Philippa Kaur

Effects on intra-event-based flood runoff and sediment characteristicsBetween the 1960s and 1990s, there was no significant change in rainfall in the Chabagou watershed35. The mean values of runoff and sediment transport in the baseline period and measurement period were calculated. Regardless of rainfall influence, the effect of conservation measures was assessed by the time series contrasting method25.Table 1 shows the statistics of the characteristics of event-based flood flows and sediment in 1961–1990 (excluding 1970). Compared with those in the baseline period, T and Tr in the measurement period increased by 16.54% and 29.21%, respectively; however, Tp decreased by 55.52% in the measurement period, which showed that the soil and water conservation measures extended the flood duration while reducing the time of increased discharge. Under identical rainfall conditions, long-duration runoff with less time for increased discharge could cause less erosion than short-duration runoff with more time for increased discharge36. Hence, the conservation measures reduced soil erosion by prolonging the flood duration and reducing the time to peak. In addition, the hydrodynamic indices qp, H and qm were 75.2%, 56.0% and 68.0% lower, respectively, in the measurement period than in the baseline period. Moreover, E in the measurement period was only 10.2% that in the baseline period. The results showed that the conservation measures greatly reduced the hydrodynamic energy and thus soil erosion. In addition, the relative erosion indicators SSY, SCE and MSCE, decreased 69.2%, 33.3%, and 11.9%, respectively, in the measurement period compared with the baseline period, which indicated that the conservation measures significantly reduced soil erosion and decreased the mean sediment concentration, although the reduction in the maximum sediment concentration was relatively small. The conservation measures, especially the engineering measures, reduced the runoff velocity, extended the flood duration, and reduced the peak discharge, which sharply reduced the runoff erosion power37,38. As a consequence of the decrease in erosive energy, soil erosion was diminished.Table 1 Descriptive statistics of the characteristics of event-based flood flows and sediment in 1961–1990 (excluding 1970).Full size tableInfluence on intra-event-based flood regimesClassification of flood events and the characteristics of baseline period flood regimesFigure 2 shows the clustering results of the flood events at the Caoping hydrological station in 1961–1969. The flood events were divided into 4 regimes with a significance level of p  More

1.Bartelink, E. J., Mackinnon, A. T., Prince-Buitenhuys, J. R., Tipple, B. J. & Chesson, L. A. Stable isotope forensics as an investigative tool in missing persons investigations BT. In Handbook of Missing Persons (eds Morewitz, S. J. & Sturdy Colls, C.) 443–462 (Springer, 2016).Chapter 

Google Scholar 
2.Ehleringer, J. R. et al. Hydrogen and oxygen isotope ratios in human hair are related to geography. Proc. Natl. Acad. Sci. USA 105, 2788–2793 (2008).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
3.Laffoon, J. E., Rojas, R. V. & Hofman, C. L. Oxygen and carbon isotope analysis of human dental enamel from the caribbean: Implications for investigating individual origins. Archaeometry 55, 742–765 (2013).CAS 
Article 

Google Scholar 
4.Glick, P. L. Patterns of enamel maturation. J. Dent. Res. 58, 883–895 (1979).CAS 
PubMed 
Article 

Google Scholar 
5.Lacruz, R. S., Habelitz, S., Wright, J. T. & Paine, M. L. Dental enamel formation and implications for oral health and disease. Physiol. Rev. 97, 939–993 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Longinelli, A. Oxygen isotopes in mammal bone phosphate: A new tool for paleohydrological and paleoclimatological research?. Geochim. Cosmochim. Acta 48, 385–390 (1984).CAS 
Article 
ADS 

Google Scholar 
7.Luz, B., Kolodny, Y. & Horowitz, M. Fractionation of oxygen isotopes between mammalian bone-phosphate and environmental drinking water. Geochim. Cosmochim. Acta 48, 1689–1693 (1984).CAS 
Article 
ADS 

Google Scholar 
8.Levinson, A. A., Luz, B. & Kolodny, Y. Variations in oxygen isotopic compositions of human teeth and urinary stones. Appl. Geochemistry 2, 367–371 (1987).CAS 
Article 

Google Scholar 
9.Dansgaard, W. Stable isotopes in precipitation. Tellus A 16, 436–468 (1964).Article 
ADS 

Google Scholar 
10.Rozanski, K., Araguás-Araguás, L. & Gonfiantini, R. Isotopic patterns in modern global precipitation in Climate Change in Continental Isotopic Records 1–36 (American Geophysical Union, 1993). 11.Terzer, S., Wassenaar, L. I., Araguás-Araguás, L. J. & Aggarwal, P. K. Global isoscapes for δ18O and δ2H in precipitation: Improved prediction using regionalized climatic regression models. Hydrol. Earth Syst. Sci. Discuss. https://doi.org/10.5194/hessd-10-7351-2013 (2013).12.Bowen, G. J. Interpolating the isotopic composition of modern meteoric precipitation. Water Resour. Res. 39, 1–13 (2003).Article 
CAS 

Google Scholar 
13.West, A. G., February, E. C. & Bowen, G. J. Spatial analysis of hydrogen and oxygen stable isotopes (“isoscapes”) in ground water and tap water across South Africa. J. Geochem. Explor. 145, 213–222 (2014).CAS 
Article 

Google Scholar 
14.Ehleringer, J. R. et al. A Framework for the incorporation of isotopes and isoscapes in geospatial forensic investigations in Isoscapes SE-17 (eds. West, J. B., Bowen, G. J., Dawson, T. E. & Tu, K. P.) 357–387 (Springer, 2010).15.Laffoon, J. E. et al. Investigating human geographic origins using dual-isotope (87Sr/86Sr, δ18O) assignment approaches. PLoS ONE 12, e0172562 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Daux, V. et al. Oxygen isotope fractionation between human phosphate and water revisited. J. Hum. Evol. 55, 1138–1147 (2008).PubMed 
Article 

Google Scholar 
17.Dotsika, E. Correlation between δ18Ow and δ18Οen for estimating human mobility and paleomobility patterns. Sci. Rep. 10, 15439 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
18.Chenery, C., Müldner, G., Evans, J., Eckardt, H. & Lewis, M. Strontium and stable isotope evidence for diet and mobility in Roman Gloucester, UK. J. Archaeol. Sci. 37, 150–163 (2010).Article 

Google Scholar 
19.Herrmann, N., Li, Z.-H., Warner, M., Weinand, D. & Soto, M. Isotopic and elemental analysis of the William Bass donated skeletal collection and other modern donated collections. Dep. Justice Rep. 1, 248669 (2015).
Google Scholar 
20.Keller, A. T., Regan, L. A., Lundstrom, C. C. & Bower, N. W. Evaluation of the efficacy of spatiotemporal Pb isoscapes for provenancing of human remains. Forensic Sci. Int. 261, 83–92 (2016).CAS 
PubMed 
Article 

Google Scholar 
21.Pollard, A. M., Pellegrini, M. & Lee-Thorp, J. A. Technical note: some observations on the conversion of dental enamel δ18O(p)values to δ18O(w)to determine human mobility. Am. J. Phys. Anthropol. 145, 499–504 (2011).CAS 
PubMed 
Article 

Google Scholar 
22.Posey, R. Development and Validation of a Spatial Prediction Model for Forensic Geographical Provenancing of Human Remains (University of East Anglia, 2011).23.Warner, M. M., Plemons, A. M., Herrmann, N. P. & Regan, L. A. Refining stable oxygen and hydrogen isoscapes for the identification of human remains in Mississippi. J. Forensic Sci. https://doi.org/10.1111/1556-4029.13575 (2018).Article 
PubMed 

Google Scholar 
24.Chenery, C. A., Pashley, V., Lamb, A. L., Sloane, H. J. & Evans, J. A. The oxygen isotope relationship between the phosphate and structural carbonate fractions of human bioapatite. Rapid Commun. Mass Spectrom. 26, 309–319 (2012).CAS 
PubMed 
Article 
ADS 

Google Scholar 
25.Bell, L. S., Lee-Thorp, J. A. & Elkerton, A. Sailing against the wind. J. Archaeol. Sci. 37, 683–686 (2010).Article 

Google Scholar 
26.Pellegrini, M., Pouncett, J., Jay, M., Pearson, M. P. & Richards, M. P. Tooth enamel oxygen “isoscapes’’ show a high degree of human mobility in prehistoric Britain. Sci. Rep. 6, 1–10 (2016).Article 
CAS 

Google Scholar 
27.Podlesak, D. W., Bowen, G. J., O’Grady, S., Cerling, T. E. & Ehleringer, J. R. δ2H and δ18O of human body water: A GIS model to distinguish residents from non-residents in the contiguous USA. Isotopes Environ. Health Stud. https://doi.org/10.1080/10256016.2012.644283 (2012).Article 
PubMed 

Google Scholar 
28.Bowen, G. J. & Wilkinson, B. Spatial distribution of δ18O in meteoric precipitation. Geology 30, 315–318 (2002).Article 
ADS 

Google Scholar 
29.Lee-Thorp, J. Two decades of progress towards understanding fossilization processes and isotopic signals in calcified tissue minerals. Archaeometry 44, 435–446 (2002).CAS 
Article 

Google Scholar 
30.Bryant, D., Koch, P. L., Froelich, P. N., Showers, W. J. & Genna, B. J. Oxygen isotope partitioning between phosphate and carbonate in mammalian apatite. Geochim. Cosmochim. Acta 60, 5145–5148 (1996).CAS 
Article 
ADS 

Google Scholar 
31.Iacumin, P., Bocherens, H., Mariotti, A. & Longinelli, A. Oxygen isotope analyses of co-existing carbonate and phosphate in biogenic apatite: A way to monitor diagenetic alteration of bone phosphate?. Earth Planet. Sci. Lett. 142, 1–6 (1996).CAS 
Article 
ADS 

Google Scholar 
32.Wright, L. E., Valdés, J. A., Burton, J. H., Douglas Price, T. & Schwarcz, H. P. The children of Kaminaljuyu: Isotopic insight into diet and long distance interaction in Mesoamerica. J. Anthropol. Archaeol. 29, 155–178 (2010).Article 

Google Scholar 
33.Chesson, L. A., Kenyhercz, M. W., Regan, L. A. & Berg, G. E. Addressing data comparability in the creation of combined data sets of bioapatite carbon and oxygen isotopic compositions. Archaeometry 61, 1193–1206 (2019).CAS 
Article 

Google Scholar 
34.International Atomic Energy Agency/World Meteorological Organization. Global Network of Isotopes in Precipitation http://www.iaea.org/water (2020).35.Bowen, G. J. The Online Isotopes in Precipitation Calculator, Version 3.1  https://wateriso.utah.edu/waterisotopes/pages/data_access/oipc.html (2018).36.Tipple, B. J. et al. Stable hydrogen and oxygen isotopes of tap water reveal structure of the San Francisco Bay Area’s water system and adjustments during a major drought. Water Res. 119, 212–224 (2017).CAS 
PubMed 
Article 

Google Scholar 
37.Wang, S. et al. Water source signatures in the spatial and seasonal isotope variation of chinese tap waters. Water Resour. Res. 54, 9131–9143 (2018).Article 
ADS 

Google Scholar 
38.Ghassemi, F. & White, I. Inter-Basin Water Transfer: Case Studies from Australia, United States, Canada, China and India (Cambridge University Press, 2007).Book 

Google Scholar 
39.World Health Organization. Progress on Sanitation and Drinking Water: 2010 Update http://apps.who.int/iris/bitstream/10665/81245/1/9789241505390_eng.pdf (2013).40.Good, S. P. et al. Patterns of local and nonlocal water resource use across the western U.S. determined via stable isotope intercomparisons. Water Resour. Res. https://doi.org/10.1002/2012WR013085 (2014).Article 

Google Scholar 
41.Bowen, G. J., Ehleringer, J. R., Chesson, L. A., Stange, E. & Cerling, T. E. Stable isotope ratios of tap water in the contiguous United States. Water Resour. Res. 43, 1–12 (2007).Article 
CAS 

Google Scholar 
42.Chesson, L. A. et al. Strontium isotopes in tap water from the coterminous USA. Ecosphere 3, 67 (2012).Article 

Google Scholar 
43.Ammer, S. T. M., Bartelink, E. J., Vollner, J. M., Anderson, B. E. & Cunha, E. M. Spatial distributions of oxygen stable isotope ratios in tap water from mexico for region of origin predictions of unidentified border crossers. J. Forensic Sci. 65, 1049–1055 (2020).CAS 
PubMed 
Article 

Google Scholar 
44.Daux, V. et al. Oxygen and hydrogen isotopic composition of tap waters in France. Geol. Soc. Lond. Spec. Publ. 507, 207 (2021).
Google Scholar 
45.Gautam, M. K., Song, B. Y., Shin, W. J., Bong, Y. S. & Lee, K. S. Spatial variations in oxygen and hydrogen isotopes in waters and human hair across South Korea. Sci. Total Environ. 726, 138365 (2020).CAS 
PubMed 
Article 
ADS 

Google Scholar 
46.Zhao, S. et al. Divergence of stable isotopes in tap water across China. Sci. Rep. 7, 43653 (2017).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
47.Ueda, M. & Bell, L. S. A city-wide investigation of the isotopic distribution and source of tap waters for forensic human geolocation ground-truthing. J. Forensic Sci. 62, 655–667 (2017).CAS 
PubMed 
Article 

Google Scholar 
48.Jameel, Y. et al. Tap water isotope ratios reflect urban water system structure and dynamics across a semiarid metropolitan area. Water Resour. Res. 52, 5891–5910 (2016).CAS 
Article 
ADS 

Google Scholar 
49.Lee-Thorp, J., Manning, L. & Sponheimer, M. Problems and prospects for carbon isotope analysis of very small samples of fossil tooth enamel. Bull. Geol. Soc. Fr. 168, 767–773 (1997).CAS 

Google Scholar 
50.Connan, M. et al. Multidimensional stable isotope analysis illuminates resource partitioning in a sub-Antarctic island bird community. Ecography 42, 1948–1959 (2019).Article 

Google Scholar 
51.Coplen, T. B. Guidelines and recommended terms for expression of stable-isotope-ratio and gas-ratio measurement results. Rapid Commun. Mass Spectrom. 25, 2538–2560 (2011).CAS 
PubMed 
Article 
ADS 

Google Scholar 
52.Metro Vancouver. Water Treatment & Supply  http://www.metrovancouver.org/services/water/Pages/default.aspx (2020).53.International Atomic Energy Agency. International Atomic Energy Agency: RCWIP (Regionalized Cluster-Based Water Isotope Prediction) Model: Gridded precipitation δ18O|δ2H| δ18O and δ2H isoscape data http://www.iaea.org/water (2014).54.Gujarati, D. N. Linear regression: A mathematical introduction. Rapid Commun. Mass Spectrom. https://doi.org/10.4135/9781071802571 (2019).Article 

Google Scholar 
55.Coplen, T. B. Normalization of oxygen and hydrogen isotope data. Chem. Geol. Isot. Geosci. Sect. 72, 293–297 (1988).CAS 
Article 

Google Scholar 
56.Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).MATH 
Article 

Google Scholar 
57.Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).MathSciNet 
MATH 
Article 

Google Scholar 
58.R Core Team. R: A Language and Environment for Statistical Computing (2020).59.Kobayashi, K. & Salam, M. U. Comparing simulated and measured values using mean squared deviation and its components. Agron. J. 92, 345–352 (2000).Article 

Google Scholar 
60.Gauch, H. G., Hwang, J. T. G. & Fick, G. W. Model evaluation by comparison of model-based predictions and measured values. Agron. J. 95, 1442–1446 (2003).Article 

Google Scholar 
61.Metro Vancouver. Metro Vancouver Growth Projections: A Backgrounder http://www.metrovancouver.org/services/regional-planning/PlanningPublications/OverviewofMetroVancouversMethodsinProjectingRegionalGrowth.pdf (2018).62.City of Kelowna. City of Kelowna Water http://www.kelowna.ca/cm/page393.aspx (2009).63.City of Prince George. Water and Watersheds  https://www.princegeorge.ca/CityServices/Pages/Environment/WaterAndWatersheds.aspx (2017).64.City of Winnipeg. Water and Waste Department http://www.winnipeg.ca/waterandwaste/water/default.stm (2017).65.City of Toronto. Drinking Water http://www1.toronto.ca/wps/portal/contentonly?vgnextoid=e73cfe4eda8ae310VgnVCM10000071d60f89RCRD (2017).66.Jacklin, J. Assessment of Vanderhoof South Drinking Water Supply: Source Water Characteristics https://www2.gov.bc.ca/assets/gov/environment/air-land-water/water/waterquality/water-quality-reference-documents/dwa-vanderhoof_south.pdf (2005).67.Ministère du Développement durable de l’Environnement et de la Lutte contre les changements climatiques. Summary Profile of the Rivière des Outaouais Watershed http://www.environnement.gouv.qc.ca/eau/bassinversant/bassins/outaouais/portrait-sommaire-en.pdf (2015).68.Municipalité de L’Isle-aux-Allumettes. Municipality of L’Isle-Aux-Allumettes http://www.isle-aux-allumettes.com/index-en.php (2012).69.Mant, M., Nagel, A. & Prowse, T. investigating residential history using stable hydrogen and oxygen isotopes of human hair and drinking water. J. Forensic Sci. 61, 884–891 (2016).CAS 
PubMed 
Article 

Google Scholar 
70.Koehler, G. & Hobson, K. A. Tracking cats revisited: Placing terrestrial mammalian carnivores on δ2H and δ18O isoscapes. PLoS ONE 14, e0221876 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Wassenaar, L. I., Athanasopoulos, P. & Hendry, M. J. Isotope hydrology of precipitation, surface and ground waters in the Okanagan Valley, British Columbia, Canada. J. Hydrol. 411, 37–48 (2011).CAS 
Article 
ADS 

Google Scholar 
72.Cameron, E. M., Hall, G. E. M., Veizer, J. & Krouse, H. R. Isotopic and elemental hydrogeochemistry of a major river system: Fraser River, British Columbia, Canada. Chem. Geol. 122, 149–169 (1995).CAS 
Article 
ADS 

Google Scholar 
73.Waterisotope Database. http://waterisotopesDB.org (2019).74.Mostapa, R., Ishak, A. K., Mohamad, K. & Demana, R. Identification of bottled zam zam water in Malaysian market using hydrogen and oxygen stable isotope ratios(d2H and d18O). J. Nucl. Relat. Technol. 11, 48–53 (2014).
Google Scholar 
75.Thompson, A. H. et al. Stable isotope analysis of modern human hair collected from Asia (China, India, Mongolia, and Pakistan). Am. J. Phys. Anthropol. https://doi.org/10.1002/ajpa.21162 (2010).Article 
PubMed 

Google Scholar 
76.Katebe, R., Musukwa, G., Mweetwa, B., Shaba, P. & Njovu, E. Assessment of Uranium in Drinking Water in Kitwe, Chambeshi and Chingola in the Copperbelt Region of Zambia (IAEA, 2015).77.Zurbrügg, R., Wamulume, J., Kamanga, R., Wehrli, B. & Senn, D. B. River-floodplain exchange and its effects on the fluvial oxygen regime in a large tropical river system (Kafue Flats, Zambia). J. Geophys. Res. Biogeosci. 117, 1–10 (2012).Article 
CAS 

Google Scholar 
78.The Government of the Hong Kong Special Administrative Region. Water Supplies Department https://www.wsd.gov.hk/en/publications-and-statistics/pr-publications/the-facts/index.html (2019).79.Demény, A., Gugora, A. D., Kesjár, D., Lécuyer, C. & Fourel, F. Stable isotope analyses of the carbonate component of bones and teeth: The need for method standardization. J. Archaeol. Sci. 109, 104979 (2019).Article 
CAS 

Google Scholar 
80.Wunder, M. B. Using Isoscapes to Model probability surfaces for determining geographic origins BT: Isoscapes: Understanding movement, pattern, and process on Earth through isotope mapping in (eds. West, J. B., Bowen, G. J., Dawson, T. E. & Tu, K. P.) 251–270 (Springer, 2010).81.Graul, C. leafletR: Interactive Web-Maps Based on the Leaflet JavaScript Library (2016).82.Pye, K. Isotope and trace element analysis of human teeth and bones for forensic purposes. Geol. Soc. Lond. Spec. Publ. 232, 215–236 (2004).CAS 
Article 
ADS 

Google Scholar  More

1.Bo, M. et al. Characteristics of a black coral meadow in the twilight zone of the Central Mediterranean Sea. Mar. Ecol. Prog. Ser. 397, 53–61 (2009).ADS 
Article 

Google Scholar 
2.Fabri, M. et al. Megafauna of vulnerable marine ecosystems in French mediterranean submarine canyons: Spatial distribution and anthropogenic impacts. Deep Sea Res. Part II Top. Stud. Oceanogr. 104, 184–207 (2014).ADS 
Article 

Google Scholar 
3.Opresko, D. M. & Baron-Szabo, R. Re-descriptions of the antipatharian corals described by E. J. C. ESPER with selected English translations of the original German text (Cnidaria, Anthozoa, Antipatharia). Senckenb. Biol. 81, 1–21 (2019).
Google Scholar 
4.Molodstova, T. N. Deep-sea fauna of European seas: An annotated species check-list of benthic invertebrates living deeper than 2000 m in the seas bordering Europe. Antipatharia. Invertebr. Zool. 11, 3–7 (2014).Article 

Google Scholar 
5.IUCN. No Title. (2020). Available at: http://www.iucn.it/scheda.php?id=-512293580. Accessed: 27th August 2020.6.Chimienti, G., Bo, M., Taviani, M. & Mastrototaro, F. 19 Occurrence and Biogeography of Mediterranean Cold-Water Corals. (2019). https://doi.org/10.1007/978-3-319-91608-8_197.Bo, M. et al. Persistence of pristine deep-sea coral gardens in the Mediterranean Sea (SW Sardinia). PLoS ONE 10, 1–21 (2015).Article 
CAS 

Google Scholar 
8.Cau, A. et al. Leiopathes glaberrima millennial forest from SW Sardinia as nursery ground for the small spotted catshark Scyliorhinus canicula. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 731–735 (2017).Article 

Google Scholar 
9.D’Onghia, G. Cold-water corals as shelter, feeding and life-history critical habitats for fish species: Ecological interactions and fishing impact. In Mediterranean cold-water corals: Past, present and future (eds. Covadonga, O. & Jiménez, C.) 335–356 (Springer, 2019).10.Etnoyer, P. J. et al. Models of habitat suitability, size, and age-class structure for the deep-sea black coral Leiopathes glaberrima in the Gulf of Mexico. Deep. Res. Part II Top. Stud. Oceanogr. 150, 218–228 (2018).ADS 
Article 

Google Scholar 
11.Vitale, S. et al. Black coral age and growth validation using 14C dating in the central Mediterranean Sea. In 2nd international radiocarbon in the environment conference (2017).12.Carlier, A. et al. Trophic relationships in a deep mediterranean cold-water coral bank (Santa Maria di Leuca, Ionian sea). Mar. Ecol. Prog. Ser. 397, 125–137 (2009).ADS 
CAS 
Article 

Google Scholar 
13.Wagner, D., Luck, D. G. & Toonen, R. J. The biology and ecology of black corals (Cnidaria: Anthozoa: Hexacorallia: Antipatharia. Adv. Mar. Biol. 63, 67–132 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Bo, M. et al. Deep coral oases in the South Tyrrhenian Sea. PLoS ONE 7, e49870 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Mytilineou, C., Smith, C., Anastasopoulou, A., Papadopoulou, K. & Christidis, G. New cold-water coral occurrences in the Eastern Ionian Sea: Results from experimental long line fishing. Deep. Res. Part I Oceanogr. Res. Pap. 99, 146–157 (2014).ADS 

Google Scholar 
16.Angeletti, L. et al. First report of live deep water cnidarian assemblages from the Malta Escarpment. Ital. J. Zool. 82, 291–297 (2015).
Google Scholar 
17.Costantini, F., Fauvelot, C. & Abbiati, M. Fine-scale genetic structuring in Corallium rubrum: Evidence of inbreeding and limited effective larval dispersal. Mar. Ecol. Prog. Ser. 340, 109–119 (2007).ADS 
CAS 
Article 

Google Scholar 
18.Miller, K. Short-distance dispersal of black coral larvae:inference from spatial analysis of colony genotypes. Mar. Ecol. Prog. Ser. 171, 225–233 (1998).ADS 
Article 

Google Scholar 
19.Bo, M. et al. The coral assemblages of an off-shore deep Mediterranean rocky bank (NW Sicily, Italy). Mar. Ecol. 35, 332–342 (2014).ADS 
Article 

Google Scholar 
20.Bo, M. et al. Fishing impact on deep Mediterranean rocky habitats as revealed by ROV investigation. Biol. Conserv. 171, 167–176 (2014).Article 

Google Scholar 
21.Deidun, A., Tsounis, G., Balzan, F. & Micallef, A. Records of black coral (Antipatharia) and red coral (Corallium rubrum) fishing activities in the Maltese Islands. Mar. Bioldivers. Rec. 3, 1–6 (2010).Article 

Google Scholar 
22.Tsounis, G. et al. The exploitation and conservation of precious corals. Oceanogr. Mar. Biol. An Annu. Rev. 48, 161–211 (2010).Article 

Google Scholar 
23.Deidun, A. et al. First characterisation of a Leiopathes glaberrima (Cnidaria: Anthozoa: Antipatharia) forest in Maltese exploited fishing grounds. Ital. J. Zool. 82, 271–280 (2015).
Google Scholar 
24.Thompson, A., Sanders, J., Tandstad, M., Carocci, F. & Fuller, M. Vulnerable Marine Ecosystems: Processes and Practices in the High Seas (2016).25.Fabri, M. C. et al. Megafauna of vulnerable marine ecosystems in French mediterranean submarine canyons: Spatial distribution and anthropogenic impacts. Deep. Res. Part II Top. Stud. Oceanogr. 104, 184–207 (2014).ADS 
Article 

Google Scholar 
26.Pearson, R. G. Species distribution modeling for conservation educators and practitioners synthesis. In (2008).27.Sundahl, H., Buhl-Mortensen, P. & Buhl-Mortensen, L. Distribution and Suitable Habitat of the Cold-Water Corals Lophelia pertusa, Paragorgia arborea, and Primnoa resedaeformis on the Norwegian continental shelf. Front. Mar. Sci. 7, 1–22 (2020).Article 

Google Scholar 
28.Tittensor, D. P. et al. Predicting global habitat suitability for stony corals on seamounts. J. Biogeogr. 36, 1111–1128 (2009).Article 

Google Scholar 
29.Davies, A. J. & Guinotte, J. M. Global habitat suitability for framework-forming cold-water corals. PLoS ONE 6, e18483 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Bargain, A. et al. Predictive habitat modeling in two Mediterranean canyons including hydrodynamic variables. Prog. Oceanogr. 169, 151–168 (2018).ADS 
Article 

Google Scholar 
31.Lauria, V. et al. Species distribution models of two critically endangered deep-sea octocorals reveal fishing impacts on vulnerable marine ecosystems in central Mediterranean Sea. Sci. Rep. 7, 1–14 (2017).CAS 
Article 

Google Scholar 
32.Massi, D. et al. Spatial distribution of the black coral Leiopathes glaberrima (Esper, 1788) (Antipatharia: Leiopathidae) in the Mediterranean: A prerequisite for protection of Vulnerable Marine Ecosystems (VMEs). Eur. Zool. J. 85, 170–179 (2018).Article 

Google Scholar 
33.Hayes, D. R. et al. Review of the circulation and characteristics of intermediate water masses of the mediterranean: Implications for cold-water coral habitats. In Mediterranean cold-water corals: Past, present and future (eds. Orejas, C. & Jiménez, C.) 195–212 (Springer, 2019).34.Pinardi, N. & Masetti, E. Variabilityofthelarge-scalegeneralcirculationofthe, MediterraneanSeafromobservationsandmodelling:areview. Palaeogeogr. Palaeoclimatol. Palaeoecol. 158, 153–173 (2000).Article 

Google Scholar 
35.Pinardi, N. et al. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: A retrospective analysis. Prog. Oceanogr. 132, 318–332 (2015).ADS 
Article 

Google Scholar 
36.Astraldi, M. et al. Water mass properties and chemical signatures in the central Mediterranean region. J. Mar. Syst. 133–134, 155–177 (2002).Article 

Google Scholar 
37.Taviani, M. et al. The “Sardinian cold-water coral province” in the context of the Mediterranean coral ecosystems. Deep. Res. Part II Top. Stud. Oceanogr. 145, 61–78 (2017).ADS 
Article 

Google Scholar 
38.EMODnet Bathymetry Consortium. EMODnet Digital Bathymetry (DTM 2016). EMODnet Bathymetry Consortium (2016). https://doi.org/10.12770/c7b53704-999d-4721-b1a3-04ec60c87238.39.McArthur, M. et al. On the use of abiotic surrogates to describe marine benthic biodiversity. Estuar. Coast. Shelf Sci. 88, 21–32 (2010).ADS 
Article 

Google Scholar 
40.Lauria, V. et al. Species distribution models of two critically endangered deep-sea octocorals reveal fishing impacts on vulnerable marine ecosystems in central Mediterranean Sea. Sci. Rep. 7, 8049 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Wilson, M. F. J., O’Connell, B., Brown, C., Guinan, J. C. & Grehan, A. J. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Mar. Geod. 30, 3–35 (2007).Article 

Google Scholar 
42.Tong, R., Purser, A., Unnithan, V. & Guinan, J. Multivariate statistical analysis of distribution of deep-water gorgonian corals in relation to seabed topography on the norwegian margin. PLoS ONE 7, 1–13 (2012).
Google Scholar 
43.Savini, A., Vertino, A., Marchese, F., Beuck, L. & Freiwald, A. Mapping cold-water coral habitats at different scales within the Northern Ionian Sea (central Mediterranean): An assessment of coral coverage and associated vulnerability. PLoS ONE 9, e102405 (2014).Article 
CAS 

Google Scholar 
44.Sbrocco, E. & Barber, P. MARSPEC: Ocean climate layers for marine spatial ecology. Ecology 94, 979 (2013).Article 

Google Scholar 
45.White, M., Mohn, C., Stigter, H. & Mottram, G. Deep-water coral development as a function of hydrodynamics and surface productivity around the submarine banks of the Rockall Trough, NE Atlantic. In eds. Freiwald, A. & Robert, JM. 503−514 (2005).46.Davies, A. J. et al. Downwelling and deep-water bottom currents as food supply mechanisms to the cold-water coral Lophelia pertusa (Scleractinia) at the Mingulay Reef complex. Limnol. Oceanogr. 54, 620–629 (2009).ADS 
Article 

Google Scholar 
47.Clementi, E. et al. Mediterranean Sea Analysis and Forecast (CMEMS MED-Currents, EAS5 system). (2019).48.Guinotte, J. Climate change and deep-sea corals. J. Mar. Educ. 21, 48–49 (2005).
Google Scholar 
49.Zuur, A. F., Ieno, E. N. & Smith, G. M. Analysing Ecological Data. (Springer2007).50.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modelling of species distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
51.Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop.) 29, 129–151 (2006).Article 

Google Scholar 
52.Tong, Y., Chen, X. & Chen, Y. Evaluating alternative management strategies for bigeye tuna, Thunnus obesus in the Indian Ocean. Sci. Mar. 77, 449–460 (2013).Article 

Google Scholar 
53.Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).Article 

Google Scholar 
54.Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).Article 

Google Scholar 
55.Bargain, A., Marchese, F., Savini, A., Taviani, M. & Fabri, M. C. Santa Maria di Leuca province (Mediterranean Sea): Identification of suitable mounds for cold-water coral settlement using geomorphometric proxies and maxent methods. Front. Mar. Sci. 4, 1–17 (2017).Article 

Google Scholar 
56.Fabri, M. C., Bargain, A., Pairaud, I., Pedel, L. & Taupier-Letage, I. Cold-water coral ecosystems in Cassidaigne Canyon: An assessment of their environmental living conditions. Deep. Res. Part II Top. Stud. Oceanogr. 137, 436–453 (2017).ADS 
Article 

Google Scholar 
57.Phillips, S. J. & Dudı, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. 161–175 (2008). https://doi.org/10.1111/j.2007.0906-7590.05203.x58.Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
59.Phillips, S. J. & Dudik, M. Modeling of species distributions withMaxent: New extensions and a comprehensive evaluation. Ecography (Cop.) 31, 161–175 (2008).Article 

Google Scholar 
60.Young, N., Lane, C. & Evangelista, P. A MaxEnt Model Tutorial (ArcGISv10) (2011).61.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. Opening the black box: an open-source release of Maxent. Ecography (Cop.) 40, 887–893 (2017).Article 

Google Scholar 
62.Mytilineou, C. et al. New cold-water coral occurrences in the Eastern Ionian Sea: Results from experimental long line fishing. Deep. Res. Part II Top. Stud. Oceanogr. 99, 146–157 (2014).ADS 
Article 

Google Scholar 
63.Chimienti, G., Bo, M., Taviani, M. & Mastrototaro, F. Coral reefs of the world. In Mediterranean cold-water corals: Past; present and future (eds. Orejas, C. & Jiménez C.) 213–243 (Springer, 2019).64.Mascle, J., Migeon, S., Coste, M., Hassoun, V. & Rouillard, P. Rocky vs sedimentary canyons around the mediterranean sea and the black sea. In Submarine canyon dynamics in the mediterranean and tributary seas—An integrated geological, oceanographic and biological perspective CIESM (ed. Briand, F.) (2015).65.Canals, M. et al. Flushing submarine canyons. Nature 444, 354–357 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
66.Huvenne, V. A. I. et al. picture on the wall: innovative mapping reveals coldwater coral refuge in submarine canyon. PLoS ONE 6, e28755 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Bo, M. et al. Coral assemblages off the Calabrian Coast (South Italy) with new observations on living colonies of Antipathes dichotoma. Ital. J. Zool. 78, 231–242 (2010).Article 

Google Scholar 
68.Freiwald, A. & Roberts, M. J. Cold-water corals and ecosystems: preface. In Cold-water corals and ecosystems (ed. Freiwald, A. R.) 1243 (Springer, 2005).69.Khripounoff, A. et al. Deep cold-water coral ecosystems in the Brittany submarine canyons (Northeast Atlantic): Hydrodynamics, particle supply, respiration, and carbon cycling. Limnol. Oceanogr. 59, 87–98 (2014).ADS 
CAS 
Article 

Google Scholar 
70.Davies, A. J., Wisshak, M., Orr, J. C. & Murray Roberts, J. Predicting suitable habitat for the cold-water coral Lophelia pertusa (Scleractinia). Deep. Res. Part I Oceanogr. Res. Pap. 55, 1048–1062 (2008).ADS 
Article 

Google Scholar 
71.Greathead, C. F., Donnan, D. W., Mair, J. M. & Saunders, G. R. The sea pens Virgularia mirabilis, Pennatula phosphorea and Funiculina quadrangularis: Distribution and conservation issues in Scottish waters. J. Mar. Biol. Assoc. UK 87, 1095–1103 (2007).Article 

Google Scholar 
72.Barry, S. & Elith, J. Error and uncertainty in habitat models. J. Appl. Ecol. 43, 413–423 (2006).Article 

Google Scholar 
73.Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 763–773 (2008).74.Parolo, G., Rossi, G. & Ferrarini, A. oward improved species niche mod-elling: arnica montana in the Alps as a case study. J. Appl. Ecol. 45, 1410–1418 (2008).Article 

Google Scholar 
75.Gogol-Prokurat, M. Predicting habitat suitability for rare plants at localspatial scales using a species distribution model. Ecol. Appl. 21, 33–47 (2011).PubMed 
Article 

Google Scholar 
76.Bean, W. T., Stafford, R. & Brashares, J. S. he effects of small sample sizeand sample bias on threshold selection and accuracy assessment of species dis-tribution models. Ecography (Cop.) 35, 250–258 (2012).Article 

Google Scholar 
77.Morales, N. S., Fernández, I. C. & Baca-González, V. MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ (2017).78.Opresko, D. M. & Försterra, G. No Title. in El Mar Mediterraneo: fauna, flora, ecologia. (ed. Hofrichter, R.) 506–509 (2004).79.Aguilar, R. & Marín, P. Mediterranean deep-sea corals : reasons protection under the Barcelona Convention. 1–18 (2013).80.FAO. International Guidelines for the Management of Deep-sea Fisheries in the High Seas. 73 (2009).81.Marin, P. & Aguilar, R. Mediterranean submarine canyons 2012: pending protection. in Mediterranean Submarine Canyons: Ecology and Governance 191–206 (IUCN, 2012).82.Thurstan, R. H., Brockington, S. & Roberts, C. M. The effects of 118 years of industrial fishing on UK bottom trawl fisheries. Nat. Commun. 1, 1–6 (2010).CAS 
Article 

Google Scholar 
83.Worm, B. & Tittensor, D. P. Range contraction in large pelagic predators. Proc. Natl. Acad. Sci. USA 108, 11942–11947 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Gross, M. Deep sea in deep trouble?. Curr. Biol. 25, 1019-R1021 (2015).Article 
CAS 

Google Scholar 
85.Morato, T., Watson, R., Pitcher, T. & Pauly, D. Fishing down the deep. Fish Fish. 7, 24–34 (2006).Article 

Google Scholar 
86.Roberts, C. M. Deep impact: The rising toll of fishing in the deep sea. Trends Ecol. Evol. 17, 242–245 (2002).MathSciNet 
Article 

Google Scholar  More

Sea urchinsSea urchins (~ 2 cm of test diameter) were transported from Dalian Haibao Fishery Company (121° 22′ E, 38° 77′ N) to the Key Laboratory of Mariculture and Stock Enhancement in North China’s Sea, Ministry of Agriculture and Rural Affairs (121° 56′ E, 38° 87′ N) in November 2020 and maintained in the fiberglass tank of 139 L (750 × 430 × 430 mm) of the recirculating system (Huixin Co., China) at ~ 14 °C. During the experiment period, the salinity of the recirculated seawater was 30.07–30.25‰. Sea urchins were fed the kelp Saccharina japonica ad libitum with aeration before the experiments. The seawater was changed every three days to remove feces and algal debris.Conspecific alarm cuesConspecific alarm cues were made from crushing one M. nudus in 50 mL of fresh seawater and then filtered using a fine silk net (260 μm of mesh size)6. Fresh conspecific alarm cues were prepared before each behavioral experiment. Five mL of conspecific alarm cues was used to simulate the natural conditions, in which a fraction of body fluids from a sea urchin instantly released into the surrounding water6.Experiment 1: whether M. nudus are attracted by the kelp exposed to conspecific alarm cuesA piece of wild fresh S. japonica (~ 5 g) was placed on the side of the device with the raceways (70 × 6 × 5 cm) and 5 mL of conspecific alarm cues were subsequently added above it in the experimental group (Fig. 4A). Five mL of conspecific alarm cues were added in the control group, while the kelp was not involved (Fig. 4B). The sea urchin was individually placed 15 cm away from the kelp for each group (Fig. 4A,B). Foraging behavior of sea urchins was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). The danger area (b area) refers to the position 15 cm away from the kelp and the escape area (a area) refers to the position 15 cm away from the sea urchin (Fig. 4A,B). The time of sea urchins spent in the danger (a area) and escape (b area) areas was calculated individually for each group. The experiment was repeated 20 times using different sea urchins for each group (N = 20). The seawater was changed and the experimental device was washed for each trial to avoid potential non-experimental effects.Figure 4Foraging devices for the experiments with Mesocentrotus nudus. Experiment 1: the kelp was attracted by sea urchins (A,B); the positions of a, c and f refer to the escape areas, while positions of b, d and e refer to the danger areas. Experiment 2: five mL of conspecific alarm cues were put in the middle of the device (C). Experiment 3 (D–F): foraging behavior of M. nudus at 70 cm in the first trial (D); five mL of conspecific alarm cues were put in the middle of the device in the second trial (E); the position of g refers to the danger area; different amounts of conspecific alarm cues (5 mL and 0.5 mL) were put in the left and right of the devices in the third trial (F); the positions of h and i refer to the safety areas close to the areas with more and less conspecific alarm cues, respectively. This figure was created using the Adobe Photoshop (version CS5) software.Full size imageExperiment 2: whether foraging behavior of fasted M. nudus is affected when they encountered conspecific alarm cuesThis experiment investigated the effects of conspecific alarm cues on the foraging behavior of fasted sea urchins. The experiment group was set to simulate that conspecific alarm cues appear on the way to kelp beds (Fig. 4C). Sea urchins were fasted for 7, 14 and 21 days in the experiment groups.An acrylic device with the raceways (70 × 6 × 5 cm) was designed according to the method of our previous study17 with some revisions. Five mL of conspecific alarm cues were added in the middle of the device, the wild fresh S. japonica (~ 10 g) was placed 15 cm away from the left side of the device, while the sea urchin was placed on 15 cm away from the right of the device (Fig. 4C). Foraging behavior of sea urchins was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). Arriving at the kelp within 20 min was defined as successfully foraging18. The danger area (e area) refers to the position between conspecific alarm cues and the sea urchin, while the escape area (f area) refers to the 15 cm position away from the right side of the sea urchin (Fig. 4C). The time of sea urchins spent in the e and f areas was calculated individually for all the groups. The individual experiment was repeated 20 times with different sea urchins for each group (N = 20).Experiment 3: whether foraging ability of M. nudus links to their responses to conspecific alarm cuesAn acrylic device with the raceways (70 × 6 × 5 cm) was designed for the measurement of foraging behavior of sea urchins. Experiment 3 included three trials.In the first trial, we measured the foraging ability of sea urchins. Ten grams of the kelp was placed on one side of the tank and one sea urchin was placed on the other side of the tank (Fig. 4D). Foraging behavior was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). Movement and velocity of sea urchins were calculated using the software ImageJ (version 1.51 n). The experiment was individually repeated 31 times using different sea urchins (N = 31). At the end of this foraging experiment, 31 sea urchins were recorded in individual cylindrical plastic cages in the tank. All the sea urchins were subsequently measured for the second and third trials (N = 31). To avoid potential fatigue influences, each behavioral experiment was carried out after another 24 h.The second trial was carried out to test the hypothesis that sea urchins with strong foraging ability are affected when conspecific alarm cues appear on the way to the kelp. A piece of wild fresh kelp (~ 10 g) was placed on the left side of the device (70 × 6 × 5 cm), while 5 mL of conspecific alarm cues were put in the middle of the device. The individual was placed on the right of the device (Fig. 4E). Foraging behavior of sea urchins was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). The danger area (g area) refers to the 15 cm position away from conspecific alarm cues. The time of the 31 sea urchins spent in the g area was individually calculated.We investigated whether different amounts of conspecific alarm cues around the kelp affected the sea urchins with strong foraging ability in the third trial. More conspecific alarm cues (5 mL) and a piece of wild fresh kelp (~ 10 g) were placed on the left side of the device (70 × 6 × 5 cm), while less conspecific alarm cues (0.5 mL) and wild fresh kelp (~ 10 g) were placed on the right side of the device (Fig. 4F). Sea urchins were individually placed in the middle of the device. Foraging behavior of sea urchins was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). The number of sea urchins that successfully foraged to areas with more and less conspecific alarm cues was recorded. The area h refers to the safety area that was 15 cm position away from the sea urchin, which was close to the side with more conspecific alarm cues. The area of i refers to the safety area that was 15 cm position away from the sea urchin, which was close to the side with less conspecifics alarm cues. The time spent in the h and i areas was individually calculated in 31 sea urchins.Statistical analysisNormal distribution and homogeneity of variance of the data were analyzed using the Kolmogorov–Smirnov test and Levene test, respectively. Independent-sample t-test was carried out to compare the differences of duration in danger (b and d) and escape (a and c) areas in experiment 1. One-way ANOVA was used to analyze the duration in danger (e) and escape (f) areas of the sea urchins fasted for 7, 14 and 21 days. Pairwise multiple comparisons were carried out using LSD test when significant differences were found in the ANOVAs. Correlations between foraging velocity and duration in the g, h and i areas were analyzed using Pearson correlation analysis. A probability level of P < 0.05 was considered significant. All data analyses were performed using SPSS 21.0 statistical software. Graphs 2−4 were performed using Origin 9.0 software (OriginLab, USA). More

These authors contributed equally: Zhongru Gu, Shengkai Pan, Zhenzhen LinKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Shengkai Pan, Zhenzhen Lin, Li Hu, Han Su, Juan Long & Xiangjiang ZhanCardiff University–Institute of Zoology Joint Laboratory for Biocomplexity Research, Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Shengkai Pan, Zhenzhen Lin, Li Hu, Han Su, Juan Long, Michael W. Bruford, Andrew Dixon & Xiangjiang ZhanUniversity of the Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Li Hu, Han Su, Juan Long, Mengru Sun & Xiangjiang ZhanSchool of Biological Sciences, University of Bristol, Bristol, UKXiaoyang DaiState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaJiang ChangKey Laboratory of RNA Biology, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, ChinaYuanchao Xue & Mengru SunWild Animal Rescue Centre, Moscow, RussiaSergey GanusevichInstitute of Plant and Animal Ecology, Ural Division Russian Academy of Sciences, Ekaterinburg, RussiaVasiliy SokolovArctic Research Station of the Institute of Plant and Animal Ecology, Ural Division Russian Academy of Sciences, Labytnangi, RussiaAleksandr Sokolov & Ivan PokrovskyDepartment of Migration, Max Planck Institute of Animal Behavior, Radolfzell, GermanyIvan PokrovskyLaboratory of Ornithology, Institute of Biological Problems of the North FEB RAS, Magadan, RussiaIvan PokrovskyState Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, ChinaFen JiSchool of Biosciences and Sustainable Places Institute, Cardiff University, Cardiff, UKMichael W. BrufordEmirates Falconers’ Club, Abu Dhabi, United Arab EmiratesAndrew DixonReneco International Wildlife Consultants, Abu Dhabi, United Arab EmiratesAndrew DixonInternational Wildlife Consultants, Carmarthen, UKAndrew DixonCenter for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, ChinaXiangjiang Zhan More

Modelling microbenthic O2 exportWe explored how microbial processes and export fluxes of their metabolic substrates and products from ancient benthic photosynthetic ecosystems were influenced by daylength, environmental conditions and various regulatory mechanisms of photosynthetic production and respiration using an in silico microbenthic model. Model scenarios were constructed and simulated using MicroBenthos software12. MicroBenthos model definitions and parameters for the described scenarios are provided with this article. The software and usage instructions are available at https://microbenthos.readthedocs.io.The modelling framework is an adaptation of de Wit et al.61. Briefly, benthic systems are constructed as a diffusive–reactive system in a 1D computational domain, with discrete cells used to represent the spatial distribution of the state and parameter variables. While the study by de Wit et al.61 focused on biomass growth running over long simulation times, our interest was to study the dynamics of process rates and solute fluxes over diel timescales. Therefore, we set a fixed biomass for the microbial groups, added a water subdomain on top of the sediment as a diffusive boundary layer and ran simulations until a diel steady state was reached (5 days). Our model domain used 5 µm cells, with an 8 mm sedimentary subdomain and 1 mm diffusive boundary layer of water on top. O2 and sulfide concentrations were the state variables that we solved for. Photosynthetically active radiation (PAR) was expressed as a percent of the maximum intensity at the diel zenith, and followed a cosinusoidal pattern similar to that of diel insolation dynamics.Raero and SOX were formulated to occur throughout the sediment. Microbial groups (cyanobacteria and SRB) were represented as biomass distributions in the sediment subdomain, and biomass-dependent metabolism kinetics were expressed as multiplications of the response functions of salient environmental and state variables. Coupled partial differential equations of the state variables (O2 and H2S) were composed from the reaction terms that accounted for sediment porosity and were solved with finite-volume numerical approximations62.Our in silico mat allowed us to explore how diffusive mass transfer shapes the interplay between illumination dynamics, gross production and consumption rates, and diel O2 export. The effect of daylength was studied by varying the period of the illumination from 12 h to 24 h, the range of estimated daylengths over Earth’s history after the earliest estimates for the origin of OP63. We report the calculated average diel net export and process rates in units of mmol m−2 h−1 because the hour is the largest temporal unit unaffected by changes in the Earth’s rotation and thus allows for comparison across daylengths.First, we explored the simplest case of O2 production, which is with light availability. Two microbial processes were considered: OP performed by cyanobacteria and Raero. The parameters for the biotic reactions were re-expressed as a biomass-specific maximal yield (Qmax). A fundamental assumption is that the photosynthesis rate is strictly correlated to the instantaneous photon flux:$${rm{OP}} = Q_{{rm{max}}}times {rm{biomass}}times {rm{sat}}left( {{rm{PAR}},,K_{{rm{PAR}}}} right),$$
(1)
where sat is a Michaelis–Menten function with KPAR = 15% and the cyanobacterial biomass with a log-normal distribution with a peak value of 12 mg cm−3 at 0.5 mm depth (Supplementary Video 1). The only source of O2 is OP, and the sinks are aerobic (sedimentary) respiration (Raero). For the production and consumption rates of Corg, we assumed a stoichiometry of:$${mathrm{H}}_2{mathrm{O}} + {mathrm{CO}}_2 to {mathrm{O}}_2 + {mathrm{CH}}_2{mathrm{O}}$$
(2)
with respect to O2 cycling rates, where CH2O refers to one Corg equivalent. Assuming that Corg is predominantly particulate, with negligible diffusional transport, diel Corg burial was thus calculated as:$${mathrm{C}}_{{mathrm{org}}} {mathrm{buried}} = smallint {mathrm{OP}}-smallint {mathrm{R}}_{{mathrm{aero}}},$$
(3)
where ∫OP and ∫Raero are the diel depth-integrated rates of O2 production and consumption and are equivalent to Corg production and consumption according to equation (2). Thus, diel burial can also be represented through the export flux of O2 at the top and bottom interfaces of the sedimentary domain:$${mathrm{C}}_{{mathrm{org}}} {mathrm{buried}} = {mathrm{O}}_{2} {mathrm{export}} = smallint {mathrm{OP}}-smallint {mathrm{R}}_{{mathrm{aero}}},$$
(4)
which allowed us to assess the dynamic steady state of the diel model when the average diel depth-integrated rates equalled the export fluxes.To calibrate the O2 productivity for unitless PAR intensities, we determined the Qmax that caused a maximum O2 export that corresponded to the median maximal flux from illuminated benthic photosynthetic systems13. A Qmax of 4.0022 mmol g−1 h−1 produced the target export flux of 5.76 mmol m−2 h−1 under a sedimentary respiration load of 0.1 mM h−1. Note that by calibrating the productivity to the maximum diel illumination, the model represents a ‘mean solar day’ of a given Earth year59. This allowed us to disentangle the effect of daylength from geological-scale changes in the insolation intensity, such as in the ‘faint young Sun’ paradigm reviewed thoroughly by Feulner23, or changes in the solar spectrum related to atmospheric composition64.Next, we explored the effect of Ranaero on the daylength dependency of the process rates and export fluxes. We used the example of sulfate reduction performed by SRB with a log-normal biomass distribution with a peak value of 2 mg cm−3 (Supplementary Video 1). The Ranaero rate was either defined as a constant rate process for the scenario ‘OP SRB constant’ as:$${mathrm{R}}_{{rm{anaero}}} = Q_{{rm{max}}}times{rm{biomass}}$$
(5)
or as an O2- and H2S-sensitive process as:$$begin{array}{rcl}{mathrm{R}}_{{rm{anaero}}} & = & Q_{{rm{max}}} times {rm{biomass}}times {rm{inhibition}}([{rm{O}}_2],,K_{{rm{max}},{{rm{O}}_2}},,K{_{{rm{half}},{{rm{O}}_2}})}\ && times {rm{inhibition}}left( [{{rm{H}}_2{rm{S}}],,K_{{rm{max}},{rm{H}}_2{rm{S}}},,K_{{rm{half}},{rm{H}}_2{rm{S}}}} right)end{array}$$
(6)
where inhibition is a function of the local H2S and O2 concentration (x) of the form:$$frac{{K_{{rm{max}}} – x}}{{2times K_{{rm{max}}} – K_{{rm{half}}} – x}}$$
(7)
when x  More

1.Prabakaran S, Lippens G, Steen H, Gunawardena J. Post‐translational modification: nature’s escape from genetic imprisonment and the basis for dynamic information encoding. Wiley Interdiscip Rev Syst Biol Med. 2012;4:565–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Khoury GA, Baliban RC, Floudas CA. Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database. Sci Rep. 2011;1:90.CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
3.den Ridder M, Daran-Lapujade P, Pabst M. Shot-gun proteomics: why thousands of unidentified signals matter. FEMS Yeast Res. 2020;20:foz088.Article 
CAS 

Google Scholar 
4.Spoel SH. Orchestrating the proteome with post-translational modifications. Oxford University Press UK. 2018;19:4499–4503.5.Varki A, Cummings RD, Esko JD, Freeze HH, Stanley P, Bertozzi CR, et al. Essentials of glycobiology. 3rd edition. (Cold Spring Harbor Laboratory Press, New York, 2015–2017).6.Varki A. Evolutionary forces shaping the Golgi glycosylation machinery: why cell surface glycans are universal to living cells. Cold Spring Harb Perspect Biol. 2011;3:a005462.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
7.Varki A, Lowe JB. Biological roles of glycans. In: Varki A. Essentials of glycobiology. 2nd edition (Cold Spring Harbor Laboratory Press, New York, 2009). pp 75–88.8.Herget S, Toukach PV, Ranzinger R, Hull WE, Knirel YA, Von der Lieth C-W. Statistical analysis of the Bacterial Carbohydrate Structure Data Base (BCSDB): characteristics and diversity of bacterial carbohydrates in comparison with mammalian glycans. BMC Struct Biol. 2008;8:1–20.Article 
CAS 

Google Scholar 
9.Schäffer C, Messner P. Emerging facets of prokaryotic glycosylation. FEMS Microbiol Rev. 2017;41:49–91.PubMed 
Article 
CAS 

Google Scholar 
10.Eichler J, Koomey M. Sweet new roles for protein glycosylation in prokaryotes. Trends Microbiol. 2017;25:662–72.CAS 
PubMed 
Article 

Google Scholar 
11.Eichler J. Extreme sweetness: protein glycosylation in archaea. Nat Rev Microbiol. 2013;11:151.CAS 
PubMed 
Article 

Google Scholar 
12.Kleikamp HB, Lin YM, McMillan DG, Geelhoed JS, Naus-Wiezer SN, Van Baarlen P, et al. Tackling the chemical diversity of microbial nonulosonic acids–a universal large-scale survey approach. Chem Sci. 2020;11:3074–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Boleij M, Kleikamp H, Pabst M, Neu TR, Van Loosdrecht MC, Lin Y. Decorating the anammox house: sialic acids and sulfated glycosaminoglycans in the extracellular polymeric substances of anammox granular sludge. Environ Sci Technol. 2020;54:5218–26.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Bucci M. A gut reaction. Nat Chem Biol. 2020;16:363-.CAS 
PubMed 
Article 

Google Scholar 
15.Conrad R. The global methane cycle: recent advances in understanding the microbial processes involved. Environ Microbiol Rep. 2009;1:285–92.CAS 
PubMed 
Article 

Google Scholar 
16.Lam P, Lavik G, Jensen MM, van de Vossenberg J, Schmid M, Woebken D, et al. Revising the nitrogen cycle in the Peruvian oxygen minimum zone. Proc Natl Acad Sci. 2009;106:4752–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Strous M, Pelletier E, Mangenot S, Rattei T, Lehner A, Taylor MW, et al. Deciphering the evolution and metabolism of an anammox bacterium from a community genome. Nature. 2006;440:790.PubMed 
Article 

Google Scholar 
18.Kartal B, Kuenen JV, Van Loosdrecht M. Sewage treatment with anammox. Science. 2010;328:702–3.CAS 
PubMed 
Article 

Google Scholar 
19.van Niftrik L, Jetten MS. Anaerobic ammonium-oxidizing bacteria: unique microorganisms with exceptional properties. Microbiol Mol Biol Rev. 2012;76:585–96.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Fuerst JA, Sagulenko E. Beyond the bacterium: planctomycetes challenge our concepts of microbial structure and function. Nat Rev Microbiol. 2011;9:403.CAS 
PubMed 
Article 

Google Scholar 
21.Van Teeseling MC, Mesman RJ, Kuru E, Espaillat A, Cava F, Brun YV, et al. Anammox Planctomycetes have a peptidoglycan cell wall. Nat Commun. 2015;6:6878.PubMed 
Article 
CAS 

Google Scholar 
22.Jeske O, Schüler M, Schumann P, Schneider A, Boedeker C, Jogler M, et al. Planctomycetes do possess a peptidoglycan cell wall. Nat Commun. 2015;6:7116.CAS 
PubMed 
Article 

Google Scholar 
23.van Teeseling MC, Maresch D, Rath CB, Figl R, Altmann F, Jetten MS, et al. The S-layer protein of the anammox bacterium Kuenenia stuttgartiensis is heavily O-glycosylated. Front Microbiol. 2016;7:1721.PubMed 
PubMed Central 

Google Scholar 
24.van Teeseling MC, de Almeida NM, Klingl A, Speth DR, den Camp HJO, Rachel R, et al. A new addition to the cell plan of anammox bacteria:“Candidatus Kuenenia stuttgartiensis” has a protein surface layer as the outermost layer of the cell. J Bacteriol. 2014;196:80–9.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Boleij M, Pabst M, Neu TR, van Loosdrecht MC, Lin Y. Identification of glycoproteins isolated from extracellular polymeric substances of full-scale anammox granular sludge. Environ Sci Technol. 2018;52:13127–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Gerbino E, Carasi P, Mobili P, Serradell M, Gómez-Zavaglia A. Role of S-layer proteins in bacteria. World J Microbiol Biotechnol. 2015;31:1877–87.CAS 
PubMed 
Article 

Google Scholar 
27.Sleytr UB, Schuster B, Egelseer E-M, Pum D. S-layers: principles and applications. FEMS Microbiol Rev. 2014;38:823–64.CAS 
PubMed 
Article 

Google Scholar 
28.Schuster B, Sleytr UB. Relevance of glycosylation of S-layer proteins for cell surface properties. Acta biomaterialia. 2015;19:149–57.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Tamir A, Eichler J N-Glycosylation is important for proper Haloferax volcanii S-layer stability and function. Appl Environ Microbiol. 2017;83:e03152-16.30.Wang F, Cvirkaite-Krupovic V, Kreutzberger MA, Su Z, de Oliveira GA, Osinski T, et al. An extensively glycosylated archaeal pilus survives extreme conditions. Nat Microbiol. 2019;4:1401–10.31.Li P-N, Herrmann J, Tolar BB, Poitevin F, Ramdasi R, Bargar JR, et al. Nutrient transport suggests an evolutionary basis for charged archaeal surface layer proteins. ISME J. 2018;12:2389–402.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Posch G, Pabst M, Brecker L, Altmann F, Messner P, Schäffer C. Characterization and scope of S-layer protein O-glycosylation in Tannerella forsythia. J Biol Chem. 2011;286:38714–24.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Benz I, Schmidt MA. Never say never again: protein glycosylation in pathogenic bacteria. Mol Microbiol. 2002;45:267–76.CAS 
PubMed 
Article 

Google Scholar 
34.Sekot G, Posch G, Messner P, Matejka M, Rausch-Fan X, Andrukhov O, et al. Potential of the Tannerella forsythia S-layer to delay the immune response. J Dent Res. 2011;90:109–14.CAS 
PubMed 
Article 

Google Scholar 
35.Szymanski CM, Burr DH, Guerry P. Campylobacter protein glycosylation affects host cell interactions. Infect Immun. 2002;70:2242.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Drickamer K, Taylor ME. Evolving views of protein glycosylation. Trends Biochem Sci. 1998;23:321–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Koomey M. O-linked protein glycosylation in bacteria: snapshots and current perspectives. Curr Opin Struct Biol. 2019;56:198–203.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Wang N, Anonsen JH, Hadjineophytou C, Reinar WB, Børud B, Vik Å, et al. Allelic polymorphisms in a glycosyltransferase gene shape glycan repertoire in the O-linked protein glycosylation system of Neisseria. Glycobiology. 2021;31:477–91.PubMed 
Article 
PubMed Central 

Google Scholar 
39.Stadlmann J, Taubenschmid J, Wenzel D, Gattinger A, Dürnberger G, Dusberger F, et al. Comparative glycoproteomics of stem cells identifies new players in ricin toxicity. Nature. 2017;549:538–42.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Polasky DA, Yu F, Teo GC, Nesvizhskii AI. Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nat Methods. 2020;17:1125–32.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Lu L, Riley NM, Shortreed MR, Bertozzi CR, Smith LM. O-Pair Search with MetaMorpheus for O-glycopeptide characterization. Nat Methods. 2020;17:1133–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Fulton KM, Li J, Tomas JM, Smith JC, Twine SM. Characterizing bacterial glycoproteins with LC-MS. Expert Rev Proteom. 2018;15:203–16.CAS 
Article 

Google Scholar 
43.Ahrné E, Müller M, Lisacek F. Unrestricted identification of modified proteins using MS/MS. Proteomics. 2010;10:671–86.PubMed 
Article 
CAS 

Google Scholar 
44.Bern M, Kil YJ, Becker C. Byonic: advanced peptide and protein identification software. Curr Protoc Bioinforma. 2012;40:13.20. 1-13.20. 14Article 

Google Scholar 
45.Na S, Bandeira N, Paek E. Fast multi-blind modification search through tandem mass spectrometry. Mol Cell Proteomics. 2012;11:1–13.46.Devabhaktuni A, Lin S, Zhang L, Swaminathan K, Gonzalez CG, Olsson N, et al. TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets. Nat Biotechnol. 2019;37:469–79.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Izaham ARA, Scott NE. Open database searching enables the identification and comparison of bacterial glycoproteomes without defining glycan compositions prior to searching. Mol Cell Proteom. 2020;19:1561–74.CAS 
Article 

Google Scholar 
48.Ahmad Izaham AR, Ang C-S, Nie S, Bird LE, Williamson NA, Scott NE. What are we missing by using hydrophilic enrichment? improving bacterial glycoproteome coverage using total proteome and FAIMS analyses. J Proteome Res. 2020;20:599–612.49.Kelstrup CD, Frese C, Heck AJ, Olsen JV, Nielsen ML. Analytical utility of mass spectral binning in proteomic experiments by SPectral Immonium Ion Detection (SPIID). Mol Cell Proteom. 2014;13:1914–24.CAS 
Article 

Google Scholar 
50.Wuhrer M, Catalina MI, Deelder AM, Hokke CH. Glycoproteomics based on tandem mass spectrometry of glycopeptides. J Chromatogr B 2007;849:115–28.CAS 
Article 

Google Scholar 
51.Hoffmann M, Marx K, Reichl U, Wuhrer M, Rapp E. Site-specific O-glycosylation analysis of human blood plasma proteins. Mol Cell Proteom. 2016;15:624–41.CAS 
Article 

Google Scholar 
52.Singh C, Zampronio CG, Creese AJ, Cooper HJ. Higher energy collision dissociation (HCD) product ion-triggered electron transfer dissociation (ETD) mass spectrometry for the analysis of N-linked glycoproteins. J proteome Res. 2012;11:4517–25.CAS 
PubMed 
Article 

Google Scholar 
53.Hoffmann M, Pioch M, Pralow A, Hennig R, Kottler R, Reichl U, et al. The fine art of destruction: a guide to in‐depth glycoproteomic analyses—exploiting the diagnostic potential of fragment ions. Proteomics 2018;18:1800282.Article 
CAS 

Google Scholar 
54.Kosma P, Wugeditsch T, Christian R, Zayni S, Messner P. Glycan structure of a heptose-containing S-layer glycoprotein of Bacillus thermoaerophilus. Glycobiology. 1995;5:791–6.CAS 
PubMed 
Article 

Google Scholar 
55.Faridmoayer A, Fentabil MA, Haurat MF, Yi W, Woodward R, Wang PG, et al. Extreme substrate promiscuity of the Neisseria oligosaccharyl transferase involved in protein O-glycosylation. J Biol Chem. 2008;283:34596–604.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Harding CM, Nasr MA, Scott NE, Goyette-Desjardins G, Nothaft H, Mayer AE, et al. A platform for glycoengineering a polyvalent pneumococcal bioconjugate vaccine using E. coli as a host. Nat Commun. 2019;10:1–11.CAS 
Article 

Google Scholar 
57.Speth DR, Guerrero-Cruz S, Dutilh BE, Jetten MS. Genome-based microbial ecology of anammox granules in a full-scale wastewater treatment system. Nat Commun. 2016;7:11172.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Lawson CE, Wu S, Bhattacharjee AS, Hamilton JJ, McMahon KD, Goel R, et al. Metabolic network analysis reveals microbial community interactions in anammox granules. Nat Commun. 2017;8:1–12.Article 
CAS 

Google Scholar 
59.Straka LL, Meinhardt KA, Bollmann A, Stahl DA, Winkler M-K. Affinity informs environmental cooperation between ammonia-oxidizing archaea (AOA) and anaerobic ammonia-oxidizing (Anammox) bacteria. ISME J. 2019;13:1997–2004.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Hu Z, Wessels HJ, van Alen T, Jetten MS, Kartal B. Nitric oxide-dependent anaerobic ammonium oxidation. Nat Commun. 2019;10:1–7.Article 
CAS 

Google Scholar 
61.Shaw DR, Ali M, Katuri KP, Gralnick JA, Reimann J, Mesman R, et al. Extracellular electron transfer-dependent anaerobic oxidation of ammonium by anammox bacteria. Nat Commun. 2020;11:1–12.Article 
CAS 

Google Scholar 
62.Lewis AL, Desa N, Hansen EE, Knirel YA, Gordon JI, Gagneux P, et al. Innovations in host and microbial sialic acid biosynthesis revealed by phylogenomic prediction of nonulosonic acid structure. Proc Natl Acad Sci. 2009;106:13552–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Fernández L, Rodríguez A, García P. Phage or foe: an insight into the impact of viral predation on microbial communities. ISME J. 2018;12:1171–9.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Wang J, Cheng B, Li J, Zhang Z, Hong W, Chen X, et al. Chemical remodeling of cell‐surface sialic acids through a palladium‐triggered bioorthogonal elimination reaction. Angew Chem Int Ed. 2015;54:5364–8.CAS 
Article 

Google Scholar 
65.Pabst M, Fischl RM, Brecker L, Morelle W, Fauland A, Köfeler H, et al. Rhamnogalacturonan II structure shows variation in the side chains monosaccharide composition and methylation status within and across different plant species. Plant J. 2013;76:61–72.CAS 
PubMed 

Google Scholar 
66.Popa I, Pons A, Mariller C, Tai T, Zanetta J-P, Thomas L, et al. Purification and structural characterization of de-N-acetylated form of GD3 ganglioside present in human melanoma tumors. Glycobiology. 2007;17:367–73.CAS 
PubMed 
Article 

Google Scholar 
67.Paschinger K, Wilson IB. Anionic and zwitterionic moieties as widespread glycan modifications in non-vertebrates. Glycoconj J. 2020;37:27–40.CAS 
PubMed 
Article 

Google Scholar 
68.Nothaft H, Scott NE, Vinogradov E, Liu X, Hu R, Beadle B, et al. Diversity in the protein N-glycosylation pathways within the Campylobacter genus. Mol Cell Proteom. 2012;11:1203–19.Article 
CAS 

Google Scholar 
69.Hadjineophytou C, Anonsen JH, Wang N, Ma KC, Viburiene R, Vik Å, et al. Genetic determinants of genus-level glycan diversity in a bacterial protein glycosylation system. PLoS Genet. 2019;15:e1008532.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Oshiki M, Satoh H, Okabe S. Ecology and physiology of anaerobic ammonium oxidizing bacteria. Environ Microbiol. 2016;18:2784–96.CAS 
PubMed 
Article 

Google Scholar 
71.Kartal B, Geerts W, Jetten MS. Cultivation, detection, and ecophysiology of anaerobic ammonium-oxidizing bacteria. Methods in enzymology. 486. Elsevier; 2011. p. 89–108.
Google Scholar 
72.Lotti T, Kleerebezem R, Lubello C, Van Loosdrecht M. Physiological and kinetic characterization of a suspended cell anammox culture. Water Res. 2014;60:1–14.CAS 
PubMed 
Article 

Google Scholar 
73.Kleikamp HB, Pronk M, Tugui C, da Silva LG, Abbas B, Lin YM, et al. Database-independent de novo metaproteomics of complex microbial communities. Cell Syst. 2021;12:375–83.74.Köcher T, Pichler P, Swart R, Mechtler K. Analysis of protein mixtures from whole-cell extracts by single-run nanoLC-MS/MS using ultralong gradients. Nat Protoc. 2012;7:882.PubMed 
Article 
CAS 

Google Scholar 
75.Lawson CE, Nuijten GH, de Graaf RM, Jacobson TB, Pabst M, Stevenson DM, et al. Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13 C and 2 H metabolic network mapping. ISME J. 2021;15:673–87.CAS 
PubMed 
Article 

Google Scholar 
76.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Laczny CC, Kiefer C, Galata V, Fehlmann T, Backes C, Keller A. BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation. Nucleic Acids Res. 2017;45:W171–W9.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
80.Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Sieber CM, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
83.Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy T, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Ciufo S, Li W. Prokaryotic genome annotation pipeline. In: The NCBI Handbook. 2nd edition. (National Center for Biotechnology Information, US, 2013). pp 131–45.85.Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
PubMed 
Article 

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

Google Scholar  More

The volatiles from fresh feces of Przewalski’s horse at the pre-oviposition, oviposition, and post-oviposition stages of G. pecorum
Throughout the stages of pre-oviposition (PREO), oviposition (OVIP), and post-oviposition (POSO) of G. pecorum, 70 volatiles were identified in fresh feces of Przewalski’s horse. Among them, 46, 48, and 52 volatiles were identified at PREO, OVIP, and POSO, respectively, and 29 volatiles were common at all three stages. In addition, 4, 5, and 9 volatiles were common between PREO and OVIP, OVIP and POSO, as well as PREO and POSO, whereas 4, 10, and 9 volatiles were unique at the single stage of PREO, OVIP, and POSO, respectively (Table 1; Fig. S1). According to relative content, the two main chemical classes of volatiles were aromatic hydrocarbons and alkenes, that is, their respective contents in a sample were both more than 25% of the total content. Except alcohols which exhibited significant difference between PREO and POSO (One-way ANOVA, F = 8.400, df = 2, P = 0.018), there was no significant difference in all other pairwise comparisons among the nine chemical classes at three stages (One-way ANOVA or Kruskal–Wallis test: P  > 0.05) (Fig. 1). Non-metric multidimensional scaling (NMDS) analysis revealed certain extent of overlap (Fig. 2), while one-way analysis of similarity (ANOSIM) indicated that there were significant differences among the three stages (R = 0.5391, P = 0.008).Table 1 The volatiles from fresh feces of Przewalski’s horse at the stages of PREO, OVIP, and POSO of Gasterophilus pecorum.Full size tableFigure 1Volatile classes detected from fresh feces of Przewalski’s horse at the stages of PREO, OVIP, and POSO of Gasterophilus pecorum. PREO, OVIP, and POSO represent fresh feces at the stages of pre-oviposition, oviposition, and post-oviposition of Gasterophilus pecorum, respectively. Data are mean (n = 3) ± SE. Different letters indicate significant differences at P  0.05). Furthermore, acetic acid was common to PREO and POSO, but no difference was observed between them (Independent t test, t = 0.137, df = 4, P = 0.897) (Table 1).Of particular concern among the eight volatiles mentioned above, ammonium acetate and butanoic acid were unique to OVIP, the critical stage of oviposition. Although not one of the five most abundant volatiles, another nine volatiles were also specific to OVIP, of which hexanoic acid, cyclopentasiloxane,decamethyl- and cyclohexene,3-methyl-6-(1-methylethyl)- were higher than 1% in relative content (Table 1).Among the 47 volatiles common to two or three stages, only six volatiles were significantly different in relative contents. Of which, D-limonene was higher at PREO than at OVIP (One-way ANOVA: F = 11.936, df = 2, P = 0.012) or POSO (P = 0.012), and 1-butanol was higher at OVIP than at PREO (One-way ANOVA: F = 8.175, df = 2, P = 0.024) or POSO (P = 0.04). Relative contents of the other four volatiles were less than 1% (Table 1).The volatiles from feces of Przewalski’s horse with different freshness states at the OVIP stage of G. pecorum
Totally, 83 volatiles were detected from fresh feces (Fresh), semi-fresh feces (Semi-fresh), and dry feces (Dry) at the OVIP stage of G. pecorum. Of which, 48, 41 and 28 volatiles were identified in Fresh, Semi-fresh and Dry, and 7 volatiles were common to all three feces with different freshness states. In addition, 14, 3 and 3, were common between Fresh and Semi-fresh, Semi-fresh and Dry, as well as Fresh and Dry, whereas 24, 17, and 15 were unique to Fresh, Semi-fresh, and Dry, respectively (Table 2; Fig. S2). Aromatic hydrocarbons and alkenes, acids and ketones, as well as alcohols and aldehydes were the two main chemical classes of Fresh, Semi-fresh, and Dry in respective. Except esters and ‘others’ which showed no significant difference in the feces, there were significant differences among other seven classes in at least one pairwise comparison of the three freshness states (One-way ANOVA, Independent t-test or Kruskal–Wallis test: P  More