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Press Release

Study in nature: protecting the ocean delivers a comprehensive solution for climate, fishing and biodiversity.

Southern Line Islands

Photograph by Southern Line Islands

Groundbreaking global study is the first to map ocean areas that, if strongly protected, would help solve climate, food and biodiversity crises

London, UK (17 March 2021) —A new study published in the prestigious peer-reviewed scientific journal Nature today offers a combined solution to several of humanity’s most pressing challenges. It is the most comprehensive assessment to date of where strict ocean protection can contribute to a more abundant supply of healthy seafood and provide a cheap, natural solution to address climate change—in addition to protecting embattled species and habitats.

An international team of 26 authors identified specific areas that, if protected, would safeguard over 80% of the habitats for endangered marine species, and increase fishing catches by more than eight million metric tons. The study is also the first to quantify the potential release of carbon dioxide into the ocean from trawling, a widespread fishing practice—and finds that trawling is pumping hundreds of millions of tons of carbon dioxide into the ocean every year, a volume of emissions similar to those of aviation.

“Ocean life has been declining worldwide because of overfishing, habitat destruction and climate change. Yet only 7% of the ocean is currently under some kind of protection,” said Dr. Enric Sala, explorer in residence at the National Geographic Society and lead author of the study, Protecting the global ocean for biodiversity, food and climate .

“In this study, we’ve pioneered a new way to identify the places that—if strongly protected—will boost food production and safeguard marine life, all while reducing carbon emissions,” Dr. Sala said. “It’s clear that humanity and the economy will benefit from a healthier ocean. And we can realize those benefits quickly if countries work together to protect at least 30% of the ocean by 2030.”

To identify the priority areas, the authors—leading marine biologists, climate experts, and economists—analyzed the world’s unprotected ocean waters based on the degree to which they are threatened by human activities that can be reduced by marine protected areas (for example, overfishing and habitat destruction). They then developed an algorithm to identify those areas where protections would deliver the greatest benefits across the three complementary goals of biodiversity protection, seafood production and climate mitigation. They mapped these locations to create a practical “blueprint” that governments can use as they implement their commitments to protect nature.

The study does not provide a single map for ocean conservation, but it offers a first-in-kind framework for countries to decide which areas to protect depending on their national priorities. However, the analysis shows that 30% is the minimum amount of ocean that the world must protect in order to provide multiple benefits to humanity.

“There is no single best solution to save marine life and obtain these other benefits. The solution depends on what society—or a given country—cares about, and our study provides a new way to integrate these preferences and find effective conservation strategies,” said Dr. Juan S. Mayorga, a report co-author and a marine data scientist with the Environmental Market Solutions Lab at UC Santa Barbara and Pristine Seas at National Geographic Society.

The study comes ahead of the 15th Conference of the Parties to the United Nations Convention on Biological Diversity, which is expected to take place in Kunming, China in 2021. The meeting will bring together representatives of 190 countries to finalize an agreement to end the world’s biodiversity crisis. The goal of protecting 30% of the planet’s land and ocean by 2030 (the “30x30” target) is expected to be a pillar of the treaty. The study follows commitments by the United States, the United Kingdom, Canada, the European Commission and others to achieve this target on national and global scales.

Safeguarding Biodiversity

The report identifies highly diverse marine areas in which species and ecosystems face the greatest threats from human activities. Establishing marine protected areas (MPAs) with strict protection in those places would safeguard more than 80% of the ranges of endangered species, up from a current coverage of less than 2%.

The authors found that the priority locations are distributed throughout the ocean, with the vast majority of them contained within the 200-mile Exclusive Economic Zones of coastal nations.

The additional protection targets are located in the high seas—those waters governed by international law. These include the Mid-Atlantic Ridge (a massive underwater mountain range), the Mascarene Plateau in the Indian Ocean, the Nazca Ridge off the west coast of South America and the Southwest Indian Ridge, between Africa and Antarctica.

"Perhaps the most impressive and encouraging result is the enormous gain we can obtain for biodiversity conservation—if we carefully chose the location of strictly protected marine areas,” said Dr. David Mouillot, a report co-author and a professor at the Université de Montpellier in France. “One notable priority for conservation is Antarctica, which currently has little protection, but is projected to host many vulnerable species in a near future due to climate change."

Shoring up the Fishing Industry

The study finds that smartly placed marine protected areas (MPAs) that ban fishing would actually boost the production of fish—at a time when supplies of wild-caught fish are dwindling and demand is rising. In doing so, the study refutes a long-held view that ocean protection harms fisheries and opens up new opportunities to revive the industry just as it is suffering from a recession due to overfishing and the impacts of global warming.

“Some argue that closing areas to fishing hurts fishing interests. But the worst enemy of successful fisheries is overfishing—not protected areas,” Dr. Sala said.

The study finds that protecting the right places could increase the catch of seafood by over 8 million metric tons relative to business as usual.

“It’s simple: When overfishing and other damaging activities cease, marine life bounces back,” said Dr. Reniel Cabral, a report co-author and assistant researcher with the Bren School of Environmental Science & Management and Marine Science Institute at UC Santa Barbara. “After protections are put in place, the diversity and abundance of marine life increase over time, with measurable recovery occurring in as little as three years. Target species and large predators come back, and entire ecosystems are restored within MPAs. With time, the ocean can heal itself and again provide services to humankind.”

Soaking up Carbon

The study is the first to calculate the climate impacts of bottom trawling, a damaging fishing method used worldwide that drags heavy nets across the ocean floor. It finds that the amount of carbon dioxide released into the ocean from this practice is larger than most countries’ annual carbon emissions, and similar to annual carbon dioxide emissions from global aviation.

“The ocean floor is the world’s largest carbon storehouse. If we’re to succeed in stopping global warming, we must leave the carbon-rich seabed undisturbed. Yet every day, we are trawling the seafloor, depleting its biodiversity and mobilizing millennia-old carbon and thus exacerbating climate change. Our findings about the climate impacts of bottom trawling will make the activities on the ocean’s seabed hard to ignore in climate plans going forward,” said Dr. Trisha Atwood of Utah State University, a co-author of the paper.

The study finds that countries with the highest potential to contribute to climate change mitigation via protection of carbon stocks are those with large national waters and large industrial bottom trawl fisheries. It calculates that eliminating 90% of the present risk of carbon disturbance due to bottom trawling would require protecting only about 4% of the ocean , mostly within national waters.

Closing a Gap

The study’s range of findings helps to close a gap in our knowledge about the impacts of ocean conservation, which to date had been understudied relative to land-based conservation.

“The ocean covers 70% of the earth—yet, until now, its importance for solving the challenges of our time has been overlooked,” said Dr. Boris Worm, a study co-author and Killam Research Professor at Dalhousie University in Halifax, Nova Scotia. “Smart ocean protection will help to provide cheap natural climate solutions, make seafood more abundant and safeguard imperiled marine species—all at the same time. The benefits are clear. If we want to solve the three most pressing challenges of our century—biodiversity loss, climate change and food shortages —we must protect our ocean.”

Additional Quotes from Supporters and Report Co-Authors

Zac Goldsmith, British Minister for Pacific and the Environment, UK

Kristen Rechberger, Founder & CEO, Dynamic Planet

Dr. William Chueng, Canada Research Chair and Professor, The University of British Columbia, Principal Investigator, Changing Ocean Research Unit, The University of British Columbia

Dr. Jennifer McGowan, Global Science, The Nature Conservancy & Center for Biodiversity and Global Change, Yale University

Dr. Alan Friedlander, Chief Scientist, Pristine Seas, National Geographic Society at the Hawai'i Institute of Marine Biology, University of Hawai'i

Dr. Ben Halpern, Director of the National Center for Ecological Analysis and Synthesis (NCEAS), UCSB

Dr. Whitney Goodell, Marine Ecologist, Pristine Seas, National Geographic Society

Dr. Lance Morgan, President and CEO, Marine Conservation Institute

Dr. Darcy Bradley, Co-Director of the Ocean and Fisheries Program at the Environmental Market Solutions Lab, UCSB

The study, Protecting the global ocean for biodiversity, food and climate , answers the question of which places in the ocean should we protect for nature and people. The authors developed a novel framework to produce a global map of places that, if protected from fishing and other damaging activities, will produce multiple benefits to people: safeguarding marine life, boosting seafood production and reducing carbon emissions. Twenty-six scientists and economists contributed to the study.

Study’s Topline Facts

• Ocean life has been declining worldwide because of overfishing, habitat destruction and climate change. Yet only 7% of the ocean is currently under some kind of protection.
• A smart plan of ocean protection will contribute to more abundant seafood and provide a cheap, natural solution to help solve climate change, alongside economic benefits.
• Humanity and the economy would benefit from a healthier ocean. Quicker benefits occur when countries work together to protect at least 30% of the ocean.
• Substantial increases in ocean protection could achieve triple benefits, not only protecting biodiversity, but also boosting fisheries’ productivity and securing marine carbon stocks.

Study’s Topline Findings

• The study is the first to calculate that the practice of bottom trawling the ocean floor is responsible for one gigaton of carbon emissions on average annually. This is equivalent to all emissions from aviation worldwide. It is, furthermore, greater than the annual emissions of all countries except China, the U.S., India, Russia and Japan.
• The study reveals that protecting strategic ocean areas could produce an additional 8 million tons of seafood.
• The study reveals that protecting more of the ocean--as long as the protected areas are strategically located--would reap significant benefits for climate, food and biodiversity.

Priority Areas for Triple Wins

• If society were to value marine biodiversity and food provisioning equally, and established marine protected areas based on these two priorities, the best conservation strategy would protect 45% of the ocean, delivering 71% of the possible biodiversity benefits, 92% of the food provisioning benefits and 29% of the carbon benefits.
• If no value were assigned to biodiversity, protecting 29% of the ocean would secure 8.3 million tons of extra seafood and 27% of carbon benefits. It would also still secure 35% of biodiversity benefits.
• Global-scale prioritization helps focus attention and resources on places that yield the largest possible benefits.
• A globally coordinated expansion of marine protected areas (MPAs) could achieve 90% of the maximum possible biodiversity benefit with less than half as much area as a protection strategy based solely on national priorities.
• EEZs are areas of the global ocean within 200 nautical miles off the coast of maritime countries that claim sole rights to the resources found within them. ( Source )

Priority Areas for Climate

• Eliminating 90% of the present risk of carbon disturbance due to bottom trawling would require protecting 3.6% of the ocean, mostly within EEZs.
• Priority areas for carbon are where important carbon stocks coincide with high anthropogenic threats, including Europe’s Atlantic coastal areas and productive upwelling areas.

Countries with the highest potential to contribute to climate change mitigation via protection of carbon stocks are those with large EEZs and large industrial bottom trawl fisheries.

Priority Areas for Biodiversity

• Through protection of specific areas, the average protection of endangered species could be increased from 1.5% to 82% and critically endangered species from 1.1% to and 87%.
• the Antarctic Peninsula
• the Mid-Atlantic Ridge
• the Mascarene Plateau
• the Nazca Ridge
• the Southwest Indian Ridge
• Despite climate change, about 80% of today’s priority areas for biodiversity will still be essential in 2050. In the future, however, some cooler waters will be more important protection priorities, whereas warmer waters will likely be too stressed by climate change to shelter as much biodiversity as they currently do. Specifically, some temperate regions and parts of the Arctic would rank as higher priorities for biodiversity conservation by 2050, whereas large areas in the high seas between the tropics and areas in the Southern Hemisphere would decrease in priority.

Priority Areas for Food Provision

• If we only cared about increasing the supply of seafood, strategically placed MPAs covering 28% of the ocean could increase food provisioning by 8.3 million metric tons.

The Campaign for Nature works with scientists, Indigenous Peoples, and a growing coalition of over 100 conservation organizations around the world who are calling on policymakers to commit to clear and ambitious targets to be agreed upon at the 15th Conference of the Parties to the Convention on Biological Diversity in Kunming, China in 2021 to protect at least 30% of the planet by 2030 and working with Indigenous leaders to ensure full respect for Indigenous rights.

Media Contact

The National Geographic Society is a global nonprofit organization that uses the power of science, exploration, education and storytelling to illuminate and protect the wonder of our world. Since 1888, National Geographic has pushed the boundaries of exploration, investing in bold people and transformative ideas, providing more than 15,000 grants for work across all seven continents, reaching 3 million students each year through education offerings, and engaging audiences around the globe through signature experiences, stories and content.

To learn more, visit www.nationalgeographic.org or follow us on Instagram , LinkedIn, and Facebook .

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April 9, 2024

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Impact of climate change on marine life shown to be much bigger than previously known

by Royal Netherlands Institute for Sea Research

Fish and invertebrate animals are far more affected by warmer and more acidic seawater than was previously known. This is the conclusion of a study co-led by NIOZ marine biologist Katharina Alter, based on a new analysis method published in Nature Communications .

Lead author Katharina Alter of the Royal Netherlands Institute for Sea Research (NIOZ) explains why it is essential to summarize and analyze the results of published studies addressing the effects of climate change . "To gain a better understanding of the overall worldwide impact of climate change, marine biologists calculate its effects on all fish or all invertebrate species lumped together."

"Yet, effects determined in different individual studies can cancel each other out: for example, if invertebrate animals such as snails profit from a certain environmental change and other invertebrates, such as sea urchins, suffer from it, the overall effect for invertebrates is concluded to be zero, although both animal groups are affected."

In fact, snails eat more due to climate change, and sea urchins eat less. Alter says, "Both effects matter and even have cascading effects: turf algae, the food for sea urchins , grow more while the growth of kelp, the food for gastropods, decreases. The difference in feeding in the two invertebrates causes a shift in the ecosystem from a kelp-dominated ecosystem to a turf algae-dominated ecosystem, consequently changing the living environment for all other animals living in this ecosystem."

Important for understanding ecological shifts

Together with colleagues from Wageningen University and 12 other research institutions from the US, France, Argentina, Italy, and Chile, dr. Alter developed a new research method that no longer cancels out seemingly contradictory results but uses both to determine the consequences of climate change on animals' fitness.

Before the use of this method, ocean warming and more acidic seawater were known to affect fish and invertebrate animals in three general ways negatively: their chance of survival is reduced, their metabolism is increased, and the skeletons of invertebrates are weakened.

Using the new method, the international group of marine researchers discovered that climate change has negative effects on additional important biological responses of fish and invertebrates: physiology, reproduction, behavior, and physical development. Alter says, "Because this may result in ecological shifts impacting marine ecosystem structures, our results suggest that climate change will likely have stronger impacts than previously thought."

Up to 100% of biological processes affected

Increasing levels of carbon dioxide in the air have been causing warmer and more acidic seawater for decades, a trend that is expected to continue in the future. However, it is unknown at which speed and to what extent.

Alter and her colleagues calculated the consequences of three projected scenarios of carbon dioxide increase, and thus of ocean warming and ocean acidification : extreme increase, moderate increase at the current speed and—due to possible measures—mitigated increase.

Alter says, "Our new approach suggests that if ocean warming and acidification continue on the current trajectory, up to 100% of the biological processes in fish and invertebrate species will be affected, while previous research methods found changes in only about 20 and 25% of all processes, respectively."

Furthermore, the research shows that measures to mitigate atmospheric carbon dioxide levels will help reduce changes in biological processes: in the low carbon dioxide scenario, 50% of responses in invertebrates and 30% in fish will be affected.

The big gain of the new method, according to Alter, is that more details become known about the effects of climate change on species. "The new calculation method weighs the significant deviation from the current state irrespective of its direction—be it beneficial or detrimental—and counts it as the impact of warming and acidifying seawater. With our new approach, you can include the broadest range of measured responses and detect impacts that were hidden in the traditional approach."

Journal information: Nature Communications

Provided by Royal Netherlands Institute for Sea Research

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Warning on Mass Extinction of Sea Life: ‘An Oh My God Moment’

A new study finds that if fossil fuel emissions continue apace, the oceans could experience a mass extinction by 2300. There is still time to avoid it.

By Catrin Einhorn

At first, the scientists chose a straightforward title for their research: “Marine Extinction Risk From Climate Warming.”

But as publication approached, something nagged at them. Their findings illustrated two drastically different outcomes for ocean life over the next three centuries depending on whether greenhouse gas emissions were sharply curbed or continued apace. Somehow it seemed the study’s name conjured only doom.

“We were about to send it in and I thought, ‘Gee, it sounds like a title that only has the dark side of the result,’” said Curtis Deutsch, a professor of geosciences at Princeton University who studies how climate change affects the ocean. “Not the bright side.”

So he and his co-author, Justin L. Penn, added an important word they hoped would highlight their finding that the grim scenario outlined by their results could still be, well, avoided.

On Thursday they published “Avoiding Ocean Mass Extinction From Climate Warming” in Science. It is the latest research that crystallizes the powerful yet paralyzed moment in which humanity finds itself. The choices made today regarding greenhouse gas emissions stand to affect the very future of life on Earth, even though the worst impacts may still feel far away.

Under the high emissions scenario that the scientists modeled, in which pollution from the burning of fossil fuels continues to climb, warming would trigger ocean species loss by 2300 that was on par with the five mass extinctions in Earth’s past. The last of those wiped out the dinosaurs.

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WSDF 2020, Mar 5-7 | Durango, Mexico

The Ocean: Life Below Water and Why it Matters

Key questions >>>

• Why does the ocean matter? How is the ocean important for sustainable development?
• What does the sustainable blue economy offer us?
• What are the ocean knowledge gaps?
• How do we need to develop a multidisciplinary ocean science?

The ocean covers around three-quarters of the earth's surface and contains more than 90% of living species on our planet. The ocean is also the single largest ecosystem in the world, and it provides food for billions of people worldwide, as well as maritime transport, renewable energies, and other goods and services like regulating, cultural and supporting services. 

Nevertheless, the ocean is not indestructible, and our footprint is very large. Overfishing, toxic pollution, invasive species, nutrient over-enrichment, habitat degradation and destruction, biodiversity loss, dependence of a growing global population on its goods and services, and coastal development, all threaten the sustainability of coastal ocean ecosystems ( Vanderweerd in Sherman and McGovern, 2011). Ocean acidification is also a growing threat that may be more important than warming, pollution and overfishing (Roberts, 2011).

Why Does the Ocean Matter?

Oceans mean different things for different people: life, passion or wonderment; vastly important; a very important source of life and energy; an incredible source of food and amazing source of biodiversity; it's wild, exciting, terrifying and exhilarating; means a lot to me, if something happens I will not have the fun I’m used to; it's a livelihood, it's been there for generations and hopefully will be there for generations to come.’ (Adapted from video excerpt, Plymouth Marine Laboratory, 2011, in Muñoz-Sevilla and Le Bail 2017).

According to the World Wildlife Fund, the ocean is currently valued at $24 trillion dollars. The goods and services from marine environments add up to an additional $2.5 trillion yearly. This means the ocean would have the seventh-largest GDP in the world.  However, the value of the ocean relies on its current output, which in turn depends on its conditions. Climate change, ocean acidification, habitat destruction, pollution and overfishing are endangering the ocean and threatening its value and the security and livelihood of the three billion people who depend on it. Most of these people live in Small Island Developing States, they are among the ones who contribute least to these issues, but they are the ones at most risk, as they’re already vulnerable. ( Hoegh-Guldberg 2015)

Agenda 2030: SDG 13 and SDG 14

A historical change has been taking place for the past 23 years, from Agenda 21 to Agenda 2030. At the Rio de Janeiro Earth Summit in 1992, more than 178 countries adopted Agenda 21. The Millennium declaration was adopted after the 2000 Millennium Summit in New York. 10 years after the Rio Earth Summit, in the Millennium Development Goals (MDGs) that were adopted during the Earth Summit in Johannesburg, ocean issues were included in the conversation for the first time. 

In 2012, at the United Nations Conference on Sustainable Development (also popularly known as Rio+20), member states adopted the document titled “The Future We Want”, which set the process of developing the sustainable development goals (SDGs) building on the MDGs. Finally, during the UN Sustainable Development Summit in 2015, seventeen SDGs were adopted which are an integral part of the 2030 Agenda.  

Progress of SDG 14 in 2019

The expansion of protected areas for marine biodiversity and existing policies and treaties that encourage responsible use of ocean resources are still insufficient to combat the adverse effects of overfishing, growing ocean acidification and worsening coastal eutrophication. As billions of people depend on oceans for their livelihood and food source, increased efforts and interventions are needed to conserve and sustainably use ocean resources at all levels. 

• Ocean acidification is caused by the uptake of atmospheric CO 2 by the ocean, which changes the chemical composition of the seawater. Long-term observations over the past 30 years have shown an average increase of acidity of 26 percent since pre-industrial times. At this rate, an increase of 100 to 150 percent is predicted by the end of the century, with serious consequences for marine life. 
• To achieve sustainable development of fisheries, fish stocks must be maintained at a biologically sustainable level. Analysis reveals that the fraction of world marine fish stocks that are within biologically sustainable levels declined from 90 percent in 1974 to 66.9 percent in 2015. 
• As of December 2018, over 24 million km 2 (17.2 per cent) of waters under national jurisdiction (0–200 nautical miles from a national border) were covered by protected areas, a significant increase from 12 percent in 2015 and more than double the extent covered in 2010. The protected areas increased from 31.2 per cent in 2000 to 44.7 per cent in 2015 and to 45.7 per cent in 2018. 
• Illegal, unreported and unregulated fishing remains one of the greatest threats to sustainable fisheries, the livelihoods of those who depend upon them and marine ecosystems. Most countries have taken measures to combat such fishing and have adopted an increasing number of fisheries management instruments in the past decade. 
• Small-scale fisheries are present in almost all countries, accounting for more than half of total production on average, in terms of both quantity and value. To promote small-scale fishers’ access to productive resources, services and markets, most countries have developed targeted regulatory and institutional frameworks. However, more than 20 per cent of countries have a low to medium level of implementation of such frameworks, particularly in Oceania and Central and South Asia.

The Ocean Decade

To recognize that more needs to be done to mitigate the global decline in ocean health, in December 2017, the UN declared 2021 to 2030 as the decade of ‘Ocean Science and Sustainable Development’. 

The Ocean Decade will strengthen international cooperation in all levels by strengthening dialogues, developing partnerships, developing capacity-building and leveraging investment, while supporting the entire 2030 Agenda for sustainable development. Other critical goals include improving ocean literacy and education to modify social norms and behaviors, and creating new models for ocean action.

The Ocean Decade aims to include science-informed mitigation and adaptation policies around the world and share knowledge with coastal communities who are most vulnerable to the changes of the ocean. (Claudet et al. 2019)

The COVID-19 Pandemic and the Ocean 

From Little Blue Letter, Glen Wright

• Marine creatures are enjoying some quiet time as underwater noise levels drop. Scientists are studying these effects on marine mammals.
• ​From Florida to Thailand, the number of sea turtles nests has increased on the now-empty beaches. The rapid recovery of marine wildlife in coastal areas shows how extensive our impacts are and highlights the importance of protected areas. 
• Fishers around the world are struggling with decreased demand, lack of sanitary conditions and logistical challenges. In some countries, like India, food security of the communities may be affected by this disruption of supply chains.
• PADI and Rash’R are producing (non-profit)  reusable face masks made from Ocean plastic , with designs based on sea animals!

Final Remarks

We can all take small steps towards protecting our ocean. Reduction of single-use plastic, responsible fish consumption, avoiding ocean harming products, and making your voice heard can all directly contribute towards a healthier ocean. However, more indirect approaches can be taken by reducing the amount of greenhouse gases produced by our daily activities and, therefore, reducing our carbon footprint. Reducing red meat consumption, consuming locally sourced products and using personal vehicles less are all examples of small steps we can take towards reducing our impact. The sum of individual actions can truly make a difference in the fate of our ocean.

Collectively, we need to form a global ocean community, acknowledging that all of our actions have an impact on the ocean (Claudet et al. 2019). And, although it is incumbent on each of us to take steps to protect the ocean, collective action is also required. New models for ocean action, which are collaborative, intergenerational, cross-cultural, and multi-sectoral, are needed in the coming decade, in order to protect our beloved ocean. 

The ocean is our life support system, it connects every one of us, you can think of the ocean as the blue heart  of this planet, but then we look after that heart and we know how we are damaging it and it needs intensive care. We know that scientists, politicians and stakeholders are talking to each other, but it isn’t just up to them, each and every one of us can make the difference, even if the difference might be small, after all individual small drops of sea water can make up the vast ocean . (Adapted from video excerpt, Plymouth Marine Laboratory 2011, in Muñoz-Sevilla and Le Bail 2017).

Bibliography

Cheung, W. et al (2013), “Signature of Ocean Warming in Global Fisheries Catch”, Nature, 497(2013): 365–368.

Claudet, J. et al (2019), “A Roadmap for Using the UN Decade of Ocean Science for Sustainable Development in Support of Science, Policy, and Action”, One Earth , 2(1): 34-42.

Halpern, B. et al (2012), “An Index to Assess the Health and Benefits of the Global Ocean, Nature , 488(2012): 615–620.

UNESCO and UNEP (United Nations Educational, Scientific and Cultural Organization and United Nations Environment Programme) (2016), Large Marine Ecosystems: Status and Trends, Summary for Policy Makers , Nairobi: UNEP.

Muñoz-Sevilla N. and M. Le Bail M (2017), “Latin American and Caribbean Regional Perspectives on Ecosystem Based Management (EBM) of Large Marine Ecosystems Goods and Services”, Environmental Development , 22(2017), 9-17.

Munoz-Sevilla N. et al (2019), UNU Ocean Institute Scoping Study Report , Tokyo: United Nations University.

Plymouth Marine Laboratory (2011), Ocean Acidification: Connecting Science, Industry, Policy and Public (A Short Film for the Natural Environment Research Council and the UK Ocean Acidification Research Programme), Plymouth Marine Laboratory

Roberts D. (2011), In: Ocean Acidification: Connecting Science, Industry, Policy and Public . A short film for the Natural Environment Research Council and the UK Ocean Acidification Research Programme. Plymouth Marine Laboratory.

Sherman, K. and G. McGovern (2011), Toward Recovery and Sustainability of the World’s Large Marine Ecosystems during Climate Change , Gland, Switzerland: International Union for Conservation of Nature.

Sherman K. et al (2017), “Sustainable Development of Latin American and the Caribbean Large Marine Ecosystems”, Environmental Development , 22(2017), 1-8.

United Nations (2015), Transforming Our World: the 2030 Agenda for Sustainable Development , New York: UN.

Wright G. (2020), “The Pandemic and the Ocean”, Email Correspondence on May 1, 2020.

Hoegh-Guldberg, O. (2015), Reviving the Ocean Economy: The Case for Action , Geneva: World Wide Fund for Nature.

Consulted on April 24th, 2020. (2019) What is the United Nations Decade of Ocean Science for Sustainable Development?. https://www.oceandecade.org/about?tab=our-story . Consulted on May 4th, 2020.

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This study shows that marine reserves can be climate reserves, especially when the protected areas are large, well-managed, in place for many years, and strict regarding what human activities are allowed.

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The results of this study, led by Dr. Callum Roberts and Dr. Bethan O’Leary from the University of York in the  United Kingdom, emphasize the urgent need for governments to implement climate-adaptation policies in order  to safeguard the oceans and societies that depend on them. According to Roberts, these findings “show that to boost the resilience of ocean ecosystems, safeguard their wildlife, protect their capacity to support fisheries,  provide coastal protection, and offer clean, healthy water, we must accelerate implementation of effective marine protected areas.”

The findings also reinforce the worldwide demand for stronger ocean protections. The International Union for Conservation of Nature (IUCN), at its 2016 World Conservation Congress, adopted a motion calling for protection of at least 30 percent of the ocean by 2030 to avert large-scale extinction of marine life. Governments are taking notice, and the movement to protect large expanses of ocean space is growing. In recent years, countries such as Chile, Mexico, Palau, the United Kingdom, and the United States have set aside huge swaths of ocean for protection.

Counteracting the combined impacts of overfishing, warming ocean waters, and rising acidity requires swift action. To date, about 5 percent of the ocean has been set aside for protection and just over 2 percent is highly protected, although there are commitments to raise the total coverage of protected areas to 10 percent by 2020. The IUCN has recommended protecting 30 percent of all marine habitats by 2030. Acting now, to put in place fully protected ocean space, is necessary to safeguard these ecosystems and the economic benefits they provide.

Marine reserves are noted to be a low-tech, cost-effective strategy in ocean conservation. This study clearly indicates that this conservation tool can yield multiple benefits beyond ocean conservation, on local, regional, and global scales. The establishment of marine reserves can help slow the effects of climate change and alleviate some of its expected hardships, such as reduced food security and sea level rise. Large, highly protected reserves can help ensure the future health of our ocean for communities whose livelihoods depend on it.

Matt Rand directs The Pew Bertarelli Ocean Legacy Project.

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• Published: 09 September 2024

Environmental stress reduces shark residency to coral reefs

• Michael J. Williamson   ORCID: orcid.org/0000-0001-9681-8920 1 , 2 , 3 ,
• Emma J. Tebbs   ORCID: orcid.org/0000-0003-0575-1236 2 ,
• David J. Curnick   ORCID: orcid.org/0000-0002-3093-1282 1 ,
• Francesco Ferretti 4 ,
• Aaron B. Carlisle 5 ,
• Taylor K. Chapple 6 ,
• Robert J. Schallert 7 ,
• David M. Tickler 8 ,
• Barbara A. Block 7 &
• David M. P. Jacoby   ORCID: orcid.org/0000-0003-2729-3811 1 , 9  

Communications Biology volume  7 , Article number:  1018 ( 2024 ) Cite this article

Metrics details

• Animal behaviour
• Behavioural ecology
• Climate-change ecology

Coral reef ecosystems are highly threatened and can be extremely sensitive to the effects of climate change. Multiple shark species rely on coral reefs as important habitat and, as such, play a number of significant ecological roles in these ecosystems. How environmental stress impacts routine, site-attached reef shark behavior, remains relatively unexplored. Here, we combine 8 years of acoustic tracking data (2013-2020) from grey reef sharks resident to the remote coral reefs of the Chagos Archipelago in the Central Indian Ocean, with a satellite-based index of coral reef environmental stress exposure. We show that on average across the region, increased stress on the reefs significantly reduces grey reef shark residency, promoting more diffuse space use and increasing time away from shallow forereefs. Importantly, this impact has a lagged effect for up to 16 months. This may have important physiological and conservation consequences for reef sharks, as well as broader implications for reef ecosystem functioning. As climate change is predicted to increase environmental stress on coral reef ecosystems, understanding how site-attached predators respond to stress will be crucial for forecasting the functional significance of altering predator behavior and the potential impacts on conservation for both reef sharks and coral reefs themselves.

Introduction

Over the past 20 years there has been a significant decline of coral cover across the world’s coral reef ecosystems due to increases in disease, tropical cyclones, and bleaching events 1 , 2 . Coral bleaching can cause increased mortality, reduced coral cover, loss of structural complexity, reduced biodiversity as well as altering species and community composition and ecosystem function 3 , 4 , 5 . Multiple shark species are reliant on coral reefs as important habitat for feeding, breeding and as social refugia 6 , 7 , 8 . Consequently, climate change induced changes in coral reef habitat have the potential to significantly impact the behaviour of predators associated with reef ecosystems, such as reef sharks 9 , 10 . Despite widespread awareness of the perilous state of global shark populations 11 , including reef sharks 12 , 13 , the link between habitat quality, changing environmental drivers, and movement ecology, as well as how these factors interact to impact population vulnerability, remains relatively unexplored 6 .

Reef sharks exhibit routine use of habitats and different ecological landscapes, as they feed, develop and reproduce 14 , 15 . Residency, defined as ‘an individual exhibiting largely uninterrupted occupancy of a limited area for a specified period of time’ 16 , is one aspect of routine animal movement which facilitates crucial ecological processes, and thus is inherently linked to habitat quality, trophic interactions and population persistence 17 , 18 . However, many of the drivers influencing residency in reef shark species, including environmental stress, are not well understood. Changes in reef shark behaviour may have significant implications for ecological processes, such as population dynamics 19 , predator-prey landscapes 20 , nutrient transfer 21 , dispersal 22 , and management and conservation 23 . As such, understanding species responses to disturbance and the longevity or lag in these responses, especially in light of increasing anthropogenic impacts, is becoming ever more important as we face the current biodiversity crisis 24 .

Grey reef sharks ( Carcharhinus amblyrhynchos ) are an Indo-Pacific distributed shark species, commonly associated with coral reefs 7 , 25 and currently listed as Endangered by the International Union for the Conservation of Nature (IUCN) 26 . Grey reef sharks are site-attached, central place foragers which move periodically and predictably from a core area of residency 27 . This behavioural trait make them a good model for evaluating residency, and particularly amenable to acoustic telemetry, where receivers can be located around coral reefs or atolls to monitor long-term space use within core areas and movements within shallow waters 6 , 28 . As reef shark species are increasingly threatened with extinction 12 , long-term data from widely distributed model species, particularly how shark movement is altered with changing environmental conditions on coral reefs, is valuable for informing conservation and management strategies.

Environmental stress can be defined as negative impacts on the growth and health of ecosystems resulting from changes or extremes in environmental variables 29 , 30 . Coral reefs are susceptible to a number of environmental stressors 31 , which in turn may impact reef shark populations. However, there can be significant inter-, and intra-regional variance in how different environmental variables drive stress on these ecosystems 32 , 33 . For example, an environmental stress index, based on satellite remote sensing data that allows assessment of multiple abiotic environmental stressors, recently found that sea surface temperature (SST), current and wind were the primary drivers of environmental stress in the Chagos Archipelago in the Indian Ocean, however depth and SST, and Degree Heating Weeks (DHW), SST, and current, were stronger drivers of stress on coral reefs in the Red Sea and the Gilbert Islands, respectively 32 . Composite indices such as this, therefore, capture interactive variables that may increase, or reduce, environmental stress, providing an opportunity to gain a more holistic understanding of how multiple environmental stressors on coral reefs can impact reef shark movement and residency.

This study aims to investigate how reef shark residency to remote coral reefs is influenced by environmental stress to the reef itself, using an index that balances the following remotely sensed environmental variables: cloud cover, current, depth, salinity, four metrics of SST (SST, DHW, SST anomaly, SST variability), and wind. As reef shark residency is likely to be in part influenced by changes in environmental conditions 15 , 34 , we hypothesise that as environmental stress on coral reefs increases, reef shark residency will decrease. We suggest this is a behavioural response designed to locate more appropriate habitat, enhance resource availability (e.g., prey, physical or thermal refugia), thus decreasing residency to coral reef ecosystems.

Following data preparation and filtering, 714,810 detections from 122 grey reef sharks (81 female, 41 male) from 52 receivers were used for analysis. Grey reef shark lengths ranged from 70–159 cm with mean (SD) = 117.9 cm (19.6) (Supplementary Data  1 ). Residency index for grey reef sharks ranged from 0.03 to 1.00 with mean (SD) = 0.34 (0.33). Environmental Stress Exposure (SE) index values, calculated at the estimated range of each acoustic receiver, varied from 0.03–0.60 with mean (SD) = 0.22 (0.09), on a scale of 0–1 (low to high stress).

Residuals of the global model were free from heteroscedasticity and temporal autocorrelation (Supplementary Fig.  1 ). Following the dredge and nesting of the global model, two candidate models were found with ΔAICc values < 2 (Supplementary Table  1 ). Relative importance values of environmental stress exposure (SE) index, season, sex, and year were all greater than 0, indicating they are important predictors for explaining residency in reef sharks (Table  1 ). Total length had a relative importance of zero and was not deemed an important predictor.

Model averaging of the two candidate models indicated that environmental SE, season, and year were all significant predictors of residency in grey reef sharks in the Chagos Archipelago (Table  1 ). A significant negative relationship between residency and combined environmental SE index was found (estimate = −0.1, z  = −10.48, p  < 0.001, Fig.  1 ), indicating that on average across the reefs of the northern atolls of the archipelago, grey reef sharks became less resident as environmental stress on reefs increased, particularly during strong El Niño conditions, albeit with a delay in these effects during the strong El Niño event. Kernel estimates (KUD) of core (50%), 75% and 95% space use all increased almost immediately during elevated periods of stress exposure, suggesting space use became more diffuse (Fig.  1B ). The variance and standard deviation of the random factors ID and station on the logit scale were 1.90 and 1.38, and 1.58 and 1.26, respectively. Marginal R 2 (R2m) was 0.02 and conditional R 2 (R2c) 0.52, suggesting high variation between stations and individuals. Results from conditional models of the random effects and their standard deviations suggest that 56% (29/52) of receivers had residency significantly different from the intercept, with some showing increased residency (Fig.  2 ). A similar relationship between residency and combined environmental SE index (estimate = −0.07, z  = −4.34, p  < 0.001) (Supplementary Table  2 ) was found even after data from El Niño periods were removed, suggesting persistence of this trend even without extreme climatic events known to cause high environmental stress to coral reefs. The median duration of time spent away from the forereefs were not stochastically equal between times of low and high stress; grey reef sharks were absent for significantly longer when stress was high (Brunner-Munzel; P̂*(1235.9) = −2.8336, p  = 0.0047). The probability that sharks would remain away from the forereef longer during times of stress was 0.4661 (Fig.  3A ).

Temporal trends in combined environmental stress exposure (SE) index experienced by coral reefs in the northern atolls of the Chagos Archipelago during a strong El Niño ‘episode’ and weaker El Niño ‘conditions’ ( A ). Grey reef shark residency (blue trend line) during the same period (Feb 2013–Feb 2021) and temporal changes in area use (km 2 ) measured as the 50% (yellow), 75% (green) and 95% (pink) kernel utilisation estimation (KUD) ( B ).

A Departures of 122 grey reef sharks from the global intercept are plotted with 95% CIs (black bars). Receivers where CIs do not cross zero indicate average residency significantly different than the average. Receivers where grey reef sharks had less than the average residency have negative global intercept values, and those that had more have positive intercept values. B Spatial distribution of acoustic receivers ( n  = 52) coloured by departure from global intercept, where pink is significantly more resident and green significantly less resident (transparent = no change in residency).

A The mean delay in log days, between detections for 122 grey reef sharks leaving the forereef (note: for clarity we represent the mean but test the median using a Brunner-Munzel test to show that the probability that sharks would remain away from the forereef longer during times of stress was 0.466). Box plot represents median mean delay in log days and the interquartile range. Whiskers extend from the hinge to the highest and lowest values within 1.5× the interquartile range. Outliers are not visualised. B The proportion of tagged 122 grey reef sharks falling within each mean residency index bin (0.0–1.0) across 14 sixth monthly periods. Yellow/red indicate El Niño conditions and blue, non-El Niño conditions.

Cross correlations of mean environmental SE index and mean residency index indicated lagged effects (Supplementary Fig.  2 ) with significant negative values at t  − 0 to t  + 16, indicating that ‘current’ environmental stress on coral reefs has a significant persistent negative impact on grey reef shark residency for up to 16 months (Fig.  1 ). Within this time period, correlation coefficient values varied between −0.16 and −0.37. There was also a significant relationship between residency and season, with grey reef sharks in the wet season less resident than during the dry season (Table  1 ) (estimate = −0.40, z  = −21.73, p  < 0.001). Residency behaviour significantly differed in all years, except 2014 and 2018, compared to the baseline year of 2013 (Table  1 ). Sex was not a significant predictor of residency.

Generalised Additive Mixed Modelling (GAMM) results indicated a significant impact of date (Supplementary Fig.  3 ) (edf = 8.9, Ref.df = 9.0, F  = 209.0, p  = <0.001), suggesting that environmental stress varied through time in the region, lowest in March 2013, January 2017 and September 2020 and peaking in May 2015 and May 2016, matching El Niño events in the region (Fig.  1 and Supplementary Fig.  3 ). The adjusted R squared value was 0.28 and deviance explained 27.6%. The proportion of sharks with low residency scores also changed throughout time, with a greater proportion of sharks decreasing residency during periods of high stress also matching these El Niño events (Fig.  3B ). Beta regression and post hoc results indicated significant differences between some atolls within year periods (Supplementary Table  3 ), with receivers at Blenheim reef experiencing significantly less environmental SE than receivers at Salomon and Peros Banhos in 2013, 2014, 2015, and 2016. Receivers at Victory Bank experienced significantly less environmental SE than receivers at Salomon in 2013, 2014, 2015, 2016 and 2018 and Peros Banhos in 2013, 2014, 2015, 2016, 2018 and 2019. Receivers at Benares Shoal experienced significantly more environmental SE than receivers at Victory Bank in 2016.

Climate change is projected to have a strong influence on marine habitats and, as such, is predicted to alter and impact the movement ecology of marine species 9 , 10 . Here, we analysed a multi-year dataset to explore the influence of environmental habitat stress, based on a composite index of nine remotely sensed environmental variables, on the residency behaviour of a site-attached shark species, found in abundance throughout the coral reefs of the Indo-Pacific. We show that increased environmental stress on coral reef habitat reduces residency in grey reef sharks, promoting more diffuse space use and extending periods away from the reef. Additionally, our results suggest that this impact has a lagged effect across the archipelago, with increased environmental stress altering residency for up to 16 months. These findings will likely have important repercussions for trophic interactions and reef ecosystem functioning 7 , 21 with potential alterations in nutrient subsidies to reefs. In addition, these results may also affect conservation and management of both grey reef sharks and coral reef ecosystems, with decreased residency potentially shifting the likelihood of interactions with both commercial and Illegal, Unregulated and Unreported (IUU) fisheries 35 . However, to date, this has yet to be examined. Interestingly, we find that these results are not ubiquitous across the whole area. Some receivers showed significant negative departures, while others showed significant positive departures, from the global mean, suggesting localised factors, such as reef resilience likely also influence residency in grey reef sharks (Fig.  2 ).

Our findings support our hypothesis that, overall, grey reef sharks reduce residency behaviour in the face of increased habitat stress. To respond, large mobile marine ectotherms must balance the behavioural trade-off between moving to escape stress, which requires increased energy expenditure and potentially increased risk, or remaining in the same area, which may become suboptimal but in doing so might conserve energy 36 . Although the energetic implications for these behaviours were not explicitly tested here, we do show an expansion of both core and broader space use, as well as increased periods of absence during times of stress. This might include moving into offshore, deeper and likely cooler waters, and as a result spending more time outside of receiver coverage. For a species well documented to maintain and regularly return to core areas of reef facilitating several important behavioural processes 8 , 27 , these results are a concern. In the short term, our results suggest an immediate ‘avoidance’ response that has implications for this species’ ecology and conservation, but in the longer term, where the benefits of departing suboptimal habitat outweigh the benefits of remaining, there are likely to be implications for the wider reef ecosystem structure (for up to 16 months post peak stress). This study does not aim to tease apart the specific mechanisms driving these short or long-term responses, which are likely different for these two processes. It does, however, offer an exciting research avenue for future studies to explore the different mechanisms influencing space use in response to environmental stress, and at different temporal scales. From a practical perspective, changes in residency may also be due to the influence of changing environmental conditions, such as wind speed, on acoustic detectability 37 , 38 in addition to coral reef health. Range testing was not feasible at this site during the period of study, so this could not be assessed. However, given the long time-series of data obtained, and the wide variation in environmental conditions throughout the study period, the impacts of varying detectability is like to be minimal.

Encouragingly, this negative association between stress and shark residency was not ubiquitous across all monitored locations. Model variance suggests that there is significant variation in residency at each receiver, with sharks more resident in some locations compared to others (Fig.  2 ). This pattern coincided with spatial and temporal variation between atolls in environmental stress exposure in this region 32 , 39 . Receivers in the north and west of Peros Banhos experienced a reduction in residency from the global average, but some receivers in the south of Peros Banhos and west of Salomon Islands experienced an increase in residency (Fig.  2B ). Interestingly, our regional pattern of residency maps with the spatial patterns of rat infestation on the islands of the northern atolls, with receivers with higher residency overlapping with rat absent and rat eradicated islands, and receivers with significantly less residency than the global average overlapping with rat present islands 40 . Although we do not explicitly explore mechanistic drivers within this study, recent research in the Chagos Archipelago has found that seabird nutrients significantly enhance fish biomass on reefs surrounding rat absent or rat eradicated islands compared to islands with rats 41 , 42 . These regional patterns clearly warrant further investigation, but could indicate grey reef shark residency is also influenced by factors that make particular reef habitats more resilient to perturbation, leading to these being more stable areas to occupy as a reef predator.

The spatial variation in residency observed could also be driven by hydrodynamic factors. Coral response to environmental stress, such as bleaching, can be highly variable, even within a reef system, and often is the result of differing fine scale environmental and biological processes 32 , 43 . There is also some congruence between areas of increased residency and areas that are sheltered from wave exposure. Shelter from wave exposure is associated with increased coral cover and quicker recovery from bleaching events 44 , another potentially important factor influencing shark behaviour. The mechanisms driving these results are clearly complex and involve a mixture of variability in shark behaviour as well as heterogeneity in coral reef response to environmental stress, and at different temporal scales.

Results from cross correlations indicate that reduced residency of grey reef sharks on coral reefs in the archipelago persists well into the future, as much as up to 16 months. Time lags in how coral reefs themselves respond to stress can be relatively short, within a few weeks 45 , 46 , or prolonged, over periods of months or even years 47 , 48 . The time lag in response to stress in other reef-reliant species can also vary from months to several years 49 . For example, Halford and Caley 50 found a time lag of 12–18 months between bleaching and change in structure of fish communities on remote reefs of north-west Australia. Declines in the abundance and diversity of coral reef fishes may be apparent more than 3 years after coral depletion in some regions, due to the delay in structural collapse of dead corals 51 . The true mechanisms underlying both the instantaneous reduction in residency and its persistence for months following increased environmental stress seen here are unknown and provide an interesting next step for this research, such as exploration of possible correlation between areas of greater stress and the persistence of reduced residency in reef sharks.

This study did not examine the precise environmental factors driving reductions in residency. Stress on coral reefs is often closely linked to SST, and other temperature metrics, such as DHW and SST variability 32 , 52 , and metrics of SST contributed considerably to the environmental SE index 32 . As such, the reduced residency found in this study could be driven by increases or changes in metrics of SST. Reef sharks are ectotherms and have been seen to exhibit behavioural thermoregulation to regulate their body temperatures and avoid physiological damage from adverse SSTs 15 . Therefore, an influence of different metrics of SST on movement is to be expected. Indeed, links with SST, SST anomalies, and SST variability and movement, residency, and presence/absence of other shark species have been seen elsewhere 53 , 54 . For example Ryan et al. 54 found that low SST anomalies increased white shark ( Carcharodon carcharias ) presence and residency, which increased the chances of attacks on the eastern Australian coast. However, little is known about these relationships in reef sharks 6 , and the few studies that have investigated these relationships have typically found that changing environmental conditions have limited impact. For example, Schlaff et al. 55 found that size and sex were the most important drivers of activity space in Australian blacktip reef sharks Carcharhinus melanopterus , with salinity and water temperature having significant but relatively low impacts, while Heupel and Simpfendorfer 56 found no relationship between activity space and environmental variables in grey reef sharks on the Great Barrier Reef. As such, these results, to our knowledge, provide the some of the first evidence of changing environmental variables impacting the movement and residency of grey reef sharks.

Season was also found to have a significant effect on residency in grey reef sharks, which supports previous research at this site that showed that grey reef sharks spent more time away from reefs during the wet season compared to the dry season 28 . These changes in residency with season could be due to environmental or ecological factors. Shark species have been seen to increase movement and decrease residency during storm events 57 , 58 , which may be increased during the wet season. Alternatively, residency changes may be due to changes in food resources, with historical fisheries known to peak in the wet season in this region 59 , 60 . In addition, our results confirm that year is a variable that should be regularly included as a predictor variable to account for temporal variation when modelling movement ecology of marine species 61 , 62 , which here is most likely linked particularly to the severity of environmental change associated with El Niño events.

As climate change continues to alter oceanic conditions, environmental stress across marine ecosystems, including coral reef habitats, is predicted to increase 63 , 64 . Although reef sharks use coral reef systems as primary habitat, they can spend significant periods of time away from reefs for foraging 28 , 65 , bringing substantial nutrients from deeper pelagic waters that could not be produced by the reefs themselves 21 . Consequently, reduced residency by reef sharks could lead to a reduction of subsidies between pelagic and reef ecosystems, with these cross-ecosystem flows of energy potentially influencing reef resilience during times of high environmental stress. In addition, reduced residency may have trophic implications on particular reefs, with species assemblage reorganisation possible due to loss of large mesopredators 66 , 67 .

As well as ecological impacts to coral reef systems, these results also have implications for the ecology and conservation of reef sharks. Reduced residency may result in increased energetic costs, with subsequent impacts on survival, growth and reproduction 68 , 69 , 70 . In the Chagos Archipelago, and other coral reef systems, grey reef sharks are under threat from Illegal, Unregulated and Unreported (IUU) fishing activity 35 , 71 , 72 , which is believed to be suppressing populations around Peros Banhos and Salomon Islands 73 . Reef shark species that are less resident on coral reef systems are more threatened with extinction risk 12 . Sharks that shift to spending more time in offshore waters may increase their vulnerability to IUU fishing, especially in a large, remote area such as the Chagos Archipelago, as increased movements in some species may increase the encounter rate with IUU vessels 35 , or commercial fisheries, as they spend less time in MPAs. Alternatively, as fishers target where shark are known to aggregate 71 , reduced residency may mean fewer aggregations and reduced impact of fishing on this species as they are less likely to find large numbers of them in one location.

The environmental SE index used in this study was chosen as it includes both reducers and enhancers of stress, therefore providing a balanced metric that helps decipher differences between reefs as well as providing a holistic view of multiple stressors on reef systems, not only those that drive bleaching 32 . Consequently, the results seen here may relate to reef shark response to environmental stress on coral systems, rather than to direct habitat damage. There are now remotely sensed products of coral bleaching available, such as the Allen Coral Atlas ( www.allencoralatlas.org ) but these were not used in this study because the data was only available for 2019 onwards. In addition, it should be noted that the environmental SE index does not contain an exhaustive list of environmental stressors. Environmental variables such as turbidity, chlorophyll- a , pH and ultra-violet light, all known to impact coral habitat quality, are not included in this index, due to a lack of appropriate products or due to low accuracy in shallow areas, such as coral reefs 32 , 74 . Furthermore, some of the variables in the environmental SE index influence habitat quality but can also have direct impacts on reef shark movement behaviour, so it is difficult to disentangle direct and indirect effects using this approach. Future work could address these issues by including additional environmental variables, currently unavailable, into the index, and when satellite-based maps of bleaching become available with sufficient temporal coverage these could be included disentangle these effects.

With climate change predicted to cause bleaching events annually by 2043 75 , changing environmental stress and disturbance on coral reefs has the potential to impact the movement and ecology of reef shark species. Here, we used 714,810 detections from acoustic tagging data for 122 grey reef sharks between 2013 and 2020, combined with satellite remote sensing data to investigate how changing environmental stress on coral reefs can impact the residency of an abundant reef shark species in the Chagos Archipelago. Environmental stress, season and year were all significant predictors of grey reef shark residency. As such, we show that increased environmental stress on coral reef ecosystems reduces grey reef shark residency, expands their space use and extends periods of absence from the reef, patterns that persist more than a year beyond the main period of stress. In addition, there is also some cause for optimism with our finding that residency varies significantly at different reef locations with some areas showing increased residency. As environmental stress on coral reef systems is predicted to increase 76 , 77 , 78 , these changes have important consequences for the ecology and ecosystem functioning of coral reefs in the region, such as altering nutrient subsidies. Furthermore, decreased residency is likely to have conservation impacts on the sharks themselves, potentially altering their interactions with IUU and commercial fishing vessels. Finally, results from the study will enhance predictions about predator responses to climate-related stress into the future.

Materials and methods

Data collection and study site.

The Chagos Archipelago is a large, remote archipelago, at the centre of the British Indian Ocean Territory Marine Protected Area (BIOT MPA) in the Central Indian Ocean. Established in 2010, the reefs are home to multiple resident and transient elasmobranch species 79 , 80 . Following El Niño events in 2015 and 2016, the coral reefs of the Chagos Archipelago experienced widespread bleaching events in response to substantial increases in environmental stress 39 , 79 . A long-term tagging programme of grey reef sharks has been undertaken in the region since 2013 to investigate the efficacy of the MPA for protecting large mobile fishes and for understanding how ecology can inform MPA enforcement 23 , 35 .

Acoustic telemetry data were collected from five atolls (Benares, Blenheim, Peros Banhos, Salomon and Victory Bank) in the Chagos Archipelago between 2013 and 2021 from an acoustic array of 54 receivers 28 (Supplementary Fig.  4 ). All receivers were far enough apart to avoid overlap in their detection range, with mean distance to the next closest receiver being 2.15 km and ranging from 0.55–4.57 km 28 . Although range testing was not undertaken for this array, due to financial and logistical constraints of vessel time in the Chagos Archipelago, other studies conducted around coral atolls in the Indian Ocean using the same or similar equipment have found detection ranges between 300 and 500 m 81 , 82 .

This study utilised tracking data from grey reef sharks carrying 10-year, V16, 69 kHz Innovasea coded acoustic transmitters between 1st March 2013 and the 30th November 2020. In total 122 grey reef (81 female, 41 male) were tagged with sharks being caught from handlines and barbless hooks. Larger animals (>1.5 m) were kept in the water, but all others were brought onto the boat and restrained. A seawater house was used to irrigate the gills, and a wet cloth placed over the eyes. Once restrained, sharks were measured and acoustic tags implanted intraperitoneally through a small incision ( ∼ 2–3 cm) just off the midline of their abdomen 23 . Total handling time was generally less than 5 min per animal. All procedures were approved by the Stanford University Administrative Panel on Laboratory Animal Care (APLAC) under permit APLAC-10765. Tags were configured to transmit an acoustic ‘ping’ containing a unique ID code with a nominal delay of 30–90 s, or 60–180 s for the duration of their battery life (~10 years), providing a long-term time-series of detection data. Differences in transmission delay between tag types were accounted for using the method by Jacoby et al. 35 to ensure that detections between the two delay types were standardised and comparable. Receivers were downloaded and serviced annually at the same time each year (March–May) with the exception of 2017, where for logistical reasons no service expedition took place.

Data preparation

To avoid false detections from unknown tagged animals in our study system, only detections from animals with known ID codes were used for the analyses. To remove the possibility of false positives in the data set three different methods were used. First, animals with a single detection were filtered from the dataset 83 , 84 . Secondly, detection gaps of less than 30 s, under the ping delay of the tags, were also removed from the data by removing the second detection. Finally, transitions (movements between two different receivers) were calculated as per Williamson et al. 28 and removed if the speed of the transition exceeded 10 times the minimum sustainable swimming speeds of 0.69 m/s for grey reef sharks, resulting in a cut-off speed of 6.9 ms −1 28 ,  85 . To reduce any impact of the stress of capture on detected behaviour 86 ,  87 , the first 24 h of data were removed for each individual 88 .

Statistics and reproducibility

There are several equations that can be used for calculating residency from acoustic telemetry 83 . In this study, a local fixed time residency index for each shark was calculated per month at each receiver by counting the number days the shark was present per receiver in that month (minimum 2 days) divided by how many days the receiver was active during that month 83 . This allows for comparisons of residency through both time and space 83 .

To explore environmental stress on the reef habitat of grey reef sharks we used the Reef Environmental Stress Exposure Toolbox (RESET) developed by Williamson et al. 32 ( https://mjw1280.users.earthengine.app/view/reef-environmental-stress-exposure-toolbox ). This study used nine environmental variables (cloud cover, current, depth, salinity, wind, and four SST based metrics), derived from satellite remote sensing and Google Earth engine (GEE), known to have an impact on stress and health of coral reef systems. As the spatial resolution of the nine variables varied (Supplementary Table  4 ), each product was resampled using bilinear interpolation to match the detection range of the receivers (500 m) 89 . These data were combined with ecological and health-based thresholds obtained from the available literature, and fuzzy logic (discontinuous functions), to develop a combined environmental SE index from satellite remote sensing data for monitoring environmental SE on coral reef systems 32 . This index was chosen, as although there are remote sensing datasets available for reef habitat and bleaching (such as the Allen Coral Atlas, https://allencoralatlas.org ), these data were only available for 2019 onwards for the Chagos Archipelago. In addition, the coral reef environmental SE index incorporates several environmental variables that both enhance and reduce stress on the reef. Consequently, the index evaluates environmental stress as a whole, rather than specifically focusing on bleaching, which is one aspect of habitat stress, and can vary spatially and temporally within reef systems 48 , 90 . This index cannot be used to directly quantify the health of coral reefs per se. Rather, it is a temporally explicit monitoring tool (i.e., to compare against various time periods from the same region) to evaluate relative changes in stress exposure on coral reef ecosystems. The environmental SE index is scored between 0–1 with 0 being low environmental SE and 1 being high environmental SE. These index values were then included as an explanatory variable for subsequent analyses. From previous research at this site, RESET scores of 0.3 or higher indicate considerable stress to the reefs in Chagos 32 .

To examine how the environmental SE index changed temporally over the study period, the environmental SE index at each receiver was included as a response variable in a GAMM, with numerical day from 01/01/2014 as an explanatory variable and ‘atoll’ as a random effect, using the ‘gam’ function in the mgcv package 91 . To assess how the environmental stress changed spatially, beta regression with environmental SE index as a response variable was used 92 , 93 , with atoll as a predictor variable, using the ‘betareg’ function in the betareg package 94 . As environmental stress can change between years, year was included as an interaction in the model. Post hoc tests were undertaken between interactions using the ‘emmeans’ function in the emmeans package 95 .

As all receivers were situated greater than 500 m apart 28 , more than the spatial resolution of the index, detections from all receivers were included in the analysis and were not grouped. To limit exploratory analyses, and prevent model overfitting, an a priori selection of additional explanatory variables and interactions based on previous research and theory were included 96 , 97 . Along with the combined environmental SE index, four additional explanatory variables were included in the model. Season was included over month as season is often a more biologically relevant driver of variability in ocean systems 98 . The Chagos Archipelago has two clear seasons (wet—October to March, dry—April to September) which influences ecological processes, such as historical fisheries 59 , 60 . During our study period, El Niño conditions varied, and with different levels of intensity, but driving bleaching events in the region in both 2015 and 2016 39 , 99 . As such, year was also included as an explanatory variable, as a factor, for the global model. As both sex and size have been shown to influence movement patterns in grey reef sharks ‘sex’ and ‘total length’ were also included as explanatory variables. Continuous variables (SE index and total length) were scaled (mean = 0, SD = 1) to aid in model fitting 97 , using the ‘scale’ function from the base package 100 . To prevent pseudoreplication, individual ID and receiver ID were included as independent random factors in the model.

All analyses were conducted in R version 4.2.2 100 . A Generalised Linear Model (GLM) was built to assess the explanatory variables for collinearity. Collinearity was assessed by producing a variance inflation factor (VIF) from the generalised linear model using the ‘check_collinearity’ function in the performance package in R 101 . No evidence of collinearity was found, with all variables having a VIF ≤ 1.05, less than the critical threshold of 5.0 102 , 103 . As such, all a priori selected explanatory variables were included in the global model.

To investigate the drivers of residency, a global model, with all explanatory variables (environmental SE index, season, year, sex, and total length) and individual ID and station as random effects, was created using a Generalised Linear Mixed Model (GLMM) (family = binomial, link = logit) with the ‘glmmTMB’ function from the glmmTMB package. To fit the GLMM with proportion as the response variable, residency index was coded in the model as a fraction (days detected/days per month) and days per month used as the ‘weights’ argument in the model to set the basis of the response proportion 104 . Residuals of the global model were checked for heteroscedasticity, autocorrelation and data were checked for binomial distribution using the functions ‘resid’, ‘fitted’, and ‘acf’ from the stats package 100 .

A model set was subsequently generated from the global model using the ‘dredge’ function, from the MuMIn package 105 , with random effects as fixed terms. Models in the set were ranked by small sample size Akaike Information Criterion (AICc) values 106 , and Akaike weights for each model in the confidence set were calculated 96 , 97 , 107 . To improve inference using AICc the ‘nested’ function from the MuMIn package was used on the model selection table to remove models which were more complex versions of others 96 , 97 . Selected models included those with ΔAICc values < 2 and that were not nested models 107 . If a single parsimonious model remained following selection, this model was fitted to the data. If no single parsimonious model subsequently resulted from the set and the weight of the best model was less than 0.9, model averaging was used 96 , and the relative importance of each predictor variable calculated by summing Akaike weights for all confidence set models containing them. Model averaging was then undertaken on all predictor variables included in the final confidence set 96 , 97 , with parameter estimates indicating the change in probability of residency as the value for continuous predictor variables increased. Categorical predictor variables were compared to the categorical variable level used as the model baseline. Positive estimates indicated an increase in residency in grey reef sharks, negative estimates a decrease in residency. It is important to note that predictors may display a high relative importance but show no significant result in the model averaged estimates, and the relative importance and model averaged estimates should be considered in combination 108 . To test whether the relationship between stress and residency were not exclusively driven by extreme stress events, a secondary analysis removing data from El Niño periods (01/07/2014–30/06/2016 and 01/07/2018–30/06/2019) was undertaken.

The effects of the fixed effects on the model, and the combination of fixed and random effects 109 , 110 , were tested by calculating the marginal R 2 (R 2 m) and conditional R 2 (R 2 c) values using ‘r.squaredGLMM’ in the MuMIn package 105 , 110 , and conditional models of the random effects, and their SDs, extracted from the top model using the ‘ranef ’ function from the lme4 package 111 .

To if there were any lagged responses in residency behaviour as a result of occupying reefs that have undergone long-term stress (in particular during El Niño events) cross correlations were calculated from the mean monthly environmental SE index and mean monthly residency using the ‘ccf’ function from the stats package 100 . These were used to identify time lags in months ( t ) between our predictor (stress) in the present ( t  = 0) and our response (residency) into the future ( t  > 0) based on autocorrelation between the two as we move into the future.

To evaluate if grey reef sharks alter their residency through changes in space and/or time during the study, Kernel Utilisation Density (KUD) estimates as well as the time intervals between consecutive detections were calculated. KUDs at 50, 75 and 95% were generated using the ‘kernelUD’ function from the adehabitatHR package 112 and plotted through time. The median number of days per month between detections across the array were calculated per individual. Median detection differences (i.e., detection gaps) were compared between El- Niño (elevated stress) and non-El Niño periods using the nonparametric Brunner-Munzel test 113 . Finally, residency indices were averaged across individuals for all 6 monthly periods, binned and bubble plots produced to show variation in residency across the population in response to changing stress through time.

Data availability

Raw data supporting the results are available from the Zenodo Digital Repository: https://zenodo.org/records/11653900 114 .

Code availability

The R code used for analyses are available from the Zenodo Digital Repository: https://zenodo.org/records/11639740 115 .

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Acknowledgements

Funding for this project was provided by the Bertarelli Foundation and contributed to the Bertarelli Programme in Marine Science. This work was also supported by the Natural Environment Research Council (Grant No. NE/L002485/1) to M.J.W., as part of the London NERC Doctoral Training Partnership at the Department of Geography, King’s College London and the Institute of Zoology, London. D.J.C. was funded by Research England. All procedures were approved by the Stanford University Administrative Panel on Laboratory Animal Care (APLAC) under permit APLAC-10765. We thank the BIOT Administration for granting us permission to undertake the research. We would like thank S. Vanovac and C. Monk for their help for producing visualisations for this manuscript.

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Michael J. Williamson, David J. Curnick & David M. P. Jacoby

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Department of Genetics, Evolution and Environment, University College London, London, UK

Michael J. Williamson

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Francesco Ferretti

School of Marine Science and Policy, University of Delaware, Lewes, DE, USA

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Hatfield Marine Science Center, Oregon State University, Newport, OR, USA

Taylor K. Chapple

Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA

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Contributions

Michael J. Williamson: conceptualisation (equal); formal analysis (lead); methodology (equal); visualisation (equal); writing—original draft (lead); writing—review & editing (lead). Emma J. Tebbs: supervision (equal); writing—review & editing (supporting). David J. Curnick: funding acquisition (equal); investigation (equal); supervision (supporting); writing—review & editing (supporting). Francesco Ferretti: investigation (equal); writing—review & editing (supporting). Aaron B. Carlisle: investigation (equal); writing—review & editing (supporting). Taylor K. Chapple: investigation (equal); writing—review & editing (supporting). Robert J. Schallert: investigation (equal). David M. Tickler: investigation (equal). Barbara A. Block: funding acquisition (equal); writing—review & editing (supporting). David M. P. Jacoby: conceptualisation (equal); funding acquisition (equal); investigation (equal); methodology (equal); supervision (equal); visualisation (equal); writing—review & editing (supporting).

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Williamson, M.J., Tebbs, E.J., Curnick, D.J. et al. Environmental stress reduces shark residency to coral reefs. Commun Biol 7 , 1018 (2024). https://doi.org/10.1038/s42003-024-06707-3

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Received : 12 December 2023

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DOI : https://doi.org/10.1038/s42003-024-06707-3

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