Philippa Kaur

Ehrlich, P. R. & Raven, P. H. Butterflies and plants: A study in coevolution. Evolution 18, 586 (1964).Article 

Google Scholar 
Raguso, R. A. et al. The raison d’être of chemical ecology. Ecology 96, 617–630 (2015).PubMed 
Article 

Google Scholar 
Kariyat, R. R. & Portman, S. L. Plant–herbivore interactions: Thinking beyond larval growth and mortality. Am. J. Bot. 103, 789–791 (2016).PubMed 
Article 

Google Scholar 
Raubenheimer, D. & Simpson, S. J. Nutritional ecology and foraging theory. Curr. Opin. Insect Sci. 27, 38–45 (2018).PubMed 
Article 

Google Scholar 
Goehring, L. & Oberhauser, K. S. Effects of photoperiod, temperature, and host plant age on induction of reproductive diapause and development time in Danaus plexippus. Ecol. Entomol. 27, 674–685 (2002).Article 

Google Scholar 
Hahn, D. A. Larval nutrition affects lipid storage and growth, but not protein or carbohydrate storage in newly eclosed adults of the grasshopper Schistocerca americana. J. Insect Physiol. 51, 1210–1219 (2005).CAS 
PubMed 
Article 

Google Scholar 
Portman, S. L., Kariyat, R. R., Johnston, M. A., Stephenson, A. G. & Marden, J. H. Cascading effects of host plant inbreeding on the larval growth, muscle molecular composition, and flight capacity of an adult herbivorous insect. Funct. Ecol. 29, 328–337 (2015).Article 

Google Scholar 
Johnson, C. G. Physiological factors in insect migration by flight. Nature 198, 423–427 (1963).Article 

Google Scholar 
Harrison, R. G. Dispersal polymorphisms in insects. Annu. Rev. Ecol. Syst. 11, 95–118 (1980).Article 

Google Scholar 
Zera, A. J. & Denno, R. F. Physiology and ecology of dispersal polymorphism in insects. Annu. Rev. Entomol. 42, 207–231 (1997).CAS 
PubMed 
Article 

Google Scholar 
Marden, J. H. et al. Weight and nutrition affect pre-mRNA splicing of a muscle gene associated with performance, energetics and life history. J. Exp. Biol. 211, 3653–3660 (2008).CAS 
PubMed 
Article 

Google Scholar 
Raguso, R. A., Ojeda-Avila, T., Desai, S., Jurkiewicz, M. A. & Arthur Woods, H. The influence of larval diet on adult feeding behaviour in the tobacco hornworm moth, Manduca sexta. J. Insect Physiol. 53, 923–932 (2007).CAS 
PubMed 
Article 

Google Scholar 
Cease, A. J. et al. Nutritional imbalance suppresses migratory phenotypes of the Mongolian locust (Oedaleus asiaticus). R. Soc. Open Sci. 4, https://doi.org/10.1098/rsos.161039 (2017).Reichstein, T., Von Euw, J., Parsons, J. A. & Rothschild, M. Heart poisons in the monarch butterfly. Science 161, 861–866 (1968).CAS 
PubMed 
Article 

Google Scholar 
Brower, L. P., Ryerson, W. N., Coppinger, L. L. & Glazier, S. C. Ecological chemistry and the palatability spectrum. Science 161, 1349–1351 (1968).CAS 
PubMed 
Article 

Google Scholar 
Young, A. M. An evolutionary-ecological model of the evolution of migratory behavior in the Monarch Butterfly, and its absence in the Queen Butterfly. Acta Biotheor. 31, 219–237 (1982).Article 

Google Scholar 
Agrawal, A. A. Monarchs and Milkweed: A Migrating Butterfly, a Poisonous Plant, and Their Remarkable Story of Coevolution. (Princeton University Press, 2017).Batalden, R. V. & Oberhauser, K. S. Potential changes in eastern north American monarch migration in response to an introduced Milkweed, Asclepias curassavica. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly 215–224 (2015).Tyler Flockhart, D. T. et al. Tracking multi-generational colonization of the breeding grounds by monarch butterflies in eastern North America. Proc. R. Soc. B Biol. Sci. 280, 20131087 (2013).Saunders, S. P., Ries, L., Oberhauser, K. S., Thogmartin, W. E. & Zipkin, E. F. Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus. Ecography. 41, 278–290 (2018).Article 

Google Scholar 
Pleasants, J. M. & Oberhauser, K. S. Milkweed loss in agricultural fields because of herbicide use: Effect on the monarch butterfly population. Insect Conserv. Divers. 6, 135–144 (2013).Article 

Google Scholar 
Borders, B. & Lee-Mäder, B. B. Project milkweed. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly. pp.190-196 (Cornell University press, 2015).Agrawal, A. A., Petschenka, G., Bingham, R. A., Weber, M. G. & Rasmann, S. Toxic cardenolides: Chemical ecology and coevolution of specialized plant-herbivore interactions. N. Phytologist 194, 28–45 (2012).CAS 
Article 

Google Scholar 
Malcolm, S. B. Milkweeds, monarch butterflies and the ecological significance of cardenolides. Chemoecology 5–6, 101–117 (1994).Article 

Google Scholar 
Pocius, V. M., Debinski, D. M., Bidne, K. G., Hellmich, R. L. & Hunter, F. K. Performance of early Instar Monarch Butterflies (Danaus plexippus L.) on nine Milkweed species native to Iowa. J. Lepid. Soc. 71, 153–161 (2017).
Google Scholar 
Ali, J. G. & Agrawal, A. A. Specialist versus generalist insect herbivores and plant defense. Trends Plant Sci. 17, 293–302 (2012).CAS 
PubMed 
Article 

Google Scholar 
Zalucki, M. P., Brower, L. P. & Alonso-M, A. Detrimental effects of latex and cardiac glycosides on survival and growth of first-instar monarch butterfly larvae Danaus plexippus feeding on the sandhill milkweed Asclepias humistrata. Ecol. Entomol. 26, 212–224 (2001).Article 

Google Scholar 
Agrawal, A. A., Hastings, A. P., Patrick, E. T. & Knight, A. C. Specificity of herbivore-induced hormonal signaling and defensive traits in five closely related milkweeds (Asclepias spp.). J. Chem. Ecol. 40, 717–729 (2014).CAS 
PubMed 
Article 

Google Scholar 
Agrawal, A. A., Ali, J. G., Rasmann, S. & Fishbein, M. Macroevolutionary trends in the defense of milkweeds against monarchs. Monarch. a Chang. World Biol. Conserv. Iconic Insect. Cornell University Press, Ithaca, NY. pp. 47–59 (2011).Pocius, V. M. et al. Milkweed matters: Monarch butterfly (Lepidoptera: Nymphalidae) survival and development on nine midwestern milkweed species. Environ. Entomol. 46, 1098–1105 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Petschenka, G. et al. Stepwise evolution of resistance to toxic cardenolides via genetic substitutions in the na+/k+-atpase of milkweed butterflies (lepidoptera: Danaini). Evolution (N. Y). 67, 2753–2761 (2013).CAS 

Google Scholar 
Agrawal, A. A. et al. Cardenolides, toxicity, and the costs of sequestration in the coevolutionary interaction between monarchs and milkweeds. Proc. Natl Acad. Sci. USA 118, e2024463118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marden, J. H. Variability in the size, composition, and function of insect flight muscles. Annu. Rev. Physiol. 62, 157–178 (2000).CAS 
PubMed 
Article 

Google Scholar 
Bicudo, J. E. P. W., Buttemer, W. A., Chappell, M. A., Pearson, J. T. & Bech, C. Ecological and Environmental Physiology of Birds. Ecological and Environmental Physiology of Birds 3 (Oxford University Press, 2010).Bailey, E. Biochemistry of Insect Flight. in Insect Biochemistry and Function. pp. 89–176 (Springer, 1975).Dudley, R. The biomechanics of insect flight: form, function, evolution. Annals of the Entomological Society of America 93 (Princeton University Press, 2000).Solensky, M. J. Overview of monarch migration. in The Monarch Butterfly: Biology and Conservation 79–83 (2004).Urquhart, F. A. & Urquhart, N. R. Monarch butterfly (Danaus plexippus L.) overwintering population in Mexico (Lep. Danaidae). Atalanta 7, 56–61 (1976).
Google Scholar 
Brower, L. P. Understanding and misunderstanding the migration of the monarch butterfly (Nymphalidae) in North America: 1857–1995. J. – Lepid. Soc. 49, 304–385 (1995).
Google Scholar 
Fisher, K. E., Adelman, J. S. & Bradbury, S. P. Employing Very High Frequency (VHF) radio telemetry to recreate monarch butterfly flight paths. Environ. Entomol. 49, 312–323 (2020).PubMed 
Article 

Google Scholar 
Reppert, S. M. & de Roode, J. C. Demystifying monarch butterfly migration. Curr. Biol. 28, R1009–R1022 (2018).CAS 
PubMed 
Article 

Google Scholar 
Zhu, H., Gegear, R. J., Casselman, A., Kanginakudru, S. & Reppert, S. M. Defining behavioral and molecular differences between summer and migratory monarch butterflies. BMC Biol. 7, 1–14 (2009).Heinze, S. & Reppert, S. M. Anatomical basis of sun compass navigation I: The general layout of the monarch butterfly brain. J. Comp. Neurol. 520, 1599–1628 (2012).PubMed 
Article 

Google Scholar 
Zhan, S. et al. The genetics of monarch butterfly migration and warning colouration. Nature 514, 317–321 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soule, A. J., Decker, L. E. & Hunter, M. D. Effects of diet and temperature on monarch butterfly wing morphology and flight ability. J. Insect Conserv. 24, 961–975 (2020).Article 

Google Scholar 
Decker, L. E., Soule, A. J., de Roode, J. C. & Hunter, M. D. Phytochemical changes in milkweed induced by elevated CO2 alter wing morphology but not toxin sequestration in monarch butterflies. Funct. Ecol. 33, 411–421 (2019).Article 

Google Scholar 
Heinrich, B. Temperature regulation of the sphinx moth, Manduca sexta. I. Flight energetics and body temperature during free and tethered flight. J. Exp. Biol. 54, 141–152 (1971).CAS 
PubMed 
Article 

Google Scholar 
Nicolson, S. W. & Louw, G. N. Simultaneous measurement of evaporative water loss, oxygen consumption, and thoracic temperature during flight in a carpenter bee. J. Exp. Zool. 222, 287–296 (1982).Article 

Google Scholar 
Rothe, U. & Nachtigall, W. Flight of the honey bee IV. J. Comp. Physiol. B 158, 711–718 (1989).Article 

Google Scholar 
Nachtigall, W., Hanauer-Thieser, U. & Mörz, M. Flight of the honey bee VII: Metabolic power versus flight speed relation. J. Comp. Physiol. B 165, 484–489 (1995).Article 

Google Scholar 
Niven, J. E. & Scharlemann, J. P. W. Do insect metabolic rates at rest and during flight scale with body mass? Biol. Lett. 1, 346–349 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
Zalucki, M. P., Parry, H. R. & Zalucki, J. M. Movement and egg laying in Monarchs: To move or not to move, that is the equation. Austral. Ecol. 41, 154–167 (2016).Article 

Google Scholar 
Marden, J. H. & Chai, Peng Aerial predation and butterfly design: How palatability, mimicry, and the need for evasive flight constrain mass allocation. Am. Nat. 138, 15–36 (1991).Article 

Google Scholar 
Levin, E., Lopez-Martinez, G., Fane, B. & Davidowitz, G. Hawkmoths use nectar sugar to reduce oxidative damage from flight. Science 355, 733–735 (2017).CAS 
PubMed 
Article 

Google Scholar 
Petschenka, G. & Agrawal, A. A. Milkweed butterfly resistance to plant toxins is linked to sequestration, not coping with a toxic diet. Proc. R. Soc. B Biol. Sci. 282, 20151865 (2015).Petschenka, G. & Agrawal, A. A. How herbivores coopt plant defenses: Natural selection, specialization, and sequestration. Curr. Opin. Insect Sci. 14, 17–24 (2016).PubMed 
Article 

Google Scholar 
Tan, W. H., Tao, L., Hoang, K. M., Hunter, M. D. & de Roode, J. C. The effects of milkweed induced defense on parasite resistance in monarch butterflies, Danaus plexippus. J. Chem. Ecol. 44, 1040–1044 (2018).CAS 
PubMed 
Article 

Google Scholar 
Brower, L. P. & Glazier, S. C. Localization of heart poisons in the monarch butterfly. Science 188, 19–25 (1975).CAS 
PubMed 
Article 

Google Scholar 
Zalucki, M. P. et al. It’s the first bites that count: Survival of first-instar monarchs on milkweeds. Austral. Ecol. 26, 547–555 (2001).Article 

Google Scholar 
Zalucki, M. P., Malcolm, S. B., Hanlon, C. C. & Paine, T. D. First-instar monarch larval growth and survival on milkweeds in Southern California: Effects of latex, leaf hairs and cardenolides. Chemoecology 22, 75–88 (2012).Article 

Google Scholar 
Ziegler, R. & Van Antwerpen, R. Lipid uptake by insect oocytes. Insect Biochem. Mol. Biol. 36, 264–272 (2006).Beenakkers, A. M. T., Van der Horst, D. J. & Van Marrewijk, W. J. A. Insect flight muscle metabolism. Insect Biochem. 14, 243–260 (1984).CAS 
Article 

Google Scholar 
Beall, G. The fat content of a butterfly, Danaus Plexippus Linn., as affected by migration. Ecology 29, 80–94 (1948).Article 

Google Scholar 
James, D. G. Phenology of weight, moisture and energy reserves of Australian monarch butterflies, Danaus plexippus. Ecol. Entomol. 9, 421–428 (1984).Article 

Google Scholar 
Briegel, H. Metabolic relationship between female body size, reserves, and fecundity of Aedes aegypti. J. Insect Physiol. 36, 165–172 (1990).Article 

Google Scholar 
Hines, W. J. W. & Smith, M. J. H. Some aspects of intermediary metabolism in the desert locust (Schistocerca gregaria Forskål). J. Insect Physiol. 9, 463–468 (1963).CAS 
Article 

Google Scholar 
Inagaki, S. & Yamashita, O. Metabolic shift from lipogenesis to glycogenesis in the last instar larval fat body of the silkworm, Bombyx mori. Insect Biochem. 16, 327–331 (1986).CAS 
Article 

Google Scholar 
Venkatesh, K. & Morrison, P. E. Studies of weight changes and amount of food ingested by the stable fly, stomoxys calcitrans (Diptera: Muscidae). Can. Entomol. 112, 141–149 (1980).Article 

Google Scholar 
Arrese, E. L. & Soulages, J. L. Insect fat body: Energy, metabolism, and regulation. Annu. Rev. Entomol. 55, 207–225 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mevi-Schütz, J. & Erhardt, A. Larval nutrition affects female nectar amino acid preference in the map butterfly (Araschnia levana). Ecology 84, 2788–2794 (2003).Article 

Google Scholar 
Wassenaar, L. I. & Hobson, K. A. Natal origins of migratory monarch butterflies at wintering colonies in Mexico: New isotopic evidence. Proc. Natl Acad. Sci. USA 95, 15436–15439 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Majewska, A. A. & Altizer, S. Exposure to Non-Native Tropical Milkweed Promotes Reproductive Development in Migratory Monarch Butterflies. Insects 10, 253 (2019).Howard, E., Aschen, H. & Davis, A. K. Citizen science observations of monarch butterfly overwintering in the Southern United States. Psyche: A Journal of Entomology 2010, https://doi.org/10.1155/2010/689301 (2010).Satterfield, D. A., Maerz, J. C. & Altizer, S. Loss of migratory behaviour increases infection risk for a butterfly host. Proc. R. Soc. B Biol. Sci. 282, 20141734 (2015).Petschenka, G. et al. Relative selectivity of plant cardenolides for Na+/K+-ATPases from the monarch butterfly and non-resistant insects. Front. Plant Sci. 9, 1424 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Jones, P. L., Petschenka, G., Flacht, L. & Agrawal, A. A. Cardenolide intake, sequestration, and excretion by the monarch butterfly along gradients of plant toxicity and larval ontogeny. J. Chem. Ecol. 45, 264–277 (2019).CAS 
PubMed 
Article 

Google Scholar 
Tao, L., Hoang, K. M., Hunter, M. D. & de Roode, J. C. Fitness costs of animal medication: antiparasitic plant chemicals reduce fitness of monarch butterfly hosts. J. Anim. Ecol. 85, 1246–1254 (2016).PubMed 
Article 

Google Scholar 
Lederhouse, R. C. The effect of female mating frequency on egg fertility in the black swallowtail, Papilio polyxenes asterius (Papilionidae). J. Lepid. Soc. 35, 266–277 (1981).
Google Scholar 
Jones, R. E., Hart, J. R. & Bull, G. D. Temperature, size and egg production in the Cabbage Butterfly, Pieris rapae L. Aust. J. Zool. 30, 159–168 (1982).Article 

Google Scholar 
Haukioja, E. & Neuvonen, S. The relationship between size and reproductive potential in male and female Epirrita autumnata (Lep., Geometridae). Ecol. Entomol. 10, 267–270 (1985).Article 

Google Scholar 
Altizer, S. M., Oberhauser, K. S. & Brower, L. P. Associations between host migration and the prevalence of a protozoan parasite in natural populations of adult monarch butterflies. Ecol. Entomol. 25, 125–139 (2000).Article 

Google Scholar 
Masters, A. R., Malcolm, S. B. & Brower, L. P. Monarch butterfly (Danaus plexippus) thermoregulatory behavior and adaptations for overwintering in Mexico. Ecology 69, 458–467 (1988).Article 

Google Scholar 
Kammer, A. E. Thoracic temperature, shivering, and flight in the monarch butterfly, Danaus plexippus (L.). Z. Vgl. Physiol. 68, 334–344 (1970).Article 

Google Scholar 
Pendar, H. & Socha, J. J. Estimation of instantaneous gas exchange in flow-through respirometry systems: A modern revision of bartholomew’s ztransform method. PLoS One 10, e0139508 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lighton, J. R. B. Measuring Metabolic Rates: A Manual for Scientists. (Oxford University Press, 2008).Alonso-Mejía, A., Rendon-Salinas, E., Montesinos-Patiño, E. & Brower, L. P. Use of lipid reserves by monarch butterflies overwintering in Mexico: Implications for conservation. Ecol. Appl. 7, 934–947 (1997).Article 

Google Scholar 
Diaz, R., Overholt, W. A., Hahn, D. & Samayoa, A. C. Diapause induction in Gratiana boliviana (Coleoptera: Chrysomelidae), a biological control agent of tropical soda apple in Florida. Ann. Entomol. Soc. Am. 104, 1319–1326 (2011).Article 

Google Scholar 
Tschinkel, W. R. Sociometry and sociogenesis of colonies of the fire ant Solenopsis invicta during one annual cycle. Ecol. Monogr. 63, 425–457 (1993).Article 

Google Scholar 
Fink, L. S. & Brower, L. P. Birds can overcome the cardenolide defence of monarch butterflies in Mexico. Nature 291, 67–70 (1981).CAS 
Article 

Google Scholar 
Ali, J. G. & Agrawal, A. A. Trade-offs and tritrophic consequences of host shifts in specialized root herbivores. Funct. Ecol. 31, 153–160 (2017).Article 

Google Scholar 
Woodson, R. E. The North American Species of Asclepias L. Ann. Mo. Bot. Gard. 41, 1 (1954).Article 

Google Scholar 
NRCS USDA. The PLANTS Database. National Plant Data Center. http://plants.usda.gov (2006).Agrawal, A. A., Salminen, J. P. & Fishbein, M. Phylogenetic trends in phenolic metabolism of milkweeds (Asclepias): Evidence for escalation. Evolution (N. Y). 63, 663–673 (2009).CAS 

Google Scholar 
Pocius, V. M. et al. Monarch butterflies show differential utilization of nine midwestern milkweed species. Front. Ecol. Evol. 6, 169 (2018).Pocius, V. M., Debinski, D. M., Pleasants, J. M., Bidne, K. G. & Hellmich, R. L. Monarch butterflies do not place all of their eggs in one basket: Oviposition on nine Midwestern milkweed species. Ecosphere 9, e02064 (2018).Article 

Google Scholar 
Ladner, D. T. & Altizer, S. Oviposition preference and larval performance of North American monarch butterflies on four Asclepias species. Entomol. Exp. Appl. 116, 9–20 (2005).Article 

Google Scholar 
Borders, B. A guide to the native milkweeds of Oregon. Xerces Soc. Invertebr. Conserv. www.xerces.org, 5, 12-23 (2012). More

The difficulties inherent in ranking global threats are due to them being context-dependent, which result from conditions and the nature of the threats themselves differing among locations, habitats, and taxa (Fig. 1). Current high-risk hotspots from habitat loss and overexploitation are primarily located in the tropics, whereas Europe is documented as a threat hotspot for pollution6. On islands, biological invasions mainly threaten biodiversity in the Pacific and Atlantic Oceans, while islands in the Indian Ocean and near the coasts of Asia are mostly threatened by overexploitation and agriculture3. Climate change affects species more at higher latitudes and altitudes because species are constrained by the physical environment (geographic barriers and mountain tops) to follow their optimal isotherms.Fig. 1: Divergence of global threat rankings across different references and international agencies.IPBES, WWF, and IUCN established global rankings of the five threats responsible for the current biodiversity crisis (B: central, yellow panel). However, the relative importance of each threat depends on the taxon, system, species’ characteristics, time, and/or the metric considered, resulting in divergences. Global biodiversity threats are represented by colors and symbols, given in the top panel. This figure encapsulates results combined from different studies detailed in Supplementary Table 1 with their associated references.Full size imageThe relative importance of threats also depends on the taxon considered. At the global scale, vertebrates are primarily threatened by habitat loss, overexploitation, and then biological invasions. But even within the vertebrates rankings differ — birds and mammals are mainly affected by overexploitation, while amphibians have a higher probability of succumbing to habitat loss6. Because of species-specific traits and adaptations, some species are likely to respond differently to global threats even within a clade. Large-bodied vertebrates are more likely to be threatened by overexploitation, whereas small-bodied vertebrates are more prone to habitat loss or pollution (Fig. 1). Threat ranking also depends on the habitat under consideration. Marine mammals are more threatened by overexploitation and pollution than terrestrial mammals for which habitat loss is the primary threat (Fig. 1). On islands, habitat loss is secondary to the pressures of biological invasions in freshwater systems, but the former is more important for terrestrial vertebrates and plants3. Another source of uncertainty is that most studies examining threats are based on well-studied taxa such as terrestrial vertebrates, which only represent a small subset of the tree of life. For instance, only 0.2% of fungi, 1.7% of invertebrates, and 10% of described plants are assessed in the IUCN update of 20197, potentially underestimating the intensity of some threats and biasing conservation priorities for these groups. Similarly, there is a bias of research effort towards regions with high-income countries, while research from low or middle-income countries is generally underrepresented8. This may give the false impression of absence of threats in some regions of the world.Likewise, period-specific global threat ranks are subject to the vagaries of temporal dynamics (Fig. 1). However, distinguishing past, current, and future threats is essential for current or future conservation interventions. Historically, overexploitation caused most of the Pleistocene megafauna extinctions, likely exacerbated by climate change. As agricultural practices intensified, habitat loss played a major role in extinctions. As humans later colonized islands, biological invasions caused the extinction of hundreds of species worldwide3. In contrast, climate change is only predicted to become major in the near future9. In fact, the effects of recent threats might be masked by delayed species’ responses, especially in under-studied regions, resulting in a large extinction debt. For instance, the severity of biological invasions often causes native species to decline rapidly to local extinction, while other threats such as habitat loss might affect species more slowly. In both cases, the eventual extinctions are ultimately if similar magnitude. More

Cox, P. & Jones, C. Climate change – Illuminating the modern dance of climate and CO2. Science 321, 1642–1644 (2008).CAS 
PubMed 
Article 

Google Scholar 
Gilmanov, T. G. et al. Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements. Glob. Biogeochem. Cycle 17, 1071 (2003).ADS 
Article 
CAS 

Google Scholar 
Running, S. W. Climate change – Ecosystem disturbance, carbon, and climate. Science 321, 652–653 (2008).CAS 
PubMed 
Article 

Google Scholar 
Sun, Z. et al. Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015. Ecol. Inform. 46, 156–165 (2018).CAS 
Article 

Google Scholar 
Running, S. W. et al. A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens. Environ. 70, 108–127 (1999).ADS 
Article 

Google Scholar 
Madani, N. et al. The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska. J. Geophys. Res.-Biogeosci. 126, e2020JG006078 (2021).ADS 
Article 

Google Scholar 
Morales, P. et al. Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Glob. Change Biol. 11, 2211–2233 (2005).ADS 
Article 

Google Scholar 
Tramontana, G., Ichii, K., Camps-Valls, G., Tomelleri, E. & Papale, D. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data. Remote Sens. Environ. 168, 360–373 (2015).ADS 
Article 

Google Scholar 
Canadell, J. G. et al. Carbon metabolism of the terrestrial biosphere: A multitechnique approach for improved understanding. Ecosystems 3, 115–130 (2000).CAS 
Article 

Google Scholar 
Fletcher, B. J. et al. Photosynthesis and productivity in heterogeneous arctic tundra: consequences for ecosystem function of mixing vegetation types at stand edges. J. Ecol. 100, 441–451 (2012).CAS 
Article 

Google Scholar 
Liu, L., Guan, L. & Liu, X. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence. Agr. Forest Meteorol. 232, 1–9 (2017).ADS 
Article 

Google Scholar 
Xu, X. et al. Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data. J. Environ. Manage. 246, 605–616 (2019).PubMed 
Article 

Google Scholar 
Baldocchi, D. D. How eddy covariance flux measurements have contributed to our understanding of Global Change Biology. Glob. Change Biol. 26, 242–260 (2020).ADS 
Article 

Google Scholar 
He, L., Chen, J. M., Liu, J., Belair, S. & Luo, X. Assessment of SMAP soil moisture for global simulation of gross primary production. J. Geophys. Res.-Biogeosci. 122, 1549–1563 (2017).Article 

Google Scholar 
Wang, S., Ibrom, A., Bauer-Gottwein, P. & Garcia, M. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agr. Forest Meteorol. 248, 479–493 (2018).ADS 
Article 

Google Scholar 
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yu, G., Fu, Y., Sun, X., Wen, X. & Zhang, L. Recent progress and future directions of ChinaFLUX. Sci. China Ser. D-Earth Sci. 49, 1–23 (2006).ADS 
Article 

Google Scholar 
McCallum, I. et al. Improved light and temperature responses for light-use-efficiency-based GPP models. Biogeosciences 10, 6577–6590 (2013).ADS 
Article 

Google Scholar 
Stocker, B. D. et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nature Geoscience 12, 264‐+ (2019).ADS 
Article 
CAS 

Google Scholar 
Cheng, S. J. et al. Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems. Agr. Forest Meteorol. 201, 98–110 (2015).ADS 
Article 

Google Scholar 
Zhang, M. et al. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China. Agr. Forest Meteorol. 151, 803–816 (2011).ADS 
Article 

Google Scholar 
Oliphant, A. J. et al. The role of sky conditions on gross primary production in a mixed deciduous forest. Agr. Forest Meteorol. 151, 781–791 (2011).ADS 
Article 

Google Scholar 
Urban, O. et al. Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation. Glob. Change Biol. 13, 157–168 (2007).ADS 
Article 

Google Scholar 
Zhou, H. et al. Large contributions of diffuse radiation to global gross primary productivity during 1981–2015. Glob. Biogeochem. Cycle 35, e2021GB006957 (2021).ADS 
CAS 
Article 

Google Scholar 
Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).ADS 
Article 

Google Scholar 
Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. USA 111, E1327–E1333 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, L. & Cheng, Z. Detection of vegetation light-use efficiency based on solar-induced chlorophyll fluorescence separated from canopy radiance spectrum. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 3, 306–312 (2010).ADS 
Article 

Google Scholar 
MacBean, N. et al. Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data (vol 8, 1973, 2018). Sci. Rep. 8, 10420 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Meroni, M. et al. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sens. Environ. 113, 2037–2051 (2009).ADS 
Article 

Google Scholar 
Zheng, T. & Chen, J. M. Photochemical reflectance ratio for tracking light use efficiency for sunlit leaves in two forest types. ISPRS-J. Photogramm. Remote Sens. 123, 47–61 (2017).ADS 
Article 

Google Scholar 
Damm, A. et al. Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP). Glob. Change Biol. 16, 171–186 (2010).ADS 
Article 

Google Scholar 
Lee, J. E. et al. Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4. Glob. Change Biol. 21, 3469–3477 (2015).ADS 
Article 

Google Scholar 
Pinto, F. et al. Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ. 39, 1500–1512 (2016).CAS 
PubMed 
Article 

Google Scholar 
Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).CAS 
PubMed 
Article 

Google Scholar 
Xie, X., Li, A., Jin, H., Yin, G. & Nan, X. Derivation of temporally continuous leaf maximum carboxylation rate (V-cmax) from the sunlit leaf gross photosynthesis productivity through combining BEPS model with light response curve at tower flux sites. Agr. Forest Meteorol. 259, 82–94 (2018).ADS 
Article 

Google Scholar 
Chen, J. M., Liu, J., Leblanc, S. G., Lacaze, R. & Roujean, J. L. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sens. Environ. 84, 516–525 (2003).ADS 
Article 

Google Scholar 
Chen, J. M. et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Glob. Biogeochem. Cycle 26, GB1019 (2012).ADS 
Article 
CAS 

Google Scholar 
Running, S. W., Thornton, P. E., Nemani, R. & Glassy, J. M. in Methods in Ecosystem Science. Ch.3 (Springer, New York, NY. Press, 2000).Wu, C., Munger, J. W., Niu, Z. & Kuang, D. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sens. Environ. 114, 2925–2939 (2010).ADS 
Article 

Google Scholar 
Makela, A. et al. Developing an empirical model of stand GPP with the LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe. Glob. Change Biol. 14, 92–108 (2008).ADS 
Article 

Google Scholar 
McCallum, I. et al. Satellite-based terrestrial production efficiency modeling. Carbon Balanc. Manag. 4, 8–8 (2009).Article 

Google Scholar 
Wang, H. et al. Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ 114, 2248–2258 (2010).ADS 
Article 

Google Scholar 
Yu, R. An improved estimation of net primary productivity of grassland in the Qinghai-Tibet region using light use efficiency with vegetation photosynthesis model. Ecol. Model. 431, 109121 (2020).Article 

Google Scholar 
Yuan, W. et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agr. Forest Meteorol. 143, 189–207 (2007).ADS 
Article 

Google Scholar 
Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

Google Scholar 
Zhang, Y. et al. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agr. Forest Meteorol. 223, 116–131 (2016).ADS 
Article 

Google Scholar 
He, M. et al. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agr. Forest Meteorol. 173, 28–39 (2013).ADS 
Article 

Google Scholar 
Zhou, Y. et al. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites. J. Geophys. Res.-Biogeosci. 121, 1045–1072 (2016).Article 

Google Scholar 
Friedlingstein, P. et al. Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks. J. Clim. 27, 511–526 (2014).ADS 
Article 

Google Scholar 
Raich, J. W. et al. Potential net primary productivity in South-America – application of a global-model. Ecol. Appl. 1, 399–429 (1991).CAS 
PubMed 
Article 

Google Scholar 
Li, J. et al. An algorithm differentiating sunlit and shaded leaves for improving canopy conductance and vapotranspiration estimates. J. Geophys. Res.-Biogeosci. 124, 807–824 (2019).ADS 
Article 

Google Scholar 
Chen, J. M., Liu, J., Cihlar, J. & Goulden, M. L. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).CAS 
Article 

Google Scholar 
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 
Article 

Google Scholar 
Korson, L., Drosthan, W. & Millero, F. J. Viscosity of water at various temperatures. J. Phys. Chem. 73, 34–39 (1969).CAS 
Article 

Google Scholar 
Olofsson, P., Van Laake, P. E. & Eklundh, L. Estimation of absorbed PAR across Scandinavia from satellite measurements Part I: Incident PAR. Remote Sens. Environ. 110, 252–261 (2007).ADS 
Article 

Google Scholar 
González, J. A. & Calbó, J. Modelled and measured ratio of PAR to global radiation under cloudless skies. Agr. Forest Meteorol. 110, 319–325 (2002).ADS 
Article 

Google Scholar 
Zhang, X., Zhang, Y. & Zhoub, Y. Measuring and modelling photosynthetically active radiation in Tibet Plateau during April–October. Agr. Forest Meteorol. 102, 207–212 (2000).ADS 
Article 

Google Scholar 
Yang, Y., Xiao, P., Feng, X. & Li, H. Accuracy assessment of seven global land cover datasets over China. ISPRS-J. Photogramm. Remote Sens. 125, 156–173 (2017).ADS 
Article 

Google Scholar 
Liu, Y., Liu, R. & Chen, J. M. GLOBMAP global Leaf Area Index since 1981. Zenodo https://doi.org/10.5281/zenodo.4700264 (2019).Vermote, E. MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD09A1.006 (2015).Deng, F., Chen, J. M., Plummer, S., Chen, M. & Pisek, J. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans. Geosci. Remote Sens. 44, 2219–2229 (2006).ADS 
Article 

Google Scholar 
Vermote, E. NOAA CDR Program. NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. LAI. NOAA National Centers for Environmental Information https://doi.org/10.7289/V5TT4P69 (2019).He, L., Chen, J. M., Pisek, J., Schaaf, C. & Strahler, A. Global clumping index map derived from the MODIS BRDF product. Remote Sens. Environ. 119, 118–130 (2012).ADS 
Article 

Google Scholar 
Liu, R. G. & Liu, Y. Generation of new cloud masks from MODIS land surface reflectance products. Remote Sens. Environ. 133, 21–37 (2013).ADS 
CAS 
Article 

Google Scholar 
Chen, J. M., Deng, F. & Chen, M. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Trans. Geosci. Remote Sens. 44, 2230–2238 (2006).ADS 
Article 

Google Scholar 
Harris, I.C. CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data. Centre for Environmental Data Analysis http://catalogue.ceda.ac.uk/uuid/863a47a6d8414b6982e1396c69a9efe8 (2019).Li, X., Liang, H. & Cheng, W. Evaluation and comparison of light use efficiency models for their sensitivity to the diffuse PAR fraction and aerosol loading in China. Int. J. Appl. Earth Obs. Geoinf. 95, 102269 (2021).
Google Scholar 
Duan, Q. Y., Sorooshian, S. & Gupta, V. Effective and efficient global optimization for conceptual rain full-runoff models. Water Resour. Res. 28, 1015–1031 (1992).ADS 
Article 

Google Scholar 
Gu, L. H. et al. Advantages of diffuse radiation for terrestrial ecosystem productivity. J. Geophys. Res.-Atmos. 107, 4050 (2002).ADS 

Google Scholar 
Bi, W. & Zhou, Y. A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020). Dryad https://doi.org/10.5061/dryad.dfn2z352k (2022).Ogutu, B. O. & Dash, J. Assessing the capacity of three production efficiency models in simulating gross carbon uptake across multiple biomes in conterminous USA. Agr. Forest Meteorol. 174, 158–169 (2013).ADS 
Article 

Google Scholar 
Cai, W. et al. Large differences in terrestrial vegetation production derived from satellite-based light use efficiency models. Remote Sens. 6, 8945–8965 (2014).ADS 
Article 

Google Scholar 
Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785–818 (2015).ADS 
Article 

Google Scholar 
Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: A global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sens. 11, 2563 (2019).ADS 
Article 

Google Scholar 
Alemohammad, S. H. et al. Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. Biogeosciences 14, 4101–4124 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).ADS 
Article 

Google Scholar 
Wang, S., Zhang, Y., Ju, W., Qiu, B. & Zhang, Z. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci. Total Environ. 755, 142569 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zheng, Y. et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data 12, 2725–2746 (2020).ADS 
Article 

Google Scholar 
Running, S., Mu, Q. & Zhao, M. MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD17A2H.006 (2015).Ciais, P. et al. A three-dimensional synthesis study of delta O-18 in atmospheric CO2 .1. Surface fluxes. J. Geophys. Res.-Atmos. 102, 5857–5872 (1997).ADS 
CAS 
Article 

Google Scholar 
Zhang, Y., Joiner, J., Gentine, P. & Zhou, S. Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Glob. Change Biol. 24, 2229–2230 (2018).ADS 
Article 

Google Scholar 
Xie, X. et al. Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models. Sci. Total Environ. 690, 1120–1130 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fang, H., Wei, S., Jiang, C. & Scipal, K. Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method. Remote Sens. Environ. 124, 610–621 (2012).ADS 
Article 

Google Scholar 
Camacho, F., Cemicharo, J., Lacaze, R., Baret, F. & Weiss, M. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sens. Environ. 137, 310–329 (2013).ADS 
Article 

Google Scholar 
Prince, S. D. & Goward, S. N. Global primary production: A remote sensing approach. J. Biogeogr. 22, 815–835 (1995).Article 

Google Scholar 
Verma, S. B. et al. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agr. Forest Meteorol. 131, 77–96 (2005).ADS 
Article 

Google Scholar 
Yan, H. et al. Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants. Ecol. Model. 297, 42–59 (2015).CAS 
Article 

Google Scholar 
Jiang, S. et al. Comparison of satellite-based models for estimating gross primary productivity in agroecosystems. Agr. Forest Meteorol. 297, 108253 (2021).ADS 
Article 

Google Scholar 
Yang, X. et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).ADS 
CAS 
Article 

Google Scholar 
Zhou, H. et al. Responses of gross primary productivity to diffuse radiation at global FLUXNET sites. Atmos. Environ. 244, 117905 (2021).CAS 
Article 

Google Scholar 
Han, J. et al. Effects of diffuse photosynthetically active radiation on gross primary productivity in a subtropical coniferous plantation vary in different timescales. Ecol. Indic. 115, 106403 (2020).Article 

Google Scholar 
Grant, I. F., Prata, A. J. & Cechet, R. P. The impact of the diurnal variation of albedo on the remote sensing of the daily mean albedo of grassland. J. Appl. Meteorol. 39, 231–244 (2000).ADS 
Article 

Google Scholar 
Singarayer, J. S., Ridgwell, A. & Irvine, P. Assessing the benefits of crop albedo bio-geoengineering. Environ. Res. Lett. 4, 045110 (2009).ADS 
Article 

Google Scholar 
Tang, S. et al. LAI inversion algorithm based on directional reflectance kernels. J. Environ. Manage. 85, 638–648 (2007).CAS 
PubMed 
Article 

Google Scholar  More

IPCC: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O. et al.] In press (2019).Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gibble, C. et al. Investigation of a largescale Common Murre (Uria aalge) mortality event in California, USA, in 2015. J. Wildl. Dis. 54, 569–574 (2018).PubMed 
Article 

Google Scholar 
Brodeur, R. D., Auth, T. D. & Phillips, A. J. Major shifts in pelagic micronekton and macrozooplankton community structure in an upwelling ecosystem related to an unprecedented marine heatwave. Front. Mar. Sci. 6, 212 (2019).Article 

Google Scholar 
Le Nohaïc, M. et al. Marine heatwave causes unprecedented regional mass bleaching of thermally resistant corals in northwestern Australia. Sci. Rep. 7, 1–11 (2017).ADS 
Article 
CAS 

Google Scholar 
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Genevier, L. G., Jamil, T., Raitsos, D. E., Krokos, G. & Hoteit, I. Marine heatwaves reveal coral reef zones susceptible to bleaching in the Red Sea. Glob. Change Biol. 25, 2338–2351 (2019).ADS 
Article 

Google Scholar 
Leggat, W. P. et al. Rapid coral decay is associated with marine heatwave mortality events on reefs. Curr. Biol. 29, 2723–2730 (2019).CAS 
PubMed 
Article 

Google Scholar 
Green, E. P. & Short, F. T. World Atlas of Seagrasses (University of California Press, 2003).Duarte, C. M. The future of seagrass meadows. Environ. Conserv. 29, 192–206 (2002).Article 

Google Scholar 
Alongi, D. M. Blue Carbon: Coastal Sequestration for Climate Change Mitigation (Springer, Berlin, 2018).Book 

Google Scholar 
Blandon, A. & ZuErmgassen, P. S. Quantitative estimate of commercial fish enhancement by seagrass habitat in southern Australia. Estuarine Coast. Shelf Sci. 141, 1–8 (2014).ADS 
Article 

Google Scholar 
Boudouresque, C. F., Mayot, N. & Pergent, G. The outstanding traits of the functioning of the Posidonia oceanica seagrass ecosystem. Biol. Mar. Medit. 13, 109–113 (2006).
Google Scholar 
Carr, J., D’odorico, P., McGlathery, K. & Wiberg, P. L. Stability and bistability of seagrass ecosystems in shallow coastal lagoons: Role of feedbacks with sediment resuspension and light attenuation. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2009JG001103 (2010).Article 

Google Scholar 
Welsh, D. T. Nitrogen fixation in seagrass meadows: regulation, plant–bacteria interactions and significance to primary productivity. Ecol. Lett. 3, 58–71. https://doi.org/10.1046/j.1461-0248.2000.00111.x (2000).Article 

Google Scholar 
Duarte, C. M. et al. Seagrass community metabolism: Assessing the carbon sink capacity of seagrass meadows. Glob. Biogeochem. Cycles. https://doi.org/10.1029/2010GB003793 (2010).Article 

Google Scholar 
Cabaço, S. & Santos, R. Human-induced changes of the seagrass Cymodocea nodosa in Ria Formosa lagoon (Southern Portugal) after a decade. Cah. Biol. Mar. 55, 101–108 (2014).
Google Scholar 
Marbà, N., Krause-Jensen, D., Masqué, P. & Duarte, C. M. Expanding Greenland seagrass meadows contribute new sediment carbon sinks. Sci. Rep. 8, 1–8 (2018).Article 
CAS 

Google Scholar 
Bañolas, G., Fernández, S., Espino, F., Haroun, R. & Tuya, F. Evaluation of carbon sinks by the seagrass Cymodocea nodosa at an oceanic island: Spatial variation and economic valuation. Ocean Coast. Manag. 187, 105112 (2020).Article 

Google Scholar 
Duarte, C. M. & Krause-Jensen, D. Export from seagrass meadows contributes to marine carbon sequestration. Front. Mar. Sci. 4, 13 (2017).
Google Scholar 
Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosci. 2, 1–8 (2005).ADS 
CAS 
Article 

Google Scholar 
Kennedy, H. et al. Seagrass sediments as a global carbon sink: Isotopic constraints. Glob. Biogeochem. Cycles https://doi.org/10.1029/2010GB003848 (2010).Article 

Google Scholar 
Orth, R. J. et al. A global crisis for seagrass ecosystems. Bioscience 56, 987–996 (2006).Article 

Google Scholar 
Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl. Acad. Sci. 106, 12377–12381 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Arias-Ortiz, A. et al. A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Change 8, 338 (2018).ADS 
CAS 
Article 

Google Scholar 
Collier, C. J. et al. Optimum temperatures for net primary productivity of three tropical seagrass species. Front. Plant Sci. 8, 1446 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
George, R., Gullström, M., Mangora, M. M., Mtolera, M. S. & Björk, M. High midday temperature stress has stronger effects on biomass than on photosynthesis: a mesocosm experiment on four tropical seagrass species. Ecol. Evol. 8, 4508–4517 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Savva, I., Bennett, S., Roca, G., Jordà, G. & Marbà, N. Thermal tolerance of Mediterranean marine macrophytes: Vulnerability to global warming. Ecol. Evol. 8, 12032–12043 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Massa, S. I., Arnaud-Haond, S., Pearson, G. A. & Serrão, E. A. Temperature tolerance and survival of intertidal populations of the seagrass Zostera noltii (Hornemann) in Southern Europe (Ria Formosa, Portugal). Hydrobiologia 619, 195–201 (2009).Article 

Google Scholar 
Bergmann, N. et al. Population-specificity of heat stress gene induction in northern and southern eelgrass Zostera marina populations under simulated global warming. Mol. Ecol. 19, 2870–2883 (2010).PubMed 
Article 

Google Scholar 
Franssen, S. U. et al. Genome-wide transcriptomic responses of the seagrasses Zostera marina and Nanozostera noltii under a simulated heatwave confirm functional types. Mar. Genomics 15, 65–73 (2014).PubMed 
Article 

Google Scholar 
Qin, L. Z. et al. Influence of regional water temperature variability on the flowering phenology and sexual reproduction of the seagrass Zostera marina in Korean coastal waters. Estuaries Coasts 43, 449–462 (2020).CAS 
Article 

Google Scholar 
Gao, Y. et al. Photosynthetic and metabolic responses of eelgrass Zostera marina L. to short-term high-temperature exposure. J. Oceanol. Limnol. 37, 199–209 (2019).ADS 
CAS 
Article 

Google Scholar 
Marín-Guirao, L. et al. Carbon economy of Mediterranean seagrasses in response to thermal stress. Mar. Pollut. Bull. 135, 617–629 (2018).PubMed 
Article 
CAS 

Google Scholar 
Costa, M. M., Silva, J., Barrote, I. & Santos, R. Heatwave effects on the photosynthesis and antioxidant activity of the seagrass Cymodocea nodosa under contrasting light regimes. Oceans 2, 448–460 (2021).Article 

Google Scholar 
de los Santos, C. et al. Recent trend reversal for declining European seagrass meadows. Nat. Commun. 10, 3356 (2019).Cunha, A. H., Assis, J. F. & Serrão, E. A. Reprint of “Seagrasses in Portugal: A most endangered marine habitat”. Aquat. Bot. 115, 3–13 (2014).Article 

Google Scholar 
Olsen, Y. S., Sánchez-Camacho, M., Marbà, N. & Duarte, C. M. Mediterranean seagrass growth and demography responses to experimental warming. Estuaries Coasts 35, 1205–1213 (2012).Article 

Google Scholar 
Marín-Guirao, L., Ruiz, J. M., Dattolo, E., Garcia-Munoz, R. & Procaccini, G. Physiological and molecular evidence of differential short-term heat tolerance in Mediterranean seagrasses. Sci. Rep. 6, 1–13 (2016).Article 
CAS 

Google Scholar 
Lüning, K. Seaweeds. Their Environment, Biogeography, and Ecophysiology (Wiley-Interscience, New York, 1990).Lee, K. S., Park, S. R. & Kim, Y. K. Effects of irradiance, temperature, and nutrients on growth dynamics of seagrasses: a review. J. Exp. Mar. Biol. Ecol. 350, 144–175 (2007).Article 

Google Scholar 
Franssen, S. U. et al. Transcriptomic resilience to global warming in the seagrass Zostera marina, a marine foundation species. Proc. Natl. Acad. Sci. 108, 19276–19281 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Winters, G., Nelle, P., Fricke, B., Rauch, G. & Reusch, T. B. H. Effects of a simulated heat wave on photophysiology and gene expression of high- and low-latitude populations of Zostera marina. Mar. Ecol. Prog. Ser. 435, 83–95 (2011).ADS 
Article 

Google Scholar 
Maxwell, K. & Johnson, G. N. Chlorophyll fluorescence—A practical guide. J. Exp. Bot. 51, 659–668 (2000).CAS 
PubMed 
Article 

Google Scholar 
Schubert, N. et al. Photoacclimation strategies in northeastern Atlantic seagrasses: Integrating responses across plant organizational levels. Sci. Rep. 8, 1–14 (2018).CAS 
Article 

Google Scholar 
Miyake, C., Yonekura, K., Kobayashi, Y. & Yokota, A. Cyclic electron flow within PSII functions in intact chloroplasts from spinach leaves. Plant Cell Physiol. 43, 951–957 (2002).CAS 
PubMed 
Article 

Google Scholar 
Rasmusson, L. M., Gullström, M., Gunnarsson, P. C. B., George, R. & Björk, M. Estimation of a whole plant Q10 to assess seagrass productivity during temperature shifts. Sci. Rep. 9, 1–9 (2019).CAS 
Article 

Google Scholar 
Buapet, P. & Björk, M. The role of O2 as an electron acceptor alternative to CO2 in photosynthesis of the common marine angiosperm Zostera marina L. Photosynth. Res. 129, 59–69 (2016).CAS 
PubMed 
Article 

Google Scholar 
Mehler, A. H. Studies on reactions of illuminated chloroplasts. II Stimulation and inhibition of the reaction with molecular oxygen. Arch. Biochem. Biophys. 34, 339–51 (1951).CAS 
PubMed 
Article 

Google Scholar 
Apel, K. & Hirt, H. Reactive oxygen species: metabolism, oxidative stress, and signal transduction. Annu. Rev. Plant Biol. 55, 373–399 (2004).CAS 
PubMed 
Article 

Google Scholar 
Chalanika De Silva, H. C. & Asaeda, T. Effects of heat stress on growth, photosynthetic pigments, oxidative damage and competitive capacity of three submerged macrophytes. J. Plant Interact. 12, 228–236 (2017).Article 
CAS 

Google Scholar 
Beer, S., Björk, M., Gademann, R. & Ralph, P. Measurements of photosynthetic rates in seagrasses. In Global Seagrass Research Methods pp. 183–198 (Elsevier Science, 2001).Brodersen, K. E., Kühl, M., Nielsen, D. A., Pedersen, O. & Larkum, A. W. Rhizome, root/sediment interactions, aerenchyma and internal pressure changes in seagrasses. In Seagrasses of Australia pp. 393–418; https://doi.org/10.1007/978-3-319-71354-0_13 (Springer, Cham, 2018).Purnama, P. R., Purnama, E. R., Manuhara, Y. S. W., Hariyanto, S. & Purnobasuki, H. Effect of high temperature stress on changes in morphology, anatomy and chlorophyll content in tropical seagrass Thalassia hemprichii. AACL Bioflux 11, 1825–1833 (2018).
Google Scholar 
Rosalina, D., Herawati, E. Y., Musa, M., Sofarini, D. & Risjani, Y. Anatomical changes in the roots, rhizomes and leaves of seagrass (Cymodocea serrulata) in response to lead. Biodiversitas 20, 2583–2588; https://doi.org/10.13057/biodiv/d200921 (2019).Beca-Carretero, P., Olesen, B., Marbà, N. & Krause-Jensen, D. Response to experimental warming in northern eelgrass populations: comparison across a range of temperature adaptations. Mar. Ecol. Progr. Ser. 589, 59–72; https://doi.org/10.3354/meps12439 (2018).Beca-Carretero, P., Guihéneuf, F., Krause-Jensen, D. & Stengel, D. B. Seagrass fatty acid profiles as a sensitive indicator of climate settings across seasons and latitudes. Mar. Env. Res. 161, 105075; https://doi.org/10.1016/j.marenvres.2020.105075 (2020).Pérez, M. & Romero, J. Photosynthetic response to light and temperature of the seagrass Cymodocea nodosa and the prediction of its seasonality. Aquat. Bot. 43, 51–62; https://doi.org/10.1016/0304-3770(92)90013-9 (1992).Saha, M. et al. Response of foundation macrophytes to near‐natural simulated marine heatwaves. Global Change Biol. 26, 417–430; https://doi.org/10.1111/gcb.14801 (2020).Tutar, O., Marín-Guirao, L., Ruiz, J. M. & Procaccini, G. Antioxidant response to heat stress in seagrasses. A gene expression study. Mar. Environ. Res. 132, 94–102; https://doi.org/10.1016/j.marenvres.2017.10.011 (2017).Moreno‐Marín, F., Brun, F. G. & Pedersen, M. F. Additive response to multiple environmental stressors in the seagrass Zostera marina L. Limnol. Oceanogr. 63, 1528–1544; https://doi.org/10.1002/lno.10789 (2018).Kim, M. et al. Influence of water temperature anomalies on the growth of Zostera marina plants held under high and low irradiance levels. Estuaries Coasts 43, 463–476; https://doi.org/10.1007/s12237-019-00578-2 (2020).Egea, L. G., Jiménez-Ramos, R., Vergara, J. J., Hernández, I. & Brun, F. G. Interactive effect of temperature, acidification and ammonium enrichment on the seagrass Cymodocea nodosa. Mar. Pollut. Bull. 134, 14–26; https://doi.org/10.1016/j.marpolbul.2018.02.029 (2018).Newton, A. & Mudge, S. M. Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa, Portugal. Estuarine Coastal Shelf Sci. 57, 73–85; https://doi.org/10.1016/S0272-7714(02)00332-3 (2003).Instituto Hidrográfico. Marés 81/82 Ria de Faro. Estudo das marés de oito estacões da Ria de Faro pp. 13 (Lisbon: Instituto Hidrográfico, 1986).Andrade, J. P. Aspectos Geomorfológicos, Ecológicos e Socioeconómicos da Ria Formosa pp. 91 (Faro: Universidade do Algarve, 1985).Hobday, A.J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238; https://doi.org/10.1016/j.pocean.2015.12.014 (2016).Hobday, A. J. et al. Categorizing and naming marine heatwaves. Oceanogr. 31, 162–173; https://doi.org/10.5670/oceanog.2018.205 (2018).Cunha, A. H., Paulo, D. S., Sousa, I. & Serrão, E. The rediscovery of Caulerpa prolifera in Ria Formosa, Portugal, 60 years after the previous record. Cah. Biol. Mar. 54, 359–364 (2013).
Google Scholar 
Huang, B. et al. Improvements of the daily optimum interpolation sea surface temperature (DOISST) Version 2.1. J. Clim. 34, 2923–2939 (2020).ADS 
Article 

Google Scholar 
Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496 (2007).ADS 
Article 

Google Scholar 
Banzon, V., Smith, T. M., Chin, T. M., Liu, C. & Hankins, W. A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modelling and environmental studies. Earth Syst. Sci. Data 8, 165–176 (2016).ADS 
Article 

Google Scholar 
Schlegel, R. W. Marine Heatwave Tracker. http://www.marineheatwaves.org/tracker; 10.5281/zenodo.3787872 (2020).Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q. (Eds.). Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change (IPCC) (Cambridge University Press, 2012).Silva, J., Barrote, I., Costa, M. M., Albano, S. & Santos, R. Physiological responses of Zostera marina and Cymodocea nodosa to light-limitation stress. PLoS One 8, e81058 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Silva, J. & Santos, R. Can chlorophyll fluorescence be used to estimate photosynthetic production in the seagrass Zostera noltii?. J. Exp. Mar. Biol. Ecol. 307, 207–216 (2004).CAS 
Article 

Google Scholar 
Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).ADS 
CAS 
Article 

Google Scholar 
Henley, W. J. Measurement and interpretation of photosynthetic light-response curves in algae in the context of photoinhibition and diel changes. J. Phycol. 29, 729–739 (1993).Article 

Google Scholar 
Genty, B., Briantais, J. M. & Baker, N. R. The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochim. Biophys. Acta 990, 87–92 (1989).CAS 
Article 

Google Scholar 
Folin, O. & Ciocalteu, V. On tyrosine and tryptophane determinations in proteins. J. Biol. Chem. 73, 627–650 (1927).CAS 
Article 

Google Scholar 
Booker, F. L. & Miller, J. E. Phenylpropanoid metabolism and phenolic composition of soybean [Glycine max (L) Merr] leaves following exposure to ozone. J. Exp. Bot. 49, 1191–1202 (1998).CAS 
Article 

Google Scholar 
Re, R. et al. Antioxidant activity applying an improved ABTS radical cation decolorization assay. Free Radical Biol. Med. 26, 1231–1237 (1999).CAS 
Article 

Google Scholar 
Gillespie, K. M., Chae, J. M. & Ainsworth, E. A. Rapid measurement of total antioxidant capacity in plants. Nat. Protoc. 2, 867–870 (2007).CAS 
PubMed 
Article 

Google Scholar 
Huang, D., Ou, B., Hampsch-Woodill, M., Flanagan, J. A. & Prior, R. L. High-Throughput Assay of Oxygen Radical Absorbance Capacity (ORAC) Using a Multichannel Liquid Handling System Coupled with a Microplate Fluorescence Reader in 96-Well Format. J. Agric. Food Chem. 50, 4437–4444 (2002).CAS 
PubMed 
Article 

Google Scholar 
Hodges, D. M., DeLong, J. M., Forney, C. F. & Prange, R. K. Improving the thiobarbituric acid-reactive-substances assay for estimating lipid peroxidation in plant tissues containing anthocyanin and other interfering compounds. Planta 207, 604–611 (1999).CAS 
Article 

Google Scholar 
Rasband, W.S. ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, 1997–2018. https://imagej.nih.gov/ij/ (1997).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2014).Devore, J. & Farnum, N. Applied Statistics for Engineers and Scientists (ed. Brooks/Cole) pp. 656 (Pacific Grove, CA, USA, 1999). More

Wheat production and yield vis-à-vis climate trendsWheat is currently grown in all six continents except Antarctica. The leading producers include China, the Russian Federation, Ukraine, Kazakhstan (RUK), India, USA, France, Canada, Pakistan, Germany, Argentina, Turkey, Australia, and United Kingdom (Fig. 1 and Supplementary Table 1). The total grain production of these twelve countries is estimated at 600 megatons (2019 data), which accounts for over 78% of the global wheat production. The top three producers are China with 133.6 megatons per year (Mt y−1), RUK with 114.1 Mt y−1, and India with 103.6 Mt y−1. RUK contains the largest harvested area of 45.8 million hectares, followed by India with 29.3 million hectares and China with 23.7 million hectares (Fig. 1A). Despite a relatively small harvested area of 10.1 million hectares (only 22% of RUK’s harvested area), the United Kingdom, France, and Germany account for the world’s highest yields per hectare, with 8.93 tons ha−1, 7.74 tons ha−1, and 7.40 tons ha−1, respectively (compared with the world’s average yield of only 3.2 tons ha−1), accounting for a total yearly production of 79.9 Mt y−1.Figure 1Global wheat area and trends in wheat yield and climate in top-twelve global wheat producers (1961–2019). (A) Worldwide wheat cropping area (%)29, total harvested area (106 hectares in 2019), and wheat production (megatons for 2019) of the top 12 global wheat producers (China, RUK—Russia, Ukraine, and Kazakhstan, India, USA—hard red winter (HRW) and hard red spring (HRS), France, Canada, Pakistan, Germany, Argentina, Turkey, Australia, and United Kingdom) (Map was generated in Python 3.8.5; http://www.python.org). (B) Changes in wheat yield (tons per hectare) and (C) climate—mean daily temperature (red dashed line; °C) and the seasonal water balance represented as potential evaporation minus precipitation (blue line; PET—P in millimeters of H2O). A positive trend in PET-P indicates an increase in water deficit. The seasonal atmospheric [CO2] in μmol CO2 per mol−1 air is also shown in the insert of C (black line). Temperature, PET-P, and [CO2] shown in C are averaged values over the wheat-growing period and the shared area of the wheat-growing areas of the top 12 global wheat producers. Decadal trends in temperature (red) and PET-P (blue) as well as the significance levels of these trends are presented in C.Full size imageWhile all these twelve major wheat producers saw an increase in yield during the last six decades (Fig. 1B), China displayed the most noteworthy increase with a nearly sevenfold higher yield in 2019 than in 1961 and a mean total increase of 5.19 tons ha−1 for the period of 1961–2019. Germany, the UK, and France reported comparable yield increases of 5.20 tons ha−1, 5.19 tons ha−1, and 4.81 tons ha−1, respectively, during this period, suggesting an approximately 1.6-fold improvement since 1961 (Fig. 1B). Australia, RUK, and Turkey reported the lowest gains with only 0.87 tons ha−1, 1.26 tons ha−1, and 1.71 tons ha−1, respectively, representing improvements of 67%, 150%, and 175% in yield per hectare since 1961.Yield increase occurred despite the steep rise in temperature (nearly 1.2 °C) in the twelve countries during the last six decades (Fig. 1C). Water deficit—calculated as the difference between potential evaporative demand and precipitation (PET—P; mm H2O y−1)—also increased by an average of (sim) 29 mm of H2O for the same period. Increases in yield since the early 1960s were likely due to breeding and agrotechnological advances, improved management, and a steep rise in atmospheric [CO2] of (sim) 98 μmol mol−1, from 315.9 μmol mol−1 in 1961 to 413.4 μmol mol−1 in 2019 (insert in Fig. 1C).Unraveling the impacts of climate and [CO2] on yieldBased on previous studies30,31, we used a log-linear model to quantify the impact of [CO2] and daily minimum (Tmin), maximum (Tmax), and mean (Tmean) temperatures, as well as seasonal water deficit (PET-P), and rainfall distribution on wheat yield. Climate variables were obtained from the TerraClimate data set32, while monthly records of [CO2] from the Mauna Loa station were used to model the effects of CO2 (see “Methods”). To quantify wheat yield as a function of climate variables and [CO2], we included all 12 countries in the regression analysis. Supplementary Table 2 presents summary statistics of all variables, while Supplementary Fig. 1 depicts trends in Tmean and PET-P per country.Since climate variables tend to be correlated over time (Supplementary Table 3), controlling for all of these variables in the model facilitates the estimation of their distinct effect on yield. We used country-specific trends to distinguish changes in wheat yield related to climate and [CO2] from those attributed to agrotechnological advancements, changes in country-specific policies, and other local-changing factors (e.g., economic and population growth; more information on how this was done can be found in “Methods”). We also included country-specific effects across all models to account for unobserved time-invariant heterogeneity at the country level, such as geographical properties, edaphic characteristics, and other local-specific features (see “Methods”).Table 1 reports the estimated regression coefficients of four models, (1) using only temperature variables (T), (2) temperature and water-related (i.e., seasonal rainfall distribution and water deficit as PET-P) variables (T + W), (3) including [CO2] (T + W + C), and (4) the interaction between [CO2] and climate variables (T + W + C + interactions).Table 1 Effects of climate variables and [CO2] on log wheat yields of the world’s major wheat producers.Full size tableAmong the temperature measures, only Tmean had a consistently significant effect on yield (p  More

Literature studyLiterature considering the effect of pasture degradation on SOC, N, and clay content, as well as bulk density (BD), was assembled by searching (i) Web of Science V.5.22.1, (ii) ScienceDirect (Elsevier B.V.) (iii) Google Scholar, and (iv) the China Knowledge Resource Integrated Database (CNKI). Search terms were “degradation gradient”, “degradation stages”, “alpine meadow”, “Tibetan Plateau”, “soil”, “soil organic carbon”, and “soil organic matter” in different combinations. The criteria for including a study in the analysis were: (i) a clear and comprehensible classification of degradation stages was presented, (ii) data on SOC, N, and/or BD were reported, (iii) a non-degraded pasture site was included as a reference to enable an effect size analysis and the calculation of SOC and N losses, (iv) sampling depths and study location were clearly presented. (v) Studies were only considered that took samples in 10 cm depth intervals, to maintain comparability to the analyses from our own study site. The degradation stages in the literature studies were regrouped into the six successive stages (S0–S5) according to the respective degradation descriptions. In total, we compiled the results of 49 publications published between 2002 and 2020.When SOM content was presented, this was converted to SOC content using a conversion factor of 2.032. SOC and N stocks were calculated using the following equation:$${{{{{rm{Elemental; stock}}}}}}=100* {{{{{rm{content}}}}}}* {{{{{rm{BD}}}}}}* {{{{{rm{depth}}}}}}$$
(1)
where elemental stock is SOC or N stock [kg ha−1]; content is SOC or N content [g kg−1]; BD is soil bulk density [g cm−3] and depth is the soil sampling depth [cm].The effect sizes of individual variables (i.e., SOC and N stocks as well as BD) were quantified as follows:$${{{{{rm{ES}}}}}}=,frac{(D-R)}{R* 100 % }$$
(2)
where ES is the effect size in %, D is the value of the corresponding variable in the relevant degradation stage and R is the value of each variable in the non-degraded stage (reference site). When ES is positive, zero, or negative, this indicates an increase, no change, or decrease, respectively, of the parameter compared to the non-degraded stage.Experimental design of the field studyLarge areas in the study region are impacted by grassland degradation. In total, 45% of the surface area of the Kobresia pasture ecosystem on the TP is already degraded2. The experiment was designed to differentiate and quantify SOC losses by erosion vs. net decomposition and identify underlying shifts in microbial community composition and link these to changes in key microbial functions in the soil C cycle. We categorized the range of Kobresia root-mat degradation from non-degraded to bare soils into six successive degradation stages (S0–S5). Stage S0 represented non-degraded root mats, while stages S1–S4 represented increasing degrees of surface cracks, and bare soil patches without root mats defined stage S5 (Supplementary Fig. 1). All six degradation stages were selected within an area of about 4 ha to ensure equal environmental conditions and each stage was sampled in four field replicates. However, the studied degradation patterns are common for the entire Kobresia ecosystem (Supplementary Fig. 1).Site descriptionThe field study was conducted near Nagqu (Tibet, China) in the late summer 2013 and 2015. The study site of about 4 ha (NW: 31.274748°N, 92.108963°E; NE: 31.274995°N, 92.111482°E; SW: 31.273488°N, 92.108906°E; SE: 31.273421°N, 92.112025°E) was located on gentle slopes (2–5%) at 4,484 m a.s.l. in the core area of the Kobresia pygmaea ecosystem according to Miehe et al.8. The vegetation consists mainly of K. pygmaea, which covers up to 61% of the surface. Other grasses, sedges, or dwarf rosette plants (Carex ivanoviae, Carex spp., Festuca spp., Kobresia pusilla, Poa spp., Stipa purpurea, Trisetum spp.) rarely cover more than 40%. The growing season is strongly restricted by temperature and water availability. At most, it lasts from mid-May to mid-September, but varies strongly depending on the onset and duration of the summer monsoon. Mean annual precipitation is 431 mm, with roughly 80% falling as summer rains. The mean annual temperature is −1.2 °C, while the mean maximum temperature of the warmest month (July) is +9.0 °C2.A characteristic feature of Kobresia pastures is their very compact root mats, with an average thickness of 15 cm at the study site. These consist mainly of living and dead K. pygmaea roots and rhizomes, leaf bases, large amounts of plant residue, and mineral particles. Intact soil is a Stagnic Eutric Cambisol (Humic), developed on a loess layer overlying glacial sediments and containing 50% sand, 33% silt, and 17% clay in the topsoil (0–25 cm). The topsoil is free of carbonates and is of neutral pH (pH in H2O: 6.8)5. Total soil depth was on average 35 cm.The site is used as a winter pasture for yaks, sheep, and goats from January to April. Besides livestock, large numbers of plateau pikas (Ochotona) are found on the sites. These animals have a considerable impact on the plant cover through their burrowing activity, in particular the soil thrown out of their burrows, which can cover and destroy the Kobresia turf.Sampling designThe vertical and horizontal extent of the surface cracks was measured for each plot (Supplementary Table 2). Vegetation cover was measured and the aboveground biomass was collected in the cracks (Supplementary Table 2). In general, intact Kobresia turf (S0) provided high resistance to penetration as measured by a penetrologger (Eijkelkamp Soil and Water, Giesbeek, NL) in 1 cm increments and four replicates per plot.Soil sampling was conducted using soil pits (30 cm length × 30 cm width × 40 cm depth). Horizons were classified and then soil and roots were sampled for each horizon directly below the cracks. Bulk density and root biomass were determined in undisturbed soil samples, using soil cores (10 cm height and 10 cm diameter). Living roots were separated from dead roots and root debris by their bright color and soft texture using tweezers under magnification, and the roots were subsequently washed with distilled water to remove the remaining soil. Because over 95% of the roots occurred in the upper 25 cm5, we did not sample for root biomass below this depth.Additional soil samples were taken from each horizon for further analysis. Microbial community and functional characterization were performed on samples from the same pits but with a fixed depth classification (0–5 cm, 5–15 cm, 15–35 cm) to reduce the number of samples.Plant and soil analysesSoil and roots were separated by sieving (2 mm) and the roots subsequently washed with distilled water. Bulk density and root density were determined by dividing the dry soil mass (dried at 105 °C for 24 h) and the dry root biomass (60 °C) by the volume of the sampling core. To reflect the root biomass, root density was expressed per soil volume (mg cm−3). Soil and root samples were milled for subsequent analysis.Elemental concentrations and SOC characteristicsTotal SOC and total N contents and stable isotope signatures (δ13C and δ15N) were analyzed using an isotope ratio mass spectrometer (Delta plus, Conflo III, Thermo Electron Cooperation, Bremen, Germany) coupled to an elemental analyzer (NA 1500, Fisons Instruments, Milano, Italy). Measurements were conducted at the Centre for Stable Isotope Research and Analysis (KOSI) of the University of Göttingen. The δ13C and δ15N values were calculated by relating the isotope ratio of each sample (Rsample = 13C/12C or 15N/14N) to the international standards (Pee Dee Belemnite 13C/12C ratio for δ13C; the atmospheric 15N/14N composition for δ15N).Soil pH of air-dried soil was measured potentiometrically at a ratio (v/v) of 1.0:2.5 in distilled water.Lignin phenols were depolymerized using the CuO oxidation method25 and analyzed with a gas chromatography-mass spectrometry (GC–MS) system (GC 7820 A, MS 5977B, Agilent Technologies, Waldbronn, Germany). Vanillyl and syringyl units were calculated from the corresponding aldehydes, ketones, and carboxylic acids. Cinnamyl units were derived from the sum of p-coumaric acid and ferulic acid. The sum of the three structural units (VSC = V + S + C) was considered to reflect the lignin phenol content in a sample.DNA extraction and PCRSamples were directly frozen on site at −20 °C and transported to Germany for analysis of microbial community structure. Total DNA was extracted from the soil samples with the PowerSoil DNA isolation kit (MoBio Laboratories Inc., Carlsbad, CA, USA) according to the manufacturer’s instructions, and DNA concentration was determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The extracted DNA was amplified with forward and reverse primer sets suitable for either t-RFLP (fluorescence marked, FAM) or Illumina MiSeq sequencing (Illumina Inc., San Diego, USA): V3 (5’-CCT ACG GGN GGC WGC AG-3’) and V4 (5’-GAC TAC HVG GGT ATC TAA TCC-3’) primers were used for bacterial 16 S rRNA genes whereas ITS1 (5’-CTT GGT CAT TTA GAG GAA GTA A-3’), ITS1-F_KYO1 (5’-CTH GGT CAT TTA GAG GAA STA A-3’), ITS2 (5’-GCT GCG TTC TTC ATC GAT GC-3’) and ITS4 (5’-TCC TCC GCT TAT TGA TAT GC-3’) were used for fungi33,34. Primers for Illumina MiSeq sequencing included adaptor sequences (forward: 5’-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG-3’; reverse: 5’-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA G-3’)33. PCR was performed with the Phusion High-Fidelity PCR kit (New England Biolabs Inc., Ipswich, MA, USA) creating a 50 µl master mix with 28.8 µl H2Omolec, 2.5 µl DMSO, 10 µl Phusion GC buffer, 1 µl of forward and reverse primer, 0.2 µl MgCl2, 1 µl dNTPs, 0.5 µl Phusion HF DNA Polymerase, and 5 µl template DNA. PCR temperatures started with initial denaturation at 98 °C for 1 min, followed by denaturation (98 °C, 45 s), annealing (48/60 °C, 45 s), and extension (72 °C, 30 s). These steps were repeated 25 times, finalized again with a final extension (72 °C, 5 min), and cooling to 10 °C. Agarose gel electrophoresis was used to assess the success of the PCR and the amount of amplified DNA (0.8% gel:1.0 g Rotigarose, 5 µl Roti-Safe Gelstain, Carl Roth GmbH & Co. KG, Karlsruhe, Germany; and 100 ml 1× TAE-buffer). PCR product was purified after initial PCR and restriction digestion (t-RFLP) with either NucleoMag 96 PCR (16 S rRNA gene amplicons, Macherey-Nagel GmbH & Co. KG, Düren, Germany) or a modified clean-up protocol after Moreau (t-RFLP)35: 3× the volume of the reaction solution as 100% ethanol and ¼x vol. 125 mM EDTA was added and mixed by inversion or vortex. After incubation at room temperature for 15 min, the product was centrifuged at 25,000 × g for 30 min at 4 °C. Afterwards the supernatant was removed, and the inverted 96-well plate was centrifuged shortly for 2 min. Seventy microliters ethanol (70%) were added and centrifuged at 25,000 × g for 30 min at 4 °C. Again, the supernatant was removed, and the pallet was dried at room temperature for 30 min. Finally, the ethanol-free pallet was resuspended in H2Omolec.T-RFLP fingerprintingThe purified fluorescence-labeled PCR products were digested with three different restriction enzymes (MspI and BstUI, HaeIII) according to the manufacturer’s guidelines (New England Biolabs Inc., Ipswich, MA, USA) with a 20 µl master mix: 16.75 µl H2Omolec, 2 µl CutSmart buffer, 0.25 or 0.5 µl restriction enzyme, and 1 µl PCR product for 15 min at 37 °C (MspI) and 60 °C (BstUI, HaeIII), respectively. The digested PCR product was purified a second time35, dissolved in Super-DI Formamide (MCLAB, San Francisco, CA, USA) and, along with Red DNA size standard (MCLAB, San Francisco, USA), analyzed in an ABI Prism 3130 Genetic Analyzer (Applied Biosystems, Carlsbad, CA, USA). Terminal restriction fragments shorter than 50 bp and longer than 800 bp were removed from the t-RFLP fingerprints.16 S rRNA gene and internal transcribed spacer (ITS) sequencing and sequence processingThe 16 S rRNA gene and ITS paired-end raw reads for the bacterial and fungal community analyses were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) and can be found under the BioProject accession number PRJNA626504. This BioProject contains 70 samples and 139 SRA experiments (SRR11570615–SRR11570753) which were processed using CASAVA software (Illumina, San Diego, CA, USA) for demultiplexing of MiSeq raw sequences (2 × 300 bp, MiSeq Reagent Kit v3).Paired-end sequences were quality-filtered with fastp (version 0.19.4)36 using default settings with the addition of an increased per base phred score of 20, base-pair corrections by overlap (-c), as well as 5′- and 3′-end read trimming with a sliding window of 4, a mean quality of 20 and minimum sequence size of 50 bp. Paired-end sequences were merged using PEAR v0.9.1137 with default parameters. Subsequently, unclipped reverse and forward primer sequences were removed with cutadapt v1.1838 with default settings. Sequences were then processed using VSEARCH (v2.9.1)39. This included sorting and size-filtering (—sortbylength,—minseqlength) of the paired reads to ≥300 bp for bacteria and ≥140 bp for ITS1, dereplication (—derep_fulllength). Dereplicated sequences were denoised with UNOISE340 using default settings (—cluster_unoise—minsize 8) and chimeras were removed (—uchime3_denovo). An additional reference-based chimera removal was performed (—uchime_ref) against the SILVA41 SSU NR database (v132) and UNITE42 database (v7.2) resulting in the final set of amplicon sequence variants (ASVs)43. Quality-filtered and merged reads were mapped to ASVs (—usearch_global–id 0.97). Classification of ASVs was performed with BLAST 2.7.1+ against the SILVA SSU NR (v132) and UNITE (v7.2) database with an identity of at least 90%. The ITS sequences contained unidentified fungal ASVs after UNITE classification, these sequences were checked (blastn)44 against the “nt” database (Nov 2018) to remove non-fungal ASVs and only as fungi classified reads were kept. Sample comparisons were performed at the same surveying effort, utilizing the lowest number of sequences by random selection (total 15,800 bacteria, 20,500 fungi). Species richness, alpha and beta diversity estimates, and rarefaction curves were determined using the QIIME 1.9.145 script alpha_rarefaction.py.The final ASV tables were used to compute heatmaps showing the effect of degradation on the community using R (Version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria) and R packages “gplots”, “vegan”, “permute” and “RColorBrewer”. Fungal community functions were obtained from the FunGuild database46. Plant mycorrhizal association types were compiled from the literature38,39,40,41,47,48,49,50. If no direct species match was available, the mycorrhizal association was assumed to remain constant within the same genus.Enzyme activityEnzyme activity was measured to characterize the functional activity of the soil microorganisms. The following extracellular enzymes, involved in C, N, and P transformations, were considered: two hydrolases (β-glucosidase and xylanase), phenoloxidase, urease, and alkaline phosphatase. Enzyme activities were measured directly at the sampling site according to protocols after Schinner et al.51. Beta-glucosidase was incubated with saligenin for 3 h at 37 °C, xylanase with glucose for 24 h at 50 °C, phenoloxidase with L-3,4-dihydroxy phenylalanine (DOPA) for 1 h at 25 °C, urease with urea for 2 h at 37 °C and alkaline phosphatase on P-nitrophenyl phosphate for 1 h at 37 °C. Reaction products were measured photometrically at recommended wavelengths (578, 690, 475, 660, and 400 nm, respectively).SOC stocks and SOC lossThe SOC stocks (in kg C m−2) for the upper 30 cm were determined by multiplying the SOC content (g C kg−1) by the BD (g cm−3) and the thickness of the soil horizons (m). SOC losses (%) were calculated for each degradation stage and horizon and were related to the mean C stock of the reference stage (S0). The erosion-induced SOC loss of the upper horizon was estimated by considering the topsoil removal (extent of vertical soil cracks) of all degraded soil profiles (S1–S5) and the SOC content and BD of the reference (S0). To calculate the mineralization-derived SOC loss, we accounted for the effects of SOC and root mineralization on both SOC content and BD. Thus, we used the SOC content and BD from each degradation stage (S1–S5) and multiplied it by the mean thickness of each horizon (down to 30 cm) from the reference site (S0). The disentanglement of erosion-derived SOC loss from mineralization-derived SOC loss was based on explicit assumptions that (i) erosion-derived SOC losses are mainly associated with losses from the topsoil, and (ii) the decreasing SOC contents in the erosion-unaffected horizons were mainly driven by mineralization and decreasing root C input.Statistical analysesStatistical analyses were performed using PASW Statistics (IBM SPSS Statistics) and R software (Version 3.6.1). Soil and plant characteristics are presented as means and standard errors (means ± SE). The significance of treatment effects (S0–S5) and depth was tested by one-way ANOVA at p  More

Study siteWe conducted this research at the Uluwatu temple site in Bali, Indonesia. Uluwatu is located on the Island’s southern coast, in the Badung Regency. The temple at Uluwatu is a Pura Luhur, which is a significant temple for Balinese Hindus across the island and is therefore visited regularly for significant regional, community, family, and household rituals by Balinese people from different regions throughout the year18. During the period of data collection hundreds of tourists also visit the Uluwatu temple each day. The temple sits on top of a promontory cliff edge, with walking paths in front of it that continue in loops to the North and South. These looping pathways surround scrub forests, which the macaques frequently inhabit but the humans rarely enter.In 2017–2018 there were five macaque groups at Uluwatu, which ranged throughout the temple complex area, and beyond. All groups are provisioned daily with a mixed diet of corn, cucumbers, and bananas by temple staff members. The two groups included in this research are the Celagi and Riting groups. We selected these groups because they previously exhibited significant differences in robbing frequencies whereby Riting was observed exhibiting robbing and bartering more frequently than Celagi1. Furthermore, both groups include the same highly trafficked tourist areas in their overlapping home ranges relative to the other groups at Uluwatu, theoretically minimizing between group differences in the contexts of human interaction1,19.Data collectionJVP collected data from May, 2017 to March, 2018 totaling 197 focal observation hours on all 13 subadult males in Celagi and Riting that were identified in May–June 2017. Subadult male long-tailed macaques exhibit characteristic patterns of incomplete canine eruption, sex organ development, and body size growth, which achieves a maximum of 80% of total adult size18. Mean sampling effort per individual was 15.2 hours (h), with a range of 1.75 h, totaling 102.75 h for Riting and 94.75 h for Celagi. The data collection protocol consisted of focal-animal sampling and instantaneous scan sampling20 on all six subadult males in the Celagi group, and all seven subadult males in the Riting group. Focal follows were 15 minutes in length. Sampling effort per individual is presented in Table 1. A random number generator determined the order of focal follows each morning. In the event a target focal animal could not be located within 10 minutes of locating the group, the next in line was located and observed. Data presented here come from focal animal sampling records of state and event behaviors. Relevant event behaviors consist of agonistic gestures used for calculating dominance relationships, including the target, or interaction partner, of all communicative event behaviors and the time of its occurrence. All changes in the focal animal’s state behavior were noted, recording the time of the change to the minute.Table 1 Focal Subadult male long-tailed macaques in Celagi and Riting at Uluwatu, Bali, Indonesia.Full size tableDuring focal samples we recorded robbing and bartering as a sequence of mixed event and state behaviors. We scored both the robbery and exchange phases as event behaviors, and the interim phase of item possession as a state behavior. We record a robbery as successful if the focal animal took an object from a human and established control of the object with their hands or teeth, and as unsuccessful if the focal animal touched the object but was not able to establish control of it. For each successful robbery we recorded the object taken. Unsuccessful robberies end the sequence, whereas successful robberies are typically followed by various forms of manipulating the object.The robbing and bartering sequence ends with one of several event behavior exchange outcomes: (1) “Successful exchanges” consist of the focal animal receiving a food reward from a human and releasing the stolen object; (2) “forced exchanges” are when a human takes the object back without a bartering event; (3) “dropped objects” describe when the macaque loses control of the object while carrying it or otherwise locomoting, and is akin to an “accidental drop”; (4) “no exchange” includes instances of the macaque releasing the object for no reward after manipulating it; and (5) “expired observation” consists of instances in which the final result of the robbing and bartering event was unobserved in the sample period (i.e., the sample period ended while the macaque still had possession of the object). A 6th exchange outcome is “rejected exchange,” which occurs when the focal animal does not drop the stolen object after being offered, or in some cases even accepting, a food reward. The “rejected exchange” outcome is unique in that it does not end the robbing and bartering sequence because a human may have one or more exchange attempts rejected before eventually facilitating a successful exchange, or before one of the other outcomes (2–5) occurs. For each successful exchange we recorded the food item the macaques received. Food items are grouped into four categories: fruits, peanuts, eggs, and human snacks. Snacks include packaged and processed food items such as candy or chips.Data analysisWe grouped the broad range of stolen items into classes of general types. “Eyewear” combines eyeglasses and sunglasses, while “footwear” combines sandals and shoes. “Ornaments” includes objects attached to and/or hanging from backpacks, such as keychains, while “accessories” includes decorative objects attached to an individual’s body or clothing like bracelets and hair ties. “Electronics” covers cellular phones and tablets. “Hats” encompasses removable forms of headwear, most typically represented by baseball-style hats or sun hats. “Plastics” is an item class consisting of lighters and bottles, which may be filled with water, soda, or juice. The “unidentified” category is used for stolen items which could not be clearly observed during or after the robbing and bartering sequence.“Robbery attempts” refers to the combined total number of successful and unsuccessful robberies. “Robbery efficiency” is a novel metric referring to the number of successful robberies divided by the total number of robbery attempts. The “Exchange Outcome Index” is calculated by dividing the number of successful exchanges by the total number of robbery attempts. We make this calculation using robbery attempts instead of successful robberies to account for total robbery effort because failed robberies still factor into an individual’s total energy expenditure toward receiving a bartered food reward and their total exposure to the risks (e.g., physical retaliation) of stealing from humans relative to achieving the desired end result of a food reward.Social rank was measured with David’s Score, calculated using dyadic agonistic interactions. We coded “winners” of contests as those who exhibited the agonistic behavior, while “losers” were the recipients of those agonistic behaviors21,22. We excluded intergroup agonistic interactions in our calculations of David’s Score.To account for potential variation in the overall patterns of interaction with humans between groups we calculated a Human Interaction Rate, which is the sum of human-directed interactions from focal animals in each group divided by the total number of observation hours on focal animals in that group.Statistical analysisWe ran statistical tests in SYSTAT software with a significance level set at 0.05. We used chi-square goodness-of-fit tests to assess the significance of differences in successful robberies between individuals for each group. To avoid having cells with values of zero, two focal subjects, Minion and Spot from Celagi, are excluded from this test because neither were observed making a successful robbery during the observation period. We also used chi-square goodness-of-fit tests to assess exchange outcome occurrences within each group, as well as a Fisher’s exact to test for significant differences in robbery outcomes between groups due to low expected counts in 40% of the cells. “Rejected exchange” events were not included in the analysis of robbery outcomes because they do not end the sequence and are therefore not mutually exclusive with the other robbery outcomes.We further tested for the effect of dominance position on robbery outcomes. Due to our small sample size and the preliminary nature of this investigation, we used Spearman correlations to assess the relationship between subadult male dominance position via David’s Score and (1) robbing efficiency and (2) the Exchange Outcome Index.Compliance with ethical standardsThis research complied with the standards and protocols for observational fieldwork with nonhuman primates and was approved by the University of Notre Dame Compliance IACUC board (protocol ID: 16-02-2932), where JVP and AF were affiliated at the time of this research. This study did not involve human subjects. This research further received a research permit from RISTEK in Indonesia (permit number: 2C21EB0881-R), and complied with local laws and customary practices in Bali. More

Springer, A. M. et al. Sequential megafaunal collapse in the North Pacific Ocean: an ongoing legacy of industrial whaling?. Proc. Natl. Acad. Sci. 100, 12223–12228. https://doi.org/10.1073/pnas.1635156100 (2003).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Estes, J. A., Heithaus, M., McCauley, D. J., Rasher, D. B. & Worm, B. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Resour. 41, 83–116. https://doi.org/10.1146/annurev-environ-110615-085622 (2016).Article 

Google Scholar 
Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572. https://doi.org/10.1111/j.1748-7692.2009.00354.x (2010).CAS 
Article 

Google Scholar 
Bowen, W. D. & Iverson, S. J. Methods of estimating marine mammal diets: a review of validation experiments and sources of bias and uncertainty. Mar. Mamm. Sci. 29, 719–754. https://doi.org/10.1111/j.1748-7692.2012.00604.x (2013).Article 

Google Scholar 
Krahn, M. M. et al. Use of chemical tracers in assessing the diet and foraging regions of eastern North Pacific killer whales. Mar. Environ. Res. 63, 91–114. https://doi.org/10.1016/j.marenvres.2006.07.002 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Remili, A. et al. Individual prey specialization drives PCBs in Icelandic killer whales. Environ. Sci. Technol. 55, 4923–4931. https://doi.org/10.1021/acs.est.0c08563 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Foote, A. D., Vester, H., Vikingsson, G. A. & Newton, J. Dietary variation within and between populations of northeast Atlantic killer whales, Orcinus orca, inferred from d13C and d15N analyses. Mar. Mamm. Sci. 28, E472–E485. https://doi.org/10.1111/j.1748-7692.2012.00563.x (2012).CAS 
Article 

Google Scholar 
Remili, A. et al. Humpback whales (Megaptera novaeangliae) breeding off Mozambique and Ecuador show geographic variation of persistent organic pollutants and isotopic niches. Environ. Pollut. 267, 115575. https://doi.org/10.1016/j.envpol.2020.115575 (2020).CAS 
Article 
PubMed 

Google Scholar 
Pinzone, M., Damseaux, F., Michel, L. N. & Das, K. Stable isotope ratios of carbon, nitrogen and sulphur and mercury concentrations as descriptors of trophic ecology and contamination sources of Mediterranean whales. Chemosphere 237, 124448. https://doi.org/10.1016/j.chemosphere.2019.124448 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bourque, J. et al. Feeding habits of a new Arctic predator: insight from full-depth blubber fatty acid signatures of Greenland, Faroe Islands, Denmark, and managed-care killer whales Orcinus orca. Mar. Ecol. Prog. Ser. 603, 1–12. https://doi.org/10.3354/meps12723 (2018).ADS 
CAS 
Article 

Google Scholar 
Krahn, M. M., Pitman, R. L., Burrows, D. G., Herman, D. P. & Pearce, R. W. Use of chemical tracers to assess diet and persistent organic pollutants in Antarctic Type C killer whales. Mar. Mamm. Sci. 24, 643–663. https://doi.org/10.1111/j.1748-7692.2008.00213.x (2008).CAS 
Article 

Google Scholar 
Groß, J. et al. Interannual variability in the lipid and fatty acid profiles of east Australia-migrating humpback whales (Megaptera novaeangliae) across a 10-year timeline. Sci. Rep. 10, 18274. https://doi.org/10.1038/s41598-020-75370-5 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jory, C. et al. Individual and population dietary specialization decline in fin whales during a period of ecosystem shift. Sci. Rep. 11, 17181. https://doi.org/10.1038/s41598-021-96283-x (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Iverson, S. J., Field, C., Bowen, W. D. & Blanchard, W. Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol. Monogr. 74, 211–235. https://doi.org/10.1890/02-4105 (2004).Article 

Google Scholar 
McKinney, M. A. et al. Global change effects on the long-term feeding ecology and contaminant exposures of East Greenland polar bears. Glob. Change Biol. 19, 2360–2372. https://doi.org/10.1111/gcb.12241 (2013).ADS 
Article 

Google Scholar 
Nordstrom, C. A., Wilson, L. J., Iverson, S. J. & Tollit, D. J. Evaluating quantitative fatty acid signature analysis (QFASA) using harbour seals Phoca vitulina richardsi in captive feeding studies. Mar. Ecol. Prog. Ser. 360, 245–263. https://doi.org/10.3354/meps07378 (2008).ADS 
Article 

Google Scholar 
Bourque, J., Atwood, T. C., Divoky, G. J., Stewart, C. & McKinney, M. A. Fatty acid-based diet estimates suggest ringed seal remain the main prey of southern Beaufort Sea polar bears despite recent use of onshore food resources. Ecol. Evol. https://doi.org/10.1002/ece3.6043 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Thiemann, G. W., Derocher, A. E. & Stirling, I. Polar bear Ursus maritimus conservation in Canada: an ecological basis for identifying designatable units. Oryx 42, 504–515. https://doi.org/10.1017/S0030605308001877 (2008).Article 

Google Scholar 
Choy, E. S. et al. A comparison of diet estimates of captive beluga whales using fatty acid mixing models with their true diets. J. Exp. Mar. Biol. Ecol. 516, 132–139. https://doi.org/10.1016/j.jembe.2019.05.005 (2019).ADS 
Article 

Google Scholar 
Kirsch, P. E., Iverson, S. J. & Bowen, W. D. Effect of a low-fat diet on body composition and blubber fatty acids of captive Juvenile Harp Seals (Phoca groenlandica). Physiol. Biochem. Zool. 73, 45–59. https://doi.org/10.1086/316723 (2000).CAS 
Article 
PubMed 

Google Scholar 
Koopman, H. N. Phylogenetic, ecological, and ontogenetic factors influencing the biochemical structure of the blubber of odontocetes. Mar. Biol. 151, 277–291. https://doi.org/10.1007/s00227-006-0489-8 (2007).Article 

Google Scholar 
Strandberg, U. et al. Stratification, composition, and function of marine mammal blubber: the ecology of fatty acids in marine mammals. Physiol. Biochem. Zool 81, 473–485. https://doi.org/10.1086/589108 (2008).CAS 
Article 
PubMed 

Google Scholar 
Choy, E. S. et al. Variation in the diet of beluga whales in response to changes in prey availability: insights on changes in the Beaufort Sea ecosystem. Mar. Ecol. Prog. Ser. 647, 195–210 (2020).ADS 
CAS 
Article 

Google Scholar 
Koopman, H. N., Iverson, S. J. & Gaskin, D. E. Stratification and age-related differences in blubber fatty acids of the male harbour porpoise (Phocoena phocoena). J. Comp. Physiol. B. 165, 628–639. https://doi.org/10.1007/BF00301131 (1996).CAS 
Article 
PubMed 

Google Scholar 
Budge, S. M., Iverson, S. J. & Koopman, H. N. Studying trophic ecology in marine ecosystems using fatty acids: a primer on analysis and interpretation. Mar. Mamm. Sci. 22, 759–801. https://doi.org/10.1111/j.1748-7692.2006.00079.x (2006).Article 

Google Scholar 
Krahn, M. M. et al. Stratification of lipids, fatty acids and organochlorine contaminants in blubber of white whales and killer whales. J. Cetacean Res. Manag. 6, 175–189 (2004).
Google Scholar 
Loseto, L. L. et al. Summer diet of beluga whales inferred by fatty acid analysis of the eastern Beaufort Sea food web. J. Exp. Mar. Biol. Ecol. 374, 12–18. https://doi.org/10.1016/j.jembe.2009.03.015 (2009).CAS 
Article 

Google Scholar 
Heide-Jørgensen, M.-P. Occurrence and hunting of killer whales in Greenland. Rit Fiskedeildar 11, 115–135 (1988).
Google Scholar 
Nøttestad, L. et al. Prey selection of offshore killer whales Orcinus orca in the Northeast Atlantic in late summer: spatial associations with mackerel. Mar. Ecol. Prog. Ser. 499, 275–283 (2014).ADS 
Article 

Google Scholar 
Nikolioudakis, N. et al. Drivers of the summer-distribution of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2011 to 2017; a Bayesian hierarchical modelling approach. ICES J. Mar. Sci. 76, 530–548. https://doi.org/10.1093/icesjms/fsy085 (2019).Article 

Google Scholar 
Olafsdottir, A. H. et al. Geographical expansion of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2007 to 2016 was primarily driven by stock size and constrained by low temperatures. Deep Sea Res. Part II 159, 152–168. https://doi.org/10.1016/j.dsr2.2018.05.023 (2019).Article 

Google Scholar 
Jansen, T. et al. Ocean warming expands habitat of a rich natural resource and benefits a national economy. Ecol. Appl. 26, 2021–2032. https://doi.org/10.1002/eap.1384 (2016).Article 
PubMed 

Google Scholar 
Ferguson, S. H., Higdon, J. W. & Westdal, K. H. Prey items and predation behavior of killer whales (Orcinus orca) in Nunavut, Canada based on Inuit hunter interviews. Aquat. Biosyst. 8, 3–3. https://doi.org/10.1186/2046-9063-8-3 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Laidre, K. L., Heide-Jørgensen, M. P. & Orr, J. R. Reactions of narwhals, Monodon monoceros, to killer whale, Orcinus orca, attacks in the eastern Canadian Arctic. Can. Field-Naturalist 120, 457–465 (2006).Article 

Google Scholar 
Willoughby, A. L., Ferguson, M. C., Stimmelmayr, R., Clarke, J. T. & Brower, A. A. Bowhead whale (Balaena mysticetus) and killer whale (Orcinus orca) co-occurrence in the U.S. Pacific Arctic, 2009–2018: evidence from bowhead whale carcasses. Polar Biol. 43, 1669–1679. https://doi.org/10.1007/s00300-020-02734-y (2020).Article 

Google Scholar 
Bloch, D. & Lockyer, C. Killer whales (Orcinus orca) in Faroese waters. Rit Fiskideildar 11, 55–64 (1988).
Google Scholar 
Pedro, S. et al. Blubber-depth distribution and bioaccumulation of PCBs and organochlorine pesticides in Arctic-invading killer whales. Sci. Total Environ. 601, 237–246. https://doi.org/10.1016/j.scitotenv.2017.05.193 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Samarra, F. I. P. et al. Prey of killer whales (Orcinus orca) in Iceland. PLoS ONE 13, 20. https://doi.org/10.1371/journal.pone.0207287 (2018).CAS 
Article 

Google Scholar 
Jourdain, E. et al. Isotopic niche differs between seal and fish-eating killer whales (Orcinus orca) in northern Norway. Ecol. Evol. 10, 4115–4127. https://doi.org/10.1002/ece3.6182 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Bromaghin, J. F., Budge, S. M., Thiemann, G. W. & Rode, K. D. Assessing the robustness of quantitative fatty acid signature analysis to assumption violations. Methods Ecol. Evol. 7, 51–59. https://doi.org/10.1111/2041-210X.12456 (2016).Article 

Google Scholar 
Jefferson, T. A., Stacey, P. J. & Baird, R. W. A review of Killer Whale interactions with other marine mammals: predation to co-existence. Mamm. Rev. 21, 151–180. https://doi.org/10.1111/j.1365-2907.1991.tb00291.x (1991).Article 

Google Scholar 
Bromaghin, J. F. QFASAR: quantitative fatty acid signature analysis with R. Methods Ecol. Evol. 8, 1158–1162. https://doi.org/10.1111/2041-210x.12740 (2017).Article 

Google Scholar 
Stewart, C., Iverson, S. & Field, C. Testing for a change in diet using fatty acid signatures. Environ. Ecol. Stat. 21, 775–792. https://doi.org/10.1007/s10651-014-0280-9 (2014).MathSciNet 
CAS 
Article 

Google Scholar 
Zhang, J. et al. Review of estimating trophic relationships by quantitative fatty acid signature analysis. J. Marine Sci. Eng. 8, 1030 (2020).Article 

Google Scholar 
Budge, S. M., Penney, S. N., Lall, S. P. & Trudel, M. Estimating diets of Atlantic salmon (Salmo salar) using fatty acid signature analyses; validation with controlled feeding studies. Can. J. Fish. Aquat. Sci. 69, 1033–1046. https://doi.org/10.1139/f2012-039 (2012).CAS 
Article 

Google Scholar 
Happel, A. et al. Evaluating quantitative fatty acid signature analysis (QFASA) in fish using controlled feeding experiments. Can. J. Fish. Aquat. Sci. 73, 1222–1229. https://doi.org/10.1139/cjfas-2015-0328 (2016).CAS 
Article 

Google Scholar 
Bromaghin, J. F. Simulating realistic predator signatures in quantitative fatty acid signature analysis. Eco. Inform. 30, 68–71. https://doi.org/10.1016/j.ecoinf.2015.09.011 (2015).Article 

Google Scholar 
Bromaghin, J. F., Budge, S. M., Thiemann, G. W. & Rode, K. D. Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data. Ecol. Evol. 7, 6103–6113. https://doi.org/10.1002/ece3.3179 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Burns, J. M., Costa, D. P., Frost, K. & Harvey, J. T. Development of body oxygen stores in harbor seals: effects of age, mass, and body composition. Physiol. Biochem. Zool. 78, 1057–1068. https://doi.org/10.1086/432922 (2005).CAS 
Article 
PubMed 

Google Scholar 
Noren, D. P. & Mocklin, J. A. Review of cetacean biopsy techniques: Factors contributing to successful sample collection and physiological and behavioral impacts. Mar. Mamm. Sci. 28, 154–199. https://doi.org/10.1111/j.1748-7692.2011.00469.x (2012).Article 

Google Scholar  More