As emerging threats such as warming temperatures, pollution and other cumulative stressors put pressure on freshwater populations and contribute to global biodiversity crises, experts have sought to assess how effective environmental protections are at curbing this decline. 

Now, researchers have revealed that long-term improvements in water quality, as well as the increased occurrence of certain aquatic species, are likely associated with the implementation of broad environmental regulations such as the Clean Air Act and the Clean Water Act.

“Rivers provide a lot of cultural, recreational and ecosystem services, and we also depend on fresh and clean water to survive,” said Casey Pennock, senior author of the study and an assistant professor in the aquatic ecology laboratory at The Ohio State University. “That’s really the motivation behind these conservation policies, to ensure that the natural environment is usable for both wildlife and all other things we care about.”

Aside from negatively impacting local biodiversity, contaminated water can destroy fishing industries and damage human health. It’s thanks to widespread environmental protections that water quality in the U.S. is better today than it was only a few decades ago, said Pennock. His team’s newfound evidence for this claim stems from water quality and aquatic species data collected between 1970 and 2023 across seven major river basins in Ohio. 

By using that data to analyze how fish, insects and freshwater mussel communities changed over time, the team’s findings showed that lower levels of pollutants in rivers — such as zinc, ammonia and lead — corresponded to increases in range for many aquatic species. These findings suggest the affected groups were those with heightened sensitivity to poor water quality.

Their observations indicated that as water quality improved, 71 fish species and 171 insect groups became more common across the state of Ohio, large river basins, with only a few species decreasing. Freshwater mussels, however, experienced mixed responses over time, with nine species increasing and 10 decreasing in occurrence. The composition of fish, aquatic insects and freshwater mussels also changed significantly over time in all seven river basins researchers studied.  

“Ecological communities are not static; they’re dynamic systems,” said Pennock. “Monitoring them is important to assessing how their trajectories change as new contaminants come online.” 

The study was recently published in the journal Ecological Indicators.

Despite how critical freshwater diversity is to the health of all of Earth’s ecosystems, it can be difficult for people to link the issue of conservation to the benefits they reap from it – such as protection from infectious diseases and a safe drinking water supply – in their everyday lives, said Lindsey Bruckerhoff, a co-author of the study and an assistant professor in the aquatic ecology laboratory at Ohio State.

“Our work revolves around trying to prevent further declines of imperiled species and keep diversity on the landscape,” she said. “So extensive research like this that highlights the positive effects of those systems on humans is incredibly rare.”

Additionally, as a lack of broad-scale monitoring efforts has made it difficult for researchers to document species-specific responses to prolonged environmental changes, this study, which compiles observations from numerous archives, is a vital addition to future conservation efforts.  

“We now have a dataset where we can actually analyze long-term biodiversity trends,” said Bruckerhoff. “It’s really exciting to be able to chart success in that way.”

According to the study, implementing nationwide policies also motivates municipalities to upgrade their own conservation initiatives. In response to the Clean Water Act, for example, Columbus instituted a $200 million municipal wastewater upgrade initiative for the Scioto River, leading to significant declines in levels of ammonia and heavy metals that continue to decrease today.

Overall, their results suggest that policies that promote conservation gains for animals like fish and insects and, by extension, protect human health, should remain in place, the researchers say. These outcomes also provide evidence of the benefits derived from environmental regulations. 

“This work shows there’s still more work to do,” said Pennock. “It tells us that if we deregulate or allow more pollution to happen, then those gains could reverse themselves, to our detriment.” 

This study was supported by the U.S. Fish and Wildlife Service and the Ohio Biodiversity Conservation Partnership. Other Ohio State co-authors include Seth Drake and Nathaniel Shoobs, as well as Robert Miltner from the Ohio Environmental Protection Agency.

After these hotspots of accelerated bird decline were revealed, researchers looked for factors that could explain the difference in the rates of decline, examining climate measures and human activity-related data.

A top predictor of where the accelerated abundance loss occurred became clear, overlapping with locations of agriculture intensity as indicated by the extent of cropland and the use of fertilizer and pesticides.

“Agriculture intensity is the main driver associated with accelerated loss of abundance, but we cannot disentangle which of these three metrics is most important because this is a correlative analysis,” said lead author François Leroy, a postdoctoral scholar in evolution, ecology and organismal biology at The Ohio State University.

“But the impact is not only on a few species with the same traits or only on farmland bird species. Twice as many species showed accelerating decline compared to decelerating decline, and the same pattern was seen at the family level. That means it is occurring at a very large taxonomic scale – a lot of different species with different functional traits are affected, and it’s systemic.”

The study is published today (Feb. 26, 2026) in Science.

Data for the primary analysis came from the North American Breeding Bird Survey, an annual multinational monitoring program tracking North American bird populations.

Using 1,033 routes from the survey data, the researchers analyzed abundance change and the rate of that change, both acceleration and deceleration, for 261 bird species, 54 avian families and 10 habitats from 1987 to 2021 – a date range providing the best combination of records covering both space and time.

Overall, the results showed a continent-wide decline in the abundance of all birds, with 122 species – 47% of those tracked – showing significant declines. Among those, accelerated decline was detected in 63 species. There was only one small region, just north of the U.S.-Canadian border, where total bird abundance had increased.

Leroy and colleagues did not focus on extrapolating figures to quantify the number of birds lost, instead estimating a 15% total loss of abundance of birds per route across North America over 35 years.

“We worked at the local scale in order to be as conservative and reliable as possible,” he said. “With this kind of change at the global scale, when you find that there is a 15% loss all across the U.S., that means something statistically. It represents a strong message.

“Basically, the decline is everywhere, and then there is a spatial variation of this acceleration – these three hotspots. And the Midwest hotspot is quite big.”

In an effort to explain the acceleration, the team turned to datasets on a range of factors with potential to affect bird abundance: mean temperature and temperature change, precipitation, land cover (grassland, tree, shrub, water), cropland cover change, fertilizer use, pesticide usage change, vegetation change and the human footprint – a metric of human impacts taking into account population density, infrastructure and energy usage.

“What we found was that agriculture intensity was the main predictor of those hotspots of accelerated decline, and that was the case for the map that was smoothed to detect patterns and even for the raw output of the model,” Leroy said.

This widespread loss of birds – through both deaths and lower birth rates – has consequences. Among birds’ most significant ecosystem services are regulating insect populations and spreading plant seeds in ways that can promote genetic diversity and increase plant resilience. They are also an important element of the food chain on which many large birds and other wildlife rely.  

That human factors are at play in bird loss is not entirely surprising: Leroy first took on the investigation of the accelerated decline in bird abundance based on metrics that show an acceleration of humans’ influence on the global environment since the 1950s.

“We know there is a relationship between human activities and biodiversity because we know that humans, like any other species, have an impact on the environment,” he said.

Even so, Leroy said he has hope for better days ahead for birds.

“Biodiversity is very dynamic,” he said. “If you let life come back, if you leave space and take the right measures, you will see results on biodiversity quickly – let’s say, in a matter of decades. If we act, we will see the impact in our lifetime.”

Co-authors of the paper were Marta Jarzyna, associate professor of evolution, ecology and organismal biology at Ohio State, and Petr Keil of the Czech University of Life Sciences.

This work was supported by the European Union, the U.S. National Science Foundation and the Czech Science Foundation.

The study, recently published in the journal Molecular Phylogenetics and Evolution, suggests that the genetic variation of two species, the Brazilian sibilator frog and the granular toad, both amphibians native to northeastern Brazil, were shaped by different processes.  

Results showed that the genetic variation in the sibilator frog was shaped mostly by population demographic events in response to habitat changes that occurred over the last 100,000 years.   In contrast, genetic diversity in the granular toad was mostly shaped by contemporary landscape factors – toads that are relatively more isolated, either by geographic distance or inhospitable habitat, were more likely to be genetically different.

While previous investigations have explored the effects of historical demographic and landscape factors on genetic diversity of these amphibians, they were conducted with separate sets of data for these factors, making it difficult to discern which was the most important. Now, researchers involved with this paper are the first to use artificial intelligence to consider how both processes shape genetic diversity equally, rather than making manual assumptions about which may have been more vital. 

“Prior to this work, we had to ask questions independently because you couldn't investigate both influences in the same framework,” said Bryan Carstens, co-author of the study and a professor in evolution, ecology and organismal biology at The Ohio State University. “What AI allows us to do is to simulate processes that are both happening ecologically in the present and during deep-time evolutionary events and compare those findings to the actual data that we collect from these frogs.”   

Due to the sheer amount of data that’s become available to geneticists and other wildlife biologists over the past few decades, it can be challenging for researchers to identify specific factors that might be important in certain experiments, said Carstens. But by integrating large swaths of information into simulations that can account for those elements in a single analysis, it’s possible to get a much more complete chronicle of a species’ development. 

“It takes a long time to build and train our AI models, but we wanted one l that could capture the range of potential variation in the species’ histories in a way that was as faithful as we could be to what we knew about the biology of the system,” said Carstens. 

For example, while the species this study investigated dwell in the same region, there are many differences in their natural histories. Despite both their eggs and larvae being fully aquatic, the sibulator frog reproduces continuously throughout the wet season and in underground chambers, while the granular toad’s reproductive events happen explosively because they are dependent on heavy rainfall. 

Combined with their machine learning approach, the researchers’ simulation determined their model scenarios were 100% supported regarding historical explanations for the sibilator frog’s expansion, and over 99% supported for those of the granular toad. 

One of the reasons their model is so accurate is due to its ability to account for recent demographic events, including measuring how events like human development or habitat change may have affected animal genetic diversity over a long period of time. 

But even when using AI, researchers have to be careful to avoid deceptive patterns in their results, said Carstens. 

“No analysis that we do is going to capture every single factor that has been important to these species over millions of years,” he said. “So we have to allow for a range of possibilities without making it so broad that essentially any model would be able to fit the data.”

That said, as technological strides allow researchers to answer niche ecological questions and test new hypotheses, their work is a precursor to creating an upgraded machine learning framework that could be applied to unique investigations of other species, said Carstens. 

“We’re likely to continue using different combinations of these AI tools in different ways to try to understand evolutionary history,” said Carstens. “And as we keep learning, the tools we’re using will change, and they’ll evolve to be even better.”

Emanuel M. Fonseca, who earned his doctorate from Ohio State in 2022, was a co-author. The study was supported by the Ohio Supercomputer Center, the U.S. National Science Foundation and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior in Brazil.

The field of imageomics aims to help explore fundamental questions about biological processes on Earth by combining images of living organisms with computer-enabled analysis and discovery. 

Wei-Lun Chao, an investigator at The Ohio State University’s Imageomics Institute and a distinguished assistant professor of engineering inclusive excellence in computer science and engineering at Ohio State, gave an in-depth presentation about the latest research advances in the field last month at the annual meeting of the American Association for the Advancement of Science. 

Chao and two other presenters described how imageomics could transform society’s understanding of the biological and ecological world by turning research questions into computable problems. Chao’s presentation focused on imageomics’ potential application for micro to macro-level problems.

“Nowadays we have many rapid advances in machine learning and computer vision techniques,” said Chao. “If we use them appropriately, they could really help scientists solve critical but laborious problems.” 

While some research problems might take years or decades to solve manually, imageomics researchers suggest that with the aid of machine and computer vision techniques – such as pattern recognition and multi-modal alignment – the rate and efficiency of next-generation scientific discoveries could be expanded exponentially. 

“If we can incorporate the biological knowledge that people have collected over decades and centuries into machine learning techniques, we can help improve their capabilities in terms of interpretability and scientific discovery,” said Chao. 

One of the ways Chao and his colleagues are working toward this goal is by creating foundation models in imageomics that will leverage data from all kinds of sources to enable various tasks. Another way is to develop machine learning models capable of identifying and even discovering traits to make it easier for computers to recognize and classify objects in images, which is what Chao’s team did. 

“Traditional methods for image classification with trait detection require a huge amount of human annotation, but our method doesn’t,” said Chao. “We were inspired to develop our algorithm through how biologists and ecologists look for traits to differentiate various species of biological organisms.”

Conventional machine learning-based image classifiers have achieved a great level of accuracy by analyzing an image as a whole, and then labeling it a certain object category. However, Chao’s team takes a more proactive approach: Their method teaches the algorithm to actively look for traits like colors and patterns in any image that are specific to an object’s class – such as its animal species – while it’s being analyzed. 

This way, imageomics can offer biologists a much more detailed account of what is and isn’t revealed in the image, paving the way to quicker and more accurate visual analysis. Most excitingly, Chao said, it was shown to be able to handle recognition tasks for very challenging fine-grained species to identify, like butterfly mimicries, whose appearance is characterized by fine detail and variety in their wing patterns and coloring. 

The ease with which the algorithm can be used could potentially also allow imageomics to be integrated into a variety of other diverse purposes, ranging from climate to material science research, he said.

Chao said that one of the most challenging parts of fostering imageomics research is integrating different parts of scientific culture to collect enough data and form novel scientific hypotheses from them. 

It’s one of the reasons why collaboration between different types of scientists and disciplines is such an integral part of the field, he said. Imageomics research will continue to evolve, but for now, Chao is enthusiastic about its potential to allow for the natural world to be seen and understood in brand-new, interdisciplinary ways. 

“What we really want is for AI to have strong integration with scientific knowledge, and I would say imageomics is a great starting point towards that,” he said. 

Chao’s AAAS presentation, titled “An Imageomics Perspective of Machine Learning and Computer Vision: Micro to Global,” was part of the session “Imageomics: Powering Machine Learning for Understanding Biological Traits.”