Ecology

1.Ryan RP, Germaine K, Franks A, Ryan DJ, Dowling DN. Bacterial endophytes: recent developments and applications. FEMS Microbiol Lett. 2008;278:1–9. https://doi.org/10.1111/j.1574-6968.2007.00918.x.Article 
PubMed 

Google Scholar 
2.Rodriguez R, White J Jr, Arnold A, Redman R. Fungal endophytes: diversity and functional roles. New Phytol. 2009;182:314–30.Article 

Google Scholar 
3.Wilson D. Endophyte: the evolution of a term, and clarification of its use and definition Oikos. 1995;73:274–6.4.Harrison JG, Griffin EA. The diversity and distribution of endophytes across biomes, plant phylogeny and host tissues: How far have we come and where do we go from here?. Environ Microbiol. 2020;22:2107–23. https://doi.org/10.1111/1462-2920.14968.Article 
PubMed 

Google Scholar 
5.Clay K, Schardl C. Evolutionary origins and ecological consequences of endophyte symbiosis with grasses. Am Nat. 2002;160:S99–S127. https://doi.org/10.1086/342161.Article 
PubMed 

Google Scholar 
6.Rudgers JA, Afkhami ME, Rúa MA, Davitt AJ, Hammer S, Huguet VM. A fungus among us: broad patterns of endophyte distribution in the grasses. Ecology. 2009;90:1531–9. https://doi.org/10.1890/08-0116.1.Article 
PubMed 

Google Scholar 
7.Clay K, Holah J. Fungal endophyte symbiosis and plant diversity in successional fields. Science. 1999;285:1742–4. https://doi.org/10.1126/science.285.5434.1742.Article 
PubMed 

Google Scholar 
8.Afkhami ME, Strauss SY. Native fungal endophytes suppress an exotic dominant and increase plant diversity over small and large spatial scales. Ecology. 2016;97:1159–69. https://doi.org/10.1890/15-1166.1.Article 
PubMed 

Google Scholar 
9.Rudgers JA, Clay K. An invasive plant–fungal mutualism reduces arthropod diversity. Ecol Lett. 2008;11:831–40. https://doi.org/10.1111/j.1461-0248.2008.01201.x.Article 
PubMed 

Google Scholar 
10.Gorischek AM, Afkhami ME, Seifert EK, Rudgers JA. Fungal symbionts as manipulators of plant reproductive biology. Am Nat. 2013;181:562–70. https://doi.org/10.1086/669606.Article 
PubMed 

Google Scholar 
11.Malloch D, Blackwell M. Dispersal of fungal diaspores. The fungal community: Its organization and role in the ecosystem. 2nd ed. New York, NY: Marcel Dekker, Inc; 1992. p. 147–71.
Google Scholar 
12.Devarajan P, Suryanarayanan T. Evidence for the role of phytophagous insects in dispersal of non-grass fungal endophytes. Fungal Divers. 2006;23:111–9.
Google Scholar 
13.Lodge DJ, Fisher P, Sutton B. Endophytic fungi of Manilkara bidentata leaves in Puerto Rico. Mycologia. 1996;88:733–8.14.Paine RT. A note on trophic complexity and community stability. Am Nat. 1969;103:91–93. https://doi.org/10.1086/282586.Article 

Google Scholar 
15.Jenkins SH, Busher PE. Castor canadensis, Mammalian Species. 1979. https://doi.org/10.2307/3503787.16.Hajishengallis G, Darveau RP, Curtis MA. The keystone-pathogen hypothesis. Nat Rev Microbiol. 2012;10:717–25. https://doi.org/10.1038/nrmicro2873.Article 
PubMed 
PubMed Central 

Google Scholar 
17.Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: How the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62. https://doi.org/10.1038/ismej.2016.174.Article 
PubMed 
PubMed Central 

Google Scholar 
18.Hassani MA, Durán P, Hacquard S. Microbial interactions within the plant holobiont. Microbiome. 2018;6:58. https://doi.org/10.1186/s40168-018-0445-0.Article 
PubMed 
PubMed Central 

Google Scholar 
19.Rockman MV. The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution. 2012;66:1–17. https://doi.org/10.1111/j.1558-5646.2011.01486.x.Article 
PubMed 

Google Scholar 
20.Beckers GJ, Conrath U. Priming for stress resistance: from the lab to the field. Curr Opin Plant Biol. 2007;10:425–31. https://doi.org/10.1016/j.pbi.2007.06.002.Article 
PubMed 

Google Scholar 
21.Hartmann A, Rothballer M, Hense BA, Schröder P. Bacterial quorum sensing compounds are important modulators of microbe-plant interactions. Front Plant Sci. 2014;5. https://doi.org/10.3389/fpls.2014.00131.22.Friesen ML, Porter SS, Stark SC, von Wettberg EJ, Sachs JL, Martinez-Romero E. Microbially mediated plant functional traits. Annu Rev Ecol Evol Syst. 2011;42:23–46. https://doi.org/10.1146/annurev-ecolsys-102710-145039.Article 

Google Scholar 
23.Hardoim PR, Van Overbeek LS, Berg G, Pirttilä AM, Compant S, Campisano A, et al. The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol Mol Biol Rev. 2015;79:293–320. https://doi.org/10.1128/MMBR.00050-14.Article 
PubMed 
PubMed Central 

Google Scholar 
24.Doty SL. Growth-promoting endophytic fungi of forest trees. In: Pirttilä AM, Frank AC, editors. Endophytes of forest trees: biology and applications. Dordrecht: Springer Netherlands; 2011;151–6.
Google Scholar 
25.Arnold AE, Herre EA. Canopy cover and leaf age affect colonization by tropical fungal endophytes: Ecological pattern and process in Theobroma cacao (Malvaceae). Mycologia. 2003;95:388–98. http://www.mycologia.org.unr.idm.oclc.org/content/95/3/388. Accessed 12 Dec 2016.Article 

Google Scholar 
26.Busby PE, Peay KG, Newcombe G. Common foliar fungi of Populus trichocarpa modify melampsora rust disease severity. New Phytol. 2016;209:1681–92. https://doi.org/10.1111/nph.13742.Article 
PubMed 

Google Scholar 
27.Christian N, Herre EA, Mejia LC, Clay K. Exposure to the leaf litter microbiome of healthy adults protects seedlings from pathogen damage. Proc R Soc B. 2017;284:20170641. https://doi.org/10.1098/rspb.2017.0641.Article 
PubMed 

Google Scholar 
28.Cheplick GP, Cho R. Interactive effects of fungal endophyte infection and host genotype on growth and storage in Lolium perenne. New Phytol. 2003;158:183–91. https://doi.org/10.1046/j.1469-8137.2003.00723.x.Article 

Google Scholar 
29.Zahn G, Amend AS. Foliar fungi alter reproductive timing and allocation in arabidopsis under normal and water-stressed conditions. 2019. https://www.biorxiv.org/content/10.1101/519678v1.30.Christian N, Herre EA, Clay K. Foliar endophytic fungi alter patterns of nitrogen uptake and distribution in Theobroma cacao. New Phytol. 2019;222:1573–83. https://doi.org/10.1111/nph.15693.Article 
PubMed 

Google Scholar 
31.Rosado BHP, Almeida LC, Alves LF, Lambais MR, Oliveira RS. The importance of phyllosphere on plant functional ecology: a phyllo trait manifesto. New Phytol. 2018;219:1145–9. https://doi.org/10.1111/nph.15235.Article 
PubMed 

Google Scholar 
32.Mejía LC, Herre EA, Sparks JP, Winter K, García MN, Van Bael SA, et al. Pervasive effects of a dominant foliar endophytic fungus on host genetic and phenotypic expression in a tropical tree. Front Microbiol. 2014;5:479.33.Dupont PY, Eaton CJ, Wargent JJ, Fechtner S, Solomon P, Schmid J, et al. Fungal endophyte infection of ryegrass reprograms host metabolism and alters development. New Phytol. 2015;208:1227–40. https://doi.org/10.1111/nph.13614.Article 
PubMed 
PubMed Central 

Google Scholar 
34.Dinkins RD, Nagabhyru P, Graham MA, Boykin D, Schardl CL. Transcriptome response of Lolium arundinaceum to its fungal endophyte Epichloë coenophiala. New Phytol. 2017;213:324–37. https://doi.org/10.1111/nph.14103.Article 
PubMed 

Google Scholar 
35.Welsh S, North American species of astragalus Linnaeus (Leguminosae): a taxonomic revision. Provo, Utah: Brigham Young University; 2007.36.Knaus BJ. Morphometric architecture of the most taxon-rich species in the U.S. Flora: Astragalus lentiginosus (Fabaceae). Am J Bot. 2010;97;1816–26. https://doi.org/10.3732/ajb.0900145.37.Baucom DL, Romero M, Belfon R, Creamer R. Two new species of undifilum, fungal endophytes of astragalus (locoweeds) in the United States. Botany. 2012;90:866–75. https://doi.org/10.1139/b2012-056.Article 

Google Scholar 
38.Woudenberg JHC, Groenewald JZ, Binder M, Crous PW. Alternaria redefined. Stud Mycol. 2013;75:171–212. https://doi.org/10.3114/sim0015.Article 
PubMed 
PubMed Central 

Google Scholar 
39.Cook D, Gardner DR, Martinez A, Robles CA, Pfister JA. Screening for swainsonine among South American astragalus species. Toxicon. 2017;139:54–7. https://doi.org/10.1016/j.toxicon.2017.09.014.Article 
PubMed 

Google Scholar 
40.Molyneux RJ, James LF. Loco intoxication: indolizidine alkaloids of spotted locoweed (Astragalus lentiginosus). Science. 1982;216:190–1. https://doi.org/10.1126/science.6801763.Article 
PubMed 

Google Scholar 
41.Cook D, Gardner DR, Ralphs MH, Pfister JA, Welch KD, Green BT. Swainsoninine concentrations and endophyte amounts of Undifilum oxytropis in different plant parts of Oxytropis sericea. J Chem Ecol. 2009;35:1272–8. https://doi.org/10.1007/s10886-009-9710-9.Article 
PubMed 

Google Scholar 
42.Harrison JG, Parchman TL, Cook D, Gardner DR, Forister ML. A heritable symbiont and host-associated factors shape fungal endophyte communities across spatial scales. J Ecol. 2018;106:2274–86. https://doi.org/10.1111/1365-2745.12967.Article 

Google Scholar 
43.Grum DS, Cook D, Baucom D, Mott IW, Gardner DR, Creamer R, et al. Production of the alkaloid swainsonine by a fungal endophyte in the host Swainsona canescens. J Nat Prod. 2013;76:1984–8. https://doi.org/10.1021/np400274n.44.Cook D, Gardner DR, Pfister JA. Swainsonine-containing plants and their relationship to endophytic fungi. J Agric Food Chem. 2014;62:7326–34. https://doi.org/10.1021/jf501674r.Article 
PubMed 

Google Scholar 
45.Panaccione DG, Beaulieu WT, Cook D. Bioactive alkaloids in vertically transmitted fungal endophytes. Funct Ecol. 2014;28:299–314. https://doi.org/10.1111/1365-2435.12076.Article 

Google Scholar 
46.Thompson DC, Knight JL, Sterling TM, Murray LW. Preference for specific varieties of woolly locoweed by a specialist weevil, Cleonidius trivittatus (Say). Southwest Entomol. 1995;20:325–325.
Google Scholar 
47.Parker JE. Effects of insect herbivory by the four-lined locoweed weevil, Cleonidius trivittatus (say) (Coleoptera: Curculionidae), on the alkaloid swainsonine in locoweeds Astragalus mollissimus and Oxytropis sericea. Ph.D. thesis. Las Cruces, New Mexico: New Mexico State University; 2008.48.Creamer R, Baucom D. Fungal endophytes of locoweeds: a commensal relationship? J Plant Physiol Pathol. 2013;1. https://doi.org/10.4172/2329-955X.1000104.49.Lu H, Quan H, Zhou Q, Ren Z, Xue R, Zhao B, et al. Endogenous fungi isolated from three locoweed species from rangeland in western China. Afr J Microbiol Res. 2017;11:155–70. https://doi.org/10.5897/AJMR2016.8392.Article 

Google Scholar 
50.Schulthess FM, Faeth SH. Distribution, abundances, and associations of the endophytic fungal community of Arizona fescue (Festuca arizonica). Mycologia. 1998;90:569–78. https://doi.org/10.1080/00275514.1998.12026945.Article 

Google Scholar 
51.Cook D, Gardner DR, Pfister JA, Stonecipher CA, Robins JG, Morgan JA. Effects of elevated CO2 on the swainsonine chemotypes of Astragalus lentiginosus and Astragalus mollissimus. J Chem Ecol. 2017;43:307–16. https://doi.org/10.1007/s10886-017-0820-5.Article 
PubMed 

Google Scholar 
52.Oldrup E, McLain-Romero J, Padilla A, Moya A, Gardner D, Creamer R. Localization of endophytic undifilum fungi in locoweed seed and influence of environmental parameters on a locoweed in vitro culture system. Botany. 2010;88:512–21. https://doi.org/10.1139/B10-026.Article 

Google Scholar 
53.Gardner DR, Molyneux RJ, Ralphs MH. Analysis of swainsonine: extraction methods, detection, and measurement in populations of locoweeds (oxytropis spp.). J Agric Food Chem. 2001;49:4573–80.Article 

Google Scholar 
54.Högberg P. 15N natural abundance in soil–plant systems. New Phytol. 1997;137:179–203. https://www.cambridge.org/core/journals/new-phytologist/article/tansley-review-no-95-15n-natural-abundance-in-soilplant-systems/304069FD5C8283EDB78D0AA594465E71. Accessed 2 Jul 2017.Article 

Google Scholar 
55.Wang Y, Qian P-Y. Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies. PLoS ONE. 2009;4:e7401. https://doi.org/10.1371/journal.pone.0007401.Article 
PubMed 
PubMed Central 

Google Scholar 
56.White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In Innis MA, Glefand DH, Sninsky JJ, White TJ, editors, PCR protocols: a guide to methods and applications. London: Academic Press; 1990.57.Harrison JG, Calder WJ, Shuman B, Buerkle CA, The quest for absolute abundance: the use of internal standards for DNA-based community ecology. Mol Ecol Resour. 2020, https://doi.org/10.1111/1755-0998.13247.58.Tourlousse DM, Yoshiike S, Ohashi A, Matsukura S, Noda N, Sekiguchi Y. Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res. 2017;45:e23–e23. https://doi.org/10.1093/nar/gkw984.Article 
PubMed 

Google Scholar 
59.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.Article 

Google Scholar 
60.Rognes T, Flouri T, Nichols B, Quince C, Mahé F, “VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016. https://doi.org/10.7717/peerj.2584.61.Edgar RC, UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. 2016. https://www.biorxiv.org/content/10.1101/081257v1.full.62.Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017:2639. https://doi.org/10.1038/ismej.2017.119.63.Edgar R, “SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. 2016. https://www.biorxiv.org/content/10.1101/074161v1.64.Nilsson RH, Larsson KH, Taylor AFS, 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/gky1022.65.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal database project: data and tools for high throughput rRNA analysis Nucleic Acids Res. 2014. 42, https://doi.org/10.1093/nar/gkt1244.66.Machida RJ, Leray M, Ho S-L, Knowlton N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci Data. 2017;4:17007 https://doi.org/10.1038/sdata.2017.27.Article 

Google Scholar 
67.Fordyce JA, Gompert Z, Forister ML, Nice CC. A hierarchical Bayesian approach to ecological count data: A flexible tool for ecologists. PLoS ONE. 2011;6;e26785. https://doi.org/10.1371/journal.pone.0026785.68.Harrison JG, Calder WJ, Shastry V, Buerkle CA. Dirichlet-multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data. Mol Ecol Resour. 2020;20:481–97. https://doi.org/10.1111/1755-0998.13128.Article 
PubMed 

Google Scholar 
69.R Core Team, R: a language and environment for statistical computing. Vienna: R Core Team; 2019.70.Harrison J, Shastry V, Calder WJ, Buerkle CA, “CNVRG: Dirichlet-multinomial modelling of relative abundance data.” Sep. 2020. https://CRAN.R-project.org/package=CNVRG. Accessed 28 Oct 2020.71.S. D. Team, Stan modeling language users guide and reference manual. 2020. https://mc-stan.org/users/documentation/72.S. D. Team, “RStan: The R interface to Stan. R package.” 2020. http://mc-stan.org/.73.Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7:457–72. http://www.jstor.org/stable/2246093. Accessed 16 Jun 2018.
Google Scholar 
74.Gloor GB, Macklaim JM, Vu M, Fernandes AD. Compositional uncertainty should not be ignored in high-throughput sequencing data analysis. Austrian J Stat. 2016;45:73–87. http://ajs.data-analysis.at/index.php/ajs/article/view/vol45-4-5. Accessed 4 Dec 2017.Article 

Google Scholar 
75.Jost L. Entropy and diversity. Oikos. 2006;113:363–75.Article 

Google Scholar 
76.Marion ZH, Fordyce JA, Fitzpatrick BM. A hierarchical Bayesian model to incorporate uncertainty into methods for diversity partitioning. Ecology. 2018;99:947–56. https://doi.org/10.1002/ecy.2174.Article 
PubMed 

Google Scholar 
77.Harrison JG, Gompert Z, Fordyce JA, Buerkle CA, Grinstead R, Jahner JP. et al. The many dimensions of diet breadth: phytochemical, genetic, behavioral, and physiological perspectives on the interaction between a native herbivore and an exotic host. PLoS ONE. 2016;11:e0147971. https://doi.org/10.1371/journal.pone.0147971.Article 
PubMed 
PubMed Central 

Google Scholar 
78.Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling,”. Proc 3rd Int workshop Distrib Stat Comput. 2003;124:1–8. 10.2003.
Google Scholar 
79.Plummer M. Rjags: Bayesian graphical models using MCMC. R package version 3-15. 2015. Https://CRAN.R-project.org/package=rjags.80.Breiman L. Random forests. Mach Learn. 2001;45:5–32. https://doi.org/10.1023/A:1010933404324.Article 

Google Scholar 
81.Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2:18–22.
Google Scholar 
82.Grum DS, Cook D, Gardner DR, Roper JM, Pfister JA, Ralphs MH. Influence of seed endophyte amounts on swainsonine concentrations in astragalus and oxytropis locoweeds. J Agric Food Chem. 2012;60:8083–9. https://doi.org/10.1021/jf3024062.Article 
PubMed 

Google Scholar 
83.Marion ZH, Fordyce JA, Fitzpatrick BM. Extending the concept of diversity partitioning to characterize phenotypic complexity. Am Nat. 2015;186:348–61.Article 

Google Scholar 
84.Arnold AE, Lutzoni F. Diversity and host range of foliar fungal endophytes: are tropical leaves biodiversity hotspots?”. Ecology. 2007;88:541–49. https://doi.org/10.1890/05-1459.Article 
PubMed 

Google Scholar 
85.Strong DR, Lawton JH, Southwood SR. Insects on plants. Community patterns and mechanisms. Oxford, UK: Blackwell Scientific Publicatons; 1984.
Google Scholar 
86.Carmona D, Lajeunesse MJ, Johnson MTJ. Plant traits that predict resistance to herbivores. Funct Ecol. 2011;25:358–67. https://doi.org/10.1111/j.1365-2435.2010.01794.x.Article 

Google Scholar 
87.Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. 2014;5. https://doi.org/10.3389/fmicb.2014.00219.88.Trosvik P, de Muinck EJ. Ecology of bacteria in the human gastrointestinal tract—identification of keystone and foundation taxa. Microbiome. 2015;3:44 https://doi.org/10.1186/s40168-015-0107-4.Article 
PubMed 
PubMed Central 

Google Scholar 
89.Banerjee S, Schlaeppi K, van der Heijden MGA, Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567. https://doi.org/10.1038/s41579-018-0024-1.90.Braun K, Romero J, Liddell C, Creamer R. Production of swainsonine by fungal endophytes of locoweed. Mycological Res. 2003;107:980–8. https://doi.org/10.1017/S095375620300813X.Article 

Google Scholar 
91.Noor AI, Nava A, Cooke P, Cook D, Creamer R. Evidence for nonpathogenic relationships of alternaria section undifilum endophytes within three host locoweed plant species. Botany. 2018;96:187–200. https://doi.org/10.1139/cjb-2017-0117.Article 

Google Scholar 
92.Kulpa SM, Leger EA. Strong natural selection during plant restoration favors an unexpected suite of plant traits. Evolut Appl. 2013;6:510–23. https://doi.org/10.1111/eva.12038.Article 

Google Scholar 
93.Leger EA, Baughman OW. What seeds to plant in the Great Basin? Comparing traits prioritized in native plant cultivars and releases with those that promote survival in the field. Nat Areas J. 2015;35:54–68. https://doi.org/10.3375/043.035.0108.Article 

Google Scholar 
94.Klypina N, Pinch M, Schutte BJ, Maruthavanan J, Sterling TM, Water-deficit stress tolerance differs between two locoweed genera (astragalus and oxytropis) with fungal endophytes. Weed Sci. 2017:1–13. https://doi.org/10.1017/wsc.2017.21.95.Stamp N. Out of the quagmire of plant defense hypotheses. Q Rev Biol. 2003;78:23–55. https://doi.org/10.1086/367580.Article 
PubMed 

Google Scholar 
96.Eades CJ, Hintz WE. Characterization of the α-mannosidase gene family in filamentous fungi: N-glycan remodelling for the development of eukaryotic expression systems. Biotechnol Bioprocess Eng. 2000;5:227. https://doi.org/10.1007/BF02942178.Article 

Google Scholar 
97.Schmid J, Day R, Zhang N, Dupont PY, Cox MP, Schardl CL, et al. Host tissue environment directs activities of an epichloë endophyte, while it induces systemic hormone and defense responses in its native perennial ryegrass host. Mol Plant Microbe Interact. 2016;30:138–49. https://doi.org/10.1094/MPMI-10-16-0215-R.98.Zamioudis C, Pieterse CMJ. Modulation of host immunity by beneficial microbes. Mol Plant-Microbe Interact. 2011;25:139–50. https://doi.org/10.1094/MPMI-06-11-0179.Article 

Google Scholar 
99.Kannadan S, Rudgers JA. Endophyte symbiosis benefits a rare grass under low water availability. Funct Ecol. 2008;22:706–13. https://doi.org/10.1111/j.1365-2435.2008.01395.x.Article 

Google Scholar 
100.Barillas JRV, Paschke MW, Ralphs MH, Child RD. White locoweed toxicity is facilitated by a fungal endophyte and nitrogen-fixing bacteria. Ecology. 2007;88:1850–6. https://doi.org/10.1890/06-0728.1.Article 

Google Scholar 
101.Isbell F, Calcagno V, Hector A, Connolly J, Harpole WS, Reich PB, et al. High plant diversity is needed to maintain ecosystem services. Nature. 2011;477:199–202. https://doi.org/10.1038/nature10282. More

This is the first comparative study of commercial scallop species in the Pacific coast of the MP combining morphological and molecular characters. Our phylogenetic analyses highlight the association between A. natans and Ad. colbecki; two members of monospecific tribes and last extant representatives of their Southern Ocean-restricted genera.These results confirm the presence of both Magallanes scallops in the MP, as well as the so-far unsuspected presence of mixed “banks” where both species occur in sympatry. The BND/VH ratio helps discriminate between two distinct entities that belong to the genetic lineage of Z. patagonica and to a different lineage, highly divergent from the former, which corresponds to A. natans. A. natans is the only species of a whole lineage with a particular phylogenetic value, therefore having developed and tested an accurate identification criterion for both scallops will allow efficient fishery management in the future.Here we discuss the phylogenetic position and the taxonomic status of both Magallanes scallops, as well as the implications of these results for the future management and conservation of Z. patagonica and A. natans in the Magallanes Region. Despite the numerous classifications built on morphological, ecological or molecular data, the relationships among pectinids are still under constant modification depending on the number of taxa, loci, length of the sequence and the selected outgroups1,4. The work of Alejandrino et al.7 is the most inclusive so far in terms of taxon sampling, with 81 species. Although Scherrat et al.25 included 143 species, the node supports of the phylogenetic trees are not provided, making it difficult to assess the robustness of this large phylogeny. In order to define the phylogenetic position of Zygochlamys patagonica and Austrochlamys natans, we included 93 pectinid taxa (43 genera) representative of tribes Chlamydini, Crassadomini, Fortipectini, Palliolini, Aequipectinini, Pectinini and Amussini. Comparing to Waller’s5 and Dijkstra’s15 classifications, only the subfamily Camptonectinae and the tribe Mesoplepini are missing. We used three ribosomal regions (one nuclear and two mitochondrial). Compared to Alejandrino et al.7, histone H3 is missing here, however this locus is among the least informative4. The family Pectinidae appears to be monophyletic with high support values (Fig. 5, S2), as previously demonstrated4,7,26,27,28. According to Dijkstra15 there are currently five subfamilies of Pectinidae, two of which are absent from our analysis: Camptonectinae and Pedinae. This topology supports the classifications of Waller5 and Dijkstra15, except for the position of the tribe Austrochlamydini.Our Magallanes scallops separated into two very divergent clades: Z. patagonica is associated with its conspecifics and congenerics in a single lineage (Fig. 5), which also contains species of Veprichlamys and Talochlamys. This lineage already appeared well supported as the sister clade to Palliolinae and Pectininae in Alejandrino7. For the first time, Talochlamys dichroa and T. gemmulata are nested with high support values into the Zygochlamys clade, making this latter genus paraphyletic (Fig. 5). These taxa are all restricted to high latitudes of the Southern Ocean. Due to phylogenetic and geographic affinities, we suggest that these three genera may constitute a tribe separate from Chlamydini. Since Dijkstra15 moved the two Atlantic ‘Crassadoma’ into the genus Talochlamys, the affinities among Talochlamys spp. had not been explored until now. Talochlamys species rather associate according to geographic affinities, splitting the genus into two highly divergent entities corresponding to European and New Zealand Talochlamys. A systematic revision of these four species would be useful.Austrochlamys natans associated with the Palliolinae, which was elevated to a subfamily rank by Waller5. Of the three extant tribes that compose this group, Mesopleplini are missing from our phylogenetic analyses. We included 4 genera (8 species) of the remaining two tribes: Adamussium (Adamussini) and Palliolum, Pseudamussium, Placopecten (Palliolini). The present sampling of Palliolini is the most inclusive to date and led to the monophyly and full support of the tribe Palliolini. Our phylogenetic results do not support any of the previous classifications of the tribe Austrochlamydini1,5,9,13,15, and introduce this monospecific tribe as a new member of the subfamily Palliolinae. Indeed, Austrochlamys natans clusters together with Adamussium colbecki, both in a sister clade to Palliolini. The first molecular characterization of Ad. colbecki did not lead to a clear classification due to the low polymorphism of the 18S26. Later, Ad. colbecki appears either as sister species to Chlamydinae or to Palliolini, depending on tribe sampling and the choice of outgroup and loci4,10,11. However, in the most recent and inclusive studies of taxon sampling7 (present study) or genomic cover29, Ad. colbecki is the sister group of the tribe Palliolini, as in the present phylogeny.The subfamily Palliolinae originated from a Chlamydinine ancestor in the Cretaceous and subsequently underwent diversification in the Northern Hemisphere1 and in the Southern Hemisphere, where the extinct genus Lentipecten spread in the Paleocene–Eocene Thermal Maximum30. The genus Adamussium derived from Lentipecten and appeared in the early Oligocene; it comprises 5 endemic Antarctic species; Ad. colbecki is the only one extant13,31,32. The genus Austrochlamys also appeared in the Oligocene and was first restricted to King George Island (South Shetlands), then spread around the north of the Antarctic Peninsula and achieved a circum-Antarctic distribution until the Pliocene13,33,34. Austrochlamys persisted during the progressive cooling of the Antarctic Continent from the Paleocene to the Pliocene, dominating the coastal areas, while Adamussium occupied the deep seas and continental platform33. The opening and deepening of the Drake Passage and the intensification of the Antarctic Circumpolar Current during the Pliocene provoked a drastic cooling and the extension of sea ice over the coastal habitat, which caused the northward movement of Austrochlamys and its subsequent disappearance from Antarctica, along with the circumpolar expansion of Ad. colbecki in Antarctic shallow waters33. The colonization of the coastal habitat has been related to the sea ice extent that provided a more stable environment and low-energy fine-grained sediment with which Adamussium was associated in the deep waters. Austrochlamys fossils appear in the Subantarctic Heard Island in late Pliocene layers (3.62–2.5 Ma35). Today Ad. colbecki is a circum-Antarctic and eurybathic species that reaches high local density in protected locations13,36, while all Austrochlamys became extinct except for A. natans, which is restricted to southern South America33. The phylogenetic affinity highlighted here between A. natans and Ad. colbecki has its origins in the Southern Ocean; the deep divergence between the lineages of these monospecific tribes attests to the long time since their common origin in the Paleogene. These results point out both species as relevant biogeographic models to address longstanding questions regarding the origin of marine biota from Southern Ocean.The nomenclature, taxonomy and ecology of both A. natans and Z. patagonica have been problematic for almost 200 years. Since its original description37, Z. patagonica, a.k.a. the “Ostión Patagónico” has been named with more than 10 synonyms, probably due to the great intra-specific morphological variability throughout its distribution19,38 (see the nomenclatural history in Supplementary Table S1). In contrast, there are very few records in the scientific literature and no genetic data on A. natans, a.k.a. the “Ostión del Sur”13,14,17,19, and some problems of nomenclature and establishing diagnostic characters persist since its description13,39. Many of the current junior synonyms of both species were described from small and juvenile specimens (under 52 mm VH39,40,41). Indeed, all deposited type material of A. natans ranges from 23.5 to 52 mm VH; the latter is half of the maximum size39. The criteria most commonly used for the identification of both scallops were number of radial primary ribs, maximum size, shell colour and presence of laminated concentric lines (Supplementary Table S1). Specimens with marked primary and secondary radial ribs alternated regularly and more whitish colouring of the right shell were attributed to Z. patagonica, while those with weaker and less markedly coloured radial ribs and the maximum size were considered as A. natans42. However, the number of radial ribs overlaps between Z. patagonica (26–4212,43) and A. natans (22–5017,19). These characters also have high variability across different environments and during ontogeny13,17. Thus the use of a taxonomy based on environment-sensitive and allometric characters has led to confusion in the morphological identification of these species13,38. The criterion used in the present study, the BND/VH ratio established by Jonkers13, discriminates the species efficiently. As attested by the narrow dispersal cluster in Fig. 3, this character has low intra-population variability13. In some cases a level of intraspecific variation can be detected, and this is mainly due to the environments where the scallop populations inhabit19 (e.g. exposed, protected, substrate type, fjord, oceanic). However, although there may be some intraspecific variability between populations, this variability does not generate problems for the identification of the two species. Individuals of A. natans generally presented a significantly greater BND/VH ratio than those of Z. patagonica. However, it is important to consider that, given that this character varies during ontogeny, it is more accurate in individuals over 25 mm VH13. Only the molecular identification was able to discriminate juvenile scallops of both species accurately.According to the literature, A. natans is restricted to interior waters of channels and is associated with kelp forests of M. pyrifera (Supplementary Table S1). Z. patagonica inhabits a wider range of environments such as bottoms of shells, sand, mud and gravel in protected and exposed areas, between 2 and 300 m depth (Supplementary Table S1), but is also associated with kelp forests in fjords with different degrees of glacial retreat12,16,44. The juveniles of both scallops recruit in kelp forests44,45. According to the local artisanal fishermen, adults of “Ostión del Sur” (A. natans) occur in fjords with glaciers (orange circles in Fig. 123). We included two sampling locations near glaciers (in Pia and Montañas fjords), where large individuals (between 46 and 86 mm) of A. natans and Z. patagonica occur in sympatry. This sympatry was previously reported in Silva Palma Fjord between 5 and 25 m depth16. In conclusion, scallop banks are not monospecific but rather mixed and Z. patagonica occurs in the interior waters of the channels and fjords. Consequently, these two species have overlapping ecology (recruiting zone and glacial affinity) in the channels and fjords, overturning a long-held view that these scallops have marked habitat segregation.The fishery for both species was established in the 1990s in the political-administrative Region of Magallanes16, despite the complexity of the morphological recognition of scallops. The distinction between species was based on shell colour and radial ribs42, two characters that, given the results of this study, do not have this diagnostic capacity. Consequently, the scallop fisheries in the Magallanes Region are currently based on inaccurately discriminative characters. Scallop banks in MP have always been considered as monospecific16,47. A great part of scallop landing has always been attributed to A. natans47, about which the scientific literature is scarce (Supplementary Table S1). Conversely, Z. patagonica, which was erroneously considered as the commercial species of southern Chile, has more scientific research (Supplementary Table S1).The difficulty to discriminate A. natans and Z. patagonica morphologically may lead to incorrect fishery statistics and uncertain conservation status of A. natans. Incorrect fishery statistics could overestimate the abundance of banks of A. natans compared to Z. patagonica. If the minimum catch size is reduced23 in the context of the fishing overuse of the last decade, A. natans may suffer a reduction of its maximum size48. Therefore, an identification criterion between species is a need to improve fishery management. We showcased a quantified criterion that is useful to identify both species. In the short-term, this method can be used, but it is difficult to enforce in practical ways. We suggest to train fishing inspectors, following three guidelines. First, the identification should consider only the right valve (RV) for species identification, since the left valve is not taxonomically informative. Second, for visual classification, check the outline of the BN, mainly because the individuals of Z. patagonica have a more arcute BN. Third, a reliable identification has to measure the depth of the byssal notch (BND) and shell height (VH) ratio. Lastly, future research and fishery monitoring should follow these criteria to carry out a correct identification and subsequently better landings statistics.Molecular tools allowed evaluating the phylogenetic relationships of scallops globally or regionally and incorporating parameters that can be used for the management and conservation of species of commercial interest49. For example, in the last few decades metrics have been developed to address conservation problems that give us a measure of the current state of particular taxa. These conservation priorities are often seen as measures for threatened species categorized by the IUCN Red List (World Conservation Union, 1980), one of the most widely and recognized systems. Although this prioritization metric incorporates phylogenetic distinctiveness (PD), this factor has been updated due to the importance of quantifying the loss of evolutionary diversity that would be implied by the extinction of a species50. The magnitude of the PD loss from any species will depend (but not exclusively) on the fate of its close relatives51. The “Ostión del Sur”, Austrochlamys natans is the last representative of its tribe (Austrochlamydini) in the Southern Ocean. Its phylogenetic position and the long branch length (i.e. the length of the branch from the tip to where it joins the tree), which represents an important amount of evolutionary change, highlights the degree of isolation of A. natans and calls attention to the possible loss of a unique genetic lineage. There is currently no conservation value for this relict species; we sought to alert the current fishery management that the “Ostión del Sur” is a distinct taxon and provide integrative evidence for further conservation studies.Finally, regarding the overlapping niche of these scallops and the conservation importance of the clade of A. natans, we propose three key recommendations for the future scallop fishery policies in the sub-Antarctic channels. First, it is necessary to assess the proportion of both species per bank and landing to generate a distribution map through the sub-Antarctic channels. For this assessment, the byssal notch depth is the most appropriate morphological character. Second, we recommend reassessments of biological and ecological parameters (e.g. size at first maturity) for A. natans across the glacial fjords, which are the most relevant fishing sites. As a final point, today there is a complete lack of knowledge of the genetic connectivity along the Subantarctic Channels. Thus we should generate more research about spatial population genetics at different temporal scales. The integration of genomic approaches (e.g. SNPs) with macro- and micro-environmental modelling approaches provide enormous opportunity to establish a new regional zoning for fishery management and conservation scallop strategy. More

Soil propertiesStone contentThe stone content ranged from (15,pm ,8%) (w/w) at V.18 to (44,pm ,13%) (w/w) at F.3 (Fig. 1a, Table 1). These differences between sites may partly be due to different sources of the raw material used to create the SIS. Further, the stone content increased with depth within the first 15 cm (V.18) and 10 cm (W.10), but remained approximately constant over depth at F.3. Hence, the stone content in the upper layers of the older SIS (W.10 and V.18) was lower than in the lower layers. These depth-related differences at each site may be related to time-dependent developments within the SIS. In the uppermost layers of V.18 and W.10, stone content was comparatively low probably due to input of fine mineral and organic particles by storm water. For the oldest SIS (V.18), this assumption is supported by the field observation of soil material lying on a bricked stone border near the inflow within the SIS.Figure 1Depth-dependent soil properties: (a) stone content [% (w/w)], (b) bulk density ((hbox {g},hbox {cm}^{-3})), (c) pH (0.01 M (hbox {CaCl}_{2})) and (d) organic carbon content (OC) [% (w/w)] of the three sites F3, W.10, V18. The error bars are the standard deviation ((hbox {n}=4)).Full size imageTable 1 Soil properties of SIS.Full size tableBulk densityThe bulk density in the upper layers of the different SIS increased in the following order: V.18 < W.10 < F.3 (Fig. 1b, Table 1). At V.18, we observed the strongest change with depth from (1.0,pm ,0.1,hbox {g},hbox {cm}^{-3}) (0–5 cm) to (1.5,pm ,0.1,hbox {g},hbox {cm}^{-3}) (15–20 cm). In contrast, we observed almost no depth-dependent change of bulk density at the youngest site of F.3 ((1.6,pm ,0.2,hbox {g},hbox {cm}^{-3})).In samples of the older sites of V.18 and W.10, low bulk densities in the uppermost layers compared to deeper layers were probably caused by the activity of macrofauna, an intensive rooting, a higher organic carbon (OC) content and the input of strongly sorted fine material. The older the SIS, the stronger the effect of these factors.At F.3 the bulk density was relatively high. Here, we supposed an uniform compaction of the soil layer under the topsoil during construction. This assumption was supported by the observation of redox characteristics (iron-red stains next to grey iron-depleted areas) in the soil at approximately 25 cm depth caused by the lack of oxygen due to accumulating water45 in compacted soil.TextureThe mean texture of fine soil at all SIS was very similar: 57–80% (w/w) sand, 16–34% (w/w) silt and 5–9% (w/w) clay, since similar textured materials were used for construction to guarantee solute retention and sufficient hydraulic conductivity31. Average clay contents of all SIS were within acceptable ranges of Best Management Practice (BMP) claimed by ATV-DVWK A-138 (( More

1.Spalding, M., Kainuma, M. & Collins, L. World Atlas of Mangroves. (Earthscan, 2010).2.Duke, N. et al. A world without mangroves?. Science 317, 41–42 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Friess, D. et al. The state of the world’s mangrove forests: past, present, and future. Annu. Rev. Env. Resour. 44, 89–115 (2019).Article 

Google Scholar 
4.Wee, et al. The integration and application of genomic information in mangrove conservation. Conserv. Biol. 33, 206–209 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Duke, N., Lo, E. & Sun, M. Global distribution and genetic discontinuities of mangroves—emerging patterns in the evolution of Rhizophora. Trees-Struct. Funct. 16, 65–79 (2002).Article 

Google Scholar 
6.Ellison, A. M., Farnsworth, E. J. & Merkt, R. E. Origins of mangrove ecosystems and the mangrove biodiversity anomaly. Global Ecol. Biogeogr. 8, 95–115 (1999).
Google Scholar 
7.Plaziat, J.-C., Cavagnetto, C., Koeniguer, J.-C. & Baltzer, F. History and biogeography of the mangrove ecosystem, based on a critical reassessment of the paleontological record. Wetl. Ecol. Manag. 9, 161–180 (2001).Article 

Google Scholar 
8.Duke, N., Ball, M. & Ellison, J. Factors influencing biodiversity and distributional gradients in mangroves. Global Ecol. Biogeogr. Lett. 7, 27–47 (1998).Article 

Google Scholar 
9.Duke, N. Genetic diversity, distributional barriers and rafting continents—more thoughts on the evolution of mangroves. Hydrobiologia 295, 167–181 (1995).Article 

Google Scholar 
10.Tomlinson, P. B. The botany of mangroves. (Cambridge University press, 1986).11.Schwarzbach, A. E. & Ricklefs, R. E. Systematic affinities of Rhizophoraceae and Anisophylleaceae, and intergeneric relationships within Rhizophoraceae, based on chloroplast DNA, nuclear ribosomal DNA, and morphology. Am. J. Bot. 87, 547–564 (2000).CAS 
PubMed 
Article 

Google Scholar 
12.Lo, E. Y. Y. Testing hybridization hypotheses and evaluating the evolutionary potential of hybrids in mangrove plant species. J. Evol. Biol. 23, 2249–2261 (2010).CAS 
PubMed 
Article 

Google Scholar 
13.Takayama, K., Tamura, M., Tateishi, Y., Webb, E. L. & Kajita, T. Strong genetic structure over the American continents and transoceanic dispersal in the mangrove genus Rhizophora (Rhizophoraceae) revealed by broad-scale nuclear and chloroplast DNA analysis. Am. J. Bot. 100, 1191–1201 (2013).CAS 
PubMed 
Article 

Google Scholar 
14.Lo, E., Duke, N. & Sun, M. Phylogeographic pattern of Rhizophora (Rhizophoraceae) reveals the importance of both vicariance and long-distance oceanic dispersal to modern mangrove distribution. BMC Evol. Biol. 14, 83 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
15.Chen, Y. et al. Applications of multiple nuclear genes to the molecular phylogeny, population genetics and hybrid identification in the mangrove genus Rhizophora. PLoS ONE 10, e0145058 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Xu, S. H. et al. The origin, diversification and adaptation of a major mangrove clade (Rhizophoreae) revealed by whole-genome sequencing. Natl. Sci. Rev. 4, 721–734 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Tyagi, A. P. Cytogenetics and reproductive biology of mangroves in Rhizophoraceae. Aust. J. Bot. 50, 601–605 (2002).Article 

Google Scholar 
18.Tyagi, A. P. Chromosomal Pairing and Pollen Viability in Rhizophora mangle and Rhizophora stylosa Hybrids. S. Pac. J. Nat. Sci. 20, 1–3 (2002).Article 

Google Scholar 
19.Tyagi, A. P. & Singh, E. V. V. Pollen fertility and intraspecific and interspecific compatibility in mangroves of Fiji. Sex. Plant Reprod. 11, 60–63 (1998).Article 

Google Scholar 
20.Steininger, F. F. & Rögl, F. Paleogeography and palinspastic reconstruction of the Neogene of the Mediterranean and Paratethys. Geol. Soc. Spec. Publ. 17, 659–668 (1984).ADS 
Article 

Google Scholar 
21.Harzhauser, M. et al. Biogeographic responses to geodynamics: a key study all around the Oligo-Miocene Tethyan Seaway. Zoo. Anz. 246, 241–256 (2007).Article 

Google Scholar 
22.Vrielynck, B., Odin, G. & Dercourt, J. Miocene palaeogeography of the Tethys Ocean; potential global correlations in the Mediterranean. Miocene stratigraphy: an integrated approach. Elsevier Science, (1997).23.Harzhauser, M., Piller, W. E. & Steininger, F. F. Circum-Mediterranean Oligo-Miocene biogeographic evolution—the gastropods’ point of view. Palaeogeogr. Palaeoclimatol. Palaeoecol. 183, 103–133 (2002).Article 

Google Scholar 
24.Dercourt, J. et al. Geological evolution of the Tethys belt from the Atlantic to the Pamirs since the LIAS. Tectonophysics 123, 241–315 (1986).ADS 
Article 

Google Scholar 
25.Marko, P. B. Fossil calibration of molecular clocks and the divergence times of geminate species pairs separated by the Isthmus of Panama. Mol. Biol. Evol. 19, 2005–2021 (2002).CAS 
PubMed 
Article 

Google Scholar 
26.Saenger, P. Mangrove vegetation: an evolutionary perspective. Mar. Freshw. Res. 49, 277–286 (1998).CAS 
Article 

Google Scholar 
27.Muller, J. & Caratini, C. Pollen of Rhizophora (Rhizophoraceae) as a guide fossil. Pollen Spores 19, 361–390 (1977).
Google Scholar 
28.Muller, J. Fossil pollen records of extant angiosperms. Bot. Rev. 47, 1–142 (1981).Article 

Google Scholar 
29.Germeraad, J. H., Hopping, C. A. & Muller, J. Palynology of tertiary sediments from tropical areas. Rev. Palaeobot. Palyno. 6, 189–348 (1968).Article 

Google Scholar 
30.Zachos, J., Pagani, H., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).ADS 
CAS 
Article 

Google Scholar 
31.Pole, M. S. & Macphail, M. K. Eocene Nypa from Regatta Point, Tasmania. Rev. Palaeobot. Palyno. 92, 55–67 (1996).Article 

Google Scholar 
32.Hornibrook, N. D. B. New Zealand Cenozoic marine paleoclimates: a review based on the distribution of some shallow water and terrestrial biota. Pacific Neogene: environment, evolution, and events, 83–106 University of Tokyo Press, (1992).33.Hou, Z. & Li, S. Tethyan changes shaped aquatic diversification. Biol. Rev. 93, 874–896 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Wee, A. K. S. et al. Genetic differentiation and phylogeography of partially sympatric species complex Rhizophora mucronata Lam. and R. stylosa Griff. using SSR markers. BMC Evol. Biol. 15, 57 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Ng, W. L. et al. Closely related and sympatric but not all the same: genetic variation of Indo-West Pacific Rhizophora mangroves across the Malay Peninsula. Conserv. Genet. 16, 137–150 (2015).Article 

Google Scholar 
36.Doyle, J. & Doyle, J. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 9, 11–15 (1987).
Google Scholar 
37.Strand, A. E., Leebens-Mack, J. & Milligan, B. G. Nuclear DNA-based markers for plant evolutionary biology. Mol. Ecol. 6, 113–118 (1997).CAS 
PubMed 
Article 

Google Scholar 
38.Cronn, R. C., Small, R. L. & Wendel, J. F. Duplicated genes evolve independently after polyploid formation in cotton. Proc. Natl. Acad. Sci. USA 96, 14406–14411 (1999).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Hayashi, K. PCR-SSCP: a simple and sensitive method for detection of mutations in the genomic DNA. Genome Res. 1, 34–38 (1991).CAS 
Article 

Google Scholar 
40.Rozas, J., Sanchez-DelBarrio, J. C., Messeguer, X. & Rozas, R. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19, 2496–2497 (2003).CAS 
Article 

Google Scholar 
41.Swofford, D.L. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Sinauer Associates, Sunderland, Massachusetts, (2002).42.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
44.Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, I37-48 (1999).Article 

Google Scholar 
45.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Heled, J. & Drummond, A. J. Bayesian inference of species trees from multilocus data. Mol. Biol. Evol. 27, 570–580 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Graham, A. Paleobotanical evidence and molecular data in reconstructing the historical phytogeography of Rhizophoraceae. Ann. Mo. Bot. Gard. 93, 325–334 (2006).Article 

Google Scholar 
48.Rambaut, A. Fig Tree v1.4. (2012). Available at http://tree.bio.ed.ac.uk/software/figtree/49.Matzke, N. J. Probabilistic historical biogeography: new models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing. Front. Biogeogr. 5, 242–248 (2013).Article 

Google Scholar 
50.Blair, C. & He, X. J. RASP 4: ancestral state reconstruction tool for multiple genes and characters. Mol. Biol. Evol. 37, 604–606 (2020).PubMed 
Article 
CAS 

Google Scholar 
51.Takayama, K., Tamura, M., Tateishi, Y. & Kajita, T. Isolation and characterization of microsatellite loci in a mangrove species, Rhizophora stylosa (Rhizophoraceae). Conserv. Genet. Resour. 1, 175–178 (2009).Article 

Google Scholar 
52.Takayama, K., Tamura, M., Tateishi, Y. & Kajita, T. Isolation and characterization of microsatellite loci in the red mangrove Rhizophora mangle (Rhizophoraceae) and its related species. Conserv. Genet. 9, 1323–1325 (2008).CAS 
Article 

Google Scholar 
53.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol. Ecol. Notes. 7, 574–578 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
Article 

Google Scholar  More

1.Voss, N. A., Nesis, K. N. & Rodhouse, P. G. The cephalopod family Histioteuthidae (Oegopsida): systematics, biology, and biogeography in Systematics and Biogeography of Cephalopods (eds. Voss, N. A., Vecchione, M., Toll, R. B. & Sweeney M. J.) 277–291 (Smithsonian Contributions to Zoology, 1998).2.Crocetta, F. et al. Biogeographical homogeneity in the eastern Mediterranean Sea – III: new records and a state of the art of Polyplacophora, Scaphopoda and Cephalopoda (Mollusca) from Lebanon. Spixiana 37(2), 183–206 (2014).
Google Scholar 
3.Guerra, A. Mollusca, cephalopoda in Fauna Iberica (eds. Ramos, M. A. et al.) 327 (Museo Nacional de Ciencias Naturales, 1992).4.Cuccu, D., Mereu, M., Loi, B., Sanna, I. & Cau, A. The squid family Histioteuthidae in the Sardinian waters. Biol. Mar. Mediterr. 13, 262–263 (2007).
Google Scholar 
5.Jereb, P. & Roper, C. F. E. Cephalopods of the world. An annotated and illustrated catalogue of cephalopod species known to date. Myopsid and Oegopsid Squids in FAO species catalogue for fishery purposes (ed. FAO) 649 (FAO, 2010).6.Quetglas, A., de Mesa, A., Ordines, F. & Grau, A. Life history of the deep-sea cephalopod family Histioteuthidae in the western Mediterranean. Deep Sea Res. Part I 57, 999–1008. https://doi.org/10.1016/j.dsr.2010.04.008 (2010).Article 

Google Scholar 
7.Oshima, T., Shimazu, T., Koyama, H. & Akahane, H. J. J. On the larvae of the genus Anisakis (Nematoda: Anisakidaae) from euphausiids. Jpn. J. Parasitol. 18, 241–248 (1969).
Google Scholar 
8.Hochberg, F. G. The parasites of cephalopods: a review. Mem. Nat. Mus. Vict. 44, 109–145. https://doi.org/10.24199/j.mmv.1983.44.10 (1983).Article 

Google Scholar 
9.Bello, G. Role of cephalopods in the diet of the swordfish, Xiphias gladius, from the eastern Mediterranean Sea. Bull. Mar. Sci. 49, 312–324 (1991).
Google Scholar 
10.Bello, G. Teuthophagous predators as collectors of oceanic cephalopods: the case of the Adriatic Sea. Boll. Malacol. 32, 71–78 (1996).
Google Scholar 
11.Santos, M. et al. Stomach contents of sperm whales Physeter macrocephalus stranded in the North Sea 1990–1996. Mar. Ecol. Prog. Ser. 183, 281–294 (1999).ADS 
Article 

Google Scholar 
12.Xavier, J. et al. Current status of using beaks to identify cephalopods: III International Workshop and training course on Cephalopod beaks, Faial island, Azores, April 2007. Arquipélago-Life Mar. Sci. 24, 41–48 (2007).
Google Scholar 
13.Marcogliese, D. J. & Cone, D. K. Food webs: a plea for parasites. Trends Ecol. Evol. 12, 320–325. https://doi.org/10.1016/S0169-5347(97)01080-X (1997).CAS 
Article 
PubMed 

Google Scholar 
14.Abollo, E. et al. Squid as trophic bridges for parasite flow within marine ecosystems: the case of Anisakis simplex (Nematoda: Anisakidae), or when the wrong way can be right. Afr. J. Mar. Sci. 20, 223–232. https://doi.org/10.2989/025776198784126575 (1998).Article 

Google Scholar 
15.Klimpel, S., Seehagen, A., Palm, H. W. & Rosenthal, H. Deep-water metazoan fish parasites of the world. (eds. Klimpel, S., Seehagen, A., Palm, H. W. & Rosenthal, H.) (Logos Verlag, 2001).16.Parker, G. A., Chubb, J. C., Ball, M. A. & Roberts, G. N. Evolution of complex life cycles in helminth parasites. Nature 425, 480–484 (2003).ADS 
CAS 
Article 

Google Scholar 
17.Santoro, M., Iaccarino, D. & Bellisario, B. Host biological factors and geographic locality influence predictors of parasite communities in sympatric sparid fishes off the southern Italian coast. Sci. Rep. 10(1), 13283. https://doi.org/10.1038/s41598-020-69628-1 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Pascual, S., González, A., Arias, C. & Guerra, A. Helminth infection in the short-finned squid Illex coindetii (Cephalopoda, Ommastrephidae) off NW Spain. Dis. Aquat. Org. 23, 71–75. https://doi.org/10.3354/dao023071 (1995).Article 

Google Scholar 
19.Petrić, M., Mladineo, I. & Šifner, S. Insight into the short-finned squid Illex coindetii (Cephalopoda: Ommastrephidae) feeding ecology: is there a link between helminth parasites and food composition? J. Parasitol. 97, 55–62. https://doi.org/10.1645/GE-2562.1 (2011).Article 
PubMed 

Google Scholar 
20.Klimpel, S. & Rückert, S. Life cycle strategy of Hysterothylacium aduncum to become the most abundant anisakid fish nematode in the North Sea. Parasitol. Res. 97, 141–149. https://doi.org/10.1007/s00436-005-1407-6 (2005).Article 
PubMed 

Google Scholar 
21.Tursi, A., D’Onghia, A., Matarrese, A., Panetta, P. & Maiorano, P. Finding of uncommon cephalopods (Ancistroteuthis lichtensteinii, Histioteuthis bonnellii, Histioteuthis reversa) and first record of Chiroteuthis veranyi in the Ionian Sea. Cah. Biol. Mar. 35, 339–346 (1994).
Google Scholar 
22.Koutsoubas, D. & Boyle, P. Histioteuthis bonnelli (Férussac, 1835) (Cephalopoda) in the Eastern Mediterranean: new record and biological considerations. J. Mollus. Stud. 65, 380–383. https://doi.org/10.1093/mollus/65.3.380 (1999).Article 

Google Scholar 
23.Bello, G. How rare is Histioeuthis bonnellii (Cephalopoda: Histioteuthidae) in the eastern Mediterranean Sea? J. Mollus. Stud. 66, 575–576. https://doi.org/10.1093/mollus/66.4.575 (2000).Article 

Google Scholar 
24.Belcari, P. & Sartor, P. Bottom trawling teuthofauna of the northern Tyrrhenian Sea. Sci. Mar. 57, 145–152 (1993).
Google Scholar 
25.Quetglas, A., Carbonell, A. & Sánchez, P. Demersal continental shelf and upper slope cephalopod assemblages from the Balearic Sea (North-Western Mediterranean). Biological aspects of some deep-sea species. Estuar. Coast. Shelf Sci. 50, 739–749. https://doi.org/10.1006/ecss.1999.0603 (2000).ADS 
Article 

Google Scholar 
26.Culurgioni, J., Cuccu, D., Mereu, M. & Figus, V. Larval anisakid nematodes of Histioteuthis reversa (Verril, 1880) and H. bonnellii (Férussac, 1835) (Cephalopoda: Teuthoidea) from Sardinian Channel (western Mediterranean). Bull. Eur. Ass. Fish Pathol. 30, 217 (2010).
Google Scholar 
27.Capua, D. I cefalopodi delle coste e dell’Arcipelago Toscano: sistematica, anatomia, fisiologia e sfruttamento delle specie presenti nel Mediterraneo. 446 (Evolver, 2004).28.Crocetta, F. et al. Bottom-trawl catch composition in a highly polluted coastal area reveals multifaceted native biodiversity and complex communities of fouling organisms on litter discharge. Mar. Environ. Res. 155, 104875. https://doi.org/10.1016/j.marenvres.2020.104875 (2020).CAS 
Article 
PubMed 

Google Scholar 
29.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
30.Meyer, C. P. Molecular systematics of cowries (Gastropoda: Cypraeidae) and diversification patterns in the tropics. Biol. J. Linn. Soc. 79, 401–459. https://doi.org/10.1046/j.1095-8312.2003.00197.x (2003).Article 

Google Scholar 
31.Morgulis, A. et al. Database indexing for production MegaBLAST searches. Bioinformatics 24, 1757–1764. https://doi.org/10.1093/bioinformatics/btn322 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Berland, B. Nematodes from some Norwegian marine fishes. Sarsia 2, 1–50. https://doi.org/10.1080/00364827.1961.10410245 (1961).Article 

Google Scholar 
33.Nagasawa, K. & Moravec, F. Larval anisakid nematodes of Japanese common squid (Todarodes pacificus) from the Sea of Japan. J. Parasitol. 81, 69–75. https://doi.org/10.2307/3284008 (1995).CAS 
Article 
PubMed 

Google Scholar 
34.Nagasawa, K. & Moravec, F. Larval anisakid nematodes from four species of squid (Cephalopoda: Teuthoidea) from the central and western North Pacific Ocean. J. Nat. Hist. 36, 8. https://doi.org/10.1080/00222930110051752 (2002).Article 

Google Scholar 
35.Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83(4), 575–583 (1997).CAS 
Article 

Google Scholar 
36.Zhu, X., Gasser, R. B., Podolska, M. & Chilton, N. Characterisation of anisakid nematodes with zoonotic potential by nuclear ribosomal DNA sequences. Int. J. Parasitol. 28, 1911–1921. https://doi.org/10.1016/S0020-7519(98)00150-7 (1998).CAS 
Article 
PubMed 

Google Scholar 
37.Nadler, S. A. & Hudspeth, D. S. Phylogeny of the Ascaridoidea (Nematoda: Ascaridida) based on three genes and morphology: hypotheses of structural and sequence evolution. J. Parasitol. 86, 380–393. https://doi.org/10.1645/0022-3395(2000)086[0380:POTANA]2.0.CO;2 (2000).CAS 
Article 
PubMed 

Google Scholar 
38.Valentini, A. et al. Genetic relationships among Anisakis species (Nematoda: Anisakidae) inferred from mitochondrial cox2 sequences, and comparison with allozyme data. J. Parasitol. 92, 156–166. https://doi.org/10.1645/GE-3504.1 (2006).CAS 
Article 
PubMed 

Google Scholar 
39.Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180. https://doi.org/10.1111/j.1096-0031.2010.00329.x (2011).Article 

Google Scholar 
40.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9(8), 772. https://doi.org/10.1038/nmeth.2109 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Guindon, S. & Gascuel, O. A simple, fast and accurate method to estimate large phylogenies by maximum-likelihood. Syst. Biol. 52, 696–704. https://doi.org/10.1080/10635150390235520 (2003).Article 
PubMed 

Google Scholar 
42.Akaike, H. Information theory and an extension of the maximum likelihood principle in Proceeding of the second international symposium on information theory (eds. Petrov, T. & Caski, F.) 267–281 (Akademiai Kiado, 1973).43.Posada, D. jModelTest: phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256. https://doi.org/10.1093/molbev/msn083 (2008).CAS 
Article 
PubMed 

Google Scholar 
44.Posada, D. & Buckley, T. R. Model selection and model averaging in phylogenetics: advantages of Akaike Information Criterion and Bayesian approaches over likelihood ratio tests. Syst. Biol. 53, 793–808. https://doi.org/10.1080/10635150490522304 (2004).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Ronquist, F. & Huelsenbeck, J. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574. https://doi.org/10.1093/bioinformatics/btg180 (2003).CAS 
Article 

Google Scholar 
46.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
Article 

Google Scholar 
47.Kumar, S. et al. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Lindgren, A. R. Molecular inference of phylogenetic relationships among Decapodiformes (Mollusca: Cephalopoda) with special focus on the squid Order Oegopsida. Mol. Phylogenet. 56(1), 77–90. https://doi.org/10.1016/j.ympev.2010.03.025 (2010).Article 

Google Scholar 
49.Taite, M., Vecchione, M., Fennell, S. & Allcock, L. A. Paralarval and juvenile cephalopods within warm-core eddies in the North Atlantic. Bul. Mar. Sci. 96(2), 235–262. https://doi.org/10.5343/bms.2019.0042 (2020).Article 

Google Scholar 
50.Guardone, L. et al. Larval ascaridoid nematodes in horned and musky octopus (Eledone cirrhosa and E. moschata) and longfin inshore squid (Doryteuthis pealeii): safety and quality implications for cephalopod products sold as fresh on the Italian market. Int. J. Food Microbiol. 333, 108812 (2020).CAS 
Article 

Google Scholar 
51.Pascual, S., Abollo, E., Mladineo, I. & Gestal, C. Metazoa and Related Diseases in Handbook of Pathogens and Diseases in Cephalopods (eds. Gestal, C., Pascual S., Guerra A., Fiorito G. & Vieites, J. M.) 169–179 (2019).52.Mattiucci, S. & Nascetti, G. Advances and trends in the molecular systematics of anisakid nematodes, with implications for their evolutionary ecology and host—parasite co-evolutionary processes. Adv. Parasitol. 66, 47–148. https://doi.org/10.1016/S0065-308X(08)00202-9 (2008).Article 
PubMed 

Google Scholar 
53.Mattiucci, S., Cipriani, P., Levsen, A., Paoletti, M. & Nascetti, G. Molecular epidemiology of Anisakis and Anisakiasis: an ecological and evolutionary road map. Adv. Parasitol. 99, 93–263. https://doi.org/10.1016/bs.apar.2017.12.001 (2018).Article 
PubMed 

Google Scholar 
54.Kie, M. Aspects of the life cycle and morphology of Hysterothylacium aduncum (Rudolphi, 1802) (Nematoda, Ascaridoidea, Anisakidae). Can. J. Zool. 71, 1289–1296. https://doi.org/10.1139/z93-178 (1993).Article 

Google Scholar 
55.Santoro, M. et al. Helminth parasites of the dwarf sperm whale Kogia sima (Cetacea: Kogiidae) from the Mediterranean Sea, with implications on host ecology. Dis. Aquat. Organ. 14, 175–182. https://doi.org/10.3354/dao03251 (2018).CAS 
Article 

Google Scholar 
56.Kawakami, T. A review of sperm whale food. Sci. Rep. Whales Res. Inst. 32, 199–218 (1980).
Google Scholar 
57.Garibaldi, F. & Podestà, M. Stomach contents of a sperm whale (Physeter macrocephalus) stranded in Italy (Ligurian Sea, northwestern Mediterranean). JMBA 94(6), 1087–1091. https://doi.org/10.1017/S0025315413000428 (2014).Article 

Google Scholar 
58.Mattiucci, S., Nascetti, G., Bullini, L., Orecchia, P. & Paggi, L. Genetic structure of Anisakis physeteris and its differentiation from the Anisakis simplex complex (Ascaridida: Anisakidae). Parasitology 93, 383–387. https://doi.org/10.1017/S0031182000051544 (1986).CAS 
Article 
PubMed 

Google Scholar 
59.Mattiucci, S. et al. Genetic divergence and reproductive isolation between Anisakis brevispiculata and Anisakis physeteris (Nematoda: Anisakidae). Int. J. Parasitol. 31, 9–14. https://doi.org/10.1016/S0020-7519(00)00125-9 (2001).CAS 
Article 
PubMed 

Google Scholar 
60.Gupta, P. C. & Masoodi, B. A. Three new and one known nematode (Family: Anisakidae) from marine fishes of India. Indian J. Parasitol. 14(2), 157–164 (1990).
Google Scholar 
61.Vicente, J. J., Mincarone, M. M. & Pint, R. M. First report of Lappetascaris lutjani Rasheed, 1965 (Nematoda, Ascaridoidea, Anisakidae) parasitizing Trachipterus arawatae (Pisces, Lampridiformes) on the Atlantic coast of Brazil. Mem. Inst. Oswaldo Cruz. 97, 93–94. https://doi.org/10.1590/s0074-02762002000100015(2002) (2002).Article 
PubMed 

Google Scholar 
62.Bruce, N. L. & Cannon, L. R. G. Hysterothylacium, Iheringascaris and Maricostula new genus, nematodes (Ascaridoidea) from Australian pelagic marine fishes. J. Nat. Hist. 23(6), 1397–1441. https://doi.org/10.1080/00222938900770771 (1989).Article 

Google Scholar 
63.Shamsi, S. Morphometric and molecular descriptions of three new species of Hysterothylacium (Nematoda: Raphidascarididae) from Australian marine fish. J. Helminthol. 91, 1–12. https://doi.org/10.1017/S0022149X16000596 (2016).CAS 
Article 

Google Scholar 
64.Li, L. et al. Molecular phylogeny and dating reveal a terrestrial origin in the early carboniferous for ascaridoid nematodes. Syst. Biol. 67(5), 888–900. https://doi.org/10.1093/sysbio/syy018 (2018).Article 
PubMed 

Google Scholar 
65.Garcia, A., Mattiucci, S., Santos, M. N., Damiano, S. & Nascetti, G. Metazoan parasites of Xiphias gladius (L. 1758) (Pisces: Xiphiidae) from the Atlantic Ocean: implications for host stock identification. ICES J. Mar. Sci. 68, 175–182 (2010).Article 

Google Scholar 
66.Klimpel, S. & Palm, H. W. Anisakid Nematode (Ascaridoidea) Life Cycles and Distribution: Increasing Zoonotic Potential in the Time of Climate Change? in Progress in Parasitology. Parasitology Research Monographs (ed. Mehlhorn, H.) https://doi.org/10.1007/978-3-642-21396-0_11 (Springer, 2011).67.Kuhn, T., Cunze, S., Kochmann, J. & Klimpel, S. Environmental variables and definitive host distribution: a habitat suitability modelling for endohelminth parasites in the marine realm. Sci. Rep. 6, 30246. https://doi.org/10.1038/srep30246 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Cipriani, P. et al. Occurrence of larval ascaridoid nematodes in the Argentinean short-finned squid Illex argentinus from the Southwest Atlantic Ocean (off Falkland Islands). Int. J. Food Microbiol. 297, 27–31. https://doi.org/10.1016/j.ijfoodmicro.2019.02.019 (2019).Article 
PubMed 

Google Scholar 
69.Cipriani, P. et al. Anisakis simplex (s.s.) larvae (Nematoda: Anisakidae) hidden in the mantle of European flying squid Todarodes sagittatus (Cephalopoda: Ommastrephidae) in NE Atlantic Ocean: food safety implications. Int. J. Food Microbiol. 339, 109021 (2021).CAS 
Article 

Google Scholar 
70.Klimpel, S., Kellermanns, E. & Palm, H. W. The role of pelagic swarm fish (Myctophidae: Teleostei) in the oceanic life cycle of Anisakis sibling species at the Mid-Atlantic Ridge, Central Atlantic. Parasitol. Res. 104, 43–53. https://doi.org/10.1007/s00436-008-1157-3 (2008).Article 
PubMed 

Google Scholar 
71.Mattiucci, S., Paoletti, M. & Webb, S. C. Anisakis nascettii n. sp. (Nematoda: Anisakidae) from beaked whales of the southern hemisphere: morphological description, genetic relationships between congeners and ecological data. Syst. Parasitol. 74, 199–217. https://doi.org/10.1007/s11230-009-9212-8 (2009).Article 
PubMed 

Google Scholar 
72.Pico-Duran, G., Pulleiro-Potel, L., Abollo, E., Pascual, S. & Munoz, P. Molecular identification of Anisakis and Hysterothylacium larvae in commercial cephalopods from the Spanish Mediterranean coast. Vet. Parasitol. 220, 47–53. https://doi.org/10.1016/j.vetpar.2016.02.020 (2016).Article 
PubMed 

Google Scholar 
73.Menconi, V. et al. Occurrence of ascaridoid nematodes in Illex coindetii, a commercially relevant cephalopod species from the Ligurian Sea (Northwest Mediterranean Sea). Food Control https://doi.org/10.1016/j.foodcont.2020.107311 (2020).Article 

Google Scholar 
74.Blazekovic, K. et al. Three Anisakis spp. isolated from toothed whales stranded along the eastern Adriatic Sea coast. Int. J. Parasitol. 45(1), 17–31. https://doi.org/10.1016/j.ijpara.2014.07.012 (2015).Article 
PubMed 

Google Scholar 
75.Santoro, M. et al. Epidemiology of Sulcascaris sulcata (Nematoda: Anisakidae) ulcerous gastritis in the Mediterranean loggerhead sea turtle (Caretta caretta). Parasitol. Res. 118, 1457–1463. https://doi.org/10.1007/s00436-019-06283-0 (2019).Article 
PubMed 

Google Scholar 
76.Bao, M., Cipriani, P., Giulietti, L., Drivenes, N. & Levsen, A. Quality issues related to the presence of the fish parasitic nematode Hysterothylacium aduncum in export shipments of fresh Northeast Arctic cod (Gadus morhua). Food Control 121, 107724. https://doi.org/10.1016/j.foodcont.2020.107724 (2020).CAS 
Article 

Google Scholar 
77.Zhang, K., Xu, Z., Chen, H. X., Guo, N. & Li, L. Anisakid and raphidascaridid nematodes (Ascaridoidea) infection in the important marine food-fish Lophius litulon (Jordan) (Lophiiformes: Lophiidae). Int. J. Food Microbiol. 284, 105–111. https://doi.org/10.1016/j.ijfoodmicro.2018.08.002 (2018).Article 
PubMed 

Google Scholar 
78.Szostakowska, B., Myjak, P., Kur, J. & Sywula, T. Molecular evaluation of Hysterothylacium auctum (Nematoda, Ascaridida, Raphidascarididae) taxonomy from fish of the southern Baltic. Acta Parasitol. 46(3), 194–201 (2001).CAS 

Google Scholar 
79.Andres, M. J., Peterson, M. S. & Overstreet, R. M. Endohelminth parasites of some midwater and benthopelagic stomiiform fishes from the northern Gulf of Mexico. Gulf Caribb. Res. 27, 11–19. https://doi.org/10.18785/gcr.2701.02 (2016).Article 

Google Scholar 
80.Li, L., Liu, Y. Y. & Zhang, L. P. Morphological and molecular identification of Hysterothylacium longilabrum sp. Nov. (Nematoda: Anisakidae) and larvae of different stages from marine fishes in the South China Sea. Parasitol. Res. 111(2), 767–777 (2012).Article 

Google Scholar 
81.Shamsi, S. et al. Occurrence of ascaridoid nematodes in selected edible fish from the Persian Gulf and description of Hysterothylacium larval type XV and Hysterothylacium persicum n. sp. (Nematoda: Raphidascarididae). Int. J. Food Microbiol. 236, 65–67. https://doi.org/10.1016/j.ijfoodmicro.2016.07.006 (2016).Article 
PubMed 

Google Scholar 
82.Chen, H. X. et al. Detection of ascaridoid nematode parasites in the important marine food-fish Conger myriaster (Brevoort) (Anguilliformes: Congridae) from the Zhoushan fishery, China. Parasit. Vectors 11, 274. https://doi.org/10.1186/s13071-018-2850-4 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
83.Liu, Y. Y., Xu, Z., Zhang, L. P. & Li, L. Redescription and genetic characterization of Hysterothylacium thalassini Bruce, 1990 (Nematoda: Anisakidae) from marine fishes in the South China Sea. J. Parasitol. 99, 655–661. https://doi.org/10.1645/12-136.1 (2013).Article 
PubMed 

Google Scholar 
84.Shamsi, S., Gasser, R. & Beveridge, I. Description and genetic characterisation of Hysterothylacium (Nematoda: Raphidascarididae) larvae parasitic in Australian marine fishes. Parasitol. Int. 62, 320–328. https://doi.org/10.1016/j.parint.2012.10.001 (2013).CAS 
Article 
PubMed 

Google Scholar 
85.Li, L., Zhao, W. T., Guo, Y. N. & Zhang, L. P. Nematode parasites infecting the starry batfish Halieutaea stellata (Vahl) (Lophiiformes: Ogcocephalidae) from the East and South China Sea. J. Fish. Dis. 39(5), 515–529. https://doi.org/10.1111/jfd.12374 (2016).CAS 
Article 
PubMed 

Google Scholar 
86.Zhao, W. T. et al. Ascaridoid parasites infecting in the frequently consumed marine fishes in the coastal area of China: a preliminary investigation. Parasitol. Int. 65(2), 87–98. https://doi.org/10.1016/j.parint.2015.11.002 (2016).Article 
PubMed 

Google Scholar 
87.Hossen, M. S. & Shamsi, S. Zoonotic nematode parasites infecting selected edible fish in New South Wales, Australia. Int. J. Food Microbiol. 308, 108306. https://doi.org/10.1016/j.ijfoodmicro.2019.108306 (2019).CAS 
Article 
PubMed 

Google Scholar 
88.Jabbar, A. et al. Mutation scanning-based analysis of anisakid larvae from Sillago flindersi from Bass Strait, Australia. Electrophoresis 33, 499–505. https://doi.org/10.1002/elps.201100438 (2012).CAS 
Article 
PubMed 

Google Scholar 
89.Jabbar, A. et al. Molecular characterization of anisakid nematode larvae from 13 species of fish from Western Australia. Int. J. Food Microbiol. 161(3), 247–253. https://doi.org/10.1016/j.ijfoodmicro.2012.12.012 (2013).CAS 
Article 
PubMed 

Google Scholar 
90.Shamsi, S., Stellar, E. & Chen, Y. New and known zoonotic nematode larvae within selected fish species from Queensland waters in Australia. Int. J. Food Microbiol. 272, 73–82. https://doi.org/10.1016/j.ijfoodmicro.2018.03.007 (2018).Article 
PubMed 

Google Scholar 
91.Arizono, N. et al. Ascariasis in Japan: is pig-derived ascaris infecting humans? Jpn. J. Infect. Dis. 63(6), 447–448 (2010).PubMed 

Google Scholar 
92.Mattiucci, S. et al. Metazoan parasitic infections of swordfish (Xiphias gladius) from the Mediterranean Sea and Atlantic Gibraltar waters: implications for stock assessment. Col. Vol. Sci. Pap. ICCAT 58(4), 1470–1482 (2005).
Google Scholar 
93.Di Azevedo, M. I. N. & Iñiguez, A. M. Nematode parasites of commercially important fish from the southeast coast of Brazil: morphological and genetic insight. Int. J. Food Microbiol. 267, 29–41. https://doi.org/10.1016/j.ijfoodmicro.2017.12.014 (2018).Article 
PubMed 

Google Scholar 
94.Pekmezci, G. Z., Yardimci, B., Onuk, E. E. & Umur, S. Molecular characterization of Hysterothylacium fabri (Nematoda: Anisakidae) from Zeus faber (Pisces: Zeidae) caught off the Mediterranean coasts of Turkey based on nuclear ribosomal and mitochondrial DNA sequences. Parasitol. Int. 63(1), 127–131. https://doi.org/10.1016/j.parint.2013.10.006 (2014).CAS 
Article 
PubMed 

Google Scholar 
95.Zhao, J. Y., Zhao, W. T., Ali, A. H., Chen, H. X. & Li, L. Morphological variability, ultrastructure and molecular characterisation of Hysterothylacium reliquens (Norris & Overstreet, 1975) (Nematoda: Raphidascarididae) from the oriental sole Brachirus orientalis (Bloch & Schneider) (Pleuronectiformes: Soleidae). Parasitol. Int. 66(1), 831–838. https://doi.org/10.1016/j.parint.2016.09.012 (2016).Article 
PubMed 

Google Scholar  More

Collection of marine organismsMarine invertebrates such as Corallium rubrum are ideal organisms to perform controlled experiments and to gather useful information on a variety of environmental conditions74. This species, whose diet is based on small zooplankton captured with the polyp tentacles, has been already used in long-term experiments74,75,76. Coral specimens were collected in March 2017 at ca. 35-m depth in the Marine Protected Area of Portofino (Punta del Faro, 44°17′41.02 N; 9°13′31.30 E) in the Ligurian Sea (North-Western Mediterranean Sea) by scuba divers (using TRIMIX blending).Experimental designAfter recovery, the coral specimens were brought to the laboratory and maintained in a tank (30 L) at in situ temperature (13 ± 0.8 °C) and subjected to the continuous flux of natural seawater filtered onto 0.7-µm pore-size membranes (micro-glass fibre paper, Munktell) by using two submersible pumps (Euronatale, 203 V, 50 Hz, 4 Watt).Sixty coral branches obtained from different colonies, with similar morphology, and a surface of ~2 cm2 each, were distributed among 12 experimental tanks, in order to have 5 coral branches per tank (12 L glass tanks, containing on average, 274 ± 26.4 coral polyps each). The corals were acclimatised for 20 days in a temperature-controlled room, and dim light conditions, before starting experiments. Each tank, filled with natural seawater, was equipped with a prefiltered (0.2 µm) channelled aeration system combined with motor-driven paddles in order to create convective currents, which allowed the resuspension of the microplastic mixture, thus ensuring as much as possible a homogeneous distribution of the polymers. This experimental system was designed and set up according to Sutherland et al.77. To assess the potential effects of increasing microplastic  microparticles L−1 (here defined as low, medium and high concentrations of microplastic particles). We also quantified the exact amount of particles actually interacting with the corals, by discounting the fractions loss due to experimental manipulations (see details in Supplementary Methods). According to the results reported in the Supplementary Results, the systems were responsible for the loss of ca. 40% of the microplastic particles, thus the corals in experimental systems were actually exposed to 60, 300 and 600 microplastic particles per litre (to which we referred the low, medium and high concentrations).The highest concentrations of microplastics (up to 600 microplastic particles per litre) can reflect future contamination on the basis of estimates obtained by numerical models9, whereas the low and medium concentrations have been selected to represent highly-contaminated marine habitats, including the areas where the corals were collected (Ligurian Sea)78,79. In particular, for the Ligurian Sea, we estimated an average value of 94 microplastic particles L−1, based on the concentrations of microplastic particles ( >200 µm) determined by Fossi et al.78,79, and the most cautionary correction factor (105) calculated by Brandon et al.10 for the unaccounted smaller fraction of microplastics (25–75% of the fragments falling approximately in the 20–100 µm dimensional class with median range: 59–116).Microplastic mixtures were also prepared considering the concentration and composition of dominant polymers in different coastal marine environments, especially in hot spots of microplastic contamination5,7,8.The microplastic mixture added to the tanks was composed of 76.6% polyethylene, 10.9% polypropylene, 7.3% polystyrene, 3.3% polyvinylchloride and 1.8% polyethylene terephthalate particles. These particles were obtained by milling plastic objects from everyday life (i.e., containers, bottles, cups, pipes) according to Paul-Pont et al.8 (Supplementary Table 5). Plastic milling was carried out under a laminar flow hood in chilled sterilized and 0.02 µm prefiltered milliQ water. All the tools used for handling plastics were pre-treated with 1% sodium hypochlorite in water and rinsed 10 times with sterilized and 0.02 µm prefiltered milliQ water, and then dried under laminar flow hood. Details on the preparation of microplastic mixtures are reported in the Supplementary Methods.The low, medium and high concentrations of microplastics were added in triplicate tanks (n = 3 for each concentration). Additional systems containing seawater and coral branches without microplastics (n = 3, here defined Controls), and seawater added with microplastics (at the highest concentration) without red corals (n = 3, here define CTRL MPs) were used as controls. Overall, the experimental setup comprised 15 tanks.The experiments for assessing the impact of microplastics on red corals started immediately after the microplastic mixture addition (time 0). During the experiment, seawater temperature (range: 13.10 ± 0.01–13.13 ± 0.05 °C), salinity (range: 38.35 ± 0.18–38.65 ± 0.18) and oxygen levels (7.10 ± 0.08–7.36 ± 0.2 mg L−1) were monitored daily in all tanks using a probe (YSI Professional Plus, USA) and corals were fed three times a week with 103 Artemia salina nauplii L−1.After ten days the condition of corals that were exposed to microplastics was deteriorating, so we collected one coral branch from each tank for molecular analyses (i.e., associated microbiome, gene expression and DNA damage). After 14 days, the experiment was stopped because the coral branches that were exposed to the medium and high microplastic particle concentrations were completely wrapped in mucus, with a large portion of damaged tissue and without polyp activity, therefore corals were defined dead (overall 12 branches, see ‘Results’ section for details). Coral branches in the controls showed no visible signs of necrosis or other macroscopic stress. The tissue remained intact, and the colour unchanged until the end of the experiment.Effects of microplastic ingestionCoral feeding activityTo assess the impact of microplastics on feeding activity of C. rubrum, analyses based on the use of Artemia salina were performed after 2 and 10 days from the start of the experiment (t0) in replicate systems (n = 3 for each treatment, n = 3 for the controls) according to standard international protocols80. The nauplii of Artemia salina were reared in the laboratory, incubating 0.5 g of cysts (Ocean Nutrition) in 1 L of seawater filtered onto 0.2-µm filter in a separatory funnel, 2 days before the analysis of feeding rate. At hatching, nauplii were counted and maintained in vials to obtain the concentration of 1000 nauplii L−1. To avoid stress, corals (one branch for each tank) were transferred underwater to beakers along with 1 L seawater of each tank. After addition of live A. salina nauplii (1000 nauplii L−1) to the 1 L beakers containing the coral branches and to the controls, three aliquots of 10 ml seawater were collected after ~30 s from the start of the experiment (t0) and after 2 and 4 h. The remaining nauplii present in each seawater aliquot were counted under a stereomicroscope at ×3.2 magnification (Zeiss Stami 2000). Mean ingestion rates (nauplii removed h−1) were determined by linear regression analysis.Accumulation of microplastics by C. rubrum
To investigate the accumulation of plastic polymers by C. rubrum polyps, the number of microplastic particles ingested by coral polyps was evaluated after 14 days of exposure to microplastic mixture, by dissolving polyps and skeleton of the corals (one for each tank at the concentration of 1000 microplastic particles L−1) using an acid/base digestion protocol36 with some modifications.To exclude biases on the estimate of the number of microplastic particles actually accumulated within the polyps, coral branches were accurately rinsed with milliQ water and checked under stereomicroscope (at ×50 magnification) for the potential presence of microplastic particles adherent to the coral tissue. Coral branches were then soaked in 5 ml of 4.5% sodium hypochlorite (NaClO) for 24 h and dissolved in 5 ml of 37% HCl for 30 min. Particulate material was retained on a 0.2-μm filter in a vacuum filtration system, and microplastic particles were counted under a stereomicroscope at ×50 magnification. The chemical composition of the polymers ingested by corals was confirmed by FT-IR analyses (Perkin Elmer, software Packages Spectrum 5.3.1). To evaluate possible damage to plastic polymers due to the use of acid/base solutions, we exposed polypropylene, polyethylene, polystyrene, polyvinylchloride and polyethylene terephthalate at the same volume and concentration of NaClO and HCl during digestion of the coral.Potential transfer of microplastics by zooplanktonWhile testing the exposure of the red corals to microplastics, we also determined the rate of microplastic ingestion by A. salina nauplii used to feed the red corals, in order to assess their role as potential vectors of microplastics. To do this, additional tanks (n = 3) were added with 0.2 µm prefiltered 12 L natural seawater, 1000 nauplii L−1 of A. salina and the same microplastic mixture used for the experiment on the red corals (at the highest concentration). Three other tanks were used as controls containing 0.2 µm prefiltered 12 L natural seawater and 1000 nauplii L−1 of A. salina.Microplastic ingestion by A. salina was determined after 2 and 10 days of experiment following the enzymatic digestion protocol previously developed81 with some modifications. Such a procedure degrades biological tissues without affecting shape, colour and composition of plastic fragments. Gut contents of 100 individuals of A. salina (n = 5) were assessed under a stereomicroscope (Leica MZ125) and light microscope (Zeiss Axiovert 200) and photographed with a Zeiss Axiocam digital camera. Afterwards, nauplii were processed immediately according to the modified enzymatic digestion protocol. Nauplii were dried in an oven for 3 h at 60 °C, transferred to glass jars containing a buffer homogenizing solution (400 mM Tris-HCl pH 8, 60 mM EDTA pH 8, 5 M NaCl, SDS 1%) incubated at 50 °C for 15 min and exposed to Proteinase K (1 mg ml−1). Then, samples were dried for 2 h at 50 °C, homogenized and re-incubated at 60 °C for 20 min and sonicated on ice (1–2 min) three times. After digestion, the microplastic-containing suspensions were placed in Utermöhl chambers and the microplastics were examined at the inverted light microscope (Leica DMI3000-Bat ×200 magnification) and counted. Microplastics obtained from nauplii digested after 10 days of incubation were also measured and categorized by colours and shape to evaluate the numbers and the size spectra of microplastics ingested by A. salina during the experiment.Physical impact on coral coenenchymaScanning electron microscopy (SEM) analysesTo investigate the physical damage of the microplastic mixture on the coral tissues, samples (one branch from each tank including the control) were collected before the start of the experiment (t0), after 7 days and at the end of the experiment and prepared for SEM analyses according to standard protocols82 with some modifications. Coral branches were stored in 0.7 µm prefiltered seawater with 4% buffered formalin. After 24 h, samples were washed with 0.7 µm prefiltered seawater and dehydrated for 3 h in 20% ethanol. After 3 h they were washed in the same way and dehydrated in ethanol 50%. After 3 h, samples were stored in 70% ethanol. Samples were stored at +4 °C. We dehydrated samples using different gradients of ethanol solutions (70–80%, 80–90%, 90–95%, 95–99% in 2 days)82. Then, samples were dried using HMDS (Hexamethyldisilazane, Aldrich 440191)83. Dried samples were mounted on aluminium stubs using Leit-C glue (conductive carbon cement, Neubauer Chemikalien) and sputter-coated with gold. Samples were examined with a Scanning Electron Microscope (Zeiss SUPRA 40). In addition, the tissue damage percentage was assessed on SEM micrographs at ×200 of magnification by using PhotoQuad v1.4 software84. Such a software for advanced image processing of 2D photographic quadrat samples, dedicated to ecological applications, was used for the analysis of three randomly selected areas from the apex to the base of each coral rotating it on three sides (n = 9). Additional analyses through random SEM observations (n = 20) at 3.00KX to 17.00KX of magnification were carried out to determine prokaryotic cell abundances around lesions of corals (n = 3) exposed to high concentrations of microplastic particles. Data were standardised to the coral surface analysed.Mucus release and trapped microplastics and prokaryotic cellsTo evaluate the first symptoms of coral stress, a photographic report was conducted daily. The abundance of microplastic particles trapped in coral mucus was estimated using an enzymatic digestion protocol81 with some modifications. Mucus produced by corals exposed to higher microplastics concentrations was dried in oven at 60 °C for 12 h. After 12 h, five ml of homogenizing solution was added to the samples and incubated at 50 °C for 15 min. Proteinase K (1 mg mL−1) was added to the samples, which subsequently were incubated at 50 °C for 2 h. Then, samples were homogenized and incubated again at 60 °C for 20 min, after that samples were sonicated three times (three 1-min treatments using a Branson Sonifier 2200; 60 W). After digestion, microplastics-containing suspension was filtered on 0.2-μm filters in a vacuum filtration system (Whatman, Nuclepore). Filters were analysed at stereomicroscope at ×50 magnification (Zeiss Stemi 2000).Stress signals at the molecular levelRNA extraction, cDNA synthesis and gene expression level by qPCRTo assess potential changes in the gene expression pattern of C. rubrum due to microplastics, total RNA was extracted from ca. 20 mg of tissue (wet weight) from one coral branch randomly collected from each treatment (n = 3) and control (n = 3) after 10 days of experiment by using Quick-RNA™ MiniPrep (Zymo Research, Freiburg, Germany) according to the manufacturer’s instructions. Total RNA was also extracted from additional samples of coral branches collected randomly at the beginning of the experiment. Once scraped by surgical disposable scalpels (Braun), coral tissues were placed in new 2 ml sterile tubes and washed three times with phosphate-buffered saline (PBS 1×). Samples were centrifuged at 1800 rpm for 10 min in an Eppendorf® 5810r refrigerated centrifuge using a swing-out rotor at 4 °C and, after removing the supernatant, were homogenized for 5 min with a RNase-free sterile glass stick in RNA lysis buffer. Contaminating DNA was degraded by treating each sample with DNase dissolved in RNase-free water included in the kit. For each sample, 250 ng of total RNA extracted was retrotranscribed with an iScript™ cDNA Synthesis kit (Bio-Rad, Milan, Italy), following the manufacturer’s instructions. The reaction was performed on the Veriti™ 96-Well Thermal Cycler (Applied Biosystem, Monza, Italy). To evaluate the efficiency of cDNA synthesis, a PCR was performed with primers of the reference gene, cytochrome oxidase I (COI, Supplementary Table 6). The reaction was carried out using MyTaq™ HS DNA Polymerase (Bioline, Luckenwalde, Germany) on the Veriti™ 96-Well Thermal Cycler (Applied Biosystem, Monza, Italy). The PCR programme consisted of a denaturation step at 95 °C for 1 min, 35 cycles at 95 °C for 45 s, 60 °C for 45 s, and 72 °C for 45 s and a final extension step at 72 °C for 10 min.The expression levels of the six genes of hsp70, hsp60, MnSOD, mtMutS, EF1 and cytb, involved in a broad range of functional responses, such as stress, detoxification processes, and DNA repair, were followed by real-time qPCR to identify potential stress of corals exposed to microplastics61. For the cytb, target-specific primer pairs were designed with the Primer 3 software (http://primer3.ut.ee85) using nucleotide sequences retrieved from the GenBank database for C. rubrum as template (https://www.ncbi.nlm.nih.gov/genbank/; Supplementary Table 6). SensiFAST™ SYBR® & Fluorescein mix (Bioline, Luckenwalde, Germany) were used for measuring the levels of mRNAs on CFX Connect™ Real-Time PCR detection system (Biorad, Milan, Italy). Fluorescence was measured using CFX Manager™ software (Biorad, Milan, Italy). All genes tested by qPCR in this study were amplified with primers purchased from Life Technologies/Thermo Fisher Scientific (Milan, Italy). The fold change in target gene mRNA expression of corals exposed to microplastics compared with the control was calculated using the comparative CT method using the 2−ΔΔCt equation86. COI was used as reference gene for normalising the gene expression analyses.DNA oxidative damageFor evaluating oxidative DNA damage potentially due to microplastic exposure on C. rubrum, the content of 8-hydroxydeoxyguanosine (8-OHdG) was analysed. DNA was extracted from 20 mg (wet weight) of tissue randomly collected from one coral branch for each treatment (n = 3) and control (n = 3) after 10 days of experiment using DNeasy Blood & Tissue Kits (Qiagen, Valencia, CA) and following the manufacturer’s protocol. Finally, samples were kept at −20 °C before subsequent analyses. Nucleic acids extracted (2 μg) were transferred into new 2-ml tubes and incubated for 5 min at 95 °C, then rapidly chilled on ice. Samples were digested to nucleosides by incubating the denatured DNA in sodium acetate 20 mM, pH 5.2 with 2 μl of nuclease P1 (6 U/μl; Merck KGaA, Darmstadt, Germany) for 2 h at 37 °C. Each sample was then incubated with 5 μl alkaline phosphatase (1 U/μl; Roche, Mannheim, Germany) in Tris-HCl 100 mM, pH 7.5 for 1 h at 37 °C. The reaction mixtures were then centrifuged for 5 min at 6000 × g and the supernatants tested for DNA oxidation with an OxiSelect™ Oxidative DNA Damage ELISA Kit (8-OHdG Quantitation; Cell Biolabs, CA, USA). As positive control, Escherichia coli genomic DNA (2 μg) was incubated in a final concentration of 50 and 100 mM H2O2 overnight at 37 °C, and subsequently tested.Prokaryotic abundance in coral mucus and surrounding seawaterTo highlight possible effects in terms of prokaryotic contamination associated with the exposure of the corals to microplastics, we determined prokaryotic abundances in the mucus released by C. rubrum and the surrounding seawater.Prokaryotic abundances in the coral mucus collected from each tank (except for the control where coral mucus was not released) after 14 days of the experiment, were analysed by epifluorescence microscopy. The extraction of prokaryotic cells from the mucus (ca. 1 mL for each tank) was performed using pyrophosphate (final concentration, 5 mM) and ultrasound treatment (three 1-min treatments using a Branson Sonifier 2200; 60 W)87. Then, samples were diluted from 50- to 100-fold with sterile water filtered onto 0.2-μm pore-size filters (Anodisc filters; black-stained polycarbonate). The filters were stained using SYBR Green I (10,000× in anhydrous dimethyl sulfoxide, Molecular Probes-Invitrogen) diluted 1:20 in prefiltered TE buffer (pH 7.5) and incubated in the dark for 20 min; a drop (20 µl) of antifade solution (composed of 50% 6.7 mmol L−1 phosphate buffer at pH 7.8 and 50% glycerol with the addition of 0.5% ascorbic acid) was laid both on a glass slide and on the filter mounted on it. Prokaryotic counts were performed under epifluorescence microscopy (magnification, ×1000; Zeiss filter set #09, 488009-9901-000, excitation BP 450–490 nm, beam splitter FT 515, emission LP 520), by examining at least 20 fields per slide and counting at least 400 cells per filter.For the determination of prokaryote abundance in seawater surrounding corals, three replicates of 10 ml of seawater were collected from each tank. Total prokaryotic abundance was determined according to Danovaro87. Samples were filtered onto 0.2-μm pore-size filters (Anodisc black-stained polycarbonate filters, Whatman) into a funnel with vacuum pressure no greater than 20 kPa (or 150 mmHg) to avoid cell damage. When the sample had passed through, filters were stained with 20 µl of SYBR Green I (10,000× in anhydrous dimethyl sulfoxide, Molecular Probes-Invitrogen) diluted 1:20 in prefiltered TE buffer (pH 7.5) and incubated in the dark for 20 min. Then, to remove the excess stain, filters were washed three times using 3 ml of Milli-Q water; a drop (20 µl) of antifading solution (composed of 50% 6.7 mmol L−1 phosphate buffer at pH 7.8 and 50% glycerol with the addition of 0.5% ascorbic acid) was laid both on a glass slide and on the filter mounted on it. Prokaryotic counts were carried out as described above.Microbiome of corals exposed to microplasticsThe coral microbiome was analysed immediately before the start of the experiment (before the addition of microplastics) and after 10 days of the experiment, both in replicated coral branches exposed to microplastics and in unexposed corals (Control t10). For the analysis of the microbiome, ca. 20 mg of tissue (wet weight) from one coral branch randomly collected from two tanks of each treatment and control was scraped from the skeleton by using surgical disposable scalpels (Braun) and DNA extraction was performed using the QIAGEN DNeasy Blood & Tissue Kit. Briefly, samples were digested with proteinase K at 56 °C overnight or until the tissue was completely lysed, then samples were processed following the manufacturer’s protocol. Finally, samples were held at −20 °C before PCR amplification and sequencing. The molecular size of the DNA extracts was analysed by agarose gel electrophoresis (1%) and the amount and purity of DNA were determined by Nanodrop spectrophotometer (ND-1000). For PCR amplification of the 16S V3 region, the Bacteria-specific primer pair 805R/341F was chosen with Illumina-specific adapters and barcodes. Sequencing was performed on an Illumina MiSeq platform by LGC Genomics GmbH (Berlin, Germany).Raw sequencing paired-end reads were first joined using the bbmerge tool from the BBMap suite88 in a two-step process: reads that did not merge in a first step were quality-trimmed to remove low-quality bases (Q  More

1.Dias, M. P. et al. Threats to seabirds: A global assessment. Biol. Cons. 237, 525–537 (2019).Article 

Google Scholar 
2.Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).PubMed 
Article 

Google Scholar 
3.Michael, P. E. et al. Illegal fishing bycatch overshadows climate as a driver of albatross population decline. Mar. Ecol. Prog. Ser. 579, 185–199 (2017).ADS 
Article 

Google Scholar 
4.Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecol. Model. 415, 108837 (2020).Article 

Google Scholar 
5.Rayner, M. J. et al. Predictive habitat modelling for the population census of a burrowing seabird: A study of the endangered Cook’s petrel. Biol. Cons. 138, 235–247 (2007).Article 

Google Scholar 
6.Habeeb, R. L., Trebilco, J., Wotherspoon, S. & Johnson, C. R. Determining natural scales of ecological systems. Ecol. Monogr. 75, 467–487 (2005).Article 

Google Scholar 
7.Li, G. D., Sun, S. A. & Fang, C. L. The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis. Landscape Urban Plan. 174, 63–77 (2018).Article 

Google Scholar 
8.Ranjeva, S. L. et al. Untangling the dynamics of persistence and colonization in microbial communities. ISME J. 13, 2998–3010 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Thurman, L. L., Barner, A. K., Garcia, T. S. & Chestnut, T. Testing the link between species interactions and species co-occurrence in a trophic network. Ecography 42, 1658–1670 (2019).Article 

Google Scholar 
10.Murcia, C. Edge effects in fragmented forests: implications for conservation. Trends Ecol. Evol. 10, 58–62 (1995).CAS 
PubMed 
Article 

Google Scholar 
11.Jonzen, N., Wilcox, C. & Possingham, H. P. Habitat selection and population regulation in temporally fluctuating environments. Am. Nat. 164, 103–114 (2004).Article 

Google Scholar 
12.Coulson, J. C. Difference in the quality of birds nesting in the centre and on the edges of a colony. Nature 217, 478–479 (1968).ADS 
Article 

Google Scholar 
13.Reid, T., Hindell, M., Lavers, J. L. & Wilcox, C. Re-examining mortality sources and population trends in a declining seabird: using Bayesian methods to incorporate existing information and new data. PLoS ONE 8(4), e58230 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Lavers, J. L., Hutton, I. & Bond, A. L. Changes in technology and imperfect detection of nest contents impedes reliable estimates of population trends in burrowing seabirds. Global Ecol. Conserv. 17, e00579 (2019).Article 

Google Scholar 
15.Priddel, D., Carlile, N., Fullagar, P., Hutton, I. & O’Neill, L. Decline in the distribution and abundance of flesh-footed shearwaters (Puffinus carneipes) on Lord Howe Island, Australia. Biol. Cons. 128, 412–424 (2006).Article 

Google Scholar 
16.Baker, G. B. & Wise, G. S. The impact of pelagic longline fishing on the flesh-footed shearwater Puffinus carneipes in Eastern Australia. Biol. Cons. 126, 306–136 (2005).Article 

Google Scholar 
17.Tuck, G. N. & Wilcox, C. Assessing the potential impacts of fishing on the Lord Howe Island population of flesh-footed shearwaters 86 (Australian Fisheries Management Authority and CSIRO Marine and Atmospheric Research, 2010).
Google Scholar 
18.Carlile, N., Priddel, D., Reid, T. & Fullager, P. Flesh-footed shearwater decline on Lord Howe Island: rebuttal to Lavers et al 2019. Global Ecol. Conserv. 20, 1–3 (2019).
Google Scholar 
19.Lavers, J. L. Population status and threats to flesh-footed shearwater (Puffinus carneipes) in Western and South Australia. ICES J. Mar. Sci. 72, 316–327 (2014).Article 

Google Scholar 
20.Carey, M. J. The effects of investigator disturbance on procellariiform seabirds: a review. N. Z. J. Zool. 36, 367–377 (2009).Article 

Google Scholar 
21.Carey, M. J. Investigator disturbance reduces reproductive success in Short-tailed Shearwaters Puffinus tenuirostris. Ibis 153, 363–372 (2011).Article 

Google Scholar 
22.Orr, J. A. et al. Towards a unified study of multiple stressors: divisions and common goals across research disciplines. Proc. R. Soc. B Biol. Sci. 287, 20200421. https://doi.org/10.1098/rspb.2020.0421 (2020).Article 

Google Scholar 
23.Piggott, J. J., Townsend, C. R. & Matthael, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5(7), 1538–1547 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
24.Ormerod, S. J., Dobson, M., Hildrew, A. G. & Townsend, C. R. Multiple stressors in freshwater ecosystems. Freshw. Biol. 55(Suppl. 1), 1–4. https://doi.org/10.1111/j.1365-2427.2009.02395.x (2010).Article 

Google Scholar 
25.Powell, C. D. L. Foraging movements and the migration trajectory of Flesh-footed Shearwaters Puffinus carneipes from the south coast of Western Australia. Mar. Ornithol. 37, 115–120 (2009).
Google Scholar 
26.Rexer-Huber, K., Parker, G. C., Ryan, P. G. & Cuthbert, R. J. Burrow occupancy and population size in the Atlantic Petrel Pterodroma incerta: a comparison of methods. Mar. Ornithol. 42, 137–141 (2014).
Google Scholar 
27.Rebstock, G. A., Boersma, P. D. & Garcia-Barbaroglu, P. Changes in habitat use and nesting density in a declining seabird Colony. Popul. Ecol. 58, 105–119 (2016).Article 

Google Scholar 
28.Ponchon, A. et al. When things go wrong: intra-season dynamics of breeding failure in a seabird. Ecosphere 5(1), 4. https://doi.org/10.1890/ES13-00233.1 (2014).Article 

Google Scholar 
29.Jackson, A. L., Bearhop, S. & Thompson, D. R. Shape can influence the rate of colony fragmentation in ground nesting seabirds. Oikos 111, 473–478 (2005).Article 

Google Scholar 
30.Martinez-Abrain, A. Why do ecologists aim to get positive results? Once again, negative results are necessary for better knowledge accumulation. Anim. Biodivers. Conserv. 36, 33–36 (2013).Article 

Google Scholar 
31.Gales, R., Brothers, N. & Reid, T. Seabird mortality in the Japanese longline tuna fishery around Australia, 1988–1995. Biol. Cons. 86, 37–56 (1997).Article 

Google Scholar 
32.Trebilco, R. et al. Characterizing seabird bycatch in the eastern Australian tuna and billfish pelagic longline fishery in relation to temporal, spatial and biological influences. Aquat. Conserv. Mar. Freshwat. Ecosyst. 20, 531–542 (2010).Article 

Google Scholar 
33.Chan, K. M. A. Value and advocacy in conservation biology: crisis discipline or discipline in crisis. Conserv. Biol. 22, 1–3 (2008).PubMed 
Article 

Google Scholar 
34.Hindwood, K. A. The birds of Lord Howe Island. Emu 40, 1–86 (1940).Article 

Google Scholar 
35.McDougall, I., Embleton, B. J. J. & Stone, D. B. Origin and evolution of Lord Howe Island, Southwest Pacific Ocean. J. Geol. Soc. Aust. 28, 155–176 (1981).CAS 
Article 

Google Scholar 
36.Pickard, J. Vegetation of Lord Howe Island. Cunninghamia 1, 133–265 (1983).
Google Scholar 
37.Marchant, S. & Higgins, P. J. (eds) Handbook of Australian, New Zealand and Antarctic Birds. Ratites to Ducks Vol. 1 (Oxford University Press, 1990).
Google Scholar 
38.Serventy, D. L. & Whittell, H. M. A Handbook of the Birds of Western Australia 2nd edn. (Paterson Brokensha Pty., Ltd, 1951).
Google Scholar 
39.Powell, C. D. L., Wooller, R. D. & Bradley, J. S. Breeding biology of the flesh-footed shearwater (Puffinus carneipes) on Woody Island, Western Australia. Emu 107, 275–283 (2007).Article 

Google Scholar 
40.Reid, T. A. et al. Nonbreeding distribution of flesh-footed shearwaters and the potential for overlap with north Pacific fisheries. Biol. Cons. 166, 3–10 (2013).Article 

Google Scholar 
41.Lombal, A. J. et al. Genetic divergence between colonies of flesh-footed shearwaters Ardenna carneipes exhibiting different foraging strategies. Conserv. Genet. 9, 27–41 (2018).Article 

Google Scholar 
42.Carlile, N. & Priddel, D. Seabird islands No. 261: Mutton Bird Island, Lord Howe Group, New South Wales. Corella 37(4), 94–96 (2013).
Google Scholar 
43.Carlile, N., Priddel, D. & Bower, H. Seabird islands No. 256: Roach Island, Lord Howe Group, New South Wales. Corella 37(4), 82–85 (2013).
Google Scholar 
44.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/. (2015)45.Wood, S. N. Generalised Additive Models: An Introduction with R (Chapman and Hall/CRC, 2006).
Google Scholar 
46.Burnham, K. R. & Anderson, D. R. Model Selection and Multimodal Inference: A Practical Information Theoretic Approach (Springer, 2002).
Google Scholar 
47.Barton, K. MuMIn: Multi-Model Inference. R package version 1.15.1. http://CRAN.R-project.org/package=MuMIn (2015).48.Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5 (2), https://cran.r-project.org/doc/Rnews/.(2005).49.Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn. (Springer, 2013).
Google Scholar  More

1.Bartz, R. & Kowarik, I. Assessing the environmental impacts of invasive alien plants: a review of assessment approaches. Neobiota https://doi.org/10.3897/neobiota.43.30122 (2019).Article 

Google Scholar 
2.Chen, C. et al. Historical introduction, geographical distribution, and biological characteristics of alien plants in China. Biodivers. Conserv. 26, 353–381. https://doi.org/10.1007/s10531-016-1246-z (2017).Article 

Google Scholar 
3.Feng, J. & Zhu, Y. Alien invasive plants in China: risk assessment and spatial patterns. Biodivers. Conserv. 19, 3489–3497. https://doi.org/10.1007/s10531-010-9909-7 (2010).Article 

Google Scholar 
4.Thapa, S., Chitale, V., Rijal, S. J., Bisht, N. & Shrestha, B. B. Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya. Plos One https://doi.org/10.1371/journal.pone.0195752 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
5.Majewska, M. L. et al. Do the impacts of alien invasive plants differ from expansive native ones? An experimental study on arbuscular mycorrhizal fungi communities. Biol. Fertil. Soils 54, 631–643. https://doi.org/10.1007/s00374-018-1283-8 (2018).Article 

Google Scholar 
6.Shi, J., Luo, Y.-Q., Zhou, F. & He, P. The relationship between invasive alien species and main climatic zones. Biodivers. Conserv. 19, 2485–2500. https://doi.org/10.1007/s10531-010-9855-4 (2010).Article 

Google Scholar 
7.Hulme, P. E. Trade, transport and trouble: managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18. https://doi.org/10.1111/j.1365-2664.2008.01600.x (2009).Article 

Google Scholar 
8.Hulme, P. E. et al. Grasping at the routes of biological invasions: a framework for integrating pathways into policy. J. Appl. Ecol. 45, 403–414. https://doi.org/10.1111/j.1365-2664.2007.01442.x (2008).Article 

Google Scholar 
9.Jara-Guerrero, A., De la Cruz, M. & Mendez, M. Seed dispersal spectrum of woody species in south ecuadorian dry forests: environmental correlates and the effect of considering species abundance. Biotropica 43, 722–730. https://doi.org/10.1111/j.1744-7429.2011.00754.x (2011).Article 

Google Scholar 
10.van Oudtshoorn, K. v. R. & van Rooyen, M. W. Dispersal biology of desert plants. (Springer 1999).11.Liu, J., Liang, S. C., Liu, F. H., Wang, R. Q. & Dong, M. Invasive alien plant species in China: regional distribution patterns. Divers. Distrib. 11, 341–347. https://doi.org/10.1111/j.1366-9516.2005.00162.x (2005).Article 

Google Scholar 
12.Caughlin, T. T., Ferguson, J. M., Lichstein, J. W., Bunyavejchewin, S. & Levey, D. J. The importance of long-distance seed dispersal for the demography and distribution of a canopy tree species. Ecology 95, 952–962. https://doi.org/10.1890/13-0580.1 (2014).Article 
PubMed 

Google Scholar 
13.Nathan, R. et al. Mechanisms of long-distance seed dispersal. Trends Ecol. Evol. 23, 638–647. https://doi.org/10.1016/j.tree.2008.08.003 (2008).Article 
PubMed 

Google Scholar 
14.Wang, R. et al. Multiple mechanisms underlie rapid expansion of an invasive alien plant. New Phytol. 191, 828–839. https://doi.org/10.1111/j.1469-8137.2011.03720.x (2011).Article 
PubMed 

Google Scholar 
15.Vittoz, P. & Engler, R. Seed dispersal distances: a typology based on dispersal modes and plant traits. Bot. Helv. 117, 109–124. https://doi.org/10.1007/s00035-007-0797-8 (2007).Article 

Google Scholar 
16.Willson, M. F., Rice, B. L. & Westoby, M. Seed dispersal spectra – a comparison of temperate plant-communities. J. Veg. Sci. 1, 547–562. https://doi.org/10.2307/3235789 (1990).Article 

Google Scholar 
17.Nilsson, C., Brown, R. L., Jansson, R. & Merritt, D. M. The role of hydrochory in structuring riparian and wetland vegetation. Biol. Rev. 85, 837–858. https://doi.org/10.1111/j.1469-185X.2010.00129.x (2010).Article 
PubMed 

Google Scholar 
18.Eminniyaz, A. et al. Dispersal Mechanisms of the Invasive Alien Plant Species Buffalobur (Solanum rostratum) in Cold Desert Sites of Northwest China. Weed Sci. 61, 557–563. https://doi.org/10.1614/ws-d-13-00011.1 (2013).CAS 
Article 

Google Scholar 
19.Soons, M. B., Heil, G. W., Nathan, R. & Katul, G. G. Determinants of long-distance seed dispersal by wind in grasslands. Ecology 85, 3056–3068. https://doi.org/10.1890/03-0522 (2004).Article 

Google Scholar 
20.Tackenberg, O. Modeling long-distance dispersal of plant diaspores by wind. Ecol. Monogr. 73, 173–189. https://doi.org/10.1890/0012-9615(2003)073[0173:mldopd]2.0.co;2 (2003).Article 

Google Scholar 
21.Wallace, H. M., Howell, M. G. & Lee, D. J. Standard yet unusual mechanisms of long-distance dispersal: seed dispersal of Corymbia torelliana by bees. Divers. Distrib. 14, 87–94. https://doi.org/10.1111/j.1472-4642.2007.00427.x (2008).Article 

Google Scholar 
22.Soons, M. B., Nathan, R. & Katul, G. G. Human effects on long-distance wind dispersal and colonization by grassland plants. Ecology 85, 3069–3079. https://doi.org/10.1890/03-0398 (2004).Article 

Google Scholar 
23.Taylor, K., Brummer, T., Taper, M. L., Wing, A. & Rew, L. J. Human-mediated long-distance dispersal: an empirical evaluation of seed dispersal by vehicles. Divers. Distrib. 18, 942–951. https://doi.org/10.1111/j.1472-4642.2012.00926.x (2012).Article 

Google Scholar 
24.Cain, M. L., Milligan, B. G. & Strand, A. E. Long-distance seed dispersal in plant populations. Am. J. Bot. 87, 1217–1227. https://doi.org/10.2307/2656714 (2000).CAS 
Article 
PubMed 

Google Scholar 
25.Thomson, F. J., Moles, A. T., Auld, T. D. & Kingsford, R. T. Seed dispersal distance is more strongly correlated with plant height than with seed mass. J. Ecol. 99, 1299–1307. https://doi.org/10.1111/j.1365-2745.2011.01867.x (2011).Article 

Google Scholar 
26.Zhu, J., Liu, M., Xin, Z., Zhao, Y. & Liu, Z. Which factors have stronger explanatory power for primary wind dispersal distance of winged diaspores: the case of Zygophyllum xanthoxylon (Zygophyllaceae)?. J Plant Ecol 9, 346–356. https://doi.org/10.1093/jpe/rtv051 (2016).Article 

Google Scholar 
27.Jones, F. A. & Muller-Landau, H. C. Measuring long-distance seed dispersal in complex natural environments: an evaluation and integration of classical and genetic methods. J. Ecol. 96, 642–652. https://doi.org/10.1111/j.1365-2745.2008.01400.x (2008).Article 

Google Scholar 
28.Snell, R. S. Simulating long-distance seed dispersal in a dynamic vegetation model. Glob. Ecol. Biogeogr. 23, 89–98. https://doi.org/10.1111/geb.12106 (2014).Article 

Google Scholar 
29.Jongejans, E. & Telenius, A. Field experiments on seed dispersal by wind in ten umbelliferous species (Apiaceae). Plant Ecol. 152, 67–78. https://doi.org/10.1023/a:1011467604469 (2001).Article 

Google Scholar 
30.Guitian, J. & Sanchez, J. M. Seed dispersal spectra of plant-communities in the Iberian Peninsula. Vegetatio 98, 157–164. https://doi.org/10.1007/bf00045553 (1992).Article 

Google Scholar 
31.Ou, H., Lu, C. & O’Toole, D. K. A risk assessment system for alien plant bio-invasion in Xiamen China. J. Environ. Sci. 20, 989–997. https://doi.org/10.1016/s1001-0742(08)62198-1 (2008).Article 

Google Scholar 
32.Huang, Q. Q., Wu, J. M., Bai, Y. Y., Zhou, L. & Wang, G. X. Identifying the most noxious invasive plants in China: role of geographical origin, life form and means of introduction. Biodivers. Conserv. 18, 305–316. https://doi.org/10.1007/s10531-008-9485-2 (2009).Article 

Google Scholar 
33.Liu, J. et al. Invasive alien plants in China: role of clonality and geographical origin. Biol. Invasions 8, 1461–1470. https://doi.org/10.1007/s10530-005-5838-x (2006).Article 

Google Scholar 
34.Xu, H., Wang, J., Qiang, S. & Wang, C. Study of key issues under the convention on biological diversity: alien species invasion, biosafety, genetic resources. (Science Press, 2004).35.Ma, J. & Li, H. The checklist of the alien invasive plants in China. (Higher Education Press, 2018).36.Wang, C., Liu, J., Xiao, H., Zhou, J. & Du, D. Floristic characteristics of alien invasive seed plant species in China. Anais Da Academia Brasileira De Ciencias 88, 1791–1797. https://doi.org/10.1590/0001-3765201620150687 (2016).Article 
PubMed 

Google Scholar 
37.Xie, Y., Li, Z. Y., Gregg, W. P. & Dianmo, L. Invasive species in China – an overview. Biodivers. Conserv. 10, 1317–1341 (2001).Article 

Google Scholar 
38.Qi, W., Liu, S. H., Zhao, M. F. & Liu, Z. China’s different spatial patterns of population growth based on the “Hu Line”. J. Geog. Sci. 26, 1611–1625. https://doi.org/10.1007/s11442-016-1347-3 (2016).Article 

Google Scholar 
39.Chen, M. X., Gong, Y. H., Li, Y., Lu, D. D. & Zhang, H. Population distribution and urbanization on both sides of the Hu Huanyong Line: answering the Premier’s question. J. Geog. Sci. 26, 1593–1610. https://doi.org/10.1007/s11442-016-1346-4 (2016).Article 

Google Scholar 
40.Pan, X. B. et al. Spatial similarity in the distribution of invasive alien plants and animals in China. Nat. Hazards 77, 1751–1764. https://doi.org/10.1007/s11069-015-1672-3 (2015).Article 

Google Scholar 
41.Yan, X. et al. The categorization and analysis on the geographic distribution patterns of Chinese alien invasive plants. Biodiv. Sci. 22, 667–676 (2014).Article 

Google Scholar 
42.Wang, G., Bai, F. & Sang, W. Spatial distribution of invasive alien animal and plant species and its influencing factors in China. Plant Sci. J. 35, 513–524 (2017).
Google Scholar 
43.Weber, E., Sun, S. G. & Li, B. Invasive alien plants in China: diversity and ecological insights. Biol. Invasions 10, 1411–1429. https://doi.org/10.1007/s10530-008-9216-3 (2008).Article 

Google Scholar 
44.Zhou, Q. et al. Geographical distribution and determining factors of different invasive ranks of alien species across China. Sci. Total Environ. 722, 137929. https://doi.org/10.1016/j.scitotenv.2020.137929 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
45.Ding, J., Mack, R. N., Lu, P., Ren, M. & Huang, H. China’s booming economy is sparking and accelerating biological invasions. Bioscience 58, 317–324. https://doi.org/10.1641/b580407 (2008).Article 

Google Scholar 
46.Pysek, P. et al. Alien plants in checklists and floras: towards better communication between taxonomists and ecologists. Taxon 53, 131–143. https://doi.org/10.2307/4135498 (2004).Article 

Google Scholar 
47.Zeng, C. & Chen, W. A forecasting model of urban underground space development intensity. Chin. J. Undergr. Space Eng. 14, 1154–1160 (2018).
Google Scholar 
48.Shao, M. N. et al. Outbreak of a new alien invasive plant Salvia reflexa in north-east China. Weed Res. 59, 201–208. https://doi.org/10.1111/wre.12357 (2019).Article 

Google Scholar 
49.Wan, F. H. et al. Invasive mechanism and control strategy of Ageratina adenophora (Sprengel). Sci. China-Life Sci. 53, 1291–1298. https://doi.org/10.1007/s11427-010-4080-7 (2010).Article 
PubMed 

Google Scholar 
50.Poudel, A. S., Jha, P. K., Shrestha, B. B. & Muniappan, R. Biology and management of the invasive weed Ageratina adenophora (Asteraceae): current state of knowledge and future research needs. Weed Res. 59, 79–92. https://doi.org/10.1111/wre.12351 (2019).Article 

Google Scholar 
51.Datta, A., Schweiger, O. & Kuehn, I. Niche expansion of the invasive plant species Ageratina adenophora despite evolutionary constraints. J. Biogeogr. 46, 1306–1315. https://doi.org/10.1111/jbi.13579 (2019).Article 

Google Scholar 
52.Guo, X., Ren, M. & Ding, J. Do the introductions by botanical gardens facilitate the invasion of Solidago canadensis (Asterceae) in China?. Weed Res. 56, 442–451. https://doi.org/10.1111/wre.12227 (2016).Article 

Google Scholar 
53.Ganneru, S., Shaik, H., Peddi, K. & Mudiam, M. K. R. Evaluating the metabolic perturbations in Mangifera indica (mango) ripened with various ripening agents/practices through gas chromatography – mass spectrometry based metabolomics. J. Sep. Sci. 42, 3086–3094. https://doi.org/10.1002/jssc.201900291 (2019).CAS 
Article 
PubMed 

Google Scholar 
54.Mahandran, V., Murugan, C. M., Marimuthu, G. & Nathan, P. T. Seed dispersal of a tropical deciduous Mahua tree, Madhuca latifolia (Sapotaceae) exhibiting bat-fruit syndrome by pteropodid bats. Glob. Ecol. Conserv. 14, e00396. https://doi.org/10.1016/j.gecco.2018.e00396 (2018).Article 

Google Scholar 
55.Weber, E. & Li, B. Plant invasions in China: What is to be expected in the wake of economic development?. Bioscience 58, 437–444. https://doi.org/10.1641/b580511 (2008).Article 

Google Scholar 
56.Jian, L., Hua, C., Kowarik, I., Zhang, Y. & Wang, R. Plant invasions in China: an emerging hot topic in invasion science. Neobiota 15, 27–41 (2012).Article 

Google Scholar  More