Philippa Kaur

Zimmer, H. D. et al. Memory for Action: A Distinct Form of Episodic Memory? (Oxford University Press, 2001).
Google Scholar 
Goswami, U. The Wiley-Blackwell Handbook of Childhood Cognitive Development (Wiley, 2013).
Google Scholar 
Fujita, K., Morisaki, A., Takaoka, A., Maeda, T. & Hori, Y. Incidental memory in dogs (Canis familiaris): Adaptive behavioral solution at an unexpected memory test. Anim. Cogn. 15, 1055–1063 (2012).Article 
PubMed 

Google Scholar 
Lind, J., Enquist, M. & Ghirlanda, S. Animal memory: A review of delayed matching-to-sample data. Behav. Processes 117, 52–58 (2015).Article 
PubMed 

Google Scholar 
Kuczaj, S. A. II. & Eskelinen, H. C. (2014) The “creative dolphin” revisited: What do dolphins do when asked to vary their behavior. Anim. Behav. Cogn. 1, 66–77 (2014).Article 

Google Scholar 
Tulving, E. Episodic and semantic memory. Organ. Mem. 1, 381–403 (1972).
Google Scholar 
Tulving, E. How many memory systems are there?. Am. Psychol. 40, 385 (1985).Article 

Google Scholar 
Fugazza, C., Pongrácz, P., Pogány, Á., Lenkei, R. & Miklósi, Á. Mental representation and episodic-like memory of own actions in dogs. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: What is it, who has it, and how did it evolve?. Science 298, 1569–1579 (2002).Article 
PubMed 

Google Scholar 
Conway, M. A. Memory and the self. J. Mem. Lang. 53, 594–628 (2005).Article 

Google Scholar 
Scagel, A. & Mercado, E. III. Do that again! Memory for self-performed actions in dogs (Canis familiaris). J. Comp. Psychol. 20, 25 (2022).
Google Scholar 
Mercado, E., Murray, S. O., Uyeyama, R. K., Pack, A. A. & Herman, L. M. Memory for recent actions in the bottlenosed dolphin (Tursiops truncatus): Repetition of arbitrary behaviors using an abstract rule. Learn. Behav. 26, 210–218 (1998).Article 

Google Scholar 
Paukner, A., Anderson, J. R., Donaldson, D. I. & Ferrari, P. F. Cued repetition of self-directed behaviors in macaques (Macaca nemestrina). J. Exp. Psychol. Anim. Behav. Process. 33, 139 (2007).Article 
PubMed 

Google Scholar 
Smeele, S. Q. et al. Memory for own behaviour in pinnipeds. Anim. Cogn. 20, 1–12 (2019).
Google Scholar 
Clayton, N. S. Episodic-like memory and mental time travel in animals. (2017).Clayton, N. S., Griffiths, D. P. & Dickinson, A. Declarative and episodic-like memory in animals: Personal musings of a Scrub Jay (2000).Clayton, N. S. & Dickinson, A. Episodic-like memory during cache recovery by scrub jays. Nature 395, 272–274 (1998).Article 
PubMed 

Google Scholar 
Tulving, E. Episodic memory and autonoesis: Uniquely human. Missing Link Cogn. Orig. Self-Reflect. Conscious 20, 3–56 (2005).
Google Scholar 
Suddendorf, T. & Corballis, M. C. Mental time travel and the evolution of the human mind. Genet. Soc. Gen. Psychol. Monogr. 123, 133–167 (1997).PubMed 

Google Scholar 
Suddendorf, T. & Corballis, M. C. The evolution of foresight: What is mental time travel, and is it unique to humans?. Behav. Brain Sci. 30, 299–313 (2007).Article 
PubMed 

Google Scholar 
Crystal, J. D. Evaluating evidence from animal models of episodic memory. J. Exp. Psychol. Anim. Learn. Cogn. 47, 337 (2021).Article 
PubMed 

Google Scholar 
Mercado, E. III., Uyeyama, R. K., Pack, A. A. & Herman, L. M. Memory for action events in the bottlenosed dolphin. Anim. Cogn. 2, 17–25 (1999).Article 

Google Scholar 
Zentall, T. R. Coding of stimuli by animals: Retrospection, prospection, episodic memory and future planning. Learn. Motiv. 41, 225–240 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Fugazza, C., Pogány, Á. & Miklósi, Á. Recall of others’ actions after incidental encoding reveals episodic-like memory in dogs. Curr. Biol. 26, 3209–3213 (2016).Article 
PubMed 

Google Scholar 
Lambert, M. L., Jacobs, I., Osvath, M. & von Bayern, A. M. Birds of a feather? Parrot and corvid cognition compared. Behaviour 156, 505–594 (2019).Article 

Google Scholar 
Emery, N. J. Cognitive ornithology: The evolution of avian intelligence. Philos. Trans. R. Soc. B Biol. Sci. 361, 23–43 (2006).Article 

Google Scholar 
Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proc. Natl. Acad. Sci. 113, 7255–7260 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Emery, N. J. & Clayton, N. S. Evolution of the avian brain and intelligence. Curr. Biol. 15, R946–R950 (2005).Article 
PubMed 

Google Scholar 
Bradbury, J. W. & Balsby, T. J. The functions of vocal learning in parrots. Behav. Ecol. Sociobiol. 70, 293–312 (2016).Article 

Google Scholar 
Baciadonna, L., Cornero, F. M., Emery, N. J. & Clayton, N. S. Convergent evolution of complex cognition: Insights from the field of avian cognition into the study of self-awareness. Learn. Behav. 49, 9–22 (2021).Article 
PubMed 

Google Scholar 
Osvath, M., Kabadayi, C. & Jacobs, I. Independent evolution of similar complex cognitive skills (2014).Zentall, T. R., Clement, T. S., Bhatt, R. S. & Allen, J. Episodic-like memory in pigeons. Psychon. Bull. Rev. 8, 685–690 (2001).Article 
PubMed 

Google Scholar 
Zentall, T. R., Singer, R. A. & Stagner, J. P. Episodic-like memory: Pigeons can report location pecked when unexpectedly asked. Behav. Processes 79, 93–98 (2008).Article 
PubMed 

Google Scholar 
Healy, S. D. & Hurly, T. A. Spatial learning and memory in birds. Brain. Behav. Evol. 63, 211–220 (2004).Article 
PubMed 

Google Scholar 
Taylor, A. H. Corvid cognition. Wiley Interdiscip. Rev. Cogn. Sci. 5, 361–372 (2014).Article 
PubMed 

Google Scholar 
Boeckle, M. & Bugnyar, T. Long-term memory for affiliates in ravens. Curr. Biol. 22, 801–806 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Marzluff, J. M., Walls, J., Cornell, H. N., Withey, J. C. & Craig, D. P. Lasting recognition of threatening people by wild American crows. Anim. Behav. 79, 699–707 (2010).Article 

Google Scholar 
Pepperberg, I. M. & Pepperberg, I. M. The Alex Studies: Cognitive and Communicative Abilities of Grey Parrots (Harvard University Press, 2009).Book 

Google Scholar 
Emery, N. J. & Clayton, N. S. Effects of experience and social context on prospective caching strategies by scrub jays. Nature 414, 443–446 (2001).Article 
PubMed 

Google Scholar 
Herzog, S. K. et al. First systematic sampling approach to estimating the global population size of the Critically Endangered Blue-throated Macaw Ara glaucogularis. Bird Conserv. Int. 31, 293–311 (2021).Article 

Google Scholar 
Auersperg, A. M. & von Bayern, A. M. Who’sa clever bird—now? A brief history of parrot cognition. Behaviour 156, 391–407 (2019).Article 

Google Scholar 
Tassin de Montaigu, C., Durdevic, K., Brucks, D., Krasheninnikova, A. & von Bayern, A. Blue-throated macaws (Ara glaucogularis) succeed in a cooperative task without coordinating their actions. Ethology 126, 267–277 (2020).Article 

Google Scholar 
Auersperg, A. M. et al. Social transmission of tool use and tool manufacture in Goffin cockatoos (Cacatua goffini). Proc. R. Soc. B Biol. Sci. 281, 20140972 (2014).Article 

Google Scholar 
Brucks, D. & von Bayern, A. M. Parrots voluntarily help each other to obtain food rewards. Curr. Biol. 30, 292–297 (2020).Article 
PubMed 

Google Scholar 
Krasheninnikova, A., Höner, F., O’Neill, L., Penna, E. & von Bayern, A. M. Economic decision-making in parrots. Sci. Rep. 8, 1–10 (2018).Article 

Google Scholar 
Jarvis, E. D. et al. Avian brains and a new understanding of vertebrate brain evolution. Nat. Rev. Neurosci. 6, 151–159 (2005).Article 
PubMed 

Google Scholar 
Gutiérrez-Ibáñez, C., Iwaniuk, A. N. & Wylie, D. R. Parrots have evolved a primate-like telencephalic-midbrain-cerebellar circuit. Sci. Rep. 8, 1–11 (2018).Article 

Google Scholar 
Smeele, S. Q. et al. Coevolution of relative brain size and life expectancy in parrots. Proc. R. Soc. B 289, 20212397 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Kirsch, J. A., Güntürkün, O. & Rose, J. Insight without cortex: Lessons from the avian brain. Conscious. Cogn. 17, 475–483 (2008).Article 
PubMed 

Google Scholar 
Dunbar, R. I. & Shultz, S. Evolution in the social brain. Science 317, 1344–1347 (2007).Article 
PubMed 

Google Scholar 
Wright, A. A. & Katz, J. S. Mechanisms of same/different concept learning in primates and avians. Behav. Processes 72, 234–254 (2006).Article 
PubMed 

Google Scholar 
Smirnova, A. A., Obozova, T. A., Zorina, Z. A. & Wasserman, E. A. How do crows and parrots come to spontaneously perceive relations-between-relations?. Curr. Opin. Behav. Sci. 37, 109–117 (2021).Article 

Google Scholar 
Schusterman, R. J. & Kastak, D. A California sea lion (Zalophus californianus) is capable of forming equivalence relations. Psychol. Rec. 43, 823–839 (1993).Article 

Google Scholar 
Kastak, D. & Schusterman, R. J. Transfer of visual identity matching-to-sample in two California sea lions (Zalophus californianus). Anim. Learn. Behav. 22, 427–435 (1994).Article 

Google Scholar 
Zentall, T. R., Wasserman, E. A., Lazareva, O. F., Thompson, R. K. & Rattermann, M. J. Concept learning in animals. Comp. Cogn. Behav. Rev. 20, 25 (2008).
Google Scholar 
Marino, L. Convergence of complex cognitive abilities in cetaceans and primates. Brain. Behav. Evol. 59, 21–32 (2002).Article 
PubMed 

Google Scholar 
Huber, L., Range, F. & Virányi, Z. Dog imitation and its possible origins. In Domestic dog Cognition and Behavior 79–100 (Springer, 2014).Chapter 

Google Scholar 
Schmidjell, T., Range, F., Huber, L. & Virányi, Z. Do owners have a Clever Hans effect on dogs? Results of a pointing study. Front. Psychol. 3, 558 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Hare, B., Brown, M., Williamson, C. & Tomasello, M. The domestication of social cognition in dogs. Science 298, 1634–1636 (2002).Article 
PubMed 

Google Scholar 
Lindqvist, C. & Jensen, P. Domestication and stress effects on contrafreeloading and spatial learning performance in red jungle fowl (Gallus gallus) and White Leghorn layers. Behav. Processes 81, 80–84 (2009).Article 
PubMed 

Google Scholar 
Pack, A. A., Herman, L. M. & Roitblat, H. L. Generalization of visual matching and delayed matching by a California sea lion (Zalophus californianus). Anim. Learn. Behav. 19, 37–48 (1991).Article 

Google Scholar 
Bennett, M. S. Five breakthroughs: A first approximation of brain evolution from early bilaterians to humans. Front. Neuroanat. 15, 25 (2021).Article 

Google Scholar 
Cisek, P. Resynthesizing behavior through phylogenetic refinement. Atten. Percept. Psychophys. 81, 2265–2287 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Toft, C. A. & Wright, T. F. Parrots of the wild. Nat. Hist. World’s Most Captiv. Birds 20, 25 (2015).
Google Scholar 
Merkle, J. A., Sigaud, M. & Fortin, D. To follow or not? How animals in fusion–fission societies handle conflicting information during group decision-making. Ecol. Lett. 18, 799–806 (2015).Article 
PubMed 

Google Scholar 
Stevens, J. R. & Gilby, I. C. A conceptual framework for nonkin food sharing: Timing and currency of benefits. Anim. Behav. 67, 603–614 (2004).Article 

Google Scholar 
Kamil, A. C. & Roitblat, H. L. The ecology of foraging behavior—Implications for animal learning and memory. Annu. Rev. Psychol. 36, 141–169 (1985).Article 
PubMed 

Google Scholar 
Ortiz, S. T., Castro, A. C., Balsby, T. J. S. & Larsen, O. N. Problem-solving in a cooperative task in peach-fronted conures (Eupsittula aurea). Anim. Cogn. 23, 265–275 (2020).Article 

Google Scholar 
Krasheninnikova, A., Brucks, D., Blanc, S. & von Bayern, A. M. Assessing African grey parrots’ prosocial tendencies in a token choice paradigm. R. Soc. Open Sci. 6, 190696 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Krasheninnikova, A. et al. Parrots do not show inequity aversion. Sci. Rep. 9, 1–12 (2019).Article 

Google Scholar 
Clayton, N. S., Griffiths, D. P., Emery, N. J. & Dickinson, A. Elements of episodic–like memory in animals. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1483–1491 (2001).Article 
PubMed 
PubMed Central 

Google Scholar 
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall, 2020).Book 

Google Scholar  More

Pitcher, T. J. et al. Seamounts: Ecology, Fisheries & Conservation (Blackwell Publishing, 2007).Book 

Google Scholar 
Harris, P. T., Macmillan-Lawler, M., Rupp, J. & Baker, E. K. Geomorphology of the oceans. Mar. Geol. 352, 4–24 (2014).Article 

Google Scholar 
Wessel, P., Sandwell, D. T. & Kim, S.-S. The global seamount census. Oceanography 23, 24–33 (2010).Article 

Google Scholar 
Etnoyer, P. J. et al. BOX 12|How large is the seamount biome?. Oceanography 23, 206–209 (2010).Article 

Google Scholar 
De Forges, B. R., Koslow, J. A. & Pooro, G. C. B. Diversity and endemism of the benthic seamount fauna in the southwest Pacific. Nature 405, 944–947 (2000).Article 
PubMed 

Google Scholar 
Rowden, A. A., Dower, J. F., Schlacher, T. A., Consalvey, M. & Clark, M. R. Paradigms in seamount ecology: Fact, fiction and future. Mar. Ecol. 31, 226–241 (2010).Article 

Google Scholar 
Pinheiro, H. T. et al. Fish biodiversity of the Vitória-Trindade seamount chain, southwestern Atlantic: An updated database. PLoS ONE 10, 1–17 (2015).Article 

Google Scholar 
Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. PNAS 107, 9711 (2010).Article 

Google Scholar 
Rowden, A. A. et al. A test of the seamount oasis hypothesis: Seamounts support higher epibenthic megafaunal biomass than adjacent slopes. Mar. Ecol. 31, 95–106 (2010).Article 

Google Scholar 
Busch, K. et al. On giant shoulders: How a seamount affects the microbial community composition of seawater and sponges. Biogeosciences 17, 3471–3486 (2020).Article 
CAS 

Google Scholar 
Zhao, Y. et al. Virioplankton distribution in the tropical western Pacific Ocean in the vicinity of a seamount. Microbiol Open 9, e1031 (2020).Article 

Google Scholar 
Arístegui, J. et al. Plankton metabolic balance at two North Atlantic seamounts. Deep-Sea Res. II 56, 2646–2655 (2009).Article 

Google Scholar 
Dower, J. F. & Mackast, D. L. “Seamount effects” in the zooplankton community near Cobb Seamount. Deep-Sea Res. I 43, 837–858 (1996).Article 

Google Scholar 
O’Hara, T. D., Rowden, A. A. & Bax, N. J. A Southern Hemisphere bathyal fauna is distributed in latitudinal bands. Curr. Biol. 21, 226–230 (2011).Article 
PubMed 

Google Scholar 
Williams, A., Althaus, F., Clark, M. R. & Gowlett-Holmes, K. Composition and distribution of deep-sea benthic invertebrate megafauna on the Lord Howe Rise and Norfolk Ridge, southwest Pacific Ocean. Deep-Sea Res. II 58, 948–958 (2011).Article 
CAS 

Google Scholar 
Bridges, A. E. H., Barnes, D. K. A., Bell, J. B., Ross, R. E. & Howell, K. L. Benthic assemblage composition of South Atlantic seamounts. Front. Mar. Sci. 8, 660648 (2021).Article 

Google Scholar 
Lapointe, A. E., Watling, L., France, S. C. & Auster, P. J. Megabenthic assemblages in the lower bathyal (700–3000 m) on the New England and corner rise seamounts Northwest Atlantic. Deep-Sea Res. I 165, 103366 (2020).Article 

Google Scholar 
Clark, M. R. & Bowden, D. A. Seamount biodiversity: High variability both within and between seamounts in the Ross Sea region of Antarctica. Hydrobiologia 761, 161–180 (2015).Article 
CAS 

Google Scholar 
McClain, C. R., Lundsten, L., Barry, J. & DeVogelaere, A. Assemblage structure, but not diversity or density, change with depth on a northeast Pacific seamount. Mar. Ecol. 31, 14–25 (2010).Article 

Google Scholar 
Long, D. J. & Baco, A. R. Rapid change with depth in megabenthic structure-forming communities of the Makapu’u deep-sea coral bed. Deep-Sea Res. II 99, 158–168 (2014).Article 

Google Scholar 
Thresher, R. et al. Strong septh-related zonation of megabenthos on a rocky continental margin (∼ 700–4000 m) off southern Tasmania Australia. PLoS ONE 9, e85872 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
O’Hara, T. D., Consalvey, M., Lavrado, H. P. & Stocks, K. I. Environmental predictors and turnover of biota along a seamount chain. Mar. Ecol. 31, 84–94 (2010).Article 

Google Scholar 
Boschen, R. E. et al. Megabenthic assemblage structure on three New Zealand seamounts: Implications for seafloor massive sulfide mining. Mar. Ecol. Prog. Ser. 523, 1–14 (2015).Article 

Google Scholar 
Caratori Tontini, F. et al. Crustal magnetization of brothers volcano, New Zealand, measured by autonomous underwater vehicles: Geophysical expression of a submarine hydrothermal system. Econ. Geol. 107, 1571–1581 (2012).Article 

Google Scholar 
Rex, M. A., Etter, R. J., Clain, A. J. & Hill, M. S. Bathymetric patterns of body size in deep-sea gastropods. Evolution (N Y) 53, 1298–1301 (1999).
Google Scholar 
O’Hara, T. D. Seamounts: Centres of endemism or species richness for ophiuroids?. Glob. Ecol. Biogeogr. 16, 720–732 (2007).Article 

Google Scholar 
Clark, M. R. et al. The ecology of seamounts: Structure, function, and human impacts. Ann. Rev. Mar. Sci. 2, 253–278 (2010).Article 
PubMed 

Google Scholar 
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).Article 
PubMed 

Google Scholar 
Levin, L. A. & Thomas, C. L. The influence of hydrodynamic regime on infaunal assemblages inhabiting carbonate sediments on central Pacific seamounts. Deep Sea Res. A 36, 1897–1915 (1989).Article 

Google Scholar 
Puerta, P. et al. Variability of deep-sea megabenthic assemblages along the western pathway of the Mediterranean outflow water. Deep-Sea Res. I 185, 103791 (2022).Article 

Google Scholar 
Tapia-Guerra, J. M. et al. First description of deep benthic habitats and communities of oceanic islands and seamounts of the Nazca Desventuradas Marine Park Chile. Sci. Rep. 11, 6209 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morgan, N. B., Goode, S., Roark, E. B. & Baco, A. R. Fine scale assemblage structure of benthic invertebrate megafauna on the North Pacific seamount Mokumanamana. Front. Mar. Sci. 6, 715 (2019).Article 

Google Scholar 
Perez, J. A. A., Kitazato, H., Sumida, P. Y. G., Sant’Ana, R. & Mastella, A. M. Benthopelagic megafauna assemblages of the Rio Grande Rise (SW Atlantic). Deep-Sea Res. I 134, 1–11 (2018).Article 

Google Scholar 
Poore, G. C. B. et al. Invertebrate diversity of the unexplored marine western margin of Australia: Taxonomy and implications for global biodiversity. Mar. Biodivers. 45, 271–286 (2015).Article 

Google Scholar 
Henry, L. A., Moreno Navas, J. & Roberts, J. M. Multi-scale interactions between local hydrography, seabed topography, and community assembly on cold-water coral reefs. Biogeosciences 10, 2737–2746 (2013).Article 

Google Scholar 
Meyer, K. S. et al. Rocky islands in a sea of mud: Biotic and abiotic factors structuring deep-sea dropstone communities. Mar. Ecol. Prog. Ser. 556, 45–57 (2016).Article 

Google Scholar 
Stratmann, T., Soetaert, K., Kersken, D. & van Oevelen, D. Polymetallic nodules are essential for food-web integrity of a prospective deep-seabed mining area in Pacific abyssal plains. Sci. Rep. 11, 12238 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Genin, A., Dayton, P. K., Lonsdale, P. F. & Spiess, F. N. Corals on seamount peaks provide evidence of current acceleration over deep-sea topography. Nature 322, 59–61 (1986).Article 

Google Scholar 
Roberts, J. M., Wheeler, A. J. & Freiwald, A. Reefs of the deep: The biology and geology of cold-water coral ecosystems. Science 1979(312), 543–547 (2006).Article 

Google Scholar 
Kutti, T., Bannister, R. J. & Fosså, J. H. Community structure and ecological function of deep-water sponge grounds in the Traenadypet MPA-Northern Norwegian continental shelf. Cont. Shelf Res. 69, 21–30 (2013).Article 

Google Scholar 
Beazley, L., Kenchington, E. L., Murillo, F. J. & Sacau, M. D. M. Deep-sea sponge grounds enhance diversity and abundance of epibenthic megafauna in the Northwest Atlantic. ICES J. Mar. Sci. 70, 1471–1490 (2013).Article 

Google Scholar 
Buhl-Mortensen, L. et al. Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins. Mar. Ecol. 31, 21–50 (2010).Article 

Google Scholar 
Victorero, L., Robert, K., Robinson, L. F., Taylor, M. L. & Huvenne, V. A. I. Species replacement dominates megabenthos beta diversity in a remote seamount setting. Sci. Rep. 8, 1–11 (2018).Article 
CAS 

Google Scholar 
Yesson, C., Clark, M. R., Taylor, M. L. & Rogers, A. D. The global distribution of seamounts based on 30 arc seconds bathymetry data. Deep-Sea Res. I 58, 442–453 (2011).Article 

Google Scholar 
ICES. Report of the ICES-NAFO Working Group on Deep-Water Ecology (WGDEC), 9–13 March 2009, ICES CM2009ACOM:23. 2009.Cárdenas, P. & Rapp, H. T. Demosponges from the Northern mid-Atlantic ridge shed more light on the diversity and biogeography of North Atlantic deep-sea sponges. J. Mar. Biol. Assoc. U.K. 95, 1475–1516 (2015).Article 

Google Scholar 
Cárdenas, P. et al. Taxonomy, biogeography and DNA barcodes of Geodia species (Porifera, Demospongiae, Tetractinellida) in the Atlantic boreo-arctic region. Zool. J. Linn. Soc. 169, 251–311 (2013).Article 

Google Scholar 
Roberts, E. M. et al. Oceanographic setting and short-timescale environmental variability at an Arctic seamount sponge ground. Deep-Sea Res. I 138, 98–113 (2018).Article 

Google Scholar 
Roberts, E. et al. Water masses constrain the distribution of deep-sea sponges in the North Atlantic Ocean and Nordic seas. Mar. Ecol. Prog. Ser. 659, 75–96 (2021).Article 

Google Scholar 
Morganti, T. M. et al. Giant sponge grounds of central Arctic seamounts are associated with extinct seep life. Nat. Commun. 13, 638 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morganti, T. M. et al. In situ observation of sponge trails suggests common sponge locomotion in the deep central Arctic. Curr. Biol. 31, R368–R370 (2021).Article 
CAS 
PubMed 

Google Scholar 
Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep-Sea Res. I 153, 103137 (2019).Article 

Google Scholar 
McIntyre, F. D., Drewery, J., Eerkes-Medrano, D. & Neat, F. C. Distribution and diversity of deep-sea sponge grounds on the Rosemary bank seamount NE Atlantic. Mar. Biol. 163, 143 (2016).Article 

Google Scholar 
Buhl-Mortensen, P. & Buhl-Mortensen, L. Diverse and vulnerable deep-water biotopes in the Hardangerfjord. Mar. Biol. Res. 10, 253–267 (2014).Article 

Google Scholar 
de Clippele, L. H. et al. The effect of local hydrodynamics on the spatial extent and morphology of cold-water coral habitats at Tisler Reef Norway. Coral Reefs 37, 253–266 (2018).Article 
PubMed 

Google Scholar 
Dunlop, K., Harendza, A., Plassen, L. & Keeley, N. Epifaunal habitat Associations on mixed and hard bottom substrates in coastal waters of Northern Norway. Front. Mar. Sci. 7, 568802 (2020).Article 

Google Scholar 
Fiore, C. L. & Cox Jutte, P. Characterization of macrofaunal assemblages associated with sponges and tunicates collected off the southeastern United States. Biology 129, 105–120 (2010).
Google Scholar 
Murillo, F. J. et al. Deep-sea sponge grounds of the Flemish Cap, Flemish Pass and the Grand Banks of Newfoundland (Northwest Atlantic Ocean): Distribution and species composition. Mar. Biol. Res. 8, 842–854 (2012).Article 

Google Scholar 
Purser, A. et al. Local variation in the distribution of benthic megafauna species associated with cold-water coral reefs on the Norwegian margin. Cont. Shelf Res. 54, 37–51 (2013).Article 

Google Scholar 
Klitgaard, A. B. & Tendal, O. S. Distribution and species composition of mass occurrences of large-sized sponges in the northeast Atlantic. Prog. Oceanogr. 61, 57–98 (2004).Article 

Google Scholar 
Klitgaard, A. B. The fauna associated with outer shelf and upper slope sponges (porifera, demospongiae) at the faroe islands, northeastern Atlantic. Sarsia 80, 1–22 (1995).Article 

Google Scholar 
Cárdenas, P. & Moore, J. A. First records of Geodia demosponges from the New England seamounts, an opportunity to test the use of DNA mini-barcodes on museum specimens. Mar. Biodivers. 49, 163–174 (2019).Article 

Google Scholar 
Schejter, L., Chiesa, I. L., Doti, B. L. & Bremec, C. Mycale (Aegogropila) magellanica (Porifera: Demospongiae) in the southwestern Atlantic Ocean: Endobiotic fauna and new distributional information. Sci. Mar. 76, 753–761 (2012).
Google Scholar 
Beaulieu, S. E. Life on glass houses: Sponge stalk communities in the deep sea. Mar. Biol. 138, 803–817 (2001).Article 

Google Scholar 
Goren, L., Idan, T., Shefer, S. & Ilan, M. Macrofauna inhabiting massive demosponges from shallow and mesophotic habitats along the Israeli Mediterranean coast. Front. Mar. Sci. 7, 612779 (2021).Article 

Google Scholar 
Kersken, D. et al. The infauna of three widely distributed sponge species (Hexactinellida and Demospongiae) from the deep Ekström Shelf in the Weddell Sea Antarctica. Deep-Sea Res. II 108, 101–112 (2014).Article 

Google Scholar 
Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep Sea Res. 1 Oceanogr. Res. Pap. 153, 103137 (2019).Article 

Google Scholar 
Bart, M. C., Hudspith, M., Rapp, H. T., Verdonschot, P. F. M. & de Goeij, J. M. A Deep-Sea Sponge Loop? Sponges transfer dissolved and particulate organic carbon and nitrogen to associated fauna. Front. Mar. Sci. 8, 604879 (2021).Article 

Google Scholar 
de Goeij, J. M. et al. Surviving in a marine desert: The sponge loop retains resources within coral reefs. Science 1979(342), 108–110 (2013).Article 

Google Scholar 
Pawlik, J. R. & Mcmurray, S. E. The emerging ecological and biogeochemical importance of sponges on coral reefs. (2019) https://doi.org/10.1146/annurev-marine-010419Wassmann, P., Slagstad, D. & Ellingsen, I. Primary production and climatic variability in the European sector of the Arctic Ocean prior to 2007: Preliminary results. Polar Biol. 33, 1641–1650 (2010).Article 

Google Scholar 
Arrigo, K. R., van Dijken, G. & Pabi, S. Impact of a shrinking Arctic ice cover on marine primary production. Geophys. Res. Lett. 35, L19603 (2008).Article 

Google Scholar 
Dunne, J. P., Sarmiento, J. L. & Gnanadesikan, A. A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor. Glob. Biogeochem. Cycles 21, GB4006 (2007).Article 

Google Scholar 
Wei, C.-L. et al. Global patterns and predictions of seafloor biomass using random forests. PLoS ONE 5, e15323 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stratmann, T. et al. The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities. Sci. Data 7, 206 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McClain, C. R., Lundsten, L., Ream, M., Barry, J. & DeVogelaere, A. Endemicity, biogeography, composition, and community structure on a Northeast Pacific seamount. PLoS ONE 4, e4141 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Walter, M., Köhler, J., Myriel, H., Steinmacher, B. & Wisotzki, A. Physical oceanography measured on water bottle samples during POLARSTERN cruise PS101 (ARK-XXX/3). PANGAEA https://doi.org/10.1594/PANGAEA.871927 (2017).van Appen, W.-J., Latarius, K. & Kanzow, T. Physical oceanography and current meter data from mooring F6–17. PANGAEA https://doi.org/10.1594/PANGAEA.870845 (2017).Ruhl, H. A. & Smith, K. L. Shifts in deep-sea community structure linked to climate and food supply. Science 1979(305), 513–515 (2004).Article 

Google Scholar 
Boetius, A. et al. Export of algal biomass from the melting Arctic sea ice. Science 1979(339), 1430–1432 (2013).Article 

Google Scholar 
Rybakova, E., Kremenetskaia, A., Vedenin, A., Boetius, A. & Gebruk, A. Deep-sea megabenthos communities of the Eurasian Central Arctic are influenced by ice-cover and sea-ice algal falls. PLoS ONE 14, e0211009 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhulay, I., Bluhm, B. A., Renaud, P. E., Degen, R. & Iken, K. Functional pattern of benthic epifauna in the Chukchi borderland Arctic deep sea. Front. Mar. Sci. 8, 609956 (2021).Article 

Google Scholar 
Boetius, A. & Purser, A. The expedition PS101 of the research vessel Polarstern to the Arctic Ocean in 2016. Berichte zur Polar-und Meeresforschung = Rep Polar Mar Res https://doi.org/10.2312/BzPM_0706_2017 (2017).Article 

Google Scholar 
Simon-Lledó, E. et al. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone. Prog. Oceanogr. 187, 102405 (2020).Article 

Google Scholar 
Simon-Lledó, E. et al. Preliminary observations of the abyssal megafauna of Kiribati. Front. Mar. Sci. 6, 1–13 (2019).Article 

Google Scholar 
Zhulay, I., Iken, K., Renaud, P. E. & Bluhm, B. A. Epifaunal communities across marine landscapes of the deep Chukchi Borderland (Pacific Arctic). Deep Sea Res. 1 Oceanogr. Res. Pap. 151, 103065 (2019).Article 

Google Scholar 
Åström, E. K. L., Sen, A., Carroll, M. L. & Carroll, J. L. Cold seeps in a warming Arctic: Insights for benthic ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00244 (2020).Article 

Google Scholar 
Pedersen, R. B. et al. Discovery of a black smoker vent field and vent fauna at the Arctic Mid-Ocean Ridge. Nat. Commun. 1, 1–6 (2010).Article 
CAS 

Google Scholar 
Åström, E. K. L. et al. Methane cold seeps as biological oases in the high-Arctic deep sea. Limnol. Oceanogr. 63, S209–S231 (2018).Article 

Google Scholar 
Rybakova Goroslavskaya, E., Galkin, S., Bergmann, M., Soltwedel, T. & Gebruk, A. Density and distribution of megafauna at the Håkon Mosby mud volcano (the Barents Sea) based on image analysis. Biogeosciences 10, 3359–3374 (2013).Article 

Google Scholar 
Sweetman, A. K., Levin, L. A., Rapp, H. T. & Schander, C. Faunal trophic structure at hydrothermal vents on the southern mohn’s ridge, arctic ocean. Mar. Ecol. Prog. Ser. 473, 115–131 (2013).Article 

Google Scholar 
Decker, C. & Olu, K. Does macrofaunal nutrition vary among habitats at the Hakon Mosby mud volcano?. Cah. Biol. Mar. 51, 361–367 (2010).
Google Scholar 
Macdonald, I. R., Bluhm, B. A., Iken, K., Gagaev, S. & Strong, S. Benthic macrofauna and megafauna assemblages in the Arctic deep-sea Canada Basin. Deep-Sea Res. II 57, 136–152 (2010).Article 

Google Scholar 
Taylor, J., Krumpen, T., Soltwedel, T., Gutt, J. & Bergmann, M. Dynamic benthic megafaunal communities: Assessing temporal variations in structure, composition and diversity at the Arctic deep-sea observatory HAUSGARTEN between 2004 and 2015. Deep Sea Res. 1 Oceanogr. Res. Pap. 122, 81–94 (2017).Article 

Google Scholar 
Vedenin, A. A. et al. Uniform bathymetric zonation of marine benthos on a Pan-Arctic scale. Prog. Oceanogr. 202, 102764 (2022).Article 

Google Scholar 
Bart, M. C. et al. A deep-sea sponge loop? Sponges transfer dissolved and particulate organic carbon and nitrogen to associated fauna. Front. Mar. Sci. 8, 604879 (2021).Article 

Google Scholar 
Guihen, D., White, M. & Lundälv, T. Temperature shocks and ecological implications at a cold-water coral reef. ANZIAM J. https://doi.org/10.1017/S1755267212000413 (2014).Article 

Google Scholar 
Strand, R. et al. The response of a boreal deep-sea sponge holobiont to acute thermal stress. Sci. Rep. 7, 1660 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hanz, U. et al. The important role of sponges in carbon and nitrogen cycling in a deep-sea biological hotspot. Funct. Ecol. 36, 2188–2199 (2022).Article 
CAS 

Google Scholar 
Maier, S. R. et al. Reef communities associated with ‘dead’ cold-water coral framework drive resource retention and recycling in the deep sea. Deep-Sea Res. I 175, 103574 (2021).Article 
CAS 

Google Scholar 
Bart, M. C. et al. Dissolved organic carbon (DOC) is essential to balance the metabolic demands of four dominant North-Atlantic deep-sea sponges. Limnol. Oceanogr. https://doi.org/10.1002/lno.11652 (2020).Article 

Google Scholar 
Bart, M. C. et al. Differential processing of dissolved and particulate organic matter by deep-sea sponges and their microbial symbionts. Sci. Rep. 10, 1–13 (2020).Article 

Google Scholar 
Maier, S. R. et al. Recycling pathways in cold-water coral reefs: Use of dissolved organic matter and bacteria by key suspension feeding taxa. Sci. Rep. 10, 9942 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
International Hydrographic Bureau. 16th meeting of the GEBCO sub-committee on undersea feature names (SCUFN). Preprint at (2003).Torres-Valdés, S., Morische, A. & Wischnewski, L. Revision of nutrient data from Polarstern expedition PS101 (ARK-XXX/3). PANGAEA https://doi.org/10.1594/PANGAEA.908179 (2019).Purser, A. et al. Ocean floor observation and bathymetry system (OFOBS): A new towed camera/sonar system for deep-sea habitat surveys. IEEE J. Ocean. Eng. 44, 87–99 (2019).Article 

Google Scholar 
Marcon, Y. & Purser, A. PAPARA(ZZ)I : An open-source software interface for annotating photographs of the deep-sea. SoftwareX 6, 69–80 (2017).Article 

Google Scholar 
Greene, H. G., Bizzarro, J. J., O’Connell, V. M. & Brylinsky, C. K. Construction of digital potential marine benthic habitat maps using a coded classification scheme and its application. Spec. Pap.: Geol. Assoc. Canada 47, 141–155 (2007).
Google Scholar 
Horton, T. et al. Recommendations for the standardisation of open taxonomic nomenclature for image-based identifications. Front. Mar. Sci. 8, 620702 (2021).Article 

Google Scholar 
Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Application (Cambridge University Press, 1997).Book 
MATH 

Google Scholar 
Rodgers, J. L. The bootstrap, the jackknife, and the randomization test: A sampling taxonomy. Multivar. Behav. Res. 34, 441–456 (1999).Article 
CAS 

Google Scholar 
Crowley, P. H. Resampling methods for computation-intensive data analysis in ecology and evolution. Annu. Rev. Ecol. Syst. 23, 405–447 (1992).Article 

Google Scholar 
Simon-Lledó, E. et al. Ecology of a polymetallic nodule occurrence gradient: Implications for deep-sea mining. Limnol. Oceanogr. 64, 1883–1894 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

Google Scholar 
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
R-Core Team. R: A language and environment for statistical computing. Preprint at https://www.r-project.org/ (2017).Oksanen, J. et al. vegan: Community ecology package. Preprint at (2017).Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260 (2013).Article 

Google Scholar 
Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. 69, 1–17 (2016).Article 

Google Scholar 
Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).Article 
CAS 
PubMed 

Google Scholar 
de Kluijver, A. Fatty acid analysis sponges. protocols.io 1, 1–14. https://doi.org/10.17504/protocols.io.bhnpj5dn (2021).Article 

Google Scholar 
de Kluijver, A. et al. Bacterial precursors and unsaturated long-chain fatty acids are biomarkers of North-Atlantic deep-sea demosponges. PLoS ONE 16, e0241095 (2021).Article 
PubMed 
PubMed Central 

Google Scholar  More

As humans warm the planet, biodiversity is plummeting. These two global crises are connected in multiple ways. But the details of the intricate feedback loops between biodiversity decline and climate change are astonishingly under-studied.It is well known that climate extremes such as droughts and heatwaves can have devastating impacts on ecosystems and, in turn, that degraded ecosystems have a reduced capacity to protect humanity against the social and physical impacts of such events. Yet only a few such relationships have been probed in detail. Even less well known is whether biodiversity-depleted ecosystems will also have a negative effect on climate, provoking or exacerbating weather extremes.For us, a group of researchers living and working mainly in Central Europe, the wake-up call was the sequence of heatwaves of 2018, 2019 and 2022. It felt unreal to watch a floodplain forest suffer drought stress in Leipzig, Germany. Across Germany, more than 380,000 hectares of trees have now been damaged (see go.nature.com/3etrrnp; in German), and the forestry sector is struggling with how to plan restoration activities over the coming decades1. What could have protected these ecosystems against such extremes? And how will the resultant damage further impact our climate?
Nature-based solutions can help cool the planet — if we act now
In June 2021, the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) published their first joint report2, acknowledging the need for more collaborative work between these two domains. And some good policy moves are afoot: the new EU Forest Strategy for 2030, released in July 2021, and other high-level policy initiatives by the European Commission, formally recognize the multifunctional value of forests, including their role in regulating atmospheric processes and climate. But much more remains to be done.To thoroughly quantify the risk that lies ahead, ecologists, climate scientists, remote-sensing experts, modellers and data scientists need to work together. The upcoming meeting of the United Nations Convention on Biological Diversity in Montreal, Canada, in December is a good opportunity to catalyse such collaboration.Buffers and responsesWhen lamenting the decline in biodiversity, most people think first about the tragedy of species driven to extinction. There are more subtle changes under way, too.For instance, a study across Germany showed that over the past century, most plant species have declined in cover, with only a few increasing in abundance3. Also affected is species functionality4 — genetic diversity, and the diversity of form and structure that can make communities more or less efficient at taking up nutrients, resisting heat or surviving pathogen attacks.When entire ecosystems are transformed, their functionality is often degraded. They are left with less capacity to absorb pollution, store carbon dioxide, soak up water, regulate temperature and support vital functions for other organisms, including humans5. Conversely, higher levels of functional biodiversity increase the odds of an ecosystem coping with unexpected events, including climate extremes. This is known as the insurance effect6.The effect is well documented in field experiments and modelling studies. And there is mounting evidence of it in ecosystem responses to natural events. A global synthesis of various drought conditions showed, for instance, that forests were more resilient when trees with a greater diversity of strategies for using and transporting water lived together7.

Dead trees near Iserlohn, Germany, in April 2020 (left) and after felling in June 2021 (right).Credit: Ina Fassbender/AFP via Getty

However, biodiversity cannot protect all ecosystems against all kinds of impacts. In a study this year across plots in the United States and Canada, for example, mortality was shown to be higher in diverse forest ecosystems8. The proposed explanation for this unexpected result was that greater biodiversity could also foster more competition for resources. When extreme events induce stress, resources can become scarce in areas with high biomass and competition can suddenly drive mortality, overwhelming the benefits of cohabitation. Whether or not higher biodiversity protects an ecosystem from an extreme is highly site-specific.Some plants respond to drought by reducing photosynthesis and transpiration immediately; others can maintain business as usual for much longer, stabilizing the response of the ecosystem as a whole. So the exact response of ecosystems to extremes depends on interactions between the type of event, plant strategies, vegetation composition and structure.Which plant strategies will prevail is hard to predict and highly dependent on the duration and severity of the climatic extreme, and on previous extremes9. Researchers cannot fully explain why some forests, tree species or individual plants survive in certain regions hit by extreme climate conditions, whereas entire stands disappear elsewhere10. One study of beech trees in Germany showed that survival chances had a genomic basis11, yet it is not clear whether the genetic variability present in forests will be sufficient to cope with future conditions.And it can take years for ecosystem impacts to play out. The effects of the two consecutive hot drought years, 2018 and 2019, were an eye-opener for many of us. In Leipzig, tree growth declined, pathogens proliferated and ash and maple trees died. The double blow, interrupted by a mild winter, on top of the long-term loss of soil moisture, led to trees dying at 4–20 times the usual rate throughout Germany, depending on the species (see go.nature.com/3etrrnp; in German). The devastation peaked in 2020.Ecosystem changes can also affect atmospheric conditions and climate. Notably, land-use change can alter the brightness (albedo) of the planet’s surface and its capacity for heat exchange. But there are more-complex mechanisms of influence.Vegetation can be a source or sink for atmospheric substances. A study published in 2020 showed that vegetation under stress is less capable of removing ozone than are unstressed plants, leading to higher levels of air pollution12. Pollen and other biogenic particles emitted from certain plants can induce the freezing of supercooled cloud droplets, allowing ice in clouds to form at much warmer temperatures13, with consequences for rainfall14. Changes to species composition and stress can alter the dynamics of these particle emissions. Plant stress also modifies the emission of biogenic volatile organic gases, which can form secondary particles. Wildfires — enhanced by drought and monocultures — affect clouds, weather and climate through the emission of greenhouse gases and smoke particles. Satellite data show that afforestation can boost the formation of low-level, cooling cloud cover15 by enhancing the supply of water to the atmosphere.Research prioritiesAn important question is whether there is a feedback loop: will more intense, and more frequent, extremes accelerate the degradation and homogenization of ecosystems, which then, in turn, promote further climate extremes? So far, we don’t know.One reason for this lack of knowledge is that research has so far been selective: most studies have focused on the impacts of droughts and heatwaves on ecosystems. Relatively little is known about the impacts of other kinds of extremes, such as a ‘false spring’ caused by an early-season bout of warm weather, a late spring frost, heavy rainfall events, ozone maxima, or exposure to high levels of solar radiation during dry, cloudless weather.Researchers have no overview, much less a global catalogue, of how each dimension of biodiversity interacts with the full breadth of climate extremes in different combinations and at multiple scales. In an ideal world, scientists would know, for example, how the variation in canopy density, vegetation age, and species diversity protects against storm damage; and whether and how the diversity of canopy structures controls atmospheric processes such as cloud formation in the wake of extremes. Researchers need to link spatiotemporal patterns of biodiversity with the responses of ecosystem processes to climate extremes.
Biodiversity needs every tool in the box: use OECMs
Creating such a catalogue is a huge challenge, particularly given the more frequent occurrence of extremes with little or no precedent16. Scientists will also need to account for the increasing likelihood of pile-ups of climate stressors. The ways in which ecosystems respond to compound events17 could be quite different. Researchers will have to study which facets of biodiversity (genetic, physiological, structural) are required to stabilize ecosystems and their functions against these onslaughts.There is at least one piece of good news: tools for data collection and analysis are improving fast, with huge advances over the past decade in satellite-based observations for both climate and biodiversity monitoring. The European Copernicus Earth-observation programme, for example — which includes the Sentinel 1 and 2 satellite fleet, and other recently launched missions that cover the most important wavelengths of the electromagnetic spectrum — offer metre-scale resolution observations of the biochemical status of plants and canopy structure. Atmospheric states are recorded in unprecedented detail, vertically and in time.Scientists must now make these data interoperable and integrate them with in situ observations. The latter is challenging. On the ground, a new generation of data are being collected by researchers and by citizen scientists18. For example, unique insights into plant responses to stress are coming from time-lapse photography of leaf orientation; accelerometer measures of movement patterns of stems have been shown to provide proxies for the drought stress of trees19.High-quality models are needed to turn these data into predictions. The development of functional ‘digital twins’ of the climate system is now in reach. These models replicate hydrometeorological processes at the metre scale, and are fast enough to allow for rapid scenario development and testing20. The analogous models for ecosystems are still in a more conceptual phase. Artificial-intelligence methods will be key here, to study links between climate extremes and biodiversity.Researchers can no longer afford to track global transformations of the Earth system in disciplinary silos. Instead, ecologists and climate scientists need to establish a joint agenda, so that humanity is properly forewarned: of the risks of removing biodiversity buffers against climate extremes, and of the risk of thereby amplifying these extremes. More

Millien, V. et al. Ecotypic variation in the context of global climate change: Revisiting the rules. Ecol. Lett. 9, 853–869 (2006).Article 
PubMed 

Google Scholar 
Calder, W. A. Size, Function and Life History (Harvard University Press, 1984).
Google Scholar 
Bergmann, C. Über die verhältnisse der wärmeökonomie der thiere zu ihrer grösse. Göttinger Stud. 3, 595–708 (1847).
Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).Article 

Google Scholar 
Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. PNAS 116, 21609–21615 (2019).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Foster, J. B. Evolution of mammals on islands. Nature 202, 234–235 (1964).Article 
ADS 

Google Scholar 
Lomolino, M. V. Body size evolution in insular vertebrates: Generality of the island rule. J. Biogeogr. 32, 1683–1699 (2005).Article 

Google Scholar 
Benítez-López, A. et al. The island rule explains consistent patterns of body size evolution in terrestrial vertebrates. Nat. Ecol. Evol. 5, 768–786 (2021).Article 
PubMed 

Google Scholar 
Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351 (2003).Article 

Google Scholar 
Meiri, S., Cooper, N. & Purvis, A. The island rule: Made to be broken?. Proc. R. Soc. B. 275, 141–148 (2008).Article 
PubMed 

Google Scholar 
Millien, V. Relative effects of climate change, isolation and competition on body-size evolution in the Japanese field mouse, Apodemus argenteus. J. Biogeogr. 31, 1267–1276 (2004).Article 

Google Scholar 
Millien, V. & Damuth, J. Climate change and size evolution in an island rodent species: New perspectives on the island rule. Evolution 58, 1353–1360 (2004).Article 
PubMed 

Google Scholar 
Lomolino, M. V., Sax, D. F., Riddle, B. R. & Brown, J. H. The island rule and a research agenda for studying ecogeographical patterns. J. Biogeogr. 33, 1503–1510 (2006).Article 

Google Scholar 
Sargis, E. J., Millien, V., Woodman, N. & Olson, L. E. Rule reversal: Ecogeographical patterns of body size variation in the common treeshrew (Mammalia, Scandentia). Ecol. Evol. 8, 1634–1645 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Barnosky, A. D., Hadly, E. A. & Bell, C. J. Mammalian response to global warming on varied temporal scales. J. Mammal. 84, 354–368 (2003).Article 

Google Scholar 
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 
ADS 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: A third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 
PubMed 

Google Scholar 
Teplitsky, C., Mills, J. A., Alho, J. S., Yarrall, J. W. & Merilä, J. Bergmann’s rule and climate change revisited: Disentangling environmental and genetic responses in a wild bird population. PNAS 105, 13492–13496 (2008).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Teplitsky, C. & Millien, V. Climate warming and Bergmann’s rule through time: Is there any evidence?. Evol. Appl. 7, 156–168 (2014).Article 
PubMed 

Google Scholar 
James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51, 385–390 (1970).Article 

Google Scholar 
Wigginton, J. D. & Dobson, F. S. Environmental influences on geographic variation in body size of western bobcats. Can. J. Zool. 77, 802–813 (1999).Article 

Google Scholar 
Yom-Tov, Y. & Geffen, E. Geographic variation in body size: The effects of ambient temperature and precipitation. Oecologia 148, 213–218 (2006).Article 
PubMed 
ADS 

Google Scholar 
Wagner, J. A. Schreber’s saugthiere, supplementband, 2. Abtheilung 1841(37–44), 553 (1841).
Google Scholar 
Hawkins, M. T. Family Tupaiidae (treeshrews). In Handbook of the Mammals of the World, Volume 8 Insectivores, Sloths and Colugos (eds Wilson, D. E. & Mittermeier, R. A.) (Lynx Edicions, 2018).
Google Scholar 
Roberts, T. E., Lanier, H. C., Sargis, E. J. & Olson, L. E. Molecular phylogeny of treeshrews (Mammalia: Scandentia) and the timescale of diversification in Southeast Asia. Mol. Phylogenet. Evol. 60, 358–372 (2011).Article 
PubMed 

Google Scholar 
Zhang, L., Yang, F., Wang, Z. K. & Zhu, W. L. Role of thermal physiology and bioenergetics on adaptation in tree shrew (Tupaia belangeri): The experiment test. Sci. Rep. 7, 41352 (2017).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Zhu, W., Zhang, H. & Wang, Z. Seasonal changes in body mass and thermogenesis in tree shrews (Tupaia belangeri): The roles of photoperiod and cold. J. Therm. Biol. 37, 479–484 (2012).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 
MATH 

Google Scholar 
South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0 (2017).Dunnington, D. ggspatial: Spatial Data Framework for ggplot2. R package version 1.1.4 (2020).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2018).Helgen, K. M. Order Scandentia. In Mammal Species of the World: A Taxonomic and Geographic Reference 3rd edn (eds Wilson, D. E. & Reeder, D. M.) (Johns Hopkins University Press, 2005).
Google Scholar 
Collins, P. M. & Tsang, W. N. Growth and reproductive development in the male tree shrew (Tupaia belangeri) from birth to sexual maturity. Biol. Reprod. 37, 261–267 (1987).Article 
CAS 
PubMed 

Google Scholar 
Heaney, L. R. Island area and body size of insular mammals: Evidence from the tri-colored squirrel (Callosciurus prevosti) of Southeast Asia. Evolution 32, 29–44 (1978).PubMed 

Google Scholar 
Husson, L., Boucher, F. C., Sarr, A. C., Sepulchre, P. & Cahyarini, S. Y. Evidence of Sundaland’s subsidence requires revisiting its biogeography. J. Biogeogr. 47, 843–853 (2020).Article 

Google Scholar 
Juman, M. M., Woodman, N., Olson, L. E. & Sargis, E. J. Ecogeographic variation and taxonomic boundaries in Large Treeshrews (Scandentia, Tupaiidae: Tupaia tana Raffles, 1821) from Southeast Asia. J. Mammal. 102, 1054–1066 (2021).Article 

Google Scholar 
Hinckley, A. et al. Challenging ecogeographical rules: Phenotypic variation in the Mountain Treeshrew (Tupaia montana) along tropical elevational gradients. PLoS ONE 17, e0268213 (2022).Article 
CAS 
PubMed 

Google Scholar 
Lomolino, M. V., Sax, D. F., Palombo, M. R. & van der Geer, A. A. Of mice and mammoths: evaluations of causal explanations for body size evolution in insular mammals. J. Biogeogr. 39, 842–854 (2011).Article 

Google Scholar 
Teta, P., de la Sancha, N. U., D’Elía, G. & Patterson, B. D. Andean rain shadow effect drives phenotypic variation in a widely distributed Austral rodent. J. Biogeogr. 49, 1767–1778 (2022).Article 

Google Scholar 
Yom-Tov, Y. & Yom-Tov, S. Climatic change and body size in two species of Japanese rodents. Biol. J. Linn. Soc. 82, 263–267 (2004).Article 

Google Scholar 
Yom-Tov, Y. & Yom-Tov, J. Global warming, Bergmann’s rule and body size in the masked shrew Sorex cinereus in Alaska. J. Anim. Ecol. 74, 803–808 (2005).Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. PNAS 105, 6668–6672 (2008).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).Article 
PubMed 

Google Scholar 
Cronk, Q. C. B. Islands: stability, diversity, conservation. Biodivers. Conserv. 6, 477–493 (1997).Article 

Google Scholar 
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. PNAS 106, 9322–9327 (2009).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Yom-Tov, Y. & Geffen, E. Recent spatial and temporal changes in body size of terrestrial vertebrates: Probable causes and pitfalls. Biol. Rev. 86, 531–541 (2011).Article 
PubMed 

Google Scholar 
Theriot, M. K., Lanier, H. C. & Olson, L. E. Harnessing natural history collections to detect trends in body-size change as a response to warming: A critique and review of best practices. Methods Ecol. Evol. (2022).Rohwer, V. G., Rohwer, Y. & Dillman, C. B. Declining growth of natural history collections fails future generations. PLoS Biol. 20, e3001613 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Reese, A. T. & Olson, L. E. Morphological distinctiveness of Javan Tupaia hypochrysa (Scandentia, Tupaiidae). J. Mammal. 94, 938–947 (2013).Article 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Reese, A. T. & Olson, L. E. Island history affects faunal composition: The treeshrews (Mammalia: Scandentia: Tupaiidae) from the Mentawai and Batu Islands, Indonesia. Biol. J. Linn. Soc. 111, 290–304 (2014).Article 

Google Scholar 
Sargis, E. J., Campbell, K. K. & Olson, L. E. Taxonomic boundaries and craniometric variation in the treeshrews (Scandentia, Tupaiidae) from the Palawan faunal region. J. Mamm. Evol. 21, 111–123 (2014).Article 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Bell, T. N. & Olson, L. E. Skeletal variation and taxonomic boundaries among mainland and island populations of the common treeshrew (Mammalia: Scandentia: Tupaiidae). Biol. J. Linn. Soc. 120, 286–312 (2017).
Google Scholar 
Juman, M. M., Olson, L. E. & Sargis, E. J. Skeletal variation and taxonomic boundaries in the Pen-tailed Treeshrew (Scandentia, Ptilocercidae: Ptilocercus lowii Gray, 1848). J. Mamm. Evol. 28, 1193–1203 (2021).Article 

Google Scholar 
Juman, M. M., Woodman, N., Miller-Murthy, A., Olson, L. E. & Sargis, E. J. Taxonomic boundaries in Lesser Treeshrews (Scandentia, Tupaiidae: Tupaia minor Günther, 1876). J. Mammal. https://doi.org/10.1093/jmammal/gyac080 (2022).Article 

Google Scholar 
Woodman, N., Miller-Murthy, A., Olson, L. E. & Sargis, E. J. Coming of age: Morphometric variation in the hand skeletons of juvenile and adult Lesser Treeshrews (Scandentia: Tupaiidae: Tupaia minor Günther, 1876). J. Mammal. 101, 1151–1164 (2020).Article 

Google Scholar 
Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L. & Ram, K. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.7.2, https://CRAN.R-project.org/package=rgbif.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data. 7, 109 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Meiyappan, P. & Jain, A. K. Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Front. Earth Sci. 6, 122–139 (2012).Article 
ADS 

Google Scholar 
Ryan, W. B. F. et al. Global multi-resolution topography synthesis. Geochem. Geophys. 10, Q03014 (2009).
Google Scholar 
van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
Clavel, J., Merceron, G. & Escarguel, G. Missing data estimation in morphometrics: How much is too much? Syst. Biol. 63, 203–218 (2014).Article 
PubMed 

Google Scholar 
Nally, R. M. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. 13, 659–660 (2004).Article 

Google Scholar 
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn. (Springer, 2013).Book 
MATH 

Google Scholar  More

Alpha diversity differences among communitiesNematode gut microbiomes were assigned into their respective species categories of E. antarcticus and P. murrayi based on 18S host data that was consistent with morphology (see Methods “Microinvertebrate haplotypes”). In contrast, due to recovery of three undiscernible 18S tardigrade haplotypes, the gut microbiomes were assigned to Tardigrada. Mat bacterial communities were significantly (Tukey’s HSD, P  0.65, χ2(1)  0.38, χ2(3)  More

Variation in environmental parameters across space and time in Bohai BayThe environmental parameters of samples collected near the Tianjin coastal area from different stations and seasons exhibited high temporal and spatial heterogeneity. The seawater temperature was 28.09 ± 0.53 °C in Aug, 17.48 ± 2.36 °C in May, and 19.55 ± 1.26 °C in Oct (Table 1). The seasonal variation in seawater temperature corresponded to the meteorological characteristics in Bohai Bay, with warm seawater in summer and relatively cool seawater in spring. The salinity was 29.69 ± 2.71‰ in Aug, 33.19 ± 0.33‰ in May, and 30.15 ± 1.63‰ in Oct. Seasonal variations in salinity may be mainly related to freshwater loading. According to the precipitation observed data of Bohai Bay in previous years, the rainfall amount and days in summer are the most19, which may lead to the increase in runoff and the relatively low salinity in summer. Chlorophyll a (Chl a) was highest in May, with lower levels in Aug and Oct. The dissolved inorganic nitrogen (DIN) was significantly higher in May and Aug than in Oct. The higher level of DIN in May and Aug may be related to terrestrial input and supply for the demand of phytoplankton growth. In October, the temperature and DIN content were both not suitable for phytoplankton growth, and Chl_a showed the lowest value. Spatially, the DIN distribution across the three seasons was rather similar, with high values observed in nearshore waters and low values in offshore waters (Dataset S1 & Fig. S1), which suggested that terrestrial input was an important source of DIN. The pH, soluble reactive phosphate (SRP) and chemical oxygen demand (COD) showed relatively higher values in October than in August and May, which may be caused by the dead phytoplankton release and terrestrial loadings through coasts and rivers. The dissolved oxygen (DO), conductivity and depth did not show significant variation among sampling times (Table 1), while the conductivity and depth had relatively higher values at offshore stations (Dataset S1) since the more remote the sampling water was, the greater the depth was in Bohai Bay and the closer it was to the open sea with higher salinity and conductivity. The ordination plot showed distinct partitioning of samples from nearshore and offshore sites along principal component axis 1 (PC1) (Fig. 1). The ordination plot could explain 73.49% of the total variation in the geo-physical–chemical parameters and revealed a linear positive correlation between different parameters (Fig. 1). AN, DIN, nitrate and Chl_a were most crucial in the partitioning of samples from May and the other 2 months; salinity, longitude, depth and conductivity were crucial for the partitioning of samples from offshore and nearshore stations; pH, COD, SRP, nitrite and temperature were crucial for the partitioning of samples from nearshore stations in August and October and samples from offshore stations. Overall, the principal component analysis (PCA) plot clearly showed both the temporal and spatial variation of the measured environmental parameters, indicating that complex biogeochemical processes and hydrodynamic conditions lead to the variation among sites and seasons.Table 1 The independent-samples t test of environmental variables and α-diversity among different months.Full size tableFigure 1Biplot of the principal component analysis (PCA) for environmental parameters in the seawater samples of the Bohai Bay coastal area across different seasons and sites. The two principal components (PC1 and PC2) explained 73.49% of the total variation in the environmental data and showed clear partitioning of offshore samples (in blue font) from other nearshore samples along PC1 and partitioning of May samples from August and October along PC2. The variables transparency and latitude were strongly correlated with PC1, and the variables ammonia nitrogen (AN), COD, pH, soluble reactive phosphate (SRP), and nitrite were strongly correlated with PC2. Chlorophyll a (Chl_a), dissolved inorganic nitrogen (DIN), nitrate and DO were mainly positively correlated with samples from May, while salinity, longitude, depth and conductivity were mainly positively correlated with offshore samples. Blue arrows represent environmental parameters, and circles in color represent sampling points.Full size imageProkaryotic α/β-diversity variationMeasures of α-diversity showed significant differences in shannon, evenness, faith_pd and OTU richness between samples from May/Aug and Oct (Fig. 2, Table 1). Principal coordinates analysis (PCoAs) based on weighted UniFrac (WUF) distance and unweighted UniFrac (UUF) distance showed that the PCC from different sampling months separated across the first and second principal coordinates (Fig. 3A-B). Both the analysis of similarity (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA/ADONIS) results indicated that the prokaryotic communities varied significantly across different sampling months when using a WUF distance metric (ANOSIM, r = 0.709, P = 0.001; ADONIS, R2 = 40.0%, P = 0.001) and UUF distance metric (ANOSIM, r = 0.934, P = 0.001; ADONIS, R2 = 38.7%, P = 0.001). At the same time, the prokaryotic α– and β-diversity both showed high within-month variability in Aug (Figs. 2, 3C–D), which indicated that the community varied greatly among different sites in Aug.Figure 2Alpha diversity of shannon, eveness, faith_pd (phylogenetic diversity) and OTU richness value of the prokaryotic community of all the samples from different stations at different sampling times.Full size imageFigure 3Principal coordinate analysis (PCoA) based on unweighted (A) and weighted (B) UniFrac distances for prokaryotic communities in the surface waters; box plots showing the unweighted (C) and weighted (D) UniFrac distances among each station at different sampling times.Full size imageCorrelation between prokaryotic α/β-diversity and physical, chemical and geographic factorsThe α-diversity measurements exhibited significant positive correlations with temperature, pH, SRP, AN and un_ionN (Dataset S2). The correlation between α-diversity indexes and geo factors (longitude and latitude) was not strong or significant both in samples across the three sampling times or from each sampling time (Dataset S2).The environmental variation significantly correlated with β-diversity among the three seasons (r_weighted = 0.4558, r_unweighted = 0.4631, P = 0.001, Table 2), with pH, AN, temperature, un_ionN, COD, nitrite, SRP, salinity, DO and DIN as the main individual determinants. However, it did not show significant correlations with β-diversity at any sampling time except in Oct (Table S1).Table 2 Spearman’s rank correlation between environmental/spatial variability (Euclidean distance) and prokaryotic β-diversity (weighted/unweighted UniFrac distance) among all samples from different season.Full size tableThe geographic distance was not correlated with prokaryotic β-diversity (variation in community composition; r  0.05; Table 2) among the three sampling times. However, samples from Aug and Oct exhibited a significant correlation between β-diversity and geographic distance (Table S1).Factors driving the PCC variationPERMANOVA using the UUF/WUF distance indicated that temperature variation explained the largest part of community variation among the investigated factors (34.90%/19.83%, P = 0.001, Dataset S3), with AN (31.84%/13.56%, P = 0.001) and salinity (12.91%/6.21%, P = 0.001) as the second and third most significant sources of variation.The variance partitioning analysis (VPA) conducted on both UUF/WUF distances showed that almost 100% percent of the variation in PCC among all three sampling times was explained by the detected environmental factors. In May, no environmental or spatial factors could be selected as significantly explain the PCC variation; in Aug, the joint effects of environmental and spatial factors could explain 49.5% of the variation; in Oct, based on WUF distance, the spatial factors could purely explain 10.5%, environmental factors could purely explain 38.8%, their joint effects could explain 28.2%, and based on UUF distance, the joint effects of environmental factors and trend could explain 13.7% of the PCC variation. These results indicated dramatic shifts in the spatial or environmental factor effects on the PCC variation at different sampling times in Bohai Bay (Table 3).Table 3 Variance partitioning analysis of prokaryotic community in Bohai Bay according to seawater environmental factors and geospatial factors. The spatial factors including linear trend and PCNM variables. Forward selection procedures were used to select the best subset of environmental, trend, and PCNM variables explaining community variation, respectively. The community variation was calculated on the weighted and unweighted UniFrac distance matrix, respectively. Monte Carlo permutation test was performed on each set without the effect of the other by permuting samples freely (999 permutations).Full size tableDistinct seasonal features at the phylum and OTU levelsThere were notable differences in the proportions of various phyla among different seasons (sampling month). In May, there was a greater proportion of Alphaproteobacteria (41.41%), Planctomycetes (6.42%), Actinobacteria (3.86%), Firmicutes (1.48%), Acidobacteria (0.45%), TM7 (0.16%), Tenericutes (0.16%), OD1 (0.13%), and WPS-2 (0.09%) than in Aug and Oct, whereas Gammaproteobacteria (44.23%), GN02 (0.08%) and SAR406 (0.04%) were depleted in May and Aug but enriched in Oct. In Aug, Bacteroidetes (13.98%), Deltaproteobacteria (6.93%), Verrucomicrobia (4.5%), Chloroflexi (0.36%), Lentisphaerae (0.97%), TM6 (0.25%), Nitrospirae (0.08%), Chlamydiae (0.07%), Chlorobi (0.07%), Spirochaetes (0.04%) and OP8 (0.03%) were significantly enriched than in the other two sampling times (Duncan test; Table S2).At the OTU level, OTUs with relative abundance  > 0.01% (1040 OTUs) were used to perform the difference analysis, and 175 OTUs in May, 281 OTUs in Aug, and 210 OTUs in Oct were identified as seasonal specific OTUs (ssOTUs). The cooccurrence network showed that the ssOTUs were clustered separately (Fig. 4A). Furthermore, the separation of the three modules contained most of the ssOTUs specific to different seasons (Fig. 4A-B). All the ssOTUs of different seasons comprised a taxonomically broad set of prokaryotes at the phylum (phylum Proteobacteria is grouped at the class level) level (Fig. 4C) belonging to various phyla with different proportions. Betaproteobacteria, Verrucomicrobia, Gemmatimonadetes, Epsilonproteobacteria, PAUC34f., and Euryarchaeota did not show significant differences among the three sampling times at the phylum level, but features belonging to these phyla showed differences at the OTU level (Fig. 4C, Dataset S4). In addition, the phylum ssOTUs belonging to, such as Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, Actinobacteria, and Deltaproteobacteria, were not only enriched at one sampling time (Dataset S4) but also enriched at the other two sampling times (Fig. 4C, Dataset S4). These results revealed that different seasons do not strictly select specific microbial lineages at the phylum level, but a finer level analysis could more strictly reflect the seasonal variation.Figure 4Co-occurrence patterns of seasonal sensitive OTUs (A). Co-occurrence network visualizing significant correlations (ρ  > 0.7, P  0.01%. Different colors represent ssOTUs in May (green), Aug (red) and Oct (blue). Cumulative relative abundance (as counts per million, CPM; y-axis in × 1000) of all the sensitive modules in the networks (B). The phylum attribution of ssOTUs in each season (C). The y-axis is the percentage of the number of OTUs that belong to a particular phylum that accounts for the total number of all the OTUs.Full size imageRegression analysis between the relative abundance of modules to which the ssOTUs belonged and the environmental factors was also conducted, and module 1 abundance, to which the Aug-ssOTUs belonged, showed a significant positive correlation with temperature (R2 = 0.77, P = 6.609e−62), AN (R2 = 0.43, P = 7.416e−25), and un_ionN (R2 = 0.75, P = 1.366e−58) and a negative correlation with SRP (R2 = 0.81, P = 6.762e-17). This may be caused by the functional role of the microbes in Aug. In the Aug-ssOTUs, Deltaproteobacteria showed a higher ratio than in the other 2 months (Fig. 4c), and in the following functional analysis, Deltaproteobacteria contributed to the genes related to nitrogen fixation, which may help to explain why there was a positive correlation of Aug-ssOTUs to AN and un_ionN. The module 2 abundance to which the May-ssOTUs belonged showed a significant negative correlation with pH (R2 = 0.65, P = 4.026e−44), temperature (R2 = 0.19, P = 2.325e−10), un_ionN (R2 = 0.025, P = 0.01779), and SRP (R2 = 0.12, P = 4.104e−07) and a positive correlation with AN (R2 = 0.26, P = 5.174e−14). In the May-ssOTUs, the ratio of Alphaproteobacteria was the highest, and Alphaproteobacteria were reported to be pH-sensitive groups in marine environments20, which prefer neutral pH environments21. In this study, the pH in May was 8.04 ± 0.07, in Aug was 8.39 ± 0.09, in Oct was 8.38 ± 0.07, and the pH in May was the closest to neutral, and the ratio decreased with increasing pH in Oct and Aug. The abundance of module 3, to which the Oct-ssOTUs belonged, showed a significant positive correlation with SRP (R2 = 0.81, P = 0.16e-10) and pH (R2 = 0.054, P = 0.00075) and a negative correlation with temperature (R2 = 0.44, P = 2.276e−25), AN (R2 = 0.75, P = 4.51e−58), and un_ionN (R2 = 0.6, P = 3.995e-39) (Fig. S2). Phosphate has been identified to limit primary productivity22, which is of great importance in the structure of dominant bacterial taxa in marine environments23. In the Oct-ssOTUs, the ratio of Gammaproteobacteria was the highest, as reported. Gammaproteobacteria was significantly explained by SRP during the seasonal variation in the Western English Channel, with Rho equal to 0.7523, which suggested the sensitivity of it to SRP, and in that study, it also showed a negative correlation between temperature and Gammaproteobacteria and a positive correlation between SRP and Gammaproteobacteria. Although the correlation was not significant, the variation trend was consistent, which indicates that the phenomenon observed in this study was not unexpected. In addition, most ammonia-oxidizing bacteria belong to the Betaproteobacteria and Gammaproteobacteria classes are chemolithoautotrophs that oxidize ammonia to nitrite24. Gammaproteobacteria and Betaproteobacteria both had higher ratios in Oct-ssOTUs, and the functional prediction results also showed that pmoA/amoA and pmoB/amoB, which encode ammonia monooxygenase, were mainly contributed by OTUs from Gammaproteobacteria and Betaproteobacteria (Dataset S10). The utilization of ammonia may explain the negative correlation between the Oct-ssOTUs and AN.Community assembly processes across different sampling months and sitesBased on the analysis of phylogenetic turnover, unweighted βNTI mostly ranged from -2 to 2 across different sites at a single sampling time in May, Aug and Oct, revealing that PCC variations across different sampling sites at a single time were mostly affected by stochastic processes. The unweighted βNTI was greater than 2 across May–Aug, May–Oct and Aug-Oct (Fig. 5A), which revealed that the variations in PCC across different sampling times were mostly affected by deterministic processes. The RCbray values across any two sampling times were equal to 1, and in each sampling time, the RCbray values ranged from − 1 to 1 (Fig. 5B). Combining the βNTI and RCbray values, the community assembly processes were quantified at each sampling time and at any two sampling times. As shown in Fig. 5C, turning over of the community during different sampling times was mainly governed by selection; among the different sites in May and Oct, it was mainly governed by “undominated” processes; community turn over in Aug was mainly governed by the influence of “Dispersal Limitation”. These results indicated that the shifts in the assembly of prokaryotic communities during different sampling times were caused by strong “heterogeneous selection” (βNTI  > 2), and the community variation at each sampling time was mainly caused by stochastic processes.Figure 5Patterns of distribution of unweighted βNTI (A) and RCbray (B) values across different sampling times. Quantification of the features that impose community assembly processes in and among different sampling times. (C) Pie charts give the percent of turnover in community composition governed primarily by Selection acting alone (white fill), Dispersal Limitation (green line fill), Homogenizing Dispersal (blue line fill) and undominated process (cyan fill).Full size imagePrediction of the metabolic potential at different sampling timesThe NSTI scores of each sample ranged from 0.033 to 0.096, with a mean of 0.058 (Dataset S5). Microbial functions were detected in all the samples from the three sampling times, and it was found that the relative abundances of 242 pathways were significantly changed between samples from May and samples from Aug (Dataset S6). The relative abundances of 321 pathways were significantly changed between samples from May and Oct (Dataset S7), and the relative abundances of 370 pathways were significantly changed between samples from Aug and Oct (Dataset S8).Genes related to energy metabolism were given more attention. For nitrogen metabolism genes relevant with nitrogen fixation (nifD, nifK) were detected only enriched in Aug, while genes relevant with nitrate reduction and denitrification (narG, narZ, nxrA, narH, narY, nxrB, narI, narV, nirD, nasA, nasB) were detected enriched in May, genes related with ammonia oxidation were both detected enriched in Oct and Aug. For sulfur metabolism, genes relevant with thiosulfate oxidation (soxA, soxB, soxC, soxX, soxY and soxZ) were only enriched in Aug, while genes relevant with sulfate and sulfite reduction (cysNC, aprA, aprB, cysJ, cysI, cysK, dsrA) were detected enriched in May and Oct (Fig. 6).Figure 6The LEfSe analysis indicated significantly differential abundances of PICRUSt predicted genes relevant to energy metabolism in different months of samples.Full size imageProkaryotic taxa contributed to the significantly varied functional genes related to nitrogen and sulfur metabolism at different sampling times. At the species level, the taxa contributing to nifK and nifD mainly belonged to Deltaproteobacteria and Firmicutes, and the taxa contributing to the sox-series genes mainly belonged to Alphaproteobacteria and Gammaproteobacteria (Fig. S3). The denitrification-related functional genes that were enriched in May were mainly contributed by members from Alphaproteobacteria and Gammaproteobacteria. The taxa contributing to dsrA, aprA and aprB were mainly from Deltaproteobacteria, including members of Desulfarculaceae, Desulfobacteraceae, Desulfobulbaceae, Desulfovibrionaceae and Syntrophobacteraceae (Fig. S4). More

Aristegui, J., Gasol, J. M., Duarte, C. M. & Herndl, G. J. Microbial oceanography of the dark ocean’s pelagic realm. Limnol. Oceanogr. 54, 1501–1529 (2009).Article 

Google Scholar 
Jannasch, H. W., Eimhjellen, K., Wirsen, C. O. & Farmanfarmaian, A. Microbial degradation of organic matter in the deep sea. Science 171, 672–675 (1971).Article 

Google Scholar 
Tamburini, C., Boutrif, M., Garel, M., Colwell, R. R. & Deming, J. W. Prokaryotic responses to hydrostatic pressure in the ocean – a review. Environ. Microbiol. 15, 1262–1274 (2013).Article 

Google Scholar 
Yayanos, A. A. Microbiology to 10,500 meters in the deep-sea. Annu. Rev. Microb. 49, 777–805 (1995).Article 

Google Scholar 
Jebbar, M., Franzetti, B., Girard, E. & Oger, P. Microbial diversity and adaptation to high hydrostatic pressure in deep-sea hydrothermal vents prokaryotes. Extremophiles 19, 721–740 (2015).Article 

Google Scholar 
Yayanos, A. A. Evolutional and ecological implications of the properties of deep-sea barophilic bacteria. Proc. Natl Acad. Sci. USA 83, 9542–9546 (1986).Article 

Google Scholar 
Nagata, T. et al. Emerging concepts on microbial processes in the bathypelagic ocean – ecology, biogeochemistry, and genomics. Deep-Sea Res. II 57, 1519–1536 (2010).Article 

Google Scholar 
Picard, A. & Daniel, I. Pressure as an environmental parameter for microbial life – a review. Biophys. Chem. 183, 30–41 (2013).Article 

Google Scholar 
Herndl, G. J. & Reinthaler, T. Microbial control of the dark end of the biological pump. Nat. Geosci. 6, 718–724 (2013).Article 

Google Scholar 
Marietou, A. & Bartlett, D. H. Effects of high hydrostatic pressure on coastal bacterial community abundance and diversity. Appl. Environ. Microbiol. 80, 5992–6003 (2014).Article 

Google Scholar 
Lauro, F. M. & Bartlett, D. H. Prokaryotic lifestyles in deep sea habitats. Extremophiles 12, 15–25 (2008).Article 

Google Scholar 
Peoples, L. M. et al. Distinctive gene and protein characteristics of extremely piezophilic Colwellia. BMC Genom. 21, 692 (2020).Article 

Google Scholar 
Reinthaler, T. et al. Prokaryotic respiration and production in the meso- and bathypelagic realm of the eastern and western North Atlantic basin. Limnol. Oceanogr. 51, 1262–1273 (2006).Article 

Google Scholar 
Steinberg, D. K. et al. Bacterial vs. zooplankton control of sinking particle flux in the ocean’s twilight zone. Limnol. Oceanogr. 53, 1327–1338 (2008).Article 

Google Scholar 
Burd, A. B. et al. Assessing the apparent imbalance between geochemical and biochemical indicators of meso- and bathypelagic biological activity: what the @$#! is wrong with present calculations of carbon budgets? Deep-Sea Res. II 57, 1557–1571 (2010).Article 

Google Scholar 
Boyd, P. W., Claustre, H., Levy, M., Siegel, D. A. & Weber, T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature 568, 327–335 (2019).Article 

Google Scholar 
Kirchman, D., Knees, E. & Hodson, R. Leucine incorporation and its potential as a measure of protein-synthesis by bacteria in natural aquatic systems. Appl. Environ. Microbiol. 49, 599–607 (1985).Article 

Google Scholar 
Nielsen, J. L., Christensen, D., Kloppenborg, M. & Nielsen, P. H. Quantification of cell-specific substrate uptake by probe-defined bacteria under in situ conditions by microautoradiography and fluorescence in situ hybridization. Environ. Microbiol. 5, 202–211 (2003).Article 

Google Scholar 
Sintes, E. & Herndl, G. J. Quantifying substrate uptake by individual cells of marine bacterioplankton by catalyzed reporter deposition fluorescence in situ hybridization combined with micro autoradiography. Appl. Environ. Microbiol. 72, 7022–7028 (2006).Article 

Google Scholar 
Garel, M. et al. Pressure-retaining sampler and high-pressure systems to study deep-sea microbes under in situ conditions. Front. Microbiol 10, 453 (2019).Article 

Google Scholar 
Peoples, L. M. et al. A full-ocean-depth rated modular lander and pressure-retaining sampler capable of collecting hadal-endemic microbes under in situ conditions. Deep-Sea Res. I 143, 50–57 (2019).Article 

Google Scholar 
Gross, M. & Jaenicke, R. Proteins under pressure – the influence of high hydrostatic pressure on structure, function and assembly of proteins and protein complexes. Eur. J. Biochem. 221, 617–630 (1994).Article 

Google Scholar 
Kirchman, D. L. Growth rates of microbes in the oceans. Annu. Rev. Mar. Sci. 8, 285–309 (2016).Article 

Google Scholar 
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).Article 

Google Scholar 
Xie, Z., Jian, H., Jin, Z. & Xiao, X. Enhancing the adaptability of the deep-sea bacterium Shewanella piezotolerans WP3 to high pressure and low temperature by experimental evolution under H2O2 stress. Appl. Environ. Microbiol. 84, e02342–02317 (2018).Article 

Google Scholar 
Tamburini, C. et al. Effects of hydrostatic pressure on microbial alteration of sinking fecal pellets. Deep-Sea Res. II 56, 1533–1546 (2009).Article 

Google Scholar 
Ivars-Martinez, E. et al. Comparative genomics of two ecotypes of the marine planktonic copiotroph Alteromonas macleodii suggests alternative lifestyles associated with different kinds of particulate organic matter. ISME J. 2, 1194–1212 (2008).Article 

Google Scholar 
Zhao, Z., Baltar, F. & Herndl, G. J. Linking extracellular enzymes to phylogeny indicates a predominantly particle-associated lifestyle of deep-sea prokaryotes. Sci. Adv. 6, eaaz4354 (2020).Article 

Google Scholar 
Bochdansky, A. B., van Aken, H. M. & Herndl, G. J. Role of macroscopic particles in deep-sea oxygen consumption. Proc. Natl Acad. Sci. USA 107, 8287–8291 (2010).Article 

Google Scholar 
Chikuma, S., Kasahara, R., Kato, C. & Tamegai, H. Bacterial adaptation to high pressure: a respiratory system in the deep-sea bacterium Shewanella violacea DSS12. FEMS Microbiol. Lett. 267, 108–112 (2007).Article 

Google Scholar 
Qin, Q. L. et al. Oxidation of trimethylamine to trimethylamine N-oxide facilitates high hydrostatic pressure tolerance in a generalist bacterial lineage. Sci. Adv. 7, eabf9941 (2021).Article 

Google Scholar 
Mestre, M. et al. Sinking particles promote vertical connectivity in the ocean microbiome. Proc. Natl Acad. Sci. USA 115, E6799–E6807 (2018).Article 

Google Scholar 
Thiele, S., Fuchs, B. M., Amann, R. & Iversen, M. H. Colonization in the photic zone and subsequent changes during sinking determine bacterial community composition in marine snow. Appl. Environ. Microbiol. 81, 1463–1471 (2015).Article 

Google Scholar 
Tada, Y. et al. Differing growth responses of major phylogenetic groups of marine bacteria to natural phytoplankton blooms in the western North Pacific Ocean. Appl. Environ. Microbiol. 77, 4055–4065 (2011).Article 

Google Scholar 
Cottrell, M. T. & Kirchman, D. L. Natural assemblages of marine proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl. Environ. Microbiol. 66, 1692–1697 (2000).Article 

Google Scholar 
Poff, K. E., Leu, A. O., Eppley, J. M., Karl, D. M. & DeLong, E. F. Microbial dynamics of elevated carbon flux in the open ocean’s abyss. Proc. Natl Acad. Sci. USA 118, e2018269118 (2021).Article 

Google Scholar 
Ducklow, H. in Microbial Ecology of the Oceans (ed. Kirchman, D. L.) Ch. 4, 85–120 (Wiley-Liss, 2000).Herndl, G. J. et al. Contribution of archaea to total prokaryotic production in the deep Atlantic Ocean. Appl. Environ. Microbiol. 71, 2303–2309 (2005).Article 

Google Scholar 
Baltar, F., Aristegui, J., Gasol, J. M. & Herndl, G. J. Prokaryotic carbon utilization in the dark ocean: growth efficiency, leucine-to-carbon conversion factors, and their relation. Aquat. Microb. Ecol. 60, 227–232 (2010).Article 

Google Scholar 
Edgcomb, V. P. et al. Comparison of Niskin vs. in situ approaches for analysis of gene expression in deep Mediterranean Sea water samples. Deep-Sea Res. II 129, 213–222 (2016).Article 

Google Scholar 
Cario, A., Oliver, G. C. & Rogers, K. L. Exploring the deep marine biosphere: challenges, innovations, and opportunities. Front. Earth Sci. 7, 225 (2019).Article 

Google Scholar 
Giering, S. L. C. et al. Reconciliation of the carbon budget in the ocean’s twilight zone. Nature 507, 480–483 (2014).Article 

Google Scholar 
Simon, M. & Azam, F. Protein content and protein synthesis rates of planktonic marine bacteria. Mar. Ecol. Prog. Ser. 51, 201–213 (1989).Article 

Google Scholar 
Gasol, J. M. et al. Mesopelagic prokaryotic bulk and single-cell heterotrophic activity and community composition in the NW Africa-Canary Islands coastal-transition zone. Prog. Oceanogr. 83, 189–196 (2009).Article 

Google Scholar 
DeLong, E. F. et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311, 496–503 (2006).Article 

Google Scholar 
Teira, E., Reinthaler, T., Pernthaler, A., Pernthaler, J. & Herndl, G. J. Combining catalyzed reporter deposition-fluorescence in situ hybridization and microautoradiography to detect substrate utilization by bacteria and archaea in the deep ocean. Appl. Environ. Microbiol. 70, 4411–4414 (2004).Article 

Google Scholar 
Woebken, D., Fuchs, B. M., Kuypers, M. M. M. & Amann, R. Potential interactions of particle-associated anammox bacteria with bacterial and archaeal partners in the Namibian upwelling system. Appl. Environ. Microbiol. 73, 4648–4657 (2007).Article 

Google Scholar 
Wand, M. P. Data-based choice of histogram bin width. Am. Stat. 51, 59–64 (1997).
Google Scholar 
Acinas, S. G. et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun. Biol. 4, 604 (2021).Article 

Google Scholar 
Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).Article 

Google Scholar 
Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).Article 

Google Scholar 
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 

Google Scholar 
Wu, Y. W., Tang, Y. H., Tringe, S. G., Simmons, B. A. & Singer, S. W. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome 2, 26 (2014).Article 

Google Scholar 
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. Peerj 7, e7359 (2019).Article 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).Article 

Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).
Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinf. 11, 119 (2010).Article 

Google Scholar 
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).Article 

Google Scholar 
Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass. Spectrom. 5, 976–989 (1994).Article 

Google Scholar 
Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).Article 

Google Scholar 
Riffle, M. et al. MetaGOmics: a web-based tool for peptide-centric functional and taxonomic analysis of metaproteomics data. Proteomes 6, 2 (2017).Article 

Google Scholar 
Reinthaler, T., van Aken, H. M. & Herndl, G. J. Major contribution of autotrophy to microbial carbon cycling in the deep North Atlantic’s interior. Deep-Sea Res. II 57, 1572–1580 (2010).Article 

Google Scholar 
Yokokawa, T., Yang, Y. H., Motegi, C. & Nagata, T. Large-scale geographical variation in prokaryotic abundance and production in meso- and bathypelagic zones of the central Pacific and Southern Ocean. Limnol. Oceanogr. 58, 61–73 (2013).Article 

Google Scholar 
Frank, A. H., Garcia, J. A., Herndl, G. J. & Reinthaler, T. Connectivity between surface and deep waters determines prokaryotic diversity in the North Atlantic Deep Water. Environ. Microbiol. 18, 2052–2063 (2016).Article 

Google Scholar 
Herndl, G. J., Bayer, B., Baltar, F. & Reinthaler, T. Prokaryotic life in the deep ocean’s water column. Annu. Rev. Mar. Sci. (in the press).Uchimiya, M., Ogawa, H. & Nagata, T. Effects of temperature elevation and glucose addition on prokaryotic production and respiration in the mesopelagic layer of the western North Pacific. J. Oceanogr. 72, 419–426 (2016).Article 

Google Scholar 
Antia, A. N. et al. Basin-wide particulate carbon flux in the Atlantic Ocean: regional export patterns and potential for atmospheric CO2 sequestration. Glob. Biogeochem. Cycles 15, 845–862 (2001).Article 

Google Scholar 
Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).Article 

Google Scholar  More

Dataset descriptionSample collectionOur research complies with all relevant ethical regulations following policies at the University of California, San Diego (UCSD). Animal samples that were sequenced were not collected at UCSD and are not for vertebrate animals research at UCSD following the UCSD Institutional Animal Care and Use Committee (IACUC). Samples were contributed by 34 principal investigators of the Earth Microbiome Project 500 (EMP500) Consortium and are samples from studies at their respective institutions (Supplementary Table 1). Relevant permits and ethics information for each parent study are described in the ‘Permits for sample collection’ section below. Samples were contributed as distinct sets referred to here as studies, where each study represented a single environment (for example, terrestrial plant detritus). To achieve more even coverage across microbial environments, we devised an ontology of sample types (microbial environments), the EMP Ontology (EMPO) (http://earthmicrobiome.org/protocols-and-standards/empo/)1, and selected samples to fill out EMPO categories as broadly as possible. EMPO recognizes strong gradients structuring microbial communities globally, and thus classifies microbial environments (level 4) on the basis of host association (level 1), salinity (level 2), host kingdom (if host-associated) or phase (if free-living) (level 3) (Fig. 1a). As we anticipated previously1, we have updated the number of levels as well as states therein for EMPO (Fig. 1b) on the basis of an important additional salinity gradient observed among host-associated samples when considering the previously unreported shotgun metagenomic and metabolomic data generated here (Fig. 3c,d). We note that although we were able to acquire samples for all EMPO categories, some categories are represented by a single study.Samples were collected following the Earth Microbiome Project sample submission guide50. Briefly, samples were collected fresh, split into 10 aliquots and then frozen, or alternatively collected and frozen, and subsequently split into 10 aliquots with minimal perturbation. Aliquot size was sufficient to yield 10–100 ng genomic DNA (approximately 107–108 cells). To leave samples amenable to chemical characterization (metabolomics), buffers or solutions for sample preservation (for example, RNAlater) were avoided. Ethanol (50–95%) was allowed as it is compatible with LC–MS/MS although it should also be avoided if possible.Sampling guidance was tailored for four general sample types: bulk unaltered (for example, soil, sediment, faeces), bulk fractionated (for example, sponges, corals, turbid water), swabs (for example, biofilms) and filters. Bulk unaltered samples were split fresh (or frozen), sampled into 10 pre-labelled 2 ml screw-cap bead beater tubes (Sarstedt, 72.694.005 or similar), ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Bulk fractionated samples were fractionated as appropriate for the sample type, split into 10 pre-labelled 2 ml screw-cap bead beater tubes, ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Swabs were collected as 10 replicate swabs using 5 BD SWUBE dual cotton swabs with wooden stick and screw cap (281130). Filters were collected as 10 replicate filters (47 mm diameter, 0.2 um pore size, polyethersulfone (preferred) or hydrophilic PTFE filters), placed in pre-labelled 2 ml screw-cap bead beater tubes, and flash frozen in liquid nitrogen (if possible). All sample types were stored at –80 °C if possible, otherwise –20 °C.To track the provenance of sample aliquots, we employed a QR coding scheme. Labels were affixed to aliquot tubes before shipping when possible. QR codes had the format ‘name.99.s003.a05’, where ‘name’ is the PI name, ‘99’ is the study ID, ‘s003’ is the sample number and ‘a05’ is the aliquot number. QR codes (version 2, 25 pixels × 25 pixels) were printed on 1.125’ × 0.75’ rectangular and 0.437’ circular cap Cryogenic Direct Thermal labels (GA International, DFP-70) using a Zebra model GK420d printer and ZebraDesigner Pro 3 software for Windows. After receipt but before aliquots were stored in freezers, QR codes were scanned into a sample inventory spreadsheet using a QR scanner.Sample metadataEnvironmental metadata were collected for all samples on the basis of the EMP Metadata Guide, which combines guidance from the Genomics Standards Consortium MIxS (Minimum Information about any Sequence) standard74 and the Qiita Database (https://qiita.ucsd.edu)51. The metadata guide provides templates and instructions for each MIxS environmental package (that is, sample type). Relevant information describing each PI submission, or study, was organized into a separate study metadata file (Supplementary Table 1).MetabolomicsLC–MS/MS sample extraction and preparationTo profile metabolites among all samples, we used LC–MS/MS, a versatile method that detects tens of thousands of metabolites in biological samples. All solvents and reactants used were LC–MS grade. To maximize the biomass extracted from each sample, the samples were prepared depending on their sampling method (for example, bulk, swabs, filter and controls). The bulk samples were transferred into a microcentrifuge tube (polypropylene, PP) and dissolved in 7:3 MeOH:H2O using a volume varying from 600 µl to 1.5 ml, depending on the amounts of sample available, and homogenized in a tissue lyser (QIAGEN) at 25 Hz for 5 min. Then, the tubes were centrifuged at 2,000 × g for 15 min, and the supernatant was collected in a 96-well plate (PP). For swabs, the swabs were transferred into a 96-well plate (PP) and dissolved in 1.0 ml of 9:1 ethanol:H2O. The prepared plates were sonicated for 30 min, and after 12 h at 4 °C, the swabs were removed from the wells. The filter samples were dissolved in 1.5 ml of 7:3 MeOH:H2O in microcentrifuge tubes (PP) and sonicated for 30 min. After 12 h at 4 °C, the filters were removed from the tubes. The tubes were centrifuged at 2,000 × g for 15 min, and the supernatants were transferred to 96-well plates (PP). The process control samples (bags, filters and tubes) were prepared by adding 3.0 ml of 2:8 MeOH:H2O and recovering 1.5 ml after 2 min. After the extraction process, all sample plates were dried with a vacuum concentrator and subjected to solid phase extraction (SPE). SPE was used to remove salts that could reduce ionization efficiency during mass spectrometry analysis, as well as the most polar and non-polar compounds (for example, waxes) that cannot be analysed efficiently by reversed-phase chromatography. The protocol was as follows: the samples (in plates) were dissolved in 300 µl of 7:3 MeOH:H2O and put in an ultrasound bath for 20 min. SPE was performed with SPE plates (Oasis HLB, hydrophilic-lipophilic-balance, 30 mg with particle sizes of 30 µm). The SPE beds were activated by priming them with 100% MeOH, and equilibrated with 100% H2O. The samples were loaded on the SPE beds, and 100% H2O was used as wash solvent (600 µl). The eluted washing solution was discarded, as it contains salts and very polar metabolites that subsequent metabolomics analysis is not designed for. The sample elution was carried out sequentially with 7:3 MeOH:H2O (600 µl) and 100% MeOH (600 µl). The obtained plates were dried with a vacuum concentrator. For mass spectrometry analysis, the samples were resuspended in 130 µl of 7:3 MeOH:H2O containing 0.2 µM of amitriptyline as an internal standard. The plates were centrifuged at 30 × g for 15 min at 4 °C. Samples (100 µl) were transferred into new 96-well plates (PP) for mass spectrometry analysis.LC–MS/MS sample analysisThe extracted samples were analysed by ultra-high performance liquid chromatography (UHPLC, Vanquish, Thermo Fisher) coupled to a quadrupole-Orbitrap mass spectrometer (Q Exactive, Thermo Fisher) operated in data-dependent acquisition mode (LC–MS/MS in DDA mode). Chromatographic separation was performed using a Kinetex C18 1.7 µm (Phenomenex), 100 Å pore size, 2.1 mm (internal diameter) × 50 mm (length) column with a C18 guard cartridge (Phenomenex). The column was maintained at 40 °C. The mobile phase was composed of a mixture of (A) water with 0.1% formic acid (v/v) and (B) acetonitrile with 0.1% formic acid. Chromatographic elution method was set as follows: 0.00–1.00 min, isocratic 5% B; 1.00–9.00 min, gradient from 5% to 100% B; 9.00–11.00 min, isocratic 100% B; followed by equilibration 11.00–11.50 min, gradient from 100% to 5% B; 11.50–12.50 min, isocratic 5% B. The flow rate was set to 0.5 ml min−1.The UHPLC was interfaced to the orbitrap using a heated electrospray ionization source with the following parameters: ionization mode, positive; spray voltage, +3,496.2 V; heater temperature, 363.90 °C; capillary temperature, 377.50 °C; S-lens RF, 60 arbitrary units (a.u.); sheath gas flow rate, 60.19 a.u.; and auxiliary gas flow rate, 20.00 a.u. The MS1 scans were acquired at a resolution (at m/z 200) of 35,000 in the m/z 100–1500 range, and the fragmentation spectra (MS2) scans at a resolution of 17,500 from 0 to 12.5 min. The automatic gain control target and maximum injection time were set at 1.0 × 106 and 160 ms for MS1 scans, and set at 5.0 × 105 and 220 ms for MS2 scans, respectively. Up to three MS2 scans in data-dependent mode (Top 3) were acquired for the most abundant ions per MS1 scans using the apex trigger mode (4–15 s), dynamic exclusion (11 s) and automatic isotope exclusion. The starting value for MS2 was m/z 50. Higher-energy collision induced dissociation (HCD) was performed with a normalized collision energy of 20, 30 and 40 eV in stepped mode. The major background ions originating from the SPE were excluded manually from the MS2 acquisition. Analyses were randomized within plate and blank samples analysed every 20 injections. A quality control mix sample assembled from 20 random samples across the sample types was injected at the beginning, the middle and the end of each plate sequence. The chromatographic shift observed throughout the batch was estimated as less than 2 s, and the relative standard deviation of ion intensity was 15% per replicate.LC–MS/MS data processingThe mass spectrometry data were centroided and converted from the proprietary format (.raw) to the m/z extensible markup language format (.mzML) using ProteoWizard (ver. 3.0.19, MSConvert tool)75. The mzML files were then processed with MZmine 2 toolbox76 using the ion-identity networking modules77 that allow advanced detection for adduct/isotopologue annotations. The MZmine processing was performed on Ubuntu 18.04 LTS 64-bits workstation (Intel Xeon E5-2637, 3.5 GHz, 8 cores, 64 Gb of RAM) and took ~3 d. The MZmine project, the MZmine batch file (.XML format) and results files (.MGF and .CSV) are available in the MassIVE dataset MSV000083475. The MZmine batch file contains all the parameters used during the processing. In brief, feature detection and deconvolution was performed with the ADAP chromatogram builder78 and local minimum search algorithm. The isotopologues were regrouped and the features (peaks) were aligned across samples. The aligned peak list was gap filled and only peaks with an associated fragmentation spectrum and occurring in a minimum of three files were conserved. Peak shape correlation analysis grouped peaks originating from the same molecule and annotated adduct/isotopologue with ion-identity networking77. Finally, the feature quantification table results (.CSV) and spectral information (.MGF) were exported with the GNPS module for feature-based molecular networking analysis on GNPS79 and with SIRIUS export modules.LC–MS/MS data annotationThe results files of MZmine (.MGF and .CSV files) were uploaded to GNPS (http://gnps.ucsd.edu)52 and analysed with the feature-based molecular networking workflow79. Spectral library matching was performed against public fragmentation spectra (MS2) spectral libraries on GNPS and the NIST17 library.For the additional annotation of small peptides, we used the DEREPLICATOR tools available on GNPS80,81. We then used SIRIUS82 (v. 4.4.25, headless, Linux) to systematically annotate the MS2 spectra. Molecular formulae were computed with the SIRIUS module by matching the experimental and predicted isotopic patterns83, and from fragmentation trees analysis84 of MS2. Molecular formula prediction was refined with the ZODIAC module using Gibbs sampling85 on the fragmentation spectra (chimeric spectra or those with poor fragmentation were excluded). In silico structure annotation using structures from biodatabase was done with CSI:FingerID86. Systematic class annotations were obtained with CANOPUS41 and used the NPClassifier ontology87.The parameters for SIRIUS tools were set as follows, for SIRIUS: molecular formula candidates retained, 80; molecular formula database, ALL; maximum precursor ion m/z computed, 750; profile, orbitrap; m/z maximum deviation, 10 ppm; ions annotated with MZmine were prioritized and other ions were considered (that is, [M+H3N+H]+, [M+H]+, [M+K]+, [M+Na]+, [M+H-H2O]+, [M+H-H4O2]+, [M+NH4]+); for ZODIAC: the features were split into 10 random subsets for lower computational burden and computed separately with the following parameters: threshold filter, 0.9; minimum local connections, 0; for CSI:FingerID: m/z maximum deviation, 10 ppm; and biological database, BIO.To establish putative microbially related secondary metabolites, we collected annotations from spectral library matching and the DEREPLICATOR+ tools and queried them against the largest microbial metabolite reference databases (Natural Products Atlas88 and MIBiG89). Molecular networking79 was then used to propagate the annotation of microbially related secondary metabolites throughout all molecular families (that is, the network component).LC–MS/MS data analysisWe combined the annotation results from the different tools described above to create a comprehensive metadata file describing each metabolite feature observed. Using that information, we generated a feature-table including only secondary metabolite features determined to be microbially related. We then excluded very low-intensity features introduced to certain samples during the gap-filling step described above. These features were identified on the basis of presence in negative controls that were universal to all sample types (that is, bulk, filter and swab) and by their relatively low per-sample intensity values. Finally, we excluded features present in positive controls for sampling devices specific to each sample type (that is, bulk, filter or swab). The final feature-table included 618 samples and 6,588 putative microbially related secondary metabolite features that were used for subsequent analysis.We used QIIME 2’s90 (v2020.6) ‘diversity’ plugin to quantify alpha-diversity (that is, feature richness) for each sample and ‘deicode’91 to quantify beta-diversity (that is, robust Aitchison distances, which are robust to both sparsity and compositionality in the data) between each pair of samples. We parameterized our robust Aitchison principal components analysis (RPCA)91 to exclude samples with fewer than 500 features and features present in fewer than 10% of samples. We used the ‘taxa’ plugin to quantify the relative abundance of microbially related secondary metabolite pathways and superclasses (that is, on the basis of NPClassifier) within each environment (that is, for each level of EMPO 4), and ‘songbird’ v1.0.492 to identify sets of microbially related secondary metabolites whose abundances were associated with certain environments. We parameterized our ‘songbird’ model as follows: epochs, 1,000,000; differential prior, 0.5; learning rate, 1.0 × 10−5; summary interval, 2; batch size, 400; minimum sample count, 0; and training on 80% of samples at each level of EMPO 4 using ‘Animal distal gut (non-saline)’ as the reference environment. Environments with fewer than 10 samples were excluded to optimize model training (that is, ‘Animal corpus (non-saline)’, ‘Animal proximal gut (non-saline)’, ‘Surface (saline)’). The output from ‘songbird’ includes a rank value for each metabolite in every environment, which represents the log fold change for a given metabolite in a given environment92. We compared log fold changes for each metabolite from this run to those from (1) a replicate run using the same reference environment and (2) a run using a distinct reference environment: ‘Water (saline)’. We found strong Spearman correlations in both cases (Supplementary Table 8), and therefore focused on results from the original run using ‘Animal distal gut (non-saline)’ as the reference environment, as it has previously been shown to be relatively unique among other habitats. In addition to summarizing the top 10 metabolites for each environment (Supplementary Table 3), we used the log fold change values in our multi-omics analyses described below.We used the RPCA biplot and QIIME 2’s90 EMPeror93 to visualize differences in composition among samples, as well as the association with samples of the 25 most influential microbially related secondary metabolite features (that is, those with the largest magnitude across the first three principal component loadings). We tested for significant differences in metabolite composition across all levels of EMPO using PERMANOVA implemented with QIIME 2’s ‘diversity’ plugin90 and using our robust Aitchison distance matrix as input. In parallel, we used the differential abundance results from ‘songbird’ described above to identify specific microbially related secondary metabolite pathways and superclasses that varied strongly across environments. We then went back to our metabolite feature-table to visualize differences in the relative abundances of those pathways and superclasses within each environment by first selecting features and calculating log-ratios using ‘qurro’94, and then plotting using the ‘ggplot2’ package95 in R96 v4.0.0. We tested for significant differences in relative abundances across environments using Kruskal–Wallis tests implemented with the base ‘stats’ package in R96.GC–MS sample extraction and preparationTo profile volatile small molecules among all samples in addition to what was captured with LC–MS/MS, we used gas chromatography coupled with mass spectrometry (GC–MS). All solvents and reactants were GC–MS grade. Two protocols were used for sample extraction, one for the 105 soil samples and a second for the 356 faecal and sediment samples that were treated as biosafety level 2. The 105 soil samples were received at the Pacific Northwest National Laboratory and processed as follows. Each soil sample (1 g) was weighed into microcentrifuge tubes (Biopur Safe-Lock, 2.0 ml, Eppendorf). H2O (1 ml) and one scoop (~0.5 g) of a 1:1 (v/v) mixture of garnet (0.15 mm, Omni International) and stainless steel (0.9–2.0 mm blend, Next Advance) beads and one 3 mm stainless steel bead (Qiagen) were added to each tube. Samples were homogenized in a tissue lyser (Qiagen) for 3 min at 30 Hz and transferred into 15 ml polypropylene tubes (Olympus, Genesee Scientific). Ice-cold water (1 ml) was used to rinse the smaller tube and combined into the 15 ml tube. Chloroform:methanol (10 ml, 2:1 v/v) was added and samples were rotated at 4 °C for 10 min, followed by cooling at −70 °C for 10 min and centrifuging at 150 × g for 10 min to separate phases. The top and bottom layers were combined into 40 ml glass vials and dried using a vacuum concentrator. Chloroform:methanol (1 ml, 2:1) was added to each large glass vial and the sample was transferred into 1.5 ml tubes and centrifuged at 1,300 × g. The supernatant was transferred into glass vials and dried for derivatization.The remaining 356 samples received from UCSD that included faecal and sediment samples were processed as follows: 100 µl of each sample was transferred to a 2 ml microcentrifuge tube using a scoop (MSP01, Next Advance). The final volume of the sample was brought to 1.5 ml, ensuring that the solvent ratio is 3:8:4 H2O:CHCl3:MeOH by adding the appropriate volumes of H2O, MeOH and CHCl3. After transfer, one 3 mm stainless steel bead (QIAGEN), 400 µl methanol and 300 µl H2O were added to each tube and the samples were vortexed for 30 s. Then, 800 µl chloroform was added and samples were vortexed for 30 s. After centrifuging at 150 × g for 10 min to separate phases, the top and bottom layers were combined in a vial and dried for derivatization.The samples were derivatized for GC–MS analysis as follows: 20 µl of a methoxyamine solution in pyridine (30 mg ml−1) was added to the sample vial and vortexed for 30 s. A bath sonicator was used to ensure that the sample was completely dissolved. Samples were incubated at 37 °C for 1.5 h while shaking at 1,000 r.p.m. N-methyl-N-trimethylsilyltrifluoroacetamide (80 µl) and 1% trimethylchlorosilane solution was added and samples were vortexed for 10 s, followed by incubation at 37 °C for 30 min, with 1,000 r.p.m. shaking. The samples were then transferred into a vial with an insert.An Agilent 7890A gas chromatograph coupled with a single quadrupole 5975C mass spectrometer (Agilent) and an HP-5MS column (30 m × 0.25 mm × 0.25 μm; Agilent) was used for untargeted analysis. Samples (1 μl) were injected in splitless mode, and the helium gas flow rate was determined by the Agilent Retention Time Locking function on the basis of analysis of deuterated myristic acid (Agilent). The injection port temperature was held at 250 °C throughout the analysis. The GC oven was held at 60 °C for 1 min after injection, and the temperature was then increased to 325 °C at a rate of 10 °C min−1, followed by a 10 min hold at 325 °C. Data were collected over the mass range of m/z 50–600. A mixture of FAMEs (C8–C28) was analysed each day with the samples for retention index alignment purposes during subsequent data analysis.GC–MS data processing and annotationThe data were converted from vendor’s format to the .mzML format and processed using GNPS GC–MS data analysis workflow (https://gnps.ucsd.edu)97. The compounds were identified by matching experimental spectra to the public libraries available at GNPS, as well as NIST 17 and Wiley libraries. The data are publicly available at the MassIVE depository (https://massive.ucsd.edu); dataset ID: MSV000083743. The GNPS deconvolution is available in GNPS (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=d5c5135a59eb48779216615e8d5cb3ac), as is the library search (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=59b20fc8381f4ee6b79d35034de81d86).GC–MS data analysisFor multi-omics analyses including GC–MS data, we first removed noisy (that is, suspected background contaminants and artifacts) features by excluding those with balance scores 1.5–2 kb DNA fragments’ (Oxford Nanopore Technologies). The resulting product consists of uniquely tagged rRNA operon amplicons. The uniquely tagged rRNA operons were amplified in a second PCR, where the reaction (100 µl) contained 2 U Platinum SuperFi DNA Polymerase High Fidelity (Thermo Fisher) and a final concentration of 1X SuperFi buffer, 0.2 mM of each dNTP, and 500 nM of each forward and reverse synthetic primer targeting the tailed primers from above. The PCR cycling parameters consisted of an initial denaturation (3 min at 95 °C) and then 25–35 cycles of denaturation (15 s at 95 °C), annealing (30 s at 60 °C) and extension (6 min at 72 °C), followed by final extension (5 min at 72 °C). The PCR product was purified using the custom bead purification protocol above. Batches of 25 amplicon libraries were barcoded and sent for PacBio Sequel II library preparation and sequencing (Sequel II SMRT Cell 8M and 30 h collection time) at the DNA Sequencing Center at Brigham Young University. Circular consensus sequencing (CCS) reads were generated using CCS v.3.4.1 (https://github.com/PacificBiosciences/ccs) using default settings. UMI consensus sequences were generated using the longread_umi pipeline (https://github.com/SorenKarst/longread_umi) with the following command: longread_umi pacbio_pipeline -d ccs_reads.fq -o out_dir -m 3500 -M 6000 -s 60 -e 60 -f CAAGCAGAAGACGGCATACGAGAT -F AGRGTTYGATYMTGGCTCAG -r AATGATACGGCGACCACCGAGATC -R CGACATCGAGGTGCCAAAC -U ‘0.75;1.5;2;0’ -c 2.Amplicon data analysisFor multi-omics analyses including amplicon sequence data, we processed each dataset for comparison of beta-diversity. For all amplicon data except that for bacterial full-length rRNA amplicons, raw sequence data were converted from bcl to fastq, and then multiplexed files for each sequencing run uploaded as separate preparations to Qiita (study: 13114).For each 16S sequencing run, in Qiita, data were demultiplexed, trimmed to 150 bp and denoised using Deblur122 to generate a feature-table of sub-operational taxonomic units (sOTUs) per sample, using default parameters. We then exported feature-tables and denoised sequences from each sequencing run, used QIIME 2’s ‘feature-table’ plugin to merge feature-tables and denoised reads across sequencing runs, and placed all denoised reads into the GreenGenes 13_8 phylogeny123 via fragment insertion using QIIME 2’s90 SATé-Enabled Phylogenetic Placement (SEPP)124 plugin to produce a phylogeny for diversity analyses. To allow for phylogenetically informed diversity analyses, reads not placed during SEPP (that is, 513 sOTUs, 0.1% of all sOTUs) were removed from the merged feature-table. We then used QIIME 2’s ‘feature-table’ plugin to exclude singleton sOTUs and rarefy the data to 5,000 reads per sample. Rarefaction depths for all amplicon analyses were chosen to best normalize sampling effort per sample while maintaining ≥75% of samples representative of Earth’s environments, and also to maintain consistency with the analyses from EMP release 1. We then used QIIME 2’s90 ‘diversity’ plugin to estimate alpha-diversity (that is, sOTU richness) and beta-diversity (that is, unweighted UniFrac distances). The final feature-table for 16S beta-diversity analysis included 681 samples and 93,260 features. We performed a comparative analysis of the data including and excluding the reads not placed during SEPP, and note that both alpha-diversity (that is, sOTU richness) and beta-diversity (that is, sample–sample RPCA distances) were highly correlated between datasets (Spearman r = 1.0) (Supplementary Fig. 5). We thus proceeded with the SEPP-filtered dataset and used phylogenetically informed diversity metrics where applicable.For 18S data, we used QIIME 2’s90 ‘demux’ plugin’s ‘emp-paired’ method125,126 to first demultiplex each sequencing run, and then the ‘cutadapt’ plugin’s127 ‘trim-paired’ method to trim sequencing primers from reads. We then exported trimmed reads, concatenated R1 and R2 read files per sample, and denoised reads using Deblur’s122,128 ‘workflow’ with default settings, trimming reads to 90 bp, and taking the ‘all.biom’ and ‘all.seqs’ output, for each sequencing run. We then used QIIME 2’s ‘feature-table’ plugin to merge feature-tables and denoised sequences across sequencing runs, and then the ‘feature-classifier’ plugin’s ‘classify-sklearn’ method to classify taxonomy for each sOTU via pre-fitted machine-learning classifiers129 and the SILVA 138 reference database130. We then used QIIME 2’s90 ‘feature-table’ plugin to exclude reads assigned to bacteria and archaea, singleton sOTUs and samples with a total frequency of More