Ecology

1.Orians, G. H. & Pearson, N. E. On the theory of central place foraging. Analysis of ecological systems. In Analysis of ecological systems Vol. 2 (eds Horn D. J., Mitchell R. D. & Stairs G. R.) 155–177 (Ohio State Univ. Press, 1979).
Google Scholar 
2.Biuw, M. et al. Variations in behavior and condition of a Southern Ocean top predator in relation to in situ oceanographic conditions. Proc. Natl. Acad. Sci. 104, 13705–13710 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Arcalís-Planas, A. et al. Limited use of sea ice by the Ross seal (Ommatophoca rossii), in Amundsen Sea, Antarctica, using telemetry and remote sensing data. Polar Biol. 38, 445–461 (2015).Article 

Google Scholar 
4.Staniland, I. J., Boyd, I. L. & Reid, K. An energy–distance trade-off in a central-place forager, the Antarctic fur seal (Arctocephalus gazella). Mar. Biol. 152, 233–241 (2007).Article 

Google Scholar 
5.Le Boeuf, B. et al. Foraging ecology of northern elephant seals. Ecol. Monogr. 70, 353–382 (2000).Article 

Google Scholar 
6.Adelung, D., Kierspel, M. A., Liebsch, N., Müller, G. & Wilson, R. P. Distribution of harbour seals in the German bight in relation to offshore wind power plants. In Offshore Wind Energy: Research on Environmental Impacts (eds Köller, J., Köppel J. & Peters, W.) 65–75 (Springer, 2006).
Google Scholar 
7.Thompson, P. M., Mackay, A., Tollit, D. J., Enderby, S. & Hammond, P. S. The influence of body size and sex on the characteristics of harbour seal foraging trips. Can. J. Zool. 76, 1044–1053 (1998).Article 

Google Scholar 
8.Wilson, R. P. et al. Options for modulating intra-specific competition in colonial pinnipeds : the case of harbour seals (Phoca vitulina) in the Wadden Sea. PeerJ 4, e957 (2015).Article 

Google Scholar 
9.Sharples, R. J., Moss, S. E., Patterson, T. A. & Hammond, P. S. Spatial variation in foraging behaviour of a marine top predator (Phoca vitulina) determined by a large-scale satellite tagging program. PLoS ONE 7, e37216 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Liebsch, N., Wilson, R. P. & Adelung, D. Utilisation of time and space by harbour seals (Phoca vitulina vitulina) determined by new remote-sensing methods. In Progress in Marine Conservation in Europe (eds von Nordheim, H., Boedeker, D. & Krause, J.C.) 179–188 (Springer, 2006).
Google Scholar 
11.Tougaard, J., Teilmann, J. & Tougaard, S. Harbour seal spatial distribution estimated from Argos satellite telemetry: overcoming positioning errors. Endanger. Species Res. 4, 113–122 (2008).Article 

Google Scholar 
12.Common Wadden Sea Secretariat. Report on the State of Conservation of the World Heritage property “ The Wadden Sea ( N1314 )” (2016).13.Brasseur, S. M. J. M. et al. Echoes from the past: regional variations in recovery within a harbour seal population. PLoS ONE 13, 1–21 (2018).Article 
CAS 

Google Scholar 
14.Wolff, W. J. Ecology of the Wadden Sea (Balkema, 1983).
Google Scholar 
15.Baumann, H., Malzahn, A. M., Voss, R. & Temming, A. The German Bight (North Sea) is a nursery area for both locally and externally produced sprat juveniles. J. Sea Res. 61, 234–243 (2009).ADS 
Article 

Google Scholar 
16.Tulp, I., Bolle, L. J. & Rijnsdorp, A. D. Signals from the shallows: in search of common patterns in long-term trends in Dutch estuarine and coastal fish. J. Sea Res. 60, 54–73 (2008).ADS 
Article 

Google Scholar 
17.Dulvy, N. K. et al. Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. J. Appl. Ecol. 45, 1029–1039 (2008).Article 

Google Scholar 
18.Birt, V., Birt, T., Goulet, D., Cairns, D. & Montevecchi, W. Ashmole’s halo: direct evidence for prey depletion by a seabird. Mar. Ecol. Prog. Ser. 40, 205–208 (1987).ADS 
Article 

Google Scholar 
19.Russell, D. J. F. et al. Intrinsic and extrinsic drivers of activity budgets in sympatric grey and harbour seals. Oikos 124, 1462–1472 (2015).Article 

Google Scholar 
20.Sparling, C. E., Fedak, M. A. & Thompson, D. Eat now, pay later? Evidence of deferred food-processing costs in diving seals. Biol. Lett. 3, 94–98 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Ramasco, V., Biuw, M. & Nilssen, K. T. Improving time budget estimates through the behavioural interpretation of dive bouts in harbour seals. Anim. Behav. 94, 117–134 (2014).Article 

Google Scholar 
22.Mikkelsen, L. et al. Long-term sound and movement recording tags to study natural behavior and reaction to ship noise of seals. Ecol. Evol. 9(5), 2588–2601 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Carter, M. I. D., Bennett, K. A., Embling, C. B., Hosegood, P. J. & Russell, D. J. F. Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds. Mov. Ecol. 4, 1–20 (2016).Article 

Google Scholar 
24.Boyd, I. L. Temporal scales of foraging in a marine predator author. Ecology 77, 426–434 (1996).Article 

Google Scholar 
25.Bjorge, A. et al. Habitat Use and Diving Behaviour of Harbour Seals in a Coastal Archipelage in Norway 211–223 (Elsevier, 1995).
Google Scholar 
26.Lesage, V., Hammill, M. O. & Kovacs, K. M. Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success. Can. J. Zool. 77, 74–87 (1999).Article 

Google Scholar 
27.Baechler, J., Beck, C. A. & Bowen, W. Dive shapes reveal temporal changes in the foraging behaviour of different age and sex classes of harbour seals (Phoca vitulina). Can. J. Zool. Can. Zool. 80, 1569–1577 (2002).Article 

Google Scholar 
28.Dragon, A. C., Bar-Hen, A., Monestiez, P. & Guinet, C. Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data. Mar. Ecol. Prog. Ser. 452, 253–267 (2012).ADS 
Article 

Google Scholar 
29.Dragon, A. C., Bar-Hen, A., Monestiez, P. & Guinet, C. Horizontal and vertical movements as predictors of foraging success in a marine predator. Mar. Ecol. Prog. Ser. 447, 243–257 (2012).ADS 
Article 

Google Scholar 
30.Thums, M. T., Bradshaw, C. J. A. & Hindell, M. A. In situ measures of foraging success and prey encounter reveal marine habitat-dependent search strategies. Ecology 92, 1258–1270 (2011).PubMed 
Article 

Google Scholar 
31.Volpov, B. L., Hoskins, A. J., Battaile, B. C. & Viviant, M. Identification of prey captures in Australian fur seals (Arctocephalus pusillus doriferus) using head-mounted accelerometers: Field validation with animal-borne video cameras. PLoS ONE 10, e0128789 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Gallon, S. et al. Identifying foraging events in deep diving southern elephant seals, Mirounga leonina, using acceleration data loggers. Deep. Res. Part II Top. Stud. Oceanogr. 88–89, 14–22 (2013).ADS 
Article 

Google Scholar 
33.Viviant, M., Trites, A. W., Rosen, D. A. S., Monestiez, P. & Guinet, C. Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers. Polar Biol. 33, 713–719 (2010).Article 

Google Scholar 
34.Watanabe, Y. Y. & Takahashi, A. Linking animal-borne video to accelerometers reveals prey capture variability. Proc. Natl. Acad. Sci. U.S.A. 110, 2199–2204 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Martín Lopez, L. M., Miller, P. J. O., Aguilar de Soto, N. & Johnson, M. Gait switches in deep-diving beaked whales: biomechanical strategies for long-duration dives. J. Exp. Biol. 218, 1325–1338 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Naito, Y., Bornemann, H., Takahashi, A., McIntyre, T. & Plötz, J. Fine-scale feeding behavior of Weddell seals revealed by a mandible accelerometer. Polar Sci. 4, 309–316 (2010).ADS 
Article 

Google Scholar 
37.Ydesen, K. S. et al. What a jerk: prey engulfment revealed by high-rate, super-cranial accelerometry on a harbour seal (Phoca vitulina). J. Exp. Biol. 217, 2239–2243 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Johnson, M., De Soto, N. A. & Madsen, P. T. Studying the behaviour and sensory ecology of marine mammals using acoustic recording tags: a review. Mar. Ecol. Prog. Ser. 395, 55–73 (2009).ADS 
Article 

Google Scholar 
39.Goldbogen, J. A., Friedlaender, A. S., Calambokidis, J., McKenna, M. F. & Simon, M. Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology. Bioscience 63, 90–100 (2013).Article 

Google Scholar 
40.Wilson, R. P. et al. All at sea with animal tracks; methodological and analytical solutions for the resolution of movement. Deep. Res. Part II(54), 193–210 (2007).ADS 
Article 

Google Scholar 
41.Baylis, A. M. M., Page, B. & Goldsworthy, S. D. Effect of seasonal changes in upwelling activity on the foraging locations of a wide-ranging central-place forager, the New Zealand fur seal. Can. J. Zool. 789, 774–789 (2008).Article 

Google Scholar 
42.McClintock, B. T., Russell, D. J. F., Matthiopoulos, J. & King, R. Combining individual animal movement and ancillary biotelemetry data to investigate population-level activity budgets. Ecology 94, 838–849 (2013).Article 

Google Scholar 
43.Siebert, U., Müller, S., Gilles, A., Sundermeyer, J. & Narberhaus, I. Chapter VII species profiles marine mammals authors: harbour porpoise red lists, conservation status and assessment. In Threatened Biodiversity in the German North and Baltic Seas-Sensitivities Towards Human Activities and the Effects of Climate Change (eds Narberhaus, I. & Krause, J.) 448–495 (Naturschutz und Biologische Vielfalt, 2012).
Google Scholar 
44.Ashmole, N. P. The regulation of numbers of tropical oceanic birds. Ibis (Lond. 1859) 103, 458–473 (1963).
Google Scholar 
45.Gaston, A. J., Ydenberg, R. C. & Smith, G. E. J. Ashmole’s halo and population regulation in seabirds. Mar. Ornithol. 35, 119–126 (2007).
Google Scholar 
46.Cronin, M., Pomeroy, P. & Jessopp, M. Size and seasonal influences on the foraging range of female grey seals in the northeast Atlantic. Mar. Biol. 160, 531–539 (2013).Article 

Google Scholar 
47.Dietz, R., Teilmann, J., Andersen, S. M., Rige, F. & Olsen, M. T. Movements and site fidelity of harbour seal (Phoca vitulina) in Kattegat, Denmark, with implications for the epidemiology of the phocine distemper virus. ICES J. Mar. Sci. 70, 186–195 (2013).Article 

Google Scholar 
48.Brasseur, S., Creuwels, J., Werf, B. & Reijnders, P. Deprivation indicates necessity for haul-out in harbor seals. Mar. Mamm. Sci. 12, 619–624 (1996).Article 

Google Scholar 
49.Lyamin, O. I., Mukhametov, L. M. & Siegel, J. M. Sleep in the northern fur seal. Curr. Opin. Neurobiol. 44, 144–151 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Härkönen, T. et al. The 1988 and 2002 phocine distemper virus epidemics in European harbour seals. Dis. Aquat. Organ. 68, 115–130 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Bodewes, R. et al. Avian influenza a(H10n7) virus–associated mass deaths among harbor seals. Emerg. Infect. Dis. 21, 720–722 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Galatius, A. et al. Trilateral Surveys of Harbour Seals in the Wadden Sea and Helgoland in 2019 (2019).53.Madsen, P. T., Wahlberg, M., Tougaard, J., Lucke, K. & Tyack, P. Wind turbine underwater noise and marine mammals: implications of current knowledge and data needs. Mar. Ecol. Prog. Ser. 309, 279–295 (2006).ADS 
Article 

Google Scholar 
54.Cremer, J. et al. EG-Seals Grey Seal Surveys in the Wadden Sea and Helgoland in 2018–2019-Steady Growth (2019).55.Christensen, J. T. & Richardson, K. Stable isotope evidence of long-term changes in the North Sea food web structure. Mar. Ecol. Prog. Ser. 368, 1–8 (2008).ADS 
Article 

Google Scholar 
56.Daan, N., Gislason, H., Pope, J. G. & Rice, J. C. Changes in the North Sea fish community: Evidence of indirect effects of fishing ?. ICES J. Mar. Sci. 62, 177–188 (2005).Article 

Google Scholar 
57.Baudron, A. R., Needle, C. L., Rijnsdorp, A. D. & Marshall, C. T. Warming temperatures and smaller body sizes: Synchronous changes in growth of North Sea fishes. Glob. Change Biol. 20, 1023–1031 (2014).ADS 
Article 

Google Scholar 
58.Hasselmeier, I., Fonfara, S., Driver, J. & Siebert, U. Differential hematology profiles of free-ranging, rehabilitated, and captive harbor seals (Phoca vitulina) of the German North Sea. Aquat. Mamm. 34, 149–156 (2008).Article 

Google Scholar 
59.Wales, B., Tarazona, L. & Bavaro, M. Snapshot positioning for low-power miniaturised spaceborne GNSS receivers. In 2010 5th ESA Work. Satell. Navig. Technol. Eur. Work. GNSS Signals Signal Process.1–6 (IEEE, 2010).60.Johnson, M. P. & Tyack, P. L. A digital acoustic recording tag for measuring the response of wild marine mammals to sound. IEEE J. Ocean. Eng. 28, 3–12 (2003).ADS 
Article 

Google Scholar 
61.Sibly, R. M., Nott, H. M. R. & Fletcher, D. J. Splitting behaviour into bouts. Anim. Behav. 39, 63–69 (1990).Article 

Google Scholar 
62.Bowen, W. D., Tully, D., Boness, D. J., Bulheier, B. M. & Marshall, G. J. Prey-dependent foraging tactics and preyprofitability in a marine mammal. Mar. Ecol. Prog. Ser. 244, 235–245 (2002).ADS 
Article 

Google Scholar 
63.Cox, S. L. et al. Processing of acceleration and dive data on-board satellite relay tags to investigate diving and foraging behaviour in free-ranging marine predators. Methods Ecol. Evol. 9, 64–77 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
64.Thompson, D. & Fedak, M. A. How long should a dive last? A simple model of foraging decisions by breath-hold divers in a patchy environment. Anim. Behav. 61, 287–296 (2001).Article 

Google Scholar 
65.Martín López, L. M., de Soto, N. A., Miller, P. & Johnson, M. Tracking the kinematics of caudal-oscillatory swimming: a comparison of two on-animal sensing methods. J. Exp. Biol. 219, 2103–2109. https://doi.org/10.1242/jeb.136242 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Sato, K. et al. Stroke frequency, but not swimming speed, is related to body size in free-ranging seabirds, pinnipeds and cetaceans. Proc. R. Soc. B Biol. Sci. 274, 471–477 (2007).Article 

Google Scholar 
67.Bartoń, K. MuMIn: Multi‐model inference. R package version 1.43.17. 75 (2020). More

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

Google Scholar 
2.Thorstad, E. B., Whoriskey, F., Rikardsen, A. H. & Aarestrup, K. Aquatic nomads: The life and migrations of the Atlantic salmon. In Atlantic Salmon Ecology (eds Aas, Ø. et al.) 1–32 (Wiley-Blackwell, 2010) https://doi.org/10.1002/9781444327755.ch1.
Google Scholar 
3.Harvey, A. C., Glover, K. A., Wennevik, V. & Skaala, Ø. Atlantic salmon and sea trout display synchronised smolt migration relative to linked environmental cues. Sci. Rep. 10, 3529 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Jensen, A. J. et al. Timing of smolt migration in sympatric populations of Atlantic salmon (Salmo salar), brown trout (Salmo trutta), and Arctic char (Salvelinus alpinus). Can. J. Fish. Aquat. Sci. 69, 711–723 (2012).Article 

Google Scholar 
5.Hansen, L. P. & Jonsson, B. Salmon ranching experiments in the River Imsa: Effect of timing of Atlantic salmon (Salmo salar) smolt migration on survival to adults. Aquaculture 82, 367–373 (1989).Article 

Google Scholar 
6.Hvidsten, N. A. et al. Influence of sea temperature and initial marine feeding on survival of Atlantic salmon (Salmo salar) post-smolts from the Rivers Orkla and Hals, Norway. J. Fish Biol. 74, 1532–1548 (2009).CAS 
PubMed 
Article 

Google Scholar 
7.Hvidsten, N. A., Heggberget, T. & Jensen, A. J. Sea water temperatures at Atlantic salmon smolt enterance. Nord. J. Freshw. Res. 74, 79–86 (1998).8.Otero, J. et al. Quantifying the ocean, freshwater and human effects on year-to-year variability of one-sea-winter Atlantic salmon angled in multiple Norwegian rivers. PLoS ONE 6, e24005 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Rikardsen, A. H. & Dempson, J. B. Dietary life-support: the food and feeding of Atlantic salmon at sea. In Atlantic Salmon Ecology (eds. Aas, Ø., Klemetsen, A., Einum, S. & Skurdal, J.) 115–143 (Wiley, 2011).10.Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
11.Piou, C. & Prévost, E. Contrasting effects of climate change in continental vs. oceanic environments on population persistence and microevolution of Atlantic salmon. Glob. Change Biol. 19, 711–723 (2013).ADS 
Article 

Google Scholar 
12.McCormick, S. D., Hansen, L. P., Quinn, T. P. & Saunders, R. L. Movement, migration, and smolting of Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 55, 77–92 (1998).Article 

Google Scholar 
13.Thorstad, E. B. et al. A critical life stage of the Atlantic salmon (Salmo salar): Behaviour and survival during the smolt and initial post-smolt migration. J. Fish Biol. 81, 500–542 (2012).CAS 
PubMed 
Article 

Google Scholar 
14.Aldrin, M., Storvik, B., Kristoffersen, A. B. & Jansen, P. A. Space-time modelling of the spread of salmon lice between and within Norwegian marine salmon farms. PLoS ONE 8, e64039 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Jansen, P. A. et al. Sea lice as a density-dependent constraint to salmonid farming. Proc. R. Soc. B Biol. Sci. 279, 2330–2338 (2012).Article 

Google Scholar 
16.Kristoffersen, A. B. et al. Large scale modelling of salmon lice (Lepeophtheirus salmonis) infection pressure based on lice monitoring data from Norwegian salmonid farms. Epidemics 9, 31–39 (2014).PubMed 
Article 

Google Scholar 
17.Bøhn, T. et al. Timing is everything: Survival of Atlantic salmon (Salmo salar) postsmolts during events of high salmon lice densities. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13612 (2020).Article 

Google Scholar 
18.Berge, Å. I. et al. Development of salinity tolerance in underyearling smolts of Atlantic salmon (Salmo salar) reared under different photoperiods. Can. J. Fish. Aquat. Sci. 52, 243–251 (1995).Article 

Google Scholar 
19.Hoar, W. S. 4 The physiology of smolting salmonids. In Fish Physiology Vol. 11 (eds Hoar, W. S. & Randall, D. J.) 275–343 (Academic Press, 1988).
Google Scholar 
20.Saunders, R. L. & Henderson, E. B. Influence of photoperiod on smolt development and growth of Atlantic salmon (Salmo solar). J. Fish. Res. Board Can. 27, 1295–1311 (1970).Article 

Google Scholar 
21.Strand, J. E. T., Hazlerigg, D. & Jørgensen, E. H. Photoperiod revisited: Is there a critical day length for triggering a complete parr-smolt transformation in Atlantic salmon (Salmo salar)?. J. Fish Biol. 93, 440–448 (2018).CAS 
PubMed 
Article 

Google Scholar 
22.Antonsson, T. & Gudjonsson, S. Variability in timing and characteristics of Atlantic salmon smolt in Icelandic rivers. Trans. Am. Fish. Soc. 131, 643–655 (2002).Article 

Google Scholar 
23.Kennedy, R. & Crozier, W. Evidence of changing migratory patterns of wild Atlantic salmon Salmo salar smolts in the River Bush, Northern Ireland, and possible associations with climate change. J. Fish Biol. 76, 1786–1805 (2010).CAS 
PubMed 
Article 

Google Scholar 
24.Hvidsten, N. A., Jensen, A. J., Vivås, H. & Bakke, Ø. Downstream migration of Atlantic salmon smolts in relation to water flow, water temperature, moon phase and social interaction. Nord. J. Freshw. Res. 70, 38–48 (1995).25.Urke, H. A., Kristensen, T., Ulvund, J. B. & Alfredsen, J. A. Riverine and fjord migration of wild and hatchery-reared Atlantic salmon smolts. Fish. Manag. Ecol. 20, 544–552 (2013).Article 

Google Scholar 
26.Carlsen, K. T., Berg, O. K., Finstad, B. & Heggberget, T. G. Diel periodicity and environmental influence on the smolt migration of Arctic charr, Salvelinus alpinus, Atlantic salmon, Salmo salar, and Brown Trout, Salmo trutta, Northern Norway. Environ. Biol. Fishes 70, 403–413 (2004).Article 

Google Scholar 
27.Birnie-Gauvin, K., Larsen, M. H., Thomassen, S. T. & Aarestrup, K. Testing three common stocking methods: Differences in smolt size, migration rate and timing of two strains of stocked Atlantic salmon (Salmo salar). Aquaculture 483, 163–168 (2018).Article 

Google Scholar 
28.Nielsen, C., Holdensgaard, G., Petersen, H. C., Bjornsson, BTh. & Madsen, S. S. Genetic differences in physiology, growth hormone levels and migratory behaviour of Atlantic salmon smolts. J. Fish Biol. 59, 28–44 (2001).CAS 
Article 

Google Scholar 
29.Orciari, R. D. & Leonard, G. H. Length characteristics of smolts and timing of downstream migration among three strains of Atlantic salmon in a southern New England stream. N. Am. J. Fish. Manag. 16, 851–860 (1996).Article 

Google Scholar 
30.Skaala, Ø. et al. An extensive common-garden study with domesticated and wild Atlantic salmon in the wild reveals impact on smolt production and shifts in fitness traits. Evol. Appl. 12, 1001–1016 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Cooke, S. J. et al. Tracking animals in freshwater with electronic tags: Past, present and future. Anim. Biotelemetry 1, 5 (2013).MathSciNet 
Article 

Google Scholar 
32.Lennox, R. J. et al. Envisioning the future of aquatic animal tracking: Technology, science, and application. Bioscience 67, 884–896 (2017).Article 

Google Scholar 
33.Finstad, B., Okland, F., Thorstad, E. B., BjOrn, P. A. & McKinley, R. S. Migration of hatchery-reared Atlantic salmon and wild anadromous brown trout post-smolts in a Norwegian fjord system. J. Fish Biol. 66, 86–96 (2005).Article 

Google Scholar 
34.McMichael, G. A. et al. The juvenile salmon acoustic telemetry system: A new tool. Fisheries 35, 9–22 (2010).Article 

Google Scholar 
35.Welch, D. W. et al. Freshwater and marine migration and survival of endangered Cultus Lake sockeye salmon (Oncorhynchus nerka) smolts using POST, a large-scale acoustic telemetry array. Can. J. Fish. Aquat. Sci. 66, 736–750 (2009).Article 

Google Scholar 
36.Nilsen, F. et al. Vurdering av lakselusindusert villfiskdødelighet per produksjonsområde i 2018. Rapp. Fra Ekspertgruppe Vurder. Av Lusepåvirkning Append 2, 62 (2018).
Google Scholar 
37.Mulcahy, D. M. Surgical implantation of transmitters into fish. ILAR J. 44, 295–306 (2003).CAS 
PubMed 
Article 

Google Scholar 
38.Cooke, S. J. & Wagner, G. N. Training, experience, and opinions of researchers who use surgical techniques to implant telemetry devices into fish. Fisheries 29, 10–18 (2004).Article 

Google Scholar 
39.Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLOS Biol. 18, e3000411 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Grün, B. & Leisch, F. FlexMix version 2: Finite mixtures with concomitant variables and varying and constant parameters. J. Stat. Softw. 28, 1–35 (2008).Article 

Google Scholar 
41.Leisch, F. FlexMix: A general framework for finite mixture models and latent class regression in R. J. Stat. Softw. 11, 1–18 (2004).Article 

Google Scholar 
42.Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer Science & Business Media, 2009).
Google Scholar 
43.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models using lme4. arXiv:1406.5823Stat (2014).44.Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

Google Scholar 
45.Laake, J. L. RMark: An R Interface for Analysis of Capture-Recapture Data with MARK. 25 http://www.afsc.noaa.gov/Publications/ProcRpt/PR2013-01.pdf (2013).46.White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).Article 

Google Scholar 
47.Burnham, K. P. Design and analysis methods for fish survival experiments based on release-recapture. Am. Fish. Soc. Monogr. 5, 1–437 (1987).
Google Scholar 
48.Michel, C. J. et al. Chinook salmon outmigration survival in wet and dry years in California’s Sacramento River. Can. J. Fish. Aquat. Sci. 72, 1749–1759 (2015).Article 

Google Scholar 
49.Persson, L., Kagervall, A., Leonardsson, K., Royan, M. & Alanärä, A. The effect of physiological and environmental conditions on smolt migration in Atlantic salmon Salmo salar. Ecol. Freshw. Fish 28, 190–199 (2019).Article 

Google Scholar 
50.Whalen, K. G., Parrish, D. L. & McCormick, S. D. Migration timing of Atlantic salmon smolts relative to environmental and physiological factors. Trans. Am. Fish. Soc. 128, 289–301 (1999).Article 

Google Scholar 
51.Haraldstad, T., Kroglund, F., Kristensen, T., Jonsson, B. & Haugen, T. O. Diel migration pattern of Atlantic salmon (Salmo salar) and sea trout (Salmo trutta) smolts: An assessment of environmental cues. Ecol. Freshw. Fish 26, 541–551 (2017).Article 

Google Scholar 
52.Skilbrei, O. T., Wennevik, V., Dahle, G., Barlaup, B. & Wiers, T. Delayed smolt migration of stocked Atlantic salmon parr. Fish. Manag. Ecol. 17, 493–500 (2010).Article 

Google Scholar 
53.Freshwater, C. et al. Individual variation, population-specific behaviours and stochastic processes shape marine migration phenologies. J. Anim. Ecol. 88, 67–78 (2019).PubMed 
Article 

Google Scholar 
54.Chapman, B. B., Brönmark, C., Nilsson, J. -Å. & Hansson, L.-A. The ecology and evolution of partial migration. Oikos 120, 1764–1775 (2011).Article 

Google Scholar 
55.Urke, H. A., Arnekleiv, J. V., Nilsen, T. O. & Nilssen, K. J. Development of seawater tolerance and subsequent downstream migration in wild and stocked young-of-the-year derived Atlantic salmon Salmo salar smolts. J. Fish Biol. 84, 178–192 (2014).CAS 
PubMed 
Article 

Google Scholar 
56.Virtanen, E., Söderholm-Tana, L., Soivio, A., Foreman, L. & Muona, M. Effect of physiological condition and smoltification status at smolt release on subsequent catches of adult salmon. Aquaculture 97, 231–257 (1991).Article 

Google Scholar 
57.Björnsson, B. T., Stefansson, S. O. & McCormick, S. D. Environmental endocrinology of salmon smoltification. Gen. Comp. Endocrinol. 170, 290–298 (2011).PubMed 
Article 
CAS 

Google Scholar 
58.Stefansson, S. O., Björnsson, B. T., Ebbesson, L. O. E. & McCormick, S. D. Smoltification. In Fish Larval Physiology (eds. Finn, R. N. & Kapoor, B. G.) 639–681 (Science Publishers, 2008).59.McCormick, S. D., Shrimpton, J. M., Nilsen, T. O. & Ebbesson, L. O. Advances in our understanding of the parr-smolt transformation of juvenile salmon: A summary of the 10th International Workshop on Salmon Smoltification. J. Fish Biol. 93, 437–439 (2018).CAS 
PubMed 
Article 

Google Scholar 
60.Simons, A. Playing smart vs. playing safe: The joint expression of phenotypic plasticity and potential bet hedging across and within thermal environments. J. Evol. Biol. 27, 1047–1056 (2014).CAS 
PubMed 
Article 

Google Scholar 
61.Finstad, B. & Jonsson, N. Factors influencing the yield of smolt releases in Norway. Nord. J. Freshw. Res. 75, 37–55 (2001).62.Diserud, O. H., Hindar, K., Karlsson, S., Glover, K. A. & Skaala, Ø. Genetic impact of escaped farmed Atlantic salmon on wild salmon populations—status 2017. NINA Rapp. 1337, 55 (2017).
Google Scholar 
63.Vollset, K. W. et al. Can the river location within a fjord explain the density of Atlantic salmon and sea trout?. Mar. Biol. Res. 10, 268–278 (2014).Article 

Google Scholar 
64.Lacroix, G. L., Knox, D. & McCurdy, P. Effects of implanted dummy acoustic transmitters on juvenile Atlantic salmon. Trans. Am. Fish. Soc. 133, 211–220 (2004).Article 

Google Scholar 
65.Newton, M. et al. Does size matter? A test of size-specific mortality in Atlantic salmon Salmo salar smolts tagged with acoustic transmitters. J. Fish Biol. 89, 1641–1650 (2016).CAS 
PubMed 
Article 

Google Scholar 
66.Hansen, L. P., Holm, M., Hoist, J. C. & Jacobsen, J. A. The ecology of post-smolts of Atlantic salmon. In Salmon at the Edge (ed. Mills, D.) 25–39 (Blackwell Science Ltd., 2003) https://doi.org/10.1002/9780470995495.ch4.
Google Scholar 
67.Gregory, S. D. et al. Atlantic salmon return rate increases with smolt length. ICES J. Mar. Sci. 76, 1702–1712 (2019).Article 

Google Scholar 
68.Bjørn, P. A. et al. Metodeutvikling for overvåkning og telling av lakselus på viltlevende laksefisk: Ekstrainnsats i 2010 med midler fra FKD. (2011).69.Riley, W. D. et al. Development of schooling behaviour during the downstream migration of Atlantic salmon Salmo salar smolts in a chalk stream: Development of schooling in Salmo salar smolts. J. Fish Biol. 85, 1042–1059 (2014).CAS 
PubMed 
Article 

Google Scholar 
70.Daniels, J., Sutton, S., Webber, D. & Carr, J. Extent of predation bias present in migration survival and timing of Atlantic salmon smolt (Salmo salar) as suggested by a novel acoustic tag. Anim. Biotelemetry 7, 16 (2019).Article 

Google Scholar 
71.Halttunen, E. et al. Migration of Atlantic salmon post-smolts in a fjord with high infestation pressure of salmon lice. Mar. Ecol. Prog. Ser. 592, 243–256 (2018).ADS 
Article 

Google Scholar 
72.Harvey, A. C. et al. Inferring Atlantic salmon post-smolt migration patterns using genetic assignment. R. Soc. Open Sci. 6, 190426 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Amato, K. R. Incorporating the gut microbiota into models of human and non-human primate ecology and evolution. Am. J. Phys. Anthropol. 159, S196–S215 (2016).PubMed 
Article 

Google Scholar 
2.Ley, R., Hamady, M. & Lozupone, C. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Walter, J. Ecological role of lactobacilli in the gastrointestinal tract: Implications for fundamental and biomedical research. Appl. Environ. Microbiol. 74, 4985–4996 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.O’Callaghan, A. & van Sinderen, D. Bifidobacteria and their role as members of the human gut microbiota. Front. Microbiol. 7, 925 (2016).PubMed 
PubMed Central 

Google Scholar 
5.Redford, K. H., Segre, J. A., Salafsky, N., Del Rio, C. M. & Mcaloose, D. Conservation and the microbiome. Conserv. Biol. 26, 195–197 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. & Relman, D. A. The application of ecological theory towards an understanding of the human microbiome. Science 336, 1255–1262 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262–1267 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
8.Kohl, K. D., Skopec, M. M. & Dearing, M. D. Captivity results in disparate loss of gut microbial diversity in closely related hosts. Conserv. Physiol. 2, cou009 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Clayton, J. B. et al. Captivity humanizes the primate microbiome. Proc. Natl. Acad. Sci. 113, 10376–10381 (2016).CAS 
PubMed 
Article 

Google Scholar 
10.McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Myers, S. P. & Hawrelak, J. A. The causes of intestinal dysbiosis: a review. Altern. Med. Rev. 9, 180–197 (2004).PubMed 

Google Scholar 
13.Knights, D. et al. Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med. 6, 107 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Perofsky, A. C., Lewis, R. J., Abondano, L. A., Fiore, A. D. & Meyers, L. A. Hierarchical social networks shape gut microbial composition in wild Verreaux ’ s sifaka. Proc. R. Soc. B Biol. Sci. 248, 20172274 (2017).Article 

Google Scholar 
15.Phillips, C. D. et al. Microbiome analysis among bats describes influences of host phylogeny, life history, physiology and geography. Mol. Ecol. 21, 2617–2627 (2012).PubMed 
Article 

Google Scholar 
16.Hale, V. L. et al. Diet versus phylogeny: a comparison of gut microbiota in captive colobine monkey species. Microb. Ecol. 75, 515–527 (2018).PubMed 
Article 

Google Scholar 
17.Delsuc, F. et al. Convergence of gut microbiomes in myrmecophagous mammals. Mol. Ecol. 23, 1301–1317 (2014).CAS 
PubMed 
Article 

Google Scholar 
18.Lambert, J. E. Primate Nutritional Ecology: Feeding Biology and Diet at Ecological and Evolutionary Scales: Primates in Perspective (Oxford University Press, 2010).
Google Scholar 
19.Gomez, A. et al. Temporal variation selects for diet-microbe co-metabolic traits in the gut of Gorilla spp. ISME J. 10, 514–526 (2016).CAS 
PubMed 
Article 

Google Scholar 
20.Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Ochman, H. et al. Evolutionary relationships of wild hominids recapitulated by gut microbial communities. PLoS Biol. 8, 3–10 (2010).Article 
CAS 

Google Scholar 
22.Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Borbón-García, A., Reyes, A., Vives-Flórez, M. & Caballero, S. Captivity shapes the gut microbiota of Andean bears: Insights into health surveillance. Front. Microbiol. 8, 1316 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
24.Kohl, K. D. & Dearing, M. D. Wild-caught rodents retain a majority of their natural gut microbiota upon entrance into captivity. Environ. Microbiol. Rep. 6, 191–195 (2014).PubMed 
Article 

Google Scholar 
25.Xiao, Y. et al. Captivity causes taxonomic and functional convergence of gut microbial communities in bats. PeerJ 7, e6844 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Prabhu, V. R., Wasimuddin, V. R., Kamalakkannan, R., Arjun, M. S. & Nagarajan, M. Consequences of domestication on gut microbiome: a comparative study between wild gaur and domestic mithun. Front. Microbiol. 11, 133 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Chaves, Ó. M., Stoner, K. E. & Arroyo-Rodríguez, V. Differences in diet between spider monkey groups living in forest fragments and continuous forest in Mexico. Biotropica 44, 105–113 (2012).Article 

Google Scholar 
28.Nakagawa, N. Determinants of the dramatic seasonal changes in the intake of energy and protein by Japanese monkeys in a cool temperate forest. Am. J. Primatol. 41, 267–288 (1997).CAS 
PubMed 
Article 

Google Scholar 
29.Amato, K. R. et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb. Ecol. 69, 434–443 (2014).PubMed 
Article 
CAS 

Google Scholar 
30.Chen, T., Li, Y., Liang, J., Li, Y. & Huang, Z. Gut microbiota of provisioned and wild rhesus macaques (Macaca mulatta) living in a limestone forest in southwest Guangxi China. Microbiologyopen 9, e981 (2020).PubMed 

Google Scholar 
31.Mackie, R. I., Sghir, A. & Gaskins, H. R. Developmental microbial ecology of the neonatal gastrointestinal tract. Am. J. Clin. Nutr. 69, 1035s–1045s (1999).CAS 
PubMed 
Article 

Google Scholar 
32.Donnet-hughes, A. et al. Potential role of the intestinal microbiota of the mother in neonatal immune education. Proc. Nutr. Soc. 69, 407–415 (2010).PubMed 
Article 

Google Scholar 
33.Orkin, J. D. et al. Seasonality of the gut microbiota of free-ranging white-faced capuchins in a tropical dry forest. ISME J. 13, 183–196 (2019).CAS 
PubMed 
Article 

Google Scholar 
34.Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2015).ADS 
Article 

Google Scholar 
35.Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. U. S. A. 102, 11070–11075 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Frank, D. N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl. Acad. Sci. U. S. A. 104, 13780–13785 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Shin, N. R., Whon, T. W. & Bae, J. W. Proteobacteria: Microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 33, 496–503 (2015).CAS 
PubMed 
Article 

Google Scholar 
38.Jang, H. B., Choi, M. K., Kang, J. H., Park, S. I. & Lee, H. J. Association of dietary patterns with the fecal microbiota in Korean adolescents. BMC Nutr. 3, 1–11 (2017).Article 

Google Scholar 
39.Wang, B. et al. Comparison of the fecal microbiomes of healthy and diarrheic captive wild boar. Microb. Pathog. 147, 104377 (2020).CAS 
PubMed 
Article 

Google Scholar 
40.Tang, J. et al. Gut microbiota in reintroduction of giant panda. Ecol. Evol. 10, 1012–1028 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Clayton, J. B. et al. The gut microbiome of nonhuman primates: Lessons in ecology and evolution. Am. J. Primatol. 80, e22867 (2018).PubMed 
Article 

Google Scholar 
42.Martínez-Mota, R., Kohl, K. D., Orr, T. J. & Denise Dearing, M. Natural diets promote retention of the native gut microbiota in captive rodents. ISME J. 14, 67–78 (2020).PubMed 
Article 
CAS 

Google Scholar 
43.Kilkenny, C. et al. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 8, e1000412 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Vargas, S. A., León, J., Ramírez, M., Galvis, N., Cifuentes, E., & Stevenson, P. R. Population density and ecological traits of highland woolly monkeys at Cueva de los Guácharos National Park, Colombia. In High altitude primates Springer, New York. 85–102 (2014)45.Stevenson, P. R. Seed dispersal by woolly monkeys (Lagothrix lagothricha) at Tinigua National Park, Colombia: dispersal distance, germination rates, and dispersal quantity. Am. J. Primatol. Off. J. Am. Soc. Primatol. 50, 275–289 (2000).CAS 

Google Scholar 
46.Botero, S., Rengifo, L. Y., Bueno, M. L. & Stevenson, P. R. How many species of woolly monkeys inhabit Colombian forests?. Am. J. Primatol. 72, 1131–1140 (2020).Article 

Google Scholar 
47.Di Fiore, A. et al. The rise and fall of a genus: Complete mtDNA genomes shed light on the phylogenetic position of yellow-tailed woolly monkeys, Lagothrix flavicauda, and on the evolutionary history of the family Atelidae (Primates: Platyrrhini). Mol. Phylogenet. Evol. 82, 495–510 (2015).PubMed 
Article 
CAS 

Google Scholar 
48.Fooden, D. A revision of the woolly monkeys (genus Lagothrix). J. Mammal. 44, 213–247 (1963).Article 

Google Scholar 
49.Botero, S. & Stevenson, P. R. Coat color is not an indicator of subspecies identity in colombian woolly monkeys. The Woolly Monkey https://doi.org/10.1007/978-1-4939-0697-0 (2014).Article 

Google Scholar 
50.Stevenson, P. R. Activity and ranging patterns of Colombian woolly monkeys in north-western Amazonia. Primates https://doi.org/10.1007/s10329-005-0172-6 (2006).Article 
PubMed 

Google Scholar 
51.Altmann, J. Observational study of behavior: sampling methods. Behaviuor. 49, 227–266 (1974).CAS 
Article 

Google Scholar 
52.Caporaso, J. G. et al. Correspondence QIIME allows analysis of high-throughput community sequencing data intensity normalization improves color calling in SOLiD sequencing. Nat. Publ. Gr 7, 335–336 (2010).CAS 

Google Scholar 
53.Liu, Z., Lozupone, C., Hamady, M., Bushman, F. D. & Knight, R. Short pyrosequencing reads suffice for accurate microbial community analysis. Nucl. Acids Res. 35, e120 (2007).PubMed 
Article 
CAS 

Google Scholar 
54.Liu, Z., Desantis, T. Z., Andersen, G. L. & Knight, R. Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers. Nucl. Acids Res. 36, 1–11 (2008).Article 
CAS 

Google Scholar 
55.Yang, B., Wang, Y. & Qian, P. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinform. 17, 1–8 (2016).CAS 
Article 

Google Scholar 
56.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
57.Andrews, S. FastQC: a quality control tool for high throughput sequence data. (2010).58.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Amir, A. et al. Deblur rapidly resolves single-. Am. Soc. Microbiol. 2, 1–7 (2017).
Google Scholar 
62.DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Douglas, G. M., Maffei, V. J., Zaneveld, J., Yurgel, S. N., Brown, J. R., Taylor, C. M., & Langille, M. G. PICRUSt2: An improved and extensible approach for metagenome inference. BioRxiv 672295 (2020).64.McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).PubMed 
Article 

Google Scholar 
66.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
67.Barton, K. MuMIn: multi-model inference. (2009). http://r-forge.R-project.org/projects/mumin/.68.Richards, S. A., Whittingham, M. J. & Stephens, P. A. Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework. Behav. Ecol. Sociobiol. 65, 77–89 (2011).Article 

Google Scholar  More

Experimental designThis experiment was conducted at the Science and Technology experimental site (125° 27′ 5″ N, 49° 33′ 35″ E) of the North Corporation of Sinograin, Nenjiang, Heilongjiang, China. The soil in the tested area was classified as Black soil according to Chinese Soil Taxonomy (as Mollisol according to USDA classification system) with a thick humus layer and clay texture. The area has a mid-temperate continental monsoon climate with an average annual temperature of − 1.4 to 0.8 °C, precipitation of 450 mm, a frost-free period of 115 days, and an effective accumulated temperature of 2150 °C. The basic physicochemical properties at 0–20 cm plow layer of soil were shown in Table 2. The experiment included two modes: maize straw mulching (FG) and straw returning combined with rotary tillage. In the test plot, there were six 10-m ridges per treatment, and each treatment area was 39 m2. Each treatment has three replicate. The experiment started in 2012 under the continuous planting of maize, and the straw was mechanically crushed and returned to the field in the fall. In accordance with the adjustment in the C/N of the straw, the amount of applied fertilizer was N 150 kg hm−2, P2O5 75 kg hm−2, and K2O 75 kg hm−2. Five treatments were included: (1) stubble remaining in the field, which served as the control (CK); (2) full straw mulching (FG); (3) full straw returning combined with rotary tillage (1 XG) ; (4) 1/3 of full straw returning combined with rotary tillage (1/3 XG); and (5) half of full straw returning combined with rotary tillage (1/2 XG).Table 2 Basic physicochemical properties of surface soil.Full size tableSample collectionSoil samples were collected after the maize (Demeiya 2) was harvested in November 2019. Five soil cores (diameter 5 cm) were randomly taken from 0 to 20 cm depth in each plot, mixed thoroughly, and packed into cloth bags. After the crop roots and other debris were removed, the samples were air-dried for analyzing the content and chemical structure of SOC.Determination of total soil organic carbonThe content of total SOC was measured using a TOC (total organic carbon) analyzer (NC2100, Jena, Germany,) after air-dried soil samples were passed through a 100-mesh sieve.Purification of soil organic carbonFive grams of air-dried soil was added to a 100 mL plastic centrifuge tube, followed by the addition of 50 mL of hydrogen fluoride (HF) solution (10% v/v). After the tube was capped, the solution was shaken for 1 h and centrifuged for 10 min (3000 r min−1), and the supernatant was removed. Subsequently, the residue in the tube was treated with HF solution and then followed the above shaking and centrifuging steps. A total of eight repeats (according to the conditions of the actual samples) were performed with different duration of shaking (4 × 1 h, 3 × 12 h, and 1 × 24 h). Lastly, the residue in the tube was washed with double-distilled water four times, mainly to remove the residual HF in the soil sample. The detailed steps were as follows: 50 mL of double-distilled water was added into tube, shaken for 10 min and centrifuged (3000 r min−1) for 10 min, and then the supernatant was removed. The purified samples with free-HF were dried in an oven at 40 °C, ground through a 60-mesh sieve, and stored in a Zip-lock bag for NMR measurement.Determination of the chemical structure of soil organic carbonThe 13C solid-state NMR spectrum was collected on a Bruker AV400 NMR spectrometer (Switzerland). The cross-polarization magic-angle spinning (CPMAS) technique was used, the 13C NMR frequency was 400.18 MHz, the magic angle spinning frequency was 8 kHz, the contact time was 2 ms, the delay time was 3 s, and the number of data points was 3000. The chemical shift was calibrated based on the external standard sodium 2, 2-dimethyl-2-silapentane-5-sulfonate (DSS), the integrated area was automatically given by the instrument, and the relative content of organic C in each functional group of SOC was expressed as the percentage of the integrated area of a chemical shift interval to the total integrated area. The C structures corresponding to the chemical shift of the main 13C signal of SOC (Table 3) were as follows: alkyl C region (0–45 ppm), alkoxy C region (45–110 ppm), aromatic C region (110–160 ppm), and carbonyl C region (160–220 ppm)4,19.Table 3 13C solid-state NMR determination of organic carbon functional groups and corresponding high-molecular-weight compounds.Full size tableData analysisNMR spectra (CPMAS 13C-NMR) were analyzed using MestReNova professional software. After analyzing and extracting the source data, Microsoft Office Excel 2010 and Origin 8.0 software were used for data processing and plotting. The data in the “Available Data” were plotted using Origin by overlapping the fitted curve, and SPSS 17.0 (SPSS Inc., Chicago, USA) statistical analysis software was used to test for significant differences (Duncan’s method) and for correlation analysis. More

1.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 
PubMed Central 

Google Scholar 
2.Mora, C., Rollo, A. & Tittensor, D. P. Comment on ‘Can we name Earth’s species before they go extinct?’. Science 341, 237 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.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 
4.May, R. & Beverton, R. J. H. How many species? Phil. Trans. R. Soc. B 330, 293–304 (1990).Article 

Google Scholar 
5.Scheffers, B. R., Joppa, L. N., Pimm, S. L. & Laurance, W. F. What we know and don’t know about Earth’s missing biodiversity. Trends Ecol. Evol. 27, 501–510 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Raven, P. H. & Wilson, E. O. A fifty-year plan for biodiversity surveys. Science 258, 1099–1100 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Whittaker, R. J. et al. Conservation biogeography: assessment and prospect. Divers. Distrib. 11, 3–23 (2005).Article 

Google Scholar 
8.Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

Google Scholar 
9.Guide to the Global Taxonomy Initiative (Secretariat of the Convention on Biological Diversity, 2010).10.Costello, M. J., May, R. M. & Stork, N. E. Response to comments on ‘Can we name Earth’s species before they go extinct?’. Science 341, 237 (2013).CAS 
PubMed 
Article 

Google Scholar 
11.Bebber, D. P., Marriott, F. H. C., Gaston, K. J., Harris, S. A. & Scotland, R. W. Predicting unknown species numbers using discovery curves. Proc. R. Soc. B 274, 1651–1658 (2007).PubMed 
Article 

Google Scholar 
12.Edie, S. M., Smits, P. D. & Jablonski, D. Probabilistic models of species discovery and biodiversity comparisons. Proc. Natl Acad. Sci. USA 114, 3666–3671 (2017).CAS 
PubMed 
Article 

Google Scholar 
13.Guenard, B., Weiser, M. D. & Dunn, R. R. Global models of ant diversity suggest regions where new discoveries are most likely are under disproportionate deforestation threat. Proc. Natl Acad. Sci. USA 109, 7368–7373 (2012).CAS 
PubMed 
Article 

Google Scholar 
14.Blackburn, T. M. & Gaston, K. J. What determines the probability of discovering a species – a study of South-American Oscine Passerine birds. J. Biogeogr. 22, 7–14 (1995).Article 

Google Scholar 
15.Costello, M. J., Lane, M., Wilson, S. & Houlding, B. Factors influencing when species are first named and estimating global species richness. Glob. Ecol. Conserv. 4, 243–254 (2015).Article 

Google Scholar 
16.Collen, B., Purvis, A. & Gittleman, J. L. Biological correlates of description date in carnivores and primates. Glob. Ecol. Biogeogr. 13, 459–467 (2004).Article 

Google Scholar 
17.Diniz-Filho, J. A. F. et al. Macroecological correlates and spatial patterns of anuran description dates in the Brazilian Cerrado. Glob. Ecol. Biogeogr. 14, 469–477 (2005).Article 

Google Scholar 
18.Costello, M. J., Houlding, B. & Joppa, L. N. Further evidence of more taxonomists discovering new species, and that most species have been named: response to Bebber et al. (2014). New Phytol. 202, 739–740 (2014).PubMed 
Article 

Google Scholar 
19.Meiri, S. Small, rare and trendy: traits and biogeography of lizards described in the 21st century. J. Zool. 299, 251–261 (2016).Article 

Google Scholar 
20.Klein, J. P. & Moeschberger, M. L. Survival Analysis: Techniques for Censored and Truncated Data.(Springer, 2003).21.Essl, F., Rabitsch, W., Dullinger, S., Moser, D. & Milasowszky, N. How well do we know species richness in a well-known continent? Temporal patterns of endemic and widespread species descriptions in the European fauna. Glob. Ecol. Biogeogr. 22, 29–39 (2013).Article 

Google Scholar 
22.Colli, G. R. et al. In the depths of obscurity: knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae). Biol. Conserv. 204, 51–62 (2016).Article 

Google Scholar 
23.Burgin, C. J., Colella, J. P., Kahn, P. L. & Upham, N. S. How many species of mammals are there? J. Mammal. 99, 1–14 (2018).Article 

Google Scholar 
24.Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Bellard, C. et al. Vulnerability of biodiversity hotspots to global change. Glob. Ecol. Biogeogr. 23, 1376–1386 (2014).Article 

Google Scholar 
26.Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).CAS 
PubMed 
Article 

Google Scholar 
27.Joppa, L. N., Roberts, D. L. & Pimm, S. L. How many species of flowering plants are there? Proc. R. Soc. B 278, 554–559 (2011).PubMed 
Article 

Google Scholar 
28.Giam, X. et al. Reservoirs of richness: least disturbed tropical forests are centres of undescribed species diversity. Proc. R. Soc. B 279, 67–76 (2012).PubMed 
Article 

Google Scholar 
29.Jetz, W. & Fine, P. V. A. Global gradients in vertebrate diversity predicted by historical area-productivity dynamics and contemporary environment. PLoS Biol. 10, e1001292 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Gouveia, S. F., Villalobos, F., Dobrovolski, R., Beltrão-Mendes, R. & Ferrari, S. F. Forest structure drives global diversity of primates. J. Anim. Ecol. 83, 1523–1530 (2014).PubMed 
Article 

Google Scholar 
31.Oliveira, B. F. & Scheffers, B. R. Vertical stratification influences global patterns of biodiversity. Ecography 42, 249–249 (2019).Article 

Google Scholar 
32.Oliveira, U. et al. The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Divers. Distrib. 22, 1232–1244 (2016).33.Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).PubMed 
Article 

Google Scholar 
34.Garnett, S. T. & Christidis, L. Taxonomy anarchy hampers conservation. Nature 546, 25–27 (2017).CAS 
PubMed 
Article 

Google Scholar 
35.Isaac, N. J. B., Mallet, J. & Mace, G. M. Taxonomic inflation: its influence on macroecology and conservation. Trends Ecol. Evol. 19, 464–469 (2004).PubMed 
Article 

Google Scholar 
36.Bremer, K., Bremer, B., Karis, P. & Källersjö, M. Time for change in taxonomy. Nature 343, 202 (1990).CAS 
PubMed 
Article 

Google Scholar 
37.Raposo, M. A. et al. What really hampers taxonomy and conservation? A riposte to Garnett and Christidis (2017). Zootaxa 4317, 179–184 (2017).Article 

Google Scholar 
38.Wake, D. B. Persistent plethodontid themes: species, phylogenies, and biogeography. Herpetologica 73, 242–251 (2017).Article 

Google Scholar 
39.Tedesco, P. A. et al. Estimating how many undescribed species have gone extinct. Conserv. Biol. 28, 1360–1370 (2014).CAS 
PubMed 
Article 

Google Scholar 
40.Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012).PubMed 
Article 

Google Scholar 
41.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).PubMed 
Article 

Google Scholar 
43.Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.González-del-Pliego, P. et al. Phylogenetic and trait-based prediction of extinction risk for data-deficient amphibians. Curr. Biol. 29, 1557–1563.e3 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Moura, M. R. et al. Geographical and socioeconomic determinants of species discovery trends in a biodiversity hotspot. Biol. Conserv. 220, 237–244 (2018).Article 

Google Scholar 
46.Gaston, K. J., Blackburn, T. M. & Loder, N. Which species are described first? The case of North-American butterflies. Biodivers. Conserv. 4, 119–127 (1995).Article 

Google Scholar 
47.Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. & Costa, G. C. AmphiBIO, a global database for amphibian ecological traits. Sci. Data 4, 170123 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Feldman, A., Sabath, N., Pyron, R. A., Mayrose, I. & Meiri, S. Body sizes and diversification rates of lizards, snakes, amphisbaenians and the tuatara. Glob. Ecol. Biogeogr. 25, 187–197 (2016).Article 

Google Scholar 
49.Hallmann, K. & Griebeler, E. M. An exploration of differences in the scaling of life history traits with body mass within reptiles and between amniotes. Ecol. Evol. 8, 5480–5494 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Slavenko, A., Itescu, Y., Ihlow, F. & Meiri, S. Home is where the shell is: predicting turtle home range sizes. J. Anim. Ecol. 85, 106–114 (2016).PubMed 
Article 

Google Scholar 
51.Regis, K. W. & Meik, J. M. Allometry of sexual size dimorphism in turtles: a comparison of mass and length data. PeerJ 5, e2914 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Itescu, Y., Karraker, N. E., Raia, P., Pritchard, P. C. H. & Meiri, S. Is the island rule general? Turtles disagree. Glob. Ecol. Biogeogr. 23, 689–700 (2014).Article 

Google Scholar 
53.Faurby, S. & Svenning, J.-C. Resurrection of the island rule: human-driven extinctions have obscured a basic evolutionary pattern. Am. Nat. 187, 812–820 (2016).PubMed 
Article 

Google Scholar 
54.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).Article 

Google Scholar 
55.Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204A, 23–31 (2016).56.Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

Google Scholar 
57.Gaston, K. J., Blackburn, T. M. & Lawton, J. H. Interspecific abundance–range size relationships: an appraisal of mechanisms. J. Anim. Ecol. 66, 579–601 (1997).Article 

Google Scholar 
58.Borregaard, M. K. & Rahbek, C. Causality of the relationship between geographic distribution and species abundance. Q. Rev. Biol. 85, 3–25 (2010).PubMed 
Article 

Google Scholar 
59.IUCN Red List of Threatened Species. Version 2018 (IUCN, 2018).60.Freitag, S., Hobson, C., Biggs, H. C. & Jaarsveld, A. S. Testing for potential survey bias: the effect of roads, urban areas and nature reserves on a southern African mammal data set. Anim. Conserv. 1, 119–127 (1998).Article 

Google Scholar 
61.Kier, G. & Barthlott, W. Measuring and mapping endemism and species richness: a new methodological approach and its application on the flora of Africa. Biodivers. Conserv. 10, 1513–1529 (2001).Article 

Google Scholar 
62.Vilela, B. & Villalobos, F. letsR: a new R package for data handling and analysis in macroecology. Methods Ecol. Evol. 6, 1229–1234 (2015).Article 

Google Scholar 
63.Papavero, N. Essays on the History of Neotropical Dipterology: With Special Reference to Collectors: 1750–1905: Vol. I (Museu de Zoologia da Universidade de São Paulo, 1971).64.Baselga, A., Lobo, J. M., Hortal, J., Jiménez-Valverde, A. & Gómez, J. F. Assessing alpha and beta taxonomy in eupelmid wasps: determinants of the probability of describing good species and synonyms. J. Zool. Syst. Evol. Res. 48, 40–49 (2010).Article 

Google Scholar 
65.Yang, W., Ma, K. & Kreft, H. Environmental and socio-economic factors shaping the geography of floristic collections in China. Glob. Ecol. Biogeogr. 23, 1284–1292 (2014).Article 

Google Scholar 
66.Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
67.R Core Team R: A Language and Environment for Statistical Computing Version 3.5.3 (R Foundation for Statistical Computing, 2019).68.Hijmans, R. J. raster: Geographic Data Analysis and Modeling https://cran.r-project.org/package=raster (2015).69.Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
70.Klein Goldewijk, K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).Article 

Google Scholar 
71.Joppa, L. N., Roberts, D. L. & Pimm, S. L. The population ecology and social behaviour of taxonomists. Trends Ecol. Evol. 26, 551–553 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
72.Wickham, H. stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.3.1 http://stringr.tidyverse.org (2018).73.Mahto, A. splitstackshape: Stack and Reshape Datasets After Splitting Concatenated Values. R package version 1.4.6 http://github.com/mrdwab/splitstackshape (2018).74.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
75.Kutner, M. H., Nachtsheim, C. J., Neter, J. & Li, W. Applied Linear Statistical Models (McGraw-Hill, 2004).
Google Scholar 
76.Naimi, B. usdm: Uncertainty Analysis for Species Distribution Models https://cran.r-project.org/package=usdm (2017).77.von Linné, C. Systema Naturae https://doi.org/10.5962/bhl.title.542 (Impensis Direct Laurentii Salvii, 1758).78.Harrell, F. E. Regression Modeling Strategies (Springer, 2001).79.George, B., Seals, S. & Aban, I. Survival analysis and regression models. J. Nucl. Cardiol. 21, 686–694 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Jackson, C. flexsurv: a platform for parametric survival modeling in R. J. Stat. Softw. 70, 1–33 (2016).Article 

Google Scholar 
81.Burnham, K. P. & Anderson, D. A. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).82.Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
Article 

Google Scholar 
83.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.6 https://cran.r-project.org/package=MuMIn (2019).84.Alexander Pyron, R. & Wiens, J. J. A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol. Phylogenet. Evol. 61, 543–583 (2011).PubMed 
Article 

Google Scholar 
85.Pyron, R. A., Burbrink, F. T. & Wiens, J. J. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol. Biol. 13, 93 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
86.Fisher, D. O. & Blomberg, S. P. Correlates of rediscovery and the detectability of extinction in mammals. Proc. R. Soc. B 278, 1090–1097 (2011).PubMed 
Article 

Google Scholar 
87.Jetz, W., Sekercioglu, C. H. & Böhning-Gaese, K. The worldwide variation in avian clutch size across species and space. PLoS Biol. 6, e303 (2008).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
88.Jetz, W. & Rubenstein, D. R. Environmental uncertainty and the global biogeography of cooperative breeding in birds. Curr. Biol. 21, 72–78 (2011).CAS 
PubMed 
Article 

Google Scholar 
89.Jetz, W. & Rahbek, C. Geographic range size and determinants of avian species richness. Science 297, 1548–1551 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Dowle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R package version 1.12.4 https://cran.r-project.org/package=data.table (2019).91.Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: elements of a synthesis. J. Biogeogr. 35, 483–500 (2008).Article 

Google Scholar 
92.Violle, C., Reich, P. B., Pacala, S. W., Enquist, B. J. & Kattge, J. The emergence and promise of functional biogeography. Proc. Natl Acad. Sci. USA 111, 13690–13696 (2014).CAS 
PubMed 
Article 

Google Scholar 
93.Database of Global Administrative Areas Version 3.6 (GADM, 2019); http://www.gadm.org More

1.Ji M, Greening C, Vanwonterghem I, Carere CR, Bay SK, Steen JA. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature. 2017;552:400–3.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Grostern A, Alvarez-Cohen L. RubisCO-based CO2 fixation and C1 metabolism in the actinobacterium Pseudonocardia dioxanivorans CB1190. Environ Microbiol. 2013;15:3040–53.CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Greening C, Biswas A, Carere CR, Jackson CJ, Taylor MC, Stott MB, et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 2016;10:761–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Bay S, Ferrari BC, Greening C. Life without water: how do bacteria generate biomass in desert ecosystems? Microbiol Austral. 2018;39:28–32.Article 

Google Scholar 
5.Ray A, Zhang E, Terauds A, Ji M, Kong W, Ferrari BC. Soil microbiomes with the genetic capacity for atmospheric chemosynthesis are widespread across the poles and are associated with moisture, carbon and nitrogen limitation. Front Microbiol. 2020;11:1–13.Article 

Google Scholar 
6.Greening C, Berney M, Hards K, Cook GM, Conrad R. A soil actinobacterium scavenges atmospheric H2 using two membrane-associated, oxygen-dependent [NiFe] hydrogenases. Proc Natl Acad Sci. 2014;111:4257–61.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Nogales B, Moore ERB, Llobet-Brossa E, Rossello-Mora R, Amann R, Timmis KN. Combined use of 16S ribosomal DNA and 16S rRNA to study the bacterial community of polychlorinated biphenyl-polluted soil. Appl Environ Microbiol. 2001;67:1874–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Nessner Kavamura V, Taketani RG, Lançoni MD, Andreote FD, Mendes R, Soares de Melo I. Water regime influences bulk soil and rhizosphere of Cereus jamacaru bacterial communities in the Brazilian Caatinga biome. PLoS ONE. 2013;8:e73606.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Serkebaeva YM, Kim Y, Liesack W, Dedysh SN. Pyrosequencing-based assessment of the bacteria diversity in surface and subsurface peat layers of a northern wetland, with focus on poorly studied phyla and candidate divisions. PLoS ONE. 2013;8:e63994.PubMed 
PubMed Central 
Article 

Google Scholar 
10.Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Holland-Moritz H, Stuart J, Lewis LR, Miller S, Mack MC, McDaniel SF, et al. Novel bacterial lineages associated with boreal moss species. Environ Microbiol. 2018;20:2625–38.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Ward LM, Cardona T, Holland-Moritz H. Evolutionary implications of anoxygenic phototrophy in the bacterial phylum Candidatus Eremiobacterota (WPS-2). Front Microbiol. 2019;10:1658.PubMed 
PubMed Central 
Article 

Google Scholar 
13.Sheremet A, Jones GM, Jarett J, Bowers RM, Bedard I, Culham C, et al. Ecological and genomic analyses of candidate phylum WPS-2 bacteria in an unvegetated soil. Environ Microbiol. 2020;22:3143–57.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Dewhirst FE, Klein EA, Thompson EC, Blanton JM, Chen T, Milella L, et al. The canine oral microbiome. PLoS ONE. 2012;7:e36067.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Camanocha A, Dewhirst FE. Host-associated bacterial taxa from Chlorobi, Chloroflexi, GN02, Synergistetes, SR1, TM7, and WPS-2 Phyla/candidate divisions. J Oral Microbiol. 2014;6. https://doi.org/10.3402/jom.v6.25468.16.Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Ji M, van Dorst J, Bissett A, Brown MV, Palmer AS, Snape I, et al. Microbial diversity at Mitchell Peninsula, Eastern Antarctica: a potential biodiversity “hotspot”. Pol Biol. 2015;39:237–49.Article 

Google Scholar 
18.Ferrari BC, Bissett A, Snape I, van Dorst J, Palmer AS, Ji M, et al. Geological connectivity drives microbial community structure and connectivity in polar, terrestrial ecosystems. Environ Microbiol. 2016;18:1834–49.PubMed 
Article 
PubMed Central 

Google Scholar 
19.Bissett A, Fitzgerald A, Meintjes T, Mele PM, Reith F, Dennis PG, et al. Introducing BASE: the biomes of Australian soil environments soil microbial diversity database. Gigascience. 2016;5:21.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Siciliano SD, Palmer AS, Winsley T, Lamb E, Bissett A, Brown MV, et al. Polar soil bacterial and fungal biodiversity survey, Ver. 1. Australian Antarctic Data Centre; 2014. https://doi.org/10.4225/15/526F42ADA05B1. Accessed 11 Feb 2021.21.Lane D. Nucleic acid techniques in bacterial systematics. In: Stackebrandt E, Goodfellow M, editors. Chichester NY: John Wiley and Sons; 1991. p. 115–75.22.Siciliano SD, Palmer A, Winsley T, Lamb E, Bissett A, Brown M, et al. Soil fertility is associated with fungal and bacterial richness, whereas pH is associated with community composition in polar soil microbial communities. Soil Biol Biochem. 2014;78:10–20.CAS 
Article 

Google Scholar 
23.Archer E. R package. 2016.24.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014. https://doi.org/10.1093/bioinformatics/btu170.25.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Berkeley, CA, United States: Lawrence Berkeley National Laboratory; 2014.27.Imelfort M, Parks D, Woodcroft BJ, Dennis P, Hugenholtz P, Tyson GW. GroopM: an automated tool for the recovery of population genomes from related metagenomes. PeerJ. 2014;2:e603.PubMed 
PubMed Central 
Article 

Google Scholar 
28.Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.CAS 

Google Scholar 
34.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Murray AE, Freudenstein J, Gribaldo S, Hatzenpichler R, Hugenholtz P, Kämpfer P, et al. Roadmap for naming uncultivated Archaea and Bacteria. Nat Microbiol. 2020;5:987–94.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
37.Tabita FR, Hanson TE, Li H, Satagopan S, Singh J, Chan S. Function, structure, and evolution of the RubisCO-like proteins and their RubisCO homologs. Microbiol Mol Biol Rev. 2007;71:576–99.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Tabita FR, Hanson TE, Satagopan S, Witte BH, Kreel NE. Phylogenetic and evolutionary relationships of RubisCO and the RubisCO-like proteins and the functional lessons provided by diverse molecular forms. Philos Trans R Soc Lond B Biol Sci. 2008;363:2629–40.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.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 
40.Sondergaard D, Pedersen CN, Greening C. HydDB: a web tool for hydrogenase classification and analysis. Sci Rep. 2016;6:34212.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Fuchs BM, Wallner G, Beisker W, Schwippl I, Ludwig W, Amann R. Flow cytometric analysis of the in situ accessibility of Escherichia coli 16S rRNA for fluorescently labeled oligonucleotide probes. Appl Environ Microbiol. 1998;64:4973–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Schramm A, Fuchs BM, Nielsen JL, Tonolla M, Stahl DA. Fluorescence in situ hybridization of 16S rRNA gene clones (Clone-FISH) for probe validation and screening of clone libraries. Environ Microbiol. 2002;4:713–20.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Lindahl V. Improved soil dispersion procedures for total bacterial counts, extraction of sandy clayey silt soil bacteria and cell survival. J Microbiol Meth. 1996;25:279–86.Article 

Google Scholar 
46.Ferrari BC, Tujula N, Stoner K, Kjelleberg S. Catalysed reporter deposition-FISH allows for enrichment independent detection of microcolony forming soil bacteria. Appl Environ Microbiol. 2006;72:918–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Kim M, Lim HS, Hyun CU, Cho A, Noh HJ, Hong SG, et al. Local-scale variation of soil bacterial communities in ice-free regions of maritime Antarctica. Soil Biol Biochem. 2019;133:165–73.CAS 
Article 

Google Scholar 
48.Islam ZF, Welsh C, Bayly K, Grinter R, Southam G, Gagen EJ, et al. A widely distributed hydrogenase oxidises atmospheric H2 during bacterial growth. ISME J. 2020;14:2649–58.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Myers MR, King GM. Isolation and characterization of Acidobacterium ailaaui sp. nov., a novel member of Acidobacteria subdivision 1, from a geothermally heated Hawaiian microbial mat. Int J Syst Evol Microbiol. 2016;66:5328–35.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Cordero PRF, Bayly K, Leung PM, Huang C, Islam ZF, Schittenhelm RB, et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 2019;13:2868–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Tremblay PL, Lovley DR. Role of the NiFe hydrogenase Hya in oxidative stress defense in Geobacter sulfurreducens. J Bacteriol. 2012;194:2248–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Greening C, Cook GM. Integration of hydrogenase expression and hydrogen sensing in bacterial cell physiology. Curr Opin Microbiol. 2014;18:30–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.English RS, Lorbach SC, Qin X, Shively JM. Isolation and characterization of a carboxysome shell gene from Thiobacillus neapolitanus. Mol Microbiol. 1994;12:647–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Bonomi HR, Toum L, Sycz G, Sieira R, Toscani AM, Gudesblat GE, et al. Xanthomonas campestris attenuates virulence by sensing light through a bacteriophytochrome photoreceptor. EMBO Rep. 2016;17:1565–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Gamiz-Hernandez AP, Kaila VRI. Conversion of light-energy into molecular strain in the photocycle of the photoactive yellow protein. Phys Chem Chem Phys. 2016;18:2802–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Zhang E, Thibaut LM, Terauds A, Raven M, Tanaka MM, van Dorst J, et al. Lifting the veil on arid-to-hyperarid Antarctic soil microbiomes: a tale of two oases. Microbiome. 2020;8:37.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Borisov VB, Gennis RB, Hemp J, Verkhovsky MI. The cytochrome bd respiratory oxygen reductases. Biochim Biophys Acta. 2011;1807:1398–413.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.McCrindle SL, Kappler U, McEwan AG. Microbial dimethylsulfoxide and trimethylamine-N-oxide respiration. Adv Micro Physiol. 2005;50:147–98.CAS 
Article 

Google Scholar 
59.Bogachev AV, Bertsova YV, Bloch DA, Verkhovsky MI. Urocanate reductase: identification of a novel anaerobic respiratory pathway in Shewanella oneidensis MR-1. Mol Microbiol. 2012;86:1452–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Hopper AC, Li Y, Cole JA. A critical role for the cccA gene product, cytochrome c2, in diverting electrons from aerobic respiration to denitrification in Neisseria gonorrhoeae. J Bacteriol. 2013;195:2518–29.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Nichols NN, Harwood CS. PcaK, a high-affinity permease for the aromatic compounds 4-hydroxybenzoate and protocatechuate from Pseudomonas putida. J Bacteriol. 1997;179:5056–61.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Fraga J, Maranha A, Mendes V, Pereira PJB, Empadinhas N, Macedo-Ribeiro S. Structure of mycobacterial maltokinase, the missing link in the essential GlgE-pathway. Sci Rep. 2015;5:8026.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Reina-Bueno M, Argandoña M, Nieto JJ, Hidalgo-García A, Iglesias-Guerra F, Delgado MJ, et al. Role of trehalose in heat and desiccation tolerance in the soil bacterium Rhizobium etli. BMC Microbiol. 2012;12:207.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Mougous JD, Petzold CJ, Senaratne RH, Lee DH, Akey DL, Lin FL, et al. Identification, function and structure of the mycobacterial sulfotransferase that initiates sulfolipid-1 biosynthesis. Nat Struct Mol Biol. 2004;11:721–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Oren A. Diversity of halophilic microorganisms: environments, phylogeny, physiology, and applications. J Ind Microbiol Biotechnol. 2002;28:56–63.CAS 
Article 

Google Scholar 
66.Cheggour A, Fanuel L, Duez C, Joris B, Bouillenne F, Devreese B, et al. The dppA gene of Bacillus subtilis encodes a new D-aminopeptidase. Mol Microbiol. 2000;38:504–13.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Geueke B, Heck T, Limbach M, Nesatyy V, Seebach D, Kohler HPE. Bacterial β-peptidyl aminopeptidases with unique substrate specificities for β-oligopeptides and mixed β,α-oligopeptides. FEBS J. 2006;273:5261–72.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Driessen AJM, van de Vossenberg JLM, Konings WN. Membrane composition and ion-permeability in extremophiles. FEMS Microbiol Rev. 1996;18:139–48.CAS 
Article 

Google Scholar 
69.Jones DS, Albrecht HL, Dawson KS, Schaperdoth I, Freeman KH, Pi Y, et al. Community genomic analysis of an extremely acidophilic sulfur-oxidizing biofilm. ISME J. 2012;6:158–70.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Nguyen NL, Yu WJ, Gwak JH, Kim SJ, Park SJ, Herbold CW, et al. Genomic insights into the acid adaptation of novel methanotrophs enriched from acidic forest soils. Front Microbiol. 2018;9:1982.PubMed 
PubMed Central 
Article 

Google Scholar 
71.Xue J, Ahring BK. Enhancing isoprene production by genetic modification of the 1-deoxy-d-xylulose-5-phosphate pathway in Bacillus subtilis. Appl Environ Microbiol. 2011;77:2399–405.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Baker-Austin C, Dopson M. Life in acid: pH homeostasis in acidophiles. Trends Microbiol. 2007;15:165–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Siebers A, Altendorf K. The K+-translocating Kdp-ATPase from Escherichia coli. Purification, enzymatic properties and production of complex- and subunit-specific antisera. Eur J Biochem. 1988;178:131–40.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Milkman R. An Escherichia coli homologue of eukaryotic potassium channel proteins. Proc Natl Acad Sci. 1994;91:3510–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Holtmann G, Bakker EP, Uozumi N, Bremer E. KtrAB and KtrCD: two K+ uptake systems in Bacillus subtilis and their role in adaptation to hypertonicity. J Bacteriol. 2003;185:1289–98.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Castanie-Cornet MP, Penfound TA, Smith D, Elliott JF, Foster JW. Control of acid resistance in Escherichia coli. J Bacteriol. 1999;181:3525–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Geisseler D, Horwath WR. Regulation of extracellular protease activity in soil in response to different sources and concentrations of nitrogen and carbon. Soil Biol Biochem. 2008;40:3040–8.CAS 
Article 

Google Scholar 
78.Einsle O, Messerschmidt A, Stach P, Bourenkov GP, Bartunik HD, Huber R, et al. Structure of cytochrome c nitrite reductase. Nature. 1999;400:476–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Simon J, Pisa R, Stein T, Eichler R, Klimmek O, Gross R. The tetraheme cytochrome c NrfH is required to anchor the cytochrome c nitrite reductase (NrfA) in the membrane of Wolinella succinogenes. Eur J Biochem. 2001;268:5776–82.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Nair RV, Bennett GN, Papoutsakis ET. Molecular characterization of an aldehyde/alcohol dehydrogenase gene from Clostridium acetobutylicum ATCC 824. J Bacteriol. 1994;176:871–85.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Axen SD, Erbilgin O, Kerfeld CA. A taxonomy of bacterial microcompartment loci constructed by a novel scoring method. PLoS Comput Biol. 2014;10:e1003898.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Erbilgin O, McDonald KL, Kerfeld CA. Characterization of a planctomycetal organelle: a novel bacterial microcompartment for the aerobic degradation of plant saccharides. Appl Environ Microbiol. 2014;80:2193–205.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
83.Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.PubMed 
PubMed Central 
Article 

Google Scholar 
84.Srinivasan V, Morowitz HJ. The canonical network of autotrophic intermediary metabolism: minimal metabolome of a reductive chemoautotroph. Biol Bull. 2009;216:126–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Nunoura T, Chikaraishi Y, Izaki R, Suwa T, Sato T, Harada T, et al. A primordial and reversible TCA cycle in a facultatively chemolithoautotrophic thermophile. Science. 2018;359:559–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Bekal S, Van Beeumen J, Samyn B, Garmyn D, Henini S, Diviès C, et al. Purification of Leuconostoc mesenteroides citrate lyase and cloning and characterization of the citCDEFG gene cluster. J Bacteriol. 1998;180:647–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Dimroth P, Jockel P, Schmid M. Coupling mechanism of the oxaloacetate decarboxylase Na(+) pump. Biochim Biophys Acta. 2001;1505:1–14.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Mulkidjanian AY, Dibrov P, Galperin MY. The past and present of sodium energetics: may the sodium-motive force be with you. Biochim Biophys Acta. 2008;1777:985–92.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Lewis Smith RI. Plant community dynamics in Wilkes Land, Antarctica, vol. 3. Proceedings of the NIPR Symposium on Polar Biology. 1990. p. 229–44.90.Seppelt RD. Plant communities at Wilkes Land. In: Geoecology of Antarctic ice-free coastal landscapes. Ecological studies (Analysis and synthesis), vol. 154. Springer; 2002. p. 233–48. More

Strains and growth conditionsSingle gene deletion strains were taken from the Keio collection [34] (Supplementary Table 1), which consists of all non-essential single gene deletions in E. coli K-12 strain BW25113. MreB and mrdA point mutant strains were from Ref. [35] (Supplementary Table 2). Plasmids pQY10 and pQY11 were created by Gibson assembly of Venus YFP A206K (for pQY10) or Venus CFP A206K (for pQY11) [31], and SpecR from pKDsgRNA-ack (gift from Kristala Prather, Addgene plasmid # 62654, http://n2t.net/addgene:62654; RRID:Addgene_62654) [36]. Plasmids pQY12 and pQY13 were created similarly but additionally with CmR from pACYC184.All E. coli experiments were performed in LB (Merck 110285, Kenilworth, New Jersey) with the appropriate antibiotics and experiments with S. cerevisae were performed in YPD [37]. All agar plates were prepared in OmniTrays (Nunc 242811, Roskilde, Denmark, 12.8 cm × 8.6 cm) or 12 cm × 12 cm square petri dishes (Greiner 688102, Kremsmuenster, Austria) filled with 70 mL media solidified with 2% Bacto Agar (BD 214010, Franklin Lakes, New Jersey). After solidifying, the plates were dried upside-down in the dark for 2 days and stored wrapped at 4 °C in the dark for 7–20 days before using.Tracking lineages with fluorescent tracer beadsIn order to track lineages, we spread fluorescent tracer beads with a similar size to the cells on the surface of an agar plate, allowed them to dry, then inoculated and grew a colony on top of the agar plate and imaged the tracer beads to track lineages. In this way, we are able to track lineages without genetic labels at low density (i.e. sparsely) in the colony so that we can distinguish individual lineages without needing high-resolution microscopy. We find that the bead trajectories track cell lineages over the course of one hour both at the colony front and behind the front (Figs. 1c, S1c, d, and S2). We chose to spread fluorescent tracer beads on the surface of the agar so that they could continue to be incorporated into the colony as it grew, which would allow us to track lineages even as existing beads and lineages get lost from the front. Even though behind the front many cells will be piled up on top of other cells rather than in contact with the agar, we don’t expect this to affect the ability of the beads to measure demographic noise, since lineages at the front (where cells are in a monolayer) are the most likely to contribute offspring to future generations [26].Fig. 1: Label-free method of measuring demographic noise in microbial colonies.a Schematic of bead-based sparse lineage tracing method for measuring demographic noise. b Schematic of existing method for measuring fraction of diversity preserved [26]. c (Top) The trajectory of a single bead (black) and the lineages of the cells neighboring it in the final-timepoint (colors) traced backwards in time in the Keio collection wild type strain. (Bottom) The deviation of the distance between the cell lineages and the bead from the final distance, backwards in time. Colors are the same as in the time series images. The gray shaded region shows a single cell width away or towards the bead. All cells that neighbor the bead in the final timepoint, except for one (orange), are neighbors of the bead in the first timepoint and stay within a single cell width of the final distance to the bead. d Example neutral mixtures of YFP and CFP tagged strains grown for 1 day and bead trajectories for strains highlighted in e. Black lines show the colony front at 12 and 23 hours. e Comparison of MSD at window size L = 50 µm to the fraction of diversity preserved for 3 E. coli strain backgrounds and 6 single gene deletions on the Keio collection wild type background (BW25113). Error bars in MSD represent the standard error of the weighted mean (N = 7–8, see Methods) and error bars in the fraction of diversity preserved represent the standard error of the weighted mean (N = 8) where weights come from uncertainties in counting the number of sectors.Full size imageFluorescent tracer beadsFor experiments with E. coli, 1 µm red fluorescent polystyrene beads from Magsphere (PSF-001UM, Pasadena, CA, USA) were diluted to 3 µg/mL in molecular grade water and 920 µL was spread on the surface of the prepared OmniTray agar plates with sterile glass beads. Excess bead solution was poured out, and the plates were dried under the flow of a class II biosafety cabinet (Nuaire, NU-425-300ES, Plymouth, MN, USA) for 45 min. The bead density was chosen to achieve ~250 beads in a 56x field of view. For experiments with S. cerervisiae, 2 µm dragon green fluorescent polystyrene beads from Bang’s labs (FSDG005, Fishers, IN, USA) were used at a similar surface density.Measurement of the distribution of demographic noiseWe randomly selected 352 single gene deletion strains from the Keio collection. For each experiment, cells were thawed from glycerol stock (see Supplementary Methods), mixed, and 5 µL was transferred into a 96-well flat bottom plate with 100 µL LB and the appropriate antibiotics. Plates were covered with Breathe-Easy sealing membrane (Diversified Biotech BEM-1, Doylestown, PA, USA) and grown for 12 h at 37 °C without shaking. A floating pin replicator (V&P Scientific, FP12, 2.36 mm pin diameter, San Diego, CA, USA) was used to inoculate a 2–3 mm droplet from each well of the liquid culture onto a prepared OmniTray covered with fluorescent tracer beads. Droplets were dried and the plates were incubated upside down at 37 °C for 12 h before timelapse imaging.To account for systematic differences between plates, we also put 8 wild type BW25113 wells in each 96-well plate in different positions on each plate. The mean squared displacement (MSD, see below) of each gene deletion colony was normalized to the weighted average MSD of the wild type BW25113 colonies on that plate, 〈MSD〉WT, and this “relative MSD” is reported. We performed three biological replicates for each strain (grown from the same glycerol stock, Fig. S3), and their measurements were averaged together weighted by the inverse of the square of their individual error in relative MSD. The reported error for the strain is the standard error of the mean. During the experiment, several experimental challenges impede our ability to measure demographic noise, including the appearance of beneficial sectors (identified as diverging bead trajectories that correspond to bulges at the colony front) either due to de novo beneficial mutations or standing variation from glycerol stock (see Supplementary Section 2.4, Figs. S4 and S5), slow growth rate leading to bead tracks that were too short for analysis, no cells transferred during inoculation with our pinning tool, inaccurate particle tracking due to beads being too close together, or out of focus images. In order to keep only the highest quality data points, we focused on the 191 strains that had at least 2 replicates free of such issues.Timelapse imaging of fluorescent beadsPlates were transferred to an ibidi stagetop incubator (Catalog number 10918, Gräfelfing, Germany) set to 37 °C for imaging. Evaporation was minimized by putting wet Kim wipes in the chamber and sealing the chamber with tape. The fluorescent tracer beads at the front of the colony were imaged with a Zeiss Axio Zoom.V16 (Oberkochen, Germany) at 56x magnification. A custom macro program written using the Open Application Development for Zen software was used to find the initial focal position for each colony and adjust for deterministic focus drift over time due to slight evaporation. Timelapse imaging was performed at an interval of 10 min for 12 h, during which time the colony grew about halfway across the field of view. Two z slices were taken for each colony and postprocessed to find the most in-focus image to adjust for additional focus drift. Subpixel-resolution particle tracking of the bead trajectory was achieved using a combination of particle image velocimetry and single particle tracking [38] and is described in detail in the Supplementary Methods.Measurement of bead trajectory mean squared displacementThe measurement of mean squared displacement (MSD) is adapted from [31] and is illustrated in Figs. 1a and S1a. Points in a trajectory that fall within a window of length L are fit to a line of best fit. The MSD is given by$$MSDleft( L right) = leftlangle {leftlangle {frac{1}{L}mathop {int}nolimits_l^{l + L} {left( {{Delta}wleft( {L^prime } right)} right)^2dL^prime } } rightrangle _{windows}} rightrangle _{trajectories}$$where Δw(L’) is the displacement of the bead trajectory from the line of best fit at each point, 〈〉windows is an average over all possible definitions of a window with length L along the trajectory (window definitions are overlapping), and 〈〉trajectories is a weighted average over all trajectories in a field of view, where the weight is the inverse squared standard error of the mean for each trajectory’s MSD(L) (Fig. S1a). We use 200 linearly spaced window sizes from L = 6 to 1152 μm. Window sizes that fit in fewer than 5 trajectories are dropped due to the noisiness in calculating the averaged MSD(L). The combined MSD(L) for all trajectories reflects that of bead trajectories at the colony front, which will have the largest contribution to the strength of demographic noise [26] (Fig. S6). Because we expect the trajectories to follow an anomalous random walk [31], the combined MSD(L) for all trajectories across the field of view is fit using weighted least squares to a power law, where the weight is the inverse square of the propagated standard error of the mean. Colonies with data in fewer than 5 window sizes are dropped due to the noisiness in fitting to a power law. The fit is extrapolated or interpolated to L = 50 µm to give a single summary statistic for each colony, and this quantity is reported as MSD(L = 50 µm) (see Supplementary Section 2.2, Figs. S7 and S8), and the error is calculated as half the difference in MSD (L = 50 µm) from using the upper and lower bounded coefficients to the fit. For calculating the distribution of demographic noise effects, only MSD values where the error is less than half of the value are kept.Measurement of phenotypic traitsFor the phenotypic trait measurements, in addition to the 191 single gene deletions, we also measured 41 additional strains of E. coli which included 4 strain backgrounds, 1 mreB knockout in the MC1000 background, 2 adhesin mutants, and 34 single gene knockouts from the Keio collection that we predicted may have large changes to demographic noise because of an altered biofilm forming ability in liquid culture [39] or altered cell shape from the wild type (using the classification on the Keio website, https://shigen.nig.ac.jp/ecoli/strain/resource/keioCollection/list). We normalized all phenotypic trait values to the average value measured from the wild type colonies on the same plate. The reported values for each strain are averages across 2–3 replicate colonies on different plates and the errors are the standard error of the mean. See the Supplementary Methods for more details of the specific phenotypic trait measurements.Measurement of neutral fraction of diversity preservedNeutral fluorescent pairs were created by transforming background strains with plasmids pQY10 (YFP, SpecR) or pQY11 (CFP, SpecR). Cells were streaked from glycerol stock and a single colony of each strain was inoculated into a 96 well plate with 600 µL LB and 120 µg/mL spectinomycin for plasmid retention. Plates were covered with Breathe-Easy sealing membrane and grown for 12 h at 37 °C without shaking. 50 µL of culture from each strain in a neutral pair were mixed and a floating pin replicator was used to inoculate a 2–3 mm droplet from the liquid culture onto a prepared OmniTray covered with fluorescent tracer beads. Droplets were dried and the plates were incubated at 37 °C.Colonies were imaged after 24 h with fluorescence microscopy using a Zeiss Axio Zoom.V16 and the number of sectors of each color was manually counted. The fraction of diversity preserved was calculated as in Ref. [26] by dividing the number of neutral sectors by one-half times the estimated initial number of cells at the inoculum front (see Fig. 1b). The factor of one-half accounts for the probability that two neighboring cells at the inoculum front share the same color label. The initial number of cells is estimated by measuring the inoculum size of each colony (manually measured by fitting a circle to a brightfield backlight image at the time of inoculation) divided by the effective cell size for E. coli (sqrt(length*width) taken to be 1.7 µm, Ref. [26]).Colony fitnessThe colony fitness coefficient between two strains was measured using a colony collision assay as described in Refs. [26, 40] by growing colonies next to one another and measuring the curvature of the intersecting arc upon collision. Cells were streaked from glycerol stock and a single colony for each strain was inoculated into LB with 120 µg/mL spectinomycin for plasmid retention and incubated at 37 °C for 15 h. The culture was back diluted 1:500 in 1 mL fresh LB with 120 µg/mL spectinomycin and grown at 37 °C for 4 h. 1 µL of the culture was then inoculated onto the prepared 12cmx12cm square petri dishes containing LB with different concentrations of chloramphenicol (0 µg/mL, 1 µg/mL, 2 µg/mL, 3 µg/mL) in pairs that were 5 mm apart, with 32 pairs per plate, then the colonies were incubated at 37 °C. After half of a day, bright field backlight images are taken and were used to fit circles to each colony to determine the distance between the two colonies. After 6 days, the colonies were imaged with fluorescence microscopy using a Zeiss Axio Zoom.V16. The radius of curvature of the intersecting arc between the two colonies was determined with image segmentation and was used to calculate the fitness coefficient between the two strains (Fig. S9a).Measurement of non-neutral establishment probabilityWe transformed 9 gene deletion strains from the Keio collection (gpmI, recB, pgm, tolQ, ychJ, lpcA, dsbA, rfaF, tatB) and 3 strain backgrounds (BW25113, MG1655, DH5α) with pQY11 (CFP, SpecR) or pQY12 (YFP, SpecR, CmR). Cells were streaked from glycerol stock and a single colony of each strain was inoculated into media with 120 µg/mL spectinomycin for plasmid retention, then incubated at 37 °C for 16 h. The culture was back-diluted 1:1000 in 1 mL fresh media with 120 µg/mL spectinomycin and grown at 37 °C for 4 h. YFP chloramphenicol-resistant and CFP chloramphenicol-sensitive cells from the same strain background were mixed respectively at approximately 1:500, 1:200, and 1:50 and distributed in a 96-well plate. A floating pin replicator was used to inoculate a 2–3 mm droplet from the liquid culture onto prepared OmniTrays with varying concentrations of chloramphenicol (0 µg/mL, 1 µg/mL, 2 µg/mL, 3 µg/mL). Droplets were dried and the plates were incubated at 37 °C for 3 days, then imaged by fluorescence microscopy using a Zeiss Axio Zoom.V16.The establishment probability of the resistant strain can be measured by counting the number of established resistant sectors normalized by the initial number of resistant cells at the inoculum front [26], which gives the probability that any given resistant cell in the inoculum escaped genetic drift and grew to a large enough size to create a sector. Briefly,$$p_{est} = N_{sectors}/N_0$$
(1)
where Nsectors is the number of resistant sectors after 3 days (counted by eye) and N0 is the estimated initial number of cells of the resistant type at the inoculum front. Because the establishment probability can only be accurately measured when the initial number of resistant cells is low enough that the resistant sectors do not interact with one another, we only keep colonies where neighboring resistant sectors are distinguishable at the colony front. In cases where we could see that a sector had coalesced from multiple sectors, we counted the number of sectors pre-coalescence. We also did not find a clear downward bias in the establishment probability as a function of initial mutant fraction (Fig. S10), suggesting that the probability of sector coalescence is low in the regime of these experimental parameters. The initial number N0 of cells of the resistant type is estimated by multiplying the initial number of cells at the inoculum front (see measurement of neutral fraction of diversity preserved) by the fraction of resistant cells in the inoculum (measured by plating and counting CFUs). More

1.Sharma VK. Adaptive significance of circadian clocks. Chronobiol Int. 2003;20:901–19.PubMed 
Article 

Google Scholar 
2.Paranjpe DA, Kumar Sharma V. Evolution of temporal order in living organisms. J Circadian Rhythms. 2005;3:1–13.Article 
CAS 

Google Scholar 
3.Nobs SP, Tuganbaev T, Elinav E. Microbiome diurnal rhythmicity and its impact on host physiology and disease risk. EMBO Rep. 2019;20:e47129.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Sartor F, Eelderink-Chen Z, Aronson B, Bosman J, Hibbert LE, Dodd AN, et al. Are there circadian clocks in non-photosynthetic bacteria? Biology. 2019;8:41.CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
5.Soriano M, Roibas B, Garcia A, Espinosa-Urgel M. Evidence of circadian rhythms in non-photosynthetic bacteria? J Circadian Rhythms. 2010;8:1–4.Article 

Google Scholar 
6.Thaiss CA, Zeevi D, Levy M, Zilberman-Schapira G, Suez J, Tengeler AC, et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell. 2014;159:514–29.CAS 
PubMed 
Article 

Google Scholar 
7.Ishiura M, Kutsuna S, Aoki S, Iwasaki H, Andersson CR, Tanabe A, et al. Expression of a gene cluster kaiABC as a circadian feedback process in cyanobacteria. Science. 1998;281:1519–23.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Aylward FO, Boeuf D, Mende DR, Wood-Charlson EM, Vislova A, Eppley JM, et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proc Natl Acad Sci USA. 2017;114:11446–51.CAS 
PubMed 
Article 

Google Scholar 
9.Dvornyk V, Vinogradova O, Nevo E. Origin and evolution of circadian clock genes in prokaryotes. Proc Natl Acad Sci USA. 2003;100:2495–500.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Maniscalco M, Nannen J, Sodi V, Silver G, Lowrey PL, Bidle KA. Light-dependent expression of four cryptic archaeal circadian gene homologs. Front Microbiol. 2014;5:79.PubMed 
PubMed Central 
Article 

Google Scholar 
11.Bernal P, Allsopp LP, Filloux A, Llamas MA. The Pseudomonas putida T6SS is a plant warden against phytopathogens. ISME J. 2017;11:972–87.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Schmelling NM, Lehmann R, Chaudhury P, Beck C, Albers S-V, Axmann IM, et al. Minimal tool set for a prokaryotic circadian clock. BMC Evol Biol. 2017;17:1–20.Article 
CAS 

Google Scholar 
13.Hong L, Vani BP, Thiede EH, Rust MJ, Dinner AR. Molecular dynamics simulations of nucleotide release from the circadian clock protein KaiC reveal atomic-resolution functional insights. Proc Natl Acad Sci USA. 2018;115:11475–84.Article 
CAS 

Google Scholar 
14.Edgar RS, Green EW, Zhao Y, Ooijen Gvan, Olmedo M, Qin X, et al. Peroxiredoxins are conserved markers of circadian rhythms. Nature. 2012;485:459–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Harmer SL, Hogenesch JB, Straume M, Chang H-S, Han B, Zhu T, et al. Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science. 2000;290:2110–3.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Farré EM, Weise SE. The interactions between the circadian clock and primary metabolism. Curr Opin Plant Biol. 2012;15:293–300.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
17.Harmer SL. The circadian system in higher plants. Annu Rev Plant Biol. 2009;60:357–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Haydon MJ, Mielczarek O, Robertson FC, Hubbard KE, Webb AAR. Photosynthetic entrainment of the Arabidopsis circadian clock. Nature. 2013;502:689–92.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.DeAngelis KM, Brodie EL, DeSantis TZ, Andersen GL, Lindow SE, Firestone MK. Selective progressive response of soil microbial community to wild oat roots. ISME J. 2009;3:168–78.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Lundberg DS, Lebeis SL, Paredes SH, Yourstone S, Gehring J, Malfatti S, et al. Defining the core Arabidopsis thaliana root microbiome. Nature. 2012;488:86–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Wu G, Tang W, He Y, Hu J, Gong S, He Z, et al. Light exposure influences the diurnal oscillation of gut microbiota in mice. Biochem Biophys Res Commun. 2018;501:16–23.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Teichman EM, O’Riordan KJ, Gahan CGM, Dinan TG, Cryan JF. When rhythms meet the blues: circadian interactions with the microbiota-gut-brain axis. Cell Metab. 2020;31:448–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Zarrinpar A, Chaix A, Yooseph S, Panda S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 2014;20:1006–17.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Leone V, Gibbons SM, Martinez K, Hutchison AL, Huang EY, Cham CM, et al. Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism. Cell Host Microbe. 2015;17:681–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Liang X, Bushman FD, FitzGerald GA. Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock. Proc Natl Acad Sci USA. 2015;112:10479–84.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Kaczmarek JL, Musaad SM, Holscher HD. Time of day and eating behaviors are associated with the composition and function of the human gastrointestinal microbiota. Am J Clin Nutr. 2017;106:1220–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Deaver JA, Eum SY, Toborek M. Circadian disruption changes gut microbiome taxa and functional gene composition. Front Microbiol. 2018;9:737.PubMed 
PubMed Central 
Article 

Google Scholar 
29.Hubbard CJ, Brock MT, van Diepen LT, Maignien L, Ewers BE, Weinig C. The plant circadian clock influences rhizosphere community structure and function. ISME J. 2018;12:400–10.PubMed 
Article 
PubMed Central 

Google Scholar 
30.Staley C, Ferrieri AP, Tfaily MM, Cui Y, Chu RK, Wang P, et al. Diurnal cycling of rhizosphere bacterial communities is associated with shifts in carbon metabolism. Microbiome. 2017;5:1–13.Article 

Google Scholar 
31.Feng J, Xu Y, Ma B, Tang C, Brookes PC, He Y, et al. Assembly of root-associated microbiomes of typical rice cultivars in response to lindane pollution. Environ Int. 2019;131:104975.CAS 
PubMed 
Article 

Google Scholar 
32.Gremion F, Chatzinotas A, Harms H. Comparative 16S rDNA and 16S rRNA sequence analysis indicates that Actinobacteria might be a dominant part of the metabolically active bacteria in heavy metal-contaminated bulk and rhizosphere soil. Environ Microbiol. 2003;5:896–907.CAS 
PubMed 
Article 

Google Scholar 
33.Lavecchia A, Curci M, Jangid K, Whitman WB, Ricciuti P, Pascazio S, et al. Microbial 16S gene-based composition of a sorghum cropped rhizosphere soil under different fertilization managements. Biol Fertil Soils. 2015;51:661–72.CAS 
Article 

Google Scholar 
34.Wang B, Zhao J, Guo Z, Ma J, Xu H, Jia Z. Differential contributions of ammonia oxidizers and nitrite oxidizers to nitrification in four paddy soils. ISME J. 2015;9:1062–75.CAS 
PubMed 
Article 

Google Scholar 
35.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
36.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:633–42.Article 
CAS 

Google Scholar 
37.Edgar RC. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. 2016. https://www.biorxiv.org/content/10.1101/074161v1.38.Deng Y, Ruan Y, Ma B, Timmons MB, Lu H, Xu X, et al. Multi-omics analysis reveals niche and fitness differences in typical denitrification microbial aggregations. Environ Int. 2019;132:105085.CAS 
PubMed 
Article 

Google Scholar 
39.Yu M, Meng J, Yu L, Su W, Afzal M, Li Y, et al. Changes in nitrogen related functional genes along soil pH, C and nutrient gradients in the charosphere. Sci Total Environ. 2019;650:626–32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–12.Article 
CAS 

Google Scholar 
41.Hamady M, Lozupone C, Knight R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 2010;4:17–27.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Zhang J, Zhang N, Liu Y-X, Zhang X, Hu B, Qin Y, et al. Root microbiota shift in rice correlates with resident time in the field and developmental stage. Sci China Life Sci. 2018;61:613–21.PubMed 
Article 
PubMed Central 

Google Scholar 
43.Liaw A, Wiener M. Classification and regression by random Forest. R News. 2002;2:18–22.
Google Scholar 
44.Breiman L. Random forests. Mach Learn. 2001;45:5–32.Article 

Google Scholar 
45.Ma B, Wang H, Dsouza M, Lou J, He Y, Dai Z, et al. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 2016;10:1891–901.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Wang B, Pourshafeie A, Zitnik M, Zhu J, Bustamante CD, Batzoglou S, et al. Network enhancement as a general method to denoise weighted biological networks. Nat Commun. 2018;9:1–8.Article 
CAS 

Google Scholar 
47.Luo F, Zhong J, Yang Y, Scheuermann RH, Zhou J. Application of random matrix theory to biological networks. Phys Lett A. 2006;357:420–3.CAS 
Article 

Google Scholar 
48.Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. Complex Syst. 2006;1695:1–9.
Google Scholar 
49.Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Icwsm. 2009;8:361–2.
Google Scholar 
50.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

Google Scholar 
51.Li H, Su J-Q, Yang X-R, Zhu Y-G. Distinct rhizosphere effect on active and total bacterial communities in paddy soils. Sci Total Environ. 2019;649:422–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Vieira S, Sikorski J, Dietz S, Herz K, Schrumpf M, Bruelheide H, et al. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. ISME J. 2019; 1–13.53.Yerushalmi S, Green RM. Evidence for the adaptive significance of circadian rhythms. Ecol Lett. 2009;12:970–81.PubMed 
Article 
PubMed Central 

Google Scholar 
54.Pedersen O, Sand‐Jensen K, Revsbech NP. Diel pulses of O2 and CO2 in sandy lake sediments inhabited by Lobelia dortmanna. Ecology. 1995;76:1536–45.Article 

Google Scholar 
55.Hernandez ME, Beck DAC, Lidstrom ME, Chistoserdova L. Oxygen availability is a major factor in determining the composition of microbial communities involved in methane oxidation. PeerJ. 2015;3:e801.PubMed 
PubMed Central 
Article 

Google Scholar 
56.Saifuddin M, Bhatnagar JM, Segrè D, Finzi AC. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat Commun. 2019;10:1–10.CAS 
Article 

Google Scholar 
57.Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 
Article 
PubMed Central 

Google Scholar 
58.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. 2019;50:145–68.Article 

Google Scholar 
59.Cozzi G, Broekhuis F, McNutt JW, Turnbull LA, Macdonald DW, Schmid B. Fear of the dark or dinner by moonlight? Reduced temporal partitioning among Africa’s large carnivores. Ecology. 2012;93:2590–9.PubMed 
Article 
PubMed Central 

Google Scholar 
60.Kohl MT, Ruth TK, Metz MC, Stahler DR, Smith DW, White PJ, et al. Do prey select for vacant hunting domains to minimize a multi-predator threat? Ecol Lett. 2019;22:1724–33.PubMed 
Article 
PubMed Central 

Google Scholar 
61.de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, et al. Soil bacterial networks are less stable under drought than fungal networks. Nat Commun. 2018;9:1–12.Article 
CAS 

Google Scholar 
62.Schmidt JE, Kent AD, Brisson VL, Gaudin ACM. Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling. Microbiome. 2019;7:1–18.Article 

Google Scholar 
63.DeCoursey PJ, Walker JK, Smith SA. A circadian pacemaker in free-living chipmunks: essential for survival? J Comp Physiol A. 2000;186:169–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Worden BD, Skemp AK, Papaj DR. Learning in two contexts: the effects of interference and body size in bumblebees. J Exp Biol. 2005;208:2045–53.PubMed 
Article 
PubMed Central 

Google Scholar 
65.Yerushalmi S, Bodenhaimer S, Bloch G. Developmentally determined attenuation in circadian rhythms links chronobiology to social organization in bees. J Exp Biol. 2006;209:1044–51.PubMed 
Article 
PubMed Central 

Google Scholar 
66.Lone SR, Sharma VK. Exposure to light enhances pre-adult fitness in two dark-dwelling sympatric species of ants. BMC Dev Biol. 2008;8:1–11.Article 

Google Scholar 
67.Yadav P, Choudhury D, Sadanandappa MK, Sharma VK. Extent of mismatch between the period of circadian clocks and light/dark cycles determines time-to-emergence in fruit flies. Insect Sci. 2015;22:569–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Yadav P, Thandapani M, Sharma VK. Interaction of light regimes and circadian clocks modulate timing of pre-adult developmental events in Drosophila. BMC Dev Biol. 2014;14:1–12.Article 
CAS 

Google Scholar 
69.Woelfle MA, Ouyang Y, Phanvijhitsiri K, Johnson CH. The adaptive value of circadian clocks: an experimental assessment in cyanobacteria. Curr Biol. 2004;14:1481–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Lambert G, Chew J, Rust MJ. Costs of clock-environment misalignment in individual cyanobacterial cells. Biophys J. 2016;111:883–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Lai AG, Doherty CJ, Mueller-Roeber B, Kay SA, Schippers JHM, Dijkwel PP. CIRCADIAN CLOCK-ASSOCIATED 1 regulates ROS homeostasis and oxidative stress responses. Proc Natl Acad Sci USA. 2012;109:17129–34.CAS 
PubMed 
Article 

Google Scholar 
72.Tanaka K, Ishikawa M, Kaneko M, Kamiya K, Kato S, Nakanishi S. The endogenous redox rhythm is controlled by a central circadian oscillator in cyanobacterium Synechococcus elongatus PCC7942. Photosynth Res. 2019;142:203–10.CAS 
PubMed 
Article 

Google Scholar 
73.Tanaka K, Nakanishi S Time-of-day dependent responses of cyanobacterial cellular viability against oxidative stress. 2019. https://www.biorxiv.org/content/10.1101/851774v2.74.Krittika S, Yadav P. Circadian clocks: an overview on its adaptive significance. Biol Rhythm Res 2019;0:1–24.CAS 

Google Scholar 
75.Koilraj AJ, Sharma VK, Marimuthu G, Chandrashekaran MK. Presence of circadian rhythms in the locomotor activity of a cave-dwelling millipede Glyphiulus cavernicolus sulu (Cambalidae, Spirostreptida). Chronobiol Int. 2000;17:757–65.CAS 
PubMed 
Article 

Google Scholar 
76.Roenneberg T, Merrow M. Life before the clock: modeling circadian evolution. J Biol Rhythms. 2002;17:495–505.PubMed 
Article 

Google Scholar 
77.Espinasa L, Jeffery WR. Conservation of retinal circadian rhythms during cavefish eye degeneration. Evol Dev. 2006;8:16–22.PubMed 
Article 

Google Scholar 
78.Hubbard CJ, McMinn RL, Weinig C. Rhizosphere microbes influence host circadian clock function. 2018. https://www.biorxiv.org/content/10.1101/444539v1. More