Philippa Kaur

Eilers, E. J., Kremen, C., Smith Greenleaf, S., Garber, A. K. & Klein, A. M. Contribution of pollinator-mediated crops to nutrients in the human food supply. PLoS ONE 6, 21363 (2011).ADS 

Google Scholar 
Williams, P. H. The dependence of crop pollination within the European Union on pollination by honey bees. Agric. Zool. Rev. 6, 229–257 (1994).
Google Scholar 
Burd, M. Bateman’s principle and plant reproduction: the role of pollen limitation in fruit and seed set. Bot. Rev. 60, 83–139 (1994).MathSciNet 

Google Scholar 
Aguilar, R., Ashworth, L., Galetto, L. & Aizen, M. A. Plant reproductive susceptibility to habitat fragmentation: Review and synthesis through a meta-analysis. Ecol. Lett. 9, 968–980 (2006).
Google Scholar 
Potts, S. G. et al. Declines of managed honey bees and beekeepers in Europe. J. Apic. Res. 49, 15–22 (2010).
Google Scholar 
van Engelsdorp, D., Hayes, J., Underwood, R. M. & Pettis, J. A survey of honey bee colony losses in the U.S., fall 2007 to spring 2008. PLoS ONE 3, e4071 (2008).ADS 

Google Scholar 
Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19, 915–918 (2009).CAS 

Google Scholar 
Van Engelsdorp, D. et al. Colony collapse disorder: A descriptive study. PLoS ONE 4, e6481 (2009).ADS 

Google Scholar 
Genersch, E. American foulbrood in honeybees and its causative agent, Paenibacillus larvae. J. Invertebr. Pathol. 103(suppl 1), 10–19 (2010).
Google Scholar 
Graystock, P., Yates, K., Darvill, B., Goulson, D. & Hughes, W. O. H. Emerging dangers: Deadly effects of an emergent parasite in a new pollinator host. J. Invertebr. Pathol. 114, 114–119 (2013).
Google Scholar 
Insolia, L. et al. Honey bee colony loss linked to parasites, pesticides and extreme weather across the United States. Sci. Rep. 12(1), 20787. https://doi.org/10.1038/s41598-022-24946-4 (2022).Article 
ADS 
CAS 

Google Scholar 
Sanchez-Bayo, F. & Goka, K. Pesticide residues and bees: A risk assessment. PLoS ONE 9(4), e94482 (2014).ADS 

Google Scholar 
Bolognesi, C. & Merlo, F. D. Pesticides: Human health effects. Encyclop. Environ. Health 1, 438–453 (2011).
Google Scholar 
Mullin, C. A. et al. High levels of miticides and agrochemicals in North American apiaries: Implications for honey bee health. PLoS ONE 1, e9754 (2015).
Google Scholar 
Calatayud-Vernich, P., Calatayud, F., Simó, E. & Picó, Y. Pesticide residues in honey bees, pollen and beeswax: Assessing beehive exposure. Environ. Pollut. 241, 106–114. https://doi.org/10.1016/j.envpol.2018.05.062 (2018).Article 
CAS 

Google Scholar 
Woodcock, B. A. et al. Impacts of neonicotinoid use on long-term population changes in wild bees in England. Nat. Commun. 2016(7), 12459 (2016).ADS 

Google Scholar 
Zhao, H. et al. Review on effects of some insecticides on honey bee health. Pestic. Biochem. Physiol. 188, 105219. https://doi.org/10.1016/j.pestbp.2022.105219 (2022).Article 
CAS 

Google Scholar 
Ludicke, J. C. & Nieh, J. C. Thiamethoxam impairs honey bee visual learning, alters decision times, and increases abnormal behaviors. Ecotoxicol. Environ. Saf. 193, 110367 (2020).CAS 

Google Scholar 
Tison, L., Duer, A., Púčiková, V., Greggers, U. & Menzel, R. Detrimental effects of clothianidin on foraging and dance communication in honey bees. PLoS ONE 15(10), e0241134 (2020).CAS 

Google Scholar 
Fent, K., Schmid, M. & Christen, V. Global transcriptome analysis reveals relevant effects at environmental concentrations of cypermethrin in honey bees (Apis mellifera). Environ. Pollut. 259, 113715 (2020).CAS 

Google Scholar 
Christen, V., Krebs, J., Bünter, I. & Fent, K. Biopesticide spinosad induces transcriptional alterations in genes associated with energy production in honey bees (Apis mellifera) at sublethal concentrations. J. Hazard. Mater. 378, 120736 (2019).CAS 

Google Scholar 
Christen, V., Krebs, J. & Fent, K. Fungicides chlorothanolin, azoxystrobin and folpet induce transcriptional alterations in genes encoding enzymes involved in oxidative phosphorylation and metabolism in honey bees (Apis mellifera) at sublethal concentrations. J. Hazard. Mater. 377, 215–226 (2019).CAS 

Google Scholar 
Fent, K., Haltiner, T., Kunz, P. & Christen, V. Insecticides cause transcriptional alterations of endocrine related genes in the brain of honey bee foragers. Chemosphere 260, 127542 (2020).ADS 
CAS 

Google Scholar 
Christen, V., Grossar, D., Charrière, J. D., Eyer, M. & Jeker, L. Correlation between increased homing flight duration and altered gene expression in the brain of honey bee foragers after acute oral exposure to thiacloprid and thiamethoxam. Insect Sci. 1, 1–15 (2021).
Google Scholar 
Mao, W., Schuler, M. A. & Berenbaum, M. R. Disruption of quercetin metabolism by fungicide affects energy production in honey bees (Apis mellifera). Proc. Natl. Acad. Sci. USA 114(10), 2538–2543 (2017).ADS 
CAS 

Google Scholar 
Christen, V., Kunz, P. Y. & Fent, K. Endocrine disruption and chronic effects of plant protection products in bees: Can we better protect our pollinators?. Environ. Pollut. 243(Pt B), 1588–1601 (2018).CAS 

Google Scholar 
Testai, E., Buratti, F. & Di Consiglio, E. Chlorpyrifos Hayes’ Handbook of Pesticide Toxicology 1505–1526 (Academic Press, 2010).
Google Scholar 
Eastmond, D. & Balakrishnan, S. Genotoxicity of Pesticides Hayes’ Handbook of Pesticide Toxicology 357–380 (Academic Press, 2010).
Google Scholar 
Urlacher, E. et al. Measurements of chlorpyrifos levels in forager bees and comparison with levels that disrupt honey bee odor-mediated learning under laboratory conditions. J. Chem. Ecol. 42(2), 127–138 (2016).CAS 

Google Scholar 
Li, Z. et al. Effects of sublethal concentrations of chlorpyrifos on olfactory learning and memory performances in two bee species, Apis mellifera and Apis cerana. Sociobiology 64, 174 (2017).
Google Scholar 
DeGrandi-Hoffman, G., Chen, Y. & Simonds, R. The effects of pesticides on queen rearing and virus titers in honey bees (Apis mellifera L.). Insects 4, 71–89 (2013).
Google Scholar 
Cutler, G. C., Purdy, J., Giesy, J. P. & Solomon, K. R. Risk to pollinators from the use of chlorpyrifos in the United States. In Ecological Risk Assessment for Chlorpyrifos in Terrestrial and Aquatic Systems in the United States Reviews of Environmental Contamination and Toxicology (eds Giesy, J. & Solomon, K.) (Springer, 2014).
Google Scholar 
Christen, V. & Fent, K. Exposure of honey bees (Apis mellifera) to different classes of insecticides exhibit distinct molecular effect patterns at concentrations that mimic environmental contamination. Environ. Pollut. 226, 48–59 (2017).CAS 

Google Scholar 
Stevenson, J. H. The acute toxicity of unformulated pesticides to worker honey bees (Apis mellifera L.). Plant Pathol. 27, 38–40 (1978).CAS 

Google Scholar 
Bartlett, D. W. et al. The strobilurin fungicides. Pest. Manag. Sci. 58, 649–662 (2002).CAS 

Google Scholar 
Ostiguy, N. et al. Honey bee exposure to pesticides: A four-year nationwide study. Insects. 10, 13 (2019).
Google Scholar 
Inoue, L. V. B., Domingues, C. E. C., Gregorc, A., Silva-Zacarin, E. C. M. & Malaspina, O. Harmful effects of pyraclostrobin on the fat body and pericardial cells of foragers of africanized honey bee. Toxics 10, 530. https://doi.org/10.3390/toxics10090530 (2022).Article 
CAS 

Google Scholar 
Nicodemo, D. et al. Mitochondrial respiratory inhibition promoted by pyraclostrobin in fungi is also observed in honey bees. Environ. Toxicol. Chem. 39, 1267–1272 (2020).CAS 

Google Scholar 
Domingues, C. E. C., Inoue, L. V. B., Silva-Zacarin, E. C. M. & Malaspina, O. Foragers of Africanized honeybee are more sensitive to fungicide pyraclostrobin than newly emerged bees. Environ. Pollut. 266, 115267 (2020).
Google Scholar 
Tadei, R. et al. Late effect of larval co-exposure to the insecticide clothianidin and fungicide pyraclostrobin in Africanized Apis mellifera. Sci. Rep 9, 3277 (2019).ADS 

Google Scholar 
Zioga, E., Kelly, R., White, B. & Stout, J. C. Plant protection product residues in plant pollen and nectar: A review of current knowledge. Environ. Res. 189, 109873 (2020).CAS 

Google Scholar 
Corona, M. et al. Vitellogenin, juvenile hormone, insulin signaling, and queen honey bee longevity. Proc. Natl. Acad. Sci. USA 104, 7128–7133 (2007).ADS 
CAS 

Google Scholar 
Winston, M. L. The Biology of the Honey Bee (Harvard University Press, 1987).
Google Scholar 
Ueno, T., Nakaoka, T., Takeuchi, H. & Kubo, T. Differential gene expression in the hypopharyngeal glands of worker honeybees (Apis mellifera L.) associated with an age-dependent role change. Zool. Sci. 8, 557–563 (2009).
Google Scholar 
Kubo, T. et al. Change in the expression of hypopharyngealgland proteins of the worker honeybees (Apis mellifera L.) with age and/or role. J. Biochem. 119, 291–295 (1996).CAS 

Google Scholar 
Ohashi, K., Sawata, M., Takeuchi, H., Natori, S. & Kubo, T. Molecular cloning of cDNA and analysis of expression of the gene for alpha-glucosidase from the hypopharyngeal gland of the honeybee Apis mellifera L. Biochem. Biophys. Res. Commun. 221, 380–385 (1996).CAS 

Google Scholar 
Ohashi, K., Natori, S. & Kubo, T. Expression of amylase and glucose oxidase in the hypopharyngeal gland with an age dependent role change of the worker honeybee (Apis mellifera L.). Eur. J. Biochem. 265, 127–133 (1999).CAS 

Google Scholar 
Chanchao, C., Padoongsupalai, R. & Sangvanich, P. Expression and characterization of α-glucosidase III in the dwarf honeybee, Apis florea (Hymenoptera: Apoidea: Apidae). Insect Sci. 14(4), 283–293 (2007).CAS 

Google Scholar 
Corby-Harris, V. & Snyder, L. A. Measuring hypopharyngeal gland acinus size in honey bee (Apis mellifera) Workers. J. Vis. Exp. 139, 58261 (2018).
Google Scholar 
Yamada, T. & Yamada, K. Comparison of long-term changes in size and longevity of bee colonies in mid-west Japan and Maui with and without exposure to pesticide, cold winters, and mites. PeerJ 8, e9505 (2020).
Google Scholar 
Rinkevich, F. D. et al. Genetics, synergists, and age affect insecticide sensitivity of the honey bee, Apis mellifera. PLoS ONE 10(10), e0139841 (2015).
Google Scholar 
Weidenmüller, A. The control of nest climate in bumblebee (Bombus terrestris) colonies: Interindividual variability and self reinforcement in fanning response. Behav. Ecol. 15(1), 120–128 (2004).MathSciNet 

Google Scholar 
Flatt, T., Tu, M. P. & Tatar, M. Hormonal pleiotropy and the juvenile hormone regulation of Drosophila development and life history. BioEssays 27, 999–1010 (2005).CAS 

Google Scholar 
Wu, M. C., Chang, Y. W., Lu, K. H. & Yang, E. C. Gene expression changes in honey bees induced by sublethal imidacloprid exposure during the larval stage. Insect. Biochem. Mol. Biol. 88, 12–20 (2017).CAS 

Google Scholar 
Ament, S. A., Corona, M., Pollock, H. S. & Robinson, G. E. Insulin signaling is involved in the regulation of worker division of labor in honey bee colonies. Proc. Natl. Acad. Sci. USA 105, 4226–4231 (2008).ADS 
CAS 

Google Scholar 
Nicodemo, D. et al. Fipronil and imidacloprid reduce honeybee mitochondrial activity. Environ. Toxicol. Chem. 33(9), 2070–2075 (2014).CAS 

Google Scholar 
Syromyatnikov, M. Y., Lopatin, A. V., Starkov, A. A. & Popov, V. N. Isolation and properties of flight muscle mitochondria of the bumblebee Bombus terrestris (L.). Biochemistry 78(8), 909–914 (2013).CAS 

Google Scholar 
Dayer, F. C. Coadaptation of colony design and worker performance in honeybees. In Diversity in the Genus Apis (ed. Smith, D. R.) 2133–2245 (Westview Press, 1991).
Google Scholar 
Simon-Delso, N., Amaral-Rogers, V. & Belzunces, L. P. Systemic insecticides (neonicotinoids and fipronil): Trends, uses, mode of action and metabolites. Environ. Sci. Pollut. Res. 22, 5–34 (2015).CAS 

Google Scholar 
Evans, J. D. et al. Immune pathways and defence mechanisms in honey bees Apis mellifera. Insect. Mol. Biol. 5, 645–656 (2006).
Google Scholar 
Pankiw, T. & Page, R. E. Response thresholds to sucrose predict foraging division of labor in honeybees. Behav. Ecol. Sociobiol. 47, 265–267 (2000).
Google Scholar  More

IUCN. The IUCN red list of threatened species. Version 2022-1. https://www.iucnredlist.org. Accessed on 17 September 2022. (2022).O’Hanlon, S., Rieux, A., Farrer, R. A. & Rosa, G. M. Recent Asian origin of chytrid fungi causing global amphibian declines. Science 360, 621–627 (2018).ADS 

Google Scholar 
Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363, 1459–1463 (2019).ADS 
CAS 

Google Scholar 
La Marca, E. et al. Catastrophic population declines and extinctions in neotropical Harlequin frogs (Bufonidae: Atelopus). Biotropica 37, 190–201 (2005).
Google Scholar 
Rovito, S. M., Parra-Olea, G., Vasquez-Almazan, C. R., Papenfuss, T. J. & Wake, D. B. Dramatic declines in neotropical salamander populations are an important part of the global amphibian crisis. Proc. Natl. Acad. Sci. U.S.A. 106, 3231–3236 (2009).ADS 
CAS 

Google Scholar 
Stegen, G. et al. Drivers of salamander extirpation mediated by Batrachochytrium salamandrivorans. Nature 544, 353–356 (2017).ADS 
CAS 

Google Scholar 
Martel, A. et al. Recent introduction of a chytrid fungus endangers western palearctic salamanders. Science 346, 630–631 (2014).ADS 
CAS 

Google Scholar 
Green, D. E., Converse, K. A. & Schrader, A. K. Epizootiology of sixty-four amphibian morbidity and mortality events in the USA, 1996–2001. Annu. NY Acad. Sci. 969, 323–339 (2002).ADS 

Google Scholar 
Duffus, A. L. J. & Cunningham, A. A. Major disease threats to European amphibians. Herpetol. J. 20, 117–127 (2010).
Google Scholar 
Teacher, A. G. F., Cunningham, A. A. & Garner, T. W. J. Assessing the long-term impact of Ranavirus infection in wild common frog populations. Anim. Conserv. 13, 514–522 (2010).
Google Scholar 
Chinchar, V. G. & Waltzek, T. B. Ranaviruses: Not just for frogs. PLoS Pathog. 10, e1003850 (2014).
Google Scholar 
Nickerson, M. A. & Mays, C. E. The hellbenders: North American giant salamanders. Milwaukee Public Mus. Publ. Biol. Geol. 1, 1–106 (1973).
Google Scholar 
Wheeler, B. A., Prosen, E., Mathis, A. & Wilkinson, R. F. Population declines of a long- lived salamander: A 20+ year study of hellbenders, Cryptobranchus alleganiensis. Biol. Conserv. 109, 151–156 (2003).
Google Scholar 
Freake, M. J. & DePerno, C. S. Importance of demographic surveys and public lands for the conservation of eastern hellbenders Cryptobranchus alleganiensis alleganiensis in southeast USA. PLoS ONE 12, e0179153 (2017).
Google Scholar 
USFWS. Endangered and threatened wildlife and plants; Endangered status for the Ozark Hellbender salamander. 50 CFR Part 23. Fed. Reg. 76, 61956–61978 (2011).
Google Scholar 
USFWS. Species status assessment report for the Eastern Hellbender (Cryptobranchus alleganiensis alleganiensis). p 104 (2018).Pugh, M., Hutchins, M., Madritch, M., Siefferman, L. & Gangloff, M. M. Land-use and local physical and chemical habitat parameters predict site occupancy by hellbender salamanders. Hydrobiologia 770, 105–116 (2015).
Google Scholar 
Bodinof-Jachowski, C. M. & Hopkins, W. A. Loss of catchment-wide riparian forest cover is associated with reduced recruitment in a long-lived amphibian. Biol. Cons. 202, 215–227 (2018).
Google Scholar 
Bodinof, C. M., Briggler, J. T. & Duncan, M. C. Historic occurrence of the amphibian chytrid fungus Batrachochytrium dendrobatidis in hellbender Cryptobranchus alleganiensis populations from Missouri. Dis. Aquat. Org. 96, 1–7 (2011).
Google Scholar 
Hardman, R. H. et al. Geographic and individual determinants of important amphibian pathogens in hellbenders (Cryptobranchus alleganiensis) in Tennessee and Arkansas, USA. J. Wildl. Dis. 56, 803–814 (2020).CAS 

Google Scholar 
Bales, E. K. et al. Pathogenic chytrid fungus Batrachochytrium dendrobatidis, but not B. salamandrivorans, detected on eastern hellbenders. PLoS ONE 10, e0116405 (2015).
Google Scholar 
Souza, M. J., Gray, M. J., Colclough, P. & Miller, D. L. Prevalence of infection by Batrachochytrium dendrobatidis and ranavirus in eastern hellbenders (Cryptobranchus alleganiensis alleganiensis) in eastern Tennessee. J. Wildl. Dis. 48, 560–566 (2012).
Google Scholar 
Gonynor, J. L., Yabsley, M. J. & Jensen, J. B. A preliminary survey of Batrachochytrium dendrobatidis exposure in hellbenders from a stream in Georgia, USA. Herpetol. Rev. 42, 58–59 (2011).
Google Scholar 
Briggler, J. T., Larson, K. A. & Irwin, K. J. Presence of the amphibian chytrid fungus (Batrachochytrium dendrobatidis) on hellbenders (Cryptobranchus alleganiensis) in the Ozark highlands. Herpetol. Rev. 39, 443–444 (2008).
Google Scholar 
Dusick, A., Flatland, B., Craig, L. & Ferguson, S. What is your diagnosis? Skin scraping from a hellbender. Vet. Clin. Pathol. 46, 183–184 (2017).
Google Scholar 
Dean, N., Ossiboff, R., Bunting, E., Schuler, K., Rothrock, A., & Roblee, K. The eastern hellbender and Batrachochytrium dendrobatidis (Bd) in western New York. In Proceedings of the 65th International Conference of the Wildlife Disease Association p. 151 (2016).Cusaac, J. P. et al. Emerging pathogens and a current-use pesticide: potential impacts on eastern hellbenders. J. Aquat. Anim. Health 33, 24–32 (2021).CAS 

Google Scholar 
Geng, Y. et al. First report of a ranavirus associated with morbidity and mortality in farmed Chinese giant salamanders (Andrias davidianus). J. Comp. Pathol. 145, 96–102 (2011).
Google Scholar 
Hardman, R. H., Irwin, K. J., Sutton, W. B. & Miller, D. L. Evaluation of severity and factors contributing to foot lesions in endangered Ozark Hellbenders, Cryptobranchus alleganiensis bishopi. Front. Vet. Sci. 7, 1–10 (2020).
Google Scholar 
Hernández-Gómez, O., Kimble, S. J. A., Briggler, J. T. & Williams, R. T. Characterization of the cutaneous bacterial communities of two giant salamander subspecies. Microb. Ecol. 73, 445–454 (2017).
Google Scholar 
Miller, B. T. & Miller, J. L. Prevalence of physical abnormalities in eastern hellbender (Cryptobranchus alleganiensis alleganiensis) populations of middle Tennessee. Southeast. Nat. 4, 513–520 (2005).
Google Scholar 
Shoemaker, V. H. & Nagy, K. Osmoregulation in amphibians and reptiles. Annu. Rev. Physiol. 39, 449–471 (1977).CAS 

Google Scholar 
Guimond, R. W. & Hutchison, V. H. Aquatic respiration: An unusual strategy in the hellbender Cryptobranchus alleganiensis alleganiensis (Daudin). Science 182, 1263–1265 (1973).ADS 
CAS 

Google Scholar 
Rollins-Smith, L. A. & Conlon, J. M. Antimicrobial peptide defenses against chytridiomycosis, an emerging infectious disease of amphibian populations. Dev. Comp. Immunol. 29, 589–598 (2005).CAS 

Google Scholar 
Brogden, K. A. Antimicrobial peptides: Pore formers or metabolic inhibitors in bacteria. Nat. Rev. Microbiol. 3, 238–250 (2005).CAS 

Google Scholar 
Xu, X. & Lai, R. The chemistry and biological activities of peptides from amphibian skin secretions. Chem. Rev. 115, 1760–1846 (2015).CAS 

Google Scholar 
Woodhams, D. C. et al. Population trends associated with antimicrobial peptide defenses against chytridiomycosis in Australian frogs. Oecologica 146, 531–540 (2006).ADS 

Google Scholar 
Rollins-Smith, L. A. et al. Antimicrobial peptide defenses of the mountain yellow-legged frog (Rana muscosa). Dev. Comp. Immunol. 30, 831–842 (2006).CAS 

Google Scholar 
Van Rooij, P., Martel, A., Haesebrouck, F. & Pasmans, F. Amphibian chytridiomycosis: A review with focus on fungus-host interactions. Vet. Res. 46, 137 (2015).
Google Scholar 
Demori, I. et al. Peptides for skin protection and healing in amphibians. Molecules 24, 347 (2019).
Google Scholar 
Wu, J. et al. A frog cathelicidin peptide effectively promotes cutaneous wound healing in mice. Biochem. J. 475, 2785–2799 (2018).CAS 

Google Scholar 
Tennessen, J. A. et al. Variations in the expressed antimicrobial peptide repertoire of northern leopard frog (Rana pipiens) populations suggest intraspecies differences in resistance to pathogens. Dev. Comp. Immunol. 33, 1247–1257 (2009).CAS 

Google Scholar 
Tatiersky, L. et al. Effect of glucocorticoids on expression of cutaneous antimicrobial peptides in northern leopard frogs (Lithobates pipiens). BMC Vet. Res. 11, 191 (2015).
Google Scholar 
Pereira, K. E. & Woodley, S. K. Skin defenses of North American salamanders against a deadly salamander fungus. Anim. Conserv. 24, 552–567 (2021).
Google Scholar 
Pereira, K. E. et al. Skin glands of an aquatic salamander vary in size and distribution and release antimicrobial secretions effective against chytrid fungal pathogens. J. Exp. Biol. 221, jeb183707 (2018).
Google Scholar 
Smith, H. K. et al. Skin mucosome activity as an indicator of Batrachochytrium salamandrivorans susceptibility in salamanders. PLoS ONE 13, e0199295 (2018).
Google Scholar 
Meng, P. et al. The first salamander defensin antimicrobial peptide. PLoS ONE 8, e83044 (2013).ADS 

Google Scholar 
Sheafor, B., Davidson, E. W., Parr, L. & Rollins-Smith, L. A. Antimicrobial peptide defenses in the salamander, Ambystoma tigrinum, against emerging amphibian pathogens. J. Wildl. Dis. 44, 226–236 (2008).CAS 

Google Scholar 
Fredericks, L. P. & Dankert, J. R. Antibacterial and hemolytic activity of the skin of the terrestrial salamander, Plethodon cinereus. J. Exp. Zool. 287, 340–345 (2000).CAS 

Google Scholar 
Pei, J. & Jiang, L. Antimicrobial peptide from mucus of Andrias davidianus: Screening and purification by magnetic cell membrane separation technique. Int. J. Antimicrob. Agents 50, 41–46 (2017).CAS 

Google Scholar 
Woodhams, D. C. et al. Adaptations of skin peptide defences and possible response to the amphibian chytrid fungus in populations of Australian green-eyed treefrogs, Litoria genimaculata. Div. Distrib. 16, 703–712 (2010).
Google Scholar 
Hernández-Gómez, O., Briggler, J. T. & Williams, R. N. Influence of immunogenetics, sex and body condition on the cutaneous microbial communities of two giant salamanders. Mol. Ecol. 27, 1915–1929 (2018).
Google Scholar 
Niyonsaba, F., Kiatsurayanon, C., Chieosilapatham, P. & Ogawa, H. Friends or foes? Host defense (antimicrobial) peptides and proteins in human skin diseases. Exp. Dermatol. 26, 989–998 (2017).CAS 

Google Scholar 
Rollins-Smith, L. A., Ramsey, J. P., Pask, J. D., Reinert, L. K. & Woodhams, D. C. Amphibian immune defenses against chytridiomycosis: Impacts of changing environments. Integr. Comp. Biol. 51, 552–562 (2011).CAS 

Google Scholar 
Chinchar, V. G. et al. Inactivation of viruses infecting ectothermic animals by amphibian and piscine antimicrobial peptides. Virology 323, 268–275 (2004).CAS 

Google Scholar 
Woodhams, D. C. et al. Interacting symbionts and immunity in the amphibian skin mucosome predict disease risk and probiotic effectiveness. PLoS ONE 9, e96375 (2014).ADS 

Google Scholar 
Becker, M. H., Brucker, R. M., Schwantes, C. R., Harris, R. N. & Minbiole, K. P. The bacterially produced metabolite violacein is associated with survival of amphibians infected with a lethal fungus. Appl. Environ. Microbiol. 75, 6635–6638 (2009).ADS 
CAS 

Google Scholar 
Bell, S. C., Garland, S. & Alford, R. A. Increased numbers of culturable inhibitory bacterial taxa may mitigate the effects of Batrachochytrium dendrobatidis in Australian wet tropics frogs. Front. Microbiol. 9, 1604 (2018).
Google Scholar 
Zhang, L. & Gallo, R. L. Antimicrobial peptides. Curr. Biol. 26, R14–R19 (2016).CAS 

Google Scholar 
Rollins-Smith, L. A. et al. Antimicrobial peptide defenses of the Tarahumara frog, Rana tarahumarae. Biochem. Biophys. Res. Commun. 297, 361–367 (2002).CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ (2013).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Hime, P. M. et al. Genomic data reveal conserved female heterogamety in giant salamanders with gigantic nuclear genomes. G3 Genes Genomes Genet. 9, 3467–3476 (2019).CAS 

Google Scholar 
Mazerolle, M. J. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 2.2–1. https://cran.r-project.org/package=AICcmodavg (2019).Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Information-Theoretic Approach 2nd edn, 454 (Springer, 2002).MATH 

Google Scholar 
Holden, W. M., Reinert, L. K., Hanlon, S. M., Parris, M. J. & Rollins-Smith, L. A. Development of antimicrobial peptide defenses of southern leopard frogs, Rana sphenocephala, against the pathogenic chytrid fungus, Batrachochytrium dendrobatidis. Dev. Comp. Immunol. 48, 65–75 (2015).CAS 

Google Scholar 
De Caceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3 (2009).
Google Scholar  More

Muller, Z. et al. Giraffa camelopardalis. The IUCN red list of threatened species 2016:e.T9194A109326950 (2018).Oconnor, D. et al. Updated geographic range maps for giraffe, Giraffa spp., throughout sub-Saharan Africa, and implications of changing distributions for conservation. Mamm. Rev. 49, 285–299. https://doi.org/10.1111/mam.12165 (2019).Article 

Google Scholar 
Brown, M. B. et al. Conservation status of giraffe: Evaluating contemporary distribution and abundance with evolving taxonomic perspectives. Ref. Module Earth Syst. Environ. Sci. https://doi.org/10.1016/B978-0-12-821139-7.00139-2 (2021).Article 

Google Scholar 
Dunn, M. E. et al. Investigating the international and pan-African trade in giraffe parts and derivatives. Conserv. Sci. Pract. 3, e390. https://doi.org/10.1111/csp2.390 (2021).Article 

Google Scholar 
Hassanin, A. et al. Mitochondrial DNA variability in Giraffa camelopardalis: Consequences for taxonomy, phylogeography and conservation of giraffes in West and Central Africa. C.R. Biol. 330, 265–274. https://doi.org/10.1016/j.crvi.2007.02.008 (2007).Article 
CAS 

Google Scholar 
Groves, C. & Grubb, P. Ungulate Taxonomy (Johns Hopkins University Press, 2011).Book 

Google Scholar 
Fennessy, J. et al. Multi-locus analyses reveal four giraffe species instead of one. Curr. Biol. 26, 1–7. https://doi.org/10.1016/j.cub.2016.07.036 (2016).Article 
CAS 

Google Scholar 
Winter, S., Fennessy, J. & Janke, A. Limited introgression supports division of giraffe into four species. Ecol. Evol. 8, 10156–10165. https://doi.org/10.1002/ece3.4490 (2018).Article 

Google Scholar 
Bercovitch, F. B. Giraffe taxonomy, geographic distribution, and conservation. Afr. J. Ecol. 58, 150–158. https://doi.org/10.1111/aje.12741 (2020).Article 

Google Scholar 
Petzold, A. & Hassanin, A. A comparative approach for species delimitation based on multiple methods of multi-locus DNA sequence analysis: A case study of the genus Giraffa (Mammalia, Cetartiodactyla). PLoS ONE 15, e0217956. https://doi.org/10.1371/journal.pone.0217956 (2020).Article 
CAS 

Google Scholar 
Petzold, A. et al. First insights into past biodiversity of giraffes based on mitochondrial sequences from museum specimens. Eur. J. Taxon. 703, L57-63. https://doi.org/10.1371/journal.pone.0217956 (2020).Article 
CAS 

Google Scholar 
Coimbra, R. T. F. et al. Whole-genome analysis of giraffe supports four distinct species. Curr. Biol. 31, 2929-2938.e5. https://doi.org/10.1016/j.cub.2021.04.033 (2021).Article 
CAS 

Google Scholar 
Muneza, A. B. et al. Giraffa camelopardalis ssp. reticulata. The IUCN Red List of Threatened Species 2018:e.T88420717A88420720 (2018).Miller, M. F. Dispersal of Acacia seeds by ungulates and ostriches in an African Savanna. J. Trop. Ecol. 12, 345–356. https://doi.org/10.1017/S0266467400009548 (1996).Article 

Google Scholar 
Palmer, T. M. et al. Breakdown of an ant-plant mutualism follows the loss of large herbivores from an African savanna. Science 319, 192–195. https://doi.org/10.1126/science.1151579 (2008).Article 
ADS 
CAS 

Google Scholar 
Kalema, G. Investigation of a skin disease in giraffe in Murchison Falls National Park. Report Submitted to Uganda National Park. Uganda National Parks. Kampala, Uganda (1996).Muneza, A. B. et al. Regional variation of the manifestation, prevalence, and severity of giraffe skin disease: A review of an emerging disease in wild and captive giraffe populations. Biol. Conserv. 198, 145–156. https://doi.org/10.1016/j.biocon.2016.04.014 (2016).Article 

Google Scholar 
Epaphras, A. M., Karimuribo, E. D., Mpanduji, D. G. & Meing’ataki, G. E. Prevalence, disease description and epidemiological factors of a novel skin disease in giraffes (Giraffa camelopardalis) in Ruaha National Park, Tanzania. Res. Opin. Anim. Vet. Sci. 2, 60–65 (2012).
Google Scholar 
Lee, D. E. & Bond, M. L. The occurrence and prevalence of giraffe skin disease in protected areas of northern Tanzania. J. Wildl. Dis. 52, 753–755. https://doi.org/10.7589/2015-09-24 (2016).Article 

Google Scholar 
Muneza, A. B. et al. Examining disease prevalence for species of conservation concern using non-invasive spatial capture–recapture techniques. J. Appl. Ecol. 54, 709–717. https://doi.org/10.1111/1365-2664.12796 (2017).Article 

Google Scholar 
Brown, M. Murchison falls giraffe project: Field report. Giraffid 9, 5–10 (2015).
Google Scholar 
Muneza, A. B. et al. Quantifying the severity of an emerging skin disease affecting giraffe populations using photogrammetry analysis of camera trap data. J. Wildl. Dis. 55, 770–781. https://doi.org/10.7589/2018-06-149 (2019).Article 

Google Scholar 
Han, S. et al. Giraffe skin disease: Clinicopathologic characterization of cutaneous filariasis in the critically endangered Nubian giraffe (Giraffa camelopardalis camelopardalis). Vet. Pathol. https://doi.org/10.1177/03009858221082606 (2022).Article 

Google Scholar 
Whittier, C. A. et al. Cutaneous filariasis in free-ranging Rothschild’s giraffes (Giraffa Camelopardalis rothschildi) in Uganda. J. Wildl. Dis. 56, 1–5. https://doi.org/10.7589/2018-09-212 (2020).Article 

Google Scholar 
Pellew, R. Food consumption and energy budgets of the giraffe. J. Appl. Ecol. 21, 141–159. https://doi.org/10.2307/2403043 (1984).Article 

Google Scholar 
Strauss, M. K. L. & Packer, C. Using claw marks to study lion predation on giraffes of the Serengeti. J. Zool. 289, 134–142. https://doi.org/10.1111/j.1469-7998.2012.00972.x (2013).Article 

Google Scholar 
Muneza, A. B. et al. Exploring the connections between giraffe skin disease and lion predation. J. Zool. https://doi.org/10.1111/jzo.12930 (2021).Article 

Google Scholar 
Lindsey, P. A. et al. The bushmeat trade in African savannas: Impacts, drivers, and possible solutions. Biol. Conserv. 160, 80–96. https://doi.org/10.1016/j.biocon.2012.12.020 (2013).Article 

Google Scholar 
Becker, M. et al. Evaluating wire-snare poaching trends and the impacts of by-catch on elephants and large carnivores. Biol. Conserv. 158, 26–36. https://doi.org/10.1016/j.biocon.2012.08.017 (2013).Article 

Google Scholar 
Mudumba, T., Jingo, S., Heit, D. & Montgomery, R. A. The landscape configuration and lethality of snare poaching of sympatric guilds of large carnivores and ungulates. Afr. J. Ecol. 59, 51–62. https://doi.org/10.1111/aje.12781 (2020).Article 

Google Scholar 
Strauss, M. K. L., Kilewo, M., Rentsch, D. & Packer, C. Food supply and poaching limit giraffe abundance in the Serengeti. Popul. Ecol. 57, 505–516. https://doi.org/10.1007/s10144-015-0499-9 (2015).Article 

Google Scholar 
Munn, J. Effects of injury on the locomotion of free-ranging chimpanzees in the Budongo Forest Reserve, Uganda. In Primates of Western Uganda: Developments in Primatology: Progress and Prospects (eds. Newton-Fisher, N. E., Notman, H., Paterson, J. D., & Reynolds, V.) 259–280 (Springer, 2006).Yersin, H., Asiimwe, C., Voordouw, M. J. & Zuberbühler, K. Impact of snare injuries on parasite prevalence in wild chimpanzees (Pan troglodytes). Int. J. Primatol. 38, 21–30. https://doi.org/10.1007/s10764-016-9941-x (2017).Article 

Google Scholar 
Dagg, A. I. Gaits of the giraffe and okapi. J. Mammal. 41, 282–282. https://doi.org/10.2307/1376381 (1960).Article 

Google Scholar 
Dagg, A. I. The role of the neck in the movements of the giraffe. J. Mammal. 43, 88–97. https://doi.org/10.2307/1376883 (1962).Article 

Google Scholar 
Dagg, A. I. & Vos, A. D. The walking gaits of some species of Pecora. J. Zool. 155, 103–110. https://doi.org/10.1111/j.1469-7998.1968.tb03031.x (1968).Article 

Google Scholar 
Alexander, R. M. N., Langman, V. A. & Jayes, A. S. Fast locomotion of some African ungulates. J. Zool. 183, 291–300. https://doi.org/10.1111/j.1469-7998.1977.tb04188.x (1977).Article 

Google Scholar 
Basu, C., Deacon, F., Hutchinson, J. R. & Wilson, A. M. The running kinematics of free-roaming giraffes, measured using a low cost unmanned aerial vehicle (UAV). PeerJ 7, e6312. https://doi.org/10.7717/peerj.6312 (2019).Article 

Google Scholar 
Basu, C., Wilson, A. M. & Hutchinson, J. R. The locomotor kinematics and ground reaction forces of walking giraffes. J. Exp. Biol. 222, jeb159277. https://doi.org/10.1242/jeb.159277 (2019).Article 

Google Scholar 
Hildebrand, M. The adaptive significance of tetrapod gait selection. Am. Zool. 20, 255–267. https://doi.org/10.1093/icb/20.1.255 (1980).Article 

Google Scholar 
Flower, F. C., Sanderson, D. J. & Weary, D. M. Hoof pathologies influence kinematic measures of dairy cow gait. J. Dairy Sci. 88, 3166–3173. https://doi.org/10.3168/jds.s0022-0302(05)73000-9 (2005).Article 
CAS 

Google Scholar 
Brown, M. B., Bolger, D. T. & Fennessy, J. All the eggs in one basket: A countrywide assessment of current and historical giraffe population distribution in Uganda. Glob. Ecol. Conserv. 19, e00612. https://doi.org/10.1016/j.gecco.2019.e00612 (2019).Article 

Google Scholar 
Foster, J. B. The giraffe of Nairobi National Park: Home range, sex ratios, the herd, and food. Afr. J. Ecol. 4, 139–148. https://doi.org/10.1111/j.1365-2028.1966.tb00889.x (1966).Article 

Google Scholar 
Bond, M. L., Strauss, M. K. L. & Lee, D. E. Soil correlates and mortality from giraffe skin disease in Tanzania. J. Wildl. Dis. 52, 953–958. https://doi.org/10.7589/2016-02-047 (2016).Article 

Google Scholar 
Dunham, N. T., McNamara, A., Shapiro, L., Hieronymus, T. & Young, J. W. A user’s guide for the quantitative analysis of substrate characteristics and locomotor kinematics in free-ranging primates. Am. J. Phys. Anthropol. 167, 569–584. https://doi.org/10.1002/ajpa.23686 (2018).Article 

Google Scholar 
Rueden, C. T. et al. Imagej 2: Imagej for the next generation of scientific image data. BMC Bioinform. 18, 529. https://doi.org/10.1186/s12859-017-1934-z (2017).Article 

Google Scholar 
Cartmill, M., Lemelin, P. & Schmitt, D. Support polygons and symmetrical gaits in mammals. Zool. J. Linn. Soc. 136, 401–420. https://doi.org/10.1046/j.1096-3642.2002.00038.x (2002).Article 

Google Scholar 
Hildebrand, M. Analysis of the symmetrical gaits of tetrapods. Folia Biotheoretica 6, 1–22. https://doi.org/10.2307/1379571 (1966).Article 

Google Scholar 
Shapiro, L. J. & Young, J. W. Kinematics of quadrupedal locomotion in sugar gliders (Petaurus breviceps): Effects of age and substrate size. J. Exp. Biol. 215, 480–496. https://doi.org/10.1242/jeb.062588 (2012).Article 

Google Scholar 
Shapiro, L. J., Young, J. W. & VandeBerg, J. L. Body size and the small branch niche: Using marsupial ontogeny to model primate locomotor evolution. J. Hum. Evol. 68, 14–31. https://doi.org/10.1016/j.jhevol.2013.12.006 (2014).Article 

Google Scholar 
Dunham, N. T., McNamara, A., Shapiro, L., Phelps, T. & Young, J. W. Asymmetrical gait kinematics of free-ranging callitrichines in response to changes in substrate diameter, orientation, and displacement. J. Exp. Biol. 223, jeb217562. https://doi.org/10.1242/jeb.217562 (2020).Article 

Google Scholar 
Robinson, R., Herzog, W. & Nigg, B. Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry. J. Manipulative Physiol. Ther. 10, 172–176 (1987).CAS 

Google Scholar 
Vanden Hole, C. et al. How innate is locomotion in precocial animals? A study on the early development of spatiotemporal gait variables and gait symmetry in piglets. J. Exp. Biol. 220, 2706–2716. https://doi.org/10.1242/jeb.157693 (2017).Article 

Google Scholar 
Jacobs, B. Y., Kloefkorn, H. E. & Allen, K. D. Gait analysis methods for rodent models of osteoarthritis. Curr. Pain Headache Rep. 18, 456–475. https://doi.org/10.1007/s11916-014-0456-x (2014).Article 

Google Scholar 
Pfau, T., Spence, A., Starke, S., Ferrari, M. & Wilson, A. Modern riding style improves horse racing times. Science 325, 289–289. https://doi.org/10.1126/science.1174605 (2009).Article 
ADS 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019). http://www.R-project.org/.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. LmerTest package: Tests in linear mixed effects models. J. Stat. Softw. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
Length, R. emmeans: Estimated marginal means, aka least‐squares means. R package version 0.9. https://CRAN.R-project.org/package=emmeans (2017).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).Article 
MathSciNet 
MATH 

Google Scholar 
Merkens, H. W. & Schamhardt, H. C. Evaluation of equine locomotion during different degrees of experimentally induced lameness I: Lameness model and quantification of ground reaction force patterns of the limbs. Equine Vet. J. 20, 99–106. https://doi.org/10.1111/j.2042-3306.1988.tb04655.x (1988).Article 

Google Scholar 
Fanchon, L. & Grandjean, D. Accuracy of asymmetry indices of ground reaction forces for diagnosis of hind limb lameness in dogs. Am. J. Vet. Res. 68, 1089–1094. https://doi.org/10.2460/ajvr.68.10.1089 (2007).Article 

Google Scholar 
Bragança, F. M. S., Rhodin, M. & van Weeren, P. R. On the brink of daily clinical application of objective gait analysis: What evidence do we have so far from studies using an induced lameness model?. Vet. J. 234, 11–23. https://doi.org/10.1016/j.tvjl.2018.01.006 (2018).Article 

Google Scholar 
Brown, M. B. & Bolger, D. T. Male-biased partial migration in a giraffe population. Front. Ecol. Evol. 7, 524. https://doi.org/10.3389/fevo.2019.00524 (2020).Article 

Google Scholar 
Dagg, A. I. Giraffe: Biology, Behaviour and Conservation (Cambridge University Press, 2014).Book 

Google Scholar 
Castles, M. P. et al. Relationships between male giraffes’ colour, age and sociability. Anim. Behav. 157, 13–25. https://doi.org/10.1016/j.anbehav.2019.08.003 (2019).Article 

Google Scholar  More

Data were analyzed from samples collected, processed, and published previously [21, 25, 29] and have been summarized here. The present analysis, which consisted of sequence data processing, the calculation of taxon-specific isotopic signatures, and subsequent analyses, reflects original work.Sample collection and isotope incubationTo generate experimental data, three replicate soil samples were collected from the top 10 cm of plant-free patches in four ecosystems along the C. Hart Merriam elevation gradient in Northern Arizona. From low to high elevation, these sites are located in the following environments: desert grassland (GL; 1760 m), piñon-pine juniper woodland (PJ; 2020 m), ponderosa pine forest (PP; 2344 m), and mixed conifer forest (MC; 2620 m). Soil samples were air-dried for 24 h at room temperature, homogenized, and passed through a 2 mm sieve before being stored at 4 °C for another 24 h. This produced three distinct but homogenous soil samples from each of the four ecosystems that were subject to experimental treatments. Three treatments were applied to bring soils to 70% water-holding capacity: water alone (control), water with glucose (C treatment; 1000 µg C g−1 dry soil), or water with glucose and a nitrogen source (CN treatment; [NH4]2SO4 at 100 µg N g−1 dry soil). To track growth through isotope assimilation, both 18O-enriched water (97 atom %) and 13C-enriched glucose (99 atom %) were used. In all treatments isotopically heavy samples were paired with matching “light” samples that received water with a natural abundance isotope signatures. For 18O incubations, this design resulted in three soil samples per ecosystem per treatment (across four ecosystems and three treatments, n = 36) while 13C incubations were limited to only C and CN treatments (n = 24). Previous analyses suggest that three replicates is sufficient to detect growth of 10 atom % 18O in microbial DNA with a power of 0.6 and a growth of 5 atom % 18O with a power of 0.3 (12 and 6 atom % respectively for 13C) [30]. All soils were incubated in the dark for one week. Following incubation, soils were frozen at −80 °C for one week prior to DNA extraction.Quantitative stable isotope probingThe procedure of qSIP (quantitative stable isotope probing) is described here but has been applied to these samples as previously published [17, 21, 25]. DNA extraction was performed on soils using a DNeasy PowerSoil HTP 96 Kit (MoBio Laboratories, Carlsbad, CA, USA) and following manufacturer’s protocol. Briefly, 0.25 g of soils from each sample were carefully added to deep, 96-well plates containing zirconium dioxide beads and a cell lysis solution with sodium dodecyl sulfate (SDS) and shaken for 20 min. Following cell lysis, supernatant was collected and centrifuged three times in fresh 96-well plates with reagents separating DNA from non-DNA organic and inorganic materials. Lastly, DNA samples were collected on silica filter plates, rinsed with ethanol and eluted into 100 µL of a 10 mM Tris buffer in clean 96-well plates. To quantify the degree of 18O or 13C isotope incorporation into bacterial DNA (excess atom fraction or EAF), the qSIP protocol [31] was used, though modified slightly as reported previously [21, 24, 32]. Briefly, microbial growth was quantified as the change in DNA buoyant density due to incorporation of the 18O or 13C isotopes through the method of density fractionation by adding 1 µg of DNA to 2.6 mL of saturated CsCl solution in combination with a gradient buffer (200 mM Tris, 200 mM KCL, 2 mM EDTA) in a 3.3 mL OptiSeal ultracentrifuge tube (Beckman Coulter, Fullerton, CA, USA). The solution was centrifuged to produce a gradient of increasingly labeled (heavier) DNA in an Optima Max bench top ultracentrifuge (Beckman Coulter, Brea, CA, USA) with a Beckman TLN-100 rotor (127,000 × g for 72 h) at 18 °C. Each post-incubation sample was thus converted from a continuous gradient into approximately 20 fractions (150 µL) using a modified fraction recovery system (Beckman Coulter). The density of each fraction was measured with a Reichart AR200 digital refractometer (Reichert Analytical Instruments, Depew, NY, USA). Fractions with densities between 1.640 and 1.735 g cm−3 were retained as densities outside this range generally did not contain DNA. In all retained fractions, DNA was cleaned and purified using isopropanol precipitation and the abundance of bacterial 16S rRNA gene copies was quantified with qPCR using primers specific to bacterial 16S rRNA genes (Eub 515F: AAT GAT ACG GCG ACC ACC GAG TGC CAG CMG CCG CGG TAA, 806R: CAA GCA GAA GAC GGC ATA CGA GGA CTA CVS GGG TAT CTA AT). Triplicate reactions were 8 µL consisting of 0.2 mM of each primer, 0.01 U µL−1 Phusion HotStart II Polymerase (Thermo Fisher Scientific, Waltham, MA), 1× Phusion HF buffer (Thermo Fisher Scientific), 3.0 mM MgCl2, 6% glycerol, and 200 µL of dNTPs. Reactions were performed on a CFX384 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) under the following cycling conditions: 95 °C at 1 min and 44 cycles at 95 °C (30 s), 64.5 °C (30 s), and 72 °C (1 min). Separate from qPCR, retained sample-fractions were subject to a similar amplification step of the 16S rRNA gene V4 region (515F: GTG YCA GCM GCC GCG GTA A, 806R: GGA CTA CNV GGG TWT CTA AT) in preparation for sequencing with the same reaction mix but differing cycle conditions – 95 °C for 2 min followed by 15 cycles at 95 °C (30 s), 55 °C (30 s), and 60 °C (4 min). The resulting 16S rRNA gene V4 amplicons were sequenced on a MiSeq sequencing platform (Illumina, Inc., San Diego, CA, USA). DNA sequence data and sample metadata have been deposited in the NCBI Sequence Read Archive under the project ID PRJNA521534.Sequence processing and qSIP analysisIndependently from previous publications, we processed raw sequence data of forward and reverse reads (FASTQ) within the QIIME2 environment [33] (release 2018.6) and denoised sequences within QIIME2 using the DADA2 pipeline [34]. We clustered the remaining sequences into amplicon sequence variants (ASVs, at 100% sequence identity) against the SILVA 138 database [35] using a pre-trained open-reference Naïve Bayes feature classifier [36]. We removed samples with less than 3000 sequence reads, non-bacterial lineages, and global singletons and doubletons. We converted ASV sequencing abundances in each fraction to the number of 16S rRNA gene copies per gram dry soil based on qPCR abundances and the known amount of dry soil equivalent added to the initial extraction. This allowed us to express absolute population densities, rather than relative abundances. Across all replicates, we identified 114 543 unique bacterial ASVs.We calculated the 18O and 13C excess atom fraction (EAF) for each bacterial ASV using R version 4.0.3 [37] and data.table [38] with custom scripts available at https://www.github.com/bramstone/. Negative enrichment values were corrected using previously published methods [17]. ASVs that appeared in less than two of the three replicates of an ecosystem-treatment combination (n = 3) and less than three density fractions within those two replicates were removed to avoid assigning spurious estimates of isotope enrichment to infrequent taxa. Any ASVs filtered out of one ecosystem-treatment group were allowed to be present in another if they met the frequency threshold. Applying these filtering criteria, we limited our analysis towards 3759 unique bacterial ASVs which accounted for a small proportion of the total diversity but represented 68.0% of all sequence reads, and encompassed most major bacterial groups (Supplementary Fig. 1).Analysis of life history strategies and nutrient responseAll statistical tests were conducted in R version 4.0.3 [37]. We assessed the ability of phylum-level assignment of life history strategy to predict growth in response to C and N addition, as proxied by the incorporation of heavy isotope during DNA replication [39, 40]. Phylum-level assignments (Table 1) were based on the most frequently observed behavior of lineages with a representative phylum (or subphylum) as compiled previously [23]. We averaged 18O EAF values of bacterial taxa for each treatment and ecosystem and then subtracted the values in control soils from values in C-amended soils to determine C response (∆18O EAFC) and from the 18O EAF of bacteria in CN-amended soils to determine C and N response (Δ18O EAFCN). Because an ASV must have a measurable EAF in both the control and treatment for a valid Δ18O EAF to be calculated, we were only able to resolve the nutrient response for 2044 bacterial ASVs – 1906 in response to C addition and 1427 in response to CN addition.We used Gaussian finite mixture modeling, as implemented by the mclust R package [41], to demarcate plausible multi-isotopic signatures for oligotrophs and copiotrophs. For each treatment, we calculated average per-taxon 13C and 18O EAF values. To compare both isotopes directly, we divided 18O EAF values by 0.6 based on the estimate that this value (designated as µ) represents the fraction of oxygen atoms in DNA derived from the 18O-water, rather than from 16O within available C sources [42]. Two mixture components, corresponding to oligotrophic and copiotrophic growth modes, were defined using the Mclust function using ellipsoids of equal volume and shape. We observed several microorganisms with high 18O enrichment but comparatively low 13C enrichment, potentially indicating growth following the depletion of the added glucose, and that were reasonably clustered as oligotrophs in our mixture model.We tested how frequently mixture model clustering of each microorganism’s growth (based on average 18O–13C EAF in a treatment) could predict its growth across replicates (n = 12 in each treatment—although individual). We applied the treatment-level mixture models defined above to the per-taxon isotope values in each replicate, recording when a microorganism’s life history strategy in a replicate agreed with the treatment-level cluster, and when it didn’t. We used exact binomial tests to test whether the number of “successes” (defined as a microorganism being grouped in the same life history category as its treatment-level cluster) was statistically significant. To account for type I error across all individual tests (one per ASV per treatment), we adjusted P values in each treatment using the false-discovery rate (FDR) method [43].To determine the extent that life history categorizations may be appropriately applied at finer levels of taxonomic resolution, we constructed several hierarchical linear models using the lmer function in the nlme package version 3.1-149 [44]. To condense growth information from both isotopes into a single analysis, 18O and 13C EAF values were combined into a single variable using principal components analysis separately for each treatment. Across the C and CN treatments, the first principal component (PC1) was able to explain – respectively – 86% and 91% of joint variation of 18O and 13C EAF values. In all cases, we applied PC1 as the response variable and treated taxonomy and ecosystem as random model terms to limit the potential of pseudo-replication to bias significance values. We used likelihood ratio analysis and Akaike information criterion (AIC) values to compare models where life history strategy was determined based on observed nutrient responses at different taxonomic levels (Eq. 1) against a model with the same random terms but without any life history strategy data (Eq. 2). Separate models were applied to each treatment. To reduce model overfitting, we removed families represented by fewer than three bacterial ASVs as well as phyla represented by only one order. In addition, we removed bacterial ASVs with unknown taxonomic assignments (following Morrissey et al. [21]). This limited our analysis to 1 049 ASVs in the C amendment and 984 in the CN amendment.$${{{{{rm{PC}}}}}}{1}_{{18{{{{{rm{O}}}}}} – 13{{{{{rm{C}}}}}}}}sim {{{{{rm{strategy}}}}}} + 1|{{{{{rm{phylum}}}}}}/{{{{{rm{class}}}}}}/{{{{{rm{order}}}}}}/{{{{{rm{family}}}}}}/{{{{{rm{genus}}}}}}/{{{{{rm{eco}}}}}}$$
(1)
$${{{{{rm{PC}}}}}}{1}_{{18{{{{{rm{O}}}}}} – 13{{{{{rm{C}}}}}}}}sim 1 + 1|{{{{{rm{phylum}}}}}}/{{{{{rm{class}}}}}}/{{{{{rm{order}}}}}}/{{{{{rm{family}}}}}}/{{{{{rm{genus}}}}}}/{{{{{rm{eco}}}}}}$$
(2)
Here, life history strategy was defined at each taxonomic level using the mixture models above and based on the mean 18O and 13C EAF values of each bacterial lineage (Supplemental Fig. 2). We compared these models with the no-strategy model (Eq. 2) directly using likelihood ratio testing. More

Pitois, S. G., Lynam, C. P., Jansen, T., Halliday, N. & Edwards, M. Bottom-up effects of climate on fish populations: data from the Continuous Plankton Recorder. Mar. Ecol. Prog. Ser. 456, 169–186 (2012).ADS 

Google Scholar 
Ruzicka, J. J. et al. Interannual variability in the Northern California Current food web structure: changes in energy flow pathways and the role of forage fish, euphausiids, and jellyfish. Prog. Oceanogr. 102, 19–41 (2012).ADS 

Google Scholar 
Lauria, V., Attrill, M. J., Brown, A., Edwards, M. & Votier, S. C. Regional variation in the impact of climate change: evidence that bottom-up regulation from plankton to seabirds is weak in parts of the Northeast Atlantic. Mar. Ecol. Prog. Ser. 488, 11–22 (2013).ADS 

Google Scholar 
Heneghan, R. F., Everett, J. D., Blanchard, J. L. & Richardson, A. J. Zooplankton are not fish: improving zooplankton realism in size-spectrum models mediates energy transfer in food webs. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00201 (2016).Lehette, P., Tovar-Sánchez, A., Duarte, C. M. & Hernández-León, S. Krill excretion and its effect on primary production. Mar. Ecol. Prog. Ser. 459, 29–38 (2012).ADS 
CAS 

Google Scholar 
Arístegui, J., Duarte, C. M., Reche, I. & Gómez-Pinchetti, J. L. Krill excretion boosts microbial activity in the Southern Ocean. PLoS ONE 9, e89391 (2014).ADS 

Google Scholar 
Tovar-Sánchez, A., Duarte, C. M., Hernández-León, S. & Sañudo-Wilhelmy, S. A. Krill as a central node for iron cycling in the Southern Ocean. Geophys. Res. Lett. 34, 1–4 (2007).Schmidt, K. et al. Seabed foraging by Antarctic krill: Implications for stock assessment, bentho-pelagic coupling, and the vertical transfer of iron. Limnol. Oceanogr. 56, 1411–1428 (2011).ADS 
CAS 

Google Scholar 
Cavan, E. L. et al. The importance of Antarctic krill in biogeochemical cycles. Nat. Commun. 10, 4742 (2019). This Review demonstrates how the dominant grazer in Antarctica plays a critical role in biogeochemical cycles.ADS 
CAS 

Google Scholar 
Ratnarajah, L., Nicol, S. & Bowie, A. R. Pelagic iron recycling in the southern ocean: exploring the contribution of marine animals. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00109 (2018).Halfter, S., Cavan, E. L., Swadling, K. M., Eriksen, R. S. & Boyd, P. W. The role of zooplankton in establishing carbon export regimes in the southern ocean – a comparison of two representative case studies in the subantarctic region. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.567917 (2020).Schmidt, K. et al. Zooplankton gut passage mobilizes lithogenic iron for ocean productivity. Curr. Biol. 26, 2667–2673 (2016).CAS 

Google Scholar 
Brun, P. et al. Climate change has altered zooplankton-fuelled carbon export in the North Atlantic. Nat. Ecol. Evol. 3, 416–423 (2019).
Google Scholar 
Chust, G. et al. Are Calanus spp. shifting poleward in the North Atlantic? A habitat modelling approach. ICES J. Mar. Sci. 71, 241–253 (2014).
Google Scholar 
Batten, S. D. & Walne, A. W. Variability in northwards extension of warm water copepods in the NE Pacific. J. Plankton Res. 33, 1643–1653 (2011).
Google Scholar 
Fu, W., Randerson, J. T. & Moore, J. K. Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models. Biogeosciences 13, 5151–5170 (2016).ADS 

Google Scholar 
Tagliabue, A. et al. Persistent uncertainties in ocean net primary production climate change projections at regional scales raise challenges for assessing impacts on ecosystem services. Front. Clim. https://doi.org/10.3389/fclim.2021.738224 (2021).Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 

Google Scholar 
Mackas, D. L. et al. Changing zooplankton seasonality in a changing ocean: comparing time series of zooplankton phenology. Prog. Oceanogr. 97-100, 31–62 (2012).ADS 

Google Scholar 
Freer, J. J., Daase, M. & Tarling, G. A. Modelling the biogeographic boundary shift of Calanus finmarchicus reveals drivers of Arctic Atlantification by subarctic zooplankton. Glob. Change Biol. 28, 429–440 (2021).
Google Scholar 
Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. Proc. Natl Acad. Sci. USA 106, 12788–12793 (2009).ADS 
CAS 

Google Scholar 
Brandão, M. C. et al. Macroscale patterns of oceanic zooplankton composition and size structure. Sci. Rep. 11, 15714 (2021). This study showed that zooplankton abundance and median size decreased towards warmer and less productive environments due to changes in copepod composition, but some groups displayed the opposite relationships potentially due to alternative feeding strategies.ADS 

Google Scholar 
Campbell, M. D. et al. Testing Bermann’s rule in marine copepods. Ecography 44, 1283–1295 (2021). This global study found that temperature better predicted copepod size than did latitude or oxygen, with body size decreasing by 43.9% across the temperature range (−1.7 to 30 °C).
Google Scholar 
Barange, M. et al. Impacts of Climate Change on Fisheries and Aquaculture. Synthesis of Current Knowledge, Adaptation, and Mitigation Options. (FAO, 2018).Atkinson, A. et al. Questioning the role of phenology shifts and trophic mismatching in a planktonic food web. Prog. Oceanogr. 137, 498–512 (2015).ADS 

Google Scholar 
Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 

Google Scholar 
Sasaki, M. & Dam, H. G. Global patterns in copepod thermal tolerance. J. Plankton Res. 43, 598–609 (2021).
Google Scholar 
Dam, H. G. et al. Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification. Nat. Clim. Change 11, 780–786 (2021).ADS 

Google Scholar 
Cooley, S. et al. Ocean and Coastal Ecosystems and their Services. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2022). This IPCC report synthesizes changes in zooplankton phenology compared to other marine life.Mackas, D. L., Goldblatt, R. & Lewis, A. G. Interdecadal variation in developmental timing of Neocalanus plumchrus populations at Ocean Station P in the subarctic North Pacific. Can. J. Fish. Aquat. Sci. 55, 1878–1893 (1998).
Google Scholar 
Edwards, M. et al. Ecological Status Report: results from the CPR survey 2007/2008. 1-12 (2009).Richardson, A. J. In hot water: zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).
Google Scholar 
Costello, J. H., Sullivan, B. K. & Gifford, D. J. A physical–biological interaction underlying variable phenological responses to climate change by coastal zooplankton. J. Plankton Res. 28, 1099–1105 (2006).
Google Scholar 
Chevillot, X. et al. Toward a phenological mismatch in estuarine pelagic food web? PLoS ONE 12, e0173752 (2017).
Google Scholar 
Ji, R., Edwards, M., Mackas, D. L., Runge, J. A. & Thomas, A. C. Marine plankton phenology and life history in a changing climate: current research and future directions. J. Plankton Res. 32, 1355–1368 (2010).
Google Scholar 
Thibodeau, P. S. et al. Long-term observations of pteropod phenology along the Western Antarctic Peninsula. Deep Sea Res. Part I: Oceanogr. Res. Pap. 166, 103363 (2020).
Google Scholar 
Beaugrand, G., Reid Philip, C., Ibañez, F., Lindley, J. A. & Edwards, M. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296, 1692–1694 (2002).ADS 
CAS 

Google Scholar 
Edwards, M. et al. North Atlantic warming over six decades drives decreases in krill abundance with no associated range shift. Commun. Biol. 4, 644 (2021). This regional study showed that ocean warming is causing a decrease in krill abundance but no poleward movement in range.
Google Scholar 
Chivers, W. J., Walne, A. W. & Hays, G. C. Mismatch between marine plankton range movements and the velocity of climate change. Nat. Commun. 8, 14434 (2017).ADS 
CAS 

Google Scholar 
Lindley, J. A. & Daykin, S. Variations in the distributions of Centropages chierchiae and Temora stylifera (Copepoda: Calanoida) in the north-eastern Atlantic Ocean and western European shelf waters. ICES J. Mar. Sci. 62, 869–877 (2005).
Google Scholar 
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019). This regional study shows that the dominant grazer in Antarctic waters, Antarctic krill is moving southward due to regional warming.ADS 

Google Scholar 
Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).ADS 
CAS 

Google Scholar 
Pakhomov, E. A., Froneman, P. W., Wassmann, P., Ratkova, T. & Arashkevich, E. Contribution of algal sinking and zooplankton grazing to downward flux in the Lazarev Sea (Southern Ocean) during the onset of phytoplankton bloom: a lagrangian study. Mar. Ecol. Prog. Ser. 233, 73–88 (2002).ADS 

Google Scholar 
Tarling, G. A., Ward, P. & Thorpe, S. E. Spatial distributions of Southern Ocean mesozooplankton communities have been resilient to long-term surface warming. Glob. Change Biol. 24, 132–142 (2017). This study shows that 16 mesozooplankton taxa in the in the southwest Atlantic sector of the Southern Ocean are resilient to ocean warming.ADS 

Google Scholar 
Atkinson, A. et al. Stepping stones towards Antarctica: switch to southern spawning grounds explains an abrupt range shift in krill. Glob. Change Biol. 28, 1359–1375 (2021).
Google Scholar 
Jonkers, L., Hillebrand, H. & Kucera, M. Global change drives modern plankton communities away from the pre-industrial state. Nature 570, 372–377 (2019).ADS 
CAS 

Google Scholar 
Yebra, L. et al. Spatio-temporal variability of the zooplankton community in the SW Mediterranean 1992–2020: Linkages with environmental drivers. Prog. Oceanogr. 209, 1–10 (2022).Cowen, T. et al. Report on the status and trends of the Southern Ocean zooplankton based on the SCAR Southern Ocean Continuous Plankton Recorder (SO-CPR) survey. (2020).Corona, S., Hirst, A., Atkinson, D. & Atkinson, A. Density-dependent modulation of copepod body size and temperature–size responses in a shelf sea. Limnol. Oceanogr. 66, 3916–3927 (2021).ADS 

Google Scholar 
Horne, C. R., Hirst, A. G., Atkinson, D., Neves, A. & Kiørboe, T. A global synthesis of seasonal temperature–size responses in copepods. Glob. Ecol. Biogeogr. 25, 988–999 (2016).
Google Scholar 
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).ADS 

Google Scholar 
Brodeur, R. D., Auth, T. D. & Phillips, A. J. Major shifts in pelagic micronekton and macrozooplankton community structure in an upwelling ecosystem related to an unprecedented marine heatwave. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00212 (2019).Lavaniegos, B. E., Jiménez-Herrera, M. & Ambriz-Arreola, I. Unusually low euphausiid biomass during the warm years of 2014–2016 in the transition zone of the California Current. Deep Sea Res. Part II: Top. Stud. Oceanogr. 169-170, 104638 (2019).
Google Scholar 
Peterson, W. T. et al. The pelagic ecosystem in the Northern California Current off Oregon during the 2014–2016 warm anomalies within the context of the past 20 years. J. Geophys. Res.: Oceans 122, 7267–7290 (2017).ADS 

Google Scholar 
O’ Loughlin, J. H. O. et al. Implications of Pyrosoma atlanticum range expansion on phytoplankton standing stocks in the Northern California Current. Prog. Oceanogr. 188, 1–9 (2020).Robertson, R. R. & Bjorkstedt, E. P. Climate-driven variability in Euphausia pacifica size distributions off northern California. Prog. Oceanogr. 188, 102412 (2020).
Google Scholar 
Stephens, J. A., Jordan, M. B., Taylor, A. H. & Proctor, R. The effects of fluctuations in North Sea flows on zooplankton abundance. J. Plankton Res. 20, 943–956 (1998).
Google Scholar 
Greene, C. H. & Pershing, A. J. The response of Calanus finmarchicus populations to climate variability in the Northwest Atlantic: basin-scale forcing associated with the North Atlantic Oscillation. ICES J. Mar. Sci. 57, 1536–1544 (2000).
Google Scholar 
Saba, G. K. et al. Winter and spring controls on the summer food web of the coastal West Antarctic Peninsula. Nat. Commun. 5, 4318 (2014).ADS 
CAS 

Google Scholar 
Steinberg, D. K. et al. Long-term (1993–2013) changes in macrozooplankton off the Western Antarctic Peninsula. Deep Sea Res. Part I: Oceanogr. Res. Pap. 101, 54–70 (2015).ADS 

Google Scholar 
Steinke, K. B., Bernard, K. S., Ross, R. M. & B, Q. L. Environmental drivers of the physiological condition of mature female Antarctic krill during the spawning season: implications for krill recruitment. Mar. Ecol. Prog. Ser. 669, 65–82 (2021).ADS 

Google Scholar 
Brodeur, R. D. et al. Rise and fall of jellyfish in the eastern Bering Sea in relation to climate regime shifts. Prog. Oceanogr. 77, 103–111 (2008).ADS 

Google Scholar 
Quiñones, J. et al. Climate-driven population size fluctuations of jellyfish (Chrysaora plocamia) off Peru. Mar. Biol. 162, 2339–2350 (2015).
Google Scholar 
Lynam, C. P., Attrill, M. J. & Skogen, M. D. Climatic and oceanic influences on the abundance of gelatinous zooplankton in the North Sea. J. Mar. Biol. Assoc. UK 90, 1153–1159 (2009).
Google Scholar 
Schmidt, K. et al. Increasing picocyanobacteria success in shelf waters contributes to long-term food web degradation. Glob. Change Biol. 26, 5574–5587 (2020).ADS 

Google Scholar 
Laglera, L. M. et al. Iron partitioning during LOHAFEX: Copepod grazing as a major driver for iron recycling in the Southern Ocean. Mar. Chem. 196, 148–161 (2017).CAS 

Google Scholar 
Cavan, E. L., Henson, S. A., Belcher, A. & Sanders, R. Role of zooplankton in determining the efficiency of the biological carbon pump. Biogeosciences 14, 177–186 (2017).ADS 
CAS 

Google Scholar 
Valdés, V. et al. Nitrogen and phosphorus recycling mediated by copepods and response of bacterioplankton community from three contrasting areas in the western tropical South Pacific (20° S). Biogeosciences 15, 6019–6032 (2018).ADS 

Google Scholar 
Steinberg, D. K. & Landry, M. R. Zooplankton and the Ocean Carbon Cycle. Annu. Rev. Mar. Sci. 9, 413–444 (2017). This Review synthesizes the role of zooplankton within the ocean carbon cycle.ADS 

Google Scholar 
Ratnarajah, L. et al. Understanding the variability in the iron concentration of Antarctic krill. Limnol. Oceanogr. 61, 1651–1660 (2016).ADS 

Google Scholar 
Bernard, K. S., Steinberg, D. K. & Schofield, O. M. Summertime grazing impact of the dominant macrozooplankton off the Western Antarctic Peninsula. Deep Sea Res. Part I: Oceanogr. Res. Pap. 62, 111–122 (2012).ADS 

Google Scholar 
Böckmann, S. et al. Salp fecal pellets release more bioavailable iron to Southern Ocean phytoplankton than krill fecal pellets. Curr. Biol. 31, 2737–2746.e2733 (2021).
Google Scholar 
Cabanes, D. J. E. et al. First Evaluation of the Role of Salp Fecal Pellets on Iron Biogeochemistry. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00289 (2017).Ratnarajah, L. Regenerated iron: how important are different zooplankton groups to oceanic productivity. Curr. Biol. 31, R848–R850 (2021).CAS 

Google Scholar 
Giering, S. L., Steigenberger, S., Achterberg, E. P., Sanders, R. & Mayor, D. J. Elevated iron to nitrogen recycling by mesozooplankton in the Northeast Atlantic Ocean. Geophys. Res. Lett. 39, 1–5 (2012).Svensen, C. et al. Zooplankton communities associated with new and regenerated primary production in the Atlantic inflow North of Svalbard. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00293 (2019).Darnis, G. & Fortier, L. Zooplankton respiration and the export of carbon at depth in the Amundsen Gulf (Arctic Ocean). J. Geophys. Res. Oceans 117, 1–12 (2012).Miquel, J.-C. et al. Downward particle flux and carbon export in the Beaufort Sea, Arctic Ocean; the role of zooplankton. Biogeosciences 12, 5103–5117 (2015).ADS 

Google Scholar 
Hernández-León, S. et al. Carbon export through zooplankton active flux in the Canary Current. J. Mar. Syst. 189, 12–21 (2019).
Google Scholar 
Gorgues, T., Aumont, O. & Memery, L. Simulated changes in the particulate carbon export efficiency due to diel vertical migration of zooplankton in the North Atlantic. Geophys. Res. Lett. 46, 5387–5395 (2019).ADS 
CAS 

Google Scholar 
Steinberg, D. K. et al. Zooplankton vertical migration and the active transport of dissolved organic and inorganic carbon in the Sargasso Sea. Deep Sea Res. Part I: Oceanogr. Res. Pap. 47, 137–158 (2000).ADS 
CAS 

Google Scholar 
Lebrato, M., Molinero, J.-C., Mychek-Londer, J. G., Gonzalez, E. M. & Jones, D. O. B. Gelatinous carbon impacts benthic megafaunal communities in a continental margin. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.902674 (2022).Lebrato, M. & Jones, D. O. B. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnol. Oceanogr. 54, 1197–1209 (2009).ADS 
CAS 

Google Scholar 
Kobari, T. et al. Impacts of ontogenetically migrating copepods on downward carbon flux in the western subarctic Pacific Ocean. Deep Sea Res. Part II: Top. Stud. Oceanogr. 55, 1648–1660 (2008).ADS 

Google Scholar 
Wilson, S. E., Steinberg, D. K. & Buesseler, K. O. Changes in fecal pellet characteristics with depth as indicators of zooplankton repackaging of particles in the mesopelagic zone of the subtropical and subarctic North Pacific Ocean. Deep Sea Res. Part II: Top. Stud. Oceanogr. 55, 1636–1647 (2008).ADS 

Google Scholar 
Laurenceau-Cornec, E. et al. The relative importance of phytoplankton aggregates and zooplankton fecal pellets to carbon export: insights from free-drifting sediment trap deployments in naturally iron-fertilised waters near the Kerguelen Plateau. Biogeosciences 12, 1007–1027 (2015).ADS 

Google Scholar 
Manno, C., Stowasser, G., Enderlein, P., Fielding, S. & Tarling, G. The contribution of zooplankton faecal pellets to deep-carbon transport in the Scotia Sea (Southern Ocean). Biogeosciences 12, 1955–1965 (2015).ADS 

Google Scholar 
Cavan, E. et al. Attenuation of particulate organic carbon flux in the Scotia Sea, Southern Ocean, is controlled by zooplankton fecal pellets. Geophys. Res. Lett. 42, 821–830 (2015).ADS 
CAS 

Google Scholar 
Lebrato, M. et al. Jelly biomass sinking speed reveals a fast carbon export mechanism. Limnol. Oceanogr. 58, 1113–1122 (2013).ADS 

Google Scholar 
Ducklow, H. W., Steinberg, D. K. & Buesseler, K. O. Upper ocean carbon export and the biological pump. Oceanography 14, 50–58 (2001).
Google Scholar 
Yebra, L. et al. Zooplankton production and carbon export flux in the western Alboran Sea gyre (SW Mediterranean). Prog. Oceanogr. 167, 64–77 (2018).ADS 

Google Scholar 
Yebra, L. et al. Mesoscale physical variability affects zooplankton production in the Labrador Sea. Deep Sea Res. Part I: Oceanogr. Res. Pap. 56, 703–715 (2009).ADS 
CAS 

Google Scholar 
Beaugrand, G., Edwards, M. & Legendre, L. Marine biodiversity, ecosystem functioning, and carbon cycles. Proc. Natl Acad. Sci. USA 107, 10120–10124 (2010).ADS 
CAS 

Google Scholar 
Benson, A. J. & Trites, A. W. Ecological effects of regime shifts in the Bering Sea and eastern North Pacific Ocean. Fish. Fish. 3, 95–113 (2002).
Google Scholar 
Coyle, K. O. & Pinchuk, A. I. Climate-related differences in zooplankton density and growth on the inner shelf of the southeastern Bering Sea. Prog. Oceanogr. 55, 177–194 (2002).ADS 

Google Scholar 
Duffy-Anderson, J. T. et al. Return of warm conditions in the southeastern Bering Sea: Phytoplankton – Fish. PLoS ONE 12, e0178955 (2017).
Google Scholar 
Odebrecht, C., Secchi, E. R., Abreu, P. C., Muelbert, J. H. & Uiblein, F. Biota of the Patos Lagoon estuary and adjacent marine coast: long-term changes induced by natural and human-related factors. Mar. Biol. Res. 13, 3–8 (2017).
Google Scholar 
Eisner, L. B. et al. Seasonal, interannual, and spatial patterns of community composition over the eastern Bering Sea shelf in cold years. Part I: zooplankton. ICES J. Mar. Sci. 75, 72–86 (2018).
Google Scholar 
Trueblood, L. A. Salp metabolism: temperature and oxygen partial pressure effect on the physiology of Salpa fusiformis from the California Current. J. Plankton Res. 41, 281–291 (2019).CAS 

Google Scholar 
Hernández-León, S. & Ikeda, T. in Respiration in aquatic ecosystems. p. 57-82 (Oxford University Press, 2005).Lewandowska, A. M. et al. Effects of sea surface warming on marine plankton. Ecol. Lett. 17, 614–623 (2014).
Google Scholar 
O’Connor, M. I., Piehler, M. F., Leech, D. M., Anton, A. & Bruno, J. F. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 7, e1000178 (2009).
Google Scholar 
Chen, B., Landry, M. R., Huang, B. & Liu, H. Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnol. Oceanogr. 57, 519–526 (2012).ADS 
CAS 

Google Scholar 
Paul, C., Matthiessen, B. & Sommer, U. Warming, but not enhanced CO2 concentration, quantitatively and qualitatively affects phytoplankton biomass. Mar. Ecol. Prog. Ser. 528, 39–51 (2015).ADS 
CAS 

Google Scholar 
Sommer, U. & Lewandowska, A. Climate change and the phytoplankton spring bloom: warming and overwintering zooplankton have similar effects on phytoplankton. Glob. Change Biol. 17, 154–162 (2010).ADS 

Google Scholar 
Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Change 9, 237–243 (2019).ADS 

Google Scholar 
Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton University Press, 2002).Matsumoto, K., Tanioka, T. & Rickaby, R. Linkages between dynamic phytoplankton C:N:P and the ocean carbon cycle under climate change. Oceanography 33, 44–52 (2020).
Google Scholar 
Finkel, Z. V. et al. Phytoplankton in a changing world: cell size and elemental stoichiometry. J. Plankton Res. 32, 119–137 (2010).CAS 

Google Scholar 
Bank, T. W. Blue Economy. https://www.worldbank.org/en/topic/oceans-fisheries-and-coastal-economies#1 (2021).Burthe, S. et al. Phenological trends and trophic mismatch across multiple levels of a North Sea pelagic food web. Mar. Ecol. Prog. Ser. 454, 119–133 (2012).ADS 

Google Scholar 
Durant, J. M. et al. Contrasting effects of rising temperatures on trophic interactions in marine ecosystems. Sci. Rep. 9, 15213 (2019).ADS 

Google Scholar 
Otero, J. et al. Basin-scale phenology and effects of climate variability on global timing of initial seaward migration of Atlantic salmon (Salmo salar). Glob. Change Biol. 20, 61–75 (2014).ADS 

Google Scholar 
Kovach, R. P., Ellison, S. C., Pyare, S. & Tallmon, D. A. Temporal patterns in adult salmon migration timing across southeast Alaska. Glob. Change Biol. 21, 1821–1833 (2014).ADS 

Google Scholar 
Chust, G. et al. Earlier migration and distribution changes of albacore in the Northeast Atlantic. Fish. Oceanogr. 28, 505–516 (2019).
Google Scholar 
McQueen, K. & Marshall, C. T. Shifts in spawning phenology of cod linked to rising sea temperatures. ICES J. Mar. Sci. 74, 1561–1573 (2017).
Google Scholar 
Kanamori, Y., Takasuka, A., Nishijima, S. & Okamura, H. Climate change shifts the spawning ground northward and extends the spawning period of chub mackerel in the western North Pacific. Mar. Ecol. Prog. Ser. 624, 155–166 (2019).ADS 

Google Scholar 
Henderson, M. E., Mills, K. E., Thomas, A. C., Pershing, A. J. & Nye, J. A. Effects of spring onset and summer duration on fish species distribution and biomass along the Northeast United States continental shelf. Rev. Fish. Biol. Fish. 27, 411–424 (2017).
Google Scholar 
Beaugrand, G., Brander, K. M., Alistair Lindley, J., Souissi, S. & Reid, P. C. Plankton effect on cod recruitment in the North Sea. Nature 426, 661–664 (2003).ADS 
CAS 

Google Scholar 
Kang, Y. S., Kim, J. Y., Kim, H. G. & Park, J. H. Long-term changes in zooplankton and its relationship with squid, Todarodes pacificus, catch in Japan/East Sea. Fish. Oceanogr. 11, 337–346 (2002).
Google Scholar 
Mackas, D. et al. Zooplankton time series from the Strait of Georgia: results from year-round sampling at deep water locations, 1990–2010. Prog. Oceanogr. 115, 129–159 (2013).ADS 

Google Scholar 
Daly, E. A., Brodeur, R. D. & Auth, T. D. Anomalous ocean conditions in 2015: impacts on spring Chinook salmon and their prey field. Mar. Ecol. Prog. Ser. 566, 169–182 (2017).ADS 

Google Scholar 
Feuilloley, G. et al. Concomitant changes in the environment and small pelagic fish community of the Gulf of Lions. Prog. Oceanogr. 186, 102375 (2020).
Google Scholar 
Yebra, L. et al. Molecular identification of the diet of Sardina pilchardus larvae in the SW Mediterranean Sea. Mar. Ecol. Prog. Ser. 617-618, 41–52 (2019).ADS 
CAS 

Google Scholar 
Record, N. et al. Copepod diapause and the biogeography of the marine lipidscape. J. Biogeogr. 45, 2238–2251 (2018).
Google Scholar 
Yebra, L. et al. Zooplankton biomass depletion event reveals the importance of small pelagic fish top-down control in the Western Mediterranean Coastal Waters. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.608690 (2020).Friedland, K. D. et al. Pathways between primary production and fisheries yields of large marine ecosystems. PLoS ONE 7, e28945 (2012).Santora, J. A. et al. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 11, 536 (2020).ADS 
CAS 

Google Scholar 
Piatt, J. et al. Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016. PLOS ONE 15, e0226087 (2020).Meyer-Gutbrod, E., Greene, C., Davies, K. & Johns, D. G. Ocean regime shift is driving collapse of the North Atlantic Right Whale Population. Oceanography 34, 22–31 (2021).
Google Scholar 
Beltran, R. S. et al. Seasonal resource pulses and the foraging depth of a Southern Ocean top predator. Proc. R. Soc. B 288, 1–9 (2021).Everett, J. D. et al. Modeling what we sample and sampling what we model: challenges for zooplankton model assessment. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00077 (2017). This article synthesizes key information required for better parameterize zooplankton in various models.Gibbs Samantha, J. et al. Algal plankton turn to hunting to survive and recover from end-Cretaceous impact darkness. Sci. Adv. 6, eabc9123 (2020).Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
CAS 

Google Scholar 
Mitra, A. et al. Bridging the gap between marine biogeochemical and fisheries sciences; configuring the zooplankton link. Prog. Oceanogr. 129, 176–199 (2014).ADS 

Google Scholar 
Gentleman, W., Leising, A., Frost, B., Strom, S. & Murray, J. Functional responses for zooplankton feeding on multiple resources: a review of assumptions and biological dynamics. Deep Sea Res. Part II: Top. Stud. Oceanogr. 50, 2847–2875 (2003).ADS 
CAS 

Google Scholar 
Chenillat, F., Rivière, P. & Ohman, M. D. On the sensitivity of plankton ecosystem models to the formulation of zooplankton grazing. PLOS ONE 16, e0252033 (2021).CAS 

Google Scholar 
Stemmann, L. & Boss, E. Plankton and particle size and packaging: from determining optical properties to driving the biological pump. Annu. Rev. Mar. Sci. 4, 263–290 (2012).ADS 
CAS 

Google Scholar 
Kiørboe, T., Saiz, E., Tiselius, P. & Andersen, K. H. Adaptive feeding behavior and functional responses in zooplankton. Limnol. Oceanogr. 63, 308–321 (2017).ADS 

Google Scholar 
Grigor, J. J. et al. Non-carnivorous feeding in Arctic chaetognaths. Prog. Oceanogr. 186, 102388 (2020).
Google Scholar 
Yeh, H. D., Questel, J. M., Maas, K. R. & Bucklin, A. Metabarcoding analysis of regional variation in gut contents of the copepod Calanus finmarchicus in the North Atlantic Ocean. Deep Sea Res. Part II: Top. Stud. Oceanogr. 180, 104738 (2020).
Google Scholar 
Novotny, A., Zamora-Terol, S. & Winder, M. DNA metabarcoding reveals trophic niche diversity of micro and mesozooplankton species. Proc. R. Soc. B 288, 1–10 (2021).Käse, L. et al. Metabarcoding analysis suggests that flexible food web interactions in the eukaryotic plankton community are more common than specific predator–prey relationships at Helgoland Roads, North Sea. ICES J. Mar. Sci. 78, 3372–3386 (2021).
Google Scholar 
Greco, M., Morard, R. & Kucera, M. Single-cell metabarcoding reveals biotic interactions of the Arctic calcifier Neogloboquadrina pachyderma with the eukaryotic pelagic community. J. Plankton Res. 43, 113–125 (2021).CAS 

Google Scholar 
Serra-Pompei, C., Soudijn, F., Visser, A. W., Kiørboe, T. & Andersen, K. H. A general size- and trait-based model of plankton communities. Prog. Oceanogr. 189, 102473 (2020).
Google Scholar 
Heneghan, R. F. et al. A functional size-spectrum model of the global marine ecosystem that resolves zooplankton composition. Ecol. Model. 435, 109265 (2020).CAS 

Google Scholar 
Ward, B. A. et al. EcoGEnIE 1.0: plankton ecology in the cGEnIE Earth system model. Geosci. Model Dev. 11, 4241–4267 (2018).ADS 
CAS 

Google Scholar 
Sosik, H. M. & Olson, R. J. Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr. Methods 5, 204–216 (2007).
Google Scholar 
Lombard, F. et al. Globally consistent quantitative observations of planktonic ecosystems. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00196 (2019).Pitois, S. G. et al. A first approach to build and test the Copepod Mean Size and Total Abundance (CMSTA) ecological indicator using in-situ size measurements from the Plankton Imager (PI). Ecol. Indic. 123, 107307 (2021).Irisson, J.-O., Ayata, S.-D., Lindsay, D. J., Karp-Boss, L. & Stemmann, L. Machine learning for the study of plankton and marine snow from images. Annu. Rev. Mar. Sci. 14, 277–301 (2022).ADS 

Google Scholar 
Cornils, A. et al. Testing the usefulness of optical data for zooplankton long-term monitoring: Taxonomic composition, abundance, biomass and size spectra from ZooScan image analysis. Limnol. Oceanogr. Methods 20, 428–450 (2022).Henson, S. A., C, B. & R, L. Observing climate change trends in ocean biogeochemistry: when and where. Glob. Change Biol. 22, 1561–1571 (2016).ADS 

Google Scholar 
García-Comas, C. et al. Zooplankton long-term changes in the NW Mediterranean Sea: Decadal periodicity forced by winter hydrographic conditions related to large-scale atmospheric changes? J. Mar. Syst. 87, 216–226 (2011).
Google Scholar 
Vucetich, J. A., Nelson, M. P. & Bruskotter, J. T. What drives declining support for long-term ecological research? BioScience 70, 168–173 (2020).
Google Scholar 
Lindenmayer, D. B. et al. Value of long-term ecological studies. Austral Ecol. 37, 745–757 (2012).
Google Scholar 
Giron-Nava, A. et al. Quantitative argument for long-term ecological monitoring. Mar. Ecol. Prog. Ser. 572, 269–274 (2017).ADS 

Google Scholar 
Hughes, B. B. et al. Long-term studies contribute disproportionately to ecology and policy. BioScience 67, 271–281 (2017).
Google Scholar 
Berline, L., Siokou-Frangou, I. & Marasovic, I. Intercomparison of six Mediterranean zooplankton time series. Prog. Oceanogr. 97-100, 76–91 (2012).ADS 

Google Scholar 
Beaugrand, G. et al. Synchronous marine pelagic regime shifts in the Northern Hemisphere. Philos. Trans. R. Soc. B: Biol. Sci. 370, 20130272 (2015).
Google Scholar 
Mackas, D. L. & Beaugrand, G. Comparisons of zooplankton time series. J. Mar. Syst. 79, 286–304 (2010).
Google Scholar 
O’Brien, T. D., Lorenzoni, L., Isensee, K. & Valdés, L. What are Marine Ecological Time Series Telling Us About The Ocean? A Status Report. (2017).Ratnarajah, L. Map of BioEco Observing networks/capability (https://eurosea.eu/download/eurosea-d1-2-bioeco-observing-networks/?wpdmdl=3580&refresh=637b1a59bb2011669012057, 2021).Wright, R. M., Le Quéré, C., Buitenhuis, E. T., Pitois, S. & Gibbons, M. J. Role of jellyfish in the plankton ecosystem revealed using a global ocean biogeochemical model. Biogeosciences 18, 1291–1320 (2021).ADS 
CAS 

Google Scholar 
Buitenhuis, E. T. et al. MAREDAT: towards a world atlas of MARine Ecosystem DATa. Earth Syst. Sci. Data 5, 227–239 (2013).ADS 

Google Scholar 
O’Brien, T. D. COPEPOD: The Global Plankton Database. An overview of the 2014 database contents, processing methods, and access interface. U.S. Dep. Commerce, NOAA Tech. Memo. NMFS-F/ST-37, 29p. (2014).Pitois, S. G., Bouch, P., Creach, V. & van der Kooij, J. Comparison of zooplankton data collected by a continuous semi-automatic sampler (CALPS) and a traditional vertical ring net. J. Plankton Res. 38, 931–943 (2016).
Google Scholar 
Wiebe, P. H. & Benfield, M. C. From the Hensen net toward four-dimensional biological oceanography. Prog. Oceanogr. 56, 7–136 (2003).ADS 

Google Scholar 
Boss, E. et al. Recommendations for plankton measurements on oceansites moorings with relevance to other observing sites. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.929436 (2022).Pollina, T. et al. PlanktoScope: affordable modular quantitative imaging platform for citizen oceanography. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.949428 (2022).Pitois, S. G. et al. Comparison of a cost-effective integrated plankton sampling and imaging instrument with traditional systems for mesozooplankton sampling in the Celtic Sea. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00005 (2018).Ohman, M. D. et al. Zooglider: an autonomous vehicle for optical and acoustic sensing of zooplankton. Limnol. Oceanogr.: Methods 17, 69–86 (2018).
Google Scholar 
Picheral, M. et al. The Underwater Vision Profiler 6: an imaging sensor of particle size spectra and plankton, for autonomous and cabled platforms. Limnol. Oceanogr. Methods 20, 115–129 (2021).
Google Scholar 
Picheral, M. et al. The Underwater Vision Profiler 5: an advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol. Oceanogr. Methods 8, 462–473 (2010).
Google Scholar 
Richardson, A. et al. in Guidelines for the study of climate change effects on HABs Vol. 88 23 (UNESCO-IOC/SCOR, 2022).Drago, L. et al. Global distribution of zooplankton biomass estimated by in situ imaging and machine learning. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.894372 (2022).Forest, A. et al. Ecosystem function and particle flux dynamics across the Mackenzie Shelf (Beaufort Sea, Arctic Ocean): an integrative analysis of spatial variability and biophysical forcings. Biogeosciences 10, 2833–2866 (2013).ADS 

Google Scholar 
Haëntjens, N. et al. Detecting mesopelagic organisms using biogeochemical-argo floats. Geophys. Res. Lett. 47, 1–10 (2020).Clayton, S. et al. Bio-GO-SHIP: the time is right to establish global repeat sections of ocean biology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.767443 (2022).Miloslavich, P. et al. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Change Biol. 24, 2416–2433 (2018).ADS 

Google Scholar 
McPhaden, M. J., Santoso, A. & Cai, W. El Niño Southern Oscillation in a Changing Climate: Glossary (John Wiley & Sons, Inc, 2021). More

Ren, W. et al. Changes of periphyton abundance and biomass driven by factors specific to flooding inflow in a river inlet area in Erhai Lake, China. Front. Environ. Sci. 9, 680718. https://doi.org/10.3389/fenvs.2021.680718 (2021).Article 

Google Scholar 
Woodruff, S. L. et al. The effects of a developing biofilm on chemical changes across the sediment-water interface in a freshwater environment. Int. Rev. Hydrobiol. 84(5), 509–532 (1999).CAS 

Google Scholar 
Muñoz, I., Real, M., Guasch, H., Navarro, E. & Sabater, S. Effects of atrazine on periphyton under grazing pressure. Aquat. Toxicol. 55(3–4), 239–249 (2001).
Google Scholar 
Hoagland, K. D., Roemer, S. C. & Rosowski, J. R. Colonization and community structure of two periphyton assemblages, with emphasis on the diatoms (Bacillariophyceae). Am. J. Bot. 69, 188–213. https://doi.org/10.2307/2443006 (1982).Article 

Google Scholar 
Steinman, A. D. & McIntire, C. D. Effects of current velocity and light energy on the structure of periphyton assemblages in laboratory streams. J. Phycol. 22, 352–361. https://doi.org/10.1111/J.1529-8817.1986.TB00035.X (1986).Article 

Google Scholar 
Tonkin, J. D., Death, R. G. & Barquín, J. Periphyton control on stream invertebrate diversity: Is periphyton architecture more important than biomass?. Mar. Freshw. Res. 65(9), 818–829 (2014).
Google Scholar 
Beck, W. S., Markman, D. W., Oleksy, I. A., Lafferty, M. H. & Poff, N. L. Seasonal shifts in the importance of bottom-up and top-down factors on stream periphyton community structure. Oikos 128, 680–691. https://doi.org/10.1111/oik.05844 (2018).Article 
CAS 

Google Scholar 
Hogsden, K. L. & Harding, J. S. Consequences of acid mine drainage for the structure and function of benthic stream communities: A review. Freshw. Sci. 31, 108–120. https://doi.org/10.1899/11-091.1 (2012).Article 

Google Scholar 
Sofi, M. S., Bhat, S. U., Rashid, I. & Kuniyal, J. C. The natural flow regime: A master variable for maintaining river ecosystem health. Ecohydrology 13(8), e2247. https://doi.org/10.1002/eco.2247 (2020).Article 

Google Scholar 
Biggs, B. J. F. Eutrophication of streams and rivers: Dissolved nutrient-chlorophyllrelationship for benthic algae. J. N. Am. Benthol. Soc. 19, 17–31 (2000).
Google Scholar 
Ormerod, S. J., Dobson, M., Hildrew, A. G. & Townsend, C. Multiple stressors in freshwater ecosystems. Freshw. Biol. 55, 1–4 (2010).
Google Scholar 
Poff, et al. The natural flow regime: A paradigm for river conservation and restoration. Bioscience 47, 769–784 (1997).
Google Scholar 
Naiman, R. J., Décamps, H., & McClain, M. E. Riparia: Ecology, Conservation and Management of Streamside Communities, (Elsevier/Academic Press, 2005).Gleick, P. H. Water use. Annu. Rev. Environ. Resour. 28, 275–314 (2003).
Google Scholar 
Jenkins, K. M. & Boulton, A. J. Connectivity in a dryland river: Short-term aquatic macroinvertebrate recruitment following floodplain inundation. Ecology 84(10), 2708–2723 (2003).
Google Scholar 
Biggs, B. J. F. Patterns in benthic algae of streams. In Algal Ecology in Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell, M. L., & Lowe, R. L.) 31–56 (Academic Press, 1996).Smolar-Žvanut, N. & Mikoš, M. The impact of flow regulation by hydropower dams on the periphyton community in the Soča River, Slovenia. Hydrol. Sci. J. 59(5), 1032–1045. https://doi.org/10.1080/02626667.2013.834339 (2014).Article 
CAS 

Google Scholar 
Curry, C. J. & Baird, D. J. Habitat type and dispersal ability influence spatial structuring of larval Odonata and Trichoptera assemblages. Freshw. Biol. 60, 2142–2152 (2015).
Google Scholar 
Wu, N., Cai, Q. & Fohrer, N. Contribution of microspatial factors to benthic diatom communities. Hydrobiologia 732, 49–60. https://doi.org/10.1007/s10750-014-1843-3 (2014).Article 
CAS 

Google Scholar 
Mueller, M., Pander, J. & Geist, J. The effects of weirs on structural stream habitat and biological communities. J. Appl. Ecol 48(6), 1450–1461. https://doi.org/10.1111/j.1365-2664.2011.02035.x (2011).Article 

Google Scholar 
Davies, P. M. et al. Flow–ecology relationships: closing the loop on effective environmental flows. Mar. Freshw. Res. 65(2), 133–141 (2013).
Google Scholar 
Jun, Y. C. et al. Spatial distribution of benthic macroinvertebrate assemblages in relation to environmental variables in Korean nationwide streams. Water 8(1), 27. https://doi.org/10.3390/w8010027 (2016).Article 

Google Scholar 
Biggs, B. J. F. & Close, M. E. Periphyton biomass dynamics in gravel bed rivers: The relative effects of flows and nutrients. Freshw. Biol. 22, 209–231 (1989).CAS 

Google Scholar 
Jowett, I. & Biggs, B. J. F. Flood and velocity effects on periphyton and silt accumulation in two New Zealand rivers. N. Zeal. J. Mar. Freshw. Res. 31, 287–300 (1997).
Google Scholar 
Biggs, B. J. F., Goring, D. G. & Nikora, V. I. Subsidy and stress responses of stream periphyton to gradients in water velocity as a function of community growth form. J. Phycol. 34, 598–607 (1998).
Google Scholar 
Malmqvist, B. & Englund, G. Effects of hydropower-induced flow perturbations on mayfly (Ephemeroptera) richness and abundance in north Swedish river rapids. Hydrobiologia 341(2), 145–158 (1996).
Google Scholar 
Poff, N. L. & Ward, J. V. Herbivory under different flow regimes: A field experiment and test of a model with a benthic stream insect. Oikos 72, 179–188 (1995).
Google Scholar 
Poff, L. N., Wellnitz, T. & Monroe, J. B. Redundancy among three herbivorous insects across an experimental current velocity gradient. Oecologia 134, 262–269. https://doi.org/10.1007/s00442-002-1086-2 (2003).Article 

Google Scholar 
Vaughn, C. C. The role of periphyton abundance and quality in the microdistribution of a stream grazer, Helicopsyche borealis (Trichoptera: Helicopsychidae). Freshw. Biol. 16, 485–493 (1986).
Google Scholar 
Francoeur, S. N. Meta-analysis of lotic nutrient amendment experiments: Detecting and quantifying subtle responses. J. N. Am. Benthol. Soc. 20, 358–368 (2001).
Google Scholar 
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).
Google Scholar 
Hillebrand, H. Meta-analysis of grazer control of periphyton biomass across aquatic ecosystems. J. Phycol. 45, 798–806 (2009).
Google Scholar 
Lamberti, G. A. The role of periphyton in benthic food webs. In Algal Ecology—Freshwater Benthic Ecosystems, 533–572 (eds. Stevenson, R. J., Bothwell, M. L. & Lowe, R. L.) (Academic Press, 1996).Lamberti, G. A. et al. Influence of grazer type and abundance on plant–herbivore interactions in streams. Hydrobiologia 306, 179–188 (1995).
Google Scholar 
Gregory, S. V. Plant–herbivore interactions in stream systems. In Stream Ecology (eds. Barnes, J. R. & Minshall, G. W.) 157–189 (Plenum, 1983).Lamberti, G. A. & Moore, J. W. Aquatic insects as primary consumers. In The Ecology of Aquatic Insects (eds Resh, V. H. & Rosenberg, D. M.) 164–195 (Praeger, 1984).
Google Scholar 
Sterner, R. W., Elser, J. J. & Hessen, D. O. Stoichiometric relationships among producers, consumers and nutrient cycling in pelagic ecosystems. Biogeochemistry 17, 49–67 (1992).CAS 

Google Scholar 
Kahlert, M. & Baunsgaard, M. T. Nutrient recycling—A strategy of a grazer community to overcome nutrient limitation. J. N. Am. Benthol. Soc. 18, 363–369 (1999).
Google Scholar 
Burkholder, J. M., Wetzel, R. G. & Klomparens, K. L. Direct comparison of phosphate uptake by adnate and loosely attached microalgae within and intact biofilm matrix. Appl. Environ. Microbiol. 56, 2882–2890 (1990).CAS 

Google Scholar 
Steinman, A. D. Effects of grazers on freshwater benthic algae. In Algal Ecology: Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell & Lowe, R. L.) 341–366 (Academic Press, 1996).Smucker, N. J. & Vis, M. L. Spatial factors contribute to benthic diatom structure in streams across spatial scales: Considerations for biomonitoring. Ecol. Indic. 11, 1191–1203 (2011).
Google Scholar 
Myers, et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).Article 
CAS 

Google Scholar 
Wang, J., Pan, F., Soininen, J., Heino, J. & Shen, J. Nutrient enrichment modifies temperature-biodiversity relationship in large scale field experiments. Nat. Commun. 7, 13 (2016).
Google Scholar 
Wu, et al. Flow regimes filter species traits of benthic diatom communities and modify the functional features of lowland streams-a nationwide scale study. Sci. Total Environ. 651, 357–366 (2019).CAS 

Google Scholar 
Nisar, M. A. Geospatial approach to study environmental characterization of a Kashmir wetland (Anchar) catchment with special reference to land use/land cover and changing climate. Ph.D Thesis, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir. Weblink. http://krishikosh.egranth.ac.in/handle/1/91309 (2012).Bhat, S. U., Sofi, A. H., Yaseen, T., Pandit, A. K. & Yousuf, A. R. Macro invertebrate community from Sonamarg streams of Kashmir Himalaya. Pak. J. Biol. Sci. 14(3), 182–194. https://doi.org/10.3923/pjbs.2011.182.194 (2011).Article 
CAS 

Google Scholar 
Baba, A. I., Sofi, A. H., Bhat, S. U., & Pandit, A. K. Periphytic algae of river Sindh in the Sonamarg area of Kashmir valley. J. Phytol. 3(6) (2011).Sofi, M. S., Rautela, K. S., Bhat, S. U., Rashid, I. & Kuniyal, J. C. Application of geomorphometric approach for the estimation of hydro-sedimentological flows and cation weathering rate: Towards understanding the sustainable land use policy for the Sindh Basin, Kashmir Himalaya. Water Air Soil Pollut. 232(7), 1–11. https://doi.org/10.1007/s11270-021-05217-w (2021).Article 
CAS 

Google Scholar 
Romshoo, S. A., & Fayaz, M. Use of high resolution remote sensing for improving environmental Friendly tourism master planning in the Alpine Himalaya: A case study of Sonamarg tourist resort, Kashmir. J. Himalayan Ecol. Sustain. Dev. 14 (2019).Biggs, B. J. F. & Kilroy, C. Stream periphyton monitoring manual. Published by NIWA for Ministry for the Environment, 226 Christchurch, New Zealand: NIWA (2000).APHA. Standard Methods for Examination of Water and Wastewater, 22nd edn. (American Public Health Association, 2012).Cox, E. J. Identification of Freshwater Diatoms from Live Material. (Chapman and Hall, 1996). https://doi.org/10.1017/S0025315400041023.Krammer, K., & Lange-Bertalot, H. Bacillariophyceae, Part 5. English and French Translation of the Keys. (VEB Gustav Fisher Verlag, 2000).Reichardt, E. A remarkable association of diatoms in a spring habitat in the Grazer Bergland, Austria. In Iconographia Diatomologica (ed. Lange-Bertalot, H.) 419–479 (2004).Żelazna-Wieczorek, J. Diatom flora in springs of Lódz Hills (Central Poland). Biodiversity, taxonomy and temporal changes of epipsammic diatom assemblages in springs affected by human impact, 419. Volume 13 of Diatom monographs. Gantner. https://books.google.co.in/books?id=bdxeewAACAAJ (2011).Stark, J. D., Boothroyd, I. K. G., Harding, J. S., Maxted, J. R. & Scarsbrook, M. R. Protocols for sampling macroinvertebrates in wadeable streams. In New Zealand Macroinvertebrate Working Group Report no. 1. Prepared for the Ministry for the Environment. Sustainable Management Fund Project, 5103 (2001).Winterbourn, M. J. Sampling stream invertebrates. In Biological Monitoring of Freshwaters. Proceedings of the Seminar. Water and Soil Miscellaneous Publication No. 83 (eds. Pridmore, R. D., Cooper, A. B.) 241–258. (National Water and Soil Conservation Authority, 1985).Barbour, M. T., Gerritsen, J., Snyder, B. D., Stribling, J. B. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates and fish, 339. (United States Environmental Protection Agency, Office of Water, 1999).Malmqvist, B. & Hoffsten, P. O. Macroinvertebrate taxonomic richness, community structure and nestedness in Swedish streams. Fundam. Appl. Limnol. 150(1), 29–54. https://doi.org/10.1127/archiv-hydrobiol/150/2000/29 (2000).Article 

Google Scholar 
Ilmonen, J. & Paasivirta, L. Benthic macrocrustacean and insect assemblages in relation to spring habitat characteristics: Patterns in abundance and diversity. Hydrobiologia 533(1–3), 99–113. https://doi.org/10.1007/s10750-004-2399-4 (2005).Article 

Google Scholar 
Munasinghe, D. S. N., Najim, M. M. M., Quadroni, S. & Musthafa, M. M. Impacts of streamflow alteration on benthic macroinvertebrates by mini-hydro diversion in Sri Lanka. Sci. Rep. 11(1), 546. https://doi.org/10.1038/s41598-020-79576-5 (2021).Article 
CAS 

Google Scholar 
Edmondson, W. T. Fresh-Water Biology, 2nd ed. 1050–1056 (Wiley, 1959).Pennak, R. W. Freshwater Invertebrates of United States. (Wiley, 1978).McCafferty, W. P., Provonsha, A. V. Aquatic entomology: The fishermen’s and ecologists’ Illustrated Guide to Insects and their Relatives. (Jones and Bartlett Publishers, 1983).Borror, D., Triplehorn, C., Johnson, N. An Introduction to the Study of Insects, 6th ed. (Saunders College Publishing, 1989).Ward, J. V. Aquatic Insect Ecology, Biology and Habitat. (Wiley, 1992).Engblom, E. & Lingdell, P.E. Analyses of Benthic Invertebrates (ed. Nyman, L.) (1999).Bouchard, R. W. Guide to Aquatic Invertebrates of the Upper Midwest: Identification Manual for Students (University of Minnesota, 2004).
Google Scholar 
Subramanian, K. A. & Sivaramakrishnan, K. G. Aquatic Insects for Biomonitoring Freshwater Ecosystems—A Methodology Manual. (Ashoka Trust for Ecology and Environment (ATREE), 2007).Thorp, J. H., & Covich, A. P. (eds.) Ecology and Classification of North American Freshwater Invertebrates. (Academic Press, 2009).Allan, J. D. & Castillo, M.M. An introduction to fluvial ecosystems. In Stream Ecology: Structure and Function of Running Waters, 1–12 (2007).Oksanen, et al. Vegan: Community ecology package. In: R package version 2.4-3.McCune, B. & Grace, B. Analysis of Ecological Communities (MjM Software Design, 2016).Clarke, K. R. & Gorley, R. N. Primer v6 Permanova+ (Primer-E Ltd., 2006).
Google Scholar 
Salazar, G. EcolUtils: Utilities for Community Ecology Analysis. R package version 0.1 software (2018).Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9(6), 683–693 (2006).
Google Scholar 
Gardener, M. Community Ecology: Analytical Methods in Using R and Excel. (Pelagic Publishing, 2014).Chao, A. & Bunge, J. Estimating the number of species in a stochastic abundance model. Biometrics 58, 531–539. https://doi.org/10.1111/j.0006-341X.2002.00531.x (2002).Article 
MATH 

Google Scholar 
Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).
Google Scholar 
Meng, X. L. et al. Responses of macroinvertebrates and local environment to short-term commercial sand dredging practices in a flood-plain lake. Sci. Total Environ. 631, 1350–1359 (2018).
Google Scholar 
Core Team, R. R: A Language and Environmental for Statistical Computing. (R Foundation for Statistical Computing, 2017).Wood, P. J. & Armitage, P. D. Biological effects of fine sediment in the lotic environment. Environ. Manag. 21, 203–217 (1997).CAS 

Google Scholar 
Marchant, R. Changes in the benthic invertebrate communities of the Thomson River, southeastern Australia, after dam construction. Regul. Rivers Res. Manag. 4, 71–89 (1989).
Google Scholar 
Gray, L. J. & Ward, J. V. Effects of sediment releases from a reservoir on stream macroinvertebrates. Hydrobiologia 96, 177–184 (1982).
Google Scholar 
Sand-Jensen, K., Moller, J. & Olesen, B. H. Biomass regulation of microbenthic algae in Danish lowland streams. Oikos 53, 332–340 (1988).
Google Scholar 
Lewis, M. A., Weber, D. E., Stanley, R. S. & Moore, J. C. Dredging impact on an urbanized Florida bayou: Effects on benthos and algal-periphyton. Environ. Pollut. 115(2), 161–171 (2001).CAS 

Google Scholar 
Biggs, B. J. Algal ecology in freshwater benthic ecosystems geology and landuse to the habitat template of periphyton in stream ecosystems. Freshw. Biol. 33, 419–438 (1995).
Google Scholar 
Taylor, et al. Can diatom-based pollution indices be used for biomonitoring in South Africa? A case study of the Crocodile West and Marico water management area. Hydrobiologia 592, 455–464 (2007).
Google Scholar 
Porter, et al. Efficacy of algal metrics for assessing nutrient and organic enrichment in flowing waters. Freshw. Biol. 53, 1036–1054 (2008).
Google Scholar 
Wetzel, R. G. & Likens, G. E. Limnological analyses, 3rd ed. In Nitrogen, Phosphorus, and Other Nutrients, 85–113. (Springer, 2000). https://doi.org/10.1007/978-1-4757-3250-4.Wetzel, R. G. Attached algal-substrata interactions: Fact or myth, and when and how? vol. 17. In Periphyton of Freshwater Ecosystems (ed. Wetzel, R.) 207–215 (Springer, 1983). https://doi.org/10.1007/978-94-009-7293-3_28.Krajenbrink, H. J. et al. Diatoms as indicators of the effects of river impoundment at multiple spatial scales. PeerJ 7, e8092. https://doi.org/10.7717/peerj.8092 (2019).Article 

Google Scholar 
Poff, N. L., Voelz, N. J., Ward, J. V. & Lee, R. E. Algal colonization under four experimentally-controlled current regimes in a high mountain stream. J. N. Am. Benthol. Soc. 9, 303–318 (1990).
Google Scholar 
Dodds, W. K. & Marra, J. L. Behaviors of the midge, Cricotopus (Diptera; Chironomidae) related to mutualism with Nostoc parmeloides (Cyanobacteria). Aquat. Insects 11, 201–208 (1989).
Google Scholar 
Tang, T., Niu, S. Q. & Dudgeon, D. Responses of epibenthic algal assemblages to water abstraction in Hong Kong streams. Hydrobiologia 703(1), 225–237. https://doi.org/10.1007/s10750-012-1362-z (2013).Article 
CAS 

Google Scholar 
Maheshwari, K., Vashistha, J., Paulose, P. V. & Agarwal, T. Seasonal changes in phytoplankton community of lake Ramgarh, India. Int. J. Curr. Microbiol. Appl. Sci. 4(11), 318–330 (2015).CAS 

Google Scholar 
Luttenton, M. R., & Baisden, C. The relationships among disturbance, substratum size and periphyton community structure. In Advances in Algal Biology: A Commemoration of the Work of Rex Lowe 111–117. (Springer, 2006).Uehlinger, U. Spatial and temporal variability of periphyton biomass in a prealpine river (Necker, Switzerland). Arch. Fur. Hydrobiol. 123, 219–237 (1991).
Google Scholar 
Hill, W. R. Effects of light. In Algal Ecology in Freshwater Benthic Ecosystems. 121–148 (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) (Academic Press, 1996).DeNichola, D. M. Periphyton responses to temperature at different ecological levels. In Algal Ecology in Freshwater Benthic Ecosystems. (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) 149–181 (Academic Press, 1996).O’Reilly, C. M. Seasonal dynamics of periphyton in a large tropical lake. Hydrobiologia 553, 293–301. https://doi.org/10.1007/s10750-005-0878-x (2006).Article 

Google Scholar 
Borduqui, M. & Ferragut, C. Factors determining periphytic algae succession in a tropical hypereutrophic reservoir. Hydrobiologia 683, 109–122. https://doi.org/10.1007/s10750-011-0943-6 (2012).Article 
CAS 

Google Scholar 
De Souza, M. L., Pellegrini, B. G. & Ferragut, C. Periphytic algal community structure in relation to seasonal variation and macrophyte richness in a shallow tropical reservoir. Hydrobiologia 755, 183–196. https://doi.org/10.1007/s10750-015-2232-2 (2015).Article 

Google Scholar 
Prowse, T. D. River-ice hydrology. In Encyclopedia of Hydrological Sciences, vol. 4 (ed. Anderson, M. G.). (Wiley, 2005).Rusanov, A. G., Stanislavskaya, E. V. & Ács, É. Periphytic algal assemblages along environmental gradients in the rivers of the Lake Ladoga basin, Northwestern Russia: Implication for the water quality assessment. Hydrobiologia 695(1), 305–327 (2012).CAS 

Google Scholar 
Sofi, M. S., Hamid, A., Bhat, S. U., Rashid, I. & Kuniyal, J. C. Impact evaluation of the run-of-river hydropower projects on the water quality dynamics of the Sindh River in the Northwestern Himalayas. Environ. Monit. Assess. 194(9), 1–6 (2022).
Google Scholar 
MCCormick, P. V. Resource competition and species coexistence in freshwater algal assemblages. In Algal ecology—Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) 229–252 (Academic, 1996).Hillebrand, H., Worm, B. & Lotze, H. K. Marine microbenthic community structure regulated by nitrogen loading and grazing pressure. Mar. Ecol. Prog. Ser. 204, 27–38 (2000).CAS 

Google Scholar  More

Study areaThe Qinghai–Tibet Plateau (26°00′-39°47′N, 73°19′-104°47′E), one of the most important pastoral areas in the world, straddles the southwest regions of China, and it includes 244 counties, which belong to six provinces: Tibet, Qinghai, Xinjiang, Gansu, Sichuan, and Yunnan. It is characterized by rich natural grassland resources, including desert steppes, alpine steppes, and alpine meadows (Fig. 1a). The grassland areas account for over 56% of this region34. The grassland plays a vital role in providing regional and national animal husbandry products and fodder35, which enables the local herders to obtain almost all of the resources required for survival36. The grazing density distribution is extremely unbalanced (Fig. 1a) owing to the high spatial heterogeneity of economic development (Fig. 1b-1) and grassland production (Fig. 1b-2), resulting from the differences in resources and environmental factors37. Over the past few decades, there has been a significant change in the number of livestock animals, and the number of sheep exceeded 160 million by 2020. Therefore, it is urgent to obtain a high-resolution gridded grazing dataset for its evaluating spatiotemporal changes and coordinating the relationship between human beings and the grassland ecosystem.Fig. 1Location of the Qinghai–Tibet Plateau: (a) grassland type and distribution, and grazing density (GD) in 244 counties; (b) spatial heterogeneity of economic development (ED) and grassland production (GP) in 244 counties. GD, ED, and GP are represented by sheep unit per grassland area per county (SU/hm2), human footprint index per pixel (HF/pixel) per county, and net primary production per grassland area per county (gC/m2), respectively.Full size imageFig. 2Methodological framework for grazing spatialization.Full size imageMethodological frameworkWe developed a methodological framework for high-resolution gridded grazing dataset mapping. The framework mainly includes four parts: (i) identifying features affecting grazing, (ii) extracting theoretical suitable grazing areas, (iii) building grazing spatialization model, and (iv) correcting the grazing spatialization dataset. Each step is explained in more detail below (Fig. 2).Step 1: Identifying features affecting grazingGrazing activities are affected by the spatial heterogeneity of resources and environmental factors, regulated by the grazing behavior of herders and the foraging behavior of herds, and restricted by ecological protection policies. Therefore, the specific implications of the 14 influencing factors from the above four aspects are presented in Table 1. These factors are necessary for spatializing the county-level grazing data.Table 1 The identified features affecting grazing.Full size tableStep 2: Extracting theoretical suitable grazing areasThe decision tree approach38 was adopted to extract the theoretical suitable grazing areas for further grazing spatialization (step 2 in Fig. 2). First, the potential grazing area was identified according to the boundary of the grassland ecosystem, because grazing behavior only occurs in the grassland. Then, the unsuitable areas for grazing, i.e., extremely-high-altitude areas and areas adjacent to towns, were removed from the potential grazing area stepwise. The areas strictly prohibited for grazing, i.e., the core areas of national nature reserves39 within grassland areas, were also deemed unsuitable for grazing. Finally, the extracted areas were the theoretically suitable grazing areas.Step 3: Building grazing spatialization model(i) Extracting cross-scale feature (CSFs)In the traditional method, the spatial resolution of the training data (i.e., the average value at the administrative level) differs from that of the predicting data (i.e., the value at the pixel level), and the trained model can only capture the characteristics within the training data. However, the extreme value of the predicting data inevitably exceeds the range of the training data, which can result in underestimation in these parts40. To reduce these mismatches, we built an improved method for CSFs extraction (Fig. 2, first part of step 3).First, the census grazing data are simply distributed from county level to pixel level using the weight of the absolute disturbance (AD) index as Eq. (1). The AD index is measured by Mahalanobis distance using Eq. (2), which is calculated according to the deviation between the potential and observed normalized difference vegetation index (NDVI) values22. Second, the distributed grazing data are graded via the hierarchical clustering method, and the optimal number of the group can be determined using the Davies–Bouldin index (DBI)41 as Eq. (3), an index for evaluating the quality of clustering algorithm. The smaller the DBI, the smaller the distance within each group. Therefore, the DBI can be used to select the best similar values to minimize the deviation within each group. Finally, we can obtain the scope of the groups within each county using the above two steps and obtain the average value of all independent variables and the dependent variable accordingly. As expected, we can decompose the average value at the county level (traditional features in Fig. 2) into the average value at the group level (improved features in Fig. 2).$$S{U}_{i}=S{U}_{j}^{C}frac{{w}_{A{D}_{i}}}{{w}_{A{D}_{j}}}$$
(1)
where SUi and (S{U}_{j}^{C}) are the grazing value for pixel i and the census grazing value for county j; ({w}_{A{D}_{i}}) is the weight of the AD index for pixel i and ({w}_{A{D}_{j}}) represents the summed weight of the AD index values for all pixels in county j.$$begin{array}{cll}A{D}_{i} & = & sqrt{{({D}_{i}-u)}^{T}co{v}^{-1}({D}_{i}-u)}\ {D}_{i} & = & NDV{I}_{i}^{T}-NDV{I}_{i}^{P}end{array}$$
(2)
where ADi is the AD index for pixel i; the vector composed of its observed NDVI (left(NDV{I}_{i}^{T}right)) and potential NDVI (left(NDV{I}_{i}^{P}right)) time-series data could be considered as two points in the feature space for pixel i, and Di and u are the difference and the mean value of the vector, respectively; cov is the covariance matrix.$$DB{I}_{k}=frac{1}{k}{sum }_{x=1}^{k}ma{x}_{yne x}left(frac{overline{{a}_{x}}+overline{{a}_{y}}}{left|{delta }_{x}-{delta }_{y}right|}right)$$
(3)
where DBIk is the DBI coefficient when the cluster number is k; (overline{{a}_{x}}) and (overline{{a}_{y}}) are the average distances of the group xth and the group yth, respectively; δx and δy are the center distance of the group xth and the group yth, respectively.Different from the traditional method, our method can decompose features into multiple features using the grading AD index. The differences among counties will not be easily averaged out. Moreover, our method is less affected by scale mismatch and can be transferred to cross-scale modeling26.(ii) Building RF model with partitioningA single model cannot accurately obtain the variation information of the Qinghai–Tibet Plateau with high spatial heterogeneity. The partition model, a widely used method for estimating population distribution and others42,43, can be incorporated into the proposed model to improve its performance. The thresholds (0.43, 0.35 and 0.21 SU/hm2), determined according to the theoretical livestock carrying capacity (equation S1), were calculated and used to separate independent variables and dependent variable for each grassland types: alpine meadow, alpine steppe and alpine desert steppe (see Section 6.1 for details). Then, the RF models were established, and the training and testing samples were randomly divided in the proportion of 3:1. It is notable that transforming the response variable using natural log prior to RF model fitting is necessary to achieve higher prediction accuracies44. Finally, the independent variables at the pixel level were inputted into the two trained RF models, and the corresponding grid grazing dataset was output by combining the two results (Fig. 2, second part of step 3).(iii) Validating the accuracy of the methodsThe performance of the grazing spatialization model was evaluated through a comparison of the predicted value with census value26. Accuracy validation indexes, including coefficients of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the performances of the proposed RF-based models (Table 2), as presented in Eq. (4).$$begin{array}{ccc}{R}^{2} & = & 1-frac{{sum }_{j=1}^{N}{left(S{U}_{j}^{C}-S{U}_{j}^{P}right)}^{2}}{{sum }_{j=1}^{N}{left(S{U}_{j}^{C}-overline{S{U}^{C}}right)}^{2}}\ RMSE & = & sqrt{frac{{sum }_{j=1}^{N}{left(S{U}_{j}^{C}-S{U}_{j}^{P}right)}^{2}}{N}}\ MAE & = & frac{{sum }_{j=1}^{N}| S{U}_{j}^{C}-S{U}_{j}^{P}| }{N}end{array}$$
(4)
where (S{U}_{j}^{C}) and (S{U}_{j}^{P}) are the census grazing value and the predicted grazing value for county j, respectively; (overline{S{U}^{C}}) is the average census data for all counties; and N is the number of all counties.Table 2 The proposed methods and their descriptions.Full size tableStep 4: Correcting grazing spatialization dataset(i) Correcting residuals of datasetCorrecting residuals is necessary to obtain datasets with higher accuracy45,46, because propagating the cross-scale relationship in the RF models will inevitably generate errors47. The residuals, calculated by the difference between the average census grazing and predicted grazing values at the administrative level, were used to calibrate the errors related to all pixels within this county. The revised dataset after residual correction is the final product provided in this study. The residual correction method is expressed by Eq. (5), and the process is shown in the fourth step in Fig. 2.$$S{U}_{i}^{RP}=S{U}_{i}^{P}+{R}_{j}$$
(5)
where (S{U}_{i}^{RP}) denotes the predicted grazing value revised by the residuals for pixel i, (S{U}_{i}^{P}) denotes the predicted grazing for pixel i, and Rj denotes the residuals calculated from the difference between census grazing and predicted grazing data for county j.(ii) Validating the accuracy of datasetTwo goodness-of-fit indexes were used to validate the consistency of spatial distribution and the temporal trend between predicted grazing data and census grazing data. Generally, the coefficient of determination (R2), defined in Eq. (4), is used to verify the consistency of spatial distribution, and the Nash–Sutcliffe efficiency (NSE, Eq. (6)) is used to verify the consistency of temporal trend. An index value closer to 1 corresponds to a more accurate dataset. Meanwhile, we also collected field grazing data from 56 sites to further validate the spatial accuracy of the dataset, and it measured using the R2 in Eq. (4).$$NSE=1-frac{{sum }_{t=1}^{T}{left(S{U}_{t}^{RP}-S{U}_{t}^{C}right)}^{2}}{{sum }_{t=1}^{T}{left(S{U}_{t}^{C}-overline{S{U}^{{C}^{{prime} }}}right)}^{2}}$$
(6)
where (S{U}_{t}^{RP}) and (S{U}_{t}^{C}) are the predicted grazing value revised by residuals and the census grazing value of all counties in year t, respectively; (overline{S{U}^{{C}^{{prime} }}}) is the average census grazing value of all years; and T is the number of time steps.(iii) Identifying uncertainties associated with datasetThe uncertainties associated with the dataset originate from the following two aspects: First, the unreasonableness of our method, owing to the errors related to cross-scale modeling or the inappropriate selection of influencing factors, is an important source of uncertainties. Second, the incompleteness of auxiliary variables also introduces uncertainties. In this instance, grassland-free areas are not accurately identified in some counties, but livestock animals are raised in these counties. These counties have no effective value for livestock density prediction. Overall, the uncertainties can be identified in terms of the mean relative error (MRE) in Eq. (7).$$MRE=frac{{sum }_{j=1}^{N}left|frac{S{U}_{j}^{C}-S{U}_{j}^{RP}}{S{U}_{j}^{C}}right|}{N}ast 100 % $$
(7)
where (S{U}_{j}^{C}) is the census grazing value for county j, (S{U}_{j}^{RP}) is the predicted grazing value revised by residuals for county j, and N is the number of counties.Data sourceCensus grazing data at county levelEight types of livestock, namely cattle, yaks, horses, donkeys, mules, camels, goats, and sheep, were considered according to the regional characteristics, and livestock stocking quantity at the end of year for each county can be determined from statistical yearbooks. However, the numbers of livestock at the county level for some years between 1982 and 2015 were not recorded. The missing data were indirectly approximated from city- or provincial-level data (e.g., interpolation using their temporal trends). Each type of livestock stocking quantity was converted into standard sheep unit (SU) according to the national standards using Eq. (8)48, namely the calculation of rangeland carrying capacity (NY/T 635-2015). Of the 244 counties of the Qinghai–Tibet Plateau, only 242 counties were considered, as the census grazing data for the other 2 counties were unavailable. The unit of grazing statistics data at the county level is defined as SU per county per year (SU·county−1·year−1).$$begin{array}{l}SU={N}_{sheep}+0.8times {N}_{goats}+5times {N}_{cattle}+5times {N}_{yaks+}+\ 6times {N}_{horses}+3times {N}_{donkeys}+6times {N}_{mules}+7times {N}_{camels}end{array}$$
(8)
where Nsheep, Ngoats, Ncattle, Nyaks, Nhorses, Ndonkeys, Nmules, Ncamels are the number of sheep, goats, cattle, yaks, horses, donkeys, mules, and camels at the year-end, respectively. SU denotes the standard sheep unit (SU·county−1·year−1).Data of grazing influencing factors at pixel levelThe types of features affecting grazing were obtained from the first step described in Methods, and the detailed information, such as original spatiotemporal resolution, format, and source, is shown in Table 3. The format (i.e., GeoTIFF), spatial resolution (i.e., 0.083°), and the number of rows and columns of the gridded features were leveraged to further produce a high-resolution grazing dataset.Table 3 Data source of grazing influence factors.Full size table More

Now consider a generalized model in which the species interactions are heterogeneous. A natural way of introducing heterogeneity in the system is by having a species diversify into subpopulations with different interaction strengths12,13,14,15. This way of modeling heterogeneity is useful as it can describe different kinds of heterogeneity. For example, the subpopulations could represent polymorphic traits that are genetically determined or result from plastic response to heterogeneous environments. A population could also be divided into local subpopulations in different spatial patches, which can migrate between patches and may face different local predators. We can also model different behavioral modes as subpopulations that, for instance, spend more time foraging for food or hiding from predators. We study several kinds of heterogeneity after we introduce a common mathematical framework. By studying these different scenarios using variants of the model, we show that our main results are not sensitive to the details of the model.We focus on the simple case where only the prey species splits into two types, (C_1) and (C_2), as illustrated in Fig. 1b. The situation is interesting when predator A consumes (C_1) more readily than predator B and B consumes (C_2) more readily than A (i.e., (a_1 / a_0 > b_1 / b_0) and (b_2 / b_0 > a_2 / a_0), which is equivalent to the condition that the nullclines of A and B cross, see section “Resources competition and nullcline analysis”). The arrows between (C_1) and (C_2) in Fig. 1b represent the exchange of individuals between the two subpopulations, which can happen by various mechanisms considered below. Such exchange as well as intraspecific competition between (C_1) and (C_2) result from the fact that the two prey types remain a single species.The system is now described by an enlarged Lotka-Volterra system with four variables, A, B, (C_1), and (C_2): $$begin{aligned} dot{A}&= varepsilon _A ,alpha _{A1} , A , C_1 + alpha _{A2} , A , C_2 – beta _A , A end{aligned}$$
(3a)
$$begin{aligned} dot{B}&= varepsilon _B , alpha _{B1} , B , C_1 + alpha _{B2} , B , C_2 – beta _B , B end{aligned}$$
(3b)
$$begin{aligned} dot{C_1}&= C_1 , (beta _C – alpha _{CC} , C)-alpha _{A1} , C_1 A-alpha _{B1} , C_1 B – sigma _1 , C_1 + sigma _2 , C_2 end{aligned}$$
(3c)
$$begin{aligned} dot{C_2}&= C_2 , (beta _C – alpha _{CC} , C) -alpha _{A2} , C_2 A -alpha _{B2} , C_2 B + sigma _1 , C_1 – sigma _2 , C_2 end{aligned}$$
(3d)
The parameters in these equations and their meanings are listed in Table 1. Here we assume that the prey types (C_1) and (C_2) have the same birth rate and intraspecific competition strength, but different interaction strengths with A and B. Note that (C_1) and (C_2) are connected by the (sigma _i) terms, which represent the exchange of individuals between these subpopulations through mechanisms studied below; these terms indicate a major difference between our model of a prey with intraspecific heterogeneity and other models of two prey species. For the convenience of analysis, we transform the variables (C_1) and (C_2) to another pair of variables C and (lambda), where (C equiv C_1 + C_2) is the total population of C as before, and (lambda equiv C_2 / (C_1 + C_2)) represents the composition of the population (Fig. 1c). After this transformation and rescaling of variables (described in “Methods”), the new dynamical system can be written as: $$begin{aligned} dot{A}&= A , big ( C , (a_1 (1-lambda ) + a_2 lambda ) – a_0 big ) end{aligned}$$
(4a)
$$begin{aligned} dot{B}&= B , big ( C , (b_1 (1-lambda ) + b_2 lambda ) – b_0 big ) end{aligned}$$
(4b)
$$begin{aligned} dot{C}&= C , big ( 1 – C – A (a_1 (1-lambda ) + a_2 lambda ) – B (b_1 (1-lambda ) + b_2 lambda ) big ) end{aligned}$$
(4c)
$$begin{aligned} dot{lambda }&= lambda (1-lambda ) , big ( A (a_1 – a_2) + B (b_1 – b_2) big ) + eta _1 (1-lambda ) – eta _2 lambda end{aligned}$$
(4d)
Here, (a_i) and (b_i) are the (rescaled) feeding rates of the predators on the prey type (C_i); (a_0) and (b_0) are the death rates of the predators as before; (eta _1) and (eta _2) are the exchange rates of the prey types (Table 1). The latter can be functions of other variables, representing different kinds of heterogeneous interactions that we study below. Notice that Eqs. (4a–4c) are equivalent to the homogeneous Eqs. (2a–2c) but with effective interaction strengths (a_text {eff} = (1-lambda ) , a_1 + lambda , a_2) and (b_text {eff} = (1-lambda ) , b_1 + lambda , b_2) that both depend on the prey composition (lambda) (Fig. 1c).Table 1 Model parameters (before/after rescaling) and their meanings.Full size tableThe variable (lambda) can be considered an internal degree of freedom within the C population. In all of the models we study below, (lambda) dynamically stabilizes to a special value (lambda ^*) (a bifurcation point), as shown in Fig. 3a. Accordingly, a new equilibrium point (P_N) appears (on the line (mathscr {L}) in Fig. 2), at which all three species coexist. For comparison, Fig. 3b shows the equilibrium points if (lambda) is held fixed at any other values, which all result in the exclusion of one of the predators. Thus, heterogeneous interactions give rise to a new coexistence phase (see Fig. 4 below) by bringing the prey composition (lambda) to the value (lambda ^*), instead of having to fine-tune the interaction strengths. The exact conditions for this new equilibrium to be stable are detailed in “Methods”.Figure 3(a) Time series of (lambda) for systems with each kind of heterogeneity. All three systems stabilize at the same (lambda ^*) value, which is the bifurcation point in panel (b). (b) Equilibrium population of each species (X = A), B, or C, with (lambda) held fixed at different values. Solid curves represent stable equilibria and dashed curves represent unstable equilibria (see Eq. (9) in “Methods”). The vertical dashed line is where (lambda = lambda ^*), which is also the bifurcation point. Notice that the equilibrium population of C is maximized at this point (for (a_1 > a_2) and (b_2 > b_1)). Parameters used here are ((a_0, a_1, a_2, b_0, b_1, b_2, rho , eta _1, eta _2, kappa ) = (0.25, 0.5, 0.2, 0.4, 0.2, 0.6, 0.5, 0.05, 0.05, 50)).Full size imageInherent heterogeneityWe first consider a scenario where individuals of the prey species are born as one of two types with a fixed ratio, such that a fraction (rho) of the newborns are (C_2) and ((1-rho )) are (C_1). This could describe dimorphic traits, such as the winged and wingless morphs in aphids12 or the horned and hornless morphs in beetles13. We call this “inherent” heterogeneity, because individuals are born with a certain type and cannot change in later stages of life. The prey type given at birth determines the individual’s interaction strength with the predators. This kind of heterogeneity can be described by Eq. (4d) with (eta _1 = rho (1-C)) and (eta _2 = (1-rho ) (1-C)) (see “Methods”).Figure 4Phase diagrams showing regions of parameter space identified by the stable equilibrium points. Yellow region represents (P_C) (predators A, B both extinct), red represents (P_A) (A excludes B), blue represents (P_B) (B excludes A), and green represents (P_N) (A, B coexist). The middle point (black dot) is where the preferences of the two predators are identical, (a_2/a_0=b_2/b_0) and (b_1/b_0=a_1/a_0). The coexistence phase appears in all three kinds of heterogeneity modeled here. (a–d) Inherent heterogeneity: Individuals of the prey population are born in two types with a fixed composition (rho). In the extreme cases of (rho = 0) and 1, the prey is homogeneous and there is no coexistence of the predators. (e–h) Reversible heterogeneity: Individual prey can switch types with fixed switching rates (eta _1) and (eta _2). As the switching rates increase, the coexistence region shrinks because the prey population becomes effectively homogeneous (the occasional green spots are numerical artifacts because the time to reach the equilibrium becomes long in this limit). (i–l) Adaptive heterogeneity: The switching rates (eta _i) dynamically adapt to the predator densities, so as to maximize the growth rate of the prey. As the sharpness (kappa) of the sigmoidal decision function is increased, the prey adapts more optimally and the region of coexistence expands. Parameters used here are ((a_0, a_1, b_0, b_2) = (0.3, 0.5, 0.4, 0.6)).Full size imageThe stable equilibrium of the system can be represented by phase diagrams that show the identities of the species at equilibrium. We plot these phase diagrams by varying the parameters (a_2) and (b_1) while keeping (a_1) and (b_2) constant. As shown in Fig. 4a–d, the equilibrium state depends on the parameter (rho). In the limit (rho = 0) or 1, we recover the homogeneous case because only one type of C is produced. The corresponding phase diagrams (Fig. 4a, d) contain only two phases where either of the predators is excluded, illustrating the competitive exclusion principle. For intermediate values of (rho), however, there is a new phase of coexistence that separates the two exclusion phases (Fig. 4b, c). There are two such regions of coexistence, which touch at a middle point and open toward the bottom left and upper right, respectively. The middle point is at ((a_2/a_0 = b_2/b_0, b_1/b_0 = a_1/a_0)), where the feeding preferences of the two predators are identical (hence their niches fully overlap). Towards the origin and the far upper right, the predators consume one type of C each (hence their niches separate). The coexistence region in the bottom left is where the feeding rates of the predators are the lowest overall. There can be a region (yellow) where both predators go extinct, if their primary prey type alone is not enough to sustain each predator. Increasing the productivity of the system by increasing the birth rate ((beta _C)) of the prey eliminates this extinction region, whereas lowering productivity causes the extinction region to take over the lower coexistence region. Because the existence and identity of the phases is determined by the configuration of the equilibrium points (Fig. 2, see also section “Mathematical methods”), the qualitative shape of the phase diagram is not sensitive to changes of parameter values.The new equilibrium is not only where the predators A and B can coexist, but also where the prey species C grows to a larger density than what is possible for a homogeneous population. This is illustrated in Fig. 3b, which shows the equilibrium population of C if we hold (lambda) fixed at different values. The point (lambda = lambda ^*) is where the system with a dynamic (lambda) is stable, and also where the population of C is maximized (when A and B prefer different prey types). That means the population automatically stabilizes at the optimal composition of prey types. Moreover, the value of (C^*) at this coexistence point can even be larger than the equilibrium population of C when there is only one predator A or B. This is discussed further in section “Multiple-predator effects and emergent promotion of prey”. These results suggest that heterogeneity in interaction strengths can potentially be a strategy for the prey population to leverage the effects of multiple predators against each other to improve survival.Reversible heterogeneityWe next consider a scenario where individual prey can switch their types. This kind of heterogeneity can model reversible changes of phenotypes, i.e., trait changes that affect the prey’s interaction with predators but are not permanent. For example, changes in coat color or camouflage14,16,17, physiological changes such as defense15, and biomass allocation among tissues18,19. One could also think of the prey types as subpopulations within different spatial patches, if each predator hunts at a preferred patch and the prey migrate between the patches20,21. With some generalization, one could even consider heterogeneity in resources, such as nutrients located in different places, that can be reached by primary consumers, such as swimming phytoplankton22. We can model this “reversible” kind of heterogeneity by introducing switching rates from one prey type to the other. In Eq. (4d), (eta _1) and (eta _2) now represent the switching rates per capita from (C_1) to (C_2) and from (C_2) to (C_1), respectively. Here we study the simplest case where both rates are fixed.In the absence of the predators, the natural composition of the prey species given by the switching rates would be (rho equiv eta _1 / (eta _1 + eta _2)), and the rate at which (lambda) relaxes to this natural composition is (gamma equiv eta _1 + eta _2). Compared to the previous scenario where we had only one parameter (rho), here we have an additional parameter (gamma) that modifies the behavior of the system. Fig. 4e–h shows phase diagrams for the system as (rho) is fixed and (gamma) varies. We again find the new equilibrium (P_N) where all three species coexist. When (gamma) is small, the system has a large region of coexistence. As (gamma) is increased, this region is squeezed into a border between the two regions of exclusion, where the slope of the border is given by (eta _1/eta _2) as determined by the parameter (rho). However, this is different from the exclusion we see in the case of inherent heterogeneity, which happens only for (rho rightarrow 0) or 1, where the borders are horizontal or vertical (Fig. 4a,d). Here the predators exclude each other despite having a mixture of prey types in the population.This special limit can be understood as follows. For a large (gamma), (lambda) is effectively set to a constant value equal to (rho), because it has a very fast relaxation rate. In other words, the prey types exchange so often that the population always maintains a constant composition. In this limit, the system behaves as if it were a homogeneous system with effective interaction strengths (a_text {eff} = (1-rho ) , a_1 + rho , a_2) and (b_text {eff} = (1-rho ) , b_1 + rho , b_2). As in a homogeneous system, there is competitive exclusion between the predators instead of coexistence. This demonstrates that having a constant level of heterogeneity is not sufficient to cause coexistence. The overall composition of the population must be able to change dynamically as a result of population growth and consumption by predators.An interesting behavior is seen when we examine a point inside the shrinking coexistence region as (gamma) is increased. Typical trajectories of the system for such parameter values are shown in Fig. 5. As (gamma) increases, the system relaxes to the line (mathscr {L}) quickly, then slowly crawls along it towards the final equilibrium point (P_N). This is because increasing (gamma) increases the speed that (lambda) relaxes to (lambda ^*), and when (lambda rightarrow lambda ^*), (mathscr {L}) becomes marginally stable. Therefore, the attraction to (mathscr {L}) in the perpendicular direction is strong, but the attraction towards the equilibrium point along (mathscr {L}) is weak. This leads to a long transient behavior that makes the system appear to reach no equilibrium in a limited time23,24. It is especially true when there is noise in the dynamics, which causes the system to diffuse along (mathscr {L}) with only a weak drift towards the final equilibrium (Fig. 5). Thus, the introduction of a fast timescale (quick relaxation of (lambda) due to a large (gamma)) actually results in a long transient.Figure 5Trajectories of the system projected in the A-B plane, with parameters inside the coexistence region (by holding the position of (P_N) fixed). As (gamma) increases, the system tends to approach the line (mathscr {L}) quickly and then crawl along it. The grey trajectory is with independent Gaussian white noise ((sim mathscr {N}(0,0.5))) added to each variable’s dynamics. Noise causes the system to diffuse along (mathscr {L}) for a long transient period before coming to the equilibrium point (P_N). Parameters used here are ((a_0, a_1, a_2, b_0, b_1, b_2) = (0.2, 0.8, 0.5, 0.2, 0.6, 0.9)), chosen to place (P_N) away from the middle of (mathscr {L}) to show the trajectory drifting toward the equilibrium.Full size imageAdaptive heterogeneityA third kind of heterogeneity we consider is the change of interactions in time. By this we mean an individual can actively change its interaction strength with others in response to certain conditions. This kind of response is often invoked in models of adaptive foraging behavior, where individuals choose appropriate actions to maximize some form of fitness25,26. For example, we may consider two behaviors, resting and foraging, as our prey types. Different predators may prefer to strike when the prey is doing different things. In response, the prey may choose to do one thing or the other depending on the current abundances of different predators. Such behavioral modulation is seen, for example, in systems of predatory spiders and grasshoppers27. Phenotypic plasticity is also seen in plant tissues in response to consumers28,29,30.This kind of “adaptive” heterogeneity can be modeled by having switching rates (eta _1) and (eta _2) that are time-dependent. Let us assume that the prey species tries to maximize its population growth rate by switching to the more favorable type. From Eq. (4c), we see that the growth rate of C depends linearly on the composition (lambda) with a coefficient (u(A,B) equiv (a_1 – a_2) A + (b_1 – b_2) B). Therefore, when this coefficient is positive, it is favorable for C to increase (lambda) by switching to type (C_2). This can be achieved by having a positive switching rate (eta _2) whenever (u(A,B) > 0). Similarly, whenever (u(A,B) < 0), it is favorable for C to switch to type (C_1) by having a positive (eta _1). In this way, the heterogeneity of the prey population constantly adapts to the predator densities. We model such adaptive switching by making (eta _1) and (eta _2) functions of the coefficient u(A, B), e.g., (eta _1(u) = 1/(1+mathrm {e}^{kappa u})) and (eta _2(u) = 1/(1+mathrm {e}^{-kappa u})). The sigmoidal form of the functions means that the switching rate in the favorable direction for C is turned on quickly, while the other direction is turned off. The parameter (kappa) controls the sharpness of this transition.Phase diagrams for the system with different values of (kappa) are shown in Fig. 4i–l. A larger (kappa) means the prey adapts its composition faster and more optimally, which causes the coexistence region to expand. In the extreme limit, the system changes its dynamics instantaneously whenever it crosses the boundary where (u(A,B) = 0), like in a hybrid system31. Such a system can still reach a stable equilibrium that lies on the boundary, if the flow on each side of the boundary points towards the other side32. This is what happens in our system and, interestingly, the equilibrium is the same three-species coexistence point (P_N) as in the previous scenarios. The region of coexistence turns out to be largest in this limit (Fig. 4l).Our results suggest that the coexistence of the predators can be viewed as a by-product of the prey’s strategy to maximize its own benefit. The time-dependent case studied here represents a strategy that involves the prey evaluating the risk posed by different predators. This is in contrast to the scenarios studied above, where the prey population passively creates phenotypic heterogeneity regardless of the presence of the predators. These two types of behavior are analogous to the two strategies studied for adaptation in varying environments, i.e., sensing and bet-hedging33,34. The former requires accessing information about the current environment to make optimal decisions, whereas the latter relies on maintaining a diverse population to reduce detrimental effects caused by environmental changes. Here the varying abundances of the predators play a similar role as the varying environment. From this point of view, the heterogeneous interactions studied here can be a strategy of the prey species that is evolutionarily favorable. More