More stories

  • in

    Physiological and molecular responses of lobe coral indicate nearshore adaptations to anthropogenic stressors

    Physiological responses
    Small fragments from five source colonies from the two experimental sites (N- and O-sites) were used to conduct a reciprocal transplant experiment in Maunalua Bay, Hawaii (Fig. 1). The results revealed clear physiological response differences between the two populations. The transplantation resulted in a significant reduction in the average tissue layer thickness (TLT) in only one treatment: O-corals transplanted to N-site (O → N) (Tukey-HSD, P-adj  2 at FDR = 0.01. Proteins associated with key GO terms were colored in different colors, and the top 10 abundant proteins in each population are annotated. The bottom bars indicate the total numbers of significantly abundant proteins for each population.

    Full size image

    Response difference in transplant to the offshore site (N → O vs. O → O)
    A total of 3236 distinct coral proteins were identified at O-site: 2217 (68.5%) were shared between the two populations, 656 unique to N → O corals, and 363 to O → O corals (Fig. S1C). GO analysis identified 35 enriched terms specific to N → O, which involved amino acid biosynthetic process, ATP metabolic process, TCA cycles, fatty acid oxidation, and monosaccharide metabolic process. There were 15 specific GO terms in O → O corals, including nucleotide monophosphate biosynthetic process, intracellular protein transport, vesicle organization, and GTP binding (SI.2B).
    Quantitative analysis on protein abundances indicated a total of 665 proteins to be significantly differentially abundant at O-site: N → O corals had 155 abundant-proteins, and O → O corals had 510 abundant-proteins (Fig. 3B). GO analysis resulted in identifying 39 enriched terms from abundant proteins in O → O corals, while only one met the cutoff in N → O corals (SI.2B). Although the number of abundant-proteins and enriched terms identified in O → O corals were relatively high, the enriched terms predominantly consisted of cellular functions related to protein translation; organonitrogen biosynthetic process and organic acid metabolic process, both leading to single child terms for BP, CC, and MF (tRNA aminoacylation for protein translation, cytosolic large ribosomal subunit, and tRNA aminoacyl ligase activity). The enriched term in N → O corals was a non-specific term of ‘extracellular region’, indicating that despite the higher number of abundant-proteins, the main functional difference between N → O and O → O corals was an enhanced protein translation activity in O → O corals.
    Response comparisons to cross transplantation
    Effects of cross transplantation yielded a more diverse proteomic stress-response in O-corals as they moved nearshore than N-corals as they were moved offshore (Fig. S2). The total number of abundant-proteins between the sites was much higher for O-corals (440, O → N vs. O → O) than N-corals (135, N → N vs. N → O) (Table S1), and the number of unique GO terms identified between the sites was also higher in O-corals (69, SI.2C) than in N-corals (46, SI.2D). The number of overlapping proteins between the sites was lower in O-corals than in N-corals (70% vs. 79%), and log-fold changes of all identified proteins between the sites were significantly larger for O-corals than N-corals (Wilcoxon Rank-Sum test, P = 6.02 × 10–9), all emphasizing the larger metabolic reshuffling needed to respond to cross transplantation in O-corals. GO enrichment analysis indicated that N-corals responded to transplantation to O-site with increased abundance of proteins involved in amino acid biosynthesis, fatty acid beta oxidation, TCA cycle, chitin catabolism, coenzyme biosynthesis and translational initiation. O-corals responded to transplantation to N-site by increasing the abundance of proteins associated with detoxification, antioxidant activity, protein complex subunit organization, and multiple metabolic processes (amino acid, fatty acid, ATP, monosaccharide, and carbohydrate derivative) (SI.2E). The shared responses between the cross-transplanted corals (N → O and O → N corals) included increased proteins involved in fatty-acid beta oxidation, TCA cycle, carbohydrate derivative catabolic process, pyridoxal phosphate binding, and ‘oxidoreductase activity acting on the CH-CH group of donors with flavin as acceptor’, likely representing the effects of transplantation to a non-native environment.
    Proteome patterns across the four treatments
    Comparing enriched GO terms across all treatments (SI.2E) highlighted the unique state of O → N corals; O → N corals had a much higher number of uniquely enriched GO terms (n = 27) compared to those in the rests (4 in O → O, 5 in N → N, and 15 in N → O corals). The most notable difference among the treatments was enrichment of detoxification and antioxidant activity exclusively in O → N corals (Fig. 4). Also, lipid oxidation was highly enriched in O → N corals with four terms associated to this category identified (Fig. 4, SI.2E).
    Figure 4

    Enriched GO terms uniquely identified to specific treatment groups. Treatment groups are shown in the right column (e.g. N-coral = N-corals at both sites, N-site = N- and O-corals at N-site, CrossT = cross transplantation). The heat-map represents P-values for the associated GO terms. The GO terms are grouped by the parent–child terms with the most parent term in bold (for values, see SI-2E).

    Full size image

    Examining the relative abundance of individual proteins associated with detoxification (‘detox-proteins’) revealed the following interesting patterns. (1) Distinct sets of proteins were abundant in different treatments, rather than all detox-proteins to be elevated in one treatment, and the direction and magnitude of responses to transplantation were protein specific and varied between populations (Fig. S4A). (2) Two peroxiredoxin (Prx) proteins, Prx-1 (m.6147) and Prx-6 (m.9595), dominated the relative abundance of detox-proteins by having over an order of magnitude higher abundance values, and they were consistently more abundant in N-corals than O-corals (ave. 44%, Kruskal Test, P = 0.004–0.01) (Fig. S4B, SI.1B). (3) Some proteins with the same or similar annotations had contrasting responses between the populations. For example, Prx-4 (m.17739), which belongs to the same subfamily as Prx-1, was significantly more abundant in O-corals at both sites (Fig. S4B, SI.2F,G), while Prx-1 was more abundant in N-corals. Similarly, seven peroxidasin (PXDN) homologs were identified, of which m.17686 was significantly more abundant in O → N corals, while m.9432 was significantly more abundant in N → N corals (Fig. S4B, SI.2F), suggesting that the two populations potentially utilize different class/kind of enzymes as primary proteins in detoxification/antioxidant pathways. Of the seven PXDN homologs, two (m.1440, m.9432) were consistently higher in N-corals, two (m.10928, m.15200) were consistently higher in O-corals, and three (m.12572, m.17686, m.9657) increased abundance at N-site in both corals, but m.12572 and m.17686 being higher in O-corals, while m.9657 higher in N-corals (Fig. S3B).
    To ascertain that the proteins with the same annotations are indeed different proteins, sequences of matched peptides were assessed for those that showed contrasting responses. The pairwise comparison of Prx-1 and Prx-4 showed only seven of the total 65 peptides (11%) were identical between the two, revealing that these protein sequences are significantly different and they each have unique peptides that be detected and quantified accurately (SI.1C1). Similarly the majority of PXDN-like proteins identified had no overlapping peptides between the contrasting pairs (0–19%, median = 0, SI.1C2), indicating that corals possess multiple types of PXDN, and N- and O-corals respond to stressors with different sets of PXDN.
    In addition to lipid oxidation being significantly enriched in O → N corals, a single term (fatty acid beta-oxidation,) was also enriched in N → O corals, which suggests that cross-transplantation had an effect on lipid oxidation processes. However, the abundances of most proteins associated with lipid oxidation were higher in O-corals than N-corals at both sites (Fig. S4A). Statistically, three proteins (medium-chain sp acyl-CoA:m.22274, very-long-chain sp. acyl-CoA:m.17984, and trifunctional enzyme subunit alpha:m.6724) showed a difference in abundance between the two populations at N-site (Fig. S4C) and one (isovaleryl-CoA dehydrogenase:m.27714) at O-site, all of which were higher in O-corals than N-corals. More

  • in

    The amphibian microbiome exhibits poor resilience following pathogen-induced disturbance

    1.
    Connell JH. Diversity in Tropical Rain Forests and Coral Reefs. Science. 1978;199:1302–10.
    CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Moreno-Mateos D, Barbier EB, Jones PC, Jones HP, Aronson J, López-López JA, et al. Anthropogenic ecosystem disturbance and the recovery debt. Nat Commun. 2017;8:14163.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    3.
    Rodil IF, Lohrer AM, Chiaroni LD, Hewitt JE, Thrush SF. Disturbance of sandflats by thin terrigenous sediment deposits: consequences for primary production and nutrient cycling. Ecol Appl. 2011;21:416–26.
    PubMed  Article  PubMed Central  Google Scholar 

    4.
    Carnell PE, Keough MJ. More severe disturbance regimes drive the shift of a kelp forest to a sea urchin barren in south-eastern Australia. Sci Rep. 2020;10:11272.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    5.
    McDowell NG, Michaletz ST, Bennett KE, Solander KC, Xu C, Maxwell RM, et al. Predicting Chronic Climate-Driven Disturbances and Their Mitigation. Trends Ecol Evol. 2018;33:15–27.
    PubMed  Article  PubMed Central  Google Scholar 

    6.
    Shade A, Peter H, Allison SD, Baho D, Berga M, Buergmann H, et al. Fundamentals of Microbial Community Resistance and Resilience. Front Microbiol. 2012;3:417.
    PubMed  PubMed Central  Article  Google Scholar 

    7.
    Allison SD, Martiny JBH. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci. 2008;105:11512–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Shade A, Read JS, Welkie DG, Kratz TK, Wu CH, McMahon KD. Resistance, resilience and recovery: aquatic bacterial dynamics after water column disturbance: Bacterial community recovery after lake mixing. Environ Microbiol. 2011;13:2752–67.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Shade A, Read JS, Youngblut ND, Fierer N, Knight R, Kratz TK, et al. Lake microbial communities are resilient after a whole-ecosystem disturbance. ISME J. 2012;6:2153–67.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    10.
    Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci. 2011;108:4554–61.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Heinsen F-A, Knecht H, Neulinger SC, Schmitz RA, Knecht C, Kühbacher T, et al. Dynamic changes of the luminal and mucosa-associated gut microbiota during and after antibiotic therapy with paromomycin. Gut Microbes. 2015;6:243–54.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    12.
    Fukuyama J, Rumker L, Sankaran K, Jeganathan P, Dethlefsen L, Relman DA, et al. Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment. PLOS Comput Biol. 2017;13:e1005706.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    13.
    Subramanian S, Huq S, Yatsunenko T, Haque R, Mahfuz M, Alam MA, et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature. 2014;510:417–21.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    14.
    Antwis RE, Garcia G, Fidgett AL, Preziosi RF. Tagging Frogs with Passive Integrated Transponders Causes Disruption of the Cutaneous Bacterial Community and Proliferation of Opportunistic Fungi. Appl Environ Microbiol. 2014;80:4779–84.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    15.
    Bates KA, Shelton JMG, Mercier VL, Hopkins KP, Harrison XA, Petrovan SO, et al. Captivity and Infection by the Fungal Pathogen Batrachochytrium salamandrivorans Perturb the Amphibian Skin Microbiome. Front Microbiol. 2019;10:1834.
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Gimblet C, Meisel JS, Loesche MA, Cole SD, Horwinski J, Novais FO, et al. Cutaneous Leishmaniasis Induces a Transmissible Dysbiotic Skin Microbiota that Promotes Skin Inflammation. Cell Host Microbe. 2017;22:13–24.e4.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    17.
    Jani AJ, Briggs CJ. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc Natl Acad Sci. 2014;111:E5049–E5058.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Kong HH, Oh J, Deming C, Conlan S, Grice EA, Beatson MA, et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 2012;22:850–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    19.
    Longcore JE, Pessier AP, Nichols DK. Batrachochytrium Dendrobatidis gen. et sp. nov., a Chytrid Pathogenic to Amphibians. Mycologia. 1999;91:219–27.
    Article  Google Scholar 

    20.
    Berger L, Speare R, Daszak P, Green DE, Cunningham AA, Goggin CL, et al. Chytridiomycosis causes amphibian mortality associated with population declines in the rain forests of Australia and Central America. Proc Natl Acad Sci. 1998;95:9031–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    Crawford AJ, Lips KR, Bermingham E. Epidemic disease decimates amphibian abundance, species diversity, and evolutionary history in the highlands of central Panama. Proc Natl Acad Sci. 2010;107:13777–82.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Lips KR, Brem F, Brenes R, Reeve JD, Alford RA, Voyles J, et al. Emerging infectious disease and the loss of biodiversity in a Neotropical amphibian community. Proc Natl Acad Sci USA. 2006;103:3165–70.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    23.
    Vredenburg VT, Knapp RA, Tunstall TS, Briggs CJ. Dynamics of an emerging disease drive large-scale amphibian population extinctions. Proc Natl Acad Sci. 2010;107:9689–94.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Bletz MC, Loudon AH, Becker MH, Bell SC, Woodhams DC, Minbiole KPC, et al. Mitigating amphibian chytridiomycosis with bioaugmentation: characteristics of effective probiotics and strategies for their selection and use. Ecol Lett. 2013;16:807–20.
    PubMed  Article  PubMed Central  Google Scholar 

    25.
    Hardy BM, Pope KL, Piovia-Scott J, Brown RN, Foley JE. Itraconazole treatment reduces Batrachochytrium dendrobatidis prevalence and increases overwinter field survival in juvenile Cascades frogs. Dis Aquat Organ. 2015;112:243–50.
    PubMed  Article  PubMed Central  Google Scholar 

    26.
    McMahon TA, Sears BF, Venesky MD, Bessler SM, Brown JM, Deutsch K, et al. Amphibians acquire resistance to live and dead fungus overcoming fungal immunosuppression. Nature. 2014;511:224–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    27.
    Harris RN, Brucker RM, Walke JB, Becker MH, Schwantes CR, Flaherty DC, et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 2009;3:818–24.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Muletz CR, Myers JM, Domangue RJ, Herrick JB, Harris RN. Soil bioaugmentation with amphibian cutaneous bacteria protects amphibian hosts from infection by Batrachochytrium dendrobatidis. Biol Conserv. 2012;152:119–26.
    Article  Google Scholar 

    29.
    Becker MH, Harris RN, Minbiole KPC, Schwantes CR, Rollins-Smith LA, Reinert LK, et al. Towards a Better Understanding of the Use of Probiotics for Preventing Chytridiomycosis in Panamanian Golden Frogs. Ecohealth. 2011;8:501–6.
    PubMed  Article  PubMed Central  Google Scholar 

    30.
    Woodhams DC, Geiger CC, Reinert LK, Rollins-Smith LA, Lam B, Harris RN, et al. Treatment of amphibians infected with chytrid fungus: learning from failed trials with itraconazole, antimicrobial peptides, bacteria, and heat therapy. Dis Aquat Organ. 2012;98:11–25.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Belden LK, Hughey MC, Rebollar EA, Umile TP, Loftus SC, Burzynski EA, et al. Panamanian frog species host unique skin bacterial communities. Front Microbiol. 2015; 6:1171.

    32.
    Bletz MC, Goedbloed DJ, Sanchez E, Reinhardt T, Tebbe CC, Bhuju S, et al. Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions. Nat Commun. 2016;7:13699.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Jani AJ, Briggs CJ. Host and Aquatic Environment Shape the Amphibian Skin Microbiome but Effects on Downstream Resistance to the Pathogen Batrachochytrium dendrobatidis Are Variable. Front Microbiol. 2018;9:487.
    PubMed  PubMed Central  Article  Google Scholar 

    34.
    Kueneman JG, Parfrey LW, Woodhams DC, Archer HM, Knight R, McKenzie VJ. The amphibian skin-associated microbiome across species, space and life history stages. Mol Ecol. 2014;23:1238–50.
    PubMed  PubMed Central  Article  Google Scholar 

    35.
    Kueneman JG, Bletz MC, McKenzie VJ, Becker CG, Joseph MB, Abarca JG, et al. Community richness of amphibian skin bacteria correlates with bioclimate at the global scale. Nat Ecol Evol. 2019;3:381–9.
    PubMed  Article  PubMed Central  Google Scholar 

    36.
    Küng D, Bigler L, Davis LR, Gratwicke B, Griffith E, Woodhams DC. Stability of Microbiota Facilitated by Host Immune Regulation: Informing Probiotic Strategies to Manage Amphibian Disease. PLoS ONE. 2014;9:e87101.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    37.
    McKenzie VJ, Bowers RM, Fierer N, Knight R, Lauber CL. Co-habiting amphibian species harbor unique skin bacterial communities in wild populations. ISME J. 2012;6:588–96.
    CAS  Article  Google Scholar 

    38.
    Prest TL, Kimball AK, Kueneman JG, McKenzie VJ. Host-associated bacterial community succession during amphibian development. Mol Ecol. 2018;27:1992–2006.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Rebollar EA, Hughey MC, Medina D, Harris RN, Ibáñez R, Belden LK. Skin bacterial diversity of Panamanian frogs is associated with host susceptibility and presence of Batrachochytrium dendrobatidis. ISME J. 2016;10:1682–95.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    40.
    Harrison XA, Price SJ, Hopkins K, Leung WTM, Sergeant C, Garner TWJ. Diversity-Stability Dynamics of the Amphibian Skin Microbiome and Susceptibility to a Lethal Viral Pathogen. Front Microbiol. 2019;10:2883.
    PubMed  PubMed Central  Article  Google Scholar 

    41.
    Jani AJ, Knapp RA, Briggs CJ. Epidemic and endemic pathogen dynamics correspond to distinct host population microbiomes at a landscape scale. Proc R Soc B-Biol Sci. 2017;284:20170944.
    Article  Google Scholar 

    42.
    Walke JB, Becker MH, Loftus SC, House LL, Teotonio TL, Minbiole KPC, et al. Community Structure and Function of Amphibian Skin Microbes: an Experiment with Bullfrogs Exposed to a Chytrid Fungus. PLOS ONE. 2015;10:e0139848.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    43.
    Knutie SA, Wilkinson CL, Kohl KD, Rohr JR. Early-life disruption of amphibian microbiota decreases later-life resistance to parasites. Nat Commun. 2017;8:86.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    44.
    Rachowicz LJ, Knapp RA, Morgan JA, Stice MJ, Vredenburg VT, Parker JM, et al. Emerging infectious disease as a proximate cause of amphibian mass mortality. Ecology. 2006;87:1671–83.
    PubMed  Article  PubMed Central  Google Scholar 

    45.
    Jones MEB, Paddock D, Bender L, Allen JL, Schrenzel MD, Pessier AP. Treatment of chytridiomycosis with reduced-dose itraconazole. Dis Aquat Organ. 2012;99:243–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    46.
    Brannelly LA. Reduced Itraconazole Concentration and Durations Are Successful in Treating Batrachochytrium dendrobatidis Infection in Amphibians. JOVE-J Vis Exp. 2014;85:e51166.
    Google Scholar 

    47.
    Hyatt AD, Boyle DG, Olsen V, Boyle DB, Berger L, Obendorf D, et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis Aquat Organ. 2007;73:175–92.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    48.
    Boyle DG, Boyle DB, Olsen V, Morgan JAT, Hyatt AD. Rapid quantitative detection of chytridiomycosis (Batrachochytrium dendrobatidis) in amphibian samples using real-time Taqman PCR assay. Dis Aquat Organ. 2004;60:141–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    49.
    Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform. Appl Environ Microbiol. 2013;79:5112–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    50.
    Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1–e1.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    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 

    53.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–596.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK, Pietroni C, et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat Commun. 2017;8:1188.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    55.
    Arisdakessian C, Cleveland SB, Belcaid M. MetaFlow|mics: Scalable and Reproducible Nextflow Pipelines for the Analysis of Microbiome Marker Data. Pract Exp Adv Res Comput. 2020. Association for Computing Machinery, New York, NY, USA, pp 120–4.

    56.
    Lozupone C, Knight R. UniFrac: a New Phylogenetic Method for Comparing Microbial Communities. Appl Environ Microbiol. 2005;71:8228–35.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    57.
    Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
    Google Scholar 

    58.
    Anderson MJ. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley statsref: statistics reference online. American Cancer Society;2017. p. 1–15.

    59.
    Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.
    PubMed  PubMed Central  Article  Google Scholar 

    60.
    Joseph MB, Knapp RA. Disease and climate effects on individuals jointly drive post-reintroduction population dynamics of an endangered amphibian. bioRxiv. 2018; 332114.

    61.
    SanMiguel AJ, Meisel JS, Horwinski J, Zheng Q, Bradley CW, Grice EA. Antiseptic Agents Elicit Short-Term, Personalized, and Body Site–Specific Shifts in Resident Skin Bacterial Communities. J Investig Dermatol. 2018;138:2234–43.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Volkman J. Sterols in microorganisms. Appl Microbiol Biotechnol. 2003;60:495–506.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    63.
    Niño DF, Cauvi DM, De Maio A. Itraconazole, a Commonly Used Antifungal, Inhibits Fcγ Receptor–Mediated Phagocytosis: Alteration of Fcγ Receptor Glycosylation and Gene Expression. Shock. 2014;42:52.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    64.
    Tang C, Kamiya T, Liu Y, Kadoki M, Kakuta S, Oshima K, et al. Inhibition of Dectin-1 Signaling Ameliorates Colitis by Inducing Lactobacillus-Mediated Regulatory T Cell Expansion in the Intestine. Cell Host Microbe. 2015;18:183–97.
    CAS  Article  Google Scholar 

    65.
    Zuo T, Wong SH, Cheung CP, Lam K, Lui R, Cheung K, et al. Gut fungal dysbiosis correlates with reduced efficacy of fecal microbiota transplantation in Clostridium difficile infection. Nat Commun. 2018;9:3663.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    66.
    Zaneveld JR, McMinds R, Vega Thurber R. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat Microbiol. 2017;2:17121.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    67.
    Wilber MQ, Jani AJ, Mihaljevic JR, Briggs CJ. Fungal infection alters the selection, dispersal and drift processes structuring the amphibian skin microbiome. Ecol Lett. 2019;23:88–98.
    PubMed  Article  PubMed Central  Google Scholar 

    68.
    Loudon AH, Woodhams DC, Parfrey LW, Archer H, Knight R, McKenzie V, et al. Microbial community dynamics and effect of environmental microbial reservoirs on red-backed salamanders (Plethodon cinereus). ISME J. 2013;8:830–40.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    69.
    Santillan E, Constancias F, Wuertz S. Press Disturbance Alters Community Structure and Assembly Mechanisms of Bacterial Taxa and Functional Genes in Mesocosm-Scale Bioreactors. mSystems. 2020;5:e00471–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Rebollar EA, Gutiérrez-Preciado A, Noecker C, Eng A, Hughey MC, Medina D, et al. The Skin Microbiome of the Neotropical Frog Craugastor fitzingeri: inferring Potential Bacterial-Host-Pathogen Interactions From Metagenomic Data. Front Microbiol. 2018;9:466.
    PubMed  Article  PubMed Central  Google Scholar 

    71.
    Mountain Yellow-legged Frog Interagency Technical Team. Interagency Conservation Strategy for Mountain Yellow-legged Frogs in the Sierra Nevada (Rana sierrae and Rana muscosa). Version 1. California Department of Fish and Wildlife, National Park Service, U.S. Fish and Wildlife Service, U.S. Forest Service; 2018. More

  • in

    Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes

    1.
    Warren J, Topping CJ, James P. A unifying evolutionary theory for the biomass–diversity–fertility relationship. Theor Ecol. 2009;2:119–26.
    Article  Google Scholar 
    2.
    Al-Mufti MM, Sydes CL, Furness SB, Grime JP, Band SR. A quantitative analysis of shoot phenology and dominance in herbaceous vegetation. J Ecol. 1977;65:759–91.
    Article  Google Scholar 

    3.
    Grace JB, Anderson TM, Seabloom EW, Borer ET, Adler PB, Harpole WS, et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature. 2016;529:390–3.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    4.
    Hooper DU, Chapin FS III, Ewel JJ, Hector A, Inchausti P, Lavorel S, et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol Monogr. 2005;75:3–35.
    Article  Google Scholar 

    5.
    Tilman D, Wedin D, Knops J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature. 1996;379:718–20.
    CAS  Article  Google Scholar 

    6.
    Grace JB. The factors controlling species density in herbaceous plant communities: an assessment. Perspect Plant Ecol. 1999;2:1–28.
    Article  Google Scholar 

    7.
    Grime JP. Plant strategies and vegetation processes. Chichester-New York-Brisbane-Toronto: John Wiley & Sons, Ltd.; 1979.

    8.
    Loreau M, Hector A. Partitioning selection and complementarity in biodiversity experiments. Nature. 2001;412:72–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Michalet R, Brooker RW, Cavieres LA, Kikvidze Z, Lortie CJ, Pugnaire FI, et al. Do biotic interactions shape both sides of the humped-back model of species richness in plant communities? Ecol Lett. 2006;9:767–73.
    PubMed  Article  PubMed Central  Google Scholar 

    10.
    Rajaniemi TK. Explaining productivity-diversity relationships in plants. Oikos. 2003;101:449–57.
    Article  Google Scholar 

    11.
    Wardle DA, Bonner KI, Barker GM, Yeates GW, Nicholson KS, Bardgett RD, et al. Plant remobals in perennial grassland: vegetation dynamics, decomposers, soil biodiversity, and ecosystem properties. Ecol Monogr. 1999;69:535–68.
    Article  Google Scholar 

    12.
    Fraser LH, Pither J, Jentsch A, Sternberg M, Zobel M, Askarizadeh D, et al. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science. 2015;349:302–5.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Adler PB, Seabloom EW, Borer ET, Hillebrand H, Hautier Y, Hector A, et al. Productivity is a poor predictor of plant species richness. Science. 2011;333:1750–3.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Bastida F, García C, Fierer N, Eldridge DJ, Bowker MA, Abades S, et al. Global ecological predictors of the soil priming effect. Nat Commun. 2019;10:3481.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    15.
    Crowther TW, van den Hoogen J, Wan J, Mayes MA, Keiser AD, Mo L, et al. The global soil community and its influence on biogeochemistry. Science. 2019;365:eaav0550.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol 2017;15:579–90.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Tedersoo L, Bahram M, Põlme S, Kõljalg U, Yorou NS, Wijesundera R, et al. Global diversity and geography of soil fungi. Science. 2014;346:1256688.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    20.
    Bardgett RD, Wardle DA. Herbivore-mediated linkages between aboveground and belowground communities. Ecology. 2003;84:2258–68.
    Article  Google Scholar 

    21.
    Wardle DA. Communities and ecosystems linking the aboveground and belowground components (MPB-34). Princeton (New Jersey): Princeton University Press; 2002.

    22.
    Geyer KM, Barrett JE. Unimodal productivity–diversity relationships among bacterial communities in a simple polar soil ecosystem. Environ Microbiol. 2019;21:2523–32.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    23.
    Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Wardle DA. A comparative assessment of factors which influence microbial biomass carbon and nitrogen levels in soil. Biol Rev. 1992;67:321–58.
    Article  Google Scholar 

    25.
    Geyer KM, Altrichter AE, Van Horn DJ, Takacs-Vesbach CD, Gooseff MN, Barrett JE. Environmental controls over bacterial communities in polar desert soils. Ecosphere. 2013;4:art127.
    Article  Google Scholar 

    26.
    Langenheder S, Prosser JI. Resource availability influences the diversity of a functional group of heterotrophic soil bacteria. Environ Microbiol. 2008;10:2245–56.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Hopkins FM, Torn MS, Trumbore SE. Warming accelerates decomposition of decades-old carbon in forest soils. Proc Natl Acad Sci USA. 2012;109:1753–61.
    Article  Google Scholar 

    28.
    Lal R. Soil carbon sequestration impacts on global climate change and food security. Science. 2004;304:1623–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Bertness MD, Callaway R. Positive interactions in communities. Trends Ecol Evol. 1994;9:191–3.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    30.
    Hammarlund SP, Harcombe WR. Refining the stress gradient hypothesis in a microbial community. Proc Natl Acad Sci USA. 2019;116:15760.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Bastida F, Torres IF, Moreno JL, Baldrian P, Ondoño S, Ruiz-Navarro A, et al. The active microbial diversity drives ecosystem multifunctionality and is physiologically related to carbon availability in Mediterranean semi-arid soils. Mol Ecol. 2016;25:4660–73.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    32.
    Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:10541.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Wagg C, Bender SF, Widmer F, van der Heijden MGA. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc Natl Acad Sci USA. 2014;111:5266–70.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    34.
    Wieder WR, Allison SD, Davidson EA, Georgiou K, Hararuk O, He Y, et al. Explicitly representing soil microbial processes in Earth system models. Glob Biogeochem Cycles. 2015;29:1782–1800.
    CAS  Article  Google Scholar 

    35.
    Glassman SI, Weihe C, Li J, Albright MBN, Looby CI, Martiny AC, et al. Decomposition responses to climate depend on microbial community composition. Proc Natl Acad Sci USA. 2018;115:11994–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    36.
    Maestre FT, Quero J, Gotelli NJ, Escudero A, Ochoa V, Delgado-baquerizo M, et al. Plant species richness and ecosystem multifunctionality in global drylands. Science. 2012;335:214–8.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Delgado-Baquerizo M, Bardgett RD, Vitousek PM, Maestre FT, Williams MA, Eldridge DJ, et al. Changes in belowground biodiversity during ecosystem development. Proc Natl Acad Sci USA. 2019;116:6891–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    38.
    Kettler TA, Doran JW, Gilbert TL. Simplified method for soil particle-size determination to accompany soil-quality analyses. Soil Science Society of America journal. vol. 65. Lincoln, Nebraska: 2001. p. 849–52. Journal Series no. 13277 of the Agric Res Div, Univ Neb, Linc, Ne.

    39.
    Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959;37:911–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Buyer JS, Sasser M. High throughput phospholipid fatty acid analysis of soils. Appl Soil Ecol. 2012;61:127–30.
    Article  Google Scholar 

    41.
    Frostegård A, Bååth E. The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil. Biol Fertil Soils. 1996;22:59–65.
    Article  Google Scholar 

    42.
    Rinnan R, Bååth E. Differential utilization of carbon substrates by bacteria and fungi in tundra soil. Appl Environ Microbiol. 2009;75:3611–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Kaiser C, Frank A, Wild B, Koranda M, Richter A. Negligible contribution from roots to soil-borne phospholipid fatty acid fungal biomarkers 18:2ω6,9 and 18:1ω9. Soil Biol Biochem. 2010;42:1650–2.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Frostegård A, Tunlid A, Bååth E. Use and misuse of PLFA measurements in soils. Soil Biol Biochem. 2011;43:1621–5.
    Article  CAS  Google Scholar 

    45.
    Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    46.
    Ramirez KS, Leff JW, Barberán A, Bates ST, Betley J, Crowther TW, et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc R Soc B. 2014;281:20141988.
    PubMed  Article  PubMed Central  Google Scholar 

    47.
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    48.
    Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    49.
    Breiman L. Random forests. Mach Learn. 2001;45:5–32.
    Article  Google Scholar 

    50.
    Delgado-Baquerizo M, Giaramida L, Reich PB, Khachane AN, Hamonts K, Edwards C, et al. Lack of functional redundancy in the relationship between microbial diversity and ecosystem functioning. J Ecol. 2016;104:936–46.
    Article  Google Scholar 

    51.
    Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. New York: Springer; 2003.

    52.
    Grace JB. Structural equation modeling and natural systems. Cambridge: Cambridge University Press; 2006.

    53.
    Quinlan JR. Combining instance-based and model-based learning. In: Proceedings of the Tenth International Conference on International Conference on Machine Learning. Amherst, MA, USA: Morgan Kaufmann Publishers Inc.; 1993.

    54.
    Delgado-Baquerizo M. Obscure soil microbes and where to find them. ISME J. 2019;13:2120–4.
    PubMed  PubMed Central  Article  Google Scholar 

    55.
    Kuhn SW, Keefer C, Coulter N. Cubist: rule- and instance-based regression modeling. R package version 0.0.19; 2016.

    56.
    Bailey VL, Peacock AD, Smith JL, Bolton H. Relationships between soil microbial biomass determined by chloroform fumigation-extraction, substrate-induced respiration, and phospholipid fatty acid analysis. Soil Biol Biochem. 2002;34:1385–9.
    CAS  Article  Google Scholar 

    57.
    Fierer N, Strickland MS, Liptzin D, Bradford MA, Cleveland CC. Global patterns in belowground communities. Ecol Lett. 2009;12:1238–49.
    PubMed  Article  PubMed Central  Google Scholar 

    58.
    Xu X, Thornton PE, Post WM. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems. Glob Ecol Biogeogr. 2013;22:737–49.
    Article  Google Scholar 

    59.
    Six J, Frey SD, Thiet RK, Batten KM. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci Soc Am J. 2006;70:555–69.
    CAS  Article  Google Scholar 

    60.
    Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;348:1–11.
    Google Scholar 

    61.
    Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:17105.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Fierer N, Jackson RB. The diversity and biogeography of soil bacterial communities. Proc Natl Acad Sci USA. 2006;103:626–31.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    63.
    Maestre FT, Delgado-Baquerizo M, Jeffries TC, Eldridge DJ, Ochoa V, Gozalo B, et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc Natl Acad Sci USA. 2015;112:15684–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    64.
    Delgado-Baquerizo M, Eldridge DJ. Cross-biome drivers of soil bacterial alpha diversity on a worldwide scale. Ecosystems. 2019;22:1220–31.
    Article  Google Scholar 

    65.
    Větrovský T, Kohout P, Kopecký M, Machac A, Man M, Bahnmann BD, et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat Commun. 2019;10:5142.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    66.
    Gaston KJ. Global patterns in biodiversity. Nature. 2000;405:220–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    67.
    Srivastava DS, Lawton JH. Why more productive sites have more species: an experimental test of theory using tree-hole communities. Am Naturalist. 1998;152:510–29.
    CAS  Article  Google Scholar 

    68.
    Storch D, Bohdalková E, Okie J. The more-individuals hypothesis revisited: the role of community abundance in species richness regulation and the productivity–diversity relationship. Ecol Lett. 2018;21:920–37.
    PubMed  Article  PubMed Central  Google Scholar 

    69.
    Paquette A, Messier C. The effect of biodiversity on tree productivity: from temperate to boreal forests. Glob Ecol Biogeogr. 2011;20:170–80.
    Article  Google Scholar 

    70.
    Dorrepaal E, Toet S, van Logtestijn RSP, Swart E, van de Weg MJ, Callaghan TV, et al. Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. Nature. 2009;460:616–9.
    CAS  Article  Google Scholar 

    71.
    Melillo JM, Butler S, Johnson J, Mohan J, Steudler P, Lux H, et al. Soil warming, carbon–nitrogen interactions, and forest carbon budgets. Proc Natl Acad Sci USA. 2011;108:9508–12.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    72.
    Crowther TW, Todd-Brown KEO, Rowe CW, Wieder WR, Carey JC, Machmuller MB, et al. Quantifying global soil carbon losses in response to warming. Nature. 2016;540:104–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    73.
    Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S. Agricultural sustainability and intensive production practices. Nature. 2002;418:671–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    74.
    Navarrete AA, Tsai SM, Mendes LW, Faust K, de Hollander M, Cassman NA, et al. Soil microbiome responses to the short-term effects of Amazonian deforestation. Mol Ecol. 2015;24:2433–48.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    75.
    Rodrigues JLM, Pellizari VH, Mueller R, Baek K, Jesus EdC, Paula FS, et al. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc Natl Acad Sci USA. 2013;110:988–93.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    76.
    Bastida F, García C, von Bergen M, Moreno JL, Richnow HH, Jehmlich N. Deforestation fosters bacterial diversity and the cyanobacterial community responsible for carbon fixation processes under semiarid climate: a metaproteomics study. Appl Soil Ecol. 2015;93:65–7.
    Article  Google Scholar 

    77.
    Huang J, Yu H, Guan X, Wang G, Guo R. Accelerated dryland expansion under climate change. Nat Clim Change. 2016;6:166–71.
    Article  Google Scholar 

    78.
    Maron PA, Sarr A, Kaisermann A, Léveque J, Mathieu O, Guigue J, et al. High microbial diversity promotes soil ecosystem functioning. Appl Environ Microbiol. 2018;84:e02738–17.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    79.
    Chen C, Chen HYH, Chen X, Huang Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat Commun. 2019;10:1332.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    80.
    Delgado-Baquerizo M, Grinyer J, Reich PB, Singh BK. Relative importance of soil properties and microbial community for soil functionality: insights from a microbial swap experiment. Funct Ecol. 2016;30:1862–73.
    Article  Google Scholar 

    81.
    Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World Map of the Köppen-Geiger climate classification updated. Meteorol. Z. 2006;15:259–63.
    Article  Google Scholar  More

  • in

    Nickel excess affects phenology and reproductive attributes of Asterella wallichiana and Plagiochasma appendiculatum growing in natural habitats

    1.
    Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Cardinale, B. J., Gonzalez, A., Allington, G. R. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conserv. 219, 175–183 (2018).
    Article  Google Scholar 

    3.
    Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    4.
    Tovar-Sánchez, E., Hernández-Plata, I., Martínez, M. S., Valencia-Cuevas, L. & Galante, P. M. Heavy metal pollution as a biodiversity threat. Heavy Met. 383 (2018).

    5.
    Das, K. K., Das, S. N. & Dhundasi, S. A. Nickel, its adverse health effects & oxidative stress. Indian J. Med. Res. 128, 412 (2008).
    CAS  PubMed  PubMed Central  Google Scholar 

    6.
    Fabiano, C., Tezotto, T., Favarin, J. L., Polacco, J. C. & Mazzafera, P. Essentiality of nickel in plants: A role in plant stresses. Front. Plant Sci. 6, 754 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    7.
    Sreekanth, T.V.M., Nagajyothi, P. C., Lee, K. D. & Prasad, T.N.V.K.V. Occurrence, physiological responses and toxicity of nickel in plants. Int.J.Environ.Sci.Technol.10(5), 1129–1140 (2013).

    8.
    Pietrini, F. et al. Evaluation of nickel tolerance in Amaranthus paniculatus L. plants by measuring photosynthesis, oxidative status, antioxidative response and metal-binding molecule content. Environ. Sci. Pollut.22, 482–494 (2015).

    9.
    Georgiadou, E. C. et al. Influence of heavy metals (Ni, Cu and Zn) on nitro-oxidative stress responses, proteome regulation and allergen production in basil (Ocimum basilicum L.) plants. Front. Plant Sci.9, 862 (2018).

    10.
    Shahid, M. et al. Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. J. Hazard. Mater. 325, 36–58 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Shen, Z. J., Chen, Y. S. & Zhang, Z. Heavy metals translocation and accumulation from the rhizosphere soils to the edible parts of the medicinal plant Fengdan (Paeonia ostii) grown on a metal mining area. China. Ecotoxicol. Environ. Saf. 143, 19–27 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Xun, E., Zhang, Y., Zhao, J. & Guo, J. Translocation of heavy metals from soils into floral organs and rewards of Cucurbita pepo: Implications for plant reproductive fitness. Ecotox. Environ. Safe. 145, 235–243 (2017).
    CAS  Article  Google Scholar 

    13.
    Meindl, G. A. & Ashman, T. L. Effects of soil metals on pollen germination, fruit production, and seeds per fruit differ between a Ni hyperaccumulator and a congeneric nonaccumulator. Plant Soil. 420, 493–503 (2017).
    CAS  Article  Google Scholar 

    14.
    Temizer, İK., Güder, A., Temel, F. A. & Esin, A. V. C. I. A comparison of the antioxidant activities and biomonitoring of heavy metals by pollen in the urban environments. Environ. Monit. Assess. 190, 462 (2018).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    15.
    Baumann, H. A., Morrison, L. & Stengel, D. B. Metal accumulation and toxicity measured by PAM—Chlorophyll fluorescence in seven species of marine macroalgae. Ecotoxicol. Environ. Saf. 72, 1063–1075 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Liang, S. et al. How Chlorella sorokiniana and its high tolerance to Pb might be a potential Pb biosorbent. Pol. J. Environ. Stud. 26, 1139–1146 (2017).
    CAS  Article  Google Scholar 

    17.
    Ares, A., Itouga, M., Kato, Y. & Sakakibara, H. Differential Metal Tolerance and Accumulation Patterns of Cd, Cu, Pb and Zn in the Liverwort Marchantia polymorpha L. B. Environ. Contam. Tox. 100, 444–450 (2018).
    CAS  Article  Google Scholar 

    18.
    Stanković, J. D., Sabovljević, A. D. & Sabovljević, M. S. Bryophytes and heavy metals: A review. Acta Bot. Croat. 77, 109–118 (2018).
    Article  Google Scholar 

    19.
    Wang, S., Zhang, Z. & Wang, Z. Bryophyte communities as biomonitors of environmental factors in the Goujiang karst bauxite, southwestern China. Sci. Total Environ. 538, 270–278 (2015).
    ADS  CAS  PubMed  Article  Google Scholar 

    20.
    Vanderpoorten, A. et al. To what extent are bryophytes efficient dispersers?. J. Ecol. 107, 2149–2154 (2019).
    Article  Google Scholar 

    21.
    Carginale, V. et al. Accumulation, localisation, and toxic effects of cadmium in the liverwort Lunularia cruciata. Protoplasma. 223, 53–61 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Yan, Y., Zhang, Q., Wang, G. G. & Fang, Y. M. Atmospheric deposition of heavy metals in Wuxi, China: Estimation based on native moss analysis. Ecotox. Environ. Safe. 188, 360 (2016).
    Google Scholar 

    23.
    Gupta, R. & Asthana, A. K. Diversity and distribution of liverworts across habitats and altitudinal gradient at Pachmarhi Biosphere Reserve (India). Plant Sci. Today 3, 354–359 (2016).
    Article  Google Scholar 

    24.
    Gao, S., Yu, H. N., Xu, R. X., Cheng, A. X. & Lou, H. X. Cloning and functional characterization of a 4-coumarate CoA ligase from liverwort Plagiochasma appendiculatum. Phytochemistry 111, 48–58 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    25.
    Wu, Y. F. et al. A bHLH Transcription factor regulates bisbibenzyl biosynthesis in the liverwort Plagiochasma appendiculatum. Plant Cell Physiol. 59, 1187–1199 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    26.
    Venugopal, M. & Nair, M. C. Bryophyte diversity of Thamarassery pass (Wayanad pass) in the Western Ghats of Kerala. Plant Sci. Today 4, 41–48 (2017).
    Article  Google Scholar 

    27.
    Pant, G. & Tewari, S. D. Bryophytes as Biogeoindicators: Bryophytic Associations of Mineral-Enriched Substrates in Kumaon Himalaya. Topics in Bryology 165–184 (Allied Publishers Ltd., New Delhi, 1998).
    Google Scholar 

    28.
    Ghate, S. & Chaphekar, S. B. Plagiochasma appendiculatum as a biotest for water quality assessment. Environ. Pollut. 108, 173–181 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Choudhary, S.P., Kanwar, M., Bhardwaj, R., Yu, J.Q. & Tran, L.S.P. Chromium stress mitigation by polyamine-brassinosteroid application involves phytohormonal and physiological strategies in Raphanus sativus L. PLoS One. 7(3) (2012).

    30.
    Bai, C., Liu, L. & Wood, B. W. Nickel affects xylem Sap RNase a and converts RNase A to a urease. BMC Plant Biol. 13, 207 (2013).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    31.
    Bai, C., Reilly, C. C. & Wood, B. W. Nickel deficiency disrupts metabolism of ureides, amino acids, and organic acids of young pecan foliage. Plant Physiol. 140, 433–443 (2006).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    Kandeler, E. & Gerber, H. Short-term assay of soil urease activity using colorimetric determination of ammonium. Biol. Fertil. Soils. 6, 68–72 (1988).
    CAS  Article  Google Scholar 

    33.
    Poonkothai, M. V. B. S. & Vijayavathi, B. S. Nickel as an essential element and a toxicant. Int. J. Environ. Sci. 1, 285–288 (2012).
    Google Scholar 

    34.
    Freitas, D. S. et al.Hidden nickel deficiency? Nickel fertilization via soil improves nitrogen metabolism and grain yield in soybean genotypes. Front. Plant Sci.9(2018).

    35.
    Rout, G. R. & Das, P. Effect of metal toxicity on plant growth and metabolism: I. Zinc. in Sustainable Agriculture (pp. 873–884). (Springer, Dordrecht, 2009).

    36.
    Myking, T. et al.Effects of Air Pollution from a Nickel-Copper Industrial Complex on Boreal Forest Vegetation in the Joint Russian-Norwegian-Finnish Border Area (2009).

    37.
    Kowalska, J. B., Mazurek, R., Gąsiorek, M. & Zaleski, T. Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination—A review. Environ. Geochem. Health. 1–26 (2018).

    38.
    Awadh, S. M., Al-Kilabi, J. A. & Khaleefah, N. H. Comparison the geochemical background, threshold and anomaly with pollution indices in the assessment of soil pollution: Al-Hawija, north of Iraq case study. Int. J. Sci. Res. 4, 2357–2363 (2015).
    Google Scholar 

    39.
    Dung, T. T. T., Cappuyns, V., Swennen, R. & Phung, N. K. From geochemical background determination to pollution assessment of heavy metals in sediments and soils. Rev. Environ. Sci. Biotechnol. 12, 335–353 (2013).
    CAS  Article  Google Scholar 

    40.
    Mazurek, R. et al. Assessment of heavy metals contamination in surface layers of Roztocze National Park forest soils (SE Poland) by indices of pollution. Chemosphere 168, 839–850 (2017).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    41.
    Čecháková, K., Motyka, O., Válová, E., Macečková, B. & Stalmachová, B. Investigation of the influence of nickel in precipitation through the surface properties of moss Pleurozium schreberi Carpath. J. Earth Environ. 9, 153–158 (2014).
    Google Scholar 

    42.
    Marchiol, L., Assolari, S., Sacco, P. & Zerbi, G. Phytoextraction of heavy metals by canola (Brassica napus) and radish (Raphanus sativus) grown on multiexcess soil. Environ. Pollut. 132, 21–27 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    43.
    Tuna, A. L., Burun, B., Yokas, I. & Coban, E. The effects of heavy metals on pollen germination and pollen tube length in the tobacco plant. Turk. J. Biol. 26, 109–113 (2002).
    CAS  Google Scholar 

    44.
    Mostofa, M. G., Hossain, M. A., Fujita, M. & Tran, L. S. P. Physiological and biochemical mechanisms associated with trehalose-induced copper-stress tolerance in rice. Sci. Rep. 5, 11433 (2015).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    Choudhury, S. & Panda, S. K. Induction of oxidative stress and ultrastructural changes in moss Taxithelium nepalense (Schwaegr.) Broth. under lead and arsenic phytotoxicity. Curr. Sci. 342–348 (2004).

    46.
    Choudhury, S. & Panda, S. K. Toxic effects, oxidative stress and ultrastructural changes in moss Taxithelium nepalense (Schwaegr.) Broth. under chromium and lead phytotoxicity. Water Air Soil Pollut.167, 73–90 (2005).

    47.
    Penny, C., Dickinson, N. M. & Lepp, N. W. The effect of heavy metal contamination on the pigment profiles of Torreya sp. in Remediation and Management of Degraded Lands. (2018).

    48.
    Rau, S., Miersch, J., Neumann, D., Weber, E. & Krauss, G. J. Biochemical responses of the aquatic moss Fontinalis antipyretica to Cd, Cu, Pb and Zn determined by chlorophyll fluorescence and protein levels. Environ. Exp. Bot. 59, 299–306 (2007).
    CAS  Article  Google Scholar 

    49.
    Foyer, C. H. & Noctor, G. Ascorbate and glutathione: The heart of the redox hub. Plant Physiol. 155, 2–18 (2011).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    50.
    Hasanuzzaman, M., Nahar, K., Anee, T. I. & Fujita, M. Glutathione in plants: biosynthesis and physiological role in environmental stress tolerance. Physiol. Mol. Biol. Plants. 23, 249–268 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    51.
    Yadav, N. S., Shukla, P. S., Jha, A., Agarwal, P. K. & Jha, B. The SbSOS1 gene from the extreme halophyte Salicornia brachiata enhances Na+ loading in xylem and confers salt tolerance in transgenic tobacco. BMC Plant Biol. 12, 188 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    Subbiah, B. V. & Asija, G. L. A rapid procedure for estimation of available nitrogen in soils. Curr Sci. 25, 259–260 (1956).
    CAS  Google Scholar 

    53.
    Olsen, S.R., Cole, C,V., Watanabe, F.S. & Dean, L. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate. U.S.D.A. Circ. 939. (U.S. Govt. Printing Office: Washington, DC) (1954).

    54.
    Qingjie, G., Jun, D., Yunchuan, X., Qingfei, W. & Liqiang, Y. Calculating pollution indices by heavy metals in ecological geochemistry assessment and a case study in parks of Beijing. J. China Univ. Geosci. 19, 230–241 (2008).
    Article  Google Scholar 

    55.
    Choudhary, S. P., Kanwar, M., Bhardwaj, R., Gupta, B. D. & Gupta, R. K. Epibrassinolide ameliorates Cr (VI) stress via influencing the levels of indole-3-acetic acid, abscisic acid, polyamines and antioxidant system of radish seedlings. Chemosphere 84, 592–600 (2011).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    56.
    Lacy, R.C., P.S. Miller & Traylor-Holzer, K. Vortex 10 User’s Manual. 1 June 2018 update. IUCN SSC Conservation Breeding Specialist Group, and Chicago Zoological Society, Apple Valley, Minnesota, USA (2018).

    57.
    Brown, P. H., Welch, R. M. & Cary, E. E. Nickel: A micronutrient essential for higher plants. Plant Physiol. 85, 801–803 (1987).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    58.
    Seregin, I. V., Kozhevnikova, A. D., Kazyumina, E. M. & Ivanov, V. B. Nickel toxicity and distribution in maize roots. Russ. J. Plant Physiol. 50, 711–717 (2003).
    CAS  Article  Google Scholar 

    59.
    Shin, R., Berg, R. H. & Schachtman, D. P. Reactive oxygen species and root hairs in Arabidopsis root response to nitrogen, phosphorus and potassium deficiency. Plant Cell Physiol. 46, 1350–1357 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    60.
    Saeed, A. I. et al. TM4a free, open-source system for microarray data management and analysis. Biotechniques 34, 374–378 (2003).
    CAS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Species traits affect phenological responses to climate change in a butterfly community

    1.
    Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Change Biol. 13, 1860–1872. https://doi.org/10.1111/j.1365-2486.2007.01404.x (2007).
    ADS  Article  Google Scholar 
    2.
    Peñuelas, J. et al. Evidence of current impact of climate change on life: A walk from genes to the biosphere. Glob. Change Biol. 19, 2303–2338. https://doi.org/10.1111/gcb.12143 (2013).
    ADS  Article  Google Scholar 

    3.
    Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Change Biol. 16, 3304–3313. https://doi.org/10.1111/j.1365-2486.2010.02165.x (2010).
    ADS  Article  Google Scholar 

    4.
    Dapporto, L. et al. Rise and fall of island butterfly diversity: Understanding genetic differentiation and extinction in a highly diverse archipelago. Divers. Distrib. 23, 1169–1181. https://doi.org/10.1111/ddi.12610 (2017).
    Article  Google Scholar 

    5.
    Hendry, A. P., Farrugia, T. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29. https://doi.org/10.1111/j.1365-294X.2007.03428.x (2008).
    Article  PubMed  Google Scholar 

    6.
    Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Change 2, 121–124. https://doi.org/10.1038/nclimate1347 (2012).
    ADS  Article  Google Scholar 

    7.
    Forister, M. L. & Shapiro, A. M. Climatic trends and advancing spring flight of butterflies in lowland California. Glob. Change Biol. 9, 1130–1135. https://doi.org/10.1046/j.1365-2486.2003.00643.x (2003).
    ADS  Article  Google Scholar 

    8.
    Altermatt, F. Tell me what you eat and I’ll tell you when you fly: Diet can predict phenological changes in response to climate change. Ecol. Lett. 13, 1475–1484. https://doi.org/10.1111/j.1461-0248.2010.01534.x (2010).
    Article  PubMed  Google Scholar 

    9.
    Stefanescu, C., Penuelas, J. & Filella, I. Effects of climatic change on the phenology of butterflies in the northwest Mediterranean Basin. Glob. Change Biol. 9, 1494–1506. https://doi.org/10.1046/j.1365-2486.2003.00682.x (2003).
    ADS  Article  Google Scholar 

    10.
    Roy, D. B. & Sparks, T. H. Phenology of British butterflies and climate change. Glob. Change Biol. 6, 407–416. https://doi.org/10.1046/j.1365-2486.2000.00322.x (2000).
    ADS  Article  Google Scholar 

    11.
    Diez, J. M. et al. Forecasting phenology: from species variability to community patterns. Ecol. Lett. 15, 545–553. https://doi.org/10.1111/j.1461-0248.2012.01765.x (2012).
    Article  PubMed  Google Scholar 

    12.
    Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479. https://doi.org/10.1890/07-1748.1 (2008).
    Article  PubMed  Google Scholar 

    13.
    Glazaczow, A., Orwin, D. & Bogdziewicz, M. Increased temperature delays the late-season phenology of multivoltine insect. Sci. Rep. https://doi.org/10.1038/srep38022 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    14.
    van der Kolk, H.-J., WallisDeVries, M. F. & van Vliet, A. J. H. Using a phenological network to assess weather influences on first appearance of butterflies in the Netherlands. Ecol. Indicators 69, 205–212, https://doi.org/10.1016/j.ecolind.2016.04.028 (2016).

    15.
    Zografou, K. et al. Signals of climate change in butterfly communities in a mediterranean protected area. PLoS ONE 9, e87245. https://doi.org/10.1371/journal.pone.0087245 (2014).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    16.
    Visser, M. Keeping up with a warming world; assessing the rate of adaptation to climate change. Proc. Biol. Sci. R. Soc. 275, 649–659, https://doi.org/10.1098/rspb.2007.0997 (2008).

    17.
    Kharouba, H. M., Paquette, S. R., Kerr, J. T. & Vellend, M. Predicting the sensitivity of butterfly phenology to temperature over the past century. Glob. Change Biol. 20, 504–514. https://doi.org/10.1111/gcb.12429 (2014).
    ADS  Article  Google Scholar 

    18.
    Roy, D. B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Glob. Change Biol. 21, 3313–3322. https://doi.org/10.1111/gcb.12920 (2015).
    ADS  Article  Google Scholar 

    19.
    Rapacciuolo, G. et al. Beyond a warming fingerprint: Individualistic biogeographic responses to heterogeneous climate change in California. Glob. Change Biol. 20, 2841–2855. https://doi.org/10.1111/gcb.12638 (2014).
    ADS  Article  Google Scholar 

    20.
    Fischer, K. & Fiedler, K. Life-history plasticity in the butterfly Lycaena hippothoe: Local adaptations and trade-offs. Biol. J. Lin. Soc. 75, 173–185. https://doi.org/10.1046/j.1095-8312.2002.00014.x (2002).
    Article  Google Scholar 

    21.
    Zografou, K. Who flies first?—Habitat-specific phenological shifts of butterflies and orthopterans in the light of climate change: A case study from the south-east Mediterranean Lepidoptera and Orthoptera phenology change. Ecol. Entomol. 40, 562–574. https://doi.org/10.1111/een.12220 (2015).
    Article  Google Scholar 

    22.
    Suggitt Andrew, J. et al. Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120, 1–8, https://doi.org/10.1111/j.1600-0706.2010.18270.x (2010).

    23.
    Dell, D., Sparks, T. & Dennis, R. Climate change and the effect of increasing spring temperatures on emergence dates of the butterfly Apatura iris (Lepidoptera: Nymphalidae). Eur. J. Entomol. 102, 161–167. https://doi.org/10.14411/eje.2005.026 (2005).
    Article  Google Scholar 

    24.
    Zipf, L., Williams, E. H., Primack, R. B. & Stichter, S. Climate effects on late-season flight times of Massachusetts butterflies. Int. J. Biometeorol. 61, 1667–1673. https://doi.org/10.1007/s00484-017-1347-8 (2017).
    ADS  CAS  Article  PubMed  Google Scholar 

    25.
    Diamond, S. E., Frame, A. M., Martin, R. A. & Buckley, L. B. Species’ traits predict phenological responses to climate change in butterflies. Ecology 92, 1005–1012. https://doi.org/10.1890/i0012-9658-92-5-1005 (2011).
    Article  PubMed  Google Scholar 

    26.
    Melero, Y., Stefanescu, C. & Pino, J. General declines in Mediterranean butterflies over the last two decades are modulated by species traits. Biol. Cons. 201, 336–342. https://doi.org/10.1016/j.biocon.2016.07.029 (2016).
    Article  Google Scholar 

    27.
    Stefanescu, C., Peñuelas, J. & Filella, I. Butterflies highlight the conservation value of hay meadows highly threatened by land-use changes in a protected Mediterranean area. Biol. Cons. 126, 234–246. https://doi.org/10.1016/j.biocon.2005.05.010 (2005).
    Article  Google Scholar 

    28.
    Sparks, T. H., Huber, K. & Dennis, R. L. H. Complex phenological responses to climate warming trends? Lessons from history. Eur. J. Entomol. 103, 379–386 (2006).
    Article  Google Scholar 

    29.
    Wong, M. K. L., Guénard, B. & Lewis, O. T. Trait-based ecology of terrestrial arthropods. Biol. Rev. 94, 999–1022. https://doi.org/10.1111/brv.12488 (2019).
    Article  PubMed  Google Scholar 

    30.
    Gutiérrez, D. & Wilson, R. J. Intra- and interspecific variation in the responses of insect phenology to climate. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13348 (2020).
    Article  PubMed  Google Scholar 

    31.
    Zografou, K. et al. Butterfly phenology in Mediterranean mountains using space-for-time substitution. Ecol. Evolut. 10, 928–939. https://doi.org/10.1002/ece3.5951 (2020).
    Article  Google Scholar 

    32.
    Steltzer, H. & Post, E. Seasons and life cycles. Science 324, 886–887. https://doi.org/10.1126/science.1171542 (2009).
    Article  PubMed  Google Scholar 

    33.
    Hale, R., Morrongiello, J. R. & Swearer, S. E. Evolutionary traps and range shifts in a rapidly changing world. Biol. Let. 12, 20160003. https://doi.org/10.1098/rsbl.2016.0003 (2016).
    Article  Google Scholar 

    34.
    Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407. https://doi.org/10.1111/j.1365-2435.2007.01283.x (2007).
    Article  Google Scholar 

    35.
    Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, 4455. https://doi.org/10.1038/s41467-019-12479-w (2019).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    36.
    Pau, S. et al. Predicting phenology by integrating ecology, evolution and climate science. Glob. Change Biol. 17, 3633–3643. https://doi.org/10.1111/j.1365-2486.2011.02515.x (2011).
    ADS  Article  Google Scholar 

    37.
    Sherry, R. A. et al. Divergence of reproductive phenology under climate warming. Proc. Natl. Acad. Sci. 104, 198. https://doi.org/10.1073/pnas.0605642104 (2007).
    ADS  CAS  Article  PubMed  Google Scholar 

    38.
    Wilson, R. J. & Fox, R. Insect responses to global change offer signposts for biodiversity and conservation. Ecol. Entomol. https://doi.org/10.1111/een.12970 (2020).
    Article  Google Scholar 

    39.
    Brooks, S. J. et al. The influence of life history traits on the phenological response of British butterflies to climate variability since the late-19th century. Ecography 40, 1152–1165. https://doi.org/10.1111/ecog.02658 (2017).
    Article  Google Scholar 

    40.
    Cayton, H. L., Haddad, N. M., Gross, K., Diamond, S. E. & Ries, L. Do growing degree days predict phenology across butterfly species?. Ecology 96, 1473–1479. https://doi.org/10.1890/15-0131.1 (2015).
    Article  Google Scholar 

    41.
    Stocker, T. F. et al. Summary for policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 3–29 (2013).

    42.
    Swengel, A. B. Effects of fire and hay management on abundance of prairie butterflies. Biol. Cons. 76, 73–85 (1996).
    Article  Google Scholar 

    43.
    Zografou, K. et al. Severe decline and partial recovery of a rare butterfly on an active military training area. Biol. Cons. 216, 43–50. https://doi.org/10.1016/j.biocon.2017.09.026 (2017).
    Article  Google Scholar 

    44.
    Gillingham, P. K., Huntley, B., Kunin, W. E. & Thomas, C. D. The effect of spatial resolution on projected responses to climate warming. Divers. Distrib. 18, 990–1000. https://doi.org/10.1111/j.1472-4642.2012.00933.x (2012).
    Article  Google Scholar 

    45.
    Roy David, B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Global Change Biol. 21, 3313–3322, https://doi.org/10.1111/gcb.12920 (2015).

    46.
    Lemoine, N. P. Climate change may alter breeding ground distributions of eastern migratory monarchs (Danaus plexippus) via range expansion of asclepias host plants. PLoS ONE 10, e0118614. https://doi.org/10.1371/journal.pone.0118614 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    47.
    Slansky, F. Phagism relationships among butterflies. J. N. Y. Entomol. Soc. 84, 91–105 (1976).
    Google Scholar 

    48.
    Morin, X., Roy, J., Sonié, L. & Chuine, I. Changes in leaf phenology of three European oak species in response to experimental climate change. New Phytol. 186, 900–910. https://doi.org/10.1111/j.1469-8137.2010.03252.x (2010).
    Article  PubMed  Google Scholar 

    49.
    Chuine, I., Morin, X. & Bugmann, H. Warming. Photoperiods Tree Phenol. 329, 277–278. https://doi.org/10.1126/science.329.5989.277-e%JScience (2010).
    Article  Google Scholar 

    50.
    Luedeling, E., Girvetz, E. H., Semenov, M. A. & Brown, P. H. Climate change affects winter chill for temperate fruit and nut trees. PLoS ONE 6, e20155. https://doi.org/10.1371/journal.pone.0020155 (2011).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    51.
    Fu, Y. S. H. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl. Acad. USA 111, 7355–7360. https://doi.org/10.1073/pnas.1321727111%JProceedingsoftheNationalAcademyofSciences (2014).
    ADS  CAS  Article  Google Scholar 

    52.
    Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182. https://doi.org/10.1146/annurev-ecolsys-110617-062535 (2018).
    Article  Google Scholar 

    53.
    Barton, K. E., Edwards, K. F. & Koricheva, J. Shifts in woody plant defence syndromes during leaf development. Funct. Ecol. 33, 2095–2104. https://doi.org/10.1111/1365-2435.13435 (2019).
    Article  Google Scholar 

    54.
    Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228. https://doi.org/10.1038/s41558-018-0067-3 (2018).
    ADS  Article  Google Scholar 

    55.
    Altermatt, F. Climatic warming increases voltinism in European butterflies and moths. Proc. R. Soc. B Biol. Sci. 277, 1281–1287. https://doi.org/10.1098/rspb.2009.1910 (2010).
    Article  Google Scholar 

    56.
    Illán, J. G., Gutiérrez, D., Díez, S. B. & Wilson, R. J. Elevational trends in butterfly phenology: Implications for species responses to climate change. Ecol. Entomol. 37, 134–144. https://doi.org/10.1111/j.1365-2311.2012.01345.x (2012).
    Article  Google Scholar 

    57.
    Nufio, C. R., McGuire, C. R., Bowers, M. D. & Guralnick, R. P. Grasshopper community response to climatic change: Variation along an elevational gradient. PLoS ONE 5, e12977. https://doi.org/10.1371/journal.pone.0012977 (2010).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    58.
    Van Dyck, H., Bonte, D., Puls, R., Gotthard, K. & Maes, D. The lost generation hypothesis: Could climate change drive ectotherms into a developmental trap?. Oikos 124, 54–61. https://doi.org/10.1111/oik.02066 (2015).
    Article  Google Scholar 

    59.
    Scott, J. A. The Butterflies of North America: A Natural History and Field Guide. (Stanford University Press, 1992).

    60.
    Division, E. Final Integrated Natural Resources Management Plan 17003–25002 (The Pennsylvania Department of Military and Veterans Affairs, Annville, 2002).
    Google Scholar 

    61.
    Shuey, J. et al. Landscape-scale response to local habitat restoration in the regal fritillary butterfly (Speyeria idalia) (Lepidoptera: Nymphalidae). J. Insect Cons. 20, 773–780. https://doi.org/10.1007/s10841-016-9908-4 (2016).
    Article  Google Scholar 

    62.
    Metzler, E., Shuey, J., Ferge, L., Henderson, R. & Goldstein, P. Contributions to the understanding of tallgrass prairie-dependent butterflies and moths (Lepidoptera) and their biogeography in the United States. Ohio Biol. Surv. Bull. New Ser. 15, 1–143 (2005).
    Google Scholar 

    63.
    PNHP. PNHP Species Lists. Pennsylvania Natural Heritage Program. http://www.naturalheritage.state.pa.us/Species.aspx (2019).

    64.
    Pollard, E. & Yates, T. J. Monitoring Butterflies for Ecology and Conservation (1993).

    65.
    Nufio, C. R., McGuire, C. R., Bowers, M. D. & Guralnick, R. P. Grasshopper community response to climatic change: Variation along an elevational gradient. PLoS ONE https://doi.org/10.1371/journal.pone.0012977 (2010).
    Article  PubMed  PubMed Central  Google Scholar 

    66.
    Glassberg, J. Butterflies through binoculars, the East. A field guide to the butterflies of Eastern North America, 242. (Oxford University Press, Inc., 1999).

    67.
    Brock, J. P. & Kaufman, K. Field Guide to Butterflies of North America., 391 (Hillstar Editions L.C, 2003).

    68.
    Brakefield, P. M. Geographical variability in, and temperature effects on, the phenology of Maniola jurtina and Pyronia tithonus (Lepidoptera, Satyrinae) in England and Wales. Ecol. Entomol. 12, 139–148. https://doi.org/10.1111/j.1365-2311.1987.tb00993.x (1987).
    Article  Google Scholar 

    69.
    de Arce Crespo, J. I. & Gutiérrez, D. Altitudinal trends in the phenology of butterflies in a mountainous area in central Spain. Eur. J. Entomol. 108, 651–658 (2011).

    70.
    Moussus, J.-P., Julliard, R. & Jiguet, F. Featuring 10 phenological estimators using simulated data. Methods Ecol. Evol. 1, 140–150. https://doi.org/10.1111/j.2041-210X.2010.00020.x (2010).
    Article  Google Scholar 

    71.
    Penny, D. The comparative method in evolutionary biology. J. Classif. 9, 169–172. https://doi.org/10.1007/BF02618482 (1992).
    MathSciNet  Article  Google Scholar 

    72.
    Earl, C., Belitz, M. et al. Spatial phylogenetics of butterflies in relation to environmental drivers and angiosperm diversity across North America. BioRxiv:2020.2007.2022.216119, https://doi.org/10.1101/2020.07.22.216119 (2020).

    73.
    PRISM. Climate Group, Parameter-elevation Regressions on Independent Slopes Model. Oregon State University, http://prism.oregonstate.edu. Accessed 24 July 2018.

    74.
    Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064. https://doi.org/10.1002/joc.1688 (2008).
    Article  Google Scholar 

    75.
    Peñuelas, J. et al. Response of plant species richness and primary productivity in shrublands along a north-south gradient in Europe to seven years of experimental warming and drought: Reductions in primary productivity in the heat and drought year of 2003. Glob. Change Biol. 13, 2563–2581. https://doi.org/10.1111/j.1365-2486.2007.01464.x (2007).
    ADS  Article  Google Scholar 

    76.
    McMaster, G. S. & Wilhelm, W. W. Growing degree-days: One equation, two interpretations. Agric. For. Meteorol. 87, 291–300. https://doi.org/10.1016/S0168-1923(97)00027-0 (1997).
    ADS  Article  Google Scholar 

    77.
    Walters, E. J., Morrell, C. H. & Auer, R. E. An investigation of the median-median method of linear regression. J. Stat. Educ. https://doi.org/10.1080/10691898.2006.11910582 (2006).
    Article  Google Scholar 

    78.
    Theil, H. in Henri Theil’s Contributions to Economics and Econometrics: Econometric Theory and Methodology (eds Baldev Raj & Johan Koerts) 345–381 (Springer Netherlands, 1992).

    79.
    Sen, P. K. Estimates of the regression coefficient based on Kendall’s Tau. J. Am. Stat. Assoc. 63, 1379–1389. https://doi.org/10.2307/2285891 (1968).
    MathSciNet  Article  MATH  Google Scholar 

    80.
    Siegel, A. F. Robust regression using repeated medians. Biometrika 69, 242–244. https://doi.org/10.2307/2335877 (1982).
    Article  MATH  Google Scholar 

    81.
    Schneider, G., Chicken, E. & Becvarik, R. NSM3: Functions and Datasets to Accompany Hollander, Wolfe, and Chicken – Nonparametric Statistical Methods, Third Edition. R Package Version 1.15. https://CRAN.R-project.org/package=NSM3. (2020).

    82.
    Patrick Bogaart, Loo, M. v. d. & Pannekoek, J. rtrim: Trends and Indices for Monitoring Data. R Package Version 2.1.1. https://CRAN.R-project.org/package=rtrim. (2020).

    83.
    Zografou, K. et al. Stable generalist species anchor a dynamic pollination network. Ecosphere 11, e03225. https://doi.org/10.1002/ecs2.3225 (2020).
    Article  Google Scholar 

    84.
    Pinheiro J, Bates D, DebRoy S & D, S. nlme: Linear and Nonlinear Mixed Effects Models. R Package v. 3.1‐117. (www document). https://CRAN.R-project.org/package=nlme. (2015).

    85.
    Felsenstein, J. Phylogenies and quantitative characters. Annu. Rev. Ecol. Syst. 19, 445–471. https://doi.org/10.1146/annurev.es.19.110188.002305 (1988).
    Article  Google Scholar  More

  • in

    The occurrence and ecology of microbial chain elongation of carboxylates in soils

    1.
    Barker HA, Taha SM. Clostridium kluyverii, an organism concerned in the formation of caproic acid from ethyl alcohol. J Bacteriol. 1942;43:347–63.
    CAS  PubMed  PubMed Central  Article  Google Scholar 
    2.
    Angenent LT, Richter H, Buckel W, Spirito CM, Steinbusch KJJ, Plugge CM, et al. Chain elongation with reactor microbiomes: open-culture biotechnology to produce biochemicals. Environ Sci Technol. 2016;50:2796–810.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    3.
    Béchamp MA. Lettre de m. A. Béchamp a m. Dumas. Ann Chim Phys 1868;4:103–11.
    Google Scholar 

    4.
    Weimer PJ, Stevenson DM. Isolation, characterization, and quantification of Clostridium kluyveri from the bovine rumen. Appl Microbiol Biotechnol. 2012;94:461–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Kenealy WR, Waselefsky DM. Studies on the substrate range of Clostridium kluyveri – the use of propanol and succinate. Arch Microbiol. 1985;141:187–94.
    CAS  Article  Google Scholar 

    6.
    Barker HA, Kamen MD, Bornstein BT. The synthesis of butyric and caproic acids from ethanol and acetic acid by Clostridium kluyveri. Proc Natl Acad Sci USA. 1945;31:373–81.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Bornstein BT, Barker HA. The energy metabolism of Clostridium kluyveri and the synthesis of fatty acids. J Biol Chem. 1948;172:659–69.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Seedorf H, Fricke WF, Veith B, Bruggemann H, Liesegang H, Strittimatter A, et al. The genome of Clostridium kluyveri, a strict anaerobe with unique metabolic features. Proc Natl Acad Sci USA. 2008;105:2128–33.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Gonzalez-Cabaleiro R, Lema JM, Rodriguez J, Kleerebezem R. Linking thermodynamics and kinetics to assess pathway reversibility in anaerobic bioprocesses. Energy Environ Sci. 2013;6:3780–9.
    CAS  Article  Google Scholar 

    10.
    Spirito CM, Richter H, Rabaey K, Stams AJM, Angenent LT. Chain elongation in anaerobic reactor microbiomes to recover resources from waste. Curr Opin Biotechnol. 2014;27:115–22.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Rittmann BE & McCarty PL. Environmental Biotechnology: Principles and Applications. McGraw-Hill Book Education: New York; 2001.

    12.
    Thauer RK, Jungermann K, Henninger H, Wenning J, Decker K. The energy metabolism of Clostridium kluyveri. Eur J Biochem. 1968;4:173–80.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Stadtman ER, Barker HA. Fatty acid synthesis by enzyme preparations of Clostridium kluyveri. I. Preparation of cell-free extracts that catalyze the conversion of ethanol and acetate to butyrate and caproate. J Biol Chem. 1949;180:1085–93.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Stadtman ER, Barker HA. Fatty acid synthesis by enzyme preparations of Clostridium kluyveri. VI. Reactions of acyl phosphates. J Biol Chem. 1950;184:769–93.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Steinbusch KJJ, Hamelers HVM, Plugge CM, Buisman CJN. Biological formation of caproate and caprylate from acetate: fuel and chemical production from low grade biomass. Energy Environ Sci. 2011;4:216–24.
    CAS  Article  Google Scholar 

    16.
    Agler MT, Spirito CM, Usack JG, Werner JJ, Angenent LT. Chain elongation with reactor microbiomes: upgrading dilute ethanol to medium-chain carboxylates. Energy Environ Sci. 2012;5:8189–92.
    CAS  Article  Google Scholar 

    17.
    Cavalcante WD, Leitao RC, Gehring TA, Angenent LT, Santaella ST. Anaerobic fermentation for n-caproic acid production: A review. Process Biochem. 2017;54:106–19.
    CAS  Article  Google Scholar 

    18.
    De Groof V, Coma M, Arnot T, Leak DJ, Lanham AB. Medium chain carboxylic acids from complex organic feedstocks by mixed culture fermentation. Molecules 2019;24:398.
    PubMed Central  Article  CAS  Google Scholar 

    19.
    Schievano A, Sciarria TP, Vanbroekhoven K, De Wever H, Puig S, Andersen SJ, et al. Electro-fermentation – merging electrochemistry with fermentation in industrial applications. Trends Biotechnol. 2016;34:866–78.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    20.
    Jourdin L, Raes SMT, Buisman CJN, Strik D. Critical biofilm growth throughout unmodified carbon felts allows continuous bioelectrochemical chain elongation from CO2 up to caproate at high current density. Front Energy Res. 2018;6:7.
    Article  Google Scholar 

    21.
    Candry P, Huang SL, Carvajal-Arroyo JM, Rabaey K, Ganigue R. Enrichment and characterisation of ethanol chain elongating communities from natural and engineered environments. Sci Rep. 2020;10:1–10.
    Article  CAS  Google Scholar 

    22.
    Conrad R. Importance of hydrogenotrophic, aceticlastic and methylotrophic methanogenesis for methane production in terrestrial, aquatic and other anoxic environments: a mini review. Pedosphere 2020;30:25–39.
    Article  Google Scholar 

    23.
    Rui JP, Peng JJ, Lu YH. Succession of bacterial populations during plant residue decomposition in rice field soil. Appl Environ Microbiol. 2009;75:4879–86.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Tsutsuki K, Ponnamperuma FN. Behavior of anaerobic decomposition in submerged soils – effect of organic material amendment, soil properties, and temperature. Soil Sci Plant Nutr. 1987;33:13–33.
    CAS  Article  Google Scholar 

    25.
    Roy R, Kluber HD, Conrad R. Early initiation of methane production in anoxic rice soil despite the presence of oxidants. FEMS Microbiol Ecol. 1997;24:311–20.
    CAS  Article  Google Scholar 

    26.
    Adeleke R, Nwangburuka C, Oboirien B. Origins, roles and fate of organic acids in soils: a review. S Afr J Bot. 2017;108:393–406.
    CAS  Article  Google Scholar 

    27.
    Mohana Rangan S, Mouti A, LaPat-Polasko L, Lowry GV, Krajmalnik-Brown R, Delgado A. Synergistic zero-valent iron (Fe0) and microbiological trichloroethene and perchlorate reductions are determined by the concentration and speciation of Fe. Environ Sci Technol. 2020;54:14422–31.
    Article  CAS  Google Scholar 

    28.
    Delgado AG, Kang D-W, Nelson KG, Fajardo-Williams D, Miceli JF, III, Done HY, et al. Selective enrichment yields robust ethene-producing dechlorinating cultures from microcosms stalled at cis-dichloroethene. PLoS ONE. 2014;9:e100654.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    29.
    Delgado AG, Fajardo-Williams D, Popat SC, Torres CI, Krajmalnik-Brown R. Successful operation of continuous reactors at short retention times results in high-density, fast-rate Dehalococcoides dechlorinating cultures. Appl Microbiol Biotechnol. 2014;98:2729–37.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    30.
    Chen TF, Delgado AG, Yavuz BM, Maldonado J, Zuo Y, Kamath R, et al. Interpreting interactions between ozone and residual petroleum hydrocarbons in soil. Environ Sci Technol. 2017;51:506–13.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Esquivel-Elizondo S, Miceli J, Torres CI, Krajmalnik-Brown R. Impact of carbon monoxide partial pressures on methanogenesis and medium chain fatty acids production during ethanol fermentation. Biotechnol Bioeng. 2018;115:341–50.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    32.
    Delgado AG, Fajardo-Williams D, Kegerreis KL, Parameswaran P, Krajmalnik-Brown R. Impact of ammonium on syntrophic organohalide-respiring and fermenting microbial communities. mSphere. 2016;1:e00053–16.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Delgado AG, Fajardo-Williams D, Bondank E, Esquivel-Elizondo S, Krajmalnik-Brown R. Coupling bioflocculation of Dehalococcoides mccartyi to high-rate reductive dehalogenation of chlorinated ethenes. Environ Sci Technol. 2017;51:11297–307.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    34.
    Esquivel-Elizondo S, Delgado AG, Krajmalnik-Brown R. Evolution of microbial communities growing with carbon monoxide, hydrogen, and carbon dioxide. FEMS Microbiol Ecol. 2017;93:fix076.
    Article  CAS  Google Scholar 

    35.
    Xiaoyu Z, Yong T, Cheng L, Xiangzhen L, Na W, Wenjie Z, et al. The synthesis of n-caproate from lactate: a new efficient process for medium-chain carboxylates production. Sci Rep. 2015;5:14360.
    Article  CAS  Google Scholar 

    36.
    Caporaso JG, Christian LL, William AW, Donna B-L, James H, Noah F, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–24.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Masella A, Bartram A, Truszkowski J, Brown D, Neufeld J. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinform. 2012;13:31.
    CAS  Article  Google Scholar 

    38.
    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–57.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    39.
    Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.
    PubMed  PubMed Central  Article  Google Scholar 

    40.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    41.
    Robeson MS, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, et al. RESCRIPt: Reproducible sequence taxonomy reference database management for the masses. bioRxiv. 2020; https://doi.org/10.1101/2020.10.05.326504.

    42.
    Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.
    PubMed  PubMed Central  Article  Google Scholar 

    43.
    Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST plus: architecture and applications. BMC Bioinform. 2009;10:1.
    Article  CAS  Google Scholar 

    44.
    Kusel K, Drake HL. Acetate synthesis in soil from a Bavarian beech forest. Appl Environ Microbiol. 1994;60:1370–3.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    Kusel K, Drake HL. Effects of environmental parameters on the formation and turnover of acetate by forest soils. Appl Environ Microbiol. 1995;61:3667–75.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    46.
    Duddleston KN, Kinney MA, Kiene RP, Hines ME. Anaerobic microbial biogeochemistry in a northern bog: Acetate as a dominant metabolic end product. Glob Biogeochem Cycles. 2002;16:11.1–9.
    Article  CAS  Google Scholar 

    47.
    Thebrath B, Mayer HP, Conrad R. Bicarbonate-dependent production and methanogenic consumption of acetate in anoxic paddy soil. FEMS Microbiol Ecol. 1992;86:295–302.
    CAS  Article  Google Scholar 

    48.
    Delgado AG, Parameswaran P, Fajardo-Williams D, Halden RU, Krajmalnik-Brown R. Role of bicarbonate as a pH buffer and electron sink in microbial dechlorination of chloroethenes. Microb Cell Fact. 2012;11:128.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    49.
    Kucek LA, Spirito CM, Angenent LT. High n-caprylate productivities and specificities from dilute ethanol and acetate: chain elongation with microbiomes to upgrade products from syngas fermentation. Energy Environ Sci. 2016;9:3482–94.
    CAS  Article  Google Scholar 

    50.
    Volker AR, Gogerty DS, Bartholomay C, Hennen-Bierwagen T, Zhu HL, Bobik TA. Fermentative production of short-chain fatty acids in Escherichia coli. Microbiology 2014;160:1513–22.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Grootscholten TIM, Steinbusch KJJ, Hamelers HVM, Buisman CJN. Chain elongation of acetate and ethanol in an upflow anaerobic filter for high rate MCFA production. Bioresour Technol. 2013;135:440–5.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    52.
    Reddy MV, Mohan SV, Chang YC. Medium-chain fatty acids (MCFA) production through anaerobic fermentation using Clostridium kluyveri: effect of ethanol and acetate. Appl Biochem Biotechnol. 2018;185:594–605.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    53.
    Scarborough MJ, Lawson CE, Hamilton JJ, Donohue TJ, Noguera DR. Metatranscriptomic and thermodynamic insights into medium-chain fatty acid production using an anaerobic microbiome. mSystems 2018;3:6.
    Article  Google Scholar 

    54.
    Bao S, Wang QY, Zhang PY, Zhang Q, Wu Y, Li F, et al. Effect of acid/ethanol ratio on medium chain carboxylate production with different VFAs as the electron acceptor: insight into carbon balance and microbial community. Energies 2019;12:3720.
    CAS  Article  Google Scholar 

    55.
    Spirito CM, Marzilli AM, Angenent LT. Higher substrate ratios of ethanol to acetate steered chain elongation toward n-caprylate in a bioreactor with product extraction. Environ Sci Technol. 2018;52:13438–47.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    56.
    Coma M, Vilchez-Vargas R, Roume H, Jauregui R, Pieper DH, Rabaey K. Product diversity linked to substrate usage in chain elongation by mixed-culture fermentation. Environ Sci Technol. 2016;50:6467–76.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Janssen PH. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl Environ Microbiol. 2006;72:1719–28.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    58.
    Spain AM, Krumholz LR, Elshahed MS. Abundance, composition, diversity and novelty of soil Proteobacteria. ISME J 2009;3:992–1000.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    59.
    Johnson JS, Spakowicz DJ, Hong BY, Petersen LM, Demkowicz P, Chen L, et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun. 2019;10:5029.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    60.
    Hollister EB, Forrest AK, Wilkinson HH, Ebbole DJ, Malfatti SA, Tringe SG, et al. Structure and dynamics of the microbial communities underlying the carboxylate platform for biofuel production. Appl Microbiol Biotechnol. 2010;88:389–99.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    61.
    Mackie RI, Aminov RI, Hu WP, Klieve AV, Ouwerkerk D, Sundset MA, et al. Ecology of uncultivated Oscillospira species in the rumen of cattle, sheep, and reindeer as assessed by microscopy and molecular approaches. Appl Environ Microbiol. 2003;69:6808–15.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    62.
    Ye TR, Cai HY, Liu X, Jiang HL. Dominance of Oscillospira and Bacteroides in the bacterial community associated with the degradation of high-concentration dimethyl sulfide under iron-reducing condition. Ann Microbiol. 2016;66:1199–206.
    CAS  Article  Google Scholar 

    63.
    Konikoff T, Gophna U. Oscillospira: a central, enigmatic component of the human gut microbiota. Trends Microbiol. 2016;24:523–4.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    64.
    Clarke RTJ. Niche in pasture-fed ruminants for the large rumen bacteria Oscillospira, Lampropedia, and Quin’s and Eadie’s ovals. Appl Environ Microbiol. 1979;37:654–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    Lee GH, Rhee MS, Chang DH, Lee J, Kim S, Yoon MH, et al. Oscillibacter ruminantium sp nov., isolated from the rumen of Korean native cattle. Int J Syst Evol Microbiol. 2013;63:1942–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    66.
    Iino T, Mori K, Tanaka K, Suzuki KI, Harayama S. Oscillibacter valericigenes gen. nov., sp nov., a valerate-producing anaerobic bacterium isolated from the alimentary canal of a Japanese corbicula clam. Int J Syst Evol Microbiol. 2007;57:1840–5.
    PubMed  Article  PubMed Central  Google Scholar 

    67.
    Gophna U, Konikoff T, Nielsen HB. Oscillospira and related bacteria – From metagenomic species to metabolic features. Environ Microbiol. 2017;19:835–41.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    68.
    Wang H-J, Dai K, Wang Y-Q, Wang H-F, Zhang F, Zeng RJ. Mixed culture fermentation of synthesis gas in the microfiltration and ultrafiltration hollow-fiber membrane biofilm reactors. Bioresour Technol. 2018;267:650–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    69.
    Fraj B, Ben Hania W, Postec A, Hamdi M, Ollivier B, Fardeau ML. Fonticella tunisiensis gen. nov., sp nov., isolated from a hot spring. Int J Syst Evol Microbiol. 2013;63:1947–50.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    70.
    Collins MD, Lawson PA, Willems A, Cordoba JJ, Fernandezgarayzabal J, Garcia P, et al. The phylogeny of the genus Clostridium – Proposal of 5 new genera and 11 new species combinations. Int J Syst Bacteriol. 1994;44:812–26.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    71.
    BS Jeon, Kim BC, Um Y, et al. BI. Production of hexanoic acid from D-galactitol by a newly isolated Clostridium sp. BS-1. Appl Microbiol Biotechnol. 2010;88:1161–7.
    Article  CAS  Google Scholar 

    72.
    Zhu XY, Zhou Y, Wang Y, Wu TT, Li XZ, Li DP, et al. Production of high-concentration n-caproic acid from lactate through fermentation using a newly isolated Ruminococcaceae bacterium CPB6. Biotechnol Biofuels. 2017;10:102.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    73.
    Robertson WJ, Bowman JP, Franzmann PD, Mee BJ. Desulfosporosinus meridiei sp nov., a spore-forming sulfate-reducing bacterium isolated from gasolene-contaminated groundwater. Int J Syst Evol Microbiol. 2001;51:133–40.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    74.
    Lee YJ, Romanek CS, Wiegel J. Desulfosporosinus youngiae sp nov., a spore-forming, sulfate-reducing bacterium isolated from a constructed wetland treating acid mine drainage. Int J Syst Evol Microbiol. 2009;59:2743–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Spatial patterns in phage-Rhizobium coevolutionary interactions across regions of common bean domestication

    1.
    Breitbart M, Rohwer F. Here a virus, there a virus, everywhere the same virus? Trends Microbiol. 2005;13:278–84.
    CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Hatfull GF. Dark matter of the biosphere: the amazing world of bacteriophage diversity. J Virol. 2015;89:8107–10.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    3.
    Bouvier T, Del Giorgio PA. Key role of selective viral-induced mortality in determining marine bacterial community composition. Environ Microbiol. 2007;9:287–97.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    4.
    Canchaya C, Fournous G, Chibani-Chennoufi S, Dillmann ML, Brüssow H. Phage as agents of lateral gene transfer. Curr Opin Microbiol. 2003;6:417–24.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Howard-Varona C, Hargreaves KR, Solonenko NE, Markillie LM, White RA, Brewer HM, et al. Multiple mechanisms drive phage infection efficiency in nearly identical hosts. ISME J. 2018;12:1605–18.
    PubMed  PubMed Central  Article  Google Scholar 

    6.
    Weinbauer MG, Rassoulzadegan F. Are viruses driving microbial diversification and diversity? Environ Microbiol. 2004;6:1–11.
    PubMed  Article  PubMed Central  Google Scholar 

    7.
    Thurber RV. Current insights into phage biodiversity and biogeography. Curr Opin Microbiol. 2009;12:582–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Chow C-ET, Suttle CA. Biogeography of viruses in the sea. Annu Rev Virol. 2015;2:41–66.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    10.
    Shkoporov AN, Khokhlova EV, Fitzgerald CB, Stockdale SR, Draper LA, Ross RP, et al. ΦCrAss001 represents the most abundant bacteriophage family in the human gut and infects Bacteroides intestinalis. Nat Commun. 2018;9:4781.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    11.
    Breitbart M, Miyake JH, Rohwer F. Global distribution of nearly identical phage-encoded DNA sequences. FEMS Microbiol Lett. 2004;236:249–56.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Dutilh BE, Cassman N, McNair K, Sanchez SE, Silva GGZ, Boling L, et al. A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes. Nat Commun. 2014;5:4498.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    13.
    Jameson E, Mann NH, Joint I, Sambles C, Mühling M. The diversity of cyanomyovirus populations along a North-South Atlantic Ocean transect. ISME J. 2011;5:1713–21.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    14.
    Delong EF, Preston CM, Mincer T, Rich V, Hallam SJ, Frigaard N, et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science. 2006;311:496–503.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Finke JF, Suttle CA. The environment and cyanophage diversity: insights from environmental sequencing of DNA polymerase. Front Microbiol. 2019;10:167.
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Hanson CA, Marston MF, Martiny JB. Biogeographic variation in host range phenotypes and taxonomic composition of marine cyanophage isolates. Front Microbiol. 2016;7:983.
    PubMed  PubMed Central  Article  Google Scholar 

    17.
    Huang S, Zhang S, Jiao N, Chen F. Marine cyanophages demonstrate biogeographic patterns throughout the global ocean. Appl Environ Microbiol. 2015;81:441–52.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Marston MF, Taylor S, Sme N, Parsons RJ, Noyes TJE, Martiny JBH. Marine cyanophages exhibit local and regional biogeography. Environ Microbiol. 2013;15:1452–63.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    20.
    Winter C, Matthews B, Suttle CA. Effects of environmental variation and spatial distance on bacteria, archaea and viruses in sub-polar and arctic waters. ISME J. 2013;7:1507–18.
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Luo E, Aylward FO, Mende DR, Delong EF. Bacteriophage distributions and temporal variability in the ocean’s interior. mBio 2017;8:e01903–17.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Brum JR, Ignacio-espinoza JC, Roux S, Doulcier G, Acinas SG, Alberti A, et al. Patterns and ecological drivers of ocean viral communities. Science. 2015;348:1261498.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    23.
    Dennehy JJ. What ecologists can tell virologists. Annu Rev Microbiol. 2014;68:117–35.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Held NL, Whitaker RJ. Viral biogeography revealed by signatures in Sulfolobus islandicus genomes. Environ Microbiol. 2009;11:457–66.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    25.
    Ashby B, Boots M. Multi-mode fluctuating selection in host–parasite coevolution. Ecol Lett. 2017;20:357–65.
    PubMed  Article  PubMed Central  Google Scholar 

    26.
    Koskella B, Brockhurst MA. Bacteria-phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol Rev. 2014;38:916–31.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    27.
    Vos M, Birkett PJ, Birch E, Griffiths RI, Buckling A. Local adaptation of bacteriophages to their bacterial hosts in soil. Science 2009;325:833.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Gomez P, Buckling A. Coevolution with phages does not influence the evolution of bacterial mutation rates in soil. ISME J. 2013;7:2242–4.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    Kraemer SA, Boynton PJ. Evidence for microbial local adaptation in nature. Mol Ecol. 2017;26:1860–76.
    PubMed  Article  PubMed Central  Google Scholar 

    30.
    Kawecki T, Ebert D. Conceptual issues in local adaptation. Ecol Lett. 2004;7:1225–41.
    Article  Google Scholar 

    31.
    Lenormand T. Gene flow and the limits to natural selection. Trends Ecol Evol. 2002;17:183–9.
    Article  Google Scholar 

    32.
    Nosil P, Egan SP, Funk DJ. Heterogeneous genomic differentiation between walking-stick ecotypes: “isolation by adaptation” and multiple roles for divergent selection. Evolution. 2008;62:316–36.
    PubMed  Article  Google Scholar 

    33.
    Orsini L, Vanoverbeke J, Swillen I, Mergeay J, De Meester L. Drivers of population genetic differentiation in the wild: Isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol Ecol. 2013;22:5983–99.
    PubMed  Article  Google Scholar 

    34.
    Zhang Q-G, Buckling A. Migration highways and migration barriers created by host–parasite interactions. Ecol Lett. 2016;19:1479–85.
    PubMed  Article  Google Scholar 

    35.
    Wang IJ, Bradburd GS. Isolation by environment. Mol Ecol. 2014;23:5649–62.
    PubMed  Article  Google Scholar 

    36.
    Buckling A, Rainey PB. Antagonistic coevolution between a bacterium and a bacteriophage. Proc Biol Sci. 2002;269:931–6.
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Kunin V, He S, Warnecke F, Peterson SB, Garcia Martin H, Haynes M, et al. A bacterial metapopulation adapts locally to phage predation despite global dispersal. Genome Res. 2008;18:293–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    38.
    Lopez Pascua L, Gandon S, Buckling A. Abiotic heterogeneity drives parasite local adaptation in coevolving bacteria and phages. J Evol Biol. 2012;25:187–95.
    CAS  PubMed  Article  Google Scholar 

    39.
    Baumann P. Biology of endosymbionts of plant sap-sucking insects. Annu Rev Microbiol. 2005;59:155–89.
    CAS  PubMed  Article  Google Scholar 

    40.
    Levy A, Gonzalez IS, Mittelviefhaus M, Clingenpeel S, Paredes SH, Miao J, et al. Genomic features of bacterial adaptation to plants. Nat Genet. 2018;50:138–50.
    CAS  Article  Google Scholar 

    41.
    Bäckhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. Host-bacterial mutualism in the human intestine. Science 2005;307:1915–20.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    42.
    Heath KD, Tiffin P. Context dependence in the coevolution of plant and rhizobial mutualists. Proc Biol Sci. 2007;274:1905–12.
    PubMed  PubMed Central  Google Scholar 

    43.
    Koch M, Delmotte N, Rehrauer H, Vorholt JA, Pessi G, Hennecke H. Rhizobial adaptation to hosts, a new facet in the legume root-nodule symbiosis. Mol Plant Microbe Interact. 2010;23:784–90.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    44.
    Aguilar OM, Riva O, Peltzer E. Analysis of Rhizobium etli and of its symbiosis with wild Phaseolus vulgaris supports coevolution in centers of host diversification. Proc Natl Acad Sci. 2004;101:13548–53.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    45.
    Bitocchi E, Bellucci E, Giardini A, Rau D, Rodriguez M, Biagetti E, et al. Molecular analysis of the parallel domestication of the common bean (Phaseolus vulgaris) in Mesoamerica and the Andes. N Phytol. 2013;197:300–13.
    CAS  Article  Google Scholar 

    46.
    Koenig R, Gepts P. Allozyme diversity in wild Phaseolus vulgaris: further evidence for two major centers of genetic diversity. Theor Appl Genet. 1989;78:809–17.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    47.
    Melkonian R, Moulin L, Béna G, Tisseyre P, Chaintreuil C, Heulin K, et al. The geographical patterns of symbiont diversity in the invasive legume Mimosa pudica can be explained by the competitiveness of its symbionts and by the host genotype. Environ Microbiol. 2014;16:2099–111.
    PubMed  Article  PubMed Central  Google Scholar 

    48.
    Tian CF, Young JPW, Wang ET, Tamimi SM, Chen WX. Population mixing of Rhizobium leguminosarum bv. viciae nodulating Vicia faba: the role of recombination and lateral gene transfer. FEMS Microbiol Ecol. 2010;73:563–76.
    CAS  PubMed  PubMed Central  Google Scholar 

    49.
    Burdon JJ, Thrall PH. Spatial and temporal patterns in coevolving plant and pathogen associations. Am Nat. 1999;153:S15–S33.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    50.
    Van Cauwenberghe J, Visch W, Michiels J, Honnay O. Selection mosaics differentiate Rhizobium-host plant interactions across nitrogen environments. Oikos 2016;125:1755–61.
    Article  Google Scholar 

    51.
    Guimarães PR, Pires MM, Jordano P, Bascompte J, Thompson JN. Indirect effects drive coevolution in mutualistic networks. Nature 2017;550:511–4.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    52.
    Heath KD, Lau JA. Herbivores alter the fitness benefits of a plant–rhizobium mutualism. Acta Oecol. 2011;37:87–92.
    Article  Google Scholar 

    53.
    Rogers HS, Buhle ER, HilleRisLambers J, Fricke EC, Miller RH, Tewksbury JJ. Effects of an invasive predator cascade to plants via mutualism disruption. Nat Commun. 2017;8:6–13.
    Article  CAS  Google Scholar 

    54.
    Delmas E, Besson M, Brice MH, Burkle LA, Dalla Riva GV, Fortin MJ, et al. Analysing ecological networks of species interactions. Biol Rev. 2019;94:16–36.
    Article  Google Scholar 

    55.
    Gaiarsa MP, Guimarães PR. Interaction strength promotes robustness against cascading effects in mutualistic networks. Sci Rep. 2019;9:1–7.
    CAS  Article  Google Scholar 

    56.
    Sih A, Crowley P, McPeek M, Petranka J, Strohmeier K. Predation, competition, and prey communities: a review of field experiments. Annu Rev Ecol Syst. 1985;16:269–311.
    Article  Google Scholar 

    57.
    Parratt SR, Barrès B, Penczykowski RM, Laine AL. Local adaptation at higher trophic levels: contrasting hyperparasite–pathogen infection dynamics in the field and laboratory. Mol Ecol. 2017;26:1964–79.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Hatcher MJ, Dick JTA, Dunn AM. How parasites affect interactions between competitors and predators. Ecol Lett. 2006;9:1253–71.
    PubMed  Article  PubMed Central  Google Scholar 

    59.
    Hutchinson MC, Bramon Mora B, Pilosof S, Barner AK, Kéfi S, Thébault E, et al. Seeing the forest for the trees: putting multilayer networks to work for community ecology. Funct Ecol. 2019;33:206–17.
    Article  Google Scholar 

    60.
    Koskella B, Taylor TB. Multifaceted impacts of bacteriophages in the plant microbiome. Annu Rev Phytopathol. 2018;56:361–80.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    61.
    Labrie SJ, Samson JE, Moineau S. Bacteriophage resistance mechanisms. Nat Rev Microbiol. 2010;8:317–27.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Evans TJ, Ind A, Komitopoulou E, Salmond GPC. Phage-selected lipopolysaccharide mutants of Pectobacterium atrosepticum exhibit different impacts on virulence. J Appl Microbiol. 2010;109:505–14.
    CAS  PubMed  PubMed Central  Google Scholar 

    63.
    Perez Carrascal OM, Vaninsberghe D, Juárez S, Polz MF. Population genomics of the symbiotic plasmids of sympatric nitrogen-fixing Rhizobium species associated with Phaseolus vulgaris. Environ Microbiol. 2016;18:2660–76.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    64.
    Santamaría RI, Bustos P, Sepúlveda-Robles O, Lozano L, Rodríguez C, Fernández JL, et al. Narrow-host-range bacteriophages that infect Rhizobium etli associate with distinct genomic types. Appl Environ Microbiol. 2014;80:446–54.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    65.
    Carlson K. Working with bacteriophages: common techniques and methodological approaches. In: Kutter E, Sulakvelidze A (eds). Bacteriophages: biology and applications. Boca Raton, FL: CRC Press; 2005). p. 437–94.

    66.
    Werle E, Schneider C, Renner M, Völker M, Fiehn W. Convenient single-step, one tube purification of PCR products for direct sequencing. Nucleic Acids Res. 1994;22:4354–5.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    67.
    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    68.
    Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-Cell sequencing. J Comput Biol. 2012;19:455–77.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Zerbino DR, Birney E. Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18:821–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    71.
    Gordon D, Green P. Consed: a graphical editor for next-generation sequencing. Bioinformatics 2013;29:2936–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    72.
    Chaudhari NM, Gupta VK, Dutta C. BPGA- an ultra-fast pan-genome analysis pipeline. Sci Rep. 2016;6:24373.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    73.
    Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3 — new capabilities and interfaces. Nucleic Acids Res. 2012;40:e115.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Richter M, Rosselló-Móra R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci. 2009;106:19126–31.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    75.
    Pritchard L, Glover RH, Humphris S, Elphinstone JG, Toth IK. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal Methods. 2016;8:12–14.
    Article  Google Scholar 

    76.
    Lopes A, Tavares P, Petit M, Guérois R, Zinn-justin S. Automated classification of tailed bacteriophages according to their neck organization. BMC Genom. 2014;15:1027.
    Article  CAS  Google Scholar 

    77.
    Hyman P, Abedon ST. Phage host range and efficiency of plating. In: Clokie MRJ, Kropinski AM (eds). Bacteriophages, methods and protocols. Vol. I: Isolation, characterization, and interactions. Totowa, NJ: Humana Press; 2009. p. 175–202.

    78.
    Hyman P, Abedon ST. Bacteriophage host range and bacterial resistance. Adv Appl Microbiol. 2010;70:217–48.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    79.
    Holmfeldt K, Solonenko N, Howard-Varona C, Moreno M, Malmstrom RR, Blow MJ, et al. Large-scale maps of variable infection efficiencies in aquatic Bacteroidetes phage-host model systems. Environ Microbiol. 2016;18:3949–61.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    80.
    Ishizawa H, Kuroda M, Morikawa M, Ike M. Evaluation of environmental bacterial communities as a factor affecting the growth of duckweed Lemna minor. Biotechnol Biofuels. 2017;10:1–10.
    Article  CAS  Google Scholar 

    81.
    Cenens W, Makumi A, Mebrhatu MT, Lavigne R, Aertsen A. Phage–host interactions during pseudolysogeny. Bacteriophage 2013;3:e25029.
    PubMed  PubMed Central  Article  Google Scholar 

    82.
    Kauffman KM, Hussain FA, Yang J, Arevalo P, Brown JM, Chang WK, et al. A major lineage of non-tailed dsDNA viruses as unrecognized killers of marine bacteria. Nature. 2018;554:118–22.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    83.
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, Glinn D, et al. Community Ecology Package. https://cran.r-project.org, https://github.com/vegandevs/vegan. 2019.

    84.
    Flores CO, Poisot T, Valverde S, Weitz JS. BiMat: a MATLAB package to facilitate the analysis of bipartite networks. Methods Ecol Evol. 2016;7:127–32.
    Article  Google Scholar 

    85.
    Consul PC. A simple urn model dependent on predetermined strategy. Sankhyā Indian J Stat Ser B. 1974;36:391–9.
    Google Scholar 

    86.
    Borcard D, Legendre P. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol Modell. 2002;153:51–68.
    Article  Google Scholar 

    87.
    Flores CO, Valverde S, Weitz JS. Multi-scale structure and geographic drivers of cross-infection within marine bacteria and phages. ISME J. 2013;7:520–32.
    PubMed  Article  PubMed Central  Google Scholar 

    88.
    Porter SS, Chang PL, Conow CA, Dunham JP, Friesen ML. Association mapping reveals novel serpentine adaptation gene clusters in a population of symbiotic Mesorhizobium. ISME J. 2016;11:248–62.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    89.
    Greenlon A, Chang PL, Damtew ZM, Muleta A, Carrasquilla-Garcia N, Kim D, et al. Global-level population genomics reveals differential effects of geography and phylogeny on horizontal gene transfer in soil bacteria. Proc Natl Acad Sci. 2019;116:15200–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    90.
    Scola V, Ramond JB, Frossard A, Zablocki O, Adriaenssens EM, Johnson RM, et al. Namib desert soil microbial community diversity, assembly, and function along a natural xeric gradient. Micro Ecol. 2018;75:193–203.
    CAS  Article  Google Scholar 

    91.
    Short CM, Suttle CA. Nearly identical bacteriophage structural gene sequences are widely distributed in both marine and freshwater environments. Appl Environ Microbiol. 2005;71:480–6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    92.
    Edwards RA, Vega AA, Norman HM, Ohaeri M, Levi K, Dinsdale EA, et al. Global phylogeography and ancient evolution of the widespread human gut virus crAssphage. Nat Microbiol. 2019;4:1727–36.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    93.
    Culley AI, Steward GF. New genera of RNA viruses in subtropical seawater, inferred from polymerase gene sequences. Appl Environ Microbiol. 2007;73:5937–44.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    94.
    Miranda-Sánchez F, Rivera J, Vinuesa P. Diversity patterns of Rhizobiaceae communities inhabiting soils, root surfaces and nodules reveal a strong selection of rhizobial partners by legumes. Environ Microbiol. 2016;18:2375–91.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    95.
    Bontemps C, Rogel MA, Wiechmann A, Mussabekova A, Moody S, Simon MF, et al. Endemic Mimosa species from Mexico prefer alphaproteobacterial rhizobial symbionts. N Phytol. 2016;209:319–33.
    CAS  Article  Google Scholar 

    96.
    Van Cauwenberghe J, Lemaire B, Stefan A, Efrose R, Michiels J, Honnay O. Symbiont abundance is more important than pre-infection partner choice in a Rhizobium – legume mutualism. Syst Appl Microbiol. 2016;39:345–9.
    PubMed  Article  PubMed Central  Google Scholar 

    97.
    Van Cauwenberghe J, Michiels J, Honnay O. Effects of local environmental variables and geographical location on the genetic diversity and composition of Rhizobium leguminosarum nodulating Vicia cracca populations. Soil Biol Biochem. 2015;90:71–9.
    Article  CAS  Google Scholar 

    98.
    Van Cauwenberghe J, Verstraete B, Lemaire B, Lievens B, Michiels J, Honnay O. Population structure of root nodulating Rhizobium leguminosarum in Vicia cracca populations at local to regional geographic scales. Syst Appl Microbiol. 2014;37:613–21.
    PubMed  Article  PubMed Central  Google Scholar 

    99.
    Hurwitz BL, Brum JR, Sullivan MB. Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome. ISME J. 2015;9:472–84.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    100.
    Mühling M, Fuller NJ, Millard A, Somerfield PJ, Marie D, Wilson WH, et al. Genetic diversity of marine Synechococcus and co-occurring cyanophage communities: evidence for viral control of phytoplankton. Environ Microbiol. 2005;7:499–508.
    PubMed  Article  PubMed Central  Google Scholar 

    101.
    Sun Y, Zhang S, Long L, Dong J, Chen F, Huang S. Genetic diversity and cooccurrence patterns of marine cyanopodoviruses and picocyanobacteria. Appl Environ Microbiol. 2018;84:e00591–18.
    CAS  PubMed  PubMed Central  Google Scholar 

    102.
    Chase AB, Arevalo P, Brodie EL, Polz MF, Karaoz U, Martiny JBH. Maintenance of sympatric and allopatric populations in free-living terrestrial bacteria. mBio. 2019;10:e02361–19.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    103.
    Flores CO, Meyer JR, Valverde S, Farr L, Weitz JS. Statistical structure of host – phage interactions. Proc Natl Acad Sci. 2011;108:E288.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    104.
    Koskella B, Thompson JN, Preston GM, Buckling A. Local biotic environment shapes the spatial scale of bacteriophage adaptation to bacteria. Am Nat. 2011;177:440–51.
    PubMed  Article  PubMed Central  Google Scholar 

    105.
    Koskella B, Parr N. The evolution of bacterial resistance against bacteriophages in the horse chestnut phyllosphere is general across both space and time. Philos Trans R Soc B Biol Sci. 2015;370:20140297.
    Article  Google Scholar 

    106.
    Morgan AD, Gandon S, Buckling A. The effect of migration on local adaptation in a coevolving host-parasite system. Nature 2005;437:253–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    107.
    Gómez P, Paterson S, De Meester L, Liu X, Lenzi L, Sharma MD, et al. Local adaptation of a bacterium is as important as its presence in structuring a natural microbial community. Nat Commun. 2016;7:12453.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    108.
    Zhang Q-G, Buckling A. Resource-dependent antagonistic coevolution leads to a new paradox of enrichment. Ecology 2016;97:1319–28.
    PubMed  Article  PubMed Central  Google Scholar 

    109.
    Lopez-Pascua LDC, Buckling A. Increasing productivity accelerates host-parasite coevolution. J Evol Biol. 2008;21:853–60.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    110.
    Gurney J, Aldakak L, Betts A, Gougat-Barbera C, Poisot T, Kaltz O, et al. Network structure and local adaptation in co-evolving bacteria–phage interactions. Mol Ecol. 2017;26:1764–77.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    111.
    Thompson JN. The geographic mosaic of coevolution. Chicago, IL: Uni. Chicago Press; 2005. More

  • in

    Metabolomic signatures of coral bleaching history

    1.
    LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580 (2018).
    CAS  PubMed  Google Scholar 
    2.
    Muscatine, L. & Porter, J. W. Reef corals: mutualistic symbioses adapted to nutrient-poor environments. BioScience 27, 454–460 (1977).
    Google Scholar 

    3.
    van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nat. Clim. Change 3, 508–511 (2013).
    Google Scholar 

    4.
    National Academies of Sciences, Engineering, and Medicine A Research Review of Interventions to Increase the Persistence and Resilience of Coral Reefs (The National Academies Press, 2019); https://doi.org/10.17226/25279

    5.
    Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. USA 110, 1387–1392 (2013).
    CAS  PubMed  Google Scholar 

    6.
    Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).
    CAS  PubMed  Google Scholar 

    7.
    Bay, R. & Palumbi, S. Rapid acclimation ability mediated by transcriptome changes in reef-building corals. Genome Biol. Evol. 7, 1602–1612 (2015).
    CAS  PubMed  PubMed Central  Google Scholar 

    8.
    Grottoli, A. G. et al. Coral physiology and microbiome dynamics under combined warming and ocean acidification. PLoS ONE 13, e0191156 (2018).
    PubMed  PubMed Central  Google Scholar 

    9.
    Ziegler, M., Seneca, F., Yum, L. & P, S.-N. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    10.
    Hillyer, K. E. et al. 13C metabolomics reveals widespread change in carbon fate during coral bleaching. Metabolomics 14, 12 (2018).
    Google Scholar 

    11.
    Hillyer, K. E. et al. Metabolite profiling of symbiont and host during thermal stress and bleaching in the coral Acropora aspera. Coral Reefs 36, 105–118 (2017).
    Google Scholar 

    12.
    Sogin, E. M., Putnam, H., Gates, R. D., Putnam, H. M. & Anderson, P. E. Metabolomic signatures of increases in temperature and ocean acidification from the reef-building coral Pocillopora damicornis. Metablomics 12, 71 (2016).
    Google Scholar 

    13.
    Hillyer, K. E., Tumanov, S., Villas-Bô As, S. & Davy, S. K. Metabolite profiling of symbiont and host during thermal stress and bleaching in a model cnidarian-dinoflagellate symbiosis. J. Exp. Biol. https://doi.org/10.1242/jeb.128660 (2016).

    14.
    Fisch, J., Drury, C., Towle, E. K., Winter, R. N. & Miller, M. W. Physiological and reproductive repercussions of consecutive summer bleaching events of the threatened Caribbean coral Orbicella faveolata. Coral Reefs 38, 863–876 (2019).
    Google Scholar 

    15.
    Pinzón, J. H. et al. Whole transcriptome analysis reveals changes in expression of immune-related genes during and after bleaching in a reef-building coral. R. Soc. Open Sci. 2, 140214 (2015).
    PubMed  PubMed Central  Google Scholar 

    16.
    Thomas, L. & Palumbi, S. R. The genomics of recovery from coral bleaching. Proc. R. Soc. B 284, 20171790 (2017).
    PubMed  Google Scholar 

    17.
    Wall, C. B. et al. Shifting baselines: repeat bleaching drives coral physiotypes through environmental legacy and cellular memory. Preprint at bioRxiv https://doi.org/10.1101/2020.04.23.056457 (2020).

    18.
    Matsuda, S. et al. Coral bleaching susceptibility is predictive of subsequent mortality within but not between coral species. Front. Ecol. Evol. 8, 178 (2020).
    Google Scholar 

    19.
    Howells, E. J., Abrego, D., Meyer, E., Kirk, N. L. & Burt, J. A. Host adaptation and unexpected symbiont partners enable reef-building corals to tolerate extreme temperatures. Glob. Change Biol. 22, 2702–2714 (2016).
    Google Scholar 

    20.
    van Oppen, M. J. H. et al. Shifting paradigms in restoration of the world’s coral reefs. Glob. Change Biol. 23, 3437–3448 (2017).
    Google Scholar 

    21.
    Anthony, K. R. N. et al. Operationalizing resilience for adaptive coral reef management under global environmental change. Glob. Change Biol. 21, 48–61 (2015).
    Google Scholar 

    22.
    da Silva, R. R., Lopes, N. P. & Silva, D. B. in Mass Spectrometry in Chemical Biology: Evolving Applications (eds da Silva, R. R. & Lopes, N. P.) 57–81 (Royal Society of Chemistry, 2017).

    23.
    Cunning, R., Ritson-Williams, R. & Gates, R. Patterns of bleaching and recovery of Montipora capitata in Kāne’ohe Bay, Hawai’i, USA. Mar. Ecol. Prog. Ser. 551, 131–139 (2016).
    CAS  Google Scholar 

    24.
    Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis: Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).
    CAS  PubMed  PubMed Central  Google Scholar 

    25.
    Rosset, S. et al. Lipidome analysis of Symbiodiniaceae reveals possible mechanisms of heat stress tolerance in reef coral symbionts. Coral Reefs 38, 1241–1253 (2019).
    Google Scholar 

    26.
    Li, Y. et al. Simultaneous structural identification of diacylglyceryl-N-trimethylhomoserine (DGTS) and diacylglycerylhydroxymethyl-N,N,N-trimethyl-β-alanine (DGTA) in microalgae using dual Li+/H+ adduct ion mode by ultra-performance liquid chromatography/quadrupole time‐of‐flight mass spectrometry. Rapid Commun. Mass Spectrom. 31, 457–468 (2017).
    CAS  PubMed  Google Scholar 

    27.
    Matthews, J. L. et al. Optimal nutrient exchange and immune responses operate in partner specificity in the cnidarian–dinoflagellate symbiosis. Proc. Natl Acad. Sci. USA 114, 13194–13199 (2017).
    CAS  PubMed  Google Scholar 

    28.
    Weis, V. M. Cellular mechanisms of cnidarian bleaching: stress causes the collapse of symbiosis. J. Exp. Biol. 211, 3059–3066 (2008).
    CAS  PubMed  Google Scholar 

    29.
    Mansour, J. S., Pollock, F. J., Díaz-Almeyda, E., Iglesias-Prieto, R. & Medina, M. Intra- and interspecific variation and phenotypic plasticity in thylakoid membrane properties across two Symbiodinium clades. Coral Reefs 37, 841–850 (2018).
    Google Scholar 

    30.
    Roach, T. N. F. et al. A multiomic analysis of in situ coral–turf algal interactions. Proc. Natl Acad. Sci. USA 117, 13588–13595 (2020).
    CAS  PubMed  Google Scholar 

    31.
    Quinn, R. A. et al. Metabolomics of reef benthic interactions reveals a bioactive lipid involved in coral defence. Proc. R. Soc. B 283, 20160469 (2016).
    PubMed  Google Scholar 

    32.
    Rosset, S., Wiedenmann, J., Reed, A. J. & D’Angelo, C. Phosphate deficiency promotes coral bleaching and is reflected by the ultrastructure of symbiotic dinoflagellates. Mar. Pollut. Bull. 118, 180–187 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    33.
    Galtier d’Auriac, I. et al. Before platelets: the production of platelet-activating factor during growth and stress in a basal marine organism. Proc. R. Soc. B 285, 20181307 (2018).
    PubMed  Google Scholar 

    34.
    Quistad, S. D. et al. Evolution of TNF-induced apoptosis reveals 550 My of functional conservation. Proc. Natl Acad. Sci. USA 111, 9567–9572 (2014).
    CAS  PubMed  Google Scholar 

    35.
    Williams, A. et al. Metabolomic shifts associated with heat stress in coral holobionts. Sci. Adv. 7, eabd4210 (2021).
    PubMed Central  Google Scholar 

    36.
    Takahashi, N. Chemistry of Plant Hormones (CRC, 1986).

    37.
    Reyes, F., Martín, R. & Fernández, R. Granulatamides A and B, cytotoxic tryptamine derivatives from the soft coral Eunicella granulata. J. Nat. Prod. 69, 668–670 (2006).
    CAS  PubMed  Google Scholar 

    38.
    Hill, R., Larkum, A. W. & Kramer, D. Light-induced dissociation of antenna complexes in the symbionts of scleractinian corals correlates with sensitivity to coral bleaching. Coral Reefs 31, 963–975 (2012).
    Google Scholar 

    39.
    Venn, A. A., Wilson, M. A., Trapido-Rosenthal, H. G., Keely, B. J. & Douglas, A. E. The impact of coral bleaching on the pigment profile of the symbiotic alga, Symbiodinium. Plant Cell Environ. 29, 2133–2142 (2006).
    CAS  PubMed  Google Scholar 

    40.
    Martin, F. J. et al. A top-down systems biology view of microbiome–mammalian metabolic interactions in a mouse model. Mol. Syst. Biol. 3, 112 (2007).
    PubMed  PubMed Central  Google Scholar 

    41.
    Quinn, R. A. et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 579, 123–129 (2020).
    CAS  PubMed  PubMed Central  Google Scholar 

    42.
    Wikoff, W. R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl Acad. Sci. USA 106, 3698–3703 (2009).
    CAS  PubMed  Google Scholar 

    43.
    Dixon, G., Abbott, E. & Matz, M. Meta-analysis of the coral environmental stress response: Acropora corals show opposing responses depending on stress intensity. Mol. Ecol. https://doi.org/10.1111/mec.15535 (2020).

    44.
    Boström-Einarsson, L. et al. Coral restoration – a systematic review of current methods, successes, failures and future directions. PLoS ONE 15, e0226631 (2020).
    PubMed  PubMed Central  Google Scholar 

    45.
    Van Oppen, M. J. H., Oliver, J. K., Putnam, H. M. & Gates, R. D. Building coral reef resilience through assisted evolution. Proc. Natl Acad. Sci. USA 112, 2307–2313 (2015).
    PubMed  Google Scholar 

    46.
    Baums, I. B. et al. Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic. Ecol. Appl. 29, e01978 (2019).
    PubMed  PubMed Central  Google Scholar 

    47.
    Bay, R., Rose, N., Logan, C. & Palumbi, S. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, e1701413 (2017).
    PubMed  PubMed Central  Google Scholar 

    48.
    Dührkop, K. et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat. Methods 16, 299–302 (2019).
    PubMed  Google Scholar 

    49.
    Pluskal, T., Castillo, S., Villar-Briones, A. & Orešič, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform. 11, 395 (2010).
    Google Scholar 

    50.
    Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    51.
    Nothias, L.-F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020).
    CAS  PubMed  Google Scholar 

    52.
    Martin, C. et al. Viscosin-like lipopeptides from frog skin bacteria inhibit Aspergillus fumigatus and Batrachochytrium dendrobatidis detected by imaging mass spectrometry. Sci. Rep. 9, 3019 (2019).
    Google Scholar 

    53.
    Cunning, R., Gillette, P., Capo, T., Galvez, K. & Baker, A. C. Growth tradeoffs associated with thermotolerant symbionts in the coral Pocillopora damicornis are lost in warmer oceans. Coral Reefs 34, 155–160 (2015).
    Google Scholar 

    54.
    Cunning, R. & Baker, A. C. Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 259–262 (2013).
    Google Scholar  More