Ecology

Ehrlich PR, Raven PH. Butterflies and plants: a study in coevolution. Evolution. 1964;18:586–608.Article 

Google Scholar 
Marston MF, Pierciey FJ Jr, Shepard A, Gearin G, Qi J, Yandava C, et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc Natl Acad Sci USA. 2012;109:4544–9.Article 
CAS 

Google Scholar 
Hall AR, Scanlan PD, Buckling A. Bacteria-phage coevolution and the emergence of generalist pathogens. Am Nat. 2011;177:44–53.Article 

Google Scholar 
Badger MR, Andrews TJ, Whitney SM, Ludwig M, Yellowlees DC, Leggat W, et al. The diversity and coevolution of Rubisco, plastids, pyrenoids, and chloroplast-based CO2-concentrating mechanisms in algae. Can J Bot. 1998;76:1052–71.CAS 

Google Scholar 
Schluter D. The ecology of adaptive radiation. Oxford, UK: University Press; 2000.Buckling A, Maclean CR, Brockhurst MA, Colegrave N. The Beagle in a bottle. Nature. 2009;457:824–9.Article 
CAS 

Google Scholar 
Thompson JN. The coevolutionary process. Chicago, USA: University of Chicago Press; 1994.Vallina SM, Follows MJ, Dutkiewicz S, Montoya JM, Cermeno P, Loreau M. Global relationship between phytoplankton diversity and productivity in the ocean. Nat Commun. 2014;5:4299.Article 
CAS 

Google Scholar 
Jürgens K, Matz C. Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Antonie Van Leeuwenhoek. 2002;81:413–34.Article 

Google Scholar 
Wildschutte H, Wolfe DM, Tamewitz A, Lawrence JG. Protozoan predation, diversifying selection, and the evolution of antigenic diversity in Salmonella. Proc Natl Acad Sci USA. 2004;101:10644–9.Article 
CAS 

Google Scholar 
Thompson JN. The geographic mosaic of coevolution. Chicago, USA: University of Chicago Press; 2005.Hahn MW, Höfle MG. Grazing of protozoa and its effect on populations of aquatic bacteria. FEMS Microbiol Ecol. 2001;35:113–21.Article 
CAS 

Google Scholar 
Fuhrman JA, Noble RT. Viruses and protists cause similar bacterial mortality in coastal seawater. Limnol Oceanogr. 1995;40:1236–42.Article 

Google Scholar 
Azam F, Fenchel T, Field JG, Gray JS, Meyer-Reil LA, Thingstad F. The ecological role of water-column microbes in the sea. Mar Ecol Prog Ser. 1983;10:257–63.Article 

Google Scholar 
Lankau RA, Strauss SY. Mutual feedbacks maintain both genetic and species diversity in a plant community. Science. 2007;317:1561–3.Article 
CAS 

Google Scholar 
Hogle SL, Hepolehto I, Ruokolainen L, Cairns J, Hiltunen T. Effects of phenotypic variation on consumer coexistence and prey community structure. Ecol Lett. 2022:25;307–19.Yoshida T, Jones LE, Ellner SP, Fussmann GF, Hairston NG Jr. Rapid evolution drives ecological dynamics in a predator-prey system. Nature. 2003;424:303–6.Article 
CAS 

Google Scholar 
McClean D, McNally L, Salzberg LI, Devine KM, Brown SP, Donohue I. Single gene locus changes perturb complex microbial communities as much as apex predator loss. Nat Commun. 2015;6:8235.Article 

Google Scholar 
Gómez P, Paterson S, De Meester L, Liu X, Lenzi L, Sharma MD, et al. Local adaptation of a bacterium is as important as its presence in structuring a natural microbial community. Nat Commun. 2016;7:12453.Article 

Google Scholar 
Middelboe M, Holmfeldt K, Riemann L, Nybroe O, Haaber J. Bacteriophages drive strain diversification in a marine Flavobacterium: Implications for phage resistance and physiological properties. Environ Microbiol. 2009;11:1971–82.Article 
CAS 

Google Scholar 
Lennon JT, Martiny JBH. Rapid evolution buffers ecosystem impacts of viruses in a microbial food web. Ecol Lett. 2008;11:1178–88.Article 

Google Scholar 
Cairns J, Jokela R, Hultman J, Tamminen M, Virta M, Hiltunen T. Construction and characterization of synthetic bacterial community for experimental ecology and evolution. Front Genet. 2018;9:312.Article 

Google Scholar 
Pascual-García A, Bell T. Community-level signatures of ecological succession in natural bacterial communities. Nat Commun. 2020;11:2386.Article 

Google Scholar 
Rivett DW, Bell T. Abundance determines the functional role of bacterial phylotypes in complex communities. Nat Microbiol. 2018;3:767–72.Article 
CAS 

Google Scholar 
Cairns J, Moerman F, Fronhofer EA, Altermatt F, Hiltunen T. Evolution in interacting species alters predator life-history traits, behaviour and morphology in experimental microbial communities. Proc Biol Sci. 2020;287:20200652.
Google Scholar 
Cooke DP, Wedge DC, Lunter G. A unified haplotype-based method for accurate and comprehensive variant calling. Nat Biotechnol. 2021;39:885–92.Article 
CAS 

Google Scholar 
Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6:80–92.Article 
CAS 

Google Scholar 
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.Article 
CAS 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 

Google Scholar 
Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–14.Article 
CAS 

Google Scholar 
Blin K, Shaw S, Steinke K, Villebro R, Ziemert N, Lee SY, et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47:W81–7.Article 
CAS 

Google Scholar 
Good BH, McDonald MJ, Barrick JE, Lenski RE, Desai MM. The dynamics of molecular evolution over 60,000 generations. Nature. 2017;551:45–50.Article 

Google Scholar 
Timonen J, Mannerström H, Vehtari A, Lähdesmäki H. lgpr: an interpretable nonparametric method for inferring covariate effects from longitudinal data. Bioinformatics. 2021;37:1860–7.Article 
CAS 

Google Scholar 
Willis AD, Martin BD. Estimating diversity in networked ecological communities. Biostatistics. 2022;23:207–22.Article 

Google Scholar 
Willis A, Bunge J, Whitman T. Improved detection of changes in species richness in high diversity microbial communities. J R Stat Soc C. 2017;66:963–77.Article 

Google Scholar 
Anderson MJ. A new method for non‐parametric multivariate analysis of variance. Austral Ecol. 2001:26;32–46.Anderson MJ. Distance-based tests for homogeneity of multivariate dispersions. Biometrics. 2006;62:245–53.Article 

Google Scholar 
Martin BD, Witten D, Willis AD. Modeling microbial abundances and dysbiosis with beta-binomial regression. Ann Appl Stat. 2020;14:94–115.Article 

Google Scholar 
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.Article 

Google Scholar 
Zhang Y, Thompson KN, Huttenhower C, Franzosa EA. Statistical approaches for differential expression analysis in metatranscriptomics. Bioinformatics. 2021;37:i34–41.Article 
CAS 

Google Scholar 
Abdi H, Williams LJ, Valentin D, Bennani-Dosse M. STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling. WIREs Comp Stat. 2012;4:124–67.Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: an R package for determining the relevant number of clusters in a data set. J Stat Softw. 2014;61:1–36.Article 

Google Scholar 
Zhu A, Ibrahim JG, Love MI. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. 2019;35:2084–92.Article 
CAS 

Google Scholar 
Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.Article 
CAS 

Google Scholar 
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.Article 
CAS 

Google Scholar 
Wichman HA, Badgett MR, Scott LA, Boulianne CM, Bull JJ. Different trajectories of parallel evolution during viral adaptation. Science. 1999;285:422–4.Article 
CAS 

Google Scholar 
Lieberman TD, Michel J-B, Aingaran M, Potter-Bynoe G, Roux D, Davis MR Jr, et al. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nat Genet. 2011;43:1275–80.Article 
CAS 

Google Scholar 
Lebeuf-Taylor E, McCloskey N, Bailey SF, Hinz A, Kassen R. The distribution of fitness effects among synonymous mutations in a gene under directional selection. Elife. 2019;8:e45952.Article 

Google Scholar 
Bailey SF, Hinz A, Kassen R. Adaptive synonymous mutations in an experimentally evolved Pseudomonas fluorescens population. Nat Commun. 2014;5:4076.Article 
CAS 

Google Scholar 
Mukherjee S, Jemielita M, Stergioula V, Tikhonov M, Bassler BL. Photosensing and quorum sensing are integrated to control Pseudomonas aeruginosa collective behaviors. PLoS Biol. 2019;17:e3000579.Article 
CAS 

Google Scholar 
Segura A, Hurtado A, Duque E, Ramos JL. Transcriptional phase variation at the flhB gene of Pseudomonas putida DOT-T1E is involved in response to environmental changes and suggests the participation of the flagellar export system in solvent tolerance. J Bacteriol. 2004;186:1905–9.Article 
CAS 

Google Scholar 
Lee X, Reimmann C, Greub G, Sufrin J, Croxatto A. The Pseudomonas aeruginosa toxin L-2-amino-4-methoxy-trans-3-butenoic acid inhibits growth and induces encystment in Acanthamoeba castellanii. Microbes Infect. 2012;14:268–72.Article 
CAS 

Google Scholar 
Montagnes DJS, Barbosa AB, Boenigk J, Davidson K, Jürgens K, Macek M, et al. Selective feeding behaviour of key free-living protists: avenues for continued study. Aquat Micro Ecol. 2008;53:83–98.Article 

Google Scholar 
Collins K, editor. Tetrahymena thermophila. New York: Academic Press, Elsevier; 2012.Ruehle MD, Orias E, Pearson CG. Tetrahymena as a unicellular model eukaryote: genetic and genomic tools. Genetics. 2016;203:649–65.Article 
CAS 

Google Scholar 
Plum K, Tarkington J, Zufall RA. Experimental evolution in Tetrahymena. Microorganisms. 2022;10:1–11.Heard E, Martienssen RA. Transgenerational epigenetic inheritance: myths and mechanisms. Cell. 2014;157:95–109.Article 
CAS 

Google Scholar 
Jones ML, Rivett DW, Pascual-García A, Bell T. Relationships between community composition, productivity and invasion resistance in semi-natural bacterial microcosms. Elife. 2021;10:1–25.Kertesz MA. Riding the sulfur cycle-metabolism of sulfonates and sulfate esters in gram-negative bacteria. FEMS Microbiol Rev. 2000;24:135–75.CAS 

Google Scholar 
Park C, Shin B, Park W. Protective role of bacterial alkanesulfonate monooxygenase under oxidative stress. Appl Environ Microbiol. 2020;86:1–14.Shatalin K, Shatalina E, Mironov A, Nudler E. H2S: a universal defense against antibiotics in bacteria. Science. 2011;334:986–90.Article 
CAS 

Google Scholar 
Ong C-LY, Beatson SA, Totsika M, Forestier C, McEwan AG, Schembri MA. Molecular analysis of type 3 fimbrial genes from Escherichia coli, Klebsiella and Citrobacter species. BMC Microbiol. 2010;10:183.Article 

Google Scholar 
McNally L, Brown SP. Building the microbiome in health and disease: niche construction and social conflict in bacteria. Philos Trans R Soc Lond B Biol Sci. 2015;370:1–8.Scheuerl T, Cairns J, Becks L, Hiltunen T. Predator coevolution and prey trait variability determine species coexistence. Proc Biol Sci. 2019;286:20190245.
Google Scholar 
Koskella B, Brockhurst MA. Bacteria-phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol Rev. 2014;38:916–31.Article 
CAS 

Google Scholar 
Wilhelm Scherer H. Sulfur in soils. J Plant Nutr Soil Sci. 2009;172:326–35.Article 

Google Scholar 
Kaya K. Chemistry and biochemistry of taurolipids. Prog Lipid Res. 1992;31:87–108.Article 
CAS 

Google Scholar 
Fukami T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu Rev Ecol Evol Syst. 2015;46:1–23.Article 

Google Scholar 
Debray R, Herbert RA, Jaffe AL, Crits-Christoph A, Power ME, Koskella B. Priority effects in microbiome assembly. Nat Rev Microbiol. 2022;20:109–21.Article 
CAS 

Google Scholar 
Price TD, Qvarnström A, Irwin DE. The role of phenotypic plasticity in driving genetic evolution. Proc Biol Sci. 2003;270:1433–40.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

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

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

Fischer, E. M. & Knutti, R. Nature Clim. Change 5, 560–564 (2015).Article 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).Article 
PubMed 

Google Scholar 
Ma, G., Hoffmann, A. A. & Ma, C.-S. J. Exp. Biol. 218, 2289–2296 (2015).PubMed 

Google Scholar 
Dillon, M. E., Wang, G. & Huey, R. B. Nature 467, 704–706 (2010).Article 
PubMed 

Google Scholar 
Garcia, R. A., Cabeza, M., Rahbek, C. & Araújo, M. B. Science 344, 1247579 (2014).Article 
PubMed 

Google Scholar  More

Dulvy, N. K., Sadovy, Y. & Reynolds, J. D. Extinction vulnerability in marine populations. Fish Fish. 4, 25–64 (2003).Article 

Google Scholar 
Fowler S. L. et al. Sharks, Rays and Chimaeras: The Status of the Chondrichthyan Fishes. IUCN/SSC Shark Specialist Group, Gland, Switzerland and Cambridge, UK (2005).Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, e00590 (2014).Article 

Google Scholar 
Lack M. & Sant G. Illegal, Unreported and Unregulated Shark Catch: A review of current knowledge and action. Department of the Environment, Water, Heritage and the Arts and TRAFFIC, Canberra http://www.traffic.org/fish/ (2008).Rose D.A. An Overview of World Trade in Sharks and Other Cartilaginous Fishes. TRAFFIC International, Cambridge, UK (1996).Lam, V. Y. & Sadovy, M. Y. The sharks of South East Asia–unknown, unmonitored and unmanaged. Fish Fish 12, 51–74 (2011).Article 

Google Scholar 
Kessel S.T. Investigation into the behaviour and population dynamics of the lemon shark (Negaprion brevirostris). Cardiff University (United Kingdom) (2010).Morrissey, J. F. & Gruber, S. H. Habitat selection by juvenile lemon sharks Negaprion brevirostris. Environ. Biol. Fishes 38, 311–319 (1993).Article 

Google Scholar 
Filmalter, J. D., Dagorn, L. & Cowley, P. D. Spatial behaviour and site fidelity of the sicklefin lemon shark Negaprion acutidens in a remote Indian Ocean atoll. Mari. Biol. 160, 2425–2436 (2013).Article 

Google Scholar 
DiBattista, J. D. et al. A genetic assessment of polyandry and breeding site fidelity in lemon sharks. Mol. Ecol. 17, 3337–3351 (2008).Article 

Google Scholar 
Wetherbee, B. M., Gruber, S. H. & Rosa, R. S. Movement patterns of juvenile lemon sharks Negaprion brevirostris within Atol das Rocas, Brazil: A nursery characterized by tidal extremes. Mar. Ecol. Prog. Seri. 343, 283–293 (2007).Article 
ADS 

Google Scholar 
Feldheim, K. A. et al. Two decades of genetic profiling yields first evidence of natal philopatry and long-term fidelity to parturition sites in sharks. Mol. Ecol. 23, 110–117 (2014).Article 

Google Scholar 
Stevens J. D. et al. Diversity, abundance and habitat utilisation of sharks and rays: Final report to West Australian Marine Science Institute. CSIRO, editor. Hobart (2009).Schultz, J. K. et al. Global phylogeography and seascape genetics of the lemon sharks (genus Negaprion). Mol. Ecol. 17, 5336–5348 (2008).Article 
CAS 

Google Scholar 
Mourier, J., Buray, N., Schultz, J. K., Clua, E. & Planes, S. Genetic network and breeding patterns of a sicklefin lemon shark (Negaprion acutidens) population in the Society Islands, French Polynesia. PLoS ONE 8, e73899 (2013).Article 
ADS 
CAS 

Google Scholar 
Speed, C. W. et al. Reef shark movements relative to a coastal marine protected area. Reg. Stud. Mar. Sci. 3, 58–66 (2016).
Google Scholar 
Huang, Z. Marine Species and Their Distribution in China’s Seas (Krieger Publishing Company, 2001).
Google Scholar 
Chang, C. W., Huang, C. S. & Wang, S. I. Species composition and sizes of fish in the lagoon of dongsha island (Pratas Island), Dongsha Atoll of the South China sea. Platax 2012, 25–32 (2012).
Google Scholar 
Pillans, R. D. et al. Long-term acoustic monitoring reveals site fidelity, reproductive migrations, and sex specific differences in habitat use and migratory timing in a large coastal shark (Negaprion acutidens). Front. Mar. Sci. 8, 616633 (2021).Article 

Google Scholar 
Daly-Engel, T. S. et al. Global phylogeography with mixed-marker analysis reveals male-mediated dispersal in the endangered scalloped hammerhead shark (Sphyrna lewini). PLoS ONE 7, e29986 (2012).Article 
ADS 
CAS 

Google Scholar 
Félix-López, D. G. et al. Possible female philopatry of the smooth hammerhead shark Sphyrna zygaena revealed by genetic structure patterns. J. Fish Biol. 94, 671–679 (2019).Article 

Google Scholar 
Nosal, A. P., Caillat, A., Kisfaludy, E. K., Royer, M. A. & Wegner, N. C. Aggregation behavior and seasonal philopatry in male and female leopard sharks Triakis semifasciata along the open coast of southern California, USA. Mar. Ecol. Prog. Ser. 499, 157–175 (2014).Article 
ADS 

Google Scholar 
Jirik, K. E. & Lowe, C. G. An elasmobranch maternity ward: Female round stingrays Urobatis halleri use warm, restored estuarine habitat during gestation. J. Fish. Biol. 80(5), 1227–1245 (2012).Article 
CAS 

Google Scholar 
Jacoby, D. M., Croft, D. P. & Sims, D. W. Social behaviour in sharks and rays: Analysis, patterns and implications for conservation. Fish Fish 13(4), 399–417 (2012).Article 

Google Scholar 
Su, S. H., Liu, S. Y. V., Liu, K. M. & Tsai, W. P. Development and characterization of novel microsatellite loci for an endangered hammerhead shark Sphyrna lewini by using shotgun sequencing. Taiwania 65(2), 261–263 (2020).
Google Scholar 
Dieringer, D. & Schlötterer, C. Microsatellite analyser (MSA): A platform independent analysis tool for large microsatellite data sets. Mol. Ecol. Notes 3, 167–169 (2003).Article 
CAS 

Google Scholar 
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. & Shipley, P. Micro-checker: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
CAS 

Google Scholar 
Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes 7, 574–578 (2007).Article 
CAS 

Google Scholar 
Earl, D. A. & VonHoldt, B. M. Structure harvester: A website and program for visualizing structure output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

Google Scholar 
Jakobsson, M. & Rosenberg, N. A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14), 1801–1806 (2007).Article 
CAS 

Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in excel population genetic software for teaching and research–an update. Bioinformatics 28, 2537–2539 (2012).Article 
CAS 

Google Scholar 
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. POPPR: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).Article 

Google Scholar 
Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-Relate: A computer program for maximum likelihood estimation of relatedness and relationship. Mol. Ecol. Resour. 6, 576–579 (2006).Article 
CAS 

Google Scholar 
Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).Article 
CAS 

Google Scholar 
Oh, B. Z. et al. Contrasting patterns of residency and space use of coastal sharks within a communal shark nursery. Mar. Freshw. Res. 68, 1501–1517 (2017).Article 

Google Scholar 
McClelland J. Genetic Assessment of Breeding Patterns and Population Size of the Sicklefin Lemon Shark Negaprion acutidens in a Tropical Marine Protected Area: Implications for Conservation and Management (Doctoral dissertation, University of York) (2020).Compagno L. J .V. FAO species catalogue Sharks of the world: An annotated and illustrated catalogue of shark species known to date. FAO Fish. Synop. No. 125 Rome 4, 1–655 (1984).Stevens, J. D. Life-history and ecology of sharks at aldabra Atoll. Indian Ocean. Proc R Soc. B 222, 79–106 (1984).ADS 

Google Scholar 
Kool, J. T., Moilanen, A. & Treml, E. A. Population connectivity: Recent advances and new perspectives. Landsc. Ecol. 28, 165–185 (2013).Article 

Google Scholar 
Ruzzante, D. E. et al. Effective number of breeders, effective population size and their relationship with census size in an iteroparous species Salvelinus fontinalis. Proc. R Soc. B 283, 20152601 (2016).Article 

Google Scholar 
Van Wyngaarden, M. et al. Identifying patterns of dispersal, connectivity and selection in the sea scallop, Placopecten magellanicus, using RADseq-derived SNPs. Evol. Appl. 10, 102–117 (2017).Article 

Google Scholar 
Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: Revised recommendations for the 50/500 rules, Red list criteria and population viability analyses. Biol. Conserv. 170, 56–63 (2014).Article 

Google Scholar 
Pazmiño, D. A., Maes, G. E., Simpfendorfer, C. A., Salinas-de-León, P. & van Herwerden, L. Genome-wide SNPs reveal low effective population size within confined management units of the highly vagile Galapagos shark (Carcharhinus galapagensis). Conserv. Genet. 18, 1151–1163 (2017).Article 

Google Scholar 
Waples, R. S. & Do, C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).Article 

Google Scholar 
Dudgeon, C. L. & Ovenden, J. R. The relationship between abundance and genetic effective population size in elasmobranchs: An example from the globally threatened zebra shark Stegostoma fasciatum within its protected range. Conserv. Genet. 16, 1443–1454 (2015).Article 

Google Scholar 
Feldheim, K. A., Gruber, S. H. & Ashley, M. V. Population genetic structure of the lemon shark (Negaprion brevirostris) in the western Atlantic: DNA microsatellite variation. Mol. Ecol. 10, 295–303 (2001).Article 
CAS 

Google Scholar 
Feldheim, K. A., Gruber, S. H. & Ashley, M. V. The breeding biology of lemon sharks at a tropical nursery lagoon. Proc. R. Soc. Lond. B 269, 1471–2954 (2002).Article 

Google Scholar 
Portnoy, D., McDowell, J. R., Thompson, K., Musick, J. A. & Graves, J. E. Isolation and characterization of five dinucleotide microsatellite loci in the sandbar shark, Carcharhinus plumbeus. Mol. Ecol. Notes 6, 431–433 (2006).Article 
CAS 

Google Scholar  More

A visionary and interdisciplinary scientist who brought a fearless passion to everything she did, inspiring all those around her.
Alma Dal Co tragically passed away on 14 November 2022 at the age of 33, doing what she loved most — spearfishing near the Italian island of Pantelleria. Alma was a visionary scientist at the beginning of what was promising to become a stellar career. As a physicist turned biologist, Alma wanted to unravel how complexity emerges from simplicity. Despite her young age, she had already made an important impact on the field by showing how the activities of microbial communities emerge from interactions between individual cells. Alma was a warm and caring friend, and a committed and inspiring mentor. She pursued science with fearless passion, creativity, vision and dedication.Alma Dal Co in 2016 in Joshua Tree National Park, California. Photograph by Simon van Vliet.Alma had an exceptionally sharp and creative mind, and an insatiable curiosity. She kept exploring new directions, working on everything from gene-regulatory circuits to microbial communities, to developmental processes. She was the embodiment of a true interdisciplinary scientist, combining state-of-the-art experiments with advanced computational approaches. The unifying theme of her work was to understand how interactions between individuals (be it fish, microorganisms or pancreatic cells) give rise to complex behaviour at higher levels of organization. She strived to derive simple, quantitative rules to explain the complexity that we see around us. Alma believed that science is a team effort: she was generous with her time, and always happy to discuss ideas and share resources. No matter where she went, she quickly connected with people, built formal and informal networks, and fostered collaborations and friendships.Alma was born in Turin and grew up in Venice, in Italy. Her true home, however, was Pantelleria, an Italian island in the Mediterranean Sea off the coast of Sicily. Alma spent her summers in the sea from an early age, developing a deep and lasting bond with it. The sea was not only a place to recharge, but also a source of inspiration: Alma became fascinated by the intricate behaviours of octopuses and schools of fish, creating a lasting sense of wonder about the natural world. Alma’s primary education focused on the humanities, but most of all music. In 2002, she was accepted to the conservatorium in Venice to study the piano. However, her love for the natural world remained and in 2007 she started studying physics in Padua. In 2011, she finished her BSc in physics and a year later her education at the conservatorium. Both a career in music and in science were an option, but Alma chose science and moved to Turin to study the physics of complex systems. Music always remained important in her life, and she played the piano whenever she could.Alma’s transition to biology started in Turin in the laboratory of Michelle Caselle, where she used mathematical models to study gene regulatory networks. She discovered how the regulation of gene expression can reduce stochastic fluctuations and provide robustness to the expression of an organism’s phenotype (A. Dal Co et al. Nucleic Acids Res. 45, 1069–1078; 2017). In 2014, she exchanged the blackboard for the wet lab, and moved to Zurich, Switzerland, to start her PhD with Martin Ackermann at ETH and the aquatic research institute Eawag. Despite the struggles of having to learn hands-on biology without formal training, she was not deterred from pursuing a highly challenging project.Alma developed an innovative approach to gain a mechanistic understanding of how metabolic interactions between individual microbial cells determine the dynamics of spatially structured communities. She quantified the growth of single cells in a synthetic microbial community and developed computational tools to infer their interaction network. She showed that cells in these communities live in a small world: they only interact with few neighbours (A. Dal Co et al. Nat. Ecol. Evol. 4, 366–375; 2020). This short interaction range limits the growth of mutually dependent microorganisms, thereby counteracting the evolution of metabolic specialization. Moreover, Alma developed a mathematical framework to quantitatively predict the dynamics of microbial communities from the molecular properties of the underlying intercellular interactions (S. van Vliet et al. PLoS Comput. Biol. 18, e1009877; 2022). Together, these works have made an important contribution to our understanding of how microbial communities function, and they have inspired numerous follow-up projects, both by Alma herself (for example, A. Dal Co et al. Phil. Trans. R. Soc. B 374, 20190080; 2019) and by others in the field (for example, J. van Gestel et al. Nat. Commun. 12, 2324; 2021).Alma finished her PhD in 2019, winning the ETH medal for an outstanding thesis. She then moved to Harvard to study developmental processes, together with Michael Brenner. She quickly developed a large network of collaborators and designed an innovative project to study pancreatic islet formation. However, COVID-19-related laboratory restrictions brought an early end to these plans, and Alma instead developed a novel computational framework that can be applied to both animal tissues and microbial communities to study how local cell–cell interactions can create spatial structure at the scale of multicellular systems.In September 2021, Alma started an assistant professorship at the University of Lausanne. At the age of 32, she was one of youngest professors ever appointed there. Thanks to her leadership, she quickly assembled a highly interdisciplinary, collaborative and cohesive team of talented young scientists. The group’s research was as varied as Alma’s interests. A major theme was to gain a quantitative understanding of how cell–cell interactions affect the function and structure of microbial communities and other multicellular systems. Her group combines state-of-the art experimental tools such as optogenetics, microfluidics and single-cell imaging, with computational approaches and mathematical modelling to study the dynamics of a wide range of model systems.During her very short career as an assistant professor, Alma was a core member of the Swiss National Research Program on microbiome research (https://nccr-microbiomes.ch); was awarded two major grants; established a large network of collaborators; and was invited to present her work at numerous international meetings. Most importantly, Alma fostered a strong sense of community, both in her group and beyond — creating an open, inclusive and interactive space to discuss science and life.Interacting with Alma was never dull: her passion and energy were infectious and her curiosity and openness a source of inspiration. She always kept you on your toes with her constant stream of pointed questions. But most of all, her easy laugh and positive energy made working with her an extraordinarily joyous experience.With Alma the world has lost a visionary scientist. We are deeply saddened that we will never see what other discoveries she would have made. However, it offers some conciliation to see how profoundly Alma has impacted the people around her, leaving a lasting impression even on those she only briefly met. Her vision, spirit and leadership have profoundly changed many around her and will continue to be a source of inspiration for many years to come. More

Hung, K.-L.J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. B 285, 20172140 (2018).Article 

Google Scholar 
Klein, A.-M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).Article 

Google Scholar 
Mashilingi, S. K., Zhang, H., Garibaldi, L. A. & An, J. Honeybees are far too insufficient to supply optimum pollination services in agricultural systems worldwide. Agr. Ecosyst. Environ. 335, 108003 (2022).Article 

Google Scholar 
Stein, K. et al. Bee pollination increases yield quantity and quality of cash crops in Burkina Faso West Africa. Sci. Rep. 7, 17691 (2017).Article 
ADS 

Google Scholar 
Neumann, P. & Carreck, N. L. Honey bee colony losses. J. Apic. Res. 49, 1–6 (2010).Article 

Google Scholar 
Pettis, J. S. & Delaplane, K. S. Coordinated responses to honey bee decline in the USA. Apidologie 41, 256–263 (2010).Article 

Google Scholar 
Potts, S. G. et al. Declines of managed honey bees and beekeepers in Europe. J. Apic. Res. 49, 15–22 (2010).Article 

Google Scholar 
Moritz, R. F. A. & Erler, S. Lost colonies found in a data mine: Global honey trade but not pests or pesticides as a major cause of regional honeybee colony declines. Agr. Ecosyst. Environ. 216, 44–50 (2016).Article 

Google Scholar 
Osterman, J. et al. Global trends in the number and diversity of managed pollinator species. Agr. Ecosyst. Environ. 322, 107653 (2021).Article 

Google Scholar 
Requier, F. et al. Trends in beekeeping and honey bee colony losses in Latin America. J. Apic. Res. 57, 657–662 (2018).Article 

Google Scholar 
Vandame, R. & Palacio, M. A. Preserved honey bee health in Latin America: a fragile equilibrium due to low-intensity agriculture and beekeeping?. Apidologie 41, 243–255 (2010).Article 

Google Scholar 
Antúnez, K., Invernizzi, C., Mendoza, Y., vanEngelsdorp, D. & Zunino, P. Honeybee colony losses in Uruguay during 2013–2014. Apidologie 48, 364–370 (2017).Article 

Google Scholar 
Castilhos, D., Bergamo, G. C. & Kastelic, J. P. Honey bee colony losses in Brazil in 2018–2019 / Perdas de colônias de abelhas no Brasil em 2018–2019. Braz. J. Anim. Environ. Res. 4, 5017–5041 (2021).
Google Scholar 
Castilhos, D., Bergamo, G. C., Gramacho, K. P. & Gonçalves, L. S. Bee colony losses in Brazil: a 5-year online survey. Apidologie 50, 263–272 (2019).Article 

Google Scholar 
Maggi, M. et al. Honeybee health in South America. Apidologie 47, 835–854 (2016).Article 

Google Scholar 
SIAP. Sistema de Información Agroalimentaria de Consulta. http://www.agricultura.gob.mx/datos-abiertos/siap (2019).Namdar-Irani, M., Sotomayor, O. & Rodrigues, M. Tendencias estructurales en la agricultura de América Latina: desafíos para las políticas públicas. 45 (2020).Torres-Ruiz, A., Jones, R. W. & Barajas, R. A. Present and Potential use of Bees as Managed Pollinators in Mexico1. Southwestern entomologist (2013).Brodschneider, R. et al. Multi-country loss rates of honey bee colonies during winter 2016/2017 from the COLOSS survey. J. Apic. Res. 57, 452–457 (2018).Article 

Google Scholar 
Gray, A. et al. Loss rates of honey bee colonies during winter 2017/18 in 36 countries participating in the COLOSS survey, including effects of forage sources. J. Apic. Res. 58, 479–485 (2019).Article 

Google Scholar 
Medina-Flores, C. A. et al. Pérdida de colonias de abejas melíferas y factores asociados en el centro-occidente de México en los inviernos del 2016 al 2019. Revista Bio Ciencias 8, 11 (2021).Article 

Google Scholar 
vanEngelsdorp, D. & Meixner, M. D. A historical review of managed honey bee populations in Europe and the United States and the factors that may affect them. J. Invert. Pathol. 103(Supplement), S80–S95 (2010).Hristov, P., Shumkova, R., Palova, N. & Neov, B. Honey bee colony losses: Why are honey bees disappearing?. Sociobiology 68, e5851–e5851 (2021).Article 

Google Scholar 
Shanahan, M. Honey bees and industrial agriculture: What researchers are missing, and why it’s a problem. J. Insect Sci. 22, 14 (2022).Article 

Google Scholar 
Nearman, A. & vanEngelsdorp, D. Water provisioning increases caged worker bee lifespan and caged worker bees are living half as long as observed 50 years ago. Sci. Rep. 12, 18660. https://doi.org/10.1038/s41598-022-21401-2 (2022).Article 
ADS 
CAS 

Google Scholar 
Ellis, J. D., Evans, J. D. & Pettis, J. Colony losses, managed colony population decline, and Colony Collapse Disorder in the United States. J. Apic. Res. 49, 134–136 (2010).Article 

Google Scholar 
Guzmán-Novoa, E., Benítez, A. C., Montaño, L. G. E. & Novoa, G. G. Colonization, impact and control of Africanized honey bees in Mexico. Veterinaria México OA 42, (2011).Becerra-Guzmán, F., Guzmán-Novoa, E., Correa-Benítez, A. & Zozaya-Rubio, A. Length of life, age at first foraging and foraging life of Africanized and European honey bee (Apis mellifera) workers, during conditions of resource abundance. J. Apic. Res. 44, 151–156 (2005).Article 

Google Scholar 
Guzman-Novoa, E. & Uribe-Rubio, J. L. Honey production by European, Africanized and hybrid honey bee (Apis mellifera) colonies in Mexico. American bee journal (2004).Guzman-Novoa, E. et al. The Process and Outcome of the Africanization of Honey Bees in Mexico: Lessons and Future Directions. Front. Ecol. Evol. 8, (2020).Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).Article 

Google Scholar 
Otto, C. R. V., Roth, C. L., Carlson, B. L. & Smart, M. D. Land-use change reduces habitat suitability for supporting managed honey bee colonies in the Northern Great Plains. PNAS 113, 10430–10435 (2016).Article 
ADS 
CAS 

Google Scholar 
Outhwaite, C. L., McCann, P. & Newbold, T. Agriculture and climate change are reshaping insect biodiversity worldwide. Nature 605, 97–102 (2022).Article 
ADS 
CAS 

Google Scholar 
Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A.-L. & Totland, Ø. How does climate warming affect plant-pollinator interactions?. Ecol. Lett. 12, 184–195 (2018).Article 

Google Scholar 
Cooper, P. D., Schaffer, W. M. & Buchmann, S. L. Temperature Regulation of Honey Bees (Apis Mellifera) Foraging in the Sonoran Desert. J. Exp. Biol. 114, 1–15 (1985).Article 

Google Scholar 
Stalidzans, E. et al. Dynamics of weight change and temperature of Apis mellifera (Hymenoptera: Apidae) Colonies in a wintering building with controlled temperature. J. Econ. Entomol. 110, 13–23 (2017).CAS 

Google Scholar 
Qu, M., Wan, J. & Hao, X. Analysis of diurnal air temperature range change in the continental United States. Weather Clim. Extremes 4, 86–95 (2014).Article 

Google Scholar 
Braganza, K., Karoly, D. J. & Arblaster, J. M. Diurnal temperature range as an index of global climate change during the twentieth century. Geophys. Res. Lett. 31, (2004).Halsch, C. A. et al. Insects and recent climate change. Proc. Natl. Acad. Sci. 118, e2002543117 (2021).Article 
CAS 

Google Scholar 
Abou-Shaara, H. F. The foraging behaviour of honey bees, Apis mellifera: A review. Vet. Med. 59, 1–10 (2014).Article 

Google Scholar 
Joshi, N. & Joshi, P. Foraging Behaviour of Apis Spp. on Apple Flowers in a Subtropical Environment. New York Sci. J. 3, (2010).Gounari, S., Proutsos, N. & Goras, G. How does weather impact on beehive productivity in a Mediterranean island? Ital. J. Agrometeorol. 65–81. https://doi.org/10.36253/ijam-1195 (2022).Delgado, D. L., Pérez, M. E., Galindo-Cardona, A., Giray, T. & Restrepo, C. Forecasting the Influence of Climate Change on Agroecosystem Services: Potential Impacts on Honey Yields in a Small-Island Developing State. Psyche J. Entomol. https://www.hindawi.com/journals/psyche/2012/951215/. https://doi.org/10.1155/2012/951215 (2012).Alves, L. H. S., Cassino, P. C. R. & Prezoto, F. Effects of abiotic factors on the foraging activity of Apis mellifera Linnaeus, 1758 in inflorescences of Vernonia polyanthes Less (Asteraceae). Acta Sci. Anim. Sci. 37, 405–409 (2015).Abou-Shaara, H. Expectations about the potential impacts of climate change on Honey Bee Colonies in Egypt. J. Apicult. 31, 157–164 (2016).Article 

Google Scholar 
Donkersley, P., Rhodes, G., Pickup, R. W., Jones, K. C. & Wilson, K. Honeybee nutrition is linked to landscape composition. Ecol. Evol. 4, 4195–4206 (2014).Article 

Google Scholar 
Michel-Cuello, C. & Aguilar-Rivera, N. Climate change effects on agricultural production systems in México. in Handbook of Climate Change Across the Food Supply Chain (eds. Leal Filho, W., Djekic, I., Smetana, S. & Kovaleva, M.) 335–353 (Springer International Publishing, 2022). https://doi.org/10.1007/978-3-030-87934-1_19.LaFevor, M. C. Spatial and temporal changes in crop species production diversity in Mexico (1980–2020). Agriculture 12, 985 (2022).Article 

Google Scholar 
Smart, M. D., Otto, C. R. V. & Lundgren, J. G. Nutritional status of honey bee (Apis mellifera L.) workers across an agricultural land-use gradient. Sci. Rep. 9, 1–10 (2019).Alaux, C., Ducloz, F., Crauser, D. & Conte, Y. L. Diet effects on honeybee immunocompetence. Biol. Lett. rsbl20090986. https://doi.org/10.1098/rsbl.2009.0986 (2010).Dolezal, A. G., Carrillo-Tripp, J., Miller, W. A., Bonning, B. C. & Toth, A. L. Intensively Cultivated Landscape and Varroa Mite Infestation Are Associated with Reduced Honey Bee Nutritional State. PLoS One 11, (2016).Pasquale, G. D. et al. Variations in the Availability of Pollen Resources Affect Honey Bee Health. PLoS ONE 11, e0162818 (2016).Article 

Google Scholar 
Kaluza, B. F. et al. Social bees are fitter in more biodiverse environments. Sci Rep 8, 1–10 (2018).Article 
CAS 

Google Scholar 
Ricigliano, V. A. et al. Honey bee colony performance and health are enhanced by apiary proximity to US Conservation Reserve Program (CRP) lands. Sci. Rep. 9, 4894 (2019).Article 
ADS 

Google Scholar 
Clermont, A., Eickermann, M., Kraus, F., Hoffmann, L. & Beyer, M. Correlations between land covers and honey bee colony losses in a country with industrialized and rural regions. Sci. Total Environ. 532, 1–13 (2015).Article 
ADS 
CAS 

Google Scholar 
Kuchling, S. et al. Investigating the role of landscape composition on honey bee colony winter mortality: A long-term analysis. Sci. Rep. 8, 1 (2018).Article 
CAS 

Google Scholar 
Dixon, D. J., Zheng, H. & Otto, C. R. V. Land conversion and pesticide use degrade forage areas for honey bees in America’s beekeeping epicenter. PLoS ONE 16, e0251043 (2021).Article 
CAS 

Google Scholar 
Mendoza-Ponce, A., Corona-Núñez, R. O., Galicia, L. & Kraxner, F. Identifying hotspots of land use cover change under socioeconomic and climate change scenarios in Mexico. Ambio 48, 336–349 (2019).Article 

Google Scholar 
Magaña, M. et al. Productividad de la apicultura en México y su impacto sobre la rentabilidad. Revista mexicana de ciencias agrícolas 7, 1103–1115 (2016).Article 

Google Scholar 
Mitchell, E. a. D. et al. A worldwide survey of neonicotinoids in honey. Science 358, 109–111 (2017).Pacheco, A. P. Identificación de residuos tóxicos en miel de diferentes procedencias en la zona centro del Estado de Veracruz / Identification of toxic residues in honey from different sources in the central zone of the State of Veracruz. CIBA Revista Iberoamericana de las Ciencias Biológicas y Agropecuarias 1, 1–42 (2014).Article 

Google Scholar 
Ruiz-Toledo, J. et al. Organochlorine Pesticides in Honey and Pollen Samples from Managed Colonies of the Honey Bee Apis mellifera Linnaeus and the Stingless Bee Scaptotrigona mexicana Guérin from Southern Mexico. Insects 9, 54 (2018).Article 

Google Scholar 
Valdovinos-Flores, C., Alcantar-Rosales, V. M., Gaspar-Ramírez, O., Saldaña-Loza, L. M. & Dorantes-Ugalde, J. A. Agricultural pesticide residues in honey and wax combs from Southeastern, Central and Northeastern Mexico. J. Apic. Res. 56, 667–679 (2017).Article 

Google Scholar 
Gómez-Escobar, E. et al. Effect of GF-120 (Spinosad) aerial sprays on colonies of the stingless Bee Scaptotrigona mexicana (Hymenoptera: Apidae) and the Honey Bee (Hymenoptera: Apidae). J. Econ. Entomol. 111, 1711–1715 (2018).Article 

Google Scholar 
Sánchez, D., Solórzano, E. D. J., Liedo, P. & Vandame, R. Effect of the natural pesticide Spinosad (GF-120 Formulation) on the Foraging behavior of Plebeia moureana (Hymenoptera: Apidae). J. Econ. Entomol. 105, 1234–1237 (2012).Article 

Google Scholar 
Cabrera-Marín, N. V., Liedo, P. & Sánchez, D. The Effect of Application Rate of GF-120 (Spinosad) and Malathion on the Mortality of Apis mellifera (Hymenoptera: Apidae) Foragers. J. Econ. Entomol. 109, 515–519 (2016).Article 

Google Scholar 
Valdovinos-Núñez, G. R. et al. Comparative toxicity of pesticides to stingless bees (Hymenoptera: Apidae: Meliponini). J. Econ. Entomol. 102, 1737–1742 (2009).Article 

Google Scholar 
ANADA. Atlas Nacional de las Abejas y Derivados Apícolas. https://atlas-abejas.agricultura.gob.mx/cap2.html#212_Enfermedades_y_Plagas_de_las_Colmenas (2021).Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).Article 

Google Scholar 
Daberkow, S., Korb, P. & Hoff, F. Structure of the U.S. beekeeping industry: 1982–2002. J. Econ. Entomol. 102, 868–886 (2009).Saunders, S. P. et al. Unraveling a century of global change impacts on winter bird distributions in the eastern United States. Glob. Change Biol. 28, 2221–2235 (2022).Article 
CAS 

Google Scholar 
CICESE. Base de datos climatológica nacional (Sistema CLICOM). http://clicom-mex.cicese.mx/ (2018).CONEVAL. Metodología para la medición de pobreza en México | CONEVAL. https://www.coneval.org.mx/Medicion/MP/Paginas/Metodologia.aspx.Wood, S. N. Generalized Additive Models: An Introduction with R, Second Edition. (CRC Press, 2017).Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R. (Springer-Verlag, 2009).Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2016).Lichstein, J. W., Simons, T. R., Shriner, S. A. & Franzreb, K. E. Spatial autocorrelation and autoregressive models in ecology. Ecol. Monogr. 72, 445–463 (2002).Article 

Google Scholar 
Döke, M. A., Frazier, M. & Grozinger, C. M. Overwintering honey bees: biology and management. Curr. Opin. Insect Sci. 10, 185–193 (2015).Article 

Google Scholar 
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. J. Open Sour. Softw. 6, 3139 (2021).Article 
ADS 

Google Scholar 
Simpson, G. L. Modelling palaeoecological time series using generalised additive models. Front. Ecol. Evol. 6, 1 (2018).Article 

Google Scholar 
Furrer, R., Nychka, D., Sain, S. & Nychka, M. D. Title Tools for spatial data. (2012). More