Philippa Kaur

1.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
ADS 

Google Scholar 
2.Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
3.Giovannoni, S. J. & Stingl, U. Molecular diversity and ecology of microbial plankton. Nature 437, 343–348 (2005).CAS 
PubMed 
ADS 

Google Scholar 
4.Kujawinski, E. B. The impact of microbial metabolism on marine dissolved organic matter. Ann. Rev. Mar. Sci. 3, 567–599 (2011).PubMed 

Google Scholar 
5.Moran, M. A. The global ocean microbiome. Science 350, aac8455 (2015).PubMed 

Google Scholar 
6.Pedrós-Alió, C. The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 15.1-15.18 (2012).
Google Scholar 
7.Salazar, G. et al. Global diversity and biogeography of deep-sea pelagic prokaryotes. ISME J. 10, 596–608 (2015).PubMed 
PubMed Central 

Google Scholar 
8.Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored ‘rare biosphere’. Proc. Natl. Acad. Sci. 103, 12115–12120 (2006).CAS 
PubMed 
PubMed Central 
ADS 

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

Google Scholar 
10.Campbell, B. J., Yu, L., Heidelberg, J. F. & Kirchman, D. L. Activity of abundant and rare bacteria in a coastal ocean. Proc. Natl. Acad. Sci. 108, 12776–12781 (2011).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
11.Salter, I. et al. Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea. ISME J. 9, 347–360 (2015).CAS 
PubMed 

Google Scholar 
12.Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).PubMed 
PubMed Central 

Google Scholar 
13.Våge, S., Storesund, J. E. & Thingstad, T. F. Adding a cost of resistance description extends the ability of virus-host model to explain observed patterns in structure and function of pelagic microbial communities. Environ. Microbiol. 15, 1842–1852 (2012).
Google Scholar 
14.Våge, S., Storesund, J. E. & Thingstad, T. F. SAR11 viruses and defensive host strains. Nature 499, 9–11 (2013).
Google Scholar 
15.Giovannoni, S., Temperton, B. & Zhao, Y. Giovannoni et al. reply. Nature 499, 9–11 (2013).
Google Scholar 
16.Zhao, Y. et al. Abundant SAR11 viruses in the ocean. Nature 494, 357–360 (2013).CAS 
PubMed 
ADS 

Google Scholar 
17.Herndl, G. J. et al. Contribution of Archaea to total prokaryotic production in the deep Atlantic Ocean. Appl. Environ. Microbiol. 71, 2303–2309 (2005).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
18.Teira, E., Lebaron, P., Van Aken, H. & Herndl, G. J. Distribution and activity of Bacteria and Archaea in the deep water masses of the North Atlantic. Limnol. Oceanogr. 51, 2131–2144 (2006).CAS 
ADS 

Google Scholar 
19.Newton, R. J. & Shade, A. Lifestyles of rarity: Understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat. Microb. Ecol. 78, 51–63 (2016).
Google Scholar 
20.Hamasaki, K., Taniguchi, A., Tada, Y., Long, R. A. & Azam, F. Actively growing bacteria in the Inland Sea of Japan, identified by combined bromodeoxyuridine immunocapture and denaturing gradient gel electrophoresis. Appl. Environ. Microbiol. 73, 2787–2798 (2007).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
21.Tada, Y., Makabe, R., Kasamatsu-Takazawa, N., Taniguchi, A. & Hamasaki, K. Growth and distribution patterns of Roseobacter/Rhodobacter, SAR11, and Bacteroidetes lineages in the Southern Ocean. Polar Biol. 36, 691–704 (2013).
Google Scholar 
22.Suttle, C. A. Marine viruses—Major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).CAS 
PubMed 

Google Scholar 
23.Pernthaler, J. Predation on prokaryotes in the water column and its ecological implications. Nat. Rev. Microbiol. 3, 537–546 (2005).CAS 
PubMed 

Google Scholar 
24.Mena, C. et al. Seasonal niche partitioning of surface temperate open ocean prokaryotic communities. Front. Microbiol. 11, 1749 (2020).PubMed 
PubMed Central 

Google Scholar 
25.Mena, C. et al. Dynamic prokaryotic communities in the dark western Mediterranean Sea. Sci. Rep. 11, 17859 (2021).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
26.Urbach, E., Vergin, K. L. & Giovannoni, S. J. Immunochemical detection and isolation of DNA from metabolically active bacteria. Appl. Environ. Microbiol. 65, 1207–1213 (1999).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
27.Hatzenpichler, R. et al. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ. Microbiol. 16, 2568–2590 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Emerson, J. et al. Schrödinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems. Micobiome 5, 86 (2017).
Google Scholar 
29.Smriga, S., Samo, T., Malfatti, F., Villareal, J. & Azam, F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat. Microb. Ecol. 72, 269–280 (2014).
Google Scholar 
30.Reichart, N. et al. Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. ISME J. 14, 2851–2861 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Bakenhus, I. et al. Composition of total and cell-proliferating bacterioplankton community in early summer in the North Sea—Roseobacters are the most active component. Front. Microbiol. 8, 1–14 (2017).
Google Scholar 
32.Morris, R. M. et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810 (2002).CAS 
PubMed 
ADS 

Google Scholar 
33.Giovannoni, S. J. SAR11 Bacteria: The most abundant plankton in the oceans. Ann. Rev. Mar. Sci. 9, 231–255 (2017).PubMed 

Google Scholar 
34.Clifford, E. L. et al. Taurine is a major carbon and energy source for marine prokaryotes in the North Atlantic Ocean off the Iberian Peninsula. Microb. Ecol. 78, 299–312 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Tripp, H. J. et al. SAR11 marine bacteria require exogenous reduced sulphur for growth. Nature 452, 741–744 (2008).CAS 
PubMed 
ADS 

Google Scholar 
36.Carlson, C. A. et al. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 3, 283–295 (2009).CAS 
PubMed 

Google Scholar 
37.Winter, C., Bouvier, T., Weinbauer, M. G. & Thingstad, T. F. Trade-offs between competition and defense specialists among unicellular planktonic organisms: The ‘Killing the Winner’ hypothesis revisited. Microbiol. Mol. Biol. Rev. 74, 42–57 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Vergin, K. L. et al. High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series Study site by phylogenetic placement of pyrosequences. ISME J. 7, 1322–1332 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Hugoni, M. et al. Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters. Proc. Natl. Acad. Sci. 110, 1–6 (2013).
Google Scholar 
40.Qin, W. et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc. Natl. Acad. Sci. 111, 12504–12509 (2014).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
41.Pollard, P. C. & Moriarty, D. J. W. Validity of the tritiated thymidine method for estimating bacterial growth rates: Measurement of isotope dilution during DNA synthesis. Appl. Environ. Microbiol. 48, 1076–1083 (1984).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
42.Wellsbury, P., Herbert, R. A. & John Parkes, R. Incorporation of [methyl-3H]thymidine by obligate and facultative anaerobic bacteria when grown under defined culture conditions. FEMS Microbiol. Ecol. 12, 87–95 (1993).CAS 

Google Scholar 
43.Clausen, A., Matakos, A., Sandrini, M. & Piskur, J. Thymidine kinases in Archaea. Nucleosides Nucleotides Nucleic Acids 25, 1159–1163 (2006).CAS 
PubMed 

Google Scholar 
44.Hamasaki, K., Long, R. A. & Azam, F. Individual cell growth rates of marine bacteria, measured by bromodeoxyuridine incorporation. Aquat. Microb. Ecol. 35, 217–227 (2004).
Google Scholar 
45.Qin, W. et al. Influence of oxygen availability on the activities of ammonia-oxidizing Archaea. Environ. Microbiol. Rep. 9, 250–256 (2017).CAS 
PubMed 

Google Scholar 
46.Reji, L., Tolar, B. B., Smith, J. M., Chavez, F. P. & Francis, C. A. Differential co-occurrence relationships shaping ecotype diversification within Thaumarchaeota populations in the coastal ocean water column. ISME J. 13, 1144–1158 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Sebastián, M. et al. Deep ocean prokaryotic communities are remarkably malleable when facing long-term starvation. Environ. Microbiol. 20, 713–723 (2018).PubMed 

Google Scholar 
48.Vergin, K. L., Done, B., Carlson, C. A. & Giovannoni, S. J. Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean. Aquat. Microb. Ecol. 71, 1–13 (2013).
Google Scholar 
49.Tada, Y., Taniguchi, A., Sato-Takabe, Y. & Hamasaki, K. Growth and succession patterns of major phylogenetic groups of marine bacteria during a mesocosm diatom bloom. J. Oceanogr. 68, 509–519 (2012).
Google Scholar 
50.Mestre, M. et al. Sinking particles promote vertical connectivity in the ocean microbiome. Proc. Natl. Acad. Sci. 115, E6799–E6807 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Ruiz-González, C. et al. Major imprint of surface plankton on deep ocean prokaryotic structure and activity. Mol. Ecol. 29, 1820–1838 (2020).PubMed 

Google Scholar 
52.Chen, X., Ma, R., Yang, Y., Jiao, N. & Zhang, R. Viral regulation on bacterial community impacted by lysis-lysogeny switch: A microcosm experiment in eutrophic coastal waters. Front. Microbiol. 10, 1763 (2019).PubMed 
PubMed Central 

Google Scholar 
53.McCarren, J. et al. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc. Natl. Acad. Sci. 107, 16420–16427 (2010).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
54.Reintjes, G., Arnosti, C., Fuchs, B. & Amann, R. Selfish, sharing and scavenging bacteria in the Atlantic Ocean: A biogeographical study of bacterial substrate utilisation. ISME J. 13, 1119–1132 (2019).CAS 
PubMed 

Google Scholar 
55.Middelboe, M. Bacterial growth rate and marine virus-host dynamics. Microb. Ecol. 40, 114–124 (2000).CAS 
PubMed 

Google Scholar 
56.Buchan, A., Lecleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: Features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 

Google Scholar 
57.Mou, X. et al. Bromodeoxyuridine labelling and fluorescence-activated cell sorting of polyamine-transforming bacterioplankton in coastal seawater. Environ. Microbiol. 17, 876–888 (2014).PubMed 

Google Scholar 
58.Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 

Google Scholar 
59.Yilmaz, P., Yarza, P., Rapp, J. Z. & Glöckner, F. O. Expanding the world of marine bacterial and archaeal clades. Front. Microbiol. 6, 1524 (2016).PubMed 
PubMed Central 

Google Scholar 
60.Coe, A. et al. Survival of Prochlorococcus in extended darkness. Limnol. Oceanogr. 61, 1375–1388 (2016).ADS 

Google Scholar 
61.Cottrell, M. T. & Kirchman, D. L. Photoheterotrophic microbes in the arctic ocean in summer and winter. Appl. Environ. Microbiol. 75, 4958–4966 (2009).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
62.Zeder, M., Peter, S., Shabarova, T. & Pernthaler, J. A small population of planktonic Flavobacteria with disproportionally high growth during the spring phytoplankton bloom in a prealpine lake. Environ. Microbiol. 11, 2676–2686 (2009).PubMed 

Google Scholar 
63.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).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
64.Banning, E. C., Casciotti, K. L. & Kujawinski, E. B. Novel strains isolated from a coastal aquifer suggest a predatory role for flavobacteria. FEMS Microbiol. Ecol. 73, 254–270 (2010).CAS 
PubMed 

Google Scholar 
65.Uchimiya, M. et al. Coupled response of bacterial production to a wind-induced fall phytoplankton bloom and sediment resuspension in the chukchi sea shelf, Western Arctic Ocean. Front. Mar. Sci. 3, 1–12 (2016).
Google Scholar 
66.Ivancic, I. et al. Seasonal variations in extracellular enzymatic activity in marine snow-associated microbial communities and their impact on the surrounding water. FEMS Microbiol. Ecol. 94, fiy198 (2018).CAS 

Google Scholar 
67.Cram, J. A. et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 9, 563–580 (2015).PubMed 

Google Scholar 
68.Manca, B. et al. Physical and biochemical averaged vertical profiles in the Mediterranean regions: An important tool to trace the climatology of water masses and to validate incoming data from operational oceanography. J. Mar. Syst. 48, 83–116 (2004).
Google Scholar 
69.Puig, P. & Palanques, A. Temporal variability and composition of settling particle fluxes on the Barcelona continental margin (Northwestern Mediterranean). J. Mar. Res. 56, 639–654 (1998).
Google Scholar 
70.Buesseler, K. O. & Boyd, P. W. Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean. Limnol. Oceanogr. 54, 1210–1232 (2009).CAS 
ADS 

Google Scholar 
71.Alonso-González, I. J., Arístegui, J., Lee, C. & Calafat, A. Regional and temporal variability of sinking organic matter in the subtropical northeast Atlantic Ocean: A biomarker diagnosis. Biogeosciences 7, 2101–2115 (2010).ADS 

Google Scholar 
72.Hunt, D. E. et al. Relationship between abundance and specific activity of bacterioplankton in open ocean surface waters. Appl. Environ. Microbiol. 79, 177–184 (2013).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
73.Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220 (2013).CAS 
PubMed 

Google Scholar 
74.Alderkamp, A. C., Sintes, E. & Herndl, G. J. Abundance and activity of major groups of prokaryotic plankton in the coastal North Sea during spring and summer. Aquat. Microb. Ecol. 45, 237–246 (2006).
Google Scholar 
75.De Corte, D., Sintes, E., Yokokawa, T. & Herndl, G. J. Comparison between MICRO-CARD-FISH and 16S rRNA gene clone libraries to assess the active versus total bacterial community in the coastal Arctic. Environ. Microbiol. Rep. 5, 272–281 (2013).PubMed 

Google Scholar 
76.Bergauer, K. et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc. Natl. Acad. Sci. 115, E400–E408 (2018).CAS 
PubMed 

Google Scholar 
77.Georges, A. A., El-Swais, H., Craig, S. E., Li, W. K. W. & Walsh, D. A. Metaproteomic analysis of a winter to spring succession in coastal northwest Atlantic Ocean microbial plankton. ISME J. 8, 1301–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat. Commun. 10, 2770 (2019).PubMed 
PubMed Central 
ADS 

Google Scholar 
79.Wu, X. et al. Culturing of ‘unculturable’ subsurface microbes: Natural organic carbon source fuels the growth of diverse and distinct bacteria from groundwater. Front. Microbiol. 11, 610001 (2020).PubMed 
PubMed Central 

Google Scholar 
80.Alonso-Sáez, L., Díaz-Pérez, L. & Morán, X. A. G. The hidden seasonality of the rare biosphere in coastal marine bacterioplankton. Environ. Microbiol. 17, 3766–3780 (2015).PubMed 

Google Scholar 
81.Liu, J., Meng, Z., Liu, X. & Zhang, X.-H. Microbial assembly, interaction, functioning, activity and diversification: A review derived from community compositional data. Mar. Life Sci. Technol. 1, 112–128 (2019).ADS 

Google Scholar 
82.Long, R. A. & Azam, F. Antagonistic interactions among marine pelagic bacteria. Appl. Environ. Microbiol. 67, 4975–4983 (2001).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
83.López-Jurado, J. L. et al. The RADMED monitoring programme as a tool for MSFD implementation: Towards an ecosystem-based approach. Ocean Sci. 11, 897–908 (2015).ADS 

Google Scholar 
84.Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis (Fisheries Research Board of Canada, 1968).
Google Scholar 
85.Grasshoff, K., Ehrhardt, M. & Kremling, K. Methods of seawater analysis (Verlag Chemie GmbH, 1983). https://doi.org/10.1002/iroh.19850700232.Book 

Google Scholar 
86.Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1962).CAS 

Google Scholar 
87.Brussaard, C. P. D. Optimization of procedures for counting viruses by flow cytometry. Appl. Environ. Microbiol. 70, 1506–1513 (2004).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
88.Gasol, J. M. & del Giorgio, P. A. Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Sci. Mar. 64, 197–224 (2000).
Google Scholar 
89.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
90.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Callahan, B. J. et al. Dada2: High-resolution sample inference from illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Katoh, K. & Standley, D. M. MAFFT Multiple sequence aligment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Eren, A. M. et al. Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013).PubMed Central 

Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.Maldonado, M. et al. in Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds. Rossi, S., Bramanti, L., Gori, A. & del Valle, C.) (Springer, 2016).2.de Goeij, J. M. et al. Surviving in a marine desert: the sponge loop retains resources within coral reefs. Science 342, 108–110 (2013).ADS 
PubMed 

Google Scholar 
3.Beazley, L., Kenchington, E., Yashayaev, I. & Murillo, F. J. Drivers of epibenthic megafaunal composition in the sponge grounds of the Sackville Spur, northwest. Atl. Deep. Res. Part I 98, 102–114 (2015).
Google Scholar 
4.Klitgaard, A. B. & Tendal, O. S. Progress in oceanography distribution and species composition of mass occurrences of large-sized sponges in the northeast Atlantic. Prog. Oceanogr. 61, 57–98 (2004).ADS 

Google Scholar 
5.Kazanidis, G. et al. Distribution of deep-sea sponge aggregations in an area of multisectoral activities and changing oceanic conditions. Front. Mar. Sci. 6, 163 (2019).
Google Scholar 
6.Hanz, U., Roberts, E. M., Duineveld, G., Davies, A. & Rapp, H. T. Long – term observations reveal environmental conditions and food supply mechanisms at an Arctic deep-sea sponge ground. J. Geophisical. Res. 126, 1–18 (2021).
Google Scholar 
7.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).ADS 

Google Scholar 
8.Cathalot, C. et al. Cold-water coral reefs and adjacent sponge grounds: hotspots of benthic respiration and organic carbon cycling in the deep sea. Front. Mar. Sci. 2, 37 (2015).
Google Scholar 
9.Kahn, A. S., Yahel, G., Chu, J. W. F., Tunnicliffe, V. & Leys, S. P. Benthic grazing and carbon sequestration by deep-water glass sponge reefs. Limnol. Oceanogr. 60, 78–88 (2015).ADS 

Google Scholar 
10.Morganti, T., Coma, R., Yahel, G. & Ribes, M. Trophic niche separation that facilitates co-existence of high and low microbial abundance sponges is revealed by in situ study of carbon and nitrogen fluxes. Limnol. Oceanogr. 62, 1963–1983 (2017).ADS 
CAS 

Google Scholar 
11.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).ADS 

Google Scholar 
12.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. 9999, 1–14 (2020).
Google Scholar 
13.Gloeckner, V. et al. The HMA-LMA dichotomy revisited: an electron microscopical survey of 56 sponge species. Biol. Bull. 227, 78–88 (2014).PubMed 

Google Scholar 
14.Bruck, T. B., Self, W. T., Reed, J. K., Nitecki, S. S. & McCarthy, P. J. Comparison of the anaerobic microbiota of deep-water Geodia spp. and sandy sediments in the Straits of Florida. ISME J. 4, 686–699 (2010).PubMed 

Google Scholar 
15.Schottner, S. et al. Relationships between host phylogeny, host type and bacterial community diversity in cold-water coral reef sponges. PLoS ONE 8, 1–11 (2013).
Google Scholar 
16.Hoffmann, F. et al. An anaerobic world in sponges. Geomicrobiol. J. 22, 1–10 (2005).
Google Scholar 
17.Schlindwein, V. & Schmid, F. Mid-ocean-ridge seismicity reveals extreme types of ocean lithosphere. Nature 535, 276–279 (2016).ADS 
CAS 
PubMed 

Google Scholar 
18.Cochran, J. R. Seamount volcanism along the Gakkel Ridge. Arct. Ocean. Geophys. J. Int. 174, 1153–1173 (2008).ADS 

Google Scholar 
19.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).ADS 

Google Scholar 
20.Wassmann, 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).
Google Scholar 
21.Wiedmann, I. et al. What feeds the Benthos in the Arctic Basins? Assembling a carbon budget for the deep Arctic Ocean. Front. Mar. Sci. 7, 224 (2020).
Google Scholar 
22.Boetius, A. & Purser, A. The Expedition PS101 of the Research Vessel POLARSTERN to the Arctic Ocean in 2016, Berichte zur Polar- und Meeresforschung = Reports on polar and marine research, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research. (2017).23.Alvizu, A., Xavier, J. R. & Rapp, H. T. Description of new chiactine-bearing sponges provides insights into the higher classification of Calcaronea (Porifera: Calcarea). Zootaxa 4615, 201–251 (2019).
Google Scholar 
24.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, 1–27 (2019).
Google Scholar 
25.Astrom, E. K. L. et al. Methane cold seeps as biological oases in the high-Arctic deep sea. Limnol. Oceanogr. 63, 209–231 (2018).
Google Scholar 
26.Sen, A., Didriksen, A., Hourdez, S., Svenning, M. M. & Rasmussen, T. L. Frenulate siboglinids at high Arctic methane seeps and insight into high latitude frenulate distribution. Ecol. Evol. 10, 1339–1351 (2020).PubMed 
PubMed Central 

Google Scholar 
27.Henrich, R. et al. Facies belts and communities of the arctic Vesterisbanken Seamount (Central Greenland Sea). Facies 27, 71 (1992).
Google Scholar 
28.Leys, S. P., Kahn, A. S., Fang, J. K. H., Kutti, T. & Bannister, R. J. Phagocytosis of microbial symbionts balances the carbon and nitrogen budget for the deep-water boreal sponge Geodia barretti. Limnol. Oceanogr. 63, 187–202 (2018).ADS 
CAS 

Google Scholar 
29.Druffel, E. R. M., Griffin, S., Glynn, C. S., Benner, R. & Walker, B. D. Radiocarbon in dissolved organic and inorganic carbon of the Arctic Ocean. Geophys. Res. Lett. 44, 2369–2376 (2017).ADS 
CAS 

Google Scholar 
30.Mehrshad, M., Rodriguez-Valera, F., Amoozegar, M. A., López-García, P. & Ghai, R. The enigmatic SAR202 cluster up close: shedding light on a globally distributed dark ocean lineage involved in sulfur cycling. ISME J. 12, 655–668 (2018).CAS 
PubMed 

Google Scholar 
31.Petersen, J. M., Wentrup, C., Verna, C., Knittel, K. & Dubilier, N. Origins and evolutionary flexibility of chemosynthetic symbionts from deep-sea animals. Biol. Bull. 223, 123–137 (2012).CAS 
PubMed 

Google Scholar 
32.Rubin-Blum, M. et al. Fueled by methane: deep-sea sponges from asphalt seeps gain their nutrition from methane-oxidizing symbionts. ISME J. 13, 1209–1225 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Bayer, K., Jahn, M. T., Slaby, B. M., Moitinho-Silva, L. & Hentschel, U. Marine sponges as chloroflexi hot spots: genomic insights and high-resolution visualization of an abundant and diverse symbiotic clade. mSystems 3, e00150–18 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Kamke, J. et al. Single-cell genomics reveals complex carbohydrate degradation patterns in poribacterial symbionts of marine sponges. ISME J. 7, 2287–2300 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Bayer, K. et al. Microbial strategies for survival in the glass sponge Vazella pourtalesii. mSystems 5, e00473–20 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Van Duyl, F. C., Hegeman, J., Hoogstraten, A. & Maier, C. Dissolved carbon fixation by sponge-microbe consortia of deep water coral mounds in the northeastern Atlantic Ocean. Mar. Ecol. Prog. Ser. 358, 137–150 (2008).ADS 

Google Scholar 
37.Leitner, A. B., Neuheimer, A. B. & Drazen, J. C. Evidence for long-term seamount-induced chlorophyll enhancements. Sci. Rep. 10, 12729 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
38.von Appen, W.-J., Latarius, K. & Kanzow, T. Physical oceanography and current meter data from mooring F6-17. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven (2017). https://doi.org/10.1594/PANGAEA.870845.39.Woodgate, R. Arctic ocean circulation: going around at the top of the world. Nat. Educ. Knowl. 4, 8 (2013).
Google Scholar 
40.White, M., Bashmachnikov, I., Arístegui, J. & Martins, A. in Seamounts: Ecology, Fisheries & Conservation (eds Pitcher, T. J. et al.) Ch. 4 (Wiley, 2007).41.Buchs, D. M., Hoernle, K. & Grevemeyer, I. In Encyclopedia of Marine Geosciences (eds Harff, J., Meschede, M., Petersen, S. & Thiede, J.) (Springer, Dordrecht, 2015). https://doi.org/10.1007/978-94-007-6644-0_34-2.42.Emerson, D. & Moyer, C. Microbiology of seamounts: common patterns observed in community structure. Oceanography 23, 148–163 (2010).
Google Scholar 
43.Rimskaya-Korsakova, N. N. et al. First discovery of pogonophora (Annelida, Siboglinidae) in the Kara Sea coincide with the area of high methane concentration. Dokl. Biol. Sci. 490, 25–27 (2020).CAS 
PubMed 

Google Scholar 
44.Cardenas, 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. Kindom 95, 1475–1516 (2015).
Google Scholar 
45.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. Res. Part I Oceanogr. Res. Pap. 153, 103137 (2019).
Google Scholar 
46.Grebmeier, J. M. et al. Ecosystem characteristics and processes facilitating persistent macrobenthic biomass hotspots and associated benthivory in the Pacific Arctic. Prog. Oceanogr. 136, 92–114 (2015).ADS 

Google Scholar 
47.Oevelen, D. Van et al. The cold-water coral community as a hot spot for carbon cycling on continental margins: a food-web analysis from Rockall Bank (northeast Atlantic). Limnol. Oceanogr. 54, 1829–1844 (2009).ADS 

Google Scholar 
48.Hammel, J. U., Herzen, J., Beckmann, F. & Nickel, M. Sponge budding is a spatiotemporal morphological patterning process: insights from synchrotron radiation-based x-ray microtomography into the asexual reproduction of Tethya wilhelma. Front. Zool. 6, 19 (2009).PubMed 
PubMed Central 

Google Scholar 
49.Witte, U. & Graf, G. Metabolism of deep-sea sponges in the Greenland- Norwegian Sea. Mar. Biol. 198, 223–235 (1996).
Google Scholar 
50.Rovelli, L. et al. Benthic O2 uptake of two cold-water coral communities estimated with the non-invasive eddy correlation technique. Mar. Ecol. Prog. Ser. 525, 97–104 (2015).ADS 

Google Scholar 
51.De Clippele, L. H. et al. Mapping cold-water coral biomass: an approach to derive ecosystem functions. Coral Reefs 40, 215–231 (2021).
Google Scholar 
52.de Kluijver, A. et al. An integrative model of carbon and nitrogen metabolism in a common deep-sea sponge (Geodia barretti). Front. Mar. Sci. 7, 1–18 (2021).
Google Scholar 
53.Lalande, C., Nothig, E.-M. & Fortier, L. Algal export in the Arctic ocean in times of global warming. Geophys. Res. Lett. 46, 1–9 (2019).
Google Scholar 
54.Boetius, A. et al. Export of algal biomass from the melting Arctic sea ice. Science 339, 1430–1433 (2013).55.Maier, S. R. et al. Survival under conditions of variable food availability: Resource utilization and storage in the cold-water coral Lophelia pertusa. Limnol. Oceanogr. 64, 1651–1671 (2019).ADS 
CAS 

Google Scholar 
56.Rix, L. et al. Heterotrophy in the earliest gut: a single-cell view of heterotrophic carbon and nitrogen assimilation in sponge-microbe symbioses. ISME J. 14, 2554–2567 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Hansell, D. A. Recalcitrant dissolved organic carbon fractions. Ann. Rev. Mar. Sci. 5, 421–445 (2013).PubMed 

Google Scholar 
58.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).
Google Scholar 
59.Anderson, L. G. & Amon, R. M. W. DOM in the Arctic Ocean. In Biogeochemistry of Marine Dissolved Organic Matter (eds Hansell, D. A. & Carlson, C. A.) Ch. 14 (Academic Press, 2015).60.Rossel, P. E., Bienhold, C., Boetius, A. & Dittmar, T. Dissolved organic matter in pore water of Arctic Ocean sediments: environmental influence on molecular composition. Org. Geochem. 97, 41–52 (2016).CAS 

Google Scholar 
61.Landry, Z., Swan, B. K., Herndl, G. J., Stepanauskas, R. & Giovannoni, S. J. SAR202 genomes from the dark ocean predict pathways for the oxidation of recalcitrant dissolved organic matter. MBio 8, e00413–e00417 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Radax, R. et al. Metatranscriptomics of the marine sponge Geodia barretti: tackling phylogeny and function of its microbial community. Environ. Microbiol. 14, 1308–1324 (2012).63.Busch, K. et al. Chloroflexi dominate the deep-sea golf ball sponges Craniella zetlandica and Craniella infrequens throughout different life stages. Front. Mar. Sci. 7, 1–13 (2020).
Google Scholar 
64.Raimundo, I. et al. Functional metagenomics reveals differential chitin degradation and utilization features across free-living and host-associated marine microbiomes. Microbiome 9, 1–18 (2021).
Google Scholar 
65.Hoffmann, F. et al. Complex nitrogen cycling in the sponge Geodia barretti. Environ. Microbiol. 11, 2228–2243 (2009).CAS 
PubMed 

Google Scholar 
66.Radax, R., Hoffmann, F., Rapp, H. T., Leininger, S. & Schleper, C. Ammonia-oxidizing archaea as main drivers of nitrification in cold-water sponges. Environ. Microbiol. 14, 909–923 (2012).CAS 
PubMed 

Google Scholar 
67.Kahn, A. S., Chu, J. W. F. & Leys, S. P. Trophic ecology of glass sponge reefs in the Strait of Georgia, British Columbia. Sci. Rep. 8, 756 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
68.Thiel, V. et al. Mid-chain branched alkanoic acids from “living fossil” demosponges: a link to ancient sedimentary lipids? Org. Geochem. 30, 1–14 (1999).CAS 

Google Scholar 
69.de Kluijver, A. et al. Bacterial precursors and unsaturated long-chain fatty acids are biomarkers of North-Atlantic deep-sea demosponges. PLoS ONE 16, 1–18 (2021).
Google Scholar 
70.Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5, e9672 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
71.Freeman, C. J. et al. Microbial symbionts and ecological divergence of Caribbean sponges: a new perspective on an ancient association. ISME J. 14, 1571–1583 (2020).PubMed 
PubMed Central 

Google Scholar 
72.Middelburg, J. J. Stable isotopes dissect aquatic food webs from the top to the bottom. Biogeosciences 11, 2357–2371 (2014).ADS 

Google Scholar 
73.Åström, E. et al. Chemosynthesis influences food web and community structure in high-Arctic benthos. Mar. Ecol. Prog. Ser. 629, 19–42 (2019).ADS 

Google Scholar 
74.Ravaux, J. et al. Comparative degradation rates of chitinous exoskeletons from deep-sea environments. Mar. Biol. 143, 405–412 (2003).CAS 

Google Scholar 
75.Gooday, G. W. The Ecology of Chitin Degradation. In Advances in Microbial Ecology, (ed. Marshall, K. C.) vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-7612-5_10.76.Schwarz, J. R., Yayanos, A. A. & Colwell, R. R. Metabolic activities of the intestinal microflora of a deep-sea invertebrate. Appl. Environ. Microbiol. 31, 46 LP–46 48 (1976).ADS 

Google Scholar 
77.Godefroy, N. et al. Sponge digestive system diversity and evolution: filter feeding to carnivory. Cell Tissue Res. 377, 341–351 (2019).PubMed 

Google Scholar 
78.Ehrlich, H. et al. First evidence of chitin as a component of the skeletal fibers of marine sponges. Part I. Verongidae (demospongia: Porifera). J. Exp. Zool. Part B Mol. Dev. Evol. 308B, 347–356 (2007).CAS 

Google Scholar 
79.Bowden, D. A. et al. Cold seep epifaunal communities on the Hikurangi Margin, New Zealand: composition, succession, and vulnerability to human activities. PLoS ONE 8, e76869 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Georgieva, M. N. et al. Identification of fossil worm tubes from Phanerozoic hydrothermal vents and cold seeps. J. Syst. Palaeontol. 17, 287–329 (2017).
Google Scholar 
81.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).CAS 
PubMed 

Google Scholar 
82.Maldonado, M. An experimental approach to the ecological significance of microhabitat-scale movement in an encrusting sponge. Mar. Ecol. Prog. Ser. 185, 239–255 (1999).ADS 

Google Scholar 
83.Rice, A. L., Thurston, M. H. & New, A. L. Dense aggregations of a hexactinellid sponge, Pheronema carpenteri, in the Porcupine Seabight (northeast Atlantic Ocean), and possible causes. Prog. Oceanogr. 24, 179–196 (1990).ADS 

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

Google Scholar 
85.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, 1–13 (2019).
Google Scholar 
86.Marcon, Y. & Purser, A. PAPARA(ZZ)I: an open-source software interface for annotating photographs of the deep-sea. SoftwareX 6, 69–80 (2017).ADS 

Google Scholar 
87.Morganti, T. M., Ribes, M., Yahel, G. & Coma, R. Size is the major determinant of pumping rates in marine sponges. Front. Physiol. 10, 1474 (2019).PubMed 
PubMed Central 

Google Scholar 
88.Zelles, L. Phospholipid fatty acid profiles in selected members of soil microbial communities. Chemosphere 35, 275–294 (1997).ADS 
CAS 
PubMed 

Google Scholar 
89.Volkman, J. K., Jeffrey, S. W., Nichols, P. D., Rogers, G. I. & Garland, C. D. Fatty acid and lipid composition of 10 species of microalgae used in mariculture. J. Exp. Mar. Bio. Ecol. 128, 219–240 (1989).CAS 

Google Scholar 
90.Koopmans, M. et al. Seasonal variation of fatty acids and stable carbon isotopes in sponges as indicators for nutrition: biomarkers in sponges identified. Mar. Biotechnol. 17, 43–54 (2015).CAS 

Google Scholar 
91.Mollenhauer, G., Grotheer, H., Gentz, T., Bonk, E. & Hefter, J. Standard operation procedures and performance of the MICADAS radiocarbon laboratory at Alfred Wegener Institute (AWI). Ger. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. At. 496, 45–51 (2021).ADS 
CAS 

Google Scholar 
92.Fallon, S. J., James, K., Norman, R., Kelly, M. & Ellwood, M. J. A simple radiocarbon dating method for determining the age and growth rate of deep-sea sponges. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. At. 268, 1241–1243 (2010).ADS 
CAS 

Google Scholar 
93.Griffith, D. R. et al. Carbon dynamics in the western Arctic Ocean: insights from full-depth carbon isotope profiles of DIC, DOC, and POC. Biogeosciences 9, 1217–1224 (2012).ADS 
CAS 

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

Google Scholar 
95.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
PubMed Central 

Google Scholar 
96.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
97.Li, D. et al. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).CAS 
PubMed 

Google Scholar 
98.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
99.Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
100.Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
101.Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).CAS 
PubMed 

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

Google Scholar 
103.De Anda, V. et al. MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle. Gigascience 6, 1–17 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
104.Benner, R., Benitez-Nelson, B., Kaiser, K. & Amon, R. M. W. Export of young terrigenous dissolved organic carbon from rivers to the Arctic Ocean. Geophys. Res. Lett. 31, 10–13 (2004).
Google Scholar 
105.Thibodeau, B., Bauch, D. & Voss, M. Nitrogen dynamic in Eurasian coastal Arctic ecosystem: Insight from nitrogen isotope. Glob. Biogeochem. Cycles 31, 836–849 (2017).ADS 
CAS 

Google Scholar 
106.Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER – Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011). More

Timeseries imaging tracks gene expression in spatial systemsRecent studies have shown it possible to identify the members of microbial consortia as well as their gene expression within spatially-structured systems30,33,34. However, these methods capture data cross-sectionally and are unable to provide temporal insight into gene expression patterning as it emerges in these cell populations. To bridge this gap, we built a fluorescent imager inside an incubator (Supplementary Fig. 1). Our framework characterizes cellular growth and gene expression in spatially-structured environments with previously unattainable time-resolution and throughput. Fluorescently labeled cells are illuminated using LEDs connected to a custom-built control system (see methods). The images are background corrected and analyzed, tracking colony growth and gene expression information (Supplementary Figs. 2, 3) straight from the spatially-structured system.In our experiments, we utilized a dual-labeled P. aeruginosa PA14 strain harboring PBad-DsRed(EC2)35 driven by L-arabinose in the plate media, which cannot be metabolized by the cells36, and PrhlAB-GFP28,37. When grown in spatial structure, the constitutive expression of DsRed provided a measure of the local density of bacteria (Supplementary Fig. 4). In all our experiments, the dynamical expression of GFP, validated by RT-qPCR (Supplementary Fig. 5) (see methods), reported on the expression of rhlAB.Using these data, we were able to characterize how the surroundings experienced by these microbes influence the dynamics of their cooperative behavior directly in a spatially-structured setting.Rhamnolipid production differs in liquid and spatial environmentsRhamnolipids are necessary for cooperative swarming behavior in P. aeruginosa and for other traits related to virulence26. Rhamnolipids can be produced in liquid culture10,20,28,38, thus rhamnolipid production is often studied in detail there. Despite recent work indicating that gene expression related to quorum signaling systems in P. aeruginosa may differ in spatial structure29, no studies assess how downstream genes, such as rhlAB, may be affected in spatially-structured colonies. Given the relevance of these diffusible inputs to the rhlAB system, we hypothesized that there could be differences between gene expression patterns in liquid and spatial environments.We compared P. aeruginosa biomass growth and gene expression in the liquid and spatial environments (Fig. 1a). Liquid culture data was collected following prior methods28. To interrogate the spatial system, we used the protocol from the classic Colony Forming Unit (CFU) assay. Cells were seeded with extreme dilution and we observed the behavior of the resultant colonies (cCFUs) across time and within the random configurations generated.Fig. 1: Rhamnolipid production differs between liquid culture and surface-attached P. aeruginosa.a Cartoon depictions of liquid and spatially-structured environments used in this study. b Optical density timeseries describing P. aeruginosa growth in liquid culture. [Blue] Biomass growth without exogenous quorum signals. [Purple] Biomass growth with exogenous quorum signals. c DsRed fluorescent timeseries generated from a custom-built imager (Supplementary Fig. 1) and custom software (Supplementary Fig. 3) describing P. aeruginosa growth in colony forming units (CFU). [Blue] Biomass growth without exogenous quorum signals [Purple] Biomass growth with exogenous quorum signals added to the plate media. [Inset] Example plate showing colonies at 48 h. Scale bar 1 cm. d Promoter activity (left[frac{{dGFP}}{{dt}}cdot frac{1}{{{OD}}_{600}}right]) of PrhlAB with respect to culture growth rate (left[frac{d{{OD}}_{600}}{{dt}}cdot frac{1}{{{OD}}_{600}}right]). [Blue] without exogenous quorum signals [Purple] with exogenous quorum signals. e Promoter activity (left[frac{{dGFP}}{{dt}}cdot frac{1}{{DsRed}}right]) of PrhlAB with respect to CFU growth rate (left[frac{{dDsRed}}{{dt}}cdot frac{1}{{DsRed}}right]). [Blue] without exogenous quorum signals [Purple] with exogenous quorum signals provided in the plate media.Full size imageWe observed differences in growth between cells grown in liquid culture (Fig. 1b) and spatial structure (Fig. 1c) with the same media composition. The growth pattern observed in liquid culture recapitulates previously reported data22,28. In comparing WT growth (dark blue data in Fig. 1b, c) between environments, we observed that both achieve a period of exponential growth, followed by a period of slowed growth. This sub-exponential growth is prolonged and no period of biomass decay is observed in the spatially-structured environment during our observation window.Quorum signal perturbation has long been an experimental tool to determine if a phenotype is responsive to social signaling9,10. rhlAB gene expression in particular is known to be downstream of both the las and rhl quorum signal systems39,40. However, it has previously been shown that liquid culture perturbation with additional C4-HSL and 3-oxo-C12-HSL, the rhl and las quorum signal system auto-inducers respectively, do not illicit significant change in growth or PrhlAB dynamics in this strain of P. aeruginosa22. We replicated this liquid culture result (Fig. 1b, purple data). In the spatially-structured system, we performed this perturbation by including both quorum signal molecules in the plate media in the same concentration by volume as previously published22. This analysis was done using biological replicates with More

1.Angel, M. V. Biodiversity of the Pelagic Ocean. Conserv. Biol. 7, 760–772 (1993).Article 

Google Scholar 
2.Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. https://doi.org/10.1038/ncomms4271 (2014).Article 
PubMed 

Google Scholar 
3.Hays, G. C. A review of the adaptive significance and ecosystem consequences of zooplankton diel vertical migrations. Hydrobiology 503, 163–170. https://doi.org/10.1023/B:HYDR.0000008476.23617.b0 (2003).Article 

Google Scholar 
4.Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873. https://doi.org/10.1038/srep19873 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
5.Hammerschlag, N., Gallagher, A. J. & Lazarre, D. M. A review of shark satellite tagging studies. J. Exp. Mar. Biol. Ecol. 398, 1–8. https://doi.org/10.1016/j.jembe.2010.12.012 (2011).Article 

Google Scholar 
6.Dulvy, N. K. et al. You can swim but you can’t hide: The global status and conservation of oceanic pelagic sharks and rays. Aquat. Conserv. 18, 459–482 (2008).Article 

Google Scholar 
7.Pacoureau, N. et al. Half a century of global decline in oceanic sharks and rays. Nature 589, 567–571. https://doi.org/10.1038/s41586-020-03173-9 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
8.Compagno, L. J. V. Pelagic elasmobranch diversity. In Sharks of the Open Ocean, 14–23 (2008).9.Howey, L. A. et al. Into the deep: The functionality of mesopelagic excursions by an oceanic apex predator. Ecol. Evol. 6, 5290–5304. https://doi.org/10.1002/ece3.2260 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Francis, M. P. et al. Oceanic nomad or coastal resident? Behavioural switching in the shortfin mako shark (Isurus oxyrinchus). Mar. Biol. 166, 5. https://doi.org/10.1007/s00227-018-3453-5 (2018).Article 

Google Scholar 
11.Skomal, G. et al. Horizontal and vertical movement patterns and habitat use of juvenile porbeagles (Lamna nasus) in the western north Atlantic. Front. Mar. Sci. 8, 16 (2021).Article 

Google Scholar 
12.Gaube, P. et al. Mesoscale eddies influence the movements of mature female white sharks in the Gulf Stream and Sargasso Sea. Sci. Rep. 8, 7363. https://doi.org/10.1038/s41598-018-25565-8 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Coelho, R., Fernandez-Carvalho, J. & Santos, M. N. Habitat use and diel vertical migration of bigeye thresher shark: Overlap with pelagic longline fishing gear. Mar. Environ. Res. 112, 91–99. https://doi.org/10.1016/j.marenvres.2015.10.009 (2015).CAS 
Article 
PubMed 

Google Scholar 
14.Arostegui, M. C. et al. Vertical movements of a pelagic thresher shark (Alopias pelagicus): Insights into the species’ physiological limitations and trophic ecology in the Red Sea. Endanger. Species Res. 43, 387–394. https://doi.org/10.3354/esr01079 (2020).Article 

Google Scholar 
15.Coffey, D. M., Carlisle, A. B., Hazen, E. L. & Block, B. A. Oceanographic drivers of the vertical distribution of a highly migratory, endothermic shark. Sci. Rep. 7, 10434. https://doi.org/10.1038/s41598-017-11059-6 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Coffey, D. M., Royer, M. A., Meyer, C. G. & Holland, K. N. Diel patterns in swimming behavior of a vertically migrating deepwater shark, the bluntnose sixgill (Hexanchus griseus). PLoS One 15, e0228253. https://doi.org/10.1371/journal.pone.0228253 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Francis, M. P., Holdsworth, J. C. & Block, B. A. Life in the open ocean: Seasonal migration and diel diving behaviour of Southern Hemisphere porbeagle sharks (Lamna nasus). Mar. Biol. 162, 2305–2323. https://doi.org/10.1007/s00227-015-2756-z (2015).Article 

Google Scholar 
18.Jorgensen, S. J. et al. Eating or meeting? Cluster analysis reveals intricacies of white shark (Carcharodon carcharias) migration and offshore behavior. PLoS One 7, e47819. https://doi.org/10.1371/journal.pone.0047819 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Nelson, D. R. et al. An acoustic tracking of a megamouth shark, Megachasma pelagios: A crepuscular vertical migrator. Environ. Biol. Fish. 49, 389–399. https://doi.org/10.1023/A:1007369619576 (1997).Article 

Google Scholar 
20.Sims, D. W., Southall, E. J., Tarling, G. A. & Metcalfe, J. D. Habitat-specific normal and reverse diel vertical migration in the plankton-feeding basking shark. J. Anim. Ecol. 74, 755–761. https://doi.org/10.1111/j.1365-2656.2005.00971.x (2005).Article 

Google Scholar 
21.Watanabe, Y. Y. & Papastamatiou, Y. P. Distribution, body size and biology of the megamouth shark Megachasma pelagios. J. Fish Biol. 95, 992–998. https://doi.org/10.1111/jfb.14007 (2019).Article 
PubMed 

Google Scholar 
22.Braun, C. D., Skomal, G. B. & Thorrold, S. R. Integrating archival tag data and a high-resolution oceanographic model to estimate basking shark (Cetorhinus maximus) movements in the Western Atlantic. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00025 (2018).Article 

Google Scholar 
23.Jorgensen, S. J. et al. Philopatry and migration of Pacific white sharks. Proc. R. Soc. B 277, 679–688. https://doi.org/10.1098/rspb.2009.1155 (2010).Article 
PubMed 

Google Scholar 
24.Lipscombe, R. S. et al. Habitat use and movement patterns of tiger sharks (Galeocerdo cuvier) in eastern Australian waters. ICES J. Mar. Sci. 77, 3127–3137. https://doi.org/10.1093/icesjms/fsaa212 (2020).Article 

Google Scholar 
25.Walker, T. I. et al. Galeorhinus galeus. The IUCN Red List of Threatened Species 2020: e.T39352A2907336. https://doi.org/10.2305/IUCN.UK.2020-2.RLTS.T39352A2907336.en (2020). (Downloaded on 18 June 2021).26.Chabot, C. L. Microsatellite loci confirm a lack of population connectivity among globally distributed populations of the tope shark Galeorhinus galeus (Triakidae). J. Fish Biol. 87, 371–385. https://doi.org/10.1111/jfb.12727 (2015).CAS 
Article 
PubMed 

Google Scholar 
27.Bester-van der Merwe, A. E. et al. Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus): Intercontinental divergence and constrained gene flow at different geographical scales. PLoS One 12, e0184481. https://doi.org/10.1371/journal.pone.0184481 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Stevens, J. D. Further results from a tagging study of pelagic sharks in the north-east Atlantic. J. Mar. Biol. Assoc. UK 70, 707–720. https://doi.org/10.1017/S0025315400058999 (1990).Article 

Google Scholar 
29.West, G. J. & Stevens, J. D. Archival tagging of school shark, Galeorhinus galeus, in Australia: Initial results. Environ. Biol. Fish. 60, 283–298 (2001).Article 

Google Scholar 
30.Thorburn, J. et al. Ontogenetic variation in movements and depth use, and evidence of partial migration in a Benthopelagic Elasmobranch. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00353 (2019).Article 

Google Scholar 
31.McMillan, M. N., Huveneers, C., Semmens, J. M. & Gillanders, B. M. Partial female migration and cool-water migration pathways in an overfished shark. ICES J. Mar. Sci. 76, 1083–1093. https://doi.org/10.1093/icesjms/fsy181 (2019).Article 

Google Scholar 
32.Walker, T. Galeorhinus galeus fisheries of the World, in: Case studies of management of elasmobranch fisheries. FAO Fish. Tech. Pap. 378, 728–773 (1999).
Google Scholar 
33.Brown, L., Bridge, N. & Walker, T. Summary of tag releases and recaptures in the Southern Shark Fishery. Mar. Freshw. Resour. Inst. Rep. 16, 60 (2000).
Google Scholar 
34.Lucifora, L., Menni, R. & Escalante, A. Reproductive biology of the school shark, Galeorhinus galeus, off Argentina: Support for a single south western Atlantic population with synchronized migratory movements. Environ. Biol. Fish. 71, 199–209. https://doi.org/10.1007/s10641-004-0305-6 (2004).Article 

Google Scholar 
35.Jaureguizar, A. J., Argemi, F., Trobbiani, G., Palma, E. D. & Irigoyen, A. J. Large-scale migration of a school shark, Galeorhinus galeus, in the Southwestern Atlantic. Neotrop. Ichthyol. https://doi.org/10.1590/1982-0224-20170050 (2018).Article 

Google Scholar 
36.Nosal, A. P. et al. Triennial migration and philopatry in the critically endangered soupfin shark Galeorhinus galeus. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13848 (2021).Article 

Google Scholar 
37.Cuevas, J., Garcia, M. & Di Giacomo, E. Diving behaviour of the critically endangered tope shark Galeorhinus galeus in the Natural Reserve of Bahia San Blas, northern Patagonia. Anim. Biotelemetry 2, 11 (2014).Article 

Google Scholar 
38.Iosilevskii, G., Papastamatiou, Y. P., Meyer, C. G. & Holland, K. N. Energetics of the yo-yo dives of predatory sharks. J. Theor. Biol. 294, 172–181. https://doi.org/10.1016/j.jtbi.2011.11.008 (2012).ADS 
MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
39.Carey, F. G., Scharold, J. V. & Kalmijn, A. J. Movements of blue sharks (Prionace glauca) in depth and course. Mar. Biol. 106, 329–342. https://doi.org/10.1007/BF01344309 (1990).Article 

Google Scholar 
40.Nakamura, I., Watanabe, Y. Y., Papastamatiou, Y. P., Sato, K. & Meyer, C. G. Yo-yo vertical movements suggest a foraging strategy for tiger sharks Galeocerdo cuvier. Mar. Ecol. Prog. Ser. 424, 237–246 (2011).ADS 
Article 

Google Scholar 
41.Thorrold, S. R. et al. Extreme diving behaviour in devil rays links surface waters and the deep ocean. Nat. Commun. https://doi.org/10.1038/ncomms5274 (2014).Article 
PubMed 

Google Scholar 
42.Braun, C. D., Gaube, P., Sinclair-Taylor, T. H., Skomal, G. B. & Thorrold, S. R. Mesoscale eddies release pelagic sharks from thermal constraints to foraging in the ocean twilight zone. Proc. Nat. Acad. Sci. 116, 17187–17192. https://doi.org/10.1073/pnas.1903067116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Andrzejaczek, S., Gleiss, A. C., Pattiaratchi, C. B. & Meekan, M. G. Patterns and drivers of vertical movements of the large fishes of the epipelagic. Rev. Fish. Biol. Fish. 29, 335–354. https://doi.org/10.1007/s11160-019-09555-1 (2019).Article 

Google Scholar 
44.Papastamatiou, Y. P. et al. Drivers of daily routines in an ectothermic marine predator: Hunt warm, rest warmer?. PLoS One 10, e0127807. https://doi.org/10.1371/journal.pone.0127807 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Proud, R., Cox, M. J. & Brierley, A. S. Biogeography of the global ocean’s mesopelagic zone. Curr. Biol. 27, 113–119. https://doi.org/10.1016/j.cub.2016.11.003 (2017).CAS 
Article 
PubMed 

Google Scholar 
46.Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. I(126), 85–102. https://doi.org/10.1016/j.dsr.2017.05.006 (2017).Article 

Google Scholar 
47.Ariza, A. et al. Vertical distribution, composition and migratory patterns of acoustic scattering layers in the Canary Islands. J. Mar. Syst. 157, 82–91. https://doi.org/10.1016/j.jmarsys.2016.01.004 (2016).Article 

Google Scholar 
48.Peña, M., Cabrera-Gámez, J. & Domínguez-Brito, A. C. Multi-frequency and light-avoiding characteristics of deep acoustic layers in the North Atlantic. Mar. Environ. Res. 154, 104842. https://doi.org/10.1016/j.marenvres.2019.104842 (2020).CAS 
Article 
PubMed 

Google Scholar 
49.Peña, M. et al. Acoustic detection of mesopelagic fishes in scattering layers of the Balearic Sea (western Mediterranean). Can. J. Fish. Aquat. Sci. 71, 1186–1197. https://doi.org/10.1139/cjfas-2013-0331 (2014).CAS 
Article 

Google Scholar 
50.Menkes, C. E. et al. Seasonal oceanography from physics to micronekton in the south-west Pacific. Deep Sea Res. II(113), 125–144. https://doi.org/10.1016/j.dsr2.2014.10.026 (2015).Article 

Google Scholar 
51.Urmy, S. S. & Horne, J. K. Multi-scale responses of scattering layers to environmental variability in Monterey Bay, California. Deep Sea Res. I(113), 22–32. https://doi.org/10.1016/j.dsr.2016.04.004 (2016).Article 

Google Scholar 
52.Korneliussen, R. J. et al. Acoustic target classification. ICES Coop. Res. Rep. 344, 110. https://doi.org/10.17895/ices.pub.4567 (2018).Article 

Google Scholar 
53.D’Elia, M. et al. Diel variation in the vertical distribution of deep-water scattering layers in the Gulf of Mexico. Deep Sea Res. I(115), 91–102. https://doi.org/10.1016/j.dsr.2016.05.014 (2016).Article 

Google Scholar 
54.Scoulding, B., Chu, D., Ona, E. & Fernandes, P. G. Target strengths of two abundant mesopelagic fish species. J. Acoust. Soc. Am. 137, 989–1000. https://doi.org/10.1121/1.4906177 (2015).ADS 
Article 
PubMed 

Google Scholar 
55.Geoffroy, M. et al. Mesopelagic sound scattering layers of the high arctic: Seasonal variations in biomass, species assemblage, and trophic relationships. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00364 (2019).Article 

Google Scholar 
56.Shea, E. K. & Vecchione, M. Ontogenic changes in diel vertical migration patterns compared with known allometric changes in three mesopelagic squid species suggest an expanded definition of a paralarva. ICES J. Mar. Sci. 67, 1436–1443. https://doi.org/10.1093/icesjms/fsq104 (2010).Article 

Google Scholar 
57.Lucifora, L. O., Garcia, V. B., Menni, R. C. & Escalante, A. H. Food habits, selectivity, and foraging modes of the school shark Galeorhinus galeus. Mar. Ecol. Prog. Ser. 315, 259–270 (2006).ADS 
Article 

Google Scholar 
58.Morato, T., Sola, E., Gros, M. P. & Menezes, G. Diets of thornback ray (Raja clavata) and tope shark (Galeorhinus galeus) in the bottom longline fishery of the Azores, northeastern Atlantic. Fish. Bull. 101, 590–602 (2003).
Google Scholar 
59.Ellis, J. R., Pawson, M. G. & Shackley, S. E. The comparative feeding ecology of six species of shark and four species of ray (Elasmobranchii) in the North-East Atlantic. J. Mar. Biol. Assoc. UK. 76, 89–106. https://doi.org/10.1017/S0025315400029039 (1996).Article 

Google Scholar 
60.Clarke, M. R., Clarke, D. C., Martins, H. R. & Silva, H. M. The diet of blue shark (Prionace glauca) in Azorean waters, Arquipélago. Life Mar. Sci. 14A, 41–56 (1996).
Google Scholar 
61.Bond, M. E., Tolentino, E., Mangubhai, S. & Howey, L. A. Vertical and horizontal movements of a silvertip shark (Carcharhinus albimarginatus) in the Fijian archipelago. Anim. Biotelemetry 3, 19. https://doi.org/10.1186/s40317-015-0055-6 (2015).Article 

Google Scholar 
62.Saba, G. K. et al. Toward a better understanding of fish-based contribution to ocean carbon flux. Limnol. Oceanogr. 66, 1–26. https://doi.org/10.1002/lno.11709 (2021).CAS 
Article 

Google Scholar 
63.Arkhipkin, A. I. Squid as nutrient vectors linking Southwest Atlantic marine ecosystems. Deep Sea Res. II(95), 7–20. https://doi.org/10.1016/j.dsr2.2012.07.003 (2013).CAS 
Article 

Google Scholar 
64.Bird, C. S. et al. A global perspective on the trophic geography of sharks. Nat. Ecol. Evol. 2, 299–305. https://doi.org/10.1038/s41559-017-0432-z (2018).Article 
PubMed 

Google Scholar 
65.Spaet, J. L. Y., Lam, C. H., Braun, C. D. & Berumen, M. L. Extensive use of mesopelagic waters by a Scalloped hammerhead shark (Sphyrna lewini) in the Red Sea. Anim. Biotelemetry 5, 20. https://doi.org/10.1186/s40317-017-0135-x (2017).Article 

Google Scholar 
66.ICES. Working Group on Elasmobranch Fishes (WGEF). ICES Sci. Rep. 2, 789. https://doi.org/10.17895/ices.pub.7470 (2020).Article 

Google Scholar 
67.Murgier, J. et al. Rebound in functional distinctiveness following warming and reduced fishing in the North Sea. Proc. R. Soc. B 288, 20201600. https://doi.org/10.1098/rspb.2020.1600 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
68.Pastoors, M. A., van Helmond, E. B., van Marlen, B., van Overzee, H. & de Graaf, E. Pelagic pilot project discard ban, 2013–2014. (IMARES, Wageningen UR, Report Number C071/14) (2014).69.Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496. https://doi.org/10.1175/2007JCLI1824.1 (2007).ADS 
Article 

Google Scholar 
70.NOAA National Geophysical Data Center. ETOPO1 1 Arc-Minute Global Relief Model. (NOAA National Centers for Environmental Information, 2009).71.Pedersen, M. W., Patterson, T. A., Thygesen, U. H. & Madsen, H. Estimating animal behaviour and residency from movement data. Oikos 120, 1281–1290. https://doi.org/10.1111/j.1600-0706.2011.19044.x (2011).Article 

Google Scholar 
72.Bauer, R. RchivalTag: Analyzing Archival Tagging Data. A set of functions to generate, access and analyze standard data products from archival tagging data. (2020). https://cran.r-project.org/package=RchivalTag.
Accessed on 8 November 2021.73.Cazelles, B. et al. Wavelet analysis of ecological time series. Oecologia 156, 287–304. https://doi.org/10.1007/s00442-008-0993-2 (2008).ADS 
Article 
PubMed 

Google Scholar 
74.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S, 4th ed. (Springer, 2002). https://doi.org/10.1007/978-0-387-21706-2.75.Wood, S. mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation and GAMMs by REML/PQL (2012). https://cran.r-project.org/package=mgcv. Accessed on 8 November 2021.76.Wood S. N. Generalized Additive Models. An Introduction with R. 2nd ed. (Chapman & Halll, 2017). https://doi.org/10.1201/9781315370279. More

1.Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).
Google Scholar 
2.Fuller, A. et al. Physiological mechanisms in coping with climate change. Physiol. Biochem. Zool. 83, 713–720 (2010).
Google Scholar 
3.Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).CAS 

Google Scholar 
4.Brawn, J. D., Benson, T. J., Stager, M., Sly, N. D. & Tarwater, C. E. Impacts of changing rainfall regime on the demography of tropical birds. Nat. Clim. Change 7, 133–136 (2016).
Google Scholar 
5.Summers, B. A. Climate change and animal disease. Vet. Pathol. 46, 1185–1186 (2009).CAS 

Google Scholar 
6.Randall, C. J. & van Woesik, R. Contemporary white-band disease in Caribbean corals driven by climate change. Nat. Clim. Change 5, 375–379 (2015).
Google Scholar 
7.Munson, L. et al. Climate extremes promote fatal co-infections during canine distemper epidemics in African lions. PLoS ONE 3, e2545 (2008).
Google Scholar 
8.Rohr, J. R. et al. Frontiers in climate change–disease research. Trends Ecol. Evol. 26, 270–277 (2011).
Google Scholar 
9.Zarnetske, P. L., Skelly, D. K. & Urban, M. C. Biotic multipliers of climate change. Science 336, 1516–1518 (2012).CAS 

Google Scholar 
10.Cohen, J. M., Sauer, E. L., Santiago, O., Spencer, S. & Rohr, J. R. Divergent impacts of warming weather on wildlife disease risk across climates. Science 370, eabb1702 (2020).CAS 

Google Scholar 
11.Cornwallis, C. K. et al. Cooperation facilitates the colonization of harsh environments. Nat. Ecol. Evol. 1, 0057 (2017).
Google Scholar 
12.Koenig, W. D. & Dickinson, J. L. (eds) Cooperative Breeding in Vertebrates: Studies of Ecology, Evolution, and Behavior (Cambridge Univ. Press, 2016).13.Groenewoud, F. & Clutton-Brock, T. Meerkat helpers buffer the detrimental effects of adverse environmental conditions on fecundity, growth and survival. J. Anim. Ecol. 90, 641–652 (2020).
Google Scholar 
14.Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol. Lett. 15, 1050–1057 (2012).
Google Scholar 
15.Vicente, J., Delahay, R. J., Walker, N. J. & Cheeseman, C. L. Social organization and movement influence the incidence of bovine tuberculosis in an undisturbed high-density badger Meles meles population. J. Anim. Ecol. 76, 348–360 (2007).CAS 

Google Scholar 
16.Bermejo, M. et al. Ebola outbreak killed 5000 gorillas. Science 314, 1564 (2006).CAS 

Google Scholar 
17.Hanya, G. et al. Mass mortality of Japanese macaques in a western coastal forest of Yakushima. Ecol. Res. 19, 179–188 (2004).
Google Scholar 
18.Angulo, E. et al. Allee effects in social species. J. Anim. Ecol. 87, 47–58 (2018).
Google Scholar 
19.Woodroffe, R., Groom, R. & McNutt, J. W. Hot dogs: high ambient temperatures impact reproductive success in a tropical carnivore. J. Anim. Ecol. 86, 1329–1338 (2017).
Google Scholar 
20.Brandell, E. E., Dobson, A. P., Hudson, P. J., Cross, P. C. & Smith, D. W. A metapopulation model of social group dynamics and disease applied to Yellowstone wolves. Proc. Natl Acad. Sci. USA 118, 33649227 (2021).
Google Scholar 
21.Clutton-Brock, T. H. & Manser, M. in Cooperative Breeding in Vertebrates: Studies of Ecology, Evolution, and Behavior (eds Koenig, W. D. & Dickinson, J. L.) 294–317 (Cambridge Univ. Press, 2016).22.Drewe, J. A. Who infects whom? Social networks and tuberculosis transmission in wild meerkats. Proc. R. Soc. B 277, 633–642 (2010).
Google Scholar 
23.Parsons, S. D. C., Drewe, J. A., van Pittius, N. C. G., Warren, R. M. & van Helden, P. D. Novel cause of tuberculosis in meerkats, South Africa. Emerg. Infect. Dis. 19, 2004–2007 (2013).
Google Scholar 
24.Duncan, C., Manser, M., & Clutton-Brock, T. H. Decline and fall: the causes of group failure in cooperatively breeding meerkats. Ecol. Evol. https://doi.org/10.1002/ece3.7655 (2021).25.Drewe, J. A., Foote, A. K., Sutcliffe, R. L. & Pearce, G. P. Pathology of Mycobacterium bovis infection in wild meerkats (Suricata suricatta). J. Comp. Pathol. 140, 12–24 (2009).CAS 

Google Scholar 
26.van Wilgen, N. J., Goodall, V. & Holness, S. Rising temperatures and changing rainfall patterns in South Africa’s national parks. Aquat. Microb. Ecol. 36, 706–721 (2016).
Google Scholar 
27.Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).CAS 

Google Scholar 
28.Fischer, E. M., Beyerle, U. & Knutti, R. Robust spatially aggregated projections of climate extremes. Nat. Clim. Change 3, 1033–1038 (2013).
Google Scholar 
29.Bourne, A. R., Cunningham, S. J., Spottiswoode, C. N. & Ridley, A. R. Hot droughts compromise interannual survival across all group sizes in a cooperatively breeding bird. Ecol. Lett. 23, 1776–1788 (2020).
Google Scholar 
30.Van de Ven, T. M. F. N., Fuller, A. & Clutton‐Brock, T. H. Effects of climate change on pup growth and survival in a cooperative mammal, the meerkat. Funct. Ecol. 34, 194–202 (2020).
Google Scholar 
31.Katale, B. Z. et al. Prevalence and risk factors for infection of bovine tuberculosis in indigenous cattle in the Serengeti ecosystem, Tanzania. BMC Vet. Res. 9, 267 (2013).
Google Scholar 
32.Paniw, M., Maag, N., Cozzi, G., Clutton-Brock, T. & Ozgul, A. Life history responses of meerkats to seasonal changes in extreme environments. Science 363, 631–635 (2019).CAS 

Google Scholar 
33.Dwyer, R. A., Witte, C., Buss, P., Goosen, W. J. & Miller, M. Epidemiology of tuberculosis in multi-host wildlife systems: implications for black (Diceros bicornis) and white (Ceratotherium simum) rhinoceros. Front. Vet. Sci. 7, 580476 (2020).
Google Scholar 
34.Patterson, S., Drewe, J. A., Pfeiffer, D. U. & Clutton-Brock, T. H. Social and environmental factors affect tuberculosis related mortality in wild meerkats. J. Anim. Ecol. 86, 442–450 (2017).
Google Scholar 
35.Dubuc, C. et al. Increased food availability raises eviction rate in a cooperative breeding mammal. Biol. Lett. 13, 20160961 (2017).
Google Scholar 
36.Maag, N., Cozzi, G., Clutton-Brock, T. H. & Ozgul, A. Density‐dependent dispersal strategies in a cooperative breeder. Ecology 99, 1932–1941 (2018).
Google Scholar 
37.Ekernas, L. S. & Cords, M. Social and environmental factors influencing natal dispersal in blue monkeys, Cercopithecus mitis stuhlmanni. Anim. Behav. 73, 1009–1020 (2007).
Google Scholar 
38.Ozgul, A., Bateman, A. W., English, S., Coulson, T. & Clutton-Brock, T. H. Linking body mass and group dynamics in an obligate cooperative breeder. J. Anim. Ecol. 83, 1357–1366 (2014).
Google Scholar 
39.Tomlinson, A. J., Chambers, M. A., Wilson, G. J., McDonald, R. A. & Delahay, R. J. Sex-related heterogeneity in the life-history correlates of Mycobacterium bovis infection in European badgers (Meles meles). Transbound. Emerg. Dis. 60, 37–45 (2013).
Google Scholar 
40.Courchamp, F., Grenfell, B. & Clutton-Brock, T. H. Population dynamics of obligate cooperators. Proc. R. Soc. B 266, 557–563 (1999).
Google Scholar 
41.Lerch, B. A., Nolting, B. C. & Abbott, K. C. Why are demographic Allee effects so rarely seen in social animals? J. Anim. Ecol. 87, 1547–1559 (2018).
Google Scholar 
42.Borg, B. L., Brainerd, S. M., Meier, T. J. & Prugh, L. R. Impacts of breeder loss on social structure, reproduction and population growth in a social canid. J. Anim. Ecol. 84, 177–187 (2015).
Google Scholar 
43.Brown, P. T. & Caldeira, K. Greater future global warming inferred from Earth’s recent energy budget. Nature 552, 45–50 (2017).CAS 

Google Scholar 
44.Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).
Google Scholar 
45.Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).CAS 

Google Scholar 
46.Blackwood, J. C., Streicker, D. G., Altizer, S. & Rohani, P. Resolving the roles of immunity, pathogenesis, and immigration for rabies persistence in vampire bats. Proc. Natl Acad. Sci. USA 110, 20837–20842 (2013).CAS 

Google Scholar 
47.Fenner, A. L., Godfrey, S. S. & Michael Bull, C. Using social networks to deduce whether residents or dispersers spread parasites in a lizard population. J. Anim. Ecol. 80, 835–843 (2011).
Google Scholar 
48.Paniw, M. et al. The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: a global analysis. J. Anim. Ecol. 90, 1398–1407 (2021).
Google Scholar 
49.McDonald, J. L. et al. Demographic buffering and compensatory recruitment promotes the persistence of disease in a wildlife population. Ecol. Lett. 19, 443–449 (2016).
Google Scholar 
50.Plowright, R. K., Sokolow, S. H., Gorman, M. E., Daszak, P. & Foley, J. E. Causal inference in disease ecology: investigating ecological drivers of disease emergence. Front. Ecol. Environ. 6, 420–429 (2008).
Google Scholar 
51.Russell, R., DiRenzo, G. V., Szymanski, J., Alger, K. & Grant, E. H. C. Principles and mechanisms of wildlife population persistence in the face of disease. Front. Ecol. Evol. 8, 344 (2020).
Google Scholar 
52.Baudouin, A. et al. Disease avoidance, and breeding group age and size condition the dispersal patterns of western lowland gorilla females. Ecology 100, e02786 (2019).
Google Scholar 
53.Townsend, A. K., Hawley, D. M., Stephenson, J. F. & Williams, K. E. G. Emerging infectious disease and the challenges of social distancing in human and non-human animals. Proc. R. Soc. B 287, 20201039 (2020).CAS 

Google Scholar 
54.Schisler, G. J., Bergersen, E. P. & Walker, P. G. Effects of multiple stressors on morbidity and mortality of fingerling rainbow trout infected with Myxobolus cerebralis. Trans. Am. Fish. Soc. 129, 859–865 (2000).
Google Scholar 
55.Härkönen, T., Harding, K., Rasmussen, T. D., Teilmann, J. & Dietz, R. Age- and sex-specific mortality patterns in an emerging wildlife epidemic: the phocine distemper in European harbour seals. PLoS ONE 2, e887 (2007).
Google Scholar 
56.Clutton-Brock, T. H. et al. Reproduction and survival of suricates (Suricata suricatta) in the southern Kalahari. Afr. J. Ecol. 37, 69–80 (1999).
Google Scholar 
57.Clutton-Brock, T. H., Hodge, S. J. & Flower, T. P. Group size and the suppression of subordinate reproduction in Kalahari meerkats. Anim. Behav. 76, 689–700 (2008).
Google Scholar 
58.Bateman, A. W., Ozgul, A., Coulson, T. & Clutton-Brock, T. H. Density dependence in group dynamics of a highly social mongoose, Suricata suricatta. J. Anim. Ecol. 81, 628–639 (2012).
Google Scholar 
59.Adler, R. F. et al. The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138 (2018).
Google Scholar 
60.Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).CAS 

Google Scholar 
61.Parding, K. M. et al. GCMeval – an interactive tool for evaluation and selection of climate model ensembles. Clim. Serv. 18, 100167 (2020).
Google Scholar 
62.Delahay, R. J., Langton, S., Smith, G. C., Clifton-Hadley, R. S. & Cheeseman, C. L. The spatio-temporal distribution of Mycobacterium bovis (bovine tuberculosis) infection in a high-density badger population. J. Anim. Ecol. 69, 428–441 (2000).
Google Scholar 
63.Delahay, R. J. et al. Long-term temporal trends and estimated transmission rates for Mycobacterium bovis infection in an undisturbed high-density badger (Meles meles) population. Epidemiol. Infect. 141, 1445–1456 (2013).CAS 

Google Scholar 
64.Buzdugan, S. N., Chambers, M. A., Delahay, R. J. & Drewe, J. A. Diagnosis of tuberculosis in groups of badgers: an exploration of the impact of trapping efficiency, infection prevalence and the use of multiple tests. Epidemiol. Infect. 144, 1717–1727 (2016).CAS 

Google Scholar 
65.Akaike, H. in Selected Papers of Hirotugu Akaike (eds Parzen, E. et al.) 199–213 (Springer, 1998).66.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B Stat. 73, 3–36 (2011).
Google Scholar 
67.Grimm, V. et al. The ODD protocol: a review and first update. Ecol. Model. 221, 2760–2768 (2010).
Google Scholar 
68.Wood, S. N. Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466, 1102–1104 (2010).CAS 

Google Scholar 
69.Fronzek, S., Carter, T. R., Räisänen, J., Ruokolainen, L. & Luoto, M. Applying probabilistic projections of climate change with impact models: a case study for sub-Arctic palsa mires in Fennoscandia. Clim. Change 99, 515–534 (2010).
Google Scholar  More

1.Westerling, A. L. R. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Phil. Trans. R. Soc. B 371, 20150178 (2016).PubMed 
PubMed Central 

Google Scholar 
2.Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Gonzalez, P. et al. Southwest: Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment (U.S. Global Change Research Program, 2018).4.McLauchlan, K. K. et al. Fire as a fundamental ecological process: research advances and frontiers. J. Ecol. 108, 2047–2069 (2020).
Google Scholar 
5.Bowman, D. M. J. S. et al. Fire in the Earth system. Science 324, 481–484 (2009).6.Davis, K. T. et al. Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration. Proc. Natl Acad. Sci. USA 116, 6193–6198 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Stephens, S. L. et al. Drought, tree mortality, and wildfire in forests adapted to frequent fire. Bioscience 68, 77–88 (2018).
Google Scholar 
8.Radeloff, V. C. et al. Rapid growth of the US wildland–urban interface raises wildfire risk. Proc. Natl Acad. Sci. USA 115, 3314–3319 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Syphard, A. D., Keeley, J. E., Pfaff, A. H. & Ferschweiler, K. Human presence diminishes the importance of climate in driving fire activity across the United States. Proc. Natl Acad. Sci. USA 114, 13750–13755 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Mietkiewicz, N. et al. In the line of fire: consequences of human-ignited wildfires to homes in the U.S. (1992–2015). Fire 3, 50 (2020).11.Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl Acad. Sci. USA 114, 2946–2951 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.McKenzie, D. & Littell, J. S. Climate change and the eco-hydrology of fire: will area burned increase in a warming western USA. Ecol. Appl. 27, 26–36 (2017).PubMed 

Google Scholar 
13.Littell, J. S., Mckenzie, D., Peterson, D. L. & Westerling, A. L. Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003. Ecol. Appl. 19, 1003–1021 (2009).PubMed 

Google Scholar 
14.Jensen, D. et al. The sensitivity of US wildfire occurrence to pre-season soil moisture conditions across ecosystems. Environ. Res. Lett. 13, 014021 (2018).PubMed 
PubMed Central 

Google Scholar 
15.Vicente-Serrano, S. M., Quiring, S. M., Peña-Gallardo, M., Yuan, S. & Domínguez-Castro, F. A review of environmental droughts: increased risk under global warming? Earth Sci. Rev. 201, 102953 (2020).
Google Scholar 
16.Ficklin, D. L. & Novick, K. A. Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res. 122, 2061–2079 (2017).
Google Scholar 
17.Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D. & Diffenbaugh, N. S. Multidimensional risk in a nonstationary climate: joint probability of increasingly severe warm and dry conditions. Sci. Adv. 4, eaau3487 (2018).PubMed 
PubMed Central 

Google Scholar 
18.Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M. & Kolden, C. A. Global patterns of interannual climate–fire relationships. Glob. Change Biol. 24, 5164–5175 (2018).
Google Scholar 
19.Williams, A. P. & Abatzoglou, J. T. Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity. Curr. Clim. Change Rep. 2, 1–14 (2016).
Google Scholar 
20.Bradstock, R. A. A biogeographic model of fire regimes in Australia: current and future implications. Glob. Ecol. Biogeogr. 19, 145–158 (2010).
Google Scholar 
21.Krawchuk, M. A. & Moritz, M. A. Constraints on global fire activity vary across a resource gradient. Ecology 92, 121–132 (2011).PubMed 

Google Scholar 
22.Scarff, F. R. et al. Effects of plant hydraulic traits on the flammability of live fine canopy fuels. Funct. Ecol. 35, 835–846 (2021).23.Ruffault, J., Martin-StPaul, N., Pimont, F. & Dupuy, J. L. How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems. Agric. For. Meteorol. 262, 391–401 (2018).
Google Scholar 
24.Pivovaro, A. L. et al. The effect of ecophysiological traits on live fuel moisture content. Fire 2, 28 (2019).25.Nolan, R. H., Hedo, J., Arteaga, C., Sugai, T. & Resco de Dios, V. Physiological drought responses improve predictions of live fuel moisture dynamics in a Mediterranean forest. Agric. For. Meteorol. 263, 417–427 (2018).26.Skelton, R. P., West, A. G. & Dawson, T. E. Predicting plant vulnerability to drought in biodiverse regions using functional traits. Proc. Natl Acad. Sci. USA 112, 5744–5749 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Ma, W. et al. Assessing climate change impacts on live fuel moisture and wildfire risk using a hydrodynamic vegetation model. Biogeosciences 18, 4005–4020 (2021).CAS 

Google Scholar 
28.McColl, K. A. et al. The global distribution and dynamics of surface soil moisture. Nat. Geosci. 10, 100–104 (2017).CAS 

Google Scholar 
29.Chuvieco, E., González, I., Verdú, F., Aguado, I. & Yebra, M. Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. Int. J. Wildland Fire 18, 430–441 (2009).30.Rao, K., Williams, A. P., Flefil, J. F. & Konings, A. G. SAR-enhanced mapping of live fuel moisture content. Remote Sens. Environ. 245, 111797 (2020).
Google Scholar 
31.Nolan, R. H., Boer, M. M., Resco De Dios, V., Caccamo, G. & Bradstock, R. A. Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia. Geophys. Res. Lett. 43, 4229–4238 (2016).
Google Scholar 
32.Dennison, P. E. & Moritz, M. A. Critical live fuel moisture in chaparral ecosystems: a threshold for fire activity and its relationship to antecedent precipitation. Int. J. Wildland Fire 18, 1021–1027 (2009).
Google Scholar 
33.Tumino, B. J., Duff, T. J., Goodger, J. Q. D. & Cawson, J. G. Plant traits linked to field-scale flammability metrics in prescribed burns in Eucalyptus forest. PLoS ONE 14, e0221403 (2019).34.Rodman, K. C. et al. A trait-based approach to assessing resistance and resilience to wildfire in two iconic North American conifers. J. Ecol. 109, 313–326 (2021).
Google Scholar 
35.Resco de Dios, V. Plant–Fire Interactions (Springer, 2020).36.Hurteau, M. D., Liang, S., Westerling, A. L. R. & Wiedinmyer, C. Vegetation–fire feedback reduces projected area burned under climate change. Sci. Rep. 9, 2838 (2019).37.Littell, J. S., McKenzie, D., Wan, H. Y. & Cushman, S. A. Climate change and future wildfire in the western United States: an ecological approach to nonstationarity. Earths Future 6, 1097–1111 (2018).38.Abatzoglou, J. T. & Kolden, C. A. Relationships between climate and macroscale area burned in the western United States. Int. J. Wildland Fire 22, 1003–1020 (2013).
Google Scholar 
39.Goss, M. et al. Climate change is increasing the likelihood of extreme autumn wildfire conditions across California. Environ. Res. Lett. 15, 094016 (2020).40.Bradshaw, L. S., Deeming, J. E., Burgan, R. E. & Cohen, J. D. The 1978 National Fire-Danger Rating System: Technical Documentation General Technical Report INT-169 (US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station,1984); https://doi.org/10.2737/INT-GTR-16941.Hardy, C. C. & Hardy, C. E. Fire danger rating in the United States of America: an evolution since 1916. Int. J. Wildland Fire 16, 217–231 (2007).42.Rabin, S. S. et al. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. Geosci. Model Dev. 10, 1175–1197 (2017).CAS 

Google Scholar 
43.Hantson, S. et al. The status and challenge of global fire modelling. Biogeosciences 13, 3359–3375 (2016).
Google Scholar 
44.Anderegg, W. R. L. Spatial and temporal variation in plant hydraulic traits and their relevance for climate change impacts on vegetation. New Phytol. 205, 1008–1014 (2015).PubMed 

Google Scholar 
45.Konings, A. G. & Gentine, P. Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23, 891–905 (2017).
Google Scholar 
46.Forkel, M. et al. Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models. Biogeosciences 16, 57–76 (2019).
Google Scholar 
47.Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).CAS 
PubMed 

Google Scholar 
48.Trugman, A. T., Anderegg, L. D. L., Shaw, J. D. & Anderegg, W. R. L. Trait velocities reveal that mortality has driven widespread coordinated shifts in forest hydraulic trait composition. Proc. Natl Acad. Sci. USA 117, 8532–8538 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Williams, A. P. et al. Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States. Int. J. Wildland Fire 24, 14–26 (2015).
Google Scholar 
50.Knapp, P. A. Spatio-temporal patterns of large grassland fires in the Intermountain West U.S.A. Glob. Ecol. Biogeogr. Lett. 7, 259–272 (1998).
Google Scholar 
51.Keeley, J. & Syphard, A. Climate change and future fire regimes: examples from California. Geosciences 6, 37 (2016).
Google Scholar 
52.Badia, A., Serra, P. & Modugno, S. Identifying dynamics of fire ignition probabilities in two representative Mediterranean wildland–urban interface areas. Appl. Geogr. 31, 930–940 (2011).
Google Scholar 
53.Fusco, E. J., Abatzoglou, J. T., Balch, J. K., Finn, J. T. & Bradley, B. A. Quantifying the human influence on fire ignition across the western USA. Ecol. Appl. 26, 2390–2401 (2016).
Google Scholar 
54.Syphard, A. D. et al. Human influence on California fire regimes. Ecol. Appl. 17, 1388–1402 (2007).PubMed 

Google Scholar 
55.Ager, A. A., Finney, M. A., Kerns, B. K. & Maffei, H. Modeling wildfire risk to northern spotted owl (Strix occidentalis caurina) habitat in central Oregon, USA. For. Ecol. Manage. 246, 45–56 (2007).56.Thomas, D., Butry, D., Gilbert, S., Webb, D. & Fung, J. The Costs and Losses of Wildfires: A Literature Survey NIST Special Publication 1215 (NIST, 2017); https://doi.org/10.6028/NIST.SP.121557.Wang, D. et al. Economic footprint of California wildfires in 2018. Nat. Sustain. 4, 252–260 (2021).
Google Scholar 
58.Burke, M. et al. The changing risk and burden of wildfire in the United States. Proc. Natl Acad. Sci. USA 118, e2011048118 (2021).59.García, M., Chuvieco, E., Nieto, H. & Aguado, I. Combining AVHRR and meteorological data for estimating live fuel moisture content. Remote Sens. Environ. 112, 3618–3627 (2008).
Google Scholar 
60.Matthews, S. Dead fuel moisture research: 1991–2012. Int. J. Wildland Fire 23, 78–92 (2014).
Google Scholar 
61.Cohen, J. D. et al. The National Fire-Danger Rating System: Basic Equations Vol. 82 (US Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station, 1985).62.Pellizzaro, G., Cesaraccio, C., Duce, P., Ventura, A. & Zara, P. Relationships between seasonal patterns of live fuel moisture and meteorological drought indices for Mediterranean shrubland species. Int. J. Wildland Fire 16, 232–241 (2007).63.Liu, L., Zhang, Y., Wu, S., Li, S. & Qin, D. Water memory effects and their impacts on global vegetation productivity and resilience. Sci. Rep. 8, 2962 (2018).PubMed 
PubMed Central 

Google Scholar 
64.Anderegg, W. R. L. et al. Woody plants optimise stomatal behaviour relative to hydraulic risk. Ecol. Lett. 21, 968–977 (2018).PubMed 

Google Scholar 
65.Meinzer, F. C., Johnson, D. M., Lachenbruch, B., McCulloh, K. A. & Woodruff, D. R. Xylem hydraulic safety margins in woody plants: coordination of stomatal control of xylem tension with hydraulic capacitance. Funct. Ecol. 23, 922–930 (2009).
Google Scholar 
66.National Fuel Moisture Database (United States Forest Service, 2018); https://www.wfas.net/nfmd/public/index.php67.Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2011).
Google Scholar 
68.Homer, C. et al. Completion of the 2006 National Land Cover Database for the conterminous United States. Photogramm. Eng. Remote Sens. 77, 858–864 (2011).69.Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earths Future 7, 892–910 (2019).
Google Scholar 
70.Boschetti, L., Roy, D., Hoffman, A. A. & Humber, M. Collection 5 MODIS Burned Area Product User Guide Version 3.0.1 (NASA EOSDIS Land Processes DAAC, 2013).71.PRISM Climate Data (Prism Climate Group, Oregon State University, accessed 16 December 2020); https://prism.oregonstate.edu72.Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. 116, G04021 (2011).73.Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Montzka, C., Herbst, M., Weihermüller, L., Verhoef, A. & Vereecken, H. A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves. Earth Syst. Sci. Data 9, 529–543 (2017).
Google Scholar 
75.Liu, S. et al. NACP MsTMIP: Unified North American Soil Map (ORNL DAAC, 2014); https://doi.org/10.3334/ornldaac/124276.Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar 
77.Martinuzzi, S. et al. The 2010 Wildland–Urban Interface of the Conterminous United States (USDA, 2015).78.Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).
Google Scholar  More