Ecology

1.
Tassi, F. et al. Low-pH waters discharging from submarine vents at Panarea Island (Aeolian Islands, southern Italy) after the 2002 gas blast: Origin of hydrothermal fluids and implications for volcanic surveillance. Appl. Geochem. 24, 246–254 (2009).
CAS  Article  Google Scholar 
2.
Boatta, F. et al. Geochemical survey of Levante Bay, Vulcano Island (Italy), a natural laboratory for the study of ocean acidification. Mar. Pollut. Bull. 73, 485–494. https://doi.org/10.1016/j.marpolbul.2013.01.029 (2013).
CAS  Article  PubMed  Google Scholar 

3.
Hall-Spencer, J. M. et al. Volcanic carbon dioxide vents show ecosystem effects of ocean acidification. Nature 454, 96–99 (2008).
ADS  CAS  Article  Google Scholar 

4.
Ricevuto, E., Kroeker, K. J., Ferrigno, F. & Gambi, M. C. Spatio-temporal variability of polychaete colonization at volcanic CO2 vents indicates high tolerance to ocean acidification. Mar. Biol. 161, 2909–2919. https://doi.org/10.1007/s00227-014-2555-y (2014).
CAS  Article  Google Scholar 

5.
Ricevuto, E., Vizzini, S. & Gambi, M. C. Ocean acidification effects on stable isotope signatures and trophic interactions of polychaete consumers and organic matter sources at a CO2 shallow vent system. J. Exp. Mar. Biol. Ecol. 468, 105–117. https://doi.org/10.1016/j.jembe.2015.03.016 (2015).
CAS  Article  Google Scholar 

6.
Foo, S.A., Byrne, M., Ricevuto, E., Gambi, M.C. The Carbon Dioxide Vents of Ischia, Italy, A Natural System to Assess Impacts of Ocean Acidification on Marine Ecosystems: An Overview of Research and Comparisons with Other Vent Systems. In Oceanography and Marine Biology An Annual Review. S. J. Hawkins, A. J. Evans, A.C. Dale, L. B. Firth, I. P. Smith eds. Taylor & Francis Group, 56 (2018).

7.
Mutalipassi, M. et al. Ocean acidification alters the responses of invertebrates to wound-activated infochemicals produced by epiphytes of the seagrassPosidonia oceanica. J. Exp. Mar. Biol. Ecol. 530–531, 151435 (2020).
Article  Google Scholar 

8.
Apostolaki, E. T., Vizzini, S., Hendriks, I. E. & Olsen, Y. S. Seagrass ecosystem response to long-term high CO2 in a Mediterranean volcanic vent. Mar. Environ. Res. 99, 9–15 (2014).
CAS  Article  Google Scholar 

9.
Vizzini, S., Apostolaki, E. T., Ricevuto, E., Polymenakou, P. & Mazzola, A. Plant and sediment properties in seagrass meadows from two Mediterranean CO2 vents: Implications for carbon storage capacity of acidified oceans. Mar. Environ. Res. 146, 101–108 (2019).
CAS  Article  Google Scholar 

10.
Beer, S., Björk, M., Beardall, J. Acquisition of carbon in marine plants. In: John Wiley & Sons eds. Photoshynthesis in the Marine Environment. Wiley Blackwell, Iowa, USA. pp: 95–124 (2014).

11.
Beer, S., Björk, M., Hellblom, F. & Axelsson, L. Inorganic carbon utilization in marine angiosperms (seagrasses). Funct. Plant Biol. 29, 349–354 (2002).
CAS  Article  Google Scholar 

12.
Koch, M., Bowes, G., Ross, C. & Zhang, X. H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132. https://doi.org/10.1111/j.1365-2486.2012.02791.x (2013).
ADS  Article  Google Scholar 

13.
Zimmerman, R. C., Kohrs, D. G., Steller, D. L. & Alberte, R. S. Impacts of CO2 enrichment on productivity and light requirements of eelgrass. Plant Physiol. 115, 599–607. https://doi.org/10.1104/pp.115.2.599 (1997).
CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Garrard, S. L. & Beaumont, N. J. The effect of ocean acidification on carbon storage and sequestration in seagrass beds; a global and UK context. Mar. Pollut. Bull. 86, 138–146 (2014).
CAS  Article  Google Scholar 

15.
Hendriks, I. E., Duarte, C. M. & Alvarez, M. A. Vulnerability of marine biodiversity to ocean acidification: a meta-analysis. Estuar. Coast. Shelf Sci. 86, 157–164 (2010).
ADS  CAS  Article  Google Scholar 

16.
Zimmerman, R. C., Hill, V. J. & Gallegos, C. L. Predicting effects of ocean warming, acidification, and water quality on Chesapeake region eelgrass. Limnol. Oceanogr. 60(2015), 1781–1804 (2015).
ADS  CAS  Article  Google Scholar 

17.
Pacella, S. R., Cheryl, A. B., George, G. W., Rochelle, G. L. & Burke, H. Seagrass habitat metabolism increases short-term extremes and long-term offset of CO2 under future ocean acidification. PNAS 115(15), 3870–3875 (2018).
ADS  CAS  Article  Google Scholar 

18.
Russell, B. D., Connell, S. D., Uthicke, S. & Hall-Spencer, J. M. Future seagrass beds: can increased productivity lead to increased carbon storage?. Mar. Pollut. Bull. 73, 463–469 (2013).
CAS  Article  Google Scholar 

19.
de los Santos, C. B., Godbold, J. A. & Solan, M. Short-term growth and biomechanical responses of the temperate seagrassCymodocea nodosato CO2 enrichment. Mar. Ecol. Prog. Ser. 572, 91–102 (2017).
ADS  CAS  Article  Google Scholar 

20.
Schneider, G. et al. Structural and physiological responses of Halodule wrightii to ocean acidification. Protoplasma 255, 629–641 (2018).
CAS  Article  Google Scholar 

21.
Radoglou, K. M. & Jarvis, P. G. The effects of CO2 enrichment and nutrient supply on growth morphology and anatomy of Phaseolus vulgaris L seedlings. Ann. Bot. 70, 245–256 (1992).
CAS  Article  Google Scholar 

22.
Epron, D., Liozon, R. & Mousseau, M. Effects of elevated CO2 concentration on leaf characteristics and photosynthetic capacity of beech (Fagus sylvatica) during the growing season. Tree Physiol. 16, 425–432 (1995).
Article  Google Scholar 

23.
Lin, J., Jach, M. E. & Ceulemans, R. Stomatal density and needle anatomy of Scots pine (Pinus sylvestris) are affected by elevated CO2. New Phytol. 150, 665–674 (2001).
Article  Google Scholar 

24.
Ruocco, M. et al. Genome-wide transcriptional reprogramming in the seagrassCymodocea nodosa under experimental ocean acidification. MolEcol 26, 4241–4259. https://doi.org/10.1111/mec.14204 (2017).
CAS  Article  Google Scholar 

25.
Olivé, I. et al. Linking gene expression to productivity to unravel long- and short-term responses of seagrasses exposed to CO2 in volcanic vents. Sci. Rep. 7, 42278 (2017).
ADS  Article  Google Scholar 

26.
Procaccini, G. et al. Depth-specific fluctuations of gene expression and protein abundance modulate the photophysiology in the seagrassPosidonia oceanica. Sci. Rep. 7, 42890. https://doi.org/10.1038/srep42890 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Kumar, M. et al. Proteome analysis reveals extensive light stress response reprogramming in the seagrassZostera muelleri (Alismatales, Zosteraceae) metabolism. Frontiers Plant Sci. 7, 2023 (2017).
Article  Google Scholar 

28.
Piro, A. et al. The modulation of leaf metabolism plays a role in salt tolerance of Cymodocea nodosa exposed to hypersaline stress in mesocosms. Front Plant Sci. 6, 464 (2015).
Article  Google Scholar 

29.
Dattolo, E. et al. Acclimation to different depths by the marine angiosperm Posidonia oceanica: transcriptomic and proteomic profiles. Front. Plant Sci. 4, 195. https://doi.org/10.3389/fpls.2013.00195 (2013).
Article  PubMed  PubMed Central  Google Scholar 

30.
Mazzuca, S. et al. Seagrass light acclimation: 2-DE protein analysis in Posidonia leaves grown inchronic low light conditions. J. Exp. Mar. Biol. Ecol. 374, 113–122 (2009).
CAS  Article  Google Scholar 

31.
Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
ADS  Article  Google Scholar 

32.
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
CAS  Article  Google Scholar 

33.
Watanabe, C. K. et al. Effects of elevated CO2 on levels of primary metabolites and transcripts of genes encoding respiratory enzymes and their diurnal patterns in Arabidopsis thaliana: possible relationships with respiratory rates. Plant Cell Physiol. 55(2), 341–357. https://doi.org/10.1093/pcp/pct185 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Lauritano, C. et al. Response of key stress-related genes of the seagrassPosidonia oceanica in the vicinity of submarine volcanic vents. Biogeosciences 12, 4947–4971 (2015).
Article  Google Scholar 

35
Neha, S., Gokhale, S. P. & Kumar, B. A. Effect of elevated [CO2] on cell structure and function in seed plants. Clim. Change Environ. Sustain. 2, 69–104. https://doi.org/10.5958/2320-642X.2014.00001.5 (2014).
Article  Google Scholar 

36.
Iuchi, S. et al. Regulation of drought tolerance by gene manipulation of 9-cis-epoxycarotenoid dioxygenase, a key enzyme in abscisic acid biosynthesis in Arabidopsis. Plant J. 27, 325–333. https://doi.org/10.1046/j.1365-313x.2001.01096.x (2001).
CAS  Article  PubMed  Google Scholar 

37.
Endo, A. et al. Drought induction of Arabidopsis 9-cis-epoxycarotenoid dioxygenase occurs in vascular parenchyma cells. Plant Physiol. 147, 1984–1993 (2008).
CAS  Article  Google Scholar 

38
Toh, S. et al. High temperature-induced abscisic acid biosynthesis and its role in the inhibition of gibberellins action in Arabidopsis seeds. Plant Physiol. 146, 1368–1385 (2008).
CAS  Article  Google Scholar 

39.
Dong, C. H. et al. ADF proteins are involved in the control of flowering and regulate F-actin organization, cell expansion, and organ growth in Arabidopsis. Plant Cell 13, 1333–1346 (2001).
CAS  Article  Google Scholar 

40.
Vantard, M. & Blanchoin, L. Actin polymerization processes in plant cells. Curr. Opin. Plant Biol. 5(6), 502–506 (2002).
CAS  Article  Google Scholar 

41.
Smertenko, A. P. et al. Ser6 in the maize actin-depolymerizing factor, ZmADF3, is phosphorylated by a calcium-stimulated protein kinase and is essential for the control of functional activity. Plant J. 14(2), 187–193 (1988).
Article  Google Scholar 

42.
Webster, J. & Stone, B. A. Isolation, structure and monosaccharide composition of the wall of vegetative parts of Heterozostera tasmanica (Martens ex Aschers) den Hartog. Aquat. Bot. 47, 39–52 (1994).
CAS  Article  Google Scholar 

43.
Olsen J.L., Rouzé, P., Verhelst, B., Lin, Y.-C., Bayer, T., Collen, J., Dattolo, E., De Paoli, E., Dittami, S., Maumus, F., et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nature 530, 331–335 (2016) https://doi.org/10.1038/nature16548.
ADS  CAS  Article  PubMed  Google Scholar 

44.
Brummel, D. A. Cell wall acidification and its role in Auxin-stimulated growth. J. Exp. Bot. 37(2), 270–276 (1986).
Article  Google Scholar 

45.
Laemmli, U. K. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680–685 (1970).
ADS  CAS  Article  Google Scholar 

46.
Shevchenko, A., Tomas, H., Havlis, J., Olsen, J. V. & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1, 2856–2860 (2007).
Article  Google Scholar 

47.
Lucini, L. & Bernardo, L. Comparison of proteome response to saline and zinc stress in lettuce. Front. Plant Sci. https://doi.org/10.3389/fpls.2015.00240 (2015).
Article  PubMed  PubMed Central  Google Scholar  More

1.
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).
CAS  Article  Google Scholar 
2.
Austin, K. G., Schwantes, A., Gu, Y. & Kasibhatla, P. S. What causes deforestation in Indonesia? Environ. Res. Lett. 14, 024007 (2019).
Article  Google Scholar 

3.
Tsujino, R., Yumoto, T., Kitamura, S., Djamaluddin, I. & Darnaedi, D. History of forest loss and degradation in Indonesia. Land use policy 57, 335–347 (2016).
Article  Google Scholar 

4.
Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).
Article  Google Scholar 

5.
Miettinen, J., Hooijer, A., Wang, J., Shi, C. & Liew, S. C. Peatland degradation and conversion sequences and interrelations in Sumatra. Reg. Environ. Change 12, 729–737 (2012).
Article  Google Scholar 

6.
Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F. & Hansen, M. C. Primary forest cover loss in Indonesia over 2000–2012. Nat. Clim. Change 4, 730 (2014).
Article  Google Scholar 

7.
Stibig, H. J., Achard, F., Carboni, S., Rasi, R. & Miettinen, J. Change in tropical forest cover of Southeast Asia from 1990 to 2010. Biogeosciences 11, 247–258 (2014).
Article  Google Scholar 

8.
Page, S. E. et al. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420, 61–65 (2002).
CAS  Article  Google Scholar 

9.
Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 26886 (2016).
CAS  Article  Google Scholar 

10.
Koplitz, S. N. et al. Public health impacts of the severe haze in Equatorial Asia in September–October 2015: demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure. Environ. Res. Lett. 11, 094023 (2016).
Article  Google Scholar 

11.
Crippa, P. et al. Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia. Sci. Rep. 6, 37074 (2016).
CAS  Article  Google Scholar 

12.
Wijaya, A. R. et al. How can Indonesia achieve its climate change mitigation goal? An analysis of potential emissions reductions from energy and land-use policies. World Resour. Inst. (Washington D.C, 2017).

13.
Cochrane, M. A. Fire science for rainforests. Nature 421, 913 (2003).
CAS  Article  Google Scholar 

14.
Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Philos. Trans. Royal Soc. B 371, 20150176 (2016).
Article  CAS  Google Scholar 

15.
Miettinen, J., Shi, C. & Liew, S. C. Fire distribution in Peninsular Malaysia, Sumatra and Borneo in 2015 with special emphasis on peatland fires. Environ. Manag. 60, 747–757 (2017).
Article  Google Scholar 

16.
Goldammer, J. G. History of equatorial vegetation fires and fire research in Southeast Asia before the 1997–98 episode: a reconstruction of creeping environmental changes. Mitig. Adapt. Strat. Glob. Chang. 12, 13–32 (2007).
Article  Google Scholar 

17.
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).
CAS  Article  Google Scholar 

18.
Baker, J. & Spracklen, D. Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front. For. Glob. Chang. 2, 47 (2019).
Article  Google Scholar 

19.
Uhl, C., Kauffman, J. B. and Cummings, D. L. Fire in the Venezuelan Amazon 2: environmental conditions necessary for forest fires in the evergreen rainforest of Venezuela. Oikos 53, 176–184 (1988).

20.
Dommain, R., Couwenberg, J., Glaser, P. H., Joosten, H. & Suryadiputra, I. N. N. Carbon storage and release in Indonesian peatlands since the last deglaciation. Quat. Sci. Rev. 97, 1–32 (2014).
Article  Google Scholar 

21.
Cole, L. E. S., Bhagwat, S. A. & Willis, K. J. Fire in the swamp forest: palaeoecological insights into natural and human-induced burning in intact tropical peatlands. Front. For. Glob. Chang. 2, 48 (2019).
Article  Google Scholar 

22.
Warren, M., Hergoualc’h, K., Kauffman, J. B., Murdiyarso, D. & Kolka, R. An appraisal of Indonesia’s immense peat carbon stock using national peatland maps: uncertainties and potential losses from conversion. Carbon balanc. management 12, 12 (2017).
Article  CAS  Google Scholar 

23.
Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. chang. biol. 17, 798–818 (2011).
Article  Google Scholar 

24.
Page, S. E. et al. A record of late pleistocene and holocene carbon accumulation and climate change from an equatorial peat bog (Kalimantan, Indonesia): implications for past, present and future carbon dynamics. J. Quat. Sci. 19, 625–635 (2004).
Article  Google Scholar 

25.
Schultz, N. M., Lawrence, P. J. & Lee, X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. 122, 903–917 (2017).
Article  Google Scholar 

26.
Sabajo, C. R. et al. Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia. Biogeosciences 14, 4619–4635 (2017).
CAS  Article  Google Scholar 

27.
McAlpine, C. A. et al. Forest loss and Borneo’s climate. Environ. Res. Lett. 13, 044009 (2018).
Article  Google Scholar 

28.
Hardwick, S. R. et al. The relationship between leaf area index and microclimate in tropical forest and oil palm plantation: forest disturbance drives changes in microclimate. Agric. For. Meteorol. 201, 187–195 (2015).
Article  Google Scholar 

29.
Jauhiainen, J., Kerojoki, O., Silvennoinen, H., Limin, S. & Vasander, H. Heterotrophic respiration in drained tropical peat is greatly affected by temperature—a passive ecosystem cooling experiment. Environ. Res. Lett. 9, 105013 (2014).
Article  CAS  Google Scholar 

30.
Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).
Article  Google Scholar 

31.
Hoscilo, A., Page, S. E., Tansey, K. J. & Rieley, J. O. Effect of repeated fires on land-cover change on peatland in southern Central Kalimantan, Indonesia, from 1973 to 2005. Int. J. Wildland Fire 20, 578–588 (2011).
Article  Google Scholar 

32.
Laurance, W. F. Do edge effects occur over large spatial scales? Trends Ecol. Evol. 15, 134–135 (2000).
CAS  Article  Google Scholar 

33.
Cochrane, M. A. & Laurance, W. F. Fire as a large-scale edge effect in Amazonian forests. J. Tropi. Ecol. 18, 311–325 (2002).
Article  Google Scholar 

34.
Laurance, W. F., Laurance, S. G. & Delamonica, P. Tropical forest fragmentation and greenhouse gas emissions. For. Ecol. Manag. 110, 173–180 (1998).
Article  Google Scholar 

35.
Curran, L. M. et al. Impact of El Nino and logging on canopy tree recruitment in Borneo. Science 286, 2184–2188 (1999).
CAS  Article  Google Scholar 

36.
Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).
CAS  Article  Google Scholar 

37.
Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 1–6 (2017).
Article  CAS  Google Scholar 

38.
Briant, G., Gond, V. & Laurance, S. G. Habitat fragmentation and the desiccation of forest canopies: a case study from eastern Amazonia. Biol. conserv. 143, 2763–2769 (2010).
Article  Google Scholar 

39.
Didham, R. K. & Lawton, J. H. Edge structure determines the magnitude of changes in microclimate and vegetation structure in tropical forest fragments. Biotropica 31, 17–30 (1999).
Google Scholar 

40.
Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053 (2012).
CAS  Article  Google Scholar 

41.
Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra, Indonesia. Geoderma 338, 410–421 (2019).
Article  Google Scholar 

42.
Cattau, M. E. et al. Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Glob. Environ. Chang. 39, 205–219 (2016).
Article  Google Scholar 

43.
Wooster, M. J., Perry, G. L. W. and Zoumas, A. Fire, drought and El Niño relationships on Borneo (Southeast Asia) in the pre-MODIS era (1980–2000). Biogeosciences 9, (2012)

44.
Spessa, A. C. et al. Seasonal forecasting of fire over Kalimantan, Indonesia. Nat. Hazards Earth Syst. Sci. 15, 429–442 (2015).
Article  Google Scholar 

45.
Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl Acad. Sci. 113, 9204–9209 (2016).
CAS  Article  Google Scholar 

46.
Langner, A. & Siegert, F. Spatiotemporal fire occurrence in Borneo over a period of 10 years. Glob. Chang. Biol. 15, 48–62 (2009).
Article  Google Scholar 

47.
Pan, X., Chin, M., Ichoku, C. M. & Field, R. D. Connecting Indonesian fires and drought with the type of El Niño and phase of the Indian Ocean Dipole during 1979–2016. J. Geophys. Res. 123, 7974–7988 (2018).
Google Scholar 

48.
Konecny, K. et al. Variable carbon losses from recurrent fires in drained tropical peatlands. Glob. Chang. Biol. 22, 1469–1480 (2016).
Article  Google Scholar 

49.
Miettinen, J., Hooijer, A., Vernimmen, R., Liew, S. C. & Page, S. E. From carbon sink to carbon source: extensive peat oxidation in insular Southeast Asia since 1990. Environ. Res. Lett. 12, 024014 (2017).
Article  CAS  Google Scholar 

50.
Langner, A., Miettinen, J. & Siegert, F. Land cover change 2002–2005 in Borneo and the role of fire derived from MODIS imagery. Glob. Chang. Biol. 13, 2329–2340 (2007).
Article  Google Scholar 

51.
van der Werf, G. R. et al. Climate regulation of fire emissions and deforestation in equatorial Asia. Proc. Natl Acad. Sci. 105, 20350–20355 (2008).
Article  Google Scholar 

52.
Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640 (2016).
Article  Google Scholar 

53.
Wahyunto, R. S. & Suparto, S. H. Maps of area of peatland distribution and carbon content in Kalimantan, 2000–2002. Wetl. Int.-Indones. Program. Wildl. Habitat Can. (WHC) Bogor. (2004).

54.
Purnomo A. Protecting Indonesia’s Forests, Pros-Cons Policy of Moratorium on Forests and Peatlands (Kepustakaan Populer Gramedia, Jakarta, Indonesia, 2012).

55.
Normile, D. Indonesia’s fires are bad, but new measures prevented them from becoming worse. Sci. Mag. https://www.sciencemag.org/news/2019/10/indonesias-fires-are-bad-new-measures-prevented-them-becoming-worse (2019).

56.
Purnomo, H. et al. Fire economy and actor network of forest and land fires in Indonesia. For. Policy Econ. 78, 21–31 (2017).
Article  Google Scholar 

57.
Seymour, F. Indonesia Reduces Deforestation, Norway to Pay Up. World Resources Institute. https://www.wri.org/blog/2019/02/indonesia-reduces-deforestation-norway-pay (2019).

58.
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, 1500052 (2015).
Article  Google Scholar 

59.
Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
Article  Google Scholar 

60.
Gaveau, D. L. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: evidence from the 2013 Sumatran fires. Sci. Rep. 4, 6112 (2014).
CAS  Article  Google Scholar 

61.
Wooster, M. et al. New tropical peatland gas and particulate emissions factors indicate 2015 Indonesian fires released far more particulate matter (but less methane) than current inventories imply. Remote Sens. 10, 495 (2018).
Article  Google Scholar 

62.
Taufik, M. et al. Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nat. Clim. Chang. 7, 428–431 (2017).
Article  Google Scholar 

63.
Margono, B. A. et al. Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010. Environ. Res. Lett. 7, 034010 (2012).
Article  Google Scholar 

64.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
CAS  Article  Google Scholar 

65.
Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).
Article  Google Scholar 

66.
Lohberger, S., Stängel, M., Atwood, E. C. & Siegert, F. Spatial evaluation of Indonesia’s 2015 fire‐affected area and estimated carbon emissions using Sentinel‐1. Glob. Chang. Biol. 24, 644–654 (2015).
Article  Google Scholar  More

Study area
This project was conducted in the downstream segment of the Karnali River basin of Nepal (Fig. 1), which is the largest of Nepal’s three major river systems and is characterized by the steep terrain of the Himalayan Mountains. The highest runoff occurs during the monsoon season (e.g., June–October), and the lowest occurs during the winter season (e.g., December–May). Below the Siwalik Mountain range (a physiographic zone, Fig. 1), a vast network of small tributaries combines to form a single narrow channel of the Karnali River with well-defined banks. Originating from the Tibetan Plateau, the Karnali River is the largest tributary to the Ganges River in India, which harbours the most significant density of GRDs in the world. The lower Karnali River basin provides the furthest upstream range for GRDs, critically endangered gharials (Gavialis gangeticus), smooth Indian otters (Lutrogale perspicillata), and 36 native fish species49. The GRD population size in the Karnali River has declined from 26 to six individuals50. Such a sharp decline in the GRD population is due to the effects of habitat degradation, mainly from water-based development projects (i.e., water diversion,51). Concurrently, several upstream development projects are proposed, under construction, or completed [e.g., planned: the Karnali Chisapani multipurpose dam, 10,800 megawatt (MW); under construction: the upper Karnali hydropower project, 900 MW, and Bhari Babai diversion project; completed: Rani Jamara Kulariya irrigation intakes] and further threaten downstream aquatic life. All projects adopt traditional preconstruction environmental impact assessments procedure to define flow proportions (generally 10–20% of natural regimes) anecdotally and unscientifically. Thus, traditional flow proportions might be inadequate to sustain native aquatic biodiversity. Our study focused on the lower catchment area of the Karnali River basin, which is downstream from all megaprojects. All measurement protocols, including dolphin observation methods, were carried out in accordance with the Department of National Parks and Wildlife Conservation, Government of Nepal, guidelines and regulations. Habitat measurement protocols, including dolphin observation methods, were approved by the Department of National Parks and Wildlife Conservation, Government of Nepal (No 1129; 12 December 2016).
Available habitat assessment
Reduced water levels during the low-water season (e.g., December–May) escalate threats to aquatic biota by limiting physical habitat availability. Here, habitat refers to the hydro-physical habitat, which is defined by the flow and depth interactions at a particular geomorphic condition over space and time. Therefore, habitat availability (i.e. the area accessible to species) is assumed to be the greatest bottleneck, critically limiting species reproduction and survival42,51. We measured the available habitats in the low-water season when suitable habitat is critically limited (i.e., December–May in 2018/2019), excluding the monsoon season (June–November). Further, to capture dynamic flow variation within the dry (i.e., low) water season, we selected three temporal periods—March (mid dry season), May (late dry season), and December (early dry season)—based on 39 years of flow records available from the Department of Hydrology, Government of Nepal. Assessing available habitat includes habitat mapping and bank and instream surveys. We divided the study area into three segments [upper segment (S1): length = 11 km, average width = 218 m; middle segment (S2): length = 29 km, average width = 121 m; and lower segment (S3): length = 10 km, average width = 198 m; Fig. 1] based on uniform flow and channel geomorphology mapped along the selected stretch of the river. The three segments vary hydrologically and structurally. S1 consists of river channels with natural flows without any infrastructural diversion. Because of water diversion operations (e.g., Rani Jamuna irrigation intake and several traditional agricultural irrigation channels) and distributaries, the natural flow volume in S2 was low compared to that in S1. S3 benefited slightly from distributaries and received more water than S2.
Within each segment, the study reach (the linear segment where cross-sections are established) was established in such a way that the length of each reach was at least higher than the mean width (so the number varies among segments) of the respective segment. We also tried to maintain relatively similar flow at the top and bottom of the reach. Within each reach, random cross-sections were established to capture the hydraulic properties based on flow variation. As the flow variability of the stream increased, the number of cross-sections increased, and each section was kept at least 300 m apart from the other sections. Therefore, the number of cross-sections was based on the flow variation within a reach instead of the length of the reach. Bank and instream surveys started in an upstream direction, wherein directional readings of the cross-sections were noted. For the bank and instream measurements, pin heights were established at either side of the cross-section using GPS and a permanent reference marker for repeated flow measurements. Water surface elevations were estimated using a total station (an optical instrument for land surveying; Leica 772737 Builder 503) across the pin heights for each cross-section required for hydrological simulation. A new benchmark was established for each effort to measure the water surface elevation at each cross-section. The total number of cross-sections examined for the available habitats was 177 (March = 60, May = 47, and December = 60). The hydraulic parameters (see habitat characterization section below) at each cross-section were measured using a RiverSurveyor S5 acoustic water current profile reader [Sontek, Acoustic Doppler Profiler (ADP S5)], which records hydro-parameters continuously at a cell size between 0.02 to 0.5 m offering complete underwater available hydro-physical profile.
Occupied habitat assessment
We conducted a GRD population survey to capture occupied (selectivity) habitat characteristics (n = 97) at three temporal scales (previously mentioned) using the developed approach24. Within each temporal scale, we conducted three replications to capture the temporal and spatial variability in the characteristics of the occupied habitat. When we first detected dolphins, we observed surfacing behaviours for at least five minutes before establishing a cross-section. The habitats that were used for at least five minutes were considered occupied habitats, and then cross-sections were established to measure habitat characteristics using the ADP. If the dolphins disappeared after the location of the first sighting in less than five minutes, we excluded those habitats from our analysis. The Dolphin observation (only observation done) protocols were approved and permitted by the Department of National Parks and Wildlife Conservation, Government of Nepal.
Data analysis
Data preparation and software
The ADP S5 hydraulic data were imported into Excel databases (Microsoft v. 2010) to format for System for Environmental Flow Analysis (SEFA, version 1.5; Aquatic Habitat Analysts Inc.) software. All the hydraulic properties [depth (m), velocity (m/s), wetted perimeter-WP (m), width (m), cross-sectional area-CSA (m2), Froude number, and discharge (m3/s)], suitability, and flow regime determination were calculated using SEFA software and analysed at the cross-section and segment levels. The average flow of each segment was used as a base flow while running the habitat simulation model for the respective segment. We found critical flows ( 417 m3/s, excess flow with a negative contribution to habitat suitability) in May. Therefore, the habitat retention hydraulic simulation model was performed only with excess flow (for May) using 39 years of 90% exceedance flow (the flow that is equaled or exceeded 90% of the time).
Habitat characterization
The cross-sectional hydro-physical parameters—width, flow, depth, velocity, wetted perimeter, cross-sectional area, and habitat (types)—were reported spatially and temporally. The habitat type (e.g., pool, run, and riffle) was classified based on the Froude number (Fr), where Froude is an index of hydraulic turbulence (the ratio of velocity by the acceleration of gravity). Points with Froude numbers exceeding 0.41 were considered riffles, points with Froude numbers less than 0.18 were considered pools, and intermediate values were classified as run habitats. The proportion of run, riffle, and pool habitats within each study reach was calculated from the Froude numbers. The GRD’s seasonal hydro-physical habitats were characterized using basic descriptive statistics (mean and 95% CI). The variation in these hydraulic parameters among seasons, habitat types, and segments was examined by an analysis of variance (ANOVA), and post hoc pairwise comparisons were performed using Tukey’s honestly significant difference (HSD) test. A two-way ANOVA test was used to investigate any interactive effects of season and habitat on hydraulic variations. The level of significance was set at p  one and zero to those categories for which w ≤ one. By assigning one and zero to each group, we developed an HSC to calculate the area weighted suitability (AWS) at each measured point. Hydraulic habitat suitability is expressed as AWS in terms of usable area in metres of width or square metres per metre of reach (m2/m).
To obtain the AWS value for the reach, we multiplied the combined suitability index (CSI, which is the product of the suitability of depth and velocity at a point) and the proportion of the reach area represented by that point. Using a 39-year average base flow of 536.11 m3/s (90% exceedance flow) in May, we predicted the fluctuation (decrease by 10%) in the currently available maximum AWS (i.e., 22.718 m2/m, AWS of May) in the range of flows from 200–900 m3/s. We simulated the AWS in this particular range because this range represents the 39-year low and maximum values of the 90% exceedance flow for the low-water season (November–May). Covering this variation over a broader scale increases the applicability of our ecological thresholds across time. Using the same base flow and range, we also estimated the minimum flows that retain various standards (%) of habitat protection. Further, we also determined the minimum flow that provides the maximum AWS for the low-water season.
Ecological thresholds using flow-ecology relationships
As water depth and velocity are the result of instream habitat features, such as pools, riffles, and runs, we only incorporated depth and velocity when estimating the hydraulic habitat suitability. Additionally, GRD habitat selection is strongly guided by the depth and velocity of a river section24,51. Generalized linear models (GLMs) using logit functions were used to examine the relationship between GRD presence and hydraulic properties (depth and velocity). Four different GLMs (depth, velocity, depth*velocity, and depth + velocity) were developed, and the Akaike information criterion (AIC) was used to select the best models. The additive effect of depth and velocity on the GRD presence was found in the model with the best performance; therefore, we further used a generalized additive model (GAM) to capture the possible non-linear influence of depth and velocity on GRD presence. Because of the possibility of both linear and non-linear relationships11, we again used a GAM to capture the functional relationships between ecology (AWS) and flow. The degree of smoothness for all the GAMs identified by the iterative approach (up to 25 smoothing factors were checked) and the selected smoothing parameter (i.e., 20 for all the GAM models) that yielded a significant covariate (at the 0.005 level of significance) explained the maximum deviance and adjusted R2. Both the GLM and GAM models were fitted using the lm and mgcv packages in R Studio. More

1.
Belden, L. K. et al. Panamanian frog species host unique skin bacterial communities. Front. Microbiol. 6, 1171 (2015).
PubMed  PubMed Central  Article  Google Scholar 
2.
Jani, A. J. & Briggs, C. J. Host and aquatic environment shape the amphibian skin microbiome but effects on downstream resistance to the pathogen Batrachochytrium dendrobatidis are variable. Front. Microbiol. 9, 487 (2018).
PubMed  PubMed Central  Article  Google Scholar 

3.
McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. USA 110, 3229–3236 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Woodhams, D. C., Bletz, M., Kueneman, J. & McKenzie, V. Managing amphibian disease with skin microbiota. Trends Microbiol. 24, 161–164 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Flechas, S. V. et al. Current and predicted distribution of the pathogenic fungus Batrachochytrium dendrobatidis in Colombia, a hotspot of amphibian biodiversity. Biotropica 49, 685–694 (2017).
Article  Google Scholar 

6.
Rollins-Smith, L. A. & Conlon, J. M. Antimicrobial peptide defenses against chytridiomycosis, an emerging infectious disease of amphibian populations. Dev. Comp. Immunol. 29, 589–598 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Jiménez, R. R. & Sommer, S. The amphibian microbiome: natural range of variation, pathogenic dysbiosis, and role in conservation. Biodiv. Conserv. 26, 763–786 (2017).
Article  Google Scholar 

8.
Bletz, M. C. et al. Host ecology rather than host phylogeny drives amphibian skin microbial community structure in the biodiversity hotspot of Madagascar. Front. Microbiol. 8, 1530 (2017).
PubMed  PubMed Central  Article  Google Scholar 

9.
Romano-Bertrand, S., Licznar-Fajardo, P., Parer, S. & Jumas-Bilak, E. Impact de l’environnement sur les microbiotes: focus sur l’hospitalisation et les microbiotes cutanés et chirurgicaux. Revue Francophone des Laboratoires 469, 75–82 (2015).
Article  Google Scholar 

10.
Woodhams, D. C. et al. Host-associated microbiomes are predicted by immune system complexity and climate. Genome Biol. 21, 23 (2020).
PubMed  PubMed Central  Article  Google Scholar 

11.
Cheng, Y. et al. The Tasmanian devil microbiome—implications for conservation and management. Microbiome 3, 1–11 (2015).
Article  Google Scholar 

12.
Lemieux-Labonté, V., Tromas, N., Shapiro, B. J. & Lapointe, F. J. Environment and host species shape the skin microbiome of captive neotropical bats. PeerJ 4, e2430 (2016).

13.
Grice, E. A. & Segre, J. A. The skin microbiome. Nat. Rev. Microbiol. 9, 244–253 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Daskin, J. H., Bell, S. C., Schwarzkopf, L. & Alford, R. A. Cool temperatures reduce antifungal activity of symbiotic bacteria of threatened amphibians–implications for disease management and patterns of decline. PLoS ONE 9, e100378 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

15.
Duellman, W. E. & Trueb, L. Integumentary, Sensory, and Visceral Systems. Biology of Amphibians (McGraw-Hill, New York, 1986).
Google Scholar 

16.
Bataille, A. et al. Susceptibility of amphibians to chytridiomycosis is associated with MHC class II conformation. Proc. R. Soc. B Biol. Sci. 282, 20143127 (2015).
Article  Google Scholar 

17.
Longo, A. V., Savage, A. E., Hewson, I. & Zamudio, K. R. Seasonal and ontogenetic variation of skin microbial communities and relationships to natural disease dynamics in declining amphibians. R. Soc. Open Sci. 2, 140377 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Kueneman, J. G. et al. The amphibian skin-associated microbiome across species, space and life history stages. Mol. Ecol. 23, 1238–1250 (2014).
PubMed  Article  Google Scholar 

19.
Kueneman, J. G. et al. Community richness of amphibian skin bacteria correlates with bioclimate at the global scale. Nat. Ecol. Evol. 3, 381–389 (2019).
PubMed  Article  Google Scholar 

20.
Rollins-Smith, L. A., Ramsey, J. P., Pask, J. D., Reinert, L. K. & Woodhams, D. C. Amphibian immune defenses against chytridiomycosis: impacts of changing environments. Integr. Comp. Biol. 51, 552–562 (2011).

21.
Sanchez, E. et al. Cutaneous bacterial communities of a poisonous salamander: a perspective from life stages, body parts and environmental conditions. Microb. Ecol. 73, 455–465 (2017).
CAS  PubMed  Article  Google Scholar 

22.
Antwis, R. E. et al. Ex situ diet influences the bacterial community associated with the skin of red-eyed tree frogs (Agalychnis callidryas). PLoS ONE 9, e85563 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Jani, A. J. & Briggs, C. J. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc. Natl. Acad. Sci. USA 111, E5049–E5058 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

24.
Medina, D. et al. Variation in metabolite profiles of amphibian skin bacterial communities across elevations in the Neotropics. Microb. Ecol. 74, 227–238 (2017).
CAS  PubMed  Article  Google Scholar 

25.
Loudon, A. H. et al. Vertebrate hosts as islands: dynamics of selection, immigration, loss, persistence, and potential function of bacteria on salamander skin. Front. Microbiol. 7, 333 (2016).
PubMed  PubMed Central  Article  Google Scholar 

26.
Bates, K. A. et al. Amphibian chytridiomycosis outbreak dynamics are linked with host skin bacterial community structure. Nat. Commun. 9, 1–11 (2018).
ADS  CAS  Article  Google Scholar 

27.
Woodhams, D. C. et al. Interacting symbionts and immunity in the amphibian skin mucosome predict disease risk and probiotic effectiveness. PLoS ONE 9, e96375 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Costa, S., Lopes, I., Proença, D. N., Ribeiro, R. & Morais, P. V. Diversity of cutaneous microbiome of Pelophylax perezi populations inhabiting different environments. Sci. Total Environ. 572, 995–1004 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

29.
Sabino-Pinto, J. et al. Composition of the cutaneous bacterial community in Japanese amphibians: effects of captivity, host species, and body region. Microb. Ecol. 72, 460–469 (2016).
PubMed  Article  Google Scholar 

30.
Kueneman, J. G. et al. Inhibitory bacteria reduce fungi on early life stages of endangered Colorado boreal toads (Anaxyrus boreas). ISME J. 10, 934–944 (2016).
PubMed  Article  PubMed Central  Google Scholar 

31.
Becker, C. G., Longo, A. V., Haddad, C. F. B. & Zamudio, K. R. Land cover and forest connectivity alter the interactions among host, pathogen and skin microbiome. Proc. R. Soc. B Biol. Sci. 284, 20170582 (2017).
Article  Google Scholar 

32.
Belasen, A. M., Bletz, M. C., Leite, D. D. S., Toledo, L. F. & James, T. Y. Long-term habitat fragmentation is associated with reduced MHC IIB diversity and increased infections in amphibian hosts. Front. Ecol. Evol. 6, 236 (2019).
Article  Google Scholar 

33.
Greenspan, S. E. et al. Arthropod–bacteria interactions influence assembly of aquatic host microbiome and pathogen defense. Proc. R. Soc. B 286, 20190924 (2019).
CAS  PubMed  Article  Google Scholar 

34.
Becker, C. G. et al. Low-load pathogen spillover predicts shifts in skin microbiome and survival of a terrestrial-breeding amphibian. Proc. Roy. Soc. B 286, 20191114 (2019).
Article  Google Scholar 

35.
Christian, K., Weitzman, C., Rose, A., Kaestli, M. & Gibb, K. Ecological patterns in the skin microbiota of frogs from tropical Australia. Ecol. Evol. 8, 10510–10519 (2018).
PubMed  PubMed Central  Article  Google Scholar 

36.
McKenzie, V. J., Bowers, R. M., Fierer, N., Knight, R. & Lauber, C. L. Co-habiting amphibian species harbor unique skin bacterial communities in wild populations. ISME J. 6, 588–596 (2012).
CAS  PubMed  Article  Google Scholar 

37.
Walke, J. B. et al. Amphibian skin may select for rare environmental microbes. ISME J. 8, 2207–2217 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Varela, B. J., Lesbarrères, D., Ibáñez, R. & Green, D. M. Environmental and host effects on skin bacterial community composition in Panamanian frogs. Front. Microbiol. 9, 298 (2018).
PubMed  PubMed Central  Article  Google Scholar 

39.
Loudon, A. H. et al. Microbial community dynamics and effect of environmental microbial reservoirs on red-backed salamanders (Plethodon cinereus). ISME J. 8, 830–840 (2014).
CAS  PubMed  Article  Google Scholar 

40.
Toledo, L. F. & Batista, R. F. Integrative study of Brazilian anurans: geographic distribution, size, environment, taxonomy, and conservation. Biotropica 44, 785–792 (2012).
Article  Google Scholar 

41.
Haddad, C. F. B. et al. Guide to the Amphibians of the Antic Forest: Diversity and Biology (Anolisbooks, São Paulo, 2013).
Google Scholar 

42.
Toledo, L. F., Becker, C. G., Haddad, C. F. & Zamudio, K. R. Rarity as an indicator of endangerment in Neotropical frogs. Biol. Conserv. 179, 54–62 (2014).
Article  Google Scholar 

43.
Sasso, T. et al. Environmental DNA characterization of amphibian communities in the Brazilian Atlantic forest: potential application for conservation of a rich and threatened fauna. Biol. Conserv. 215, 225–232 (2017).
Article  Google Scholar 

44.
Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J. & Hirota, M. M. The Brazilian Atlantic Forest: how much is left, and how is the remaining forest distributed? Implications for conservation. Biol. Conserv. 142, 1141–1153 (2009).
Article  Google Scholar 

45.
Ledru, M. P., Montade, V., Blanchard, G. & Hély, C. Long-term spatial changes in the distribution of the Brazilian Atlantic Forest. Biotropica 48, 159–169 (2016).
Article  Google Scholar 

46.
Joly, C. A., Metzger, J. P. & Tabarelli, M. Experiences from the Brazilian Atlantic F orest: ecological findings and conservation initiatives. New Phytol. 204, 459–473 (2014).
PubMed  Article  PubMed Central  Google Scholar 

47.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
ADS  CAS  Article  Google Scholar 

48.
Eterovick, P. C. et al. Amphibian declines in Brazil: an overview 1. Biotropica 37, 166–179 (2005).
Article  Google Scholar 

49.
Becker, C. G., Fonseca, C. R., Haddad, C. F. B., Batista, R. F. & Prado, P. I. Habitat split and the global decline of amphibians. Science 318, 1775–1777 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Both, C. et al. Widespread occurrence of the American bullfrog, Lithobates catesbeianus (Shaw, 1802) (Anura: Ranidae), Brazil. S. Am. J. Herpetol. 6, 127–134 (2011).
Article  Google Scholar 

51.
Carvalho, T., Becker, C. G. & Toledo, L. F. Historical amphibian declines and extinctions in Brazil linked to chytridiomycosis. Proc. R. Soc. B Biol. Sci. 284, 20162254 (2017).
Article  Google Scholar 

52.
Haddad, C. F., Toledo, L. F. & Prado, C. P. Anfíbios da Mata Atlântica: guia dos anfíbios anuros da Mata Atlântica. Editora Neotropica (2008).

53.
Carnaval, A. C. O. Q., Toledo, L. F., Haddad, C. F. B. & Britto, F. B. (2005). Chytrid fungus infects high-altitude stream-dwelling Hylodes magalhaesi (Leptodactylidae) in the Brazilian Atlantic rainforest. Froglog 70, 3–4 (2005).

54.
Carnaval, A. C. O., Puschendorf, R., Peixoto, O. L., Verdade, V. K. & Rodrigues, M. T. Amphibian chytrid fungus broadly distributed in the Brazilian Atlantic Rain Forest. EcoHealth 3, 41–48 (2006).
Article  Google Scholar 

55.
Toledo, L. F., Britto, F. B., Araújo, O. G., Giasson, L. M. & Haddad, C. F. The occurrence of Batrachochytrium dendrobatidis in Brazil and the inclusion of 17 new cases of infection. S. Am. J. Herpetol. 1, 185–191 (2006).
Article  Google Scholar 

56.
Toledo, L. F., Haddad, C. F. B., Carnaval, A. C. O. Q. & Britto, F. B. A Brazilian anuran (Hylodes magalhaesi: Leptodactylidae) infected by Batrachochytrium dendrobatidis: a conservation concern. Amphib. Reptile Conserv. 4, 17–21 (2006).
Google Scholar 

57.
de Oliveira Ramalho, A. C., De Paula, C. D., Catao-Dias, J. L., Vilarinho, B. & Brandao, R. A. First record of Batrachochytrium dendrobatidis in two endemic Cerrado hylids, Bokermannohyla pseudopseudis and Bokermannohyla sapiranga, with comments on chytridiomycosis spreading in Brazil. North West. J. Zool. 9, 145–150 (2013).
Google Scholar 

58.
Rodriguez, D., Becker, C. G., Pupin, N. C., Haddad, C. F. B. & Zamudio, K. R. Long-term endemism of two highly divergent lineages of the amphibian-killing fungus in the Atlantic F orest of B razil. Mol. Ecol. 23, 774–787 (2014).
CAS  PubMed  Article  Google Scholar 

59.
Preuss, J. F., Lambertini, C., da Silva Leite, D., Toledo, L. F. & Lucas, E. M. Batrachochytrium dendrobatidis in near threatened and endangered amphibians in the southern Brazilian Atlantic Forest. North West. J. Zool 11, 360–362 (2015).
Google Scholar 

60.
Preuss, J. F., Lambertini, C., Leite, D. D. S., Toledo, L. F. & Lucas, E. M. Crossing the threshold: an amphibian assemblage highly infected with Batrachochytrium dendrobatidis in the southern Brazilian Atlantic forest. Stud. Neotrop. Fauna E 51, 68–77 (2016).
Article  Google Scholar 

61.
Valencia-Aguilar, A., Toledo, L. F., Vital, M. V. & Mott, T. Seasonality, environmental factors, and host behavior linked to disease risk in stream-dwelling tadpoles. Herpetologica 72, 98–106 (2016).
Article  Google Scholar 

62.
Becker, C. G., Rodriguez, D., Lambertini, C., Toledo, L. F. & Haddad, C. F. Historical dynamics of Batrachochytrium dendrobatidis in Amazonia. Ecography 39, 954–960 (2016).
Article  Google Scholar 

63.
Skerratt, L. F. et al. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. EcoHealth 4, 125–134 (2007).
Article  Google Scholar 

64.
Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

65.
Bataille, A., Lee-Cruz, L., Tripathi, B. & Waldman, B. Skin bacterial community reorganization following metamorphosis of the fire-bellied toad (Bombina orientalis). Microb. Ecol. 75, 505–514 (2018).
CAS  PubMed  Article  Google Scholar 

66.
Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363, 1459–1463 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

67.
Scheele, B. C. et al. Response to Comment on “Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity”. Science 367, eaay2905 (2020).
CAS  PubMed  Article  Google Scholar 

68.
Woodhams, D. C. et al. Antifungal isolates database of amphibian skin-associated bacteria and function against emerging fungal pathogens: ecological archives E096–059. Ecology 96, 595 (2015).
Article  Google Scholar 

69.
Harris, R. N. et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 3, 818–824 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Muletz-Wolz, C. R. et al. Inhibition of fungal pathogens across genotypes and temperatures by amphibian skin bacteria. Front. Microbiol. 8, 1551 (2017).
PubMed  PubMed Central  Article  Google Scholar 

71.
Niederle, M. V. et al. Skin-associated lactic acid bacteria from North American bullfrogs as potential control agents of Batrachochytrium dendrobatidis. PLoS ONE 14, e0223020 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
Becker, M. H. et al. Composition of symbiotic bacteria predicts survival in Panamanian golden frogs infected with a lethal fungus. Proc. R. Soc. B Biol. Sci. 282, 20142881 (2015).
Article  CAS  Google Scholar 

73.
Rebollar, E. A. et al. Skin bacterial diversity of Panamanian frogs is associated with host susceptibility and presence of Batrachochytrium dendrobatidis. ISME J. 10, 1682–1695 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Longo, A. V. & Zamudio, K. R. Environmental fluctuations and host skin bacteria shift survival advantage between frogs and their fungal pathogen. ISME 11, 349–361 (2017).
Article  Google Scholar 

75.
Longo, A. V. & Zamudio, K. R. Temperature variation, bacterial diversity and fungal infection dynamics in the amphibian skin. Mol. Ecol. 26, 4787–4797 (2017).
PubMed  Article  PubMed Central  Google Scholar 

76.
Lambertini, C. et al. Biotic and abiotic determinants of Batrachochytrium dendrobatidis infections in amphibians of the Brazilian Atlantic Forest. Fung. Ecol. 49, 100995 (2021).
Article  Google Scholar 

77.
Becker, C. G. et al. Variation in phenotype and virulence among enzootic and panzootic amphibian chytrid lineages. Fung. Ecol. 26, 45–50 (2017).
Article  Google Scholar 

78.
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103, 626–631 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

79.
Lozupone, C. A. & Knight, R. Global patterns in bacterial diversity. Proc. Natl. Acad. Sci. USA 104, 11436–11440 (2007).
ADS  CAS  PubMed  Article  Google Scholar 

80.
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

81.
Boyle, A. H. D. et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis. Aquat. Organ. 73, 175–192 (2007).
PubMed  Article  Google Scholar 

82.
Boyle, D. G., Boyle, D. B., Olsen, V., Morgan, J. A. T. & Hyatt, A. D. Rapid quantitative detection of chytridiomycosis (Batrachochytrium dendrobatidis) in amphibian samples using real-time Taqman PCR assay. Dis. Aquat. Organ. 60, 141–148 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

83.
Lambertini, C., Rodriguez, D., Brito, F. B., Leite, D. S. & Toledo, L. F. Diagnóstico do fungo Quitrídio: Batrachochytrium dendrobatidis. Herpetol. Bras. 2, 12–17 (2013).
Google Scholar 

84.
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Envrion. Microbiol. 79, 5112–5120 (2013).
CAS  Article  Google Scholar 

85.
Bletz, M. C. et al. Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions. Nat. Commun. 7, 13699 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

86.
Kriger, K. M. & Hero, J. M. The chytrid fungus Batrachochytrium dendrobatidis is non-randomly distributed across amphibian breeding habitats. Divers. Distrib. 13, 781–788 (2007).
Article  Google Scholar 

87.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. J. R. Meteorol. Soc. 25, 1965–78 (2005).
Article  Google Scholar 

88.
Kwon, S., Park, S., Lee, B. & Yoon, S. In-depth analysis of interrelation between quality scores and real errors in illumina reads. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 635–638 (2013).

89.
Rideout, J. R. et al. Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences. PeerJ 2, e545 (2014).
PubMed  PubMed Central  Article  Google Scholar 

90.
Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems 2, e00191–16 (2017).

91.
Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).
CAS  PubMed  Article  Google Scholar 

92.
Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).
CAS  PubMed  Article  Google Scholar 

93.
Team, R. C. R: A Language and Environment for Statistical Computing. (2013).

94.
Wickham, H. Ggplot: Using the Grammar of Graphics with R. (2009)

95.
Calcagno, V., Calcagno, M. V., Java, S. & Suggests, M. A. S. S. Package ‘glmulti’ (2020).

96.
Bates, K. A. et al. Captivity and infection by the fungal pathogen Batrachochytrium salamandrivorans perturb the amphibian skin microbiome. Front. Microbiol. 10, 1834 (2019).
PubMed  PubMed Central  Article  Google Scholar 

97.
Lin, D., Foster, D. P. & Ungar, L. H. VIF regression: a fast regression algorithm for large data. J. Am. Stat. Assoc. 106, 232–247 (2011).
MathSciNet  CAS  MATH  Article  Google Scholar 

98.
Kruskal, J. B. Nonmetric multidimensional scaling: a numerical method. Psychometrika 29, 115–129 (1964).
MathSciNet  MATH  Article  Google Scholar 

99.
Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27, 326–349 (1957).
Article  Google Scholar 

100.
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P. & Minchin, P. R. In 2012: Vegan: Community Ecology Package. R Package Version 2.0-5 (eds. Hara, O. et al.) (2014).

101.
De Caceres, M., Jansen, F. & De Caceres, M. M. Indicspecies: relationship between species and groups of sites. R package Version 1, (2016).

102.
Longo, A. V., Burrowes, P. A. & Zamudio, K. R. Genomic studies of disease-outcome in host–pathogen dynamics. Am. Zool. 54, 427–438 (2014).
Google Scholar 

103.
Assis, A. B. D., Barreto, C. C. & Navas, C. A. Skin microbiota in frogs from the Brazilian Atlantic forest: species, forest type, and potential against pathogens. PLoS ONE 12, e0179628 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

104.
Estrada, A. et al. Skin bacterial communities of neotropical treefrogs vary with local environmental conditions at the time of sampling. PeerJ 7, e7044 (2019).
PubMed  PubMed Central  Article  Google Scholar 

105.
Muletz-Wolz, C. R., Fleischer, R. C. & Lips, K. R. Fungal disease and temperature alter skin microbiome structure in an experimental salamander system. Mol. Ecol. 28, 2917–2931 (2019).
CAS  PubMed  Google Scholar 

106.
Xu, L. L. et al. Changes in the community structure of the symbiotic microbes of wild amphibians from the eastern edge of the Tibetan Plateau. Microbiol. Open 9, e1004 (2020).
Article  Google Scholar 

107.
Puschendorf, R. et al. Environmental refuge from disease-driven amphibian extinction. Conserv. Biol. 25, 956–964 (2011).
PubMed  Article  Google Scholar 

108.
Whitfield, S. M., Kerby, J., Gentry, L. R. & Donnelly, M. A. Temporal variation in infection prevalence by the amphibian chytrid fungus in three species of frogs at La Selva, Costa Rica. Biotropica 44, 779–784 (2012).
Article  Google Scholar 

109.
Ruggeri, J. et al. Seasonal variation in population abundance and chytrid infection in stream-dwelling frogs of the Brazilian Atlantic forest. PLoS ONE 10, e0130554 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

110.
Longo, A. V., Burrowes, P. A. & Joglar, R. L. Seasonality of Batrachochytrium dendrobatidis infection in direct-developing frogs suggests a mechanism for persistence. Dis. Aquat. Organ. 92, 253–260 (2010).
PubMed  Article  PubMed Central  Google Scholar 

111.
Ellison, S., Knapp, R. A., Sparagon, W., Swei, A. & Vredenburg, V. T. Reduced skin bacterial diversity correlates with increased pathogen infection intensity in an endangered amphibian host. Mol. Ecol. 28, 127–140 (2019).
PubMed  Article  PubMed Central  Google Scholar 

112.
Piovia-Scott, J. et al. Greater species richness of bacterial skin symbionts better suppresses the amphibian fungal pathogen Batrachochytrium dendrobatidis. Microb. Ecol. 74, 217–226 (2017).
PubMed  Article  PubMed Central  Google Scholar 

113.
Flechas, S. V. et al. Surviving chytridiomycosis: differential anti-Batrachochytrium dendrobatidis activity in bacterial isolates from three lowland species of Atelopus. PLoS ONE 7, e44832 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

114.
Muletz, C. R., Myers, J. M., Domangue, R. J., Herrick, J. B. & Harris, R. N. Soil bioaugmentation with amphibian cutaneous bacteria protects amphibian hosts from infection by Batrachochytrium dendrobatidis. Biol. Conserv. 152, 119–126 (2012).
Article  Google Scholar 

115.
Woodhams, D. C., Ramsey, J. P. & Rollins-Smith, L. A. Effects of cold temperature on antimicrobial peptide synthesis and release in northern leopard frogs, Rana pipiens. Integr. Comp. Biol. 45, 1099–1099 (2005).
Google Scholar 

116.
Bovo, R. P. et al. Physiological responses of Brazilian amphibians to an enzootic infection of the chytrid fungus Batrachochytrium dendrobatidis. Dis. Aquat. Organ. 117, 245–252 (2016).
PubMed  Article  PubMed Central  Google Scholar 

117.
Familiar López, M., Rebollar, E. A., Harris, R. N., Vredenburg, V. T. & Hero, J. M. Temporal variation of the skin bacterial community and Batrachochytrium dendrobatidis infection in the terrestrial cryptic frog Philoria loveridgei. Front. Microbiol. 8, 2535 (2017).
PubMed  PubMed Central  Article  Google Scholar  More

1.
Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 220–229 (2016).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).
PubMed  Article  Google Scholar 

3.
Cameron, S. A. & Sadd, B. M. Global trends in bumble bee health. Annu. Rev. Entomol. 65, 209–232 (2020).
CAS  PubMed  Article  Google Scholar 

4.
Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).
PubMed  Article  CAS  Google Scholar 

5.
Steffan-Dewenter, I., Münzenberg, U., Bürger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432 (2002).
Article  Google Scholar 

6.
Winfree, R., Aguilar, R., Vázquez, D. P., LeBuhn, G. & Aizen, M. A. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90, 2068–2076 (2009).
PubMed  Article  Google Scholar 

7.
Grozinger, C. M. & Flenniken, M. L. Bee viruses: Ecology, pathogenicity, and impacts. Annu. Rev. Entomol. 64, 205–226 (2019).
CAS  PubMed  Article  Google Scholar 

8.
Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. R. Soc. B Biol. Sci. 108, 662–667 (2011).
CAS  Google Scholar 

9.
Tokarev, Y. S. et al. A formal redefinition of the genera Nosema and Vairimorpha (Microsporidia: Nosematidae) and reassignment of species based on molecular phylogenetics. J. Invertebr. Pathol. 169, 107279 (2020).
CAS  PubMed  Article  Google Scholar 

10.
Levitt, A. L. et al. Cross-species transmission of honey bee viruses in associated arthropods. Virus Res. 176, 232–240 (2013).
CAS  PubMed  Article  Google Scholar 

11.
Radzevičiūtė, R. et al. Replication of honey bee-associated RNA viruses across multiple bee species in apple orchards of Georgia, Germany and Kyrgyzstan. J. Invertebr. Pathol. 146, 14–23 (2017).
PubMed  Article  CAS  Google Scholar 

12.
Fürst, M. A., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Dolezal, A. G. et al. Honey bee viruses in wild bees: Viral prevalence, loads, and experimental inoculation. PLoS ONE 11, 11 (2016).
Google Scholar 

14.
Douglas, M. R., Sponsler, D. B., Lonsdorf, E. V. & Grozinger, C. M. County-level analysis reveals a rapidly shifting landscape of insecticide hazard to honey bees (Apis mellifera) on US farmland. Sci. Rep. 10, 1–11 (2020).
Article  CAS  Google Scholar 

15.
Blacquiere, T., Smagghe, G., Van Gestel, C. A. & Mommaerts, V. Neonicotinoids in bees: A review on concentrations, side-effects and risk assessment. Ecotoxicology 21, 973–992 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Aliouane, Y. et al. Subchronic exposure of honeybees to sublethal doses of pesticides: effects on behavior. Environ. Toxicol. Chem. 28, 113–122 (2009).
CAS  PubMed  Article  Google Scholar 

17.
Whitehorn, P. R., O’connor, S., Wackers, F. L. & Goulson, D. Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science 336, 351–352 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

18.
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

19.
Dolezal, A. G. & Toth, A. L. Feedbacks between nutrition and disease in honey bee health. Curr. Opin. Insect Sci. 26, 114–119 (2018).
PubMed  Article  Google Scholar 

20.
DeGrandi-Hoffman, G. & Chen, Y. Nutrition, immunity and viral infections in honey bees. Curr. Opin. Insect Sci. 10, 170–176 (2015).
PubMed  Article  Google Scholar 

21.
DeGrandi-Hoffman, G., Chen, Y., Huang, E. & Huang, M. H. The effect of diet on protein concentrcation, hypopharyngeal gland development and virus load in worker honey bees (Apis mellifera L.). J. Insect Physiol. 56, 1184–1191 (2010).
CAS  PubMed  Article  Google Scholar 

22.
Di Pasquale, G. et al. Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8, 8 (2013).
Google Scholar 

23.
Manley, R., Boots, M. & Wilfert, L. Condition-dependent virulence of slow bee paralysis virus in Bombus terrestris: Are the impacts of honeybee viruses in wild pollinators underestimated?. Oecologia 184, 305–315 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

24.
Ricigliano, V. A. et al. Honey bee colony performance and health are enhanced by apiary proximity to US Conservation Reserve Program (CRP) lands. Sci. Rep. 9, 1–11 (2019).
CAS  Article  Google Scholar 

25.
O’Neal, S. T., Anderson, T. D. & Wu-Smart, J. Y. Interactions between pesticides and pathogen susceptibility in honey bees. Curr. Opin. Insect Sci. 26, 57–62 (2018).
PubMed  Article  Google Scholar 

26.
Di Prisco, G. V. et al. Neonicotinoid clothianidin adversely affects insect immunity and promotes replication of a viral pathogen in honey bees. Proc. Natl. Acad. Sci. 110, 18466–18471 (2013).
ADS  PubMed  Article  CAS  Google Scholar 

27.
O’Neal, S. T., Swale, D. R. & Anderson, T. D. ATP-sensitive inwardly rectifying potassium channel regulation of viral infections in honey bees. Sci. Rep. 7, 8668 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Fine, J. D., Cox-Foster, D. L. & Mullin, C. A. An inert pesticide adjuvant synergizes viral pathogenicity and mortality in honey bee larvae. Sci. Rep. 7, 40499 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Pettis, J. S., Johnson, J. & Dively, G. Pesticide exposure in honey bees results in increased levels of the gut pathogen Nosema. Naturwissenschaften 99, 153–158 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Pettis, J. S., Lichtenberg, E. M., Andree, M., Stitzinger, J. & Rose, R. Crop pollination exposes honey bees to pesticides which alters their susceptibility to the gut pathogen Nosema ceranae. PLoS ONE 8, e70182 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
McArt, S. H., Fersch, A. A., Milano, N. J., Truitt, L. L. & Böröczky, K. High pesticide risk to honey bees despite low focal crop pollen collection during pollination of a mass blooming crop. Sci. Rep. 7, 46554 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
McArt, S. H., Koch, H., Irwin, R. E. & Adler, L. S. Arranging the bouquet of disease: Floral traits and the transmission of plant and animal pathogens. Ecol. Lett. 17, 624–636 (2014).
PubMed  Article  Google Scholar 

33.
Piot, N. et al. Establishment of wildflower fields in poor quality landscapes enhances micro-parasite prevalence in wild bumble bees. Oecologia 189, 149–158 (2019).
ADS  PubMed  Article  Google Scholar 

34.
Bailes, E. J. et al. Host density drives viral, but not trypanosome, transmission in a key pollinator. Proc. R. Soc. B Biol. Sci. 287, 20191969 (2020).
Article  Google Scholar 

35.
Singh, R. et al. RNA viruses in hymenopteran pollinators: Evidence of inter-taxa virus transmission via pollen and potential impact on non-Apis hymenopteran species. PLoS ONE 5, e14357 (2010).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

36.
Manley, R., Boots, M. & Wilfert, L. Emerging viral disease risk to pollinating insects: Ecological, evolutionary and anthropogenic factors. J. Appl. Ecol. 52, 331–340 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Meeus, I., Pisman, M., Smagghe, G. & Piot, N. Interaction effects of different drivers of wild bee decline and their influence on host–pathogen dynamics. Curr. Opin. Insect Sci. 26, 136–141 (2018).
PubMed  Article  Google Scholar 

38.
Huang, Z. Pollen nutrition affects honey bee stress resistance. Terr. Arthropod. Rev. 5, 175–189 (2012).
Article  Google Scholar 

39.
Smart, M., Pettis, J., Rice, N., Browning, Z. & Spivak, M. Linking measures of colony and individual honey bee health to survival among apiaries exposed to varying agricultural land use. PLoS ONE 11, 3 (2016).
Google Scholar 

40.
Danihlík, J., Aronstein, K. & Petřivalský, M. Antimicrobial peptides: a key component of honey bee innate immunity: Physiology, biochemistry, and chemical ecology. J. Apic. Res. 54, 123–136 (2015).
Article  Google Scholar 

41.
Meeus, I., Brown, M. J., De Graaf, D. C. & Smagghe, G. U. Y. Effects of invasive parasites on bumble bee declines. Conserv. Biol. 25, 662–671 (2011).
PubMed  Article  Google Scholar 

42.
Vaudo, A. D., Tooker, J. F., Grozinger, C. M. & Patch, H. M. Bee nutrition and floral resource restoration. Curr. Opin. Insect Sci. 10, 133–141 (2015).
PubMed  Article  Google Scholar 

43.
Sánchez-Bayo, F. et al. Are bee diseases linked to pesticides?—A brief review. Environ. Int. 89, 7–11 (2016).
PubMed  Article  CAS  Google Scholar 

44.
Beck, M. A. & Levander, O. A. Host nutritional status and its effect on a viral pathogen. J. Infect. Dis. 182, 93–96 (2000).
Article  Google Scholar 

45.
Hing, S., Narayan, E. J., Thompson, R. A. & Godfrey, S. S. The relationship between physiological stress and wildlife disease: Consequences for health and conservation. Wildl. Res. 43, 51–60 (2016).
Article  Google Scholar 

46.
Graystock, P., Goulson, D. & Hughes, W. O. Parasites in bloom: Flowers aid dispersal and transmission of pollinator parasites within and between bee species. Proc. R. Soc. B Biol. Sci. 282, 20151371 (2015).
Article  Google Scholar 

47.
Sponsler, D. B., Shump, D., Richardson, R. T. & Grozinger, C. M. Characterizing the floral resources of a North American metropolis using a honey bee foraging assay. Ecosphere 11, e03102 (2020).
Article  Google Scholar 

48.
Williams, N. M., Regetz, J. & Kremen, C. Landscape-scale resources promote colony growth but not reproductive performance of bumble bees. Ecology 93, 1049–1058 (2012).
PubMed  Article  Google Scholar 

49.
Steffan-Dewenter, I. & Tscharntke, T. Resource overlap and possible competition between honey bees and wild bees in central Europe. Oecologia 122, 288–296 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

50.
Tehel, A., Brown, M. J. & Paxton, R. J. Impact of managed honey bee viruses on wild bees. Curr. Opin. Virol. 19, 16–22 (2016).
PubMed  Article  Google Scholar 

51.
Sponsler, D. B. et al. Pesticides and pollinators: A socioecological synthesis. Sci. Total Environ. 662, 1012–1027 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

52.
McCaskill, G. L. et al. Pennsylvania’s Forests 2009 (U.S Forest Service, Washington, DC, 2009).
Google Scholar 

53.
Park, M. G., Blitzer, E. J., Gibbs, J., Losey, J. E. & Danforth, B. N. Negative effects of pesticides on wild bee communities can be buffered by landscape context. Proc. R. Soc. B Biol. Sci. 282, 20150299 (2015).
Article  CAS  Google Scholar 

54.
Koh, I. et al. Modeling the status, trends, and impacts of wild bee abundance in the United States. Proc. R. Soc. B Biol. Sci. 113, 140–145 (2016).
CAS  Google Scholar 

55.
Williams, P. H., Thorp, R. W., Richardson, L. L. & Colla, S. R. Bumble Bees of North America: An Identification Guide (Princeton University Press, Princeton, 2014).
Google Scholar 

56.
National Research Council. Under the Weather: Climate, Ecosystems, and Infectious Disease (National Academy Press, Washington, DC, 2001).
Google Scholar 

57.
Polgreen, P. M. & Polgreen, E. L. Infectious diseases, weather, and climate. Clin. Infect. Dis. 66, 815–817 (2018).
PubMed  Article  Google Scholar 

58.
Retschnig, G., Williams, G. R., Schneeberger, A. & Neumann, P. Cold ambient temperature promotes Nosema spp. intensity in honey bees (Apis mellifera). Insects 8, 20 (2017).
PubMed Central  Article  PubMed  Google Scholar 

59.
Dalmon, A., Peruzzi, M. L., Conte, Y., Alaux, C. & Pioz, M. Temperature-driven changes in viral loads in the honey bee Apis mellifera. J. Invertebr. Pathol. 160, 87–94 (2019).
PubMed  Article  Google Scholar 

60.
Gardner, W. A., Sutton, R. M. & Noblet, R. Persistence of Beauveria bassiana, Nomuraea rileyi, and Nosema necatrix on Soyhean Foliage. Environ. Entomol. 6, 616–618 (1977).
Article  Google Scholar 

61.
Neidel, V., Steyer, C. S. & C., & Hoch, G. ,. Simulation of rain enhances horizontal transmission of the microsporidium Nosema lymantriae via infective feces. J. Invertebr. Pathol. 149, 56–58 (2017).
PubMed  Article  Google Scholar 

62.
Rangel, J. et al. Prevalence of Nosema species in a feral honey bee population: A 20-year survey. Apidologie 47, 561–571 (2017).
Article  Google Scholar 

63.
Leather, S. R. “Ecological Armageddon”-more evidence for the drastic decline in insect numbers. Ann. Appl. Biol. 172, 1–3 (2017).
Article  Google Scholar 

64.
Scheper, J. et al. Local and landscape-level floral resources explain effects of wildflower strips on wild bees across four European countries. J. Appl. Ecol. 52, 1165–1175 (2015).
Article  Google Scholar 

65.
Rodríguez, J. P., Brotons, L., Bustamante, J. & Seoane, J. The application of predictive modelling of species distribution to biodiversity conservation. Divers. Distrib. 13, 243–251 (2017).
Article  Google Scholar 

66.
Young, B. E. et al. Using citizen science data to support conservation in environmental regulatory contexts. Biol. Conserv. 237, 57–62 (2019).
Article  Google Scholar 

67.
Lesley, J. P. A Summary Description of the Geology of Pennsylvania (Board of Commissioners for the Geological Survey, Pennsylvania, 1892).
Google Scholar 

68.
Dyer, J. Revisiting the Deciduous Forests of Eastern North America. Bioscience 56, 341–352 (2006).
Article  Google Scholar 

69.
Wherry, E. T., Fogg, Jr., J. M., & Wahl. H. A. Atlas of the Flora of Pennsylvania. (University of Pennsylvania, Pennsylvania, 1979).

70.
Albright, T. A. Forests of Pennsylvania, 2017. Resource Update FS-175. (U.S. Department of Agriculture, Forest Service, 2017).

71.
Wickham, J. et al. The multi-resolution land characteristics (MRLC) consortium—20 years of development and integration. Remote Sens. 6, 7424–7441 (2014).
ADS  Article  Google Scholar 

72.
Shannon, C. E. A mathematical theory of communication. Bell Labs Tech. J. 27, 379–423 (1948).
MathSciNet  MATH  Article  Google Scholar 

73.
Plischuk, S. et al. South American native bumblebees (Hymenoptera: Apidae) infected by Nosema ceranae (Microsporidia), an emerging pathogen of honeybees (Apis mellifera). Environ. Microbiol. Rep. 1, 131–135 (2009).
PubMed  Article  Google Scholar 

74.
Chu, C. C. & Cameron, S. A. A scientific note on Nosema bombi infection intensity among different castes within a Bombus auricomus nest. Apidologie 48, 141–143 (2017).
Article  Google Scholar 

75.
vanEngelsdorp, D. et al. Colony collapse disorder: A descriptive study. PLoS ONE 4, e6481–e6481 (2009).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

76.
Simmons, W. R. & Angelini, D. R. Chronic exposure to a neonicotinoid increases expression of antimicrobial peptide genes in the bumblebee Bombus impatiens. Sci. Rep. 7, 44773 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Muller, C. B. & Schmid-Hempel, P. Variation in life-history pattern in relation to worker mortality in the bumble-bee, Bombus lucorum. Funct. Ecol. 6, 48–56 (1992).
Article  Google Scholar 

78.
Hijmans, R. J. & van Etten, J. Raster: Geographic analysis and modeling with raster data. R package version 2.0-12. http://CRAN.R-project.org/package=raster (2012).

79.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/index.html (2019).

80.
Knight, M. E. et al. Bumblebee nest density and the scale of available forage in arable landscapes. Insect Conserv. Diver. 2, 116–124 (2009).
Article  Google Scholar 

81.
Darvill, B., Knight, M. E. & Goulson, D. Use of genetic markers to quantify bumblebee foraging range and nest density. Oikos 107, 471–478 (2004).
Article  Google Scholar 

82.
Desjardins, È. C. & De Oliveira, D. Commercial bumble bee Bombus impatiens (Hymenoptera: Apidae) as a pollinator in lowbush blueberry (Ericale: Ericaceae) fields. J. Econ. Entomol. 99, 443–449 (2006).
PubMed  Article  Google Scholar 

83.
Natural Capital Project. InVEST: Crop Pollination Model. Version 3.1.0. http://naturalcapitalproject.org/models/crop_pollination.html (2014).

84.
Kammerer, M. A., Biddinger, D. J., Joshi, N. K., Rajotte, E. G. & Mortensen, D. A. Modeling local spatial patterns of wild bee diversity in Pennsylvania apple orchards. Landsc. Ecol. 31, 2459–2469 (2016).
Article  Google Scholar 

85.
Johnson, D. M. & Mueller, R. The 2009 cropland data layer. Photogramm. Eng. Remote. Sens. 76, 1201–1205 (2010).
Google Scholar 

86.
PRISM Climate Group. PRISM Gridded Climate Data. Oregon State University, Corvallis Oregon, USA. http://prism.oregonstate.edu (2019).

87.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference—A Practical Information-Theoretic Approach (Springer, New York, 2002).
Google Scholar 

88.
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

89.
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, New York, 2009).
Google Scholar 

90.
Sokal, R. R. & Rohlf, F. J. The Principles and Practice of Statistics in Biological Research (W.H Freeman and Company, New York, 1969).
Google Scholar  More

1.
Moreira, X. et al. Specificity of induced defenses, growth, and reproduction in lima bean (Phaseolus lunatus) in response to multispecies herbivory. Am. J. Bot. 102, 1300–1308 (2015).
CAS  PubMed  Article  Google Scholar 
2.
Danell, K., Bergström, R. & Edenius, L. Effects of large mammalian browsers on architecture, biomass, and nutrients of woody plants. J. Mammal. 75, 833–844 (1994).
Article  Google Scholar 

3.
Kaitaniemi, P., Neuvonen, S. & Nyyssönen, T. Effects of cumulative defoliations on growth, reproduction, and insect resistance in mountain birch. Ecology 80, 524–532 (1999).
Article  Google Scholar 

4.
den Herder, M., Bergström, R., Niemelä, P., Danell, K. & Lindgren, M. Effects of natural winter browsing and simulated summer browsing by moose on growth and shoot biomass of birch and its associated invertebrate fauna. Ann. Zool. Fennici 46, 63–74 (2009).
Article  Google Scholar 

5.
Wallgren, M., Bergquist, J., Bergström, R. & Eriksson, S. Effects of timing, duration, and intensity of simulated browsing on Scots pine growth and stem quality. Scand. J. For. Res. 29, 734–746 (2014).
Article  Google Scholar 

6.
Schwenk, W. S. & Strong, A. M. Contrasting patterns and combined effects of moose and insect herbivory on striped maple (Acer pensylvanicum). Basic Appl. Ecol. 12, 64–71 (2011).
Article  Google Scholar 

7.
Muiruri, E. W., Milligan, H. T., Morath, S. & Koricheva, J. Moose browsing alters tree diversity effects on birch growth and insect herbivory. Funct. Ecol. 29, 724–735 (2015).
Article  Google Scholar 

8.
van Zandt, P. A. & Agrawal, A. A. Community-Wide impacts of herbivore-induced plant responses in milkweed (Asclepias syriaca). Ecology 85, 2616–2629 (2004).
Article  Google Scholar 

9.
Erb, M., Robert, C. A. M., Hibbard, B. E. & Turlings, T. C. J. Sequence of arrival determines plant-mediated interactions between herbivores. J. Ecol. 99, 7–15 (2011).
Article  Google Scholar 

10.
Kafle, D., Hänel, A., Lortzing, T., Steppuhn, A. & Wurst, S. Sequential above- and belowground herbivory modifies plant responses depending on herbivore identity. BMC Ecol. 17, 1–10 (2017).
Article  Google Scholar 

11.
Stephens, A. E. A., Srivastava, D. S. & Myers, J. H. Strength in numbers? Effects of multiple natural enemy species on plant performance. Proc. R. Soc. B Biol. Sci. 280, 20122756 (2013).
Article  Google Scholar 

12.
Gagic, V. et al. Interactive effects of pests increase seed yield. Ecol. Evol. 6, 2149–2157 (2016).
PubMed  PubMed Central  Article  Google Scholar 

13.
Strauss, S. Y. Direct, indirect, and cumulative effects of three native herbivores on a shared host plant. Ecology 72, 543–558 (1991).
Article  Google Scholar 

14.
Gómez, J. M. & González-Megías, A. Asymmetrical interactions between ungulates and phytophagous insects: being different matters. Ecology 83, 203–211 (2002).
Article  Google Scholar 

15.
Ohgushi, T. Indirect interaction webs: herbivore-induced effects through trait change in plants. Annu. Rev. Ecol. Evol. Syst. 36, 81–105 (2005).
Article  Google Scholar 

16.
Mauch-Mani, B., Baccelli, I., Luna, E. & Flors, V. Defense priming: an adaptive part of induced resistance. Annu. Rev. Plant Biol. 68, 485–512 (2017).
CAS  PubMed  Article  Google Scholar 

17.
Hilker, M. et al. Priming and memory of stress responses in organisms lacking a nervous system. Biol. Rev. 91, 1118–1133 (2016).
PubMed  Article  Google Scholar 

18.
Lyytikäinen-Saarenmaa, P. The responses of scots pine, Pinus silvestris, to natural and artificial defoliation stress. Ecol. Appl. 9, 469–474 (1999).
Article  Google Scholar 

19.
Ericsson, A., Larsson, S. & Tenow, O. Effects of early and late season defoliation on growth and carbohydrate dynamics in scots pine. J. Appl. Ecol. 17, 747–769 (1980).
Article  Google Scholar 

20.
Edenius, L. Browsing by moose on Scots pine in relation to plant resource availability. Ecology 74, 2261–2269 (1993).
Article  Google Scholar 

21.
Nordkvist, M. et al. Trait-mediated indirect interactions: Moose browsing increases sawfly fecundity through plant-induced responses. Ecol. Evol. 9, 10615–10629 (2019).
PubMed  PubMed Central  Article  Google Scholar 

22.
Edenius, L., Danell, K. & Nyquist, H. Effects of simulated moose browsing on growth, mortality, and fecundity in Scots pine: relations to plant productivity. Can. J. For. Res. 25, 529–535 (1995).
Article  Google Scholar 

23.
Honkanen, T., Haukioja, E. & Kitunen, V. Responses of Pinus sylvestris branches to simulated herbivory are modified by tree sink/source dynamics and by external resources. Funct. Ecol. 13, 126–140 (1999).
Article  Google Scholar 

24.
Persson, I. L., Bergström, R. & Danell, K. Browse biomass production and regrowth capacity after biomass loss in deciduous and coniferous trees: Responses to moose browsing along a productivity gradient. Oikos 116, 1639–1650 (2007).
Article  Google Scholar 

25.
Belsky, A. J. Does herbivory benefit plants? A review of the evidence. Am. Nat. 127, 870–892 (1986).
Article  Google Scholar 

26.
Bergman, M. Can saliva from moose, Alces alces, affect growth responses in the salow, Salix caprea?. Oikos 96, 164–168 (2002).
Article  Google Scholar 

27.
Ohse, B. et al. Salivary cues: simulated roe deer browsing induces systemic changes in phytohormones and defence chemistry in wild-grown maple and beech saplings. Funct. Ecol. 31, 340–349 (2017).
Article  Google Scholar 

28.
Kollberg, I. et al. Temperature affects insect outbreak risk through tritrophic interactions mediated by plant secondary compounds. Ecosphere 6, 1–17 (2015).
Article  Google Scholar 

29.
Lyytikäinen-Saarenmaa, P. & Tomppo, E. Impact of sawfly defoliation on growth of Scots pine Pinus sylvestris (Pinaceae) and associated economic losses. Bull. Entomol. Res. 92, 137–140 (2002).
PubMed  Article  Google Scholar 

30.
Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165–1183 (1998).
Article  Google Scholar 

31.
Edenius, L., Bergman, M., Ericsson, G. & Danell, K. The role of moose as a disturbance factor in managed boreal forests. Silva Fennica 36, 57–67 (2002).
Article  Google Scholar 

32.
Hódar, J. A., Zamora, R., Castro, J., Gómez, J. M. & García, D. Biomass allocation and growth responses of Scots pine saplings to simulated herbivory depend on plant age and light availability. Plant Ecol. 197, 229–238 (2008).
Article  Google Scholar 

33.
Bergström, R. & Hjeljord, O. Moose and vegetation interactions in northwestern Europe and Poland. Swedish Wildl. Res. Suppl. 1, 213–228 (1987).
Google Scholar 

34.
Nilsson, U., Berglund, M., Bergquist, J., Holmström, H. & Wallgren, M. Simulated effects of browsing on the production and economic values of Scots pine (Pinus sylvestris) stands. Scand. J. For. Res. 31, 279–285 (2016).
Article  Google Scholar 

35.
Långsström, B. & Hellqvist, C. Effects of different pruning regimes on growth and sapwood area of Scots pine. For. Ecol. Manag. 44, 239–254 (1991).
Article  Google Scholar 

36.
Mathisen, K. M., Milner, J. M. & Skarpe, C. Moose-tree interactions: rebrowsing is common across tree species. BMC Ecol. 17, 1–15 (2017).
Article  Google Scholar 

37.
Bergqvist, G., Bergström, R. & Edenius, L. Effects of moose (Alces alces) rebrowsing on damage development in young stands of Scots pine (Pinus sylvestris). For. Ecol. Manag. 176, 397–403 (2003).
Article  Google Scholar 

38.
Bergqvist, G., Bergström, R. & Edenius, L. Patterns of stem damage by moose (Alces alces) in young Pinus sylvestris stands in Sweden. Scand. J. For. Res. 16, 363–370 (2001).
Article  Google Scholar 

39.
Riipi, M., Lempa, K., Haukioja, E., Ossipov, V. & Pihlaja, K. Effects of simulated winter browsing on mountain birch foliar chemistry and on the performance of insect herbivores. Oikos 111, 221–234 (2005).
Article  Google Scholar 

40.
Kupferschmid, A. D. & Bugmann, H. Timing, light availability and vigour determine the response of Abies alba saplings to leader shoot browsing. Eur. J. For. Res. 132, 47–60 (2013).
Article  Google Scholar 

41.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).

42.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. _nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1–142. https://CRAN.R-project.org/package=nlme (2019).

43.
Fox, J. & Weisberg, F. An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. (2019).

44.
Darling, E. S., Mcclanahan, T. R. & Côté, I. M. Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conserv. Lett. 3, 122–130 (2010).
Article  Google Scholar 

45.
Bansal, S., Hallsby, G., Löfvenius, M. O. & Nilsson, M. C. Synergistic, additive and antagonistic impacts of drought and herbivory on Pinus sylvestris: leaf, tissue and whole-plant responses and recovery. Tree Physiol. 33, 451–463 (2013).
CAS  PubMed  Article  Google Scholar  More

1.
Neuenschwander, S. M., Pernthaler, J., Posch, T. & Salcher, M. M. Seasonal growth potential of rare lake water bacteria suggest their disproportional contribution to carbon fluxes. Environ. Microbiol. 17(3), 781–795 (2015).
CAS  PubMed  Article  Google Scholar 
2.
Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313(5790), 1068–1072 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

3.
United Nations Environment Programme. GEO Year Book 2004/5: An Overview of Our Changing Environment (2004). https://www.unep.org/resources/report/geo-year-book-20045-overview-our-changing-environment.

4.
Lindström, E. S. Bacterioplankton community composition in five lakes differing in trophic status and humic content. Microb. Ecol. 40(2), 104–113 (2000).
PubMed  Article  Google Scholar 

5.
Ávila, M. P., Staehr, P. A., Barbosa, F. A., Chartone-Souza, E. & Nascimento, A. Seasonality of freshwater bacterioplankton diversity in two tropical shallow lakes from the Brazilian Atlantic Forest. FEMS Microbiol. Ecol. 93, fw218 (2017).
Article  CAS  Google Scholar 

6.
Zhang, H. et al. Biogeographic distribution patterns of algal community in different urban lakes in China: insights into the dynamics and co-existence. J. Environ. Sci. 100, 216–227 (2021).
Article  Google Scholar 

7.
Ji, B. et al. Bacterial communities of four adjacent fresh lakes at different trophic status. Ecotoxicol. Environ. Safe 157, 388–394 (2018).
CAS  Article  Google Scholar 

8.
Iliev, I. et al. Metagenomic profiling of the microbial freshwater communities in two Bulgarian reservoirs. J. Basic Microb. 57(8), 669–679 (2017).
CAS  Article  Google Scholar 

9.
Linz, A. M. et al. Bacterial community composition and dynamics spanning five years in freshwater bog lakes. mSphere 2(3), e00169 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Hartmann, A., Goldscheider, N., Wagener, T., Lange, J. & Weiler, M. Karst water resources in a changing world: review of hydrological modeling approaches. Rev. Geophys. 52(3), 218–242 (2014).
ADS  Article  Google Scholar 

11.
Yu, S. et al. Spatial and temporal dynamics of bacterioplankton community composition in a subtropical dammed karst river of southwestern China. Microbiol. Open 8(9), e00849 (2019).
Article  CAS  Google Scholar 

12.
Li, Q., Sun, H., Han, J., Liu, Z. & Yu, L. High-resolution study on the hydrochemical variations caused by the dilution of precipitation in the epikarst spring: an example spring of Landiantang at Nongla, Mashan, China. Environ. Geol. 54(2), 347–354 (2008).
ADS  CAS  Article  Google Scholar 

13.
Song, A., Yue, M. L. & Li, Q. Influence of precipitation on bacterial structure in a typical karst spring, SW China. J. Groundw. Sci. Eng. 6(3), 193–204 (2018).
Google Scholar 

14.
Gray, C. J. & Engel, A. S. Microbial diversity and impact on carbonate geochemistry across a changing geochemical gradient in a karst aquifer. ISME J. 7(2), 325–337 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Shabarova, T. et al. Bacterial community structure and dissolved organic matter in repeatedly flooded subsurface karst water pools. FEMS Microbiol. Ecol. 89(1), 111–126 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Li, Q. et al. Contribution of aerobic anoxygenic phototrophic bacteria to total organic carbon pool in aquatic system of subtropical karst catchments, Southwest China: evidence from hydrochemical and microbiological study. FEMS Microbiol. Ecol. 93, fix065 (2017).
Google Scholar 

17.
Stevanović, Z. & Milanović, P. Engineering challenges in karst. Acta Carsol. 44(3), 381–399 (2015).
Article  Google Scholar 

18.
Lu, X. X. et al. Water chemistry and characteristics of dissolved organic carbon during the wet season in Wulixia Reservoir, SW China. Huanjing Kexue 39(5), 2075–2085 (2018) (in Chinese with English abstract).
PubMed  PubMed Central  Google Scholar 

19.
Xin, S. L. et al. Relationship between the bacterial abundance and production with environmental factors in a subtropical karst reservoir. Huanjing Kexue 39(12), 5647–5656 (2018) (in Chinese with English abstract).
PubMed  PubMed Central  Google Scholar 

20.
National Research Council. Assessing the TMDL Approach to Water Quality Management (National Academy Press, Washington, DC, 2001).
Google Scholar 

21.
Cunha, D. G. F., do Carmo Calijuri, M. & Lamparelli, M. C. A trophic state index for tropical/subtropical reservoirs (TSItsr). Ecol. Eng. 60, 126–134 (2013).
Article  Google Scholar 

22.
Lorenzen, C. J. Determination of chlirophyll and pheo-pigments: spectrophotometric equations. Limnol. Oceanogr. 12(2), 343–346 (1967).
ADS  CAS  Article  Google Scholar 

23.
Tamaki, H. et al. Analysis of 16S rRNA amplicon sequencing options on the Roche/454 next-generation titanium sequencing platform. PLoS ONE 6(9), e25263 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Kuczynski, J. et al. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr. Protoc. Microbiol. 27(1), 1–20 (2012).
Google Scholar 

25.
Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).
PubMed  PubMed Central  Article  Google Scholar 

26.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral. J. Ecol. 18(1), 117–143 (1993).
Article  Google Scholar 

27.
Palmer, M. W., McGlinn, D. J., Westerberg, L. & Milberg, P. Indices for detecting differences in species composition: some simplifications of RDA and CCA. Ecology 89(6), 1769–1771 (2008).
PubMed  Article  Google Scholar 

28.
Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6(2), 343–351 (2012).
PubMed  Article  CAS  Google Scholar 

29.
Sanchez, G. PLS Path Modeling with R (Trowchez Editions, Berkeley, 2013).
Google Scholar 

30.
Lopez-Chicano, M., Bouamama, M., Vallejos, A. & Pulido-Bosch, A. Factors which determine the hydrogeochemical behaviour of karstic springs. A case study from the Betic Cordilleras, Spain. Appl. Geochem. 16(9–10), 1179–1192 (2001).
CAS  Article  Google Scholar 

31.
Stumm, W. & Morgan, J. J. Aquatic chemistry: chemical equilibria and rates in natural waters. In Environmental Science and Technology (eds Stumm, W. & Morgan, J. J.) (Wiley, New York, 2012).
Google Scholar 

32.
Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. R. 75, 14–49 (2011).
CAS  Article  Google Scholar 

33.
Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2(1), 589 (2011).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

34.
Li, D. et al. Microbial community evolution during simulated managed aquifer recharge in response to different biodegradable dissolved organic carbon (BDOC) concentrations. Water Res. 47(7), 2421–2430 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Miranda, C. D. & Zemelman, R. Bacterial resistance to oxytetracycline in Chilean salmon farming. Aquaculture 212(1–4), 31–47 (2002).
CAS  Article  Google Scholar 

36.
Dul’tseva, N. M., Chernitsina, S. M. & Zemskaya, T. I. Isolation of bacteria of the genus Variovorax from the Thioploca mats of Lake Baikal. Microbiology 81(1), 67–78 (2012).
Article  CAS  Google Scholar 

37.
Mohiuddin, M. M., Salama, Y., Schellhorn, H. E. & Golding, G. B. Shotgun metagenomic sequencing reveals freshwater beach sands as reservoir of bacterial pathogens. Water Res. 115, 360–369 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Fuentes, S., Méndez, V., Aguila, P. & Seeger, M. Bioremediation of petroleum hydrocarbons: catabolic genes, microbial communities, and applications. Appl. Microbiol. Biotechnol. 98(11), 4781–4794 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Gomes, B. C. et al. Analysis of a microbial community associated with polychlorinated biphenyl degradation in anaerobic batch reactors. Biodegradation 25(6), 797–810 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Cai, J. et al. Characterization of bacterial and microbial eukaryotic communities associated with an ephemeral hypoxia event in Taihu Lake, a shallow eutrophic Chinese lake. Environ. Sci. Pollut. R. 25(31), 31543–31557 (2018).
CAS  Article  Google Scholar 

41.
Zhang, S. et al. Characterization of a novel bacteriophage specific to Exiguobacterium indicum isolated from a plateau eutrophic lake. J. Basic Microb. 59(2), 206–214 (2019).
CAS  Article  Google Scholar 

42.
Li, S., Luo, Z. & Ji, G. Seasonal function succession and biogeographic zonation of assimilatory and dissimilatory nitrate-reducing bacterioplankton. Sci. Total Environ. 637, 1518–1525 (2018).
ADS  PubMed  Article  CAS  Google Scholar 

43.
Savio, D. et al. Spring water of an alpine karst aquifer is dominated by a taxonomically stable but discharge-responsive bacterial community. Front. Microbiol. 10, 28 (2019).
PubMed  PubMed Central  Article  Google Scholar 

44.
Freedman, Z. & Zak, D. R. Soil bacterial communities are shaped by temporal and environmental filtering: evidence from a long-term chronosequence. Environ. Microbiol. 17(9), 3208–3218 (2015).
PubMed  Article  Google Scholar 

45.
Subramani, T., Elango, L. & Damodarasamy, S. R. Groundwater quality and its suitability for drinking and agricultural use in Chithar River Basin, Tamil Nadu, India. Environ. Geol. 47(8), 1099–1110 (2005).
CAS  Article  Google Scholar 

46.
Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88(10), 2427–2439 (2007).
PubMed  Article  Google Scholar 

47.
Niño-García, J. P., Ruiz-González, C. & del Giorgio, P. A. Interactions between hydrology and water chemistry shape bacterioplankton biogeography across borssseal freshwater networks. ISME J. 10(7), 1755 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar  More

1.
Paul, E. A. (ed.) Soil Microbiology, Ecology and Biochemistry (Academic Press, Amsterdam, 2015).
Google Scholar 
2.
Nannipieri, P. et al. Microbial diversity and soil functions. Eur. J. Soil. Sci. 54, 655–670 (2003).
Article  Google Scholar 

3.
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil. Biol. Biochem. 83, 184–199 (2015).
CAS  Article  Google Scholar 

4.
Berg, G. & Smalla, K. Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol. Ecol. 68, 1–13 (2009).
CAS  Article  Google Scholar 

5.
Bais, H. P., Park, S.-W., Weir, T. L., Callaway, R. M. & Vivanco, J. M. How plants communicate using the underground information superhighway. Trends Plant. Sci. 9, 26–32 (2004).
CAS  Article  Google Scholar 

6.
Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. https://doi.org/10.1111/1574-6976.12028 (2013).
CAS  Article  PubMed  Google Scholar 

7.
Praeg, N., Pauli, H. & Illmer, P. Microbial diversity in bulk and rhizosphere soil of Ranunculus glacialis along a high-alpine altitudinal gradient. Front. Microbiol. 10, 1429. https://doi.org/10.3389/fmicb.2019.01429 (2019).
Article  PubMed  PubMed Central  Google Scholar 

8.
Nacke, H. et al. Pyrosequencing-based assessment of bacterial community structure along different management types in German forest and grassland soils. PLoS ONE 6, e17000. https://doi.org/10.1371/journal.pone.0017000 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Jackson, R. B., Solomon, E. I., Canadell, J. G., Cargnello, M. & Field, C. B. Methane removal and atmospheric restoration. Nat. Sustain. 2, 436–438. https://doi.org/10.1038/s41893-019-0299-x (2019).
Article  Google Scholar 

10.
Ciais, P. et al. Climate Change 2013: The Physical Science Basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, Cambridge, 2013).
Google Scholar 

11.
Adam, P. S., Borrel, G., Brochier-Armanet, C. & Gribaldo, S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 11, 2407. https://doi.org/10.1038/ismej.2017.122 (2017).
Article  PubMed  PubMed Central  Google Scholar 

12.
Knief, C. Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as molecular marker. Front. Microbiol. 6, 1346 (2015).
Article  Google Scholar 

13.
Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).
CAS  Article  Google Scholar 

14.
Op den Camp, H. J. M. et al. Environmental, genomic and taxonomic perspectives on methanotrophic Verrucomicrobia. Environ. Microbiol. Rep. 1, 293–306. https://doi.org/10.1111/j.1758-2229.2009.00022.x (2009).
CAS  Article  PubMed  Google Scholar 

15.
Knief, C., Lipski, A. & Dunfield, P. F. Diversity and activity of methanotrophic bacteria in different upland soils. Appl. Environ. Microbiol. 69, 6703–6714. https://doi.org/10.1128/AEM.69.11.6703-6714.2003 (2003).
CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Kolb, S. The quest for atmospheric methane oxidizers in forest soils. Environ. Microbiol. Rep. 1, 336–346 (2009).
CAS  Article  Google Scholar 

17.
Plesa, I. et al. Effects of drought and salinity on European Larch (Larix decidua Mill.) seedlings. Forests 9, 320. https://doi.org/10.3390/f9060320 (2018).
Article  Google Scholar 

18.
Falk, W., Bachmann-Gigl, U. & Kölling, C. Die Europäische Lärche im Klimawandel. In Beiträge zur Europäischen Lärche (ed. Schmidt, O.) 19–27 (Bayrische Landesanstalt für Wald und Forstwirtschaft, Freising, 2012).
Google Scholar 

19.
Obojes, N. et al. Water stress limits transpiration and growth of European larch up to the lower subalpine belt in an inner-alpine dry valley. New Phytol. 220, 460–475 (2018).
Article  Google Scholar 

20.
Wieser, G. (ed.) Trees at Their Upper Limit. Treelife Limitation at the Alpine Timberline (Springer, Dordrecht, 2007).
Google Scholar 

21.
Dedysh, S. N. et al. Methylocapsa palsarum sp. nov., a methanotroph isolated from a subArctic discontinuous permafrost ecosystem. Int. J. Syst. Evol. Microbiol. 65, 3618–3624. https://doi.org/10.1099/ijsem.0.000465 (2015).
CAS  Article  PubMed  Google Scholar 

22.
Praeg, N., Wagner, A. O. & Illmer, P. Plant species, temperature, and bedrock affect net methane flux out of grassland and forest soils. Plant Soil 410, 193–206 (2017).
CAS  Article  Google Scholar 

23.
Lladó, S., López-Mondéjar, R. & Baldrian, P. Forest soil bacteria: diversity, involvement in ecosystem processes, and response to global change. Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/MMBR.00063-16 (2017).
Article  PubMed  PubMed Central  Google Scholar 

24.
Urbanová, M., Šnajdr, J. & Baldrian, P. Composition of fungal and bacterial communities in forest litter and soil is largely determined by dominant trees. Soil. Biol. Biochem. 84, 53–64. https://doi.org/10.1016/j.soilbio.2015.02.011 (2015).
CAS  Article  Google Scholar 

25.
Liu, J. et al. Characteristics of bulk and rhizosphere soil microbial community in an ancient Platycladus orientalis forest. Appl. Soil Ecol. 132, 91–98. https://doi.org/10.1016/j.apsoil.2018.08.014 (2018).
ADS  Article  Google Scholar 

26.
Uroz, S. et al. Specific impacts of beech and Norway spruce on the structure and diversity of the rhizosphere and soil microbial communities. Sci. Rep. 6, 27756. https://doi.org/10.1038/srep27756 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Štursová, M., Bárta, J., Šantrůčková, H. & Baldrian, P. Small-scale spatial heterogeneity of ecosystem properties, microbial community composition and microbial activities in a temperate mountain forest soil. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw185 (2016).
Article  PubMed  Google Scholar 

28.
Ferrari, B., Winsley, T., Ji, M. & Neilan, B. Insights into the distribution and abundance of the ubiquitous candidatus Saccharibacteria phylum following tag pyrosequencing. Sci. Rep. 4, 3957. https://doi.org/10.1038/srep03957 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122. https://doi.org/10.1186/s40168-018-0499-z (2018).
Article  PubMed  PubMed Central  Google Scholar 

30.
Brewer, T. E., Handley, K. M., Carini, P., Gilbert, J. A. & Fierer, N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat. Microbiol. 2, 16198. https://doi.org/10.1038/nmicrobiol.2016.198 (2016).
CAS  Article  PubMed  Google Scholar 

31.
Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 744. https://doi.org/10.3389/fmicb.2016.00744 (2016).
Article  PubMed  PubMed Central  Google Scholar 

32.
Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
Article  Google Scholar 

33.
Johnston-Monje, D., Lundberg, D. S., Lazarovits, G., Reis, V. M. & Raizada, M. N. Bacterial populations in juvenile maize rhizospheres originate from both seed and soil. Plant Soil 405, 337–355. https://doi.org/10.1007/s11104-016-2826-0 (2016).
CAS  Article  Google Scholar 

34.
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Nat. Acad. Sci. USA 109, 21390–21395. https://doi.org/10.1073/pnas.1215210110 (2012).
ADS  Article  PubMed  Google Scholar 

35.
Kottke, I. & Oberwinkler, F. Comparative studies on the mycorrhization of Larix decidua and Picea abies by Suillus grevillei. Trees https://doi.org/10.1007/BF00196758 (1988).
Article  Google Scholar 

36.
Uroz, S., Buée, M., Murat, C., Frey-Klett, P. & Martin, F. Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil. Environ. Microbiol. Rep. 2, 281–288. https://doi.org/10.1111/j.1758-2229.2009.00117.x (2010).
CAS  Article  PubMed  Google Scholar 

37.
Mapelli, F. et al. The stage of soil development modulates rhizosphere effect along a High Arctic desert chronosequence. ISME J. 12, 1188. https://doi.org/10.1038/s41396-017-0026-4 (2018).
Article  PubMed  PubMed Central  Google Scholar 

38.
Mello, B. L., Alessi, A. M., McQueen-Mason, S., Bruce, N. C. & Polikarpov, I. Nutrient availability shapes the microbial community structure in sugarcane bagasse compost-derived consortia. Sci. Rep. 6, 38781. https://doi.org/10.1038/srep38781 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Turnbull, G. A., Morgan, J. A. W., Whipps, J. M. & Saunders, J. R. The role of bacterial motility in the survival and spread of Pseudomonas fluorescens in soil and in the attachment and colonisation of wheat roots. FEMS Microbiol. Ecol. 36, 21–31. https://doi.org/10.1111/j.1574-6941.2001.tb00822.x (2001).
CAS  Article  PubMed  Google Scholar 

40.
Rees, D. C., Johnson, E. & Lewinson, O. ABC transporters: the power to change. Nat. Rev. Mol. Cell. Biol. 10, 218–227. https://doi.org/10.1038/nrm2646 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Aronson, E. L., Allison, S. D. & Helliker, B. R. Environmental impacts on the diversity of methane-cycling microbes and their resultant function. Front. Microbiol. 4, 225. https://doi.org/10.3389/fmicb.2013.00225 (2013).
Article  PubMed  PubMed Central  Google Scholar 

42.
Dalal, R. C., Allen, D. E., Livesley, S. J. & Richards, G. Magnitude and biophysical regulators of methane emission and consumption in the Australian agricultural, forest, and submerged landscapes. A review. Plant Soil 309, 43–76 (2008).
CAS  Article  Google Scholar 

43.
Martins, C. S. C., Nazaries, L., Macdonald, C. A., Anderson, I. C. & Singh, B. K. Water availability and abundance of microbial groups are key determinants of greenhouse gas fluxes in a dryland forest ecosystem. Soil Biol. Biochem. 86, 5–16. https://doi.org/10.1016/j.soilbio.2015.03.012 (2015).
CAS  Article  Google Scholar 

44.
Praeg, N., Schwinghammer, L. & Illmer, P. Larix decidua and additional light affect the methane balance of forest soil and the abundance of methanogenic and methanotrophic microorganisms. FEMS Microbiol Lett. https://doi.org/10.1093/femsle/fnz259 (2020).
Article  Google Scholar 

45.
Ström, L., Mastepanov, M. & Christensen, T. R. Species-specific effects of vascular plants on carbon turnover and methane emissions from wetlands. Biogeochemistry 75, 65–82 (2005).
Article  Google Scholar 

46.
Borrel, G. et al. Genome sequence of “Candidatus Methanomassiliicoccus intestinalis” Issoire-Mx1, a third thermoplasmatales-related methanogenic archaeon from human feces. Genome Announc. 1, e004523. https://doi.org/10.1128/genomeA.00453-13 (2013).
Article  Google Scholar 

47.
Deng, Y., Liu, P. & Conrad, R. Effect of temperature on the microbial community responsible for methane production in alkaline NamCo wetland soil. Soil Biol. Biochem. 132, 69–79. https://doi.org/10.1016/j.soilbio.2019.01.024 (2019).
CAS  Article  Google Scholar 

48.
Söllinger, A. et al. Phylogenetic and genomic analysis of Methanomassiliicoccales in wetlands and animal intestinal tracts reveals clade-specific habitat preferences. FEMS Microbiol. Ecol. 92, 149. https://doi.org/10.1093/femsec/fiv149 (2016).
CAS  Article  Google Scholar 

49.
Berghuis, B. A. et al. Hydrogenotrophic methanogenesis in archaeal phylum Verstraetearchaeota reveals the shared ancestry of all methanogens. Proc. Natl. Acad. Sci. U.S.A. 116, 5037. https://doi.org/10.1073/pnas.1815631116 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Cai, Y., Zheng, Y., Bodelier, P. L. E., Conrad, R. & Jia, Z. Conventional methanotrophs are responsible for atmospheric methane oxidation in paddy soils. Nat. Commun. 7, 11728 (2016).
ADS  CAS  Article  Google Scholar 

51.
Henckel, T., Jäckel, U., Schnell, S. & Conrad, R. Molecular analyses of novel methanotrophic communities in forest soil that oxidize atmospheric methane. Appl. Environ. Microbiol. 60, 1801–1808 (2000).
Article  Google Scholar 

52.
Ricke, P., Kolb, S. & Braker, G. Application of a newly developed ARB software-integrated tool for in silico terminal restriction fragment length polymorphism analysis reveals the dominance of a novel pmoA cluster in a forest soil. Appl. Environ. Microbiol. 71, 1671–1673. https://doi.org/10.1128/AEM.71.3.1671-1673.2005 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Pratscher, J., Dumont, M. G. & Conrad, R. Assimilation of acetate by the putative atmospheric methane oxidizers belonging to the USCα clade. Environ. Microbiol. 13, 2692–2701. https://doi.org/10.1111/j.1462-2920.2011.02537.x (2011).
CAS  Article  PubMed  Google Scholar 

54.
Cai, Y., Zhou, X., Shi, L. & Jia, Z. Atmospheric methane oxidizers are dominated by upland soil cluster alpha in 20 forest soils of China. Microb. Ecol. 80, 859–871. https://doi.org/10.1007/s00248-020-01570-1 (2020).
CAS  Article  PubMed  Google Scholar 

55.
Täumer, J. et al. Divergent drivers of the microbial methane sink in temperate forest and grassland soils. Glob. Change Biol. https://doi.org/10.1111/gcb.15430 (2020).
Article  Google Scholar 

56.
Andreote, F. D. et al. Culture-independent assessment of Rhizobiales-related alphaproteobacteria and the diversity of Methylobacterium in the rhizosphere and rhizoplane of transgenic eucalyptus. Microb. Ecol. 57, 82–93. https://doi.org/10.1007/s00248-008-9405-8 (2009).
Article  PubMed  Google Scholar 

57.
Iguchi, H., Yurimoto, H. & Sakai, Y. Interactions of methylotrophs with plants and other heterotrophic bacteria. Microorganisms 3, 137–151. https://doi.org/10.3390/microorganisms3020137 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Ho, A. et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front. Microbiol. 7, 1285. https://doi.org/10.3389/fmicb.2016.01285 (2016).
Article  PubMed  PubMed Central  Google Scholar 

59.
Iguchi, H., Yurimoto, H. & Sakai, Y. Stimulation of methanotrophic growth in cocultures by cobalamin excreted by rhizobia. Appl. Environ. Microbiol. 77, 8509–8515. https://doi.org/10.1128/AEM.05834-11 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Veraart, A. J. et al. Living apart together—bacterial volatiles influence methanotrophic growth and activity. ISME J. 12, 1163–1166 (2018).
CAS  Article  Google Scholar 

61.
Karlsson, A. E., Johansson, T. & Bengtson, P. Archaeal abundance in relation to root and fungal exudation rates. FEMS Microbiol. Ecol. 80, 305–311 (2012).
CAS  Article  Google Scholar 

62.
Haichar, F. E. Z. et al. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2, 1221–1230. https://doi.org/10.1038/ismej.2008.80 (2008).
CAS  Article  PubMed  Google Scholar 

63.
Tkacz, A., Cheema, J., Chandra, G., Grant, A. & Poole, P. S. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. ISME J. 9, 2349–2359. https://doi.org/10.1038/ismej.2015.41 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

64.
Schinner, F. et al. (eds) Methods in Soil Biology (Springer, Berlin, 1996).
Google Scholar 

65.
Barillot, C. D. C., Sarde, C.-O., Bert, V., Tarnaud, E. & Cochet, N. A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system. Ann. Microbiol. 63, 471–476 (2013).
CAS  Article  Google Scholar 

66.
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Nat. Acad. Sci. U.S.A. 108(Suppl 1), 4516–4522. https://doi.org/10.1073/pnas.1000080107 (2011).
ADS  Article  Google Scholar 

67.
Ihrmark, K. et al. New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677. https://doi.org/10.1111/j.1574-6941.2012.01437.x (2012).
CAS  Article  PubMed  Google Scholar 

68.
White, T. J., Bruns, T., Lee, S. & Taylor, J. W. Amplification and direct sequencing of fungal ribosomal RNA Genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, Cambridge, 1990).
Google Scholar 

69.
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  Article  Google Scholar 

70.
Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 25, 914–919. https://doi.org/10.1111/2041-210X.12073 (2013).
Article  Google Scholar 

71.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mah, F. VSEARCH. A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Article  Google Scholar 

72.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).
CAS  Article  PubMed  Google Scholar 

73.
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277. https://doi.org/10.1111/mec.12481 (2013).
CAS  Article  PubMed  Google Scholar 

74.
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/AEM.00062-07 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

75.
Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).
CAS  Google Scholar 

76.
Martin, A. P. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl. Environ. Microbiol. 68, 3673–3682. https://doi.org/10.1128/AEM.68.8.3673-3682.2002 (2002).
CAS  Article  PubMed  PubMed Central  Google Scholar 

77.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
Article  Google Scholar 

78.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017). http://www.R-project.org. Accessed 24 Sept 2018.

79.
Oksanen, J. et al. vegan. Community Ecology Package. R package version 2.4–4 (2017). https://CRAN.R-project.org/package=vegan. Accessed 24 Sept 2018.

80.
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 44, W3–W10. https://doi.org/10.1093/nar/gkw343 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

81.
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16SrRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
CAS  Article  Google Scholar 

82.
White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, e1000352. https://doi.org/10.1371/journal.pcbi.1000352 (2009).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

83.
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

84.
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).
CAS  Article  PubMed  Google Scholar 

85.
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Article  Google Scholar 

86.
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
CAS  Article  PubMed  PubMed Central  Google Scholar 

87.
Pratscher, J., Vollmers, J., Wiegand, S., Dumont, M. G. & Kaster, A.-K. Unravelling the identity, metabolic potential and global biogeography of the atmospheric methane-oxidizing upland soil cluster α. Environ. Microbiol. 20, 1016–1029. https://doi.org/10.1111/1462-2920.14036 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

88.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

89.
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).
CAS  Article  PubMed  Google Scholar  More