Philippa Kaur

Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518. https://doi.org/10.1038/nature06970 (2008).Article 
ADS 
CAS 

Google Scholar 
Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts?. Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).Article 
ADS 
CAS 

Google Scholar 
Lafferty, K. D., Dobson, A. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. 103, 11211–11216 (2006).Article 
ADS 
CAS 

Google Scholar 
Amundsen, P. A. et al. Food web topology and parasites in the pelagic zone of a subarctic lake. J. Anim. Ecol. 78, 563–572. https://doi.org/10.1111/j.1365-2656.2008.01518.x (2009).Article 

Google Scholar 
Thompson, R. M., Mouritsen, K. N. & Poulin, R. Importance of parasites and their life cycle characteristics in determining the structure of a large marine food web. J. Anim. Ecol. 74, 77–85. https://doi.org/10.1111/j.1365-2656.2004.00899.x (2005).Article 

Google Scholar 
Thieltges, D. W. et al. Parasites as prey in aquatic food webs: Implications for predator infection and parasite transmission. Oikos 122, 1473–1482. https://doi.org/10.1111/j.1600-0706.2013.00243.x (2013).Article 

Google Scholar 
Sato, T. et al. Nematomorph parasites drive energy flow through a riparian ecosystem. Ecology 92, 201–207 (2011).Article 

Google Scholar 
Lafferty, K. D. & Kuris, A. M. Trophic strategies, animal diversity and body size. Trends Ecol. Evol. 17, 507–513 (2002).Article 

Google Scholar 
Goedknegt, M. A. et al. Trophic relationship between the invasive parasitic copepod Mytilicola orientalis and its native blue mussel (Mytilus edulis) host. Parasitology 145, 814–821. https://doi.org/10.1017/S0031182017001779 (2018).Article 
CAS 

Google Scholar 
Timi, J. T. & Poulin, R. Why ignoring parasites in fish ecology is a mistake. Int. J. Parasitol. 50, 755–761. https://doi.org/10.1016/j.ijpara.2020.04.007 (2020).Article 

Google Scholar 
Barber, I. & Svensson, P. A. Effects of experimental Schistocephalus solidus infections on growth, morphology and sexual development of female three-spined sticklebacks Gasterosteus aculeatus. Parasitology 126, 359–367. https://doi.org/10.1017/s0031182002002925 (2003).Article 
CAS 

Google Scholar 
Scharsack, J. P., Koch, K. & Hammerschmidt, K. Who is in control of the stickleback immune system: Interactions between Schistocephalus solidus and its specific vertebrate host. Proc. Biol. Sci. 274, 3151–3158. https://doi.org/10.1098/rspb.2007.1148 (2007).Article 

Google Scholar 
Hopkins, C. A. Studies on cestode metabolism. I. glycogen metabolism in Schistocephalus solidus In vivo. J. Parasitol. 36, 384–390 (1950).Article 
CAS 

Google Scholar 
Körting, W. & Barrett, J. Carbohydrate catabolism in the plerocercoids of Schistocephalus solidus (Cestoda: Pseudophyllidea). Int. J. Parasitol. 7, 411–417 (1977).Article 

Google Scholar 
Hebert, F. O., Grambauer, S., Barber, I., Landry, C. R. & Aubin-Horth, N. Major host transitions are modulated through transcriptome-wide reprogramming events in Schistocephalus solidus, a threespine stickleback parasite. Mol. Ecol. 26, 1118–1130. https://doi.org/10.1111/mec.13970 (2017).Article 
CAS 

Google Scholar 
Berger, C. S. et al. The parasite Schistocephalus solidus secretes proteins with putative host manipulation functions. Parasites Vectors 14, 436. https://doi.org/10.1186/s13071-021-04933-w (2021).Article 
CAS 

Google Scholar 
Jolles, J. W., Mazue, G. P. F., Davidson, J., Behrmann-Godel, J. & Couzin, I. D. Schistocephalus parasite infection alters sticklebacks’ movement ability and thereby shapes social interactions. Sci. Rep. 10, 12282. https://doi.org/10.1038/s41598-020-69057-0 (2020).Article 
ADS 
CAS 

Google Scholar 
Scharsack, J. P. et al. Climate change facilitates a parasite’s host exploitation via temperature-mediated immunometabolic processes. Glob. Change Biol. 27, 94–107. https://doi.org/10.1111/gcb.15402 (2021).Article 
ADS 
CAS 

Google Scholar 
Kochneva, A., Borvinskaya, E. & Smirnov, L. Zone of interaction between the parasite and the host: Protein profile of the body cavity fluid of Gasterosteus aculeatus L. infected with the Cestode Schistocephalus solidus (Muller, 1776). Acta Parasitol. 66, 569–583. https://doi.org/10.1007/s11686-020-00318-8 (2021).Article 
CAS 

Google Scholar 
Barber, I. & Scharsack, J. P. The three-spined stickleback-Schistocephalus solidus system: An experimental model for investigating host-parasite interactions in fish. Parasitology 137, 411–424. https://doi.org/10.1017/S0031182009991466 (2010).Article 
CAS 

Google Scholar 
Weber, J. N., Steinel, N. C., Shim, K. C. & Bolnick, D. I. Recent evolution of extreme cestode growth suppression by a vertebrate host. Proc. Natl. Acad. Sci. U. S. A. 114, 6575–6580. https://doi.org/10.1073/pnas.1620095114 (2017).Article 
ADS 
CAS 

Google Scholar 
Sabadel, A. J. M., Stumbo, A. D. & MacLeod, C. D. Stable-isotope analysis: A neglected tool for placing parasites in food webs. J. Helminthol. 93, 1–7. https://doi.org/10.1017/S0022149X17001201 (2019).Article 
CAS 

Google Scholar 
Hayes, J. M. Factors controlling 13C contents of sedimentary organic compounds: Principles and evidence. Mar. Geol. 113, 111–125 (1993).Article 
ADS 
CAS 

Google Scholar 
France, R. L. Differentiation between littoral and pelagic food webs in lakes using stable carbon isotopes. Limnol. Oceanogr. 40, 1310–1313 (1995).Article 
ADS 

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

Google Scholar 
O’Connell, T. C. ‘Trophic’ and ‘source’ amino acids in trophic estimation: A likely metabolic explanation. Oecologia 184, 317–326. https://doi.org/10.1007/s00442-017-3881-9 (2017).Article 
ADS 
CAS 

Google Scholar 
McMahon, K. W., Fogel, M. L., Elsdon, T. S. & Thorrold, S. R. Carbon isotope fractionation of amino acids in fish muscle reflects biosynthesis and isotopic routing from dietary protein. J. Anim. Ecol. 79, 1132–1141. https://doi.org/10.1111/j.1365-2656.2010.01722.x (2010).Article 

Google Scholar 
Liu, H.-z, Luo, L. & Cai, D.-l. Stable carbon isotopic analysis of amino acids in a simplified food chain consisting of the green alga Chlorella spp., the calanoid copepod Calanus sinicus, and the Japanese anchovy (Engraulis japonicus). Can. J. Zool. 96, 23–30. https://doi.org/10.1139/cjz-2016-0170 (2018).Article 
CAS 

Google Scholar 
Wang, Y. V. et al. Know your fish: A novel compound-specific isotope approach for tracing wild and farmed salmon. Food Chem. 256, 380–389. https://doi.org/10.1016/j.foodchem.2018.02.095 (2018).Article 
CAS 

Google Scholar 
Whiteman, J. P., Kim, S. L., McMahon, K. W., Koch, P. L. & Newsome, S. D. Amino acid isotope discrimination factors for a carnivore: Physiological insights from leopard sharks and their diet. Oecologia 188, 977–989. https://doi.org/10.1007/s00442-018-4276-2 (2018).Article 
ADS 

Google Scholar 
Rogers, M., Bare, R., Gray, A., Scott-Moelder, T. & Heintz, R. Assessment of two feeds on survival, proximate composition, and amino acid carbon isotope discrimination in hatchery-reared Chinook salmon. Fish. Res. 219, 105303. https://doi.org/10.1016/j.fishres.2019.06.001 (2019).Article 

Google Scholar 
Choy, K., Smith, C. I., Fuller, B. T. & Richards, M. P. Investigation of amino acid δ13C signatures in bone collagen to reconstruct human palaeodiets using liquid chromatography–isotope ratio mass spectrometry. Geochim. Cosmochim. Acta 74, 6093–6111. https://doi.org/10.1016/j.gca.2010.07.025 (2010).Article 
ADS 
CAS 

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).Article 
CAS 

Google Scholar 
Raghavan, M., McCullagh, J. S., Lynnerup, N. & Hedges, R. E. Amino acid delta13C analysis of hair proteins and bone collagen using liquid chromatography/isotope ratio mass spectrometry: Paleodietary implications from intra-individual comparisons. Rapid Commun. Mass Spectrom. 24, 541–548. https://doi.org/10.1002/rcm.4398 (2010).Article 
ADS 
CAS 

Google Scholar 
Honch, N. V., McCullagh, J. S. & Hedges, R. E. Variation of bone collagen amino acid delta13C values in archaeological humans and fauna with different dietary regimes: Developing frameworks of dietary discrimination. Am. J. Phys. Anthropol. 148, 495–511. https://doi.org/10.1002/ajpa.22065 (2012).Article 

Google Scholar 
Mora, A. et al. High-resolution palaeodietary reconstruction: Amino acid δ 13 C analysis of keratin from single hairs of mummified human individuals. Quatern. Int. 436, 96–113. https://doi.org/10.1016/j.quaint.2016.10.018 (2017).Article 

Google Scholar 
Matos, M. P. V., Konstantynova, K. I., Mohr, R. M. & Jackson, G. P. Analysis of the (13)C isotope ratios of amino acids in the larvae, pupae and adult stages of Calliphora vicina blow flies and their carrion food sources. Anal. Bioanal. Chem. 410, 7943–7954. https://doi.org/10.1007/s00216-018-1416-9 (2018).Article 
CAS 

Google Scholar 
Bontempo, L. et al. Bulk and compound-specific stable isotope ratio analysis for authenticity testing of organically grown tomatoes. Food Chem. 318, 126426. https://doi.org/10.1016/j.foodchem.2020.126426 (2020).Article 
CAS 

Google Scholar 
Gaye-Siessegger, J., McCullagh, J. S. & Focken, U. The effect of dietary amino acid abundance and isotopic composition on the growth rate, metabolism and tissue delta13C of rainbow trout. Br. J. Nutr. 105, 1764–1771. https://doi.org/10.1017/S0007114510005696 (2011).Article 
CAS 

Google Scholar 
Newsome, S. D., Fogel, M. L., Kelly, L. & del Rio, C. M. Contributions of direct incorporation from diet and microbial amino acids to protein synthesis in Nile tilapia. Funct. Ecol. 25, 1051–1062. https://doi.org/10.1111/j.1365-2435.2011.01866.x (2011).Article 

Google Scholar 
Larsen, T. et al. Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. PLoS ONE 8, e73441. https://doi.org/10.1371/journal.pone.0073441 (2013).Article 
ADS 
CAS 

Google Scholar 
Thieltges, D. W., Goedknegt, M. A., O’Dwyer, K., Senior, A. M. & Kamiya, T. Parasites and stable isotopes: A comparative analysis of isotopic discrimination in parasitic trophic interactions. Oikos 128, 1329–1339. https://doi.org/10.1111/oik.06086 (2019).Article 

Google Scholar 
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. Camb. Philos. Soc. 87, 545–562. https://doi.org/10.1111/j.1469-185X.2011.00208.x (2011).Article 

Google Scholar 
Wang, Y. V., Wan, A. H. L., Krogdahl, A., Johnson, M. & Larsen, T. (13)C values of glycolytic amino acids as indicators of carbohydrate utilization in carnivorous fish. PeerJ 7, e7701. https://doi.org/10.7717/peerj.7701 (2019).Article 

Google Scholar 
Hesse, T. et al. Insights into amino acid fractionation and incorporation by compound-specific carbon isotope analysis of three-spined sticklebacks. Sci. Rep. 12, 11690. https://doi.org/10.1038/s41598-022-15704-7 (2022).Article 
ADS 
CAS 

Google Scholar 
Riekenberg, P. M. et al. Stable nitrogen isotope analysis of amino acids as a new tool to clarify complex parasite–host interactions within food webs. Oikos 130, 1650–1664. https://doi.org/10.1111/oik.08450 (2021).Article 
CAS 

Google Scholar 
Carleton, S. A. & Del Rio, C. M. Growth and catabolism in isotopic incorporation: A new formulation and experimental data. Funct. Ecol. 24, 805–812. https://doi.org/10.1111/j.1365-2435.2010.01700.x (2010).Article 

Google Scholar 
Perga, M. E. & Gerdeaux, D. ‘Are fish what they eat’ all year round?. Oecologia 144, 598–606. https://doi.org/10.1007/s00442-005-0069-5 (2005).Article 
ADS 
CAS 

Google Scholar 
Grey, J. Trophic fractionation and the effects of diet switch on the carbon stable isotopic ‘signatures’ of pelagic consumers. SIL Proc. 1922–2010(27), 3187–3191. https://doi.org/10.1080/03680770.1998.11898266 (2000).Article 

Google Scholar 
Danfaer, A. Nutrient metabolism and utilization in the liver. Livest. Prod. Sci. 39, 115–127 (1994).Article 

Google Scholar 
Read, C. P. & Simmons, J. E. Biochemistry and physiology of tapeworms. Physiol. Rev. 43, 263–305 (1963).Article 
CAS 

Google Scholar 
Nachev, M. et al. Understanding trophic interactions in host-parasite associations using stable isotopes of carbon and nitrogen. Parasites Vectors 10, 1–9. https://doi.org/10.1186/s13071-017-2030-y (2017).Article 
CAS 

Google Scholar 
Kanaya, G. et al. Application of stable isotopic analyses for fish host–parasite systems: An evaluation tool for parasite-mediated material flow in aquatic ecosystems. Aquat. Ecol. 53, 217–232. https://doi.org/10.1007/s10452-019-09684-6 (2019).Article 
CAS 

Google Scholar 
Gilbert, B. M. et al. You are how you eat: differences in trophic position of two parasite species infecting a single host according to stable isotopes. Parasitol. Res. 119, 1393–1400. https://doi.org/10.1007/s00436-020-06619-1 (2020).Article 

Google Scholar 
Gilbert, B. M. et al. Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host—Parasite system from the Vaal River System, South Africa. Int. J. Parasitol. Parasites Wildl. 12, 134–141. https://doi.org/10.1016/j.ijppaw.2020.05.011 (2020).Article 

Google Scholar 
Felig, P. The glucose-alanine cycle. Metabolism 22, 179–207 (1973).Article 
CAS 

Google Scholar 
Dale, R. A. Catabolism of threonine in mammals by coupling of L-threonine 3-dehydrogenase with 2-amino-3-oxobutyrate-CoA ligase. Biochem. Biophys. Acta. 544, 496–503 (1978).Article 
CAS 

Google Scholar 
Jordan, P. M. & Akhtar, M. The mechanism of action of serine Transhydroxymethylase. Biochem. J. 116, 277–286 (1970).Article 
CAS 

Google Scholar 
Linstead, D. J., Klein, R. A. & Cross, G. A. M. Threonine catabolism in Trypanosoma brucei. J. Gen. Microbiol. 101, 243–251 (1977).Article 
CAS 

Google Scholar 
Hare, P. E., Fogel, M. L., Stafford, T. W. Jr., Mitchell, A. D. & Hoering, T. C. The isotopic composition of carbon and nitrogen in individual amino acids isolated from modern and fossil proteins. J. Archaeol. Sci. 18, 277–292 (1991).Article 

Google Scholar 
Petzke, K. J., Boeing, H., Klaus, S. & Metges, C. C. Carbon and nitrogen stable isotopic composition of hair protein and amino acids can be used as biomarkers for animal-derived dietary protein intake in humans. J. Nutr. 135, 1515–1520 (2005).Article 
CAS 

Google Scholar 
McMahon, K. W., Polito, M. J., Abel, S., McCarthy, M. D. & Thorrold, S. R. Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo penguin (Pygoscelis papua). Ecol. Evol. 5, 1278–1290. https://doi.org/10.1002/ece3.1437 (2015).Article 

Google Scholar 
Fuller, B. T. & Petzke, K. J. The dietary protein paradox and threonine (15) N-depletion: Pyridoxal-5’-phosphate enzyme activity as a mechanism for the delta (15) N trophic level effect. Rapid Commun. Mass Spectrom. 31, 705–718. https://doi.org/10.1002/rcm.7835 (2017).Article 
ADS 
CAS 

Google Scholar 
Bowyer, A. et al. Structure and function of the l-threonine dehydrogenase (TkTDH) from the hyperthermophilic archaeon Thermococcus kodakaraensis. J. Struct. Biol. 168, 294–304. https://doi.org/10.1016/j.jsb.2009.07.011 (2009).Article 
CAS 

Google Scholar 
Kikuchi, G., Motokawa, Y., Yoshida, T. & Hiraga, K. Glycine cleavage system: Reaction mechanism, physiological significance and hyperglycinemia. Proc. Jpn. Acad. https://doi.org/10.2183/pjab/84.246 (2008).Article 

Google Scholar 
Locasale, J. W. Serine, glycine and one-carbon units: Cancer metabolism in full circle. Nat. Rev. Cancer 13, 572–583. https://doi.org/10.1038/nrc3557 (2013).Article 
CAS 

Google Scholar 
Kalhan, S. C. & Hanson, R. W. Resurgence of serine: An often neglected but indispensable amino Acid. J. Biol. Chem. 287, 19786–19791. https://doi.org/10.1074/jbc.R112.357194 (2012).Article 
CAS 

Google Scholar 
Larsen, T., Wang, Y. V. & Wan, A. H. L. Tracing the Trophic fate of aquafeed macronutrients with carbon isotope ratios of amino acids. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.813961 (2022).Article 

Google Scholar 
Sweeting, C. J., Polunin, N. V. & Jennings, S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun. Mass Spectrom. 20, 595–601. https://doi.org/10.1002/rcm.2347 (2006).Article 
ADS 
CAS 

Google Scholar 
Tarallo, A., Bailey, C., Agnisola, C. & D’Onofrio, G. A theoretical evaluation of the respiration rate partition in the Gasterosteus aculeatus-Schistocephalus solidus host-parasite system. Int. Aquat. Res. 13, 185. https://doi.org/10.22034/IAR.2021.1924974.1142 (2021).Article 

Google Scholar 
Takizawa, Y. et al. A new insight into isotopic fractionation associated with decarboxylation in organisms: Implications for amino acid isotope approaches in biogeoscience. Progress Earth Planet. Sci. https://doi.org/10.1186/s40645-020-00364-w (2020).Article 

Google Scholar 
Ron-Harel, N. et al. T cell activation depends on extracellular alanine. Cell Rep. 28, 3011-3021.e4. https://doi.org/10.1016/j.celrep.2019.08.034 (2019).Article 
CAS 

Google Scholar 
Wang, W. et al. Glycine metabolism in animals and humans: Implications for nutrition and health. Amino Acids 45, 463–477. https://doi.org/10.1007/s00726-013-1493-1 (2013).Article 
CAS 

Google Scholar 
Mathis, D. & Shoelson, S. E. Immunometabolism: An emerging frontier. Nat. Rev. Immunol. 11, 81. https://doi.org/10.1038/nri2922 (2011).Article 
CAS 

Google Scholar 
Guo, C. et al. Live Edwardsiella tarda vaccine enhances innate immunity by metabolic modulation in zebrafish. Fish Shellfish Immunol. 47, 664–673. https://doi.org/10.1016/j.fsi.2015.09.034 (2015).Article 
CAS 

Google Scholar 
Peuss, R. et al. Adaptation to low parasite abundance affects immune investment and immunopathological responses of cavefish. Nat. Ecol. Evol. 4, 1416–1430. https://doi.org/10.1038/s41559-020-1234-2 (2020).Article 

Google Scholar 
Smyth, J. D. Fertilization of Schistocephalus solidus in vitro. Exp. Parasitol. 3, 64–71 (1954).Article 
CAS 

Google Scholar 
Schärer, L. & Wedekind, C. Lifetime reproductive output in a hermaphrodite cestode when reproducing alone or in pairs. Evol. Ecol. 13, 381–394 (1999).Article 

Google Scholar 
McCullagh, J. S. Mixed-mode chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 483–494. https://doi.org/10.1002/rcm.4322 (2010).Article 
ADS 
CAS 

Google Scholar 
Dunn, P. J., Honch, N. V. & Evershed, R. P. Comparison of liquid chromatography-isotope ratio mass spectrometry (LC/IRMS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) for the determination of collagen amino acid delta13C values for palaeodietary and palaeoecological reconstruction. Rapid Commun. Mass Spectrom. 25, 2995–3011. https://doi.org/10.1002/rcm.5174 (2011).Article 
ADS 
CAS 

Google Scholar 
Fry, B., Carter, J. F., Yamada, K., Yoshida, N. & Juchelka, D. Position-specific (13) C/(12) C analysis of amino acid carboxyl groups—Automated flow-injection-analysis based on reaction with ninhydrin. Rapid Commun. Mass Spectrom. https://doi.org/10.1002/rcm.8126 (2018).Article 

Google Scholar 
Marks, R. G. H., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. How to couple LC-IRMS with HRMS─A proof-of-concept study. Anal Chem 94, 2981–2987 (2022).Article 
CAS 

Google Scholar 
Sun, Y. et al. A method for stable carbon isotope measurement of underivatized individual amino acids by multi-dimensional high-performance liquid chromatography and elemental analyzer/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 34, e8885. https://doi.org/10.1002/rcm.8885 (2020).Article 
CAS 

Google Scholar 
Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).Article 
ADS 
CAS 

Google Scholar 
Köster, D., Villalobos, I. M. S., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. New concepts for the determination of oxidation efficiencies in liquid chromatography-isotope ratio mass spectrometry. Anal. Chem. 91, 5067–5073. https://doi.org/10.1021/acs.analchem.8b05315 (2019).Article 
CAS 

Google Scholar 
Boschker, H. T., Moerdijk-Poortvliet, T. C., van Breugel, P., Houtekamer, M. & Middelburg, J. J. A versatile method for stable carbon isotope analysis of carbohydrates by high-performance liquid chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 22, 3902–3908. https://doi.org/10.1002/rcm.3804 (2008).Article 
ADS 
CAS 

Google Scholar  More

Duke, N. C. et al. Mar. Freshw. Res. 68, 1816–1829 (2017).Article 

Google Scholar 
Li, W. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-01020-5 (2023).Article 

Google Scholar 
Potapov, P. et al. Ecol. Soc. 13, 51 (2008).Article 

Google Scholar 
Hancock, S. et al. Earth Space Sci. 6, 294–310 (2019).Article 

Google Scholar 
Wade, C. M. et al. Forests 11, 539 (2020).Article 

Google Scholar 
Abhilash, P. C. Land 10, 201 (2021).Article 

Google Scholar 
Biermann, F., Kanie, N. & Kim, R. E. Curr. Opin. Environ. Sustain. 26–27, 26–31 (2017).Article 

Google Scholar 
den Elzen, M. et al. Energy Policy 126, 238–250 (2019).Article 

Google Scholar 
Betts, M. G. et al. Nature 547, 441–444 (2017).Article 
CAS 

Google Scholar 
Watson, J. E. M. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0490-x (2018).Article 

Google Scholar  More

Research areaThe YREB covers Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Guizhou, and Yunnan. It includes the Yangtze River Delta urban agglomerations (YRDUA), Yangtze River midstream urban agglomeration (YRMUA), and Chengdu-Chongqing urban agglomeration (CCUA). With a regional area of 2.05 million km2, the YREB runs through the eastern, central and western regions in China32. In 2019, the total GDP of YREB is 45.8 trillion yuan, accounting for 46.2% of the national GDP. The YREB plays a pivotal strategic support and leading role in the overall situation of stable economic growth in China33. At the same time, the contradiction between the shortage of land resources and economic growth in the YREB is very prominent. Therefore, this paper selects 107 cities in YREB as the research sample. The specific geographic locations are shown in Fig. 2. This article uses ARCGIS 10.2 version to draw the map. The URL link is http://demo.domain.com:6080/arcgis/services.Figure 2The geographic location of the YREB in China.Full size imageResearch methodsGlobal Malmquist–Luenberger indexUGLUE refers to the effective utilization degree of land elements under certain input of other elements. The green utilization of urban land mainly comes from three aspects: first, improve the utilization intensity of the existing actual input land, that is, increase the input intensity of other elements of the unit land area. Second, reduce the input of land in the production process to avoid excessive waste of land. Third, promote the optimal allocation of land elements among production units. Technical efficiency refers to the maximum degree that all factor inputs need to expand or shrink in equal proportion when all production units reach the production frontier. However, for production units with high technical efficiency, the factor allocation structure may not be reasonable. The land factors may still have the problem of under-input or over-input, resulting in the reduction of UGLUE.Pastor and Lovell34 proposed a global index, which uses all the inspection periods of each decision-making unit as a benchmark to construct the production frontier. According to the current benchmark construction period t, the production possibility set reference set is defined as follows:$$P_{C}^{t} (x^{t} ) = left{ {left. {(y^{t} ,b^{t} )} right|x^{t} {kern 1pt} can{kern 1pt} , produce{kern 1pt} , b^{t} ,y^{t} } right}$$
(1)
The global benchmark is defined as: (P_{G} = P_{C}^{1} , cup ,P_{C}^{2} , cup , cdots ,P_{C}^{t}), The subscripts C and G represent the current benchmark and the global benchmark respectively. The ML index of decision-making unit i is calculated based on the current reference benchmark:$$ML^{S} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{C}^{S} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{C}^{S} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$
(2)
Among them, the superscript S indicates two adjacent periods, t period and t + 1 period. The subscript C indicates the current benchmark, which is a simplified directional distance function. (ML^{s} > 1), indicates that the productivity increases. (ML^{s} < 1), indicates that the productivity decreases.According to Hofmann et al.35, the GMLI is defined as follows:$$GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$ (3) Among them, (D_{G}^{T} (x,y,b) = max left{ {alpha |(y - alpha y,b - alpha b) in P_{G} (x)} right}). (GMLI^{t,t + 1} > 1) indicates that the productivity has increased. (GMLI^{t,t + 1} < 1) indicates that the productivity decreases. The GMLI is further broken down as follows:$$begin{aligned} & GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} \ & quad = frac{{1 + D_{G}^{t} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{t + 1} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} times left[ {frac{{(1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} ))/(1 + D_{C}^{T} (x^{t} ,y^{t} ,b^{t} ))}}{{(1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))/(1 + D_{C}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))}}} right] \ & quad = frac{{TE^{t + 1} }}{{TE^{t} }} times left( {frac{{BPG_{t + 1}^{t + 1} }}{{BPG_{t}^{t + 1} }}} right) = EC_{t}^{t + 1} times BPC_{t}^{t + 1} \ end{aligned}$$ (4) Among them, TE is the change of technological progress. EC is the change of technological efficiency. The change of technological progress reflects the change of the highest technical level. The improvement of the highest technical level often requires the introduction and innovation of advanced technology, which often requires a large amount of investment. The change of technical efficiency reflects the gap with the highest technical level. Narrowing the gap with the highest technical level often requires improvements in internal management and governance structures. (BPG_{t}^{t + 1}) is the “best practitioner gap” between the current period and overall technological frontier. (BPC_{t}^{t + 1}) measures the changes in the “best practitioner gap” between two periods (technological changes). (BPC_{t}^{t + 1} , > , 1 ,) indicates technological progress. (BPC_{t}^{t + 1} < 1) indicates technology regress.Econometric techniques of industrial agglomeration on UGLUEIn recent years, many scholars used the traditional SPM for empirical analysis, which is a basic measurement model suitable for panel data. Therefore, this article firstly uses the traditional SPM to analyze the impact of industrial agglomeration on UGLUE. The formula is:$$begin{aligned} ln UGLUE_{it} & = alpha_{0} + alpha_{1} ln RZI_{it} + alpha_{2} ln RZI_{it} *ln RZI_{it} + alpha_{3} ln RDI_{it} + alpha_{4} ln EC_{it} \ & quad + alpha_{5} ln GDP_{it} + alpha_{6} ln TEC_{it} + alpha_{7} ln ROAD_{it} + alpha_{8} ln GOV_{it} + varepsilon_{it} \ end{aligned}$$ (5) Among them, ε is the disturbance term. i represents the city, i in this paper involves 107 cities in YREB. t represents the time, and the range of t in this paper is from 2007 to 2016. UGLUE is the explained variable, which represents the UGLUE. RZI and RDI are explanatory variables, representing industrial specialization agglomeration and industrial diversification agglomeration. EC is the industrial structure. GDP is the level of economic development. TEC is the level of technology. ROAD is the level of infrastructure. GOV is the degree of government intervention. (alpha_{1}) to (alpha_{8}) is the coefficient of each variable.Formula (5) assumes that the UGLUE changes with the changes of various influencing factors in the current period. That is, there is no time lag effect. But in reality, land use often has a time lag effect. The previous level has a non-negligible impact on the current results. Therefore, this paper selects the dynamic panel model for empirical analysis. However, there is often a two-way causal relationship between industrial agglomeration and UGLUE, which may cause endogenous bias. For example, cities with higher UGLUE levels tend to have higher levels of economic development, which promotes industrial agglomeration in this city. Therefore, this paper adopts the method of system GMM for regression analysis of dynamic panel model. Compared with mixed OLS, system GMM can make full use of sample information, select appropriate lag terms as instrumental variables36. It can effectively solve the endogeneity problem between industrial agglomeration and UGLUE. Based on the above analysis, this paper introduces the first-order lag term of UGLUE on the basis of formula (5). The DPM is as follows:$$begin{aligned} ln UGLUE_{it} & = beta_{0} + tau ln UGLUE_{i(t - 1)} + beta_{1} ln RZI_{it} + beta_{2} ln RZI_{it} times ln RZI_{it} + beta_{3} ln RDI_{it} \ & quad + beta_{4} ln EC_{it} + beta_{5} ln GDP_{it} + beta_{6} ln TEC_{it} + beta_{7} ln ROAD_{it} + beta_{8} ln GOV_{it} + varepsilon_{{{text{it}}}} \ end{aligned}$$ (6) Among them, (tau) is the first-order lag coefficient of UGLUE, reflecting the time lag effect of UGLUE.Variable descriptionExplained variableThe GMLI is used to measure the UGLUE of 107 cities in YREB. According to existing research37, the following core evaluation index of UGLUE are selected (see Table 1). Regarding input indicators, we mainly choose land element input M, labor element input L, and capital element input K as input indicators. Regarding output indicators, we choose the added value of the secondary and tertiary industries in the municipal area as the expected output, and use the GDP deflator to convert it into a comparable value. At the same time, pollution indicators such as industrial wastewater emissions, industrial sulfur dioxide emissions, and industrial smoke (dust) emissions are selected as undesired output. Since the GMLI reflects the growth rate of UGLUE, this paper assumes that the GMLI in 2006 is 1, and then multiplies the calculated GMLI year by year to obtain the development level of UGLUE in each city from 2007 to 2016.Table 1 Input and output index.Full size tableExplanatory variablesIndustrial specialization index ZI is usually used to measure the specialization level of urban industries. The specialization index is represented by the share of the employment of the industry in the total employment of the city:$$ZI_{i} = Max_{j} (S_{ij} )$$ (7) Nextly, we use the relative specialization index to make a horizontal comparison of the specialization level between different cities:$$RZI_{i} = Max(S_{ij} /S_{j} )$$ (8) The most common measure of the level of industrial diversification is the Herfindahl–Hirschman Index (HHI). For city i, the HHI is the sum of the square sum of employment shares of all industries in the city. The diversification index is the reciprocal of the HHI:$$DZ_{i} = frac{1}{{sumlimits_{j} {S_{ij}^{2} } }}$$ (9) The expression of relative diversification index is as follows:$$RDI_{i} = {1 mathord{left/ {vphantom {1 {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}} right. kern-0pt} {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}$$ (10) Among them, Sij is the employment proportion of j industry in city i, and Sj is the proportion of the total employment of the national j industry. The greater value of RZI and RDI, the higher level of industrial specialization and diversification.Control variablesRegarding control variables, we choose the following variables as control variables.Industrial structure (EC): The continuous optimization of industrial structure promotes the improvement of UGLUE through three aspects: saving land, increasing land income and promoting the optimal allocation of land resources. This paper selects the added value of the tertiary industry as a percentage of GDP (take the logarithm) to express.Technological level (TEC): The higher the technological innovation level of a city is, the more it promotes the use of input elements and the transformation of innovation results, thereby improving the UGLUE. This paper selects the proportion of science and technology expenditure to fiscal expenditure (take the logarithm) to represent.Economic development level (GDP): The continuous economic development promote the rational allocation of various production factors and increase the level of urban land output, thereby improving the UGLUE. This paper selects GDP per capita (take the logarithm) to express.Road infrastructure level (ROAD): The continuous improvement of infrastructure reduces transportation costs and transaction costs, and promotes communication externalities between producers, consumers, and between producers and consumers. This paper selects the average road area per capita (take the logarithm) to express.Government behavior (GOV): Fiscal expenditure is an important means for the government to carry out macro-control. Appropriate fiscal expenditure makes up for market shortages, improves factor flow and resource allocation efficiency, and realizes positive economic externalities. This paper selects the proportion of fiscal expenditure to GDP (take the logarithm) to express. We can see the meaning of specific variables from Table 2.Table 2 The descriptive statistics of variables.Full size tableData sourceThe object of this thesis is the 107 cities in YREB from 2007 to 2016. The urban construction land area data comes from the "China Urban Construction Statistical Yearbook", and the rest of the index data all come from the "China City Statistical Yearbook". The URL link is https://www.cnki.net/. In order to maintain the integrity of the data, this article uses the average method to fill in the missing values. In addition, because Chaohu City began to be under the jurisdiction of Hefei City in 2011, Bijie City and Tongren City in Guizhou Province only became prefecture-level cities in 2011. The three cities and Pu'er City are taken from the sample to maintain the continuity of data. More

Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).Article 
ADS 

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

Google Scholar 
Harris, R. M. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).Article 
ADS 

Google Scholar 
Till, A., Rypel, A. L., Bray, A. & Fey, S. B. Fish die-offs are concurrent with thermal extremes in north temperate lakes. Nat. Clim. Change 9, 637–641 (2019).Article 
ADS 

Google Scholar 
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 
ADS 

Google Scholar 
Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612 (2014).Article 

Google Scholar 
Ma, G., Rudolf, V. H. & Ma, C. Extreme temperature events alter demographic rates, relative fitness, and community structure. Glob. Change Biol. 21, 1794–1808 (2015).Article 
ADS 

Google Scholar 
Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Ecological and evolutionary impacts of changing climatic variability. Biol. Rev. 92, 22–42 (2017).Article 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).Article 
CAS 

Google Scholar 
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).Article 
ADS 
CAS 

Google Scholar 
Power, S. B. & Delage, F. P. Setting and smashing extreme temperature records over the coming century. Nat. Clim. Change 9, 529–534 (2019).Article 
ADS 

Google Scholar 
Fischer, E. M., Sippel, S. & Knutti, R. Increasing probability of record-shattering climate extremes. Nat. Clim. Change 11, 689–695 (2021).Article 
ADS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).Article 
ADS 

Google Scholar 
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).Article 
ADS 
CAS 

Google Scholar 
McKechnie, A. E. & Wolf, B. O. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol. Lett. 6, 253–256 (2010).Article 

Google Scholar 
Maxwell, S. L. et al. Conservation implications of ecological responses to extreme weather and climate events. Divers. Distrib. 25, 613–625 (2019).Article 

Google Scholar 
Seneviratne, S. I. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) Ch. 11, 1571–1759 (Cambridge Univ. Press, 2021).Mora, C. et al. Global risk of deadly heat. Nat. Clim. Change 7, 501–506 (2017).Article 
ADS 

Google Scholar 
Battisti, D. S. & Naylor, R. L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009).Article 
CAS 

Google Scholar 
Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science 360, 791–795 (2018).Article 
CAS 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).Article 
ADS 
CAS 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).Article 
ADS 
CAS 

Google Scholar 
Ma, G., Hoffmann, A. A. & Ma, C.-S. Daily temperature extremes play an important role in predicting thermal effects. J. Exp. Biol. 218, 2289–2296 (2015).
Google Scholar 
Paaijmans, K. P. et al. Temperature variation makes ectotherms more sensitive to climate change. Glob. Change Biol. 19, 2373–2380 (2013).Article 
ADS 

Google Scholar 
Bütikofer, L. et al. The problem of scale in predicting biological responses to climate. Glob. Change Biol. 26, 6657–6666 (2020).Article 
ADS 

Google Scholar 
Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).Article 
ADS 
CAS 

Google Scholar 
Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Change Biol. 22, 3829–3842 (2016).Article 
ADS 

Google Scholar 
Garcia, R. A., Cabeza, M., Rahbek, C. & Araújo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579 (2014).Article 

Google Scholar 
Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).Article 
ADS 

Google Scholar 
Tamarin-Brodsky, T., Hodges, K., Hoskins, B. J. & Shepherd, T. G. Changes in Northern Hemisphere temperature variability shaped by regional warming patterns. Nat. Geosci. 13, 414–421 (2020).Article 
ADS 
CAS 

Google Scholar 
Schär, C. et al. The role of increasing temperature variability in European summer heatwaves. Nature 427, 332–336 (2004).Article 
ADS 

Google Scholar 
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).Article 
ADS 
CAS 

Google Scholar 
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).Article 
ADS 
CAS 

Google Scholar 
Perkins, S. E. & Alexander, L. V. On the measurement of heat waves. J. Clim. 26, 4500–4517 (2013).Article 
ADS 

Google Scholar 
Sunday, J. et al. Thermal tolerance patterns across latitude and elevation. Philos. Trans. R. Soc. B 374, 20190036 (2019).Article 

Google Scholar 
Hoffmann, A. A. Physiological climatic limits in Drosophila: patterns and implications. J. Exp. Biol. 213, 870–880 (2010).Article 
CAS 

Google Scholar 
Buckley, L. B. & Huey, R. B. How extreme temperatures impact organisms and the evolution of their thermal tolerance. Integr. Comp. Biol. 56, 98–109 (2016).Article 

Google Scholar 
Cohen, J. M., Fink, D. & Zuckerberg, B. Avian responses to extreme weather across functional traits and temporal scales. Glob. Change Biol. 26, 4240–4250 (2020).Article 
ADS 

Google Scholar 
Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 117, 19656–19657 (2020).Article 
ADS 
CAS 

Google Scholar 
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).Article 
ADS 
CAS 

Google Scholar 
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).Article 

Google Scholar 
Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).Article 
ADS 
CAS 

Google Scholar 
Welbergen, J. A., Klose, S. M., Markus, N. & Eby, P. Climate change and the effects of temperature extremes on Australian flying-foxes. Proc. R. Soc. B 275, 419–425 (2008).Article 

Google Scholar 
McKechnie, A. E., Rushworth, I. A., Myburgh, F. & Cunningham, S. J. Mortality among birds and bats during an extreme heat event in eastern South Africa. Austral Ecol. 46, 687–691 (2021).Article 

Google Scholar 
Thompson, R. M., Beardall, J., Beringer, J., Grace, M. & Sardina, P. Means and extremes: building variability into community-level climate change experiments. Ecol. Lett. 16, 799–806 (2013).Article 

Google Scholar 
Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).Article 
ADS 
CAS 

Google Scholar 
Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. R. Soc. B 276, 1939–1948 (2009).Article 

Google Scholar 
Kingsolver, J. G., Diamond, S. E. & Buckley, L. B. Heat stress and the fitness consequences of climate change for terrestrial ectotherms. Funct. Ecol. 27, 1415–1423 (2013).Article 

Google Scholar 
R. Kearney, M. Activity restriction and the mechanistic basis for extinctions under climate warming. Ecol. Lett. 16, 1470–1479 (2013).Article 

Google Scholar 
Rezende, E. L., Bozinovic, F., Szilágyi, A. & Santos, M. Predicting temperature mortality and selection in natural Drosophila populations. Science 369, 1242–1245 (2020).Article 
ADS 
CAS 
MATH 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
ADS 
CAS 

Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).Article 
ADS 

Google Scholar 
Levy, O., Dayan, T., Porter, W. P. & Kronfeld-Schor, N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol. Monogr. 89, e01334 (2019).Article 

Google Scholar 
Faurby, S. & Araújo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).Article 
ADS 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).Article 
ADS 
CAS 

Google Scholar 
Scheffers, B. R., Edwards, D. P., Diesmos, A., Williams, S. E. & Evans, T. A. Microhabitats reduce animal’s exposure to climate extremes. Glob. Change Biol. 20, 495–503 (2014).Article 
ADS 

Google Scholar 
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. B 367, 1665–1679 (2012).Article 

Google Scholar 
Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer “cold-blooded” animals against climate warming. Proc. Natl Acad. Sci. USA 106, 3835–3840 (2009).Article 
ADS 
CAS 

Google Scholar 
Morley, S. A., Peck, L. S., Sunday, J. M., Heiser, S. & Bates, A. E. Physiological acclimation and persistence of ectothermic species under extreme heat events. Glob. Ecol. Biogeogr. 28, 1018–1037 (2019).Article 

Google Scholar 
Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).Article 

Google Scholar 
Lewis, F. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 147–1926 (Cambridge Univ. Press, 2021).Thakur, M. P., Bakker, E. S., Veen, G. C. & Harvey, J. A. Climate extremes, rewilding, and the role of microhabitats. One Earth 2, 506–509 (2020).Article 
ADS 

Google Scholar 
Albright, T. P. et al. Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration. Proc. Natl Acad. Sci. USA 114, 2283–2288 (2017).Article 
ADS 
CAS 

Google Scholar 
Thrasher, B. et al. NASA Global daily downscaled projections, CMIP6. Sci. Data 9, 262 (2022).Article 

Google Scholar 
Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci. 16, 3309–3314 (2012).Article 
ADS 

Google Scholar 
Jin, Z. et al. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Glob. Change Biol. 22, 3112–3126 (2016).Article 
ADS 

Google Scholar 
Zhang, L., Yang, B., Li, S., Hou, Y. & Huang, D. Potential rice exposure to heat stress along the Yangtze River in China under RCP8.5 scenario. Agric. Forest Meteorol. 248, 185–196 (2018).Article 
ADS 

Google Scholar 
Al-Bakri, J. et al. Assessment of climate changes and their impact on barley yield in Mediterranean environment using NEX-GDDP downscaled GCMs and DSSAT. Earth Syst. Environ. 5, 751–766 (2021).Semakula, H. M. et al. Prediction of future malaria hotspots under climate change in sub-Saharan Africa. Clim. Change 143, 415–428 (2017).Article 
ADS 
CAS 

Google Scholar 
Iwamura, T., Guzman-Holst, A. & Murray, K. A. Accelerating invasion potential of disease vector Aedes aegypti under climate change. Nat. Commun. 11, 2130 (2020).Article 
ADS 
CAS 

Google Scholar 
Jones, A. E. et al. Bluetongue risk under future climates. Nat. Clim. Change 9, 153–157 (2019).Article 
ADS 

Google Scholar 
Obradovich, N. & Fowler, J. H. Climate change may alter human physical activity patterns. Nat. Hum. Behav. 1, 0097 (2017).Article 

Google Scholar 
Obradovich, N., Migliorini, R., Mednick, S. C. & Fowler, J. H. Nighttime temperature and human sleep loss in a changing climate. Sci. Adv. 3, e1601555 (2017).Article 
ADS 

Google Scholar 
Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).Article 
ADS 

Google Scholar 
Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W. & Zelinka, M. Climate simulations: recognize the ‘hot model’ problem. Nature 605, 26–29 (2022).O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).Article 
ADS 

Google Scholar 
IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).IUCN Red List of Threatened Species Version 2017, 3 (IUCN, 2017).Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677 (2017).Article 

Google Scholar 
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007).Article 
ADS 
CAS 

Google Scholar 
Maclean, I. M. Predicting future climate at high spatial and temporal resolution. Glob. Change Biol. 26, 1003–1011 (2020).Article 
ADS 

Google Scholar 
Warren, R. et al. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat. Clim. Change 3, 678–682 (2013).Article 
ADS 

Google Scholar 
Jiguet, F. et al. Thermal range predicts bird population resilience to extreme high temperatures. Ecol. Lett. 9, 1321–1330 (2006).Article 

Google Scholar 
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).Article 
ADS 

Google Scholar 
Laufkötter, C., Zscheischler, J. & Frölicher, T. L. High-impact marine heatwaves attributable to human-induced global warming. Science 369, 1621–1625 (2020).Article 
ADS 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).Article 
ADS 

Google Scholar 
Oliver, E. C. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).Article 
ADS 

Google Scholar 
Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2012).Woolway, R. I. et al. Lake heatwaves under climate change. Nature 589, 402–407 (2021).Article 
ADS 
CAS 

Google Scholar 
Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes and compound events in the ocean. Nature 600, 395–407 (2021).Article 
ADS 
CAS 

Google Scholar 
Cahill, A. E. et al. Causes of warm-edge range limits: systematic review, proximate factors and implications for climate change. J. Biogeogr. 41, 429–442 (2014).Article 

Google Scholar 
Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).Article 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).Article 

Google Scholar 
Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).Article 
ADS 
CAS 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 
ADS 

Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

Google Scholar 
Louthan, A. M., Doak, D. F. & Angert, A. L. Where and when do species interactions set range limits? Trends Ecol. Evol. 30, 780–792 (2015).Article 

Google Scholar 
Barbarossa, V. et al. Threats of global warming to the world’s freshwater fishes. Nat. Commun. 12, 1701 (2021).Article 
ADS 
CAS 

Google Scholar 
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).Article 

Google Scholar 
Qu, Y.-F. & Wiens, J. J. Higher temperatures lower rates of physiological and niche evolution. Proc. R. Soc. B 287, 20200823 (2020).Article 

Google Scholar 
Pither, J. Climate tolerance and interspecific variation in geographic range size. Proc. R. Soc. Lond. B 270, 475–481 (2003).Article 

Google Scholar 
Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).Article 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); http://www.R-project.org/Chen, H., Sun, J., Lin, W. & Xu, H. Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci. Bull. 65, 1415–1418 (2020).Article 

Google Scholar  More

Steffen, W. Introducing the Anthropocene: The human epoch. Ambio 50, 1784–1787 (2021).Article 

Google Scholar 
Keys, P. W. et al. Anthropocene risk. Nat. Sustain. 2, 667–673 (2019).Article 

Google Scholar 
Bell, G. Evolutionary rescue and the limits of adaptation. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120080 (2013).Article 

Google Scholar 
Byrne, M. & Przeslawski, R. Multistressor impacts of warming and acidification of the ocean on marine invertebrates’ life histories. Integr. Comp. Biol. 53, 582–596 (2013).Article 
CAS 

Google Scholar 
Feely, R. A. et al. Impact of anthropogenic CO2 on the CaCO3 system in the oceans. Science 305, 362–366 (2004).Article 
CAS 

Google Scholar 
Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: the other CO2 problem. Ann. Rev. Mar. Sci. 1, 169–192 (2009).Article 

Google Scholar 
Hill, T. S. & Hoogenboom, M. O. The indirect effects of ocean acidification on corals and coral communities. Coral Reefs https://doi.org/10.1007/s00338-022-02286-z (2022).Biagi, E. et al. Patterns in microbiome composition differ with ocean acidification in anatomic compartments of the Mediterranean coral Astroides calycularis living at CO2 vents. Sci. Total Environ. 724, 138048 (2020).Article 
CAS 

Google Scholar 
Chen, B. et al. Microbiome community and complexity indicate environmental gradient acclimatisation and potential microbial interaction of endemic coral holobionts in the South China Sea. Sci. Total Environ. 765, 142690 (2021).Article 
CAS 

Google Scholar 
Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).Article 
CAS 

Google Scholar 
Wood, R. The ecological evolution of reefs. Annu. Rev. Ecol. Syst. 29, 179–206 (1998).Article 

Google Scholar 
Drake, J. L. et al. How corals made rocks through the ages. Glob. Chang. Biol. 26, 31–53 (2020).Article 

Google Scholar 
Stanley, G. D. Photosymbiosis and the evolution of modern coral reefs. Science 312, 857–858 (2006).Article 
CAS 

Google Scholar 
Kitahara, M. V., Cairns, S. D., Stolarski, J., Blair, D. & Miller, D. J. A comprehensive phylogenetic analysis of the scleractinia (Cnidaria, Anthozoa) based on mitochondrial CO1 sequence data. PLoS One. 5, e11490 (2010).Article 

Google Scholar 
Dubinsky, Z. & Jokiel, P. Ratio of energy and nutrient fluxes regulates symbiosis between zooxanthellae and corals. Pac. Sci. 48, 313–324 (1994).
Google Scholar 
Falkowski, P. G., Dubinsky, Z., Muscatine, L. & Porter, J. W. Light and the bioenergetics of a symbiotic coral. Bioscience 34, 705–709 (1984).Article 
CAS 

Google Scholar 
Frankowiak, K., Roniewicz, E. & Stolarski, J. Photosymbiosis in Late Triassic scleractinian corals from the Italian Dolomites. PeerJ 9, e11062 (2021).Article 

Google Scholar 
Davy, S. K., Allemand, D. & Weis, V. M. Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev. 76, 229–261 (2012).Article 
CAS 

Google Scholar 
Kremer, P. Ingestion and elemental budgets for Linuche unguiculata, a scyphomedusa with zooxanthellae. J. Mar. Biol. Assoc. UK. 85, 613–625 (2005).Article 

Google Scholar 
Welsh, D. T., Dunn, R. J. K. & Meziane, T. Oxygen and nutrient dynamics of the upside down jellyfish (Cassiopea sp.) and its influence on benthic nutrient exchanges and primary production. Hydrobiologia 635, 351–362 (2009).Article 
CAS 

Google Scholar 
Muscatine, L., McCloskey, L. R. & Marian, R. E. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611 (1981).Article 
CAS 

Google Scholar 
Ferrier‐Pagès, C. & Leal, M. C. Stable isotopes as tracers of trophic interactions in marine mutualistic symbioses. Ecol. Evol. 9, 723–740 (2019).Article 

Google Scholar 
Teixidó, N. et al. Ocean acidification causes variable trait shifts in a coral species. Glob. Chang. Biol. 26, 6813–6830 (2020).Article 

Google Scholar 
Fantazzini, P. et al. Gains and losses of coral skeletal porosity changes with ocean acidification acclimation. Nat. Commun. 6, 7785 (2015).Article 
CAS 

Google Scholar 
Prada, F. et al. Coral micro- and macro-morphological skeletal properties in response to life-long acclimatization at CO2 vents in Papua New Guinea. Sci. Rep. 11, 19927 (2021).Article 
CAS 

Google Scholar 
Kerrison, P., Hall-Spencer, J. M., Suggett, D. J., Hepburn, L. J. & Steinke, M. Assessment of pH variability at a coastal CO2 vent for ocean acidification studies. Estuar. Coast. Shelf Sci. 94, 129–137 (2011).Article 
CAS 

Google Scholar 
Johnson, V. R., Russell, B. D., Fabricius, K. E., Brownlee, C. & Hall-Spencer, J. M. Temperate and tropical brown macroalgae thrive, despite decalcification, along natural CO2 gradients. Glob. Chang. Biol. 18, 2792–2803 (2012).Article 

Google Scholar 
Caroselli, E. et al. Low and variable pH decreases recruitment efficiency in populations of a temperate coral naturally present at a CO2 vent. Limnol. Oceanogr. 64, 1059–1069 (2019).Article 
CAS 

Google Scholar 
González-Delgado, S. & Hernández, J. C. The importance of natural acidified systems in the study of ocean acidification: what have we learned? Adv. Mar. Biol. 80, 57–99 (2018).Article 

Google Scholar 
Capaccioni, B., Tassi, F., Vaselli, O., Tedesco, D. & Poreda, R. Submarine gas burst at Panarea Island (southern Italy) on 3 November 2002: A magmatic versus hydrothermal episode. J. Geophys. Res. 112, B05201 (2007).
Google Scholar 
Reggi, M. et al. Biomineralization in mediterranean corals: The role of the intraskeletal organic matrix. Cryst. Growth Des. 14, 4310–4320 (2014).Article 
CAS 

Google Scholar 
Prada, F. et al. Ocean warming and acidification synergistically increase coral mortality. Sci. Rep. 7, 1–10 (2017).Article 

Google Scholar 
Goffredo, S. et al. Biomineralization control related to population density under ocean acidification. Nat. Clim. Chang. 4, 593–597 (2014).Article 
CAS 

Google Scholar 
Wall, M. et al. Linking internal carbonate chemistry regulation and calcification in corals growing at a Mediterranean CO2 vent. Front. Mar. Sci. 6, 699 (2019).Article 

Google Scholar 
Zohary, T., Erez, J., Gophen, M., Berman-Frank, I. & Stiller, M. Seasonality of stable carbon isotopes within the pelagic food web of Lake Kinneret. Limnol. Oceanogr. 39, 1030–1043 (1994).Article 
CAS 

Google Scholar 
Xu, S. et al. Spatial variations in the trophic status of Favia palauensis corals in the South China Sea: Insights into their different adaptabilities under contrasting environmental conditions. Sci. China Earth Sci. 64, 839–852 (2021).Article 

Google Scholar 
Horwitz, R., Borell, E. M., Yam, R., Shemesh, A. & Fine, M. Natural high pCO2 increases autotrophy in Anemonia viridis (Anthozoa) as revealed from stable isotope (C, N) analysis. Sci. Rep. 5, 1–9 (2015).Article 

Google Scholar 
Chen, B., Zou, D., Zhu, M. & Yang, Y. Effects of CO2 levels and light intensities on growth and amino acid contents in red seaweed Gracilaria lemaneiformis. Aquac. Res. 48, 2683–2690 (2017).Article 
CAS 

Google Scholar 
Winters, G., Beer, S., Zvi, B., Brickner, I. & Loya, Y. Spatial and temporal photoacclimation of Stylophora pistillata: zooxanthella size, pigmentation, location and clade. Mar. Ecol. Prog. Ser. 384, 107–119 (2009).Article 

Google Scholar 
Fitt, W. K., McFarland, F. K., Warner, M. E. & Chilcoat, G. C. Seasonal patterns of tissue biomass and densities of symbiotic dinoflagellates in reef corals and relation to coral bleaching. Limnol. Oceanogr. 45, 677–685 (2000).Article 
CAS 

Google Scholar 
Wangpraseurt, D., Larkum, A. W. D., Ralph, P. J. & Kühl, M. Light gradients and optical microniches in coral tissues. Front. Microbiol. 3, 1–9 (2012).Article 

Google Scholar 
Krief, S. et al. Physiological and isotopic responses of scleractinian corals to ocean acidification. Geochim. Cosmochim. Acta. 74, 4988–5001 (2010).Article 
CAS 

Google Scholar 
Scucchia, F., Malik, A., Zaslansky, P., Putnam, H. M. & Mass, T. Combined responses of primary coral polyps and their algal endosymbionts to decreasing seawater pH. Proc. R. Soc. B Biol. Sci. 288, 20210328 (2021).Article 
CAS 

Google Scholar 
Anthony, K. R. N., Connolly, S. R. & Willis, B. L. Comparative analysis of energy allocation to tissue and skeletal growth in corals. Limnol. Oceanogr. 47, 1417–1429 (2002).Article 

Google Scholar 
LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580.e6 (2018).Article 
CAS 

Google Scholar 
Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Chang. 2, 116–120 (2012).Article 

Google Scholar 
Brading, P. et al. Differential effects of ocean acidification on growth and photosynthesis among phylotypes of Symbiodinium (Dinophyceae). Limnol. Oceanogr. 56, 927–938 (2011).Article 
CAS 

Google Scholar 
Takahashi, T., Broecker, W. S. & Langer, S. Redfield ratio based on chemical data from isopycnal surfaces. J. Geophys. Res. 90, 6907 (1985).Article 
CAS 

Google Scholar 
Xu, Z. et al. Changes of carbon to nitrogen ratio in particulate organic matter in the marine mesopelagic zone: A case from the South China Sea. Mar. Chem. 231, 103930 (2021).Article 
CAS 

Google Scholar 
Crawford, D. W. et al. Low particulate carbon to nitrogen ratios in marine surface waters of the Arctic. Glob. Biogeochem. Cycles. 29, 2021–2033 (2015).Article 
CAS 

Google Scholar 
Kikumoto, R. et al. Nitrogen isotope chemostratigraphy of the Ediacaran and Early Cambrian platform sequence at Three Gorges, South China. Gondwana Res. 25, 1057–1069 (2014).Article 
CAS 

Google Scholar 
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).Article 
CAS 

Google Scholar 
Benavides, M., Bednarz, V. N. & Ferrier-Pagès, C. Diazotrophs: Overlooked key players within the coral symbiosis and tropical reef ecosystems? Front. Mar. Sci. 4, 10 (2017).Article 

Google Scholar 
Wannicke, N., Frey, C., Law, C. S. & Voss, M. The response of the marine nitrogen cycle to ocean acidification. Glob. Chang. Biol. 24, 5031–5043 (2018).Article 

Google Scholar 
Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).Article 
CAS 

Google Scholar 
Palladino, G. et al. Metagenomic shifts in mucus, tissue and skeleton of the coral Balanophyllia europaea living along a natural CO2 gradient. ISME Commun. 2, 65 (2022).Article 

Google Scholar 
Muscatine, L. et al. Stable isotopes (δ13C and δ15N) of organic matrix from coral skeleton. Proc. Natl Acad. Sci. 102, 1525–1530 (2005).Article 
CAS 

Google Scholar 
Lesser, M. P. et al. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar. Ecol. Prog. Ser. 346, 143–152 (2007).Article 
CAS 

Google Scholar 
Alamaru, A., Loya, Y., Brokovich, E., Yam, R. & Shemesh, A. Carbon and nitrogen utilization in two species of Red Sea corals along a depth gradient: Insights from stable isotope analysis of total organic material and lipids. Geochim. Cosmochim. Acta. 73, 5333–5342 (2009).Article 
CAS 

Google Scholar 
Lesser, M. P., Mazel, C. H., Gorbunov, M. Y. & Falkowski, P. G. Discovery of symbiotic nitrogen-fixing cyanobacteria in corals. Science 305, 997–1000 (2004).Article 
CAS 

Google Scholar 
Lesser, M. P., Morrow, K. M., Pankey, S. M. & Noonan, S. H. C. Diazotroph diversity and nitrogen fixation in the coral Stylophora pistillata from the Great Barrier Reef. ISME J. 12, 813–824 (2018).Article 
CAS 

Google Scholar 
Marcelino, V. R., Morrow, K. M., Oppen, M. J. H., Bourne, D. G. & Verbruggen, H. Diversity and stability of coral endolithic microbial communities at a naturally high pCO2 reef. Mol. Ecol. 26, 5344–5357 (2017).Article 
CAS 

Google Scholar 
Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 23, 490–497 (2015).Article 

Google Scholar 
Santos, H. F. et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 8, 2272–2279 (2014).Article 

Google Scholar 
Olson, N. D., Ainsworth, T. D., Gates, R. D. & Takabayashi, M. Diazotrophic bacteria associated with Hawaiian Montipora corals: Diversity and abundance in correlation with symbiotic dinoflagellates. J. Exp. Mar. Bio. Ecol. 371, 140–146 (2009).Article 
CAS 

Google Scholar 
Zheng, X. et al. Effects of ocean acidification on carbon and nitrogen fixation in the hermatypic coral Galaxea fascicularis. Front. Mar. Sci. 8, 644965 (2021).Article 

Google Scholar 
Lewis, E. & Wallace, D. Program developed for CO2 system calculations. Ornl/Cdiac-105 1–21 (1998).Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep Sea Res. Part A. Oceanogr. Res. Pap. 34, 1733–1743 (1987).Article 
CAS 

Google Scholar 
Dickson, A. G. Thermodynamics of the dissociation of boric acid in potassium chloride solutions from 273.15 to 318.15 K. J. Chem. Eng. Data. 35, 253–257 (1990).Article 
CAS 

Google Scholar 
Mehrbach, C., Culberson, C. H., Hawley, J. E. & Pytkowicx, R. M. Measurement of the apparent dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnol. Oceanogr. 18, 897–907 (1973).Article 
CAS 

Google Scholar 
Ivancic, I. & Degobbis, D. An optimal manual procedure for ammonia analysis in natural waters by the indophenol blue method. Water Res. 18, 1143–1147 (1984).Article 
CAS 

Google Scholar 
Parson, T. R., Maita, Y. & Llli, C. M. A manual of chemical & biological methods for seawater analysis. (Elsevier, 1984). https://doi.org/10.1016/C2009-0-07774-5Schreiber, U. Pulse-Amplitude-Modulation (PAM) fluorometry and saturation pulse method: an overview. in Chlorophyll a Fluorescence 1367, 279–319 (Springer Netherlands, 2004).Grover, R., Maguer, J. F., Reynaud-Vaganay, S. & Ferrier-Pagès, C. Uptake of ammonium by the scleractinian coral Stylophora pistillata: Effect of feeding, light, and ammonium concentrations. Limnol. Oceanogr. 47, 782–790 (2002).Article 

Google Scholar 
Tremblay, P., Grover, R., Maguer, J. F., Hoogenboom, M. & Ferrier-Pagès, C. Carbon translocation from symbiont to host depends on irradiance and food availability in the tropical coral Stylophora pistillata. Coral Reefs. 33, 1–13 (2014).Article 

Google Scholar 
Pupier, C. A. et al. Productivity and carbon fluxes depend on species and symbiont density in soft coral symbioses. Sci. Rep. 9, 17819 (2019).Article 

Google Scholar 
Ritchie, R. J. Universal chlorophyll equations for estimating chlorophylls a, b, c, and d and total chlorophylls in natural assemblages of photosynthetic organisms using acetone, methanol, or ethanol solvents. Photosynthetica 46, 115–126 (2008).Article 
CAS 

Google Scholar 
Goffredo, S., Arnone, S. & Zaccanti, F. Sexual reproduction in the Mediterranean solitary coral Balanophyllia europaea (Scleractinia, Dendrophylliidae). Mar. Ecol. Prog. Ser. 229, 83–94 (2002).Article 

Google Scholar 
Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. 110, 1387–1392 (2013).Article 
CAS 

Google Scholar 
Moore, R. B. Highly organized structure in the non-coding region of the psbA minicircle from clade C Symbiodinium. Int. J. Syst. Evol. Microbiol. 53, 1725–1734 (2003).Article 
CAS 

Google Scholar 
LaJeunesse, T. C. & Thornhill, D. J. Improved resolution of reef-coral endosymbiont (Symbiodinium) species diversity, ecology, and evolution through psbA non-coding region genotyping. PLoS One. 6, e29013 (2011).Article 
CAS 

Google Scholar 
LaJeunesse, T. C. et al. Revival of Philozoon Geddes for host-specialized dinoflagellates, ‘zooxanthellae’, in animals from coastal temperate zones of northern and southern hemispheres. Eur. J. Phycol. 57, 166–180 (2022).Article 

Google Scholar 
Anderson, M. J. PERMANOVA: a FORTRAN computer program for permutational multivariate analysis of variance. Wiley StatsRef: Statistics Reference Online (2005). More

Battin, T. J. et al. The boundless carbon cycle. Nat. Geosci. 2, 598–600 (2009).Article 
CAS 

Google Scholar 
Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013).Article 
CAS 

Google Scholar 
Hotchkiss, E. R. et al. Sources of and processes controlling CO2 emissions change with the size of streams and rivers. Nat. Geosci. 8, 696–699 (2015). Important study conceptualizing (on the basis of a data synthesis) how the sources and magnitude of CO2 evasion flux change along a stream–river continuum.Ciais, P. et al. in Climate Change 2013 The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Ch. 6 (Cambridge Univ. Press, 2013).Friedlingstein, P. et al. Global carbon budget 2021. Earth Syst. Sci. Data 14, 1917–2005 (2022).Article 

Google Scholar 
Cole, J. J. et al. Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10, 172–185 (2007). A pioneering study showing the role of inland waters for large-scale carbon fluxes and highlighting them as ‘reactors’ rather than ‘passive pipes’.Article 

Google Scholar 
Drake, T. W., Raymond, P. A. & Spencer, R. G. M. Terrestrial carbon inputs to inland waters: a current synthesis of estimates and uncertainty. Limnol. Oceanogr. Lett. 3, 132–142 (2018).Article 
CAS 

Google Scholar 
Odum, H. T. Primary production in flowing waters. Limnol. Oceanogr. 1, 102–117 (1956).Article 

Google Scholar 
Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, 99–118 (2018). A synthesis of the predominant drivers and constraints on metabolic regimes of stream and river ecosystems.Article 

Google Scholar 
Barnes, A. D. et al. Energy flux: the link between multitrophic biodiversity and ecosystem functioning. Trends Ecol. Evol. 33, 186–197 (2018).Article 

Google Scholar 
Costanza, R. & Mageau, M. What is a healthy ecosystem? Aquat. Ecol. 33, 105–115 (1999).Article 

Google Scholar 
Blöschl, G. et al. Changing climate both increases and decreases European river floods. Nature 573, 108–111 (2019).Article 

Google Scholar 
Gudmundsson, L. et al. Globally observed trends in mean and extreme river flow attributed to climate change. Science 371, 1159–1162 (2021).Article 
CAS 

Google Scholar 
Yang, X., Pavelsky, T. M. & Allen, G. H. The past and future of global river ice. Nature 577, 69–73 (2020).Article 
CAS 

Google Scholar 
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).Article 
CAS 

Google Scholar 
Belletti, B. et al. More than one million barriers fragment Europe’s rivers. Nature 588, 436–441 (2020).Article 
CAS 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).Article 
CAS 

Google Scholar 
Cooley, S. W., Ryan, J. C. & Smith, L. C. Human alteration of global surface water storage variability. Nature 591, 78–81 (2021).Article 
CAS 

Google Scholar 
Jaramillo, F. & Destouni, G. Local flow regulation and irrigation raise global human water consumption and footprint. Science 350, 1248–1251 (2015).Article 
CAS 

Google Scholar 
Quinton, J. N., Govers, G., Oost, K. V. & Bardgett, R. D. The impact of agricultural soil erosion on biogeochemical cycling. Nat. Geosci. 3, 311–314 (2010).Article 
CAS 

Google Scholar 
Mekonnen, M. M. & Hoekstra, A. Y. Global anthropogenic phosphorus loads to freshwater and associated grey water footprints and water pollution levels: a high‐resolution global study. Water Resour. Res. 54, 345–358 (2018).Article 
CAS 

Google Scholar 
Regnier, P. et al. Anthropogenic perturbation of the carbon fluxes from land to ocean. Nat. Geosci. 6, 597–607 (2013). The first study showing the extent to which human activities have altered the magnitude of contemporary lateral carbon fluxes from land to ocean.Article 
CAS 

Google Scholar 
Rüegg, J. et al. Thinking like a consumer: linking aquatic basal metabolism and consumer dynamics. Limnol. Oceanogr. Lett. 6, 1–17 (2021).Article 

Google Scholar 
Fernández-Martínez, M. et al. Global trends in carbon sinks and their relationships with CO2 and temperature. Nat. Clim. Change 9, 73–79 (2019).Article 

Google Scholar 
Behrenfeld, M. J. et al. Climate-driven trends in contemporary ocean productivity. Nature 444, 752–755 (2006).Article 
CAS 

Google Scholar 
Phillips, J. S. Time‐varying responses of lake metabolism to light and temperature. Limnol. Oceanogr. 65, 652–666 (2020).Article 
CAS 

Google Scholar 
Uehlinger, U. Annual cycle and inter‐annual variability of gross primary production and ecosystem respiration in a floodprone river during a 15‐year period. Freshw. Biol. 51, 938–950 (2006).Article 
CAS 

Google Scholar 
Uehlinger, U. & Naegeli, M. W. Ecosystem metabolism, disturbance, and stability in a prealpine gravel bed river. J. North Am. Benthol. Soc. 17, 165–178 (1998).Article 

Google Scholar 
Mulholland, P. J. et al. Inter-biome comparison of factors controlling stream metabolism. Freshw. Biol. 46, 1503–1517 (2001).Article 
CAS 

Google Scholar 
Roberts, B. J., Mulholland, P. J. & Hill, W. R. Multiple scales of temporal variability in ecosystem metabolism rates: results from 2 years of continuous monitoring in a forested headwater stream. Ecosystems 10, 588–606 (2007).Article 
CAS 

Google Scholar 
Appling, A. P., Hall, R. O., Yackulic, C. B. & Arroita, M. Overcoming equifinality: leveraging long time series for stream metabolism estimation. J. Geophys. Res. Biogeosci. 123, 624–645 (2018).Article 
CAS 

Google Scholar 
Appling, A. P. et al. The metabolic regimes of 356 rivers in the United States. Sci. Data 5, 180292 (2018).Article 
CAS 

Google Scholar 
Canadell, M. B. et al. Regimes of primary production and their drivers in Alpine streams. Freshw. Biol. 66, 1449–1463 (2021).Article 

Google Scholar 
Myrstener, M., Gómez‐Gener, L., Rocher‐Ros, G., Giesler, R. & Sponseller, R. A. Nutrients influence seasonal metabolic patterns and total productivity of Arctic streams. Limnol. Oceanogr. 66, S182–S196 (2021).Article 
CAS 

Google Scholar 
Savoy, P. et al. Metabolic rhythms in flowing waters: an approach for classifying river productivity regimes. Limnol. Oceanogr. 64, 1835–1851 (2019).Article 

Google Scholar 
Kirk, L., Hensley, R. T., Savoy, P., Heffernan, J. B. & Cohen, M. J. Estimating benthic light regimes improves predictions of primary production and constrains light-use efficiency in streams and rivers. Ecosystems 24, 825–839 (2021).Article 

Google Scholar 
Bernhardt, E. S. et al. Light and flow regimes regulate the metabolism of rivers. Proc. Natl Acad. Sci. USA 119, e2121976119 (2022).Article 
CAS 

Google Scholar 
Savoy, P. & Harvey, J. W. Predicting light regime controls on primary productivity across CONUS river networks. Geophys. Res. Lett. 48, e2020GL092149 (2021).Article 

Google Scholar 
Julian, J. P., Stanley, E. H. & Doyle, M. W. Basin-scale consequences of agricultural land use on benthic light availability and primary production along a sixth-order temperate river. Ecosystems 11, 1091–1105 (2008).Article 

Google Scholar 
Hall, R. O. et al. Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon. Limnol. Oceanogr. 60, 512–526 (2015).Article 

Google Scholar 
Hosen, J. D. et al. Enhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams. Limnol. Oceanogr. 64, 1458–1472 (2019).Article 

Google Scholar 
Allen, A. P., Gillooly, J. F. & Brown, J. H. Linking the global carbon cycle to individual metabolism. Funct. Ecol. 19, 202–213 (2005).Article 

Google Scholar 
Demars, B. O. L. et al. Temperature and the metabolic balance of streams. Freshw. Biol. 56, 1106–1121 (2011).Article 

Google Scholar 
Song, C. et al. Continental-scale decrease in net primary productivity in streams due to climate warming. Nat. Geosci. 11, 415–420 (2018).Article 
CAS 

Google Scholar 
Hood, J. M. et al. Increased resource use efficiency amplifies positive response of aquatic primary production to experimental warming. Glob. Change Biol. 24, 1069–1084 (2018).Article 

Google Scholar 
Schindler, D. E., Carpenter, S. R., Cole, J. J., Kitchell, J. F. & Pace, M. L. Influence of food web structure on carbon exchange between lakes and the atmosphere. Science 277, 248–251 (1997).Article 
CAS 

Google Scholar 
Iannucci, F. M., Beneš, J., Medvedeff, A. & Bowden, W. B. Biogeochemical responses over 37 years to manipulation of phosphorus concentrations in an Arctic river: The Upper Kuparuk River Experiment. Hydrol. Process. 35, e14075 (2021).Article 
CAS 

Google Scholar 
Rosemond, A. D. et al. Experimental nutrient additions accelerate terrestrial carbon loss from stream ecosystems. Science 347, 1142–1145 (2015). A key study explaining how nutrient excess can accelerate terrestrial carbon loss from stream ecosystems.Article 
CAS 

Google Scholar 
Arroita, M., Elosegi, A. & Hall, R. O. Jr Twenty years of daily metabolism show riverine recovery following sewage abatement. Limnol. Oceanogr. 64, 77–92 (2019).Article 

Google Scholar 
Battin, T. J. et al. Biophysical controls on organic carbon fluxes in fluvial networks. Nat. Geosci. 1, 95–100 (2008). An important article conceptualizing how physical and biological processes combine to shape metabolic dynamics and carbon fluxes in fluvial networks.Article 
CAS 

Google Scholar 
Hoellein, T. J., Bruesewitz, D. A. & Richardson, D. C. Revisiting Odum (1956): a synthesis of aquatic ecosystem metabolism. Limnol. Oceanogr. 58, 2089–2100 (2013).Article 
CAS 

Google Scholar 
Marzolf, N. S. & Ardón, M. Ecosystem metabolism in tropical streams and rivers: a review and synthesis. Limnol. Oceanogr. 66, 1627–1638 (2021).Article 

Google Scholar 
Gounand, I., Little, C. J., Harvey, E. & Altermatt, F. Cross-ecosystem carbon flows connecting ecosystems worldwide. Nat. Commun. 9, 4825 (2018).Article 

Google Scholar 
Ciais, P. et al. Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration. Natl Sci. Rev. 8, nwaa145 (2020).Article 

Google Scholar 
Bauer, J. E. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013). Important review on the sources, exchange and fates of carbon in the coastal ocean and how human activities have altered the coastal carbon cycle.Article 
CAS 

Google Scholar 
Reichert, P., Uehlinger, U. & Acuña, V. Estimating stream metabolism from oxygen concentrations: effect of spatial heterogeneity. J. Geophys. Res. Biogeosci. 114, G03016 (2009).Article 

Google Scholar 
Koenig, L. E. et al. Emergent productivity regimes of river networks. Limnol. Oceanogr. Lett. 4, 173–181 (2019).Article 

Google Scholar 
Rodríguez-Castillo, T., Estévez, E., González-Ferreras, A. M. & Barquín, J. Estimating ecosystem metabolism to entire river networks. Ecosystems 22, 892–911 (2019).Article 

Google Scholar 
Segatto, P. L., Battin, T. J. & Bertuzzo, E. The metabolic regimes at the scale of an entire stream network unveiled through sensor data and machine learning. Ecosystems 24, 1792–1809 (2021).Article 
CAS 

Google Scholar 
Loreau, M., Mouquet, N. & Holt, R. D. Meta‐ecosystems: a theoretical framework for a spatial ecosystem ecology. Ecol. Lett. 6, 673–679 (2003).Article 

Google Scholar 
Mastrandrea, M. D. et al. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties (Intergovernmental Panel on Climate Change (IPCC), 2010).Tank, S. E., Fellman, J. B., Hood, E. & Kritzberg, E. S. Beyond respiration: controls on lateral carbon fluxes across the terrestrial‐aquatic interface. Limnol. Oceanogr. Lett. 3, 76–88 (2018). Important synthesis on the mechanisms and controls of organic and inorganic carbon flows across terrestrial–aquatic interfaces.Article 

Google Scholar 
Aitkenhead, J. A. & McDowell, W. H. Soil C:N ratio as a predictor of annual riverine DOC flux at local and global scales. Global Biogeochem. Cycles 14, 127–138 (2000).Article 
CAS 

Google Scholar 
Regnier, P., Resplandy, L., Najjar, R. G. & Ciais, P. The land-to-ocean loops of the global carbon cycle. Nature 603, 401–410 (2022).Article 
CAS 

Google Scholar 
van Hoek, W. J. et al. Exploring spatially explicit changes in carbon budgets of global river basins during the 20th century. Environ. Sci. Technol. 55, 16757–16769 (2021). A global quantitative assessment of river carbon fluxes in the twentieth century, highlighting the combined influence of environmental and anthropogenic controls on the long-term patterns of global carbon export.Article 

Google Scholar 
Abril, G. & Borges, A. V. Ideas and perspectives: carbon leaks from flooded land: do we need to replumb the inland water active pipe? Biogeosciences 16, 769–784 (2019). Important review emphasizing the role of flooding for inland water carbon cycling at the global scale.Article 
CAS 

Google Scholar 
Lauerwald, R., Regnier, P., Guenet, B., Friedlingstein, P. & Ciais, P. How simulations of the land carbon sink are biased by ignoring fluvial carbon transfers: a case study for the Amazon Basin. One Earth 3, 226–236 (2020).Article 

Google Scholar 
Raymond, P. A., Saiers, J. E. & Sobczak, W. V. Hydrological and biogeochemical controls on watershed dissolved organic matter transport: pulse‐shunt concept. Ecology 97, 5–16 (2016).Article 

Google Scholar 
Catalán, N., Marcé, R., Kothawala, D. N. & Tranvik, L. J. Organic carbon decomposition rates controlled by water retention time across inland waters. Nat. Geosci. 9, 501–504 (2016).Article 

Google Scholar 
Maavara, T., Lauerwald, R., Regnier, P. & Cappellen, P. V. Global perturbation of organic carbon cycling by river damming. Nat. Commun. 8, 15347 (2017).Article 
CAS 

Google Scholar 
Mendonça, R. et al. Organic carbon burial in global lakes and reservoirs. Nat. Commun. 8, 1694–1697 (2017).Article 

Google Scholar 
Downing, J. A. et al. Sediment organic carbon burial in agriculturally eutrophic impoundments over the last century. Global Biogeochem. Cycles 22, GB1018 (2008).Article 

Google Scholar 
Deemer, B. R. et al. Greenhouse gas emissions from reservoir water surfaces: a new global synthesis. Bioscience 66, 949–964 (2016).Article 

Google Scholar 
Abril, G. et al. Amazon River carbon dioxide outgassing fuelled by wetlands. Nature 505, 395–398 (2014).Article 
CAS 

Google Scholar 
Dodds, W. K. et al. Abiotic controls and temporal variability of river metabolism: multiyear analyses of Mississippi and Chattahoochee River data. Freshw. Sci. 32, 1073–1087 (2013).Article 

Google Scholar 
Ros, G. R., Sponseller, R. A., Bergström, A. K., Myrstener, M. & Giesler, R. Stream metabolism controls diel patterns and evasion of CO2 in Arctic streams. Glob. Change Biol. 26, 1400–1413 (2020).Article 

Google Scholar 
Rasilo, T., Hutchins, R. H. S., Ruiz-González, C. & Del Giorgio, P. A. Transport and transformation of soil-derived CO2, CH4 and DOC sustain CO2 supersaturation in small boreal streams. Sci. Total Environ. 579, 902–912 (2017).Article 
CAS 

Google Scholar 
Aho, K. S., Hosen, J. D., Logozzo, L. A., McGillis, W. R. & Raymond, P. A. Highest rates of gross primary productivity maintained despite CO2 depletion in a temperate river network. Limnol. Oceanogr. Lett. 6, 200–206 (2021).Article 
CAS 

Google Scholar 
Wehrli, B. Conduits of the carbon cycle. Nature 503, 346–347 (2013).Article 
CAS 

Google Scholar 
Sarmiento, J. L. & Sundquist, E. T. Revised budget for the oceanic uptake of anthropogenic carbon dioxide. Nature 356, 589–593 (1992).Article 
CAS 

Google Scholar 
Lacroix, F., Ilyina, T., Laruelle, G. G. & Regnier, P. Reconstructing the preindustrial coastal carbon cycle through a global ocean circulation model: was the global continental shelf already both autotrophic and a CO2 sink? Glob. Biogeochem. Cycles 35, e2020GB006603 (2021).Article 
CAS 

Google Scholar 
Jacobson, A. R., Fletcher, S. E. M., Gruber, N., Sarmiento, J. L. & Gloor, M. A joint atmosphere‐ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global‐scale fluxes. Global Biogeochem. Cycles 21, GB1019 (2007).
Google Scholar 
Resplandy, L. et al. Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nat. Geosci. 11, 504–509 (2018).Article 
CAS 

Google Scholar 
Lee, L.-C. et al. Unusual roles of discharge, slope and SOC in DOC transport in small mountainous rivers, Taiwan. Sci. Rep. 9, 1574 (2019).Article 

Google Scholar 
Reddy, S. K. K. et al. Export of particulate organic carbon by the mountainous tropical rivers of Western Ghats, India: variations and controls. Sci. Total Environ. 751, 142115 (2021).Article 
CAS 

Google Scholar 
Zhang, X., Tarpley, D. & Sullivan, J. T. Diverse responses of vegetation phenology to a warming climate. Geophys. Res. Lett. 34, L19405 (2007).Article 

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

Google Scholar 
Heathcote, A. J., Anderson, N. J., Prairie, Y. T., Engstrom, D. R. & del Giorgio, P. A. Large increases in carbon burial in northern lakes during the Anthropocene. Nat. Commun. 6, 10016 (2015).Article 
CAS 

Google Scholar 
Guillemette, F., Berggren, M., Giorgio, P. Adel. & Lapierre, J.-F. Increases in terrestrially derived carbon stimulate organic carbon processing and CO2 emissions in boreal aquatic ecosystems. Nat. Commun. 4, 2972 (2013).Article 

Google Scholar 
Hastie, A., Lauerwald, R., Ciais, P., Papa, F. & Regnier, P. Historical and future contributions of inland waters to the Congo Basin carbon balance. Earth Syst. Dyn. 12, 37–62 (2020).Article 

Google Scholar 
Nakhavali, M. et al. Leaching of dissolved organic carbon from mineral soils plays a significant role in the terrestrial carbon balance. Glob. Change Biol. 27, 1083–1096 (2021).Article 
CAS 

Google Scholar 
Tian, H. et al. Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: current status and future directions. Global Biogeochem. Cycles 29, 775–792 (2015).Article 
CAS 

Google Scholar 
Öquist, M. G. et al. The full annual carbon balance of boreal forests is highly sensitive to precipitation. Environ. Sci. Technol. Lett. 1, 315–319 (2014).Article 

Google Scholar 
Jones, J. B.Jr, Stanley, E. H. & Mulholland, P. J. Long‐term decline in carbon dioxide supersaturation in rivers across the contiguous United States. Geophys. Res. Lett. 30, 1495 (2003).Article 

Google Scholar 
Raymond, P. A. & Oh, N.-H. Long term changes of chemical weathering products in rivers heavily impacted from acid mine drainage: insights on the impact of coal mining on regional and global carbon and sulfur budgets. Earth Planet. Sci. Lett. 284, 50–56 (2009).Article 
CAS 

Google Scholar 
Ran, L. et al. Substantial decrease in CO2 emissions from Chinese inland waters due to global change. Nat. Commun. 12, 1730 (2021).Article 
CAS 

Google Scholar 
Zarnetske, J. P., Bouda, M., Geophysical, B. A., Saiers, J. & Raymond, P. Generality of hydrologic transport limitation of watershed organic carbon flux across ecoregions of the United States. Geophys. Res. Lett. 45, 11,702–11,711 (2018).Article 
CAS 

Google Scholar 
Liu, S. et al. The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers. Proc. Natl Acad. Sci. USA 119, e2106322119 (2022).Article 
CAS 

Google Scholar 
Nydahl, A. C., Wallin, M. B. & Weyhenmeyer, G. A. No long‐term trends in pCO2 despite increasing organic carbon concentrations in boreal lakes, streams, and rivers. Global Biogeochem. Cycles 31, 985–995 (2017).Article 
CAS 

Google Scholar 
Raymond, P. A. & Hamilton, S. K. Anthropogenic influences on riverine fluxes of dissolved inorganic carbon to the oceans. Limnol. Oceanogr. Lett. 3, 143–155 (2018).Article 
CAS 

Google Scholar 
Ulseth, A. J., Bertuzzo, E., Singer, G. A., Schelker, J. & Battin, T. J. Climate-induced changes in spring snowmelt impact ecosystem metabolism and carbon fluxes in an Alpine stream network. Ecosystems 21, 373–390 (2018).Article 
CAS 

Google Scholar 
Berghuijs, W. R., Woods, R. A. & Hrachowitz, M. A precipitation shift from snow towards rain leads to a decrease in streamflow. Nat. Clim. Change 4, 583–586 (2014).Article 

Google Scholar 
Drake, T. W. et al. Mobilization of aged and biolabile soil carbon by tropical deforestation. Nat. Geosci. 12, 541–546 (2019).Article 
CAS 

Google Scholar 
Wit, F. et al. The impact of disturbed peatlands on river outgassing in Southeast Asia. Nat. Commun. 6, 10155 (2015).Article 
CAS 

Google Scholar 
Moore, S., Gauci, V., Evans, C. D. & Page, S. E. Fluvial organic carbon losses from a Bornean blackwater river. Biogeosciences 8, 901–909 (2011).Article 
CAS 

Google Scholar 
Masese, F. O., Salcedo-Borda, J. S., Gettel, G. M., Irvine, K. & McClain, M. E. Influence of catchment land use and seasonality on dissolved organic matter composition and ecosystem metabolism in headwater streams of a Kenyan river. Biogeochemistry 132, 1–22 (2017).Article 
CAS 

Google Scholar 
Bernot, M. J. et al. Inter‐regional comparison of land‐use effects on stream metabolism. Freshw. Biol. 55, 1874–1890 (2010). Among the first studies showing how land use alters ecosystem metabolism across geographic regions.Article 

Google Scholar 
Griffiths, N. A. et al. Agricultural land use alters the seasonality and magnitude of stream metabolism. Limnol. Oceanogr. 58, 1513–1529 (2013).Article 
CAS 

Google Scholar 
Sweeney, B. W. et al. Riparian deforestation, stream narrowing, and loss of stream ecosystem services. Proc. Natl Acad. Sci. 101, 14132–14137 (2004).Article 
CAS 

Google Scholar 
Roley, S. S., Tank, J. L., Griffiths, N. A., Hall, R. O. Jr & Davis, R. T. The influence of floodplain restoration on whole-stream metabolism in an agricultural stream: insights from a 5-year continuous data set. Freshw. Sci. 33, 1043–1059 (2014).Article 

Google Scholar 
Crawford, J. T., Stanley, E. H., Dornblaser, M. M. & Striegl, R. G. CO2 time series patterns in contrasting headwater streams of North America. Aquat. Sci. 79, 473–486 (2016).Article 

Google Scholar 
Blackburn, S. R. & Stanley, E. H. Floods increase carbon dioxide and methane fluxes in agricultural streams. Freshw. Biol. 66, 62–77 (2021).Article 
CAS 

Google Scholar 
Robertson, G. P., Paul, E. A. & Harwood, R. R. Greenhouse gases in intensive agriculture: contributions of individual gases to the radiative forcing of the atmosphere. Science 289, 1922–1925 (2000).Article 
CAS 

Google Scholar 
Min, S.-K., Zhang, X., Zwiers, F. W. & Hegerl, G. C. Human contribution to more-intense precipitation extremes. Nature 470, 378–381 (2011).Article 
CAS 

Google Scholar 
Yin, J. et al. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat. Commun. 9, 4389 (2018).Article 
CAS 

Google Scholar 
Myhre, G. et al. Sensible heat has significantly affected the global hydrological cycle over the historical period. Nat. Commun. 9, 1922 (2018).Article 
CAS 

Google Scholar 
Messager, M. L. et al. Global prevalence of non-perennial rivers and streams. Nature 594, 391–397 (2021).Article 
CAS 

Google Scholar 
Ward, A. S., Wondzell, S. M., Schmadel, N. M. & Herzog, S. P. Climate change causes river network contraction and disconnection in the H.J. Andrews Experimental Forest, Oregon, USA. Front. Water 2, 7 (2020).Article 

Google Scholar 
Sabater, S., Timoner, X., Borrego, C. & Acuña, V. Stream biofilm responses to flow intermittency: from cells to ecosystems. Front. Environ. Sci. 4, 14 (2016).Article 

Google Scholar 
Gómez-Gener, L., Lupon, A., Laudon, H. & Sponseller, R. A. Drought alters the biogeochemistry of boreal stream networks. Nat. Commun. 11, 1795 (2020).Article 

Google Scholar 
Marcé, R. et al. Emissions from dry inland waters are a blind spot in the global carbon cycle. Earth Sci. Rev. 188, 240–248 (2019).Article 

Google Scholar 
Blaszczak, J. R., Delesantro, J. M., Urban, D. L., Doyle, M. W. & Bernhardt, E. S. Scoured or suffocated: urban stream ecosystems oscillate between hydrologic and dissolved oxygen extremes. Limnol. Oceanogr. 64, 877–894 (2019).Article 
CAS 

Google Scholar 
Reisinger, A. J. et al. Recovery and resilience of urban stream metabolism following Superstorm Sandy and other floods. Ecosphere 8, e01776 (2017).Article 

Google Scholar 
O’Donnell, B. & Hotchkiss, E. R. Coupling concentration‐ and process‐discharge relationships integrates water chemistry and metabolism in streams. Water Resour. Res. 55, 10179–10190 (2019).Article 

Google Scholar 
Thellman, A. et al. The ecology of river ice. J. Geophys. Res. Biogeosci. 126, e2021JG006275 (2021).Article 

Google Scholar 
Maavara, T. et al. River dam impacts on biogeochemical cycling. Nat. Rev. Earth Environ. 1, 103–116 (2020).Article 

Google Scholar 
Rosentreter, J. A. et al. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nat. Geosci. 14, 225–230 (2021).Article 
CAS 

Google Scholar 
Barros, N. et al. Carbon emission from hydroelectric reservoirs linked to reservoir age and latitude. Nat. Geosci. 4, 593–596 (2011).Article 
CAS 

Google Scholar 
Keller, P. S., Marcé, R., Obrador, B. & Koschorreck, M. Global carbon budget of reservoirs is overturned by the quantification of drawdown areas. Nat. Geosci. 14, 402–408 (2021).Article 
CAS 

Google Scholar 
Calamita, E. et al. Unaccounted CO2 leaks downstream of a large tropical hydroelectric reservoir. Proc. Natl Acad. Sci. USA 118, e2026004118 (2021).Article 
CAS 

Google Scholar 
Park, J.-H. et al. Reviews and syntheses: anthropogenic perturbations to carbon fluxes in Asian river systems – concepts, emerging trends, and research challenges. Biogeosciences 15, 3049–3069 (2018).Article 
CAS 

Google Scholar 
Rosamond, M. S., Thuss, S. J. & Schiff, S. L. Dependence of riverine nitrous oxide emissions on dissolved oxygen levels. Nat. Geosci. 5, 715–718 (2012).Article 
CAS 

Google Scholar 
Stanley, E. H. et al. The ecology of methane in streams and rivers: patterns, controls, and global significance. Ecol. Monogr. 86, 146–171 (2016). Key paper highlighting the role of streams and rivers for methane production and emissions and developing a conceptual framework on the environmental drivers of methane dynamics in fluvial ecosystems.Article 

Google Scholar 
Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018).Article 

Google Scholar 
Jane, S. F. et al. Widespread deoxygenation of temperate lakes. Nature 594, 66–70 (2021).Article 
CAS 

Google Scholar 
Triska, F. J., Kennedy, V. C., Avanzino, R. J., Zellweger, G. W. & Bencala, K. E. Retention and transport of nutrients in a third‐order stream in northwestern California: hyporheic processes. Ecology 70, 1893–1905 (1989).Article 

Google Scholar 
Carter, A. M., Blaszczak, J. R., Heffernan, J. B. & Bernhardt, E. S. Hypoxia dynamics and spatial distribution in a low gradient river. Limnol. Oceanogr. 66, 2251–2265 (2021).Article 

Google Scholar 
Kadygrov, N. et al. On the potential of the ICOS atmospheric CO2 measurement network for estimating the biogenic CO2 budget of Europe. Atmos. Chem. Phys. 15, 12765–12787 (2015).Article 
CAS 

Google Scholar 
Hanson, P. C., Weathers, K. C. & Kratz, T. K. Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change. Inland Waters 6, 543–554 (2018).Article 

Google Scholar 
Claustre, H., Johnson, K. S. & Takeshita, Y. Observing the global ocean with biogeochemical-Argo. Annu. Rev. Mar. Sci. 12, 23–48 (2019).Article 

Google Scholar 
Jankowski, K. J., Mejia, F. H., Blaszczak, J. R. & Holtgrieve, G. W. Aquatic ecosystem metabolism as a tool in environmental management. Wiley Interdiscip. Rev. Water 8, e1521 (2021).Article 

Google Scholar 
Mao, F. et al. Moving beyond the technology: a socio-technical roadmap for low-cost water sensor network applications. Environ. Sci. Technol. 54, 9145–9158 (2020).Article 
CAS 

Google Scholar 
Park, J., Kim, K. T. & Lee, W. H. Recent advances in information and communications technology (ICT) and sensor technology for monitoring water quality. Water 12, 510 (2020).Article 
CAS 

Google Scholar 
Yamazaki, D. et al. MERIT Hydro: a high‐resolution global hydrography map based on latest topography dataset. Water Resour. Res. 55, 5053–5073 (2019).Article 

Google Scholar 
Lin, P., Pan, M., Wood, E. F., Yamazaki, D. & Allen, G. H. A new vector-based global river network dataset accounting for variable drainage density. Sci. Data 8, 28 (2021).Article 

Google Scholar 
Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–587 (2018).Article 
CAS 
MATH 

Google Scholar 
Durand, M. et al. An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope. Water Resour. Res. 52, 4527–4549 (2016).Article 

Google Scholar 
Frasson, R. P. M. et al. Exploring the factors controlling the error characteristics of the surface water and ocean topography mission discharge estimates. Water Resour. Res. 57, e2020WR028519 (2021).Article 

Google Scholar 
Dethier, E. N., Renshaw, C. E. & Magilligan, F. J. Rapid changes to global river suspended sediment flux by humans. Science 376, 1447–1452 (2022).Article 
CAS 

Google Scholar 
Campbell, A. D. et al. A review of carbon monitoring in wet carbon systems using remote sensing. Environ. Res. Lett. 17, 025009 (2022).Article 

Google Scholar 
Allen, G. H. et al. Similarity of stream width distributions across headwater systems. Nat. Commun. 9, 610 (2018).Article 

Google Scholar 
Rodriguez-Iturbe, I. & Rinaldo, A. Fractal River Basins: Chance and Self-organization (Cambridge Univ. Press, 2001). Game-changing oeuvre formalizing the structure and function of river networks.Bertuzzo, E., Helton, A. M., Hall, Robert, O. & Battin, T. J. Scaling of dissolved organic carbon removal in river networks. Adv. Water Resour. 110, 136–146 (2017).Article 
CAS 

Google Scholar 
Marzadri, A., Dee, M. M., Tonina, D., Bellin, A. & Tank, J. L. Role of surface and subsurface processes in scaling N2O emissions along riverine networks. Proc. Natl Acad. Sci. USA 114, 4330–4335 (2017).Article 
CAS 

Google Scholar 
Marzadri, A. et al. Global riverine nitrous oxide emissions: the role of small streams and large rivers. Sci. Total Environ. 776, 145148 (2021).Article 
CAS 

Google Scholar 
Botter, G. & Durighetto, N. The stream length duration curve: a tool for characterizing the time variability of the flowing stream length. Water Resour. Res. 56, e2020WR027282 (2020).Article 
CAS 

Google Scholar 
Wollheim, W. M. et al. River network saturation concept: factors influencing the balance of biogeochemical supply and demand of river networks. Biogeochemistry 141, 503–521 (2018).Article 
CAS 

Google Scholar 
Durighetto, N., Vingiani, F., Bertassello, L. E., Camporese, M. & Botter, G. Intraseasonal drainage network dynamics in a headwater catchment of the Italian Alps. Water Resour. Res. 56, e2019WR02556 (2020).Article 

Google Scholar 
Montgomery, D. R. & Dietrich, W. E. Source areas, drainage density, and channel initiation. Water Resour. Res. 25, 1907–1918 (1989).Article 

Google Scholar 
Fatichi, S., Ivanov, V. Y. & Caporali, E. A mechanistic ecohydrological model to investigate complex interactions in cold and warm water‐controlled environments: 1. Theoretical framework and plot‐scale analysis. J. Adv. Model. Earth. Syst. 4, M05002 (2012).
Google Scholar 
Ulseth, A. J. et al. Distinct air–water gas exchange regimes in low- and high-energy streams. Nat. Geosci. 12, 259–263 (2019).Article 
CAS 

Google Scholar 
Hall, R. O. in Streams and Ecosystems in a Changing Environment (eds. Jones, J. J. & Stanley, E. H.) 151–180 (Academic, 2016).Butman, D. & Raymond, P. A. Significant efflux of carbon dioxide from streams and rivers in the United States. Nat. Geosci. 4, 839–842 (2011).Article 
CAS 

Google Scholar 
Duvert, C., Butman, D. E., Marx, A., Ribolzi, O. & Hutley, L. B. CO2 evasion along streams driven by groundwater inputs and geomorphic controls. Nat. Geosci. 11, 813–818 (2018).Article 
CAS 

Google Scholar 
Zhang, L. et al. Significant methane ebullition from alpine permafrost rivers on the East Qinghai–Tibet Plateau. Nat. Geosci. 13, 349–354 (2020).Article 

Google Scholar  More

Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).Article 
ADS 

Google Scholar 
Polyakov, I. V. et al. Borealization of the Arctic Ocean in response to anomalous advection from sub-Arctic seas. Front. Mar. Sci. 7, 983–992 (2020).Article 

Google Scholar 
Lannuzel, D. et al. The future of Arctic sea-ice biogeochemistry and ice-associated ecosystems. Nat. Clim. Change. 10, 983–992 (2020).Article 
ADS 

Google Scholar 
Macias-Fauria, M. & Post, E. Effects of sea ice on Arctic biota: An emerging crisis discipline. Biol. Lett. 14, 20170702 (2018).Article 

Google Scholar 
Kohlbach, D. et al. The importance of ice algae-produced carbon in the central Arctic Ocean ecosystem: Food web relationships revealed by lipid and stable isotope analyses. Limnol. Oceanogr. 61, 2027–2044 (2016).Article 
ADS 

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

Google Scholar 
Slagstad, D., Wassmann, P. F. J. & Ellingsen, I. Physical constrains and productivity in the future Arctic Ocean. Front. Mar. Sci. 2015, 2 (2015).
Google Scholar 
FISCAO. Final Report of the Fifth Meeting of Scientific Experts on Fish Stocks in the Central Arctic Ocean. https://apps-afsc.fisheries.noaa.gov/documents/Arctic_fish_stocks_fifth_meeting/508_Documents/508_Final_report_of_the_505th_FiSCAO_meeting.pdf (2018).David, C. et al. Under-ice distribution of polar cod Boreogadus saida in the central Arctic Ocean and their association with sea-ice habitat properties. Polar Biol. 39, 981–994 (2016).Article 

Google Scholar 
Gradinger, R. Vertical fine structure of the biomass and composition of algal communities in Arctic pack ice. Mar. Biol. 133, 745–754 (1999).Article 

Google Scholar 
Kosobokova, K. N., Hopcroft, R. R. & Hirche, H.-J. Patterns of zooplankton diversity through the depths of the Arctic’s central basins. Mar Biodivers. 41, 29–50 (2011).Article 

Google Scholar 
Mumm, N. et al. Breaking the ice: Large-scale distribution of mesozooplankton after a decade of Arctic and transpolar cruises. Polar Biol. 20, 189–197 (1998).Article 

Google Scholar 
Snoeijs-Leijonmalm, P. et al. Unexpected fish and squid in the central Arctic deep scattering layer. Sci. Adv. 8, 7536 (2022).Article 

Google Scholar 
David, C., Lange, B., Rabe, B. & Flores, H. Community structure of under-ice fauna in the Eurasian central Arctic Ocean in relation to environmental properties of sea-ice habitats. Mar. Ecol. Prog. Ser. 522, 15–32 (2015).Article 
ADS 

Google Scholar 
Gosselin, M., Levasseur, M., Wheeler, P. A., Horner, R. A. & Booth, B. C. New measurements of phytoplankton and ice algal production in the Arctic Ocean. Deep-Sea Res. Part II(44), 1623–1644 (1997).Article 
ADS 

Google Scholar 
Ardyna, M. & Arrigo, K. R. Phytoplankton dynamics in a changing Arctic Ocean. Nat. Clim. Change. 10, 892–903 (2020).Article 
ADS 
CAS 

Google Scholar 
Hays, G. C. In Migrations and Dispersal of Marine Organisms. (eds Jones, M. B. et al.) 163–170 (Springer).Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).Article 
ADS 

Google Scholar 
Geoffroy, M. et al. Mesopelagic sound scattering layers of the high arctic: Seasonal variations in biomass, species assemblage, and trophic relationships. Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Gjøsæter, H., Wiebe, P. H., Knutsen, T. & Ingvaldsen, R. B. Evidence of Diel vertical migration of mesopelagic sound-scattering organisms in the Arctic. Front. Mar. Sci. 2017, 4 (2017).
Google Scholar 
Knutsen, T., Wiebe, P. H., Gjøsæter, H., Ingvaldsen, R. B. & Lien, G. High latitude epipelagic and mesopelagic scattering layers—a reference for future arctic ecosystem change. Front. Mar. Sci. 2017, 4 (2017).
Google Scholar 
Priou, P. et al. Dense mesopelagic sound scattering layer and vertical segregation of pelagic organisms at the Arctic-Atlantic gateway during the midnight sun. Prog. Oceanogr. 196, 102611 (2021).Article 

Google Scholar 
Snoeijs-Leijonmalm, P. et al. A deep scattering layer under the North Pole pack ice. Prog. Oceanogr. 194, 102560 (2021).Article 

Google Scholar 
St-John, M. A. et al. A dark hole in our understanding of marine ecosystems and their services: Perspectives from the mesopelagic community. Front. Mar. Sci. 2016, 3 (2016).
Google Scholar 
Fransson, A. et al. Joint cruise 2-2 2021: Cruise report. The Nansen Legacy Report Series, 30/2022. https://doi.org/10.7557/nlrs.6413 (2022).Rudels, B. et al. Observations of water masses and circulation with focus on the Eurasian Basin of the Arctic Ocean from the 1990s to the late 2000s. Ocean Sci. 9, 147–169 (2013).Article 
ADS 

Google Scholar 
Krumpen, T. et al. Arctic warming interrupts the Transpolar Drift and affects long-range transport of sea ice and ice-rafted matter. Sci. Rep. 9, 5459 (2019).Article 
ADS 

Google Scholar 
Aagaard, K. A synthesis of the Arctic Ocean circulation. Rapp. P.-V. Rcun. Cons. int. Explor. Mer. 188, 11–22 (1989).
Google Scholar 
Perez-Hernandez, M. D. et al. The Atlantic Water boundary current north of Svalbard in late summer. J. Geophys. Res. 122, 2269–2290 (2017).Article 
ADS 

Google Scholar 
Våge, K. et al. The Atlantic Water boundary current in the Nansen Basin: Transport and mechanisms of lateral exchange. J. Geophys. Res. 121, 6946–6960 (2016).Article 
ADS 

Google Scholar 
Crews, L., Sundfjord, A., Albretsen, J. & Hattermann, T. Mesoscale Eddy Activity and Transport in the Atlantic Water Inflow Region North of Svalbard. J. Geophys. Res. 123, 201–215 (2018).Article 
ADS 

Google Scholar 
Kolås, E. H., Koenig, Z., Fer, I., Nilsen, F. & Marnela, M. Structure and Transport of Atlantic Water North of Svalbard From Observations in Summer and Fall 2018. J. Geophys. Res. 125, 6174 (2020).Article 

Google Scholar 
Basedow, S. L. et al. Seasonal variation in transport of zooplankton into the arctic basin through the atlantic gateway. Fram Strait. Front. Mar. Sci. 2018, 5 (2018).
Google Scholar 
Vernet, M., Carstensen, J., Reigstad, M. & Svensen, C. Editorial: Carbon bridge to the Arctic. Front. Mar. Sci. 2020, 7 (2020).
Google Scholar 
Wassmann, P. et al. The contiguous domains of Arctic Ocean advection: Trails of life and death. Prog. Oceanogr. 139, 42–65 (2015).Article 
ADS 

Google Scholar 
Wassmann, P., Slagstad, D. & Ellingsen, I. Advection of mesozooplankton into the northern svalbard shelf region. Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Auel, H. Egg size and reproductive adaptations among Arctic deep-sea copepods (Calanoida, Paraeuchaeta). Helgol. Mar. Res. 58, 147–153 (2004).Article 
ADS 

Google Scholar 
Gluchowska, M. et al. Zooplankton in Svalbard fjords on the Atlantic-Arctic boundary. Polar Biol. 39, 1785–1802 (2016).Article 

Google Scholar 
Wang, Y.-G., Tseng, L.-C., Lin, M. & Hwang, J.-S. Vertical and geographic distribution of copepod communities at late summer in the Amerasian Basin. Arctic Ocean. Plos One. 14, e0219319 (2019).Article 
CAS 

Google Scholar 
Gislason, A. & Silva, T. Abundance, composition, and development of zooplankton in the Subarctic Iceland Sea in 2006, 2007, and 2008. ICES J. Mar. Sci. 69, 1263–1276 (2012).Article 

Google Scholar 
Zhukova, N. G., Nesterova, V. N., Prokopchuk, I. P. & Rudneva, G. B. Winter distribution of euphausiids (Euphausiacea) in the Barents Sea (2000–2005). Deep-Sea Res. Part II(56), 1959–1967 (2009).Article 
ADS 

Google Scholar 
Dalpadado, P. & Skjoldal, H. R. Abundance, maturity and growth of the krill species Thysanoessa inermis and T. longicaudata in the Barents Sea. Mar. Ecol. Prog. Ser. 144, 175–183 (1996).Article 
ADS 

Google Scholar 
Koszteyn, J., Timofeev, S., Węsławski, J. M. & Malinga, B. Size structure of Themisto abyssorum Boeck and Themisto libellula (Mandt) populations in European Arctic seas. Polar Biol. 15, 85–92 (1995).Article 

Google Scholar 
Dalpadado, P., Borkner, N., Bogstad, B. & Mehl, S. Distribution of Themisto (Amphipoda) spp. in the Barents Sea and predator-prey interactions. ICES J. Mar. Sci. 58, 876–895 (2001).Article 

Google Scholar 
Macnaughton, M. O., Thormar, J. & Berge, J. Sympagic amphipods in the Arctic pack ice: Redescriptions of Eusirus holmii Hansen, 1887 and Pleusymtes karstensi (Barnard, 1959). Polar Biol. 30, 1013–1025 (2007).Article 

Google Scholar 
Kraft, A., Graeve, M., Janssen, D., Greenacre, M. & Falk-Petersen, S. Arctic pelagic amphipods: Lipid dynamics and life strategy. J. Plankton Res. 37, 790–807 (2015).Article 
CAS 

Google Scholar 
Kreibich, T., Hagen, W. & Saborowski, R. Food utilization of two pelagic crustaceans in the Greenland Sea: Meganyctiphanes norvegica (Euphausiacea) and Hymenodora glacialis (Decapoda, Caridea). Mar. Ecol. Prog. Ser. 413, 105–115 (2010).Article 
ADS 
CAS 

Google Scholar 
Geoffroy, M. et al. Increased occurrence of the jellyfish Periphylla periphylla in the European high Arctic. Polar Biol. 41, 2615–2619 (2018).Article 

Google Scholar 
Grigor, J. J., Søreide, J. E. & Varpe, Ø. Seasonal ecology and life-history strategy of the high-latitude predatory zooplankter Parasagitta elegans. Mar. Ecol. Prog. Ser. 499, 77–88 (2014).Article 
ADS 

Google Scholar 
Maclennan, D. N., Fernandes, P. G. & Dalen, J. A consistent approach to definitions and symbols in fisheries acoustics. ICES J. Mar. Sci. 59, 365–369 (2002).Article 

Google Scholar 
Gjøsæter, H. & Ushakov, N. G. Acoustic estimates of the Barents Sea Arctic cod Stock (Boreogadus saida). Forage Fishes in Marine Ecosystems. Alaska Sea Grant Collage Program, University of Alaska Fairbanks 97:01, 485–504 (1997).Raskoff, K. A., Hopcroft, R. R., Kosobokova, K. N., Purcell, J. E. & Youngbluth, M. Jellies under ice: ROV observations from the Arctic 2005 hidden ocean expedition. Deep-Sea Res. Part II(57), 111–126 (2010).Article 
ADS 

Google Scholar 
Bluhm, B. A. et al. The Pan-Arctic continental slope: Sharp gradients of physical processes affect pelagic and benthic ecosystems. Front. Mar. Sci. 2020, 7 (2020).
Google Scholar 
Hop, H. et al. Pelagic ecosystem characteristics across the atlantic water boundary current from Rijpfjorden, Svalbard, to the Arctic Ocean During Summer (2010–2014). Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Mumm, N. Composition and distribution of mesozooplankton in the Nansen Basin, Arctic Ocean, during summer. Polar Biol. 13, 451–461 (1993).Article 

Google Scholar 
Ona, E. & Nielsen, J. Acoustic detection of the Greenland shark (Somniosus microcephalus) using multifrequency split beam echosounder in Svalbard waters. Prog. Oceanogr. 206, 102842 (2022).Article 

Google Scholar 
Gjøsæter, H., Ingvaldsen, R. & Christiansen, J. S. Acoustic scattering layers reveal a faunal connection across the Fram Strait. Prog. Oceanogr. 185, 102348 (2020).Article 

Google Scholar 
Ingvaldsen, R. B., Gjosaeter, H., Ona, E. & Michalsen, K. Atlantic cod (Gadus morhua) feeding over deep water in the high Arctic. Polar Biol. 40, 2105–2111 (2017).Article 

Google Scholar 
Chawarski, J., Klevjer, T. A., Coté, D. & Geoffroy, M. Evidence of temperature control on mesopelagic fish and zooplankton communities at high latitudes. Front. Mar. Sci. 2022, 9 (2022).
Google Scholar 
Chernova, N. V. Catching of Greenland halibut Reinhardtius hippoglossoides (Pleuronectidae) on the shelf edge of the Laptev and East Siberian Seas. J. Ichthyol. 57, 219–227 (2017).Article 

Google Scholar 
Benzik, A. N., Budanova, L. K. & Orlov, A. M. Hard life in cold waters: Size distribution and gonads show that Greenland halibut temporarily inhabit the Siberian Arctic. Water Biol. Secur. 1, 100037 (2022).Article 

Google Scholar 
Olsen, L. M. et al. A red tide in the pack ice of the Arctic Ocean. Sci. Rep. 9, 9536 (2019).Article 
ADS 

Google Scholar 
Assmy, P. et al. Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice. Sci. Rep. 7, 40850 (2017).Article 
ADS 
CAS 

Google Scholar 
Leu, E., Søreide, J. E., Hessen, D. O., Falk-Petersen, S. & Berge, J. Consequences of changing sea-ice cover for primary and secondary producers in the European Arctic shelf seas: Timing, quantity, and quality. Prog. Oceanogr. 90, 18–32 (2011).Article 
ADS 

Google Scholar 
Drivdal, M. et al. Connections to the deep: Deep vertical migrations, an important part of the life cycle of Apherusa glacialis, an arctic ice-associated amphipod. Front. Mar. Sci. 2021, 8 (2021).
Google Scholar 
Scoulding, B., Chu, D., Ona, E. & Fernandes, P. G. Target strengths of two abundant mesopelagic fish species. J. Acoust. Soc. Am. 137, 989–1000 (2015).Article 
ADS 

Google Scholar 
Popova, E. E., Yool, A., Aksenov, Y. & Coward, A. C. Role of advection in Arctic Ocean lower trophic dynamics: A modeling perspective. J. Geophys. Res. 118, 1571–1586 (2013).Article 
ADS 

Google Scholar 
Saunders, R. A., Ingvarsdóttir, A., Rasmussen, J., Hay, S. J. & Brierley, A. S. Regional variation in distribution pattern, population structure and growth rates of Meganyctiphanes norvegica and Thysanoessa longicaudata in the Irminger Sea, North Atlantic. Prog. Oceanogr. 72, 313–342 (2007).Article 
ADS 

Google Scholar 
Tarling, G. A. et al. Can a key boreal Calanus copepod species now complete its life-cycle in the Arctic? Evidence and implications for Arctic food-webs. Ambio 51, 333–344 (2022).Article 

Google Scholar 
Purcell, J. E., Juhl, A. R., Manko, M. K. & Aumack, C. F. Overwintering of gelatinous zooplankton in the coastal Arctic Ocean. Mar. Ecol. Prog. Ser. 591, 281–286 (2018).Article 
ADS 

Google Scholar 
Purcell, J. E., Hopcroft, R. R., Kosobokova, K. N. & Whitledge, T. E. Distribution, abundance, and predation effects of epipelagic ctenophores and jellyfish in the western Arctic Ocean. Deep-Sea Res. Part II(57), 127–135 (2010).Article 
ADS 

Google Scholar 
Solvang, H. K. et al. Distribution of rorquals and Atlantic cod in relation to their prey in the Norwegian high Arctic. Polar Biol. 44, 761–782 (2021).Article 

Google Scholar 
Ingvaldsen, R. B. et al. Physical manifestations and ecological implications of Arctic Atlantification. Nat. Rev. Earth Environ. 2, 874–889 (2021).Article 
ADS 

Google Scholar 
Flores, H. et al. Macrofauna under sea ice and in the open surface layer of the Lazarev Sea, Southern Ocean. Deep-Sea Res. Part II(58), 1948–1961 (2011).Article 
ADS 

Google Scholar 
Godø, O. R., Valdemarsen, J. W. & Engås, A. Comparison of efficiency of standard and experimental juvenile gadoid sampling trawls. ICES Mar. Sci. Symp. 196, 196–201 (1993).
Google Scholar 
Klevjer, T. et al. Micronekton biomass distribution, improved estimates across four north Atlantic basins. Deep-Sea Res. Part II. 180, 104691 (2020).Article 

Google Scholar 
Krafft, B. A. et al. Distribution and demography of Antarctic krill in the Southeast Atlantic sector of the Southern Ocean during the austral summer 2008. Polar Biol. 33, 957–968 (2010).Article 

Google Scholar 
Foote, K. G. Maintaining precision calibrations with optimal copper spheres. J. Acoust. Soc. Am. 73, 1054–1063 (1983).Article 
ADS 

Google Scholar 
Korneliussen, R. J. et al. Acoustic identification of marine species using a feature library. Methods Oceanogr. 17, 187–205 (2016).Article 

Google Scholar 
Lavergne, T. et al. Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records. Cryosphere. 13, 49–78 (2019).Article 
ADS 

Google Scholar 
Firing, E., Ramada, J. & Caldwell, P. Processing ADCP Data with the CODAS Software System Version 3.1. Joint Institute for Marine and Atmospheric Research University of Hawaii. http://currents.soest.hawaii.edu/docs/adcp_doc/index.html (1995).Padman, L. & Erofeeva, S. A barotropic inverse tidal model for the Arctic Ocean. Geophys. Res. Lett. 31, 256 (2004).Article 

Google Scholar  More

Taranu, Z. E. et al. Acceleration of cyanobacterial dominance in north temperate-subarctic lakes during the Anthropocene. Ecol. Lett. 18, 375–384 (2015).Article 

Google Scholar 
Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).Article 
CAS 

Google Scholar 
Monchamp, M. E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324 (2018).Article 

Google Scholar 
Chorus, I. & Bartram, J. Toxic Cyanobacteria in Water. A Guide to Their Public Health Consequences, Monitoring, and Management. In: World Health Organization (eds. Chorus I. & Bertram J.) (CRC Press, 1999).Rabalais, N. N. et al. Dynamics and distribution of natural and human-caused hypoxia. Biogeosciences 7, 585–619 (2010).Article 
CAS 

Google Scholar 
Carmichael, W. W. Health effects of toxin-producing cyanobacteria: “The CyanoHABs”. Hum. Ecol. Risk Assess. Int. J. 7, 1393–1407 (2001).Article 

Google Scholar 
Whitton, B. A. Ecology of Cyanobacteria II: Their Diversity in Space and Time (Springer, 2012).Smol, J. P., Birks, H. J. B. & Last, W. M. Tracking Environmental Change Using Lake Sediments. Volume 4: Zoological Indicators, Developments in Paleoenvironmental Research. (Springer, 2002).Domaizon, I., Winegardner, A., Capo, E., Gauthier, J. & Gregory-Eaves, I. DNA-based methods in paleolimnology: new opportunities for investigating long-term dynamics of lacustrine biodiversity. J. Paleolimnol. 52, 1–21 (2017).Article 

Google Scholar 
Livingstone, D. & Jaworski, G. H. M. The viability of akinetes of blue-green algae recovered from the sediments of rostherne mere. Br. Phycol. J. 15, 357–364 (1980).Article 

Google Scholar 
van Geel, B., Mur, L. R., Ralska-Jasiewiczowa, M. & Goslar, T. Fossil akinetes of Aphanizomenon and Anabaena as indicators for medieval phosphate-eutrophication of Lake Gosciaz (Central Poland). Rev. Palaeobot. Palynol. 83, 97–105 (1994).Article 

Google Scholar 
Hillbrand, M., van Geel, B., Hasenfratz, A., Hadorn, P. & Haas, J. N. Non-pollen palynomorphs show human- and livestock-induced eutrophication of Lake Nussbaumersee (Thurgau, Switzerland) since Neolithic times (3840 bc). Holocene 24, 559–568 (2014).Article 

Google Scholar 
Gosling, W. D. et al. Human occupation and ecosystem change on Upolu (Samoa) during the Holocene. J. Biogeogr. 47, 600–614 (2020).Article 

Google Scholar 
Hertzberg, S., Liaaen-Jensen, S. & Siegelman, H. W. The carotenoids of blue-green algae. Phytochemistry 10, 3121–3127 (1971).Article 
CAS 

Google Scholar 
Leavitt, P. R. & Findlay, D. L. Comparison of fossil pigments with 20 years of phytoplankton data from eutrophic Lake 227, Experimental Lakes Area, Ontario. Can. J. Fish. Aquat. Sci. 51, 2286–2299 (1994).Article 
CAS 

Google Scholar 
Kaiser, J., Ön, B., Arz, H. & Akçer-Ön, S. Sedimentary lipid biomarkers in the magnesium-rich and highly alkaline Lake Salda (south-western Anatolia). J. Limnol. 75, 581–596 (2016).
Google Scholar 
Bauersachs, T., Talbot, H. M., Sidgwick, F., Sivonen, K. & Schwark, L. Lipid biomarker signatures as tracers for harmful cyanobacterial blooms in the Baltic Sea. PLoS ONE 12, e0186360 (2017).Article 

Google Scholar 
Domaizon, I. et al. DNA from lake sediments reveals the long-term dynamics and diversity of Synechococcus assemblages. Biogeosci. Discuss. 10, 2515–2564 (2013).
Google Scholar 
Britton, G., Liaaen-Jensen, S. & Pfander, H. in Carotenoids (eds. Britton, G., Liaaen-Jensen, S., Pfander, H.). Vol. 4, 1–6 (Birkhäuser Press, 2008).Capo, E. et al. Lake sedimentary dna research on past terrestrial and aquatic biodiversity: overview and recommendations. Quaternary 4, 6 (2021).Article 

Google Scholar 
Monchamp, M. E., Walser, J. C., Pomati, F. & Spaak, P. Sedimentary DNA reveals cyanobacterial community diversity over 200 years in two perialpine lakes. Appl. Environ. Microbiol. 82, 6472–6482 (2016).Article 
CAS 

Google Scholar 
Nwosu, E. C. et al. Evaluating sedimentary DNA for tracing changes in cyanobacteria dynamics from sediments spanning the last 350 years of Lake Tiefer See, NE Germany. J. Paleolimnol. 66, 279–296 (2021).Article 

Google Scholar 
Zhang, J. et al. Pre-industrial cyanobacterial dominance in Lake Moon (NE China) revealed by sedimentary ancient DNA. Quat. Sci. Rev. 261, 106966 (2021).Article 

Google Scholar 
Brauer, A., Schwab, M. J., Brademann, B., Pinkerneil, S. & Theuerkauf, M. Tiefer See–a key site for lake sediment research in NE Germany. DEUQUA Spec. Publ. 2, 89–93 (2019).Article 

Google Scholar 
Dräger, N. et al. Varve microfacies and varve preservation record of climate change and human impact for the last 6000 years at Lake Tiefer See (NE Germany). Holocene 27, 450–464 (2017).Article 

Google Scholar 
Dräger, N. et al. Hypolimnetic oxygen conditions influence varve preservation and δ13C of sediment organic matter in Lake Tiefer See, NE Germany. J. Paleolimnol. 62, 181–194 (2019).Article 

Google Scholar 
Theuerkauf, M., Dräger, N., Kienel, U., Kuparinen, A. & Brauer, A. Effects of changes in land management practices on pollen productivity of open vegetation during the last century derived from varved lake sediments. Holocene 25, 733–744 (2015).Article 

Google Scholar 
Heinrich, I. et al. Interdisciplinary geo-ecological research across time scales in the Northeast German Lowland Observatory (TERENO-NE). Vadose Zone J. 17, 1–25 (2018).Article 

Google Scholar 
Roeser, P. et al. Advances in understanding calcite varve formation: new insights from a dual lake monitoring approach in the southern Baltic lowlands. Boreas 50, 419–440 (2021).Article 

Google Scholar 
Nwosu, E. C. et al. From water into sediment—tracing freshwater Cyanobacteria via DNA analyses. Microorganisms 9, 1778 (2021).Article 
CAS 

Google Scholar 
Schmidt, J. -P. Ein Fremdling im Nordischen Kreis Jungbronzezeitliche Funde aus dem Flachen See bei Sophienhof, Lkr. Mecklenburgische Seenplatte. In: D. Brandherm/B. Nessel (Hrsg.), Phasenübergänge und Umbrüche im bronzezeitlichen Europa. Beiträge zur Sitzung der Arbeitsgemeinschaft Bronzezeit auf der 80. Jahrestagung des Nordwestdeutschen Verbandes für Altertumskunde. Vol. 297, 271–281. (Universitätsforschungen zur Prähistorischen Archäologie, 2017).Raese, H. & Schmidt, J. -P. Zur Besiedlung Mecklenburg-Vorpommernswährend des Spätneolithikums und der frühenBronzezeit (2500–1500 v. Chr.). In: Siedlungsarchäologie des Endneolithikums und der frühen Bronzezeit. 11. Mitteldeutscher Archäologentag (eds. Meller, H., Friedderich, S., Küßner, M., Stäuble, H. & Risch, R.) 621–634 (2019).Kienel, U., Dulski, P., Ott, F., Lorenz, S. & Brauer, A. Recently induced anoxia leading to the preservation of seasonal laminae in two NE-German lakes. J. Paleolimnol. 50, 535–544 (2013).Article 

Google Scholar 
Callieri, C. & Stockner, J. Picocyanobacteria success in oligotrophic lakes: fact or fiction? J. Limnol. 59, 72–76 (2000).Article 

Google Scholar 
Sollai, M. et al. The Holocene sedimentary record of cyanobacterial glycolipids in the Baltic Sea: an evaluation of their application as tracers of past nitrogen fixation. Biogeosciences 14, 5789–5804 (2017).Article 

Google Scholar 
Mur, L. R., Skulberg, O. M. & Utkilen, H. In: Toxic Cyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring, and Management. (eds. Chorus, I. and Bartram, J.) 15–40 (St Edmundsbury Press, 1999).Schmidt, J.-P. Ein bronzenes Hallstattschwert der Periode VI aus dem Flachen See bei Sophienhof, Lkr. Mecklenburgische Seenplatte. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 26, 26–34 (2019).
Google Scholar 
Schmidt, J.-P. “Aller guten Dinge sind drei!”–Ein weiteres bronzezeitliches Schwert aus dem Flachen See bei Lütgendorf, Lkr. Mecklenburgische Seenplatte. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 27, 49–55 (2020).
Google Scholar 
Küster, M., Stöckmann, M., Fülling, A. & Weber, R. Kulturlandschaftselemente, Kolluvien und Flugsande als Archive der spätholozänen Landschaftsentwicklung im Bereich des Messtischblattes Thurow (Müritz-Nationalpark, Mecklenburg). In: Neue Beiträge zum Naturraum und zur Landschaftsgeschichte im Teilgebiet. (Geozon Science Media, 2015).Feeser, I., Dörfler, W., Kneisel, J., Hinz, M. & Dreibrodt, S. Human impact and population dynamics in the Neolithic and Bronze Age: Multi-proxy evidence from north-western Central Europe. Holocene 29, 1596–1606 (2019).Article 

Google Scholar 
Alsleben, A. In How’s Life? Living Conditions in the 2nd and 1st Millennia BCE. Scales of Transformation in Prehistoric and Archaic Societies (eds. Dal Corso, M. et al.) 85–102 (Sidestone Press, 2019).Kneisel, J., Bork, H.-R. & Czebreszuk, J. In Defensive Structures from Central Europe to the Aegean in the 3rd and 2nd Millennia bc (eds. Czebreszuk, J., Kadrow, S. & Müller, J.) 155–170 (Habelt, 2008).Haas, J. N. & Wahlmüller, N. Floren-, Vegetations- und Milieuveränderungen im Zuge der bronzezeitlichen Besiedlung von Bruszczewo (Polen) und der landwirtschaftlichen Nutzung der umliegenden Gebiete. In: Ausgrabungen und Forschungen in einer prähistorischen Siedlungskammer Großpolens. (eds. Müller, J., Czebreszuk, J. & Kneisel, J.) Studien zur Archäologie in Ostmitteleuropa Vol. 6.1, 50–81 (Bonn, 2010).Theuerkauf, M. et al. Holocene lake-level evolution of Lake Tiefer See, NE Germany, caused by climate and land cover changes. Boreas 51, 299–316 (2021).Article 

Google Scholar 
Büntgen, U. et al. 2500 years of European climate variability and human susceptibility. Science 331, 578–582 (2011).Article 

Google Scholar 
Büntgen, U. et al. Cooling and societal change during the Late Antique Little Ice Age from 536 to around 660 AD. Nat. Geosci. 9, 231–236 (2016).Article 

Google Scholar 
Kienel, U. et al. Effects of spring warming and mixing duration on diatom deposition in deep Tiefer See, NE Germany. J. Paleolimnol. 57, 37–49 (2017).Article 

Google Scholar 
Monchamp, M. E., Spaak, P. & Pomati, F. High dispersal levels and lake warming are emergent drivers of cyanobacterial community assembly in peri-Alpine lakes. Sci. Rep. 9, 7366 (2019).Article 

Google Scholar 
Erratt, K. et al. Paleolimnological evidence reveals climate-related preeminence of cyanobacteria in a temperate meromictic lake. Can. J. Fish. Aquat. Sci. 79, 558–565 (2021).Article 

Google Scholar 
Schmidt, J.-P. ders., Kein Ende in Sicht? Neue Untersuchungen auf dem Feuerstellenplatz von Naschendorf, Lkr. Nordwestmecklenburg. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 19, 26–46 (2012).
Google Scholar 
Marcott, S. A., Shakun, J. D., Clark, P. U. & Mix, A. C. A reconstruction of regional and global temperature for the past 11,300 years. Science 339, 1198–1201 (2013).Article 
CAS 

Google Scholar 
Wanner, H. et al. Holocene climate variability and change; a data-based review. J. Geol. Soc. Lond. 172, 254–263 (2015).Article 

Google Scholar 
Rigosi, A., Carey, C. C., Ibelings, B. W. & Brookes, J. D. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol. Oceanogr. 59, 99–114 (2014).Article 

Google Scholar 
Dittmann, E., Fewer, D. P. & Neilan, B. A. Cyanobacterial toxins: Biosynthetic routes and evolutionary roots. FEMS Microbiol. Rev. 37, 23–43 (2013).Article 
CAS 

Google Scholar 
Dolman, A. M. et al. Cyanobacteria and cyanotoxins: the influence of nitrogen versus phosphorus. PLoS ONE 7, e38757 (2012).Article 
CAS 

Google Scholar 
Kurmayer, R., Christiansen, G., Fastner, J. & Börner, T. Abundance of active and inactive microcystin genotypes in populations of the toxic cyanobacterium Planktothrix spp. Environ. Microbiol. 6, 831–841 (2004).Article 
CAS 

Google Scholar 
Liu, A., Zhu, T., Lu, X. & Song, L. Hydrocarbon profiles and phylogenetic analyses of diversified cyanobacterial species. Appl. Energy 11, 383–393 (2013).Article 

Google Scholar 
Coates, R. C. et al. Characterization of cyanobacterial hydrocarbon composition and distribution of biosynthetic pathways. PLoS ONE 9, e85140 (2014).Article 

Google Scholar 
Marciniak, S. et al. Ancient human genomics: the methodology behind reconstructing evolutionary pathways. J. Hum. Evol. 79, 21–34 (2015).Article 

Google Scholar 
Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. MapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. in. Bioinformatics 29, 1682–1684 (2013).Article 

Google Scholar 
Borry, M., Hübner, A., Rohrlach, A. B. & Warinner, C. PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly. PeerJ 9, e11845 (2021).Article 

Google Scholar 
Murchie, T. J. et al. Optimizing extraction and targeted capture of ancient environmental DNA for reconstructing past environments using the PalaeoChip Arctic-1.0 bait-set. Quat. Res. (U. S.) 99, 305–328 (2021).Article 
CAS 

Google Scholar 
Armbrecht, L., Hallegraeff, G., Bolch, C. J. S., Woodward, C. & Cooper, A. Hybridisation capture allows DNA damage analysis of ancient marine eukaryotes. Sci. Rep. 11, 3220 (2021).Article 
CAS 

Google Scholar 
Wulf, S. et al. Holocene tephrostratigraphy of varved sediment records from Lakes Tiefer See (NE Germany) and Czechowskie (N Poland). Quat. Sci. Rev. 132, 1–14 (2016).Article 

Google Scholar 
Sugita, S. Theory of quantitative reconstruction of vegetation I: Pollen from large sites REVEALS regional vegetation composition. Holocene 17, 2 (2007).Article 

Google Scholar 
Epp, L. S., Zimmermann, H. H. & Stoof-Leichsenring, K. R. In: Ancient DNA. Methods in Molecular Biology (eds. Shapiro B., Barlow A., Heintzman P., Hofreiter M., Paijmans J., Soares A.) Vol. 1963, 31–44 (Humana Press, 2019).Janse, I., Meima, M., Kardinaal, W. E. A. & Zwart, G. High-resolution differentiation of Cyanobacteria by using rRNA-internal transcribed spacer denaturing gradient gel electrophoresis. Appl. Environ. Microbiol. 69, 6634–6643 (2003).Article 
CAS 

Google Scholar 
Nwosu, E. C. et al. Species-level spatio-temporal dynamics of cyanobacteria in a hard-water temperate lake in the Southern Baltics. Front. Microbiol. 12, https://doi.org/10.3389/fmicb.2021.761259 (2021).Savichtcheva, O. et al. Quantitative PCR enumeration of total/toxic Planktothrix rubescens and total cyanobacteria in preserved DNA isolated from lake sediments. Appl. Environ. Microbiol. 77, 8744–8753 (2011).Article 
CAS 

Google Scholar 
Coolen, M. J. L. et al. Ancient DNA derived from alkenone-biosynthesizing haptophytes and other algae in Holocene sediments from the Black Sea. Paleoceanography 21, PA1005 (2006).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina 7 amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Kieser, S., Brown, J., Zdobnov, E. M., Trajkovski, M. & McCue, L. A. ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinformat. 21, 257 (2020).Article 

Google Scholar 
Yilmaz, P. et al. The SILVA and ‘all-species Living Tree Project (LTP)’ taxonomic frameworks. Nucleic Acids Res. 42, 643–648 (2014).Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).Article 
CAS 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformat. 11, 119–119 (2010).Article 

Google Scholar 
Huerta-Cepas, J. et al. EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).Article 
CAS 

Google Scholar 
Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msab293 (2021).Shen, W. & Ren, H. TaxonKit: a practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics. 48, 844–850 (2021).Article 

Google Scholar 
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 29, 471–482 (2001).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R Package Version 2.5-2. Cran R (2019).Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).CAS 

Google Scholar 
Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).Article 

Google Scholar  More