Tadeo Ramirez-Parada studied the timing of plant flowering for his PhD — but he didn’t touch a single petal. Instead, he developed a machine-learning algorithm to analyse the digitized captions of one million herbarium specimens, which showed him how flowering times are changing with rising temperatures.

Ramirez-Parada’s work has helped to solve an important mystery in ecology — showing that as temperatures change, plants shift their flowering times to cope with the heat, rather than adapting through natural selection1. Yet his work so far has been almost entirely computer-based. “I have had to do very little experimental or field work,” says Ramirez-Parada, who did his PhD at the University of California, Santa Barbara.

Ecologists: don’t lose touch with the joy of fieldwork

Ramirez-Parada’s work is typical of a change that is reaching into every part of ecology. Whatever scientists are analysing — digitized specimens, images of the natural world, DNA samples, or data streaming in from sensors — many are doing it indoors.

The technologies are creating a world that can be monitored at times, places and scales that were previously unimaginable. We are moving towards the “fully automated monitoring of ecological communities”, wrote Marc Besson, a marine scientist at the Sorbonne University Ocean Observatory in Banyuls-sur-Mer, France, in a 2022 paper2.

Many ecologists say this revolution offers huge potential for understanding the biodiversity crisis and discerning patterns of global change.

But some ecologists are dismayed. They feel that the discipline is losing intimacy with its subject matter. They argue that field experience is in decline, and that this loss could lead to error, bias and oversimplification of results.

“If it becomes a world where you don’t actually have to go out in order to become an ecologist, we kind of lose sight of what the actual world is like,” says Bill Sutherland, who studies conservation biology at the University of Cambridge, UK.

Always on

Like scientists everywhere, ecologists are grappling with how to make the most of a torrent of data.

Natural-history museums and herbariums around the world have digitized more than one billion specimens over the past few decades, some with accompanying DNA records.

Meanwhile, citizen scientists and researchers alike have been feeding databases such as iNaturalist with hundreds of millions of observations, which are absorbed into the Global Biodiversity Information Facility (GBIF), a central database for natural history.

There is also a stream of data from sensors such as camera traps — which take pictures when activated by movement — microphones, animal-tracking devices, drones, satellites and DNA samplers.

Such sensors can run for years without intervention. Once, a remotely planted camera trap would eventually run out of power: now, the energy consumption of such a device is minimal and it can rely on solar or wind energy. Bandwidth is no longer an obstacle to data being transmitted 24 hours a day.

And computer science is more than keeping up3. Artificial-intelligence systems are already identifying species from these data; they are also being used for more complicated tasks such as building species-distribution models and ancestry trees. Some ecologists predict that generative AI, which creates new content based on learning from huge data sets, will soon be able to make more complex models, leading the way to understanding ecological processes and forecasting how species will respond to environmental changes.

There are already at least 100 laboratories that would label their work as ‘AI for nature’, according to Tanya Berger-Wolf, a computational ecologist at the Ohio State University in Columbus.

The approach is starting to bear fruit. One European project, called CamAlien, is tracking invasive species using high-resolution cameras with machine-learning processing power, affixed to cars, boats and trains. As they speed along, they rapidly photograph the sides of roads and tracks, analyse the images in situ and upload alerts about alien invasive plants to a Europe-wide online map.

The system shows how, just in the past few years, new technologies combined with AI have “gone from mostly demonstrating potential to actually beginning to deliver real implementations”, says Toke Thomas Høye, an ecologist at Aarhus University in Denmark, who co-developed CamAlien. Some 16 European countries are trying out the technology to assess the distribution of invasive alien species.

Similarly, amid the steep declines in some insects, a consortium of researchers has finessed camera-trap technology, originally designed to spot mammals, so that it can identify and monitor insect species, which are much more numerous. Automated insect monitoring didn’t exist five years ago, says Høye. Thanks to developments in AI, scientists can distinguish between thousands of species.

“It’s opening up a door to part of our natural world that is so much more diverse compared to what camera traps have been used for previously,” says Høye. He and his group think that making insect monitoring easier and less labour-intensive will shed light on the state of insect populations around the globe.

Another group has deployed a system of microphones in search of a more detailed understanding of migration as birds fly across Europe from Norway to the Mediterranean coast of Spain. Known as the TABMON project, it is now streaming real-time soundscape data, day and night. An AI tool analyses the data and converts them into commonly used biodiversity indicators.

“Having standardized ecological data on continental scales is extremely rare,” says Sarab Sethi, who studies ecosystem sensing at Imperial College London, and led the design of the microphones, “especially when it’s on the fine-scale temporal resolution that acoustics gives, across a wide range of species, and across multiple years”. The project has yet to report its first results.

Extinction of experience

Few would dispute the benefits of more data and detail, but there is an ominous side effect, says Kevin Gaston at the University of Exeter, UK, who studies people’s relationship with nature: field experience is on the wane.

Gaston and his co-author Masashi Soga, who studies the loss of human–nature interactions at the University of Tokyo, argued in a March 2025 paper4 that there has been an ‘extinction of experience’: a widespread decline in fieldwork-based research and education, with knock-on effects on the depth of ecological understanding. They also flagged other dangers, such as reduced engagement with local communities — a practice known to be crucial for successful conservation.

Others have expressed concern about ‘AI colonialism’, a practice in which data, collected remotely in poorer countries, are siphoned off for analysis in well-equipped labs elsewhere.

In India’s forests, ecologists harness the power of fieldwork

There are few quantitative data available to support or challenge Gaston and Soga’s argument. One analysis5 of ecological studies published between 1980 and 2014 found that fieldwork-based studies decreased by 20% (as a proportion of the total), whereas modelling and data analyses increased by 600% and 800%, respectively. But these are relative changes, rather than absolute numbers, and the data set ends more than a decade ago.

Anecdotally, however, Gaston and Soga’s paper struck a nerve. Since publication, a number of groups have cited it while warning that a lack of outdoor research is hindering studies on subjects ranging from solitary bees to dinosaur fossils.

There’s also anecdotal evidence that more computer scientists have entered ecology, excited about what they can offer, but lacking field experience. That was the case for Berger-Wolf, considered a founder of computational ecology. She completed her PhD in theoretical computer science, but, being married to an ecologist, says she would chat to others in the ecology community and walk away “with a feeling like, oh, there’s got to be a different way of answering this question”.

Berger-Wolf changed tack in 2003, and by 2005 was developing algorithms for dynamic network analysis to depict the social interactions of zebras in the Kenyan Serengeti. Field colleagues urged her to go and see her data but she always refused: “I’m a city girl. And I don’t like dust and bugs. And my answer was: ‘no, my data looks beautiful on my screen.’”

Sethi is another convert to ecology, having arrived in the field with an engineering background. In 2016, he decided to apply acoustic monitoring to ecology for his PhD — but the self-confessed metrophile quickly found himself out of his depth in a rainforest in Malaysian Borneo.

“I did what I now realize was the extremely dumb thing of trying to develop a new technology and for its first deployment to be in a tropical forest on the other side of the world,” Sethi grins. On the first night, he lay under a mosquito net in a pitch-dark hut on stilts, wide awake, while his ecologist colleagues dozed comfortably amid the sounds of the rainforest. He remembers thinking: “My God, is this just like a joke that’s gone a bit too far?” Now he values his field experiences but works mostly from the lab.

Some ecologists have gone the other way, coming in from outdoors to embrace big data. Laura Pollock at McGill University in Montreal, Canada, began her career as a field ecologist, first in the swamps of New Orleans, Louisiana, and then in isolated mountain regions in Australia. She saw a need for ecologists to do better data analysis, and now she uses machine learning to do predictive modelling of biodiversity across landscapes.

“I rarely get outside,” she says. “I’m trying, but it’s really hard because there’s so much technology creating so much data that we need people who have these data-science skills to analyse this.”

But Besson has embraced technology without diminishing his hours in the field. He says that he is spending as much time outside as he did before automation arrived. “Cameras and hydrophones can capture things in addition to my own eyes and ears, and they can stay in the field when I need to go back to the lab ... and when I need to sleep.”

Perfect storm

There are also many systemic forces driving ecologists indoors, argues Gaston.

There’s a widespread perception that funding for field studies is in decline — although the data are not often differentiated into grants for fieldwork versus those for lab-based projects. Scientists who run long-term ecological studies, in particular, report that they struggle to find funding.

Biodiversity research requires more boots on the ground

Other contributing forces include the fact that research institutes are increasingly in urban areas; that more scientists have childcare responsibilities that deter them from doing long or far-flung trips; that many feel the need to reduce their carbon footprint and that others want to avoid ‘helicoptering’ in and out of a country to do fieldwork that local scientists could do.

Another major issue, says Sutherland, is that the fast track to career-boosting publications is to analyse, rather than physically collect, data.

“Supposing you do your PhD and you spend all your time doing fieldwork,” he says. “And the person sitting next to you has been extracting data [from day one]”. After three years, he says, they might have published in increasingly highly ranked journals, while “you’re still in the Amazon catching fish”.