Ecology

1.Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 
Article 

Google Scholar 
2.Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science (80-.) 31, 787–790 (2006).ADS 
Article 
CAS 

Google Scholar 
3.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
4.Pandolfi, J. M. et al. Global trajectories of the long-term decline of coral reef ecosystems. Science (80-.) 301, 955–958 (2003).ADS 
CAS 
Article 

Google Scholar 
5.Teixidó, N., Casas, E., Cebrián, E., Linares, C. & Garrabou, J. Impacts on coralligenous outcrop biodiversity of a dramatic coastal storm. PLoS ONE 8, e53742 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Martin, C. S. et al. Coralligenous and maërl habitats: Predictive modelling to identify their spatial distributions across the Mediterranean Sea. Sci. Rep. 4, 1–9 (2014).Article 
CAS 

Google Scholar 
7.Bianchi, C. N. Bioconstruction in marine ecosystems and Italian marine biology. Biol. Mar. Medit. 8, 112–130 (2001).
Google Scholar 
8.Garrabou, J. & Ballesteros, E. Growth of Mesophyllum alternans and Lithophyllum frondosum (Corallinales, Rhodophyta) in the northwestern Mediterranean. Eur. J. Phycol. 35, 1–10 (2000).Article 

Google Scholar 
9.Ballesteros, E., Avançats, E. & Csic, D. B. Mediterannean coralligenous assemblages: A synthesis of present knowledge. Oceanogr. Mar. Biol. Annu. Rev. 44, 123–195 (2006).
Google Scholar 
10.Guidetti, P., Terlizzi, A., Fraschetti, S. & Boero, F. Spatio-temporal variability in fish assemblages associated with coralligenous formations in south eastern Apulia (SE Italy). Ital. J. Zool. 69, 325–331 (2002).Article 

Google Scholar 
11.Casellato, S. & Stefanon, A. Coralligenous habitat in the northern Adriatic Sea: An overview. Mar. Ecol. 29, 321–341 (2008).ADS 
Article 

Google Scholar 
12.Bavestrello, G., Cerrano, C., Zanzi, D. & Cattaneo-Vietti, R. Damage by fishing activities to the Gorgonian coral Paramuricea clavata in the Ligurian Sea. Aquat. Conserv. Mar. Freshw. Ecosyst. 7, 253–262 (1997).Article 

Google Scholar 
13.Piazzi, L., Gennaro, P. & Balata, D. Effects of nutrient enrichment on macroalgal coralligenous assemblages. Mar. Pollut. Bull. 62, 1830–1835 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Sala, E., Garrabou, J. & Zabala, M. Effects of diver frequentation on Mediterranean sublittoral populations of the bryozoan Pentapora fascialis. Mar. Biol. 126, 451–459 (1996).Article 

Google Scholar 
15.García-Rubies, A. & Zabalai Limousin, M. Effects of total fishing prohibition on the Mediterranean), rocky fish assemblages of Medes Islands marine reserve (NW Mediterranean). Sci. Mar. 54, 317–328 (1990).
Google Scholar 
16.Martin, S. & Gattuso, J.-P. Response of Mediterranean coralline algae to ocean acidification and elevated temperature. Glob. Change Biol. 15, 2089–2100 (2009).ADS 
Article 

Google Scholar 
17.Zapata-Ramírez, P. A. et al. Innovative study methods for the Mediterranean coralligenous habitats. Adv. Oceanogr. Limnol. 4, 102–119 (2013).Article 

Google Scholar 
18.Gatti, G., Bianchi, C. N., Morri, C., Montefalcone, M. & Sartoretto, S. Coralligenous reefs state along anthropized coasts: Application and validation of the COARSE index, based on a rapid visual assessment (RVA) approach. Ecol. Indic. 52, 567–576 (2015).Article 

Google Scholar 
19.Kipson, S. et al. Rapid biodiversity assessment and monitoring method for highly diverse benthic communities: A case study of Mediterranean coralligenous outcrops. PLoS ONE 6, e27103 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Sartoretto, S. et al. An integrated method to evaluate and monitor the conservation state of coralligenous habitats: The INDEX-COR approach. Mar. Pollut. Bull. 120, 222–231 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Deter, J., Descamp, P., Ballesta, L., Boissery, P. & Holon, F. A preliminary study toward an index based on coralligenous assemblages for the ecological status assessment of Mediterranean French coastal waters. Ecol. Indic. 20, 345–352 (2012).Article 

Google Scholar 
22.Deter, J., Descamp, P., Boissery, P., Ballesta, L. & Holon, F. A rapid photographic method detects depth gradient in coralligenous assemblages. J. Exp. Mar. Bio. Ecol. 418–419, 75–82 (2012).Article 

Google Scholar 
23.Gibb, R., Browning, E., Glover-Kapfer, P. & Jones, K. E. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol. Evol. 10, 169–185 (2019).Article 

Google Scholar 
24.Mooney, T. A. et al. Listening forward: Approaching marine biodiversity assessments using acoustic methods. R. Soc. Open Sci. 7, (2020).25.Di Iorio, L. et al. ‘Posidonia meadows calling’: A ubiquitous fish sound with monitoring potential. Remote Sens. Ecol. Conserv. 4, 248–263 (2018).Article 

Google Scholar 
26.Parsons, M. J. G., Salgado Kent, C. P., Recalde-Salas, A. & McCauley, R. D. Fish choruses off Port Hedland Western, Australia. Bioacoustics 26, 135–152 (2017).Article 

Google Scholar 
27.Ladich, F. Sound Communication In Fishes (Springer, 2015).Book 

Google Scholar 
28.Amorim, M. C. P. Diversity of sound production in fish. Diversity 1, 71–105 (2006).
Google Scholar 
29.Carriço, R. et al. Temporal dynamics in diversity patterns of fish sound production in the Condor seamount (Azores, NE Atlantic). Deep Sea Res. Part I Oceanogr. Res. Pap. 164, 103357 (2020).Article 

Google Scholar 
30.Desiderà, E. et al. Acoustic fish communities: Sound diversity of rocky habitats reflects fish species diversity and beyond?. Mar. Ecol. Prog. Ser. 608, 183–197 (2019).ADS 
Article 

Google Scholar 
31.Ladich, F. Acoustic communication in fishes: Temperature plays a role. Fish Fish. 19, 598–612 (2018).Article 

Google Scholar 
32.Rabin, L. A. & Greene, C. M. Changes to acoustic communication systems in human-altered environments. J. Comp. Psychol. 116, 137–141 (2002).PubMed 
Article 

Google Scholar 
33.Sueur, J., Krause, B. & Farina, A. Climate change is breaking earth’s beat. Trends Ecol. Evol. 34, 971–973 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Whittaker, R. J. et al. Conservation biogeography: Assessment and prospect. Divers. Distrib. 11, 3–23 (2005).Article 

Google Scholar 
35.Frainer, A. et al. Climate-driven changes in functional biogeography of Arctic marine fish communities. Proc. Natl. Acad. Sci. U.S.A. 114, 12202–12207 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Olden, J. D. et al. Conservation biogeography of freshwater fishes: Recent progress and future challenges. Divers. Distrib. 16, 496–513 (2010).Article 

Google Scholar 
37.Lomolino, M. V., Pijanowski, B. C. & Gasc, A. The silence of biogeography. J. Biogeogr. 42, 1187–1196 (2015).Article 

Google Scholar 
38.Kéver, L., Lejeune, P., Michel, L. N. & Parmentier, E. Passive acoustic recording of Ophidion rochei calling activity in Calvi Bay (France). Mar. Ecol. 37, 1315–1324 (2016).ADS 
Article 

Google Scholar 
39.Picciulin, M. et al. Diagnostics of noctural calls of Sciena umbra (L., fam. Sciaenidae) in a nearshore Mediterranean marine reserve. Bioacoustics 22, 109–120 (2012).Article 

Google Scholar 
40.Bolgan, M., Picciulin, M., Di Iorio, L. & Parmentier, E. Passive acoustic monitoring of fishes in the Mediterranean Sea: from single species to whole communities monitoring. Ecoacoustics Congress, Urbino (Italy) (2021).
Google Scholar 
41.Tricas, T. C. & Boyle, K. S. Acoustic behaviors in Hawaiian coral reef fish communities. Mar. Ecol. Prog. Ser. 511, 1–16 (2014).ADS 
Article 

Google Scholar 
42.Bertucci, F. et al. Local sonic activity reveals potential partitioning in a coral reef fish community. Oecologia 193, 125–134 (2020).ADS 
PubMed 
Article 

Google Scholar 
43.Virgilio, M. & Airoldi, Æ. L. Spatial and temporal variations of assemblages in a Mediterranean coralligenous reef and relationships with surface orientation. Coral Reefs 25, 265–272 (2006).ADS 
Article 

Google Scholar 
44.Doxa, A. et al. Mapping biodiversity in three-dimensions challenges marine conservation strategies: The example of coralligenous assemblages in North-Western Mediterranean Sea. Ecol. Indic. 61, 1042–1054 (2015).
Google Scholar 
45.Casas-Güell, E. et al. Structure and biodiversity of coralligenous assemblages dominated by the precious red coral Corallium rubrum over broad spatial scales. Sci. Rep. 6, (2016).46.Sartoretto, S., Verlaque, M. & Laborel, J. Age of settlement and accumulation rate of submarine ‘coralligène’ (−10 to −60 m) of the northwestern Mediterranean Sea; relation to Holocene rise in sea level. Mar. Geol. 130, 317–331 (1996).ADS 
Article 

Google Scholar 
47.Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. In Ecosystem Management (eds Samson, F. B. & Knopf, F. L.) 130–147 (Springer, 1994).Chapter 

Google Scholar 
48.Rossi, S. & Bramanti, L. Perspectives on the Marine Animal Forests of the World (Springer, 2020).Book 

Google Scholar 
49.Bolgan, M. et al. Fish biophony in a Mediterranean submarine canyon. J. Acoust. Soc. Am. 147, 2466–2477 (2020).ADS 
PubMed 
Article 

Google Scholar 
50.Sebastianutto, L., Picciulin, M., Costantini, M., Rocca, M. & Ferrero, E. A. Four type of sounds for one winner: vocalizations during territorial behavior in the red-mouthed goby Gobius cruentatus (Pisces Gobiidae). Acta Ethol. 11, 115–121 (2008).Article 

Google Scholar 
51.Bertucci, F., Lejeune, P., Payrot, J. & Parmentier, E. Sound production by dusky grouper Epinephelus marginatus at spawning aggregation sites. J. Fish Biol. 87, 400–421 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Kéver, L. et al. Sexual dimorphism of sonic apparatus and extreme intersexual variation of sounds in Ophidion rochei (Ophidiidae): First evidence of a tight relationship between morphology and sound characteristics in Ophidiidae. Front. Zool. 9, 1–16 (2012).Article 

Google Scholar 
53.Ladich, F. Ontogenetic Development of Sound Communication in Fishes 127–148 (Springer, 2015).
Google Scholar 
54.Bolgan, M. et al. Sea chordophones make the mysterious /Kwa/ sound: Identification of the emitter of the dominant fish sound in Mediterranean seagrass meadows. J. Exp. Biol. 222, jeb196931 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Picciulin, M., Costantini, M., Hawkins, A. D. & Ferrero, E. A. Sound emissions of Mediterranean damselfish Chromis chromis (Pomacentraidae). Bioacoustics 12, 236–238 (2002).Article 

Google Scholar 
56.Dufossé, M. Recherches sur les bruits et les sons expressifs que font entendre les poissons d’Europe et sur les organes producteurs de ces phénomènes acoustiques ainsi que sur les appareils de l’audition de plusieurs de ces animaux. Ann. Sci. Nat. 20, 1–134 (1874).
Google Scholar 
57.Pardini, R., De Souza, S. M., Braga-Neto, R. & Metzger, J. P. The role of forest structure, fragment size and corridors in maintaining small mammal abundance and diversity in an Atlantic forest landscape. Biol. Conserv. 124, 253–266 (2005).Article 

Google Scholar 
58.Brokovich, E., Einbinder, S., Shashar, N., Kiflawi, M. & Kark, S. Descending to the twilight-zone: Changes in coral reef fish assemblages along a depth gradient down to 65 m. Mar. Ecol. Prog. Ser. 371, 253–262 (2008).ADS 
Article 

Google Scholar 
59.Jain, M. & Balakrishnan, R. Does acoustic adaptation drive vertical stratification? A test in a tropical cricket assemblage. Behav. Ecol. 23, 343–354 (2012).Article 

Google Scholar 
60.Rodriguez, A. et al. Temporal and spatial variability of animal sound within a neotropical forest. Ecol. Inform. 21, 133–143 (2014).Article 

Google Scholar 
61.Jankowski, M., Graham, N. & Jones, G. Depth gradients in diversity, distribution and habitat specialisation in coral reef fishes: Implications for the depth-refuge hypothesis. Mar. Ecol. Prog. Ser. 540, 203–215 (2015).ADS 
Article 

Google Scholar 
62.Garrabou, J., Ballesteros, E. & Zabala, M. Structure and dynamics of north-western Mediterranean rocky benthic communities along a depth gradient. Estuar. Coast. Shelf Sci. 55, 493–508 (2002).ADS 
Article 

Google Scholar 
63.Gervaise, C., Lossent, J., Di Iorio, L. & Boissery, P. Réseau CALME Caractérisation Acoustique du Littoral Méditerranéen et de ses Ecosystèmes Synthèse des travaux réalisés pour la période [01/01/2015–01/08/2018] 1–109 (Rapp. Sci. Agence l’Eau Rhône, 2019).
Google Scholar 
64.McCauley, R. D. & Cato, D. H. Patterns of fish calling in a nearshore environment in the Great Barrier Reef. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 355, 1289–1293 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Ladich, F. Acoustic communication in fishes: Temperature plays a role. Fish Fish. 19, 598–612 (2018).Article 

Google Scholar 
66.Desiderà, E. Reproductive behaviours of groupers (Epinephelidae) in the Tavolara-Punta Coda Cavallo Marine protected area (NW Mediterranean Sea). (PhD thesis 2019). http://paduaresearch.cab.unipd.it/12786/.67.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).Article 

Google Scholar 
68.Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).Article 

Google Scholar 
69.ter Braak, C. J. F. Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

Google Scholar 
70.Chambers, J. M. Linear models. In Statistical Models in S (eds Chambers, J. M. & Hastie, T. J.) (Wadsworth & Brooks/Cole, 1992).MATH 

Google Scholar 
71.Lepareur F. Evaluation de l’état de conservation des habi,tats naturels marins à l’échelle d’un site Natura 2000 – Guide méthodologique – Version 1. Février 2011. (Rapport SPN 2011 / 3, MNHN, Paris, 2011). http://spn.mnhn.fr/spn_rapports/archivage_rapports/2011/SPN%202011%20-%203%20-%20Rapport_EC_habmar_V1final2.pdf.72.Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). In Wiley StatsRef: Statistics Reference Online (eds Balakrishnan, N. et al.) 1–15 (Wiley, 2017).
Google Scholar  More

1.Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, et al. Ecology: biodiversity and ecosystem functioning: current knowledge and future challenges. Science (80-). 2001;294:804–8.CAS 
Article 

Google Scholar 
2.Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, et al. Biodiversity loss and its impact on humanity. Nature. 2012;486:59–67.CAS 
PubMed 
Article 

Google Scholar 
3.Jochum M, Fischer M, Isbell F, Roscher C, van der Plas F, Boch S, et al. The results of biodiversity-ecosystem functioning experiments are realistic. Nat Ecol Evol. 2020;4:1485–94.PubMed 
Article 

Google Scholar 
4.Balvanera P, Pfisterer AB, Buchmann N, He JS, Nakashizuka T, Raffaelli D, et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol Lett. 2006;9:1146–56.PubMed 
Article 

Google Scholar 
5.Cardinale BJ, Matulich KL, Hooper DU, Byrnes JE, Duffy E, Gamfeldt L, et al. The functional role of producer diversity in ecosystems. Am J Bot. 2011;98:572–92.PubMed 
Article 

Google Scholar 
6.Weißbecker C, Heintz-Buschart A, Bruelheide H, Buscot F, Wubet T. Linking soil fungal generality to tree richness in young subtropical Chinese forests. Microorganisms. 2019; https://doi.org/10.3390/microorganisms7110547.7.Prada-Salcedo LD, Wambsganss J, Bauhus J, Buscot F, Goldmann K. Low root functional dispersion enhances functionality of plant growth by influencing bacterial activities in European forest soils. Env Microbiol. 2020; https://doi.org/10.1111/1462-2920.15244.8.Prada-Salcedo LD, Goldmann K, Heintz-Buschart A, Reitz T, Wambsganss J, Bauhus J, et al. Fungal guilds and soil functionality respond to tree community traits rather than to tree diversity in European forests. Mol Ecol. 2021;30:572–91.CAS 
PubMed 
Article 

Google Scholar 
9.Baldrian P. The known and the unknown in soil microbial ecology. FEMS Microbiol Ecol. 2019; https://doi.org/10.1093/femsec/fiz005.10.van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet. 2014;30:418–26.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Smith SE, Read DJ. Mycorrhizal symbiosis. 2010. Academic press.12.Chen W, Koide RT, Eissenstat DM, Field K. Nutrient foraging by mycorrhizas: from species functional traits to ecosystem processes. Funct Ecol. 2018;32:858–69.Article 

Google Scholar 
13.Bahadur A, Batool A, Nasir F, Jiang S, Mingsen Q, Zhang Q, et al. Mechanistic insights into arbuscular mycorrhizal fungi-mediated drought stress tolerance in plants. Int J Mol Sci. 2019; https://doi.org/10.3390/ijms20174199.14.Pena R, Polle A. Attributing functions to ectomycorrhizal fungal identities in assemblages for nitrogen acquisition under stress. ISME J. 2014;8:321–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.He X-H, Critchley C, Bledsoe C. Nitrogen transfer within and between plants through common mycorrhizal networks (CMNs). CRC Crit Rev Plant Sci. 2003;22:531–67.Article 

Google Scholar 
16.Aerts R. The role of various types of mycorrhizal fungi in nutrient cycling and plant competition. In: Mycorrhizal Ecol. Springer; 2003. p. 117–33.17.Phillips RP, Brzostek E, Midgley MG. The mycorrhizal-associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. New Phytol. 2013;199:41–51.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hodge A, Campbell CD, Fitter AH. An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature. 2001;413:297–9.CAS 
PubMed 
Article 

Google Scholar 
19.Koide RT, Kabir Z. Extraradical hyphae of the mycorrhizal fungus Glomus intraradices can hydrolyse organic phosphate. New Phytol. 2000;148:511–7.CAS 
PubMed 
Article 

Google Scholar 
20.Martin F, Kohler A, Murat C, Veneault-Fourrey C, Hibbett DS. Unearthing the roots of ectomycorrhizal symbioses. Nat Rev Microbiol. 2016;14:760–73.CAS 
PubMed 
Article 

Google Scholar 
21.Teste FP, Jones MD, Dickie IA. Dual-mycorrhizal plants: their ecology and relevance. New Phytol. 2020;225:1835–51.PubMed 
Article 

Google Scholar 
22.Heklau H, Schindler N, Buscot F, Eisenhauer N, Ferlian O, Prada Salcedo LD, et al. Mixing tree species associated with arbuscular or ectotrophic mycorrhizae reveals dual mycorrhization and interactive effects on the fungal partners. Ecol Evol. 2021.23.Regvar M, Likar M, Piltaver A, Kugonič N, Smith JE. Fungal community structure under goat willows (Salix caprea L.) growing at metal polluted site: the potential of screening in a model phytostabilisation study. Plant Soil. 2010;330:345–56.CAS 
Article 

Google Scholar 
24.Waldrop MP, Zak DR, Blackwood CB, Curtis CD, Tilman D. Resource availability controls fungal diversity across a plant diversity gradient. Ecol Lett. 2006;9:1127–35.PubMed 
Article 

Google Scholar 
25.Hooper DU, Bignell DE, Brown VK, Brussard L, Mark Dangerfield J, Wall DH, et al. Interactions between aboveground and belowground biodiversity in terrestrial ecosystems: patterns, mechanisms, and feedbacks: we assess the evidence for correlation between aboveground and belowground diversity and conclude that a variety of mechanisms co. Bioscience. 2000;50:1049–61.Article 

Google Scholar 
26.Montesinos‐Navarro A, Segarra‐Moragues JG, Valiente‐Banuet A, Verdú M. The network structure of plant–arbuscular mycorrhizal fungi. New Phytol. 2012;194:536–47.PubMed 
Article 

Google Scholar 
27.Bahram M, Harend H, Tedersoo L. Network perspectives of ectomycorrhizal associations. Fungal Ecol. 2014;7:70–7.Article 

Google Scholar 
28.Querejeta J, Egerton-Warburton LM, Allen MF. Topographic position modulates the mycorrhizal response of oak trees to interannual rainfall variability. Ecology. 2009;90:649–62.PubMed 
Article 

Google Scholar 
29.Bergmann J, Weigelt A, van der Plas F, Laughlin DC, Kuyper TW, Guerrero-Ramirez N, et al. The fungal collaboration gradient dominates the root economics space in plants. Sci Adv. 2020;6:eaba3756.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Weißbecker C, Wubet T, Lentendu G, Kühn P, Scholten T, Bruelheide H, et al. Experimental evidence of functional group-dependent effects of tree diversity on soil fungi in subtropical forests. Front Microbiol. 2018;9:2312.PubMed 
PubMed Central 
Article 

Google Scholar 
31.Ferlian O, Cesarz S, Craven D, Hines J, Barry KE, Bruelheide H, et al. Mycorrhiza in tree diversity–ecosystem function relationships: conceptual framework and experimental implementation. Ecosphere. 2018;9:e02226.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Tedersoo L, May TW, Smith ME. Ectomycorrhizal lifestyle in fungi: global diversity, distribution, and evolution of phylogenetic lineages. Mycorrhiza. 2010;20:217–63.PubMed 
Article 

Google Scholar 
33.Kolaříková Z, Kohout P, Krüger C, Janoušková M, Mrnka L, Rydlová J. Root-associated fungal communities along a primary succession on a mine spoil: distinct ecological guilds assemble differently. Soil Biol Biochem. 2017;113:143–52.Article 
CAS 

Google Scholar 
34.Dang P, Vu NH, Shen Z, Liu J, Zhao F, Zhu H, et al. Changes in soil fungal communities and vegetation following afforestation with Pinus tabulaeformis on the Loess Plateau. Ecosphere. 2018;9:e02401.Article 

Google Scholar 
35.Kalucka IL, Jagodzinski AM. Successional traits of ectomycorrhizal fungi in forest reclamation after surface mining and agricultural disturbances: A review. Dendrobiology. 2016;76:91–104.Article 

Google Scholar 
36.Jones MD, Durall DM, Cairney JWG. Ectomycorrhizal fungal communities in young forest stands regenerating after clearcut logging. New Phytol. 2003;157:399–422.PubMed 
Article 

Google Scholar 
37.Rog I, Rosenstock NP, Korner C, Klein T. Share the wealth: trees with greater ectomycorrhizal species overlap share more carbon. Mol Ecol. 2020;29:2321–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Chagnon PL, Bradley RL, Maherali H, Klironomos JN. A trait-based framework to understand life history of mycorrhizal fungi. Trends Plant Sci. 2013;18:484–91.CAS 
PubMed 
Article 

Google Scholar 
39.Ohsowski BM, Zaitsoff PD, Öpik M, Hart MM. Where the wild things are: looking for uncultured Glomeromycota. New Phytol. 2014;204:171–9.PubMed 
Article 
PubMed Central 

Google Scholar 
40.Öpik M, Metsis M, Daniell TJ, Zobel M, Moora M. Large-scale parallel 454 sequencing reveals host ecological group specificity of arbuscular mycorrhizal fungi in a boreonemoral forest. New Phytol. 2009;184:424–37.PubMed 
Article 
CAS 

Google Scholar 
41.Buscot F. Implication of evolution and diversity in arbuscular and ectomycorrhizal symbioses. J Plant Physiol. 2015;172:55–61.CAS 
PubMed 
Article 

Google Scholar 
42.Hiiesalu I, Pärtel M, Davison J, Gerhold P, Metsis M, Moora M, et al. Species richness of arbuscular mycorrhizal fungi: associations with grassland plant richness and biomass. New Phytol. 2014;203:233–44.CAS 
PubMed 
Article 

Google Scholar 
43.Nguyen NH, Williams LJ, Vincent JB, Stefanski A, Cavender-Bares J, Messier C, et al. Ectomycorrhizal fungal diversity and saprotrophic fungal diversity are linked to different tree community attributes in a field‐based tree experiment. Mol Ecol. 2016;25:4032–46.PubMed 
Article 

Google Scholar 
44.Burrows RL, Pfleger FL. Arbuscular mycorrhizal fungi respond to increasing plant diversity. Can J Bot. 2002;80:120–30.Article 

Google Scholar 
45.Eisenhauer N, Lanoue A, Strecker T, Scheu S, Steinauer K, Thakur MP, et al. Root biomass and exudates link plant diversity with soil bacterial and fungal biomass. Sci Rep. 2017;7:44641.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Lange M, Eisenhauer N, Sierra CA, Bessler H, Engels C, Griffiths RI, et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat Commun. 2015;6:1–8.
Google Scholar 
47.Klironomos JN, McCune J, Hart M, Neville J. The influence of arbuscular mycorrhizae on the relationship between plant diversity and productivity. Ecol Lett. 2000;3:137–41.Article 

Google Scholar 
48.Saks Ü, Davison J, Öpik M, Vasar M, Moora M, Zobel M. Root-colonizing and soil-borne communities of arbuscular mycorrhizal fungi in a temperate forest understorey. Botany. 2013;92:277–85.Article 

Google Scholar 
49.Molina R, Horton TR. Mycorrhiza specificity: its role in the development and function of common mycelial networks BT – Mycorrhizal Networks. In: Horton TR, editor. Springer Netherlands, Dordrecht; 2015. p. 1–39.50.van der Linde S, Suz LM, Orme C, Cox F, Andreae H, Asi E, et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature. 2018;558:243–8.PubMed 
Article 
CAS 

Google Scholar 
51.Rasmussen AL, Busby RR, Hoeksema JD. Host preference of ectomycorrhizal fungi in mixed pine–oak woodlands. Can J For Res. 2017;48:153–9.Article 
CAS 

Google Scholar 
52.Soudzilovskaia NA, Vaessen S, van’t Zelfde M, Raes N. Global patterns of mycorrhizal distribution and their environmental drivers. In: Biogeogr. mycorrhizal symbiosis. Springer; 2017. p. 223–35.53.Simard SW, Jones MD, Durall DM. Carbon and nutrient fluxes within and between mycorrhizal plants BT – Mycorrhizal Ecology. In: van der Heijden MGA, Sanders IR, editors. Springer Berlin Heidelberg, Berlin, Heidelberg; 2003. p. 33–74.54.Allen EB, Allen MF, Helm DJ, Trappe JM, Molina R, Rincon E. Patterns and regulation of mycorrhizal plant and fungal diversity. Plant Soil. 1995;170:47–62.CAS 
Article 

Google Scholar 
55.Dickie IA, Koide RT, Fayish AC. Vesicular–arbuscular mycorrhizal infection of Quercus rubra seedlings. New Phytol. 2001;151:257–64.PubMed 
Article 
PubMed Central 

Google Scholar 
56.Davison J, Öpik M, Daniell TJ, Moora M, Zobel M. Arbuscular mycorrhizal fungal communities in plant roots are not random assemblages. FEMS Microbiol Ecol. 2011;78:103–15.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Singavarapu B, Beugnon R, Bruelheide H, Cesarz S, Du J, Eisenhauer N, et al. Tree mycorrhizal type and tree diversity shape the forest soil microbiota. Environ Microbiol. 2021.58.Altermann M, Rinklebe J, Merbach I, Körschens M, Langer U, Hofmann B. Chernozem—soil of the year 2005. J Plant Nutr Soil Sci. 2005;168:725–40.CAS 
Article 

Google Scholar 
59.Wang B, Qiu Y-L. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza. 2006;16:299–363.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Vierheilig H, Schweiger P, Brundrett M. An overview of methods for the detection and observation of arbuscular mycorrhizal fungi in roots. Physiol Plant. 2005;125:393–404.CAS 

Google Scholar 
61.Giovannetti M, Mosse B. An evaluation of techniques for measuring vesicular arbuscular mycorrhizal infection in roots. New Phytol. 1980; 489–500.62.White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: PCR Protoc. a Guid. to methods Appl. San Diego; 1990. p. 315–22.63.Wahdan SFM, Reitz T, Heintz-Buschart A, Schädler M, Roscher C, Breitkreuz C, et al. Organic agricultural practice enhances arbuscular mycorrhizal symbiosis in correspondence to soil warming and altered precipitation patterns. Environ Microbiol. 2021. https://doi.org/10.1111/1462-2920.15492.64.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Weißbecker C, Schnabel B, Heintz-Buschart A. Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology. Gigascience. 2020. https://doi.org/10.1093/gigascience/giaa135.66.Opik M, Vanatoa A, Vanatoa E, Moora M, Davison J, Kalwij JM, et al. The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota). New Phytol. 2010;188:223–41.CAS 
PubMed 
Article 

Google Scholar 
67.Katoh K, Asimenos G, Toh H. Multiple alignment of DNA sequences with MAFFT. In: Bioinforma. DNA Seq. Anal. Springer; 2009. p. 39–64.68.Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22:2688–90.CAS 
PubMed 
Article 

Google Scholar 
69.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Nilsson RH, Larsson KH, Taylor A, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2018. https://doi.org/10.1093/nar/gky1022.71.Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.Article 

Google Scholar 
72.Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33:2938–40.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Chytrý M, Tichý L, Holt J, Botta‐Dukát Z. Determination of diagnostic species with statistical fidelity measures. J Veg Sci. 2002;13:79–90.Article 

Google Scholar  More

In total 48 larval specimens of H. tenuicornis were obtained and analysed. We identified 30 larvae of the first instar, 14 of the second instar and 4 of the third instar. Two larvae and one adult specimen of H. tenuicornis were used for molecular phylogenetic placement of the genus within the subfamily Silphinae. The phylogenetic tree was obtained using Bayesian analysis from the concatenated partial 16S (434 bp) and COI (609 bp) sequences (Fig. 1).Figure 1Phylogenetic tree based on Bayesian analysis. Numbers above branches show the posterior probability and bootstrap values (BI)/maximal parsimony (PAUP)/Maximum likelihood (MEGA). Scaphidium quadrimaculatum Olivier, 1790 and Aleochara curtula (Goeze, 1777) (both Staphylinidae) were selected as outgroups.Full size imageSpecies identification based on genetic distancesThe calculated p-distances between concatenated sequences of 16S and COI of larval and adult specimens of H. tenuicornis were between 0.0029 and 0.0078 (the mean calculated p-distance within Heterotemna specimens was 0.01). Conversely, the distance between different species of Silpha was shown to be higher (mean calculated p-distance within the Silpha species was 0.08), thus the larval specimens were confirmed as belonging to the same species as the adult specimen, H. tenuicornis (SM1).Phylogenetic analysesThe Bayesian analysis (posterior probability 99), maximum parsimony bootstrap (84) and maximum likelihood bootstrap (93) strongly supported a clade of the genera Silpha, Heterotemna, Ablattaria and Phosphuga, suggesting close relationships of these genera with Heterotemna inside the genus Silpha, which makes the genus Silpha paraphyletic. The position of H. tenuicornis as a sister lineage to S. tristis Illiger, 1798 was strongly supported by the Bayesian analysis (97) but not strongly supported by the other analyses. The results confirmed the monophyly of the genera Thanatophilus Leach, 1815, Necrodes Leach, 1815, and Oiceoptoma Leach, 1815 within the subfamily Silphinae (Fig. 1).MorphometryThe two commonly used measurements for instar identification, head width and width of protergum , are applicable in the case of H. tenuicornis (Fig. 2c, d) as these two measurements do not overlap between the instars and show significant differences. More specifically, the following measurements were very different between instars; head width (F statistic = 231 on 2, df = 45, p value  More

1.Arnold, M. L. Natural Hybridization and Evolution (Oxford University Press, 1997).
Google Scholar 
2.Stelkens, R. & Seehausen, O. Genetic distance between species predicts novel trait expression in their hybrids. Evolution 63, 884–897 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Grant, P. R. & Grant, B. R. Hybridization of bird species. Science 256, 193–197 (1992).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Saino, N. & Villa, S. Pair composition and reproductive success across a hybrid zone of carrion crows and hooded crows. Auk 109, 543–555 (1992).
Google Scholar 
5.Good, T. P., Ellis, J. C., Annett, C. A. & Pierotti, R. Bounded hybrid superiority in an avian hybrid zone: effects of mate, diet, and habitat choice. Evolution 54, 1774–1783 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Bartley, D. M., Rana, K. & Immink, A. J. The use of inter-specific hybrids in aquaculture and fisheries. Rev. Fish Biol. Fisher. 10, 325–337 (2001).Article 

Google Scholar 
7.Rosenfield, J. A., Nolasco, S., Lindauer, S., Sandoval, C. & Kodric-Brown, A. The role of hybrid vigor in the replacement of Pecos pupfish by its hybrids with sheepshead minnow. Conserv. Biol. 18, 1589–1598 (2004).Article 

Google Scholar 
8.Sun, Y. et al. Comparative transcriptomic study of muscle provides new insights into the growth superiority of a novel grouper hybrid. PLoS ONE 11, e0168802 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Scribner, K. T., Page, K. S. & Bartron, M. L. Hybridization in freshwater fishes: A review of case studies and cytonuclear methods of biological inference. Rev. Fish Biol. Fisher. 10, 293–323 (2001).Article 

Google Scholar 
10.Ottová, E. et al. Evolution and trans-species polymorphism of MHC class IIB genes in cyprinid fish. Fish Shellfish Immun. 18, 199–222 (2005).Article 
CAS 

Google Scholar 
11.Šimková, A. et al. Does invasive Chondrostoma nasus shift the parasite community structure of endemic Parachondrostoma toxostoma in sympatric zones?. Parasite. Vector. 5, 200 (2012).Article 

Google Scholar 
12.Klein, J. & OhUigin, C. MHC polymorphism and parasites. Philos. Trans. R. Soc. Lond. B Biol. Sci. 346, 351–358 (1994).ADS 
CAS 
PubMed 
Article 

Google Scholar 
13.Klein, J., Klein, D., Figueroa, F., OhUigin, C. & Sato, A. Major histocompatibility complex genes in the study of fish phylogeny. In Molecular Systematic of Fishes (eds Kocher, T. D. & Stepien, C. A.) 271–283 (Academic Press, 1997).Chapter 

Google Scholar 
14.Hughes, A. L. & Nei, M. Nucleotide substitution at major histocompatibility complex class II loci: Evidence for overdominant selection. Proc. Nat. Acad. Sci. USA 56, 958–962 (1989).ADS 
Article 

Google Scholar 
15.Klein, J. & OhUigin, C. Composite origin of major histocompatibility complex genes. Curr. Opin. Genet. Dev. 3, 923–930 (1993).CAS 
PubMed 
Article 

Google Scholar 
16.Hughes, A. L. & Nei, M. Models of host-parasite interactions and MHC polymorphism. Genetics 132, 863–864 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Klein, J. Of HLA, tryps, and selection? An essay on coevolution of MHC and parasites. Hum. Immunol. 30, 247–258 (1991).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hughes, A. L., Hughes, M. K., Howell, C. Y. & Nei, M. Natural selection at the class II major histocompatibility complex loci of mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 346, 359–367 (1994).ADS 
CAS 
Article 

Google Scholar 
19.Hedrick, P. W. Pathogen resistence and genetic variation at MHC loci. Evolution 56, 1902–1908 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Nowak, M. A., Tarczy-Hornoch, K. & Austyn, J. M. The optimal number of major histocompatibility complex molecules in an individual. Proc. Nat. Acad. Sci. U.S.A. 89, 10896–10899 (1992).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Wegner, K. M., Reusch, T. B. H. & Kalbe, M. Multiple parasites are driving major histocompatibility complex polymorphism in the wild. J. Evol. Biol. 16, 224–232 (2003).CAS 
PubMed 
Article 

Google Scholar 
22.Eizaguirre, C., Lenz, T. L., Traulsen, A. & Milinski, M. Speciation accelerated and stabilized by pleiotropic major histocompatibility complex immunogenes. Ecol. Lett. 12, 5–12 (2009).PubMed 
Article 

Google Scholar 
23.Nadachowska-Brzyska, K., Zielinski, P., Radwan, J. & Babiks, W. Interspecific hybridization increases MHC class II diversity in two sister species of newts. Mol. Ecol. 21, 887–906 (2012).CAS 
PubMed 
Article 

Google Scholar 
24.Wegner, K. M. & Eizaguirre, C. New(t)s and views from hybridizing MHC genes: Introgression rather than trans-species polymorphism may shape allelic repertoires. Mol. Ecol. 21, 779–781 (2012).CAS 
PubMed 
Article 

Google Scholar 
25.Dudek, K., Gaczorek, T. S., Zielinski, P. & Babik, W. Massive introgression of major histocompatibility complex (MHC) genes in newt hybrid zones. Mol. Ecol. 28, 4798–4810 (2019).CAS 
PubMed 
Article 

Google Scholar 
26.Šimková, A., Civáňová, K., Gettová, L. & Gilles, A. Genomic porosity between invasive Chondrostoma nasus and endangered endemic Parachondrostoma toxostoma (Cyprinidae): The evolution of MHC IIB genes. PLoS ONE 8, e65883 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Zhang, S., Wang, Z. & Wang, H. Maternal immunity in fish. Dev. Comp. Immunol. 39, 72–78 (2013).ADS 
PubMed 
Article 
CAS 

Google Scholar 
28.Šimková, A., Vojtek, L., Halačka, K., Hyršl, P. & Vetešník, L. The effect of hybridization on fish physiology, immunity and blood biochemistry: A case study in hybridizing Cyprinus carpio and Carassius gibelio (Cyprinidae). Aquaculture 435, 381–389 (2015).Article 
CAS 

Google Scholar 
29.Cowx, I. G. The biology of bream, Abramis brama (L), and its natural hybrid with roach, Rutilus rutilus (L), in the River Exe. J. Fish Biol. 22, 631–646 (1983).Article 

Google Scholar 
30.Economidis, P. S. & Wheeler, A. Hybrids of Abramis brama with Scardinius erythrophthalmus and Rutilus rutilus from Lake Volvi, Macedonia, Greece. J. Fish Biol. 35, 295–299 (1989).Article 

Google Scholar 
31.Toscano, B. J. et al. An ecomorphological framework for the coexistence of two cyprinid fish and their hybrids in a novel environment. Biol. J. Linn. Soc. 99, 768–783 (2010).Article 

Google Scholar 
32.Hayden, B. et al. Hybridisation between two cyprinid fishes in a novel habitat: Genetics, morphology and life-history traits. BMC Evol. Biol. 10, 169 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Kuparinen, A., Vinni, M., Teacher, A. G. F., Kähkönen, K. & Merilä, J. Mechanism of hybridization between bream Abramis brama and roach Rutilus rutilus in their native range. J. Fish Biol. 84, 237–242 (2014).CAS 
PubMed 
Article 

Google Scholar 
34.Konopinski, M. K. & Amirowicz, A. Genetic composition of a population of natural common bream Abramis brama x roach Rutilus rutilus hybrids and their morphological characteristics in comparison with parent species. J. Fish Biol. 92, 365–385 (2018).CAS 
PubMed 
Article 

Google Scholar 
35.Krasnovyd, V., Vetešník, L., Gettová, L., Civáňová, K. & Šimková, A. Patterns of parasite distribution in the hybrids of non-congeneric cyprinid fish species: Is asymmetry in parasite infection the result of limited coadaptation?. Int. J. Parasitol. 47, 471–483 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Hayden, B. et al. Trophic dynamics within a hybrid zone—interactions between an abundant cyprinid hybrid and sympatric parental species. Freshwater Biol. 56, 1723–1735 (2011).Article 

Google Scholar 
37.Nzau Matondo, B. et al. Hybridization success of three common European cyprinid species, Rutilus rutilus, Blicca bjoerkna and Abramis brama and larval resistance to stress tests. Fish. Sci. 73, 1137–1146 (2007).Article 
CAS 

Google Scholar 
38.Hayden, B., McLoone, P., Coyne, J. & Caffrey, J. M. Extensive hybridization between roach, Rutilus rutilus L., and common bream, Abramis brama L. Irish lakes and rivers. Biol. Environ. 114B, 35–39 (2014).
Google Scholar 
39.Eizaguirre, C. et al. Parasite diversity, patterns of MHC II variation and olfactory based mate choice in diverging threespined stickleback ecotypes. Evol. Ecol. 25, 605–622 (2011).Article 

Google Scholar 
40.Hubbs, C. L. Hybridization between fish species in nature. Syst. Zool. 4, 1–20 (1955).Article 

Google Scholar 
41.Rauch, G., Kalbe, M. & Reusch, T. B. H. Relative importance of MHC and genetic background for parasite load in a field experiment. Evol. Ecol. Res. 8, 373–386 (2006).
Google Scholar 
42.Eizaguirre, C., Lenz, T. L., Kalbe, M. & Milinski, M. Divergent selection on locally adapted major histocompatibility complex immune genes experimentally proven in the field. Ecol. Lett. 15, 723–731 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
43.Šimková, A., Dávidová, M., Papoušek, I. & Vetešník, L. Does interspecies hybridization affect the host specificity of parasites in cyprinid fish?. Parasite. Vector. 6, 95 (2013).Article 

Google Scholar 
44.Seifertová, M., Jarkovský, J. & Šimková, A. Does the parasite-mediated selection drive the MHC class IIB diversity in wild populations of European chub (Squalius cephalus)?. Parasitol. Res. 115, 1401–1415 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Nzau Matondo, B., Ovidio, M., Philippart, J. C. & Poncin, P. Reproductive behaviour and sexual production in the first-generation hybrids of roach Rutilus rutilus L. × common bream Abramis brama L. J. Appl. Ichthyol. 27, 859–867 (2011).Article 

Google Scholar 
46.Graser, R., OhUigin, C., Vincek, V., Meyer, A. & Klein, J. Trans-species polymorphism of class II Mhc loci in danio fishes. Immunogenetics 44, 36–48 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Figueroa, F. et al. MHC class IIB gene evolution in East African cichlid fishes. Immunogenetics 51, 556–575 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Migalska, M., Sebastian, A. & Radwan, J. Major histocompatibility complex class I diversity limits the repertoire of T cell
receptors.Proc. Natl. Acad. Sci. USA 116, 5021–5026 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Šimková, A., Košař, M., Vetešník, L. & Vyskočilová, M. MHC genes and parasitism in Carassius gibelio, a diploid-triploid fish species with dual reproduction strategies. BMC Evol. Biol. 13, 122 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Borghans, J. A. M., Beltman, J. B. & De Boer, J. B. MHC polymorphism under host-pathogen coevolution. Immunogenetics 55, 732–739 (2004).CAS 
PubMed 
Article 

Google Scholar 
51.Ejsmond, M. J. & Radwan, J. Red queen processes drive positive selection on major histocompatibility complex (MHC) genes. PLoS Comput. Biol. 11, e1004627 (2015).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Phillips, K. P. et al. Immunogenetic novelty confers a selective advantage in host-pathogen coevolution. Proc. Natl. Acad. Sci. USA 115, 1552–1557 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Gaigher, A., Burri, R., San-Jose, L. M., Roulin, A. & Fumagalli, L. Lack of statistical power as a major limitation in understanding MHC-mediated immunocompetence in wild vertebrate populations. Mol. Ecol. 28, 5115–5132 (2019).PubMed 
Article 

Google Scholar 
54.Šimková, A., Ottová, E. & Morand, S. MHC variability, life-traits and parasite diversity of European cyprinid fish. Evol. Ecol. 20, 465–477 (2006).Article 

Google Scholar 
55.Clarke, B. & Kirby, D. R. S. Maintenance of histocompatibility polymorphisms. Nature 211, 999–1000 (1966).ADS 
CAS 
PubMed 
Article 

Google Scholar 
56.Meglécz, E. et al. SESAME (SEquence Sorter & AMplicon Explorer): Genotyping based on high throughput multiplex amplicon sequencing. Bioinformatics 27, 277–278 (2011).PubMed 
Article 
CAS 

Google Scholar 
57.Zagalska-Neubauer, M. et al. 454 sequencing reveals extreme complexity of the class II major histocompatibility complex in the collared flycatcher. BMC Evol. Biol. 10, 395 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Van Erp, S. H. M., Egberts, E. & Stet, R. J. M. Characterization of class II A and B genes in a gynogenetic carp clone. Immunogenetics 44, 192–202 (1996).PubMed 
Article 

Google Scholar 
59.Šimková, A. Major histocompatibility complex genes and parasites in cyprinid fish. Vie Milieu 67, 139–148 (2017).
Google Scholar 
60.Klein, J. et al. Nomenclature for the major histocompatibility complexes of different species: A proposal. Immunogenetics 31, 217–219 (1990).CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Dixon, B., Nagelkerke, L. A. J., Sibbing, F. A., Egberts, E. & Stet, R. J. M. Evolution of MHC class II beta chain-encoding genes in the Lake Tana barbel species flock (Barbus intermedius complex). Immunogenetics 44, 419–431 (1996).CAS 
PubMed 

Google Scholar 
62.Rakus, K. L. et al. Major histocompatibility (MH) class IIB gene polymorphism influences disease resistance of common carp (Cyprinus carpio L). Aquaculture 288, 44–50 (2009).CAS 
Article 

Google Scholar 
63.Seifertová, M. & Šimková, A. Structure, diversity and evolutionary patterns of expressed MHC class IIB genes in chub (Squalius cephalus), a cyprinid fish species from Europe. Immunogenetics 63, 167–181 (2011).PubMed 
Article 

Google Scholar 
64.Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Darriba, D., Taboala, G. L., Doallo, R. & Posada, D. J. ModelTest2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Yang, Z. H. PAML4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).CAS 
PubMed 
Article 

Google Scholar 
67.Doledec, S. & Chessel, D. Co-inertia analysis—an alternative method for studying species environment relationships. Freshwater Biol. 31, 277–294 (1994).Article 

Google Scholar 
68.Dray, S., Chessel, D. & Thioulouse, J. Co-inertia analysis and the linking of ecological data tables. Ecology 84, 3078–3089 (2003).Article 

Google Scholar 
69.Deter, J. et al. Association between the DQA MHC class II gene and puumala virus infection in Myodes glareolus, the bank vole. Infect. Genet. Evol. 8, 450–458 (2008).CAS 
PubMed 
Article 

Google Scholar 
70.Evans, M. L. & Neff, B. D. Major histocompatibility complex heterozygote advantage and widespread bacterial infections in populations of Chinook salmon (Oncorhynchus tshawytscha). Mol. Ecol. 18, 4716–4729 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Zuur, A. et al. Mixed Effects Models and Extensions in Ecology With R (Springer, 2009).MATH 
Book 

Google Scholar 
72.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
73.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/(2018).74.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
75.Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.15.1. http://CRAN.R-project.org/package=MuMIn (2018).76.Thioulouse, J. & Dray, S. Interactive multivariate data analysis in R with the ade4 and ade4tkgui packages. J. Stat. Softw. 22, 1–14 (2007).Article 

Google Scholar  More

The location, diameter, height and circumference of the coral were measured (Table 1, Fig. 2). The Porites was brown to cream in colour and hemispherical in shape (Fig. 2). It was identified as either Porites lutea (Hump or Pore coral) or P. lobata (Lobe coral)14.The primary habitat on the Porites was live coral (70%), followed by sponge, live coral rock and a small amount of macroalgae (Table 2). No recently dead coral, coral rubble or sand was recorded (Table 2). We observed competition between the Porites and other species of coral and invertebrate including encrusting sponge, plating and branching Acropora spp., Montipora, Chlorodesmis, soft coral and zoanthids (Table 2, Figs. 3, 4).Table 2 Reef Health Impact Survey (RHIS) of habitat and species categories on Porites sp.Full size tableFigure 3Detail of the sub-habitats and competitive interactions Porites sp. and boring sponge Cliona viridis (left) and live coral Porites sp. and Montipora sp. (right) along interspecific contact zones.Full size imageFigure 4Detail of Reef Health Impact Survey (RHIS) of Porites.Full size imageThe boring sponge, Cliona viridis, is abundant on the Great Barrier Reef15. It is a common bioeroding species advancing laterally at around 1 cm and to a depth of 1.2 cm per annum15. Abundance of Cliona viridis is often correlated to substrate availability and water energy with the greatest abundance often on the windward side of bommies15. This correlates to our observations as the large proportion of the substrate estimated to cover the bommie (15%) was on the windward side. The sponge’s advances will likely continue to compromise the colony size and health.We recorded marine debris at the base of the Porites. The debris was 2–3 m of rope that appeared to have been wrapped around the base of an adjacent coral. Adjacent to the bommie were three concrete blocks.How big is the Porites coral at Goolboodi compared to other big corals in the GBR, and the world? Potts et al.6 reported a very large, rounded Porites colony, 6.9 m in diameter which is 3.1 m smaller than this study. Lough et al.16 reported coral cores from colonies between 1.6–8.0 m in height with the largest corals of 6.0 m at Havannah, North Molle and Masthead Islands, 7.5 m at Abraham Reef and 8.0 m at Sanctuary Reef. Recognising the limitations of published data, the Porites coral at Goolboodi is the largest diameter coral that has been measured, and the 6th tallest in the GBR. It is unknown if the other corals are still alive or dead.Other comparatively large massive Porites have previously been located throughout the Pacific. These have included multiple bommies measuring more than 10 m4 and one exceptionally large colony observed measuring 17 m × 12 m in American Samoa17. Additionally, large Porites sp. bommies have been observed at Green Island, 30 km east of Taiwan18 as well as an 11 m diameter Porites australiensis at Sesoko Island, Okinawa, Japan19.How old is this massive Porites? In discussions with the Australian Institute of Marine Science (AIMS), there is a robust, linear relationship ( > 80% variance explained) between Porites average linear extension rate and average annual sea surface temperature (SST)20,21 that provides an estimate of colony age from its height. Using average annual SST at 18.5S, 146.5E of 26.12C (from HadiSST data set), the estimated linear extension rate is determined by (2.97 × 26.12) − 65.46 = 1.21 cm/year. Given the colony height of 5.1–5.3 m, this gives an estimated age of 421–438 years. This is well before European exploration and settlement of Australia. AIMS has investigated over 328 colonies of massive Porites corals from 69 reefs along the GBR and has aged them as being from 10–436 years21. AIMS has not investigated this coral (pers. comm Neal Cantin). Based on limitations of published data, the Porites coral at Goolboodi is one of the oldest corals on the GBR.Why is the Porites partially dead on top and living on the side? The proportion of live coral tissue on a colony reflects the cumulative, integrated effect of both beneficial and adverse environmental factors. Substantial portions of coral tissue can die from exposure to sun at low tides or warm water without lethal consequences to the colony as a whole10. Partial mortality of large bommies provides available real estate for opportunistic, fast growing sessile organisms. In this instance, multiple species of tabulate and branching Acropora sp., encrusting Montipora sp. and encrusting sponges are among the benthic organisms to have colonised 30% of the coral bommies’ surface area. Intraspecific competition is also evident from the skeletal barriers created along contact zones22 (Fig. 3). There was no observation of disease or coral bleaching.The Porites is located in a relatively remote, rarely visited and highly protected Marine National Park (green) zone. Its location had not been previously reported and there is no existing database for significant corals in Australia or globally. Cataloguing the location of massive and long-lived corals can have multiple benefits. Scientific benefits include geochemical and isotopic analyses in coral skeletal cores which can help understand century-scale changes in oceanographic events and can be used to verify climate models. Social and economic benefits can include diving tourism, citizen science23 culture and stewardship. Perhaps the Significant Trees Register, which was designed by the National Trust24 to protect and celebrate Australia’s heritage could be considered as a model. There are risks of cataloguing the location of massive corals. It could be damaged by direct and indirect human uses including anchoring, research and pollution.Indigenous languages are an integral part of Indigenous culture, spirituality, and connection to country. We consulted Manbarra Traditional Owners about protocol and an appropriate cultural name for the Porites and they considered: Big (Muga), Home (Wanga), Coral reef (Muugar), Coral (Dhambi), Old (Anki, Gurgu), Old man (Gulula) and Old person (Gurgurbu)25. The recommendation by Manbarra Traditional Owners is that the Porites is named as Muga dhambi (Big coral). The feedback from the process of consultation was very positive with acknowledgement of the respect that the scientists have demonstrated to acknowledge Traditional Owners in this way.The large Porites coral at Goolboodi (Orpheus) Island is unusually rare and resilient. It has survived coral bleaching, invasive species, cyclones, severely low tides and human activities for almost 500 years. In an attempt to contextualise the resilience of these individual Porites we have reviewed major historic disturbances such as coral bleaching which has occurred since at least 1575 and potentially 99 bleaching events in the GBR over the past 400 plus years26. Other indicators such as high-density ‘stress bands’ were recorded from 1877 and are significantly more frequent in the late twentieth and early twenty-first centuries in accordance with rising temperatures from anthropogenic global warming27. In addition there have been an average of 1–2 tropical cyclones per decade (40–80 in total) that have potentially impacted the coral adjacent to Goolboodi Island28,29; 46 tropical cyclones impacted the area between Ingham and Townsville from 1858 to 200830. The cumulative impact of almost 100 bleaching events and up to 80 major cyclones over a period of four centuries, plus declining nearshore water quality contextualise the high resilience of this Porites coral. Looking to the future there is real concern for corals in the GBR due to many impacts including climate change, declining water quality, overfishing and coastal development31,32. This field note provides important geospatial, environmental, and cultural information of a rare coral that can be monitored, appreciated, potentially restored and hopefully inspire future generations to care more for our reefs and culture. More

1.Chapman, B. B. et al. Partial migration in fishes: Causes and consequences. J. Fish Biol. 81, 456–478 (2012).CAS 
Article 

Google Scholar 
2.Araújo, C. V. M. et al. Habitat fragmentation caused by contaminants: Atrazine as a chemical barrier isolating fish populations. Chemosphere 193, 24–31 (2018).ADS 
Article 

Google Scholar 
3.Flitcroft, R. L., Arismendi, I. & Santelmann, M. V. A review of habitat connectivity research for pacific salmon in marine, estuary, and freshwater environments. J. Am. Water Resour. Assoc. 55, 430–441 (2019).ADS 
Article 

Google Scholar 
4.Maire, A., Thierry, E., Viechtbauer, W. & Daufresne, M. Poleward shift in large-river fish communities detected with a novel meta-analysis framework. Freshw. Biol. 64, 1143–1156 (2019).Article 

Google Scholar 
5.van Vliet, M. T. H. et al. Coupled daily streamflow and water temperature modelling in large river basins. Hydrol. Earth Syst. Sci. 16, 4303–4321 (2012).ADS 
Article 

Google Scholar 
6.Palmer, M. A. et al. Climate change and the world’s river basins: Anticipating management options. Front. Ecol. Environ. 6, 81–89 (2008).Article 

Google Scholar 
7.Jonsson, B. & Jonsson, N. A review of the likely effects of climate change on anadromous Atlantic salmon Salmo salar and brown trout Salmo trutta, with particular reference to water temperature and flow. J. Fish Biol. 75, 2381–2447 (2009).CAS 
Article 

Google Scholar 
8.Arevalo, E. et al. An innovative bivariate approach to detect joint temporal trends in environmental conditions: Application to large French rivers and diadromous fish. Sci. Total Environ. 748, 141260 (2020).ADS 
CAS 
Article 

Google Scholar 
9.Hughes, L. Biological consequences of global warming: Is the signal already apparent?. Trends Ecol. Evol. 15, 56–61 (2000).CAS 
Article 

Google Scholar 
10.Tesch, F.-W. & Bartsch, P. The Eel (Blackwell Science, 2003).Book 

Google Scholar 
11.Durif, C. M. F. et al. Age of European silver eels during a period of declining abundance in Norway. Ecol. Evol. 10, 4801–4815 (2020).Article 

Google Scholar 
12.Poole, R. W. & Reynolds, J. D. Growth rate and age at migration of Anguilla anguilla. J. Fish Biol. 48, 633–642 (1996).
Google Scholar 
13.Durif, C. M. F., Travade, F., Rives, J., Elie, P. & Gosset, C. Relationship between locomotor activity, environmental factors, and timing of the spawning migration in the European eel Anguilla anguilla. Aquat. Living Resour. 21, 163–170 (2008).Article 

Google Scholar 
14.Fontaine, M. Physiological mechanisms in the migration of marine and amphihaline fish. Adv. Mar. Biol. 13, 241–355 (1975).Article 

Google Scholar 
15.Bruijs, M. C. M. & Durif, C. M. F. Silver Eel migration and behaviour. Spawning Migr. Eur. Eel https://doi.org/10.1007/978-1-4020-9095-0_4 (2009).Article 

Google Scholar 
16.ICES. Workshop on the temporal migration patterns of European eel (WKEELMIGRATION). vol. 2 http://doi.org/https://doi.org/10.17895/ices.pub.5993 (2020).17.Vøllestad, L. A., Jonsson, B., Hvidsten, N. A. & Naesje, T. F. Experimental test of environmental factors influencing the seaward migration of European silver eels. J. Fish Biol. 45, 641–651 (1994).Article 

Google Scholar 
18.Sandlund, O. T. et al. Timing and pattern of annual silver eel migration in two European watersheds are determined by similar cues. Ecol. Evol. 7, 5956–5966 (2017).Article 

Google Scholar 
19.Drouineau, H. et al. Freshwater eels: A symbol of the effects of global change. Fish Fish. 19, 903–930 (2018).Article 

Google Scholar 
20.Pike, C., Crook, V., & Gollock, M. Anguilla anguilla. The IUCN Red List of Treatened Species 2020. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2020-539 2.RLTS.T60344A152845178.en (2020).21.Dankers, R. & Feyen, L. Climate change impact on flood hazard in Europe: An assessment based on high-resolution climate simulations. J. Geophys. Res. Atmos. 113, 1–17 (2008).Article 

Google Scholar 
22.Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).Article 

Google Scholar 
23.Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Chang. 4, 17–22 (2014).ADS 
Article 

Google Scholar 
24.Durif, C., Elie, P., Gosset, C. & Rives, J. Behavioral Study of Downstream Migrating Eels by Radio-telemetry at a Small Hydroelectric Power Plant. Am. Fish. Soc. Symp. 1–14 (2002).25.Flitcroft, R. L. et al. Linking hydroclimate to fish phenology and habitat use with ichthyographs. PLoS ONE 11, 1–12 (2016).Article 

Google Scholar 
26.Bossard, M., Feranec, J. & Othael, J. CORINE Land Cover Technical Guide – Addendum 2000. European Environment Agency. Technical Report. Available online at: http://www.eea.europa.eu/publications/tech40add. (2000).27.de Eyto, E. et al. Characterisation of salmonid food webs in the rivers and lakes of an Irish peatland ecosystem. Biol. Environ. Proc. R. Irish Acad. 120, 1–17 (2020).
Google Scholar 
28.Poole, W. R., Reynolds, J. D. & Moriarty, C. Observations on the Silver Eel Migrations of the Burrishoole River System, Ireland, 1959 to 1988. Int. Rev. der gesamten Hydrobiol. und Hydrogr 75, 807–815 (1990).Article 

Google Scholar 
29.Poole, W. R. et al. Long-term variation in numbers and biomass of silver eels being produced in two European river systems. ICES J. Mar. Sci. 75, 1627–1637 (2018).Article 

Google Scholar 
30.Chacón, J. E. & Duong, T. Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices. TEST 19, 375–398 (2010).MathSciNet 
Article 

Google Scholar 
31.Lechowicz, M. The sampling characteristics of electivity indices. Oecologia 52, 22–30 (1982).ADS 
Article 

Google Scholar 
32.Ivlev, V. S. Experimental ecology of the feeding fishes (Yale University Press, 1961).
Google Scholar 
33.R Development Core Team. R: A Language and Environment for Statistical Computing. (2020).34.Drouineau, H., Arevalo, E., Lassalle, G., Tétard, S. & Maire, A. chocR: Exploring the temporal CHange of OCcurence of events in multivariate time series. R package version 0.0.0.9000. (2020).35.Hutchinson, G. E. Concluding Remarks. in Cold Spring Harbor Symposia on Quantitative Biology 415–442 (1957).36.Schneider, C., Laizé, C. L. R., Acreman, M. C. & Flörke, M. How will climate change modify river flow regimes in Europe?. Hydrol. Earth Syst. Sci. 17, 325–339 (2013).ADS 
Article 

Google Scholar 
37.Hannaford, J., Laize, C. L. R. & Marsh, T. J. An assessment of runoff trends in undisturbed catchments in the Celtic regions of North West Europe. IAHS-AISH Publ. 78–85 (2007).38.Engen-Skaugen, T. Refinement of dynamically downscaled precipitation and temperature scenarios. Clim. Change 84, 365–382 (2007).ADS 
Article 

Google Scholar 
39.Lawrence, D. & Hisdal, H. Hydrological projections for floods in Norway under a future climate. NVE Report http://webby.nve.no/publikasjoner/report/2011/report2011_05.pdf (2011).40.Woolway, R. I. et al. Substantial increase in minimum lake surface temperatures under climate change. Clim. Change 155, 81–94 (2019).ADS 
CAS 
Article 

Google Scholar 
41.Fealy, R. et al. RESCALE : Review and Simulate Climate and Catchment Responses at Burrishoole. Review Literature and Arts of the Americas (2014).42.Als, T. D. et al. All roads lead to home: Panmixia of European eel in the Sargasso Sea. Mol. Ecol. 20, 1333–1346 (2011).Article 

Google Scholar 
43.Acou, A., Laffaille, P., Legault, A. & Feunteun, E. Migration pattern of silver eel (Anguilla anguilla, L.) in an obstructed river system. Ecol. Freshw. Fish 17, 432–442 (2008).Article 

Google Scholar 
44.Fernandes, W. P. A. et al. Does relatedness influence migratory timing and behaviour in Atlantic salmon smolts?. Anim. Behav. 106, 191–199 (2015).Article 

Google Scholar 
45.Vøllestad, L. A. et al. Environmental Factors Regulating the Seaward Migration of European Silver Eels (Anguilla anguilla). Can. J. Fish. Aquat. Sci. 43, 1909–1916 (1986).Article 

Google Scholar 
46.Daverat, F. et al. One century of eel growth: Changes and implications. Ecol. Freshw. Fish 21, 325–336 (2012).Article 

Google Scholar 
47.Vøllestad, L. A. Geographic variation in age and length at metamorphosis of maturing European eel: Environmental effects and phenotypic plasticity. J. Anim. Ecol. 61, 41 (1992).Article 

Google Scholar 
48.Vaughan, L. et al. Growth rates in a European eel Anguilla anguilla (L., 1758) population show a complex relationship with temperature over a seven-decade otolith biochronology. ICES J. Mar. Sci. (2021).49.Lassalle, G. & Rochard, E. Impact of twenty-first century climate change on diadromous fish spread over Europe, North Africa and the Middle East. Glob. Chang. Biol. 15, 1072–1089 (2009).ADS 
Article 

Google Scholar 
50.Monteiro, R. M. et al. Migration and escapement of silver eel males, Anguilla anguilla, from a southwestern European river. Ecol. Freshw. Fish https://doi.org/10.1111/eff.12545 (2020).Article 

Google Scholar 
51.Mateo, M. et al. Cause or consequence? Exploring the role of phenotypic plasticity and genetic polymorphism in the emergence of phenotypic spatial patterns of the European eel. Can. J. Fish. Aquat. Sci. 74, 987–999 (2017).Article 

Google Scholar  More

1.Irwin AJ, Oliver MJ. Are ocean deserts getting larger? Geophys Res Lett. 2009;36:L18609.Article 

Google Scholar 
2.McClain CR, Signorini SR, Christian JR. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Topical Stud Oceanogr. 2004;51:281–301.CAS 
Article 

Google Scholar 
3.Signorini SR, Franz BA, McClain CR. Chlorophyll variability in the oligotrophic gyres: Mechanisms, seasonality and trends. Front Mar Sci. 2015;2:1–11.Article 

Google Scholar 
4.Polovina JJ, Howell EA, Abecassis M. Ocean’s least productive waters are expanding. Geophys Res Lett. 2008;35:L03618.Article 

Google Scholar 
5.Sharma P, Marinov I, Cabre A, Kostadinov T, Singh A. Increasing biomass in the warm oceans: unexpected new insights from SeaWIFS. Geophys Res Lett. 2019;46:3900–10.Article 

Google Scholar 
6.Flombaum P, Wang W-L, Primeau FW, Martiny AC. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat Geosci. 2020;13:116–20.CAS 
Article 

Google Scholar 
7.Carr M-E, Friedrichs MAM, Schmeltz M, Noguchi Aita M, Antoine D, Arrigo KR, et al. A comparison of global estimates of marine primary production from ocean color. Deep Sea Res Part II Topical Stud Oceanogr. 2006;53:741–70.Article 

Google Scholar 
8.Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
PubMed 
Article 

Google Scholar 
9.DeVries T, Primeau F, Deutsch C. The sequestration efficiency of the biological pump. Geophys Res Lett. 2012;39:L13601.Article 
CAS 

Google Scholar 
10.Cabré A, Marinov I, Leung S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 earth system models. Clim Dyn. 2015;45:1253–80.Article 

Google Scholar 
11.Behrenfeld MJ, O’Malley RT, Boss ES, Westberry TK, Graff JR, Halsey KH, et al. Revaluating ocean warming impacts on global phytoplankton. Nat Clim Change. 2015;6:323–30.Article 

Google Scholar 
12.Richardson K, Bendtsen J. Vertical distribution of phytoplankton and primary production in relation to nutricline depth in the open ocean. Mar Ecol Prog Ser. 2019;620:33–46.CAS 
Article 

Google Scholar 
13.Roshan S, DeVries T. Efficient dissolved organic carbon production and export in the oligotrophic ocean. Nat Commun. 2017;8:1–8.CAS 
Article 

Google Scholar 
14.Marañón E, Holligan PM, Barciela R, González N, Mouriño B, Pazó MJ, et al. Patterns of phytoplankton size structure and productivity in contrasting open-ocean environments. Mar Ecol Prog Ser. 2001;216:43–56.Article 

Google Scholar 
15.Pérez V, Fernández E, Marañón E, Morán XAG, Zubkov MV. Vertical distribution of phytoplankton biomass, production and growth in the Atlantic subtropical gyres. Deep Sea Res Part I Oceanographic Res Pap. 2006;53:1616–34.Article 

Google Scholar 
16.Teira E, Mouriño B, Marañón E, Pérez V, Pazó MJ, Serret P, et al. Variability of chlorophyll and primary production in the Eastern North Atlantic subtropical gyre: potential factors affecting phytoplankton activity. Deep Sea Res Part I Oceanographic Res Pap. 2005;52:569–88.CAS 
Article 

Google Scholar 
17.Chisholm SW, Frankel SL, Goericke R, Olson RJ, Palenik B, Waterbury JB, et al. Prochlorococcus marinus nov. Gen. Nov. Sp.: an oxyphototrophic marine prokaryote containing divinyl chlorophyll a and b. Arch Microbiol. 1992;157:297–300.CAS 
Article 

Google Scholar 
18.Flombaum P, Gallegos JL, Gordillo RA, Rincón J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine cyanobacteria Prochlorococcus and Synechococcus. PNAS. 2013;110:9824–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Partensky F, Hess WR, Vaulot D. Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiol Mol Biol Rev. 1999;63:106–27.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Li WK. Primary production of prochlorophytes, cyanobacteria, and eucaryotic ultraphytoplankton: Measurements from flow cytometric sorting. Limnol Oceanogr. 1994;39:169–75.CAS 
Article 

Google Scholar 
21.Jardillier L, Zubkov MV, Pearman J, Scanlan DJ. Significant CO2 fixation by small prymnesiophytes in the subtropical and tropical Northeast Atlantic Ocean. ISME J. 2010;4:1180–92.CAS 
PubMed 
Article 

Google Scholar 
22.Irion S, Christaki U, Berthelot H, L’Helguen S, Jardillier L. Small phytoplankton contribute greatly to CO2-fixation after the diatom bloom in the Southern Ocean. ISME J. 2021;15:1–14.Article 
CAS 

Google Scholar 
23.Liu K, Suzuki K, Chen B, Liu H. Are temperature sensitivities of Prochlorococcus and Synechococcus impacted by nutrient availability in the subtropical Northwest Pacific? Limnol Oceanogr. 2020;66:639–51.Article 
CAS 

Google Scholar 
24.D’Hondt S, Spivack AJ, Pockalny R, Ferdelman TG, Fischer JP, Kallmeyer J, et al. Subseafloor sedimentary life in the South Pacific gyre. PNAS. 2009;106:11651–6.PubMed 
PubMed Central 
Article 

Google Scholar 
25.Longhurst A, Sathyendranath S, Platt T, Caverhill C. An estimate of global primary production in the ocean from satellite radiometer data. J Plankton Res. 1995;17:1245–71.Article 

Google Scholar 
26.Morel A, Gentili B, Claustre H, Babin M, Bricaud A, Ras J, et al. Optical properties of the “clearest” natural waters. Limnol Oceanogr. 2007;52:217–29.CAS 
Article 

Google Scholar 
27.Halm H, Lam P, Ferdelman TG, Lavik G, Dittmar T, LaRoche J, et al. Heterotrophic organisms dominate nitrogen fixation in the south pacific gyre. ISME J. 2012;6:1238–49.CAS 
PubMed 
Article 

Google Scholar 
28.Raimbault P, Garcia N. Evidence for efficient regenerated production and dinitrogen fixation in nitrogen-deficient waters of the South Pacific Ocean: impact on new and export production estimates. Biogeosciences. 2008;5:323–38.CAS 
Article 

Google Scholar 
29.Shiozaki T, Bombar D, Riemann L, Sato M, Hashihama F, Kodama T, et al. Linkage between dinitrogen fixation and primary production in the oligotrophic South Pacific Ocean. Glob Biogeochem Cyc. 2018;32:1028–44.CAS 
Article 

Google Scholar 
30.Reintjes G, Tegetmeyer HE, Bürgisser M, Orlić S, Tews I, Zubkov M, et al. On-site analysis of bacterial communities of the ultraoligotrophic South Pacific gyre. Appl Environ Microbiol. 2019;85:e00184–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zielinski O, Henkel R, Voß D, Ferdelman TG. Physical oceanography during Sonne cruise SO245 (Ultrapac). PANGAEA. 2018. https://doi.org/10.1594/PANGAEA.890394.32.Ferdelman TG, Klockgether G, Downes P, Lavik G. Nutrient data from CTD Nisken bottles from Sonne expedition SO-245 “Ultrapac”. PANGAEA. 2019. https://doi.org/10.1594/PANGAEA.899228.33.Arar EJ, Collins GB. Method 445.0: In vitro determination of chlorophyll a and pheophytin a in marine and freshwater algae by fluorescence: U.S. Environmental Protection Agency, Washington, DC; 1997. https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NERL&dirEntryId=309417.34.Welschmeyer N, Naughton S. Improved chlorophyll a analysis: single fluorometric measurement with no acidification. Lake Reserv Manag. 1994;9:123.
Google Scholar 
35.Osterholz H, Kilgour D, Storey DS, Lavik G, Ferdelman T, Niggemann J, et al. Accumulation of DOC in the South Pacific subtropical gyre from a molecular perspective. Mar Chem. 2021;231:103955.CAS 
Article 

Google Scholar 
36.Voß D, Henkel R, Wollschläger J, Zielinski O. Hyperspectral underwater light field measured during the cruise SO245 with R/V Sonne. PANGAEA. 2020. https://doi.org/10.1594/PANGAEA.911558.37.Martínez-Pérez C, Mohr W, Löscher CR, Dekaezemacker J, Littmann S, Yilmaz P, et al. The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle. Nat Microbiol. 2016;1:1–7.Article 
CAS 

Google Scholar 
38.Marra J. Net and gross productivity: weighing in with 14C. Aquat Microb Ecol. 2009;56:123–31.Article 

Google Scholar 
39.Ribeiro CG, Marie D, Santos ALD, Brandini FP, Vaulot D. Estimating microbial populations by flow cytometry: comparison between instruments. Limnol Oceanogr Methods. 2016;14:750–8.Article 

Google Scholar 
40.Pernthaler A, Pernthaler J, Amann R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol. 2002;68:3094–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.West NJ, Schönhuber WA, Fuller NJ, Amann RI, Rippka R, Post AF, et al. Closely related Prochlorococcus genotypes show remarkably different depth distributions in two oceanic regions as revealed by in situ hybridization using 16 S rRNA-targeted oligonucleotides. Microbiology. 2001;147:1731–44.CAS 
PubMed 
Article 

Google Scholar 
42.Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MMM. Look@NanoSIMS—a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.CAS 
PubMed 
Article 

Google Scholar 
43.Verity PG, Robertson CY, Tronzo CR, Andrews MG, Nelson JR, Sieracki ME. Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton. Limnol Oceanogr. 1992;37:1434–46.CAS 
Article 

Google Scholar 
44.Khachikyan A, Milucka J, Littmann S, Ahmerkamp S, Meador T, Könneke M, et al. Direct cell mass measurements expand the role of small microorganisms in nature. Appl Environ Microbiol. 2019;85:AEM00493–19.Article 

Google Scholar 
45.Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, et al. Improved bacterial 16 S rRNA gene (v4 and v4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. MSystems. 2016;1:e00009–15.PubMed 
Article 

Google Scholar 
46.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rrna primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Comeau AM, Douglas GM, Langille MG. Microbiome helper: a custom and streamlined workflow for microbiome research. MSystems. 2017;2:e00127–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. Qiime allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Haas S, Desai DK, LaRoche J, Pawlowicz R, Wallace DW. Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater. Environ Microbiol. 2019;21:3927–52.CAS 
PubMed 
Article 

Google Scholar 
50.Zhang J, Kobert K, Flouri T, Stamatakis A. Pear: a fast and accurate Illumina paired-end read merger. Bioinformatics. 2013;30:614–20.51.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. Vsearch: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
52.Kopylova E, Noé L, Touzet H. Sortmerna: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
PubMed 
Article 

Google Scholar 
53.Mercier C, Boyer F, Bonin A, Coissac E (eds). Sumatra and Sumaclust: fast and exact comparison and clustering of sequences. SeqBio 2013 Workshop 2013: (abstract).54.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16 S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Decelle J, Romac S, Stern RF, Bendif EM, Zingone A, Audic S, et al. PhytoREF: A reference database of the plastidial 16 S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Molec Ecol Res. 2015;15:1435–45.CAS 
Article 

Google Scholar 
57.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The protist ribosomal reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2012;4:D597–604.Article 
CAS 

Google Scholar 
58.Del Campo J, Kolisko M, Boscaro V, Santoferrara LF, Nenarokov S, Massana R, et al. EukRef: phylogenetic curation of ribosomal RNA to enhance understanding of eukaryotic diversity and distribution. PLoS Biol. 2018;16:e2005849.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: A software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Gruber-Vodicka HR, Seah BK, Pruesse E. Phyloflash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. Msystems. 2020;5:e00920.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Farrant GK, Doré H, Cornejo-Castillo FM, Partensky F, Ratin M, Ostrowski M, et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. PNAS. 2016;113:E3365–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Oggerin de Orube M, Fuchs BM. Personal communication: Unpublished shotgun metagenomes collected from in situ pump samples during R/V Sonne expedition SO245. Bremen, Germany. 2021.63.Schlitzer R. Ocean Data View. Bremerhaven, Germany. 2021. https://odv.awi.de.64.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. 2017. https://www.R-project.org/.65.Wickham H. Ggplot2: elegant graphics for data analysis. Springer-Verlag, New York. 2016.66.McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package: community ecology package. R package version 2.5–7. 2019. https://CRAN.R-project.org/package=vegan.68.Chaigneau A, Pizarro O. Surface circulation and fronts of the South Pacific Ocean, east of 120°W. Geophys Res Lett. 2005;32:L08605.69.Logares R, Sunagawa S, Salazar G, Cornejo‐Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16 S rRNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ Microbiol. 2014;16:2659–71.CAS 
PubMed 
Article 

Google Scholar 
70.Shi XL, Lepère C, Scanlan DJ, Vaulot D. Plastid 16 s rRNA gene diversity among eukaryotic picophytoplankton sorted by flow cytometry from the South Pacific Ocean. PLOS ONE. 2011;6:e18979.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Fuller NJ, Campbell C, Allen DJ, Pitt FD, Zwirglmaier K, Le Gall F, et al. Analysis of photosynthetic picoeukaryote diversity at open ocean sites in the Arabian Sea using a pcr biased towards marine algal plastids. Aquat Micro Ecol. 2006;43:79–93.Article 

Google Scholar 
72.Raes EJ, Bodrossy L, Kamp JVD, Bissett A, Ostrowski M, Brown MV, et al. Oceanographic boundaries constrain microbial diversity gradients in the South Pacific Ocean. PNAS. 2018;115:E8266–75.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Campbell L, Liu H, Nolla HA, Vaulot D. Annual variability of phytoplankton and bacteria in the subtropical North Pacific Ocean at station ALOHA during the 1991-4 ENSO event. Deep Sea Res Part I Oceanogr Res Pap. 1997;44:167–92.CAS 
Article 

Google Scholar 
74.Viviani DA, Church MJ. Decoupling between bacterial production and primary production over multiple time scales in the North Pacific subtropical gyre. Deep Sea Res Part I Oceanogr Res Pap. 2017;121:132–42.CAS 
Article 

Google Scholar 
75.Rii YM, Duhamel S, Bidigare RR, Karl DM, Repeta DJ, Church MJ. Diversity and productivity of photosynthetic picoeukaryotes in biogeochemically distinct regions of the south east pacific ocean. Limnol Oceanogr. 2016;61:806–24.Article 

Google Scholar 
76.Shi XL, Marie D, Jardillier L, Scanlan DJ, Vaulot D. Groups without cultured representatives dominate eukaryotic picophytoplankton in the oligotrophic South East Pacific Ocean. PLOS ONE. 2009;4:e7657.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Kirkham AR, Lepere C, Jardillier LE, Not F, Bouman H, Mead A, et al. A global perspective on marine photosynthetic picoeukaryote community structure. ISME J. 2013;7:922–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Lepère C, Vaulot D, Scanlan DJ. Photosynthetic picoeukaryote community structure in the South East Pacific Ocean encompassing the most oligotrophic waters on earth. Environ Microbiol. 2009;11:3105–17.PubMed 
Article 
CAS 

Google Scholar 
79.Bender ML, Jönsson B. Is seasonal net community production in the South Pacific subtropical gyre anomalously low? Geophys Res Lett. 2016;43:9757–63.Article 

Google Scholar 
80.Montégut CDB, Madec G, Fischer AS, Lazar A, Iudicone D. Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology. J Geophys Res Oceans. 2004;109:C12003.Article 

Google Scholar 
81.Liu Q, Lu Y. Role of horizontal density advection in seasonal deepening of the mixed layer in the subtropical Southeast Pacific. Adv Atmospher Sci. 2016;33:442–51.Article 

Google Scholar 
82.Sato K, Suga T. Structure and modification of the South Pacific eastern subtropical mode water. J Phys Oceanogr. 2009;39:1700–14.Article 

Google Scholar 
83.Jung J, Furutani H, Uematsu M. Atmospheric inorganic nitrogen in marine aerosol and precipitation and its deposition to the north and south pacific oceans. J Atmospher Chem. 2011;68:157–81.CAS 
Article 

Google Scholar 
84.Pavia FJ, Anderson RF, Winckler G, Fleisher MQ. Atmospheric dust inputs, iron cycling, and biogeochemical connections in the South Pacific Ocean from thorium isotopes. Glob Biogeochem Cycles. 2020;34:e2020GB006562.CAS 

Google Scholar 
85.Bonnet S, Guieu C, Bruyant F, Prášil O, Van Wambeke F, Raimbault P, et al. Nutrient limitation of primary productivity in the Southeast Pacific (Biosope Cruise). Biogeosciences. 2008;5:215–25.CAS 
Article 

Google Scholar 
86.Mahaffey C, Björkman KM, Karl DM. Phytoplankton response to deep seawater nutrient addition in the North Pacific subtropical gyre. Mar Ecol Prog Ser. 2012;460:13–34.CAS 
Article 

Google Scholar 
87.Grob C, Jardillier L, Hartmann M, Ostrowski M, Zubkov MV, Scanlan DJ. Cell-specific CO2 fixation rates of two distinct groups of plastidic protists in the Atlantic Ocean remain unchanged after nutrient addition. Environ Microbiol Rep. 2015;7:211–8.CAS 
PubMed 
Article 

Google Scholar 
88.Vaulot D, Marie D, Olson RJ, Chisholm SW. Growth of Prochlorococcus, a photosynthetic prokaryote, in the equatorial pacific ocean. Science. 1995;268:1480–2.CAS 
PubMed 
Article 

Google Scholar 
89.Grob C, Hartmann M, Zubkov MV, Scanlan DJ. Invariable biomass-specific primary production of taxonomically discrete picoeukaryote groups across the Atlantic Ocean. Environ Microbiol. 2011;13:3266–74.PubMed 
Article 

Google Scholar 
90.Berthelot H, Duhamel S, L’Helguen S, Maguer J-F, Wang S, Cetinić I, et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 2019;13:651.CAS 
PubMed 
Article 

Google Scholar 
91.Zubkov MV, Fuchs BM, Tarran GA, Burkill PH, Amann R. High rate of uptake of organic nitrogen compounds by Prochlorococcus cyanobacteria as a key to their dominance in oligotrophic oceanic waters. Appl Environ Microbiol. 2003;69:1299–304.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Muñoz-Marín MC, Gómez-Baena G, López-Lozano A, Moreno-Cabezuelo JA, Díez J, García-Fernández JM. Mixotrophy in marine picocyanobacteria: use of organic compounds by Prochlorococcus and Synechococcus. ISME J. 2020;14:1065–73.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
93.Timmermans K, Van der Wagt B, Veldhuis M, Maatman A, De Baar H. Physiological responses of three species of marine pico-phytoplankton to ammonium, phosphate, iron and light limitation. J Sea Res. 2005;53:109–20.CAS 
Article 

Google Scholar 
94.Vaulot D, Eikrem W, Viprey M, Moreau H. The diversity of small eukaryotic phytoplankton (≤ 3 μm) in marine ecosystems. FEMS Microbiol Rev. 2008;32:795–820.CAS 
PubMed 
Article 

Google Scholar 
95.Worden AZ, Janouskovec J, McRose D, Engman A, Welsh RM, Malfatti S, et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr Biol. 2012;22:R675–7.CAS 
PubMed 
Article 

Google Scholar 
96.Le Gall F, Rigaut-Jalabert F, Marie D, Garczarek L, Viprey M, Gobet A, et al. Picoplankton diversity in the South-east Pacific Ocean from cultures. Biogeosciences. 2008;5:203–14.Article 

Google Scholar 
97.NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data; Reprocessing. NASA OB.DAAC, Greenbelt, MD, USA. 2018. https://oceancolor.gsfc.nasa.gov/data/10.5067/AQUA/MODIS/L3M/CHL/2018/ Accessed 2019/08/01. More

Model A: abundances and δ13C of short alkanesConsidering the cleavage at position m (between the no. m and no. m + 1 carbon atoms) of an n-alkyl chain with n carbon atoms (1 ≤ m 15), the isotopic compositions of gas products are insensitive to the initial kerogen side chain length distribution. For initial values, a δ13C value of −35‰ is applied. The initial chain length is in a normal distribution with a peak of C17 and a standard deviation of σ = 2 carbon atoms. The initial alkane concentrations are assumed to be 0.For simplicity, we assume that since there is no isotopic fractionation within or between the alkyl chains at the beginning of hydrogenolysis, the probability of 13C substitution at any position of any side chain is identical and determined by the initial carbon isotopic composition δ13C. Multiple 13C substitutions on a C–C chain are omitted because consideration of multiple substitutions would drastically increase the modelling complexity. This approximation is valid when the C–C chain is not too long. For example, the ratio between the probabilities of double and single 13C substitution in a C20 chain is ({left[{left(begin{array}{c}20\ 2end{array}right)}{{left(frac{{,}^{13}{{{{{rm{C}}}}}}}{{,}^{12}{{{{{rm{C}}}}}}}right)}}^{2}right]}/{left[{left(begin{array}{c}20\ 1end{array}right)}{left(frac{{,}^{13}{{{{{rm{C}}}}}}}{{,}^{12}{{{{{rm{C}}}}}}}right)}right]}) ≈ 10% for 13C/12C ~ 0.01. Such a chain is long enough that the δ13C of gas products is insensitive to C–C chain length. Numerical simulation was conducted with Mathworks MATLAB 2020a.Model B: bulk and clumped isotopic fractionations of CH4
Conversion of methylene in a long C–C chain to methane is generalised into two steps:$${{{{{rm{R}}}}}}{mbox{-}}{{{{{{rm{CH}}}}}}}_{2}{mbox{-}}{{{{{rm{R}}}}}}{^prime} mathop{longrightarrow}limits^{{{{{{{rm{r}}}}}}}_{a}}_{+{{{{{rm{H}}}}}}}{{{{{rm{R}}}}}}{mbox{-}}{{{{{{rm{CH}}}}}}}_{3}mathop{longrightarrow}limits^{{{{{{{rm{r}}}}}}}_{b}}_{+{{{{{rm{H}}}}}}}{{{{{{rm{CH}}}}}}}_{4}$$
(8)
The first step (step a) is the conversion of the methylene group R-CH2-R′ to a methyl group (RCH3) by accepting a capping hydrogen atom from the hydrogen donor (activated H2); the second step (step b) is the conversion of the methyl group to methane by accepting another capping hydrogen atom. This scheme is highly generalised, and each step may involve multiple elementary biochemical reaction steps, such as the binding of H2 and long alkyl chains to the enzyme, activation of H–H and C–C bonds, and release of the short alkane products from the enzyme. It is beyond the scope of this work to discuss the detailed biochemical reaction steps. But the cleavage and formation of chemical bonds in these steps should be constrained by the observed isotopic patterns.Due to the computational complexity, we did not use the random scission model (Model A) in the simulation involving clumped isotopic fractionation, as explained in the following. A conventional kinetic model of the decomposition of organic matter without considering the constraints of C–C chain lengths is a zero-dimensional problem. Modelling the random cutting of long C–C chains without considering isotopes is a one-dimensional problem, and modelling bulk carbon isotopic fractionation during random cutting (Model A) is a two-dimensional problem. If 13C–13C coupling is included in random cutting, the modelling is a three-dimensional problem; a complex Monte Carlo method has been applied to deal with this problem19. If the 13C–D or D–D coupling is included in Model B, as we wish, it is a problem above the fourth dimension. The complexity of programming and the difficulty of computation make the model unattainable; even if it is achievable, solving this problem is far beyond the scope of this work.Reaction equation Eq. 8 is expanded to the scheme in Fig. 3a to quantify the five most abundant isotopologues in methane (three or more substitutions such as 13CH2D2 or 12CHD3 are ignored due to their low abundances). For the subscripts in Fig. 3a (m, i, and j in ramij or rbmij), the first digit (m = 0 or 1) is the number of 13C atoms involved in the reaction, the second digit (i = 0, 1, or 2) is the number of deuterium atoms connected in the methylene or methyl group, and the third digit (j = 0 or 1) is the number of deuterium atoms in the hydrogen donor.Clumped isotopic compositions of methylene and methane are defined as the following:$$left{begin{array}{l}{{Delta}} {{{{{rm{R}}}}}}{,}^{13}{{{{{rm{C}}}}}}{{{{{rm{HDR}}}}}}^{prime} =frac{({{{{{rm{R}}}}}}{,}^{13}{{{{{rm{C}}}}}}{{{{{rm{HDR}}}}}}^{prime} )({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} )}{({{{{{rm{R}}}}}}{,}^{13}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} )({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{rm{HDR}}}}}}^{prime} )}-1hfill\ {{Delta}} {{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{D}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} =4frac{({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{D}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} )({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} )}{{({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{rm{HDR}}}}}}^{prime} )}^{2}}-1hfill\ {{Delta}} {,}^{13}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{3}{{{{{rm{D}}}}}}=frac{({,}^{13}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{3}{{{{{rm{D}}}}}})({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{4})}{({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{3}{{{{{rm{D}}}}}})({,}^{13}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{4})}-1hfill\ {{Delta}} {,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{{rm{D}}}}}}}_{2}=frac{8}{3} frac{({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{{rm{D}}}}}}}_{2})({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{4})}{{({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{3}{{{{{rm{D}}}}}})}^{2}}-1 hfillend{array}right.$$
(9)
Note that the isotopic compositions here are expressed in decimals; they should be multiplied by 1000 to give per mil values.The deuterium isotope ratio between the hydrogen donor (denoted with subscript B) and the methylene group (subscript A) is expressed as:$${alpha }_{{{{{{rm{A}}}}}}}^{{{{{{rm{B}}}}}}}=frac{1+{{{delta }}{{{{{rm{D}}}}}}}_{{{{{{rm{B}}}}}}}}{1+{{{delta }}{{{{{rm{D}}}}}}}_{{{{{{rm{A}}}}}}}}$$
(10)
For each reaction step in Fig. 3a, the corresponding rate constants are denoted as kamij for step a or kbmij for step b. Kinetic fractionation factors αkamij = kamij/ka000 and αkbmij = kbmij/kb000 define KIEs. Note that a DKIE is often expressed as kH/kD, which is the reciprocal of the αk nomenclature here. A DKIE may be primary or secondary; a primary DKIE results in αka001 ≠ 1 and αkb001 ≠ 1, and a secondary one results in αka010 ≠ 1 and αkb010 ≠ 1. Kinetic clumped isotope fractionation factors γamij = αkamij/(αka100mαka010iαka001j) and γbmij = αkbmij/ (αkb100mαkb010iαkb001j) define the excessive KIE due to isotope clumping in steps a and b, respectively30.Conversion of the reactant R-CH2-R′ is defined as 1 − f, where f is the residual fraction of R-CH2-R′:$$f=({{{{{rm{R}}}}}}{mbox{-}}{{{{{{rm{CH}}}}}}}_{2}{mbox{-}}{{{{{rm{R}}}}}}{^prime} )/{({{{{{rm{R}}}}}}{mbox{-}}{{{{{{rm{CH}}}}}}}_{2}{mbox{-}}{{rm{R}}}{^prime} )}_{{{{{{rm{initial}}}}}}}$$
(11)
Considering the isotope abundance of D  1 or γb011  > 1, as shown by the Δ12CH2D2 expression in Eq. (13). With this prerequisite, either an inverse primary DKIE (1° DKIE, αka001  > 1, αkb001  > 1) or an inverse secondary DKIE (2° DKIE, αka010  > 1, αkb010  > 1) is necessary, and through numerical simulation, we found that only the inverse 1° DKIE satisfies the above-mentioned δDA, δDB, and Δ12CH2D2 values.Two scenarios (one is the pure stochastic condition, the other is with an inverse 1° DKIE) are modelled (Fig. 3). The parameters are listed in Table 1. For comparison, analytical solutions at the beginning and end of reactions from Eqs. (12) and (13) are presented. The numerical and analytical solutions are nearly identical at the beginning of conversion. There are small differences between the numerical and analytical solutions at the endpoint because the abundance of the hydrogen donor is not extremely excessive. A weak 13C fractionation between the organic precursor and the methane product is obtained with the KIE parameters (Fig. 3b). With such a weak 13C KIE, Δ13CH3D is nearly constant for reaction extent (Fig. 3c). Note that we applied an inverse 13C KIE, as required by the δ13C distribution of the alkane gases (Method 1, Model A). The δD and Δ12CH2D2 values are independent of 13C KIE. Both the bulk and clumped isotopic compositions of methane within the range of reported values are obtained at the organic precursor conversion of 0.65–0.70 as constrained by Fig. 2. More