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

Figure 2

Patch characteristics for NPC with (N=20). (a) individual patch connectivity versus normalized patch area parameter (beta _i). (b) pair-wise connectivity for the homogeneous patch case with (beta _i=0.5) (forall) i, versus normalized inter-patch distances (dfrac{D_{ij}}{L}) (forall) (i ne j).

Full size image

In the following section, we start by discussing some important characteristics of NPC. In “Homogeneous patches” section, we look at the system behavior for homogeneous patch parameters and contrast between different connectivity modes, namely (1) all-to-all connected, (2) fixed NBM, and (3) rewiring NBM. We study the homogeneous patch case first to focus on the influence of NPC on system dynamics without any confounding effects of patch heterogeneity. In “Heterogeneous patches” section, we discuss some general results for NBMs with heterogeneous patches, expressed through different patch carrying capacities.
NPC characteristics
For the case of (N=20) patches, individual patch (C_i) and pair-wise patch (C_{ij}) connectivity estimates are shown as functions of patch areas (beta _i) and inter-patch distances (D_{ij}), in Fig. 2a,b respectively. We observe a nearly linear growth in (C_i) as a function of patch areas in Fig. 2a, and a nonlinear (exponential) decrease in (C_{ij}) as a function of inter-patch distances in Fig. 2b. These results nicely illustrate the essential features of the NPC as formulated in Eq. (3): (a) larger patches have more incoming/outgoing connections as compared to smaller patches, and (b) closely located patches connect more frequently as compared to distant ones.
Homogeneous patches
The influence of dispersal rate d on species persistence are investigated and compared for the (1) all-to-all connected model, (2) fixed NBM, and (3) rewiring NBM. For uniformity in comparison, we consider the dispersal efficiency parameter (delta =1) in the following “Probability of persistence” and “Influence of patch density and network rewiring rates on persistence” sections.
Probability of persistence
Figure 3

Persistence probabilities (P_{mathrm{per}}) as a function of dispersal rate d, for different connectivity configurations with (N=20) and dispersal efficiency (delta =1). Curves: all-to-all connected (black), fixed NBM (green), and rewiring NBM (red) (rewiring every 100 time units). The ensemble sizes (number of network realizations) for these calculations are fixed at (N_{text{ensemble}}=100) for all three cases. Standard error values (mathrm{SE}(P_{mathrm{per}})), for NBM calculations are highlighted by error bars every four data points to avoid graph overcrowding. Corresponding biomass calculations can be seen in the Online Appendix Fig. A.1.

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Figure 3 highlights the influence of NPC on the system dynamics for the (N=20) case. For the (1) deterministic all-to-all connected case, species persistence (P_{{rm per}}=1) in the entire d range, and is mathematically expected to stay at the same value for arbitrarily high (d rightarrow infty) values—which is extremely counter intuitive considering the physical bounds and limitations in natural systems. Notably for an all-to-all connected system with (delta =1), the second term in Eq. (1) vanishes due to the parameter symmetry and the diffusive mean field nature of the term. Consequently, the dynamics of this system is essentially independent of the dispersal rate d, and therefore not affected by any changes in d. Hence in this case, local populations in each patch independently follow their logistic growth, and settle on the respective carrying capacity (K_i = K) (forall) i—which constitutes the stable equilibrium for the system. On the contrary, for the other two cases of (2) fixed and (3) rewiring NBMs, we observe decreasing (P_{mathrm{per}}) estimates for increasing d values. These results suggest that several network realizations in the NPC ensembles can lead to species extinction with higher dispersal rates in the NBMs. Furthermore, for rewiring NBM, (P_{mathrm{per}}) approaches very low values for higher d, before exhibiting species extinction (P_{mathrm{per}}=0) for (d approx 28) (not shown). On the contrary, we do not observe species extinction (P_{mathrm{per}}=0) for quite high d values for the fixed NBM case. Please see Online Appendix: Fig. A.1 for the corresponding average biomass calculations.
Influence of patch density and network rewiring rates on persistence
Figure 4

Persistence probabilities (P_{mathrm{per}}) as a function of the dispersal rate d for (a) fixed, and (b) rewiring (every 100 time units) NBMs. (N_{text{ensemble}}=100) network realizations were used for the simulations, for (N=10) (blue), (N=20) (red) and (N=40) (black) network sizes. Standard error values (mathrm{SE}(P_{mathrm{per}})) for these calculations are highlighted by error bars every four data points. Related biomass calculations can be seen in Online Appendix Fig. A.2.

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Figure 4 shows the relationship between species persistence probability (P_{mathrm{per}}), and dispersal rate d for fixed and rewiring NBMs. Since the value of largest possible distance in the system (L) is fixed (fixed landscape) for all calculations, an increase in number of patches N corresponds to an increase in patch density in the meta-population. The results suggest that a higher patch density supports species persistence in the system for higher dispersal rates d. For (N=10), (P_{mathrm{per}} > 0) (non-zero) for the fixed NBM case over the entire investigated range of d, extending to even higher d values (not shown here). This implies that there are always some network realizations in the ensemble that support species persistence. In contrast, the species go extinct at (d approx 5) for the rewiring NBM case. For (N=20), both fixed and rewiring NBMs yield non-zero (P_{mathrm{per}}) estimates in the investigated d range. However we observe a steeper decrease in persistence with increasing d, and consequently, extinction for (d approx 28) (not shown) for the rewiring NBM. For higher number of patches: (N=40), (P_{mathrm{per}} approx 1) for the entire d range for both fixed and rewiring NBMs, and this behavior extends to even higher d values above the range shown in Fig. 4. Our calculations suggest an exponential growth in the total number of connections as a function of number of patches N for a fixed landscape, and hence for increasing patch densities (see Online Appendix B, Fig. B.1). Therefore, we can conclude that higher patch densities tend to shift the (P_{mathrm{per}}) behavior closer to a highly connected case, which yields (P_{mathrm{per}}) results similar to the all-to-all connected situation. Overall, (N=20) seems to provide an appropriate trade-off between a system which is neither too sparse, nor a densely filled landscape, where a sparse system might lead to a higher proportion of isolated patches, and a dense system can mask the effects of spatially explicit connectivity. Therefore, we use (N=20) as the standard meta-population size in most of our calculations for the given set of parameter values.
For the case of rewiring NBMs, persistence probability (P_{mathrm{per}}) is also influenced by the rewiring time intervals of the connectivity matrix. Results in Fig. 5 compare the (P_{mathrm{per}}) estimates for fixed, and rewiring NBMs with different rewiring rates—every 10 and 100 time units. These results show that a faster rewiring NBM promotes species persistence in comparison to the fixed and slower rewiring NBMs, for a range of dispersal rates (d in (5,10))—a faster rewiring provides higher (P_{mathrm{per}}) estimates in this d range. However, the average biomass estimates for all these three cases are quite similar in this dispersal rate range (Online Appendix: Fig. A.3). However, for higher d values, species persistence for both rewiring NBMs is lower compared to the fixed NBM. Eventually, the rewiring NBMs exhibit species extinction for higher d, whereas, the fixed NBM still yields non-vanishing (P_{mathrm{per}}) estimates (not shown).
Figure 5

(P_{mathrm{per}}) estimates for (N=20), as a function of dispersal rate d, and (mathrm{SE}(P_{mathrm{per}})) are highlighted by error bars every four data points, for identical patches with fixed (black) versus different rewiring rates—every 10 (green) and 100 (red) time units. (N_{text{ensemble}}=100) network realizations were used for these calculations. Corresponding biomass estimates are provided in Online Appendix Fig. A.3.

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To better understand the mechanism of species extinction in the meta-population, we need to focus on the critical role of the between-patch interaction (second) term in Eq. (1), i.e. (-dleft( x_i-dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_jright)). As mentioned before, this term assumes that the dispersal process is diffusive in nature, and the diffusion occurs along a gradient from higher to lower values. With this assumption, the following situations can occur for any (d >0): (1) average input from other patches is higher than the patch population, i.e. (dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_j > x_i) (implies) (dleft( dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_j – x_iright) >0), the interaction term is positive implying an increase in the number of individuals within the patch via dispersal, i.e. net species movement is directed into the patch due to the population gradient, (2) average input from other patches is less than the patch population, i.e. (x_i >dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_j) (implies) (dleft( dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_j – x_iright) 0), the underlying NPC has a strong influence on the observed dynamics. The underlying connectivity matrix can lead to a situation where some patches have no incoming connections—such a situation is more likely in a sparsely connected network and/or for a network with low patch density. Absence of any incoming connections will lead to case (3) as discussed above, implying to net loss in the local biomass. Consequently, local populations will go extinct in these source-only patches once the dispersal rate is higher than the species growth rate for case (3). This extinction will decrease the biomass flux from these patches to the connected sink patches. With increasing dispersal rates, this will lead to case (2) for these sink patches and eventually they will also experience local extinction, thereby, enabling a cascade which leads to the extinction of the entire meta-population. In terms of bifurcation analysis, this extinction corresponds to a transcritical bifurcation (stability exchange) between equilibria with species persistence and extinction. An important point to consider is that for a fixed landscape, different NPC realizations can give rise to completely different persistence/extinction scenarios. Considering an NPC realization where all patches have at least one incoming connection, and another realization where one/some patches have no incoming connections (source-only case) or are isolated, the described mechanism indicates that species can persist for comparatively higher d values in the former case, as compared to the latter.
Figure 6

(i) (P_{mathrm{per}}) projection on the ((d,delta )) plane for homogeneous patches, and (ii) for the heterogeneous patch case. [(a.i),(a.ii)] correspond to all-to-all connected system, [(b.i),(b.ii)] to fixed NBM, and [(c.i),(c.ii)] to rewiring NBM with a rewire every 100 time units. The blue shaded area corresponds to meta-population extinction, i.e. (P_{mathrm{per}}=0), whereas red regimes correspond to (P_{mathrm{per}}=1). The boundary between persistence and extinction for the all-to-all connected system is indicated by a yellow curve which for comparison, is indicated in all panels. (N_{text{ensemble}}=100) for these calculations. Corresponding biomass calculations are shown in Online Appendix Fig. A.4.

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Figure 7

(i) (mathrm{SE}(P_{mathrm{per}})) projection on the ((d,delta )) plane for homogeneous patches, and (ii) for the heterogeneous patch case. [(a.i),(a.ii)] correspond to all-to-all connected system, [(b.i),(b.ii)] to fixed NBM, and [(c.i),(c.ii)] to NBM rewiring every 100 time units. The blue shaded area corresponds to regimes where the error is zero. Positive values are highlighted by colors as per the attached color bar. The boundary between persistence and extinction for the all-to-all connected system is again indicated by a yellow curve for comparison in all the panels. (N_{text{ensemble}}=100) for these calculations. The dynamical differences between all-to-all connected, and NBMs are even more obvious in these calculations.

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Dependence of persistence patterns on dispersal rate and efficiency
So far, the results were presented for cases with a dispersal efficiency (delta _i = delta = 1). However, an assumption of 100% efficiency of dispersal is unrealistic. Losses during the dispersal process are likely to occur in natural systems and there is a chance of the species not establishing after arrival in a new patch. To take these losses into account, we introduced (delta) in our system Eq. (1). In the following, we investigate species persistence for different connectivity scenarios for (delta in [0,1]), and (d in [0,20]). (P_{mathrm{per}}) ((mathrm{SE}(P_{mathrm{per}}))) behaviors for the homogeneous case are shown in the top row of Fig. 6 (Fig. 7), for (a.i) all-to-all connected, (b.i) fixed NBM, and (c.i) rewiring NBM, respectively.
Starting with the all-to-all connected case, we do not observe any transitions to (P_{mathrm{per}}=0) for (delta =1). The reason is related to parameter symmetries leading to a vanishing interaction term in Eq. (1) as discussed before. Therefore, the species in their respective patches can grow to the patch carrying capacities (K_i = K) (forall) i—see also the average biomass estimates in Online Appendix Fig. A.4. On considering dispersal efficiency (delta < 1), the interaction term in Eq. (1) does not vanish, and therefore, species extinction can arise, as seen in Fig. 6(a.i), where red (blue) areas correspond to persistence probabilities of one (zero). Linear stability analysis of the extinction equilibrium, (mathbf{x} ^*=mathbf{0} implies (x_1^*=0,x_2^*=0,ldots , x_N^*=0)^T), and any general (N ge 2) yields the following eigenspectrum, $$begin{aligned} begin{aligned} lambda _i={left{ begin{array}{ll}(r-d) + ddelta , &{}quad i=1 (largest) \ (r-d)-dfrac{ddelta }{(N-1)} &{}quad i=2,ldots ,N. end{array}right. } end{aligned} end{aligned}$$ (4) Note that these eigenvalues (lambda _i)s are independent of patch carrying capacities, therefore the persistence–extinction boundary is unaffected by parameter mismatch in the carrying capacities. Using the largest eigenvalue (lambda _1), one can follow the transition boundary of the transcritical bifurcation between persistence and extinction in the ((d,delta )) plane, which satisfies the expression (lambda _1 = (r-d) + ddelta =0). This boundary (yellow curve) is highlighted in Fig. 6(a.i) and is in excellent agreement with the theoretical estimates of this transition boundary (see Online Appendix Fig. A.4 for biomass calculations). Additionally, corresponding (mathrm{SE}(P_{mathrm{per}})) calculations in Fig. 7(a.i) show that the error values for the homogeneous all-to-all connected case are uniformly vanishing in the entire considered ((d,delta )) plane. This is due to the behavior of (P_{mathrm{per}}) which is 1 before the bifurcation boundary and 0 after the bifurcation leading to meta-population extinction. For the spatially explicit Fig. 6(b.i) fixed NBM, and Fig. 6(c.i) rewiring NBM cases, we observe that the persistence–extinction transition boundary is not as sharp as for the all-to-all connected case. For comparison, the transition boundary for the all-to-all case is indicated by a yellow curve in these figures. For the fixed NBM case, starting from lower (delta) and d values, the transition becomes more uncertain as we increase the value of (delta)—which can be seen by the presence of lighter colored regimes corresponding to low, non-zero persistence probabilities. This ambiguity between persistence and extinction increases even further for higher (delta). These results suggest that for high (delta), and d values, species extinction is possible for the fixed NBM case contrary to the all-to-all connected network, where extinction is impossible in the similar parameter regime. The reasoning behind these results is quite straightforward considering how (P_{mathrm{per}}) is estimated. For high (delta) and d values, there is a proportion of connectivity configurations which lead to species extinction following the mechanism discussed in “Influence of patch density and network rewiring rates on persistence” section. Due to these configurations, we obtain (0< P_{mathrm{per}} < 1) in this case. This effect is even more pronounced for the rewiring network, where the blue (extinction) regimes extend to even higher (delta) and d values, thereby comparatively reducing the regimes of persistence in the parameter space. These results are quite contrary to the all-to-all connected system where the persistence is ensured with (P_{mathrm{per}} = 1) along the entire range of d values for high (delta). The dynamical differences between the all-to-all connected and NBMs are even more conspicuous in the corresponding (mathrm{SE}(P_{mathrm{per}})) calculations, see Fig. 7(b.i),(c.i). For the parameter regimes where (P_{mathrm{per}} = 1 (0)), the (mathrm{SE}(P_{mathrm{per}})) values are either zero or very small. For (0< P_{mathrm{per}} < 1), (mathrm{SE}(P_{mathrm{per}})) exhibit higher values implying a higher variability in the (P_{mathrm{per}}) estimates for NBMs. This comparison also highlights the differences between the fixed and rewiring NBMs. In correspondence to (P_{mathrm{per}}) estimates, fixed NBM results exhibit a higher variability for high (delta) and high d values, as compared to the rewiring NBM case. This is due to the fact that in these ranges of high variability for fixed NBM, the rewiring NBM predicts extinction of the meta-population. It is quite natural that a meta-population with a local finite growth rate r and a biomass loss during dispersal, cannot sustain a population for ever increasing dispersal rates. For lower (delta) regimes, all three connectivity scenarios follow this reasoning. For higher (delta), the all-to-all connected system can sustain the meta-population for arbitrary high (d rightarrow infty) values with (P_{mathrm{per}} = 1). In comparison, our NBM implementations yield reasonable estimates of (P_{mathrm{per}} < 1) in high (delta) and d ranges. Additionally, rewiring NBMs show a higher likelihood of extinction than the fixed NBM—which highlights the fact that, everything else being constant, it is highly likely for a meta-population to exhibit extinction depending on the changes in underlying connectivity alone, which here, correspond to different NPC realizations. Heterogeneous patches Results for the heterogeneous patch case are shown in the bottom row of Fig. 6 for (a.ii) all-to-all connected, (b.ii) fixed NBM, and (c.ii) rewiring NBM. Here we investigate these three configurations for different patch areas (beta _i) (in) [0.3, 0.7], and consequently different patch carrying capacities, (K_i in [K_{min },K_{max }]), assigned to the patches in increments of (left( K_{max }-K_{min }right) /N). For our calculations in Fig. 6(a.ii),(b.ii),(c.ii), we chose (K_{min }=1.5), (K_{max }=3.5), and (N=20). We observe that the results for heterogeneous patches are quite similar to the results with identical carrying capacities. The similarity in the transition boundary can be explained by looking at the eigenspectrum in Eq. (4). The eigenspectrum for the all-to-all connected system is independent of the patch carrying capacities for the extinction equilibrium and therefore, the extinction threshold is not affected by the dissimilarity in the carrying capacities. Accordingly, for the all-to-all connected cases in Fig. 6(a.i),(a.ii), there are no differences, and (P_{mathrm{per}}) behaves identically in both the homogeneous [Fig. 6(a.i)] and heterogeneous patch [Fig. 6(a.ii)] cases. Like for the homogeneous case, (mathrm{SE}(P_{mathrm{per}})) calculations in Fig. 7(a.ii) again show that the error values for the heterogeneous all-to-all connected case are uniformly vanishing in the entire considered ((d,delta )) plane. For the fixed NPC case [Fig. 6(b.i),(b.ii)], we observe some differences for the transition boundaries for higher (delta) values. The (P_{mathrm{per}}) estimates at the transition boundaries for high (delta) and d values are lower (lighter blue dots) in the heterogeneous case [Fig. 6(b.ii)] when compared to the homogeneous case [Fig. 6(b.i)], thereby signifying that in the heterogeneous case, more realizations in the ensemble close to the transition lead to extinction. At the same time, we observe that patch heterogeneity shifts the extinction threshold towards higher (delta) values, as compared to the homogeneous case. This implies that for the case of heterogeneous patches, we will observe species extinction for higher (delta) values where the homogeneous system still supports persistence. A similar pattern can be observed for the homogeneous [Fig. 6(c.i)] and heterogeneous [Fig. 6(c.ii)] rewiring SEMs. Similar to the homogeneous case, dynamical differences between all-to-all and SEMs are yet again more obvious in (mathrm{SE}(P_{mathrm{per}})) calculations in Fig. 7(b.ii),(c.ii). These observations suggest that unlike in the all-to-all connected case, patch heterogeneity appears to play an essential role in determining the extinction threshold for meta-populations as a function of d and (delta) in fixed, as well as rewiring NBMs. More

1.
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260. https://doi.org/10.1034/j.1600-0706.2003.12559.x (2003).
Article  Google Scholar 
2.
Sampath, K. Studies on the ecology of shorebirds (Aves: Charadriifonnes) of the Great Vedaranyam Salt Swamp and the Pichavaram Mangroves of India. Ph.D. Thesis, submitted to Annamalai University, South India 202 (1989).

3.
Sampath, K. & Krishnamurthy, K. Shorebirds (Charadriiformes) of the Pichavaram Mangroves Tamilnadu, India. Wader Study Group Bull. 58, 24–27 (1990).
Google Scholar 

4.
Balachandran, S. Avian Diversity in Coastal Wetlands of India and their Conservation Needs 155–163 (International Day for biological diversity, Marine biodiversity, 2012).
Google Scholar 

5.
Sandilyan, S. & Kathiresan, K. Decline of mangroves–a threat of heavy metal poisoning in Asia. Ocean Coast. Manag. 102, 161–168 (2014).
Article  Google Scholar 

6.
Pandiyan, J. & Asokan, S. Habitat use pattern of tidal mud and sand flats by shorebirds (Charadriiformes) wintering in southern India. J. Coast. Cons. 20(1), 1–11 (2015).
Google Scholar 

7.
CAF Report. For a Preliminary List of Regional and National Activities That Contribute to Migratory Waterbird and Habitat Conservation in the CAF Region. https://www.cms.int/sites/default/files/document/CAF_action_plan_e_0.pdf (2005).

8.
Bamford, M., Watkins, D., Bancroft, W., Tischler, G. & Wahl, J. Migratory Shorebirds of the East Asian—Australasian Flyway Population Estimates and Internationally Important Sites (Wetlands International – Oceania, Canberra, 2008).
Google Scholar 

9.
Agoramoorthy, G., Chen, F. A. & Hsu, M. J. Threat of heavy metal pollution in halophytic and mangrove plants of Tamil Nadu India. Environ. Pollut 155(2), 320–326 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Agoramoorthy, G. & Pandiyan, J. Toxic pollution threatens migratory shorebirds in India. Environ. Sci. Pollut. Res. 23(15), 15771–15772 (2016).
Article  Google Scholar 

11.
Salzano, R. & Angelone, M. Reactivity of urban environments towards legislative actions. The case of Roma (Italy). In E3S Web of Conferences.1, 22003 EDP Sciences (2013).

12.
Ullah, K., Hashmi, M. Z. & Malik, R. N. Heavy-metal levels in feathers of cattle egret and their surrounding environment: a case of the Punjab province, Pakistan. Arch. Environ. Contam. Toxicol. 66, 139–153 (2014).
CAS  PubMed  Article  Google Scholar 

13.
Wilson, E. O. Threats to biodiversity. Am Sci. 261, 108–116 (1989).
Article  Google Scholar 

14.
Huettmann, F. & Czech, B. The steady state economy for global shorebird and habitat conservation. Endang. Species Res. 2, 89–92 (2006).
Article  Google Scholar 

15.
Taber, R. D. & Payne, N. F. Wildlife, Conservation, and Human Welfare: A United States and Canadian Perspective (Krieger Publishing Company, Malabar, Florida, 2003).
Google Scholar 

16.
Rogers, D., Piersma, T., Lavaleye, M., Pearson, G. B. & de Goeij, P. Life Along Land’s Edge: Wildlife on the Shores of Roebuck Bay, Broome (Dept. of Conservation and Land Management, Western Kensington, 2003).
Google Scholar 

17.
Custer, C. M., Custer, T. W., Michael, J. A., Alan, D. A. & David, E. W. Trace elements in Lesser Scaup (Aythyaaffinis) from the Mississippi flyway. Ecotoxicology 12, 47–54 (2003).
CAS  PubMed  Article  Google Scholar 

18.
Johansen, P., Asmund, G. & Riget, F. High human exposure to lead through consumption of birds hunted with lead shot. Environ. Pollut. 127, 125–129 (2004).
CAS  PubMed  Article  Google Scholar 

19.
Mansouri, N. E. et al. Research on the suitability of organosolv semi-chemical triticale fibres as reinforcement for recycled HDPE composites. Bio. Resources. 7(4), 5032–5047 (2012).
Google Scholar 

20.
Syed, J. H. & Malik, R. N. Occurrence and source identification of organochlorine pesticides in the surrounding surface soils of the Ittehad Chemical Industries Kalashah Kaku, Pakistan. Environ. Earth Sci. 62(6), 1311–1321 (2011).
CAS  Article  Google Scholar 

21.
Eqani, S. et al. Distribution and risk assessment of organochlorine contaminants in surface water from River Chenab, Pakistan. J. Environ. Monit. 14(6), 1645–1654 (2012).
CAS  PubMed  Article  Google Scholar 

22.
Qadir, A. & Malik, R. N. Heavy metals in eight edible fish species from two polluted tributaries (Aik and Palkhu) of the River Chenab, Pakistan. Biol. Trace Elem. Res. 143, 1524–1540 (2011).
CAS  PubMed  Article  Google Scholar 

23.
Hashmi, H. Z., Malik, R. N. & Shahbaz, M. Heavy metals in eggshells of cattle egret (Bubulcus ibis) and little egret (Egretta garzetta) from the Punjab province, Pakistan. Ecotoxicol Environ. Saf. 89, 158–165 (2013).
CAS  PubMed  Article  Google Scholar 

24.
Shahbaz, M., Khan, S. & Tahir, M. I. The dynamic links between energy consumption, economic growth, financial development and trade in China: fresh evidence from multivariate framework analysis. Energy Econ. 40, 8–21 (2013).
Article  Google Scholar 

25.
Kim, J. & Koo, T. H. Heavy metal concentrations in feathers of Korean shorebirds. Arch. Environ. Contam. Toxicol. 55, 122–128 (2008).
CAS  PubMed  Article  Google Scholar 

26.
Boncompagni, E. et al. Egrets as monitors of trace metal contamination in wetland of Pakistan. Arch. Environ. Contam. Toxicol. 45, 399–406 (2003).
CAS  PubMed  Article  Google Scholar 

27.
Nagajyoti, P. C., Lee, K. D. & Sreekanth, T. V. M. Heavy metals, occurrence and toxicity for plants: a review. Environ Chem. Lett. 8(3), 199–216 (2010).
CAS  Article  Google Scholar 

28.
Jaishankar, M., Mathew, B. B., Shah, M. S., Murthy, K. T. P. & Gowda, S. K. R. Biosorption of few heavy metal ions using agricultural wastes. J. Environ. Pollut. Hum. Health. 2, 1–6 (2014).
Google Scholar 

29.
Deng, H., Zhang, Z., Chang, C. & Wang, Y. Trace metal concentration in Great Tit (Parus major) and Greenfinch (Carduelissinica) at the Western Mountains of Beijing, China. Environ. Poll. 148, 620–626 (2007).
CAS  Article  Google Scholar 

30.
Kim, J. & Koo, T. H. The use of feathers to monitor heavy metal contamination in herons, KOrea. Arch. Environ. Contam. Toxicol. 53, 435–441 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Burger, J. & Gochfeld, M. Effects of lead and exercise on endurance and learning in young herring gulls. Ecotoxicol. Environ. Saf. 57, 136–144 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Scheifler, R., Coeurdassier, M. & Morilhat, C. Lead concentrations in feathers and blood of common blackbirds (Turdusmerula) and in earthworm inhabiting unpolluted and moderately polluted urban areas. Sci Total Environ. 371, 197–205 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Burger, J. Heavy metals in avian eggshells: another excretion method. J. Ecotoxicol. Environ. Health 41(2), 207–220 (1994).
CAS  Article  Google Scholar 

34.
Furness, R. W. Birds as monitors of environmental change 102–120 (Chapman, New Yok, 1993).
Google Scholar 

35.
Bostan, N., Ashrif, M., Mumtaz, A. S. & Ahmad, I. Diagnosis of heavy metal contamination in agro-ecology of Gujranwala, Pakistan using cattle egret as bioindicator. Ecotoxicology 6, 247–251 (2007).
Article  CAS  Google Scholar 

36.
Lee, C. S. L., Li, X., Shi, W., Cheung, S. C. & Thornton, I. Metal contamination in urban, suburban and country park soils of Hong Kong: a study based on GIS and multivariate statistics. Sci. Total Environ. 356, 45–61 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Balachandran, S., Sathiyaselvam, P. & Panda, S. Bird atlas of Chilka (ed. BNHS) 1–326 (BNHS, 2009).

38.
Sugathan, R. Observations on Spoon billed Sandpiper (Eurynorhynchus pygmaeus) in its wintering ground at Point Calimere, Thanjavur District, Tamil Nadu. J. Bombay Nat. Hist. Soc. 82(2), 407–409 (1985).
Google Scholar 

39.
Spoon-billed Sandpiper Task Force. News Bull. No 19 (2018).

40.
Pandiyan, J. & Jagadheesan, R. Population characteristics of migratory shorebirds in the Point Calimere Wildlife Sanctuary, Tamil Nadu, India. J. Sci. Trans. Environ. Technov. 10(1), 31–36 (2016).
Google Scholar 

41.
Manikannan, R. Diversity of Water birds in the point Calimere wildlife sanctuary, Tamil Nadu, India. Ph.D. thesis submitted to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India 264 (2011)

42.
Kathiresan, K. A review of studies on Pichavaram mangrove, southeast India. Hydrobiologia 430(1–3), 185–205 (2000).
Article  Google Scholar 

43.
Godhantaraman, N. Seasonal variations in species composition, abundance, biomass and estimated production rates of tintinnids at tropical estuarine and mangrove waters, Parangipettai, southeast coast of India. J. Mar. Syst. 36, 161–171 (2002).
Article  Google Scholar 

44.
Rajendran, N. & Kathiresan, K. How to increase juvenile shrimps in mangrove waters?. Wetlands Ecol. Manage. 12–3, 179–188 (2004).
Article  Google Scholar 

45.
Nagarajan, R. & Thiyagesan, K. Waterbirds and substrate quality of the Pichavaram wetlands, southern India. Ibis. 138, 710–721 (1996).
Article  Google Scholar 

46.
Burger, J. & Gochfeld, M. Metals in Laysan Albatrosses from Midway Atoll. Arch Environ Contamin. Toxicol. 38, 254–259 (2000).
CAS  Article  Google Scholar 

47.
Dauwe, T., Bervoets, L., Blust, R., Pinxten, R. & Eens, M. Can excrements and feathers of nestling songbirds be used as a biomonitor for heavy metal pollution. Arch. Environ Contam Toxicol. 39, 541–546 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Nyholm, N. E. Y. Monitoring of terrestrial environmental metal pollution by means of free-living insectivorous birds. Ann. Chim. 85, 343–351 (1995).
CAS  Google Scholar 

49.
Kim, J. & Oh, J. M. Monitoring of heavy metal contaminants using feathers of shorebirds Korea. J. Environ. Monit. 14, 651 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Dauwe, T. et al. Great and blue tit feathers as biomonitors for heavy metal pollution. Ecol. Ind. 1, 227–234 (2002).
CAS  Article  Google Scholar 

51.
Sokal, R. R. & Rohlf, F. I. Biometry: The Principles and Practice of Statistics in Biological Research 1–776 (W.H. Freeman, New York, 2012).
Google Scholar 

52.
Sandilyan, S. Habitat quality and waterbird utilization pattern of Pichavaram wetlands southern India. Ph.D. Thesis, Bharathidasan University, Tiruchirapalli, India 287 (2009)

53.
Loska, K. & Wiechuła, D. Application of principal component analysis for the estimation of source of heavy metal contamination in surface sediments from the Rybnik Reservoir. Chemosphere 51(8), 723–773 (2003).
ADS  CAS  PubMed  Article  Google Scholar 

54.
Balachandran, S. Avian Diversity in Coastal Wetlands of India and their Conservation Needs. International Day for biological diversity 155–163 (Uttar Pradesh State Biodiversity Board, Lucknow, 2012).
Google Scholar 

55.
Worrall, D. H. Diet of the Dunlin Calidris alpina in the Severn estuary. Bird Study 31(3), 203–212 (1984).
Article  Google Scholar 

56.
Pienkowski, M. W. Aspects of the ecology and behaviour of ringed and grey plovers charadrius hiaticula and pluvialis squatarola, Durham theses, Durham University. Durham E-Theses Online. http://etheses.dur.ac.uk/7868/ (1980).

57.
Hayman, P., Marchant, J., Prater, T. & Helm, C. Book for Shorebirds (1986)

58.
Higgins, P. J. & Davies, S. J. J. F. (eds) Handbook of Australian, New Zealand and Antarctic Birds Snipe to Pigeons (. Oxford University Press, Oxford, 1996).
Google Scholar 

59.
Ali, S. The Book of Indian Birds 1–326 (Bombay Natural History Society and Oxford University Press, Oxford, Mumbai, 2002).
Google Scholar 

60.
Everaarts, J. M. et al. Copper, zinc and cadmium in benthic organisms from the Java Sea and estuarine and coastal areas around East Java. Netherl. J. Sea Res. 23(4), 415–426 (1989).
ADS  CAS  Article  Google Scholar 

61.
Philips, D. J. H. The common mussels Mytilus edulis as an indicator of pollution by zinc, cadmium, lead, and copper. Effects of environmental variables on uptake of metals. Mar. Biol. 38, 59–69 (1976).
Article  Google Scholar 

62.
Michael, H. Trace metals in the tissues and shells of Tympanotonus Fuscatus var Radula from the Mangrove Swamps of the Bukuma Oil Field, Niger Delta. Eur. J. Sci. Res. 24(4), 468–547 (2008).
Google Scholar 

63.
Howarth, D. M., Hulbert, A. J. & Horning, D. A comparative Study of Heavy Metal Accumulation in Tissues of the Crested Tern, Sterna bergii, Breeding near Industrialized and Non-Industrialized Areas. Austr. Wildl. Res. 8, 665–672 (1981).
CAS  Article  Google Scholar 

64.
Zdziarski, J. M., Mattix, M. & Bush, R. M. Zinc toxicosis in diving ducks. J. Zool. Wildl. Med. 25, 438–445 (1994).
Google Scholar 

65.
Vanderzee, J., Zwart, P. & Schotman, A. J. H. Zinc poisoning in a Nicobar pigeon. J. Zool. Anim. Med. 16, 68–69 (1985).
Article  Google Scholar 

66.
Bamford, M., Watkins, D., Bancroft, W., Tischler, G. & Wahl, J. Migratory Shorebirds of the East Asian—Australasian Flyway; Population Estimates and Internationally Important Sites (Wetlands International – Oceania, Canberra, 2008).
Google Scholar 

67.
Balachandaran. International Day for Biological diversity. Marin Diversity. 155–163 (2006).

68.
Jerez, S. et al. Concentration of trace elements in feathers of three Antarctic penguins: geographical and interspecific differences. Environ. Pollut. 159, 2412–2419 (2011).
CAS  PubMed  Article  Google Scholar 

69.
Markowski, M. et al. Avian feathers as bioindicators of the exposure to heavy metal contamination of food. Bull. Environ. Contam. Toxicol. 91, 302–305 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Lim, H. C. & Posa, C. Distribution and prey of migratory shorebirds on the northern coastline of Singapore. Raffles Bull. Zool. 62, 701–717 (2014).
Google Scholar 

71.
Battley, P. F., Rogers, D. I., Piersma, T. & Koolhaas, A. Behavioural evidence for heat-load problems in great knots in tropical Australia fuelling for long-distance flight. Emu. 103(2), 97–103 (2003).
Article  Google Scholar 

72.
Hockey, B. A., & Rayner, M. Comparison of grammar-based and statistical language models trained on the same data. In Proceedings of the AAAI Workshop on Spoken Language Understanding (2005).

73.
Mado-Filho, G. M. et al. Heavy metals in benthic organisms from Todosos Santos Bay, Brazil. Braz. J. Biol. 68(1), 95–100 (2008).
Article  Google Scholar 

74.
Flora, G., Deepesh, G. & Archana, T. Toxicity of lead: a review with recent updates. Interdiscip. Toxicol. 5(2), 47–58 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
Metcheva, R., Yurukova, L., Teodorova, S. & Nikolova, E. The penguin feathers as bioindicator of Antarctic environmental state. Environ. Monit. Assess. 362, 259–265 (2006).
CAS  Google Scholar 

76.
Goss-Custard, J. D. & Jones, R. E. The diets of redshank and curlew. Bird Study 23(3), 233–243 (1976).
Article  Google Scholar 

77.
Dhanakumar, S., Solaraj, G. & Mohanraj, R. Heavy metal partitioning in sediments and bioaccumulation in commercial fish species of three major reservoirs of river Cauvery delta region India Ecotoxicol. Environ. Saf. 113, 145–151 (2015).
CAS  Article  Google Scholar 

78.
Eagles-Smith, C. A., Suchanek, T. H., Colwell, A. E. & Anderson, N. L. Mercury trophic transfer in a eutrophic lake: the importance of habitat-specific foraging. Ecol. Appl. 18, A196–A212 (2008).
PubMed  Article  PubMed Central  Google Scholar 

79.
Eagles-Smith, C. A., Suchanek, T. H., Colwell, A. E. & Moyle, P. B. Changes in fish diets and food web mercury bioaccumulation induced by an invasive planktivorous fish. Ecol. Appl. 18, A213–A226 (2008).
PubMed  Article  PubMed Central  Google Scholar 

80.
Wyn, B., Kidd, K. A., Burgess, N. M. & Curry, R. A. Mercury bio-magnification in the food webs of acidic lakes in Kejimkujik National Park and National Historic Site, Nova Scotia. Can. J. Fish Aquat. Sci. 66, 1532–1545 (2009).
CAS  Article  Google Scholar 

81.
Morel, F. M. M., Kraepiel, A. M. L. & Amyot, M. The chemical cycle and bioaccumulation of mercury. Annu Rev Ecol Syst. 29, 543–566 (1998).
Article  Google Scholar 

82.
Wolfe, M., Schwarzbach, S. & Sulaiman, R. A. Effects of Mercury on wildlife: a comprehensive review. Toxicol. Chem. 17, 146–160 (1998).
CAS  Article  Google Scholar 

83.
Eisler, R. Polycyclic Aromatic Hydrocarbon Hazards to Fish, Wildlife, and Invertebrates: A Synoptic Review (No. 11). Fish and Wildlife Service, US Department of the Interior. (1987).

84.
Arcas, J. Diet and prey selection of Common Sandpiper Actitis hypoleucos during winter. Int. J. Ornithol. 51(1), 203–213 (2004).
Google Scholar 

85.
Zhang, L., Khaloo, S. S., Kuban, P. & Hauser, P. C. Analysis of electroplating baths by capillary electrophoresis with high voltage contactless conductivity detection. Meas. Sci. Technol. 17(12), 3317 (2006).
ADS  CAS  Article  Google Scholar 

86.
Abdullah, M. et al. Avian feathers as a non-destructive bio-monitoring tool of trace metals signatures: a case study from severely contaminated areas. Chemosphere 119, 553–561 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

87.
Astorga España, M. S., Rodríguez Rodríguez, E. M. & Díaz Romero, C. Manganese, nickel, selenium and cadmium in molluscs from the Magellan Strait, Chile. Food Addit. Contamin. 21(8), 768–773 (2004).
Article  CAS  Google Scholar 

88.
Dange, S. & Manoj, K. Bioaccumulation of heavy metals in sediment, polychaetes (annelid) worms, mud skipper and mud crab at Purna River Estuary, Navsari, Gujarat, India. Int. J. Curr. Microbiol. Appl. Sci. 4(9), 571–575 (2015).
CAS  Google Scholar 

89.
Honda, K., Lee, D. P. & Tatsukawa, R. Lead poisoning in swans in Japan. Environ. Pollut. 65(3), 209–218 (1990).
CAS  PubMed  Article  PubMed Central  Google Scholar 

90.
Youssef, M. & El-Sorogy, A. Environmental assessment of heavy metal contamination in bottom sediments of Al-Kharrar lagoon, Rabigh, Red Sea, Saudi Arabia. Arab. J. Geosci. 9, 474 (2016).
Article  CAS  Google Scholar 

91.
Youssef, M., Madkour, H., Mansour, A., Alharbi, W. & El-Taher, A. Invertebrate shells (mollusca, foraminifera) as pollution indicators, Red Sea Coast, Egypt. J. Afr. Earth Sci. 133, 74–85 (2017).
ADS  CAS  Article  Google Scholar 

92.
Roginski, E. E. & Mertz, W. A biphasic response of rats to cobalt. J. Nutr. 107, 1537–1542 (1977).
CAS  PubMed  Article  Google Scholar 

93.
Catsiki, V. A., Katsilieri, Ch. & Gialamas, V. Chromium distribution in benthic species from a gulf receiving tannery wastes (Gulf of Geras—Lesbos island, Greece). Sci. Total Environ. 145(2), 173–185 (1994).
ADS  CAS  Article  Google Scholar 

94.
Ghani, A. Effect of chromium toxicity on growth, chlorophyll and some mineral nutrients of Brassica juncea L. Egypt. Acad. J. Biol. Sci. 2(1), 9–15 (2011).
Google Scholar 

95.
Hon, M. et al. Speciation study of chromium, copper and nickel in coastal estuarine sediments polluted by domestic and industrial effluents. Mar. Pollut. Bull. 34(11), 949–959 (1997).
Article  Google Scholar 

96.
Burger, J. et al. Mercury, lead, cadmium, arsenic, chromium and selenium in feathers of shorebirds during migrating through Delaware Bay, New Jersey: comparing the 1990s and 2011/2012. Toxics 3, 63–74 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

97.
Janssens, E., Dauwe, T., Bervoets, L. & Eens, M. Inter and intraclutch variability in heavy metals in feathers of Great tit nestlings (Parus major) along a pollution gradient. Arch. Environ. Contam. Toxicol. 43, 323–329 (2002).
CAS  PubMed  Article  Google Scholar 

98.
Burger, J. & Gochfeld, M. Metal levels in feathers of 12 species of seabirds from MidwayAtoll in the northern Pacific Ocean. Sci. Total Environ. 257, 37–52 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

99.
Mullin, D. W., Graham, A., Schoenjahn, M. J. & Walter, G. H. Phylogeography of the are Australian endemic Grey Falcon Falco hypoleucos: implications for conservation. Bird Conserv. Int. 30, 447–455 (2020).
Article  Google Scholar 

100.
Lacerda, L. D., Bidone, E. D., Guimaraes, A. F. & Pfeiffer, W. C. Mercury concentrations in fish from the Itacaiúnas-Parauapebas River system, Carajás region, Amazon. Anais Acad. Bras. Ciênc. 66(3), 373–379 (1994).
CAS  Google Scholar  More

1.
Law, K. & Thompson, R. C. Microplastics in the seas—concern is rising about widespread contamination of the marine environment by microplastics. Science (80-.) 345, 144–145 (2014).
ADS  CAS  Article  Google Scholar 
2.
Amaral-Zettler, L. A. et al. The biogeography of the plastisphere: implications for policy. Front. Ecol. Environ. 13, 541–546 (2015).
Article  Google Scholar 

3.
Sussarellu, R. et al. Oyster reproduction is affected by exposure to polystyrene microplastics. Proc. Natl. Acad. Sci. U. S. A. 113, 2430–2435 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Della Torre, C. et al. Accumulation and embryotoxicity of polystyrene nanoparticles at early stage of development of sea urchin embryos Paracentrotus lividus. Environ. Sci. Technol. 48, 12302–12311 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

5.
Cole, M. & Galloway, T. S. Ingestion of nanoplastics and microplastics by pacific oyster larvae. Environ. Sci. Technol. 49, 14625–14632 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

6.
Cole, M. et al. Microplastic ingestion by zooplankton. Environ. Sci. Technol. 47, 6646–6655 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Browne, M. A., Dissanayake, A., Galloway, T. S., Lowe, D. M. & Thompson, R. C. Ingested microscopic plastic translocates to the circulatory system of the mussel, Mytilus edulis (L.). Environ. Sci. Technol. 42, 5026–5031 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Browne, M. A., Niven, S. J., Galloway, T. S., Rowland, S. J. & Thompson, R. C. Microplastic moves pollutants and additives to worms, reducing functions linked to health and biodiversity. Curr. Biol. 23, 2388–2392 (2013).
CAS  PubMed  Article  Google Scholar 

9.
Van Cauwenberghe, L., Claessens, M., Vandegehuchte, M. B. & Janssen, C. R. Microplastics are taken up by mussels (Mytilus edulis) and lugworms (Arenicola marina) living in natural habitats. Environ. Pollut. 199, 10–17 (2015).
PubMed  Article  CAS  Google Scholar 

10.
Green, D. S. Effects of microplastics on European flat oysters, Ostrea edulis and their associated benthic communities. Environ. Pollut. 216, 95–103 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Lenz, R., Enders, K. & Nielsen, T. G. Microplastic exposure studies should be environmentally realistic. Proc. Natl. Acad. Sci. U. S. A. 113, E4121–E4122 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Connors, K. A., Dyer, S. D. & Belanger, S. E. Advancing the quality of environmental microplastic research. Environ. Toxicol. Chem. 36, 1697–1703 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Burns, E. E. & Boxall, A. B. A. Microplastics in the aquatic environment: evidence for or against adverse impacts and major knowledge gaps. Environ. Toxicol. Chem. 37, 2776–2796 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Council, N. R. Risk Assessment in the Federal Government: managing the process. (1983).

15.
Suter II, G. W. Ecological Risk Assessment (CRC Press, Boca Raton, 2007).
Google Scholar 

16.
Beiras, R. Marine Pollution. Sources, fate and effects of pollutants in coastal ecosystems. (Elsevier, 2018).

17.
Adam, V., Yang, T. & Nowack, B. Toward an ecotoxicological risk assessment of microplastics: comparison of available hazard and exposure data in freshwaters. Environ. Toxicol. Chem. 38, 436–447 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
US EPA. Guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms and their uses. PB85–227049. Environ. Prot. 105 (2010).

19.
European Commission. Common Implementation Strategy for the WFD (2000/60.EC), Guidance Document No . 27, Technical Guidance For Deriving Environmental Quality Standards. (2011). https://doi.org/10.2779/43816.

20.
de Lucia, G. A. et al. Amount and distribution of neustonic micro-plastic off the western Sardinian coast (Central-Western Mediterranean Sea). Mar. Environ. Res. 100, 10–16 (2014).
PubMed  Article  CAS  PubMed Central  Google Scholar 

21.
Law, K. L. et al. Plastic accumulation in the North Atlantic subtropical gyre. Science (80-.) 329, 1185–1188 (2010).
ADS  CAS  Article  Google Scholar 

22.
Goldstein, M. C., Rosenberg, M. & Cheng, L. Increased oceanic microplastic debris enhances oviposition in an endemic pelagic insect. Biol. Lett. 8, 817–820 (2012).
PubMed  PubMed Central  Article  Google Scholar 

23.
Carpenter, E. J. & Smith, K. L. Plastics on the Sargasso sea surface. Science (80-.) 175, 1240–1241 (1972).
ADS  CAS  Article  Google Scholar 

24.
Doyle, M. J., Watson, W., Bowlin, N. M. & Sheavly, S. B. Plastic particles in coastal pelagic ecosystems of the Northeast Pacific ocean. Mar. Environ. Res. 71, 41–52 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Collignon, A. et al. Neustonic microplastic and zooplankton in the North Western Mediterranean Sea. Mar. Pollut. Bull. 64, 861–864 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Reisser, J. et al. Marine plastic pollution in waters around Australia: Characteristics, concentrations, and pathways. PLoS ONE 8 (2013).

27.
Law, K. L. et al. Distribution of surface plastic debris in the eastern Pacific ocean from an 11-year data set. Environ. Sci. Technol. 48, 4732–4738 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Collignon, A., Hecq, J. H., Galgani, F., Collard, F. & Goffart, A. Annual variation in neustonic micro- and meso-plastic particles and zooplankton in the Bay of Calvi (Mediterranean-Corsica). Mar. Pollut. Bull. 79, 293–298 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Cózar, A. et al. Plastic accumulation in the mediterranean sea. PLoS ONE 10, 1–12 (2015).
Article  CAS  Google Scholar 

30.
Panti, C. et al. Occurrence, relative abundance and spatial distribution of microplastics and zooplankton NW of Sardinia in the Pelagos Sanctuary Protected Area Mediterranean Sea. Environ. Chem. 12, 618–626 (2015).
CAS  Article  Google Scholar 

31.
Pedrotti, M. L. et al. Changes in the floating plastic pollution of the mediterranean sea in relation to the distance to land. PLoS ONE 11, 1–14 (2016).
Article  CAS  Google Scholar 

32.
Suaria, G. et al. The Mediterranean Plastic Soup: Synthetic polymers in Mediterranean surface waters. Sci. Rep. 6 (2016).

33.
Isobe, A., Uchiyama-Matsumoto, K., Uchida, K. & Tokai, T. Microplastics in the Southern Ocean. Mar. Pollut. Bull. 114, 623–626 (2017).
CAS  PubMed  Article  Google Scholar 

34.
Cózar, A. et al. The Arctic Ocean as a dead end for floating plastics in the North Atlantic branch of the Thermohaline Circulation. Sci. Adv. 3, 1–9 (2017).
Article  CAS  Google Scholar 

35.
Jensen, L. H., Motti, C. A., Garm, A. L., Tonin, H. & Kroon, F. J. Sources, distribution and fate of microfibres on the Great Barrier Reef Australia. Sci. Rep. 9, 1–15 (2019).
Article  CAS  Google Scholar 

36.
Mu, J. et al. Microplastics abundance and characteristics in surface waters from the Northwest Pacific, the Bering Sea, and the Chukchi Sea. Mar. Pollut. Bull. 143, 58–65 (2019).
CAS  PubMed  Article  Google Scholar 

37.
Poulain, M. et al. Small microplastics as a main contributor to plastic mass balance in the north atlantic subtropical gyre. Environ. Sci. Technol. 53, 1157–1164 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

38.
Ivar do Sul, J. A., Costa, M. F., Barletta, M. & Cysneiros, F. J. A. Pelagic microplastics around an archipelago of the Equatorial Atlantic. Mar. Pollut. Bull. 75, 305–309 (2013).
CAS  PubMed  Article  Google Scholar 

39.
Desforges, J. P. W., Galbraith, M., Dangerfield, N. & Ross, P. S. Widespread distribution of microplastics in subsurface seawater in the NE Pacific Ocean. Mar. Pollut. Bull. 79, 94–99 (2014).
CAS  PubMed  Article  Google Scholar 

40.
Lusher, A. L., Burke, A., O’Connor, I. & Officer, R. Microplastic pollution in the Northeast Atlantic Ocean: Validated and opportunistic sampling. Mar. Pollut. Bull. 88, 325–333 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Lusher, A. L., Tirelli, V., O’Connor, I. & Officer, R. Microplastics in Arctic polar waters: The first reported values of particles in surface and sub-surface samples. Sci. Rep. 5, 1–9 (2015).
Article  CAS  Google Scholar 

42.
Enders, K., Lenz, R., Stedmon, C. A. & Nielsen, T. G. Abundance, size and polymer composition of marine microplastics ≥10 μm in the Atlantic Ocean and their modelled vertical distribution. Mar. Pollut. Bull. 100, 70–81 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Amélineau, F. et al. Microplastic pollution in the Greenland Sea: background levels and selective contamination of planktivorous diving seabirds. Environ. Pollut. 219, 1131–1139 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

44.
Choy, C. A. et al. The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column. Sci. Rep. 9, 1–9 (2019).
Article  CAS  Google Scholar 

45.
Pabortsava, K. & Lampitt, R. S. High concentrations of plastic hidden beneath the surface of the Atlantic Ocean. Nat. Commun. 11, 1–11 (2020).
Article  CAS  Google Scholar 

46.
Rist, S. et al. Quantification of plankton-sized microplastics in a productive coastal Arctic marine ecosystem. Environ. Pollut. 266, 115248 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Cózar, A. et al. Plastic debris in the open ocean. Proc. Natl. Acad. Sci. U. S. A. 111, 10239–10244 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Zhao, S., Zhu, L., Wang, T. & Li, D. Suspended microplastics in the surface water of the Yangtze Estuary System, China: first observations on occurrence, distribution. Mar. Pollut. Bull. 86, 562–568 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Yamashita, R. & Tanimura, A. Floating plastic in the Kuroshio Current area, western North Pacific Ocean. Mar. Pollut. Bull. 54, 464–488 (2007).
Article  CAS  Google Scholar 

50.
Kukulka, T., Proskurowski, G., Morét-Ferguson, S., Meyer, D. W. & Law, K. L. The effect of wind mixing on the vertical distribution of buoyant plastic debris. Geophys. Res. Lett. 39, 1–6 (2012).
Article  Google Scholar 

51.
Isobe, A., Uchida, K., Tokai, T. & Iwasaki, S. East Asian seas: a hot spot of pelagic microplastics. Mar. Pollut. Bull. 101, 618–623 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Frias, J. P. G. L., Otero, V. & Sobral, P. Evidence of microplastics in samples of zooplankton from Portuguese coastal waters. Mar. Environ. Res. 95, 89–95 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Gouin, T. et al. Toward the development and application of an environmental risk assessment framework for microplastic. Environ. Toxicol. Chem. 38, 2087–2100 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Erni-Cassola, G., Zadjelovic, V., Gibson, M. I. & Christie-Oleza, J. A. Distribution of plastic polymer types in the marine environment A meta-analysis. J. Hazard. Mater. 369, 691–698 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Jeong, C. B. et al. Adverse effects of microplastics and oxidative stress-induced MAPK/Nrf2 pathway-mediated defense mechanisms in the marine copepod Paracyclopina nana. Sci. Rep. 7, 1–11 (2017).
ADS  Article  CAS  Google Scholar 

56.
Fernández, B. & Albentosa, M. Insights into the uptake, elimination and accumulation of microplastics in mussel. Environ. Pollut. 249, 321–329 (2019).
PubMed  Article  CAS  PubMed Central  Google Scholar 

57.
Al-Sid-Cheikh, M. et al. Uptake, whole-body distribution, and depuration of nanoplastics by the scallop pecten maximus at environmentally realistic concentrations. Environ. Sci. Technol. 52, 14480–14486 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Baumann, L., Schmidt-Posthaus, H., Segner, H. & Wolf, J. C. Comment on ‘uptake and accumulation of polystyrene microplastics in zebrafish (Danio rerio) and toxic effects in liver’. Environ. Sci. Technol. 50, 12521–12522 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
González-Pleiter, M. et al. Secondary nanoplastics released from a biodegradable microplastic severely impact freshwater environments. Environ. Sci. Nano 6, 1382–1392 (2019).
Article  Google Scholar 

60.
Enfrin, M. et al. Release of hazardous nanoplastic contaminants due to microplastics fragmentation under shear stress forces. J. Hazard. Mater. 384, 121393 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

61.
Song, Y. K. et al. Large accumulation of micro-sized synthetic polymer particles in the sea surface microlayer. Environ. Sci. Technol. 48, 9014–9021 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Colton, J. B., Knapp, F. D. & Burns, B. R. Plastic particles in surface waters of the Northwestern Atlantic. Science (80-.) 185, 491–497 (1974).
ADS  Article  Google Scholar 

63.
Ryan, P. G. The characteristics and distribution of plastic particles at the sea-surface off the southwestern Cape Province South Africa. Mar. Environ. Res. 25, 249–273 (1988).
Article  Google Scholar 

64.
Cole, M. et al. Isolation of microplastics in biota-rich seawater samples and marine organisms. Sci. Rep. 4, 1–8 (2014).
Google Scholar 

65.
Beer, S., Garm, A., Huwer, B., Dierking, J. & Nielsen, T. G. No increase in marine microplastic concentration over the last three decades—a case study from the Baltic Sea. Sci. Total Environ. 621, 1272–1279 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Sjollema, S. B., Redondo-Hasselerharm, P., Leslie, H. A., Kraak, M. H. S. & Vethaak, A. D. Do plastic particles affect microalgal photosynthesis and growth?. Aquat. Toxicol. 170, 259–261 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Gambardella, C. et al. Ecotoxicological effects of polystyrene microbeads in a battery of marine organisms belonging to different trophic levels. Mar. Environ. Res. 141, 313–321 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Gardon, T., Reisser, C., Soyez, C., Quillien, V. & Le Moullac, G. Microplastics affect energy balance and gametogenesis in the pearl oyster pinctada margaritifera. Environ. Sci. Technol. 52, 5277–5286 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Durán, I. & Beiras, R. Acute water quality criteria for polycyclic aromatic hydrocarbons, pesticides, plastic additives, and 4-Nonylphenol in seawater. Environ. Pollut. 224, 384–391 (2017).
PubMed  Article  CAS  PubMed Central  Google Scholar 

70.
van Straalen, N. M. & Denneman, C. A. J. Ecotoxicological evaluation of soil quality criteria. Ecotoxicol. Environ. Saf. 18, 241–251 (1989).
PubMed  Article  Google Scholar 

71.
SCHEER. Scientific advice on Guidance document n°27: Technical guidance for deriving environmental quality standards. Scientific Committee on Health, Environmental and Emerging Risks (2017).

72.
Pfister, R., Schwarz, K. A., Janczyk, M. & Rick Daleand, J. B. F. Good things peak in pairs: a note on the bimodality coefficient. Front. Psychol. 4, 1–3 (2013).
Google Scholar 

73.
Padfield, D. & Matheson, G. nls.multstart: Robust Non-Linear Regression using AIC Scores. R package version 1.0.0. (2018).

74.
Delignette-Muller, M. L. & Dutang, C. fitdistrplus: an R package for fitting distributions. J. Stat. Softw. 64, 1–34 (2015).
Article  Google Scholar 

75.
R Core Team. R: A language and environment for statistical computing. R fundation, Vienna Austria (2019). More

1.
Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Ellis, E. C., Goldewijk, K. K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).
Google Scholar 

3.
Concepción, E. D., Moretti, M., Altermatt, F., Nobis, M. P. & Obrist, M. K. Impacts of urbanisation on biodiversity: the role of species mobility, degree of specialisation and spatial scale. Oikos 124, 1571–1582 (2015).
Article  Google Scholar 

4.
Lowry, H., Lill, A. & Wong, B. B. M. Behavioural responses of wildlife to urban environments. Biol. Rev. 88, 537–549 (2013).
PubMed  Article  Google Scholar 

5.
Callaghan, C. T. et al. Generalists are the most urban-tolerant of birds: a phylogenetically controlled analysis of ecological and life history traits using a novel continuous measure of bird responses to urbanization. Oikos 128, 845–858 (2019).
Article  Google Scholar 

6.
Ducatez, S., Sayol, F., Sol, D. & Lefebvre, L. Are urban vertebrates city specialists, artificial habitat exploiters, or environmental generalists? Integr. Comp. Biol. 58, 929–938 (2018).
PubMed  Article  Google Scholar 

7.
Murray, M. H. et al. City sicker? A meta-analysis of wildlife health and urbanization. Front. Ecol. Environ. 17, 575–583 (2019).
Article  Google Scholar 

8.
Lyons, J., Mastromonaco, G., Edwards, D. B. & Schulte-Hostedde, A. I. Fat and happy in the city: eastern chipmunks in urban environments. Behav. Ecol. 28, 1464–1471 (2017).
Article  Google Scholar 

9.
Meillère, A. et al. Corticosterone levels in relation to trace element contamination along an urbanization gradient in the common blackbird (Turdus merula). Sci. Total Environ. 566–567, 93–101 (2016).
ADS  PubMed  Article  CAS  Google Scholar 

10.
Soto-Calderón, I., Acevedo-Garcés, Y., Álvarez-Cardona, J., Hernandez, C. & García, G. Physiological and parasitological implications of living in a city: the case of the white-footed tamarin (Saguinus leucopus). Am. J. Primatol. 78, (2016).

11.
Sillero-Zubiri, C., Sukumar, R. & Treves, A. Living with wildlife: the roots of conflict and the solutions. In Key Topics in Conservation Biology (eds. MacDonald, D. & Service, K.) 255–272 (2006).

12.
Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Hanning, I. & Diaz-Sanchez, S. The functionality of the gastrointestinal microbiome in non-human animals. Microbiome 3, 51 (2015).
PubMed  PubMed Central  Article  Google Scholar 

14.
Tremaroli, V. & Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature 489, 242–249 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

15.
Pickard, J. M., Zeng, M. Y., Caruso, R. & Núñez, G. Gut microbiota: role in pathogen colonization, immune responses, and inflammatory disease. Immunol. Rev. 279, 70–89 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Mockler, B. K., Kwong, W. K., Moran, N. A. & Koch, H. Microbiome structure influences infection by the parasite Crithidia bombi in bumble bees. Appl. Environ. Microbiol. 84, e02335-e2417 (2018).
PubMed  PubMed Central  Article  Google Scholar 

17.
Suzuki, T. A. Links between natural variation in the microbiome and host fitness in wild mammals. Integr. Comp. Biol. 57, 756–769 (2017).
CAS  PubMed  Article  Google Scholar 

18.
Kirchoff, N. S., Udell, M. A. & Sharpton, T. J. The gut microbiome correlates with conspecific aggression in a small population of rescued dogs (Canis familiaris). PeerJ 7, e6103 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

19.
Walter, J. Ecological role of lactobacilli in the gastrointestinal tract: implications for fundamental and biomedical research. Appl. Environ. Microbiol. 74, 4985–4996 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Teyssier, A. et al. Inside the guts of the city: urban-induced alterations of the gut microbiota in a wild passerine. Sci. Total Environ. 612, 1276–1286 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

21.
Murray, M. H. et al. Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird. PLoS ONE 15, 1–16 (2020).
Google Scholar 

22.
Phillips, J. N., Berlow, M. & Derryberry, E. P. The effects of landscape urbanization on the gut microbiome: an exploration into the gut of urban and rural white-crowned sparrows. Front. Ecol. Evol. 6, 148 (2018).
Article  Google Scholar 

23.
Teyssier, A. et al. Diet contributes to urban-induced alterations in gut microbiota: experimental evidence from a wild passerine. Proc. R. Soc. B Biol. Sci. 287, (2020).

24.
Stothart, M. R., Palme, R. & Newman, A. E. M. It’s what’s on the inside that counts: stress physiology and the bacterial microbiome of a wild urban mammal. Proc. R. Soc. B Biol. Sci. 286, (2019).

25.
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 

26.
Bestion, E. et al. Climate warming reduces gut microbiota diversity in a vertebrate ectotherm. Nat. Ecol. Evol. 1, 0161 (2017).
Article  Google Scholar 

27.
Barelli, C. et al. Habitat fragmentation is associated to gut microbiota diversity of an endangered primate: implications for conservation. Sci. Rep. 5, 14862 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: a call for the consideration of host-associated microbiota in wildlife management practices. Proc. R. Soc. B Biol. Sci. 286, (2019).

29.
Nelson, T. M., Rogers, T. L., Carlini, A. R. & Brown, M. V. Diet and phylogeny shape the gut microbiota of Antarctic seals: a comparison of wild and captive animals. Environ. Microbiol. 15, 1132–1145 (2013).
CAS  PubMed  Article  Google Scholar 

30.
Wasimuddin, et al. Gut microbiomes of free-ranging and captive Namibian cheetahs: diversity, putative functions and occurrence of potential pathogens. Mol. Ecol. 26, 5515–5527 (2017).
CAS  PubMed  Article  Google Scholar 

31.
Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).
CAS  PubMed  Article  Google Scholar 

32.
Gehrt, S. D. & Riley, S. P. D. Coyotes (Canis latrans). in Urban Carnivores: Ecology, Conflict, and Conservation (eds. Gehrt, S. D., Riley, S. P. D. & Cypher, B. L.) 79–95 (2010).

33.
Breck, S. W., Poessel, S. A., Mahoney, P. & Young, J. K. The intrepid urban coyote: a comparison of bold and exploratory behavior in coyotes from urban and rural environments. Sci. Rep. 9, 2104 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Gier, H. T. Coyotes in Kansas. (1968).

35.
Murray, M. H. et al. Greater consumption of protein-poor anthropogenic food by urban relative to rural coyotes increases diet breadth and potential for human-wildlife conflict. Ecography 38, 001–008 (2015).
Article  Google Scholar 

36.
Massolo, A., Liccioli, S., Budke, C. & Klein, C. Echinococcus multilocularis in North America: the great unknown. Parasite 21, 73 (2014).
PubMed  PubMed Central  Article  Google Scholar 

37.
Murray, M. H., Edwards, M. A., Abercrombie, B. & St. Clair, C. C. Poor health is associated with use of anthropogenic resources in an urban carnivore. Proc. R. Soc. B Biol. Sci. 282, 20150009 (2015).

38.
Murray, M. H., Hill, J., Whyte, P. & St. Clair, C. C. Urban compost attracts coyotes, contains toxins, and may promote disease in urban-adapted wildlife. Ecohealth 13, 285–292 (2016).

39.
Luong, L. T., Chambers, J. L., Moizis, A., Stock, T. & St. Clair, C. Helminth parasites and zoonotic risk associated with urban coyotes (Canis latrans) in Alberta, Canada. J. Helminthol. 94, e25 (2020).

40.
Corbin, E. et al. Spleen mass as a measure of immune strength in mammals. Mamm. Rev. 38, 108–115 (2008).
Article  Google Scholar 

41.
Newsome, S. D., Ralls, K., Van Horn Job, C., Fogel, M. L. & Cypher, B. L. Stable isotopes evaluate exploitation of anthropogenic foods by the endangered San Joaquin kit fox (Vulpes macrotis mutica). J. Mammol. 91, 1313–1321 (2010).

42.
Huot, J., Poulle, M. & Crate, M. Evaluation of several indices for assessment of coyote (Canis latrans) body composition. Can. J. Zool. 73, 1620–1624 (1995).
Article  Google Scholar 

43.
Tucker, C. M. et al. A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biol. Rev. 92, 698–715 (2016).
PubMed  Article  Google Scholar 

44.
Reese, A. T. & Dunn, R. R. Drivers of microbiome biodiversity: a review of general rules, feces, and ignorance. MBio 9, e01294-e1318 (2018).
PubMed  PubMed Central  Article  Google Scholar 

45.
Pilla, R. & Suchodolski, J. S. The role of the canine gut microbiome and metabolome in health and gastrointestinal disease. Front. Vet. Sci. 6, 498 (2020).
PubMed  PubMed Central  Article  Google Scholar 

46.
Conlon, M. A. & Bird, A. R. The impact of diet and lifestyle on gut microbiota and human health. Nutrition 7, 17–44 (2015).
Google Scholar 

47.
Makki, K., Deehan, E. C., Walter, J. & Bäckhed, F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705–715 (2018).
CAS  PubMed  Article  Google Scholar 

48.
Schnorr, S. L. et al. Gut microbiome of the Hadza hunter-gatherers. Nat. Commun. 5, 3654 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Vieco-Saiz, N. et al. Benefits and inputs from lactic acid bacteria and their bacteriocins as alternatives to antibiotic growth promoters during food-animal production. Front. Microbiol. 10, 1–17 (2019).
Article  Google Scholar 

50.
Karasov, W. H. & Douglas, A. E. Comparative digestive physiology. Comp. Physiol. 3, 741–783 (2013).
Google Scholar 

51.
Wang, T. et al. Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J. 6, 320–329 (2012).
CAS  PubMed  Article  Google Scholar 

52.
AlShawaqfeh, M. K. et al. A dysbiosis index to assess microbial changes in fecal samples of dogs with chronic inflammatory enteropathy. FEMS Microbiol. Ecol. 93, 1–8 (2017).
Article  CAS  Google Scholar 

53.
Beldomenico, P. M. & Begon, M. Disease spread, susceptibility and infection intensity: vicious circles? Trends Ecol. Evol. 25, 21–27 (2010).
PubMed  Article  Google Scholar 

54.
Newsome, S. D., Garbe, H. M., Wilson, E. C. & Gehrt, S. D. Individual variation in anthropogenic resource use in an urban carnivore. Oecologia 178, 115–128 (2015).
ADS  PubMed  Article  Google Scholar 

55.
Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Brennan, C. A. & Garrett, W. S. Fusobacterium nucleatum – symbiont, opportunist and oncobacterium. Nat. Rev. Microbiol. 17, 156–166 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Bermingham, E. N., Maclean, P., Thomas, D. G., Cave, N. J. & Young, W. Key bacterial families (Clostridiaceae, Erysipelotrichaceae and Bacteroidaceae) are related to the digestion of protein and energy in dogs. PeerJ 5, e3019 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

58.
Alessandri, G. et al. Metagenomic dissection of the canine gut microbiota: insights into taxonomic, metabolic and nutritional features. Environ. Microbiol. 21, 1331–1343 (2019).
CAS  PubMed  Article  Google Scholar 

59.
Schmidt, M. et al. The fecal microbiome and metabolome differs between dogs fed Bones and Raw Food (BARF) diets and dogs fed commercial diets. PLoS ONE 13, e0201279 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

60.
Sandri, M., Dal Monego, S., Conte, G., Sgorlon, S. & Stefanon, B. Raw meat based diet influences faecal microbiome and end products of fermentation in healthy dogs. BMC Vet. Res. 13, 1–11 (2017).
Google Scholar 

61.
Moon, C. D., Cookson, A. L., Young, W., Maclean, P. H. & Bermingham, E. N. Metagenomic insights into the roles of Proteobacteria in the gastrointestinal microbiomes of healthy dogs and cats. Microbiologyopen 7, e677 (2018).
Article  Google Scholar 

62.
Wu, X. et al. Analysis and comparison of the wolf microbiome under different environmental factors using three different data of next generation sequencing. Sci. Rep. 7, 11332 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

63.
Wang, B. & Wang, X.-L. Species diversity of fecal microbial flora in Canis lupus familiaris infected with canine parvovirus. Vet. Microbiol. 237, 108390 (2019).
PubMed  Article  Google Scholar 

64.
Chen, L. et al. NLRP12 attenuates colon inflammation by maintaining colonic microbial diversity and promoting protective commensal bacterial growth. Nat. Immunol. 18, 541–551 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Martínez, I. et al. Gut microbiome composition is linked to whole grain-induced immunological improvements. ISME J. 7, 269–280 (2013).
PubMed  Article  CAS  Google Scholar 

66.
Liu, Y. et al. Splenectomy leads to amelioration of altered gut microbiota and metabolome in liver cirrhosis patients. Front. Microbiol. 9, 1–13 (2018).
Article  Google Scholar 

67.
Demas, G. E., Zysling, D. A., Beechler, B. R., Muehlenbein, M. P. & French, S. S. Beyond phytohaemagglutinin: assessing vertebrate immune function across ecological contexts. J. Anim. Ecol. 80, 710–730 (2011).
PubMed  Article  Google Scholar 

68.
Sugden, S. A., St. Clair, C. C. & Stein, L. Y. Individual and site-specific variation in a biogeographical profile of the coyote intestinal microbiota. Microb. Ecol. (2020).

69.
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

70.
Leung, J. M., Graham, A. L. & Knowles, S. C. L. Parasite-microbiota interactions with the vertebrate gut: synthesis through an ecological lens. Front. Microbiol. 9, 843 (2018).
PubMed  PubMed Central  Article  Google Scholar 

71.
Ezenwa, V. O., Gerardo, N. M., Inouye, D. W., Medina, M. & Xavier, J. B. Animal behavior and the microbiome. Science 338, 198–199 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

72.
Stewart, R. E. A., Stewart, B. E., Stirling, I. & Street, E. Counts of growth layer groups in cementum and dentine in ringed seals. Mar. Mammal Sci. 12, 383–401 (1996).
Article  Google Scholar 

73.
Linhart, S. B. & Knowlton, F. F. Determining age of coyotes by tooth cementum layers. J. Wildl. Manage. 31, 362–365 (1967).
Article  Google Scholar 

74.
Jahren, A. H. & Kraft, R. A. Carbon and nitrogen stable isotopes in fast food: signatures of corn and confinement. Proc. Natl. Acad. Sci. 105, 17855–17860 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

75.
Parnell, A. C. simmr: a stable isotope mixing model. (2019).

76.
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

77.
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Article  Google Scholar 

78.
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
PubMed  PubMed Central  Article  Google Scholar 

79.
Trachsel, D., Deplazes, P. & Mathis, A. Identification of taeniid eggs in the faeces from carnivores based on multiplex PCR using targets in mitochondrial DNA. Parasitology 134, 911–920 (2007).
CAS  PubMed  Article  Google Scholar 

80.
R Core Team. R: A language and environment for statistical computing. (2019).

81.
Chao, A. et al. Rarefaction and extrapolation of phylogenetic diversity. Methods Ecol. Evol. 6, 380–388 (2015).
Article  Google Scholar 

82.
Kembel, S. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
CAS  Article  Google Scholar 

83.
Giam, X. & Olden, J. D. Quantifying variable importance in a multimodel inference framework. Methods Ecol. Evol. 7, 388–397 (2016).
Article  Google Scholar 

84.
Cade, B. S. Model averaging and muddled multimodel inferences. Ecology 96, 2370–2382 (2015).
PubMed  Article  Google Scholar 

85.
Fernandes, A., Macklaim, J. M., Linn, T., Reid, G. & Gloor, G. B. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS ONE 8, e67019 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar  More

Ethnographic accounts from around the world have reported the widespread use of insects as food by people1,2,3. In some cases, such as among the Shoshone and other Great Basin tribes of the U.S., swarms of grasshoppers and crickets were driven into pits and blankets4, while among the Paiute the larvae of Pandora moths (Coloradia pandora lindseyi) were smoked out of trees to fall into prepared trenches, where they would be cooked5. Across the world, insects could be mass-harvested, often seasonally, offering high nutritional value especially in fat, protein and vitamins6. The harvesting of insects in the past has ranged from opportunities to feed large communal gatherings during times of plenty, to more individualistic economic pursuits such as in the search for delicacies or the exploitation of low-ranked resources when other foods were scarce or depleted7,8,9. Irrespective of the catch, insects often represented an important component of the diet, and of the reliability and thus dependability of locales as resource zones, with implications for social scheduling and cultural practice. However, a paucity of archaeological studies of insect food remains has resulted in a downplay or omission of the use of insects from archaeological narratives and deep-time community histories10.
In Australia, a wide range of insects is known to have been eaten by Aboriginal groups, in particular the larvae (‘witchetty grubs’) of cossid moths (especially Endoxyla leucomochla) in arid and semi-arid areas11,12,13. Of particular interest to archaeologists and behavioural ecologists has been the seasonal consumption of Bogong moths by mass gatherings of Aboriginal groups in the southern portions of the Eastern Uplands14 (Fig. 1). However, no conclusive archaeological evidence has ever been reported for the processing or use of Bogong moths.
Figure 1

(A) Bogong moth, Agrotis infusa (photo: Ajay Narendra). (B) Thousands of moths per square metre aestivating on a rock surface (photo: Eric Warrant).

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The Cloggs Cave grindstone
Cloggs Cave is located 72 m above sea level in the southern foothills of the Australian Alps, in the lands of the Krauatungalung clan of the GunaiKurnai Aboriginal peoples of southeastern Australia (Fig. 2). The cave is a small, 12 m long × 5 m wide × 6.8 m high limestone karst formation that is today entered through a walk-through opening on the side of a cliff (Fig. 3). Indirect sunlight dimly illuminates the cave for much of the day (Supplementary Fig. S1).
Figure 2

Location of Cloggs Cave and the area of the GunaiKurnai Land and Waters Aboriginal Corporation, at the southern foothills of the Australian Alps. Esri ArcMap 10.5 (https://desktop.arcgis.com/en/arcmap/) and Adobe Illustrator CC 2017 (21.0) (https://helpx.adobe.com/au/illustrator/release-note/illustrator-cc-2017-21-0-release-notes.html) were used by CartoGIS Services, College of Asia and the Pacific at the Australian National University, to create the map.

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Figure 3

Cloggs Cave cliffline above the Buchan River flood plain, showing location of cave entrance (white rectangle) (photo: Bruno David).

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Archaeological excavations were first undertaken in 1971–197214, followed by a new program of excavations in 2019–2020, initiated by the GunaiKurnai Land and Waters Aboriginal Corporation and directed by Bruno David. The new excavations were aimed at better determining the site’s stratigraphy and the antiquity of Aboriginal occupation (Supplementary Fig. S2). An intensive dating programme showed that the oldest excavated evidence for human activity dates to between 19,330–19,730 cal BP (median age of 19,530 cal BP; cal BP = before AD1950) and 20,590–23,530 cal BP (median age of 21,690 cal BP) (all calibrated radiocarbon ages in the text are presented at 95.4% probability range. See “Methods”; Supplementary Fig. S3)15,16,17.
During the 2019 excavations, a small, flat grindstone was found. The finely stratified hearth layers of stratigraphic unit (SU) 2 in which it was found were radiocarbon-dated to 1567–1696 cal BP at their top (uncalibrated: 1724 ± 16 BP; median age of 1632 cal BP) and 2002–2117 cal BP at their base (uncalibrated: 2091 ± 16 BP; median age of 2062 cal BP). The grindstone therefore dates to between 1600 and 2100 years ago (see “Methods”; Supplementary Figs. S3 and S4)17. No other grindstone has been found at Cloggs Cave.
The grindstone is a tabular fragment of sandstone with two flat and parallel ground surfaces (Surfaces A and B), in the form of a flat dish (Fig. 4). It measures 10.5 cm long × 8.3 cm wide × 2.2 cm thick and weighs 304 g. The outer, intact margin is elliptical in plan view; the other three margins indicate old breaks that have been subsequently worn from use. Therefore, prior to its deposition at Cloggs Cave, the grindstone had been used in its current form.
Figure 4

The Cloggs Cave grindstone. (A) Surface A, with the accretion that formed across parts of the surface after its use. (B) Surface B. (C) Margin A. (D) Margin B. (E) Narrow end. The numbers in circles are the residue sample numbers; the ‘control’ samples are in areas where grinding did not take place (photos: Richard Fullagar).

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To understand how the grindstone was used, we undertook use-wear and residue analyses (see “Methods”). The central area of both its surfaces contain fine unidirectional striations (Supplementary Figs. S5A and S5B), a lowered but not levelled topography, and areas of missing or ripped quartz grains (Supplementary Figs. S5C and S5D). Its use to shape ground stone axes is an unlikely function because the Cloggs Cave grindstone surfaces are relatively flat with only very slight concavities, and the lowered surface topography (Fig. 4) lacks broad grooves typical of axe grinding.
When viewed at lower (up to 5 ×) magnification under a stereozoom microscope with a point source of light, each surface appears relatively rough compared with grindstones used for processing seeds, which, in Australia, tend to be highly smoothed and polished18,19. There are numerous ‘pits’ where sand grains have been plucked from the surface during use (Supplementary Fig. S5D). The presence of a lowered surface topography (Supplementary Fig. S5C) with a lack of smooth, developed polish suggests that the stone was not used to process siliceous plants.
The repeated mechanical action of grinding has been shown to force residues into the voids and interstitial spaces of ground surfaces, where they become trapped20,21,22. Residue analyses conducted on grindstones worldwide have relied on microscopic observations of individual residue morphologies. However, visually diagnostic features can be altered by the mechanical forces of grinding, heat, and contact with water and various environmental factors, which can cause residues to swell or become amorphous21,22,23,24. The distinctiveness of residue identifications can be enhanced significantly with the introduction of biochemical staining that can be observed under high-power microscopy and is best used in conjunction with microscopic use-wear analysis and identification of residue morphologies22.
We extracted nine samples, or ‘lifts’, for residue analysis from across Surface A and Surface B of the Cloggs Cave grindstone, including a control sample from an unworked part of each surface (Fig. 4; see “Methods”). These samples were analysed using a recently developed biochemical staining technique that enables residues to be identified from colorimetric changes occurring at a cellular level, rather than relying solely on structural features (see “Methods”)22. We used the collagen stain Picrosirius Red (PSR) to differentiate between plant and animal residues (see “Methods”). When PSR comes into contact with collagen (a protein unique to animals), it reacts to produce clear and distinctive staining and enhanced birefringence in cross-polarised light22,25.
Residues extracted from the grindstone
A range of residues were identified in the lifts, including amorphous collagen, collagen fibres, collagen structures, partially woven collagen, possible bone-like fragments, moth wing segments, a possible moth hind leg, amorphous cellulose, wood-like structures with pits, carbonised material, bordered pits and minerals (see below).
We found collagenous residues in mid-range densities across Samples 1 and 4 from Surface B and across Sample 5 from Surface A (Supplementary Fig. S6). These extractions were taken from central areas across each modified surface. In all cases, the frequency of the collagenous residues was approximately three times greater than the collagenous residues associated with the control samples. Residues include damaged collagen fibres of varying thicknesses, including some reticular fibres.
Woven collagen structures clearly show birefringence in cross-polarised light across Sample 1. Woven collagen, which forms quickly, is mechanically weak and usually associated with immature bone. Although woven collagen may persist as tendon and ligament attachments to bone, it is generally replaced by organised parallel collagen fibre bundles at skeleton maturity26. Collagen fibrils are found in the connective tissues of vertebrates as well as in invertebrates such as insects27, and may be present as individual strands, woven structures or parallel bundles, including among the Lepidoptera (moths and butterflies)28.
The density and combination of collagenous residues on the Cloggs Cave grindstone indicates that it was used to process fauna. A variety of collagenous materials (including woven collagen) were found in association with carbonised residues across Sample 2, which was extracted from a crystalline layer. The residues present on Samples 1 and 2 suggest that an insect or immature vertebrate was prepared and cooked using the grindstone.
We identified a moderate density of carbonised plant residues across Sample 2, in particular, wood-like structures with pits. These ranged from being partially to completely carbonised. Partially carbonised residues were also seen across Sample 4. In addition, bordered pits in small clusters were identified, along with pits within the carbonised structures. Bordered pits are cavities that are essential components in the water-transport system of higher-order plants and are found in the lignified cell walls of xylem conduits (vessels and tracheids). The pit membrane allows water to pass between xylem conduits, but limits the spread of embolism and vascular pathogens in the xylem29. Small quantities of lignin were also present (see “Methods”). Lignin is found in the cell walls of vascular plants (especially in wood and bark) and is responsible for the rigidity of plant structures.
The residues identified via biochemical staining are consistent with the use of twigs and bark as fuel for fires such as those of the microstratified ashy layers in which the grindstone was found (see Supplementary Fig. S3)17. Partially carbonised wood-like material was also noted across Sample 5. The density and distribution of carbonised residues varies across extractions. Our observations suggest either that: (a) the stone has been placed in or near fires; or (b) ash, embers or fires of varying heat were placed or lit across the stone, for varied durations of time.
We identified especially high densities (frequency of residue particles per unit volume of sample) of amorphous cellulose across Samples 1, 2, 4 and 5 (Supplementary Fig. S7). The presence of partially carbonised amorphous cellulose indicates that the plant residues were associated with fire. While the high density is indicative of a plant-processing event, there is no evidence of combinations of plant residues normally expected from plant processing. In particular, no starch grain or phytolith was seen in any of the extractions. While low heat can damage starch and cause its structure to be disrupted and its characteristic extinction-cross to be lost, low heat does not completely destroy starch visibility30. Similarly, phytoliths can be reshaped but not destroyed by fire31. The presence of animal and mineral residues but absence of starches and phytoliths is thus interpreted as a true absence of plant processing activities rather than a taphonomic effect of environmental factors negatively impacting their preservation.
We found a high density of variably carbonised insect wings in Sample 6 (Surface A), and lower densities in Samples 2 and 4. These wing fragments contain regular patterning or structure and exhibit distinct birefringence in cross-polarised light. A portion of proteinaceous material was associated with a ‘tangle’ of these structures (Fig. 5). To assess whether the insect remains were those of the Bogong moth, we compared the residues on Samples 2, 4 and 6 with a comparative reference sample (see “Methods”). All 26 cases of wing segments from the grindstone matched the metrical and morphological characteristics of those from Bogong moths in the reference material. The recorded damage on the archaeological wing segments, such as ripped wing structures, small rectangular wing fragments and tearing in various states of carbonisation, is what would be expected from ethnohistoric accounts of Bogong moth processing. Aboriginal people from across the region are known to have cooked Bogong moths on heated earth during the early and mid-nineteenth century. The moths were stirred during cooking, causing the wings and legs to be broken off by friction and heat. Some of the moths were pounded and ground into a paste which could then be smoked to preserve the food for weeks1,2.
Figure 5

Examples of Bogong moth segments from lifted samples (all at × 400 magnification). (A) Partially carbonised wing structures from Sample 2 (pp). (B) Partially carbonised wing structure and carbonised material from Sample 2 (pp). (C) Partially carbonised moth wing segment from Sample 4 (pp). (D–E) Damaged moth wing segment from Sample 6 (D pp; E xp). (F–G) Damaged moth wing segment from Sample 6 (F pp; G xp). (H) Damaged moth wing segment with proteinaceous material, from Sample 6 (pp). (I) Unburnt moth wing segment from Sample 4 (pp). (J) Damaged moth wing segment with attachment, from Sample 6 (pp). (K) Damaged moth wing segments from Sample 6 (pp). (L–M) Probable moth hind leg from Sample 6 (L pp; M xp). (N) Damaged moth wing segment from Sample 6 (pp). (O) Damaged moth wing segment with attachment, from Sample 6 (pp). Light source = plane (pp), part polarised (part pol) and cross-polarised (xp) (photos: Birgitta Stephenson).

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Though the time and place of the origin of this bloom is unknown, the presumable causes of it were high temperatures, abundant nutrients, low tidal amplitude, and little current. According to fishermen, these bioluminescent blooms were first seen about 15 nautical miles offshore of the Mandapam coast between India and Sri Lanka on 6th September, and subsequently moved towards the shore (Fig. 2). Bloom of N. scintillans in 2008 was reported to affect all the marine organisms including corals in GoM12. On 14th September, our preliminary assessment revealed that corals in Shingle and Krusadai islands were possibly affected by the bloom. A great multitude of N. scintillans cells were found settled on corals and other benthic organisms in the affected areas. A greenish settlement was observable on live coral colonies and other benthic organisms including macro algae, coralline algae and sponges etc.(Fig. S2). Settling of N. scintillans on benthic organisms has been reported to cause significant damage to the reef organisms through asphyxiation12. At Shingle Island, the area of significant impact was about 8.1 hectares on the shoreward side of the Island (79°14′14.38″E, 9°14′44.23″N) at depths between 1 and 3 m (Fig. 3). At Krusadai Island, an area of 2.1 hectares in the shoreward side was found affected by the bloom (79°13′20.78″E, 9°15′00.88″N) at depths between 1 and 2 m. The rest of the reef areas in both of these islands were healthy without any impact. The settled cells of N. scintillans were found to be washed ashore during subsequent surveys. In addition to dead fishes, a multitude of benthic communities such as crustaceans, mollusks and echinoderms were also found dead on the bottom in the impacted areas. Surveys between 15 and 18th September 2019 confirmed that corals in other islands (Pullivasal, Poomarichan, Manoliputti, Manoli and Hare) were in good health, and without any noticeable impact due to the bloom. Shingle and Krusadai islands occur closest to the mainland, and the concentrated bloom appeared to get trapped by currents between the mainland shore and islands.
Figure 2

(a) Green tide of Noctiluca scintillans in the Gulf of Mannar; (b) image of N.scintillans cells; size of the grid is 1 mm2 (N. scintillans exhibits bioluminescence when disturbed).

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Figure 3

Map showing the affected islands in the Mandapam group shown in Fig. 1. Base map was prepared by digitizing the georeferred Toposheet of Survey of India (http://www.surveyofindia.gov.in/) and field data using Open source GIS software (QGIS 3.10.6; https://qgis.org/en/site/forusers/download.html).

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On 14th September, coral mortality was not observed in the affected areas though the colonies were observed to be disturbed by the settling N. scintillans cells. Low dissolved oxygen levels have been reported to be the primary cause of benthic mortality during algal blooms22. Dissolved oxygen levels were 1.48 mg l−1 at Shingle Island and 2.02 mg l−1 at Krusadai Island in the affected areas. This compares to ‘normal’ levels for coral reefs of 5–8 mg l−1, and Haas et al.11 found that dissolved oxygen content less than 4 mg l−1 is detrimental to acroporid corals. Moreover, branching coral forms have been reported to be more susceptible to hypoxic episodes than spherical or massive forms5. Corals are routinely exposed to fluctuations in oxygen levels at the tissue level due to photosynthesis and respiration processes of endosymbionts7, but are negatively impacted when (sub-) lethal thresholds of hypoxia exposure are exceeded1,5,11. Lethal hypoxia thresholds appear to differ considerably between coral species, ranging between 0.5 and 4 mg O2 l−11,5,11, while sub-lethal hypoxia thresholds for corals are almost entirely unknown5.
Seawater temperature can significantly impact dissolved oxygen levels23,24. Water temperature was 29.9 and 29.8º C (Table 1) at Shingle and Krusadai islands respectively and these levels are marginally higher than the normal levels for this particular time of the year. Apart from the summer months (April to June), temperature levels in GoM do not go higher than 29º C20. The concentration of N. scintillans was 43.4 × 105 and 27.3 × 105 cells l−1 at Shingle and Krusadai Islands respectively; pH and TDS were also high in the affected area (Table 1). Dissolved oxygen levels in other sites of these two islands and in other five islands were higher than 5 mg l−1.
Table 1 Environmental characterization at the affected sites in Shingle and Krusadai Islands.
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During the next assessment on 17th of September 2019, severe coral mortality was observed at the affected sites. At Shingle Island, overall coral colony density was 134.25 (SE ± 3.28) no.100 m−2 (n = 537) within ten 20 m belt transects which is dominated by Acropora (64%) followed by Montipora (15%). Out of total 537 colonies, 33.52% (n = 180) were found dead (Fig. 4), which include 34.5 (SE ± 1.05) no.100 m−2 (n = 138) of Acropora, 7.75 (SE ± 0.75) no.100 m−2 (n = 31) of Montipora and 2.75 (SE ± 0.35) no.100 m−2 (n = 11) of Pocillopora. The death of coral colonies was so rapid that the coral tissue was intact on the colony surface and still had its natural colour (Fig. 5). When wafted with water by hand or with scuba air, the tissue peeled off exposing the skeleton (Supplementary video). Other observed genera such as Dipsastraea, Favites, Porites, Hydnophora, Goniastrea, Echinopora, Turbinaria, Platygyra, Goniopora and Symphyllia in the same site were all alive (Fig. S3), though with excess mucus production. This may be explained by differential lethal thresholds for oxygen levels at species and growth form levels5,19. At Krusadai Island, the overall coral density on 17th September was 66 (SE ± 2.54) no.100 m−2 (n = 132), dominated by Acropora. Among the counted colonies, 6 (SE ± 1.03) no.100 m−2 of Acropora were found recently dead while mortality was not observed in other available genera such as Montipora, Pocillopora, Dipsastraea, Favites, Porites and Turbinaria. Dissolved oxygen levels had increased to 3.78 mg l−1 at Shingle Island and to 4.02 mg l−1 at Krusadai Island at the affected sites and the water had started to become clear. The concentration of N. scintillans had reduced to 1.63 × 103 cells l−1 and 0.88 × 103 cells l−1 at Shingle and Krusadai Islands, respectively (Table 1).
Figure 4

Density of live and dead colonies of affected coral genera (Acropora, Montipora and Pocillopora) in Shingle Island, by date; the green line indicates the drastic decline of Acropora density between 17.09.2019 and 27.09.2019.

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Figure 5

Rapid mortality of corals presumably due to low oxygen levels caused by Noctiluca scintillans; (a, b) Acropora; (c) Montipora; (d) Pocillopora.

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Assessment on 27th September 2019 at the impacted area in Shingle Island, showed that the overall density of coral colonies within ten 20 m transects was 135.75 (SE ± 2.82) no.100 m−2 (n = 543) and of them 70.35% (n = 382) of colonies belonging to Acropora, Montipora and Pocillopora were found dead revealing that the impact of algal bloom was more severe than expected (Fig. 4). It was almost two weeks since the corals had died and hence secondary algae had started colonizing the dead colonies. On the same day at the impacted area of Krusadai Island, overall coral density within five belt transects was 65.5 (SE ± 1.83) no.100 m−2 (n = 131), of which 9.09% (n = 12) of colonies belonging to Acropora were found dead. By 27th September, dissolved oxygen levels had increased to 6.02 and 5.73 mg l−1 respectively at the affected areas of Shingle and Krusadai islands (Table 1). N. scintillans cells were absent in all the sites indicating the end of bloom. On 04th October 2019, the overall coral colony density within 20 m belt transects was 138 (SE ± 2.08) no.100 m−2 (n = 552) and of them 71.23% (n = 393) colonies belonging to Acropora, Montipora and Pocillopora were found dead at the area of impact in Shingle Island (Fig. 4). No further mortality was witnessed in the affected area of Krusadai Island. Secondary algae have completely overgrown the dead coral colonies making the reef look green (Fig. S4). Dissolved oxygen levels were reasonably high at 7.13 and 7.24 mg l−1 respectively at Shingle and Krusadai Islands during this time (Table 1).
Coral mortality due to algal bloom and consequent hypoxia has rarely been reported12,13,25. The present study reports that the impact of blooms can be severe on corals. Different coral species respond differently to low oxygen levels according to their respiration and photosynthesis5,26. Thus, low oxygen levels can orchestrate the coral mortality by affecting coral’s productivity and respiration7. Further, fast growing corals such as Acropora and Pocillopora have been reported to be more susceptible to low oxygen levels11,13,27. Fast growing coral species have faster metabolism rates28 and hence metabolic oxygen requirements are higher11,29. Thus, the mortality of fast growing species in the present study was presumably due to the low oxygen levels induced by N.scintillans bloom.
Bleaching episodes in 2010 and 2016 had also caused significant mortality to these fast growing species in GoM19,20. Corals in GoM start to bleach when water temperature exceeds 30º C and the temperature levels during this bloom period ranged between 28.4 and 29.9º C. Though bleaching was not observed, heat stress might also have played its role in coral mortality along with low oxygen levels as the temperature level almost reached 30º C. Similar temperature levels were reported during the bloom of N. scintillans in 2008 in GoM12.
Corals in Gulf of Mannnar are still recovering from the 2016 bleaching episode20 and hence the present decline is significant. Phase shifts on coral reefs are predominantly associated with shifts from hard coral-dominated communities to macroalgae-dominated ones30. Space competition between corals and other organisms such as algae and sponges has been reported to negatively impact the corals of GoM after the 2016 bleaching event20,31. At present, secondary algae have completely occupied the dead coral colonies, which will affect the coral recovery by hindering the attachment of new coral recruits during the next spawning season32. Recent studies suggest hypoxia increases coral susceptibility to bleaching27, and may increase disease prevalence and algal proliferation7. Thus algal blooms add to the existing array of threats to corals of GoM that needs to be understood more with further focused research.
On account of the problems related to climate change, there has been a steady and severe decline of coral reefs in the past two decades. Bleaching and diseases have been reported to cause mass coral mortalities within a very short time. The observations of the present study alert us to possible mass mortality due to short-term hypoxic condition caused by algal blooms. Algal blooms and hypoxic conditions are predicted to occur more frequently in the future due to climate change14. Hence, it is likely that shallow water coral reefs will be affected more frequently by temporary low oxygen levels caused by algal blooms. More studies are, however, required to understand the mechanism of coral mortality due to algal blooms, impacts on community composition and the potential for subsequent recovery. More