Philippa Kaur

1.
Sears, M. W., Raskin, E. & Angilletta, M. J. Jr. The world is not flat: defining relevant thermal landscapes in the context of climate change. Integr. Comp. Biol. 51, 666–675 (2011).
PubMed  Google Scholar 
2.
du Plessis, K. L., Martin, R. O., Hockey, P. A. R., Cunningham, S. J. & Ridley, A. R. The costs of keeping cool in a warming world: implications of high temperatures for foraging, thermoregulation and body condition of an arid-zone bird. Glob. Change Biol. 18, 3063–3070 (2012).
ADS  Google Scholar 

3.
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
PubMed  Google Scholar 

4.
Speakman, J. R. & Król, E. Maximal heat dissipation capacity and hyperthermia risk: neglected key factors in the ecology of endotherms. J. Anim. Ecol. 79, 726–746 (2010).
PubMed  Google Scholar 

5.
Daghir, N. J. Poultry production in hot climates 2nd edn. (CAB International, Wallingford, 2008).
Google Scholar 

6.
Nyoni, N. M. B., Grab, S. & Archer, E. R. M. Heat stress and chickens: climate risk effects on rural poultry farming in low-income countries. Clim. Dev. 11, 83–90. https://doi.org/10.1080/17565529.2018.1442792 (2018).
Article  Google Scholar 

7.
Laszlo, A. The effects of hyperthermia on mammalian cell structure and function. Cell Prolif. 25, 59–87 (1992).
CAS  PubMed  Google Scholar 

8.
Roti Roti, J. L. Cellular responses to hyperthermia (40–46 C): Cell killing and molecular events. Int. J. Hyperthermia 24, 3–15 (2008).
ADS  PubMed  Google Scholar 

9.
Feder, M. E. & Hofmann, G. E. Heat-shock proteins, molecular chaperones, and the stress response: evolutionary and ecological physiology. Annu. Rev. Physiol. 61, 243–282 (1999).
CAS  PubMed  Google Scholar 

10.
Hochachka, P. W. & Somero, G. N. Biochemical Adaptation (Princeton University Press, Princeton, 1984).
Google Scholar 

11.
Pörtner, H. Climate change and temperature-dependent biogeography: oxygen limitation of thermal tolerance in animals. Naturwissenschaften 88, 137–146 (2001).
ADS  PubMed  Google Scholar 

12.
Pörtner, H.-O. Oxygen-and capacity-limitation of thermal tolerance: a matrix for integrating climate-related stressor effects in marine ecosystems. J. Exp. Biol. 213, 881–893 (2010).
PubMed  Google Scholar 

13.
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).
PubMed  Google Scholar 

14.
McKechnie, A. E. & Wolf, B. O. The physiology of heat tolerance in small endotherms. Physiology 34, 302–313 (2019).
CAS  PubMed  Google Scholar 

15.
Arad, Z. & Marder, J. Strain differences in heat resistance to acute heat stress, between the bedouin desert fowl, the white leghorn and their crossbreeds. Comp. Biochem. Physiol. A 72, 191–193 (1982).
Google Scholar 

16.
Randall, W. C. Factors influencing the temperature regulation of birds. Am. J. Physiol. 139, 56–63 (1943).
Google Scholar 

17.
Tieleman, B. I., Williams, J. B., LaCroix, F. & Paillat, P. Physiological responses of Houbara bustards to high ambient temperatures. J. Exp. Biol. 205, 503–511 (2002).
PubMed  Google Scholar 

18.
Chappell, M. A. & Bartholomew, G. A. Activity and thermoregulation of the antelope ground squirrel Ammospermophilus leucurus in winter and summer. Physiol. Zool. 54, 215–223 (1981).
Google Scholar 

19.
Lovegrove, B. G., Heldmaier, G. & Ruf, T. Perspectives of endothermy revisited: the endothermic temperature range. J. Therm. Biol 16, 185–197 (1991).
Google Scholar 

20.
Cory Toussaint, D. & McKechnie, A. E. Interspecific variation in thermoregulation among three sympatric bats inhabiting a hot, semi-arid environment. J. Comp. Physiol. B 182, 1129–1140 (2012).
PubMed  Google Scholar 

21.
Dawson, W. R. In University of California Publications in Zoology Vol. 59 (eds Bartholomew, G. A. et al.) 81–123 (University of California Press, California, 1954).
Google Scholar 

22.
Paulissen, M. A. Ontogenetic comparison of body temperature selection and thermal tolerance of Cnemidophorus sexlineatus. J. Herpetol. 22, 473–476 (1988).
Google Scholar 

23.
Weathers, W. W. Energetics and thermoregulation by small passerines of the humid, lowland tropics. Auk 114, 341–353 (1997).
Google Scholar 

24.
Southwick, E. E. Remote sensing of body temperature in a captive 25-g bird. Condor 75, 464–466 (1973).
Google Scholar 

25.
Elliott, C. C. H. In Quelea quelea: Africa’s bird pest (eds Bruggers, R. L. & Elliott, C. C. H.) (Oxford University Press, Oxford, 1989).
Google Scholar 

26.
Craig, A. J. F. K. In Roberts birds of southern Africa (eds Hockey, P. A. R. et al.) 1025–1026 (The Trustees of the John Voelcker Bird Book Fund, Cape Town, 2005).
Google Scholar 

27.
Whitfield, M. C., Smit, B., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: scaling of heat tolerance and evaporative cooling capacity in three southern African arid-zone passerines. J. Exp. Biol. 218, 1705–1714 (2015).
PubMed  Google Scholar 

28.
McKechnie, A. E. et al. Avian thermoregulation in the heat: efficient evaporative cooling allows for extreme heat tolerance in four southern Hemisphere columbids. J. Exp. Biol. 219, 2145–2155 (2016).
PubMed  Google Scholar 

29.
Smith, E. K., O’Neill, J. J., Gerson, A. R., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: resting metabolism, evaporative cooling and heat tolerance in Sonoran Desert songbirds. J. Exp. Biol. 220, 3290–3300 (2017).
PubMed  Google Scholar 

30.
Smit, B. et al. Avian thermoregulation in the heat: phylogenetic variation among avian orders in evaporative cooling capacity and heat tolerance. J. Exp. Biol. 221, jeb174870 (2018).
PubMed  Google Scholar 

31.
Karasov, W. H. In Studies in Avian Biology (eds Morrison, M. L. et al.) 391–415 (Cooper Ornithological Society, California, 1990).
Google Scholar 

32.
Swanson, D. L., Drymalski, M. W. & Brown, J. R. Sliding vs static cold exposure and the measurement of summit metabolism in birds. J. Therm. Biol 21, 221–226 (1996).
Google Scholar 

33.
Kemp, R. & McKechnie, A. E. Thermal physiology of a range-restricted desert lark. J. Comp. Physiol. B 189, 131–141. https://doi.org/10.1007/s00360-018-1190-1 (2019).
Article  PubMed  Google Scholar 

34.
Lighton, J. R. B. Measuring Metabolic Rates: A Manual for Scientists (Oxford University Press, Oxford, 2008).
Google Scholar 

35.
Walsberg, G. E. & Wolf, B. O. Variation in the respirometry quotient of birds and implications for indirect calorimetry using measurements of carbon dioxide production. J. Exp. Biol. 198, 213–219 (1995).
CAS  PubMed  Google Scholar 

36.
Tracy, C. R., Welch, W. R., Pinshow, B. & Porter, W. P. Properties of air: a manual for use in biophysical ecology. 4th Ed. The University of Wisconsin Laboratory for Biophysical Ecology: Technical Report (2010).

37.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).

38.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3. 57. (2009).

39.
Muggeo, V. M. R. Segmented: an R package to fit regression models with broken-line relationships. R News 8(1), 20–25 (2008).
Google Scholar 

40.
McKechnie, A. E. et al. Avian thermoregulation in the heat: evaporative cooling in five Australian passerines reveals within-order biogeographic variation in heat tolerance. J. Exp. Biol. 220, 2436–2444 (2017).
PubMed  Google Scholar 

41.
O’Connor, R. S., Wolf, B. O., Brigham, R. M. & McKechnie, A. E. Avian thermoregulation in the heat: efficient evaporative cooling in two southern African nightjars. J Comp Physiol B 187, 477–491 (2017).
PubMed  Google Scholar 

42.
Hoffmann, A. A., Chown, S. L. & Clusella-Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).
Google Scholar 

43.
Tieleman, B. I., Williams, J. B. & Bloomer, P. Adaptation of metabolic rate and evaporative water loss along an aridity gradient. Proc. R. Soc. Lond. 270, 207–214 (2003).
Google Scholar 

44.
Xie, S., Tearle, R. & McWhorter, T. J. Heat shock protein expression is upregulated after acute heat exposure in three species of Australian desert birds. Avian Biol. Res. 11, 263–273 (2018).
Google Scholar 

45.
Czenze, Z. J. et al. Regularly-drinking desert birds have greater evaporative cooling capacity and higher heat tolerance limits than non-drinking species. Funct. Ecol. https://doi.org/10.1111/1365-2435.13573 (2020).
Article  Google Scholar 

46.
Midtgård, U. Scaling of the brain and the eye cooling system in birds: a morphometric analysis of the rete ophthalmicum. J. Exp. Zool. 225, 197–207 (1983).
PubMed  Google Scholar 

47.
Kilgore, D. L., Bernstein, M. H. & Hudson, D. M. Brain temperatures in birds. J Comp Physiol 110, 209–215 (1976).
Google Scholar 

48.
Bernstein, M. H., Curtis, M. B. & Hudson, D. M. Independence of brain and body temperatures in flying American kestrels, Falco sparverius. Am. J. Physiol. 237, R58–R62 (1979).
CAS  PubMed  Google Scholar 

49.
Kregel, K. C. Invited review: heat shock proteins: modifying factors in physiological stress responses and acquired thermotolerance. J. Appl. Physiol. 92, 2177–2186 (2002).
CAS  PubMed  Google Scholar 

50.
McKechnie, A. E. et al. Avian thermoregulation in the heat: evaporative cooling capacity in an archetypal desert specialist, Burchell’s sandgrouse (Pterocles burchelli). J. Exp. Biol. 219, 2137–2144 (2016).
PubMed  Google Scholar 

51.
Talbot, W. A., McWhorter, T. J., Gerson, A. R., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: evaporative cooling capacity of arid-zone Caprimulgiformes from two continents. J. Exp. Biol. 220, 3488–3498 (2017).
PubMed  Google Scholar 

52.
McWhorter, T. J. et al. Avian thermoregulation in the heat: evaporative cooling capacity and thermal tolerance in two Australian parrots. J. Exp. Biol. 221, jeb168930 (2018).
PubMed  Google Scholar 

53.
Talbot, W. A., Gerson, A. R., Smith, E. K., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: metabolism, evaporative cooling and gular flutter in two small owls. J. Exp. Biol. 221, jeb171108 (2018).
PubMed  Google Scholar 

54.
Smith, E. K., O’Neill, J., Gerson, A. R. & Wolf, B. O. Avian thermoregulation in the heat: resting metabolism, evaporative cooling and heat tolerance in Sonoran Desert doves and quail. J. Exp. Biol. 218, 3636–3646 (2015).
PubMed  Google Scholar  More

1.
El-Naggar, M. Y. et al. Electrical transport along bacterial nanowires from Shewanella oneidensis MR-1. Proc. Natl. Acad. Sci. USA 107, 18127–18131 (2010).
ADS  CAS  PubMed  Google Scholar 
2.
Du Toit, A. Exporting electrons. Nat. Rev. Microbiol. 16, 657 (2018).
PubMed  Google Scholar 

3.
Lovley, D. R. Microbial fuel cells: novel microbial physiologies and engineering approaches. Curr. Opin. Biotechnol. 17, 327–332 (2006).
CAS  PubMed  Google Scholar 

4.
Myers, J. M. & Myers, C. R. Role for outer membrane cytochromes OmcA and OmcB of Shewanella putrefaciens MR-1 in reduction of manganese dioxide. Appl. Environ. Microbiol. 67, 260–269 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

5.
Bond, D. R. & Lovley, D. R. Electricity production by Geobacter sulfurreducens attached to electrodes. Appl. Environ. Microbiol. 69, 1548–1555 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

6.
Rabaey, K., Boon, N., Siciliano, S. D., Verhaege, M. & Verstraete, W. Biofuel cells select for microbial consortia that self-mediate electron transfer. Appl. Environ. Microbiol. 70, 5373–5382 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Rabaey, K., Boon, N., Höfte, M. & Verstraete, W. Microbial phenazine production enhances electron transfer in biofuel cells. Environ. Sci. Technol. 39, 3401–3408 (2005).
ADS  CAS  PubMed  Google Scholar 

8.
Potter, M. C. Electrical effects accompanying the decomposition of organic compounds. Proc. R. Soc. B Biol. Sci. 84, 260–276 (1911).
ADS  Google Scholar 

9.
Logan, B. E. & Regan, J. M. Microbial fuel cells—challenges and applications. Environ. Sci. Technol. 40, 5172–5180 (2006).
ADS  CAS  PubMed  Google Scholar 

10.
Trapero, J. R., Horcajada, L., Linares, J. J. & Lobato, J. Is microbial fuel cell technology ready? An economic answer towards industrial commercialization. Appl. Energy 185, 698–707 (2017).
CAS  Google Scholar 

11.
Reimers, C. E., Tender, L. M., Fertig, S. & Wang, W. Harvesting energy from the marine sediment−water interface. Environ. Sci. Technol. 35, 192–195 (2001).
ADS  CAS  PubMed  Google Scholar 

12.
Tender, L. M. et al. Harnessing microbially generated power on the seafloor. Nat. Biotechnol. 20, 821–825 (2002).
CAS  PubMed  Google Scholar 

13.
Kubota, K. et al. Operation of sediment microbial fuel cells in Tokyo Bay, an extremely eutrophicated coastal sea. Bioresour. Technol. Rep. 6, 39–45 (2019).
Google Scholar 

14.
Chun, C. L., Payne, R. B., Sowers, K. R. & May, H. D. Electrical stimulation of microbial PCB degradation in sediment. Water Res. 47, 141–152 (2013).
CAS  PubMed  Google Scholar 

15.
Gajda, I., Greenman, J. & Ieropoulos, I. A. Recent advancements in real-world microbial fuel cell applications. Curr. Opin. Electrochem. 11, 78–83 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

16.
Bond, D. R., Holmes, D. E., Tender, L. M. & Lovley, D. R. Electrode-reducing microorganisms that harvest energy from marine sediments. Science 295, 483–485 (2002).
ADS  CAS  PubMed  Google Scholar 

17.
Froelich, P. N. et al. Early oxidation of organic matter in pelagic sediments of the eastern equatorial Atlantic: suboxic diagenesis. Geochim. Cosmochim. Acta 43, 1075–1090 (1979).
ADS  CAS  Google Scholar 

18.
Hasvold, Ø et al. Sea-water battery for subsea control systems. J. Power Sources 65, 253–261 (1997).
ADS  CAS  Google Scholar 

19.
Li, H. et al. Pilot-scale benthic microbial electrochemical system (BMES) for the bioremediation of polluted river sediment. J. Power Sources 356, 430–437 (2017).
ADS  CAS  Google Scholar 

20.
Sherafatmand, M. & Ng, H. Y. Using sediment microbial fuel cells (SMFCs) for bioremediation of polycyclic aromatic hydrocarbons (PAHs). Bioresour. Technol. 195, 122–130 (2015).
CAS  PubMed  Google Scholar 

21.
Sajana, T. K., Ghangrekar, M. M. & Mitra, A. Application of sediment microbial fuel cell for in situ reclamation of aquaculture pond water quality. Aquac. Eng. 57, 101–107 (2013).
Google Scholar 

22.
Sajana, T. K., Ghangrekar, M. M. & Mitra, A. Effect of operating parameters on the performance of sediment microbial fuel cell treating aquaculture water. Aquac. Eng. 61, 17–26 (2014).
Google Scholar 

23.
Giles, H. Using Bayesian networks to examine consistent trends in fish farm benthic impact studies. Aquaculture 274, 181–195 (2008).
Google Scholar 

24.
Karakassis, I., Tsapakis, M., Hatziyanni, E., Papadopoulou, K. N. & Plaiti, W. Impact of cage farming of fish on the seabed in three Mediterranean coastal areas. ICES J. Mar. Sci. 57, 1462–1471 (2000).
Google Scholar 

25.
Nøhr Glud, R., Gundersen, J. K., Barker Jørgensen, B., Revsbech, N. P. & Schulz, H. D. Diffusive and total oxygen uptake of deep-sea sediments in the eastern South Atlantic Ocean: in situ and laboratory measurements. Deep. Res. I 41, 1767–1788 (1994).
Google Scholar 

26.
Van Duyl, F. C., Kop, A. J., Kok, A. & Sandee, A. J. J. The impact of organic matter and macrozoobenthos on bacterial and oxygen variables in marine sediment boxcosms. Neth. J. Sea Res. 29, 343–355 (1992).
Google Scholar 

27.
Brooks, K. M. & Mahnken, C. V. Interactions of Atlantic salmon in the Pacific northwest environment II. Organic wastes. Fish. Res. 62, 255–293 (2003).
Google Scholar 

28.
Mackin, J. E. & Swider, K. T. Organic matter decomposition pathways and oxygen consumption in coastal marine sediments. J. Mar. Res. 47, 681–716 (1989).
CAS  Google Scholar 

29.
Holmer, M. & Kristensen, E. Impact of marine fish cage farming on metabolism and sulfate reduction of underlying sediments. Mar. Ecol. Prog. Ser. 80, 191–201 (1992).
ADS  CAS  Google Scholar 

30.
Carroll, M. L., Cochrane, S., Fieler, R., Velvin, R. & White, P. Organic enrichment of sediments from salmon farming in Norway: Environmental factors, management practices, and monitoring techniques. Aquaculture https://doi.org/10.1016/S0044-8486(03)00475-7 (2003).
Article  Google Scholar 

31.
Hargrave, B. T. Empirical relationships describing benthic impacts of salmon aquaculture. Aquac. Environ. Interact. 1, 33–46 (2010).
Google Scholar 

32.
Bagarinao, T. Sulfide as an environmental factor and toxicant: tolerance and adaptations in aquatic organisms. Aquat. Toxicol. 24, 21–62 (1992).
CAS  Google Scholar 

33.
Hargrave, B. T., Holmer, M. & Newcombe, C. P. Towards a classification of organic enrichment in marine sediments based on biogeochemical indicators. Mar. Pollut. Bull. 56, 810–824 (2008).
CAS  PubMed  Google Scholar 

34.
Ryckelynck, N., Stecher, H. A. & Reimers, C. E. Understanding the anodic mechanism of a seafloor fuel cell: Interactions between geochemistry and microbial activity. Biogeochemistry 76, 113–139 (2005).
Google Scholar 

35.
Ishii, S. et al. Identifying the microbial communities and operational conditions for optimized wastewater treatment in microbial fuel cells. Water Res. 47, 7120–7130 (2013).
CAS  PubMed  Google Scholar 

36.
Fader, G.B.J. & Miller, R.O. Surficial Geology, Halifax Harbour, Nova Scotia. Bulletin of the Geological Survey of Canada (2008).

37.
Grant, J., Emerson, C. W., Hargrave, B. T. & Shortle, J. L. Benthic oxygen consumption on continental shelves off eastern Canada. Cont. Shelf Res. 11, 1083–1097 (1991).
ADS  Google Scholar 

38.
Logan, B. E. Microbial fuel cells. In Treatise on Water Science, Vol. 4 (ed. Wilderer, P.) 641–665 (Wiley, New York, 2010).
Google Scholar 

39.
Taillefert, M. et al. Early diagenesis in the sediments of the Congo deep-sea fan dominated by massive terrigenous deposits: part II—Iron—sulfur coupling. Deep. Res. II Top. Stud. Oceanogr. 142, 151–166 (2017).
ADS  CAS  Google Scholar 

40.
Canfield, D. E., Raiswell, R. & Bottrell, S. The reactivity of sedimentary iron minerals toward sulfide. Am. J. Sci. 292, 659–683 (1992).
ADS  CAS  Google Scholar 

41.
Boudreau, B. P. Diagenetic models and their implementation: modelling transport and reactions in aquatic sediments (Springer, Berlin Heidelberg, 1996).
Google Scholar 

42.
Glud, R. N. Oxygen dynamics of marine sediments. Mar. Biol. Res. 4, 243–289 (2008).
Google Scholar 

43.
Berg, P., Risgaard-petersen, N. & Silkeborg, D. Interpretation of measured concentration profiles in sediment pore water. Limnol. Oceanogr. 43, 1500–1510 (1998).
ADS  CAS  Google Scholar 

44.
Hargrave, B. T. Seasonal changes in oxygen uptake by settled particulate matter and sediments in a marine bay. J. Fish. Res. Board Can. 35, 1621–1628 (1978).
CAS  Google Scholar 

45.
Viggi, C. C. et al. Bridging spatially segregated redox zones with a microbial electrochemical snorkel triggers biogeochemical cycles in oil-contaminated River Tyne (UK) sediments. Water Res. 127, 11–21 (2017).
CAS  PubMed  Google Scholar 

46.
Brüchert, V. & Arnosti, C. Anaerobic carbon transformation: Experimental studies with flow-through cells. Mar. Chem. 80, 171–183 (2003).
Google Scholar 

47.
Arnosti, C. Microbial extracellular enzymes and their role in dissolved organic matter cycling. Aquat. Ecosyst. https://doi.org/10.1016/b978-012256371-3/50014-7 (2003).
Article  Google Scholar 

48.
Lehman, R. M. & O’Connell, S. P. Comparison of extracellular enzyme activities and community composition of attached and free-living bacteria in porous medium columns. Appl. Environ. Microbiol. 68, 1569–1575 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

49.
Reimers, C. E. et al. Microbial fuel cell energy from an ocean cold seep. Geobiology 4, 123–136 (2006).
CAS  Google Scholar 

50.
Jørgensen, B. B., Findlay, A. J. & Pellerin, A. The biogeochemical sulfur cycle of marine sediments. Front. Microbiol. 10, 849 (2019).
PubMed  PubMed Central  Google Scholar 

51.
Lovley, D. R. Happy together: Microbial communities that hook up to swap electrons. ISME J. 11, 327–336 (2017).
CAS  PubMed  Google Scholar 

52.
Finster, K., Liesack, W. & Thamdrup, B. Elemental sulfur and thiosulfate disproportionation by Desulfocapsa sulfoexigens sp. nov., a new anaerobic bacterium isolated from marine surface sediment. Appl. Environ. Microbiol. 64, 119–125 (1998).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Kelly, D. P., Shergill, J. K., Lu, W. P. & Wood, A. P. Oxidative metabolism of inorganic sulfur compounds by bacteria. Int. J. Gen. Mol. Microbiol. 71, 95–107 (1997).
CAS  Google Scholar 

54.
Keeley, N. B., Forrest, B. M. & Macleod, C. K. Novel observations of benthic enrichment in contrasting flow regimes with implications for marine farm monitoring and management. Mar. Pollut. Bull. 66, 105–116 (2013).
CAS  PubMed  Google Scholar 

55.
Cranford, P., Brager, L., Elvines, D., Wong, D. & Law, B. A revised classification system describing the ecological quality status of organically enriched marine sediments based on total dissolved sulfides. Mar. Pollut. Bull. 154, 111088 (2020).
CAS  PubMed  Google Scholar 

56.
Soetaert, K., Hofmann, A. F., Middelburg, J. J., Meysman, F. J. R. & Greenwood, J. The effect of biogeochemical processes on pH. Mar. Chem. 105, 30–51 (2007).
CAS  Google Scholar 

57.
Seitaj, D. et al. Cable bacteria generate a firewall against euxinia in seasonally hypoxic basins. Proc. Natl. Acad. Sci. USA 112, 13278–13283 (2015).
ADS  CAS  PubMed  Google Scholar 

58.
Di Toro, D. M. et al. Acid volatile sulfide predicts the acute toxicity of cadmium and nickel in sediments. Environ. Sci. Technol. 26, 96–101 (1992).
ADS  Google Scholar 

59.
Brooks, K. M. & Mahnken, C. V. W. Interactions of Atlantic salmon in the Pacific Northwest environment. III. Accumulation of zinc and copper. Fish. Res. 62, 295–305 (2003).
Google Scholar 

60.
Fitridge, I., Dempster, T., Guenther, J. & de Nys, R. The impact and control of biofouling in marine aquaculture: A review. Biofouling 28, 649–669 (2012).
PubMed  Google Scholar 

61.
FOA. The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all (2016).

62.
Millero, F. J., Plese, T. & Fernandez, M. The dissociation of hydrogen sulfide in seawater. Limnol. Oceanogr. 33, 269–274 (1988).
ADS  CAS  Google Scholar  More

1.
Wilga, C. A. D., Nishiguchi, M. K. & Tsukimura, B. Integr. Comp. Biol. 57, 7–17 (2017).
Article  Google Scholar 
2.
Rodriguez, J. E., Campbell, K. M. & Pololi, L. H. BMC Med. Educ. 15, 6 (2015).
Article  Google Scholar 

3.
Dashper, K. Gender Work Organ. 26, 541–557 (2017).
Article  Google Scholar 

4.
Timmers, T. M., Willemsen, T. M. & Tijdens, K. G. High. Educ. 59, 719–735 (2010).
Article  Google Scholar 

5.
Vangerven, B. et al. Omega 81, 38–47 (2018).
Article  Google Scholar 

6.
Fritz, C., Yankelevich, M., Zarubin, A. & Barger, P. J. Appl. Psychol. 95, 977–983 (2010).
Article  Google Scholar 

7.
Sonnentag, S. & Frese, M. Comprehensive Handbook of Psychology (eds Borman, W. C. et al.) 453–491 (Wiley, 2003).

8.
Smith, W. A., Allen, W. R. & Danley, L. L. Am. Behav. Sci. 51, 551–578 (2007).
Article  Google Scholar 

9.
Avey, J. B., Reichard, R. J., Luthans, F. & Mhatre, K. H. Hum. Resour. Dev. Q. 22, 127–152 (2011).
Article  Google Scholar 

10.
Nielsen, M. W., Bloch, C. W. & Schiebinger, L. Nat. Hum. Behav. 2, 726–734 (2018).
Article  Google Scholar 

11.
Johnson, E. J. & Goldstein, D. Science 302, 1338–1339 (2003).
CAS  Article  Google Scholar  More

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

1.
Keller W. Implications of climate warming for Boreal Shield lakes: a review and synthesis. Environ Rev. 2007;15:99–112.
CAS  Google Scholar 
2.
Teodoru CR, del Giorgio PA, Prairie YT, Camire M. Patterns in p CO2 in boreal streams and rivers of northern Quebec, Canada. Glob Biogeochem Cycles. 2009;23:GB2012.
Google Scholar 

3.
Anas MUM, Scott KA, Wissel B. Carbon budgets of boreal lakes: state of knowledge, challenges, and implications. Environ Rev. 2015;23:275–87.
CAS  Google Scholar 

4.
Schiff SL, Tsuji JM, Wu L, Venkiteswaran JJ, Molot LA, Elgood RJ, et al. Millions of Boreal Shield lakes can be used to probe Archaean Ocean biogeochemistry. Sci Rep. 2017;7:46708.
CAS  PubMed  PubMed Central  Google Scholar 

5.
Poulton SW, Canfield DE. Ferruginous conditions: a dominant feature of the ocean through Earth’s history. Elements. 2011;7:107–12.
CAS  Google Scholar 

6.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  Google Scholar 

7.
Karhunen J, Arvola L, Peura S, Tiirola M. Green sulphur bacteria as a component of the photosynthetic plankton community in small dimictic humic lakes with an anoxic hypolimnion. Aquat Microb Ecol. 2013;68:267–72.
Google Scholar 

8.
Imhoff JF. The family Chromatiaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The Prokaryotes. Berlin Heidelberg: Springer; 2014. pp. 151–78.
Google Scholar 

9.
Imhoff JF. The family Chlorobiaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The Prokaryotes. Berlin Heidelberg: Springer; 2014. pp. 501–14.
Google Scholar 

10.
Ehrenreich A, Widdel F. Anaerobic oxidation of ferrous iron by purple bacteria, a new type of phototrophic metabolism. Appl Environ Microbiol. 1994;60:4517–26.
CAS  PubMed  PubMed Central  Google Scholar 

11.
Griffin BM, Schott J, Schink B. Nitrite, an electron donor for anoxygenic photosynthesis. Science. 2007;316:1870.
CAS  PubMed  Google Scholar 

12.
Kulp TR, Hoeft SE, Asao M, Madigan MT, Hollibaugh JT, Fisher JC, et al. Arsenic(III) fuels anoxygenic photosynthesis in hot spring biofilms from Mono Lake, California. Science. 2008;321:967–70.
CAS  PubMed  Google Scholar 

13.
Thiel V, Tank M, Bryant DA. Diversity of chlorophototrophic bacteria revealed in the omics era. Annu Rev Plant Biol. 2018;69:21–49.
CAS  PubMed  Google Scholar 

14.
Heising S, Richter L, Ludwig W, Schink B. Chlorobium ferrooxidans sp. nov., a phototrophic green sulfur bacterium that oxidizes ferrous iron in coculture with a “Geospirillum” sp. strain. Arch Microbiol. 1999;172:116–24.
CAS  PubMed  Google Scholar 

15.
Llirós M, García–Armisen T, Darchambeau F, Morana C, Triadó–Margarit X, Inceoğlu Ö, et al. Pelagic photoferrotrophy and iron cycling in a modern ferruginous basin. Sci Rep. 2015;5:13803.
PubMed  PubMed Central  Google Scholar 

16.
Laufer K, Niemeyer A, Nikeleit V, Halama M, Byrne JM, Kappler A. Physiological characterization of a halotolerant anoxygenic phototrophic Fe(II)-oxidizing green-sulfur bacterium isolated from a marine sediment. FEMS Microbiol Ecol. 2017;93:fix054.
Google Scholar 

17.
Lambrecht N. Insights into early Earth ocean biogeochemistry from intensive monitoring of two ferruginous meromictic lakes. PhD thesis. Iowa, USA:Iowa State University;2019.

18.
Frigaard N-U, Bryant DA. Genomic insights into the sulfur metabolism of phototrophic green sulfur bacteria. In: Hell R, Dahl DC, Knaff D, Leustek T, editors. Sulfur metabolism in phototrophic organisms. Netherlands: Springer; 2008. pp. 337–55.
Google Scholar 

19.
Camacho A, Walter XA, Picazo A, Zopfi J. Photoferrotrophy: remains of an ancient photosynthesis in modern environments. Front Microbiol. 2017;8:323.
PubMed  PubMed Central  Google Scholar 

20.
Koeksoy E, Halama M, Konhauser KO, Kappler A. Using modern ferruginous habitats to interpret Precambrian banded iron formation deposition. Int J Astrobiol. 2016;15:205–17.
CAS  Google Scholar 

21.
He S, Barco RA, Emerson D, Roden EE. Comparative genomic analysis of neutrophilic iron(II) oxidizer genomes for candidate genes in extracellular electron transfer. Front Microbiol. 2017;8:1584.
PubMed  PubMed Central  Google Scholar 

22.
Melton ED, Swanner ED, Behrens S, Schmidt C, Kappler A. The interplay of microbially mediated and abiotic reactions in the biogeochemical Fe cycle. Nat Rev Microbiol. 2014;12:797–808.
CAS  PubMed  Google Scholar 

23.
Kato S, Ohkuma M, Powell DH, Krepski ST, Oshima K, Hattori M, et al. Comparative genomic insights into ecophysiology of neutrophilic, microaerophilic iron oxidizing bacteria. Front Microbiol. 2015;6:1265.
PubMed  PubMed Central  Google Scholar 

24.
Gupta D, Sutherland MC, Rengasamy K, Meacham JM, Kranz RG, Bose A. Photoferrotrophs produce a PioAB electron conduit for extracellular electron uptake. mBio. 2019;10:e02668–19.
CAS  PubMed  PubMed Central  Google Scholar 

25.
Castelle C, Guiral M, Malarte G, Ledgham F, Leroy G, Brugna M, et al. A new iron-oxidizing/O2-reducing supercomplex spanning both inner and outer membranes, isolated from the extreme acidophile Acidithiobacillus ferrooxidans. J Biol Chem. 2008;283:25803–11.
CAS  PubMed  PubMed Central  Google Scholar 

26.
Barco RA, Emerson D, Sylvan JB, Orcutt BN, Meyers MEJ, Ramírez GA, et al. New insight into microbial iron oxidation as revealed by the proteomic profile of an obligate iron-oxidizing chemolithoautotroph. Appl Environ Microbiol. 2015;81:5927–37.
CAS  PubMed  PubMed Central  Google Scholar 

27.
McAllister SM, Polson SW, Butterfield DA, Glazer BT, Sylvan JB, Chan CS. Validating the Cyc2 neutrophilic iron oxidation pathway using meta-omics of Zetaproteobacteria iron mats at marine hydrothermal vents. mSystems. 2020;5:e00553–19.
PubMed  PubMed Central  Google Scholar 

28.
Crowe SA, Hahn AS, Morgan-Lang C, Thompson KJ, Simister RL, Llirós M, et al. Draft genome sequence of the pelagic photoferrotroph Chlorobium phaeoferrooxidans. Genome Announc. 2017;5:e01584–16.
PubMed  PubMed Central  Google Scholar 

29.
Thompson KJ, Simister RL, Hahn AS, Hallam SJ, Crowe SA. Nutrient acquisition and the metabolic potential of photoferrotrophic Chlorobi. Front Microbiol. 2017;8:1212.
PubMed  PubMed Central  Google Scholar 

30.
Bryce C, Blackwell N, Straub D, Kleindienst S, Kappler A. Draft genome sequence of Chlorobium sp. strain N1, a marine Fe(II)-oxidizing green sulfur bacterium. Microbiol Resour Announc. 2019;8:e00080–19.
PubMed  PubMed Central  Google Scholar 

31.
Schindler DW, Armstrong FAJ, Holmgren SK, Brunskill GJ. Eutrophication of Lake 227, Experimental Lakes Area, northwestern Ontario, by addition of phosphate and nitrate. J Fish Res Board Can. 1971;28:1763–82.
CAS  Google Scholar 

32.
Campbell P. Phosphorus budgets and stoichiometry during the open-water season in two unmanipulated lakes in the Experimental Lakes Area, northwestern Ontario. Can J Fish Aquat Sci. 1994;51:2739–55.
CAS  Google Scholar 

33.
White RA III, Brown J, Colby S, Overall CC, Lee J-Y, Zucker J, et al. ATLAS (Automatic Tool for Local Assembly Structures) – a comprehensive infrastructure for assembly, annotation, and genomic binning of metagenomic and metatranscriptomic data. PeerJ Prepr. 2017;5:e2843v1.
Google Scholar 

34.
Kieser S, Brown J, Zdobnov EM, Trajkovski M, McCue LA. ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinform. 2020;21:257.
Google Scholar 

35.
Jeans C, Singer SW, Chan CS, VerBerkmoes NC, Shah M, Hettich RL, et al. Cytochrome 572 is a conspicuous membrane protein with iron oxidation activity purified directly from a natural acidophilic microbial community. ISME J. 2008;2:542–50.
CAS  PubMed  Google Scholar 

36.
Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539.
PubMed  PubMed Central  Google Scholar 

37.
Eddy SR. Accelerated profile HMM searches. PLOS Comput Biol. 2011;7:e1002195.
CAS  PubMed  PubMed Central  Google Scholar 

38.
Talavera G, Castresana J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol. 2007;56:564–77.
CAS  PubMed  Google Scholar 

39.
Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.
CAS  PubMed  Google Scholar 

40.
Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.
CAS  PubMed  PubMed Central  Google Scholar 

41.
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.
CAS  PubMed  PubMed Central  Google Scholar 

42.
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  Google Scholar 

43.
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.
PubMed  PubMed Central  Google Scholar 

44.
Rissman AI, Mau B, Biehl BS, Darling AE, Glasner JD, Perna NT. Reordering contigs of draft genomes using the Mauve Aligner. Bioinformatics. 2009;25:2071–3.
CAS  PubMed  PubMed Central  Google Scholar 

45.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
CAS  PubMed  PubMed Central  Google Scholar 

46.
Armstrong FAJ, Schindler DW. Preliminary chemical characterization of waters in the Experimental Lakes Area, northwestern Ontario. J Fish Res Board Can. 1971;28:171–87.
CAS  Google Scholar 

47.
Schindler DW. Eutrophication and recovery in experimental lakes: implications for lake management. Science. 1974;184:897–9.
CAS  PubMed  Google Scholar 

48.
Schindler D. The coupling of elemental cycles by organisms: evidence from whole-lake chemical perturbations. In: Stumm W, editor. Chemical processes in lakes. New York, NY: John Wiley and Sons; 1985. pp. 225–250.
Google Scholar 

49.
Schindler DW. The dilemma of controlling cultural eutrophication of lakes. Proc R Soc B Biol Sci. 2012;279:4322–33.

50.
Curtis PJ, Schindler DW. Hydrologic control of dissolved organic matter in low-order Precambrian Shield lakes. Biogeochemistry. 1997;36:125–38.
Google Scholar 

51.
Hegler F, Posth NR, Jiang J, Kappler A. Physiology of phototrophic iron(II)-oxidizing bacteria: implications for modern and ancient environments. FEMS Microbiol Ecol. 2008;66:250–60.
CAS  PubMed  Google Scholar 

52.
Milucka J, Kirf M, Lu L, Krupke A, Lam P, Littmann S, et al. Methane oxidation coupled to oxygenic photosynthesis in anoxic waters. ISME J. 2015;9:1991–2002.
CAS  PubMed  PubMed Central  Google Scholar 

53.
Clavier CGJ, Boucher G. The use of photosynthesis inhibitor (DCMU) for in situ metabolic and primary production studies on soft bottom benthos. Hydrobiologia. 1992;246:141–5.
Google Scholar 

54.
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.
PubMed  PubMed Central  Google Scholar 

55.
Lee MD. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics. 2019;35:4162–4.
CAS  PubMed  PubMed Central  Google Scholar 

56.
Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.
CAS  PubMed  Google Scholar 

57.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.
CAS  PubMed  PubMed Central  Google Scholar 

58.
Bergstrand LH, Cardenas E, Holert J, Hamme JDV, Mohn WW. Delineation of steroid-degrading microorganisms through comparative genomic analysis. mBio. 2016;7:e00166–16.
CAS  PubMed  PubMed Central  Google Scholar 

59.
Bryant DA, Liu Z, Li T, Zhao F, Costas AMG, Klatt CG, et al. Comparative and functional genomics of anoxygenic green bacteria from the taxa Chlorobi, Chloroflexi, and Acidobacteria. In: Burnap R, Vermaas W, editors. Functional genomics and evolution of photosynthetic systems. Netherlands, Dordrecht: Springer; 2012. pp. 47–102.
Google Scholar 

60.
Tourova TP, Kovaleva OL, Gorlenko VM, Ivanovsky RN. Use of genes of carbon metabolism enzymes as molecular markers of Chlorobi phylum representatives. Microbiology. 2013;82:784–93.
CAS  Google Scholar 

61.
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.
Google Scholar 

62.
Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front Microbiol. 2013;4:291.
PubMed  PubMed Central  Google Scholar 

63.
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:1–11.
Google Scholar 

64.
Petrenko P, Lobb B, Kurtz DA, Neufeld JD, Doxey AC. MetAnnotate: function-specific taxonomic profiling and comparison of metagenomes. BMC Biol. 2015;13:1–8.
Google Scholar 

65.
Garber AI, Nealson KH, Okamoto A, McAllister SM, Chan CS, Barco RA, et al. FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genome and metagenome assemblies. Front Microbiol. 2020;11:37.
PubMed  PubMed Central  Google Scholar 

66.
Pfennig N. Rhodocyclus purpureus gen. nov. and sp. nov., a ring-shaped, vitamin B12-requiring member of the family Rhodospirillaceae. Int J Syst Evol Microbiol. 1978;28:283–8.
CAS  Google Scholar 

67.
Tank M, Bryant DA. Nutrient requirements and growth physiology of the photoheterotrophic Acidobacterium, Chloracidobacterium thermophilum. Front Microbiol. 2015;6:226.

68.
Peng C, Bryce C, Sundman A, Borch T, Kappler A. Organic matter complexation promotes Fe(II) oxidation by the photoautotrophic Fe(II)-oxidizer Rhodopseudomonas palustris TIE-1. ACS Earth Space Chem. 2019;3:531–6.

69.
Stookey LL. Ferrozine—a new spectrophotometric reagent for iron. Anal Chem. 1970;42:779–81.
CAS  Google Scholar 

70.
Verschoor MJ, Molot LA. A comparison of three colorimetric methods of ferrous and total reactive iron measurement in freshwaters. Limnol Oceanogr Methods. 2013;11:113–25.
CAS  Google Scholar 

71.
Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods. 2015;12:7–8.
CAS  PubMed  PubMed Central  Google Scholar 

72.
Overmann J, Coolen MJL, Tuschak C. Specific detection of different phylogenetic groups of chemocline bacteria based on PCR and denaturing gradient gel electrophoresis of 16S rRNA gene fragments. Arch Microbiol. 1999;172:83–94.
CAS  PubMed  Google Scholar 

73.
Mori Y, Purdy KJ, Oakley BB, Kondo R. Comprehensive detection of phototrophic sulfur bacteria using PCR primers that target reverse dissimilatory sulfite reductase gene. Microbes Environ. 2010;25:190–6.
PubMed  Google Scholar 

74.
Finneran KT, Johnsen CV, Lovley DR. Rhodoferax ferrireducens sp. nov., a psychrotolerant, facultatively anaerobic bacterium that oxidizes acetate with the reduction of Fe(III). Int J Syst Evol Microbiol. 2003;53:669–73.
CAS  PubMed  Google Scholar 

75.
Falagán C, Johnson DB. Acidibacter ferrireducens gen. nov., sp. nov.: an acidophilic ferric iron-reducing gammaproteobacterium. Extremophiles. 2014;18:1067–73.
PubMed  Google Scholar 

76.
Watanabe T, Kojima H, Fukui M. Complete genomes of freshwater sulfur oxidizers Sulfuricella denitrificans skB26 and Sulfuritalea hydrogenivorans sk43H: genetic insights into the sulfur oxidation pathway of betaproteobacteria. Syst Appl Microbiol. 2014;37:387–95.
CAS  PubMed  Google Scholar 

77.
Kuever J. The family Desulfobacteraceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin Heidelberg: Springer; 2014. pp. 45–73.
Google Scholar 

78.
van Grinsven S, Damsté JSS, Asbun AA, Engelmann JC, Harrison J, Villanueva L. Methane oxidation in anoxic lake water stimulated by nitrate and sulfate addition. Environ Microbiol. 2019;22:766–82.
Google Scholar 

79.
van Grinsven S, Damsté JSS, Harrison J, Villanueva L. Impact of electron acceptor availability on methane-influenced microorganisms in an enrichment culture obtained from a stratified lake. Front Microbiol. 2020;11:715.
PubMed  PubMed Central  Google Scholar 

80.
Kappler A, Bryce C. Cryptic biogeochemical cycles: unravelling hidden redox reactions. Environ Microbiol. 2017;19:842–6.
PubMed  Google Scholar 

81.
Walter XA, Picazo A, Miracle MR, Vicente E, Camacho A, Aragno M, et al. Phototrophic Fe(II)-oxidation in the chemocline of a ferruginous meromictic lake. Front Microbiol. 2014;5:713.
PubMed  PubMed Central  Google Scholar 

82.
Allen JWA, Sawyer EB, Ginger ML, Barker PD, Ferguson SJ. Variant c-type cytochromes as probes of the substrate specificity of the E. coli cytochrome c maturation (Ccm) apparatus. Biochem J. 2009;419:177–86.
CAS  PubMed  Google Scholar 

83.
He S, Lau MP, Linz AM, Roden EE, McMahon KD. Extracellular electron transfer may be an overlooked contribution to pelagic respiration in humic-rich freshwater lakes. mSphere. 2019;4:e00436–18.
CAS  PubMed  PubMed Central  Google Scholar 

84.
Guzman MS, Rengasamy K, Binkley MM, Jones C, Ranaivoarisoa TO, Singh R, et al. Phototrophic extracellular electron uptake is linked to carbon dioxide fixation in the bacterium Rhodopseudomonas palustris. Nat Commun. 2019;10:1355.
PubMed  PubMed Central  Google Scholar 

85.
Croal LR, Jiao Y, Kappler A, Newman DK. Phototrophic Fe(II) oxidation in an atmosphere of H2: implications for Archean banded iron formations. Geobiology. 2009;7:21–4.
CAS  PubMed  PubMed Central  Google Scholar 

86.
Chan C, McAllister SM, Garber A, Hallahan BJ, Rozovsky S. Fe oxidation by a fused cytochrome-porin common to diverse Fe-oxidizing bacteria. bioRxiv. 2018. https://doi.org/10.1101/228056.

87.
Daye M, Klepac-Ceraj V, Pajusalu M, Rowland S, Farrell-Sherman A, Beukes N, et al. Light-driven anaerobic microbial oxidation of manganese. Nature. 2019;576:311–4.
PubMed  Google Scholar 

88.
Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8:785–6.
CAS  PubMed  Google Scholar 

89.
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004;25:1605–12.
CAS  PubMed  Google Scholar  More

Study site and subjects
We tested innovativeness in four neighboring spotted hyena clans within the Maasai Mara National Reserve, Kenya between June 2016 and November 2017. These clans ranged in size from 30 to 55 adult hyenas. Spotted hyena clans represent distinct social groups that are made up of multiple unrelated females, their offspring, and adult immigrant males. Clans are structured by strict linear dominance hierarchies, with an alpha female and her offspring at the top, followed by lower-ranking females and their offspring, with adult immigrant males occupying the lowest rank positions. Births occur year-round and unrelated females raise their offspring together at a communal den. Female hyenas stay in their natal clan throughout their lives, whereas male hyena usually disperse to join new clans when they are 24–60 months old, after they reach sexual maturity43,44.
All subjects were identified by their unique spot patterns and ear damage. Hyenas of all age classes and both sexes were included in the study. All subjects were sexed within the first few months of life based on genital morphology40. Age classes were based on life history stage45. Cubs were defined as hyenas that were still dependent on the communal den for shelter; on average, Mara cubs become den-independent around 9–12 months of age45. Subadults were hyenas who were den-independent but had not yet reached sexual maturity. Adults were hyenas that had reached sexual maturity. In females, sexual maturity was determined by the observation of mating, visual evidence of first parturition, or the female reaching three years of age, whichever came first46. In males, sexual maturity was determined by dispersal status, males who were still present in their natal clan at testing were classified as subadults and immigrant males were classified as adults.
Multi-access box paradigm for measuring repeated innovation
We tested innovativeness in wild spotted hyenas using a multi-access box designed for use with mammalian carnivores24. The multi-access box (hereafter, ‘the MAB’) is a problem-solving paradigm, also known as an artificial or novel extractive foraging task, where subjects must solve a novel problem to obtain a food reward. In contrast to traditional problem-solving tasks, MAB paradigms typically offer multiple solutions to the same puzzle, each requiring its own unique behavior pattern. As a condensed battery of tasks, the MAB paradigm allows researchers to measure innovation, not just once, but multiple times across different solutions47. We chose to use a MAB paradigm because it allowed us to compare reliability across repeated trials within the same solution to reliability across different solutions. Reliable success with the same solution across trials may be a result of individual learning rather than a result of a stable cognitive trait. However, if individuals reliably innovate by opening multiple unique solutions to the MAB this would suggest that innovativeness is a stable cognitive trait. The MAB in the current study was a steel box, measuring 40.64 × 40.64 × 40.64 cm (length × width × height), with four unique doors, each requiring a different motor behavior, that could be used to access a common interior baited with a food reward (Fig. 1. We used this MAB previously to test repeated innovation in captive hyenas; for more information about the design specifications see Johnson-Ulrich et al.24.
Test protocol
We conducted all testing between 0630 to 1000 h and 1700 to 1830 h, the daylight hours during which hyenas are most active. We deployed the MAB anytime a suitable group of hyenas was located within the territories of our study clans. A suitable group was defined as one containing five or fewer hyenas within 100 m or within visible range that were either walking or resting but not engaged in hunting, border patrol, mating, courtship, or nursing behaviors. We used our research vehicle as a mobile blind to shield the researchers from the view of hyenas while we baited and deployed the MAB on the opposite side of the vehicle from hyenas. We baited the MAB with approximately 200 g of either goat or beef muscle, skin, or organ meat. During familiarization trials we also used full cream milk powder in addition to, or in place of, meat. We deployed the MAB approximately 20 m away from the nearest hyena and after MAB deployment we drove the research vehicle to a distance of 20–50 m away from the MAB. We began videotaping immediately after we deployed the MAB and we ended videotaping when we collected the MAB.
During familiarization trials we deployed the MAB with the top removed to acclimate subjects to the MAB and allow them to learn to associate the MAB with bait. Familiarization trials began when a hyena came within 5 m of the MAB and ended upon successful food retrieval (a “feed” trial) or when the hyena moved more than 5 m away from the MAB for more than 5 min. We recorded hyenas that approached the MAB, but did not make contact, as not participating in the trial. Average duration of familiarization trials was 11.7 ± 12.3 min.
If a hyena had a “feed” familiarization trial or successful test trial, and if it had moved at least 5 m away from the MAB, we immediately rebaited the MAB for successive testing. We gave hyenas successive trials as long as the testing conditions remained suitable, as described above, or until researchers ran out of bait. We did not administer successive trials following trials where every hyena that participated was unsuccessful because unsuccessful hyenas were those that had moved beyond 5 m from the MAB for more than five minutes without opening the MAB and these hyenas were extremely unlikely to spontaneously re-approach the MAB for another trial. On average, hyenas received 1.53 ± 1.25 trials per testing session and completed testing across 6.31 ± 2.58 separate sessions (Supplementary Fig. S1). Most sessions were separated by a median of 1 day (mean ± SD = 24.18 ± 60.30 days, range = 0–321 days).
We divided test trials into four different phases of testing. During Phase 1, we presented the MAB to hyenas with all four doors accessible. After a hyena had reached completion criterion for Phase 1, defined by success with the same door in three out of four consecutive trials, it progressed to Phase 2. During Phase 2, we blocked the door used in Phase 1 by bolting it shut. The same criteria for progression applied to subsequent phases until a hyena reached the criteria for progression with all four doors. We gave hyenas trials until they either reached criterion for all four doors or failed five consecutive trials during any phase of testing. We did not include hyenas that participated in fewer than five trials, of which none were successful, in our analysis. On average, hyenas participated in 9.64 ± 5.61 trials. Hyenas completed Phase 1 in 7.43 ± 2.93 trials (N = 72) either by reaching the criterion for progression or by failure, Phase 2 in 3.67 ± 1.11 trials (N = 15), Phase 3 in 4.08 ± 1.32 trials (N = 13) and Phase 4 in 4.25 ± 1.96 trials (N = 12).
We aimed to give every hyena two familiarization trials prior to being given the option to participate in test trials. On average we gave hyenas the opportunity to participate in 1.60 ± 1.54 (mean ± standard deviation) familiarization trials prior to their first Phase 1 trial, but hyenas only fed from the MAB on an average of 0.94 ± 1.11 familiarization trials prior to their first Phase 1 trial.
When we presented a group of hyenas with the MAB, we configured the MAB for the hyena at the most advanced phase of testing. For example, if one hyena in the group had progressed to Phase 3, but all the others were still on Phase 1, we would configure the MAB for the hyena on Phase 3 and block the doors that hyena had used in Phases 1 and 2. Overall, there were only five trials in total in which a hyena solved the MAB in a trial during the ‘wrong’ phase of testing by joining a trial where we configured the MAB for a group mate rather than itself. The average ‘trial group size’ per hyena per trial was 3.89 ± 3.71 hyenas (median = 3, range = 1–29). We calculated trial group size as a count of all hyenas that participated in a trial by contacting the MAB at any point in time during the trial. Overall, trial group size had a positive and significant effect on hyena participation; hyenas were slightly more likely to contact the box if there were other hyenas contacting the box (Binomial GLMM: z = 9.19, P  More

1.
Li, B. V. & Pimm, S. L. China’s endemic vertebrates sheltering under the protective umbrella of the giant panda. Conserv. Biol. 30, 329–339 (2016).
Article  Google Scholar 
2.
Swaisgood, R., Wang, D. & Wei, F. Panda downlisted but not out of the woods. Conserv. Lett. 11, e12355 (2018).
Article  Google Scholar 

3.
Díaz, S. et al. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019); https://www.ipbes.net/sites/default/files/downloads/spm_unedited_advance_for_posting_htn.pdf

4.
Li, J. et al. Role of Tibetan Buddhist monasteries in snow leopard conservation. Conserv. Biol. 28, 87–94 (2013).
Article  Google Scholar 

5.
Gong, H., Zeng, Z., Wang, X., Zhao, K. & Zhang, Q. Current status and analysis of national protected animal species in Shaanxi Province of China. J. Shaanxi Norm. Univ. 37, 58–65 (2009).
Google Scholar 

6.
Schaller, G. B. The Last Panda (Univ. of Chicago Press, 1994).

7.
Johnson, K. G., Wang, W., Reid, D. G. & Hu, J. Food habits of Asiatic leopards (Panthera pardus fusea) in Wolong Reserve, Sichuan, China. J. Mammal. 74, 646–650 (1993).
Article  Google Scholar 

8.
Laguardia, A. et al. The current distribution and status of leopards Panthera pardus in China. Oryx 51, 153–159 (2017).
Article  Google Scholar 

9.
Mainka, S. A., Xianmeng, Q., Tingmei, H. & Appel, M. J. Serologic survey of giant pandas (Ailuropoda melanoleuca), and domestic dogs and cats in the Wolong Reserve, China. J. Wildl. Dis. 30, 86–89 (1994).
CAS  Article  Google Scholar 

10.
Connor, T., Hull, V. & Liu, J. Telemetry research on elusive wildlife: a synthesis of studies on giant pandas. Integr. Zool. 11, 295–307 (2016).
Article  Google Scholar 

11.
Johansson, O. et al. Sex-specific seasonal variation in puma and snow leopard home range utilization. Ecosphere 9, e02371 (2018).
Article  Google Scholar 

12.
Xu, W. et al. Reassessing the conservation status of the giant panda using remote sensing. Nat. Ecol. Evol. 1, 1635–1638 (2017).
Article  Google Scholar 

13.
Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).
Article  Google Scholar 

14.
Lambeck, R. J. Focal species: a multi-species umbrella for nature conservation. Conserv. Biol. 11, 849–856 (1997).
Article  Google Scholar 

15.
Roberge, J. & Angelstam, P. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18, 76–85 (2004).
Article  Google Scholar 

16.
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
CAS  Article  Google Scholar 

17.
Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl Acad. Sci. USA 114, E6089–E6096 (2017).
CAS  Article  Google Scholar 

18.
Xu, W. et al. Transforming protected area management in China. Trends Ecol. Evol. 34, 762–766 (2019).
Article  Google Scholar 

19.
Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).
CAS  Article  Google Scholar 

20.
Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).
CAS  Article  Google Scholar 

21.
Gompper, M. E., Belant, J. L. & Kays, R. Carnivore coexistence: America’s recovery. Science 347, 382–383 (2015).
CAS  Article  Google Scholar 

22.
Athreya, V., Odden, M., Linnell, J. D., Krishnaswamy, J. & Karanth, U. Big cats in our backyards: persistence of large carnivores in a human dominated landscape in India. PLoS ONE 8, e57872 (2013).
CAS  Article  Google Scholar 

23.
Perino, A. et al. Rewilding complex ecosystems. Science 364, eaav5570 (2019).
CAS  Article  Google Scholar 

24.
The Giant Pandas of China: Status Quo—Major Findings of the Fourth National Survey on Giant Panda (State Forestry Administration of P. R. China, 2015); http://www.forestry.gov.cn/main/69/content-743562.html

25.
Wang, D., Li, S., McShea, W. J. & Li, M. Use of remote-trip cameras for wildlife surveys and evaluating the effectiveness of conservation activities at a nature reserve in Sichuan Province, China. Environ. Manage. 38, 942–951 (2006).
Article  Google Scholar 

26.
Li, S., Wang, D., Gu, X. & McShea, W. J. Beyond pandas, the need for a standardized monitoring protocol for large mammals in Chinese nature reserves. Biodivers. Conserv. 19, 3195–3206 (2010).
Article  Google Scholar 

27.
Karanth, K. U., Nichols, J. D., Kumar, N. S., Link, W. A. & Hines, J. E. Tigers and their prey: predicting carnivore densities from prey abundance. Proc. Natl Acad. Sci. USA 101, 4854–4858 (2004).
CAS  Article  Google Scholar 

28.
Steenweg, R. et al. Scaling-up camera traps: monitoring the planet’s biodiversity with networks of remote sensors. Front. Ecol. Environ. 15, 26–34 (2017).
Article  Google Scholar 

29.
Carbone, C. et al. The use of photographic rates to estimate densities of tigers and other cryptic mammals. Anim. Conserv. 4, 75–79 (2001).
Article  Google Scholar 

30.
Kays, R. An empirical evaluation of camera trap study design: how many, how long, and when? Methods Ecol. Evol. https://doi.org/10.1111/2041‐210X.13370 (2020).

31.
Li, S. et al. Gauging the impact of management expertise on the distribution of large mammals across protected areas. Divers. Distrib. 18, 1166–1176 (2012).
Article  Google Scholar 

32.
Shi, B. & Zhao, E. (eds) Sichuan Fauna Economica Vol. 1 (Sichuan People’s Publishing House, 1982).

33.
Zheng, S. & Song, S. (eds) Mammals in Qingling Mountain Area (China Forestry Publishing House, 2010). More

1.
Hansell, D. A., Carlson, C. A., Repeta, D. J. & Schlitzer, R. Dissolved organic matter in the ocean: a controversy stimulates new insights. Oceanography 22, 202–211 (2009).
Google Scholar 
2.
Hansell, D. A. & Carlson, C. A. Biogeochemistry of Marine Dissolved Organic Matter (Academic Press, Cambridge, 2014).
Google Scholar 

3.
Pomeroy, L. R., Williams, P. J. L., Azam, F. & Hobbie, J. E. The microbial loop. Oceanography 20, 28–33 (2007).
Google Scholar 

4.
Jiao, N. et al. Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat. Rev. Microbiol. 8, 593 (2010).
CAS  PubMed  Google Scholar 

5.
Arrieta, J. M. et al. Dilution limits dissolved organic carbon utilization in the deep ocean. Science 348, 331–333 (2015).
ADS  CAS  PubMed  Google Scholar 

6.
Dittmar, T. Biogeochemistry of Marine Dissolved Organic Matter 369–388 (Elsevier, Amsterdam, 2015).
Google Scholar 

7.
Noriega-Ortega, B. E. et al. Does the chemodiversity of bacterial exometabolomes sustain the chemodiversity of marine dissolved organic matter?. Front. Microbiol. 10, 215 (2019).
PubMed  PubMed Central  Google Scholar 

8.
Zark, M. & Dittmar, T. Universal molecular structures in natural dissolved organic matter. Nat. Commun. 9, 1–8 (2018).
CAS  Google Scholar 

9.
Lechtenfeld, O. J., Hertkorn, N., Shen, Y., Witt, M. & Benner, R. Marine sequestration of carbon in bacterial metabolites. Nat. Commun. 6, 1–8 (2015).
Google Scholar 

10.
Roshan, S. & DeVries, T. Efficient dissolved organic carbon production and export in the oligotrophic ocean. Nat. Commun. 8, 2036 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

11.
Longhurst, A., Sathyendranath, S., Platt, T. & Caverhill, C. An estimate of global primary production in the ocean from satellite radiometer data. J. Plankton Res. 17, 1245–1271 (1995).
Google Scholar 

12.
Zubkov, M. V. Faster growth of the major prokaryotic versus eukaryotic CO2 fixers in the oligotrophic ocean. Nat. Commun. 5, 3776 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

13.
Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782 (2007).
CAS  PubMed  Google Scholar 

14.
Laws, E. A., Falkowski, P. G., Smith, W. O. Jr., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Glob. Biogeochem. Cycles 14, 1231–1246 (2000).
ADS  CAS  Google Scholar 

15.
Moran, M. A. et al. Deciphering ocean carbon in a changing world. Proc. Natl. Acad. Sci. 113, 3143–3151 (2016).
ADS  CAS  PubMed  Google Scholar 

16.
Sheik, A. R. et al. Responses of the coastal bacterial community to viral infection of the algae Phaeocystis globosa. ISME J. 8, 212 (2014).
CAS  PubMed  Google Scholar 

17.
Nelson, C. E. & Carlson, C. A. Tracking differential incorporation of dissolved organic carbon types among diverse lineages of Sargasso Sea bacterioplankton. Environ. Microbiol. 14, 1500–1516 (2012).
CAS  PubMed  Google Scholar 

18.
Ogawa, H., Amagai, Y., Koike, I., Kaiser, K. & Benner, R. Production of refractory dissolved organic matter by bacteria. Science 292, 917–920 (2001).
ADS  CAS  PubMed  Google Scholar 

19.
Kawasaki, N. & Benner, R. Bacterial release of dissolved organic matter during cell growth and decline: molecular origin and composition. Limnol. Oceanogr. 51, 2170–2180 (2006).
ADS  CAS  Google Scholar 

20.
Lara, R. J. & Thomas, D. N. Formation of recalcitrant organic matter: humification dynamics of algal derived dissolved organic carbon and its hydrophobic fractions. Mar. Chem. 51, 193–199 (1995).
CAS  Google Scholar 

21.
Gruber, D. F., Simjouw, J.-P., Seitzinger, S. P. & Taghon, G. L. Dynamics and characterization of refractory dissolved organic matter produced by a pure bacterial culture in an experimental predator-prey system. Appl. Environ. Microbiol. 72, 4184–4191 (2006).
CAS  PubMed  PubMed Central  Google Scholar 

22.
Middelboe, M. & Jørgensen, N. O. Viral lysis of bacteria: an important source of dissolved amino acids and cell wall compounds. J. Mar. Biol. Assoc. UK 86, 605–612 (2006).
CAS  Google Scholar 

23.
Stenson, A. C., Landing, W. M., Marshall, A. G. & Cooper, W. T. Ionization and fragmentation of humic substances in electrospray ionization Fourier transform-ion cyclotron resonance mass spectrometry. Anal. Chem. 74, 4397–4409. https://doi.org/10.1021/ac020019f (2002).
CAS  Article  PubMed  Google Scholar 

24.
Koch, B. & Dittmar, T. From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter. Rapid Commun. Mass Spectrom. 20, 926–932 (2006).
ADS  CAS  Google Scholar 

25.
Osterholz, H., Niggemann, J., Giebel, H.-A., Simon, M. & Dittmar, T. Inefficient microbial production of refractory dissolved organic matter in the ocean. Nat. Commun. 6, 1–8 (2015).
Google Scholar 

26.
Vorobev, A. et al. Identifying labile DOM components in a coastal ocean through depleted bacterial transcripts and chemical signals. Environ. Microbiol. 20, 3012–3030 (2018).
CAS  PubMed  Google Scholar 

27.
Dittmar, T. & Arnosti, C. In Microbial Ecology of the Oceans (eds Gasol, J. M. & Kirchman, D. L.) 189–230 (Wiley, New York, 2018).
Google Scholar 

28.
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686 (2014).
CAS  PubMed  Google Scholar 

29.
Suttle, C. A. Marine viruses—major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801 (2007).
CAS  PubMed  Google Scholar 

30.
Redfield, A. C. The biological control of chemical factors in the environment. Am. Sci. 46, 230A-A221 (1958).
Google Scholar 

31.
Martiny, A. C. et al. Strong latitudinal patterns in the elemental ratios of marine Plankton and organic matter. Nat. Geosci. 6, 279 (2013).
ADS  CAS  Google Scholar 

32.
Letscher, R. T. & Moore, J. K. Preferential remineralization of dissolved organic phosphorus and non-redfield DOM dynamics in the global ocean: Impacts on marine productivity, nitrogen fixation, and carbon export. Glob. Biogeochem. Cycles 29(3), 325–340 (2015).
ADS  CAS  Google Scholar 

33.
Hopkinson, C. S. Jr., Vallino, J. J. & Nolin, A. Decomposition of dissolved organic matter from the continental margin. Deep Sea Res. Part II Top. Stud. Oceanogr. 49, 4461–4478 (2002).
ADS  CAS  Google Scholar 

34.
Kim, T.-H. & Kim, G. Factors controlling the C:N:P stoichiometry of dissolved organic matter in the N-limited, cyanobacteria-dominated East/Japan Sea. J. Mar. Syst. 115, 1–9 (2013).
CAS  Google Scholar 

35.
Letscher, R. T., Hansell, D. A., Carlson, C. A., Lumpkin, R. & Knapp, A. N. Dissolved organic nitrogen in the global surface ocean: distribution and fate. Glob. Biogeochem. Cycles 27, 141–153 (2013).
ADS  CAS  Google Scholar 

36.
Kirchman, D. L., Suzuki, Y., Garside, C. & Ducklow, H. W. High turnover rates of dissolved organic carbon during a spring phytoplankton bloom. Nature 352, 612 (1991).
ADS  CAS  Google Scholar 

37.
Martinez-Perez, C. et al. The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle. Nat. Microbiol. 1, 16163 (2016).
CAS  PubMed  Google Scholar 

38.
Weiss, M. et al. Molecular architecture and electrostatic properties of a bacterial porin. Science 254, 1627–1630 (1991).
ADS  CAS  PubMed  Google Scholar 

39.
Wienhausen, G. et al. The Exometabolome of two model strains of the roseobacter group: a marketplace of microbial metabolites. Front. Microbiol. 8, 1985 (2017).
PubMed  PubMed Central  Google Scholar 

40.
Paerl, R. W. et al. Use of plankton-derived vitamin B1 precursors, especially thiazole-related precursor, by key marine picoeukaryotic phytoplankton. ISME J. 11(3), 753–765 (2017).
CAS  PubMed  Google Scholar 

41.
Sun, L. et al. Light-induced aggregation of microbial exopolymeric substances. Chemosphere 181, 675–681 (2017).
ADS  CAS  PubMed  Google Scholar 

42.
Hopkinson, C. S. Jr. & Vallino, J. J. Efficient export of carbon to the deep ocean through dissolved organic matter. Nature 433, 142 (2005).
ADS  CAS  PubMed  Google Scholar 

43.
Burkhardt, B. G., Watkins-Brandt, K. S., Defforey, D., Paytan, A. & White, A. E. Remineralization of phytoplankton-derived organic matter by natural populations of heterotrophic bacteria. Mar. Chem. 163, 1–9 (2014).
CAS  Google Scholar 

44.
Letscher, R. T., Moore, J. K., Teng, Y.-C. & Primeau, F. Variable C:N:P stoichiometry of dissolved organic matter cycling in the community earth system model. Biogeosciences 12, 209–221 (2015).
ADS  CAS  Google Scholar 

45.
Somes, C. J. & Oschlies, A. On the influence of “non-Redfield” dissolved organic nutrient dynamics on the spatial distribution of N2 fixation and the size of the marine fixed nitrogen inventory. Global Biogeochem. Cycles 29, 973–993 (2015).
ADS  CAS  Google Scholar 

46.
Kellerman, A. M. et al. Unifying concepts linking dissolved organic matter composition to persistence in aquatic ecosystems. Environ. Sci. Technol. 52, 2538–2548 (2018).
ADS  CAS  PubMed  Google Scholar 

47.
Zark, M., Christoffers, J. & Dittmar, T. Molecular properties of deep-sea dissolved organic matter are predictable by the central limit theorem: evidence from tandem FT-ICR-MS. Mar. Chem. 191, 9–15 (2017).
CAS  Google Scholar 

48.
Landa, M. et al. Phylogenetic and structural response of heterotrophic bacteria to dissolved organic matter of different chemical composition in a continuous culture study. Environ. Microbiol. 16(6), 1668–1681 (2014).
CAS  PubMed  Google Scholar 

49.
Arnosti, C. et al. Anoxic carbon degradation in Arctic sediments: Microbial transformations of complex substrates. Geochim. Cosmochim. Acta 69(9), 2309–2320 (2005).
ADS  CAS  Google Scholar 

50.
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

51.
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl. Acids Res. 41, D590–D596 (2012).
PubMed  Google Scholar 

54.
Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

55.
Dittmar, T., Koch, B., Hertkorn, N. & Kattner, G. A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnol. Oceanogr. Methods 6, 230–235 (2008).
CAS  Google Scholar 

56.
Riedel, T. & Dittmar, T. A method detection limit for the analysis of natural organic matter via Fourier transform ion cyclotron resonance mass spectrometry. Anal. Chem. 86, 8376–8382. https://doi.org/10.1021/ac501946m (2014).
CAS  Article  PubMed  Google Scholar 

57.
Riedel, T., Biester, H. & Dittmar, T. Molecular fractionation of dissolved organic matter with metal salts. Environ. Sci. Technol. 46, 4419–4426. https://doi.org/10.1021/es203901u (2012).
ADS  CAS  Article  PubMed  Google Scholar  More