Philippa Kaur

Affiliations

The Intergovernmental Authority on Development Climate Prediction and Applications Centre (ICPAC), Nairobi, Kenya
Abubakr A. M. Salih, Marta Baraibar, Kenneth Kemucie Mwangi & Guleid Artan

Authors
Abubakr A. M. Salih

Marta Baraibar

Kenneth Kemucie Mwangi

Guleid Artan

Corresponding author
Correspondence to Abubakr A. M. Salih. More

1.
Byrne, M. Impact of ocean warming and ocean acidification on marine invertebrate life history stages: vulnerabilities and potential for persistence in a changing ocean. Oceanogr. Mar. Biol. An Annu. Rev. 49, 1–42 (2011).
Google Scholar 
2.
Pineda, M. C. et al. Tough adults, frail babies: an analysis of stress sensitivity across early life-history stages of widely introduced marine invertebrates. PLoS ONE 7, e46672 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

3.
Gaines, S. & Roughgarden, J. Larval settlement rate: a leading determinant of structure in an ecological community of the marine intertidal zone. Proc. Natl. Acad. Sci. 82, 3707–3711 (1985).
ADS  CAS  PubMed  Google Scholar 

4.
Pecorino, D., Lamare, M. D., Barker, M. F. & Byrne, M. How does embryonic and larval thermal tolerance contribute to the distribution of the sea urchin Centrostephanus rodgersii (Diadematidae) in New Zealand?. J. Exp. Mar. Bio. Ecol. 445, 120–128 (2013).
Google Scholar 

5.
Pörtner, H. O. & Farrell, A. P. Physiology and climate change. Science 322, 690–692 (2008).
PubMed  Google Scholar 

6.
O’Connor, M. I. et al. Temperature control of larval dispersal and the implications for marine ecology, evolution, and conservation. Proc. Natl. Acad. Sci. U.S.A. 104, 1266–1271 (2007).
ADS  PubMed  PubMed Central  Google Scholar 

7.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Chang. 2, 686–690 (2012).
ADS  Google Scholar 

8.
Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: the other CO2 problem. Ann. Rev. Mar. Sci. 1, 169–192 (2009).
PubMed  Google Scholar 

9.
Fabry, V. J., Seibel, B. A., Feely, R. A., Fabry, J. C. O. & Fabry, V. J. Impacts of ocean acidification on marine fauna and ecosystem processes. ICE J. Mar. Sci. 65, 414–432 (2008).
CAS  Google Scholar 

10.
Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).
PubMed  Google Scholar 

11.
Beadle, B. Y. L. C. The effect of salinity changes on the water content and respiration of marine invertebrates. J. Exp. Biol. 8, 211–227 (1931).
Google Scholar 

12.
Cheng, B. S., Chang, A. L., Deck, A. & Ferner, M. C. Atmospheric rivers and the mass mortality of wild oysters: Insight into an extreme future?. Proc. R. Soc. B Biol. Sci. 283, 20161462 (2016).
Google Scholar 

13.
Przeslawski, R., Byrne, M. & Mellin, C. A review and meta-analysis of the effects of multiple abiotic stressors on marine embryos and larvae. Global Change Biol. 21, 2122–2140 (2015).
ADS  Google Scholar 

14.
Byrne, M. & Przeslawski, R. Multistressor impacts of warming and acidification of the ocean on marine invertebrates’ life histories. Integ. Comp. Biol. 53, 582–596 (2013).
CAS  Google Scholar 

15.
Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Global Change Biol. 19, 1884–1896 (2013).
ADS  Google Scholar 

16.
Bindoff, N. L., et al. Chapter 5: Changing ocean, marine ecosystems, and dependent communities. Intergovernmental panel of climate change. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate 447–587 (2019).

17.
Feely, R. A. et al. Present and future changes in seawater chemistry due to ocean acidification. Geophys. Monogr. Ser. 183, 175–188 (2009).
CAS  Google Scholar 

18.
Rhein, M. et al. Observations: ocean. In Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, Cambridge, 2013). https://doi.org/10.1017/CBO9781107415324.010.
Google Scholar 

19.
Narita, D., Rehdanz, K. & Tol, R. S. J. Economic costs of ocean acidification: a look into the impacts on global shellfish production. Clim. Change 113, 1049–1063 (2012).
ADS  Google Scholar 

20.
Beck, M. W. et al. Oyster reefs at risk and recommendations for conservation, restoration, and management. Bioscience 61, 107–116 (2011).
Google Scholar 

21.
Blake, B. & Bradbury, A. Washington Department of Fish and Wildlife Plan for Rebuilding Olympia Oyster (Ostrea lurida ) Populations in Puget Sound with a Historical and Contemporary Overview. (2012).

22.
Hettinger, A. et al. The influence of food supply on the response of Olympia oyster larvae to ocean acidification. Biogeosciences 10, 6629–6638 (2013).
ADS  Google Scholar 

23.
Hettinger, A. et al. Larval carry-over effects from ocean acidification persist in the natural environment. Global. Change Biol. 19, 3317–3326 (2013).
Google Scholar 

24.
Li, J. et al. The potential of ocean acidification on suppressing larval development in the Pacific oyster Crassostrea gigas and blood cockle Arcain flata Reeve*. Chin. J. Oceanol. Limnol. 32, 1307–1313 (2014).
ADS  CAS  Google Scholar 

25.
Talmage, S. C. & Gobler, C. J. Effects of elevated temperature and carbon dioxide on the growth and survival of larvae and juveniles of three species of northwest Atlantic bivalves. PLoS ONE 6, e26941 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

26.
Waldbusser, G. G. et al. Saturation-state sensitivity of marine bivalve larvae to ocean acidification. Nat. Clim. Change 5, 273–280 (2015).
ADS  CAS  Google Scholar 

27.
Dekshenieks, M. M., Hofmann, E. E., Powell, E. N. & Powell, E. N. Environmental effects on the growth and development of eastern Oyster, Crassostrea virginica ( Gmelin, 1791), Larvae : a modeling study. J. Shellfish Res. 12, 241–254 (1993).
Google Scholar 

28.
Ko, G. W. K. et al. Interactive effects of ocean acidification, elevated temperature, and reduced salinity on early-life stages of the pacific oyster. Environ. Sci. Technol. 48, 10079–10088 (2014).
ADS  CAS  PubMed  Google Scholar 

29.
Shanks, A. L., Grantham, B. A. & Carr, M. H. Propagule dispersal distance and the size and spacing of marine reserves. Ecol. Appl. 13, 159–169 (2003).
Google Scholar 

30.
Shanks, A. L. Pelagic larval duration and dispersal distance revisited. Biol. Bull. 216, 373–385 (2009).
PubMed  Google Scholar 

31.
Pineda, J., Hare, J. A. & Sponaugle, S. Larval transport and dispersal in the coastal ocean and consequences for population connectivity. Oceanography 20, 22–39 (2007).
Google Scholar 

32.
Barros, P., Sobral, P., Range, P., Chícharo, L. & Matias, D. Effects of sea-water acidification on fertilization and larval development of the oyster Crassostrea gigas. J. Exp. Mar. Biol. Ecol. 440, 200–206 (2013).
Google Scholar 

33.
Hettinger, A. et al. Persistent carry-over effects of planktonic exposure to ocean acidification in the Olympia oyster. Ecology 93, 2758–2768 (2012).
PubMed  Google Scholar 

34.
Barton, A. et al. Impacts of coastal acidification on the Pacific Northwest shellfish industry and adaptation strategies implemented in response. Oceanography 28, 146–159 (2015).
Google Scholar 

35.
Wasson, K. et al. Coast-wide recruitment dynamics of Olympia oysters reveal limited synchrony and multiple predictors of failure. Ecology https://doi.org/10.1002/ecy.1602 (2016).
Article  PubMed  Google Scholar 

36.
Cole, V. J. et al. Effects of multiple climate change stressors: ocean acidification interacts with warming, hyposalinity, and low food supply on the larvae of the brooding flat oyster Ostrea angasi. Mar. Biol. 163, 1–17 (2016).
CAS  Google Scholar 

37.
Havenhand, J., Dupont, S. & Quinn, G. P. Chapter 4: Designing ocean acidification experiments to maximise inference. in Guide to Best Practices for Ocean Acidification Research and Data Reporting 67–80 (2010).

38.
Mcintyre, B. A., McPhee-Shaw, E. E., Hatch, M. B. & Arellano, S. M. Location matters: passive and active factors affect the vertical distribution of Olympia oyster (Ostrea lurida) larvae. Estuaries Coasts https://doi.org/10.1007/s12237-020-00771-8 (2020).
Article  Google Scholar 

39.
Davis, H. C. On cultivation of larvae of Ostrea lurida. Anat. Rec. 105, 111 (1949).
Google Scholar 

40.
Loosanoff, V. L. & Davis, H. C. Rearing of bivalve mollusks. Adv. Mar. Biol. 1, 1–136 (1963).
Google Scholar 

41.
Hofmann, E. E., Powell, E. N., Bochenek, E. A. & Klinck, J. M. A modelling study of the influence of environment and food supply on survival of Crassostrea gigas larvae. ICES J. Mar. Sci. 61, 596–616 (2004).
Google Scholar 

42.
Barber, J. S., Dexter, J. E., Grossman, S. K., Greiner, C. M. & Mcardle, J. T. Low temperature brooding of Olympia Oysters (Ostrea lurida) in Northern Puget sound. J. Shellfish Res. 35, 351–357 (2016).
Google Scholar 

43.
Hopkins, A. E. Experimental observations on spawining, larval development, and setting in the Olympia oyster Ostrea lurida. Bull U.S.A. Bur. Fish. 48, 439–503 (1937).
Google Scholar 

44.
Pritchard, C., Shanks, A., Rimler, R., Oates, M. & Rumrill, S. The Olympia Oyster Ostrea lurida : recent advances in natural history, ecology, and restoration. J. Shellfish Res. 34, 259–271 (2015).
Google Scholar 

45.
Bible, J. M. et al. Timing of stressors alters interactive effects on a coastal foundation species. Ecology 98, 2468–2478 (2017).
PubMed  Google Scholar 

46.
Waldbusser, G. G. et al. Slow shell building, a possible trait for resistance to the effects of acute ocean acidification. Limnol. Oceanogr. 61, 1969–1983 (2016).
ADS  CAS  Google Scholar 

47.
Lucey, N. M. et al. To brood or not to brood: are marine invertebrates that protect their offspring more resilient to ocean acidification?. Sci. Rep. 5, 1–7 (2015).
Google Scholar 

48.
Barton, A., Hales, B., Waldbusser, G. G., Langdon, C. & Feelyd, R. A. The Pacific oyster, Crassostrea gigas, shows negative correlation to naturally elevated carbon dioxide levels: Implications for near-term ocean acidification effects. Limnol. Oceanogr. 57, 698–710 (2012).
ADS  CAS  Google Scholar 

49.
Miller, A. W., Reynolds, A. C., Sobrino, C. & Riedel, G. F. Shellfish face uncertain future in high CO2 world: influence of acidification on oyster larvae calcification and growth in estuaries. PLoS ONE 4, e5661 (2009).
ADS  PubMed  PubMed Central  Google Scholar 

50.
Khangaonkar, T. et al. Analysis of hypoxia and sensitivity to nutrient pollution in Salish Sea. J. Geophys. Res. Ocean. 123, 4735–4761 (2018).
ADS  CAS  Google Scholar 

51.
Spencer, L. H. et al. Carryover effects of temperature and pCO2 across multiple Olympia oyster populations. Ecol. Appl. 30, e02060 (2020).
PubMed  Google Scholar 

52.
Scheltema, R. S. Larval dispersal as a means of genetic exchange between geographically separated populations of shallow-water benthic marine gastropods. Biol. Bull. 140, 284–322 (1971).
Google Scholar 

53.
Pechenik, J. A. On the advantages and disadvantages of larval stages in benthic marine invertebrate life cycles. Mar. Ecol. Prog. Ser. 177, 269–297 (1999).
ADS  Google Scholar 

54.
Stick, D. A. Identification of Optimal Broodstock for Pacific Northwest Oysters (Oregon State University, Oregon, 2011).
Google Scholar 

55.
Silliman, K. Population structure, genetic connectivity, and adaptation in the Olympia oyster (Ostrea lurida) along the west coast of North America. Evol. Appl. 12, 923–939 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

56.
Hatch, M. B. A., College, N. I. & Wyllie-echeverria, S. Historic distribution of Ostrea lurida (Olympia oyster) in the San Juan Archipelago. Washington State. 1, 38–45 (2008).
Google Scholar 

57.
Polson, M. P. & Zacherl, D. C. Geographic distribution and intertidal population status for the Olympia Oyster, Ostrea lurida Carpenter 1864, from Alaska to Baja. J. Shellfish Res. 28, 69–77 (2009).
Google Scholar 

58.
Dekshenieks, M. M., Hofmann, E. E., Klinck, J. M. & Powell, E. N. Quantifying the effects of environmental change on an Oyster population: a modeling study. Estuaries 23, 593–610 (2000).
Google Scholar 

59.
Powell, E. N., Klinck, J. M., Hofmann, E. E. & Ray, S. M. Modeling oyster populations. IV: Rates of mortality, population crashes, and management. Fish. Bull. 92, 347–373 (1994).
Google Scholar 

60.
Chan, F. et al. Persistent spatial structuring of coastal ocean acidification in the California Current System. Sci. Rep. 7, 1–7 (2017).
Google Scholar 

61.
Long, W. & Khangaonkar, T. Approach for Simulating Acidification and the Carbon Cycle in the Salish Sea to Distinguish Regional Source Impacts. Washington Department of Ecology (2014).

62.
Love, B. A., Olson, M. B. & Wuori, T. Technical note: a minimally invasive experimental system for pCO2 manipulation in plankton cultures using passive gas exchange (atmospheric carbon control simulator). Biogeosciences 14, 2675–2684 (2017).
ADS  CAS  Google Scholar 

63.
Strathmann, M. F. Reproduction and Development of Marine Invertebrates of the Northern Pacific Coast (University of Washington Press, Seattle, 1987).
Google Scholar 

64.
Ko, G. W. K. et al. Larval and post-larval stages of pacific Oyster (Crassostrea gigas) are resistant to elevated CO2. PLoS ONE 8, 1–12 (2013).
Google Scholar 

65.
Buckham, S. Ocean acidification affects larval swimming in Ostrea lurida but not Crassostrea gigas. WWU Graduate School Collection. https://cedar.wwu.edu/wwuet/451 (2015).

66.
Dickson, A., Sabine, C. & Christian, J. (eds). Guide to Best Practices for Ocean CO2 Measurements. In PICES Special Publication 3 191 (2007).

67.
Pelletier, G., Lewis, E. & Wallace, D. co2.sys2.1.xls, a Calculator for the CO2System in Seawater for Microsoft Excel/VBA, Washington State Department of Ecology, Olympia, WA, Brookhaven National Laboratory, Upton, NY. (2012).

68.
Millero, F. J., Graham, T. B., Huang, F., Bustos-Serrano, H. & Pierrot, D. Dissociation constants of carbonic acid in seawater as a function of salinity and temperature. Mar. Chem. 100, 80–94 (2006).
CAS  Google Scholar 

69.
Waldbusser, G. G. et al. Ocean acidification has multiple modes of action on bivalve larvae. PLoS ONE 10, e0128376 (2015).
PubMed  PubMed Central  Google Scholar 

70.
Gazeau, F. et al. Impacts of ocean acidification on marine shelled molluscs. Mar. Biol. 160, 2207–2245 (2013).
CAS  Google Scholar 

71.
Khangaonkar, T., Nugraha, A., Xu, W. & Balaguru, K. Salish Sea response to global climate change, sea level rise, and future nutrient loads. J. Geophys. Res. Ocean. https://doi.org/10.1029/2018JC014670 (2019).
Article  Google Scholar 

72.
Loosanoff, V. L., Davis, H. C. & Chalney, P. E. Dimensions and shapes of larvae of some marine bivalve mollusks. Malacologia 4, 351–435 (1966).
Google Scholar 

73.
Brink, L. A. Molluscs: Bivalvia. Identification Guide to Larval Marine Invertebrates ofthe Pacific Northwest 129–149 (2001).

74.
Hori, J. On the development of the Olympia oyster, Ostrea lurida carpenter, transplanted from United States to Japan. Bull. Jpn. Soc. Sci. Fish 1, 269–276 (1933).
Google Scholar  More

1.
Olesen, J. M., Bascompte, J., Elberling, H. & Jordano, P. Temporal dynamics in a pollination network. Ecology 89, 1573–1582 (2008).
PubMed  Google Scholar 
2.
Petchey, O. L., Brose, U. & Rall, B. C. Predicting the effects of temperature on food web connectance. Philos. Trans. R. Soc. B 365, 2081–2091 (2010).
Google Scholar 

3.
Menke, S., Böhning-Gaese, K. & Schleuning, M. Plant–frugivore networks are less specialized and more robust at forest–farmland edges than in the interior of a tropical forest. Oikos 121, 1553–1566 (2012).
Google Scholar 

4.
Aizen, M. A., Morales, C. L. & Morales, J. M. Invasive mutualists erode native pollination webs. PLoS Biol. 6, e31 (2008).
PubMed  PubMed Central  Google Scholar 

5.
Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).
ADS  CAS  PubMed  Google Scholar 

6.
Vázquez, D. P. et al. Species abundance and asymmetric interaction strength in ecological networks. Oikos 116, 1120–1127 (2007).
Google Scholar 

7.
Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: why ecological interaction networks vary through space and time. Oikos 124, 243–251 (2015).
Google Scholar 

8.
Holt, R. D. & Kotler, B. P. Short-term apparent competition. Am. Nat. 130, 412–430 (1987).
Google Scholar 

9.
May, R. M. Will a large complex system be stable? Nature 238, 413 (1972).
ADS  CAS  PubMed  Google Scholar 

10.
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).
ADS  PubMed  Google Scholar 

11.
de Ruiter, P. C., Wolters, V., Moore, J. C. & Winemiller, K. O. Food web ecology: playing jenga and beyond. Science 309, 68–71 (2005).
PubMed  Google Scholar 

12.
Ings, T. C. et al. Ecological networks–beyond food webs. J. Anim. Ecol. 78, 253–269 (2009).
PubMed  Google Scholar 

13.
Simanonok, M. P. & Burkle, L. A. Partitioning interaction turnover among alpine pollination networks: spatial, temporal, and environmental patterns. Ecosphere 5, 1–17 (2014).
Google Scholar 

14.
CaraDonna, P. J. et al. Interaction rewiring and the rapid turnover of plant–pollinator networks. Ecol. Lett. 20, 385–394 (2017).
PubMed  Google Scholar 

15.
Petanidou, T., Kallimanis, A. S., Tzanopoulos, J., Sgardelis, S. P. & Pantis, J. D. Long-term observation of a pollination network: fluctuation in species and interactions, relative invariance of network structure and implications for estimates of specialization. Ecol. Lett. 11, 564–575 (2008).
PubMed  Google Scholar 

16.
Kaiser-Bunbury, C. N., Memmott, J. & Müller, C. B. Community structure of pollination webs of mauritian heathland habitats. Perspect. Plant Ecol. Evol. Sys. 11, 241–254 (2009).
Google Scholar 

17.
MacLeod, M., Genung, M. A., Ascher, J. S. & Winfree, R. Measuring partner choice in plant–pollinator networks: using null models to separate rewiring and fidelity from chance. Ecology 97, 2925–2931 (2016).
PubMed  Google Scholar 

18.
Alarcón, R., Waser, N. M. & Ollerton, J. Year-to-year variation in the topology of a plant–pollinator interaction network. Oikos 117, 1796–1807 (2008).
Google Scholar 

19.
Ponisio, L. C., Gaiarsa, M. P. & Kremen, C. Opportunistic attachment assembles plant–pollinator networks. Ecol. Lett. 20, 1261–1272 (2017).
PubMed  Google Scholar 

20.
Burkle, L. A., Marlin, J. C. & Knight, T. M. Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function. Science 339, 1611–1615 (2013).
ADS  CAS  PubMed  Google Scholar 

21.
Cirtwill, A. R., Roslin, T., Rasmussen, C., Olesen, J. M. & Stouffer, D. B. Between-year changes in community composition shape species roles in an arctic plant–pollinator network. Oikos 127, 1163–1176 (2018).
Google Scholar 

22.
Bascompte, J. & Stouffer, D. B. The assembly and disassembly of ecological networks. Philos. Trans. R. Soc. B 364, 1781–1787 (2009).
Google Scholar 

23.
Jordano, P., Bascompte, J. & Olesen, J. M. Invariant properties in coevolutionary networks of plant–animal interactions. Ecol. Lett. 6, 69–81 (2003).
Google Scholar 

24.
Díaz-Castelazo, C. et al. Changes of a mutualistic network over time: reanalysis over a 10-year period. Ecology 91, 793–801 (2010).
PubMed  Google Scholar 

25.
Tylianakis, J. M., Martínez-García, L. B., Richardson, S. J., Peltzer, D. A. & Dickie, I. A. Symmetric assembly and disassembly processes in an ecological network. Ecol. Lett. 21, 896–904 (2018).
PubMed  Google Scholar 

26.
Gravel, D., Massol, F., Canard, E., Mouillot, D. & Mouquet, N. Trophic theory of island biogeography. Ecol. Lett. 14, 1010–1016 (2011).
PubMed  Google Scholar 

27.
Dáttilo, W., Guimarães, P. R. & Izzo, T. J. Spatial structure of ant–plant mutualistic networks. Oikos 122, 1643–1648 (2013).
Google Scholar 

28.
Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. The dissimilarity of species interaction networks. Ecol. Lett. 15, 1353–1361 (2012).
PubMed  Google Scholar 

29.
Bramon Mora, B., Gravel, D., Gilarranz, L. J., Poisot, T. & Stouffer, D. B. Identifying a common backbone of interactions underlying food webs from different ecosystems. Nat. Commun. 9, 2603 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

30.
Stouffer, D. B., Sales-Pardo, M., Sirer, M. I. & Bascompte, J. Evolutionary conservation of species roles in food webs. Science 335, 1489–1492 (2012).
ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

31.
Baker, N. J., Kaartinen, R., Roslin, T. & Stouffer, D. B. Species roles in food webs show fidelity across a highly variable oak forest. Ecography 38, 130–139 (2015).
Google Scholar 

32.
CaraDonna, P. J. & Waser, N. M. Temporal flexibility in the structure of plant–pollinator interaction networks. Oikos https://doi.org/10.1111/oik.07526 (2020).

33.
Dunne, J. A., Williams, R. J. & Martinez, N. D. Food-web structure and network theory: the role of connectance and size. Proc. Natl Acad. Sci. USA 99, 12917–12922 (2002).
ADS  CAS  PubMed  Google Scholar 

34.
Bascompte, J., Jordano, P., Melián, C. J. & Olesen, J. M. The nested assembly of plant–animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).
ADS  CAS  PubMed  Google Scholar 

35.
Chacoff, N. P., Resasco, J. & Vázquez, D. P. Interaction frequency, network position, and the temporal persistence of interactions in a plant–pollinator network. Ecology 99, 21–28 (2018).
PubMed  Google Scholar 

36.
Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018 (2009).
ADS  CAS  PubMed  Google Scholar 

37.
Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).
PubMed  Google Scholar 

38.
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).
PubMed  Google Scholar 

39.
Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072 (2011).
PubMed  Google Scholar 

40.
Goldwasser, L. & Roughgarden, J. Sampling effects and the estimation of food-web properties. Ecology 78, 41–54 (1997).
Google Scholar 

41.
Westphal, C., Steffan-Dewenter, I. & Tscharntke, T. Mass flowering crops enhance pollinator densities at a landscape scale. Ecol. Lett. 6, 961–965 (2003).
Google Scholar 

42.
Miele, V., Ramos-Jiliberto, R. & Vázquez, D. P. Core–periphery dynamics in a plant–pollinator network. Preprint at https://doi.org/10.1101/543637 (2019).

43.
Hackett, T. D. et al. Reshaping our understanding of species’ roles in landscape-scale networks. Ecol. Lett. 22, 1367–1377 (2019).
PubMed  Google Scholar 

44.
Schwarz, B. et al. Temporal scale-dependence of plant–pollinator networks. Oikos https://doi.org/10.1111/oik.07303 (2020).

45.
Bascompte, J. & Melián, C. J. Simple trophic modules for complex food webs. Ecology 86, 2868–2873 (2005).
Google Scholar 

46.
Kondoh, M. Building trophic modules into a persistent food web. Proc. Natl Acad. Sci. USA 105, 16631–16635 (2008).
ADS  CAS  PubMed  Google Scholar 

47.
Vázquez, D. P., Blüthgen, N., Cagnolo, L. & Chacoff, N. P. Uniting pattern and process in plant–animal mutualistic networks: a review. Ann. Bot. 103, 1445–1457 (2009).
PubMed  PubMed Central  Google Scholar 

48.
Cagnolo, L., Salvo, A. & Valladares, G. Network topology: patterns and mechanisms in plant-herbivore and host-parasitoid food webs. J. Anim. Ecol. 80, 342–351 (2011).
PubMed  Google Scholar 

49.
Aizen, M. A. et al. The phylogenetic structure of plant–pollinator networks increases with habitat size and isolation. Ecol. Lett. 19, 29–36 (2016).
PubMed  Google Scholar 

50.
Junker, R. R., Höcherl, N. & Blüthgen, N. Responses to olfactory signals reflect network structure of flower-visitor interactions. J. Anim. Ecol. 79, 818–823 (2010).
PubMed  Google Scholar 

51.
Coux, C., Rader, R., Bartomeus, I. & Tylianakis, J. M. Linking species functional roles to their network roles. Ecol. Lett. 19, 762–770 (2016).
PubMed  Google Scholar 

52.
Bartomeus, I. et al. A common framework for identifying linkage rules across different types of interactions. Funct. Ecol. 30, 1894–1903 (2016).
Google Scholar 

53.
Weinstein, B. G. & Graham, C. H. Persistent bill and corolla matching despite shifting temporal resources in tropical hummingbird-plant interactions. Ecol. Lett. 20, 326–335 (2017).
PubMed  Google Scholar 

54.
Weinstein, B. G. & Graham, C. H. On comparing traits and abundance for predicting species interactions with imperfect detection. Food Webs 11, 17–25 (2017).
Google Scholar 

55.
Eklöf, A. et al. The dimensionality of ecological networks. Ecol. Lett. 16, 577–583 (2013).
PubMed  Google Scholar 

56.
Olito, C. & Fox, J. W. Species traits and abundances predict metrics of plant–pollinator network structure, but not pairwise interactions. Oikos 124, 428–436 (2015).
Google Scholar 

57.
Hart, D. R., Stone, L. & Berman, T. Seasonal dynamics of the lake kinneret food web: the importance of the microbial loop. Limnol. Oceanogr. 45, 350–361 (2000).
ADS  CAS  Google Scholar 

58.
Pilosof, S., Fortuna, M. A., Vinarski, M. V., Korallo-Vinarskaya, N. P. & Krasnov, B. R. Temporal dynamics of direct reciprocal and indirect effects in a host–parasite network. J. Anim. Ecol. 82, 987–996 (2013).
PubMed  Google Scholar 

59.
Holme, P. & Saramäki, J. Temporal networks. Phys. Rep. 519, 97–125 (2012).
ADS  Google Scholar 

60.
Tylianakis, J. M. & Morris, R. J. Ecological networks across environmental gradients. Annu. Rev. Ecol. Evol. Syst. 48, 25–48 (2017).
Google Scholar 

61.
CaraDonna, P. J. Temporal variation in plant-pollinator interactions, Rocky Mountain Biological Laboratory, CO, USA, 2013 – 2015 ver 1. Environmental Data Initiative, https://doi.org/10.6073/pasta/27dc02fe1655e3896f20326fed5cb95f (2020).

62.
Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).
ADS  CAS  PubMed  Google Scholar 

63.
Bramon Mora, B., Cirtwill, A. R. & Stouffer, D. B. pymfinder: a tool for the motif analysis of binary and quantitative complex networks. Preprint at https://doi.org/10.1101/364703 (2018).

64.
Pons, P. & Latapy, M. Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10, 191–218 (2006).
MathSciNet  MATH  Google Scholar 

65.
Danon, L., Diaz-Guilera, A., Duch, J. & Arenas, A. Comparing community structure identification. J. Stat. Mech.: Theory E 2005, P09008 (2005).
MATH  Google Scholar 

66.
Koster, J. & McElreath, R. Multinomial analysis of behavior: statistical methods. Behav. Ecol. Sociobiol. 71, 138 (2017).
PubMed  PubMed Central  Google Scholar 

67.
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, London, 2018).

68.
Team, S. D. et al. RStan: the R interface to Stan (The R Foundation, 2019). More

Microorganisms and medium composition
C. acetobutylicum DSM 792 and E. aerogenes DSM 30053 were used for all experiments. A modified Clostridium-specific medium without yeast extract was used for growth of mono-culture C. acetobutylicum as previously described in detail elsewhere71. The medium was prepared containing (per L): 0.5 g of KH2PO4, 0.5 g of K2HPO4 and 2.2 g of NH4CH3COO and glucose or cellobiose were added at a concentration of 999 C-mmol. The pH was arranged with 1 mol L−1 NaOH to 6.8. Trace elements solution was prepared as stock 100× solution containing (per L): 0.2 g of MgSO4·7 H2O, 0.01 g of MnSO4·7H2O, 0.01 g of FeSO4·7H2O, 0.01 g of NaCl. Vitamin solution was prepared as stock 200× solution containing (per L): 0.9 g of thiamine, 0.002 g of biotin and 0.2 g of 4-aminobenzoic acid. The trace elements solution and the vitamin solution were used for all experiments. Mono-culture of E. aerogenes was grown in a defined Enterobacter-specific medium, as described elsewhere72. The Enterobacter-specific medium was prepared containing (per L): 13.3 g K2HPO4, 4 g (NH4)2HPO4, 8 mg EDTA and trace elements (2.5 mg CoCl2·6H2O, 15 mg MnCl2·4H2O, 1.5 g CuCl2·4H2O; 3 mg H3BO3; 2.5 mg Na2MoO4·2H2O, 13 mg of Zn(CH3COO)2·2H2O). Glucose and cellobiose were prepared as stock solutions. Media, trace element solution, glucose and cellobiose solutions were flushed with sterile N2 to make the solutions anaerobic and sterilized separately at 121 °C for 20 min. Sterile anaerobic solutions of glucose or cellobiose, trace elements solution and filter sterilized vitamin solution were added into the media before the inoculation inside the sterilized biological safety cabinet (BH-EN 2005, Faster Srl, Ferrara, Italy).
Design of experiments
A mutual medium accommodating the nutritional requirements of both organisms was designed by using the DoE approach. The buffer compositions of two species specific media described above were analysed and the optimum concentrations of AC (NH4Cl), SA (Na+ acetate) and PB (KH2PO4/K2HPO4) capacity were investigated. The setting of DoE for concentration effect of AC, SA and PB capacity was based on 29 randomized runs within concentration range from 3–30 mmol L−1 of AC, 3–150 mmol L−1 of KH2PO4 and 10–120 mmol L−1 of SA (Table 1). Each experiment was performed in triplicates (n = 3), except for set E of the DoE experiment (centre points), which were performed in pentaplicate (n = 5). The DoE experiments were performed twice (N = 2). The end of the exponential growth phase of E. aerogenes and C. acetobutylicum was reached at 45 and 51.5 h, respectively. For modelling, these time points were used. The reason for providing an acetate source in the medium was due to the possibility to add an acetate oxidizing microorganism to the co-culture consortium, which was not performed in the context of this study.
Closed batch cultivations
Cultures of E. aerogenes and C. acetobutylicum were grown anaerobically at 0.3 bar in a 100 Vol.-% N2 atmosphere in a closed batch set-up33. Mono-culture and consortium closed batch experiments were conducted with the final volume of 50 mL medium in 120 mL serum bottles (Ochs Glasgerätebau, Langerwehe, Germany). Each serum bottle contained 45 mL Clostridium-specific medium, Enterobacter-specific medium or E-medium, 0.25 mL vitamin solution, 3.0 mL glucose or cellobiose stock solution, 0.5 mL trace elements solution and 1.25 mL inoculum. The serum bottles were sealed with rubber stoppers (20 mm butyl ruber, Chemglass Life Science LLC, Vineland, USA). For consortium experiments, different inoculum ratios were tested and initial cell concentrations were arranged with the ratios of (E. aerogenes : C. acetobutylicum) 1:2, 1:10, 1:100, 1:1000, 1:10,000 and 1:100,000 at a temperature of 37 °C. Pre-culture of E. aerogenes was diluted in DoE E-medium (Table 1) to inoculate the organism at cell densities of aforementioned ratios. The pressure in the headspace of the serum bottles were measured individually using a manometer (digital manometer LEO1-Ei,−1…3 bar, Keller, Germany). After each measurement, the pressure was released completely from the headspace of serum bottle by penetrating the butyl rubber stopper with a sterile needle. The pressure values were added up to reveal total produced pressure (cumulative pressure). Experiments were performed three times (N = 3) and each set was performed in quadruplicates (n = 4).
Cell counting, absorption measurements, DNA extraction and qPCR
A volume of 1 mL of liquid sample was collected by using sterile syringes at regular intervals for monitoring biomass growth by measuring the absorbance (optical density at 600 nm (OD600)) using a spectrophotometer (Beckman Coulter Fullerton, CA, USA). Every sampling operation was done inside the sterilized biological safety cabinet (BH-EN 2005, Faster Srl, Ferrara, Italy).
E. aerogenes and C. acetobutylicum cells were counted using a Nikon Eclipse 50i microscope (Nikon, Amsterdam, Netherlands) at each liquid/biomass sampling point. The samples for cell count were taken from each individual closed batch run using syringes (Soft-Ject, Henke Sass Wolf, Tuttlingen, Germany) and hypodermic needles (Sterican size 14, B. Braun, Melsungen, Germany). Ten microlitres of sample were applied onto a Neubauer improved cell counting chamber (Superior Marienfeld, Lauda-Königshofen, Germany) with a grid depth of 0.1 mm.
DNA for qPCR was extracted from 1 mL culture samples by centrifugation at 4 °C and 13,400 r.p.m. for 30 min. The following steps were applied for DNA extraction; (1) cells were resuspended in pre-warmed (65 °C) 1% sodium dodecyl sulfate (SDS) extraction buffer and (2) transferred to Lysing Matrix E tubes (MP Biomedicals, Santa Ana, CA, USA) containing an equal volume of phenol/chloroform/isoamylalcohol (25:24:1). (3) Cell lysis was performed in a FastPrep-24 (MP Biomedicals, NY, USA) device with speed setting 4 for 30 s and the lysate was centrifuged at 13,400 r.p.m. for 10 min. (4) An equal volume of chloroform/isoamylalcohol (24:1) was added to the supernatant of the lysate, followed by centrifugation at 13,400 r.p.m. for 10 min and collection of the aqueous phase. (5) Nucleic acids were precipitated with double volume of polyethylenglycol (PEG) solution (30% PEG, 1.6 mol L−1 NaCl) and 1 μL glycogen (20 mg mL−1) as carrier, incubated for 2 h at room temperature. (6) Following centrifugation at 13,400 r.p.m. for 1 h, nucleic acid pellets were washed with 1 mL cold 70% ethanol, dried at 30 °C using a SpeedVac centrifuge (Thermo Scientific, Dreieich, Germany), eluted in Tris-EDTA buffer and stored at −20 °C until further analysis. Nucleic acid quantification was performed with NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). qPCR assays were developed for quantifying E. aerogenes and C. acetobutylicum in consortium. The primer pairs were designed by targeting species specific genes (Supplementary Table 6) to prevent false-positive amplification and sequences of genes were compared for identifying optimal primer using the ClustalW2 multiple sequence alignment programme (http://www.ebi.ac.uk/Tools/clustalw2/). qPCR assays were performed in Eppendorf Mastercycler epgradientS realplex2 (Eppendorf, Hamburg, Germany). The PCR mixture (20 μL) contained 10 μL SYBR Green labelled Luna Universal qPCR Master Mix (M3003L, New England Biolabs), 0.5 μL of forward and 0.5 μL reverse primer, 8 μL sterile DEPC water and 1 μL of DNA template. Negative controls containing sterile diethyl pyrocarbonate (DEPC) water as a replacement for the DNA templates and DNA template of the non-targeted species were included separately in each run. The amplification protocol started with an initial denaturation at 95 °C for 2 min, followed by 45 cycles of denaturation at 95 °C for 30 s, annealing and fluorescence acquisition at 60 °C for 30 s and elongation at 72 °C for 30 s. A melting-curve analysis (from 60 °C to 95 °C at a transition rate of 1 °C every 10 s) was performed to determine the specificity of the amplification. All amplification reactions were performed in triplicates. A standard curve was generated as described elsewhere29. Culture samples of each organism were collected at different time intervals for cell count and genomic DNA extraction cell density of each strain were determined by cell counting under microscope during growth and subsequent gDNA extraction was applied to reflect absolute quantification. Six tenfold dilution standards were prepared and a linear regression analysis was performed between qPCR reads and cell counts and OD600 measurements.
Quantification of gas composition
Gas chromatography (GC) measurements were performed from serum bottles that remained without any manipulation after inoculation until the first time point GC measurement. After every GC measurement, remaining gas was released completely from the serum bottles by penetrating the butyl rubber stopper using a sterile needle. The pressure of serum bottles headspace was determined to examine whether there was any remaining overpressure by using a manometer (digital manometer LEO1-Ei,−1…3 bar, Keller, Germany). The gas compositions were analysed by using a GC (7890 A GC System, Agilent Technologies, Santa Clara, USA) with a 19808 Shin Carbon ST Micropacked Column (Restek GmbH, Bad Homburg, Germany) and provided with a gas injection and control unit (Joint Analytical System GmbH, Moers, Germany) as described before73,74,75. The standard test gas employed in GC comprised the following composition: 0.01 Vol.-% CH4; 0.08 Vol.-% CO2 in N2 (Messer GmbH, Wien, Austria). All chemicals were of highest grade available. H2, CO2, N2, 20 Vol.-% H2 in CO2 and 20 Vol.-% CO2 in N2 were of test gas quality (Air Liquide, Schwechat, Austria).
Quantification of liquid metabolites
Quantification of sugars, volatile fatty acids and alcohols were performed with high-performance liquid chromatography (HPLC) system (Agilent 1100), consisting of a G1310A isocratic pump, a G1313A ALS autosampler, a Transgenomic ICSep ICE-ION-300 column, a G1316A column thermostat set at 45 °C and a G1362A RID refractive index detector, measuring at 45 °C (all modules were from Agilent 1100 (Agilent Technologies, CA, USA). The measurement was performed with 0.005 mol L−1 H2SO4 as solvent, with a flow rate of 0.325 mL min−1 and a pressure of 48–49 bar. The injection volume was 40 µL.
Data analysis
For the quantitative analysis, the maximum specific growth rate (µmax [h−1]) and mean specific growth rate (µmean [h−1]) were calculated as follows: N = N0·eµt with N, cell number [cells ml−1]; N0, initial cell number [cells ml−1]; t, time [h] and e, Euler’s number. According to the delta cell counts in between sample points, µ was assessed. The Y(H2/S) [mol mol−1], HER [mmol L−1 h−1], CER [mmol L−1 h−1] and the specific H2 production rate (qH2) [mmol g−1 h−1]32 were calculated from the intervals between each time point and the gas composition in the headspace of serum bottle was determined using the GC. The elementary composition of the corresponding biomass59 was used for the calculation of the mean molar weight, carbon balance and the DoR balance. Yields of byproducts were determined after HPLC measurement. Values were normalized according to the zero control. Moreover, the Shannon diversity index (H) was calculated to interpret the changes in microbial diversity, accounting for both richness (S), the number of species present and abundance of different species. Relative abundance of two species was evaluated according to the calculated evenness (EH) values76. Global substrate uptake rate, byproduct production rates and the mass balance analyses of the mono-cultures and consortium on glucose and cellobiose were calculated between the first and last time point.
Fluorescence in situ hybridization
For FISH, samples of 2 mL were collected for cell fixation. The samples were centrifuged in micro-centrifuge (5415-R, Eppendorf, Hamburg, Germany) for 10 min at 13,200 r.p.m. and pellets were resuspended in 0.5 mL phosphate-buffered saline (PBS) (10 mmol L−1 of Na2HPO4/NaH2PO, 130 mmol L−1 of NaCl, pH of 7.2–7.4). After repeating this procedure twice, 0.5 mL ice-cold absolute ethanol was added to the 0.5 mL PBS/cell mixture. The ethanol fixed samples were thoroughly mixed and then stored at −20 °C. Poly-l-lysine solution (0.01 % (v/v)) was used for coating the microscope slides (76 × 26 × 1 mm, Marienfeld-Superior, Lauda-Königshofen, Germany) containing ten reaction wells separated by an epoxy layer. After dipping the slide into the solution for 5 min, residual poly-l-lysine from the slides was removed by draining the well, followed by air-drying for several minutes. Cells were immobilized on prepared slides by adding samples (1–10 µL) on each well and air-drying. For cell dehydration, the slides were impregnated with ethanol concentrations of 50% (v/v), 80% (v/v) and 96% (v/v), respectively. The slides were dipped into each solution for 3 min, starting from the lowest concentration.
The EUB338 probe77 was used to target specific 16S rRNA found in almost all organisms belonging to the domain of bacteria78. The GAM42a probe specifically binds to target regions of gammaproteobacterial 23S rRNA79 (Supplementary Table 7). Both probes were diluted with DEPC water to a certain extent depending on the fluorescence label. Cy3-labelled EUB338 was diluted to a probe concentration of 30 ng DNA μL−1, whereas FLUOS-labelled GAM42a was adjusted to a final concentration of 50 ng DNA μL−1. For hybridization of the probe, 20 µL of hybridization buffer (900 mmol L−1 NaCl, 20 mmol L−1 Tris/HCl, 30% formamide (v/v), 0.01% SDS (v/v)) and 2 µL of diluted probe solution were added into each well. The hybridization reaction (46 °C, overnight) was facilitated using an airtight hybridization chamber (50 mL centrifuge tube) to prevent dehydration.
A stringent washing step was performed at 48 °C for 10 min in pre-warmed 50 mL washing buffer (100 mmol L−1 NaCl, 20 mmol L−1 Tris/HCl, 5 mmol L−1 EDTA). Afterwards, the slides were dried up and a mounting medium (Antifade Mounting Medium, Vectashield Vector Laboratories, CA, USA) was added to each well. The slides were sealed with a cover glass and examined under phase-contrast microscope (Nikon Eclipse Ni equipped with Lumen 200 Fluorescence Illumination Systems) using filter sets TRITC (557/576) (maximum excitation/emission in nm) for cy3-labelled EUB338 probe and FITC (490/525) for FLUOS-labelled GAM42a probes by a 100 × 1.45 numerical aperture microscope objective (CFI Plan Apo Lambda DM ×100 Oil; Nikon Corp., Japan).
Statistics and reproducibility
DoE experiments were designed and analysed using Design Expert version 11.1.2.0 (Stat-Ease, Inc. USA). Analysis of variation was performed at α = 0.05. The p-values for each test are indicated in the ‘Results’ section. All closed batch experiments were reproduced three times (N = 3) and each replication contained quadruplicate (n = 4). qPCR and FISH experiments, which applied all of the mentioned replicates, were performed in technical triplicates (n = 3). DoE experiments were conducted twice (N = 2) and each replication contained triplicate experiments for corner points (n = 3), except the set E (centre points), which was performed in biological pentaplicates (n = 5).
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.
Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120, 321–326 (2011).
Article  Google Scholar 
2.
Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).
Article  Google Scholar 

3.
Aizen, M., Garibaldi, L. A., Cunningham, S. & Klein, A. M. Long-term global trends in crop yield and production reveal no current pollination shortage but increasing pollinator dependency. Curr. Biol. 18, 1572–1575 (2008).
CAS  Article  Google Scholar 

4.
Aizen, M., Garibaldi, L. A., Cunningham, S. & Klein, A. M. How much does agriculture depend on pollinators? Lessons from long-term trends in crop production. Ann. Bot. 103, 1579–1588 (2009).
Article  Google Scholar 

5.
Kremen, C., Williams, N. M. & Thorp, R. W. Crop pollination from native bees at risk from agricultural intensification. Proc. Natl. Acad. Sci. USA. 99, 16812–16816 (2002).
ADS  CAS  Article  Google Scholar 

6.
Klein, A. M., Steffan-Dewenter, I. & Tscharntke, T. Fruit set of highland coffee increases with the diversity of pollinating bees. Proc. R. Soc. B Biol. Sci. 270, 955–961 (2003).
Article  Google Scholar 

7.
Hoehn, P., Tscharntke, T., Tylianakis, J. M. & Steffan-Dewenter, I. Functional group diversity of bee pollinators increases crop yield. Proc. R. Soc. B Biol. Sci. 275, 2283–2291 (2008).
Article  Google Scholar 

8.
Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611 (2013).
ADS  CAS  Article  Google Scholar 

9.
Potts, S., Imperatriz-Fonseca, V. & Ngo, H. The assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination and food production. (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2016). https://doi.org/10.5281/zenodo.3402856

10.
Leong, J. M. & Thorp, R. W. Colour-coded sampling: the pan trap colour preferences of oligolectic and nonoligolectic bees associated with a vernal pool plant. Ecol. Entomol. 24, 329–335 (1999).
Article  Google Scholar 

11.
Westphal, C. et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 78, 653–671 (2008).
Article  Google Scholar 

12.
Wilson, J. S., Griswold, T. & Messinger, O. J. Sampling bee communities (Hymenoptera: Apiformes) in a desert landscape: are pan traps sufficient?. J. Kansas Entomol. Soc. 81, 288–300 (2008).
Article  Google Scholar 

13.
Toler, T. R., Evans, E. W. & Tepedino, V. J. Pan-trapping for bees (Hymenoptera: Apiformes) in Utah’s west desert: The importance of color diversity. Pan-Pac. Entomol. 81, 103–113 (2005).
Google Scholar 

14.
Nielsen, A. et al. Assessing bee species richness in two Mediterranean communities: importance of habitat type and sampling techniques. Ecol. Res. 26, 969–983 (2011).
Article  Google Scholar 

15.
Saunders, M. E. & Luck, G. W. Pan trap catches of pollinator insects vary with habitat. Aust. J. Entomol. 52, 106–113 (2013).
Article  Google Scholar 

16.
Allen-Wardell, G. et al. The potential consequences of pollinator declines on the conservation of biodiversity and stability of food crop yields. Conserv. Biol. 12, 8–17 (1998).
Article  Google Scholar 

17.
Kearns, C., Inouye, D. & Waser, N. Endangered mutualisms: the conservation of plant-pollinator interactions. Annu. Rev. Ecol. Syst. 29, 83–112 (1998).
Article  Google Scholar 

18.
Brunet, J. Pollinator decline: implications for food security and environment. Sci. Glob. https://doi.org/10.33548/scientia371 (2019).
Article  Google Scholar 

19.
Popic, T. J., Davila, Y. C. & Wardle, G. M. Evaluation of common methods for sampling invertebrate pollinator assemblages: net sampling out-perform pan traps. PLoS ONE 8, e66665 (2013).
ADS  CAS  Article  Google Scholar 

20.
Bauer, A. A., Clayton, M. K. & Brunet, J. Floral traits influencing plant attractiveness to three bee species: consequences for plant reproductive success. Am. J. Bot. 104, 1–10 (2017).
Article  Google Scholar 

21.
Brunet, J. & Stewart, C. M. Impact of bee species and plant density on alfalfa pollination and potential for gene flow. Psyche A J. Entomol. 2010, 1–7 (2010).
Article  Google Scholar 

22.
Wang, X. et al. Biodiversity of wild alfalfa pollinators and their temporal foraging characters in Hexi Corridor Northwest China. Entomol. Fenn. 23, 4–12 (2012).
Article  Google Scholar 

23.
Chen, M., Zhao, X. Y. & Zuo, X. A. Pollinator activity and pollination success of Medicago sativa L. in a natural and a managed population. Ecol. Evol. 8, 9007–9016 (2018).
Article  Google Scholar 

24.
Cane, J. H. Pollinating bees (Hymenoptera: Apiformes) of U.S. alfalfa compared for rates of pod and seed set. J. Econ. Entomol. 95, 22–27 (2002).
Article  Google Scholar 

25.
Bohart, G. E. Alfalfa pollinators with special reference to species other than honey bees. In Proceedings of the 10th International Congress of Entomology, Vol. 4, pp. 929–937 (1958).

26.
Brookes, B., Small, E., Lefkovitch, L. P., Damman, H. & Fairey, D. T. Attractiveness of alfalfa (Medicago sativa L.) to wild pollinators in relation to wildflowers. Can. J. Plant Sci. 74, 779–783 (1994).
Article  Google Scholar 

27.
Bohart, G. E. Pollination of alfalfa and red clover. Annu. Rev. Entomol. 2, 355–380 (1957).
Article  Google Scholar 

28.
Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).
Article  Google Scholar 

29.
Hall, H. G. Color preferences of bees captured in pan traps. J. Kansas Entomol. Soc. 89, 273–276 (2016).
Article  Google Scholar 

30.
Campbell, J. W. & Hanula, J. L. Efficiency of Malaise traps and colored pan traps for collecting flower visiting insects from three forested ecosystems. J. Insect Conserv. 11, 399–408 (2007).
Article  Google Scholar 

31.
Heneberg, P. & Bogusch, P. To enrich or not to enrich? Are there any benefits of using multiple colors of pan traps when sampling aculeate Hymenoptera?. J. Insect Conserv. 18, 1123–1136 (2014).
Article  Google Scholar 

32.
Moreira, E. F. et al. Are pan traps colors complementary to sample community of potential pollinator insects?. J. Insect Conserv. 20, 583–596 (2016).
Article  Google Scholar 

33.
Burd, M. Bateman’s principle and plant reproduction: the role of pollen limitation in fruit and seed set. Bot. Rev. 60, 83–139 (1994).
MathSciNet  Article  Google Scholar 

34.
Herrera, C. M. Pollinator abundance, morphology, and flower visitation rate: analysis of the ‘quantity’ component in a plant-pollinator system. Oecologia 80, 241–248 (1989).
ADS  Article  Google Scholar 

35.
Riday, H., Reisen, P., Raasch, J. A., Santa-Martinez, E. & Brunet, J. Selfing rate in an alfalfa seed production field pollinated with leafcutter bees. Crop Sci. 55, 1087–1095 (2015).
Article  Google Scholar 

36.
McGregor, S. Insect Pollination of Cultivated Crop Plants. (USDA, 1976). https://doi.org/10.1093/besa/23.1.104

37.
Grundel, R., Frohnapple, K. J., Jean, R. P. & Pavlovic, N. B. Effectiveness of bowl trapping and netting for inventory of a bee community. Environ. Entomol. 40, 374–380 (2011).
Article  Google Scholar 

38.
Oksanen, J. et al. Vegan: community ecology package. R package version 2.5–5. https://CRAN.R-project.org/package=vegan (2019).

39.
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).

40.
Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).
Article  Google Scholar 

41.
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Article  Google Scholar 

42.
Signorell, A. & Al, E. DescTools: tools for descriptive statistics. R package version 0.99.28. (2019). More

1.
Breitbart M, Thompson L, Suttle C, Sullivan M. Exploring the vast diversity of marine viruses. Oceanography. 2007;20:135–9.
Google Scholar 
2.
Fuhrman JA. Marine viruses and their biogeochemical and ecological effects. Nature. 1999;399:541–8.
CAS  PubMed  Google Scholar 

3.
Weinbauer MG. Ecology of prokaryotic viruses. FEMS Microbiol Rev. 2004;28:127–81.
CAS  PubMed  Google Scholar 

4.
Howard-Varona C, Lindback M, Bastien G, Solonenko N, Zayed A, Jang HB, et al. Phage-specific metabolic reprogramming of virocells. ISME J. 2020;14:881–95.
PubMed  PubMed Central  Google Scholar 

5.
Sullivan M, Lindell D, Lee J, Thompson L, Bielawski J, Chisholm S. Prevalence and evolution of core photosystem II genes in marine cyanobacterial viruses and their hosts. PLoS Biol. 2006;4:1344–57.
CAS  Google Scholar 

6.
Lindell D, Jaffe JD, Coleman ML, Futschik ME, Axmann IM, Rector T. Genome-wide expression dynamics of a marine virus and host reveal features of co-evolution. Nature. 2007;449:83–6.
CAS  PubMed  Google Scholar 

7.
Hurwitz BL, Hallam SJ, Sullivan MB. Metabolic reprogramming by viruses in the sunlit and dark ocean. Genome Biol. 2013;14:R123.
PubMed  PubMed Central  Google Scholar 

8.
Ahlgren NA, Fuchsman C, Rocap G, Fuhrman JA. Discovery of several novel, widespread, and ecologically distinct marine Thaumarchaeota viruses that encode amoC nitrification genes. ISME J. 2019;13:618–31.
CAS  PubMed  Google Scholar 

9.
Zeng Q, Chisholm SW. Marine viruses exploit their host’s two-component regulatory system in response to resource limitation. Curr Biol. 2012;22:124–8.
CAS  PubMed  Google Scholar 

10.
Anantharaman K, Duhaime MB, Breier JA, Wendt KA, Toner BM, Dick GJ. Sulfur oxidation genes in diverse deep-sea viruses. Science. 2014;344:757–60.
CAS  PubMed  Google Scholar 

11.
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.
CAS  PubMed  Google Scholar 

12.
Sullivan MB, Coleman ML, Weigele P, Rohwer F, Chisholm SW. Three Prochlorococcus cyanophage genomes: signature features and ecological interpretations. PLoS Biol. 2005;14:e144.
Google Scholar 

13.
Dwivedi B, Xue B, Lundin D, Edwards R, Breitbart M. A bioinformatic analysis of ribonucleotide reductase genes in phage genomes and metagenomes. BMC Evol Biol. 2013;13:33.
CAS  PubMed  PubMed Central  Google Scholar 

14.
Hagay E, Mandel-Gutfreund Y, Béjà O. Comparative metagenomics analyses reveal viral-induced shifts of host metabolism towards nucleotide biosysnthesis. Microbiome. 2014;2:9.
Google Scholar 

15.
Breitbart M. Marine viruses: truth or dare. Annu Rev Mar Sci. 2012;4:425–48.
Google Scholar 

16.
Chen LX, Méheust R, Crits-Christoph A, McMahon KD, Nelson TC, Warren LA et al. Large freshwater phages with the potential to augment aerobic methane oxidation. BioRxiv 2020.02.13.942896; https://doi.org/10.1101/2020.02.13.942896.

17.
Feiner R, Argov T, Rabinovich L, Sigal N, Borovok I, Herskovits AA. A new perspective on lysogeny: prophages as active regulatory switches of bacteria. Nat Rev Microbiol. 2015;10:641–50.
Google Scholar 

18.
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.
CAS  PubMed  Google Scholar 

19.
Roux S, Hawley AK, Torres Beltran M, Scofield M, Schwientek P, Stepanauskas R, et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta- genomics. eLife. 2014;3:e03125.
PubMed  PubMed Central  Google Scholar 

20.
Edgcomb VP, Orsi W, Bunge J, Jeon SO, Christen R, Leslin C, et al. Protistan microbial observatory in the Cariaco Basin, Caribbean. I. Pyrosequencing vs Sanger insights into species richness. J Int Soc. Micro Ecol. 2011;5:1344–56.
CAS  Google Scholar 

21.
Bertagnolli AD, Stewart FJ. Microbial niches in marine oxygen minimum zones. Nat Rev Microbiol. 2018;16:723–729.22.
CAS  PubMed  Google Scholar 

22.
Cassman N, Prieto-Davó A, Walsh K, Silva GG, Angly F, Akhter S, et al. Oxygen minimum zones harbour novel viral communities with low diversity. Environ Microbiol. 2012;4:3043–65.
Google Scholar 

23.
Schmidtko S, Stramma L, Visbeck M. Decline in global oceanic oxygen content during the past five decades. Nature. 2017;542:335–9.
CAS  PubMed  Google Scholar 

24.
Scranton MI, Sayles FL, Bacon MP, Brewer PG. Temporal changes in the hydrography and chemistry of the Cariaco Trench. Deep-Sea Res. 1987;34:945–63.
CAS  Google Scholar 

25.
Taylor GT, Iabichella M, Ho TY, Scranton MI, Thunell MC, Muller-Karger F, et al. Chemoautotrophy in the redox transition zone of the Cariaco Basin: a significant midwater source of organic carbon production. Limnol Oceanogr. 2001;46:148–63.
CAS  Google Scholar 

26.
Scranton MI, Astor Y, Bohrer R, Ho TY, Muller-Karger F. Controls on temporal variability of the geochemistry of the deep Cariaco Basin. Deep-Sea Res. 2001;48:1605–25.
CAS  Google Scholar 

27.
Scranton MI, Taylor GT, Thunell R, Benitez-Nelson C, Muller-Karger F, Fanning K, et al. Interannual and decadal variability in the nutrient geochemistry of the Cariaco Basin. Oceanography. 2014;27:148–59.
Google Scholar 

28.
Peterson LC, Overpeck JT, Kipp NG, Imbrie J. A high-resolution late quaternary upwelling record from the anoxic Cariaco Basin, Venezuela. Paleoceanography. 1991;6:99–119.
Google Scholar 

29.
Scranton MI, Novelli PC, Loud PA. The distribution and cycling of hydrogen gas in the waters of two marine environments. Limnol Oceanogr. 1984;29:993–1003.
CAS  Google Scholar 

30.
Madrid V, Taylor GT, Scranton MI, Chistoserdov AY. Phylogenetic diversity of bacterial and archaeal communities in the anoxic zone of the Cariaco Basin. Appl Environ Microbiol. 2001;67:1663–74.
CAS  PubMed  PubMed Central  Google Scholar 

31.
Wakeham SG, Turich C, Schubotz F, Podlaska A, Li XN, Varela R, et al. Biomarkers, chemistry and microbiology show chemoautotrophy in a multilayer chemocline in the Cariaco Basin. Deep-Sea Res. 2012;63:133–56.
CAS  Google Scholar 

32.
Suter EA, Pachiadaki M, Taylor GT, Astor Y, Edgcomb VP. Free-living chemoautotrophic and particle-attached heterotrophic prokaryotes dominate microbial assemblages along a pelagic redox gradient. Environ Microbiol. 2018;20:693–712.
CAS  PubMed  Google Scholar 

33.
Taylor GT, Hein C, Iabichella M. Temporal variations in viral distributions in the anoxic Cariaco Basin. Aquat Micro Ecol. 2003;30:103–16.
Google Scholar 

34.
Astor YM, Lorenzoni L, Scranton MI (eds). Handbook of methods for the analysis of oceanographic parameters at the Cariaco Time Series Station. Cariaco Time Series Study. Caracas, Venezuela: Fundación La Salle de Ciencias Naturales; 2013.

35.
John SG, Mendez CB, Deng L, Poulos B, Kauffamn AKM, Kern S, et al. A simple and efficient method for concentration of ocean viruses by chemical flocculation. Environ Microbiol Rep. 2011;3:195–202.
CAS  PubMed  PubMed Central  Google Scholar 

36.
Ohio Supercomputer Center 1987. Ohio Supercomputer Center. Columbus OH: Ohio Supercomputer Center. http://osc.edu/ark:/19495/f5s1ph73.

37.
Duhaime MB, Sullivan MB. Ocean viruses: rigorously evaluating the metagenomic sample-to-sequence pipeline. Virology. 2012;434:181–6.
CAS  PubMed  Google Scholar 

38.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for illumina se-quence data. Bioinformatics. 2014;30:2114–20.
CAS  PubMed  PubMed Central  Google Scholar 

39.
Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A, Lapidus A et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In: Deng M, Jiang R, Sun F, Zhang X (eds). Research in computational molecular biology. Berlin, Germany: Springer Verlag; 2013 p. 158–70.

40.
Sullivan MJ, Petty NK, Beatson SA. Easyfig: a genome comparison visualizer. Bioinformatics. 2011;27:1009–10.
CAS  PubMed  PubMed Central  Google Scholar 

41.
Garneau J, Depardieu F, Fortier LC, Bikard D, Monot M. PhageTerm: a fast and user-friendly software to determine bacteriophage termini and packaging mode using randomly fragmented NGS data. Sci Rep. 2017;7:8292.
PubMed  PubMed Central  Google Scholar 

42.
Roux S, Emerson JB, Eloe-Fadrosh EA, Sullivan MB. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ. 2017;5:e3817.
PubMed  PubMed Central  Google Scholar 

43.
Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.
PubMed  PubMed Central  Google Scholar 

44.
Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5:69.
PubMed  PubMed Central  Google Scholar 

45.
Cambuy DD, Coutinho FH, Dutilh BE. Contig annotation tool CAT robustly classifies assembled metagenomic contigs and long sequences. BioRxiv 2016;072868:1–8.
Google Scholar 

46.
Arndt D, Grant J, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.
CAS  PubMed  PubMed Central  Google Scholar 

47.
Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from Pole to Pole. Cell. 2019;177:1109–23.
CAS  PubMed  PubMed Central  Google Scholar 

48.
Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C. Versatile and open software for comparing large genomes. Genome Biol. 2004;5:R12.
PubMed  PubMed Central  Google Scholar 

49.
Hyatt D, LoCascio PF, Hauser LJ, Uberbacher EC. Gene and translation initiation site prediction in metagenomics sequences. Bioinformatics. 2012;28:2223–30.
CAS  PubMed  Google Scholar 

50.
Daly RA, Borton MA, Wilkins MJ, Hoyt DW, Kountz DJ, Wolfe RA, et al. Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales. Nat Microbiol. 2016;1:16146.
CAS  PubMed  Google Scholar 

51.
Cock PA, Chang AT, Chapman BA, Cox CJ, Dalke A, Friedberg I, et al. Biophython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25:1422–3.
CAS  PubMed  PubMed Central  Google Scholar 

52.
Solovyev V, Salamov A 2011. Automatic annotation of microbial genomes and metagenomic sequences In: Li RW, editor. Metagenomics and its applications in agriculture biomedicine and environmental studies. NY, USA: Nova Science Publishers, Hauppauge; p. 61–78.

53.
Umarov RK, Solovyev VV. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks. PLoS One. 2017;12:e0171410.
PubMed  PubMed Central  Google Scholar 

54.
Sigrist CJA, de Castro E, Cerutti L, Cuche BA, Hulo N, Bridge A, et al. New and continuing developments at PROSITE. Nucleic Acids Res. 2012;41:D344–7.
PubMed  PubMed Central  Google Scholar 

55.
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE. The Phyre2 web portal for protein modelling, prediction and analysis. Nat Protoc. 2015;10:845–58.
CAS  PubMed  PubMed Central  Google Scholar 

56.
Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
Google Scholar 

57.
Suzuki R, Shimodaira H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics. 2006;22:1540–2.
CAS  PubMed  Google Scholar 

58.
Jang HB, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol. 2019;37:632–9.
Google Scholar 

59.
Gregory AC, Solonenko SA, Ignacio-Espinoza JC, LaButti K, Copeland A, Sudek S, et al. Genomic differentiation among wild cyanophages despite widespread horizontal gene transfer. BMC Genomics. 2016;17:930.
PubMed  PubMed Central  Google Scholar 

60.
Duhaime MB, Solonenko N, Roux S, Verberkmoes NC, Wichels A, Sullivan MB. Comparative omics and trait analyses of marine Pseudoalteromonas phages advance the phage OTU concept. Front Microbiol. 2017;8:1241.
PubMed  PubMed Central  Google Scholar 

61.
Roux S, Adriaenssens EM, Dutlith BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum information about an uncultivated virus genome (MIUViG): a community consensus on standards and best practices for describing genome sequences from uncultivated viruses. Nat Biotechnol. 2019;37:29–37.
CAS  PubMed  Google Scholar 

62.
Brum JR, Ignacio-Espinoza JC, Roux S, Doulcier G, Acinas SG, Alberti A, et al. Patterns and ecological drivers of ocean viral communities. Science. 2015;348:1261498.
PubMed  Google Scholar 

63.
Haegeman B, Hamelin J, Moriarty J, Neal P, Dushoff J, Weitz JS. Robust estimation of microbial diversity in theory and in practice. ISME J. 2013;7:1092–101.
PubMed  PubMed Central  Google Scholar 

64.
Sullivan MB, Huang KH, Ignacio-Espinoza JC, Berlin AM, Kelly L, Weigele PR, et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ Microbiol. 2010;12:3035–56.
CAS  PubMed  PubMed Central  Google Scholar 

65.
Jones-Mortimer MC. Mapping of structural genes for the enzymes of cysteine biosynthesis in Escherichia coli K12 and Salmonella typhimurium LT2. Heredity. 1973;31:213–LT221.
CAS  PubMed  Google Scholar 

66.
Grote J, Schott T, Bruckner CG, Glöckner FO, Jost G, Teeling H, et al. Genome and physiology of a model Epsilonproteobacterium responsible for sulfide detoxification in marine oxygen depletion zones. PNAS. 2012;109:506–10.
CAS  PubMed  Google Scholar 

67.
Shapiro JA. Molecular model for the transposition and replication of bacteriophage Mu and other transposable elements. PNAS. 1979;76:1933–7.
CAS  PubMed  Google Scholar 

68.
Pato ML Bactioriophage Mu. In: Howe M, Berg D (eds). Mobile DNA. Washington DC, USA: ASM Press; 1989 p. 23–52.

69.
Mhammedi-Alaoui A, Pato M, Gama MJ, Toussaint A. A new component of bacteriophage Mu replicative transposition machinery: the Escherichia coli ClpX protein. Mol Microbiol. 1994;11:1109–16.
CAS  PubMed  Google Scholar 

70.
Howe MM. Prophage deletion mapping of bacteriophage Mu-1. Virology. 1973;54:93–101.
CAS  PubMed  Google Scholar 

71.
Fogg PC, Hynes AP, Digby E, Lang AS, Beatty JT. Characterization of a newly discovered Mu-like bacteriophage, RcapMu, in Rhodobacter capsulatus strain SB1003. Virology. 2011;421:211–21.
CAS  PubMed  Google Scholar 

72.
Lang AS, Zhaxybayeva O, Beatty JT. Gene transfer agents: phage-like elements of genetic exchange. Nat Rev Microbiol. 2012;10:472–82.
CAS  PubMed  PubMed Central  Google Scholar 

73.
Mosig G. Recombination and recombination-dependent DNA replication in bacteriophage T4. Annu Rev Genet. 1998;32:379–413.
CAS  PubMed  Google Scholar 

74.
Mosig G, Gewin J, Luder A, Colowick N, Vo D. Two recombination-dependent DNA replication pathways of bacteriophage T4, and their roles in mutagenesis and horizontal gene transfer. PNAS. 2001;98:8306–831.
CAS  PubMed  Google Scholar 

75.
Bragg JG, Chisholm SW. Modeling the fitness consequences of a cyanophage-encoded photosynthesis gene. PLoS One. 2008;14:e3550.
Google Scholar 

76.
Shapiro JA. A role for the Clp protease in activating Mu-mediated DNA rearrangements. J Bacteriol. 1993;175:2625–31.
CAS  PubMed  PubMed Central  Google Scholar 

77.
Hurwitz BL, Brum JR, Sullivan MB. Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome. ISME J. 2015;9:472–84.
CAS  PubMed  Google Scholar 

78.
Derelle E, Ferraz C, Escande ML, Eychenie S, Cooke R, Piganeau G, et al. Life-cycle and genome of OtV5, a large DNA virus of the pelagic marine unicellular green alga Ostreococcus tauri. PLoS One. 2008;3:e2250.
PubMed  PubMed Central  Google Scholar 

79.
Madsen JS, Hylling O, Jacquiod S, Pécastaings S, Hansen LH, Riber L, et al. An intriguing relationship between the cyclic diguanylate signaling system and horizontal gene transfer. ISME J. 2018;12:2330–4.
CAS  PubMed  PubMed Central  Google Scholar 

80.
Hengge R. Principles of c-di-GMP signalling in bacteria. Nat Rev Microbiol. 2009;7:263–73.
CAS  PubMed  Google Scholar 

81.
Taylor GT, Thunell RC, Varela R, Benitez-Nelson C, Scranton MI. Hydrolytic ectoenzyme activity associated with suspended and sinking organic particles above and within the anoxic Cariaco Basin. Deep-Sea Res. 2009;56:1266–83.
CAS  Google Scholar 

82.
Nothaft H, Szymanski CM. Protein glycosylation in bacteria: sweeter than ever. Nat Rev Microbiol. 2010;8:765–78.
CAS  PubMed  Google Scholar 

83.
Chen CR, Makhatadze GI. Molecular determinant of the effects of hydrostatic pressure on protein folding stability. Nat Commun. 2017;8:14561.
CAS  PubMed  PubMed Central  Google Scholar 

84.
Lee HS, Qi Y, Im W. Effects of N-glycosylation on protein conformation and dynamics: Protein Data Bank analysis and molecular dynamics simulation study. Sci Rep. 2015;5:8926.
PubMed  PubMed Central  Google Scholar 

85.
Mills DC, Jervis AJ, Abouelhadid S, Yates LE, Cuccui J, Linton D, et al. Functional analysis of N-linking oligosaccharyl transferase enzymes encoded by deep-sea vent proteobacteria. Glycobiology. 2016;26:398–409.
CAS  PubMed  Google Scholar 

86.
Xu C, Ng DTW. Glycosylation-directed quality control of protein folding. Nat Rev Mol Cell Biol. 2015;16:742–52.
CAS  PubMed  Google Scholar 

87.
Kandiba L, Eichler J. Archaeal S-layer glycoproteins: post-translational modification in the face of extremes. Front Microbiol. 2014;5:661.
PubMed  PubMed Central  Google Scholar 

88.
Wolfe AJ. The acetate switch. Microbiol Mol Biol Rev. 2005;69:12–50.
CAS  PubMed  PubMed Central  Google Scholar 

89.
Schilling B, Christensen D, Davis R, Sahu AK, Hul LI, Walker‐Peddakotla A. Protein acetylation dynamics in response to carbon overflow in Escherichia coli. Mol Micro. 2015;98:847–63.
CAS  Google Scholar 

90.
Marine R, Nasko D, Wray J, Polson SW, Wommack E. Novel chaperonins are prevalent in the virioplankton and demonstrate links to viral biology and ecology. ISME J. 2017;11:2479–91.
PubMed  PubMed Central  Google Scholar 

91.
Philosof A, Yutin N, Flores-Uribe J, Sharon I, Koonin EV, Béjà O. Novel abundant oceanic viruses of uncultured marine Group II Euryarchaeota. Curr Biol. 2017;2:1362–8.
Google Scholar 

92.
Nishimura Y, Watai H, Honda T, Mihara T, Omae K, Roux S, et al. Environmental viral genomes shed new light on virus-host interactions in the ocean. mSphere. 2017;2:e00359–16.
CAS  PubMed  PubMed Central  Google Scholar 

93.
Vik DR, Roux S, Brum JR, Bolduc B, Emerson JB, Padilla CC, et al. Putative archaeal viruses from the mesopelagic ocean. PeerJ. 2017;5:e3428.
PubMed  PubMed Central  Google Scholar 

94.
Ho TY, Scranton MI, Taylor GT, Varela R, Thunell RC, Muller‐Karger F. Acetate cycling in the water column of the Cariaco Basin: seasonal and vertical variability and implication for carbon cycling. Limnol Oceanogr. 2002;47:1119–28.
CAS  Google Scholar 

95.
Sharon I, Battchikova N, Aro EM, Giglione C, Meinnel T, Glaser F, et al. Comparative metagenomics of microbial traits within oceanic viral communities. ISME J. 2011;5:1178–1190.
CAS  PubMed  PubMed Central  Google Scholar 

96.
Johnson DC, Dean DR, Smith AD, Johnson MK. Structure, function, and formation of biological iron–sulfur clusters. Annu Rev Biochem. 2005;74:247–81.
CAS  PubMed  Google Scholar 

97.
Zhao D, Curatti L, Rubio LM. Evidence for nifU and nifS participation in the biosynthesis of the iron-molybdenum cofactor of nitrogenase. J Biol Chem. 2007;282:37016–25.
CAS  PubMed  Google Scholar 

98.
Iyer LM, Babu MM, Aravind L. The HIRAN domain and recruitment of chromatin remodeling and repair activities to damaged DNA. Cell Cycle. 2006;5:775–82.
CAS  PubMed  Google Scholar 

99.
Peters DL, McCutcheon JG, Stothard P, Dennis JJ. Novel Stenotrophomonas maltophilia temperate phage DLP4 is capable of lysogenic conversion. BMC Genomics. 2019;20:300.
PubMed  PubMed Central  Google Scholar 

100.
Sullivan MB, Krastins B, Hughes JL, Kelly L, Chase M, Sarracino D, et al. The genome and structural proteome of an ocean siphovirus: a new window into the cyanobacterial ‘mobilome’. Environ Microbiol. 2009;11:2935–51.
CAS  PubMed  PubMed Central  Google Scholar  More

1.
Bertin, G. & Averbeck, D. Cadmium: cellular effects, modifications of biomolecules, modulation of DNA repair and genotoxic consequences (a review). Biochimie 88, 1549–1559 (2006).
CAS  Article  Google Scholar 
2.
Nawrot, T. et al. Environmental exposure to cadmium and risk of cancer: a prospective population-based study. Lancet Oncol. 7, 119–126 (2006).
CAS  Article  Google Scholar 

3.
Horiguchi, H. et al. Hypoproduction of erythropoietin contributes to anemia in chronic cadmium intoxication: clinical study on Itai-itai disease in Japan. Arch. Toxicol. 68, 632–636 (1994).
CAS  Article  Google Scholar 

4.
Zhao, F. J., Ma, Y., Zhu, Y. G., Tang, Z. & McGrath, S. P. Soil contamination in China: current status and mitigation strategies. Environ. Sci. Technol. 49, 750–759 (2015).
ADS  CAS  Article  Google Scholar 

5.
Clemens, S. & Ma, J. F. Toxic heavy metal and metalloid accumulation in crop plants and foods. Annu. Rev. Plant Biol. 67, 489–512 (2016).
CAS  Article  Google Scholar 

6.
Wang, W., Yamaji, N. & Ma, J. F. Molecular Mechanism of Cadmium Accumulation in Rice (eds Himeno, S. & Aoshima, K.) 115–124 (Springer, 2019).

7.
Sasaki, A., Yamaji, N., Yokosho, K. & Ma, J. F. Nramp5 is a major transporter responsible for manganese and cadmium uptake in rice. Plant Cell 24, 2155–2167 (2012).
CAS  Article  Google Scholar 

8.
Yan, H. et al. Variation of a major facilitator superfamily gene contributes to differential cadmium accumulation between rice subspecies. Nat. Commun. 10, 2562 (2019).
ADS  Article  Google Scholar 

9.
Ueno, D. et al. Gene limiting cadmium accumulation in rice. Proc. Natl Acad. Sci. USA 107, 16500–16505 (2010).
ADS  CAS  Article  Google Scholar 

10.
Yamaji, N., Xia, J. X., Mitani-Ueno, N., Yokosho, K. & Ma, J. F. Preferential delivery of zinc to developing tissues in rice is mediated by P-type heavy metal ATPase OsHMA2. Plant Physiol. 162, 927–939 (2013).
CAS  Article  Google Scholar 

11.
Uraguchi, S. et al. Low-affinity cation transporter (OsLCT1) regulates cadmium transport into rice grains. Proc. Natl Acad. Sci. USA 108, 20959–20964 (2011).
ADS  CAS  Article  Google Scholar 

12.
Hao, X. et al. A node-expressed transporter OsCCX2 is involved in grain cadmium accumulation of rice. Front. Plant Sci. 9, 476 (2018).
ADS  Article  Google Scholar 

13.
Luo, J. S. et al. A defensin-like protein drives cadmium efflux and allocation in rice. Nat. Commun. 9, 645 (2018).
ADS  Article  Google Scholar 

14.
Schulte, D. et al. The international barley sequencing consortium—at the threshold of efficient access to the barley genome. Plant Physiol. 149, 142–147 (2009).
CAS  Article  Google Scholar 

15.
Codex General Standards for Contaminants and Toxins in Food and Feed (Codex Stan 193-1995) (Codex Alimentarius, FAO & WHO, 2019).

16.
Chen, F. et al. Identification of barley genotypes with low grain Cd accumulation and its interaction with four microelements. Chemosphere 67, 2082–2088 (2007).
ADS  CAS  Article  Google Scholar 

17.
Wu, D., Sato, K. & Ma, J. F. Genome-wide association mapping of cadmium accumulation in different organs of barley. New Phytol. 208, 817–829 (2015).
CAS  Article  Google Scholar 

18.
Saisho, D., Myoraku, E., Kawasaki, S., Sato, K. & Takeda, K. Construction and characterization of a bacterial artificial chromosome (BAC) library from the Japanese malting barley variety ‘Haruna Nijo’. Breed. Sci. 57, 29–38 (2007).
Article  Google Scholar 

19.
Huang, C. F., Yamaji, N., Chen, Z. & Ma, J. F. A tonoplast-localized half-size ABC transporter is required for internal detoxification of aluminum in rice. Plant J. 69, 857–867 (2012).
CAS  Article  Google Scholar 

20.
Yan, J. et al. A loss-of-function allele of OsHMA3 associated with high cadmium accumulation in shoots and grain of Japonica rice cultivars. Plant Cell Environ. 39, 1941–1954 (2016).
CAS  Article  Google Scholar 

21.
Shirasu, K., Schulman, A. H., Lahaye, T. & Schulze-Lefert, P. A contiguous 66-kb barley DNA sequence provides evidence for reversible genome expansion. Genome Res. 10, 908–915 (2000).
CAS  Article  Google Scholar 

22.
Kartal-Alacam, G., Yilmaz, S., Marakli, S. & Gozukirmizi, N. Sukkula retrotransposon insertion polymorphisms in barley. Russ. J. Plant Physiol. 61, 828–833 (2014).
CAS  Article  Google Scholar 

23.
Wicker, T. et al. A unified classification system for eukaryotic transposable elements. Nat. Rev. Genet. 8, 973–982 (2007).
CAS  Article  Google Scholar 

24.
Pereira, J. F. & Ryan, P. R. The role of transposable elements in the evolution of aluminium resistance in plants. J. Exp. Bot. 70, 41–54 (2019).
CAS  Article  Google Scholar 

25.
Zhang, L. et al. A high-quality apple genome assembly reveals the association of a retrotransposon and red fruit colour. Nat. Commun. 10, 1494 (2019).
ADS  Article  Google Scholar 

26.
Fujii, M. et al. Acquisition of aluminium tolerance by modification of a single gene in barley. Nat. Commun. 3, 713 (2012).
ADS  Article  Google Scholar 

27.
Kashino-Fujii, M. et al. Retrotransposon insertion and DNA methylation regulate aluminum tolerance in European barley accessions. Plant Physiol. 178, 716–727 (2018).
CAS  Article  Google Scholar 

28.
Laxa, M. et al. The 5′UTR intron of Arabidopsis GGT1 aminotransferase enhances promoter activity by recruiting RNA polymerase II. Plant Physiol. 172, 313–327 (2016).
CAS  Article  Google Scholar 

29.
Yokosho, K., Yamaji, N., Fujii-Kashino, M. & Ma, J. F. Retrotransposon-mediated aluminum tolerance through enhanced expression of the citrate transporter OsFRDL4. Plant Physiol. 172, 2327–2336 (2016).
CAS  Article  Google Scholar 

30.
Lisch, D. How important are transposons for plant evolution? Nat. Rev. Genet. 14, 49–61 (2013).
CAS  Article  Google Scholar 

31.
Negi, P., Rai, A. N. & Suprasanna, P. Moving through the stressed genome: emerging regulatory roles for transposons in plant stress response. Front. Plant Sci. 7, 1448 (2016).
PubMed  PubMed Central  Google Scholar 

32.
Pourkheirandish, M. et al. Evolution of the grain dispersal system in barley. Cell 162, 527–539 (2015).
CAS  Article  Google Scholar 

33.
Sasaki, A., Yamaji, N. & Ma, J. F. Overexpression of OsHMA3 enhances Cd tolerance and expression of Zn transporter genes in rice. J. Exp. Bot. 65, 6013–6021 (2014).
CAS  Article  Google Scholar 

34.
Cai, H., Huang, S., Che, J., Yamaji, N. & Ma, J. F. The tonoplast-localized transporter OsHMA3 plays an important role in maintaining Zn homeostasis in rice. J. Exp. Bot. 70, 2717–2725 (2019).
CAS  Article  Google Scholar 

35.
Close, T. J. et al. Development and implementation of high-throughput SNP genotyping in barley. BMC Genom. 10, 582 (2009).
Article  Google Scholar 

36.
Wang, S., Basten, C. J. & Zeng, Z. B. Windows QTL Cartographer 2.5 (Department of Statistics, North Carolina State University, 2012); http://statgen.ncsu.edu/qtlcart/WQTLCart.htm

37.
Fuse, T., Sasaki, T. & Yano, M. Ti-plasmid vectors useful for functional analysis of rice genes. Plant Biotech. 18, 219–222 (2001).
CAS  Article  Google Scholar 

38.
Tsutsui, T., Yamaji, N. & Ma, J. F. Identification of a cis-acting element of ART1, a C2H2-type zinc-finger transcription factor for aluminum tolerance in rice. Plant Physiol. 156, 925–931 (2011).
CAS  Article  Google Scholar 

39.
Chen, S. et al. A highly efficient transient protoplast system for analyzing defence gene expression and protein–protein interactions in rice. Mol. Plant Pathol. 7, 417–427 (2006).
ADS  CAS  Article  Google Scholar 

40.
Hiei, Y., Ishida, Y., Kasaoka, K. & Komari, T. Improved frequency of transformation in rice and maize by treatment of immature embryos with centrifugation and heat prior to infection with Agrobacterium tumefaciens. Plant Cell Tiss. Org. Cult. 87, 233–243 (2006).
Article  Google Scholar 

41.
Hiei, Y. & Komari, T. Improved protocols for transformation of indica rice mediated by Agrobacterium tumefaciens. Plant Cell Tiss. Org. Cult. 85, 271–283 (2006).
CAS  Article  Google Scholar 

42.
Hensel, G., Valkov, V., Middlefell-Williams, J. & Kumlehn, J. Efficient generation of transgenic barley: the way forward to modulate plant–microbe interactions. J. Plant Physiol. 165, 71–82 (2008).
CAS  Article  Google Scholar 

43.
Miki, D. & Shimamoto, K. Simple RNAi vectors for stable and transient suppression of gene function in rice. Plant Cell Physiol. 45, 490–495 (2004).
CAS  Article  Google Scholar  More

1.
Nerini, F. F. et al. Connecting climate action with other Sustainable Development Goals. Nat. Sustainability 2, 674–680 (2019).
Article  Google Scholar 
2.
Palmer, T. & Stevens, B. The scientific challenge of understanding and estimating climate change. Proc. Natl Acad. Sci. USA 116, 24390–24395 (2019).
ADS  CAS  Article  Google Scholar 

3.
IPCC. in Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 151 (IPCC, Geneva, Switzerland, 2014).

4.
Cook, J. et al. Consensus on consensus: a synthesis of consensus estimates on human-caused global warming. Environ. Res. Lett. 11, https://doi.org/10.1088/1748-9326/11/4/048002 (2016).

5.
IPCC. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Ch. AI, 1311–1394 (Cambridge University Press, 2013).

6.
Ipcc. in Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Barros, V. R. et al.) (Cambridge University Press, 2014).

7.
Knutti, R., Rugenstein, M. A. A. & Hegerl, G. C. Beyond equilibrium climate sensitivity. Nat. Geosci. 10, 727 (2017).
ADS  CAS  Article  Google Scholar 

8.
Palmer, T. N. A. CERN for climate change. Phys. World 24, 14 (2011).
Article  Google Scholar 

9.
Stainforth, D. A., Downing, T. E., Washington, R., Lopez, A. & New, M. Issues in the interpretation of climate model ensembles to inform decisions. Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci. 365, 2163–2177 (2007).
ADS  Article  Google Scholar 

10.
Shepherd, T. G. Storyline approach to the construction of regional climate change information. P. Roy Soc A 475, 20190013 (2019).

11.
McWilliams, J. C. Irreducible imprecision in atmospheric and oceanic simulations. Proc. Natl Acad. Sci. USA 104, 8709–8713 (2007).
ADS  CAS  Article  Google Scholar 

12.
Frigg, R., Bradley, S., Du, H. & Smith, L. A. Laplace’s demon and the adventures of his apprentices. Philos. Sci. 81, 31–39 (2014).

13.
Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8, 895 (2018).
ADS  CAS  Article  Google Scholar 

14.
Moore, F. C., Baldos, U., Hertel, T. & Diaz, D. New science of climate change impacts on agriculture implies higher social cost of carbon. Nat. Commun. 8, https://doi.org/10.1038/s41467-017-01792-x (2017).

15.
Tol, R. S. J. A social cost of carbon for (almost) every country. Energy Econ. 83, 555–566 (2019).
Article  Google Scholar 

16.
Weitzman, M. L. On modeling and interpreting the economics of catastrophic climate change. Rev. Econ. Stat. 91, 1–19 (2009).
Article  Google Scholar 

17.
Calel, R., Stainforth, D. A. & Dietz, S. Tall tales and fat tails: the science and economics of extreme warming. Climatic Change 132, 127–141 (2015).
ADS  Article  Google Scholar 

18.
Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527, 235 (2015).
ADS  CAS  Article  Google Scholar 

19.
Lemoine, D. Estimating the consequences of climate change from variation in weather. National Bureau of Economic Research Working Paper Series No. 25008, https://doi.org/10.3386/w25008 (2018).

20.
Hazeleger, W. et al. Tales of future weather. Nat. Clim. Change 5, 107 (2015).

21.
Shepherd, T. G. et al. Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change 151, 555–571 (2018).
ADS  Article  Google Scholar 

22.
Dessai, S. et al. Building narratives to characterise uncertainty in regional climate change through expert elicitation. Environ. Res. Lett. 13, https://doi.org/10.1088/1748-9326/aabcdd (2018).

23.
Pall, P. et al. Diagnosing conditional anthropogenic contributions to heavy Colorado rainfall in September 2013. Weather Clim. Extremes 17, 1–6 (2017).
Article  Google Scholar 

24.
Zappa, G. & Shepherd, T. G. Storylines of atmospheric circulation change for european regional climate impact assessment. J. Clim. 30, 6561–6577 (2017).
ADS  Article  Google Scholar 

25.
Bhave, A. G., Conway, D., Dessai, S. & Stainforth, D. A. Water resource planning under future climate and socioeconomic uncertainty in the Cauvery River Basin in Karnataka, India. Water Resour. Res. 54, 708–728 (2018).
ADS  Article  Google Scholar 

26.
Daron, J. D. & Stainforth, D. A. On predicting climate under climate change. Environ. Res. Lett. 8, 034021 (2013). More