Ecology

Bryant, S. V., Endo, T. & Gardiner, D. M. Vertebrate limb regeneration and the origin of limb stem cells. Int. J. Dev. Biol. 46, 887–896 (2004).
Google Scholar 
Fleming, P. A., Muller, D. & Bateman, P. W. Leave it all behind: A taxonomic perspective of autotomy in invertebrates. Biol. Rev. 82, 481–510 (2007).PubMed 
Article 

Google Scholar 
Bely, A. E. & Nyberg, K. G. Evolution of animal regeneration: Re-emergence of a field. Trends Ecol. Evol. 25, 161–170 (2010).PubMed 
Article 

Google Scholar 
Lindsay, S. M. Frequency of injury and the ecology of regeneration in marine benthic invertebrates. Integr. Comp. Biol. 50, 479–493 (2010).PubMed 
Article 

Google Scholar 
Wilson, B. S. Tail injuries increase the risk of mortality in free-living lizards (Uta stansburiana). Oecologia 92, 145–152 (1992).ADS 
PubMed 
Article 

Google Scholar 
Chapple, D. G. & Swain, R. Inter-populational variation in the cost of autotomy in the metallic skink (Niveoscincus metallicus). J. Zool. 264, 411–418 (2004).Article 

Google Scholar 
Tyler, R. K., Winchell, K. M. & Revell, L. J. Tails of the city: Caudal autotomy in the tropical lizard, Anolis cristatellus, in urban and natural areas of Puerto Rico. J. Herpetol. 50, 435–441 (2016).Article 

Google Scholar 
Griffen, B. D., Cannizzo, Z. J., Carver, J. & Meidell, M. Reproductive and energetic costs of injury in the mangrove tree crab. Mar. Ecol. Prog. Ser. 640, 127–137 (2020).ADS 
Article 

Google Scholar 
Smith, L. D. & Hines, A. H. Autotomy in blue crab (Callinectes sapidus Rathbun) populations: Geographic, temporal, and ontogenetic variation. Biol. Bull. 180, 416–431 (1991).CAS 
PubMed 
Article 

Google Scholar 
Maginnis, T. L. The costs of autotomy and regeneration in animals: A review and framework for future research. Behav. Ecol. 17, 857–872 (2006).Article 

Google Scholar 
Suma Gupta, N. V., Kurup, K. N. P., Adiyodi, R. G. & Adiyodi, K. G. The antagonism between somatic growth and testicular activity during different phases in intermoult (stage C4) in sexually mature freshwater crab, Paratelphusa hydrodromous. Invertebr. Reprod. Dev. 16, 195–203 (1989).Article 

Google Scholar 
Devi, S. & Adiyodi, R. G. Effect of multiple limb autotomy on oogenesis and somatic growth in Paratelphusa hydromous. Trop. Freshw. Biol. 9, 43–56 (2000).
Google Scholar 
Juanes, F. & Smith, L. D. The ecological consequences of limb damage and loss in decapod crustaceans: A review and prospectus. J. Exp. Mar. Biol. Ecol. 193, 197–223 (1995).Article 

Google Scholar 
Cheng, J. H. & Chang, E. S. Determinants of postmolt size in the American lobster (Homarus americanus). I. D13 is the critical stage. Can. J. Fish. Aquat. Sci. 50, 2106–2111 (1993).Article 

Google Scholar 
Kuris, A. M. & Mager, M. Effect of limb regeneration on size increase at molt of the shore crabs Hemigrapsus oregonensis and Pachygrapsus crassipes. J. Exp. Zool. 193, 353–359 (1975).CAS 
PubMed 
Article 

Google Scholar 
Ballinger, R. E. & Tinkle, D. W. On the cost of tail regeneration to body growth in lizards. J. Herpetol. 13, 374–375 (1979).Article 

Google Scholar 
Hopkins, P. M. & Das, S. Regeneration in crustaceans. Nat. Hist. Crustacea 4, 168–198 (2015).
Google Scholar 
Lai, A. G. & Aboobaker, A. A. EvoRegen in animals: Time to uncover deep conservation or convergence of adult stem cell evolution and regenerative processes. Dev. Biol. 433, 118–131 (2018).CAS 
PubMed 
Article 

Google Scholar 
Boudreau, S. A. & Worm, B. Ecological role of large benthic decapods in marine ecosystems: A review. Mar. Ecol. Prog. Ser. 469, 195–213 (2012).ADS 
Article 

Google Scholar 
Turner, J. T. The importance of small planktonic copepods and their roles in pelagic marine food webs. Zool. Stud. 43, 255–266 (2004).
Google Scholar 
Bondad-Reantaso, M. G., Subasinghe, R. P., Josupeit, H., Cai, J. & Zhou, X. The role of crustacean fisheries and aquaculture in global food security: Past, present and future. J. Invertebr. Pathol. 110, 158–165 (2012).PubMed 
Article 

Google Scholar 
Galil, B. S., Clark, P. F. & Carleton, J. T. In the Wrong Place—Alien Marine Crustaceans: Distribution, Biology, and Impacts (Springer, 2011).Book 

Google Scholar 
Gallien, L., Münkemüller, T., Albert, C. H., Boulangeat, I. & Thuiller, W. Predicting potential distributions of invasive species: Where to go from here?. Divers. Distrib. 16, 331–342 (2010).Article 

Google Scholar 
Barbet-Massin, M., Rome, Q., Villemant, C. & Courchamp, F. Can species distribution models really predict the expansion of invasive species?. PLoS One 13, e0193085 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Griffen, B. D., van den Akker, D., DiNuzzo, E. R., Anderson, L. & Vernier, A. Comparing methods for predicting the impacts of invasive species. Biol. Invasions 23, 491–505 (2021).Article 

Google Scholar 
Williams, A. B. & McDermott, J. J. An eastern United States record for the western Indo-Pacific crab, Hemigrapsus sanguineus (Crustacea: Decapoda: Grapsidae). Proc. Biol. Soc. Wash. 103, 108–109 (1990).
Google Scholar 
Blakeslee, A. M. et al. Reconstructing the invasion history of the Asian shorecrab, Hemigrapsus sanguineus (De Haan 1835) in the Western Atlantic. Mar. Biol. 164, 1–19 (2017).
Google Scholar 
Griffen, B. D. & Delaney, D. G. Species invasion shifts the importance of predator dependence. Ecology 88, 3012–3021 (2007).PubMed 
Article 

Google Scholar 
Epifanio, C. E. Invasion biology of the Asian shore crab Hemigrapsus sanguineus: A review. J. Exp. Mar. Biol. Ecol. 441, 33–49 (2013).Article 

Google Scholar 
Gerard, V. A., Cerrato, R. M. & Larson, A. A. Potential impacts of a western Pacific grapsid crab on intertidal communities of the northwestern Atlantic Ocean. Biol. Invasions 1, 353–361 (1999).Article 

Google Scholar 
Kraemer, G. P., Sellberg, M., Gordon, A. & Main, J. Eight-year record of Hemigrapsus sanguineus (Asian shore crab) invasion in western Long Island Sound estuary. Northeast. Nat. 14, 207–224 (2007).Article 

Google Scholar 
Davis, J. L. et al. Autotomy in the Asian shore crab (Hemigrapsus sanguineus) in a non-native area of its range. J. Crust. Biol. 25, 655–660 (2005).Article 

Google Scholar 
Delaney, D. G., Griffen, B. D. & Leung, B. Does consumer injury modify invasion impact?. Biol. Invasions 13, 2935–2945 (2011).Article 

Google Scholar 
Jensen, G. C., McDonald, P. S. & Armstrong, D. A. East meets west: Competitive interactions between green crab Carcinus maenas, and native and introduced shore crab Hemigrapsus spp. Mar. Ecol. Prog. Ser. 225, 251–262 (2002).ADS 
Article 

Google Scholar 
Lohrer, A. M. & Whitlatch, R. B. Interactions among aliens: Apparent replacement of one exotic species by another. Ecology 83, 719–732 (2002).Article 

Google Scholar 
Griffen, B. D. & Williamson, T. Influence of predator density on nonindependent effects of multiple predator species. Oecologia 155, 151–159 (2008).ADS 
PubMed 
Article 

Google Scholar 
Vernier, A. & Griffen, B. D. Physiological effects of limb loss on the Asian shore crab Hemigrapsus sanguineus. Northeast. Nat. 26, 761–771 (2019).Article 

Google Scholar 
Lohrer, A. M. & Whitlatch, R. B. Relative impacts of two exotic brachyuran species on blue mussel populations in Long Island Sound. Mar. Ecol. Prog. Ser. 227, 135–144 (2002).ADS 
Article 

Google Scholar 
Goldstein, J. S. & Carloni, J. T. Assessing the implications of live claw removal on Jonah crab (Cancer borealis), an emerging fishery in the Northwest Atlantic. Fish. Res. 243, 106046 (2021).Article 

Google Scholar 
Hines, A. H. Allometric constraints and variables of reproductive effort in brachyuran crabs. Mar. Biol. 69, 309–320 (1982).Article 

Google Scholar 
Pörtner, H. O. Oxygen-and capacity-limitation of thermal tolerance: A matrix for integrating climate-related stressor effects in marine ecosystems. J. Exp. Biol. 213, 881–893 (2010).PubMed 
Article 

Google Scholar 
Sokolova, I. M. Energy-limited tolerance to stress as a conceptual framework to integrate the effects of multiple stressors. Integr. Comp. Biol. 53, 597–608 (2013).PubMed 
Article 

Google Scholar 
Prestholdt, T. et al. Tradeoffs associated with autotomy and regeneration and their potential role in the evolution of regenerative abilities. Behav. Ecol. 33, 518–525 (2022).Article 

Google Scholar 
McDermott, J. J. The western Pacific brachyuran Hemigrapsus sanguineus (Grapsidae) in its new habitat along the Atlantic coast of the United States: Reproduction. J. Crustac. Biol. 18, 308–316 (1998).Article 

Google Scholar 
Depledge, M. H. Hemigrapsus sanguineus (De Haan). Asian Mar. Biol. 1, 115–123 (1984).
Google Scholar 
Saigusa, M. & Kawagoye, O. Circatidal rhythm of an intertidal crab, Hemigrapsus sanguineus: Synchrony with unequal tide height and involvement of a light-response mechanism. Mar. Biol. 129, 87–96 (1997).Article 

Google Scholar 
Choy, S. C. A rapid method for removing and counting eggs from fresh and preserved decapod crustaceans. Aquaculture 48, 369–372 (1985).Article 

Google Scholar 
Rosa, R., Calado, R., Narciso, L. & Nunes, M. L. Embryogenesis of decapod crustaceans with different life history traits, feeding ecologies and habitats: A fatty acid approach. Mar. Biol. 151, 935–947 (2007).Article 

Google Scholar 
Griffen, B. D. & Mosblack, H. Predicting diet and consumption rate differences between and within species using gut ecomorphology. J. Anim. Ecol. 80, 854–863 (2011).PubMed 
Article 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Zero-truncated and zero-inflated models for count data. In Mixed Effects Models and Extensions in Ecology with R 261–293 (Springer, 2009).MATH 
Chapter 

Google Scholar 
Griffen, B. D. Linking individual diet variation and fecundity in an omnivorous marine consumer. Oecologia 174, 121–130 (2014).ADS 
PubMed 
Article 

Google Scholar  More

Saba GK, Fraser WR, Saba VS, Iannuzzi RA, Coleman KE, Doney SC, et al. Winter and spring controls on the summer food web of the coastal West Antarctic Peninsula. Nat Commun. 2014;5:4318.CAS 
PubMed 
Article 

Google Scholar 
Behrenfeld MJ, Randerson JT, McClain CR, Feldman GC, Los SO, Tucker CJ, et al. Biospheric primary production during an ENSO transition. Science. 2001;291:2594–7.CAS 
PubMed 
Article 

Google Scholar 
Amin SA, Parker MS, Armbrust EV. Interactions between diatoms and bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cho BC, Azam F. Major role of bacteria in biogeochemical fluxes in the ocean’s interior. Nature. 1988;332:441–3.CAS 
Article 

Google Scholar 
Amin S, Hmelo L, Van Tol H, Durham B, Carlson L, Heal K, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98–101.CAS 
PubMed 
Article 

Google Scholar 
Durham BP, Sharma S, Luo H, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci. 2015;112:453–7.CAS 
PubMed 
Article 

Google Scholar 
Mühlenbruch M, Grossart HP, Eigemann F, Voss M. Mini‐review: Phytoplankton‐derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. Environ Microbiol. 2018;20:2671–85.PubMed 
Article 

Google Scholar 
Seymour JR, Amin SA, Raina J-B, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat Microbiol. 2017;2:1–12.Article 

Google Scholar 
Azam F, Fenchel T, Field JG, Gray JS, Meyer-Reil L-A, Thingstad F. The ecological role of water-column microbes in the sea. Marine ecology progress series. 1983;10:257–63.Ratnarajah L, Blain S, Boyd PW, Fourquez M, Obernosterer I, Tagliabue A. Resource colimitation drives competition between phytoplankton and bacteria in the Southern Ocean. Geophys Res Lett. 2021;48:e2020GL088369.PubMed 
PubMed Central 
Article 

Google Scholar 
Oulhen N, Schulz BJ, Carrier TJ. English translation of Heinrich Anton de Bary’s 1878 speech, ‘Die Erscheinung der Symbiose’ (‘De la symbiose’). Symbiosis. 2016;69:131–9.Article 

Google Scholar 
Cooper MB, Smith AG. Exploring mutualistic interactions between microalgae and bacteria in the omics age. Curr Opin Plant Biol. 2015;26:147–53.PubMed 
Article 

Google Scholar 
Croft MT, Lawrence AD, Raux-Deery E, Warren MJ, Smith AG. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature. 2005;438:90–3.CAS 
PubMed 
Article 

Google Scholar 
Cole JJ. Interactions between bacteria and algae in aquatic ecosystems. Ann Rev Ecol Syst. 1982;13:291–314.Article 

Google Scholar 
Durham B. Deciphering metabolic currencies that support marine microbial networks. mSystems. 2021;6:e00763-21.Bell W, Mitchell R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol Bull. 1972;143:265–77.Article 

Google Scholar 
Baker LJ, Kemp PF. Exploring bacteria–diatom associations using single-cell whole genome amplification. Aquat Microb Ecol. 2014;72:73–88.Article 

Google Scholar 
Graff JR, Rines JE, Donaghay PL. Bacterial attachment to phytoplankton in the pelagic marine environment. Mar Ecol Prog Ser. 2011;441:15–24.Article 

Google Scholar 
Baker LJ, Alegado RA, Kemp PF. Response of diatom-associated bacteria to host growth state, nutrient concentrations, and viral host infection in a model system. Environ Microbiol Rep. 2016;8:917–27.PubMed 
Article 

Google Scholar 
Shibl AA, Isaac A, Ochsenkühn MA, Cárdenas A, Fei C, Behringer G, et al. Diatom modulation of select bacteria through use of two unique secondary metabolites. Proc Natl Acad Sci. 2020;117:27445–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leinweber K, Kroth PG. Capsules of the diatom Achnanthidium minutissimum arise from fibrillar precursors and foster attachment of bacteria. PeerJ. 2015;3:e858.PubMed 
PubMed Central 
Article 

Google Scholar 
Guo S, Stevens CA, Vance TDR, Olijve LLC, Graham LA, Campbell RL, et al. Structure of a 1.5-MDa adhesin that binds its Antarctic bacterium to diatoms and ice. Sci Adv. 2017;3:e1701440.PubMed 
PubMed Central 
Article 

Google Scholar 
Rao D, Webb JS, Kjelleberg S. Microbial colonization and competition on the Marine Alga Ulva australis. Appl Environ Microbiol. 2006;72:5547–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou J, Chen G-F, Ying K-Z, Jin H, Song J-T, Cai Z-H, et al. Phycosphere microbial succession patterns and assembly mechanisms in a marine Dinoflagellate bloom. Appl Environ Microbiol. 2019;85:e00349–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seyedsayamdost MR, Case RJ, Kolter R, Clardy J. The Jekyll-and-Hyde chemistry of Phaeobacter gallaeciensis. Nat Chem. 2011;3:331–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frölicher TL, Sarmiento JL, Paynter DJ, Dunne JP, Krasting JP, Winton M. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J Clim. 2015;28:862–86.Article 

Google Scholar 
Strzepek RF, Hunter KA, Frew RD, Harrison PJ, Boyd PW. Iron‐light interactions differ in Southern Ocean phytoplankton. Limnol Oceanogr. 2012;57:1182–200.CAS 
Article 

Google Scholar 
Andrew SM, Strzepek RF, M Whitney S, Chow WS, Ellwood MJ. Divergent physiological and molecular responses of light‐and iron‐limited Southern Ocean phytoplankton. Limnol Oceanogr Lett. 2022;7:150–8.CAS 
Article 

Google Scholar 
Bertrand EM, Saito MA, Rose JM, Riesselman CR, Lohan MC, Noble AE, et al. Vitamin B12 and iron colimitation of phytoplankton growth in the Ross Sea. Limnol Oceanogr. 2007;52:1079–93.CAS 
Article 

Google Scholar 
Bertrand EM, McCrow JP, Moustafa A, Zheng H, McQuaid JB, Delmont TO, et al. Phytoplankton–bacterial interactions mediate micronutrient colimitation at the coastal Antarctic sea ice edge. Proc Natl Acad Sci. 2015;112:9938–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bates SSB, Hubbard KA, Lundholm N, Montresor M, Leaw CP. Pseudo-nitzschia, Nitzschia, and domoic acid: new research since 2011. Harmful Algae. 2018;79:3–43.PubMed 
Article 

Google Scholar 
Almandoz GO, Ferreyra GA, Schloss IR, Dogliotti AI, Rupolo V, Paparazzo FE, et al. Distribution and ecology of Pseudo-nitzschia species (Bacillariophyceae) in surface waters of the Weddell Sea (Antarctica). Polar Biol. 2008;31:429–42.Article 

Google Scholar 
Jabre LJ, Allen AE, McCain JSP, McCrow JP, Tenenbaum N, Spackeen JL, et al. Molecular underpinnings and biogeochemical consequences of enhanced diatom growth in a warming Southern Ocean. Proc Natl Acad Sci. 2021;118:e2107238118.Malviya S, Scalco E, Audic S, Vincent F, Veluchamy A, Poulain J, et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc Natl Acad Sci. 2016;113:E1516–25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moreno CM, Lin Y, Davies S, Monbureau E, Cassar N, Marchetti A. Examination of gene repertoires and physiological responses to iron and light limitation in Southern Ocean diatoms. Polar Biol. 2018;41:679–96.Article 

Google Scholar 
Ellis KA, Cohen NR, Moreno C, Marchetti A. Cobalamin-independent methionine synthase distribution and influence on vitamin B12 growth requirements in marine diatoms. Protist. 2017;168:32–47.CAS 
PubMed 
Article 

Google Scholar 
Price NM, Harrison GI, Hering JG, Hudson RJ, Nirel PM, Palenik B, et al. Preparation and chemistry of the artificial algal culture medium Aquil. Biol Oceanogr. 1989;6:443–61.Article 

Google Scholar 
Hubbard KA, Rocap G, Armbrust EV. Inter- and intraspecific community structure within the diatom genus Pseudo-nitzschia (Bacillariophyceae). J Phycol. 2008;44:637–49.CAS 
Article 

Google Scholar 
Madeira F, Park YM, Lee J, Buso N, Gur T, Madhusoodanan N, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 2019;47:W636–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
PubMed 
Article 

Google Scholar 
Brand LE, Guillard RR, Murphy LS. A method for the rapid and precise determination of acclimated phytoplankton reproduction rates. J Plankton Res. 1981;3:193–201.Article 

Google Scholar 
Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ. Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009;25:1189–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nguyen L-T, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 
PubMed 
Article 

Google Scholar 
Kalyaanamoorthy S, Minh BQ, Wong TK, Von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hoang DT, Chernomor O, Von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 
Article 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rodriguez-R LM, Gunturu S, Harvey WT, Rosselló-Mora R, Tiedje JM, Cole JR, et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucl Acids Res. 2018;46:W282–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:1–8.Article 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
PubMed 
Article 

Google Scholar 
Noble RT, Fuhrman JA. Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria. Aquat Microb Ecol. 1998;14:113–8.Article 

Google Scholar 
Alcamán-Arias ME, Fuentes-Alburquenque S, Vergara-Barros P, Cifuentes-Anticevic J, Verdugo J, Polz M, et al. Coastal bacterial community response to glacier melting in the Western Antarctic Peninsula. Microorganisms. 2021;9:88.PubMed Central 
Article 

Google Scholar 
Bowman JP, Gosink JJ, McCAMMON SA, Lewis TE, Nichols DS, Nichols PD, et al. Colwellia demingiae sp. nov., Colwellia hornerae sp. nov., Colwellia rossensis sp. nov. and Colwellia psychrotropica sp. nov.: psychrophilic Antarctic species with the ability to synthesize docosahexaenoic acid (22: ω63). Int J Syst Evol Microbiol. 1998;48:1171–80.CAS 

Google Scholar 
Reisch CR, Moran MA, Whitman WB. Bacterial catabolism of dimethylsulfoniopropionate (DMSP). Front Microbiol. 2011;2:172.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Diaz J, Ingall E, Benitez-Nelson C, Paterson D, de Jonge MD, McNulty I, et al. Marine polyphosphate: a key player in geologic phosphorus sequestration. Science. 2008;320:652–5.CAS 
PubMed 
Article 

Google Scholar 
Nichols CM, Bowman JP, Guezennec J. Olleya marilimosa gen. nov., sp. nov., an exopolysaccharide-producing marine bacterium from the family Flavobacteriaceae, isolated from the Southern Ocean. Int J Syst Evol Microbiol. 2005;55:1557–61.CAS 
PubMed 
Article 

Google Scholar 
von Scheibner M, Sommer U, Jürgens K. Tight coupling of Glaciecola spp. and diatoms during cold-water Phytoplankton spring blooms. Front Microbiol. 2017;8:27.Holmstrom C, Kjelleberg S. Marine Pseudoalteromonas species are associated with higher organisms and produce biologically active extracellular agents. FEMS Microbiol Ecol. 1999;30:285–93.CAS 
PubMed 
Article 

Google Scholar 
Methe BA, Nelson KE, Deming JW, Momen B, Melamud E, Zhang X, et al. The psychrophilic lifestyle as revealed by the genome sequence of Colwellia psychrerythraea 34H through genomic and proteomic analyses. Proc Natl Acad Sci. 2005;102:10913–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kirchman DL. The ecology of Cytophaga–Flavobacteria in aquatic environments. FEMS Microbiol Ecol. 2002;39:91–100.CAS 
PubMed 

Google Scholar 
Hong Z, Lai Q, Luo Q, Jiang S, Zhu R, Liang J, et al. Sulfitobacter pseudonitzschiae sp. nov., isolated from the toxic marine diatom Pseudo-nitzschia multiseries. Int J Syst Evol Microbiol. 2015;65:95–100.CAS 
PubMed 
Article 

Google Scholar 
Brussaard CPD, Riegman R. Influence of bacteria on phytoplankton cell mortality with phosphorus or nitrogen as the algal-growth-limiting nutrient. Aqua Microb Ecol. 1998;14:271–80.Article 

Google Scholar 
Cohen NR, A. Ellis K, Burns WG, Lampe RH, Schuback N, Johnson Z, et al. Iron and vitamin interactions in marine diatom isolates and natural assemblages of the Northeast Pacific Ocean. Limnol Oceanogr. 2017;62:2076–96.CAS 
Article 

Google Scholar 
Hunken M, Harder J, Kirst G. Epiphytic bacteria on the Antarctic ice diatom Amphiprora kufferathii Manguin cleave hydrogen peroxide produced during algal photosynthesis. Plant Biol. 2008;10:519–26.CAS 
PubMed 
Article 

Google Scholar 
Gourinchas G, Etzl S, Winkler A. Bacteriophytochromes–from informative model systems of phytochrome function to powerful tools in cell biology. Curr Opin Struct Biol. 2019;57:72–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gourion B, Rossignol M, Vorholt JA. A proteomic study of Methylobacterium extorquens reveals a response regulator essential for epiphytic growth. Proc Natl Acad Sci. 2006;103:13186–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mukherjee S, Bassler BL. Bacterial quorum sensing in complex and dynamically changing environments. Nat Rev Microbiol. 2019;17:371–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dong YH, Zhang LH. Quorum sensing and quorum-quenching enzymes. J Microbiol. 2005;43:101–9.CAS 
PubMed 

Google Scholar 
Núñez-Montero K, Barrientos L. Advances in Antarctic research for antimicrobial discovery: a comprehensive narrative review of bacteria from Antarctic environments as potential sources of novel antibiotic compounds against human pathogens and microorganisms of industrial importance. Antibiotics. 2018;7:90.Kieft B, Li Z, Bryson S, Hettich RL, Pan C, Mayali X, et al. Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits. Proc Natl Acad Sci. 2021;118:e2101178118.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maranger R, Bird DF. Viral abundance in aquatic systems: a comparison between marine and fresh waters. Mar Ecol Prog Ser. 1995;121:217–26.Article 

Google Scholar 
Sharpe GC, Gifford SM, Septer AN. A model roseobacter, Ruegeria pomeroyi DSS-3, employs a diffusible killing mechanism to eliminate competitors. Msystems. 2020;5:e00443–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cude WN, Mooney J, Tavanaei AA, Hadden MK, Frank AM, Gulvik CA, et al. Production of the antimicrobial secondary metabolite indigoidine contributes to competitive surface colonization by the marine roseobacter Phaeobacter sp. strain Y4I. Appl Environ Microbiol. 2012;78:4771–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Long RA, Rowley DC, Zamora E, Liu J, Bartlett DH, Azam F. Antagonistic interactions among marine bacteria impede the proliferation of Vibrio cholerae. Appl Environ Microbiol. 2005;71:8531–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bruhn JB, Gram L, Belas R. Production of antibacterial compounds and biofilm formation by Roseobacter species are influenced by culture conditions. Appl Environ Microbiol. 2007;73:442–50.CAS 
PubMed 
Article 

Google Scholar 
Gromek SM, Suria AM, Fullmer MS, Garcia JL, Gogarten JP, Nyholm SV, et al. Leisingera sp. JC1, a bacterial isolate from Hawaiian bobtail squid eggs, produces indigoidine and differentially inhibits vibrios. Front Microbiol. 2016;7:1342.PubMed 
PubMed Central 
Article 

Google Scholar 
Sharifah EN, Eguchi M. The phytoplankton Nannochloropsis oculata enhances the ability of Roseobacter clade bacteria to inhibit the growth of fish pathogen Vibrio anguillarum. PLoS One. 2011;6:e26756.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kerwin AH, Gromek SM, Suria AM, Samples RM, Deoss DJ, O’Donnell K, et al. Shielding the next generation: symbiotic bacteria from a reproductive organ protect bobtail squid eggs from fungal fouling. MBio. 2019;10:e02376–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tonelli M, Signori CN, Bendia A, Neiva J, Ferrero B, Pellizari V, et al. Climate projections for the southern ocean reveal impacts in the marine microbial communities following increases in sea surface temperature. Front Mar Sci. 2021;8:636226.Andrew SM, Morell HT, Strzepek RF, Boyd PW, Ellwood MJ. Iron availability influences the tolerance of southern ocean phytoplankton to warming and elevated irradiance. Front Mar Sci. 2019;6:681.Andrew SM, Strzepek RF, Branson O, Ellwood MJ. Ocean acidification reduces the growth of two Southern Ocean phytoplankton. Mar Ecol Prog Ser. 2022;682:51–64.CAS 
Article 

Google Scholar 
Blin K, Shaw S, Kloosterman AM, Charlop-Powers Z, van Weezel GP, Medema MH, et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucl Acids Res. 2021;49:W29–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ferrer-González FX, Widner B, Holderman NR, Glushka J, Edison AS, Kujawinski EB, et al. Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME J. 2021;15:762–73.PubMed 
Article 

Google Scholar  More

Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on earth and in the ocean? PLoS Biol. 9, e1001127 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Costello, M. J., May, R. M. & Stork, N. E. Can we name earth’s species before they go extinct? Science 339, 413–416 (2013).CAS 
PubMed 
Article 

Google Scholar 
Corlett, R. T. Plant diversity in a changing world: status, trends, and conservation needs. Plant Divers. 38, 10–16 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Baldrian, P., Větrovský, T., Lepinay, C. & Kohout, P. High-throughput sequencing view on the magnitude of global fungal diversity. Fungal Divers. 114, 539–547 (2022).CAS 
Article 

Google Scholar 
Taylor, D. L. et al. A first comprehensive census of fungi in soil reveals both hyperdiversity and fine-scale niche partitioning. Ecol. Monogr. 84, 3–20 (2014).Article 

Google Scholar 
Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schopf, J. W. Disparate rates, differing fates: tempo and mode of evolution changed from the Precambrian to the Phanerozoic. Proc. Natl Acad. Sci. USA 91, 6735–6742 (1994).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seager, S., Huang, J., Petkowski, J. J. & Pajusalu, M. Laboratory studies on the viability of life in H2-dominated exoplanet atmospheres. Nat. Astron. 4, 802–806 (2020).Article 

Google Scholar 
Halme, P., Holec, J. & Heilmann-Clausen, J. The history and future of fungi as biodiversity surrogates in forests. Fungal Ecol. 27, 193–201 (2017).Article 

Google Scholar 
Arnolds, E. Decline of ectomycorrhizal fungi in Europe. Agric. Ecosyst. Environ. 35, 209–244 (1991).Article 

Google Scholar 
Boddy, L. in The Fungi (eds Watkinson, S. C. et al.) 361–400 (Elsevier, 2016); https://doi.org/10.1016/B978-0-12-382034-1.00011-6Zimmerman, M. The mushroom message. Sun 11A (1992).Bader, P., Jansson, S. & Jonsson, B. G. Wood-inhabiting fungi and substratum decline in selectively logged boreal spruce forests. Biol. Conserv. 72, 355–362 (1995).Article 

Google Scholar 
Weinbauer, M. G. & Rassoulzadegan, F. Extinction of microbes: evidence and potential consequences. Endanger. Species Res. 3, 205–215 (2007).Article 

Google Scholar 
Chomicki, G., Kiers, E. T. & Renner, S. S. The evolution of mutualistic dependence. Annu. Rev. Ecol. Evol. Syst. 51, 409–432 (2020).Article 

Google Scholar 
Blaser, M. J. The theory of disappearing microbiota and the epidemics of chronic diseases. Nat. Rev. Immunol. 17, 461–463 (2017).CAS 
PubMed 
Article 

Google Scholar 
Carthey, A. J., Blumstein, D. T., Gallagher, R. V., Tetu, S. G. & Gillings, M. R. Conserving the holobiont. Funct. Ecol. 34, 764–776 (2020).Article 

Google Scholar 
Schapheer, C., Pellens, R. & Scherson, R. Arthropod-microbiota integration: its importance for ecosystem conservation. Front. Microbiol. 12, 2094 (2021).Article 

Google Scholar 
Zhou, Z., Wang, C. & Luo, Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat. Commun. 11, 3072 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anthony, M. A., Stinson, K. A., Moore, J. A. M. & Frey, S. D. Plant invasion impacts on fungal community structure and function depend on soil warming and nitrogen enrichment. Oecologia 194, 659–672 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lilleskov, E., Hobbie, E. A. & Horton, T. Conservation of ectomycorrhizal fungi: exploring the linkages between functional and taxonomic responses to anthropogenic N deposition. Fungal Ecol. 4, 174–183 (2011).Article 

Google Scholar 
Gibbons, S. M. et al. Invasive plants rapidly reshape soil properties in a grassland ecosystem. mSystems 2, e00178-16 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Certini, G., Moya, D., Lucas-Borja, M. E. & Mastrolonardo, G. The impact of fire on soil-dwelling biota: a review. For. Ecol. Manage. 488, 118989 (2021).Article 

Google Scholar 
Caruso, T., Hempel, S., Powell, J. R., Barto, E. K. & Rillig, M. C. Compositional divergence and convergence in arbuscular mycorrhizal fungal communities. Ecology 93, 1115–1124 (2012).CAS 
PubMed 
Article 

Google Scholar 
Anthony, M., Frey, S. & Stinson, K. Fungal community homogenization, shift in dominant trophic guild, and appearance of novel taxa with biotic invasion. Ecosphere 8, e01951 (2017).Article 

Google Scholar 
Guerra, C. A. et al. Global projections of the soil microbiome in the Anthropocene. Glob. Ecol. Biogeogr. 30, 987–999 (2021).PubMed 
Article 

Google Scholar 
Enright, D. J., Frangioso, K. M., Isobe, K., Rizzo, D. M. & Glassman, S. I. Mega‐fire in redwood tanoak forest reduces bacterial and fungal richness and selects for pyrophilous taxa that are phylogenetically conserved. Mol. Ecol. 31, 2475–2493 (2022).CAS 
PubMed 
Article 

Google Scholar 
Anthony, M. A. et al. Forest tree growth is linked to mycorrhizal fungal composition and function across Europe. ISME J. 16, 1327–1336 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).CAS 
PubMed 
Article 

Google Scholar 
Ceballos, G. et al. Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Novacek, M. J. & Cleland, E. E. The current biodiversity extinction event: scenarios for mitigation and recovery. Proc. Natl Acad. Sci. USA 98, 5466–5470 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 
Article 

Google Scholar 
Guerra, C. A. et al. Blind spots in global soil biodiversity and ecosystem function research. Nat. Commun. 11, 3870 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cameron, E. K. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019).PubMed 
Article 

Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).PubMed 
Article 

Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).CAS 
PubMed 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).CAS 
PubMed 
Article 

Google Scholar 
Peixoto, R. S. et al. Harnessing the microbiome to prevent global biodiversity loss. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01173-1 (2022).Box, G. E. P. & Draper, N. R. Empirical Model-building and Response Surfaces (Wiley, 1987).Box, G. E. P., Hunter, W. G. & Hunter, J. S. Statistics for Experimenters: an Introduction to Design, Data Analysis, and Model Building (Wiley, 1978).Kothamasi, D., Spurlock, M. & Kiers, E. T. Agricultural microbial resources: private property or global commons? Nat. Biotechnol. 29, 1091–1093 (2011).CAS 
PubMed 
Article 

Google Scholar 
Davison, J. et al. Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science 349, 970–973 (2015).CAS 
PubMed 
Article 

Google Scholar 
van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).PubMed 
Article 

Google Scholar 
Davison, J. et al. Temperature and pH define the realised niche space of arbuscular mycorrhizal fungi. New Phytol. 231, 763–776 (2021).CAS 
PubMed 
Article 

Google Scholar 
Ramirez, K. S. et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat. Microbiol. 3, 189–196 (2018).CAS 
PubMed 
Article 

Google Scholar 
Wild, S. Quest to map Africa’s soil microbiome begins. Nature 539, 152 (2016).CAS 
PubMed 
Article 

Google Scholar 
Bissett, A. et al. Introducing BASE: the Biomes of Australian Soil Environments soil microbial diversity database. GigaScience 5, 21 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Pan, K., Guo, Z. & Liu, J. Building and materializing of China Soil Microbiome Data Platform. Acta Pedol. Sin. 56, 1023–1033 (2019).
Google Scholar 
Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A. & Fernández‐Ugalde, O. LUCAS Soil, the largest expandable soil dataset for Europe: a review. Eur. J. Soil Sci. 69, 140–153 (2018).Article 

Google Scholar 
Hinckley, E. S. et al. The soil and plant biogeochemistry sampling design for The National Ecological Observatory Network. Ecosphere 7, e01234 (2016).Article 

Google Scholar 
Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 228 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Jackson, F. Sustainable agriculture and a low carbon future: are we missing out on mycelium? Forbes https://www.forbes.com/sites/feliciajackson/2021/12/02/sustainable-agriculture-and-a-low-carbon-future-are-we-missing-out-on-mycelium/?sh=3dc1a6d076ed (2021).Gilbert, J. A., Jansson, J. K. & Knight, R. The Earth Microbiome project: successes and aspirations. BMC Biol. 12, 69 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Fedrowitz, K. et al. Can retention forestry help conserve biodiversity? A meta‐analysis. J. Appl. Ecol. 51, 1669–1679 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Schmidt, R., Mitchell, J. & Scow, K. Cover cropping and no-till increase diversity and symbiotroph:saprotroph ratios of soil fungal communities. Soil Biol. Biochem. 129, 99–109 (2019).CAS 
Article 

Google Scholar 
Status of the World’s Soil Resources: Main Report (FAO, 2015).Aronson, J., Goodwin, N., Orlando, L., Eisenberg, C. & Cross, A. T. A world of possibilities: six restoration strategies to support the United Nation’s Decade on Ecosystem Restoration. Restor. Ecol. 28, 730–736 (2020).Article 

Google Scholar 
Seymour, F. Seeing the forests as well as the (trillion) trees in corporate climate strategies. One Earth 2, 390–393 (2020).Article 

Google Scholar 
Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Philipson, C. D. et al. Active restoration accelerates the carbon recovery of human-modified tropical forests. Science 369, 838–841 (2020).CAS 
PubMed 
Article 

Google Scholar 
Coleman, E. A. et al. Limited effects of tree planting on forest canopy cover and rural livelihoods in Northern India. Nat. Sustain. 4, 997–1004 (2021).Article 

Google Scholar 
Neuenkamp, L., Prober, S. M., Price, J. N., Zobel, M. & Standish, R. J. Benefits of mycorrhizal inoculation to ecological restoration depend on plant functional type, restoration context and time. Fungal Ecol. 40, 140–149 (2019).Article 

Google Scholar 
Koziol, L. et al. Manipulating plant microbiomes in the field: native mycorrhizae advance plant succession and improve native plant restoration. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.14036 (2021).Wubs, E. R. J., van der Putten, W. H., Bosch, M. & Bezemer, T. M. Soil inoculation steers restoration of terrestrial ecosystems. Nat. Plants 2, 16107 (2016).PubMed 
Article 

Google Scholar 
Bever, J. & Schultz, P. Prairie mycorrhizal fungi inoculant may increase native plant diversity on restored sites (Illinois). Ecol. Restor. 21, 311–312 (2003).
Google Scholar 
Vahter, T. et al. Co-introduction of native mycorrhizal fungi and plant seeds accelerates restoration of post-mining landscapes. J. Appl. Ecol. 57, 1741–1751 (2020).CAS 
Article 

Google Scholar 
Egan, C. P. et al. Restoration of the mycobiome of the endangered Hawaiian mint Phyllostegia kaalaensis increases its resistance to a common powdery mildew. Fungal Ecol. 52, 101070 (2021).Article 

Google Scholar 
Wubs, E. R. J. et al. Single introductions of soil biota and plants generate long‐term legacies in soil and plant community assembly. Ecol. Lett. 22, 1145–1151 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Abrego, N. et al. Reintroduction of threatened fungal species via inoculation. Biol. Conserv. 203, 120–124 (2016).Article 

Google Scholar 
Salomon, M. J. et al. Global evaluation of commercial arbuscular mycorrhizal inoculants under greenhouse and field conditions. Appl. Soil Ecol. 169, 104225 (2022).Article 

Google Scholar 
Maltz, M. R. & Treseder, K. K. Sources of inocula influence mycorrhizal colonization of plants in restoration projects: a meta-analysis: mycorrhizal inoculation in restoration. Restor. Ecol. 23, 625–634 (2015).Article 

Google Scholar 
Busby, P. E., Newcombe, G., Neat, A. S. & Averill, C. Facilitating reforestation through the plant microbiome: perspectives from the phyllosphere. Annu. Rev. Phytopathol. https://doi.org/10.1146/annurev-phyto-021320-010717 (2022).van der Heijden, M. G. A., Martin, F. M., Selosse, M.-A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).PubMed 
Article 

Google Scholar 
Crowther, T. W. et al. Predicting the responsiveness of soil biodiversity to deforestation: a cross-biome study. Glob. Change Biol. 20, 2983–2994 (2014).Article 

Google Scholar 
Lilleskov, E. A., Kuyper, T. W., Bidartondo, M. I. & Hobbie, E. A. Atmospheric nitrogen deposition impacts on the structure and function of forest mycorrhizal communities: a review. Environ. Pollut. 246, 148–162 (2019).CAS 
PubMed 
Article 

Google Scholar 
Smith, G. R., Steidinger, B. S., Bruns, T. D. & Peay, K. G. Competition–colonization tradeoffs structure fungal diversity. ISME J. 12, 1758–1767 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ceballos, I. et al. The in vitro mass-produced model mycorrhizal fungus, Rhizophagus irregularis, significantly increases yields of the globally important food security crop cassava. PLoS ONE 8, e70633 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buysens, C., César, V., Ferrais, F., de Boulois, H. D. & Declerck, S. Inoculation of Medicago sativa cover crop with Rhizophagus irregularis and Trichoderma harzianum increases the yield of subsequently-grown potato under low nutrient conditions. Appl. Soil Ecol. 105, 137–143 (2016).Article 

Google Scholar 
Antunes, P. M. et al. Influence of commercial inoculation with Glomus intraradices on the structure and functioning of an AM fungal community from an agricultural site. Plant Soil 317, 257–266 (2009).CAS 
Article 

Google Scholar 
Emam, T. Local soil, but not commercial AMF inoculum, increases native and non‐native grass growth at a mine restoration site. Restor. Ecol. 24, 35–44 (2016).Article 

Google Scholar 
Hoeksema, J. D. et al. A meta-analysis of context-dependency in plant response to inoculation with mycorrhizal fungi. Ecol. Lett. 13, 394–407 (2010).PubMed 
Article 

Google Scholar 
Policelli, N., Horton, T. R., Hudon, A. T., Patterson, T. R. & Bhatnagar, J. M. Back to roots: the role of ectomycorrhizal fungi in boreal and temperate forest restoration. Front. For. Glob. Change 3, 97 (2020).Article 

Google Scholar 
Hoeksema, J. D. et al. Ectomycorrhizal plant-fungal co-invasions as natural experiments for connecting plant and fungal traits to their ecosystem consequences. Front. Glob. Change 3, 84 (2020).Article 

Google Scholar 
Land Use Statistics and Indicators. Global, Regional and Country Trends 1990– 2019 FAOSTAT Analytical Brief Series No. 28 (FAO, 2021).Stewart, W. M., Dibb, D. W., Johnston, A. E. & Smyth, T. J. The contribution of commercial fertilizer nutrients to food production. Agron. J. 97, 1–6 (2005).Article 

Google Scholar 
Harlander, S. K. The evolution of modern agriculture and its future with biotechnology. J. Am. Coll. Nutr. 21, 161S–165S (2002).PubMed 
Article 

Google Scholar 
Cooper, J. & Dobson, H. The benefits of pesticides to mankind and the environment. Crop Prot. 26, 1337–1348 (2007).CAS 
Article 

Google Scholar 
Zsögön, A., Peres, L. E. P., Xiao, Y., Yan, J. & Fernie, A. R. Enhancing crop diversity for food security in the face of climate uncertainty. Plant J. https://doi.org/10.1111/tpj.15626 (2021).IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).McDonald, B. A. & Stukenbrock, E. H. Rapid emergence of pathogens in agro-ecosystems: global threats to agricultural sustainability and food security. Phil. Trans. R. Soc. Lond. B 371, 20160026 (2016).Article 

Google Scholar 
Avelino, J. et al. The coffee rust crises in Colombia and Central America (2008–2013): impacts, plausible causes and proposed solutions. Food Sec. 7, 303–321 (2015).Article 

Google Scholar 
Goss, E. M. et al. The Irish potato famine pathogen Phytophthora infestans originated in central Mexico rather than the Andes. Proc. Natl Acad. Sci. USA 111, 8791–8796 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ploetz, R. C. Panama disease: a classic and destructive disease of banana. Plant Health Prog. https://doi.org/10.1094/PHP-2000-1204-01-HM (2000).Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).PubMed 
Article 

Google Scholar 
Thibaut, L. M. & Connolly, S. R. Understanding diversity–stability relationships: towards a unified model of portfolio effects. Ecol. Lett. 16, 140–150 (2013).PubMed 
Article 

Google Scholar 
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).CAS 
PubMed 
Article 

Google Scholar 
Prieto, I. et al. Complementary effects of species and genetic diversity on productivity and stability of sown grasslands. Nat. Plants 1, 15033 (2015).CAS 
PubMed 
Article 

Google Scholar 
Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).PubMed 
Article 

Google Scholar 
Cornell, C. et al. Do bioinoculants affect resident microbial communities? A meta-analysis. Front. Agron. 3, 753474 (2021).Article 

Google Scholar 
Manning, L. Groundwork BioAg raises $11m to expand mycorrhizal inputs business. AgFunder Network https://agfundernews.com/groundwork-bioag-raises-11m-to-expand-mycorrhizal-inputs-business (2021).Egidi, E. et al. A few Ascomycota taxa dominate soil fungal communities worldwide. Nat. Commun. 10, 2369 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Olle, M. & Williams, I. H. Effective microorganisms and their influence on vegetable production—a review. J. Hortic. Sci. Biotechnol. 88, 380–386 (2013).Article 

Google Scholar 
Mayer, J., Scheid, S., Widmer, F., Fließbach, A. & Oberholzer, H.-R. How effective are ‘Effective microorganisms® (EM)’? Results from a field study in temperate climate. Appl. Soil Ecol. 46, 230–239 (2010).Article 

Google Scholar 
Kodippili, K. P. A. N. & Nimalan, J. Effect of homemade effective microorganisms on the growth and yield of chilli (Capsicum annuum) MI-2. AGRIEAST J. Agric. Sci. https://doi.org/10.4038/agrieast.v12i2.57 (2018).de Araujo Avila, G. M., Gabardo, G., Clock, D. C. & de Lima Junior, O. S. Use of efficient microorganisms in agriculture. Res. Soc. Dev. https://doi.org/10.33448/rsd-v10i8.17515 (2021).Saleem, M., Hu, J. & Jousset, A. More than the sum of its parts: microbiome biodiversity as a driver of plant growth and soil health. Annu. Rev. Ecol. Evol. Syst. 50, 145–168 (2019).Article 

Google Scholar 
Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).PubMed 
Article 

Google Scholar 
Romero-Olivares, A. L., Allison, S. D. & Treseder, K. K. Soil microbes and their response to experimental warming over time: a meta-analysis of field studies. Soil Biol. Biochem. 107, 32–40 (2017).CAS 
Article 

Google Scholar 
Klironomos, J. N. Variation in plant response to native and exotic arbuscular mycorrhizal fungi. Ecology 84, 2292–2301 (2003).Article 

Google Scholar 
Veen, C. G. F., Snoek, B. L., Bakx-Schotman, T., Wardle, D. A. & van der Putten, W. H. Relationships between fungal community composition in decomposing leaf litter and home-field advantage effects. Funct. Ecol. 33, 1524–1535 (2019).Article 

Google Scholar 
Wang, Q., Zhong, M. & He, T. Home-field advantage of litter decomposition and nitrogen release in forest ecosystems. Biol. Fertil. Soils 49, 427–434 (2013).CAS 
Article 

Google Scholar 
Hawkes, C. V., Waring, B. G., Rocca, J. D. & Kivlin, S. N. Historical climate controls soil respiration responses to current soil moisture. Proc. Natl Acad. Sci. USA 114, 6322–6327 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morriën, E. et al. Soil networks become more connected and take up more carbon as nature restoration progresses. Nat. Commun. 8, 14349 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittebolle, L. et al. Initial community evenness favours functionality under selective stress. Nature 458, 623–626 (2009).CAS 
PubMed 
Article 

Google Scholar 
de Graaff, M.-A., Adkins, J., Kardol, P. & Throop, H. A meta-analysis of soil biodiversity impacts on the carbon cycle. Soil 1, 257–271 (2015).Article 

Google Scholar 
Gao, J. et al. Assessing the effect of leaf litter diversity on the decomposition and associated diversity of fungal assemblages. Forests 6, 2371–2386 (2015).Article 

Google Scholar 
Selosse, M.-A., Bouchard, D., Martin, F. & Tacon, F. L. Effect of Laccaria bicolor strains inoculated on Douglas-fir (Pseudotsuga menziesii) several years after nursery inoculation. Can. J. Res. 30, 360–371 (2000).Article 

Google Scholar 
Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).PubMed 
PubMed Central 
Article 

Google Scholar  More

Benton MJ, Donoghue PCJ. Paleontological evidence to date the tree of life. Mol Biol Evol. 2006;24:26–53.PubMed 

Google Scholar 
Roll U, Feldman A, Novosolov M, Allison A, Bauer AM, Bernard R, et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat Ecol Evol. 2017;1:1677–82.PubMed 

Google Scholar 
IUCN, The IUCN Red List of Threatened Species. Version 2021-3. 2021.Medicine, N.L.o., NCBI Genome. 2022, National Center for Biotechnology Information.Hotaling S, Kelley JL, Frandsen PB. Toward a genome sequence for every animal: Where are we now? Proc Natl Acad Sci. 2021;118:e2109019118.PubMed 
PubMed Central 

Google Scholar 
Shi M, Lin XD, Chen X, Tian JH, Chen LJ, Li K, et al. The evolutionary history of vertebrate RNA viruses. Nature. 2018;556:197–202.PubMed 

Google Scholar 
Parry R, Wille M, Turnbull OMH, Geoghegan JL, Holmes EC. Divergent influenzalike viruses of amphibians and fish support an ancient evolutionary association. Viruses. 2020;12:1042.PubMed Central 

Google Scholar 
Peck KM, Lauring AS, Christopher S, Complexities of viral mutation rates. J Virol. 92: e01031-17.Latney LV, Klaphake E. Selected emerging infectious diseases of amphibians. Vet Clin N Am—Exotic Animal Pract. 2020;23:397–412.
Google Scholar 
Zhang J, Finlaison DS, Frost MJ, Gestier S, Gu X, Hall J, et al. Identification of a novel nidovirus as a potential cause of large scale mortalities in the endangered Bellinger River snapping turtle (Myuchelys georgesi). PLOS ONE. 2018;13:e0205209.PubMed 
PubMed Central 

Google Scholar 
Parrish K, Kirkland PD, Skerratt LF, Ariel E. Nidoviruses in reptiles: a review. Front Vet Sci. 2021;8:733404.PubMed 
PubMed Central 

Google Scholar 
Chang WS, Li CX, Hall J, Eden JS, Hyndman TH, Holmes EC, et al. Metatranscriptomic discovery of a divergent circovirus and a chaphamaparvovirus in captive reptiles with proliferative respiratory syndrome. Viruses. 2020;12:1073.PubMed Central 

Google Scholar 
Mendoza-Roldan JA, Mendoza-Roldan MA, Otranto D. Reptile vector-borne diseases of zoonotic concern. Int J Parasitol: Parasites Wildl. 2021;15:132–42.
Google Scholar 
Essbauer S, Ahne W. Viruses of lower vertebrates. J Vet Med Ser B. 2001;48:403–75.
Google Scholar 
Mercer LK, Harding EF, Yan GJH, White PA. Novel viruses discovered in the transcriptomes of agnathan fish. J Fish Dis. 2022;45:931–8.PubMed 
PubMed Central 

Google Scholar 
Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat biotechnol. 2011;29:644–52.PubMed 
PubMed Central 

Google Scholar 
Harding EF, Russo AG, Yan GJH, Waters PD, White PA. Ancient viral integrations in marsupials: a potential antiviral defence. Virus Evol. 2021;7:veab076.PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.PubMed 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.PubMed 
PubMed Central 

Google Scholar 
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.PubMed 
PubMed Central 

Google Scholar 
Kelly AG, Netzler NE, White PA. Ancient recombination events and the origins of hepatitis E virus. BMC Evol Biol. 2016;16:210.PubMed 
PubMed Central 

Google Scholar 
Rector A, Van, Ranst M. Animal papillomaviruses. Virology. 2013;445:213–23.PubMed 

Google Scholar 
Blahak S, Jenckel M, Höper D, Beer M, Hoffmann B, Schlottau K. Investigations into the presence of nidoviruses in pythons. Virol J. 2020;17:6.PubMed 
PubMed Central 

Google Scholar 
Marschang RE. Viruses infecting reptiles. Viruses. 2011;3:2087–126.PubMed 
PubMed Central 

Google Scholar 
Horie M, Akashi H, Kawata M, Tomonaga K. Identification of a reptile lyssavirus in Anolis allogus provided novel insights into lyssavirus evolution. Virus Genes. 2021;57:40–49.PubMed 

Google Scholar 
Stenglein MD, Sanders C, Kistler AL, Ruby JG, Franco JY, Reavill DR, et al. Identification, characterization, and in vitro culture of highly divergent arenaviruses from boa constrictors and annulated tree boas: candidate etiological agents for snake inclusion body disease. mBio. 2012;3:e00180–12.PubMed 
PubMed Central 

Google Scholar 
Garver KA, Leskisenoja K, Macrae R, Hawley LM, Subramaniam K, Waltzek TB, et al. An alloherpesvirus infection of european perch perca fluviatilis in Finland. Dis Aquat Org. 2018;128:175–85.
Google Scholar 
Hellebuyck T, Couck L, Ducatelle R, Broeck WV, Marschang RE. Cheilitis associated with a novel herpesvirus in two panther chameleons (Furcifer pardalis). J Comp Pathol. 2021;182:58–66.PubMed 

Google Scholar 
Altan E, Kubiski SV, Burchell J, Bicknese E, Deng X, Delwart E. The first reptilian circovirus identified infects gut and liver tissues of black-headed pythons. Vet Res. 2019;50:35.PubMed 
PubMed Central 

Google Scholar 
Russo AG, Harding EF, Yan GJH, Selechnik D, Ducatez S, DeVore JL, et al. Discovery of novel viruses associated with the invasive cane toad (Rhinella marina) in its native and introduced ranges. Front Microbiol. 2021;12:733631.PubMed 
PubMed Central 

Google Scholar 
Chen X-X, Wu W-C, Shi M. Discovery and characterization of actively replicating DNA and retro-transcribing viruses in lower vertebrate hosts based on RNA sequencing. Viruses. 2021;13:1042.PubMed 
PubMed Central 

Google Scholar 
Russo AG, Eden JS, Tuipulotu DE, Shi M, Selechnik D, Shine R, et al. Viral discovery in the invasive Australian cane toad (Rhinella marina) using metatranscriptomic and genomic approaches. J Virol. 2018;92:e00768–18.PubMed 
PubMed Central 

Google Scholar 
López-Bueno A, Mavian C, Labella AM, Castro D, Borrego JJ, Alcami A, et al. Concurrence of Iridovirus, Polyomavirus, and a unique member of a new group of fish Papillomaviruses in Lymphocystis disease-affected gilthead sea bream. Journal of virology. 2016;90:8768–79.PubMed 
PubMed Central 

Google Scholar 
Bentley K, Evans DJ. Mechanisms and consequences of positive-strand RNA virus recombination. J Gen Virol. 2018;99:1345–56.PubMed 

Google Scholar 
Diemer GS, Stedman KM. A novel virus genome discovered in an extreme environment suggests recombination between unrelated groups of RNA and DNA viruses. Biol Direct. 2012;7:13.PubMed 
PubMed Central 

Google Scholar 
Welch, NL, MJ Tisza, GJ Starrett, AK Belford, DV Pastrana, Y-YS Pang, et al. Identification of Adomavirus Virion proteins. bioRxiv. 2020:341131. https://doi.org/10.1101/341131Dill JA, Camus AC, Leary JH, Ng TFF, Zheng Z-M, Meng X-J. Microscopic and Molecular Evidence of the First Elasmobranch Adomavirus, the Cause of Skin Disease in a Giant Guitarfish, Rhynchobatus djiddensis. mBio. 2018;9:e00185–18.PubMed 
PubMed Central 

Google Scholar 
Yang J-X, Chen X, Li Y-Y, Song T-Y, Ge J-Q. Isolation of a novel adomavirus from cultured American eels, Anguilla rostrata, with haemorrhagic gill necrosis disease. J Fish Dis. 2021;44:1811–8.PubMed 

Google Scholar 
King AMQ, Adams MJ, Carstens EB & Lefkowitz EJ, Order – Nidovirales, in virus taxonomy: classification and nomenclature of viruses. 2012, Elsevier/Academic Press: San Diego.Lyu S, Yuan X, Zhang H, Shi W, Hang X, Liu L, et al. Complete genome sequence and analysis of a new lethal arterivirus, Trionyx sinensis hemorrhagic syndrome virus (TSHSV), amplified from an infected Chinese softshell turtle. Arch Virol. 2019;164:2593–7.PubMed 
PubMed Central 

Google Scholar 
Sinzelle L, Carradec Q, Paillard E, Bronchain OJ, Pollet N. Characterization of a Xenopus tropicalis endogenous retrovirus with developmental and stress-dependent expression. J Virol. 2011;85:2167–79.PubMed 

Google Scholar 
Wei X, Chen Y, Duan G, Holmes EC, Cui J. A reptilian endogenous foamy virus sheds light on the early evolution of retroviruses. Virus Evol. 2019;5:vez001.PubMed 
PubMed Central 

Google Scholar 
Debat HJ, Ng TFF. Complete genome sequence of a divergent strain of Tibetan frog hepatitis B virus associated with a concave-eared torrent frog (Odorrana tormota). Arch Virol. 2019;164:1727–32.PubMed 

Google Scholar 
Reuter G, Boros Á, Tóth Z, Gia Phan T, Delwart E, Pankovics P. A highly divergent picornavirus in an amphibian, the smooth newt (Lissotriton vulgaris). J Gen Virol. 2015;96:2607–13.PubMed 
PubMed Central 

Google Scholar 
ICTV. Subfamily: Secondpapillomavirinae. 2021 [cited 2022 15/06/2022]; Virus Taxonomy: 2021 Release:[Available from: https://talk.ictvonline.org/ictv-reports/ictv_online_report/dsdna-viruses/w/papillomaviridae/894/subfamilysecondpapillomavirinae.Willemsen A, Bravo IG. Origin and evolution of papillomavirus (onco)genes and genomes. Philos Trans R Soc B: Biol Sci. 2019;374:20180303.
Google Scholar 
Agius JE, Phalen DN, Rose K, Eden J-S. New insights into Sauropsid Papillomaviridae evolution and epizootiology: discovery of two novel papillomaviruses in native and invasive Island geckos. Virus Evol. 2019;5:vez051.PubMed 
PubMed Central 

Google Scholar 
Bienentreu J-F, Lesbarrères D. Amphibian disease ecology: are we just scratching the surface? Herpetologica. 2020;76:153–66.
Google Scholar 
Mashkour N, Jones K, Wirth W, Burgess G, Ariel E. The concurrent detection of Chelonid Alphaherpesvirus 5 and Chelonia mydas Papillomavirus 1 in tumoured and non-tumoured green turtles. Animals. 2021;11:697.PubMed 
PubMed Central 

Google Scholar 
Hoon-Hanks LL, Layton ML, Ossiboff RJ, Parker JSL, Dubovi EJ, Stenglein MD. Respiratory disease in ball pythons (Python regius) experimentally infected with ball python nidovirus. Virology. 2018;517:77–87.PubMed 

Google Scholar 
Dervas E, Hepojoki J, Smura T, Prähauser B, Windbichler K, Blümich S, et al. Serpentoviruses: More than respiratory pathogens. J Virol. 2020;94:e00649–20.PubMed 
PubMed Central 

Google Scholar 
O’Dea MA, Jackson B, Jackson C, Xavier P, Warren K. Discovery and Partial Genomic Characterisation of a Novel Nidovirus Associated with Respiratory Disease in Wild Shingleback Lizards (Tiliqua rugosa). PloS One. 2016;11:e0165209.PubMed 
PubMed Central 

Google Scholar 
Dervas E, Hepojoki J, Laimbacher A, Romero-Palomo F, Jelinek C, Keller S, et al. Nidovirus-associated proliferative pneumonia in the green tree python (Morelia viridis). J Virol. 2017;91:e00718–17.PubMed 
PubMed Central 

Google Scholar 
Oberhuber M, Schopf A, Hennrich AA, Santos-Mandujano R, Huhn AG, Seitz S, et al. Glycoproteins of predicted amphibian and reptile lyssaviruses can mediate infection of mammalian and reptile cells. Viruses. 2021;13:1726.PubMed 
PubMed Central 

Google Scholar 
Ritchie BW, Niagro FD, Lukert PD, Steffens WL, Latimer KS. Characterization of a new virus from cockatoos with psittacine beak and feather disease. Virology. 1989;171:83–88.PubMed 

Google Scholar 
Eleni C, Corteggio A, Altamura G, Meoli R, Cocumelli C, Rossi G, et al. Detection of Papillomavirus DNA in cutaneous squamous cell carcinoma and multiple papillomas in captive reptiles. J Comp Pathol. 2017;157:23–26.PubMed 

Google Scholar 
Tessier TM, Dodge MJ, MacNeil KM, Evans AM, Prusinkiewicz MA, Mymryk JS. Almost famous: Human adenoviruses (and what they have taught us about cancer). Tumour. Virus Res. 2021;12:200225.
Google Scholar 
Chen XX, Wu WC, Shi M. Discovery and characterization of actively replicating dna and retro-transcribing viruses in lower vertebrate hosts based on rna sequencing. Viruses. 2021;13:1042.PubMed 
PubMed Central 

Google Scholar 
Liu W, Zhang Y, Ma J, Jiang N, Fan Y, Zhou Y, et al. Determination of a novel parvovirus pathogen associated with massive mortality in adult tilapia. PLOS Pathogens. 2020;16:e1008765.PubMed 
PubMed Central 

Google Scholar  More

Sampling location and sediment coringSamples were collected during IODP Exp. 382 ‘Iceberg Alley and Subantarctic Ice and Ocean Dynamics’ on-board RV Joides Resolution between 20 March and 20 May 2019. Specifically, we collected samples at Site U1534 (Falkland Plateau, 606 m water depth), U1536 (Dove Basin, Scotia Sea, 3220 m water depth), and Site U1538 (Pirie Basin, Scotia Sea, 3130 m water depth) (Fig. 1). Site U1534 is located at the Subantarctic Front on a contourite drift at the northern limit of the Scotia Sea. This setting is ideal to study the poorly understood role of Antarctic Intermediate Water (AAIC) and its impact on the Atlantic Meridional Overturning Circulation (AMOC) along the so-called ‘cold water route’ that connects to the Pacific Ocean through the Drake Passage, as opposed to the ‘warm water route’ that connects to the Indian Ocean via the Agulhas Current42. Sites U1536 and U1538 are located in the southern and central Scotia Sea, respectively, and were drilled to study the Neogene flux of icebergs through ‘Iceberg Alley’, the main pathway along which icebergs calved from the margin of the AIS travel as they move equatorward into the warmer waters of the Antarctic Circumpolar Current (ACC)23. sedaDNA samples collected at Site U1534 were from Hole C, at Site U1536 from Hole B, and at Site U1538 from Holes C and D (Table 1), and in the following we refer to site names only. IODP Expedition proposals undergo a rigorous environmental protection and safety review, which is approved by the IODP’s Environmental Protection and Safety Panel (EPSP) and/or the Safety Panel. The same procedure was applied to IODP Exp. 382 and approval was provided by the EPSP. Sediment samples for sedaDNA analyses were imported to Australia under Import Permit number 0002658554 provided by the Australian Government Department for Agriculture and Water Resources (date of issue: 19 September 2018), and were stored and extracted at a quarantine approved facility (AA Site No. S1253, Australian Centre for Ancient DNA). No ethical approval was required for this study.Table 1 Sampling location and sample detailsFull size tableSample age determinationAge control for Site U1534 is based on tuning of benthic foraminifera δ18O to the LR04 stack43. Wherever present specimens of Uvigerina bifurcata were picked from samples at 10 cm intervals. During warmer periods when U. bifurcata was not present, Melonis affinis and/or Hoeglundina elegans were analysed. Sedimentation rates over the intervals sampled for sedaDNA typically range between 6 and 30 cm/kyr, with rates exceeding 100 cm/kyr during the Last Glacial Maximum ~20,000 years ago (20 ka). For our deepest sample, U1534C-10H-6_115cm (90.95 mbsf), we only have biostratigraphically assigned ages available (shipboard data), which date this sample as early Pleistocene (~2.5–0.7 million years ago, Ma44).Low-resolution age control for both Sites U1536 and U1538 was established using shipboard magneto- and biostratigraphy21,23. Average sedimentation rates are ~10 cm/kyr for Site U1536, with elevated values (up to 20 cm/kyr) in the upper ~80 mbsf (the last ~400 ka). Site U1538 average sedimentation rates are twice as high, averaging ~20 cm/kyr. Especially in the upper ~430 mbsf (the last 1.8 Ma), rates are up to 40 cm/kyr. Higher resolution age models are based on dust climate couplings, correlating sedimentary dust proxy records such as magnetic susceptibility and sedimentary Ca and Fe records to ice-core dust proxy records over the last 800 ka45 and to a benthic isotopic stack26 before that. These age models were established for Site U1537 (adjacent to Site U1536) and provide orbital to millennial scale resolution. For this study we correlated sedimentary cycles of Sites U1536 and U1538 to U1537 to achieve similar resolution and to be able to determine if a sample originates from a glacial or interglacial period (Table 1).Sampling of sedaDNAA detailed description of sedaDNA sampling methods can be found in ref. 24. In brief, we used advanced piston coring (APC) to acquire sediment cores, which recovers the least disturbed sediments46,47,48 and is thus the preferred technique for sedaDNA sampling. All samples were taken on the ship’s ‘catwalk’, where, once the core was on deck, the core liners were wiped clean twice (3% sodium hypochlorite, ‘bleach’) at each cutting point. Core cutting tools were sterilised before each cut (3% bleach and 80% ethanol) of the core in 1 m sections. The outer ~3 mm of surface material were removed from the bottom of each core section to be sampled, using sterilised scrapers (~4 cm wide; bleach and ethanol treated). A cylindrical sample was taken from the core centre using a sterile (autoclaved) 10 mL cut-tip syringe, providing ~5 cm3 of sediment material. The syringe was placed in a sterile plastic bag (Whirl-Pak) and immediately frozen at −80 °C. The mudline (sediment/seawater interface) was transferred from the core liner into a sterile bucket (3% bleach treated), and 10 mL sample was retained in a sterile 15 mL centrifuge tube (Falcon) and frozen at −80 °C. Samples were collected at various depth intervals depending on the site to span the Holocene up to ~1 million years (Table 1). This lower depth/age limit was determined by switching coring system from APC to the extended core barrel (XCB) system.To test for potential airborne contamination, at least one air control was taken during the sedaDNA sampling process per site. For this, an empty syringe was held for a few seconds in the sampling area and then transferred into a sterile plastic bag and frozen at −80 °C. The air controls were processed, sequenced and analysed alongside the sediment samples.Contamination control using perfluoromethyldecalin tracersAs part of the APC process, drill fluid (basically, seawater) is pumped into the borehole to trigger the hydraulic coring system, therefore, the potential for contamination exists due to drill fluid making contact with the core liner. To assess the latter, we added the non-toxic chemical tracer perfluoromethyldecalin (PFMD) to the drill fluid at a rate of ~0.55 mL min−1 for cores collected at Sites U1534 and U153649. As we found that PFMD concentrations were very low at these sites (Results section), the infusion rate was doubled prior to sedaDNA sampling at Site U1538 to ensure low PFMD concentrations represent low contamination and not delivery failure of PFMD to the core. At each sedaDNA sampling depth, one PFMD sample was taken from the periphery of the core (prior to scraping, to test whether drill fluid reached the core pipe), and one next to the sedaDNA sample in the centre of the core (after scraping, to minimise differences to the sedaDNA sample, and testing if drill fluid had reached the core centre). We transferred ~3 cm3 of sediment using a disposable, autoclaved 5 mL cut-tip syringe into a 20 mL headspace vial with metal caps and Teflon seals. We also collected a sample of the tracer-infused drill fluid at each site, by transferring ~10 mL of the fluid collected at the injection pipe on the rig floor via a sterile plastic bottle into a 15 mL centrifuge tube (inside a sterile plastic bag) and freezing it at −80 °C. These drill fluid controls were processed and analysed in the same way as the sedaDNA samples including sequencing. Samples were analysed using gas chromatography (GC-µECD; Hewlett-Packard 6890).A detailed description of the PFMD GC measurements is provided in ref. 24. Briefly, PFMD measurements were undertaken in batches per site for U1534, U1536 and U1538. This included the analyses of PFMD samples collected at two additional holes at these sites, U1534D and U1536C, from which we also collected sedaDNA samples but that are not part of this study. PFMD is categorised as the stereoisomers of PFMD (C11F20), which add up to 87-88% (and with the remaining 12% being additional perfluoro compounds unable to be separated by the manufacturer). We exclusively refer to the first and measurable PFMD category, calibrating for the 88% in bottle concentrations during concentration calculations. Each GC analysis run included the measurement of duplicate blanks and duplicate PFMD standards. Due to a large sample number, PFMD at Site U1538 was measured in three separate runs, with the first and last run including triplicate blank and triplicate PFMD standards (duplicates in the second run), and the last run also containing a drill-fluid sample. To blank-correct PFMD concentrations, we subtracted the average PFMD concentration of all blanks per run from PFMD measurements in that run. To determine the detection limit of PFMD, we used three times the standard deviation of the average blank PFMD values per run; due to all blank values for the U1538 runs being 0, we used three times the standard deviation of the lowest PFMD standard for this site in this calculation. This provided us with a PFMD detection limit of 0.2338 ng mL−1. Any PFMD measurements of samples below this limit were rejected.
sedaDNA extractions and metagenomic library preparationsA total of 80 sedaDNA extracts and metagenomic shotgun libraries (Table 1) were prepared following8,10. For the sedaDNA extractions, we randomised our samples and controls and extracted sedaDNA in batches of 16 extracts/libraries at a time, with each batch including at least one air control and one extraction blank control (EBC), and the last batch including mudline and PFMD samples to avoid contamination of the sedaDNA samples. In brief, we used 20 µL sedaDNA extracts in a repair reaction (using T4 DNA polymerase, New England Biolabs, USA; 15 min, 25 °C), then purified the sedaDNA (MinElute Reaction Cleanup Kit, Qiagen, Germany), ligated adaptors (T4 DNA ligase, Fermentas, USA, where truncated Illumina-adaptor sequences containing two unique 7 base-pair (bp) barcodes were attached to the double-stranded DNA; 60 min, 22 °C), purified the sedaDNA again (MinElute Reaction Cleanup Kit, Qiagen), and then added a fill-in reaction with adaptor sequences (Bst DNA polymerase, New England Biolabs, USA; 30 min, 37 °C, with polymerase deactivation for 10 min, 80 °C). We amplified the barcoded libraries using IS7/IS8 primers50 (8 replicates per sample, where each replicate was a 25 µL reaction containing 3 µL DNA template; using 22 cycles), purified (AxyPrep magnetic beads, Axygen Biosciences, USA; 1:1.8 library:beads) and quantified them (Qubit dsDNA HS Assay, Invitrogen, Molecular Probes, USA). We amplified the libraries (8 replicates per sample, 13 amplification cycles) using IS4 and GAII Indexing Primers50, purified (AxyPrep magnetic beads, at a ratio of 1:1.1 library:beads), quantified and quality-checked using Qubit (dsDNA HS Assay, Invitrogen, USA) and TapeStation (Agilent Technologies, USA). We combined the libraries into an equimolar pool (volume of 68 µL in total), diluted this pool with nuclease-free H2O to 100 µL, and performed a ‘reverse’ AxyPrep clean-up to retain only the small DNA fragments typical for ancient DNA (≤ 500 bp; initial library:beads ratio of 1:0.6, followed by 1:1.1, and double-eluted in 30 µL nuclease-free H2O8,51). We added one more AxyPrep clean-up to remove primer-dimer (library:beads ratio of 1:1.05) and checked sedaDNA quantity and quality via TapeStation and qPCR (QuantStudio, Applied Biosystems, USA). The libraries sequenced at the Garvan Institute for Medical Research, Sydney, Australia (Illumina NovaSeq 2 × 100 bp).
sedaDNA data processingThe sequencing data was processed and filtered as described in detail in refs. 8, 10. Briefly, data filtering involved the removal of sequences More

For several years, systematic research has been carried out on the pollution of the night sky by artificial light in the city of Toruń11,36,38. The main objective is to monitor this phenomenon, including its spatial and temporal variability and the most important factors affecting it. Based on previous experience, an in-house measurement device was constructed to automate the process of data acquisition.Genesis of the projectThe first measurements pertaining to the phenomenon of night sky pollution in Toruń were made in autumn 2017, followed by regular observations using a handheld SQM photometer (Unihedron, Canada) as part of a project implemented in 2017–2018. To this end, a permanent measurement network distributed throughout the city was established, consisting of 24 locations. During a one night measurement session taking place during the astronomical night (there is no such period at the latitude of Toruń in the summer), sky brightness was measured at all sites. The results of spot monitoring were plotted using interpolation methods and visualisation tools available in GIS systems11,39, which helped to determine the spatial distribution and extent of night sky light pollution. The intensity of this phenomenon at each of the surveyed points was also explored in relation to the distinguished landcover categories and types of urban development.Repeatable measurements performed regularly over such a long period of time were characterised by significant limitations. One measurement session was very time-consuming, as it lasted about two hours, during which time all measurement locations were visited, covering a distance of almost 50 km each night by car. Despite the observance of all time frames and sticking to the plan of fieldwork, measurements were not carried out simultaneously at all the locations, which affected the results, especially during the night with changing cloud cover. Although the measurements were carried out with great consistency and care, they were performed in a spatial buffer of about 5 m, which could unintentionally slightly affect the obtained results. An additional limitation was also a one-time night measurement at one point, instead of a whole series of measurements at specific time intervals. Inaccuracies in the readings within a single session could have been caused by sudden changes in meteorological parameters. In the adopted procedure, it was not possible to carry out simultaneous measurements in identical time and weather conditions at all the locations, not to mention the involvement of the personnel in each tour of the measurement network points.Using the experience gained and after an analysis of the identified constraints and the technical capabilities at hand, work began in 2019 on developing a network for automatic remote monitoring of light pollution of the night sky in Toruń, based on designed in-house recording devices.Design, functional and utility features of the deviceTo enhance the research on light pollution in urban space, work has begun on the construction of a device that would perform automatic measurements, would be mobile, battery-powered and use long-range wireless communication. All the aforementioned features are in line with the strategy of Industry 4.0 and modern solutions proposed as part of the Smart City concept.The concept of Industry 4.0 assumes the more and more common use of process automation as well as the processing and exchange of data with the use of new transmission technologies26. LoRaWAN is one of the solutions used for communication of Internet of Things (IoT) devices, which supports the development of Smart Cities in the Smart Environment area. As a result, the interactivity, frequency, and scope of measurements carried out in urbanized areas are increased40,41.According to the developed project, the device was to serve as a meter of very low intensity light observed in the night sky. In this respect, it was necessary to use a sensor with technical parameters suitable for very accurate measurements of light intensity. To locally verify the weather conditions occurring during the operation of the device, it was decided to carry out additional simultaneous measurements of other environmental parameters—temperature and moisture content. The analysis of the spatial coverage of the study area indicated that 36 measurement devices should be deployed to provide full coverage of Toruń. The concept of creating an urban measurement network assumes the selection of points covering the whole city relatively evenly and representing different types of housing development and elements of land cover. It was assumed that measurements will be made only at night, between 21 p.m. and 6 a.m. on the following day, at 15 min intervals, and in addition, weather conditions will be recorded twice a day.Construction and technical parameters of the device, and selected characteristics of its componentsA prototype device meeting all the predefined functionalities was constructed based on available electronic modules. The B-L072Z-LRWAN Discovery developmental board from STMicroelectronics42 was selected as the main electronic component providing wireless communication. This board has an integrated LoRa communication module, enabling low-power wireless messaging, and also allows the board to enter a low-power state during hibernation, and thus target long-term battery-powered operation. This module is fully programmable, which enables future expansion of the set with other functionalities. The TSL2591 light sensor from AMS, which has high sensitivity and registration accuracy, was selected as a component implementing the light intensity measurement. Its great advantage is a wide measurement range of 188 μlx to 88 000 lx, sensitivity reaching 0.000377 lx, and a wide dynamic range (WDR) of 600 M:143. The sensor used has two diodes with different spectral properties. One of them registers visible light together with infrared (in the range from 400 to 1 100 nm), while the other is responsible for the registration of infrared light (between 500 and 1 100 nm). Thanks to this solution, we can use the results in various ways. The use of the formula provided by the manufacturer allows us to obtain spectral characteristics similar to the human eye. The presence of a compensating diode makes a difference compared to the sensor used in the SQM device, so the results obtained in the measurements may be slightly different.To measure additional environmental parameters, the X-NUCLEO-IKS01A2 development board from STMicroelectronics was used, which is connected to the STM32 microcontroller via the I2C interface44. This board enables the recording of a number of parameters, however, in the constructed device it is only responsible for reading the temperature and humidity of the environment. This results from the necessity to limit the size of the message packets sent, while at the same time improving the operating range and reducing the power consumption of the device.Once all the components had been selected, tested and integrated, the process of final connection and programming was carried out. The base of the device, i.e. the B-L072Z-LRWAN development board was connected to the X-NUCLEO-IKS01A2 environmental sensor board, using Arduino connectors. Using standard wires, a TSL2591 light sensor was added by connecting the corresponding I2C (SCL and SDA), power supply (VIN), sensor ground (GND) pins and the X-NUCLEO-IKS01A2 board.All components used were placed in a standard external casing with dimensions of 8.0 × 5.4 × 15.8 cm. In its lower part an opening was made for an external antenna, while in the upper part a specifically selected opening was cut out, protected with a glass pane, through which measurements are performed by the light sensor (Fig. 2).Figure 2(photo by Dominika Karpińska).Constructed device viewFull size imageFollowing the above steps, an automatic device was constructed to record light intensity in the lower troposphere, i.e. to measure the pollution of the night sky by artificial light coming from the Earth’s surface. Selected technical parameters of the device are presented in Table 1.Table 1 Selected technical parameters of the device for measuring light pollution of the night sky.Full size tableFlowchart of the system operationAfter constructing the device and writing the control software, the construction of the entire measurement system was started. Each of the measuring instruments is ultimately connected to the communication gateway using LoRa technology. A MultiTech communication gateway with a LoRaWAN module was used as an access device. To successfully connect the gateway to the measuring device, it was necessary to configure the communication gateway software. To this end, the information about the unique device number (Dev EUI) and the application key and its number (App EUI and App Key) was used. Once the unit is configured, it is possible to send data to the communication gateway and read them using NodeRED, a programming tool where data are redirected to a selected server, which stores all measurement results. Figure 3 shows a schematic representation of the constructed measurement system.Figure 3Schematic diagram of the measurement system.Full size image More

Elmhagen, B. & Rushton, S. P. Trophic control of mesopredators in terrestrial ecosystems: Top-down or bottom-up?. Ecol. Lett. 10, 197–206 (2007).PubMed 
Article 

Google Scholar 
Newsome, T. M. et al. Top predators constrain mesopredator distributions. Nat. Commun. 8, 15469 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Prugh, L. R. & Sivy, K. J. Enemies with benefits: Integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. 23, 902–918 (2020).PubMed 
Article 

Google Scholar 
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
Suraci, J. P., Clinchy, M., Dill, L. M., Roberts, D. & Zanette, L. Y. Fear of large carnivores causes a trophic cascade. Nat. Commun. 7, 10698 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol. Lett. 22, 1578–1586 (2019).PubMed 
Article 

Google Scholar 
Selva, N., Jȩdrzejewska, B., Jȩdrzejewski, W. & Wajrak, A. Factors affecting carcass use by a guild of scavengers in European temperate woodland. Can. J. Zool. 83, 1590–1601 (2005).Article 

Google Scholar 
McArthur, C., Banks, P. B., Boonstra, R. & Forbey, J. S. The dilemma of foraging herbivores: Dealing with food and fear. Oecologia 176, 667–689 (2014).ADS 
Article 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 
Article 

Google Scholar 
Kuijper, D. P. J. et al. Paws without claws? Ecological effects of large carnivores in anthropogenic landscapes. Proc. R. Soc. B Biol. Sci. 283, 20161625 (2016).Article 

Google Scholar 
Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolfes, elk, and bison: Reestablishing the ‘landscape of fear’ in Yellowstone National Park, U.S.A. Can. J. Zool. 79, 1401–1409 (2001).Article 

Google Scholar 
Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: Spatial patterns of risk perception and response. Trends Ecol. Evol. 34, 355–368 (2019).PubMed 
Article 

Google Scholar 
Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. Ecol. Lett. 12, 982–998 (2009).PubMed 
Article 

Google Scholar 
Leo, V., Reading, R. P. & Letnic, M. Interference competition: Odours of an apex predator and conspecifics influence resource acquisition by red foxes. Oecologia 179, 1033–1040 (2015).ADS 
PubMed 
Article 

Google Scholar 
Clinchy, M. et al. Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore. Behav. Ecol. 27, 1826–1832 (2016).
Google Scholar 
Haswell, P. M., Jones, K. A., Kusak, J. & Hayward, M. W. Fear, foraging and olfaction: how mesopredators avoid costly interactions with apex predators. Oecologia 187, 573–583 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Switalski, T. A. Coyote foraging ecology and vigilance in response to gray wolf reintroduction in Yellowstone National Park. Can. J. Zool. 81, 985–993 (2003).Article 

Google Scholar 
Wikenros, C., Jarnemo, A., Frisén, M., Kuijper, D. P. J. & Schmidt, K. Mesopredator behavioral response to olfactory signals of an apex predator. J. Ethol. 35, 161–168 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Palomares, F., Ferreras, P., Fedriani, J. M. & Delibes, M. Spatial relationships between Iberian Lynx and other carnivores in an area of south-western Spain. J. Appl. Ecol. 33, 5–13 (1996).Article 

Google Scholar 
Salo, P., Nordström, M., Thomson, R. L. & Korpimäki, E. Risk induced by a native top predator reduces alien mink movements. J. Anim. Ecol. 77, 1092–1098 (2008).PubMed 
Article 

Google Scholar 
Haswell, P. M., Kusak, J. & Hayward, M. W. Large carnivore impacts are context-dependent. Food Webs 12, 3–13 (2017).Article 

Google Scholar 
Parsons, M. H. et al. Biologically meaningful scents: A framework for understanding predator–prey research across disciplines. Biol. Rev. 93, 98–114 (2018).PubMed 
Article 

Google Scholar 
Sivy, K. J., Pozzanghera, C. B., Grace, J. B. & Prugh, L. R. Fatal attraction? Intraguild facilitation and suppression among predators. Am. Nat. 190, 663–679 (2017).PubMed 
Article 

Google Scholar 
Ruprecht, J. et al. Variable strategies to solve risk-reward tradeoffs in carnivore communities. Proc. Natl. Acad. Sci. USA. 118, e2101614118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jędrzejewska, B. & Jędrzejewski, W. Predation in Vertebrate Communities Vol. 135 (Springer, 1998).Book 

Google Scholar 
Jȩdrzejewski, W. et al. Kill rates and predation by wolves on ungulate populations in Białowieża primeval forest (Poland). Ecology 83, 1341–1356 (2002).
Google Scholar 
Selva, N. The role of scavenging in the predator community of Białowieża Primeval Forest (Poland). PhD Thesis. (University of Sevilla, 2004).Kowalczyk, R., Zalewski, A., Jędrzejewska, B., Ansorge, H. & Bunevich, A. N. Reproduction and mortality of invasive raccoon dogs (Nyctereutes procyonoides) in the Biatowieža Primeval Forest (eastern Poland). Ann. Zool. Fennici 46, 291–303 (2009).Article 

Google Scholar 
Ballard, W. B., Carbyn, L. N. & Smith, D. W. Wolf interactions with non-prey. In Wolves: Behavior, Ecology, and Conservation (eds Mech, D. & Boitani, L.) 259–271 (University of Chicago Press, 2003).
Google Scholar 
Brown, J. S. Patch use as an indicator of habitat preference, predation risk, and competition. Behav. Ecol. Sociobiol. 22, 37–47 (1988).Article 

Google Scholar 
Bedoya-Perez, M. A., Carthey, A. J. R., Mella, V. S. A., McArthur, C. & Banks, P. B. A practical guide to avoid giving up on giving-up densities. Behav. Ecol. Sociobiol. 67, 1541–1553 (2013).Article 

Google Scholar 
Kwiatkowski, W. Vegetation landscapes of Białowieża Forest. Phytocoen. Suppl. Cart. Geobot 6, 35–87 (1994).
Google Scholar 
European Court of Justice Judgment of the Court (Grand Chamber) of 17 April 2018. European Commission vs. Republic of Poland. Case C-441/17. https://curia.europa.eu/jcms/upload/docs/application/pdf/2018-04/cp180048en.pdf.Bubnicki, J. W., Churski, M., Schmidt, K., Diserens, T. A. & Kuijper, D. P. J. Linking spatial patterns of terrestrial herbivore community structure to trophic interactions. Elife 8, e44937 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kowalczyk, R., Bunevich, A. N. & Jędrzejewska, B. Badger density and distribution of setts in Bialowieza Primeval Forest (Poland and Belarus) compared to other Eurasian populations. Acta Theriol. 45, 395–408 (2000).Article 

Google Scholar 
Jędrzejewski, W., Schmidt, K., Theuerkauf, J., Jędrzejewska, B. & Kowalczyk, R. Territory size of wolves Canis lupus: Linking local (Białowieża Primeval Forest, Poland) and holarctic-scale patterns. Ecography 30, 66–67 (2007).
Google Scholar 
Schmidt, K., Jędrzejewski, W., Okarma, H. & Kowalczyk, R. Spatial interactions between grey wolves and Eurasian lynx in Białowieża Primeval Forest, Poland. Ecol. Res. 24, 207–214 (2009).Article 

Google Scholar 
Bytheway, J. P., Carthey, A. J. R. & Banks, P. B. Risk vs reward: How predators and prey respond to aging olfactory cues. Behav. Ecol. Sociobiol. 67, 715–725 (2013).Article 

Google Scholar 
Carthey, A. J. R. & Banks, P. B. Naiveté is not forever: responses of a vulnerable native rodent to its long term alien predators. Oikos 125, 918–926 (2016).Article 

Google Scholar 
Blanchard, C. D. & Blanchard, R. J. Antipredator DEFENSE. In The Behavior of the Laboratory Rat: A Handbook with Tests (eds Whishaw, I. Q. & Kolb, B.) 335–343 (Oxford University Press, 2004).Chapter 

Google Scholar 
Masini, C. V., Sauer, S. & Campeau, S. Ferret odor as a processive stress model in rats: Neurochemical, behavioral, and endocrine evidence. Behav. Neurosci. 119, 280–292 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bubnicki, J. W., Churski, M. & Kuijper, D. P. J. Trapper: An open source web-based application to manage camera trapping projects. Methods Ecol. Evol. 7, 1209–1216 (2016).Article 

Google Scholar 
Jędrzejewski, W., Schmidt, K., Theuerkauf, J., Jędrzejewska, B. & Okarma, H. Daily movements and territory use by radio-collared wolves (Canis lupus) in Bialowieza Primeval Forest in Poland. Can. J. Zool. 79, 1993–2004 (2001).Article 

Google Scholar 
Theuerkauf, J., Jędrzejewski, W., Schmidt, K. & Gula, R. Spatiotemporal segregation of wolves from humans in the Bialowieza Forest (Poland). J. Wildl. Manage. 67, 706–716 (2003).Article 

Google Scholar 
Theuerkauf, J., Rouys, S. & Jędrzejewski, W. Selection of den, rendezvous, and resting sites by wolves in the Bialowieza Forest, Poland. Can. J. Zool. 81, 163–167 (2003).Article 

Google Scholar 
Miller, B. J., Harlow, H. J., Harlow, T. S., Biggins, D. & Ripple, W. J. Trophic cascades linking wolves (Canis lupus), coyotes (Canis latrans), and small mammals. Can. J. Zool. 90, 70–78 (2012).Article 

Google Scholar 
Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: An R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).Article 

Google Scholar 
Zoller, H. & Drygala, F. Activity patterns of the invasive raccoon dog (Nyctereutes procyonoides) in North East Germany. Folia Zool. 62, 290–296 (2013).Article 

Google Scholar 
Díaz-Ruiz, F., Caro, J., Delibes-Mateos, M., Arroyo, B. & Ferreras, P. Drivers of red fox (Vulpes vulpes) daily activity: Prey availability, human disturbance or habitat structure?. J. Zool. 298, 128–138 (2016).Article 

Google Scholar 
Mukherjee, S., Zelcer, M. & Kotler, B. P. Patch use in time and space for a meso-predator in a risky world. Oecologia 159, 661–668 (2009).ADS 
PubMed 
Article 

Google Scholar 
Tredennick, A. T., Hooker, G., Ellner, S. P. & Adler, P. B. A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology 102, e03336 (2021).PubMed 
Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).Magnusson, A. et al. R Package ‘glmmTMB’. (2020).Hartig, F. R Package ‘DHARMa: Residual Diagnostics for Hierarchical (Multi-level/Mixed) Regression Models’ (2021).Fox, J. et al. R Package ‘effects’. (2020).Hawlena, D. & Schmitz, O. J. Physiological stress as a fundamental mechanism linking predation to ecosystem functioning. Am. Nat. 176, 537–556 (2010).PubMed 
Article 

Google Scholar 
Diserens, T. A. et al. Fossoriality in a risky landscape: Badger sett use varies with perceived wolf risk. J. Zool. 313, 76–85 (2021).Article 

Google Scholar 
Lima, S. L. & Bednekoff, P. A. Temporal variation in danger drives antipredator behavior: The predation risk allocation hypothesis. Am. Nat. 153, 649–659 (1999).PubMed 
Article 

Google Scholar 
Scheinin, S., Yom-Tov, Y., Motro, U. & Geffen, E. Behavioural responses of red foxes to an increase in the presence of golden jackals: A field experiment. Anim. Behav. 71, 577 (2006).Article 

Google Scholar 
Vanak, A. T., Thaker, M. & Gompper, M. E. Experimental examination of behavioural interactions between free-ranging wild and domestic canids. Behav. Ecol. Sociobiol. 64, 279–287 (2009).Article 

Google Scholar 
Creel, S., Winnie, J. A., Christianson, D. & Liley, S. Time and space in general models of antipredator response: Tests with wolves and elk. Anim. Behav. 76, 1139–1146 (2008).Article 

Google Scholar 
Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128 (2017).PubMed 
Article 

Google Scholar 
Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Holker, F., Wolter, C., Perkin, E. K. & Tockner, K. Light pollution as a biodiversity threat. Trends Ecol. Evol. 25, 681–682. https://doi.org/10.1016/j.tree.2010.09.007 (2010).Article 
PubMed 

Google Scholar 
Kyba, C., Mohar, A. & Posch, T. How bright is moonlight?. Astron. Geophys. 58, 1.31-1.32 (2017).
Google Scholar 
Hölker, F. et al. The dark side of light: A transdisciplinary research agenda for light pollution policy. Ecol. Soc. 15, 150413 (2010).
Google Scholar 
Sanders, D., Frago, E., Kehoe, R., Patterson, C. & Gaston, K. J. A meta-analysis of biological impacts of artificial light at night. Nat. Ecol. Evol. 5, 74–81 (2021).PubMed 

Google Scholar 
Gaston, K. J., Bennie, J., Davies, T. W. & Hopkins, J. The ecological impacts of nighttime light pollution: A mechanistic appraisal. Biol. Rev. 88, 912–927. https://doi.org/10.1111/brv.12036 (2013).Article 
PubMed 

Google Scholar 
Gaston, K. J. & Bennie, J. Demographic effects of artificial nighttime lighting on animal populations. Environ. Rev. 22, 323–330. https://doi.org/10.1139/er-2014-0005 (2014).Article 

Google Scholar 
Gaston, K. J., Visser, M. E. & Hoelker, F. The biological impacts of artificial light at night: The research challenge. R. Soc. Philos. Trans. Biol. Sci. 370, 20140133–20140133 (2015).
Google Scholar 
Ouyang, J. Q. et al. Stressful colours: Corticosterone concentrations in a free-living songbird vary with the spectral composition of experimental illumination. Biol. Lett. https://doi.org/10.1098/rsbl.2015.0517 (2016).Article 

Google Scholar 
Ouyang, J. Q., Davies, S. & Dominoni, D. Hormonally mediated effects of artificial light at night on behavior and fitness: Linking endocrine mechanisms with function. J. Exp. Biol. https://doi.org/10.1242/jeb.156893 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Dominoni, D., Quetting, M. & Partecke, J. Artificial light at night advances avian reproductive physiology. Proc. Biol. Sci. 280(1756), 20123017. https://doi.org/10.1098/rspb.2012.3017 (2012).CAS 
Article 

Google Scholar 
Ayalon, I. et al. Coral gametogenesis collapse under artificial light pollution. Curr. Biol. 31, 413–419 (2021).CAS 
PubMed 

Google Scholar 
Ayalon, I., de Barros Marangoni, L. F., Benichou, J. I., Avisar, D. & Levy, O. Red Sea corals under Artificial Light Pollution at Night (ALAN) undergo oxidative stress and photosynthetic impairment. Glob. Change Biol. 25, 4194–4207 (2019).ADS 

Google Scholar 
Amichai, E. & Kronfeld-Schor, N. Artificial light at night promotes activity throughout the night in nesting common swifts (Apus apus). Sci. Rep. 9, 11052 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kronfeld-Schor, N. et al. Drivers of infectious disease seasonality: Potential implications for COVID-19. J. Biol. Rhythms 36, 35–54 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Philos. Trans. R. Soc. Lond. B https://doi.org/10.1098/rstb.2016.0248 (2017).Article 

Google Scholar 
Kronfeld-Schor, N. et al. Chronobiology by moonlight. Proc. R. Soc. B 280, 20123088 (2013).PubMed 
PubMed Central 

Google Scholar 
Stevenson, T. J. et al. Disrupted seasonal biology impacts health, food security and ecosystems. Proc. R. Soc. Lond. B. https://doi.org/10.1098/rspb.2015.1453 (2015).Article 

Google Scholar 
Kaniewska, P., Alon, S., Karako-Lampert, S., Hoegh-Guldberg, O. & Levy, O. Signaling cascades and the importance of moonlight in coral broadcast mass spawning. eLife 4, e09991 (2015).PubMed 
PubMed Central 

Google Scholar 
Liu, J. A., Meléndez-Fernández, O. H., Bumgarner, J. R. & Nelson, R. J. Effects of light pollution on photoperiod-driven seasonality. Horm. Behav. 141, 105150. https://doi.org/10.1016/j.yhbeh.2022.105150 (2022).Article 
PubMed 

Google Scholar 
Grubisic, M. et al. Light pollution, circadian photoreception, and melatonin in vertebrates. Sustainability 11, 6400 (2019).CAS 

Google Scholar 
Stevenson, T. J. & Prendergast, B. J. Photoperiodic time measurement and seasonal immunological plasticity. Front. Neuroendocrinol. 37, 76–88. https://doi.org/10.1016/j.yfrne.2014.10.002 (2015).Article 
PubMed 

Google Scholar 
Bumgarner, J. R. & Nelson, R. J. Light at night and disrupted circadian rhythms alter physiology and behavior. Integr. Comp. Biol. 61, 1160–1169 (2021).PubMed 
PubMed Central 

Google Scholar 
Mishra, I. et al. Light at night disrupts diel patterns of cytokine gene expression and endocrine profiles in zebra finch (Taeniopygia guttata). Sci. Rep. 9, 1–12 (2019).
Google Scholar 
Grunst, M. L. et al. Early-life exposure to artificial light at night elevates physiological stress in free-living songbirds. Environ. Pollut. 259, 113895 (2020).CAS 
PubMed 

Google Scholar 
Bedrosian, T., Galan, A., Vaughn, C., Weil, Z. M. & Nelson, R. J. Light at night alters daily patterns of cortisol and clock proteins in female Siberian hamsters. J. Neuroendocrinol. 25, 590–596 (2013).CAS 
PubMed 

Google Scholar 
Touzot, M. et al. Artificial light at night alters the sexual behaviour and fertilisation success of the common toad. Environ. Pollut. 259, 113883 (2020).CAS 
PubMed 

Google Scholar 
de Jong, M. et al. Effects of nocturnal illumination on life-history decisions and fitness in two wild songbird species. Philos. Trans. R. Soc. B 370, 20140128 (2015).
Google Scholar 
Spoelstra, K. et al. Experimental illumination of natural habitat: An experimental set-up to assess the direct and indirect ecological consequences of artificial light of different spectral composition. Philos. Trans. R. Soc. Lond. B 370, 20140129 (2015).
Google Scholar 
Hattar, S., Liao, H. W., Takao, M., Berson, D. M. & Yau, K. W. Melanopsin-containing retinal ganglion cells: Architecture, projections, and intrinsic photosensitivity. Science 295, 1065–1070. https://doi.org/10.1126/science.1069609 (2002).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gutman, R., Dayan, T., Levy, O., Schubert, I. & Kronfeld-Schor, N. The effect of the lunar cycle on fecal cortisol metabolite levels and foraging ecology of nocturnally and diurnally active spiny mice. PLoS ONE 6, e23446 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dhairykar, M., Singh, K. P., Kumar Jadav, K. & Rajput, N. Comparison of cortisol level in Asian elephants of different tiger reserves of Madhya Pradesh. Int. J. Vet. Sci. Anim. Husb. 5, 152–155 (2020).
Google Scholar 
Sosnowski, M. J., Benítez, M. E. & Brosnan, S. F. Endogenous cortisol correlates with performance under pressure on a working memory task in capuchin monkeys. Sci. Rep. 12, 1–10. https://doi.org/10.1038/s41598-022-04986-6 (2022).CAS 
Article 

Google Scholar 
Bewick, V., Cheek, L. & Ball, J. Statistics review 12: survival analysis. Crit. care 8, 1–6 (2004).
Google Scholar 
Shkolnik, A. Studies in the Comparative Biology of Israel’s Two Species of Spiny Mice (genus Acomys). Hebrew (1966).Shkolnik, A. Diurnal activity in a small desert rodent. Int. J. Biometeorol. 15, 115–120 (1971).ADS 
CAS 
PubMed 

Google Scholar 
Levy, O., Dayan, T. & Kronfeld-Schor, N. The relationship between the golden spiny mouse circadian system and its diurnal activity: An experimental field enclosures and laboratory study. Chronobiol. Int. 24, 599–613. https://doi.org/10.1080/07420520701534640 (2007).Article 
PubMed 

Google Scholar 
Levy, O., Dayan, T. & Kronfeld-Schor, N. Interspecific competition and torpor in golden spiny mice: Two sides of the energy-acquisition coin. Integr. Comp. Biol. 51, 441–448. https://doi.org/10.1093/icb/icr071 (2011).Article 
PubMed 

Google Scholar 
Jones, M. & Dayan, T. Foraging behavior and microhabitat use by spiny mice, Acomys cahirinus and A. russatus, in the presence of Blanford’s fox (Vulpes cana) odor. J. Chem. Ecol. 26, 455–469 (2000).CAS 

Google Scholar 
Jones, M., Mandelik, Y. & Dayan, T. Coexistence of temporally partitioned spiny mice: Roles of habitat structure and foraging behavior. Ecology 82, 2164–2176 (2001).
Google Scholar 
Kronfeld, N., Dayan, T., Zisapel, N. & Haim, A. Coexisting populations of Acomys cahirinus and A. russatus: A preliminary report. Isr. J. Zool. 40, 177–183 (1994).
Google Scholar 
Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181. https://doi.org/10.1146/annurev.ecolsys.34.011802.132435 (2003).Article 

Google Scholar 
Kronfeld-Schor, N. & Dayan, T. The dietary basis for temporal partitioning: Food habits of coexisting Acomys species. Oecologia 121, 123–128 (1999).ADS 
PubMed 

Google Scholar 
Pinter-Wollman, N., Dayan, T., Eilam, D. & Kronfeld-Schor, N. Can aggression be the force driving temporal separation between competing common and golden spiny mice?. J. Mammal. 87, 48–53 (2006).
Google Scholar 
Shargal, E., Kronfeld-Schor, N. & Dayan, T. Population biology and spatial relationships of coexisting spiny mice (Acomys) in Israel. J. Mammal. 81, 1046–1052 (2000).
Google Scholar 
Pasco, R., Gardner, D. K., Walker, D. W. & Dickinson, H. A superovulation protocol for the spiny mouse (Acomys cahirinus). Reprod. Fertil. Dev. 24, 1117–1122 (2012).CAS 
PubMed 

Google Scholar 
Lee, T. E., Watkins, J. F. & Cash, C. G. Acomys russatus. Mammal. Species 550, 1–4 (1998).
Google Scholar 
Dominoni, D., Quetting, M. & Partecke, J. Artificial light at night advances avian reproductive physiology. Proc. R. Soc. B 280, 20123017 (2013).PubMed 
PubMed Central 

Google Scholar 
Kempenaers, B., Borgström, P., Loës, P., Schlicht, E. & Valcu, M. Artificial night lighting affects dawn song, extra-pair siring success, and lay date in songbirds. Curr. Biol. 20, 1735–1739. https://doi.org/10.1016/j.cub.2010.08.028 (2010).CAS 
Article 
PubMed 

Google Scholar 
Le Tallec, T., Théry, M. & Perret, M. Melatonin concentrations and timing of seasonal reproduction in male mouse lemurs (Microcebus murinus) exposed to light pollution. J. Mammal. 97, 753–760 (2016).
Google Scholar 
Vonshak, M., Dayan, T. & Kronfeld-Schor, N. Arthropods as a prey resource: Patterns of diel, seasonal, and spatial availability. J. Arid Environ. 73, 458–462. https://doi.org/10.1016/j.jaridenv.2008.11.013 (2009).ADS 
Article 

Google Scholar 
Levy, O., Dayan, T. & Kronfeld-Schor, N. Adaptive thermoregulation in golden spiny mice: The influence of season and food availability on body temperature. Physiol. Biochem. Zool. 84, 175–184 (2011).PubMed 

Google Scholar 
Levy, O., Dayan, T., Rotics, S. & Kronfeld-Schor, N. Foraging sequence, energy intake and torpor: An individual-based field study of energy balancing in desert golden spiny mice. Ecol. Lett. 15, 1240–1248. https://doi.org/10.1111/j.1461-0248.2012.01845.x (2012).Article 
PubMed 

Google Scholar 
Katz, N., Dayan, T. & Kronfeld-Schor, N. Fitness effects of interspecific competition between two species of desert rodents. Zoology 128, 62–68 (2018).PubMed 

Google Scholar 
Brzezinski, A. Melatonin in humans. N. Engl. J. Med. 336, 186–195 (1997).CAS 
PubMed 

Google Scholar 
Hastings, M., Vance, G. & Maywood, E. Some reflections on the phylogeny and function of the pineal. Experientia 45, 903–909 (1989).CAS 
PubMed 

Google Scholar 
Oster, H. et al. The circadian rhythm of glucocorticoids is regulated by a gating mechanism residing in the adrenal cortical clock. Cell Metab. 4, 163–173 (2006).CAS 
PubMed 

Google Scholar 
Mora, F., Segovia, G., Del Arco, A., de Blas, M. & Garrido, P. Stress, neurotransmitters, corticosterone and body–brain integration. Brain Res. 1476, 71–85 (2012).CAS 
PubMed 

Google Scholar 
Farrell, M. R. Sex Differences and Stress Effects in Corticolimbic Structure and Function (Indiana University, 2013).
Google Scholar 
Son, G. H., Chung, S. & Kim, K. The adrenal peripheral clock: Glucocorticoid and the circadian timing system. Front. Neuroendocrinol. 32, 451–465 (2011).CAS 
PubMed 

Google Scholar 
Schradin, C. Seasonal changes in testosterone and corticosterone levels in four social classes of a desert dwelling sociable rodent. Horm. Behav. 53, 573–579 (2008).CAS 
PubMed 

Google Scholar 
Zatra, Y. et al. Seasonal changes in plasma testosterone and cortisol suggest an androgen mediated regulation of the pituitary adrenal axis in the Tarabul’s gerbil Gerbillus tarabuli (Thomas, 1902). Gen. Comp. Endocrinol. 258, 173–183 (2018).CAS 
PubMed 

Google Scholar 
Richardson, C. S., Heeren, T. & Kunz, T. H. Seasonal and sexual variation in metabolism, thermoregulation, and hormones in the big brown bat (Eptesicus fuscus). Physiol. Biochem. Zool. 91, 705–715 (2018).PubMed 

Google Scholar 
Touitou, S., Heistermann, M., Schülke, O. & Ostner, J. Triiodothyronine and cortisol levels in the face of energetic challenges from reproduction, thermoregulation and food intake in female macaques. Horm. Behav. 131, 104968 (2021).CAS 
PubMed 

Google Scholar 
Rotics, S., Dayan, T. & Kronfeld-Schor, N. Effect of artificial night lighting on temporally partitioned spiny mice. J. Mammal. 92, 159–168. https://doi.org/10.1644/10-mamm-a-112.1 (2011).Article 

Google Scholar 
Rotics, S., Dayan, T., Levy, O. & Kronfeld-Schor, N. Light masking in the field: An experiment with nocturnal and diurnal spiny mice under semi-natural field conditions. Chronobiol. Int. 28, 70–75. https://doi.org/10.3109/07420528.2010.525674 (2011).Article 
PubMed 

Google Scholar 
Padgett, D. A. & Glaser, R. How stress influences the immune response. Trends Immunol. 24, 444–448 (2003).CAS 
PubMed 

Google Scholar 
Khansari, D. N., Murgo, A. J. & Faith, R. E. Effects of stress on the immune system. Immunol. Today 11, 170–175 (1990).CAS 
PubMed 

Google Scholar 
Zozaya, S. M., Alford, R. A. & Schwarzkopf, L. Invasive house geckos are more willing to use artificial lights than are native geckos. Austral. Ecol. 40, 982–987 (2015).
Google Scholar 
Komine, H., Koike, S. & Schwarzkopf, L. Impacts of artificial light on food intake in invasive toads. Sci. Rep. 10, 1–8 (2020).
Google Scholar 
Murphy, S., Vyas, D., Sher, A. & Grenis, K. Light pollution affects invasive and native plant traits important to plant competition and herbivorous insects. Biol. Invasions 24, 599–602. https://doi.org/10.1007/s10530-021-02670-w (2022).Article 

Google Scholar 
Murphy, S. M. et al. Streetlights positively affect the presence of an invasive grass species. Ecol. Evol. 11, 10320–10326 (2021).PubMed 
PubMed Central 

Google Scholar  More