Human Impacts on the Environment

Humans impact the physical environment in many ways: overpopulation, pollution, burning fossil fuels, and deforestation. Changes like these have triggered climate change, soil erosion, poor air quality, and undrinkable water. These negative impacts can affect human behavior and can prompt mass migrations or battles over clean water.

Help your students understand the impact humans have on the physical environment with these classroom resources.

Earth Science, Geology, Geography, Physical Geography

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What on Earth?

human impact on nature essay

Gorillas are some of the most endangered animals on the planet. They aren't the only species struggling with the effects of humans on their habitat. Image:  Eric Kilby /

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Humans are causing life on Earth to vanish

Ecosystems, the fabric of life on which we all depend, are declining rapidly because of human actions. But there is still time to save them.

Human pressure on nature has soared since the 1970s. We have been using more and more natural resources, and this has come at a cost.

If we lose large portions of the natural world, human quality of life will be severely reduced and the lives of future generations will be threatened unless effective action is taken.

Over the last 50 years, nature's capacity to support us has plummeted. Air and water quality are reducing, soils are depleting, crops are short of pollinators, and coasts are less protected from storms.

Prof Andy Purvis, a Museum research leader,  has spent three years studying human interactions with nature. Alongside experts from more than 50 different countries, he has produced the most comprehensive review ever of the worldwide state of nature, with a summary published in the journal Science .

It was coordinated by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), an independent body that provides policymakers with objective scientific assessments about the state of knowledge regarding the planet’s biodiversity.

The latest report paints a shocking picture. We are changing nature on a global scale and the impacts of our actions are being distributed unequally.

'It was terrifying to see how close we are to playing Russian roulette with the only world we have,' says Andy. 'But it's also been inspiring, because there is a way out of this.

'What has given hope to the many scientists who worked on this report has been the way the public are fully aware of the dangers and want action. We just need to make sure the politicians remember that too.'

A diagram showing the risk of extinction in different groups

A diagram from the report showing the risk of extinction in different groups of species, assuming that species with limited or no data are equally threatened as other species in their taxonomic group.

Nature feeling the squeeze

Since the 1970s, Earth's population has doubled, and consumption has increased by 45% per capita.

The world is increasingly managed in a way that maximises the flow of material from nature, to meet rising human demands for resources like food, energy and timber.

As a result, humans have directly altered at least 70% of Earth's land, mainly for growing plants and keeping animals. These activities necessitate deforestation, the degradation of land, loss of biodiversity and pollution, and they have the biggest impacts on land and freshwater ecosystems.

About 77% of rivers longer than 1,000 kilometres no longer flow freely from source to sea, despite supporting millions of people.

The main cause of ocean change is overfishing, but 66% of the ocean's surface has also been affected by other processes like runoff from agriculture and plastic pollution.

Live coral cover on reefs has nearly halved in the past 150 years and is predicted to disappear completely within the next 80 years. Coral reefs are home to some of the most diverse ecosystems on the planet.  

The number of alien species - species found outside their natural range - has risen, as humans move organisms around the world, which disrupts and often diminishes the richness of local biodiversity. This, combined with human-driven changes in habitat, also threatens many endemic species.

In addition, fewer varieties of plants and animals are being preserved due to standardisations in farming practices, market preferences, large-scale trade and loss of local and indigenous knowledge.

Nature also benefits humans in non-material ways. We learn from it and are inspired by it. It gives us physical and psychological experiences and supports our identity and sense of place. But its capacity to provide these services has also diminished.

What's causing it?

The loss of ecosystems is caused mainly by changes in land and sea use, exploitation, climate change, pollution and the introduction of invasive species.

Some things have a direct impact on nature, like the dumping of waste into the ocean.

Other causes are indirect. Those include demographic, economic, political and institutional arrangements underpinned by social values, and they interact with one another.

For example, vast areas of land managed by Indigenous Peoples are experiencing a decline in ecosystems at a slower rate than everywhere else. But the rights of Indigenous Peoples are being threatened, which could result in faster deterioration of these areas. This would have a detrimental impact on wider ecosystems and societies.

A bleached reef

Coral reefs are bleaching at an unprecedented rate

Trading overseas has increased by 900% since the start of the post-industrial era and the extraction of living materials from nature has risen by 200%.

The growing physical distance between supply and demand means people don't see the destruction caused by their consumption.

'Before the Industrial Revolution, people had to look after the environment around them because that's where they got their products from,' says Andy. 'If they didn't look after it, they would face the consequences.

'Now with globalisation, we have massive environmental impacts a long way from where we live. But we are insulated from these impacts, so they are abstract to us.'

Overseas trading also creates and increases inequality. The pressure for material goods comes mostly from middle and high-income countries and is often met by low to middle-income countries.

For example, Japan, US and Europe alone consumed 64% of the world's imports of fish products. High income countries have their own fisheries but most of these have collapsed. Fishing now takes place in previously unexploited or underexploited fisheries, most of which belong to low-income countries.

'With the massive increase in trade, there is no longer that imperative to make sustainable choices,' says Andy. 'We can overexploit natural resources somewhere else in the world and the magnitudes of our choices are invisible to us.'

What does the future hold?

The report analysed in detail how the world will look under three very different scenarios.

  • Global sustainability: the whole world shifts towards sustainability by respecting environmental boundaries and making sure economic development includes everyone. Wealth is distributed evenly, resources and energy are used less, and emphasis is on economic growth and human wellbeing.
  • Regional competition: there is a rise in nationalism with the focus mostly on domestic issues. There is less investment in education, particularly in the developing world. High-income countries will continue exporting the damage, resulting in some strong and lasting environmental destruction for future generations to deal with.
  • Economic optimism: the world puts faith in new and innovative technologies that are still to be invented, which help us cope with environmental problems. Emissions will continue, but with the idea that technology will mitigate them. There will be stronger investment in health and education, and global markets are reasonably integrated with shared goals.

Combating the loss of ecosystems is going to be complex and will require a nexus approach. This means thinking about how different components of the problem such as nature, politics and socioeconomics all interact with one another.

An example of a nexus approach would be to reduce biodiversity loss by changing how we farm, while at the same time making sure people have enough food, their livelihoods are not undermined, and social conflicts are not aggravated.

The way to avoid some of these issues may be to focus on regenerating and restoring high-carbon ecosystems such as forests and wetlands. Similarly the need for food could be met by changing dietary choices and reducing waste.

Switching to clean energy is an important step which would allow other changes to happen more easily. Obtaining coal and gas involves destroying vast amounts of land and seascapes as well as polluting the environment beyond extraction.

But in order to achieve this fully, the world needs to revaluate current political structures and societal norms, which tend not to value nature. One way of doing that is by improving existing environmental policies and regulations, as well as removing and reforming harmful policies.

'I hope people can see that this is not a drill,' says Andy. 'This really is an emergency and I hope they act on it.'

The Parties to the United Nations Convention on Biological Diversity (CBD) have decided that the IPBES Global Assessment Report will form the scientific and technical evidence base for the intergovernmental negotiations in 2020, to agree on a global biodiversity framework for the next decade and to replace the Aichi Biodiversity Targets that expire next year.

IPBES Chair Anna Maria Hernandez concludes, 'This new article makes it even more clear that we need profound, system-wide change and that this requires urgent action from policymakers, business, communities and every individual.

'Working in tandem with other knowledge systems, such as Indigenous and local knowledge, science has spoken, and nobody can say that they did not know. There is literally no time to waste.'

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human impact on nature essay

How are climate change and biodiversity loss linked?

The climate crisis and biodiversity loss are closely connected but the good news is, so are the solutions.

human impact on nature essay

The world is in trouble: one million animals and plants face extinction

Humanity is eroding its own life-support system.

human impact on nature essay

What is the Anthropocene and why does it matter?

We are living in the age of humans.

human impact on nature essay

Wildlife populations have crashed by 69% within less than a lifetime

We know the problems, but we also know how to fix them. 

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Mind & Body Articles & More

What happens when we reconnect with nature, research is discovering all the different ways that nature benefits our well-being, health, and relationships..

Humans have long intuited that being in nature is good for the mind and body. From indigenous adolescents completing rites of passage in the wild, to modern East Asian cultures taking “forest baths,” many have looked to nature as a place for healing and personal growth.

Why nature? No one knows for sure; but one hypothesis derived from evolutionary biologist E. O. Wilson’s “ biophilia ” theory suggests that there are evolutionary reasons people seek out nature experiences. We may have preferences to be in beautiful, natural spaces because they are resource-rich environments—ones that provide optimal food, shelter, and comfort. These evolutionary needs may explain why children are drawn to natural environments and why we prefer nature to be part of our architecture.

Now, a large body of research is documenting the positive impacts of nature on human flourishing—our social, psychological, and emotional life. Over 100 studies have shown that being in nature, living near nature, or even viewing nature in paintings and videos can have positive impacts on our brains, bodies, feelings, thought processes, and social interactions. In particular, viewing nature seems to be inherently rewarding, producing a cascade of position emotions and calming our nervous systems. These in turn help us to cultivate greater openness, creativity, connection, generosity, and resilience.

human impact on nature essay

In other words, science suggests we may seek out nature not only for our physical survival, but because it’s good for our social and personal well-being.

Waterfall awe

How nature helps us feel good and do good

The naturalist John Muir once wrote about the Sierra Nevada Mountains of California: “We are now in the mountains and they are in us, kindling enthusiasm, making every nerve quiver, filling every pore and cell of us.” Clearly, he found nature’s awe-inspiring imagery a positive, emotive experience.

But what does the science say? Several studies have looked at how viewing awe-inspiring nature imagery in photos and videos impacts emotions and behavior. For example, in one study participants either viewed a few minutes of the inspiring documentary Planet Earth , a neutral video from a news program, or funny footage from Walk on the Wild Side . Watching a few minutes of Planet Earth led people to feel 46 percent more awe and 31 percent more gratitude than those in the other groups. This study and others like it tell us that even brief nature videos are a powerful way to feel awe , wonder, gratitude , and reverence—all positive emotions known to lead to increased well-being and physical health.

Positive emotions have beneficial effects upon social processes, too—like increasing trust, cooperation, and closeness with others. Since viewing nature appears to trigger positive emotions, it follows that nature likely has favorable effects on our social well-being.

This has been robustly confirmed in research on the benefits of living near green spaces. Most notably, the work of Frances Kuo and her colleagues finds that in poorer neighborhoods of Chicago people who live near green spaces—lawns, parks, trees—show reductions in ADHD symptoms and greater calm, as well as a stronger sense of connection to neighbors, more civility, and less violence in their neighborhoods. A later analysis confirmed that green spaces tend to have less crime.

Viewing nature in images and videos seems to shift our sense of self, diminishing the boundaries between self and others, which has implications for social interactions. In one study , participants who spent a minute looking up into a beautiful stand of eucalyptus trees reported feeling less entitled and self-important. Even simply viewing Planet Earth for five minutes led participants to report a greater sense that their concerns were insignificant and that they themselves were part of something larger compared with groups who had watched neutral or funny clips.

Need a dose of nature?

A version of this essay was produced in conjunction with the BBC's newly released Planet Earth II : an awe-inspiring tour of the world from the viewpoint of animals.

Several studies have also found that viewing nature in images or videos leads to greater “prosocial” tendencies—generosity, cooperation, and kindness. One illustrative study found that people who simply viewed 10 slides of really beautiful nature (as opposed to less beautiful nature) gave more money to a stranger in an economic game widely used to measure trust.

All of these findings raise the intriguing possibility that, by increasing positive emotions, experiencing nature even in brief doses leads to more kind and altruistic behavior.

How nature helps our health

Besides boosting happiness, positive emotion, and kindness, exposure to nature may also have physical and mental health benefits.

The benefits of nature on health and well-being have been well-documented in different European and Asian cultures. While Kuo’s evidence suggests a particular benefit for those from nature-deprived communities in the United States, the health and wellness benefits of immersion in nature seem to generalize across all different class and ethnic backgrounds.

Why is nature so healing? One possibility is that having access to nature—either by living near it or viewing it—reduces stress. In a study by Catharine Ward Thompson and her colleagues, the people who lived near larger areas of green space reported less stress and showed greater declines in cortisol levels over the course of the day.

In another study , participants who viewed a one-minute video of awesome nature rather than a video that made them feel happy reported feeling as though they had enough time “to get things done” and did not feel that “their lives were slipping away.” And studies have found that people who report feeling a good deal of awe and wonder and an awareness of the natural beauty around them actually show lower levels of a biomarker (IL-6) that could lead to a decreased likelihood of cardiovascular disease, depression, and autoimmune disease. 

Though the research is less well-documented in this area than in some others, the results to date are promising. One early study by Roger Ulrich found that patients recovered faster from cardiovascular surgery when they had a view of nature out of a window, for example.

A more recent review of studies looking at different kinds of nature immersion—natural landscapes during a walk, views from a window, pictures and videos, and flora and fauna around residential or work environments—showed that nature experiences led to reduced stress, easier recovery from illness, better physical well-being in elderly people, and behavioral changes that improve mood and general well-being.

Why we need nature

All of these findings converge on one conclusion: Being close to nature or viewing nature improves our well-being. The question still remains…how?

There is no question that being in nature—or even viewing nature pictures—reduces the physiological symptoms of stress in our bodies. What this means is that we are less likely to be anxious and fearful in nature, and thereby we can be more open to other people and to creative patterns of thought.

Also, nature often induces awe, wonder, and reverence, all emotions known to have a variety of benefits, promoting everything from well-being and altruism to humility to health.

There is also some evidence that exposure to nature impacts the brain. Viewing natural beauty (in the form of landscape paintings and video, at least) activates specific reward circuits in the brain associated with dopamine release that give us a sense of purpose, joy, and energy to pursue our goals.

But, regrettably, people seem to be spending less time outdoors and less time immersed in nature than before. It is also clear that, in the past 30 years, people’s levels of stress and sense of “busyness” have risen dramatically. These converging forces have led environmental writer Richard Louv to coin the term “ nature deficit disorder ”—a form of suffering that comes from a sense of disconnection from nature and its powers.

Perhaps we should take note and try a course corrective. The 19th century philosopher Ralph Waldo Emerson once wrote about nature, “There I feel that nothing can befall me in life—no disgrace, no calamity (leaving me my eyes), which nature cannot repair.” The science speaks to Emerson’s intuition. It’s time to realize nature is more than just a material resource. It’s also a pathway to human health and happiness.

About the Authors

Kristophe Green

Kristophe Green

Uc berkeley.

Kristophe Green is a senior Psychology major at UC Berkeley. He is fascinated with the study of positive emotions and how they inform pro-social behavior such as empathy, altruism and compassion.

Dacher Keltner

Dacher Keltner

Dacher Keltner, Ph.D. , is the founding director of the Greater Good Science Center and a professor of psychology at the University of California, Berkeley. He is the author of The Power Paradox: How We Gain and Lose Influence and Born to Be Good , and a co-editor of The Compassionate Instinct .

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Middle school Earth and space science - NGSS

Course: middle school earth and space science - ngss   >   unit 5, human impacts on the environment.

  • Apply: human impacts on the environment
  • Humans impact the environment through their activities. Examples of human activities include land and water use, deforestation, and the burning of fossil fuels.
  • In many cases, the impacts of human activities are negative. For example, when humans clear forests, it causes habitat loss and puts other species at risk.
  • Negative human impacts increase as the population grows. They also increase as the average person uses more natural resources.
  • Science can help identify solutions to reduce our impacts on the environment. However, it is up to us—as individuals and as a society—to put these solutions into action.

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Climate change impacts on nature

The impacts of climate change are already being felt around the world - from more frequent and severe storms, floods, droughts, and wildfires - threatening our cities, communities, crops, water, and wildlife. Climate change poses a fundamental threat to nature, species, and people – but it’s not too late to take collective action.

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About climate change and nature

Climate change has already altered marine , terrestrial and freshwater ecosystems all around the world, causing species losses and declines in key ecosystem services. These climate-driven impacts on ecosystems have caused measurable economic and livelihood losses around the world.

The Paris Climate Agreement is a commitment of the international community to keep global warming well below 2°C and to pursue efforts to limit warming to 1.5°C. However, the latest IPCC report shows greenhouse gas emissions continue to rise. Current plans to address climate change are not ambitious enough to limit warming to 1.5°C above pre-industrial levels—a threshold scientists believe is necessary to avoid even more catastrophic impacts. Above the 1.5 °C limit, the risks of extreme weather and collapsing ecosystems grow.

The actions of the international community between now and 2030 will determine whether we can collectively slow warming enough to avoid the worst impacts of climate change.

Heading 37%

of the mitigation needed between now and 2030 to meet the 2°C Paris goal can be provided by nature-based solutions.

Heading > 80%

of ecological processes that form the foundation for life on Earth are impacted by climate change.

How is IUCN limiting climate change impacts on nature?

Supporting a just and equitable transition.

IUCN works to accelerate a just and equitable transition to clean energy and a low carbon future , for the protection of people and the planet, especially including:

  • promoting a global transition to clean and renewable energy sources ,
  • engaging a suite of climate mitigation actions such as eliminating the use of coal for energy and reducing fossil fuel consumption,
  • transforming agriculture and food systems,
  • and halting deforestation .

Promoting Nature-based Solutions

In addition to cutting emissions, IUCN strongly advocates for a worldwide use of Nature-based Solutions , such as restoring ecosystems to absorb and sequester carbon already emitted or implementing  ecosystem-based adaptation to increase the resilience of ecosystems.

The latest IPCC report demonstrated that reducing the destruction of forests and other ecosystems, restoring them, and improving the management of working lands, such as farms — are among the top five most effective strategies for mitigating carbon emissions by 2030 .

IUCN engages on this issue from multiple perspectives, from assessing the risks that climate change poses to biodiversity, to advancing practical nature-based solutions for both climate mitigation and adaptation, centred on the better conservation, management and restoration of the world’s ecosystems, including:

  • Enhance nature’s ability to store carbon across forests, drylands, and oceans by deploying Nature-based Solutions for climate mitigation, such as reducing emissions from deforestation and forest degradation (REDD+) and promoting forest landscape restoration and blue carbon initiatives.
  • Catalyse the uptake of renewable energy best practices and supporting new, low-carbon technologies.
  • Secure community resilience through Nature-based Solutions to adaptation, such as restoring mangroves and wetlands which reduce the impact of storms and floods, as well as hybrid solutions, such as green-grey infrastructure and integrated adaptation technologies .
  • Support best practices in climate investments to minimise risks of maladaptation and ancillary negative impacts on people and biodiversity, such as developing best practices for the use of Nature-based Solutions as carbon offsets.
  • Mobilize enhanced finance through multiple revenue streams to enable the implementation of Nature-based Solutions for climate change, such as through the Global EbA Fund , the Blue Natural Capital Financing Facility , the Subnational Climate Finance initiative, and the Nature+ Accelerator Fund.

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Laura Mallonee

Photo Essay: The Uneasy Relationship Between Humans and Nature

Image may contain Vegetation Plant Tree Woodland Forest Outdoors Nature Land Human Person Jungle and Grove

There’s no place on earth untouched by human activity: This was clear as Lucas Foglia whizzed across the vast, white expanse of Alaska's Juneau Ice Field last summer. He was riding an old pair of skis towed by scientist Uwe Hofmann, who periodically stopped his snowmobile to measure the rapidly melting glacier.

“It was an unforgettable experience,” says Foglia , a photographer featured in WIRED’s December issue . "Being in a place that big and wild made me feel small in a way I had never felt before, yet I knew that humans as a whole were changing that landscape.”

Foglia explores this tension in his stunning new book Human Nature . It features nearly 60 photographs that illustrate the varying ways nature impacts humans and humans impact nature—for better or worse. "It focuses on our relationship with nature, how we need wild places even if they have been shaped by us," Foglia says. "I think of each photo in the book as the tip of the iceberg that hopefully points viewers to the larger story underneath the surface of the image."

Foglia grew up on a farm in rural Long Island. Watching the surrounding fields slowly being swallowed up by housing tracts inspired his work documenting the natural environment—a focus that grew in intensity after Hurricane Sandy slammed into the eastern seaboard in 2012. “Climate change is on the news every day these days, but I realized I didn’t know what the science looked like.” he says. “I felt like photography could clearly describe the process of the science.”

Over the next five years, Foglia trailed scientists in five countries with his medium format digital camera as they took samples of air pollution, studied geysers, and launched ozone balloons into the atmosphere. He also examined governmental efforts to mitigate the effects of climate change. The Singapore Green Plan, for instance, requires developers to include green spaces in new buildings, while the Agricultural Experiment Station in New York helps farmers develop crops that can withstand changing weather patterns (more on that here ).

These programs matter not only because people need nature to survive. They also matter because people need nature to thrive. Foglia learned this while documenting the research of David Strayer, a University of Utah neuroscientist who hooks participants up to EEG caps and facial electrodes as they spend time in rugged landscapes. His research shows that unplugging in nature actually increases cognitive function, helping people better solve creative problems. "He said that, in his opinion, time in wild places is part of human nature," Foglia says.

Strayer's idea reverberates throughout Human Nature . It explains the feeling of wonder and freedom Foglia felt while gliding across a remote Alaskan ice field—and further underscores the need to preserve places like it.

Human Nature is out this month from Nazraeli Press .

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Scientific Consensus

human impact on nature essay

It’s important to remember that scientists always focus on the evidence, not on opinions. Scientific evidence continues to show that human activities ( primarily the human burning of fossil fuels ) have warmed Earth’s surface and its ocean basins, which in turn have continued to impact Earth’s climate . This is based on over a century of scientific evidence forming the structural backbone of today's civilization.

NASA Global Climate Change presents the state of scientific knowledge about climate change while highlighting the role NASA plays in better understanding our home planet. This effort includes citing multiple peer-reviewed studies from research groups across the world, 1 illustrating the accuracy and consensus of research results (in this case, the scientific consensus on climate change) consistent with NASA’s scientific research portfolio.

With that said, multiple studies published in peer-reviewed scientific journals 1 show that climate-warming trends over the past century are extremely likely due to human activities. In addition, most of the leading scientific organizations worldwide have issued public statements endorsing this position. The following is a partial list of these organizations, along with links to their published statements and a selection of related resources.

American Scientific Societies

Statement on climate change from 18 scientific associations.

"Observations throughout the world make it clear that climate change is occurring, and rigorous scientific research demonstrates that the greenhouse gases emitted by human activities are the primary driver." (2009) 2

American Association for the Advancement of Science

"Based on well-established evidence, about 97% of climate scientists have concluded that human-caused climate change is happening." (2014) 3

AAAS emblem

American Chemical Society

"The Earth’s climate is changing in response to increasing concentrations of greenhouse gases (GHGs) and particulate matter in the atmosphere, largely as the result of human activities." (2016-2019) 4

ACS emblem

American Geophysical Union

"Based on extensive scientific evidence, it is extremely likely that human activities, especially emissions of greenhouse gases, are the dominant cause of the observed warming since the mid-20th century. There is no alterative explanation supported by convincing evidence." (2019) 5

AGU emblem

American Medical Association

"Our AMA ... supports the findings of the Intergovernmental Panel on Climate Change’s fourth assessment report and concurs with the scientific consensus that the Earth is undergoing adverse global climate change and that anthropogenic contributions are significant." (2019) 6

AMA emblem

American Meteorological Society

"Research has found a human influence on the climate of the past several decades ... The IPCC (2013), USGCRP (2017), and USGCRP (2018) indicate that it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-twentieth century." (2019) 7

AMS emblem

American Physical Society

"Earth's changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe. While natural sources of climate variability are significant, multiple lines of evidence indicate that human influences have had an increasingly dominant effect on global climate warming observed since the mid-twentieth century." (2015) 8

APS emblem

The Geological Society of America

"The Geological Society of America (GSA) concurs with assessments by the National Academies of Science (2005), the National Research Council (2011), the Intergovernmental Panel on Climate Change (IPCC, 2013) and the U.S. Global Change Research Program (Melillo et al., 2014) that global climate has warmed in response to increasing concentrations of carbon dioxide (CO2) and other greenhouse gases ... Human activities (mainly greenhouse-gas emissions) are the dominant cause of the rapid warming since the middle 1900s (IPCC, 2013)." (2015) 9

GSA emblem

Science Academies

International academies: joint statement.

"Climate change is real. There will always be uncertainty in understanding a system as complex as the world’s climate. However there is now strong evidence that significant global warming is occurring. The evidence comes from direct measurements of rising surface air temperatures and subsurface ocean temperatures and from phenomena such as increases in average global sea levels, retreating glaciers, and changes to many physical and biological systems. It is likely that most of the warming in recent decades can be attributed to human activities (IPCC 2001)." (2005, 11 international science academies) 1 0

U.S. National Academy of Sciences

"Scientists have known for some time, from multiple lines of evidence, that humans are changing Earth’s climate, primarily through greenhouse gas emissions." 1 1

UNSAS emblem

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U.s. global change research program.

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1. K. Myers, et al, "Consensus revisited: quantifying scientific agreement on climate change and climate expertise among Earth scientists 10 years later", Environmental Research Letters Vol.16 No. 10, 104030 (20 October 2021); DOI:10.1088/1748-9326/ac2774 M. Lynas, et al, "Greater than 99% consensus on human caused climate change in the peer-reviewed scientific literature", Environmental Research Letters Vol.16 No. 11, 114005 (19 October 2021); DOI:10.1088/1748-9326/ac2966 J. Cook et al., "Consensus on consensus: a synthesis of consensus estimates on human-caused global warming", Environmental Research Letters Vol. 11 No. 4, (13 April 2016); DOI:10.1088/1748-9326/11/4/048002 J. Cook et al., "Quantifying the consensus on anthropogenic global warming in the scientific literature", Environmental Research Letters Vol. 8 No. 2, (15 May 2013); DOI:10.1088/1748-9326/8/2/024024 W. R. L. Anderegg, “Expert Credibility in Climate Change”, Proceedings of the National Academy of Sciences Vol. 107 No. 27, 12107-12109 (21 June 2010); DOI: 10.1073/pnas.1003187107 P. T. Doran & M. K. Zimmerman, "Examining the Scientific Consensus on Climate Change", Eos Transactions American Geophysical Union Vol. 90 Issue 3 (2009), 22; DOI: 10.1029/2009EO030002 N. Oreskes, “Beyond the Ivory Tower: The Scientific Consensus on Climate Change”, Science Vol. 306 no. 5702, p. 1686 (3 December 2004); DOI: 10.1126/science.1103618

2. Statement on climate change from 18 scientific associations (2009)

3. AAAS Board Statement on Climate Change (2014)

4. ACS Public Policy Statement: Climate Change (2016-2019)

5. Society Must Address the Growing Climate Crisis Now (2019)

6. Global Climate Change and Human Health (2019)

7. Climate Change: An Information Statement of the American Meteorological Society (2019)

8. American Physical Society (2021)

9. GSA Position Statement on Climate Change (2015)

10. Joint science academies' statement: Global response to climate change (2005)

11. Climate at the National Academies

12. Fourth National Climate Assessment: Volume II (2018)

13. IPCC Fifth Assessment Report, Summary for Policymakers, SPM 1.1 (2014)

14. IPCC Fifth Assessment Report, Summary for Policymakers, SPM 1 (2014)

15. IPCC Sixth Assessment Report, Working Group 1 (2021)

16. IPCC Sixth Assessment Report, Working Group 2 (2022)

17. IPCC Sixth Assessment Report, Working Group 3 (2022)

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  • Published: 18 March 2024

Mammal responses to global changes in human activity vary by trophic group and landscape

  • A. Cole Burton   ORCID: 1 , 2   na1 ,
  • Christopher Beirne 1   na1 ,
  • Kaitlyn M. Gaynor 2 , 3 , 4 ,
  • Catherine Sun 1 ,
  • Alys Granados 1 ,
  • Maximilian L. Allen   ORCID: 5 ,
  • Jesse M. Alston   ORCID: 6 ,
  • Guilherme C. Alvarenga   ORCID: 7 ,
  • Francisco Samuel Álvarez Calderón   ORCID: 8 ,
  • Zachary Amir   ORCID: 9 ,
  • Christine Anhalt-Depies   ORCID: 10 ,
  • Cara Appel   ORCID: 11 ,
  • Stephanny Arroyo-Arce   ORCID: 12 ,
  • Guy Balme 13 ,
  • Avi Bar-Massada   ORCID: 14 ,
  • Daniele Barcelos   ORCID: 7 ,
  • Evan Barr 15 ,
  • Erika L. Barthelmess   ORCID: 16 ,
  • Carolina Baruzzi   ORCID: 17 ,
  • Sayantani M. Basak   ORCID: 18 ,
  • Natalie Beenaerts   ORCID: 19 ,
  • Jonathan Belmaker   ORCID: 20 ,
  • Olgirda Belova 21 ,
  • Branko Bezarević   ORCID: 22 ,
  • Tori Bird   ORCID: 23 ,
  • Daniel A. Bogan 24 ,
  • Neda Bogdanović   ORCID: 25 ,
  • Andy Boyce   ORCID: 26 ,
  • Mark Boyce   ORCID: 27 ,
  • LaRoy Brandt   ORCID: 28 ,
  • Jedediah F. Brodie   ORCID: 29 , 30 ,
  • Jarred Brooke 31 ,
  • Jakub W. Bubnicki 32 ,
  • Francesca Cagnacci   ORCID: 33 , 34 ,
  • Benjamin Scott Carr   ORCID: 35 ,
  • João Carvalho   ORCID: 36 ,
  • Jim Casaer 37 ,
  • Rok Černe 38 ,
  • Ron Chen   ORCID: 39 ,
  • Emily Chow 40 ,
  • Marcin Churski   ORCID: 32 ,
  • Connor Cincotta 41 ,
  • Duško Ćirović 25 ,
  • T. D. Coates 42 ,
  • Justin Compton   ORCID: 43 ,
  • Courtney Coon 44 ,
  • Michael V. Cove   ORCID: 45 ,
  • Anthony P. Crupi   ORCID: 46 ,
  • Simone Dal Farra 33 ,
  • Andrea K. Darracq 15 ,
  • Miranda Davis   ORCID: 47 ,
  • Kimberly Dawe 48 ,
  • Valerie De Waele 49 ,
  • Esther Descalzo 50 ,
  • Tom A. Diserens 32 , 51 ,
  • Jakub Drimaj   ORCID: 52 ,
  • Martin Duľa   ORCID: 52 , 53 ,
  • Susan Ellis-Felege 54 ,
  • Caroline Ellison   ORCID: 55 ,
  • Alper Ertürk   ORCID: 56 ,
  • Jean Fantle-Lepczyk 57 ,
  • Jorie Favreau 41 ,
  • Mitch Fennell   ORCID: 1 ,
  • Pablo Ferreras   ORCID: 50 ,
  • Francesco Ferretti 34 , 58 ,
  • Christian Fiderer 59 , 60 ,
  • Laura Finnegan   ORCID: 61 ,
  • Jason T. Fisher   ORCID: 62 ,
  • M. Caitlin Fisher-Reid   ORCID: 63 ,
  • Elizabeth A. Flaherty   ORCID: 31 ,
  • Urša Fležar 38 , 64 ,
  • Jiří Flousek 65 ,
  • Jennifer M. Foca   ORCID: 27 ,
  • Adam Ford 66 ,
  • Barbara Franzetti   ORCID: 67 ,
  • Sandra Frey 62 ,
  • Sarah Fritts 68 ,
  • Šárka Frýbová 69 ,
  • Brett Furnas 70 ,
  • Brian Gerber   ORCID: 71 ,
  • Hayley M. Geyle   ORCID: 72 ,
  • Diego G. Giménez 73 ,
  • Anthony J. Giordano   ORCID: 73 ,
  • Tomislav Gomercic   ORCID: 74 ,
  • Matthew E. Gompper   ORCID: 75 ,
  • Diogo Maia Gräbin 7 ,
  • Morgan Gray   ORCID: 76 ,
  • Austin Green 77 ,
  • Robert Hagen 78 , 79 ,
  • Robert (Bob) Hagen 80 ,
  • Steven Hammerich 76 ,
  • Catharine Hanekom   ORCID: 81 ,
  • Christopher Hansen 82 ,
  • Steven Hasstedt   ORCID: 83 ,
  • Mark Hebblewhite   ORCID: 29 ,
  • Marco Heurich   ORCID: 59 , 60 , 84 ,
  • Tim R. Hofmeester   ORCID: 85 ,
  • Tru Hubbard 86 ,
  • David Jachowski 87 ,
  • Patrick A. Jansen   ORCID: 88 , 89 ,
  • Kodi Jo Jaspers 90 ,
  • Alex Jensen   ORCID: 87 ,
  • Mark Jordan   ORCID: 91 ,
  • Mariane C. Kaizer   ORCID: 92 ,
  • Marcella J. Kelly   ORCID: 93 ,
  • Michel T. Kohl 35 ,
  • Stephanie Kramer-Schadt   ORCID: 79 , 94 ,
  • Miha Krofel   ORCID: 64 ,
  • Andrea Krug 95 ,
  • Kellie M. Kuhn   ORCID: 83 ,
  • Dries P. J. Kuijper 32 ,
  • Erin K. Kuprewicz   ORCID: 47 ,
  • Josip Kusak 74 ,
  • Miroslav Kutal   ORCID: 52 , 53 ,
  • Diana J. R. Lafferty   ORCID: 86 ,
  • Summer LaRose 96 ,
  • Marcus Lashley 97 ,
  • Richard Lathrop 98 ,
  • Thomas E. Lee Jr   ORCID: 99 ,
  • Christopher Lepczyk   ORCID: 57 ,
  • Damon B. Lesmeister 100 ,
  • Alain Licoppe   ORCID: 49 ,
  • Marco Linnell 100 ,
  • Jan Loch 101 ,
  • Robert Long 90 ,
  • Robert C. Lonsinger   ORCID: 102 ,
  • Julie Louvrier   ORCID: 79 ,
  • Matthew Scott Luskin   ORCID: 9 ,
  • Paula MacKay 90 ,
  • Sean Maher   ORCID: 103 ,
  • Benoît Manet   ORCID: 49 ,
  • Gareth K. H. Mann 13 ,
  • Andrew J. Marshall   ORCID: 104 ,
  • David Mason   ORCID: 97 ,
  • Zara McDonald 44 ,
  • Tracy McKay 61 ,
  • William J. McShea 26 ,
  • Matt Mechler 105 ,
  • Claude Miaud   ORCID: 106 ,
  • Joshua J. Millspaugh 82 ,
  • Claudio M. Monteza-Moreno 107 ,
  • Dario Moreira-Arce   ORCID: 108 ,
  • Kayleigh Mullen 23 ,
  • Christopher Nagy 109 ,
  • Robin Naidoo   ORCID: 110 ,
  • Itai Namir 20 ,
  • Carrie Nelson 111 ,
  • Brian O’Neill 112 ,
  • M. Teague O’Mara   ORCID: 113 ,
  • Valentina Oberosler   ORCID: 114 ,
  • Christian Osorio   ORCID: 115 ,
  • Federico Ossi   ORCID: 33 , 34 ,
  • Pablo Palencia   ORCID: 116 , 117 ,
  • Kimberly Pearson 118 ,
  • Luca Pedrotti 119 ,
  • Charles E. Pekins 120 ,
  • Mary Pendergast 121 ,
  • Fernando F. Pinho 7 ,
  • Radim Plhal 52 ,
  • Xochilt Pocasangre-Orellana 8 ,
  • Melissa Price 122 ,
  • Michael Procko   ORCID: 1 ,
  • Mike D. Proctor   ORCID: 123 ,
  • Emiliano Esterci Ramalho   ORCID: 7 ,
  • Nathan Ranc 33 , 124 ,
  • Slaven Reljic   ORCID: 74 ,
  • Katie Remine 90 ,
  • Michael Rentz 125 ,
  • Ronald Revord 96 ,
  • Rafael Reyna-Hurtado 126 ,
  • Derek Risch   ORCID: 122 ,
  • Euan G. Ritchie 127 ,
  • Andrea Romero   ORCID: 112 ,
  • Christopher Rota   ORCID: 128 ,
  • Francesco Rovero 114 , 129 ,
  • Helen Rowe   ORCID: 130 , 131 ,
  • Christian Rutz   ORCID: 132 ,
  • Marco Salvatori   ORCID: 114 , 129 ,
  • Derek Sandow 133 ,
  • Christopher M. Schalk   ORCID: 134 ,
  • Jenna Scherger 66 ,
  • Jan Schipper   ORCID: 135 ,
  • Daniel G. Scognamillo 136 ,
  • Çağan H. Şekercioğlu 77 , 137 ,
  • Paola Semenzato 138 ,
  • Jennifer Sevin 139 ,
  • Hila Shamon 26 ,
  • Catherine Shier   ORCID: 140 ,
  • Eduardo A. Silva-Rodríguez   ORCID: 141 ,
  • Magda Sindicic 74 ,
  • Lucy K. Smyth 13 , 142 ,
  • Anil Soyumert   ORCID: 56 ,
  • Tiffany Sprague 130 ,
  • Colleen Cassady St. Clair   ORCID: 27 ,
  • Jennifer Stenglein   ORCID: 10 ,
  • Philip A. Stephens   ORCID: 143 ,
  • Kinga Magdalena Stępniak   ORCID: 144 ,
  • Michael Stevens 145 ,
  • Cassondra Stevenson 27 ,
  • Bálint Ternyik   ORCID: 143 , 146 ,
  • Ian Thomson 12 ,
  • Rita T. Torres   ORCID: 36 ,
  • Joan Tremblay 47 ,
  • Tomas Urrutia 115 ,
  • Jean-Pierre Vacher 106 ,
  • Darcy Visscher   ORCID: 147 ,
  • Stephen L. Webb   ORCID: 148 ,
  • Julian Weber 149 ,
  • Katherine C. B. Weiss   ORCID: 135 ,
  • Laura S. Whipple 150 ,
  • Christopher A. Whittier   ORCID: 151 ,
  • Jesse Whittington   ORCID: 152 ,
  • Izabela Wierzbowska   ORCID: 18 ,
  • Martin Wikelski   ORCID: 107 , 153 ,
  • Jacque Williamson   ORCID: 154 ,
  • Christopher C. Wilmers 155 ,
  • Todd Windle 156 ,
  • Heiko U. Wittmer   ORCID: 157 ,
  • Yuri Zharikov 158 ,
  • Adam Zorn 159 &
  • Roland Kays   ORCID: 45 , 160  

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  • Behavioural ecology
  • Conservation biology
  • Population dynamics

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.

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With the global human population size now past 8 billion and the associated human footprint covering much of the Earth’s surface 1 , survival of wild animals in the Anthropocene requires that they adapt to physical changes to the landscape and to increasing human presence. Animals often perceive humans as threats and subsequently adjust behaviours to avoid people in space or time 2 . Conversely, some animals are attracted to people to obtain resource subsidies or protection from predators 3 , 4 . These contrasting responses to humans shape the prospects for human–wildlife coexistence, with consequences for the capacity of human-influenced ecosystems to support robust animal populations and communities.

Variation in animal responses to human activity can be driven by intrinsic factors such as species’ ecological and life-history traits (Table 1 ) 5 . For instance, small-bodied generalist species may be more tolerant of human presence, as they can be less conspicuous than larger species and more capable of shifting resource use within their broader niches than are specialists 6 . Wide-ranging, large-bodied carnivores face considerable risk of mortality from humans 7 and so may exhibit more risk-averse responses to human activity. Animal responses may also be heavily influenced by the type of human activity (for example, hunting versus hiking 8 ) and by extrinsic factors such as landscape context. Animals may be warier of people in open or human-modified environments relative to areas with abundant vegetation cover or minimal human landscape modification 9 . Conversely, animals in heavily modified landscapes could habituate to human presence and thus be less likely to respond to changes in human activity. Our ability to resolve such hypotheses about the interacting influences of species traits and landscape characteristics has been limited by the focus of previous studies on few species and contexts, with indirect measures of human activity and weaker correlative inferences. Ultimately, anticipating and managing impacts to wild animals requires stronger inferences from experimental manipulations of human activity and concurrent monitoring of people and animals across a range of species and environmental contexts.

Government policies during the early months of the COVID-19 pandemic (henceforth, pandemic) resulted in widespread changes to human activity that provided a quasi-experimental opportunity to study short-term behavioural responses of wild animals 10 . Early observations of animal responses to this ‘anthropause’ 11 relied on qualitative or opportunistic sightings prone to bias (for example, contributed by volunteers 12 ), or focused on small spatial scales and few species, reporting a mix of positive and negative responses that make it difficult to reach more general conclusions 13 . Furthermore, measures of human activity have typically been coarse and indirect 14 , yet changes to human activity during the pandemic appeared highly variable at the fine scales that affect animal behaviour (Fig. 1 ). For example, some natural areas experienced increases in human visitation while others were closed to visitors 15 and the strength of government restrictions changed over time 14 . It is thus important for studies using the pandemic as an unplanned experiment to have localized information on human activity that matches their animal data and to tackle context-dependency by using robust, standardized methods across several species and landscapes.

figure 1

a , Location of camera-trap projects included in the analysis ( n  = 102). b , c , Examples for two projects: Edmonton, Canada ( b ) and Danum Valley, Malaysia ( c ) showing time series of human detections for the two types of comparisons used to assess the effects of higher human activity on animals. b , A between-year comparison with increased human activity during the COVID-19 pandemic (treatment, red shading) relative to the same time period the year before (control, blue shading). c , A within-year comparison with decreased human activity during the pandemic (control, blue shading) relative to the prepandemic period (treatment, red shading).

The widespread use of camera traps to survey terrestrial mammals 16 provides a unique opportunity to take advantage of the pandemic experiment and improve our understanding of animal responses to changes in human activity. Thousands of cameras are deployed around the world 17 , providing standardized animal sampling while simultaneously quantifying local human activity 15 , 18 . We harnessed this opportunity to examine relationships between detections of people and mammals across gradients in land use and habitat type—spanning 102 survey sites (projects) in 21 countries (predominantly in Europe and North America) with 5,400 camera-trap locations sampling for 311,208 camera-days before and during the pandemic (Fig. 1 ; Methods ). Some sites experienced a decrease in human activity during the pandemic, consistent with the notion of an anthropause, while there was an increase or no change at others. We focused our analysis on those sites with some change in human activity (either increase or decrease) and standardized our comparisons to be between periods of relatively lower to higher human activity (either across years or within 2020; Fig. 1 ; Methods ) to mimic the general trend of increasing human presence in the Anthropocene. We examined site-level changes in animal detection rates and nocturnality across populations of 163 mammal species (body mass ≥ 1 kg; range 1–65 populations per species; Supplementary Table 1 ) as measures of the relative amount and timing of animal activity ( Methods ). We then used meta-analytic mixed-effects models to quantify the extent to which variation in animal responses across sites was explained by species traits, landscape modification and other site characteristics and the magnitude of change in human activity (Table 1 ; Methods ).

Results and discussion

Our camera-trap measures of human activity varied widely under COVID-19 lockdowns (occurring between March 2020 and January 2021), from 100-fold decreases to 10-fold increases within sites between comparison periods (Fig. 1 and Supplementary Fig. 1 ). These changes were not predicted by coarser measures of human activity based on the stringency of lockdowns (Supplementary Fig. 1 ), highlighting the complementary value of finer-scaled monitoring of human activity.

Changes in amount of animal activity

Animals did not show consistent, negative responses to greater human activity; instead, responses were highly variable among species and sites (Figs. 2 and 3 ). Across 1,065 estimated responses (one per species per project, that is, population), changes in animal detection rates (reflecting the intensity of habitat use; Methods ) varied from 139-fold increases to 36-fold decreases, with a near-zero mean change overall (−0.04, 95% confidence interval (CI) = −0.11–0.03; Fig. 2b ). Trophic group (combining body mass and trophic level) was the strongest predictor of changes in animal activity in response to increasing human use, with large herbivores showing the largest increases in activity and carnivores showing the strongest decreases (Fig. 2c , Supplementary Table 2 and Supplementary Fig. 3 ). This is consistent with carnivore avoidance of higher mortality risk from encounters with people 7 and with increased herbivore activity due to either more frequent disturbance by people or attraction to human activity driven by reduced risk of predation (human shield hypothesis 3 ).

figure 2

a , Interpretation of effects. b , Estimated effect sizes (black points) and variances (coloured lines) for all populations included in the analysis ( n  = 1,065 project–species combinations from 102 independent projects; two example species highlighted) with the global mean (and 95% quantiles) plotted in black to the right. c , Estimated model coefficients (points) and 95% CIs (lines; n  = 1,065 project–species combinations from 102 independent projects) for additive factors (with complete data; Methods ) hypothesized to influence changes in the amount of animal activity when human activity is higher, where: intercept is diurnal, large herbivore in closed habitat type with a seasonal comparison and all other effects are contrasts. d , Model predictions for the interaction between trophic group and HMI.

figure 3

a , Interpretation of effects. b , Estimated effect sizes (black points) and variances (coloured lines) for all populations included in the analysis ( n  = 499 project–species combinations from 100 independent projects; two example species highlighted) with the global mean (with 95% quantiles) plotted in black to the right. c , Estimated model coefficients (points) and 95% CIs (lines; n  = 499 project–species combinations from 100 independent projects) for additive factors (with complete data; Methods ) hypothesized to influence changes in animal nocturnality when human activity is higher, where: intercept is nocturnal, large herbivore in closed habitat type with a seasonal comparison and all other effects are contrasts. d , Model predictions for interaction between trophic group and human modification index. e , Model predictions for interaction between hunting and HMI.

Animal activity in more developed areas (that is, higher human modification index (HMI) measured at the site level; Table 1 ) generally increased (+25%) with higher levels of human activity, while animals in less-developed areas decreased their activity (−6%) when human activity was higher (Fig. 2c ; coefficient = 0.077; 95% CI = −0.001–0.156). This contrast highlights an important interaction between human modification of a landscape and human activity therein—between human footprint and footfalls—which we posit could be the result of two factors. First, local extirpations of sensitive species (species ‘filtering’ 19 ) would result in only human-tolerant species persisting in developed areas—for example, sensitive wolverine ( Gulo gulo ) were absent from sites with intermediate to high human modification. Second, species found across the gradient, such as mule deer ( Odocoileus hemionus ), could become habituated to benign human presence in more developed landscapes and therefore be less fearful of human activity than their conspecifics in less-developed areas 20 . Notably, this relationship with landscape modification varied predictably across trophic groups (Fig. 2d and Supplementary Table 3 ). Small and large carnivores, small herbivores and small omnivores increased their activity with higher human activity in developed areas (increasing by an average of 54%), while the response was much weaker for large herbivores and in fact opposite for large omnivores, which decreased activity when human activity increased in more modified landscapes (50% decrease; Fig. 2d ). This negative response was common across all of the frequently detected large omnivores—wild boar ( Sus scrofa ), American black bear ( Ursus americanus ) and brown bear ( Ursus arctos )—and could be driven by their attraction to anthropogenic food resources (for example garbage and fruit trees) that may be less risky to access when human activity is reduced 21 .

Animal detections were also more likely to decline with higher human activity in more open habitat types such as grasslands or deserts, relative to closed habitats such as forests (Fig. 2c ; coefficient = −0.172; 95% CI = −0.3428 to −0.0018). This is consistent with predictions under the landscape of fear framework that suggest that animal perceptions of risk are influenced by availability of cover 22 . Contrary to our expectations, we did not find strong evidence that the magnitude of change in human activity (measured by camera traps or the stringency index; Table 1 ) affected animal responses or that hunted populations changed their amount of activity more than non-hunted ones (Supplementary Tables 2 , 4 and 5 ). We also did not find strong support for the hypothesis that species with relatively larger brains—as an index of behavioural plasticity 23 —would show more pronounced responses to changes in human activity (Supplementary Table 5 ).

Changes in timing of animal activity

Whether or not animals change their intensity of use of an area, they could shift their timing of activity to minimize overlap with increasing human activity (Fig. 3a ) 24 . We measured changes in animal nocturnality (proportion of night time detections) across 499 populations ( Methods ) and found considerable variation in animal responses to increasing human activity (though generally less than for amount of activity): from fivefold increases in nocturnality to sixfold decreases (mean change in proportion of nocturnal detections = 0.008; 95% CI = −0.02–0.04; Fig. 3b ). The strongest predictor of changes in nocturnality was the degree of landscape modification (HMI): in more developed areas, animals tended to become more nocturnal as human activity increased (19.3% increase in nocturnality; Fig. 3c , coefficient = 0.047; 95% CI = 0.026–0.069; Supplementary Table 6 ). This is consistent with previous evidence of increasing wildlife nocturnality in the face of growing human impacts 24 and highlights the importance of the temporal refuge provided by night time cover for human–wildlife coexistence in increasingly human-dominated environments 25 .

Paralleling our findings about changes in the amount of animal activity, trophic group was also an important predictor of changes in nocturnality, with large carnivores becoming notably more nocturnal than other groups (+5.3%; Fig. 3c and Supplementary Table 6 ). Again, we found support for an interaction between human modification and trophic group: most groups had stronger increases in nocturnality along the disturbance gradient as human activity increased (mean +22.6%), whereas the increases in nocturnality for large carnivores did not vary with land-use disturbance (Fig. 3d and Supplementary Table 7 ). This finding could reflect greater sensitivity of large carnivores to the increased risk of conflict associated with more human presence 26 , such that they shift timing of activity to minimize overlap regardless of landscape context. Other groups increased night time activity only in landscapes with higher risk of human encounters (that is, more modification), which may in turn enable the increases in amount of activity observed for many of these species (Fig. 2d ).

Unlike for the amount of activity, changes in the timing of animal activity were mediated by the hunting status of species in an area, whereby hunted animals showed stronger increases in nocturnal behaviour at higher levels of landscape modification (+26.6%) relative to their non-hunted counterparts (+13.5%; Fig. 3e and Supplementary Table 8 ). We did not find strong evidence that relative brain size was associated with shifts in animal nocturnality, nor that the magnitude of change in the amount of human activity explained variation in animal responses (Fig. 3c and Supplementary Tables 6 and 9 ). We did find an effect of our comparison type such that, on average, comparisons between years showed larger shifts in nocturnality than within-year comparisons (Fig. 3c and Supplementary Table 6 ), underscoring the importance of temporal matching to minimize influence of other factors such as seasonal changes in activity patterns.

Implications for human–wildlife coexistence

Contrary to popular narratives of animals roaming more widely while people sheltered in place during early stages of the COVID-19 pandemic, our results reveal tremendous variation and complexity in animal responses to dynamic changes in human activity. Using a unique synthesis of simultaneous camera-trap sampling of people and hundreds of mammal species around the world, combined with a powerful before–after quasi-experimental design, we quantified how animals change their behaviours under higher levels of human activity across gradients of human footprint. As the human population continues to grow, the persistence of wild animals will depend on their responses to increasing human presence in both highly and moderately modified landscapes. It may thus be encouraging that many animal populations did not show dramatic changes in the amount or timing of their activity under conditions of higher human activity. Indeed, mean changes across all populations assessed were close to zero, suggesting that there was no global systematic shift in animal activity during the pandemic, consistent with other recent observations of highly variable animal responses 13 , 27 . Nevertheless, we saw stronger responses to human activity for certain species and contexts and these patterns can help us better understand and mitigate negative impacts of people on wildlife communities.

One striking pattern is that animal responses to human activity varied with the degree of human landscape modification. Our results imply that risk tolerance and associated behaviours vary between wildlife in more- versus less-developed contexts. As human activity increased, many species in more modified landscapes surprisingly had higher overall activity, although this activity was more nocturnal, suggesting that animals persisting in these developed environments may be attracted to anthropogenic resource subsidies but still seek ways to minimize encounters with people through partitioning time 28 . Wildlife managers in such modified environments should anticipate some animal habituation and manage the timing of human activity to protect night time refuges that promote human–wildlife coexistence—particularly for hunted species that showed the strongest shifts toward nocturnality. On the other hand, regulating the amount of human activity may be more important in less-developed landscapes where we detected the greatest declines in animal activity with increasing human activity. Such remote landscapes are often spatial refuges for sensitive species that may be filtered out as human modification increases; yet these areas face increasing demands from popular pursuits, such as outdoor recreation and nature-based tourism 18 , and may also be more difficult to protect from illegal hunting, encroachment or resource extraction 29 .

The sensitivity of species to human footprint and footfalls varied by trophic group and body size, as did the interplay of space and time in behavioural responses. Both large and small carnivore species were among the more sensitive to changes in human activity, generally reducing their activity levels and exhibiting more nocturnality with higher human activity. This motivates a continued emphasis on carnivore behaviour and management as a key challenge for human–wildlife coexistence, given the threatened status of many carnivores, the risk of negative outcomes of human–carnivore encounters and the ecological importance of carnivores as strongly interacting species 7 , 30 . Avoidance of people by carnivores could be beneficial if it reduces human–carnivore conflict 25 , 28 but it could also lead to different types of conflict if it results in lower predation rates on herbivores near people, as seen in overbrowsing by habituated deer 4 . Indeed, large herbivores showed the strongest increases in activity with higher human activity in our study, consistent with habituation and increased risk of conflict. Large omnivores, such as bear and boar, were unique in both spatially and temporally avoiding higher human activity in more developed environments, underscoring that management efforts to regulate human activity and create spatial or temporal refuges may lead to outcomes that differ by species and setting. Managers must pay particular attention to the prospect that such differential responses can alter species interactions and cause knock-on effects with broader consequences for ecosystem functions and services 31 , 32 .

Our study highlights the value of learning from unplanned ‘experiments’ caused by rapid changes in human activity 33 and other extreme events (for example, ref. 34 ). These insights are enabled by sampling methods, such as camera trapping, that facilitate standardized, continuous monitoring of diverse animal assemblages and humans across varied landscape contexts. While many studies of the anthropause focused on wildlife observations by volunteers in more accessible urban environments (for example, ref. 35 ), our results emphasize that animal responses to changes in human activity differ between more- and less-developed landscapes. This context-dependency should be a focus of further research, including expanded assessment of contexts and species under-represented in our sample, such as those in tropical regions subjected to different pressures during the pandemic 36 . Many geographic and taxonomic gaps in global biodiversity monitoring remain and must be filled by cost-effective networks that gather reliable evidence across several scales; standardized camera-trap programmes and infrastructure are helping to do so 37 , 38 . As the cumulative effects of the human enterprise put pressure on ecosystems worldwide 39 , bending the curve of biodiversity loss will require context-specific knowledge on ecological responses to human actions that can guide locally appropriate and globally effective conservation solutions.

Data collection

We issued a call in September 2020 to camera-trap researchers around the world for contributions of camera-trap data from before and during the onset of the COVID-19 pandemic and associated restrictions on human activity 10 , 11 . This initial call included a social media post (Twitter, now X) and targeted emails to 143 researchers in 37 countries. We requested datasets that adhered to global camera-trap metadata standards (Wildlife Insights 38 ) and received submissions from 146 projects. Submitted data were summarized using a standardized script and evaluated according to the following key criteria: (1) most or all camera-trap stations were deployed in the same area of interest (hereafter site) before and during COVID-19-related restrictions; (2) a minimum of seven unique camera-trap deployment locations (stations) were sampled; (3) a minimum sampling effort of at least 7 days per camera period (see below); and (4) trends in human detections were recorded from camera-trap data (that is, detections of humans) or human activity for a given sampling area was available from other sources (for example, lockdown dates and local knowledge).

We only included detections of wild mammal species ≥1 kg (mean species body mass in kg obtained from ref. 40 ; we excluded domestic animals, which represented only 6% of overall detections and were associated with humans) and humans (excluding research personnel servicing cameras). Our full dataset for the next step of analysis included 112 projects sampling across 5,653 cameras for 329,535 camera-days (see below for data included in specific models). The mean number of camera locations per project was 42 (range 6–300) and mean camera-days per project was 2,945 (range 348–27,986). Camera locations were considered independent within projects, as no paired cameras were included (see Supplementary Table 10 for more details on camera deployments and spacing).

Experimental design

For each project, we first reviewed site-level trends in independent detection events of humans (using a standardized 30 min interval: that is, a detection was considered independent if >30 min from previous detection at the same camera station) to identify whether there were changes in human activity associated with COVID-19 restrictions in 2020. We sought to identify two comparable sampling periods that differed in human activity but were otherwise similar (for example, in camera locations and sampling effort) and thus could be used as a quasi-experimental comparison to assess wildlife responses to the change in human activity. We initially anticipated that human activity would be reduced during COVID-19 lockdowns (that is, the anthropause 11 ) but observed a wide variety of patterns of human detections across datasets, including decreases, increases and no change in human detections between sampling before and during COVID-19 (Supplementary Fig. 1 ). Since our primary interest was in evaluating wildlife responses to changes in human activity and in general we anticipate increases in human activity during the Anthropocene, we standardized our treatments to represent increases in human activity. In other words, we defined a ‘control’ period as one with lower human activity and a ‘treatment’ period as one with higher human activity, regardless of which occurred before or during the COVID-19 pandemic (Fig. 1 ).

We identified start and end dates for each period on the basis of clear changes in human detections (determined from visual inspection of daily detections; Fig. 1 ). For some projects, dates corresponded to known dates of local COVID-19 lockdowns or changes in study design (for example, dates of camera placement or removal). We prioritized comparison between years when data were collected in similar periods in years before 2020 ( n  = 95 projects). If multiyear data were not available, we selected comparison periods before and after the onset of lockdowns around March 2020 (with specific dates chosen according to local lockdown conditions; n  = 17). If there were several potential treatment periods, we prioritized periods on the basis of the following ordered criteria: (1) the fewest seasonal or ecological confounds; (2) the most similar study design; (3) the greatest sampling effort; and (4) the most recent time period. Of the 95 projects for which we made comparisons between 2020 and a previous year, we used 2019 for 88 projects, 2018 for 6 and 2017 for 1.

In cases where there was no noticeable difference in human detections between candidate periods, or there were insufficient human detections from camera traps, we used other data or local knowledge of changes in human activity (for example, lockdown dates and visitor use data) from co-authors responsible for the particular project. Of the 112 projects included in our initial analyses, 15 used this expert opinion to determine changes in human activity. After completing our initial categorization of comparison periods, we shared details with all data contributors for review and adjustment, if necessary, based on expert knowledge of a given study area. Contributors were asked whether our delineation of sampling periods as being high versus low in human activity corresponded with their knowledge of the study system. We also asked them to consider whether other sources of environmental variation (for example, fire, drought, seasonal or interannual variation) or sampling design could confound the attribution of changes in wildlife detections to changes in human activity. After this evaluation and review, we retained 102 project datasets that had a detectable change in human activity between a treatment and control period for subsequent statistical modelling. These projects spanned 21 countries, mostly in North America and Europe but with some representation from South America, Africa and Southeast Asia (Fig. 1 and Supplementary Table 10 ).

Our paired treatment–control design makes several assumptions. For instance, we assumed that either: (1) changes in human activity occurred in the same direction throughout the entire study area within the treatment period; (2) the direction of the average effect was more important than variation in direction across camera sites; (3) variation in human activity within a study area was lower than differences in human activity between the treatment (higher activity) and control (lower activity) periods. By standardizing our treatment to be the period of higher human activity, we also assumed that the temporal direction of change did not affect animal responses.

Data analysis

We compared two response variables between treatment and control periods to assess wildlife responses to changes in human activity: the amount of animal activity and the timing of animal activity (described below). We used a two-stage approach in which we first estimated the direction and magnitude of change in these responses between periods for each species and then used a meta-analytical approach to evaluate the degree to which a set of candidate predictor variables explained variation in estimated responses. All data manipulation and analysis were done using R statistical software (v.4.1.3; ref. 41 ).

Amount of animal activity

To evaluate changes in the amount of animal activity, we quantified detection rates for each mammal species (and humans) at each camera for the treatment and control periods of each project. Specifically, we calculated the number of independent detections for a given species and camera station using a standardized 30 min interval (that is, detection was considered independent if >30 min from previous detection of the same species at the same camera station), while controlling for variation in sampling effort (log of camera-days included as an offset in models). We assumed that this detection rate (sometimes termed relative abundance index 16 ) measured the relative intensity of habitat use by a species at a camera station, which reflects both the local abundance of the species (number of individuals in sampled area) and the movement patterns of individuals.

To quantify the magnitude of change in the amount of animal activity, we first ran single-species models to estimate changes in detection rates for species and humans between the comparison periods for each project. The response variable was the count of independent detection events, modelled as negative binomial, with an offset for active camera-days. Treatment was included as a fixed effect and a random intercept was included for camera station where the same camera locations were sampled in both periods (no random effect was included if a project used different camera locations between periods). All models were implemented using the glmmTMB package 42 . These models produced a regression coefficient (effect size) for each project–species population (humans and animals) representing the estimated magnitude of change in the amount of activity between the control period and the treatment period (and its corresponding sampling variance).

Timing of animal activity

To assess changes in timing of animal activity, we first classified each independent detection of a given species within a given project as ‘day’ or ‘night’. We used the lutz package to convert all local times to UTC 43 . We calculated the angle of the sun at the time of the first image in each detection using the sunAngle function in the oce package 44 , based on the UTC time and latitude and longitude of the camera deployment location. Negative sun angles corresponded to ‘night’ (between sunset and sunrise) and positive sun angles to ‘day’ (between sunrise and sunset). Following ref. 24 , we calculated an index of nocturnality, N , as the proportion of independent camera-trap detections that occurred during the night ( N  = detections during night/ (detections during night + detections during day)) for all species which had ten or more detections in both the control and treatment periods. We then calculated the log risk ratio, RR and its corresponding sampling variance (weighted by sample size) between the treatment and control periods, pooled across all camera traps within a given study using the escalc() function within the metafor package 45 . This effect size compared the percentage of animal detections that occurred at night with high human activity ( N h ) to night time animal activity under low human activity ( N l ), with RR = ln( N h /N l )). A positive RR indicated a relatively greater degree of nocturnality in response to human activity, while a negative RR indicated reduced nocturnality.

Hypothesized explanatory variables

We identified and calculated a set of variables that we hypothesized would affect species responses to changes in human activity. These fell into four general classes: (1) species traits, (2) habitat (that is, vegetation) structure, (3) anthropogenic landscape modification and (4) magnitude of human change (Table 1 ). We did not include any covariates reflecting differences in camera-trap sampling protocols between projects, as our estimates of species responses were made within projects (that is, comparing treatment versus control periods) and thus sampling methods were internally consistent within projects (for example, camera placement and settings).

Species traits

We hypothesized that species with the following traits would be more sensitive to changes in human activity (that is, more vulnerable or risk averse): larger body mass 46 , higher trophic level 46 , narrower diet and habitat breadth 47 , diurnal activity 46 and smaller relative brain size 48 . We extracted variables for each species from the COMBINE database 40 , the most comprehensive archive of several mammal traits curated to date (representing 6,234 species). Given that some traits in the database were imputed, we reviewed the designations for plausibility and cross-referenced the traits with other widely used databases—specifically Elton Traits 49 and PanTHERIA 50 —and made the following corrections to the ‘activity cycle’ trait (diurnal, nocturnal and cathemeral): diurnal to cathemeral— Mellivora capensis, Neofelis nebulosa, Neofelis diardi ; diurnal to nocturnal— Meles meles; nocturnal to diurnal— Phacochoerus africanus; nocturnal to cathemeral— Ursus americanus . To calculate relative brain size we divided log-transformed brain mass by log-transformed body mass (as in ref. 48 ). We combined body mass and trophic level into a new variable ‘trophic group’ (consisting of small- or large-bodied categories for each of the three trophic levels, Table 1 ). Dietary and habitat breadth are described in ref. 40 .

We further hypothesized that animals in hunted populations would be more sensitive to changes in human activity. We requested that all data contributors complete a survey indicating whether a given species was hunted within their project survey area, from which we created a binary factor representing hunting status for each population (1 = hunted; 0 = not hunted).

Habitat structure

Camera-trap surveys included in our analysis covered an extensive range of biogeographic areas and habitat types. We made the simplifying assumption that species responses to changes in human activity would be most influenced by the degree of openness of habitat (that is, vegetation structure) in a sampling area. More specifically, we hypothesized that areas with more open habitat types would have higher visibility and thus less security cover for animals and thus that animals in these open habitats would be more sensitive to increases in human activity than would animals in more closed habitats with greater security cover 51 . We used the Copernicus Global Land Cover dataset (100 m resolution 52 ) via Google Earth Engine to extract land cover class at each camera station. We then used the percentage canopy cover of the mode class across all cameras in a given project to define if the survey occurred in primarily closed (>70% canopy cover) or open habitat types (0–70% canopy cover).

Land cover disturbance

We posited that animal responses to changes in human activity would differ according to the degree of anthropogenic landscape modification (that is, human footprint 1 , 53 ). More specifically, we identified two hypotheses that could underlie variation in species responses as a function of land cover disturbance. On the one hand, our ‘habituation hypothesis’ predicts that animals in more disturbed landscapes may be less sensitive to changes in human activity (relative to animals in undisturbed landscapes) and thus show less of a negative response or even a positive response as they have already behaviourally adapted to tolerate co-occurrence with people 22 . On the other hand, our ‘plasticity hypothesis’ predicts that the ability of animals to coexist with people in disturbed landscapes may be dependent on plasticity in animal behaviour 22 , such that animals in these landscapes may show more pronounced and rapid responses to changes in human activity (for example, avoidance of areas and times with greater chance of encountering people).

We initially characterized landscape disturbance using three variables accessed via Google Earth Engine: Gridded Population of the World (1 km resolution 54 ), road density (m km −2 , 8 km resolution; Global Roads Inventory Project 55 ) and HMI (for 2016 at 1 km resolution), which represents a cumulative measure of the proportion of a landscape modified by 13 anthropogenic stressors 53 . Point values were extracted for each camera station in each site, then the project-level medians were used in analysis. As the median values of these three variables were highly correlated across projects (Supplementary Fig. 2 ), we only used HMI in our subsequent models.

Magnitude of human change

We expected that animal responses would be more pronounced in areas that underwent greater changes in human activity and we used two measures to assess the magnitude of those changes. At a coarse scale, we used the COVID-19 stringency index 14 , which characterizes the policies restricting human activities within a given geographic region at a daily time scale and has been widely used in studies of COVID-19 on human mobility and the environment (for example, ref. 13 ). We used the finest-scale regional data available for each project, which was usually at the country level, with the exception of three countries with province- or state-level data (Brazil, Canada and the United States). When projects spanned several countries, provinces or states, we used the stringency index for the region in which most cameras were located. For each region, we calculated the median stringency for the treatment and control sampling periods.

At a finer scale, we used the effect size for the modelled change in camera-trap detection rates of humans across all cameras in a project (as described above under ‘amount of animal activity’). Models with this variable excluded 15 projects that either did not detect humans with camera traps or the number of humans detected on cameras was not perceived by the data contributor to be an accurate reflection of change in human use for the sampled area.

Meta-analysis models

To understand which factors mediated the effect of increasing human use on animal activity, we ran mixed-effect meta-analytic models using the function of the metafor package 45 on the effect sizes and sampling variances of the two response variables described above (amount and timing of animal activity). Our unit of observation for modelling was the estimated response for each project–species combination (that is, each animal population) and we included random intercepts for project and for species nested within family, to account for repeated observations within each of those higher-level groups and for phylogenetic relatedness within families. All continuous predictor variables (Table 1 ) were standardized to unit variance with a mean of zero using the stdize function in the MuMIn package 56 . We tested pairwise correlations among all predictor variables and found that none were highly correlated (that is, all below a threshold of Pearson | r | < 0.6; Supplementary Fig. 2 ) and thus all were retained for modelling.

We performed our analysis in three steps for each of the two wildlife response variables. First, we fit a global model including all hypothesized predictor variables for which we had complete data (excluding hunting status, relative brain size and empirical magnitude of human change, for which we had incomplete data and thus included in analysis of subsets of data, described below). Second, we used model selection to test for plausible interactions and nonlinear effects. Third, we used model selection on subsets of the full data to compare the global and interactions models with candidate models adding three more predictor variables with incomplete data.

Global model

As all of our predictor variables were independent, we used a global model approach that included additive fixed effects for all predictor variables (Table 1 ). We interpreted the P value of each effect contrast to indicate statistically significant support (at P  < 0.05 or marginal support at P  < 0.10) for a consistent effect direction of a given predictor and we used the estimated effect size as a measure of effect magnitude. We calculated the pseudo- R 2 to estimate the total variation explained by our global models. We also calculated the I 2 (ref. 57 ) of each global model to determine the amount of heterogeneity observed between the random effect levels; consistent variation in the response terms between projects, families and species would result in higher I 2 values compared to the null model with no fixed effects. To aid interpretation, we present effect sizes in terms of the proportional change (%) in model-predicted responses across lowest-to-highest values for continuous predictors (for example, HMI) or between two categories of interest (for example, trophic groups).

Model selection of plausible interactions and nonlinear terms

To explore the possibility of context-specific effects of the predictors of wildlife responses to changes in human activity, we assessed a suite of ecologically plausible interaction and nonlinear (quadratic) terms through adding them in turn to the global model and using Akaike’s Information Criterion (corrected for small sample size, AICc) to find the most parsimonious model. We assessed the following terms: (1) ‘HMI * habitat_closure’, to evaluate the potential for habitat structure to mediate responses to human landscape modification; (2) ‘ trophic_group * HMI’, to evaluate the potential for different trophic groups to respond to human modification in different ways; (3) ‘trophic_group * habitat_closure’, to evaluate the potential for different trophic groups to respond to habitat structure in different ways; and (4) HMI 2 , to assess nonlinear effects of wildlife responses to human modification. Models including the candidate interaction or nonlinear terms were compared to the global model without interaction terms using AICc (in the MuMIn package 56 ) and were discussed above if they were within 2 AICc of the best-supported model and there was no simpler, nested model with more support.

Model selection on subsets of data

We had a small amount of missing information in the data available for assessing the effects of population hunting status, species relative brain size and empirical (that is, camera-trap-based) magnitude of change in human activity (91.7%, 98.8% and 86.5% of project–species had data for these variables, respectively). Therefore, we ran the same global model used for the full dataset on the subsetted data along with candidate models including each of these predictor variables and all plausible interactions of interest (as above). These additional candidate models were compared to the global model (run on the same partial dataset) using AICc and were discussed in the results if they resulted in a lower AICc value (that is, had more support than the global model, which was a simpler nested model).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data used in this paper are available in Figshare, with the identifier: .

Code availability

The code used to analyse the data and create the figures in this paper are available in Figshare, with the identifier: .

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We recognize the tragic consequences of the COVID-19 pandemic and would like to acknowledge all people impacted. Full acknowledgements are provided in the Supplementary Information . This synthesis project was funded by the Natural Sciences and Engineering Research Council of Canada (Canada Research Chair 950-231654 and Discovery Grant RGPIN-2018-03958 to A.C.B. and RGPIN-2022-03096 to K.M.G.) and the National Center for Ecological Analysis and Synthesis (Director’s Postdoc Fellowship to K.M.G.). Additional funding sources for component subprojects are listed in the Supplementary Information .

Author information

These authors contributed equally: A. Cole Burton, Christopher Beirne.

Authors and Affiliations

Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada

A. Cole Burton, Christopher Beirne, Catherine Sun, Alys Granados, Mitch Fennell & Michael Procko

Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada

A. Cole Burton & Kaitlyn M. Gaynor

Departments of Zoology and Botany, University of British Columbia, Vancouver, British Columbia, Canada

Kaitlyn M. Gaynor

National Center for Ecological Analysis and Synthesis, Santa Barbara, CA, USA

Illinois Natural History Survey, Prairie Research Institute, University of Illinois, Champaign, IL, USA

Maximilian L. Allen

School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA

Jesse M. Alston

Instituto de Desenvolvimento Sustentável Mamirauá, Tefé, Brazil

Guilherme C. Alvarenga, Daniele Barcelos, Diogo Maia Gräbin, Fernando F. Pinho & Emiliano Esterci Ramalho

Fundación Naturaleza El Salvador, San Salvador, El Salvador

Francisco Samuel Álvarez Calderón & Xochilt Pocasangre-Orellana

School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia

Zachary Amir & Matthew Scott Luskin

Wisconsin Department of Natural Resources, Madison, WI, USA

Christine Anhalt-Depies & Jennifer Stenglein

College of Agricultural Sciences, Oregon State University, Corvallis, OR, USA

Coastal Jaguar Conservation, Heredia, Costa Rica

Stephanny Arroyo-Arce & Ian Thomson

Panthera, New York, NY, USA

Guy Balme, Gareth K. H. Mann & Lucy K. Smyth

Department of Biology and Environment, University of Haifa at Oranim, Kiryat Tivon, Israel

Avi Bar-Massada

Watershed Studies Institute, Murray State University, Murray, KY, USA

Evan Barr & Andrea K. Darracq

St. Lawrence University, Canton, NY, USA

Erika L. Barthelmess

School of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, FL, USA

Carolina Baruzzi

Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, Poland

Sayantani M. Basak & Izabela Wierzbowska

Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium

Natalie Beenaerts

School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel

Jonathan Belmaker & Itai Namir

Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry, Kėdainių, Lithuania

Olgirda Belova

National Park Tara, Mokra Gora, Serbia

Branko Bezarević

Hogle Zoo, Salt Lake City, UT, USA

Tori Bird & Kayleigh Mullen

Siena College, Loudonville, NY, USA

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Faculty of Biology, University of Belgrade, Belgrade, Serbia

Neda Bogdanović & Duško Ćirović

Smithsonian’s National Zoo and Conservation Biology Institute, Washington, DC, USA

Andy Boyce, William J. McShea & Hila Shamon

Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

Mark Boyce, Jennifer M. Foca, Colleen Cassady St. Clair & Cassondra Stevenson

Lincoln Memorial University, Harrogate, TN, USA

LaRoy Brandt

Division of Biological Sciences & Wildlife Biology Program, University of Montana, Missoula, MT, USA

Jedediah F. Brodie & Mark Hebblewhite

Institute of Biodiversity and Environmental Conservation, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia

Jedediah F. Brodie

Purdue University, West Lafayette, IN, USA

Jarred Brooke & Elizabeth A. Flaherty

Mammal Research Institute, Polish Academy of Sciences, Białowieża, Poland

Jakub W. Bubnicki, Marcin Churski, Tom A. Diserens & Dries P. J. Kuijper

Animal Ecology Unit, Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy

Francesca Cagnacci, Simone Dal Farra, Federico Ossi & Nathan Ranc

National Biodiversity Future Center (NBFC), Palermo, Italy

Francesca Cagnacci, Francesco Ferretti & Federico Ossi

Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA

Benjamin Scott Carr & Michel T. Kohl

Department of Biology and Centre for Environmental and Marine Studies, University of Aveiro, Aveiro, Portugal

João Carvalho & Rita T. Torres

Research Institute for Nature and Forest, Brussels, Belgium

Slovenia Forest Service, Ljubljana, Slovenia

Rok Černe & Urša Fležar

Hamaarag, Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv, Israel

British Columbia Ministry of Forests, Cranbrook, British Columbia, Canada

Paul Smith’s College, Paul Smiths, NY, USA

Connor Cincotta & Jorie Favreau

Royal Botanic Gardens Victoria, Melbourne, Victoria, Australia

T. D. Coates

Springfield College, Springfield, MA, USA

Justin Compton

Felidae Conservation Fund, Mill Valley, CA, USA

Courtney Coon & Zara McDonald

North Carolina Museum of Natural Sciences, Raleigh, NC, USA

Michael V. Cove & Roland Kays

Alaska Department of Fish and Game, Juneau, AK, USA

Anthony P. Crupi

University of Connecticut, Storrs, CT, USA

Miranda Davis, Erin K. Kuprewicz & Joan Tremblay

Quest University Canada, Squamish, British Columbia, Canada

Kimberly Dawe

Service Public of Wallonia, Gembloux, Belgium

Valerie De Waele, Alain Licoppe & Benoît Manet

Instituto de Investigación en Recursos Cinegéticos, Ciudad Real, Spain

Esther Descalzo & Pablo Ferreras

Faculty of Biology, University of Warsaw, Warsaw, Poland

Tom A. Diserens

Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic

Jakub Drimaj, Martin Duľa, Miroslav Kutal & Radim Plhal

Friends of the Earth Czech Republic, Carnivore Conservation Programme, Olomouc, Czech Republic

Martin Duľa & Miroslav Kutal

University of North Dakota, Grand Forks, ND, USA

Susan Ellis-Felege

Texas Parks and Wildlife Department, Austin, TX, USA

Caroline Ellison

Hunting and Wildlife Program, Kastamonu University, Kastamonu, Turkey

Alper Ertürk & Anil Soyumert

College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL, USA

Jean Fantle-Lepczyk & Christopher Lepczyk

Department of Life Sciences, University of Siena, Siena, Italy

Francesco Ferretti

Bavarian Forest National Park, Grafenau, Germany

Christian Fiderer & Marco Heurich

University of Freiburg, Breisgau, Germany

fRI Research, Hinton, Alberta, Canada

Laura Finnegan & Tracy McKay

University of Victoria, Victoria, British Columbia, Canada

Jason T. Fisher & Sandra Frey

Bridgewater State University, Bridgewater, MA, USA

M. Caitlin Fisher-Reid

Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia

Urša Fležar & Miha Krofel

Krkonoše Mountains National Park, Vrchlabí, Czech Republic

Jiří Flousek

Department of Biology, University of British Columbia, Kelowna, British Columbia, Canada

Adam Ford & Jenna Scherger

Italian Institute for Environmental Protection and Research, Rome, Italy

Barbara Franzetti

Texas State University, San Marcos, TX, USA

Sarah Fritts

Department of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic

Šárka Frýbová

California Department of Fish and Wildlife, Sacramento, CA, USA

Brett Furnas

University of Rhode Island, Kingstown, RI, USA

Brian Gerber

Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia

Hayley M. Geyle

Society for the Preservation of Endangered Carnivores and their International Ecological Study (S.P.E.C.I.E.S.), Ventura, CA, USA

Diego G. Giménez & Anthony J. Giordano

Faculty of Veterinary Medicine, University of Zagreb, Zagreb, Croatia

Tomislav Gomercic, Josip Kusak, Slaven Reljic & Magda Sindicic

New Mexico State University, Las Cruces, NM, USA

Matthew E. Gompper

Pepperwood, Santa Rosa, CA, USA

Morgan Gray & Steven Hammerich

University of Utah, Salt Lake City, UT, USA

Austin Green & Çağan H. Şekercioğlu

Agricultural Center for Cattle, Grassland, Dairy, Game and Fisheries of Baden-Württemberg, Aulendorf, Germany

Robert Hagen

Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany

Robert Hagen, Stephanie Kramer-Schadt & Julie Louvrier

University of Kansas, Lawrence, KS, USA

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Ezemvelo KZN Wildlife, Pietermartizburg, South Africa

Catharine Hanekom

University of Montana, Missoula, MT, USA

Christopher Hansen & Joshua J. Millspaugh

US Air Force Academy, Colorado Springs, CO, USA

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Inland Norway University, Hamar, Norway

Marco Heurich

Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden

Tim R. Hofmeester

Northern Michigan University, Marquette, MI, USA

Tru Hubbard & Diana J. R. Lafferty

Clemson University, Clemson, SC, USA

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Smithsonian Tropical Research Institute, Balboa, Republic of Panama

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Kodi Jo Jaspers, Robert Long, Paula MacKay & Katie Remine

Seattle University, Seattle, WA, USA

Mark Jordan

National Institute of the Atlantic Forest, Santa Teresa, Brazil

Mariane C. Kaizer

Virginia Tech, Blacksburg, VA, USA

Marcella J. Kelly

Institute of Ecology, Technische Universität Berlin, Berlin, Germany

Stephanie Kramer-Schadt

BUND Niedersachsen, Hanover, Germany

Andrea Krug

University of Missouri, Columbia, MO, USA

Summer LaRose & Ronald Revord

Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA

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Rutgers University, New Brunswick, NJ, USA

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Abilene Christian University, Abilene, TX, USA

Thomas E. Lee Jr

United States Department of Agriculture Forest Service, Pacific Northwest Research Station, Corvallis, OR, USA

Damon B. Lesmeister & Marco Linnell

Scientific Laboratory of Gorce National Park, Niedźwiedź, Poland

South Dakota State University, Brookings, SD, USA

Robert C. Lonsinger

Missouri State University, Springfield, MO, USA

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Andrew J. Marshall

City of Issaquah, Issaquah, WA, USA

Matt Mechler

CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France

Claude Miaud & Jean-Pierre Vacher

Department of Migration, Max Planck Institute of Animal Behaviour, Konstanz, Germany

Claudio M. Monteza-Moreno & Martin Wikelski

Universidad de Santiago de Chile (USACH) and Institute of Ecology and Biodiversity (IEB), Santiago, Chile

Dario Moreira-Arce

Mianus River Gorge, Bedford, MA, USA

Christopher Nagy

World Wildlife Fund—USA, Washington, DC, USA

Robin Naidoo

Effigy Mounds National Monument, Harper’s Ferry, WV, USA

Carrie Nelson

University of Wisconsin-Whitewater, Whitewater, WI, USA

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Museo delle Scienze (MUSE), Trento, Italy

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Carnivoros Australes, Talca, Chile

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University of Castilla-La Mancha Instituto de Investigación en Recursos Cinegéticos, Ciudad Real, Spain

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Department of Veterinary Sciences, University of Torino, Turin, Italy

Parks Canada—Waterton Lakes National Park, Waterton Park, Alberta, Canada

Kimberly Pearson

Stelvio National Park, Bormio, Italy

Luca Pedrotti

United States Army, Fort Hood, TX, USA

Charles E. Pekins

Sageland Collaborative, Salt Lake City, UT, USA

Mary Pendergast

University of Hawai’i at Manoa, Honolulu, HI, USA

Melissa Price & Derek Risch

Noble Research Institute, LLC, Ardmore, OK, USA

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Université de Toulouse, INRAE, CEFS, Castanet-Tolosan, France

Nathan Ranc

Iowa State University, Ames, IA, USA

Michael Rentz

El Colegio de la Frontera Sur, Campeche, Mexico

Rafael Reyna-Hurtado

Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Victoria, Australia

Euan G. Ritchie

West Virginia University, Morgantown, WV, USA

Christopher Rota

Department of Biology, University of Florence, Florence, Italy

Francesco Rovero & Marco Salvatori

McDowell Sonoran Conservancy, Scottsdale, AZ, USA

Helen Rowe & Tiffany Sprague

Northern Arizona University, Flagstaff, AZ, USA

Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK

Christian Rutz

Northern and Yorke Landscape Board, Clare, South Australia, Australia

Derek Sandow

United States Department of Agriculture Forest Service, Southern Research Station, Nacogdoches, TX, USA

Christopher M. Schalk

Arizona State University, West, Glendale, AZ, USA

Jan Schipper & Katherine C. B. Weiss

Stephen F Austin State University, Nacogdoches, TX, USA

Daniel G. Scognamillo

Koç University, Istanbul, Turkey

Çağan H. Şekercioğlu

Research, Ecology and Environment Dimension (D.R.E.A.M.), Pistoia, Italy

Paola Semenzato

University of Richmond, Richmond, VA, USA

Jennifer Sevin

Planning and Environmental Services, City of Edmonton, Edmonton, Alberta, Canada

Catherine Shier

Instituto de Conservación, Biodiversidad y Territorio & Programa Austral Patagonia, Facultad de Ciencias Forestales y Recursos Naturales, Universidad Austral de Chile, Valdivia, Chile

Eduardo A. Silva-Rodríguez

iCWild, Department of Biological Sciences, University of Cape Town, Cape Town, South Africa

Lucy K. Smyth

Conservation Ecology Group, Department of Biosciences, Durham University, Durham, UK

Philip A. Stephens & Bálint Ternyik

Department of Ecology, Institute of Functional Biology and Ecology, Faculty of Biology, University of Warsaw, Warsaw, Poland

Kinga Magdalena Stępniak

Parks Victoria, Melbourne, Victoria, Australia

Michael Stevens

United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, UK

Bálint Ternyik

The King’s University, Edmonton, Alberta, Canada

Darcy Visscher

Natural Resources Institute and Department of Rangeland, Wildlife and Fisheries Management, Texas A&M University, College Station, TX, USA

Stephen L. Webb

Oeko-Log Freilandforschung, Friedrichswalde, Germany

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University of Illinois, Urbana, IL, USA

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Tufts University, Grafton, MA, USA

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Parks Canada, Banff, Alberta, Canada

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Department of Biology, University of Konstanz, Konstanz, Germany

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Wildlife Habitat Council, Silver Spring, MD, USA

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Environmental Studies Department, University of California Santa Cruz, Santa Cruz, CA, USA

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Victoria University of Wellington, Wellington, New Zealand

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A.C.B., C. Beirne, R.K., K.M.G., C. Sun and A. Granados conceived this work. A.C.B., C. Beirne, R.K., K.M.G., A. Granados, C. Sun and F.C. were responsible for data curation. C. Beirne and K.M.G. conducted the formal analysis. A.C.B., R.K. and K.M.G. acquired funding. A.C.B., C. Beirne, K.M.G., C. Sun, A. Granados, M.L.A., J.M.A., G.C.A., F.S.Á.C., Z.A., C.A.-D., C.A., S.A.-A., G.B., A.B.-M., D.B., E.B., E.L.B., C. Baruzzi, S.M.B., N. Beenaerts, J. Belmaker, O.B., B.B., T.B., D.A.B., N. Bogdanović, A.B., M.B., L.B., J.F.B., J. Brooke, J.W.B., F.C., B.S.C., J. Carvalho, J. Casaer, R. Černe, R. Chen, E.C., M.C., C. Cincotta, D.Ć., T.D.C., J. Compton, C. Coon, M.V.C., A.P.C., S.D.F., A.K.D., M. Davis, K.D., V.D.W., E.D., T.A.D., J.D., M. Duľa, S.E.-F., C.E., A.E., J.F.-L., J. Favreau, M.F., P.F., F.F., C.F., L.F., J.T.F., M.C.F.-R., E.A.F., U.F., J.F.l., J.M.F., A.F., B. Franzetti, S. Frey, S. Fritts, Š. Frýbová, B. Furnas, B.G., H.M.G., D.G.G., A.J.G., T.G., M.E.G., D.M.G., M.G., A. Green, R.H., R.(B.)H., S. Hammerich, C. Hanekom, C. Hansen, S. Hasstedt, M. Hebblewhite, M. Heurich, T.R.H., T.H., D.J., P.A.J., K.J.J., A.J., M.J., M.C.K., M.J.K., M.T.K., S.K.-S., M. Krofel, A.K., K.M.K., D.P.J.K., E.K.K., J.K., M. Kutal, D.J.R.L., S.L., M. Lashley, R. Lathrop, T.E.L.J., C.L., D.B.L., A.L., M. Linnell, J. Loch, R. Long, R.C.L., J. Louvrier, M.S.L., P.M., S.M., B.M., G.K.H.M., A.J.M., D.M., Z.M., T.M., W.J.M., M.M., C.M., J.J.M., C.M.M.-M., D.M.-A., K.M., C. Nagy, R.N., I.N., C. Nelson, B.O., M.T.O., V.O., C.O., F.O., P.P., K.P., L.P., C.E.P., M. Pendergast, F.F.P., R.P., X.P.-O., M. Price, M. Procko, M.D.P., E.E.R., N.R., S.R., K.R., M.R., R.R., R.R.-H., D.R., E.G.R., A.R., C. Rota, F.R., H.R., C. Rutz, M. Salvatori, D.S., C.M.S., J. Scherger, J. Schipper, D.G.S., Ç.H.Ş., P.S., J. Sevin, H.S., C. Shier, E.A.S.-R., M. Sindicic, L.K.S., A.S., T.S., C.C.S.C., J. Stenglein, P.A.S., K.M.S., M. Stevens, C. Stevenson, B.T., I.T., R.T.T., J.T., T.U., J.-P.V., D.V., S.L.W., J. Weber, K.C.B.W., L.S.W., C.A.W., J. Whittington, I.W., M.W., J. Williamson, C.C.W., T.W., H.U.W., Y.Z., A.Z. and R.K. carried out the investigations. A.C.B., R.K. and F.C. were responsible for project administration. A.C.B., C. Beirne, R.K., K.M.G., C. Sun and A. Granados wrote the original draft manuscript. A.C.B., C. Beirne, K.M.G., C. Sun, A. Granados, M. Lashley, J.M.A., G.C.A., F.S.Á.C., Z.A., C.A.-D., C.A., S.A.-A., G.B., A.B.-M., D.B., E.B., E.L.B., C. Baruzzi, S.M.B., N. Beenaerts, J. Belmaker, O.B., B.B., T.B., D.A.B., N. Bogdanović, A.B., M.B., L.B., J.F.B., J. Brooke, J.W.B., F.C., B.S.C., J. Carvalho, J. Casaer, R. Černe, R. Chen, E.C., M.C., C. Cincotta, D.Ć., T.D.C., J. Compton, C. Coon, M.V.C., A.P.C., S.D.F., A.K.D., M. Davis, K.D., V.D.W., E.D, T.A.D., J.D., M. Duľa, S.E.-F., C.E., A.E., J.F.-L., J. Favreau, M.F., P.F., F.F., C.F., L.F., J.T.F., M.C.F.-R., E.A.F., U.F., J.F.l., J.M.F., A.F., B. Franzetti, S. Frey, S. Fritts, Š. Frýbová, B. Furnas, B.G., H.M.G., D.G.G., A.J.G., T.G., M.E.G., D.M.G., M.G., A. Green, R.H., R.(B.)H., S. Hammerich, C. Hanekom, C. Hansen, S. Hasstedt, M. Hebblewhite, M. Heurich, T.R.H., T.H., D.J., P.A.J., K.J.J., A.J., M.J., M.C.K., M.J.K., M.T.K., S.K.-S., M. Krofel, A.K., K.M.K., D.P.J.K., E.K.K., J.K., M. Kutal, D.J.R.L., S.L., M. Lashley, R. Lathrop, T.E.L.J., C.L., D.B.L., A.L., M. Linnell, J. Loch, R. Long, R.C.L., J. Louvrier, M.S.L., P.M., S.M., B.M., G.K.H.M., A.J.M., D.M., Z.M., T.M., W.J.M., M.M., C.M., J.J.M., C.M.M.-M., D.M.-A., K.M., C. Nagy, R.N., I.N., C. Nelson, B.O., M.T.O., V.O., C.O., F.O., P.P., K.P., L.P., C.E.P., M. Pendergast, F.F.P., R.P., X.P.-O., M. Price, M. Procko, M.D.P., E.E.R., N.R., S.R., K.R., M.R., R.R., R.R.-H., D.R., E.G.R., A.R., C. Rota, F.R., H.R., C. Rutz, M. Salvatori, D.S., C.M.S., J. Scherger, J. Schipper, D.G.S., Ç.H.Ş., P.S., J. Sevin, H.S., C. Shier, E.A.S.-R., M. Sindicic, L.K.S., A.S., T.S., C.C.S.C., J. Stenglein, P.A.S., K.M.S., M. Stevens, C. Stevenson, B.T., I.T., R.T.T., J.T., T.U., J.-P.V., D.V., S.L.W., J. Weber, K.C.B.W., L.S.W., C.A.W., J. Whittington, I.W., M.W., J. Williamson, C.C.W., T.W., H.U.W., Y.Z., A.Z. and R.K. were involved in reviewing and editing the final manuscript.

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Burton, A.C., Beirne, C., Gaynor, K.M. et al. Mammal responses to global changes in human activity vary by trophic group and landscape. Nat Ecol Evol (2024).

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Published : 18 March 2024


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Will the demise of insects that pollinate crops slash productivity? Could groundwater depletion threaten the boom in data centers? (More on that below.) Will policies to stop ocean pollution impact how companies produce plastic?

Hundreds of companies have already committed to start reporting their nature-related risks in their financial disclosures, and they will start rolling them out in the next few months.

“We’ve got to change the mindset around nature being something we can take for granted,” Tony Goldner, the executive director of the Taskforce on Nature-Related Financial Disclosures, which produced the framework the companies are using. “It’s a risk we have to actively manage. And the resilience of nature underpins the resilience of business.”

Here are a few ways companies are trying to chart the complexities of nature, and why it matters.

A practical money problem

Disclosing more corporate information may seem like a baby step. But experts say more transparency can help stop companies from greenwashing their environmental claims, as we’ve reported in a previous newsletter , and guide nature-conscious investors like New York state’s pension fund .

It may also make sense for the bottom line. Half of the world’s gross domestic product is moderately or highly dependent on nature, according to a 2020 World Economic Forum report .

A few examples: The struggling aquifers in parts of Arizona may be a major obstacle for the data centers being built there . The demise of Europe’s bumblebees , which pollinate crops such as strawberries and tomatoes, will likely make it harder for supermarkets to source products. A European law that creates obstacles for companies buying palm oil and other products from deforested areas, as my colleague Patricia Cohen has reported, can significantly affect companies based in tropical nations .

“Every single company depends on nature, whether it’s directly or through indirect links,” said Sebastian Bekker, who is developing a tool to help assess nature-related risks for the United Nations Environment Program World Conservation Monitoring Centre.

How it’s done

The Norges Bank Investment Management, which is responsible for investing money from Norway’s trillion-dollar sovereign fund , published a report about nature-related risk a few weeks ago. The fund, largely created by profits from the country’s fossil fuel exports, is the largest in the world.

Snorre Gjerde, who works on the bank’s responsible investment strategy, told me Norges Bank’s experience shows that understanding nature-related risks can be a lot more complex than accounting for climate alone.

When the bank wants to figure out how a company contributes to climate change, it’s relatively straightforward to measure greenhouse gas emissions. “One ton of emissions anywhere in the world have the same impact globally,” he said.

Impact on nature is far more complex. First, he said, there isn’t a global unit to measure nature. Second, a company’s impact on ecosystems will vary according to the location of a factory or a farm. Drawing water from a healthy river isn’t the same as depending on a nearly dry aquifer, and deforesting a biodiverse ecosystem doesn’t have the same impact as razing trees in an area that doesn’t host as many species.

“How do you account for those nuances? I don’t have an answer to that yet,” he said.

The fund owns about 1.5 percent of the entire global stock market, or “a small slice of the global economy,” Gjerde told me.

“Our mandate is to manage the fund for the benefit of the current, but also future, generations,” he added. “In the very long run, then, our financial returns will be dependent on sustainable development in economic terms, but also social and environmental terms.”

A big surge in electricity demand

Something unusual is happening in America. Demand for electricity, which has stayed largely flat for two decades, has begun to surge .

Over the past year, electric utilities have nearly doubled their forecasts of how much additional power they’ll need by 2028. Peak demand is projected to grow by 38,000 megawatts nationwide in the next five years, equivalent to adding another California to the grid.

Utilities are confronting an unexpected explosion in the number of data centers, an abrupt resurgence in manufacturing, and millions of plug-in electric vehicles.

The stakes are high. If more power isn’t brought online relatively soon, large portions of the country could risk blackouts, according to a recent report by the North American Electric Reliability Corporation, which monitors the health of the nation’s electric grids.

In an ironic twist, the swelling appetite for more electricity could also jeopardize the country’s plans to fight climate change.

To meet spiking demand, utilities in states like Georgia, North Carolina, South Carolina, Tennessee and Virginia are proposing to build dozens of natural gas-burning power plants over the next 15 years. In Kansas, one utility has postponed the retirement of a coal plant to help power a giant electric-car battery factory.

Burning more gas and coal runs counter to President Biden’s pledge to halve the nation’s planet-warming greenhouse gases by 2035.

“I can’t recall the last time I was so alarmed about the country’s energy trajectory,” said Tyler H. Norris, a former solar developer and expert in power systems. If a wave of new gas-fired plants gets approved by state regulators, he said, “it is game over for the Biden administration’s 2035 decarbonization goal.” — Brad Plumer and Nadja Popovich

Read the whole article here.

More climate news

The southern section of the Great Barrier Reef is facing what could be the worst bleaching event on record, The Washington Post reports . CBS explained why coral reefs are a lifeline for cancer patients .

Asthma inhalers emit a greenhouse gas that is up to 3,000 more potent than carbon dioxide, the public radio station WBUR reports .

A flood of Chinese solar panels are driving down prices, making it difficult for U.S. producers to establish a domestic clean energy supply chain, the Financial Times reports .

The Wall Street Journal shows that the world’s largest plane could transform wind energy by carrying enormous blades to far-flung places .

The European Commission is finalizing a series of legislative proposals, seen by Politico , that would severely weaken environmental requirements for farmers.

Manuela Andreoni is a Times climate and environmental reporter and a writer for the Climate Forward newsletter. More about Manuela Andreoni

Learn More About Climate Change

Have questions about climate change? Our F.A.Q. will tackle your climate questions, big and small .

Decades of buried trash in landfills is releasing methane , a powerful greenhouse gas, at higher rates than previously estimated, a study says.

Ocean Conservation Namibia is disentangling a record number of seals, while broadcasting the perils of marine debris in a largely feel-good way. Here’s how .

To decarbonize the electrical grid, companies are finding creative ways to store energy during periods of low demand in carbon dioxide storage balloons .

New satellite-based research reveals how land along the East Coast is slumping into the ocean, compounding the danger from global sea level rise . A major culprit: overpumping of groundwater.

Did you know the ♻ symbol doesn’t mean something is actually recyclable ? Read on about how we got here, and what can be done.

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What is the impact of nature on human health? A scoping review of the literature

Rachel m nejade.

1 Department of Infectious Disease Epidemiology, Imperial College London, London, UK

Daniel Grace

2 Abertawe Bro Morgannwg University Health Board, NHS Wales, Swansea, UK

Leigh R Bowman

Associated data.

The burden of non-communicable diseases (including poor mental health) is increasing, and some practitioners are turning to nature to provide the solution. Nature-based interventions (NBIs) could offer cost-effective solutions by reconnecting individuals with nature, but the success of these interventions depends partially on the way in which people engage with blue and green spaces.

We conducted a scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Cochrane guidelines to establish the evidence base for treating poor mental and physical health with NBIs. We searched five databases and the grey literature. Exposure was the active engagement with natural environments. The primary outcome was mental health and the secondary outcome was physical health defined using established metrics. All data were extracted to a charting table and reported as a narrative synthesis.

952 studies were identified, of which 39 met the inclusion criteria. 92% demonstrated consistent improvements across any health outcome where individuals engaged with natural outdoor environments. Mental health outcomes improved across 98% of studies while physical and cognitive health outcomes showed improvement across 83% and 75% of studies respectively. Additionally, we identified 153 factors affecting engagement with nature, 78% of which facilitated engagement compared with 22% that reduced engagement. Aspects such as the sense of wilderness, accessibility, opportunities for physical activity and the absence of noise/ air pollution all increased engagement.


Further research (accompanied by a global improvement in study design) is needed to establish the magnitude and relative effect of nature-based interventions, and to quantify the compounding effect of factors that improve engagement with green and blue spaces. Nevertheless, this review has documented the increasing body of evidence in support of NBIs as effective tools to improve mental, physical, and cognitive health outcomes, and highlighted key factors that improve engagement with the natural world.


Open Science Framework: .

It is estimated that 10% of the global population lives with a diagnosed mental health disorder, leading to negative health and economic impacts for both individuals and the broader society [ 1 ]. Of those affected, 10%-20% are children, half of whom are already suffering from a mental disorder by the age of 14 [ 2 , 3 ]. Neuropsychiatric and developmental disorders such as attention deficit hyperactivity disorder (ADHD) and autism spectrum disorders (ASDs) are particularly common [ 4 ], while depression and anxiety are more prevalent among adults [ 1 ]. As individuals age into retirement, the risk of mental health illnesses increases, partly due to social exclusion, loneliness, changes to physical health, and the passing of friends and relatives [ 5 ]. If population estimates are correct, the global fraction of those aged >60 years will have increased from 12% to 22% by 2050 [ 5 ]. In the absence of effective interventions, the global burden of poor mental health will continue to climb.

In financial terms, the combined direct and indirect cost of mental health disorders across the UK in 2013 was estimated at between £70-100 billion annually [ 6 ]. Within the European Union (EU), these costs were estimated to be around €798 billion each year [ 7 ]. Worldwide, governments and international agencies such as the World Health Organization (WHO) have responded to the mental health epidemic by increasing funding for mental health research and services [ 8 , 9 ], yet first-line treatment for conditions such as depression, ADHD, and generalised anxiety disorder (GAD) still rely heavily on medications and psychotherapeutic treatments, such as cognitive behavioural therapy (CBT) [ 10 , 11 ]. Although these strategies are often effective, medications come with a long list of potential side effects [ 12 , 13 ], not to mention financial barriers to access [ 14 , 15 ]; there are also often shortages of skilled mental health practitioners to match the demand for long-term individualised CBT.

In contrast to medicated interventions, there has been renewed interest in “natural” therapies, which are seen as less intrusive and more cost-effective [ 16 ]. Meditation, lifestyle changes such as increased physical exercise, community-based activities and engagement with natural environments are emerging as potential alternatives to complement or replace other forms of treatment [ 16 - 18 ]. Indeed, there is growing evidence suggesting that nature-based health interventions (NBIs) can improve mental and physical health outcomes while also addressing the growing demand for less intrusive and more cost-effective treatments [ 16 , 19 ]. However, challenges exist; NBIs must take place in natural outdoor environments (NOEs), defined as “any environment in which green vegetation or blue water resources can be found”, access to which is becoming increasingly difficult [ 20 , 21 ]. Indeed, many geographical, financial, and cultural barriers affect the way we interact with NOEs, and without significant changes to the way humans live, they will likely be compounded by increasing migration away from wild spaces, and further concentration of human populations within urban areas, where 68% of the world’s population is expected to reside by 2050 [ 22 ].

Through conducting a scoping review, we aimed to set a baseline for the impact of NBIs on mental and physical health outcomes and to help with understanding the factors that magnify or diminish engagement with NOEs.

Aim and objectives

We aimed to collate and assess the evidence base for NBIs and to define and assess the effect of enablers on engagement with natural outdoor environments. More specifically, we intended to locate and review the evidence base for nature-based interventions for mental and physical health outcomes, identify the enablers of, and barriers to, engagement with natural outdoor environments, and understand whether these enablers and barriers impact the effectiveness of nature-based interventions on mental and physical health outcomes.

Study design

We conducted this scoping review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, and the Cochrane guidelines for scoping reviews [ 23 , 24 ]. A scoping review was considered the most appropriate method to answer the research question, due to its capacity to answer broad questions and summarise findings from a heterogeneous body of knowledge [ 25 ].

Study protocol

The protocol for this scoping review was drafted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Protocols (PRISMA-P) and was revised by the academic team [ 26 ]. It was disseminated through MedRxiv, the preprint server for health sciences on July 4, 2020 [ 19 ].

Search strategy

The search includes terms relating to NBIs: a) green care, b) blue care, c) mental health, d) physical health, e) environmental determinants of NOE use, and f) socio-economic determinants of NOE use. The primary outcome of interest was mental health, defined using a number of key metrics. The secondary outcome was physical health, based on a number of physiological variables [ 27 ]. Several NBI studies have used physical health measures either as the main outcome (eg, obesity) or as an objective measure to confirm mental health outcomes obtained from self-reporting (eg, the link between stress and cortisol) [ 28 - 50 ]. All the keywords used for the literature search can be found in Figure S1 in the Online Supplementary Document .

The terminology used in the literature search for green and blue care reflects the varied positions held by researchers and the lack of consensus surrounding their application.

We used the search terms to identify studies from the following five databases: PubMed, The Cochrane Library, Web of Science, Scopus, and OVID (including Embase, PsycINFO, Global Health, MEDLINE, Health Management Information Consortium (HMIC), Transport Database). All search terms were grouped using the Boolean “OR” and were then all combined using the Boolean “AND”, to produce the final number of relevant studies identified by each database. We also performed snowballing (or the search of reference lists from included articles). To limit the effect of publication bias, we searched grey literature through Google Scholar, and governmental and institutional websites (eg, Public Health England (PHE)). Mendeley and the Covidence software were used to store, organise, and manage all references. To promote transparency and ensure reproducibility, the full search strategy used for the PubMed database is available in Table S1 in the Online Supplementary Document .

Study selection criteria

The study selection was done based on the pre-defined inclusion and exclusion criteria and conducted in two stages: 1) title and abstract screening, and 2) full-text screening. If a dispute occurred on the inclusion of a study, a decision was made on the inclusion/exclusion when a consensus was achieved. We backtracked existing reviews so that any study included in both the existing review and our study was excluded from our analysis. Duplicates were removed from the search before the article screening.

As this is an emerging field, we kept the inclusion criteria for this scoping review intentionally broad. We included human studies and peer-reviewed articles on green spaces and blue spaces, with physical or mental health outcomes. Any study design was accepted. NOE exposure was based on participants’ presence in nature, whether that was confirmed through participants’ observation, interviews in nature, or through an intervention using activities in NOEs. We included any review including at least one study for which NOE exposure was confirmed by these means.

Considering the contemporary topic of this scoping review, the search included all results from 1980 onwards. Studies written in both English and French were included. We excluded any studies or reviews not pertaining to health, green spaces, and blue spaces, or that were solely descriptive in nature (eg, commentaries) and studies that only defined NOE exposure based on geospatial indicators (eg, normalised difference vegetation index (NDVI)). To avoid complexities associated with recall bias, we excluded any study that used self-reported measures of engagement with nature (eg, “number of visits to parks in the last week”) [ 51 , 52 ]. However, this restriction was not applied to our main outcomes when these were found in studies using self-reporting scores such as GAD-7 and General Health Questionnaire (GHQ), as the validity of these measures to assess mental or physical health outcomes has been widely accepted within the scientific community. Additionally, this exclusion criterion would also have greatly reduced the number of available studies [ 28 - 50 ]. The full inclusion and exclusion criteria can be found in Figure 1 .

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Inclusion and exclusion criteria.

Data extraction and analysis

We performed data extraction (or charting) using a standardised data extraction form, adapted for this scoping review to address the research questions and objectives (Table S2 in the Online Supplementary Document ). Content analysis was used to group findings in categories based on similarities to create a narrative synthesis of the existing evidence informed by the data charting process.

Study Selection

The results of the literature search across the five databases and the grey literature were reported using a PRISMA flow diagram ( Figure 2 ). From the original 952 articles, 824 unique studies were identified for title and abstract screening, after the removal of 128 duplicates. Through title and abstract screening, 352 full-text articles were selected and downloaded for a full-text review (ie, eliminating 472 studies). 313 studies failed to meet the inclusion criteria at full-text screening (reasons detailed in Figure S2 in the Online Supplementary Document ). A total of 39 articles were selected for the final analysis [ 53 - 91 ].

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PRISMA flow diagram.

Descriptive characteristics

A summary of each charted study can be found in Table S3 in the Online Supplementary Document . A total of 39 studies were included in the final analysis, 11 of which were observational, seven used qualitative methods [ 55 - 57 , 60 , 74 , 83 , 84 ], three used quantitative methods [ 63 , 85 , 88 ], and only one used mixed methods [ 75 ]. Among the 14 interventional studies, only one used qualitative methods [ 66 ], nine used quantitative methods [ 58 , 59 , 61 , 62 , 64 , 65 , 67 , 81 , 91 ], and four used mixed methods [ 53 , 54 , 87 , 89 ]. Finally, among the remaining 14 reviews, ten included systematic reviews [ 70 - 72 , 76 - 79 , 82 , 86 , 90 ], one was a scoping review [ 80 ] and three were literature reviews [ 68 , 69 , 73 ]. All studies were written in English, except for one that was written in French [ 82 ]. Additionally, all studies were carried out in the past five years, with the oldest study dating back to 2015 [ 57 ].

Most studies (85%) were conducted in higher-income countries (defined using the World Bank classification based on countries’ gross national income (GNI) per capita) [ 92 ]. Few studies were conducted in upper-middle-income countries: one observational study in Mexico [ 88 ], two interventional studies from China [ 67 ] and South Africa [ 61 ], and three reviews including Chinese [ 76 , 82 ] and Bulgarian studies [ 76 , 80 ] ( Figure 3 ).

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Bar chart depicting the countries included in reviews, interventional and observational studies, grouped by study design.

Twenty out of the 39 studies (51%) assessed the effects of engagement with NOEs on mental and physical health across all age groups, with only ten studies (26%) focusing specifically on adults (18-60 years) [ 54 - 56 , 58 , 59 , 62 , 63 , 67 , 81 , 91 ], four (10%) on the elderly (age 60+) [ 57 , 73 , 74 , 83 ], as well as four (10%) on children [ 53 , 66 , 72 , 88 ] and one (3%) on adolescents (11 to 18 years) [ 61 ].

Overall, eight studies (20%) selected participants based on age group [ 53 , 57 , 61 , 72 , 74 , 76 , 86 , 88 ], two (5%) based on sex (in favour of women) [ 62 , 91 ], and six (15%) from volunteering [ 54 , 59 , 63 , 67 , 81 , 84 ]. Four other studies (10%) recruited local residents [ 58 , 60 , 65 , 75 ]. Moreover, eight studies (20%) included patient populations with pre-existing conditions [ 90 ]. These looked at people with autism [ 66 ], neurological disabilities [ 73 , 78 ], mental disorders [ 75 , 84 , 87 ], or those undergoing stroke rehabilitation [ 64 ]. Notably, some studies selected participants based on their existing use of natural environments, such as regular swimmers or members of outdoor associations in blue spaces [ 55 , 56 , 83 ], or through involvement in the conservation of green spaces [ 89 ]. Finally, eight reviews (20%) did not specify any sample populations [ 68 - 71 , 77 , 79 , 80 , 82 ].

Taxonomy for natural outdoor environments

Overall, three types of NOEs were identified across all studies: green spaces (51% (n = 20)), blue spaces (13% (n = 5)), and a mix of both (36% (n = 14)).

Green spaces encompassed both urban and rural environments, and most studies described green spaces as urban parks [ 57 , 62 , 65 , 69 , 74 , 82 , 85 , 88 , 91 ], natural environments [ 63 , 68 , 70 , 72 , 86 ], urban forests [ 53 , 62 , 78 , 81 ], or as gardens [ 64 , 73 , 74 , 78 ]. Other areas or features of green spaces were used less often, such as farms [ 53 , 66 , 78 ], micro-features [ 57 , 74 ], national parks or reserves [ 60 , 89 ], a game reserve [ 61 ], urban stream corridors [ 55 ], playgrounds [ 72 ], meadows [ 54 ], bogs [ 89 ], or neighbourhood greenness [ 77 ]. Similarly, blue spaces also covered urban and rural environments and were characterised by the terms: sea [ 56 , 90 ], blue environments [ 70 , 86 ], river [ 53 ], fountain/ seawall [ 74 ], coastal area [ 59 ], loch [ 61 ], wetlands [ 87 ], wilderness [ 90 ], ocean and beaches [ 83 ]. Finally, grey areas were typically considered as urban environments: urban city [ 54 , 62 , 65 , 91 ], built environment [ 58 , 79 ], urban sidewalk [ 59 ], shopping mall [ 62 ], hospital [ 64 ], urban landscape [ 72 ], roadside [ 81 ], home [ 91 ], swimming pools [ 83 ], and a field near a housing development [ 89 ].

Nature-based health interventions

All NBIs and their related activities reported across the selected studies were categorised ( Figure 4 ). Six types of NBI were identified: educational intervention, physical activity, wilderness therapy, leisure activity, gardening, and changes to the built environment.

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All types of nature-based health interventions found in the selected studies. Green – green spaces, blue – blue spaces, and yellow – both green and blues spaces.

Health outcomes and nature-based interventions

All reported outcomes and their associated enablers are listed in Table S4 in the Online Supplementary Document . Almost all of the studies included at least one mental health outcome [ 53 , 54 , 56 - 66 , 68 - 87 , 89 - 91 ], except for three that focused solely on physical activity [ 55 , 88 ] and cardiovascular outcomes [ 67 ]. Many studies used multiple outcomes, and each of these is reviewed and discussed in the following order: mental health outcomes, physical health/physiological outcomes, and cognitive health outcomes.

Overall, there are clear positive trends between NOE engagement (through voluntary participation or primary care intervention) and psychological, physical, and cognitive health outcomes (described in Figure 5 by the bars labelled “positive findings”). In applicable studies [ 56 , 73 - 75 , 83 , 87 ], a decrease in the measurable outcome was considered a “positive finding” where this resulted in a gain for the individual eg, a reduction in social isolation. The studies displayed as “negative findings” refers to studies where health outcomes led to mixed or no positive effects [ 59 , 70 , 71 , 76 , 81 , 82 , 87 , 91 ].

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Percentage of positive and negative findings stratified by health outcomes.

Mental health

Mental health was the most commonly studied outcome (62%). There were improvements across all mental health outcomes when engaging with nature (98%), with only one study reporting no effect (2%) [ 71 ]. No negative effects were found.

Engagement with NOEs led to an improved quality of life in 4% of all studies looking at mental health outcomes, as assessed by measures of Health-Related Quality of Life (HRQoL) [ 53 , 64 ] or Quality of Life (QoL) surveys [ 69 , 86 ]. Only one study reported improved “perceived mental health” (ie, restoration) of stream-corridor users, assessed using qualitative interviews [ 57 ]. Outcomes related to measures of well-being were the most studied ones and were usually positively associated with NOE engagement. It was measured differently across studies and relied on measures of hedonic and eudaimonic well-being [ 71 ], perceived well-being [ 56 , 73 - 75 , 83 , 90 ], and general well-being [ 54 , 58 , 63 , 68 , 71 , 76 , 86 , 87 ]. Only one systematic review reported mixed effects, which the authors attributed to poor study design and quality [ 71 ]. Finally, measures of depression [ 63 , 65 , 78 , 80 ] and anxiety [ 64 , 65 , 78 , 81 , 87 ] decreased when engaging with NOEs.

There was also a positive effect of NOE engagement on measures of emotional health outcomes across all studies. Most reported improved affect [ 58 , 64 , 70 , 73 , 81 - 83 , 86 , 87 ], mood [ 62 , 65 , 79 , 80 , 89 , 91 ], self-esteem [ 61 , 73 , 80 , 84 , 90 ], self-confidence [ 75 ], and vitality [ 62 , 66 ]. Others reported decreases in negative affect [ 63 , 81 , 83 , 86 ], mood disturbances [ 65 ], agitation [ 73 , 78 ], and behavioural problems (eg, hyperactivity or violence) [ 72 , 73 , 80 , 82 ].

Overall, engagement with NOEs led to improved social health across 100% of the fourteen studies that assessed their effects. Six studies reported reduced social isolation [ 56 , 73 - 75 , 83 , 87 ] and one found reduced social discomfort [ 91 ] following engagement with NOEs; seven noted increased social connectedness between individuals [ 66 , 68 , 78 , 82 - 84 , 90 ].

Finally, several studies assessed the effects of engagement with NOEs on stress. All studies reported positive associations with psychological resistance [ 54 , 56 , 90 ], perceived restoration [ 59 , 60 , 62 , 65 , 82 , 91 ], and stress reduction [ 54 , 63 , 66 , 73 , 81 - 84 , 89 ]. Only one study found a decrease in psychological distress [ 80 ], and three found decreases in perceived stress [ 63 , 86 , 87 ], which all translated into health benefits.

Physical/physiological health

83.3% of the studies considering physical and physiological health outcomes found benefits across a range of outcomes; 16.7% yielded no or negative effects for measures of obesity [ 70 , 76 , 82 , 87 ], heart rate [ 65 ], systolic and diastolic blood pressure (BP) [ 67 ], and heart rate variability (HRV) (2%) [ 79 ].

All measures of physical activity in natural environments demonstrated that engaging in NOEs led to increased physical activity. This was measured in several ways. Some studies used measurements of leisure-time physical activity [ 55 ] or reported use after urban green spaces interventions [ 69 , 74 , 82 ]. Others focused on increased exertion post-engagement with NOEs, using measures of moderate to vigorous physical activity (MVPA) [ 79 , 88 ]. Similar methodologies used measures of perceived physical activity [ 56 , 57 , 68 ] and physical fitness [ 90 ], or more broadly an increase in the use of NOEs for various activities like swimming [ 56 ] or walking in nature [ 60 , 76 ]. Finally, decreasing sedentary time was used as a measure in children populations [ 88 ].

One systematic review assessed the effect of engagement with green spaces on sleep during a walking intervention and found that engagement led to improvements in sleep quality and quantity [ 77 ]. Similarly, one study reported improved recovery from mental disorders after engaging in therapeutic horticulture as part of a recovery program [ 75 ].

Motor functioning was assessed differently by two studies [ 64 , 74 ]. Ottoni et al. [ 74 ] reported improved mobility after walking interventions in green spaces, while Pálsdóttir et al. [ 64 ] reported reductions in disability after engaging in horticulture therapy for post-stroke patients. Overall, improvements in disability were reported in both intervention and control groups, suggesting that the therapy itself may facilitate recovery more than the type of environment [ 64 ].

All studies measuring physical health outcomes found a positive association between physical health and NOE engagement when measured by GHQ [ 72 , 80 , 87 ]. Pálsdóttir et al. [ 64 ] used post-stroke fatigue (PSF) as their main outcome, which decreased following horticulture therapy. Importantly, both the intervention and control groups experienced decreases in PSF, thereby reducing the importance of the intervention in this context over other mainstream standards of care.

Four studies reported little to no effects on obesity (measured using body mass index) after engagement with NOEs [ 70 , 76 , 82 , 88 ]. Regarding mortality, only two studies investigated how NOE engagement affected all-cause mortality [ 70 , 79 ]. Both studies found a decrease in mortality following changes to the built environment [ 70 ] and after engaging in physical activity in nature [ 79 ].

Cardiovascular health was measured using diastolic and systolic BP [ 62 , 65 , 67 ], baseline resting heart rate [ 54 , 65 , 67 , 69 ], and HRV [ 62 , 79 , 91 ]. Heart rate was found to decrease in 80% of studies looking at this measure, except for one [ 65 ]. Similarly, BP was found to decrease in three studies, except for one by Ana et al. [ 67 ], which found no changes in BP after forest bathing. Results were also inconclusive for HRV, which tended to increase after NOEs exposure in two studies [ 62 , 91 ], but had no effects in another [ 79 ].

Physiological measures of stress were determined using cortisol samples; in two studies, there was a decrease in cortisol levels after engaging in NOEs [ 62 , 82 ].

Cognitive health

Although not initially included, cognitive health outcomes were identified on several occasions (8%) during the analytical process and were considered important for this review. Overall, NOE engagement had positive effects on cognitive health (58%), by reducing ADHD symptoms (8%) [ 72 ], and by improving cognitive functioning (50%) [ 53 , 54 , 66 , 72 , 79 ], except in one study (8%) [ 59 ]. Findings on memory were inconclusive (32%) [ 72 , 78 , 81 , 91 ].

Cognitive functioning was reported using measures of science, technology, engineering, and math (STEM)-capacity [ 53 ], attention restoration [ 54 , 72 ], and attention retention [ 59 , 66 , 79 , 82 ]. In 86% of these studies, cognitive functioning was positively associated with NOE engagement, except for one study which reported no change in attention retention [ 59 ]. However, attention retention was improved after exposure to natural environments in three studies [ 66 , 79 , 82 ], along with attention restoration [ 54 , 72 ]. One study also showed an improvement in children’s STEM-capacity following a nature-based education (NBE) intervention [ 53 ].

Memory was only assessed in four studies and yielded mixed findings [ 72 , 78 , 81 , 91 ]. While one found a positive association between spatial working memory and engaging in NOEs [ 72 ], the other found no effects [ 91 ]. Similarly, for executive functioning, one study found no effects [ 81 ], while the other saw improvements in executive memory [ 78 ].

During a wilderness expedition, trained therapists noticed a decrease in ADHD symptoms for children living with autism after exposure to animals and the natural environment [ 66 ] – which was supported by McCormick [ 72 ] in her systematic review.

Engagement with natural outdoor environments

Several factors influencing engagement were identified throughout the selected studies. These factors were divided into those that facilitated engagement (enablers ( ~ 78%)), vs those that hindered engagement (barriers: (22%)) ( Figure 6 ). These included environmental, social, individual, and structural processes, along with opportunities for physical activity and stress reduction. Poor study design and quality were considered barriers across all studies. A description of each enablers’ category can be found in Figure S3 in the Online Supplementary Document .

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Number and percentage of enablers and barriers for each health outcome. Enablers in green; barriers in orange.

Environmental processes

Most enablers focused on environmental processes (38%), the most common being the type of environment (66%), where natural environments facilitated health benefits over built environments (ie, swimming pools, city centres, shopping malls, etc.) [ 53 - 56 , 58 , 59 , 62 , 63 , 65 - 67 , 70 , 72 , 73 , 77 - 84 , 86 - 91 ]. Although the variety of green space and blue space descriptors makes comparing studies difficult, some studies have found that urban forests were better than urban parks, as they reduced cortisol levels [ 62 ], BP [ 54 , 62 , 65 ], and heart rate [ 62 , 67 , 79 ], while increasing HRV [ 62 , 91 ]. Interestingly, one study even found that heart rate benefits were amplified if that forest was made of maple trees as opposed to birch or oak trees, while BP would not change if the temperature, humidity, and light spectrum (ie, green/blue light ratio) were too high [ 67 ]. Similarly, blue space users preferred wilder and more available water environments (eg, ocean) as they amplified psychological health benefits through increases in well-being and social health benefits by reducing social isolation [ 56 , 83 ].

Typically, biodiversity was shown to facilitate well-being [ 87 ], psychological restoration [ 85 ], social connectedness [ 87 ], positive affect [ 83 ], and overall health [ 87 ], while reducing anxiety [ 87 ] and stress [ 87 ]. Notably, biodiversity may also present a barrier if perceived as threatening or harmful (for example, due to the presence of sharks in blue spaces [ 83 ]). Other environmental processes such as good weather [ 83 ], heat reduction [ 70 ], seasons [ 65 ], perceived aesthetics [ 68 ], nature connectedness [ 58 , 71 ], the presence of farm animals for autistic children [ 66 ], and sensory qualities of the environment (ie, sound) [ 59 , 60 ] were all found to also improve mental, physical, physiological and cognitive health; however, other detrimental environmental processes such as air and noise-related pollution [ 62 , 76 ] negated these positive effects.

Structural processes

Structural processes were the second most common enablers discussed in this scoping review (37%). Among them, good accessibility was most commonly reported (24%), as it facilitated improvements in perceived mental health [ 57 ], overall health [ 72 ], positive affect [ 70 ], physical activity in NOEs [ 55 , 57 , 74 , 82 ], and attention restoration [ 72 ], while reducing social isolation [ 74 ], motor disability [ 74 ], behavioural problems, and psychological distress [ 80 ]. Similarly, geographic proximity to NOEs was also mentioned several times (11%) as facilitating well-being [ 76 ], physical activity [ 60 , 69 , 76 ], cognitive functioning and spatial working memory [ 72 ].

The type of intervention was also reported by six studies (16%) as facilitating the health benefits gained from engaging in NOEs. Britton et al. [ 83 ] and Ottoni et al. [ 90 ] recognised that activities in blue spaces, such as surfing or swimming, contribute to rehabilitation, stress reduction, and health promotion, and complementary evidence demonstrates that therapeutic horticulture led to improvements in PSF [ 64 ] and reductions in agitation for older adults [ 74 ]. Additionally, viewing nature decreased BP [ 62 ] and improved executive memory [ 78 ]. Interestingly, the outcomes improved with increases in the length of the activity [ 53 , 61 , 87 , 88 ]. One study found that activities performed in the afternoon instead of the morning improved sleep quality and quantity, believed to be caused by a two-process model where sleep and waking are regulated by circadian rhythms and homeostasis [ 77 , 93 ]. Good group organisation, transportation, and staff attitudes and knowledge were also considered enablers of the associations between health and nature [ 87 ]. However, when NBIs have limited resources, the strength of these associations is reduced [ 73 , 90 ], and hence, good NBI quality and design can amplify the health benefits gained from nature.

The quality and design of NOEs were also found to amplify health benefits when engaging with nature, as the presence of micro-features of the environments (eg, benches) was found on several occasions to improve well-being and self-esteem while reducing social isolation and stress in individuals with dementia [ 73 ]. Older adults also found that benches could help decrease social isolation [ 74 ] and improve their mobility and physical activity in NOEs [ 74 ]. Other studies also found general increases in physical activity and positive affect when these features were present [ 55 , 70 , 80 ]. Overall, positive changes to the environment through the implementation of micro-features were found to facilitate engagement in NOEs.

Individual processes

Most individual processes across the selected studies were considered barriers (74%) as opposed to enablers (26%).

Safety concerns were the most common barriers to engaging in NOEs (24%), as they worsened perceived mental health [ 57 ], positive affect [ 70 , 73 ], perceived restoration [ 60 ], physical activity [ 55 , 57 ], well-being and self-esteem [ 73 ] while increasing social isolation and stress [ 73 ]. Stigma was another recurrent barrier found across studies (12%) that diminished perceived well-being [ 73 , 90 ], physical activity [ 56 ], physical fitness, social connectedness and psychological resistance [ 90 ], as well as positive affect and self-esteem [ 73 ], while increasing social isolation [ 73 ] and stress [ 73 ].

Other barriers such as social prejudice [ 73 ], fear [ 56 , 90 ], negative self-perceptions [ 57 , 73 ], poor self-confidence [ 73 ], individual factors (eg, time pressure, changing identities) [ 74 , 77 ], and deprivation [ 80 , 84 ] were also detected. Conversely, some individual processes were found to facilitate the relationship between nature and health. These included cognitive functioning [ 72 ], some intrapersonal processes (ie, individual preferences) [ 68 ], gender (whereby women tended to benefit more than men) [ 61 , 74 ], and age (since younger adults and children had increased health benefits from engaging in NOEs due higher engagement in physical activity than older adults) [ 82 ].

Lower socio-economic status (SES) and ethnicity were identified as both enablers and barriers. While one study found that being South Asian and living in the UK led to worse health outcomes than being British white [ 80 ], another found that Arab women benefited more than Jewish women when engaging in NOEs [ 91 ]. The latter was thought to be influenced by levels of comfort at home, where Jewish women reported feeling more comfortable in their home than Arab women did and therefore gained fewer marginal improvements than Arab women when engaging in NOEs [ 91 ]. Similarly, lower SES was found to increase health gains through NOE engagement [ 82 ], whereas another found it led to worse health outcomes [ 76 ].

Opportunities for physical activity

Opportunities for physical activity were the third most frequent enabler found across studies (11%). They included physical activity (72%) and active engagement in NOEs (18%), as both were found to magnify the benefits for mental health [ 56 , 58 , 63 , 71 - 73 , 75 , 79 - 82 , 89 ], physical health [ 56 , 72 , 75 , 80 ], physiological health [ 79 ], and even cognitive health [ 72 , 78 , 79 ]. However, these benefits would be reduced if participants were injured or had mobility difficulties [ 74 , 78 ]. Physical activity could therefore be another mechanism by which nature positively influences health.

Social processes

Social processes were not as common as other enablers (7%), but were found to influence the nature’s impact on health. The presence of other people was the most common enabler (29%) and barrier (29%) across studies considering social processes. Indeed, two studies reported that sharing the experience of engaging in NOEs with others could facilitate gains in physical activity [ 55 ], recovery from mental disorders [ 75 ], social connectedness, self-esteem, and self-confidence [ 84 ] while reducing social isolation [ 75 ]. However, if other individuals were perceived as safety risks, well-being and physical activity would decrease, while stress would increase [ 63 ].

Additionally, social interactions, interpersonal processes, group membership, and the presence of caregivers also facilitated positive gains in psychological [ 68 , 78 , 89 ], social [ 68 , 83 ] and physical health [ 68 , 89 ]. Therefore, social processes are other mechanisms through which health benefits can be gained from nature.

Opportunities for stress reduction

Despite abundant evidence from the literature review, only 1% of all enablers focused on opportunities for stress reduction. Stressful life events were perceived as barriers, as they decreased the quality of life, well-being, positive affect, psychological resistance, and STEM capacity for children [ 52 , 63 ], while worsening depression in adults [ 63 ]. However, engaging in NOEs was shown to reduce stress in all studies looking at stress-related outcomes, considered measures of psychological health in this review [ 53 , 63 , 66 , 73 , 81 - 84 , 89 ]. Therefore, evidence for stress reduction as a mechanism in the relationship between health and nature is moderate, but not as conclusive as other enablers.

Study quality and design

Methodological choices when conducting studies (9%), such as the study design (44%), study quality (44%) or the choice of measurements (12%) were all found to negate the relationship between health and nature across selected studies [ 59 , 70 , 71 , 76 , 81 , 82 ]. They were responsible for the lack of evidence between NOE engagement and obesity [ 70 , 76 , 82 ], well-being [ 71 ], HRV [ 79 ], and on measures of memory [ 81 ] and cognitive functioning [ 59 ]. Therefore, the methods used within studies also act as potential mechanisms on nature and health.

This scoping review synthesised heterogeneous research documenting the impact of nature on health. Of the 39 included studies, nature-based interventions were found to have improved mental, physical/ physiological and cognitive health outcomes across 98%, 83%, and 75% of articles, respectively ( Figure 5 ). Furthermore, this study identified a breadth of factors that affect the level of engagement with NOEs, and by extension the likely success of nature-based interventions ( Figure 7 ).

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Impact of natural outdoor environment (NOE) engagement and enablers on health.

Nature-based interventions and health

As a species, humans have become increasingly sedentary. Offices, schools, homes, and public spaces have been designed to optimise and prioritise efficiency. At least in part, this relatively new lifestyle (by historical standards) is driving an increase in non-communicable diseases, including poor mental health [ 94 ]. As individuals continue to seek work in urban areas, the opportunity to interact with green and blue spaces diminishes. Current estimates indicate that over 50% of people worldwide live in urban areas projected to increase to >68% by 2050 [ 22 , 95 ].

Considering this, it is not surprising that the reintroduction of nature into a person’s life, irrespective of baseline physical and mental health characteristics, can have a positive influence [ 96 ]. Research shows that individuals living in urban areas with more green space have both lower mental distress and higher well-being scores [ 97 ]. “Forest-bathing” (“shinrin-yoku” in Japanese) in Japan has been shown to significantly lower salivary and serum cortisol levels when compared to control groups [ 98 ], while Niedermeier et al. [ 99 ]f ound that hiking resulted in a statistically significant increase in “affective valence” (ie, pleasure) when compared to a sedentary control group and an indoor exercise group.

One theory that might begin to explain these mechanisms is that, when in natural outdoor environments, individuals experience a reduction in “rumination” – a maladaptive pattern of self-referential thought that is associated with heightened risk for depression and other mental illnesses [ 95 ]. Indeed, data suggest this might be plausible: functional magnetic resonance imaging (MRI) scanning performed on individuals who had spent 90 minutes on a nature walk showed reduced neural activity in the subgenual prefrontal cortex (sgPFC) – an area of the brain that displays increased activity during sadness and rumination. However, participants who went on an urban walk did not show these effects [ 95 ].

Such data makes clear the physiological responses that NBIs elicit in humans, and while further granular data are required, the mounting body of evidence generally supports nature-based interventions for the prevention and treatment of physical/ mental health ailments. Indeed, science is beginning to inform public health policy via the introduction of “green prescriptions”, which are clinically prescribed NBIs for treating physical and mental health disorders [ 100 , 101 ].

The broad evidence base uncovered by this scoping review demonstrates the positive impact of NBIs on mental, physical, and cognitive health outcomes. Indeed, the findings support national policies that integrate NBIs as effective preventative and curative tools for public health [ 16 , 19 , 100 , 101 ].

Factors impacting engagement with natural outdoor environments

Biodiversity and wilderness.

Our findings on the importance of biodiversity and wilderness as drivers of impactful NOE engagement provide support for a broader interconnectedness between humans and wild spaces. This applies to all projects at any scale, from school expeditions through urban greening to broader rewilding. Enabling interaction with NOEs through accessibility (both geographic proximity and improved infrastructure) magnifies the health benefits of NOEs [ 55 , 57 , 60 , 69 , 70 , 72 , 74 , 76 , 80 , 82 ] and facilitates interaction between the public and natural ecological systems [ 102 ], promoting greater understanding and awareness of nature’s importance. The creation and maintenance of long-distance trails [ 102 ], increasing the sense of “wild” in urban green spaces [ 83 , 85 , 87 ], and a departure from meticulous park management [ 55 , 70 , 73 , 74 , 80 ] are examples of practices that result in increased “quality”, accessibility, and biodiversity, leading to plausible health gains through greater NOE engagement [ 55 , 70 , 73 , 74 , 80 , 83 , 85 , 87 , 102 ]. This recommendation fits within the broader International Union for Conservation of Nature (IUCN) vision for human interactions and ecosystem health to “[…] protect, sustainably manage, and restore natural or modified ecosystems, that address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits” [ 103 ].

Air and noise pollution

Our findings also support wider initiatives targeting reductions in air and noise pollution, as these were found to negatively impact the time that users would spend practising physical activities in NOEs [ 62 , 76 ]. Cleaner, greener environments would also encourage physical exercise and contribute to national and global targets to mitigate climate change [ 62 , 76 , 104 ]. Indeed nature-based initiatives, such as de-pollution and re-naturalisation of urban sites, are currently under consideration by the EU Commission as methods to achieve an increase in the number of publicly available green spaces, and reverse social inequalities [ 104 , 105 ].

Socio-economic status and stigma

Cultural and ethnic differences, as well as deprivation, were found to limit the health benefits gained from engagement with NOEs. Minority groups living in more deprived areas with poorer access to, and lower quality of, green spaces, had more behavioural difficulties than non-minority groups [ 80 , 91 ]. Despite mixed findings in this review [ 76 , 82 ], existing inequalities concerning access to urban green infrastructure remain, along with inequalities in the exposure to health hazards (eg, air and noise pollution), particularly for vulnerable groups such as children, the elderly, and individuals of lower socio-economic status [ 106 ]. These inequalities are well-documented in urban areas across many European countries and likely exist globally, highlighting the need for urban greening initiatives that work towards reducing social barriers to access, and increasing the use of green and blue environments [ 106 , 107 ].

Geographic proximity and opportunities for physical activity

The sedentary lifestyle characterising modern society has also led to a clear reduction in physical activity across age groups [ 102 ]. As regular physical activity has been shown to reduce certain health risks (such as cardiovascular diseases or symptoms of depression and anxiety), health agencies such as the WHO have urged governments to promote physical activity to their populations as a way to limit the growing burden of ill health [ 27 , 108 ].

The results from this review support the need for enhanced engagement in physical activity, especially when practised in green or blue environments, as these environs likely magnify the mental, physical and cognitive gains. Importantly, structural enablers such as good accessibility [ 55 , 57 , 74 , 82 ] and closer geographic proximity to NOEs [ 60 , 69 , 76 ] led to increased physical activity. This is important for policymakers, as it highlights the need to consider access and proximity to green and blue spaces when designing health interventions that promote physical activity.


Methodologically, the exclusion of studies based on self-reported measures of exposure (eg, number of visits in the last month) could have precluded the inclusion of additional relevant studies to this review. However, this was deemed necessary to limit the inherent risk of recall bias in these studies, which could have impacted the strength of the results. The absence of critical appraisal of individual sources of evidence precluded the possibility for our results to lead to statistically significant conclusions. Nevertheless, scoping reviews as per PRISMA-ScR guidelines do not necessarily require a critical appraisal of the evidence for structural integrity; as a minimum, they promote a stronger evidence base [ 23 ].

The comparison between health outcomes and types of green spaces or blue spaces was made difficult due to the variety of terms used to describe these areas. Similarly, for nature-based interventions, direct quantitative comparisons were difficult due to the absence of magnitudes, relative effects, varied heterogeneous study designs, and sample sizes.


Further research is still needed to establish the magnitude and relative effect of nature-based interventions, as well as to quantify the compounding effect of factors that improve engagement with green and blue spaces. This must be accompanied by a global improvement in study design. Nevertheless, this review has documented the increasing body of heterogeneous evidence in support of NBIs as effective tools to improve mental, physical and cognitive health outcomes. Enablers that facilitate greater engagement with natural outdoor environments, such as improved biodiversity, a sense of wilderness, and accessibility, as well as opportunities for physical activity and an absence of pollution, will likely improve health outcomes and further reduce public health inequalities.

Additional material


Ethics statement: All data used were from published, secondary sources. No ethical clearance required.

Data availability: All data are available directly within the article or as supplementary data. The original research protocol is available on Open Science Framework

Funding: This project was undertaken as part of an Imperial College London MSc thesis. No funding was allocated. The Article Processing Charge was funded by Imperial College Open Access Fund.

Authorship contributions: Conceptualisation, L.R.B.; methodology, L.R.B., R.M.N. and D.G.; data curation, R.M.N and D.G.; formal analysis, R.M.N.; visualisation R.M.N.; supervision L.R.B. and D.G., project administration L.R.B.; funding acquisition, none. All authors have read and agreed to the published version of the manuscript.

Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests.


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