly in training and inference processes. For instance, their proprietary techniques in sparse activation and optimized