More stories

  • in

    Solutions in microbiome engineering: prioritizing barriers to organism establishment

    1.Inda ME, Broset E, Lu TK, de la Fuente-Nunez C. Emerging frontiers in microbiome engineering. Trends Immunol. 2019;40:952–73.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    2.Lawson CE, Harcombe WR, Hatzenpichler R, Lindemann SR, Loffler FE, O’Malley MA, et al. Common principles and best practices for engineering microbiomes. Nat Rev Microbiol. 2019;17:725–41.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    3.Qiu ZG, Egidi E, Liu HW, Kaur S, Singh BK. New frontiers in agriculture productivity: optimised microbial inoculants and in situ microbiome engineering. Biotechnol Adv. 2019;37:107371.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    4.Enam F, Mansell TJ. Prebiotics: tools to manipulate the gut microbiome and metabolome. J Ind Microbiol Biotechnol. 2019;46:1445–59.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    5.Ke J, Wang B, Yoshikuni Y. Microbiome engineering: synthetic biology of plant-associated microbiomes in sustainable agriculture. Trends Biotechnol. 2021;39:244–61.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    6.Markowiak P, Slizewska K. Effects of probiotics, prebiotics, and synbiotics on human health. Nutrients 2017;9:1021.PubMed Central 
    Article 
    CAS 

    Google Scholar 
    7.Finkel OM, Castrillo G, Paredes SH, Gonzalez IS, Dangl JL. Understanding and exploiting plant beneficial microbes. Curr Opin Plant Biol. 2017;38:155–63.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Kaminsky LM, Trexler RV, Malik RJ, Hockett KL, Bell TH. The inherent conflicts in developing soil microbial inoculants. Trends Biotechnol. 2019;37:140–51.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    9.Kolar CS, Lodge DM. Progress in invasion biology: predicting invaders. Trends Ecol Evol. 2001;16:199–204.PubMed 
    Article 

    Google Scholar 
    10.Cairns J, Heckman JR. Restoration ecology: the state of an emerging field. Annu Rev Environ Resour. 1996;21:167–89.
    Google Scholar 
    11.Wainwright CE, Staples TL, Charles LS, Flanagan TC, Lai HR, Loy X, et al. Links between community ecology theory and ecological restoration are on the rise. J Appl Ecol. 2018;55:570–81.Article 

    Google Scholar 
    12.Mallon CA, Le Roux X, van Doorn GS, Dini-Andreote F, Poly F, Salles JF. The impact of failure: unsuccessful bacterial invasions steer the soil microbial community away from the invader’s niche. ISME J. 2018;12:728–41.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    13.Enders M, Hutt MT, Jeschke JM. Drawing a map of invasion biology based on a network of hypotheses. Ecosphere. 2018;9:e02146.Article 

    Google Scholar 
    14.Catford JA, Jansson R, Nilsson C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers Distrib. 2009;15:22–40.
    Google Scholar 
    15.Wittmann MJ, Metzler D, Gabriel W, Jeschke JM. Decomposing propagule pressure: the effects of propagule size and propagule frequency on invasion success. Oikos 2014;123:441–50.Article 

    Google Scholar 
    16.Hulvey KB, Leger EA, Porensky LM, Roche LM, Veblen KE, Fund A, et al. Restoration islands: a tool for efficiently restoring dryland ecosystems? Restor Ecol. 2017;25:S124–S34.Article 

    Google Scholar 
    17.Funk JL, Hoffacker MK, Matzek V. Summer irrigation, grazing and seed addition differentially influence community composition in an invaded serpentine grassland. Restor Ecol. 2015;23:122–30.Article 

    Google Scholar 
    18.Jones ML, Ramoneda J, Rivett DW, Bell T. Biotic resistance shapes the influence of propagule pressure on invasion success in bacterial communities. Ecology 2017;98:1743–9.PubMed 
    Article 

    Google Scholar 
    19.Albright MBN, Sevanto S, Gallegos Graves LV, Dunbar J. Biotic interactions are more important than propagule pressure in microbial community invasions. Mbio 2020;11:e02089–20.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Vila JCC, Jones ML, Patel M, Bell T, Rosindell J. Uncovering the rules of microbial community invasions. Nat Ecol Evol. 2019;3:1162–71.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Simberloff D. The role of propagule pressure in biological invasions. Annu Rev Ecol Evol Syst. 2009;40:81–102.Article 

    Google Scholar 
    22.Zhou JZ, Ning DL. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:e00002–17.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Comeau Y, Greer CW, Samson R. Role of inoculum preparation and density on the bioremediation of 2,4-D-contaminated soil by bioaugmentation. Appl Microbiol Biotechnol. 1993;38:681–7.Article 
    CAS 

    Google Scholar 
    24.Choudhary S, Schmidt-Dannert C. Applications of quorum sensing in biotechnology. Appl Microbiol Biotechnol. 2010;86:1267–79.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    25.Kreitschitz A, Haase E, Gorb SN. The role of mucilage envelope in the endozoochory of selected plant taxa. Sci Nat-Heidelb. 2021;108:2.Article 
    CAS 

    Google Scholar 
    26.Gornish E, Arnold H, Fehmi J. Review of seed pelletizing strategies for arid land restoration. Restor Ecol. 2019;27:1206–11.Article 

    Google Scholar 
    27.Ali M, Oshiki M, Rathnayake L, Ishii S, Satoh H, Okabe S. Rapid and successful start-up of anammox process by immobilizing the minimal quantity of biomass in PVA-SA gel beads. Water Res. 2015;79:147–57.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    28.Gallien L, Mazel F, Lavergne S, Renaud J, Douzet R, Thuiller W. Contrasting the effects of environment, dispersal and biotic interactions to explain the distribution of invasive plants in alpine communities. Biol Invasions. 2015;17:1407–23.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Cadotte MW, Campbell SE, Li SP, Sodhi DS, Mandrak NE. Preadaptation and naturalization of nonnative species: Darwin’s two fundamental insights into species invasion. Annu Rev Plant Biol. 2018;69:661–84.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    30.Fick SE, Day N, Duniway MC, Hoy-Skubik S, Barger NN. Microsite enhancements for soil stabilization and rapid biocrust colonization in degraded drylands. Restor Ecol. 2020;28:S139–S49.Article 

    Google Scholar 
    31.Vasquez E, Sheley R, Svejcar T. Creating invasion resistant soils via nitrogen management. Invas Plant Sci Man. 2008;1:304–14.Article 
    CAS 

    Google Scholar 
    32.Zhao X, Wang W, Blaine A, Kane ST, Zijlstra RT, Ganzle MG. Impact of probiotic Lactobacillus sp. on autochthonous lactobacilli in weaned piglets. J Appl Microbiol. 2019;126:242–54.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    33.Muthukrishnan R, Hansel-Welch N, Larkin DJ. Environmental filtering and competitive exclusion drive biodiversity-invasibility relationships in shallow lake plant communities. J Ecol. 2018;106:2058–70.Article 

    Google Scholar 
    34.Pereira FC, Berry D. Microbial nutrient niches in the gut. Environ Microbiol. 2017;19:1366–78.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Thompson IP, van der Gast CJ, Ciric L, Singer AC. Bioaugmentation for bioremediation: the challenge of strain selection. Environ Microbiol. 2005;7:909–15.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    36.Bell TH, Bell T. Many roads to bacterial generalism. Fems Microbiol Ecol. 2021;97:fiaa240.37.Campieri M, Rizzello F, Venturi A, Poggioli G, Ugolini F, Helwig U, et al. Combination of antibiotic and probiotic treatment is efficacious in prophylaxis of post-operative recurrence of Crohn’s disease: a randomized controlled study vs mesalamine. Gastroenterology 2000;118:A781–A.Article 

    Google Scholar 
    38.Frese SA, Hutton AA, Contreras LN, Shaw CA, Palumbo MC, Casaburi G, et al. Persistence of supplemented Bifidobacterium longum subsp. infantis EVC001 in breastfed infants. Msphere. 2017;2:e00501–17.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Sasse J, Martinoia E, Northen T. Feed your friends: do plant exudates shape the root microbiome? Trends Plant Sci. 2018;23:25–41.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    40.Shepherd ES, DeLoache WC, Pruss KM, Whitaker WR, Sonnenburg JL. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature 2018;557:434–8.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    41.Shaw AJ, Lam FH, Hamilton M, Consiglio A, MacEwen K, Brevnova EE, et al. Metabolic engineering of microbial competitive advantage for industrial fermentation processes. Science. 2016;353:583–6.PubMed 
    Article 
    CAS 

    Google Scholar 
    42.Umu OCO, Rudi K, Diep DB. Modulation of the gut microbiota by prebiotic fibres and bacteriocins. Micro Ecol Health Dis. 2017;28:1348886.
    Google Scholar 
    43.Sriswasdi S, Yang CC, Iwasaki W. Generalist species drive microbial dispersion and evolution. Nat Commun. 2017;8:1162.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.McNally L, Brown SP. Building the microbiome in health and disease: niche construction and social conflict in bacteria. Philos Trans R Soc B. 2015;370:20140298.Article 

    Google Scholar 
    45.Shahab RL, Brethauer S, Luterbacher JS, Studer MH. Engineering of ecological niches to create stable artificial consortia for complex biotransformations. Curr Opin Biotechnol. 2020;62:129–36.PubMed 
    Article 
    CAS 

    Google Scholar 
    46.Shade A, Peter H, Allison SD, Baho DL, Berga M, Burgmann H, et al. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:417.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Upton RN, Bach EM, Hofmockel KS. Spatio-temporal microbial community dynamics within soil aggregates. Soil Biol Biochem. 2019;132:58–68.Article 
    CAS 

    Google Scholar 
    48.Bezkorovainy A. Probiotics: determinants of survival and growth in the gut. Am J Clin Nutr. 2001;73:399s–405s.PubMed 
    Article 
    CAS 

    Google Scholar 
    49.Tripathi S, Srivastava P, Devi R, Bhadouria R. Influence of synthetic fertilizers and pesticides on soil health and soil microbiology. In: Prasad MNV (ed). Agrochemicals detection, treatment and remediation. (Butterworth-Heinemann, 2020) pp 25-54.50.Dykhuizen DE, Hartl DL. Selection in chemostats. Microbiol Rev. 1983;47:150–68.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Zhao D, Wu SG, Feng WW, Jakovlic I, Tran NT, Xiong F. Adhesion and colonization properties of potentially probiotic Bacillus paralicheniformis strain FA6 isolated from grass carp intestine. Fish Sci. 2020;86:153–61.Article 
    CAS 

    Google Scholar 
    52.Wang XY, Cao ZP, Zhang MM, Meng L, Ming ZZ, Liu JY. Bioinspired oral delivery of gut microbiota by self-coating with biofilms. Sci Adv. 2020;6:eabb1952.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    53.Ali SA, Singh P, Tomar SK, Mohanty AK, Behare P. Proteomics fingerprints of systemic mechanisms of adaptation to bile in Lactobacillus fermentum. J Proteom. 2020;213:103600.Article 
    CAS 

    Google Scholar 
    54.Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, et al. The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev. 2013;88:15–30.PubMed 
    Article 

    Google Scholar 
    55.Funk JL, Cleland EE, Suding KN, Zavaleta ES. Restoration through reassembly: plant traits and invasion resistance. Trends Ecol Evol. 2008;23:695–703.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Northfield TD, Laurance SGW, Mayfield MM, Paini DR, Snyder WE, Stouffer DB, et al. Native turncoats and indirect facilitation of species invasions. Proc Biol Sci. 2018;285:20171936.PubMed 
    PubMed Central 

    Google Scholar 
    57.Gagnon K, Rinde E, Bengil EGT, Carugati L, Christianen MJA, Danovaro R, et al. Facilitating foundation species: the potential for plant-bivalve interactions to improve habitat restoration success. J Appl Ecol. 2020;57:1161–79.Article 

    Google Scholar 
    58.Suez J, Zmora N, Zilberman-Schapira G, Mor U, Dori-Bachash M, Bashiardes S, et al. Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT. Cell 2018;174:1406–23.PubMed 
    Article 
    CAS 

    Google Scholar 
    59.Garcia-Bayona L, Comstock LE. Bacterial antagonism in host-associated microbial communities. Science. 2018;361:eaat2456.PubMed 
    Article 
    CAS 

    Google Scholar 
    60.Maynard DS, Crowther TW, Bradford MA. Competitive network determines the direction of the diversity-function relationship. Proc Natl Acad Sci USA. 2017;114:11464–9.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    61.Feichtmayer J, Deng L, Griebler C. Antagonistic microbial interactions: contributions and potential applications for controlling pathogens in the aquatic systems. Front Microbiol. 2017;8:2192.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Fuchslin HP, Schneider C, Egli T. In glucose-limited continuous culture the minimum substrate concentration for growth, s(min), is crucial in the competition between the enterobacterium Escherichia coli and Chelatobacter heintzii, an environmentally abundant bacterium. ISME J. 2012;6:777–89.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    63.Beaury EM, Finn JT, Corbin JD, Barr V, Bradley BA. Biotic resistance to invasion is ubiquitous across ecosystems of the United States. Ecol Lett. 2020;23:476–82.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Eisenhauer N, Schulz W, Scheu S, Jousset A. Niche dimensionality links biodiversity and invasibility of microbial communities. Funct Ecol. 2013;27:282–8.Article 

    Google Scholar 
    65.Panigrahi P, Parida S, Nanda NC, Satpathy R, Pradhan L, Chandel DS, et al. A randomized synbiotic trial to prevent sepsis among infants in rural India. Nature. 2017;548:407–12.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    66.Perez-Gutierrez RA, Lopez-Ramirez V, Islas A, Alcaraz LD, Hernandez-Gonzalez I, Olivera BCL, et al. Antagonism influences assembly of a Bacillus guild in a local community and is depicted as a food-chain network. ISME J. 2013;7:487–97.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    67.Safferman RS, Morris ME. Evaluation of natural products for algicidal properties. Appl Microbiol. 1962;10:289–92.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    68.Russel J, Roder HL, Madsen JS, Burmolle M, Sorensen SJ. Antagonism correlates with metabolic similarity in diverse bacteria. Proc Natl Acad Sci USA. 2017;114:10684–8.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    69.Long RA, Rowley DC, Zamora E, Liu JY, Bartlett DH, Azam F. Antagonistic interactions among marine bacteria impede the proliferation of Vibrio cholerae. Appl Environ Microbiol. 2005;71:8531–6.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    70.Hecht AL, Casterline BW, Earley ZM, Goo YA, Goodlett DR, Wardenburg JB. Strain competition restricts colonization of an enteric pathogen and prevents colitis. EMBO Rep. 2016;17:1281–91.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    71.Lopez-Igual R, Bernal-Bayard J, Rodriguez-Paton A, Ghigo JM, Mazel D. Engineered toxin-intein antimicrobials can selectively target and kill antibiotic-resistant bacteria in mixed populations. Nat Biotechnol. 2019;37:755–60.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    72.Koskella B. New approaches to characterizing bacteria-phage interactions in microbial communities and microbiomes. Environ Microbiol Rep. 2019;11:15–6.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    73.Soundararajan M, von Bunau R, Oelschlaeger TA. K5 Capsule and lipopolysaccharide are important in resistance to T4 phage attack in probiotic E. coli strain nissle 1917. Front Microbiol. 2019;10:2783.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Thingstad TF. Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems. Limnol Oceanogr. 2000;45:1320–8.Article 

    Google Scholar 
    75.Marsh P, Wellington EMH. Phage-host interactions in soil. FEMS Microbiol Ecol. 1994;15:99–107.Article 
    CAS 

    Google Scholar 
    76.Balogh B, Jones JB, Iriarte FB, Momol MT. Phage therapy for plant disease control. Curr Pharm Biotechnol. 2010;11:48–57.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    77.Foster KR, Bell T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol. 2012;22:1845–50.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    78.Piccardi P, Vessman B, Mitri S. Toxicity drives facilitation between 4 bacterial species. Proc Natl Acad Sci USA. 2019;116:15979–84.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    79.Pascual-Garcia A, Bonhoeffer S, Bell T. Metabolically cohesive microbial consortia and ecosystem functioning. Philos Trans R Soc B. 2020;375:20190245.Article 
    CAS 

    Google Scholar 
    80.Martinez-Harms MJ, Bryan BA, Balvanera P, Law EA, Rhodes JR, Possingham HP, et al. Making decisions for managing ecosystem services. Biol Conserv. 2015;184:229–38.Article 

    Google Scholar 
    81.Kildisheva OA, Dixon KW, Silveira FAO, Chapman T, Di Sacco A, Mondoni A, et al. Dormancy and germination: making every seed count in restoration. Restor Ecol. 2020;28:S256–S65.Article 

    Google Scholar 
    82.Maslo B, Handel SN, Pover T. Restoring beaches for Atlantic coast piping plovers (Charadrius melodus): a classification and regression tree analysis of nest-site selection. Restor Ecol. 2011;19:194–203.Article 

    Google Scholar 
    83.Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50.PubMed 
    Article 
    CAS 

    Google Scholar 
    84.Carr A, Diener C, Baliga NS, Gibbons SM. Use and abuse of correlation analyses in microbial ecology. ISME J. 2019;13:2647–55.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Estes JA, Terborgh J, Brashares JS, Power ME, Berger J, Bond WJ, et al. Trophic downgrading of planet Earth. Science. 2011;333:301–6.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    86.Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. 2014;5:219.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Herren CM, McMahon KD. Keystone taxa predict compositional change in microbial communities. Environ Microbiol. 2018;20:2207–17.PubMed 
    Article 

    Google Scholar 
    88.Trosvik P, de Muinck EJ. Ecology of bacteria in the human gastrointestinal tract-identification of keystone and foundation taxa. Microbiome. 2015;3:44.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Kopp-Hoolihan L. Prophylactic and therapeutic uses of probiotics: a review. J Am Diet Assoc. 2001;101:229–41.PubMed 
    Article 
    CAS 

    Google Scholar 
    90.Woo SL, Pepe O. Microbial consortia: promising probiotics as plant biostimulants for sustainable agriculture. Front Plant Sci. 2018;9:1801.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    91.Wood-Charlson EM, Anubhav, Auberry D, Blanco H, Borkum MI, Corilo YE, et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat Rev Microbiol. 2020;18:313–4.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    92.Brussow H. Probiotics and prebiotics in clinical tests: an update. F1000Res. 2019;8:1157.93.van Nood E, Vrieze A, Nieuwdorp M, Fuentes S, Zoetendal EG, de Vos WM, et al. Duodenal infusion of donor feces for recurrent Clostridium difficile. N Engl J Med. 2013;368:407–15.PubMed 
    Article 
    CAS 

    Google Scholar 
    94.Weingarden AR, Chen C, Bobr A, Yao D, Lu YW, Nelson VM, et al. Microbiota transplantation restores normal fecal bile acid composition in recurrent Clostridium difficile infection. Am J Physiol Gastrointest Liver Physiol. 2014;306:G310–G9.PubMed 
    Article 
    CAS 

    Google Scholar 
    95.Hutchinson MI, Bell TAS, Gallegos-Graves L, Dunbar J, Albright M. Merging fungal and bacterial community profiles via an internal control. Microb Ecol. 2021; e-pub ahead of print 2021; https://doi.org/10.1007/s00248-020-01638-y.96.Nayfach S, Roux S, Seshadri R. A genomic catalog of Earth’s micobiomes. Nat Biotechnol. 2021;39:499–509. al. ePubMed 
    Article 
    CAS 

    Google Scholar 
    97.Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    98.Azubuike CC, Chikere CB, Okpokwasili GC. Bioremediation techniques-classification based on site of application: principles, advantages, limitations and prospects. World J Microbiol Biotechnol. 2016;32:180.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    99.Henze M, Gujer W, Mino T, Van Loosdrecht MCM. Activated sludge models ASM1, ASM2, ASM2d and ASM, Vol 121. 2000. IWA Scientific and Technical Report 9, IWA publishing, London.100.Orozco-Mosqueda MD, Rocha-Granados MD, Glick BR, Santoyo G. Microbiome engineering to improve biocontrol and plant growth-promoting mechanisms. Microbiol Res. 2018;208:25–31.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar  More

  • in

    A strategic sampling design revealed the local genetic structure of cold-water fluvial sculpin: a focus on groundwater-dependent water temperature heterogeneity

    Almodóvar A, Nicola GG, Ayllón D, Elvira B (2012) Global warming threatens the persistence of Mediterranean brown trout. Glob Chang Biol 18:1549–1560Article 

    Google Scholar 
    Arscott DB, Tockner K, Ward JV (2001) Thermal heterogeneity along a braided floodplain river (Tagliamento River, northeastern Italy). Can J Fish Aquat Sci 58:2359–2373Article 

    Google Scholar 
    Baker DJ, Garnett ST, O’Connor J, Ehmke G, Clarke RH, Woinarski JCZ et al. (2019) Conserving the abundance of nonthreatened species. Conserv Biol 33:319–328PubMed 
    Article 

    Google Scholar 
    Blanchet S, Prunier JG, Paz-Vinas I, Saint-Pe K, Rey O, Raffard A et al. (2020) A river runs through it: the causes, consequences, and management of intraspecific diversity in river networks. Evol Appl 13:1195–1213PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Blondel L, Paterson IG, Bentzen P, Hendry AP (2021) Resistance and resilience of genetic and phenotypic diversity to “black swan” flood events: a retrospective analysis with historical samples of guppies. Mol Ecol 30:1017–1028PubMed 
    Article 

    Google Scholar 
    Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brown LE, Milner AM, Hannah DM (2007) Groundwater influence on alpine stream ecosystems. Freshw Biol 52:878–890Article 

    Google Scholar 
    Caissie D (2006) The thermal regime of rivers: a review. Freshw Biol 51:1389–1406Article 

    Google Scholar 
    Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dennenmoser S, Rogers SM, Vamosi SM (2014) Genetic population structure in prickly sculpin (Cottus asper) reflects isolation-by-environment between two life-history ecotypes. Biol J Linn Soc 113:943–957Article 

    Google Scholar 
    Diniz-Filho JAF, De Campos Telles MP (2002) Spatial autocorrelation analysis and the identification of operational units for conservation in continuous populations. Conserv Biol 16:924–935Article 

    Google Scholar 
    Diniz-Filho JAF, Soares TN, Lima JS, Dobrovolski R, Landeiro VL, Telles MP, de C et al. (2013) Mantel test in population genetics. Genet Mol Biol 36:475–485PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dray S, Bauman D, Blanchet G, Borcard D, Clappe S, Guenard G et al. (2020) adespatial: Multivariate Multiscale Spatial Analysis. R package version 0.3-8. https://CRAN.R-project.org/package=adespatialEarl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361Article 

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

    Google Scholar 
    Gilbert B, Bennett JR (2010) Partitioning variation in ecological communities: do the numbers add up? J Appl Ecol 47:1071–1082Article 

    Google Scholar 
    Gilbert B, Lechowicz MJ (2004) Neutrality, niches, and dispersal in a temperate forest understory. Proc Natl Acad Sci USA 101:7651–7656CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Goslee S, Urban D (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw 22:1–19Article 

    Google Scholar 
    Goto A (1998) Life-history variations in the fluvial sculpin, Cottus nozawae (Cottidae), along the course of a small mountain stream. Environ Biol Fishes 52:203–212Article 

    Google Scholar 
    Goudet J (1995) FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered 86:485–486Article 

    Google Scholar 
    Griffith DA, Peres-Neto PR (2006) Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87:2603–2613PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Guillot G, Rousset F (2013) Dismantling the Mantel tests. Methods Ecol Evol 4:336–344Article 

    Google Scholar 
    Hänfling B, Weetman D (2006) Concordant genetic estimators of migration reveal anthropogenically enhanced source-sink population structure in the river sculpin, Cottus gobio. Genetics 173:1487–1501PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Harmon LJ, Glor RE (2010) Poor statistical performance of the mantel test in phylogenetic comparative analyses. Evolution 64:2173–2178PubMed 
    PubMed Central 

    Google Scholar 
    Heino J, Grönroos M, Ilmonen J, Karhu T, Niva M, Paasivirta L (2013) Environmental heterogeneity and β diversity of stream macroinvertebrate communities at intermediate spatial scales. Freshw Sci 32:142–154Article 

    Google Scholar 
    Hohenlohe PA, Funk WC, Rajora OP (2021) Population genomics for wildlife conservation and management. Mol Ecol 30:62–82PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    Iles DT, Williams NM, Crone EE (2018) Source-sink dynamics of bumblebees in rapidly changing landscapes. J Appl Ecol 55:2802–2811Article 

    Google Scholar 
    Ito N, Gotoh RO, Shirakuma T, Araki Y, Hanzawa N (2018) Genetic structure of glacial-relict populations of a freshwater sculpin, Cottus nozawae, in Yamagata area of the Tohoku district. Biogeography 20:96–102
    Google Scholar 
    Junker J, Peter A, Wagner CE, Mwaiko S, Germann B, Seehausen O et al. (2012) River fragmentation increases localized population genetic structure and enhances asymmetry of dispersal in bullhead (Cottus gobio). Conserv Genet 13:545–556Article 

    Google Scholar 
    Kanno Y, Vokoun JC, Letcher BH (2011) Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks. Mol Ecol 20:3711–3729PubMed 
    Article 

    Google Scholar 
    Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation. Ecol Lett 7:1225–1241Article 

    Google Scholar 
    Koizumi I (2011) Integration of ecology, demography and genetics to reveal population structure and persistence: a mini review and case study of stream-dwelling Dolly Varden. Ecol Freshw Fish 20:352–363Article 

    Google Scholar 
    Koizumi I, Maekawa K (2004) Metapopulation structure of stream-dwelling Dolly Varden charr inferred from patterns of occurrence in the Sorachi River basin, Hokkaido, Japan. Freshw Biol 49:973–981Article 

    Google Scholar 
    Koizumi I, Yamamoto S, Maekawa K (2006) Decomposed pairwise regression analysis of genetic and geographic distances reveals a metapopulation structure of stream-dwelling Dolly Varden charr. Mol Ecol 15:3175–3189CAS 
    PubMed 
    Article 

    Google Scholar 
    Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) CLUMPAK: a program for identifying clustering modes and packaging population structure inferences across. K Mol Ecol Resour 15:1179–1191CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Lamphere BA, Blum MJ (2012) Genetic estimates of population structure and dispersal in a benthic stream fish. Ecol Freshw Fish 21:75–86Article 

    Google Scholar 
    Legendre P, Fortin MJ, Borcard D (2015) Should the Mantel test be used in spatial analysis? Methods Ecol Evol 6:1239–1247Article 

    Google Scholar 
    Legendre P, Legendre L (2012) Numerical Ecology, 3rd edn. Elsevier, Amsterdam
    Google Scholar 
    Lichstein JW (2007) Multiple regression on distance matrices: a multivariate spatial analysis tool. Plant Ecol 188:117–131Article 

    Google Scholar 
    Lucek K, Keller I, Nolte AW, Seehausen O (2018) Distinct colonization waves underlie the diversification of the freshwater sculpin (Cottus gobio) in the Central European Alpine region. J Evol Biol 31:1254–1267PubMed 
    Article 

    Google Scholar 
    Meirmans PG (2012) The trouble with isolation by distance. Mol Ecol 21:2839–2846PubMed 
    Article 

    Google Scholar 
    Meirmans PG (2014) Nonconvergence in Bayesian estimation of migration rates. Mol Ecol Resour 14:726–733PubMed 
    Article 

    Google Scholar 
    Meirmans PG (2015) Seven common mistakes in population genetics and how to avoid them. Mol Ecol 24:3223–3231PubMed 
    Article 

    Google Scholar 
    Middaugh CR, Kessinger B, Magoulick DD (2018) Climate-induced seasonal changes in smallmouth bass growth rate potential at the southern range extent. Ecol Freshw Fish 27:19–29Article 

    Google Scholar 
    Ministry of the Environment Government of Japan (2020) Red List of Japan. http://www.env.go.jp/press/107905.html. (In Japanese)Mussmann SM, Douglas MR, Chafin TK, Douglas ME (2019) BA3-SNPs: contemporary migration reconfigured in BayesAss for next-generation sequence data. Methods Ecol Evol 10:1808–1813Article 

    Google Scholar 
    Myers EA, Xue AT, Gehara M, Cox CL, Davis Rabosky AR, Lemos-Espinal J et al. (2019) Environmental heterogeneity and not vicariant biogeographic barriers generate community-wide population structure in desert-adapted snakes. Mol Ecol 28:4535–4548PubMed 
    Article 

    Google Scholar 
    Nagasaka A, Sugiyama S (2010) Factors affecting the summer maximum stream temperature of small streams in northern Japan. Bull Hokkaido Res Inst 47:35–43. (In Japanese with English abstract)
    Google Scholar 
    Nakajima S, Hirota SK, Matsuo A, Suyama Y, Nakamura F (2020) Genetic structure and population demography of white-spotted charr in the upstream watershed of a large dam. Water 12:2406CAS 
    Article 

    Google Scholar 
    Nakamura F, Yamada H (2005) Effects of pasture development on the ecological functions of riparian forests in Hokkaido in northern Japan. Ecol Eng 24:539–550Article 

    Google Scholar 
    Natsumeda T (2003) Effects of a severe flood on the movements of Japanese fluvial sculpin. Environ Biol Fishes 68:417–424Article 

    Google Scholar 
    Nosil P, Vines TH, Funk DJ (2005) Perspective: reproductive isolation caused by natural selection against immigrants from divergent habitats. Evolution 59:705–719PubMed 

    Google Scholar 
    Oksanen JF, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D et al. (2019) vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=veganOkumura N, Goto A (1996) Genetic variation and differentiation of the two river sculpins, Cottus nozawae and C. amblystomopsis, deduced from allozyme and restriction enzyme-digested mtDNA fragment length polymorphism analyses. Ichthyol Res 43:399–416Article 

    Google Scholar 
    Olden JD, Naiman RJ (2010) Incorporating thermal regimes into environmental flows assessments: modifying dam operations to restore freshwater ecosystem integrity. Freshw Biol 55:86–107Article 

    Google Scholar 
    Orsini L, Vanoverbeke J, Swillen I, Mergeay J, De Meester L (2013) Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol Ecol 22:5983–5999PubMed 
    Article 

    Google Scholar 
    Paris JR, Stevens JR, Catchen JM (2017) Lost in parameter space: a road map for STACKS. Methods Ecol Evol 8:1360–1373Article 

    Google Scholar 
    Peacock MM, Gustin MS, Kirchoff VS, Robinson ML, Hekkala E, Pizzarro-Barraza C et al. (2016) Native fishes in the Truckee River: are in-stream structures and patterns of population genetic structure related? Sci Total Environ 563–564:221–236PubMed 
    Article 
    CAS 

    Google Scholar 
    Peakall R, Smouse PE (2012) GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28:2537–2539CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Poff NL, Richter BD, Arthington AH, Bunn SE, Naiman RJ, Kendy E et al. (2010) The ecological limits of hydrologic alteration (ELOHA): A new framework for developing regional environmental flow standards. Freshw Biol 55:147–170Article 

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

    Google Scholar 
    Raufaste N, Francois R (2001) Are partial Mantel tests adequate? Evolution 55:1703–1705CAS 
    PubMed 
    Article 

    Google Scholar 
    Richardson JL, Brady SP, Wang IJ, Spear SF (2016) Navigating the pitfalls and promise of landscape genetics. Mol Ecol 25:849–864PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Rousset F (2002) Partial Mantel tests: reply to Castellano and Balletto. Evolution 56:1874–1875Article 

    Google Scholar 
    Ruppert JLW, James PMA, Taylor EB, Rudolfsen T, Veillard M, Davis CS et al. (2017) Riverscape genetic structure of a threatened and dispersal limited freshwater species, the Rocky Mountain Sculpin (Cottus sp.). Conserv Genet 18:925–937CAS 
    Article 

    Google Scholar 
    Sexton JP, Hangartner SB, Hoffmann AA (2014) Genetic isolation by environment or distance: Which pattern of gene flow is most common? Evolution 68:1–15CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Sexton JP, Hufford MB, Bateman AC, Lowry DB, Meimberg H, Strauss SY et al. (2016) Climate structures genetic variation across a species’ elevation range: a test of range limits hypotheses. Mol Ecol 25:911–928PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Suyama Y, Matsuki Y (2015) MIG-seq: an effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. Sci Rep. 5:1–12Article 
    CAS 

    Google Scholar 
    Suzuki K, Ishiyama N, Koizumi I, Nakamura F (2021) Combined effects of summer water temperature and current velocity on the distribution of a cold-water-adapted scupin (Cottus nozawae). Water 13:975Article 

    Google Scholar 
    Tague CL, Farrell M, Grant G, Lewis S, Rey S (2007) Hydrogeologic controls on summer stream temperatures in the McKenzie River basin, Oregon. Hydrol Process 21:3288–3300Article 

    Google Scholar 
    Thomaz AT, Christie MR, Knowles LL (2016) The architecture of river networks can drive the evolutionary dynamics of aquatic populations. Evolution 70:731–739PubMed 
    Article 

    Google Scholar 
    Thorpe RS, Surget-Groba Y, Johansson H (2008) The relative importance of ecology and geographic isolation for speciation in anoles. Philos Trans R Soc B Biol Sci 363:3071–3081Article 

    Google Scholar 
    Tsuda Y, Nakao K, Ide Y, Tsumura Y (2015) The population demography of Betula maximowicziana, a cool-temperate tree species in Japan, in relation to the last glacial period: Its admixture-like genetic structure is the result of simple population splitting not admixing. Mol Ecol 24:1403–1418CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Uno H (2016) Stream thermal heterogeneity prolongs aquatic-terrestrial subsidy and enhances riparian spider growth. Ecology 97:2547–2553PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Wang IJ, Bradburd GS (2014) Isolation by environment. Mol Ecol 23:5649–5662PubMed 
    Article 

    Google Scholar 
    Wang IJ, Summers K (2010) Genetic structure is correlated with phenotypic divergence rather than geographic isolation in the highly polymorphic strawberry poison-dart frog. Mol Ecol 19:447–458PubMed 
    Article 

    Google Scholar 
    Watz J, Otsuki Y, Nagatsuka K, Hasegawa K, Koizumi I (2019) Temperature-dependent competition between juvenile salmonids in small streams. Freshw Biol 64:1534–1541Article 

    Google Scholar 
    Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    White SL, Hanks EM, Wagner T (2020) A novel quantitative framework for riverscape genetics. Ecol Appl 30:e02147PubMed 

    Google Scholar 
    Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wright S (1943) Isolation by Distance. Genetics 28:114–138CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yagami T, Goto A (2000) Patchy distribution of a fluvial sculpin, Cottus nozawae, in the Gakko River system at the southern margin of its native range. Ichthyol Res 47:277–286Article 

    Google Scholar 
    Zeileis A, Hothorn T (2002) Diagnostic checking in regression relationships. R News 2:7–10
    Google Scholar 
    Zeller KA, Creech TG, Millette KL, Crowhurst RS, Long RA, Wagner HH et al. (2016) Using simulations to evaluate Mantel-based methods for assessing landscape resistance to gene flow. Ecol Evol 6:4115–4128PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Environmental DNA preserved in marine sediment for detecting jellyfish blooms after a tsunami

    Ethics statementField research, including sediment collection, was approved by the Harbormaster of Maizuru Bay (Permission Number 31 issued on July 1, 2016). All the experiments were performed in accordance with the guidelines on the Regulation on Animal Experimentation of Kyoto University (https://www.kyoto-u.ac.jp/en/research/research-compliance-ethics/animal-experiments), the Kyoto Prefecture Fishery Management Rules (http://www.pref.kyoto.jp/reiki/reiki_honbun/a300RG00000634.html), and the ARRIVE guidelines (https://arriveguidelines.org). Fish (15 individuals of jack mackerel juveniles) were anesthetized prior to length and weight measurements using 0.05% of 2-phenoxyethanol, and all individuals recovered from the anesthesia. No fish were sacrificed or injured in the present study. The research plan was approved by the institutional review boards at the Maizuru Fisheries Research Station (MFRS) of Kyoto University.Detection of eDNA in the water and sediment in experimental tanksOn July 6, 2016, natural marine sediment was collected at a depth of 47 m off the shore of Kyoto in the Sea of Japan (35.5544° N, 135.3210° E), using a Smith–McIntyre bottom sampler. Approximately 100 L of sediment was collected from 13 casts and was preserved in four large, covered containers at room temperature until the experiment began. Four sub-samples, 3 g from each container, were used for eDNA extraction and detection of jack mackerel using the method described below, and we confirmed that none of them contained the DNA of this species. The median particle diameter of the sediment was 47.7 μm, and mud content was 61.9% based on analyses using a laser diffraction particle size analyzer (SALD-2200, Shimadzu, Kyoto, Japan). Jack mackerel juveniles were collected by hook-and-line fishing from the pier of the MFRS. This species is the most abundant fish in this area and is typically found in waters 14 °C or warmer43.Four 200 L polycarbonate tanks (66 cm in bottom diameter) were set in the rearing facility of MFRS; three were used as test tanks, and the fourth was used as a control (blank) tank. The marine sediment (24 L, 7 cm in thick layer) was placed in each tank. Fine-filtered seawater was provided 2 d after the sediment had settled. The seawater used was pumped from 6 m depth offshore from the MFRS and filtered by passing through coarse polyvinyl fabric and fine sand of ca. 0.6 mm in diameter (5G-ST, Nikkiso Eiko, Japan; www.nikkiso-eiko.co.jp). Water was supplied at the rate of 490 mL min−1 (four cycles of circulation per day) and was drained from the center of each tank, filtered through a 2 mm mesh net. Aeration was performed at a rate of 600 mL min−1. This flow-through system was maintained throughout the experimental period.Five individual jack mackerel (70.7 ± 4.4 mm in total length and 3.26 ± 0.67 g in wet mass, mean ± SD) were introduced in each of the three test tanks on August 8, 7 days after the sediment was introduced. Defrosted krill Euphausia pacifica (5 g per tank) was fed to the fish between 16:00 and 17:00 every day. Fish were removed from the tanks 14 days after introduction using two hand nets, taking care not to disturb the sediment. Water temperature was recorded using a digital thermometer at 10:00 every day while the fish were kept in the tanks, for the following 14 days after their removal, and once a week thereafter. The water temperature ranged from 24.9 to 29.5 °C (mean = 27.9 °C) during the first four weeks of the experiment and from 9.4 to 27.9 °C (mean = 17.8 °C) during the following months. These conditions are similar to the natural condition that would be undergone by eDNA in sediment; for the last 19 years, the recorded bottom water temperature in the area from which the jack mackerel had been collected ranged from 8.5 to 29.6 °C, with a mean of 18.2 °C (Masuda 200843 with updated data). All rearing equipment was either newly purchased or bleached with 0.1% sodium hypochlorite and rinsed well with tap water before use.Water and sediment samples were collected immediately before the introduction of fish (day 0) and on days 1, 2, 4, 7, and 14 after their introduction. Sampling was also conducted on days 0, 1, 2, 4, 7, and 14, as well as in months 1, 2, 4, 8, and 12 after the removal of fish. Three 1 L water samples and three sediment samples (3 g) were collected from each tank on each sampling day. Water was collected from the drainage outlet in plastic bottles, and sediment was collected in petri dishes (inner diameter of 58 mm and depth of 21 mm). The sediment was collected by pushing the open end of a petri dish onto the surface of the sediment and securing the cover from underneath. Sediment sampling was conducted with a pair of prebleached long-sleeved gloves. One sample was obtained from the central tank area, and another two from near the peripheral tank area. Repetitive collection from the same location was avoided by marking each sampling location with a piece of PVC pipe (similar in diameter to the petri dishes and 3 cm in height).Water was filtered using glass fiber filters (0.7 μm mesh; GF/F 47 mm, GE Healthcare Japan, Tokyo, Japan). This mesh size, along with 0.45 μm, are two of the most commonly used filters in macroorganism eDNA studies44. The amount of eDNA detected using a 0.7 μm mesh is equivalent to that by a 0.45 μm mesh30. Contamination was evaluated by filtering 1 L of reverse osmosis water at the end of each sampling day. The filtered paper was wrapped in aluminum foil and preserved at − 20 °C.Sediment core samplingSediment core samples were collected at four locations (St. 1–4) in and around Nishi-Moune Bay, Kesennuma, Miyagi, Japan (38.8919–38.8932°N, 141.6235–141.6262°E; Fig. 1) on May 20, 2017. St. 1 was in the inner part of the bay where the tsunami impact was assumed to be the highest, with a run-up height of 15 m. St. 2 was located along a shallow rocky shore where the tsunami impact was limited. St. 3 was located at the mouth of the bay, and St. 4 was outside the bay. Average depths of the seafloor where cores were collected were 8.1, 9.6, 23.0, and 14.0 m at stations 1, 2, 3, and 4, respectively. Seafloor temperatures ranged from 9.9 to 11.5 °C. An acrylic pipe (inner diameter of 54 mm, length of 50 cm, and thickness of 3 mm) was pushed into the bottom sediment by a scuba diver. A silicon cap (59 and 52 mm in upper and lower diameter, respectively, and 45 mm in height) was placed on the top of the pipe, and the diver slowly pulled the pipe up and put another cap on the bottom. Three cores were collected from each location and transferred to a boat at the sea surface. Sediment core samples were kept vertical to avoid disturbing the layers and protected from direct sunlight. Cores were immediately transferred to the laboratory within 10 min, and were prepared for the cutting process.The core samples (1 cm thickness) were cut by layers as follows: after removing the bottom cap, a core sample pipe was placed on a stage that pushed the sediment inside. Seawater in the upper part of the pipe was discarded until the top of the sediment appeared on the surface. A thin acrylic plate was used to cut the core, and the cut specimen was placed in a small vinyl bag and preserved at -20 °C. All 12 collected cores were used for eDNA analysis, and one at St. 1 (inner bay) and all three at St. 3 (bay mouth) were used for the analysis of PAHs.DNA extractionDNA extraction from the glass fiber filter was performed following the method described in Yamamoto et al.6 using a DNeasy Blood and Tissue Kit (Qiagen, Hilde, Germany) and a Salivette tube (Sarstedt, Nümbrecht, Germany). Total eDNA was eluted in 100 μL AE buffer and preserved at − 20 °C.DNA extraction from sediment was conducted using a combination of alkaline DNA extraction45 and ethanol precipitation, using a commercial soil DNA extraction kit (Power Soil DNA Isolation Kit, QIAGEN, Hilden, Germany), as described in Sakata et al.26. Wet sediment (ca. 3 g) was placed in a 15 mL tube. Triplicate samples were obtained from each petri dish in the tank experiment, and a single sample was obtained from each layer in the sediment cores. We added 6 mL of 0.33 M NaOH and 3 mL of 10 mM TE buffer (pH = 6.7) to the tube and mixed well using Voltex. The samples were incubated at 94 °C for 50 min, and during this time, they were inverted at 15 and 30 min of incubation. After the incubation, the samples were cooled for several minutes and then centrifuged at 5,000 × g for 30 s. Supernatants (7.5 mL) were collected in 50 mL tubes and 7.5 mL of 1 M Tris HCL buffer (pH = 9.0 in the tank experiment and pH = 6.7 in the core samples), 1.5 mL of 3 M sodium acetate (pH = 5.2), and 30 mL of 99.5% ethanol were added, and mixed well by inversion. Ethanol precipitation was achieved by incubating the mixture for 1 h at − 20 °C. As a negative control of extraction, 3 mL of pure water was treated in the same manner. Sediment in the tank experiment was processed up to the ethanol precipitation on the same day as sampling, whereas core samples were defrosted at room temperature prior to analysis and then preserved as precipitate.The ethanol-precipitated sediment sample was centrifuged at 5,350 × g for 20 min, after which the supernatant was discarded. The precipitate was moved to the PowerBead Tube of the Power Soil Isolation Kit using a microspatula. The debris left in the centrifuged tube was also transferred by dissolving it in 100 μL of pure water. The following procedure was performed according to the protocol of Power Soil. The total eluted DNA (100 μL) was stored at − 20 °C. All the spatulas were bleached prior to use, and brand-new centrifugation tubes were used for the procedure.Quantitative PCRDNA was quantified using real-time TaqMan PCR with a LightCycler 96 Real-Time PCR System (Roche, Basel, Switzerland). Species-specific sets of primers and probes were used to quantify the eDNA of jack mackerel, moon jellyfish, and sea nettle (Supplementary Table S5). For the specimens in the tank experiment, each reaction contained 2 μL of extracted eDNA solution, a final concentration of 900 nM of forward and reverse primers, and 125 nM of TaqMan probe in 1 × PCR master mix (FastStart Essential DNA Master; Roche, Basel, Switzerland). PCR was performed under the following conditions: 10 min at 95 °C, 50 cycles of 10 s at 95 °C, and 1 min at 60 °C. For the core samples, each reaction contained 5 μL of extracted eDNA solution, a final concentration of 900 nM of forward and reverse primers, and 125 nM of TaqMan probe in 1 × TaqMan Environmental Master Mix 2.0 (Thermo Fisher Scientific, Massachusetts, USA). PCR was performed under the following conditions: 2 min at 50 °C, 10 min at 95 °C, 60 cycles of 15 s at 95 °C, and 1 min at 60 °C. PCR was performed in triplicates for each extracted DNA sample. Triplicates of pure water instead of the eDNA solution were used for each PCR performance as a PCR negative control. All PCR negative controls were below the detection level.As a standard for quantification, we used a linearized plasmid containing synthesized artificial DNA fragments of the cytochrome b (CytB) gene sequence of jack mackerel or cytochrome C oxidase subunit I (COI) gene sequences of moon jellyfish and Pacific sea nettle, including target regions. The dilution series of 3.0 × 101–3.0 × 104 was run in PCR in triplicate to obtain quantification curves. Quantification was accepted only when the fitted R2 value was above 0.99 on the quantification curve. The average of the PCR replicates was used to represent the eDNA concentration in each sample. eDNA concentrations were expressed as the number of copies per gram of samples in both water and sediment. As contamination precautions, water filtration, DNA extraction, and PCR reactions were performed in separate rooms, and persons entering one of the above three rooms were not permitted to enter the other rooms.Evaluation of PCR inhibitorsSediment often contains chemicals that inhibit the PCR process. An analysis using an internal positive control (IPC) was conducted to confirm that the eDNA extraction kit successfully removed such inhibitive chemicals. DNA of lambda phage that was not present in the environment was used as the IPC46. Water and sediment samples (n = 12 for each) in the experimental tanks on day 14 after the introduction of fish were used for this experiment. We placed 300 copies of lambda phage DNA in the extracted eDNA with the primer–probe set in the test group, whereas pure water (instead of extracted eDNA) was placed in the control group (n = 3). PCR amplification of the test and control groups was compared, defining delta Ct as the difference in the number of threshold cycles (Ct values) in the PCR between samples with and without extracted eDNA. Delta Ct in the water samples ranged from − 0.49 to + 2.93 cycles, and from − 0.39 to + 0.28 cycles in the sediment samples (Supplementary Table S6). These values were less than + 3 cycles, previously proposed as criteria of inhibition12, and thus the inhibition was negligible in the present method.Analysis of polycyclic aromatic hydrocarbons (PAHs) for detecting tsunami signatureThe sampled sediment cores (one at St. 1 and three at St. 3) were analyzed to quantify PAHs as a tsunami signature. Specimens from every two layers were used for the analysis.Five hundred microliters of mixed acetone solution containing 5 μg mL−1 each of naphthalene-d8, acenaphthene-d10, fluorene-d10, anthracene-d10, fluoranthene-d10, pyrene-d10, and chrysene-d12 as surrogate standards was added to a centrifuge tube containing 1 g of sediment. The analytes were extracted twice by shaking for 10 min with acetone (10 mL). Supernatants mixed with 60 mL of saturated NaCl solution were transferred to a separatory funnel. The analytes were extracted twice with 10 mL hexane, and the organic layer was combined. This layer was then dried over anhydrous Na2SO4 and concentrated to trace level using a rotary evaporator. The solution was concentrated to 1 mL under a nitrogen atmosphere and cleaned using a Florisil Sep-Pak column (Waters Association Co., Ltd.). The Florisil Sep-Pak cartridge for clean-up was washed with 10 mL of hexane. A hexane solution containing the analytes followed by 10 mL of hexane/acetone (99/1) solution were passed through the prewashed cartridge. After the addition of 100 μL of 1 mg L−1 atradine-d5 as an internal standard, the eluate was carefully evaporated with a stream of nitrogen up to 1 mL. The analytes were determined using gas chromatography–mass spectrometry (GC/MS).A Hewlett-Packard 6890 series gas chromatograph equipped with a mass spectrometer (5973 N) was used for PAH analysis. The separation was carried out in a capillary column coated with 5% phenyl methyl silicone (J&W Scientific Co., 30 m length × 0.25 mm i.d., 0.25 μm film thickness). The column temperature was maintained at 50 °C for the first minute and then increased to 290 °C at 20 °C min−1 and to 310 °C at 10 °C min−1. Finally, the column temperature was maintained at 310 °C for 10 min. The interface temperature, ion source temperature, and ion energy were 280 °C, 230 °C, and 70 eV, respectively. Selected ion monitoring was operated under this program. The monitoring ions of 128 (127) for naphthalene, 152 (151) for acenaphthylene, 153 (152) for acenaphthene, 166 (165) for fluorene, 178 (176) for phenanthrene and anthracene, 202 (203) for fluoranthene and pyrene, 228 (229) for benzo[a]anthracene and chrysene, 252 (253) for benzo[b]fluoranthene, 252 (281) for benzo[k]fluoranthene, and benzo[a]pyrene, 276 (207) for dibenzo[a,h]anthracene, indeno[1,2,3-cd]pyrene, and benzo[g,h,i]perylene, were used to quantify the concentrations of PAHs; qualifier ions are indicated in parentheses. One microliter of the sample was injected by splitless injection.Data analysisConcentration of eDNA in the water and sediment of experimental tanks after the introduction of fish was analyzed by repeated-measures (rm) ANOVA; ‘days after the introduction of fish’ was defined as the explanatory variable, ‘concentration of eDNA’ as the response variable, and the ‘triplicates of petri dishes’ as a random factor. Then, eDNA concentrations among days were compared using Tukey’s HSD test. Homoscedasticity in eDNA content was improved by log 10 (x + 1) transformation. The decrease in eDNA in water and sediment samples after the removal of fish was also analyzed by rm ANOVA in both the test and control tanks. A comparison of the eDNA concentrations between the test and control tanks was also conducted by rm ANOVA after the removal of fish. All analyses were performed in R ver. 3.4.2 (using the packages of lmerTest and multcomp)47,48,49.Concentration of eDNA in water and sediment samples after introduction and removal of fish was fitted to eight candidate models as log X (y = a + b * ln(x)), log Y (y = exp(a + b * x)), asymptotic (y = a * x/(1 + b * x)), reciprocal (y = a + b/x), power law (y = a * x ^ b), exponential (y = a * exp(b * x)), and exponential decay (y = a + b * exp(c * x)) using the “nls” function of R. Models with the lowest AIC values were listed, and regression lines were drawn by Kaleida Graph 4.5 (Hulinks, Tokyo, Japan).We tested whether the concentration of jellyfish eDNA was highest in the layers immediately above the signature of the tsunami in the sediment cores collected at St. 3. The depth of peak PAHs was identified in each core, and this was considered to represent the timing of the tsunami. Core samples of eDNA were then divided into the following three parts: (1) upper, including the upper half of the core above the PAH peak, representing recent sedimentation; (2) middle, including the lower half of the core above the PAH peak, representing sedimentation immediately after the tsunami; and (3) lower, including the layers of PAH peak and below, representing sedimentation at the timing of or prior to the tsunami. Concentrations of jellyfish eDNA of each species were compared among these three parts by nested ANOVA (layers nested in triplicate cores) followed by Tukey’s HSD test. More

  • in

    Biogeography of acoustic biodiversity of NW Mediterranean coralligenous reefs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • in

    Distinct effects of host and neighbour tree identity on arbuscular and ectomycorrhizal fungi along a tree diversity gradient

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Google Scholar  More

  • in

    Description of larval morphology and phylogenetic relationships of Heterotemna tenuicornis (Silphidae)

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

  • in

    Identities, concentrations, and sources of pesticide exposure in pollen collected by managed bees during blueberry pollination

    Active ingredients detected in bee collected pollenAll 188 pollen samples had at least 12 active ingredients detected in each sample, with a maximum of 31 AIs and an average of 22.0 ± 0.3 per sample. Over both years, 80 of the 259 screened pesticide active ingredients were detected in the pollen. These included 28 fungicides, 26 insecticides, 21 herbicides, two miticides, and one rodenticide. We also detected one synthetic antioxidant and one pesticide synergist (Table S1). We detected approximately twice as many AIs in pollen collected by honey bees (68 AIs) in 2019 than in pollen collected by bumble bees (32). All AIs detected in pollen from bumble bees were also collected by honey bees in either 2018 or 2019. Honey bee collected pollen also had significantly more AIs on average detected at each site (35.0 ± 0.9 S.E. AIs per site) compared to bumble bees (18.6 ± 0.6) in 2019 (R2m = 0.73; X2 = 68.2, df = 1, p  More

  • in

    Short-term cell death in tissues of Pulsatilla vernalis seeds from natural and ex situ conserved populations

    1.Zielińska, K. M., Kiedrzynski, M., Grzyl, A. & Rewicz, A. Forest roadsides harbour less competitive habitats for a relict mountain plant (Pulsatilla vernalis) in lowlands. Sci. Rep. https://doi.org/10.1038/srep31913 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Zielińska, K. M., Kiedrzynski, M., Grzyl, A. & Tomczyk, P. P. Anthropogenic sites maintain the last individuals during the rapid decline of the lowland refugium of the alpine-arctic plant Pulsatilla vernalis (L.) Mill. Pak. J. Bot. 50, 211–215 (2018).3.Grzyl, A. & Ronikier, M. Pulsatilla vernalis (Ranunculaceae) in the Polish lowlands: Current population resources of a declining species. Pol. Bot. J. 56, 185–194 (2011).
    Google Scholar 
    4.Åström, S. & Stridh, B. The present status of Pulsatilla vernalis in Sweden. Sven. Bot. Tidskr. 97, 117–126 (2003).
    Google Scholar 
    5.Chappuis, E. Pulsatilla vernalis. The IUCN Red List of Threatened Species 2014: e.T55730086A55730098. (2014). https://doi.org/10.2305/IUCN.UK.2014-1.RLTS.T55730086A55730098.en. Downloaded on 02 December 2020 >.6.Ronikier, M. et al. Phylogeography of Pulsatilla vernalis (L.) Mill. (Ranunculaceae): Chloroplast DNA reveals two evolutionary lineages across central Europe and Scandinavia. J. Biogeogr. 35, 1650–1664. https://doi.org/10.1111/j.1365-2699.2008.01907.x (2008).7.Kiedrzyński, M., Zielińska, K. M., Kiedrzyńska, E. & Rewicz, A. Refugial debate: On small sites according to their function and capacity. Evol. Ecol. 31, 815–827. https://doi.org/10.1007/s10682-017-9913-4 (2017).Article 

    Google Scholar 
    8.Betz, C., Scheuerer, M. & Reisch, C. Population reinforcement—A glimmer of hope for the conservation of the highly endangered Spring Pasque flower (Pulsatilla vernalis). Biol. Conserv. 168, 161–167. https://doi.org/10.1016/j.biocon.2013.10.004 (2013).Article 

    Google Scholar 
    9.Nawrocka-Grześkowiak, U. & Frydel, K. Spring pasque-flower (Pulsatilla vernalis (L.) Miller) localities in the Kaliska Forest District. Zarządzanie Ochroną Przyrody w Lasach 6, 77–84 (2012).10.Gutterman, Y. In Seeds: The Ecology of Regeneration in Plant Communities (ed M. Fenner) 59–84 (CAB International, 2000).11.Luzuriaga, A. L., Escudero, A. & Perez-Garcia, F. Environmental maternal effects on seed morphology and germination in Sinapis arvensis (Cruciferae). Weed Res. 46, 163–174. https://doi.org/10.1111/j.1365-3180.2006.00496.x (2006).Article 

    Google Scholar 
    12.Rao, N. K., Dulloo, M. E. & Engels, J. M. M. A review of factors that influence the production of quality seed for long-term conservation in genebanks. Genet. Resour. Crop Evol. 64, 1061–1074. https://doi.org/10.1007/s10722-016-0425-9 (2017).CAS 
    Article 

    Google Scholar 
    13.Doniak, M., Barciszewska, M. Z., Kaźmierczak, J. & Kaźmierczak, A. The crucial elements of the ‘last step’ of programmed cell death induced by kinetin in root cortex of V. faba ssp. minor seedlings. Plant Cell Rep. 33, 2063–2076. https://doi.org/10.1007/s00299-014-1681-9 (2014).14.Doniak, M., Byczkowska, A. & Kaźmierczak, A. Kinetin-induced programmed death of cortex cells is mediated by ethylene and calcium ions in roots of Vicia faba ssp minor. Plant Growth Regul. 78, 335–343. https://doi.org/10.1007/s10725-015-0096-0 (2016).CAS 
    Article 

    Google Scholar 
    15.Doniak, M., Kaźmierczak, A., Byczkowska, A. & Glińska, S. Reactive oxygen species and sugars may be the messengers in kinetin-induced death of field bean root cortex cells. Biol. Plant. 61, 178–186. https://doi.org/10.1007/s10535-016-0654-y (2017).CAS 
    Article 

    Google Scholar 
    16.Tudela-Isanta, M. et al. Habitat-related seed germination traits in alpine habitats. Ecol. Evol. 8, 150–161. https://doi.org/10.1002/ece3.3539 (2018).Article 
    PubMed 

    Google Scholar 
    17.Baskin, J. M. & Baskin, C. C. A classification system for seed dormancy. Seed Sci. Res. 14, 1–16. https://doi.org/10.1079/ssr2003150 (2004).ADS 
    Article 

    Google Scholar 
    18.Finch-Savage, W. E. & Leubner-Metzger, G. Seed dormancy and the control of germination. New Phytol. 171, 501–523. https://doi.org/10.1111/j.1469-8137.2006.01787.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    19.Latrasse, D., Benhamed, M., Bergounioux, C., Raynaud, C. & Delarue, M. Plant programmed cell death from a chromatin point of view. J. Exp. Bot. 20, 5887–5900 (2016).Article 

    Google Scholar 
    20.Baskin, J. M., Baskin, C. C. & Li, X. Taxonomy, anatomy and evolution of physical dormancy in seeds. Plant Species Biol. 15, 139–152. https://doi.org/10.1046/j.1442-1984.2000.00034.x (2000).Article 

    Google Scholar 
    21.Grzyl, A. Biology and Ecology of Isolated Populations of Pulsatilla vernalis (L.) Mill. on the Eastern Limits of its RANGE in Poland. (PhD thesis. University of Lodz, Department of Geobotany and Plant Ecology, 2012).22.Grzyl, A., Kiedrzynski, M., Zielinska, K. M. & Rewicz, A. The relationship between climatic conditions and generative reproduction of a lowland population of Pulsatilla vernalis: The last breath of a relict plant or a fluctuating cycle of regeneration?. Plant Ecol. 215, 457–466. https://doi.org/10.1007/s11258-014-0316-0 (2014).Article 

    Google Scholar 
    23.Oostermeijer, J. G. B., Vaneijck, M. W. & Dennijs, J. C. M. Offspring fitness in relation to population size and genetic variation in the rare perennial plant species Gentiana pneumonanthe (Gentianaceae). Oecologia 97, 289–296. https://doi.org/10.1007/bf00317317 (1994).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    24.Ouborg, N. J. & Vantreuren, R. Variation in fitness-related characters among small and large populations of Salvia pratensis. J. Ecol. 83, 369–380. https://doi.org/10.2307/2261591 (1995).Article 

    Google Scholar 
    25.Fischer, M. & Matthies, D. RAPD variation in relation to population size and plant fitness in the rare Gentianella germanica (Gentianaceae). Am. J. Bot. 85, 811–819. https://doi.org/10.2307/2446416 (1998).CAS 
    Article 
    PubMed 

    Google Scholar 
    26.Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics. (Cambridge University Press, 2002).27.Hensen, I., Oberprieler, C. & Wesche, K. Genetic structure, population size, and seed production of Pulsatilla vulgaris Mill. (Ranunculaceae) in Central Germany. Flora 200, 3–14. https://doi.org/10.1016/j.flora.2004.05.001 (2005).28.Jakobsson, A. & Eriksson, O. A comparative study of seed number, seed size, seedling size and recruitment in grassland plants. Oikos 88, 494–502. https://doi.org/10.1034/j.1600-0706.2000.880304.x (2000).Article 

    Google Scholar 
    29.Melser, C. & Klinkhamer, P. G. L. Selective seed abortion increases offspring survival in Cynoglossum officinale (Boraginaceae). Am. J. Bot. 88, 1033–1040. https://doi.org/10.2307/2657085 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    30.Meyer, K. M., Soldaat, L. L., Auge, H. & Thulke, H. H. Adaptive and selective seed abortion reveals complex conditional decision making in plants. Am. Nat. 183, 376–383. https://doi.org/10.1086/675063 (2014).Article 
    PubMed 

    Google Scholar 
    31.Bochenková, M., Hejcman, M. & Karlík, P. Effect of plant community on recruitment of Pulsatilla pratensis in dry grassland. Sci. Agric. Bohem. 2012, 127–133. https://doi.org/10.7160/sab.2012.430402 (2012).Article 

    Google Scholar 
    32.Ghazoul, J. & Satake, A. Nonviable seed set enhances plant fitness: The sacrificial sibling hypothesis. Ecology 90, 369–377. https://doi.org/10.1890/07-1436.1 (2009).Article 
    PubMed 

    Google Scholar 
    33.Laitinen, P. The Effects of Forest Fires on the Persistence of Pulsatilla vernalis (L.) Mill. edn, (Ms. thesis, University of Jyväskylä, Faculty of Mathematics and Science, Department of Biological and Environmental Science, 2008) [in Finnish with an English abstract].34.Skalická, R., Karlík, P., Hejcman, M. & Bochenková, M. In 17th Symposium of the European Grassland Federation. 388–390.35.Arathi, H. S., Ganeshaiah, K. N., Shaanker, R. U. & Hedge, S. G. Seed abortion in Pongamia pinnata (Fabaceae). Am. J. Bot. 86, 659–662. https://doi.org/10.2307/2656574 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Brookes, R. H., Jesson, L. K. & Burd, M. A test of simultaneous resource and pollen limitation in Stylidium armeria. New Phytol. 179, 557–565. https://doi.org/10.1111/j.1469-8137.2008.02453.x (2008).Article 
    PubMed 

    Google Scholar 
    37.Yang, C. F., Sun, S. G. & Guo, Y. H. Resource limitation and pollen source (self and outcross) affecting seed production in two louseworts, Pedicularis siphonantha and P. longiflora (Orobanchaceae). Bot. J. Linn. Soc. 147, 83–89. https://doi.org/10.1111/j.1095-8339.2005.00363.x (2005).38.Cendán, C., Sampedro, L. & Zas, R. The maternal environment determines the timing of germination in Pinus pinaster. Environ. Exp. Bot. 94, 66–72. https://doi.org/10.1016/j.envexpbot.2011.11.022 (2013).Article 

    Google Scholar 
    39.Li, R. et al. Effects of cultivar and maternal environment on seed quality in Vicia sativa. Front. Plant Sci. 8. https://doi.org/10.3389/fpls.2017.01411 (2017).40.Valencia-Diaz, S. & Montaña, C. Temporal variability in the maternal environment and its effect on seed size and seed quality in Flourensia cernua DC. (Asteraceae). J. Arid Environ. 63, 686–695. https://doi.org/10.1016/j.jaridenv.2005.03.024 (2005).41.Chinnusamy, V., Gong, Z. Z. & Zhu, J. K. Abscisic acid-mediated epigenetic processes in plant development and stress responses. J. Integr. Plant Biol. 50, 1187–1195. https://doi.org/10.1111/j.1744-7909.2008.00727.x (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Butuzova, O. G. Peculiarities of seed formation in Pulsatilla vulgaris and Helleborus niger (Ranunculaceae) with embryo postdevelopment. Botanicheskii Zhurnal (St. Petersburg) 103, 313—330 (2018) [in Russian].43.Duncan, C., Schultz, N., Lewandrowski, W., Good, M. K. & Cook, S. Lower dormancy with rapid germination is an important strategy for seeds in an arid zone with unpredictable rainfall. PLoS ONE 14. https://doi.org/10.1371/journal.pone.0218421 (2019).44.Gremer, J. R., Kimball, S. & Venable, D. L. Within and among year germination in Sonoran Desert winter annuals: bet hedging and predictive germination in a variable environment. Ecol. Lett. 19, 1209–1218. https://doi.org/10.1111/ele.12655 (2016).Article 
    PubMed 

    Google Scholar 
    45.Venable, D. L. Bet hedging in a guild of desert annuals. Ecology 88, 1086–1090. https://doi.org/10.1890/06-1495 (2007).Article 
    PubMed 

    Google Scholar 
    46.Evans, M. E. K. & Dennehy, J. J. Germ banking: Bet-hedging and variable release from egg and seed dormancy. Q. R. Biol. 80, 431–451. https://doi.org/10.1086/498282 (2005).Article 

    Google Scholar 
    47.Goldberg, R. B., de Paiva, G. & Yadegari, R. Plant embryogenesis – zygote to seed. Science 266, 605–614. https://doi.org/10.1126/science.266.5185.605 (1994).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    48.Lester, R. N. & Kang, J. H. Embryo and endosperm function and failure in Solanum species and hybrids. Ann. Bot. 82, 445–453. https://doi.org/10.1006/anbo.1998.0695 (1998).Article 

    Google Scholar 
    49.Lopes, M. A. & Larkins, B. A. Endosperm origin, development, and function. Plant Cell 5, 1383–1399. https://doi.org/10.1105/tpc.5.10.1383 (1993).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Yan, D. W., Duermeyer, L., Leoveanu, C. & Nambara, E. The functions of the endosperm during seed germination. Plant Cell Physiol. 55, 1521–1533. https://doi.org/10.1093/pcp/pcu089 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    51.Willis, C. G. et al. The evolution of seed dormancy: Environmental cues, evolutionary hubs, and diversification of the seed plants. New Phytol. 203, 300–309. https://doi.org/10.1111/nph.12782 (2014).Article 
    PubMed 

    Google Scholar 
    52.Poisot, T., Bever, J. D., Nemri, A., Thrall, P. H. & Hochberg, M. E. A conceptual framework for the evolution of ecological specialisation. Ecol. Lett. 14, 841–851. https://doi.org/10.1111/j.1461-0248.2011.01645.x (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Pfeifer, E., Holderegger, R., Matthies, D. & Rutishauser, R. Investigation on the population biology of a flagship species of dry meadows: Pulsatilla vulgaris Mill. in north-eastern Switzerland. Bot. Helvet. 112, 153–171 (2002).54.Gargiulo, R. et al. Conservation of the threatened species, Pulsatilla vulgaris Mill. (Pasqueflower), is aided by reproductive system and polyploidy. J. Hered. 110, 618–628. https://doi.org/10.1093/jhered/esz035 (2019).55.Seglias, A. E., Williams, E., Bilge, A. & Kramer, A. T. Phylogeny and source climate impact seed dormancy and germination of restoration-relevant forb species. PLoS ONE 13. https://doi.org/10.1371/journal.pone.0191931 (2018).56.Byczkowska, A., Kunikowska, A. & Kaźmierczak, A. Determination of ACC-induced cell-programmed death in roots of Vicia faba ssp. minor seedlings by acridine orange and ethidium bromide staining. Protoplasma 250, 121–128. https://doi.org/10.1007/s00709-012-0383-9 (2013).57.Dray, S. & Dufour, A. B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20. https://doi.org/10.18637/jss.v022.i04 (2007).58.Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra (2020).59.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

    Google Scholar 
    60.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan (2019).61.Fox, F. & Weisberg, S. An {R} Companion to Applied Regression, Third Edition. (Sage, 2019). https://socialsciences.mcmaster.ca/jfox/Books/Companion/. More