Philippa Kaur

1.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Article  Google Scholar 
2.
Tomlinson, S. et al. Applications and implications of ecological energetics. Trends Ecol. Evol. 29, 280–290 (2014).
PubMed  Article  PubMed Central  Google Scholar 

3.
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies; a reappraisal. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13040 (2018).
Article  PubMed  PubMed Central  Google Scholar 

4.
Stearns, S. C. The Evolution of Life Histories (OUP Oxford, Oxford, 1992).
Google Scholar 

5.
Green, J. A., Boyd, I. L., Woakes, A. J., Warren, N. L. & Butler, P. J. Evaluating the prudence of parents: Daily energy expenditure throughout the annual cycle of a free-ranging bird, the macaroni penguin Eudyptes chrysolophus. J. Avian Biol. 40, 529–538 (2009).
Article  Google Scholar 

6.
Halsey, L. G. et al. Flexibility, variability and constraint in energy management patterns across vertebrate taxa revealed by long-term heart rate measurements. Funct. Ecol. 33, 260–272 (2019).
Article  Google Scholar 

7.
Butler, P. J., Green, J. A., Boyd, I. L. & Speakman, J. R. Measuring metabolic rate in the field: The pros and cons of the doubly labelled water and heart rate methods. Funct. Ecol. 18, 168–183 (2004).
Article  Google Scholar 

8.
Green, J. A. The heart rate method for estimating metabolic rate: Review and recommendations. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 158, 287–304 (2011).
PubMed  Article  CAS  PubMed Central  Google Scholar 

9.
Green, J. A., Halsey, L. G., Wilson, R. P. & Frappell, P. B. Estimating energy expenditure of animals using the accelerometry technique: Activity, inactivity and comparison with the heart-rate technique. J. Exp. Biol. 212, 471–482 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Speakman, J. R. Doubly Labelled Water: Theory and Practice (Chapman and Hall, London, 1997).
Google Scholar 

11.
Yoda, K. et al. A new technique for monitoring the behaviour of free-ranging Adélie penguins. J. Exp. Biol. 204, 685–690 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

12.
Wilson, R. P. et al. Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: The case of the cormorant. J. Anim. Ecol. 75, 1081–1090 (2006).
PubMed  Article  PubMed Central  Google Scholar 

13.
Gleiss, A. C., Wilson, R. P. & Shepard, E. L. C. Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods Ecol. Evol. 2, 23–33 (2011).
Article  Google Scholar 

14.
Ropert-Coudert, Y. & Wilson, R. P. Trends and perspectives in animal-attached remote sensing. Front. Ecol. Environ. 3, 437–444 (2005).
Article  Google Scholar 

15.
Gatt, M. C., Quetting, M., Cheng, Y. & Wikelski, M. Dynamic body acceleration increases by 20% during flight ontogeny of Greylag Geese (Anser anser). J. Avian Biol. 1, 2235 (2019).
Google Scholar 

16.
Van Walsum, T. A. et al. Exploring the relationship between flapping behaviour and accelerometer signal during ascending flight, and a new approach to calibration. Ibis (Lond. 1859) 162, 13–26 (2020).
Article  Google Scholar 

17.
Elliott, K. H. Measurement of flying and diving metabolic rate in wild animals: Review and recommendations. Comparat. Biochem. Physiol. Part A Mol. Integr. Physiol. 202, 63–77 (2016).
CAS  Article  Google Scholar 

18.
Fahlman, A., Svärd, C., Rosen, D. S., Jones, D. R. & Trites, A. W. Metabolic costs of foraging and the management of O2 and CO2 stores in Steller sea lions. J. Exp. Biol. 211, 3573–3580 (2008).
PubMed  Article  PubMed Central  Google Scholar 

19.
Payne, N. L. et al. Accelerometry estimates field metabolic rate in giant Australian cuttlefish Sepia apama during breeding. J. Anim. Ecol. 80, 422–430 (2011).
PubMed  Article  PubMed Central  Google Scholar 

20.
Wright, S., Metcalfe, J. D., Hetherington, S. & Wilson, R. P. Estimating activity-specific energy expenditure in a teleost fish, using accelerometer loggers. Mar. Ecol. Prog. Ser. 496, 19–32 (2014).
ADS  Article  Google Scholar 

21.
Bidder, O. R. et al. Does the treadmill support valid energetics estimates of field locomotion?. Integr. Comp. Biol. 57, 301–319 (2017).
PubMed  Article  PubMed Central  Google Scholar 

22.
Pagano, A. M. & Williams, T. M. Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water. Ecol. Evol. 00, 1–10 (2019).
Google Scholar 

23.
Jeanniard-du-Dot, T., Trites, A. W., Arnould, J. P. Y., Speakman, J. R. & Guinet, C. Activity-specific metabolic rates for diving, transiting, and resting at sea can be estimated from time–activity budgets in free-ranging marine mammals. Ecol. Evol. 7, 2969–2976 (2017).
PubMed  PubMed Central  Article  Google Scholar 

24.
Hicks, O. et al. Validating accelerometry estimates of energy expenditure across behaviours using heart rate data in a free-living seabird. J. Exp. Biol. 220, 1875–1881 (2017).
PubMed  PubMed Central  Article  Google Scholar 

25.
Elliott, K. H., Le Vaillant, M., Kato, A., Speakman, J. R. & Ropert-Coudert, Y. Accelerometry predicts daily energy expenditure in a bird with high activity levels. Biol. Lett. 9, 1–4 (2013).
Article  Google Scholar 

26.
Bishop, C. M. et al. The roller coaster flight strategy of bar-headed geese conserves energy during Himalayan migrations. Science 147, 250–254 (2015).
ADS  Article  CAS  Google Scholar 

27.
Stothart, M. R., Elliott, K. H., Wood, T., Hatch, S. A. & Speakman, J. R. Counting calories in cormorants: Dynamic body acceleration predicts daily energy expenditure measured in pelagic cormorants. J. Exp. Biol. 219, 2192–2200 (2016).
PubMed  Article  PubMed Central  Google Scholar 

28.
Karasov, W. H. Daily energy expenditure and the cost of activity in mammals. Integr. Comp. Biol. 32, 238–248 (1992).
Google Scholar 

29.
Lovvorn, J. R. Thermal substitution and aerobic efficiency: Measuring and predicting effects of heat balance on endotherm diving energetics. Philos. Trans. R. Soc. B Biol. Sci. 362, 2079–2093 (2007).
CAS  Article  Google Scholar 

30.
Lewden, A., Enstipp, M. R., Picard, B., Van Walsum, T. & Handrich, Y. High peripheral temperatures in king penguins while resting at sea: Thermoregulation versus fat deposition. J. Exp. Biol. 220, 3084–3094 (2017).
PubMed  Article  PubMed Central  Google Scholar 

31.
Halsey, L. G., Shepard, E. L. C. & Wilson, R. P. Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 158, 305–314 (2011).
Article  CAS  Google Scholar 

32.
Elliott, K. H. Measurement of flying and diving metabolic rate in wild animals: Review and recommendations. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 202, 63–77 (2016).
CAS  Article  Google Scholar 

33.
Wilson, R. P. & Culik, B. M. The cost of a hot meal: Facultative specific dynamic action may ensure temperature homeostasis in post-ingestive endotherms. Comp. Biochem. Physiol. Part A Physiol. 100, 151–154 (1991).
CAS  Article  Google Scholar 

34.
Halsey, L. G. et al. Assessing the validity of the accelerometry technique for estimating the energy expenditure of diving double-crested cormorants Phalacrocorax auritus. Physiol. Biochem. Zool. 84, 230–237 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Ladds, M. A., Rosen, D. A. S., Slip, D. J. & Harcourt, R. G. Proxies of energy expenditure for marine mammals: An experimental test of ‘the time trap’. Sci. Rep. 7, 1–10 (2017).
CAS  Article  Google Scholar 

36.
Qasem, L. et al. Tri-axial dynamic acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector?. PLoS ONE 7, e31187 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Sparling, C. E., Thompson, D., Fedak, M. A., Gallon, S. L. & Speakman, J. R. Estimating field metabolic rates of pinnipeds: Doubly labelled water gets the seal of approval. Funct. Ecol. 22, 245–254 (2008).
Article  Google Scholar 

38.
Ropert-Coudert, Y. et al. Two recent massive breeding failures in an adélie penguin colony call for the creation of a Marine Protected area in D’Urville Sea/Mertz. Front. Mar. Sci. 5, 1–7 (2018).
Article  Google Scholar 

39.
Chappell, M. A., Shoemaker, V. H., Janes, D. N., Maloney, S. K. & Bucher, T. L. Energetics of foraging in breeding Adélie penguins. Ecology 74, 2450–2461 (1993).
Article  Google Scholar 

40.
Nagy, K. A. & Obst, B. S. Food and energy requirements of Adelie penguins (Pygoscelis adeliae) on the Antarctic Peninsula. Physiol. Zool. 65, 1271–1284 (1992).
Article  Google Scholar 

41.
Culik, B. Y. B. & Wilson, R. P. Swimming energetics and performance of instrumented Adélie penguins (Pygoscelis Adeliae). J. Exp. Biol. 158, 355–368 (1991).
Google Scholar 

42.
Kooyman, G. L., Gentry, R. L., Bergman, W. P. & Hammel, H. T. Heat loss in penguins during immersion and compression. Comp. Biochem. Physiol. Part A Physiol. 54, 75–80 (1976).
CAS  Article  Google Scholar 

43.
Fahlman, A., Wilson, R., Svärd, C., Rosen, D. A. S. & Trites, A. W. Activity and diving metabolism correlate in Steller sea lion Eumetopias jubatus. Aquat. Biol. 2, 75–84 (2008).
Article  Google Scholar 

44.
Gleiss, A. C., Dale, J. J., Holland, K. N. & Wilson, R. P. Accelerating estimates of activity-specific metabolic rate in fishes: Testing the applicability of acceleration data-loggers. J. Exp. Mar. Bio. Ecol. 385, 85–91 (2010).
Article  Google Scholar 

45.
Halsey, L. G., Jones, T. T., Jones, D. R., Liebsch, N. & Booth, D. T. Measuring energy expenditure in sub-adult and hatchling sea turtles via accelerometry. PLoS ONE 6, 2 (2011).
Article  CAS  Google Scholar 

46.
Halsey, L. G. et al. The relationship between oxygen consumption and body acceleration in a range of species. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 152, 197–202 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Halsey, L. G. et al. Acceleration versus heart rate for estimating energy expenditure and speed during locomotion in animals: Tests with an easy model species, Homo sapiens. Zoology. https://doi.org/10.1016/j.zool.2007.07.011 (2008).
Article  PubMed  PubMed Central  Google Scholar 

48.
Gleiss, A. C., Gruber, S. H. & Wilson, R. P. Multi-channel data-logging: Towards determination of behaviour and metabolic rate in free-swimming sharks. Tag. Track. Mar. Anim. Electron. Dev. 9, 211–228 (2009).
Google Scholar 

49.
Gómez-Laich, A., Wilson, R. P., Quintana, F. & Shepard, E. Identification of imperial cormorant Phalacrocorax atriceps behaviour using accelerometers. Endanger. Species Res. 10, 29–37 (2008).
Article  Google Scholar 

50.
Jeanniard-du-Dot, T., Guinet, C., Arnould, J. P. Y., Speakman, J. R. & Trites, A. W. Accelerometers can measure total and activity-specific energy expenditures in free-ranging marine mammals only if linked to time-activity budgets. Funct. Ecol. 31, 377–386 (2016).
Article  Google Scholar 

51.
Gómez-Laich, A., Wilson, R. P., Gleiss, A. C., Shepard, E. L. C. & Quintana, F. Use of overall dynamic body acceleration for estimating energy expenditure in cormorants. J. Exp. Mar. Bio. Ecol. 399, 151–155 (2011).
Article  Google Scholar 

52.
Fahlman, A., Schmidt, A., Handrich, Y., Woakes, A. J. & Butler, P. J. Metabolism and thermoregulation during fasting in king penguins, Aptenodytes patagonicus, in air and water. Am. J. Physiol. Integr. Comp. Physiol. 289, R670–R679 (2005).
CAS  Article  Google Scholar 

53.
Ciancio, J. E., Quintana, F., Sala, J. E. & Wilson, R. P. Cold birds under pressure: Can thermal substitution ease heat loss in diving penguins?. Mar. Biol. 163, 1–15 (2016).
Article  CAS  Google Scholar 

54.
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13040 (2019).
Article  PubMed  PubMed Central  Google Scholar 

55.
Lifson, N. & McClintock, R. Theory of use of the turnover rates of body water for measuring energy and material balance. J. Theor. Biol. 12, 46–74 (1966).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Speakman, J. R. & Król, E. Comparison of different approaches for the calculation of energy expenditure using doubly labeled water in a small mammal. Physiol. Biochem. Zool. 78, 650–667 (2005).
PubMed  Article  PubMed Central  Google Scholar 

57.
Beaulieu, M. et al. Sex-specific parental strategies according to the sex of offspring in the Adélie penguin. Behav. Ecol. 20, 878–883 (2009).
Article  Google Scholar 

58.
Berman, E. S. F. et al. Direct analysis of δ2H and δ18O in natural and enriched human urine using laser-based, off-axis integrated cavity output spectroscopy. Anal. Chem. 84, 9768–9773 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Nagy, K. A. Doubly labeled water method (3 HH 18 O): A guide to its use. Oecologia 59, 1–45 (1983).
Article  Google Scholar 

60.
Speakman, J. R. How should we calculate CO2 production in DLW studies of mammals. Funct. Ecol. 7, 746–750 (1993).
Google Scholar 

61.
Visser, G. H. & Schekkerman, H. Validation of the doubly labeled water method in growing precocial birds: The importance of assumptions concerning evaporative water loss. Physiol. Biochem. Zool. 72, 740–749 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Van Trigt, R. et al. Validation of the DLW method in Japanese quail at different water fluxes using laser and IRMS. J. Appl. Physiol. 93, 2147–2154 (2002).
PubMed  Article  PubMed Central  Google Scholar 

63.
Culik, B. et al. Energy requirements of Adélie penguin (Pygoscelis adeliae) chicks. J. Comp. Physiol. B 160, 61–70 (1990).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Wilson, R. P. et al. Long-term attachment of transmitting and recording devices to penguins and other seabirds. Wildl. Soc. Bull. 25, 101–105 (1997).
Google Scholar 

65.
Collins, P. M. et al. Interpreting behaviors from accelerometry: A method combining simplicity and objectivity. Ecol. Evol. 5, 4642–4654 (2015).
PubMed  PubMed Central  Article  Google Scholar 

66.
Patterson, A., Gilchrist, H. G., Chivers, L., Hatch, S. & Elliott, K. A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Ecol. Evol. https://doi.org/10.1002/ece3.4740 (2019).
Article  PubMed  PubMed Central  Google Scholar 

67.
R Core Team. R: A language and environment for statistical computing. (2019). More

On a summer morning in 2019, Andy Suhrbier pilots a small aluminium boat out to a mussel raft in a quiet cove on the eastern shore of Puget Sound in Washington State. As the boat approaches, a mother seal and her pup resting on the raft slip into the water. Suhrbier climbs from his boat onto the raft; the only sign of life is a vague smell.
Suhrbier tugs on a couple of ropes attached to one of the raft’s beams. Soon, a mesh-lined plastic cage emerges with water and silt pouring out of it. He picks off several sea stars and tosses them back into the water, then flips open the lid like a pirate opening a treasure chest.
Inside is more dark sediment — mostly waste from the mussels, the source of the smell. Suhrbier sifts through it. He is looking for something.
“Look at this monster!” he says, holding up a sea cucumber nearly a foot long. Its deep red body covered in orange bumps stands out from the muck like a gold doubloon. “That’s definitely market size.”

Suhrbier is a biologist with the Pacific Shellfish Institute in Olympia, Washington, a non-profit research organization that works to promote healthy wild shellfish populations and sustainable shellfish aquaculture along the US west coast. Two years earlier, he had put sea cucumbers in cages and suspended them beneath the mussel raft, as part of an effort to develop aquaculture in Puget Sound. The hefty size of the cucumbers is a promising sign.
Suhrbier and his colleagues think that sea-cucumber farming could have two benefits. First, the animals could help to prevent excess waste from building up underneath aquaculture installations, such as mussel rafts or net pens used to hold bony fish such as salmon. (Sea cucumbers, soft-bodied animals related to sea urchins, move slowly over the sea floor eating detritus — the vacuum cleaners of the ocean.) Second, a ready source of farmed sea cucumbers could reduce the poaching of wild stocks to feed the growing market in east and southeast Asia.
Globally, aquaculture produced 82.1 million tonnes of aquatic animals in 2018, and wild fisheries produced 97 million tonnes, according to the United Nations’ Food and Agriculture Organization (FAO). But the value of farmed fish was higher, around US$250 billion compared with $151 billion for wild-caught fish. Aquaculture production of animals is projected to increase by one-third by 2030, reaching 109 million tonnes, and will supply the majority of aquatic protein in people’s diets by 2050.
“We need to grow the amount of seafood available, as world populations grow, to provide enough protein for everybody,” says Monica Jain, founder of Fish2.0 in Carmel, California, an organization that promotes investment in sustainable seafood businesses. With the catches from wild fisheries remaining largely flat and some stocks already overexploited, “aquaculture is really the only way to do that”. But as the industry grows, Jain and other aquaculture advocates want to make sure that it does so sustainably.
Double alchemy
Aquaculture is a relatively small proportion of the global food system — terrestrial meat production (both livestock and wild game) totalled around 342 million tonnes in 2018, and production of grains and cereals was 2.7 billion tonnes. However, aquaculture is more diverse, particularly in terms of the animals farmed. These range widely across taxonomic groups, including bony fish (carp, tilapia and salmon, for example), crustaceans (shrimp, prawns and crayfish), molluscs (clams, oysters and mussels) and echinoderms (sea cucumbers). Various species of seaweed are also gathered. There are freshwater, saltwater, brackish water and self-contained terrestrial aquaculture systems. And each has its own sustainability benefits and challenges.
One subsector that offers huge environmental advantages and has no equivalent in terrestrial agriculture is non-fed aquaculture. Marine bivalves, such as clams, mussels and oysters, get their nourishment by filtering microscopic plants, detritus and nutrients from the water that surrounds them. They require minimal inputs and can even improve the water quality. In this sense, Suhrbier’s sea cucumbers represent a kind of double alchemy: non-fed aquaculture species grown on the wastes of other non-fed aquaculture species.
Similarly, cultivated seaweeds can remove excess nutrients, such as nitrogen, that contribute to the formation of areas of oxygen-poor water where marine life has difficulty surviving, known as dead zones. By taking up carbon they can also help to alleviate ocean acidification at a local scale. Moreover, “seaweeds are incredibly nutritious,” says Alecia Bellgrove, head of the DeakinSeaweed Research Group at Deakin University in Melbourne, Australia. “They are, for example, fantastic sources of trace minerals, which are often lacking in our diets based on terrestrial foods.”

Sea cucumbers are retrieved from the mesh-lined cages at Puget Sound in Washington state. Credit: Sarah DeWeerdt

Aquatic animals that require feed — mainly prawns and bony fish — also have an environmental advantage over animals raised in terrestrial agriculture. Because most are cold-blooded, they convert food into body mass more efficiently than birds and mammals, which need energy to help regulate their body temperature. So it takes less feed to produce a kilogram of salmon, for example, than it does to produce a kilogram of, say, beef or pork.
However, some of the most lucrative aquaculture species are carnivorous, and therefore sit higher in the food chain than any terrestrial species raised in agriculture. Take the Atlantic salmon (Salmo salar), for example. In the mid-2000s, salmon aquaculture, now a $15.4-billion industry, was growing rapidly. Feeding the salmon demanded an increasing share of the world’s fish meal and fish oil, which was sourced from small forage fish, such as anchovies, sardines and capelin. But while demand from the salmon farms grew, fishing yield for the forage species remained relatively flat. It took at least 4 kilograms of wild-caught forage fish to produce just 1 kilogram of salmon.
From an environmental point of view, “It made no sense,” says Scott Nichols, founder of Food’s Future, a consultancy in West Chester, Pennsylvannia, which promotes the development of sustainable aquaculture businesses. As a biochemist working at US chemical company DuPont in the mid 2000s, Nichols helped to develop a way to produce omega-3 fatty acids from yeast. The fatty acids were then incorporated into salmon feed to replace some of the wild-fish component. The new feed was tested through a partnership between DuPont and the aquaculture firm AquaChile based in Puerto Montt, Chile, in the form of the salmon producer Verlasso in Miami, Florida.
“We were able, after a couple of years of production, to get to the point where for every kilo of salmon that was produced, we were using less than a kilo of wild-caught fish,” Nichols says. “So our farming practices resulted in the net production of fish on the planet.”
Other companies soon joined in, producing omega-3s in genetically engineered canola oil or single-celled algae. Meanwhile, fish-oil and fish-meal producers are increasingly making use of fish trimmings and other by-products that previously went to waste. Fish meal and fish oil, which are still used in a variety of aquaculture feeds, as well as in products such as food supplements, accounted for around 10% of the world’s total fish production in 2018, according to the FAO. But nonetheless, Nichols takes heart from developments. “What looked on the face of it to be dismal in 2006 now looks to be very promising,” he says.
Disease detectives
An increasingly important threat to aquaculture sustainability is disease, which affects all subsectors of aquaculture and causes an estimated $6 billion worth of aquatic animal losses every year. Diseases include parasites called sea lice in salmon; white spot syndrome virus in prawns, which emerged in the early 1990s and devastated prawn farming throughout Asia before spreading to the Americas; and tilapia lake virus, which threatens the economic and nutritional gains that freshwater aquaculture has made possible in many low- and middle-income countries.
As aquaculture is scaled up, the problem of disease will also become greater. “As you expand the volume of production, you are going to get significant losses,” says Grant Stentiford, a pathologist and head of aquatic animal health at the Centre for Environment Fisheries and Aquaculture Science, Weymouth, UK. “You’ve used up potentially large amounts of resource to get absolutely nowhere.”
To deal with such threats, some large producers who supply the export market are moving to self-contained, land-based systems. Others are moving away from the coast into deeper waters that might dilute the threat of disease. Vaccines have also made a difference, reducing not only the threat of many fish diseases, but also antibiotic use — another major environmental concern about the industry. And high-throughput sequencing of the microbial DNA in aquaculture systems could provide early warning of disease outbreaks.
But many of these solutions are expensive and, therefore, out of reach for the small and medium-sized producers who make up the majority of the global aquaculture industry, producing food for subsistence or local markets in low- and middle-income countries. Moreover, diseases that threaten aquaculture are emerging every three to five years on average. The dearth of knowledge about aquatic pathogens makes diseases hard to predict and spot.
It can also be a challenge to deduce their cause. For example, ice-ice disease results in bleaching of Kappaphycus seaweed, which is grown in large amounts in southeast Asia and Tanzania for the production of food additives, such as the thickening agent carrageenan. The disease has caused yields to plummet over the past decade, but “the causative agent is still not known”, says Valéria Montalescot, senior project manager for GlobalSeaweedSTAR, a four-year research project based at the Scottish Association for Marine Science in Oban, UK, which aims to boost knowledge about seaweed cultivation in low- and middle-income countries. Kappaphycus is usually grown from cuttings, so the whole crop across multiple countries might be the result of just a few clones, possibly making it more vulnerable to disease, Montalescot adds.
Diverse yields
Climate change is complicating efforts to fight disease. Higher water temperatures can alter the microbial community of a body of water, encouraging the growth of pathogens, as well as stressing organisms and making them more vulnerable to disease. One suggested cause of ice-ice disease is that temperature-stressed seaweeds release compounds that attract bacteria, for example.
And temperature is not the only issue. Both increased rainfall and salinity intrusion from sea-level rise can alter water chemistry in ways that are detrimental to aquaculture organisms. Storms can destroy aquaculture crops or infrastructure in the water and on land. “From an environmental point of view I think climate change is the greatest challenge” for the sustainability of aquaculture systems, says Nesar Ahmed, who studies global seafood sustainability at Deakin University.
Climate change also intersects with aquaculture’s pressure on water and land resources. Inland aquaculture demands 429 cubic kilometres of fresh water each year — much less than the demand from terrestrial agriculture, but still enough to pose a strain on increasingly drought-prone areas.
In south and southeast Asia, prawn cultivation has contributed to the destruction of 38% of the world’s mangrove habitats, which have a variety of important ecological functions, including sequestering carbon and buffering coastlines from storms and sea-level rise. The loss of mangroves has also resulted in saltwater intrusion rendering inland areas unsuitable for terrestrial agriculture.
Some farmers are now producing prawns among intact mangrove stands. Although there are concerns that this practice might also damage the health of the mangroves, it is part of a larger trend to create aquaculture systems that include multiple species and involve interrelationships more like the ones that keep natural ecosystems in balance.
Some examples of this integrated aquaculture are long-established, such as stocking rice paddy fields with fish or prawns. The animals eat pests and fertilize the rice crop, increasing rice yields and providing an extra source of protein or income for small-scale farmers, Ahmed says. Growing two species in a single body of water also reduces overall water use.
This type of rice–fish system has been practised for hundreds of years in China and has been designated a Globally Important Agricultural Heritage System by the FAO — a designation that aims to preserve agricultural knowledge that can contribute to a more sustainable and resilient food system. Large-scale aquaculture operations, such as Cooke Aquaculture based in Blacks Harbour, Canada, have also been experimenting with multi-species systems. The company keeps salmon in net pens near both mussel and kelp rafts in the Bay of Fundy, Canada.

In theory, integrated aquaculture can help to increase yields, decrease risk by diversifying operations, and is generally a more environmentally sound form of aquaculture. But in practice, it can be difficult to quantify these benefits. For example, because nitrogen moves freely through water, it is difficult to track uptake of excess nitrogen produced by bony fish by seaweed growing nearby. And then there are the complexities of managing an operation with multiple species — not just producing them but also harvesting, processing and marketing them.
Suhrbier knows such difficulties well. The sea cucumbers he and his team harvested from under the mussel raft were the right size, weight and colour for the export market, but the mussel producer he was working with was unable to renew its permit at that location. The raft was lost, and with it Suhrbier’s chance of follow-up experiments to develop sea-cucumber aquaculture techniques. “I was really shocked and saddened to see that go because it was one of those places where it just makes a lot of sense for sea cucumbers to be,” Suhrbier says. The new location of the producer’s rafts isn’t a good habitat for sea cucumbers.
Suhrbier is still experimenting growing sea cucumbers alongside other types of aquaculture operation around the Puget Sound area. But, like an increasing number of aquaculture researchers, he is beginning to think that producing the animals needs to move in a simpler and more radical direction. Growing sea cucumbers in cages is labour intensive. What if the animals are placed in the vicinity of aquaculture operations and left to roam freely — like a marine equivalent of a ranch or even a permaculture system?
“If we could mainly enhance the wild population around these areas, I think that would be a great benefit for everybody,” Suhrbier says. “I’m trying to have something that fits in: easy, cost effective and as passive as it can be.” More

1.
Gause, G. F. The Struggle for Existence (Williams & Wilkins, Baltimore, 1934).
Google Scholar 
2.
Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).
Article  Google Scholar 

3.
Volterra, V. Variations and fluctuations of the number of individuals in marine intertidal species living together. J. Conseil. 3, 3–51 (1928).
Article  Google Scholar 

4.
Darwin, C. On the Origin of Species by Natural Selection, or the Preservation of Favoured Races in the Struggle for Life (John Murray, London, 1859).
Google Scholar 

5.
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
ADS  CAS  Article  Google Scholar 

6.
Alley, T. R. Competition theory, evolution, and the concept of an ecological niche. Acta Biotheor. 31, 165–179 (1982).
CAS  PubMed  Article  Google Scholar 

7.
Chase, J. M. & Leibold, M. A. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, Chicago, 2003).
Google Scholar 

8.
Pianka, E. R. Competition and niche theory. In Theoretical Ecology: Principles and Applications (ed. May, R. M.) 114–141 (W.B. Saunders, Philadelphia, 1976).
Google Scholar 

9.
Gerking, S. D. The Feeding Ecology of Fish (Academic, San Diego, 1994).
Google Scholar 

10.
Ross, S. T. Resource partitioning in fish assemblages: a review of field studies. Copeia 2, 352–388 (1986).
Article  Google Scholar 

11.
Persson, L. Asymmetrical competition: are larger animals competitively superior?. Am. Nat. 126, 261–266 (1985).
Article  Google Scholar 

12.
Boecklen, W. J., Yarnes, C. T., Cook, B. A. & James, A. C. On the use of stable isotopes in trophic ecology. Annu. Rev. Ecol. Evol. Syst. 42, 411–440 (2011).
Article  Google Scholar 

13.
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: an overview of analytical tools. Biol. Rev. Camb. Philos. 87, 545–562 (2012).
Article  Google Scholar 

14.
Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A. & MacLeod, H. Determining trophic niche width: a novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).
Article  Google Scholar 

15.
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER-stable isotope Bayesian ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).
PubMed  Article  Google Scholar 

16.
Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratios provide for community-wide measures of trophic structure?. Ecology 88, 42–48 (2007).
PubMed  Article  Google Scholar 

17.
Newsome, S. D., del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436 (2007).
Article  Google Scholar 

18.
Hette-Tronquart, N. Isotopic niche is not equal to trophic niche. Ecol. Lett. 22, 1987–1989 (2019).
PubMed  Article  Google Scholar 

19.
Parnell, C. A. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399 (2013).
MathSciNet  Google Scholar 

20.
Phillips, D. L. et al. Best practices for use of stable isotope mixing models in food-web studies. Can. J. Zool. 92, 823–835 (2014).
Article  Google Scholar 

21.
Knickle, D. C. & Rose, G. A. Dietary niche partitioning in sympatric gadid species in coastal Newfoundland: evidence from stomachs and C-N isotopes. Environ. Biol. Fish. 97, 343–355 (2014).
Article  Google Scholar 

22.
Simpson, S. J., Sims, D. W. & Trueman, C. M. Ontogenetic trends in resource partitioning and trophic geography of sympatric skates (Rajidae) inferred from stable isotope composition across eye lenses. Mar. Ecol. Prog. Ser. 624, 103–116 (2019).
ADS  CAS  Article  Google Scholar 

23.
Bearhop, S. et al. Stable isotopes indicate sex-specific and long-term individual foraging specialisation in diving seabirds. Mar. Ecol. Prog. Ser. 311, 157–164 (2006).
ADS  Article  Google Scholar 

24.
Young, H. S., McCauley, D. J., Dirzo, R., Dunbar, R. B. & Shaffer, S. A. Niche partitioning among and within sympatric tropical seabirds revealed by stable isotope analysis. Mar. Ecol. Prog. Ser. 416, 285–294 (2010).
ADS  CAS  Article  Google Scholar 

25.
Botta, S. et al. Isotopic niche overlap and partition among three Antarctic seals from the Western Antarctic Peninsula. Deep-Sea Res. II(149), 240–249 (2018).
Google Scholar 

26.
Kiszka, J. et al. Ecological niche segregation within a community of sympatric dolphins around a tropical island. Mar. Ecol. Prog. Ser. 433, 273–288 (2011).
ADS  Article  Google Scholar 

27.
Ogloff, W. R., Yurkowski, D. J., Davoren, G. K. & Ferguson, S. H. Diet and isotopic niche overlap elucidate competition potential between seasonally sympatric phocids in the Canadian Arctic. Mar. Biol. 166, 103 (2019).
Article  CAS  Google Scholar 

28.
Dubois, S., Orvain, F., Marin-Léal, J. C., Ropert, M. & Lefebvre, S. Small-scale spatial variability of food partitioning between cultivated oysters and associated suspension feeding species, as revealed by stable isotopes. Mar. Ecol. Prog. Ser. 336, 151–160 (2007).
ADS  CAS  Article  Google Scholar 

29.
Karlson, A. M. L., Gorokhova, E. & Elmgren, R. Do deposit-feeders compete? Isotopic niche analysis of an invasion in a species-poor system. Sci. Rep. 5, 9715 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Taupp, T., Hellmann, C., Gergs, R., Winkelmann, C. & Wetzel, M. A. Life under exceptional conditions—isotopic niches of benthic invertebrates in the estuarine maximum turbidity zone. Estuar. Coast. 40, 502–512 (2017).
CAS  Article  Google Scholar 

31.
Bennice, C. O., Rayburn, A. R., Brooks, W. R. & Hanlon, R. T. Fine-scale habitat partitioning facilitates sympatry between two octopus species in a shallow Florida lagoon. Mar. Ecol. Prog. Ser. 609, 151–161 (2019).
Article  Google Scholar 

32.
Matias, R. S. et al. Show your beaks and we tell you what you eat: different ecology in sympatric Antarctic benthic octopods under a climate change context. Mar. Environ. Res. 150, 104757 (2019).
CAS  PubMed  Article  Google Scholar 

33.
Rosas-Luis, R., Navarro, J., Sánchez, P. & del Río, J. L. Assessing the trophic ecology of three sympatric squid in the marine ecosystem off the Patagonian Shelf by combining stomach content and stable isotopic analyses. Mar. Biol. Res. 12, 402–411 (2016).
Article  Google Scholar 

34.
Boyle, P. R. & Rodhouse, P. G. Cephalopods: Ecology and Fisheries (Wiley-Blackwell, Oxford, 2005).
Google Scholar 

35.
Rodhouse, P. G. & Nigmatullin, Ch. M. Role as consumers. Philos. Trans. R. Soc. B 351, 1003–1022 (1996).
ADS  Article  Google Scholar 

36.
Jereb, P. & Roper, C.F.E. Cephalopods of the world. An annotated and illustrated catalogue of cephalopod species known to date. Volume 1. Chambered nautiluses and sepioids (Nautilidae, Sepiidae, Sepiolidae, Sepiadariidae, Idiosepiidae and Spirulidae). FAO Species Catalogue for Fishery Purposes, No. 4. Rome: FAO (2005).

37.
Golikov, A. V., Sabirov, R. M., Lubin, P. A. & Jørgensen, L. L. Changes in distribution and range structure of Arctic cephalopods due to climatic changes of the last decades. Biodiversity 14, 28–35 (2013).
Article  Google Scholar 

38.
Nesis, K. N. Cephalopod mollusks of the Arctic Ocean and its seas. In Fauna and Distribution of Molluscs: North Pacific and Arctic Basin (ed. Kafanov, A. I.) 115–136 (USSR Academy of Sciences, Vladivostok, 1987) (in Russian).
Google Scholar 

39.
Xavier, J. C. et al. A review on the biodiversity, distribution and trophic role of cephalopods in the Arctic and Antarctic marine ecosystems under a changing ocean. Mar. Biol. 165, 93 (2018).
Article  Google Scholar 

40.
Golikov, A. V. et al. Reproductive biology and ecology of the boreoatlantic armhook squid Gonatus fabricii (Cephalopoda: Gonatidae). J. Mollus. Stud. 85, 287–299 (2019).
Article  Google Scholar 

41.
Golikov, A. V. et al. Food spectrum and trophic position of an Arctic cephalopod, Rossia palpebrosa (Sepiolida), inferred by stomach contents and stable isotope (δ13C and δ15N) analyses. Mar. Ecol. Prog. Ser. 632, 131–144 (2019).
ADS  Article  Google Scholar 

42.
Golikov, A. V. et al. Ontogenetic changes in stable isotope (δ13C and δ15N) values in squid Gonatus fabricii (Cephalopoda) reveal its important ecological role in the Arctic. Mar. Ecol. Prog. Ser. 606, 65–78 (2018).
ADS  CAS  Article  Google Scholar 

43.
Golikov, A. V., Sabirov, R. M. & Lubin, P. A. First assessment of biomass and abundance of cephalopods Rossia palpebrosa and Gonatus fabricii in the Barents Sea. J. Mar. Biol. Assoc. UK 97, 1605–1616 (2017).
Article  Google Scholar 

44.
Nesis, K. N. Oceanic Cephalopods: Distribution, Life Forms, Evolution (Nauka, Moscow, 1985) (in Russian).
Google Scholar 

45.
Overland, J. E., Wang, M., Walsh, J. E. & Stroeve, J. C. Future Arctic climate changes: adaptation and mitigation time scales. Earth’s Future 2, 68–74 (2014).
ADS  Article  Google Scholar 

46.
Dalpadado, P. et al. Climate effects on temporal and spatial dynamics of phytoplankton and zooplankton in the Barents Sea. Prog. Oceanogr. 185, 102320 (2020).
Article  Google Scholar 

47.
Laidre, K. L. et al. Arctic marine mammal population status, sea ice habitat loss, and conservation recommendations for the 21st century. Conserv. Biol. 29, 724–737 (2015).
PubMed  PubMed Central  Article  Google Scholar 

48.
Mercer, M.C. Systematics of the Sepiolid Squid Rossia Owen 1835 in Canadian Waters with a Preliminary Review of the Genus and Notes on Biology (MSc thesis). St. Johns: Memorial University of Newfoundland (1968).

49.
Golikov, A.V. Distribution and reproductive biology of ten-armed cephalopods (Sepiolida, Teuthida) in the Barents Sea and adjacent areas (PhD thesis). Moscow: Moscow State University (2015) (in Russian).

50.
Golikov, A.V., Sabirov, R.M., Gudmundsson, G. Cephalopoda (Smokkdýr), Rossia megaptera Verrill, 1881. (2018). http://www.ni.is/biota/animalia/mollusca/cephalopoda/rossia-megaptera. Accessed 04 June 2020.

51.
Golikov, A. V., Morov, A. R., Sabirov, R. M., Lubin, P. A. & Jørgensen, L. L. Functional morphology of reproductive system of Rossia palpebrosa (Cephalopoda, Sepiolida) in Barents Sea. Proc. Kazan Univ. Nat. Sci. Ser. 155, 116–129 (2013) (in Russian with English abstract).
Google Scholar 

52.
Cherel, Y., Ducatez, S., Fontaine, C., Richard, P. & Guinet, C. Stable isotopes reveal the trophic position and mesopelagic fish diet of female southern elephant seals breeding on the Kerguelen Islands. Mar. Ecol. Prog. Ser. 370, 239–247 (2008).
ADS  Article  Google Scholar 

53.
Cherel, Y. & Hobson, K. A. Stable isotopes, beaks and predators: a new tool to study the trophic ecology of cephalopods, including giant and colossal squids. Proc. R. Soc. B. 272, 1601–1607 (2005).
PubMed  Article  Google Scholar 

54.
Golikov, A. V. et al. The first global deep-sea stable isotope assessment reveals the unique trophic ecology of Vampire Squid Vampyroteuthis infernalis (Cephalopoda). Sci. Rep. 9, 19099 (2019).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Cherel, Y., Fontaine, C., Jackson, G. D., Jackson, C. H. & Richard, P. Tissue, ontogenic and sex-related differences in δ13C and δ15N values of the oceanic squid Todarodes filippovae (Cephalopoda: Ommastrephidae). Mar. Biol. 156, 699–708 (2009).
Article  Google Scholar 

56.
Zar, J. H. Biostatistical Analysis 5th edn. (Prentice Hall, Upper Saddle River, 2010).
Google Scholar 

57.
Ruiz-Cooley, R. I., Garcia, K. Y. & Hetherington, E. D. Effects of lipid removal and preservatives on carbon and nitrogen stable isotope ratios of squid tissues: implications for ecological studies. J. Exp. Mar. Biol. Ecol. 407, 101–107 (2011).
CAS  Article  Google Scholar 

58.
Hobson, K. A. & Cherel, Y. Isotopic reconstruction of marine food webs using cephalopod beaks: new insight from captively raised Sepia officinalis. Can. J. Zool. 84, 766–770 (2006).
Article  Google Scholar 

59.
Post, D. M. Using stable isotopes to estimate trophic position: models, methods and assumptions. Ecology 83, 703–718 (2002).
Article  Google Scholar 

60.
Hobson, K. A. et al. A stable isotope (δ13C, δ15N) model for the North Water food web: implications for evaluating trophodynamics and the flow of energy and contaminants. Deep-Sea Res. II 49, 5131–5150 (2002).
ADS  CAS  Article  Google Scholar 

61.
Van der Zanden, M. J., Cabana, G. & Rasmussen, J. B. Comparing trophic position of freshwater fish calculated using stable nitrogen isotope ratios (δ15N) and literature dietary data. Can. J. Fish. Aquat. Sci. 54, 1142–1158 (1997).
Article  Google Scholar 

62.
Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17, 239–250 (2014).
PubMed  Article  Google Scholar 

63.
Hussey, N. E. et al. Corrigendum to Hussey et al. (2014). Ecol. Lett. 17, 768 (2014).
PubMed Central  Article  PubMed  Google Scholar 

64.
Linnebjerg, J. F. et al. Deciphering the structure of the West Greenland marine food web using stable isotopes (δ13C, δ15N). Mar. Biol. 163, 230 (2016).
Article  Google Scholar 

65.
Søreide, J. E. et al. Sympagic-pelagic-benthic coupling in Arctic and Atlantic waters around Svalbard revealed by stable isotopic and fatty acid tracers. Mar. Biol. Res. 9, 831–850 (2013).
Article  Google Scholar 

66.
Sokolowski, A. et al. Trophic structure of the macrobenthic community of Hornsund, Spitsbergen, based on the determination of stable carbon and nitrogen isotopic signatures. Polar Biol. 37, 1247–1260 (2014).
Article  Google Scholar 

67.
Tamelander, T. et al. Trophic relationships and pelagic-benthic coupling during summer in the Barents Sea marginal ice zone, revealed by stable carbon and nitrogen isotope measurements. Mar. Ecol. Prog. Ser. 310, 33–46 (2006).
ADS  CAS  Article  Google Scholar 

68.
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. (2019). http://www.r-project.org/. Accessed 04 June 2020.

69.
Syväranta, J., Lensu, A., Marjomaki, T. J., Oksanen, S. & Jones, R. I. An empirical evaluation of the utility of convex hull and standard ellipse areas for assessing population niche widths from stable isotope data. PLoS ONE 8, e56094 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

70.
Langton, R. W. Diet overlap between Atlantic cod, Gadus morphua, silver hake, Merluccius bilinearis, and fifteen other northwest Atlantic finfish. Fish. B NOAA 80, 745–759 (1982).
Google Scholar 

71.
Parnell, C.A. simmr: A Stable Isotope Mixing Model. Version 0.4.1. (2019). https://cran.r-project.org/web/packages/simmr/. Accessed 04 June 2020.

72.
Smith, J. A., Mazumder, D., Suthers, I. M. & Taylor, M. D. To fit or not to fit: evaluating stable isotope mixing models using simulated mixing polygons. Methods Ecol. Evol. 4, 612–618 (2013).
Article  Google Scholar 

73.
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001).
Google Scholar 

74.
Gruber, N. et al. Spatiotemporal patterns of carbon-13 in the global surface oceans and the oceanic Suess effect. Glob. Biogeochem. Cycl. 13, 307–335 (1999).
ADS  CAS  Article  Google Scholar 

75.
Yurkowski, D. J., Hussey, N. E., Ferguson, S. H. & Fisk, A. T. A temporal shift in trophic diversity among a predator assemblage in a warming Arctic. R. Soc. Open. Sci. 5, 180259 (2018).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Guerra, A. et al. Life-history traits of the giant squid Architeuthis dux revealed from stable isotope signatures recorded in beaks. ICES J. Mar. Sci. 67, 1425–1431 (2010).
Article  Google Scholar 

77.
Queirós, J. P. et al. Ontogenic changes in habitat and trophic ecology in the Antarctic squid Kondakovia longimana derived from isotopic analysis on beaks. Polar Biol. 41, 2409–2421 (2018).
Article  Google Scholar 

78.
Queirós, J. P. et al. Ontogenetic changes in habitat and trophic ecology of the giant Antarctic octopus Megaleledone setebos inferred from stable isotope analyses in beaks. Mar. Biol. 167, 56 (2020).
Article  CAS  Google Scholar 

79.
Hansen, H. J., Hedeholm, R. B., Sünksen, K., Christensen, J. T. & Grønkjær, P. Spatial variability of carbon (δ13C) and nitrogen (δ15N) stable isotope ratios in an Arctic marine food web. Mar. Ecol. Prog. Ser. 467, 47–59 (2012).
ADS  CAS  Article  Google Scholar 

80.
Cherel, Y., Ridoux, V., Spitz, J. & Richard, P. Stable isotopes document the trophic structure of a deep-sea cephalopod assemblage including giant octopod and giant squid. Biol. Lett. 5, 364–367 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

81.
Chouvelon, T. et al. Revisiting the use of δ15N in meso-scale studies of marine food webs by considering spatio-temporal variations in stable isotopic signatures—the case of an open ecosystem: the Bay of Biscay (North-East Atlantic). Prog. Oceanogr. 101, 92–105 (2012).
ADS  Article  Google Scholar 

82.
Das, K., Lepoint, G., Leroy, Y. & Bouquegneau, J. M. Marine mammals from the southern North Sea: feeding ecology data from δ13C and δ15N measurements. Mar. Ecol. Prog. Ser. 263, 287–298 (2003).
ADS  Article  Google Scholar 

83.
Gong, Y., Ruiz-Cooley, R. I., Hunsicker, M. E., Li, Y. & Chen, X. Sexual dimorphism in feeding apparatus and niche partitioning in juvenile jumbo squid Dosidicus gigas. Mar. Ecol. Prog. Ser. 607, 99–112 (2018).
ADS  CAS  Article  Google Scholar 

84.
Trasviña-Carrillo, L. D. et al. Spatial and trophic preferences of jumbo squid Dosidicus gigas (D’Orbigny, 1835) in the central Gulf of California: ecological inferences using stable isotopes. Rapid Commun. Mass. Spectrom. 32, 1225–1236 (2018).
ADS  PubMed  Article  CAS  Google Scholar 

85.
Guerreiro, M. et al. Habitat and trophic ecology of Southern Ocean cephalopods from stable isotope analyses. Mar. Ecol. Prog. Ser. 530, 119–134 (2015).
ADS  CAS  Article  Google Scholar 

86.
Kato, Y. et al. Stable isotope analysis of the gladius to investigate migration and trophic patterns of the neon flying squid (Ommastrephes bartramii). Fish. Res. 173, 169–174 (2016).
Article  Google Scholar  More

1.
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).
CAS  Article  Google Scholar 
2.
Mehrabi, Z. & Ramankutty, N. Synchronized failure of global crop production. Nat. Ecol. Evol. 3, 780–786 (2019).
Article  Google Scholar 

3.
Cottrell, R. S. et al. Food production shocks across land and sea. Nat. Sustain. 2, 130–137 (2019).
Article  Google Scholar 

4.
The Food and Agriculture Organization of the United Nations Statistics (FAO, accessed 22 November 2019); https://www.fao.org/faostat/en/#data/.

5.
Marshall, M. G. Codebook: Major Episodes of Political Violence (MEPV) and Conflict Regions, 1946–2015. http://www.systemicpeace.org/inscr/MEPVcodebook2016.pdf (2016).

6.
Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. New anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).
ADS  Article  Google Scholar 

7.
Sacks, W. J., Deryng, D. & Foley, J. A. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).
Google Scholar 

8.
Willmott, C. J. & Matsuura, K. Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1950–1999). http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html (2001).

9.
Food balance sheets: A handbook (FAO, 2001).

10.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

11.
Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).
Article  Google Scholar 

12.
Hallett, L. M. et al. codyn : An R package of community dynamics metrics. Methods Ecol. Evol. 7, 1146–1151 (2016).
Article  Google Scholar 

13.
Khoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).
ADS  CAS  Article  Google Scholar 

14.
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

15.
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).
ADS  Article  Google Scholar  More

We thank the Bren School of Environment Science and Management of the University of California Santa Barbara for support leading to the initial publication. This work was also supported by a grant overseen by the French ‘Programme Investissement d’Avenir’ as part of the ‘Make Our Planet Great Again’ programme (reference: 17-MPGA-0004) and by a National Science Foundation grant (LTER-1831944). More

1.
Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: croplands from 1700 to 1992. Glob. Biogeochem. Cycles 13, 997–1027 (1999).
ADS  CAS  Article  Google Scholar 
2.
Krausmann, F. et al. Growth in global materials use, GDP and population during the 20th century. Ecol. Econ. 68, 2696–2705 (2009).
Article  Google Scholar 

3.
Matthews, E. The Weight of Nations: Material Outflows from Industrial Economies (World Resources Inst., 2000).

4.
Smil, V. Harvesting the Biosphere: What We Have Taken from Nature (MIT Press, 2013).

5.
Smil, V. Making the Modern World: Materials and Dematerialization (John Wiley & Sons, 2013).

6.
Haff, P. K. Technology as a geological phenomenon: implications for human well-being. Geol. Soc. Lond. Spec. Publ. 395, 301–309 (2014).
ADS  Article  Google Scholar 

7.
Zalasiewicz, J. et al. Scale and diversity of the physical technosphere: a geological perspective. Anthropocene Rev. 4, 9–22 (2017).
Article  Google Scholar 

8.
Zalasiewicz, J., Waters, C. N., Williams, M. & Summerhayes, C. The Anthropocene as a Geological Time Unit: A Guide to the Scientific Evidence and Current Debate (Cambridge Univ. Press, 2018).

9.
Stephens, L. et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Erb, K.-H. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

11.
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
CAS  PubMed  Article  Google Scholar 

12.
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

13.
Reddington, C. L. et al. Air quality and human health improvements from reductions in deforestation-related fire in Brazil. Nat. Geosci. 8, 768–771 (2015).
ADS  CAS  Article  Google Scholar 

14.
Ceballos, G. & Ehrlich, P. R. Mammal population losses and the extinction crisis. Science 296, 904–907 (2002).
ADS  CAS  PubMed  Article  Google Scholar 

15.
WWF. Living Planet Report–2018: Aiming Higher (WWF, 2018).

16.
Bar-On, Y. M. & Milo, R. Towards a quantitative view of the global ubiquity of biofilms. Nat. Rev. Microbiol. 17, 199–200 (2019).
CAS  PubMed  Article  Google Scholar 

17.
Pauliuk, S. & Hertwich, E. G. Socioeconomic metabolism as paradigm for studying the biophysical basis of human societies. Ecol. Econ. 119, 83–93 (2015).
Article  Google Scholar 

18.
Haberl, H. et al. Contributions of sociometabolic research to sustainability science. Nat. Sustainability 2, 173–184 (2019).
Article  Google Scholar 

19.
Fischer-Kowalski, M. et al. Methodology and indicators of economy-wide material flow accounting. J. Ind. Ecol. 15, 855–876 (2011).
Article  Google Scholar 

20.
Krausmann, F., Schandl, H., Eisenmenger, N., Giljum, S. & Jackson, T. Material flow accounting: measuring global material use for sustainable development. Annu. Rev. Environ. Resour. 42, 647–675 (2017).
Article  Google Scholar 

21.
Krausmann, F. et al. Global socioeconomic material stocks rise 23-fold over the 20th century and require half of annual resource use. Proc. Natl Acad. Sci. USA 114, 1880–1885 (2017).
CAS  PubMed  Article  Google Scholar 

22.
Krausmann, F., Lauk, C., Haas, W. & Wiedenhofer, D. From resource extraction to outflows of wastes and emissions: the socioeconomic metabolism of the global economy, 1900–2015. Glob. Environ. Change 52, 131–140 (2018).
PubMed  PubMed Central  Article  Google Scholar 

23.
Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the Anthropocene: the great acceleration. Anthropocene Rev. 2, 81–98 (2015).
Article  Google Scholar 

24.
Vitousek, P. M., Ehrlich, P. R., Ehrlich, A. H. & Matson, P. A. Human appropriation of the products of photosynthesis. Bioscience 36, 368–373 (1986).
Article  Google Scholar 

25.
Haberl, H. et al. Quantifying and mapping the human appropriation of net primary production in Earth’s terrestrial ecosystems. Proc. Natl Acad. Sci. USA 104, 12942–12947 (2007).
ADS  CAS  PubMed  Article  Google Scholar 

26.
Haberl, H., Erb, K.-H. & Krausmann, F. Human appropriation of net primary production: patterns, trends, and planetary boundaries. Annu. Rev. Environ. Resour. 39, 363–391 (2014).
Article  Google Scholar 

27.
Vitousek, P. M. Human domination of Earth’s ecosystems. Science 277, 494–499 (1997).
CAS  Article  Google Scholar 

28.
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

29.
Crutzen, P. J. in Earth System Science in the Anthropocene (eds. Ehlers, E. & Kraft, T.) 13–18 (Springer, 2006).

30.
Steffen, W., Crutzen, J. & McNeill, J. R. The Anthropocene: are humans now overwhelming the great forces of Nature? Ambio 36, 614–621 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Lewis, S. L. & Maslin, M. A. Defining the Anthropocene. Nature 519, 171–180 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Waters, C. N. et al. The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science 351, aad2622 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

33.
Krausmann, F. et al. Economy-wide Material Flow Accounting. Introduction and Guide Version 1, Social Ecology Working Paper 151 (Alpen-Adria Univ., 2015).

34.
Miatto, A., Schandl, H., Fishman, T. & Tanikawa, H. Global patterns and trends for non-metallic minerals used for construction. J. Ind. Ecol. 21, 924–937 (2017).
Article  Google Scholar 

35.
Cooper, A. H., Brown, T. J., Price, S. J., Ford, J. R. & Waters, C. N. Humans are the most significant global geomorphological driving force of the 21st century. Anthropocene Rev. 5, 222–229 (2018).
Article  Google Scholar 

36.
Food and Agriculture Organization of the United Nations. Global Forest Resources Assessment 2010: Main Report (FAO, 2010).

37.
Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Chang. 5, 470–474 (2015).
ADS  Article  Google Scholar 

38.
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).
ADS  Article  Google Scholar 

39.
Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).
ADS  Article  Google Scholar 

40.
Food and Agriculture Organization of the United Nations FAOSTAT http://faostat.fao.org.

41.
Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11, 707–717 (2018).
ADS  CAS  Article  Google Scholar 

42.
Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).
ADS  CAS  Article  Google Scholar 

43.
Melton, J. R. & Arora, V. K. Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0. Geosci. Model Dev. 9, 323–361 (2016).
ADS  CAS  Article  Google Scholar 

44.
Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).
ADS  Article  Google Scholar 

45.
Tian, H. et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Clim. Change 129, 413–426 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Meiyappan, P., Jain, A. K. & House, J. I. Increased influence of nitrogen limitation on CO2 emissions from future land use and land use change. Glob. Biogeochem. Cycles 29, 1524–1548 (2015).
ADS  CAS  Article  Google Scholar 

47.
Mauritsen, T. et al. Developments in the MPI-M Earth System Model version1.2 (MPI-ESM1.2) and its response to increasing CO2. J. Adv. Model. Earth Syst. 11, 998–1038 (2019).
ADS  PubMed  PubMed Central  Article  Google Scholar 

48.
Clark, D. B. et al. The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).
ADS  Article  Google Scholar 

49.
Poulter, B., Frank, D. C., Hodson, E. L. & Zimmermann, N. E. Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO2 airborne fraction. Biogeosciences 8, 2027–2036 (2011).
ADS  CAS  Article  Google Scholar 

50.
Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).
ADS  Article  Google Scholar 

51.
Lienert, S. & Joos, F. A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions. Biogeosciences 15, 2909–2930 (2018).
ADS  CAS  Article  Google Scholar 

52.
Zaehle, S. & Friend, A. D. Carbon and nitrogen cycle dynamics in the O-CN land surface model: 1. Model description, site-scale evaluation, and sensitivity to parameter estimates. Glob. Biogeochem. Cycles 24, GB1005 (2010).
ADS  Google Scholar 

53.
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere–biosphere system. Glob. Biogeochem. Cycles 19, GB1015 (2005).
ADS  Article  CAS  Google Scholar 

54.
Goll, D. S. et al. Carbon–nitrogen interactions in idealized simulations with JSBACH (version 3.10). Geosci. Model Dev. 10, 2009–2030 (2017).
ADS  CAS  Article  Google Scholar 

55.
Walker, A. P. et al. The impact of alternative trait-scaling hypotheses for the maximum photosynthetic carboxylation rate (V cmax) on global gross primary production. New Phytol. 215, 1370–1386 (2017).
CAS  PubMed  Article  Google Scholar 

56.
Kato, E., Kinoshita, T., Ito, A., Kawamiya, M. & Yamagata, Y. Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model. J. Land Use Sci. 8, 104–122 (2013).
Article  Google Scholar 

57.
Tang, Z. et al. Patterns of plant carbon, nitrogen, and phosphorus concentration in relation to productivity in China’s terrestrial ecosystems. Proc. Natl Acad. Sci. USA 115, 4033–4038 (2018).
PubMed  Article  Google Scholar 

58.
Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).
CAS  PubMed  Article  Google Scholar 

59.
Heldal, M., Norland, S. & Tumyr, O. X-ray microanalytic method for measurement of dry matter and elemental content of individual bacteria. Appl. Environ. Microbiol. 50, 1251–1257 (1985).
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
von Stockar, U. & Liu, J. Does microbial life always feed on negative entropy? Thermodynamic analysis of microbial growth. Biochim. Biophys. Acta 1412, 191–211 (1999).
Article  Google Scholar 

61.
Guo, L., Lin, H., Fan, B., Cui, X. & Chen, J. Impact of root water content on root biomass estimation using ground penetrating radar: evidence from forward simulations and field controlled experiments. Plant Soil 371, 503–520 (2013).
CAS  Article  Google Scholar 

62.
Glass, S. V. & Zelinka, S. L. in Wood Handbook: Wood as an Engineering Material Vol. 190, 4.1–4.19 (US Department of Agriculture, 2010).

63.
Loveys, B. R. et al. Thermal acclimation of leaf and root respiration: an investigation comparing inherently fast- and slow-growing plant species. Glob. Change Biol. 9, 895–910 (2003).
ADS  Article  Google Scholar 

64.
Sheremetev, S. N. Herbs on the Soil Moisture Gradient (Water Relations and the Structural-Functional Organization) (KMK, 2005).

65.
Michaletz, S. T. & Johnson, E. A. A heat transfer model of crown scorch in forest fires. Can. J. For. Res. 36, 2839–2851 (2006).
Article  Google Scholar 

66.
Messier, J., McGill, B. J. & Lechowicz, M. J. How do traits vary across ecological scales? A case for trait-based ecology. Ecol. Lett. 13, 838–848 (2010).
PubMed  Article  Google Scholar 

67.
Boucher, F. C., Thuiller, W., Arnoldi, C., Albert, C. H. & Lavergne, S. Unravelling the architecture of functional variability in wild populations of Polygonum viviparum L. Funct. Ecol. 27, 382–391 (2013).
PubMed  PubMed Central  Article  Google Scholar 

68.
Dahlin, K. M., Asner, G. P. & Field, C. B. Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. Proc. Natl Acad. Sci. USA 110, 6895–6900 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

69.
Kattge, J. et al. TRY–a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).
ADS  Article  Google Scholar 

70.
Lebigot, E. O. Uncertainties: a Python package for calculations with uncertainties. https://pythonhosted.org/uncertainties/ (2010).

71.
Wiedenhofer, D., Fishman, T., Lauk, C., Haas, W. & Krausmann, F. Integrating material stock dynamics into economy-wide material flow accounting: concepts, modelling, and global application for 1900–2050. Ecol. Econ. 156, 121–133 (2019).
Article  Google Scholar  More

1.
Umanzor, S., Ladah, L. & Calderon-aguilera, L. E. Testing the relative importance of intertidal seaweeds as ecosystem engineers across tidal heights. J. Exp. Mar. Bio. Ecol. 511, 100–107 (2018).
Article  Google Scholar 
2.
Smale, D. A., Burrows, M. T., Moore, P., O’Connor, N. & Hawkins, S. J. Threats and knowledge gaps for ecosystem services provided by kelp forests: a northeast Atlantic perspective. Ecol. Evol. 3, 4016–4038 (2013).
Article  PubMed  PubMed Central  Google Scholar 

3.
Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742 (2016).
ADS  CAS  Article  Google Scholar 

4.
King, N. G., McKeown, N. J., Smale, D. A. & Moore, P. J. The importance of phenotypic plasticity and local adaptation in driving intraspecific variability in thermal niches of marine macrophytes. Ecography (Cop.) 41, 1469–1484 (2018).
Article  Google Scholar 

5.
Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annu. Rev. Ecol. Evol. Syst. 37, 373–404 (2006).
Article  Google Scholar 

6.
Fernández, Á. et al. Additive effects of emersion stressors on the ecophysiological performance of two intertidal seaweeds. Mar. Ecol. Prog. Ser. 536, 135–147 (2015).
ADS  Article  Google Scholar 

7.
IPCC. Summary for Policymakers. In: Climate Change 2014, Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Edenhofer, O. et al.). (Cambridge University Press, Cambridge, 2014).

8.
Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).
ADS  CAS  Article  PubMed  Google Scholar 

9.
Koffi, B. & Koffi, E. Heat waves across Europe by the end of the 21st century: multiregional climate simulations. Clim. Res. 36, 153–168 (2008).
Article  Google Scholar 

10.
Lüning, K. Temperature, salinity and other abiotic factors in Seaweeds. Their Environment, Biogeography and Ecophysiology (Wiley, New York, 1990).
Google Scholar 

11.
Pang, S. J., Jin, Z. H., Sun, J. Z. & Gao, S. Q. Temperature tolerance of young sporophytes from two populations of Laminaria japonica revealed by chlorophyll fluorescence measurements and short-term growth and survival performances in tank culture. Aquaculture 262, 493–503 (2007).
Article  Google Scholar 

12.
Nielsen, S. L., Nielsen, H. D. & Pedersen, M. F. Juvenile life stages of the brown alga Fucus serratus L. Are more sensitive to combined stress from high copper concentration and temperature than adults. Mar. Biol. 161, 1895–1904 (2014).
CAS  Article  Google Scholar 

13.
Schonbeck, M. W. & Norton, T. A. The effects on intertidal fucoid algae of exposure to air under various conditions. Bot. Mar. 23, 141–148 (1980).
Article  Google Scholar 

14.
Pearson, G. A., Lago-Leston, A. & Mota, C. Frayed at the edges: Selective pressure and adaptive response to abiotic stressors are mismatched in low diversity edge populations. J. Ecol. 97, 450–462 (2009).
Article  Google Scholar 

15.
Ferreira, J. G., Arenas, F., Martínez, B., Hawkins, S. J. & Jenkins, S. R. Physiological response of fucoid algae to environmental stress: comparing range centre and southern populations. New Phytol. 202, 1157–1172 (2014).
Article  PubMed  Google Scholar 

16.
Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. Proc. R. Soc. B. 280, 20122829 (2013).
Article  PubMed  Google Scholar 

17.
Jueterbock, A. et al. Thermal stress resistance of the brown alga Fucus serratus along the North-Atlantic coast: Acclimatization potential to climate change. Mar. Genomics 13, 27–36 (2014).
Article  PubMed  Google Scholar 

18.
Hurd, K., Harrison, P.J., Bischof, K. & Lobban, C.S. Light and photosynthesis in Seaweed Ecology and Physiology (eds. Hurd, K., Harrison, P.J., Bischof, K. & Lobban, C.S.) 176–237. (Cambridge University Press, Cambridge, 2014).

19.
Mota, C. F. et al. Differentiation in fitness-related traits in response to elevated temperatures between leading and trailing edge populations of marine macrophytes. PLoS ONE 13, 1–17 (2018).
Article  CAS  Google Scholar 

20.
Martínez, B. et al. Physical factors driving intertidal macroalgae distribution: physiological stress of a dominant fucoid at its southern limit. Oecologia 170, 341–353 (2012).
ADS  Article  PubMed  Google Scholar 

21.
Pereira, T. R., Engelen, A. H., Pearson, G. A., Valero, M. & Serrão, E. A. Response of kelps from different latitudes to consecutive heat shock. J. Exp. Mar. Bio. Ecol. 463, 57–62 (2015).
Article  Google Scholar 

22.
Madsen, T. V. & Maberly, S. C. A comparison of air and water as environments for photosynthesis by the intertidal alga Fucus spiralis (Phaeophyta). J. phycol. 26(1), 24–30 (1990).
Article  Google Scholar 

23.
Contreras-Porcia, L., López-Cristoffanini, Meynard, A., & Kumar, M. Tolerance pathways to desiccation stress in seaweeds. In Systems Biology of Marine Ecosystems (eds. Kumar, M. & Ralhp, P.) 13–29. (Springer, Berlin, 2017).

24.
Helmuth, B. et al. Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015–1017 (2002).
ADS  CAS  Article  PubMed  Google Scholar 

25.
King, N. G. et al. Cumulative stress restricts niche filling potential of habitat-forming kelps in a future climate. Funct. Ecol. 32, 288–299 (2017).
Article  PubMed  PubMed Central  Google Scholar 

26.
Hereward, H. F. R., King, N. G. & Smale, D. A. Intra-annual variability in responses of a canopy forming kelp to cumulative low tide heat stress: implications for populations at the trailing range edge. J. Phycol. 56, 146–158 (2019).
Article  PubMed  Google Scholar 

27.
Fernández, C. The retreat of large brown seaweeds on the north coast of Spain: the case of Saccorhiza polyschides. Eur. J. Phycol. 46, 352–360 (2011).
Article  Google Scholar 

28.
Méndez-Sandín, M. & Fernández, C. Changes in the structure and dynamics of marine assemblages dominated by Bifurcaria bifurcata and Cystoseira species over three decades (1977–2007). Estuar. Coast. Shelf Sci. 175, 46–56 (2016).
ADS  Article  Google Scholar 

29.
Wilson, K. L., Skinner, M. A. & Lotze, H. K. Projected 21st-century distribution of canopy-forming seaweeds in the Northwest Atlantic with climate change. Divers. Distrib. 25, 582–602 (2019).
Article  Google Scholar 

30.
Martínez, B., Viejo, R. M., Carreño, F. & Aranda, S. C. Habitat distribution models for intertidal seaweeds: Responses to climatic and non-climatic drivers. J. Biogeogr. 39, 1877–1890 (2012).
Article  Google Scholar 

31.
Nyström, M. et al. Confronting feedbacks of degraded marine ecosystems. Ecosystems 15, 695–710 (2012).
Article  Google Scholar 

32.
O’Brien, B. S., Mello, K., Litterer, A. & Dijkstra, J. A. Seaweed structure shapes trophic interactions: a case study using a mid-trophic level fish species. J. Exp. Mar. Biol. Ecol. 506, 1–8 (2018).
Article  Google Scholar 

33.
Voerman, S. E., Llera, E. & Rico, J. M. Climate driven changes in subtidal kelp forest communities in NW Spain. Mar. Environ. Res. 90, 119–127 (2013).
CAS  Article  PubMed  Google Scholar 

34.
Duarte, L. et al. Recent and historical range shifts of two canopy-forming seaweeds in North Spain and the link with trends in sea surface temperature. Acta Oecol. 51, 1–10 (2013).
ADS  Article  Google Scholar 

35.
Viejo, R. M., Martínez, B., Arrontes, J., Astudillo, C. & Herna, L. Reproductive patterns in central and marginal populations of a large brown seaweed: drastic changes at the southern range limit. Ecography 34, 75–84 (2011).
Article  Google Scholar 

36.
Duarte, L. & Viejo, R. M. Environmental and phenotypic heterogeneity of populations at the trailing range-edge of the habitat-forming macroalga Fucus serratus. Mar. Environ. Res. 136, 16–26 (2018).
CAS  Article  PubMed  Google Scholar 

37.
Thomsen, M. S. et al. Local extinction of bull kelp (Durvillaea spp.) due to a marine heatwave. Front. Mar. Sci. 6, 1–10 (2019).
Article  Google Scholar 

38.
Darling, E. S. & Côté, I. M. Quantifying the evidence for ecological synergies. Ecol. Lett. 11, 1278–1286 (2008).
Article  Google Scholar 

39.
Gómez-Gesteira, M. et al. The state of climate in NW Iberia. Clim. Res. 48, 109–144 (2011).
Article  Google Scholar 

40.
Kersting, D.K. Cambio Climático en El Medio Marino Español: Impactos, Vulnerabilidad y Adaptación. Oficina Española de Cambio Climático, Ministerio de Agricultura, Alimentación y Medio Ambiente. Madrid. http://cort.as/-HXq9 (2016).

41.
Philippart, C. J. M. et al. Impacts of climate change on European marine ecosystems: observations, expectations and indicators. J. Exp. Mar. Biol. Ecol. 400, 52–69 (2011).
Article  Google Scholar 

42.
Lima, F. P., Ribeiro, P. A., Queiroz, N., Hawkins, S. J. & Santos, A. M. Do distributional shifts of northern and southern species of algae match the warming pattern?. Glob. Chang. Biol. 13, 2592–2604 (2007).
ADS  Article  Google Scholar 

43.
Díez, I., Muguerza, N., Santolaria, A., Ganzedo, U. & Gorostiaga, J. M. Seaweed assemblage changes in the eastern Cantabrian Sea and their potential relationship to climate change. Estuar. Coast. Shelf Sci. 99, 108–120 (2012).
ADS  Article  Google Scholar 

44.
Piñeiro-Corbeira, C., Barreiro, R. & Cremades, J. Decadal changes in the distribution of common intertidal seaweeds in Galicia (NW Iberia). Mar. Environ. Res. 113, 106–115 (2016).
Article  CAS  PubMed  Google Scholar 

45.
García-Fernández, A. & Bárbara, I. Studies of Cystoseira assemblages in Northern Atlantic Iberia. Ann. del Jard. Bot. Madrid 73, 1–21 (2016).
Google Scholar 

46.
García, A. G., Olabarria, C., Arrontes, J., Álvarez, Ó. & Viejo, R. M. Spatio-temporal dynamics of Codium populations along the rocky shores of N and NW Spain. Mar. Environ. Res. 140, 394–402 (2018).
Article  CAS  PubMed  Google Scholar 

47.
Fernández, C. Current status and multidecadal biogeographical changes in rocky intertidal algal assemblages: the northern Spanish coast. Estuar. Coast. Shelf Sci. 171, 35–40 (2016).
ADS  Article  Google Scholar 

48.
Eggert, A. Seaweed responses to temperature. In Seaweed Biology. Novel Insights into Ecophysiology, Ecology and Utilization (eds. Wiencke, C. & Bischof, K) 47–66 (Springer, Berlin, 2012).

49.
Karsten, U. Seaweed acclimation to salinity and desiccation stress. In Seaweed biology. Novel Insights into Ecophysiology, Ecology and Utilization (eds. Wiencke, C. & Bischof, K) 87–108 (Springer, Berlin, 2012).

50.
Phelps, C. M., Boyce, M. C. & Huggett, M. J. Future climate change scenarios differentially affect three abundant algal species in southwestern Australia. Mar. Environ. Res. 126, 69–80 (2017).
CAS  Article  PubMed  Google Scholar 

51.
Olabarria, C., Arenas, F., Fernández, Á., Troncoso, J. S. & Martínez, B. Physiological responses to variations in grazing and light conditions in native and invasive fucoids. Mar. Environ. Res. 139, 151–161 (2018).
CAS  Article  PubMed  Google Scholar 

52.
Schagerl, M. & Möstl, M. Drought stress, rain and recovery of the intertidal seaweed. Fucus spiralis. Mar. Biol. 158, 2471–2479 (2011).
Article  Google Scholar 

53.
Lamela-Silvarrey, C., Fernández, C., Anadón, R. & Arrontes, J. Fucoid assemblages on the north coast of Spain: Past and present (1977–2007). Bot. Mar. 55, 199–207 (2012).
Article  Google Scholar 

54.
Martínez, B., Arenas, F., Trilla, A., Viejo, R. M. & Carreño, F. Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae. Glob. Change Biol. 21, 1422–1433 (2014).
ADS  Article  Google Scholar 

55.
Piñeiro-Corbeira, C., Barreiro, R., Cremades, J. & Arenas, F. Seaweed assemblages under a climate change scenario: Functional responses to temperature of eight intertidal seaweeds match recent abundance shifts. Sci. Rep. 8, 1–9 (2018).
Article  CAS  Google Scholar 

56.
Figueroa, F. L. et al. Yield losses and electron transport rate as indicators of thermal stress in Fucus serratus (Ochrophyta). Algal Res. 41, 101560 (2019).
Article  Google Scholar 

57.
Davison, I. R. & Pearson, G. A. Stress tolerance in intertidal seaweeds. J. Phycol. 32, 197–211 (1996).
Article  Google Scholar 

58.
Schreiber, U., Bilger, W. & Neubauer, C. Chlorophyll Fluorescence as a Nonintrusive Indicator for Rapid Assessment of In Vivo Photosynthesis. In Ecophysiology of photosynthesis (eds. Schulze, E.D. & Caldwell, M.M.) 49–70 (Springer, Berlin, 2003).

59.
Hurd, K., Harrison, P.J., Bischof, K. & Lobban, C.S. Physico-chemical factors as environmental stressors in seaweed biology. In Seaweed Ecology and Physiology (eds. Hurd, K., Harrison, P.J., Bischof, K. & Lobban, C.S.) 294–348 (Cambridge University Press, Cambridge, 2014).

60.
Kumar, M. et al. Desiccation induced oxidative stress and its biochemical responses in intertidal red alga Gracilaria corticata (Gracilariales, Rhodophyta). Environ. Exp. Bot. 72, 194–201 (2011).
CAS  Article  Google Scholar 

61.
Bischof, K. & Rautenberger, R. Seaweed responses to environmental stress: Reactive oxygen and antioxidative strategies. In Seaweed biology. Novel insights into Ecophysiology, Ecology and Utilization (eds. Wiencke, C., Bischof, K.) 109–134 (Springer, Berlin, 2012).

62.
Allakhverdiev, S. I. et al. Heat stress: An overview of molecular responses in photosynthesis. Photosynth. Res. 98, 541–550 (2008).
CAS  Article  PubMed  Google Scholar 

63.
Mota, C. F., Engelen, A. H., Serrão, E. A. & Pearson, G. A. Some don’t like it hot: Microhabitat-dependent thermal and water stresses in a trailing edge population. Funct. Ecol. 29, 640–649 (2015).
Article  Google Scholar 

64.
Hunt, L. J. H. & Denny, M. W. Desiccation protection and disruption: A trade-off for an intertidal marine alga. J. Phycol. 44, 1164–1170 (2008).
Article  PubMed  Google Scholar 

65.
Staehr, P. A. & Wernberg, T. Physiological responses of Ecklonia radiata (Laminariales) to a latitudinal gradient in ocean temperature. J. Phycol. 45, 91–99 (2009).
CAS  Article  PubMed  Google Scholar 

66.
Davison, I. R. & Davison, J. O. The effect of growth temperature on enzyme activities in the brown alga Laminaria saccharina. Br. Phycol. J. 22, 77–87 (1987).
Article  Google Scholar 

67.
Young, E. B., Dring, M. J., Savidge, G., Birkett, D. A. & Berges, J. A. Seasonal variations in nitrate reductase activity and internal N pools in intertidal brown algae are correlated with ambient nitrate concentrations. Plant, Cell Environ. 30, 764–774 (2007).
CAS  Article  Google Scholar 

68.
Monteiro, C. et al. Canopy microclimate modification in central and marginal populations of a marine macroalga. Mar. Biodivers. 49, 415–424 (2019).
Article  Google Scholar 

69.
Dawes, C. J. Physiological ecology. In Marine Botany (ed. Dawes, C.J.) (Wiley, New York, 1998).

70.
Rohde, S., Hiebenthal, C., Wahl, M., Karez, R. & Bischof, K. Decreased depth distribution of Fucus vesiculosus (Phaeophyceae) in the Western Baltic: Effects of light deficiency and epibionts on growth and photosynthesis. Eur. J. Phycol. 43, 143–150 (2008).
Article  Google Scholar 

71.
Lüning, K. Seaweed vegetation of the cold and warm temperate regions of the northern hemisphere. In Seaweeds. Their Environment, Biogeography and Ecophysiology. (ed. Lüning, K.) (Wiley, New York, 1990).

72.
Schiel, D. R., Lilley, S. A., South, P. M. & Coggins, J. H. J. Decadal changes in sea surface temperature, wave forces and intertidal structure in New Zealand. Mar. Ecol. Prog. Ser. 548, 77–95 (2016).
ADS  Article  Google Scholar 

73.
Fernández de la Hoz, C. F. et al. OCLE: a European open access database on climate change effects on littoral and oceanic ecosystems. Prog. Oceanogr. 168, 222–231 (2018).
ADS  Article  Google Scholar 

74.
Fernández de la Hoz, C., Ramos, E., Puente, A. & Juanes, J. A. Climate change induced range shifts in seaweeds distributions in Europe. Mar. Environ. Res. 148, 1–11 (2019).
Article  CAS  Google Scholar 

75.
Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).
Article  PubMed  PubMed Central  Google Scholar 

76.
Halekoh, U., Højsgaard, S. & Yan, J. The R package geepack for generalized estimating equations. J. Stat. Softw. 15, 1–11 (2006).
Article  Google Scholar 

77.
Fox, J. Applied Regression Analysis and Generalized Linear Models 3rd edn. (Sage, Thousand Oaks, 2016).
Google Scholar  More

To produce our global Forest Landscape Integrity Index (FLII), we combined four sets of spatially explicit datasets representing: (i) forest extent23; (ii) observed pressure from high impact, localized human activities for which spatial datasets exist, specifically: infrastructure, agriculture, and recent deforestation27; (iii) inferred pressure associated with edge effects27, and other diffuse processes, (e.g., activities such as hunting and selective logging)27 modeled using proximity to observed pressures; and iv) anthropogenic changes in forest connectivity due to forest loss27 (see Supplementary Table 1 for data sources). These datasets were combined to produce an index score for each forest pixel (300 m), with the highest scores reflecting the highest forest integrity (Fig. 1), and applied to forest extent for the start of 2019. We use globally consistent parameters for all elements (i.e., parameters do not vary geographically). All calculations were conducted in Google Earth Engine (GEE)60.
Forest extent
We derived a global forest extent map for 2019 by subtracting from the Global Tree Cover product for 200023 annual Tree Cover Loss 2001–2018, except for losses categorized by Curtis and colleagues24 as those likely to be temporary in nature (i.e., those due to fire, shifting cultivation and rotational forestry). We applied a canopy threshold of 20% based on related studies e.g.31,61, and resampled to 300 m resolution and used this resolution as the basis for the rest of the analysis (see Supplementary Note 1 for further methods).
Observed human pressures
We quantify observed human pressures (P) within a pixel as the weighted sum of impact of infrastructure (I; representing the combined effect of 41 types of infrastructure weighted by their estimated general relative impact on forests (Supplementary Table 3), agriculture (A) weighted by crop intensity (indicated by irrigation levels), and recent deforestation over the past 18 years (H; excluding deforestation from fire, see Discussion). Specifically, for pixel i:

$${mathrm{P}}_{mathrm{i}} = {mathrm{exp}}left( { – {upbeta}_1{mathrm{I}}_{mathrm{i}}} right) + {mathrm{exp}}left( { – {upbeta}_2{mathrm{A}}_{mathrm{i}}} right) + {mathrm{exp}}left( { – {upbeta}_3{mathrm{H}}_{mathrm{i}}} right)$$
(1)

whereby the values of β were selected so that the median of the non-zero values for each component was 0.75. This use of exponents is a way of scaling variables with non-commensurate units so that they can be combined numerically, while also ensuring that the measure of observed pressure is sensitive to change (increase or decrease) in the magnitude of any of the three components, even at large values of I, A, or H. This is an adaptation of the Human Footprint methodology62. See Supplementary Note 3 for further details.
Inferred human pressures
Inferred pressures are the diffuse effects of a set of processes for which directly observed datasets do not exist, that include microclimate and species interactions relating to the creation of forest edges63 and a variety of intermittent or transient anthropogenic pressures such as selective logging, fuelwood collection, hunting; spread of fires and invasive species, pollution, and livestock grazing64,65,66. We modeled the collective, cumulative impacts of these inferred effects through their spatial association with observed human pressure in nearby pixels, including a decline in effect intensity according to distance, and partitioning into stronger short-range and weaker long-range effects. The inferred pressure (P′) on pixel i from source pixel j is:

$$Pprime _{i,j} = P_jleft( {w_{i,j} + v_{i,j}} right)$$
(2)

where wi,j is the weighting given to the modification arising from short-range pressure, as a function of distance from the source pixel, and vi,j is the weighting given to the modification arising from long-range pressures.
Short-range effects include most of the processes listed above, which together potentially affect most biophysical features of a forest, and predominate over shorter distances. In our model, they decline exponentially, approach zero at 3 km, and are truncated to zero at 5 km (see Supplementary Note 4).

$$begin{array}{l}{mathrm{w}}_{i,j} = alpha ,{mathrm{exp}}( – lambda {mathrm{d}}_{i,j}),,,,,,[{mathrm{for}},{mathrm{d}}_{{mathrm{i,j}}} le {mathrm{5km}}]\ {mathrm{w}}_{i,j} = {mathrm{0}},,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[{mathrm{for}},{mathrm{d}}_{i,j} > {mathrm{5km}}]end{array}$$
(3)

where α is a constant set to ensure that the sum of the weights across all pixels in the range is 1.85 (see below), λ is a decay constant set to a value of 1 (see67 and other references in Supplementary Note 4) and di,j is the Euclidean distance between the centers of pixels i and j expressed in units of km.
Long-range effects include over-exploitation of high socio-economic value animals and plants, changes to migration and ranging patterns, and scattered fire and pollution events. We modeled long-range effects at a uniform level at all distances below 6 km and they then decline linearly with distance, conservatively reaching zero at a radius of 12 km65,68 (and other references in Supplementary Note 4):

$$begin{array}{l}{mathrm{v}}_{i,j} = gamma ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[for,d_{i,j} le 6km]\ {mathrm{v}}_{i,j} = gamma left( {12 – d_{i,j}} right)/6,,,,[{mathrm{for}},6{mathrm{km}}, < ,{mathrm{d}}_{i,j} le 12{mathrm{km}}]\ {mathrm{v}}_{i,j} = 0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[for,{mathrm{d}}_{i,j} > 12{mathrm{km}}]end{array}$$
(4)

where γ is a constant set to ensure that the sum of the weights across all pixels in the range is 0.15 and di,j is the Euclidean distance between the centers of pixels i and j, expressed in kilometers.
The form of the weighting functions for short- and long-range effects and the sum of the weights (α + γ) were specified based on a hypothetical reference scenario where a straight forest edge is adjacent to a large area with uniform human pressure, and ensuring that in this case total inferred pressure immediately inside the forest edge is equal to the pressure immediately outside, before declining with distance. γ is set to 0.15 to ensure that the long-range effects conservatively contribute no more than 5% to the final index in the same scenario, based on expert opinion and supported e.g., Berzaghi et al.69 regarding the approximate level of impact on values that would be affected by severe defaunation and other long-range effects.
The aggregate effect from inferred pressures (Q) on pixel i from all n pixels within range (j = 1 to j = n) is then the sum of these individual, normalized, distance-weighted pressures, i.e.,

$$Q_i = mathop {sum}_{j=1}^{n} {P{prime}_{i,j}}$$
(5)

Loss of forest connectivity
Average connectivity of forest around a pixel was quantified using a method adapted from Beyer et al.70. The connectivity Ci around pixel i surrounded by n other pixels within the maximum radius (numbered j = 1, 2…n) is given by:

$${mathrm{C}}_i = mathop {sum}_{j=1}^{n} {left( {{mathrm{F}}_j{mathrm{G}}_{i,j}} right)}$$
(6)

where Fj is the forest extent is a binary variable indicating if forested (1) or not (0) and Gi,j is the weight assigned to the distance between pixels i and j. Gi,j uses a normalized Gaussian curve, with σ = 20 km and distribution truncated to zero at 4σ for computational convenience (see Supplementary Note 2). The large value of σ captures landscape connectivity patterns operating at a broader scale than processes captured by other data layers. Ci ranges from 0 to 1 (Ci∈[0,1]).
Current Configuration (CCi) of forest extent in pixel i was calculated using the final forest extent map and compared to the Potential Configuration (PC) of forest extent without extensive human modification, so that areas with naturally low connectivity, e.g., coasts and natural vegetation mosaics, are not penalized. PC was calculated from a modified version of the map of Laestadius et al38. and resampled to 300 m resolution (see Supplementary Note 2 for details). Using these two measures, we calculated Lost Forest Configuration (LFC) for every pixel as:

$${mathrm{LFC}}_i = 1 – left( {{mathrm{CC}}_i/{mathrm{PC}}_i} right)$$
(7)

Values of CCi/PCi  > 1 are assigned a value of 1 to ensure that LFC is not sensitive to apparent increases in forest connectivity due to inaccuracy in estimated potential forest extent – low values represent least loss, high values greatest loss (LFCi∈[0,1]).
Calculating the Forest Landscape Integrity Index
The three constituent metrics, LFC, P, and Q, all represent increasingly modified conditions the larger their values become. To calculate a forest integrity index in which larger values represent less degraded conditions we, therefore, subtract the sum of those components from a fixed large value (here, 3). Three was selected as our assessment indicates that values of LFC + P + Q of 3 or more correspond to the most severely degraded areas. The metric is also rescaled to a convenient scale (0-10) by multiplying by an arbitrary constant (10/3). The FLII for forest pixel i is thus calculated as:

$${mathrm{FLII}}_i = left[ {10/3} right] (3 – {mathrm{min}}(3,,[P_i + Q_i + {mathrm{LFC}}_i]))$$
(8)

where FLIIi ranges from 0 to 10, forest areas with no modification detectable using our methods scoring 10 and those with the most scoring 0.
Illustrative forest integrity classes
Whilst a key strength of the index is its continuous nature, the results can also be categorized for a range of purposes. In this paper three illustrative classes were defined, mapped, and summarized to give an overview of broad patterns of integrity in the world’s forests. The three categories were defined as follows.
High Forest Integrity (scores ≥ 9.6) Interiors and natural edges of more or less unmodified naturally regenerated (i.e., non-planted) forest ecosystems, comprised entirely or almost entirely of native species, occurring over large areas either as continuous blocks or natural mosaics with non-forest vegetation; typically little human use other than low-intensity recreation or spiritual uses and/or low-intensity extraction of plant and animal products and/or very sparse presence of infrastructure; key ecosystem functions such as carbon storage, biodiversity, and watershed protection and resilience expected to be very close to natural levels (excluding any effects from climate change) although some declines possible in the most sensitive elements (e.g., some high value hunted species).
Medium Forest Integrity (scores  > 6.0 but More