More stories

  • in

    Bacterial associations in the healthy human gut microbiome across populations

    1.
    Sender, R., Fuchs, S. & Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLOS Biol. 14, e1002533 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 
    2.
    Kho, Z. Y. & Lal, S. K. The human gut microbiome—A potential controller of wellness and disease. Front. Microbiol. 9, 1835 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    3.
    Kostic, A. D., Xavier, R. J. & Gevers, D. The microbiome in inflammatory bowel disease: Current status and the future ahead. Gastroenterology 146, 1489–1499 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    4.
    Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).
    CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

    5.
    Das, B. & Nair, G. B. Homeostasis and dysbiosis of the gut microbiome in health and disease. J. Biosci. 44, 117 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    6.
    Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 31, 69–75 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    7.
    Petersen, C. & Round, J. L. Defining dysbiosis and its influence on host immunity and disease: How changes in microbiota structure influence health. Cell. Microbiol. 16, 1024–1033 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    8.
    Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    9.
    Koren, O. et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl. Acad. Sci. 108, 4592–4598 (2011).
    CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

    10.
    Karlsson, F. H. et al. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 3, 1245 (2012).
    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

    11.
    Chatelier, L. M. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    12.
    Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Becker, C., Neurath, M. F. & Wirtz, S. The intestinal microbiota in inflammatory bowel disease. ILAR J. 56, 192–204 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14, 207–215 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    15.
    The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
    PubMed Central  Article  ADS  CAS  Google Scholar 

    16.
    Johnson, A. J. et al. Daily sampling reveals personalized diet–microbiome associations in humans. Cell Host Microbe 25, 789-802.e5 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Villmones, H. C. et al. Species level description of the human ileal bacterial microbiota. Sci. Rep. 8, 1–9 (2018).
    CAS  Article  Google Scholar 

    18.
    Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    19.
    David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
    CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

    20.
    Zhou, J., Deng, Y., Luo, F., He, Z. & Yang, Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. MBio 2, e00122-e211 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Lupatini, M. et al. Network topology reveals high connectance levels and few key microbial genera within soils. Front. Environ. Sci. 2, 10 (2014).
    Article  Google Scholar 

    22.
    Eiler, A., Heinrich, F. & Bertilsson, S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 6, 330–342 (2012).
    CAS  PubMed  Article  Google Scholar 

    23.
    Kara, E. L., Hanson, P. C., Hu, Y. H., Winslow, L. & McMahon, K. D. A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J. 7, 680–684 (2013).
    PubMed  Article  Google Scholar 

    24.
    Shetty, S. A., Hugenholtz, F., Lahti, L., Smidt, H. & de Vos, W. M. Intestinal microbiome landscaping: Insight in community assemblage and implications for microbial modulation strategies. FEMS Microbiol. Rev. 41, 182–199 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    25.
    Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl. Acad. Sci. 115, E11951–E11960 (2018).
    CAS  PubMed  Article  Google Scholar 

    26.
    Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: Surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8, 15–25 (2010).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    27.
    Fox, G. E., Magrum, L. J., Balcht, W. E., Wolfef, R. S. & Woese, C. R. Classification of methanogenic bacteria by 16S ribosomal RNA characterization (comparative oligonucleotide cataloging/phylogeny/molecular evolution). Evolution (N.Y.) 74, 4537–4541 (1977).
    CAS  Google Scholar 

    28.
    Venter, J. C. et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66 (2004).
    CAS  PubMed  Article  ADS  Google Scholar 

    29.
    Větrovský, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE 8, e57923 (2013).
    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

    30.
    Edgar, R. C. Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ 6, 1–29 (2018).
    Google Scholar 

    31.
    Ranjan, R., Rani, A., Metwally, A., McGee, H. S. & Perkins, D. L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 469, 967–977 (2016).
    CAS  PubMed  Article  Google Scholar 

    32.
    Laudadio, I. et al. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. Omi. A J. Integr. Biol. 22, 248–254 (2018).
    CAS  Article  Google Scholar 

    33.
    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 1–6 (2017).
    Article  Google Scholar 

    34.
    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    35.
    Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).
    CAS  PubMed  Article  Google Scholar 

    36.
    Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 40 (1982).
    MathSciNet  MATH  Google Scholar 

    37.
    Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, 1–25 (2015).
    Article  CAS  Google Scholar 

    38.
    Friedman, J., Hastie, T. & Tibshirani, R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432–441 (2008).
    PubMed  MATH  Article  Google Scholar 

    39.
    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
    CAS  PubMed  Article  ADS  Google Scholar 

    40.
    Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).
    MathSciNet  MATH  Article  Google Scholar 

    41.
    Su, W., Bogdan, M., Candès, E. & Candes, E. False discoveries occur early on the lasso path. Ann. Stat. 45, 2133–2150 (2017).
    MathSciNet  MATH  Article  Google Scholar 

    42.
    Saunders, A. M., Albertsen, M., Vollertsen, J. & Nielsen, P. H. The activated sludge ecosystem contains a core community of abundant organisms. ISME J. 10, 11–20 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    43.
    Tsvetovat, M. & Kouznetsov, A. Social network analysis for startups. Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki (2011).

    44.
    Stadtfeld, C., Takács, K. & Vörös, A. The emergence and stability of groups in social networks. Soc. Netw. 60, 129–145 (2020).
    Article  Google Scholar 

    45.
    Cordasco, G. & Gargano, L. Community detection via semi-synchronous label propagation algorithms. 2010 IEEE Int. Work. Bus. Appl. Soc. Netw. Anal. BASNA 2010 (2010). https://doi.org/10.1109/BASNA.2010.5730298.

    46.
    Prettejohn, B. J., Berryman, M. J. & McDonnell, M. D. Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists. Front. Comput. Neurosci. 5, 11 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    47.
    Guimerà, R., Sales-Pardo, M. & Amaral, L. A. N. Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70, 25101 (2004).
    Article  ADS  CAS  Google Scholar 

    48.
    Trosvik, P. & de Muinck, E. J. Ecology of bacteria in the human gastrointestinal tract—Identification of keystone and foundation taxa. Microbiome 3, 44 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    49.
    Verster, A. J. & Borenstein, E. Competitive lottery-based assembly of selected clades in the human gut microbiome. Microbiome 6, 186 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    50.
    Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    51.
    Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    52.
    Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    53.
    Hamilton, W. D. The genetical evolution of social behaviour. I. J. Theor. Biol. 7, 1–16 (1964).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
    CAS  PubMed  Article  ADS  Google Scholar 

    55.
    Jackson, M. A. et al. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations. PeerJ 6, e4303 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    56.
    Darcy, J. L. et al. A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome. ISME J. 14, 1359–1368 (2020).
    PubMed  Article  Google Scholar 

    57.
    Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).
    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

    58.
    Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430 (2002).
    CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

    59.
    Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Mark Welch, J. L., Hasegawa, Y., McNulty, N. P., Gordon, J. I. & Borisy, G. G. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc. Natl. Acad. Sci. 114, E9105–E9114 (2017).
    CAS  PubMed  Article  Google Scholar 

    61.
    Fung, T. C., Artis, D. & Sonnenberg, G. F. Anatomical localization of commensal bacteria in immune cell homeostasis and disease. Immunol. Rev. 260, 35–49 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    62.
    Donaldson, G. P., Lee, S. M. & Mazmanian, S. K. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20–32 (2016).
    CAS  PubMed  Article  Google Scholar 

    63.
    Stachowicz, J. J. Mutualism, facilitation, and the structure of ecological communities. Bioscience 51, 235 (2001).
    Article  Google Scholar 

    64.
    Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    65.
    Lynd, L. R., Weimer, P. J., van Zyl, W. H. & Pretorius, I. S. Microbial cellulose utilization: Fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66, 72 (2002).
    Article  Google Scholar 

    66.
    Turroni, F. et al. Glycan cross-feeding activities between bifidobacteria under in vitro conditions. Front. Microbiol. 6, 1030 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    67.
    Hall, C. V. et al. Co-existence of network architectures supporting the human gut microbiome. iScience 22, 380–391 (2019).
    PubMed  PubMed Central  Article  ADS  Google Scholar 

    68.
    Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).
    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

    69.
    Jones, M. B. et al. Library preparation methodology can influence genomic and functional predictions in human microbiome research. Proc. Natl. Acad. Sci. 112, 14024–14029 (2015).
    CAS  PubMed  Article  ADS  Google Scholar 

    70.
    Lahti, L., Salojärvi, J., Salonen, A., Scheffer, M. & de Vos, W. M. Tipping elements in the human intestinal ecosystem. Nat. Commun. 5, 4344 (2014).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    71.
    Dhakan, D. B. et al. The unique composition of Indian gut microbiome, gene catalogue, and associated fecal metabolome deciphered using multi-omics approaches. Gigascience 8, giz004 (2019).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    72.
    Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    73.
    Yachida, S. et al. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat. Med. 25, 968–976 (2019).
    CAS  PubMed  Article  Google Scholar 

    74.
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    75.
    Langmead, B. & Salzberg, S. Bowtie2. Nat. Methods 9, 357–359 (2013).
    Article  CAS  Google Scholar 

    76.
    O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).
    PubMed  Article  CAS  Google Scholar 

    77.
    Xia, L. C., Cram, J. A., Chen, T., Fuhrman, J. A. & Sun, F. Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS ONE 6, e27992 (2011).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    78.
    Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    79.
    Hyatt, D. et al. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
    Article  CAS  Google Scholar 

    80.
    Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    81.
    Haft, D. H. TIGRFAMs: A protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    82.
    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    83.
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
    Google Scholar 

    84.
    Zhao, T., Liu, H., Roeder, K., Lafferty, J. & Wasserman, L. The huge package for high-dimensional undirected graph estimation in R. J. Mach. Learn. Res. 13, 6 (2016).
    MathSciNet  MATH  Google Scholar 

    85.
    Liu, H., Roeder, K. & Wasserman, L. Stability approach to regularization selection (stars) for high dimensional graphical models. Advances in Neural Information Processing Systems (2010).

    86.
    Hagberg, A., Swart, P. & Chult, D. S. Exploring network structure, dynamics, and function using NetworkX. No. LA-UR-08-05495; LA-UR-08-5495 (Los Alamos National Lab. (LANL), Los Alamos, 2008).

    87.
    Newman, M. E. J. Networks: An Introduction 168–234 (Oxford University Press, Oxford, 2010).
    Google Scholar 

    88.
    Newman, M. E. J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006).
    CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

    89.
    Newman, M. E. J. Mixing patterns in networks. Phys. Rev. E 67, 26126 (2003).
    MathSciNet  CAS  Article  ADS  Google Scholar 

    90.
    Fortunato, S. Community detection in graphs. Phys. Rep. 486, 75–174 (2010).
    MathSciNet  Article  ADS  Google Scholar 

    91.
    Brandes, U. A faster algorithm for betweenness centrality*. J. Math. Sociol. 25, 163–177 (2001).
    MATH  Article  Google Scholar  More

  • in

    Functional traits explain crayfish invasive success in the Netherlands

    1.
    Keller, R. P., Geist, J., Jeschke, J. M. & Kühn, I. Invasive species in Europe: ecology, status, and policy. Environ. Sci. Eur. 23, 1–17 (2011).
    Article  Google Scholar 
    2.
    Parker, M., Thompson, J. N. & Weller, S. G. The population biology of invasive species. Annu. Rev. Ecol. Syst. 32, 305–332 (2001).
    Article  Google Scholar 

    3.
    Allendorf, F. W. & Lundquist, L. L. Introduction: population biology, evolution, and control of invasive species. Conserv. Biol. 17, 24–30 (2003).
    Article  Google Scholar 

    4.
    Crowl, T. A., Crist, T. O., Parmenter, R. R., Belovsky, G. & Lugo, A. E. The spread of invasive species and infectious disease as drivers of ecosystem change. Front. Ecol. Environ. 6, 238–246 (2008).
    Article  Google Scholar 

    5.
    van der Veer, G. & Nentwig, W. Environmental and economic impact assessment of alien and invasive fish species in Europe using the generic impact scoring system. Ecol. Freshw. Fish 24, 646–656 (2015).
    Article  Google Scholar 

    6.
    Clavero, M. & García-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110 (2005).
    PubMed  Article  PubMed Central  Google Scholar 

    7.
    Scalera, R. How much is Europe spending on invasive alien species?. Biol. Invasions 12, 173–177 (2010).
    Article  Google Scholar 

    8.
    Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    McLellan, R., Iyengar, L., Jeffries, B. & Oerlemans, N. Living Planet Report 2014: Species and Spaces, People and Places (WWF International, Gland, 2014).
    Google Scholar 

    10.
    García-Berthou, E. et al. Introduction pathways and establishment rates of invasive aquatic species in Europe. Can. J. Fish. Aquat. Sci. 62, 453–463 (2005).
    Article  Google Scholar 

    11.
    Karatayev, A. Y., Burlakova, L. E., Padilla, D. K., Mastitsky, S. E., & Olenin, S. Invaders are not a random selection of species. Biol. Invasions, 11, 2009. https://doi.org/10.1007/s10530-009-9498-0 (2009).
    Article  Google Scholar 

    12.
    Verdonschot, R. C. M., Vos, J. H., & Verdonschot, P. F. M. Exotische macrofauna en macrofyten in de Nederlandse zoete wateren: voorkomen en beleid in 2012. (WOt-werkdocument 334) (Wettelijke Onderzoekstaken Natuur & Milieu, 2013).

    13.
    Holdich, D. M., Reynolds, J. D., Souty-Grosset, C. & Sibley, P. J. A review of the ever increasing threat to European crayfish from non-indigenous crayfish species. Knowl. Manag. Aquat. Ecosyst. 394–395, 11 (2009).
    Article  Google Scholar 

    14.
    Chucholl, C. Invaders for sale: trade and determinants of introduction of ornamental freshwater crayfish. Biol. Invasions 15, 125–141 (2013).
    Article  Google Scholar 

    15.
    Barbaresi, S. & Gherardi, F. The invasion of the alien crayfish Procambarus clarkii in Europe, with particular reference to Italy. Biol. Invasions 2, 259–264 (2000).
    Article  Google Scholar 

    16.
    Gherardi, F. Crayfish invading Europe: the case study of Procambarus clarkii. Mar. Freshw. Behav. Physiol. 39, 175–191 (2006).
    Article  Google Scholar 

    17.
    Kouba, A., Petrusek, A. & Kozák, P. Continental-wide distribution of crayfish species in Europe: update and maps. Knowl. Manag. Aquat. Ecosyst. 413, 5 (2014).
    Article  Google Scholar 

    18.
    Lowe, S., Browne, M., Boudjelas, S., & De Poorter, M. 100 of the world’s worst invasive alien species: a selection from the global invasive species database in Aliens vol. 12 (Invasive Species Specialist Group, 2000).

    19.
    Padilla, D. K. & Williams, S. L. Beyond ballast water: aquarium and ornamental trades as sources of invasive species in aquatic ecosystems. Front. Ecol. Environ. 2, 131–138 (2004).
    Article  Google Scholar 

    20.
    Faulkes, Z. The global trade in crayfish as pets. Crustacean Res. 44, 75–92 (2015).
    Article  Google Scholar 

    21.
    Soes, D. M., & Koese, B. Invasive Crayfish in the Netherlands: A Preliminary Risk Analysis. (Bureau Waardenburg bv, Stichting EIS-Nederland, Invasive Alien Species Team, 2010).

    22.
    Chucholl, C. & Wendler, F. Positive selection of beautiful invaders: long-term persistence and bio-invasion risk of freshwater crayfish in the pet trade. Biol. Invasions 19, 197–208 (2017).
    Article  Google Scholar 

    23.
    Zeng, Y., Chong, K. Y., Grey, E. K., Lodge, D. M. & Yeo, D. C. Disregarding human pre-introduction selection can confound invasive crayfish risk assessments. Biol. Invasions 17, 2373–2385 (2015).
    Article  Google Scholar 

    24.
    Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).
    PubMed  Article  Google Scholar 

    25.
    Statzner, B., Bonada, N. & Dolédec, S. Biological attributes discriminating invasive from native European stream macroinvertebrates. Biol. Invasions 10, 517–530 (2008).
    Article  Google Scholar 

    26.
    Whitney, K. D. & Gabler, C. A. Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Divers. Distrib. 14, 569–580 (2008).
    Article  Google Scholar 

    27.
    Kolar, C. S. & Lodge, D. M. Progress in invasion biology: predicting invaders. Trends Ecol. Evol. 16, 199–204 (2001).
    PubMed  Article  Google Scholar 

    28.
    Marchetti, M. P., Moyle, P. B. & Levine, R. Invasive species profiling? Exploring the characteristics of non-native fishes across invasion stages in California. Freshw. Biol. 49, 646–661 (2004).
    Article  Google Scholar 

    29.
    Grabowski, M., Bacela, K. & Konopacka, A. How to be an invasive gammarid (Amphipoda: Gammaroidea)-comparison of life history traits. Hydrobiologia 590, 75–84 (2007).
    Article  Google Scholar 

    30.
    Thiébaut, G. Invasion success of non-indigenous aquatic and semi-aquatic plants in their native and introduced ranges. A comparison between their invasiveness in North America and in France. Biol. Invasions 9, 1–12 (2007).
    Article  Google Scholar 

    31.
    Swart, C., Visser, V. & Robinson, T. B. Patterns and traits associated with invasions by predatory marine crabs. NeoBiota 39, 79 (2018).
    Article  Google Scholar 

    32.
    Larson, E. R. & Olden, J. D. Latent extinction and invasion risk of crayfishes in the southeastern United States. Conserv. Biol. 24, 1099–1110 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    33.
    Tricarico, E., Vilizzi, L., Gherardi, F. & Copp, G. H. Calibration of FI-ISK, an invasiveness screening tool for nonnative freshwater invertebrates. Risk Anal. Int. J. 30, 285–292 (2010).
    Article  Google Scholar 

    34.
    Larson, E. R. & Olden, J. D. Using avatar species to model the potential distribution of emerging invaders. Glob Ecol. Biogeogr. 21, 1114–1125 (2012).
    Article  Google Scholar 

    35.
    Veselý, L., Buřič, M. & Kouba, A. Hardy exotics species in temperate zone: can “warm water” crayfish invaders establish regardless of low temperatures?. Sci. Rep. 5, 16340 (2015).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    36.
    Jaklič, M. & Vrezec, A. The first tropical alien crayfish species in European waters: the redclaw Cherax quadricarinatus (Von Martens, 1868) (Decapoda, Parastacidae). Crustaceana 84, 651–665 (2011).
    Article  Google Scholar 

    37.
    Colautti, R. I., Grigorovich, I. A. & MacIsaac, H. J. Propagule pressure: a null model for biological invasions. Biol. Invasions 8, 1023–1037 (2006).
    Article  Google Scholar 

    38.
    Marchetti, M. P., Moyle, P. B. & Levine, R. Alien fishes in California watersheds: characteristics of successful and failed invaders. Ecol. Appl. 14, 587–596 (2004).
    Article  Google Scholar 

    39.
    Bennett, S. N., Olson, J. R., Kershner, J. L. & Corbett, P. Propagule pressure and stream characteristics influence introgression: cutthroat and rainbow trout in British Columbia. Ecol. Appl. 20, 263–277 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    40.
    Cruz, M. J. & Rebelo, R. Colonization of freshwater habitats by an introduced crayfish, Procambarus clarkii Southwest Iberian Peninsula. Hydrobiologia 575, 191–201 (2007).
    Article  Google Scholar 

    41.
    Lynas, J., Storey, A. W. & Knott, B. Aggressive interactions between three species of freshwater crayfish of the genus Cherax (Decapoda: Parastacidae). Mar. Freshw. Behav. Physiol. 40, 105–116 (2007).
    Article  Google Scholar 

    42.
    Corey, S. Comparative fecundity of four species of crayfish in southwestern Ontario, Canada (Decapoda, Astacidea). Crustaceana 52(3), 276–286 (1987).
    Article  Google Scholar 

    43.
    Somers, K. M. Characterizing size-specific fecundity in crustaceans. Crustacean Egg Prod. 7, 357–378 (1991).
    Google Scholar 

    44.
    Maguire, I., Klobučar, G. I. V. & Erben, R. The relationship between female size and egg size in the freshwater crayfish Austropotamobius torrentium. Bulletin Français de la Pêche et de la Pisciculture 376–377, 777–785 (2005).
    Article  Google Scholar 

    45.
    Pilotto, F. et al. The invasive crayfish Faxonius limosus in Lake Varese: estimating abundance and population size structure in the context of habitat and methodological constraints. J. Crustacean Biol. 28, 633–640 (2008).
    Article  Google Scholar 

    46.
    Hobbs Jr, H. H. A checklist of the North and Middle American crayfishes (Decapoda: Astacidae and Cambaridae). Smithsonian Contrib. Zool. 166, 1–161 (1974).
    Google Scholar 

    47.
    Mrugała, A. et al. Trade of ornamental crayfish in Europe as a possible introduction pathway for important crustacean diseases: crayfish plague and white spot syndrome. Biol. Invasions 17, 1313–1326 (2015).
    Article  Google Scholar 

    48.
    Svoboda, J., Mrugała, A., Kozubíková-Balcarová, E. & Petrusek, A. Hosts and transmission of the crayfish plague pathogen Aphanomyces astaci: a review. J. Fish Dis. 40, 127–140 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    49.
    Grandjean, F. et al. Status of Pacifastacus leniusculus and its role in recent crayfish plague outbreaks in France: improving distribution and crayfish plague infection patterns. Aquat. Invasions, 12, 541–549 (2017).
    Article  Google Scholar 

    50.
    Crandall, K. A. & De Grave, S. An updated classification of the freshwater crayfishes (Decapoda: Astacidea) of the world, with a complete species list. J. Crustacean Biol. 37, 615–653 (2017).
    Article  Google Scholar 

    51.
    Freshwater Crayfish: A Global Overview. (ed. Kawai, T., Faulkes, Z., & Scholtz, G.) (CRC Press, Boca Raton, 2015).

    52.
    Buřič, M., Kouba, A. & Kozak, P. Reproductive plasticity in freshwater invader: from long-term sperm storage to parthenogenesis. PLoS ONE 8, e77597. https://doi.org/10.1371/journal.pone.0077597 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    53.
    Kaldre, K., Meženin, A., Paaver, T., & Kawai, T. A preliminary study on the tolerance of marble crayfish Procambarus fallax f. virginalis to low temperature in Nordic climate in Freshwater crayfish: global overview, 54–62 (2016).

    54.
    Vogt, G. Marmorkrebs: natural crayfish clone as emerging model for various biological disciplines. J. Biosci. 36, 377–382 (2011).
    PubMed  Article  Google Scholar 

    55.
    Chucholl, C. Predicting the risk of introduction and establishment of an exotic aquarium animal in Europe: insights from one decade of Marmorkrebs (Crustacea, Astacida, Cambaridae) releases. Biol. Invasions 5, 309–318 (2014).
    Article  Google Scholar 

    56.
    Chucholl, C., Morawetz, K. & Groß, H. The clones are coming–strong increase in Marmorkrebs [Procambarus fallax (Hagen, 1870) f. virginalis] records from Europe. Aquat. Invasions 7, 511–519 (2012).
    Article  Google Scholar 

    57.
    Soes, D. M. & van Eekelen, R. Rivierkreeften, een oprukkend probleem?. De Levende Natuur 107, 56–59 (2006).
    Google Scholar 

    58.
    Mauvisseau, Q., Tönges, S., Andriantsoa, R., Lyko, F. & Sweet, M. Early detection of an emerging invasive species: eDNA monitoring of a parthenogenetic crayfish in freshwater systems. Manag. Biol. Invasions 10, 461 (2019).
    Article  Google Scholar 

    59.
    Strand, D. A. et al. Monitoring a Norwegian freshwater crayfish tragedy: eDNA snapshots of invasion, infection and extinction. J. Appl. Ecol. 56, 1661–1673 (2019).
    CAS  Article  Google Scholar 

    60.
    Beentjes, K. K., Speksnijder, A. G., Schilthuizen, M., Schaub, B. E. & van der Hoorn, B. B. The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands. Metabarcoding Metagenom. 2, e26744 (2018).
    Article  Google Scholar 

    61.
    Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: a literature review and spatial analysis of evidence. Ecol. Model. 415, 108837 (2020).
    Article  Google Scholar 

    62.
    Zhang, Z. et al. Impacts of climate change on the global potential distribution of two notorious invasive crayfishes. Freshw. Biol. 65, 353–365 (2020).
    Article  Google Scholar 

    63.
    Capinha, C., Leung, B. & Anastácio, P. Predicting worldwide invasiveness for four major problematic decapods: an evaluation of using different calibration sets. Ecography 34, 448–459 (2011).
    Article  Google Scholar 

    64.
    Havel, J. E., Kovalenko, K. E., Thomaz, S. M., Amalfitano, S. & Kats, L. B. Aquatic invasive species: challenges for the future. Hydrobiologia 750, 147–170 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    65.
    Früh, D., Stoll, S. & Haase, P. Physicochemical and morphological degradation of stream and river habitats increases invasion risk. Biol. Invasions 14, 2243–2253 (2012).
    Article  Google Scholar 

    66.
    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 (2007).
    Article  Google Scholar 

    67.
    Scalici, M. et al. The new threat to Italian inland waters from the alien crayfish “gang”: the Australian Cherax destructor Clark, 1936. Hydrobiologia 632, 341–345 (2009).
    Article  Google Scholar 

    68.
    Koese, B. & Evers, C. H. M. A National Inventory of Invasive Freshwater Crayfish in the Netherlands in 2010 (EIS, Stichting European Invertebrate Survey Nederland, 2011).
    Google Scholar 

    69.
    Clement, J., & van Puijenbroek, P. Basiskaart Aquatisch: de Watertypenkaart Het oppervlaktewater in de TOP10NL geclassificeerd naar watertype (No. 500067004). (Planbureau voor de Leefomgeving 2010).

    70.
    Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. Discuss. 4, 439–473 (2007).
    ADS  Google Scholar 

    71.
    Lyko, F. The marbled crayfish (Decapoda: Cambaridae) represents an independent new species. Zootaxa 4363(4), 544–552 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    72.
    Usseglio-Polatera, P. & Tachet, H. Theoretical habitat templets, species traits, and species richness: Plecoptera and Ephemeroptera in the Upper Rhône River and its floodplain. Freshw. Biol. 31, 357–375 (1994).
    Article  Google Scholar 

    73.
    Poff, N. L. et al. Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. J. North Am. Benthological. Soc. 25, 730–755 (2006).
    Article  Google Scholar 

    74.
    Wyse, S. V. et al. A quantitative assessment of shoot flammability for 60 tree and shrub species supports rankings based on expert opinion. Int. J. Wildland Fire 25, 466–477 (2016).
    Article  Google Scholar 

    75.
    Hill, M. O. TWINSPAN. A FORTRAN program for arranging multivariate data in an ordered two-way table by classification of the individuals and attributes. (Ecology and Systematics, Cornell University, 1979).

    76.
    Hu, G. et al. Regeneration of different plant functional types in a Masson pine forest following pine wilt disease. PLoS ONE 7, e36432. https://doi.org/10.1371/journal.pone.0036432 (2012).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    77.
    Agir, S. U., Kutbay, H. G. & Surmen, B. Plant diversity along coastal dunes of the Black Sea (North of Turkey). Rendiconti Lincei 27, 443–453 (2016).
    Article  Google Scholar 

    78.
    Andrej, P. & Andraž, Č. Functional response traits and plant community strategy indicate the stage of secondary succession. Hacquetia 11, 209–225 (2012).
    Article  Google Scholar 

    79.
    Hill, M.O. & Šmilauer, P. TWINSPAN for Windows version 2.3. (Centre for Ecology and Hydrology & University of South Bohemia, Huntingdon & Ceske Budejovice, 2005).

    80.
    Roleček, J., Tichý, L., Zelený, D. & Chytrý, M. Modified TWINSPAN classification in which the hierarchy respects cluster heterogeneity. J. Veg. Sci. 20, 596–602 (2009).
    Article  Google Scholar  More

  • in

    Extreme temperatures compromise male and female fertility in a large desert bird

    1.
    Angilletta, M. J. Thermal Adaptation: A Theoretical And Empirical Analysis (Oxford University Press, 2009).
    2.
    Chown, S. L., Sinclair, B. J., Leinaas, H. P. & Gaston, K. J. Hemispheric asymmetries in biodiversity—a serious matter for ecology. PLoS Biol. 2, e406 (2004).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    3.
    Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
    ADS  Article  Google Scholar 

    4.
    Kellermann, V., van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).
    ADS  CAS  PubMed  Article  Google Scholar 

    5.
    Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
    PubMed  Article  Google Scholar 

    6.
    García-Robledo, C., Kuprewicz, E. K., Staines, C. L., Erwin, T. L. & Kress, W. J. Limited tolerance by insects to high temperatures across tropical elevational gradients and the implications of global warming for extinction. Proc. Natl Acad. Sci. USA 113, 680–685 (2016).
    ADS  PubMed  Article  CAS  Google Scholar 

    7.
    Geerts, A. N. et al. Rapid evolution of thermal tolerance in the water flea, Daphnia. Nat. Clim. Change 5, 665–668 (2015).
    ADS  Article  Google Scholar 

    8.
    Iossa, G. Sex-specific differences in thermal fertility limits. Trends Ecol. Evol. 34, 490–492 (2019).
    PubMed  Article  Google Scholar 

    9.
    Walsh, B. S. et al. The impact of climate change on fertility. Trends Ecol. Evol. 34, 249–259 (2019).
    PubMed  Article  Google Scholar 

    10.
    Vasudeva, R. et al. Adaptive thermal plasticity enhances sperm and egg performance in a model insect. eLife 8, e49452 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    11.
    Hurley, L. L., McDiarmid, C. S., Friesen, C. R., Griffith, S. C. & Rowe, M. Experimental heatwaves negatively impact sperm quality in the zebra finch. Proc. R. Soc. B 285, 20172547 (2018).
    PubMed  Article  Google Scholar 

    12.
    Dahlke, F., Wohlrab, S., Butzin, M. & Pörtner, H. Thermal bottlenecks in the lifecycle define climate vulnerability of fish. Science 369, 65–70 (2020).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    13.
    Bathiany, S., Dakos, V., Scheffer, M. & Lenton, T. M. Climate models predict increasing temperature variability in poor countries. Sci. Adv. 4, 1–11 (2018).
    Article  Google Scholar 

    14.
    Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Ecological and evolutionary impacts of changing climatic variability. Biol. Rev. 92, 22–42 (2017).
    PubMed  Article  Google Scholar 

    15.
    Chevin, L.-M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    16.
    Sgrò, C. M. & Hoffmann, A. A. Genetic correlations, tradeoffs and environmental variation. Heredity 93, 241–248 (2004).
    PubMed  Article  Google Scholar 

    17.
    Wood, C. W. & Brodie, E. D. Environmental effects on the structure of the G-matrix. Evolution 69, 2927–2940 (2015).
    PubMed  Article  Google Scholar 

    18.
    Brommer, J. E., Merila, J., Sheldon, B. C. & Gustavsson, L. Natural selection and genetic variation for reproductive reaction norms in a wild bird population. Evolution 59, 1362–1371 (2005).
    PubMed  Article  Google Scholar 

    19.
    Brommer, J. E., Rattiste, K. & Wilson, A. J. Exploring plasticity in the wild: laying date–temperature reaction norms in the common gull Larus canus. Proc. R. Soc. B 275, 687–693 (2008).
    PubMed  Article  Google Scholar 

    20.
    Nussey, D. H., Postma, E., Gienapp, P., Visser, M. E. & Gienapp, P. Selection on heritable phenotypic plasticity in a wild bird population. Science 310, 304–306 (2005).
    ADS  CAS  PubMed  Article  Google Scholar 

    21.
    Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008).
    ADS  CAS  PubMed  Article  Google Scholar 

    22.
    Matthysen, E., Adriaensen, F. & Dhondt, A. A. Multiple responses to increasing spring temperatures in the breeding cycle of blue and great tits (Cyanistes caeruleus, Parus major). Glob. Change Biol. 17, 1–16 (2011).
    ADS  Article  Google Scholar 

    23.
    Both, C. & Visser, M. E. Adjustment to climate change is constrained by arrival date in a long-distance migrant bird. Nature 411, 296–298 (2001).
    ADS  CAS  PubMed  Article  Google Scholar 

    24.
    Schiegg, K., Pasinelli, G., Walters, J. R. & Daniels, S. J. Inbreeding and experience affect response to climate change by endangered woodpeckers. Proc. R. Soc. B 269, 1153–1159 (2002).
    PubMed  Article  Google Scholar 

    25.
    Wilson, S., Norris, D. R., Wilson, A. G. & Arcese, P. Breeding experience and population density affect the ability of a songbird to respond to future climate variation. Proc. R. Soc. B 274, 2539–2545 (2007).
    PubMed  Article  Google Scholar 

    26.
    Dunn, P. O. & Winkler, D. W. Climate change has affected the breeding date of tree swallows throughout North America. Proc. R. Soc. B 266, 2487–2490 (1999).
    CAS  Article  Google Scholar 

    27.
    Hällfors, M. H. et al. Shifts in timing and duration of breeding for 73 boreal bird species over four decades. Proc. Natl Acad. Sci. USA 117, 18557–18565 (2020).
    PubMed  Article  CAS  Google Scholar 

    28.
    Gienapp, P., Postma, E. & Visser, M. E. Why breeding time has not responded to selection for earlier breeding in a songbird population. Evolution 60, 2381 (2006).
    PubMed  Article  Google Scholar 

    29.
    Jàrvinen, A. Global warming and egg size of birds. Ecography 17, 108–110 (1994).
    Article  Google Scholar 

    30.
    Kitaysky, A. S. & Golubova, E. G. Climate change causes contrasting trends in reproductive performance of planktivorous and piscivorous alcids. J. Anim. Ecol. 69, 248–262 (2000).
    Article  Google Scholar 

    31.
    Julliard, R., Clavel, J., Devictor, V., Jiguet, F. & Couvet, D. Spatial segregation of specialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244 (2006).
    PubMed  Article  Google Scholar 

    32.
    Weatherhead, P. J. Effects of climate variation on timing of nesting, reproductive success, and offspring sex ratios of red-winged blackbirds. Oecologia 144, 168–175 (2005).
    ADS  PubMed  Article  Google Scholar 

    33.
    Auer, S. K. & Martin, T. E. Climate change has indirect effects on resource use and overlap among coexisting bird species with negative consequences for their reproductive success. Glob. Change Biol. 19, 411–419 (2013).
    ADS  Article  Google Scholar 

    34.
    Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. Proc. Natl Acad. Sci. USA116, 21609–21615 (2019).
    CAS  PubMed  Article  Google Scholar 

    35.
    Visser, M. E., Van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. B 265, 1867–1870 (1998).
    Article  Google Scholar 

    36.
    Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).
    ADS  CAS  PubMed  Article  Google Scholar 

    37.
    Magige, F. J., Stokke, B. G., Sortland, R. & Røskaft, E. Breeding biology of ostriches (Struthio camelus) in the Serengeti ecosystem, Tanzania. Afr. J. Ecol. 47, 400–408 (2009).
    Article  Google Scholar 

    38.
    Bertram, B. C. R. The Ostrich Communal Nesting System (Princeton University Press, New Jersey, 1992).

    39.
    Kimwele, C. N. & Graves, J. A. A molecular genetic analysis of the communal nesting of the ostrich (Struthio camelus). Mol. Ecol. 12, 229–236 (2003).
    CAS  PubMed  Article  Google Scholar 

    40.
    Maloney, S. K. Thermoregulation in ratites: a review. Aust. J. Exp. Agric. 48, 1293–1301 (2008).
    Article  Google Scholar 

    41.
    Hassan, S. M., Siam, A. A., Mady, M. E. & Cartwright, A. L. Egg storage period and weight effects on hatchability of ostrich (Struthio camelus) eggs. Poult. Sci. 84, 1908–1912 (2005).
    CAS  PubMed  Article  Google Scholar 

    42.
    Gonzalez, A., Satterlee, D. G., Moharer, F. & Cadd, G. G. Factors affecting ostrich egg hatchability. Poult. Sci. 78, 1257–1262 (1999).
    CAS  PubMed  Article  Google Scholar 

    43.
    Roff, D. A. & Wilson, A. J. Quantifying genotype-by-environment interactions in laboratory systems. In Genotype‐by‐Environment Interactions and Sexual Selection (eds. Hunt, J. & Hosken, D.) 100–136 (John Wiley & Sons, Ltd, 2014).

    44.
    Christians, J. K. Avian egg size: variation within species and inflexibility within individuals. Biol. Rev. Camb. Philos. Soc. 77, 1–26 (2002).
    PubMed  Article  Google Scholar 

    45.
    Lack, D. The Natural Regulation of Animal Numbers (Clarendon Press, 1954).

    46.
    Perrins, C. M. The timing of birds‘ breeding seasons. Ibis 112, 242–255 (1970).
    Article  Google Scholar 

    47.
    Sales, K. et al. Experimental heatwaves compromise sperm function and cause transgenerational damage in a model insect. Nat. Commun. 9, 1–11 (2018).
    ADS  CAS  Article  Google Scholar 

    48.
    McAfee, A. et al. Vulnerability of honey bee queens to heat-induced loss of fertility. Nat. Sustain 3, 367–376 (2020).
    Article  Google Scholar 

    49.
    Pérez-Crespo, M., Pintado, B. & Gutiérrez-Adán, A. Scrotal heat stress effects on sperm viability, sperm DNA integrity, and the offspring sex ratio in mice. Mol. Reprod. Dev. 75, 40–47 (2008).
    PubMed  Article  CAS  Google Scholar 

    50.
    Hansen, P. J. Effects of heat stress on mammalian reproduction. Philos. Trans. R. Soc. B 364, 3341–3350 (2009).
    Article  Google Scholar 

    51.
    Moreno, R. D., Lagos-Cabre, R., Bunay, J., Urzua, N. & Bustamante-Marin, X. Molecular basis of heat stress damage in mammalian testis. In Testis: Anatomy, Physiology and Pathology (eds. Nemoto, Y. & Inaba, N.) 127–155 (Nova Science, 2012).

    52.
    Karaca, A. G., Parker, H. M., Yeatman, J. B. & McDaniel, C. D. The effects of heat stress and sperm quality classification on broiler breeder male fertility and semen ion concentrations. Br. Poult. Sci. 43, 621–628 (2002).
    CAS  PubMed  Article  Google Scholar 

    53.
    Mita, P., Hinton, B. T. & Dufour, J. M. The blood–testis and blood–epididymis barriers are more than just their tight junctions. Biol. Reprod. 84, 851–858 (2011).
    Article  CAS  Google Scholar 

    54.
    Smith, C. C. & Fretwell, S. D. The optimal balance between size and number of offspring. Am. Nat. 108, 499–506 (1974).
    Article  Google Scholar 

    55.
    Ojanen, M. Composition of the eggs of the great tit (Parus major) and pied flycatcher (Ficedula hypoleuca). Ann. Zool. Fenn. 20, 57–63 (1983).
    Google Scholar 

    56.
    Krist, M. Egg size and offspring quality: a meta-analysis in birds. Biol. Rev. 86, 692–716 (2011).
    PubMed  Article  Google Scholar 

    57.
    Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics (Pearson, 1996).

    58.
    Lynch, M. & Gabriel, W. Environmental tolerance. Am. Nat. 129, 283–303 (1987).
    Article  Google Scholar 

    59.
    Gilchrist, G. W. Specialists and generalists in changing environments. I. Fitness landscapes of thermal sensitivity. Am. Nat. 146, 252–270 (1995).
    Article  Google Scholar 

    60.
    Whitlock, M. C. The red queen beats the jack-of-all-trades: the limitations on the evolution of phenotypic plasticity and niche breadth. Am. Nat. 148, S65 (1996).
    Article  Google Scholar 

    61.
    Pen, I. & Weissing, F. J. Towards a unified theory of cooperative breeding: the role of ecology and life history re-examined. Proc. R. Soc. B 267, 2411–2418 (2000).
    Article  Google Scholar 

    62.
    Emlen, S. T. The evolution of helping. I. An ecological constraints model. Am. Nat. 119, 29–39 (1982).
    Article  Google Scholar 

    63.
    Rubenstein, D. R. Spatiotemporal environmental variation, risk aversion, and the evolution of cooperative breeding as a bet-hedging strategy. Proc. Natl Acad. Sci. USA 108, 10816–10822 (2011).
    ADS  CAS  PubMed  Article  Google Scholar 

    64.
    Cornwallis, C. K. et al. Cooperation facilitates the colonization of harsh environments. Nat. Ecol. Evol. 1, 0057 (2017).
    Article  Google Scholar 

    65.
    Rubenstein, D. R. & Lovette, I. J. Temporal environmental variability drives the evolution of cooperative breeding in birds. Curr. Biol. 17, 1414–1419 (2007).
    CAS  PubMed  Article  Google Scholar 

    66.
    Albright, T. P. et al. Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration. Proc. Natl Acad. Sci. USA 114, 201613625 (2017).
    Google Scholar 

    67.
    Vincze, O. et al. Parental cooperation in a changing climate: fluctuating environments predict shifts in care division. Glob. Ecol. Biogeogr. 26, 347–358 (2017).
    Article  Google Scholar 

    68.
    Nord, A. & Nilsson, J. Å. Heat dissipation rate constrains reproductive investment in a wild bird. Funct. Ecol. 33, 250–259 (2019).
    Article  Google Scholar 

    69.
    Cloete, S. W. P. et al. Variance components for live weight, body measurements and reproductive traits of pair-mated ostrich females. Br. Poult. Sci. 47, 147–158 (2006).
    CAS  PubMed  Article  Google Scholar 

    70.
    Rybnik, P. K., Horbanczuk, J. O., Naranowicz, H., Lukaszewicz, E. & Malecki, I. A. Semen collection in the ostrich (Struthio camelus) using a dummy or a teaser female. Br. Poult. Sci. 48, 635–643 (2007).
    CAS  PubMed  Article  Google Scholar 

    71.
    Brand, T. S., Olivier, T. R. & Gous, R. M. The response in food intake and reproductive parameters of breeding ostriches to increasing dietary energy. South Afr. J. Anim. Sci. 40, 434–437 (2010).
    Google Scholar 

    72.
    Brand, T. S., Olivier, T. R. & Gous, R. M. The reproductive response of female ostriches to dietary protein. Br. Poult. Sci. 56, 232–238 (2015).
    CAS  PubMed  Article  Google Scholar 

    73.
    Martin, P. A., Reimers, T. J., Lodge, J. R. & Dziuk, P. J. The effect of ratios and numbers of spermatozoa mixed from two males on proportions of offspring. J. Reprod. Fertil. 39, 251–258 (1974).
    CAS  PubMed  Article  Google Scholar 

    74.
    Birkhead, T. R. & Møller, A. P. Sperm Competition and Sexual Selection (Academic Press, 1998).

    75.
    Birkhead, T. R. & Biggins, J. D. Sperm competition mechanisms in birds: models and data. Behav. Ecol. 9, 253–260 (1998).
    Article  Google Scholar 

    76.
    Soley, J. T. & Roberts, J. C. Ultrastructure of ostrich (Struthio camelus) spermatozoa. II. Scanning electron microscopy. Onderstepoort J. Vet. Res. 61, 239–246 (1994).
    CAS  PubMed  Google Scholar 

    77.
    Lake, P. E. & Stewart, J. M. Artificial Insemination in Poultry. Ministry of Agriculture Fisheries and Food, Bulletin 213 (Her Majesty’s Stationery Office, 1978).

    78.
    Bonato, M., Malecki, I. A., Rybnik-Trzaskowska, P. K., Cornwallis, C. K. & Cloete, S. W. P. Predicting ejaculate quality and libido in male ostriches: effect of season and age. Anim. Reprod. Sci. 151, 49–55 (2014).
    PubMed  Article  Google Scholar 

    79.
    Bonato, M., Rybnik, P. K., Malecki, I. A., Cornwallis, C. K. & Cloete, S. W. P. Twice daily collection yields greater semen output and does not affect male libido in the ostrich. Anim. Reprod. Sci. 123, 258–264 (2011).
    PubMed  Article  Google Scholar 

    80.
    Muvhali, P. T. et al. Ostrich ejaculate characteristics and male libido around equinox and solstice dates. Trop. Anim. Health and Prod. 52, 2609–2619 (2020).
    CAS  Article  Google Scholar 

    81.
    Brand, Z., Cloete, S. W. P., Brown, C. R. & Malecki, I. A. Systematic factors that affect ostrich egg incubation traits. South Afr. J. Anim. Sci. 38, 315–325 (2008).
    Google Scholar 

    82.
    Bronneberg, R. G. G. et al. The relation between ultrasonographic observations in the oviduct and plasma progesterone, luteinizing hormone and estradiol during the egg laying cycle in ostriches. Domest. Anim. Endocrinol. 32, 15–28 (2007).
    CAS  PubMed  Article  Google Scholar 

    83.
    Van Schalkwyk, S. J., Cloete, S. W. P. & De Kock, J. A. Repeatability and phenotypic correlations for body weight and reproduction in commercial ostrich breeding pairs. Br. Poult. Sci. 37, 953–962 (1996).
    PubMed  Article  Google Scholar 

    84.
    Jones, R. C. & Lin, M. Spermatogenesis in birds. In Oxford Reviews of Reproductive Biology, Vol. 15 (ed. Milligan, S. R.) (Oxford University Press, 1993).

    85.
    R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2020).

    86.
    Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
    Article  Google Scholar 

    87.
    Araya-Ajoy, Y. G. & Dingemanse, N. J. Repeatability, heritability, and age-dependence of seasonal plasticity in aggressiveness in a wild passerine bird. J. Anim. Ecol. 86, 227–238 (2017).
    PubMed  Article  Google Scholar 

    88.
    Araya-Ajoy, Y. G., Mathot, K. J. & Dingemanse, N. J. An approach to estimate short-term, long-term and reaction norm repeatability. Methods Ecol. Evol. 6, 1462–1473 (2015).
    Article  Google Scholar 

    89.
    Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).
    Article  Google Scholar 

    90.
    Wilson, A. J. Why h2 does not always equal VA/VP. J. Evol. Biol. 21, 647–650 (2008).
    CAS  PubMed  Article  Google Scholar 

    91.
    de Villemereuil, P., Morrissey, M. B., Nakagawa, S. & Schielzeth, H. Fixed-effect variance and the estimation of repeatabilities and heritabilities: Issues and solutions. J. Evol. Biol. 31, 621–632 (2018).
    PubMed  Article  Google Scholar 

    92.
    de Villemereuil, P., Schielzeth, H., Nakagawa, S. & Morrissey, M. General methods for evolutionary quantitative genetic inference from generalized mixed models. Genetics 204, 1281–1294 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    93.
    BirdLife International. BirdLife International and Handbook of the Birds of the World. Bird Species Distribution Maps of the World (BirdLife International, 2019).

    94.
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Article  Google Scholar  More

  • in

    Genomic evidence of prevalent hybridization throughout the evolutionary history of the fig-wasp pollination mutualism

    1.
    Taylor, S. A. & Larson, E. L. Insights from genomes into the evolutionary importance and prevalence of hybridization in nature. Nat. Ecol. Evol. 3, 170–177 (2019).
    PubMed  Article  PubMed Central  Google Scholar 
    2.
    Payseur, B. A. & Rieseberg, L. H. A genomic perspective on hybridization and speciation. Mol. Ecol. 25, 2337–2360 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    3.
    Arnold, M. L. & Kunte, K. Adaptive genetic exchange: a tangled history of admixture and evolutionary innovation. Trends Ecol. Evol. 32, 601–611 (2017).
    PubMed  Article  Google Scholar 

    4.
    Mallet, J. Hybrid speciation. Nature 446, 279–283 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Abbott, R. et al. Hybridization and speciation. J. Evol. Biol. 26, 229–246 (2013).
    CAS  PubMed  Article  Google Scholar 

    6.
    Gross, B. L. & Rieseberg, L. H. The ecological genetics of homoploid hybrid speciation. J. Hered. 96, 241–252 (2005).
    CAS  PubMed  Article  Google Scholar 

    7.
    Schumer, M., Rosenthal, G. G. & Andolfatto, P. How common is homoploid hybrid speciation? Evolution 68, 1553–1560 (2014).
    PubMed  Article  Google Scholar 

    8.
    Grant, V. Pollination systems as isolating mechanisms in angiosperms. Evolution 3, 82–97 (1949).
    CAS  PubMed  Article  Google Scholar 

    9.
    Kay, K. M. & Sargent, R. D. The role of animal pollination in plant speciation: Integrating ecology, geography, and genetics. Annu. Rev. Ecol. Evol. Syst. 40, 637–656 (2009).
    Article  Google Scholar 

    10.
    Serrano-Serrano, M. L., Rolland, J., Clark, J. L., Salamin, N. & Perret, M. Hummingbird pollination and the diversification of angiosperms: an old and successful association in Gesneriaceae. Proc. R. Soc. B Biol. Sci. 284, https://doi.org/10.1098/rspb.2016.2816 (2017).

    11.
    Thompson, J. N. Specific hypotheses on the geographic mosaic of coevolution. Am. Nat. 153, S1–S14 (1999).
    Article  Google Scholar 

    12.
    Van der Niet, T., Peakall, R. & Johnson, S. D. Pollinator-driven ecological speciation in plants: new evidence and future perspectives. Ann. Bot. 113, 199–211 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    13.
    Armbruster, W. S. The specialization continuum in pollination systems: diversity of concepts and implications for ecology, evolution and conservation. Funct. Ecol. 31, 88–100 (2017).
    Article  Google Scholar 

    14.
    Ayasse, M., Stokl, J. & Francke, W. Chemical ecology and pollinator-driven speciation in sexually deceptive orchids. Phytochemistry 72, 1667–1677 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Machado, C. A., Robbins, N., Gilbert, M. T. P. & Herre, E. A. Critical review of host specificity and its coevolutionary implications in the fig/fig-wasp mutualism. Proc. Natl Acad. Sci. USA 102, 6558–6565 (2005).
    ADS  CAS  PubMed  Article  Google Scholar 

    16.
    Kawakita, A. Evolution of obligate pollination mutualism in the tribe Phyllantheae (Phyllanthaceae). Plant Species Biol. 25, 3–19 (2010).
    Article  Google Scholar 

    17.
    Ramirez, W. Host specificity of fig wasps (Agaonidae). Evolution 24, 680–691 (1970).
    Article  Google Scholar 

    18.
    Schiestl, F. P. & Schluter, P. M. Floral isolation, specialized pollination, and pollinator behavior in orchids. Annu. Rev. Entomol. 54, 425–446 (2009).
    CAS  PubMed  Article  Google Scholar 

    19.
    Ramirez, S. R. et al. Asynchronous diversification in a specialized plant-pollinator mutualism. Science 333, 1742–1746 (2011).
    ADS  CAS  PubMed  Article  Google Scholar 

    20.
    Cruaud, A. et al. An extreme case of plant-insect co-diversification: figs and fig-pollinating wasps. Syst. Biol. 61, 1029–1047 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Berg, C. C. & Corner, E. J. H. in Flora Malesiana Series I -Seed Plants Vol. 17 (ed. Nooteboom, H. P.) 1–702 (Nationaal Herbarium, Nederland, 2005).

    22.
    Wang, G., Cannon, C. H. & Chen, J. Pollinator sharing and gene flow among closely related sympatric dioecious fig taxa. Proc. R. Soc. B Biol. Sci. 283, https://doi.org/10.1098/rspb.2015.2963 (2016).

    23.
    Machado, C. A., Jousselin, E., Kjellberg, F., Compton, S. G. & Herre, E. A. Phylogenetic relationships, historical biogeography and character evolution of fig-pollinating wasps. Proc. R. Soc. B Biol. Sci. 268, 685–694 (2001).
    CAS  Article  Google Scholar 

    24.
    Harrison, R. D. Figs and the diversity of tropical rainforests. Bioscience 55, 1053–1064 (2005).
    Article  Google Scholar 

    25.
    Grison-Pigé, L., Bessière, J. M. & Hossaert-McKey, M. Specific attraction of fig-pollinating wasps: Role of volatile compounds released by tropical figs. J. Chem. Ecol. 28, 283–295 (2002).
    PubMed  Article  PubMed Central  Google Scholar 

    26.
    Herre, E. A. et al. Molecular phylogenies of figs and their pollinator wasps. J. Biogeogr. 23, 521–530 (1996).
    Article  Google Scholar 

    27.
    Molbo, D., Machado, C. A., Sevenster, J. G., Keller, L. & Herre, E. A. Cryptic species of fig-pollinating wasps: Implications for the evolution of the fig-wasp mutualism, sex allocation, and precision of adaptation. Proc. Natl Acad. Sci. USA 100, 5867–5872 (2003).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Rasplus, J. Y. in The Biodiversity of African Plants (eds van der Maesen, L. J. G. et al.) 639–649 (Springer, 1996).

    29.
    Yang, L.-Y. et al. The incidence and pattern of co-pollinator diversification in dioecious and monoecious figs. Evolution 69, 294–304 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    30.
    Cornille, A. et al. Floral volatiles, pollinator sharing and diversification in the fig-wasp mutualism: insights from Ficus natalensis, and its two wasp pollinators (South Africa). Proc. R. Soc. B Biol. Sci. 279, 1731–1739 (2012).
    CAS  Article  Google Scholar 

    31.
    Compton, S. G. A collapse of host specificity in some African fig wasps. S. Afr. J. Sci. 86, 39–40 (1990).
    Google Scholar 

    32.
    Renoult, J. P., Kjellberg, F., Grout, C., Santoni, S. & Khadari, B. Cyto-nuclear discordance in the phylogeny of Ficus section Galoglychia and host shifts in plant-pollinator associations. BMC Evol. Biol. 9, 248 (2009).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    33.
    Satler, J. D. et al. Inferring processes of coevolutionary diversification in a community of Panamanian strangler figs and associated pollinating wasps. Evolution 73, 2295–2311 (2019).

    34.
    Jackson, A. P., Machado, C. A., Robbins, N. & Herre, E. A. Multi-locus phylogenetic analysis of neotropical figs does not support co-speciation with the pollinators: the importance of systematic scale in fig/wasp cophylogenetic studies. Symbiosis 45, 57–72 (2008).
    CAS  Google Scholar 

    35.
    Parrish, T. L., Koelewijn, H. P., van Dijk, P. J. & Kruijt, M. Genetic evidence for natural hybridization between species of dioecious Ficus on island populations. Biotropica 35, 333–343 (2003).
    Article  Google Scholar 

    36.
    Ramirez, W. Hybridization of Ficus religiosa with F. septica and F. aurea (Moraceae). Rev. Biol. Trop. 42, 339–342 (1994).
    Google Scholar 

    37.
    Wei, Z. D., Kobmoo, N., Cruaud, A. & Kjellberg, F. Genetic structure and hybridization in the species group of Ficus auriculata: can closely related sympatric Ficus species retain their genetic identity while sharing pollinators? Mol. Ecol. 23, 3538–3550 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    38.
    Bruun-Lund, S., Clement, W. L., Kjellberg, F. & Rønsted, N. First plastid phylogenomic study reveals potential cyto-nuclear discordance in the evolutionary history of Ficus L. (Moraceae). Mol. Phylogenet. Evol. 109, 93–104 (2017).
    PubMed  Article  Google Scholar 

    39.
    Zhang, X. et al. Genomes of the Banyan tree and pollinator wasp provide insights into fig-wasp coevolution. Cell 183, 875–889 (2020).
    CAS  PubMed  Article  Google Scholar 

    40.
    Mirarab, S. & Warnow, T. ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes. Bioinformatics 31, 44–52 (2015).
    Article  CAS  Google Scholar 

    41.
    Rønsted, N., Weiblen, G. D., Clement, W. L., Zerega, N. J. C. & Savolainen, V. Reconstructing the phylogeny of figs (Ficus, Moraceae) to reveal the history of the fig pollination mutualism. Symbiosis 45, 45–55 (2008).
    Google Scholar 

    42.
    Ane, C., Larget, B., Baum, D. A., Smith, S. D. & Rokas, A. Bayesian estimation of concordance among gene trees. Mol. Biol. Evol. 24, 412–426 (2007).
    CAS  PubMed  Article  Google Scholar 

    43.
    Larget, B. R., Kotha, S. K., Dewey, C. N. & Ane, C. BUCKy: Gene tree/species tree reconciliation with Bayesian concordance analysis. Bioinformatics 26, 2910–2911 (2010).
    CAS  PubMed  Article  Google Scholar 

    44.
    Baum, D. A. Concordance trees, concordance factors, and the exploration of reticulate genealogy. Taxon 56, 417–426 (2007).
    Article  Google Scholar 

    45.
    Solis-Lemus, C., Bastide, P. & Ane, C. PhyloNetworks: a package for phylogenetic networks. Mol. Biol. Evol. 34, 3292–3298 (2017).
    CAS  PubMed  Article  Google Scholar 

    46.
    Soraggi, S., Wiuf, C. & Albrechtsen, A. Powerful inference with the D-statistic on low-coverage whole-genome data. G3 (Bethesda) 8, 551–566 (2018).

    47.
    Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    48.
    Degnan, J. H. & Rosenberg, N. A. Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends Ecol. Evol. 24, 332–340 (2009).
    PubMed  Article  Google Scholar 

    49.
    Conow, C., Fielder, D., Ovadia, Y. & Libeskind-Hadas, R. Jane: a new tool for the cophylogeny reconstruction problem. Algorithms Mol. Biol. 5, https://doi.org/10.1186/1748-7188-5-16 (2010).

    50.
    Ramsey, A. J. & Mandel, J. R. When one genome is not enough: organellar heteroplasmy in plants. Annual Plant Reviews 2, 619–658 (2019).
    Article  Google Scholar 

    51.
    Zhang, Q. & Liu, Y. & Sodmergen. Examination of the cytoplasmic DNA in male reproductive cells to determine the potential for cytoplasmic inheritance in 295 angiosperm species. Plant Cell Physiol. 44, 941–951 (2003).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    52.
    Hu, Y. C., Zhang, Q. & Rao, G. Y. & Sodmergen. Occurrence of plastids in the sperm cells of Caprifoliaceae: Biparental plastid inheritance in angiosperms is unilaterally derived from maternal inheritance. Plant Cell Physiol. 49, 958–968 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    53.
    Mayr, E. Animal Species and Evolution 1–811 (Belknap Press, 1963).

    54.
    Wu, C. I. The genic view of the process of speciation. J. Evol. Biol. 14, 851–865 (2001).
    Article  Google Scholar 

    55.
    Sun, M. et al. Deep phylogenetic incongruence in the angiosperm clade Rosidae. Mol. Phylogenet. Evol. 83, 156–166 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    56.
    Folk, R. A., Soltis, P. S., Soltis, D. E. & Guralnick, R. New prospects in the detection and comparative analysis of hybridization in the tree of life. Am. J. Bot. 105, 364–375 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    57.
    Jiao, X., Flouri, T., Rannala, B. & Yang, Z. The impact of cross-species gene flow on species tree estimation. Syst. Biol. 69, 830–847 (2020).

    58.
    Jousselin, E. et al. One fig to bind them all: host conservatism in a fig wasp community unraveled by cospeciation analyses among pollinating and nonpollinating fig wasps. Evolution 62, 1777–1797 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    59.
    Moe, A. M. & Weiblen, G. D. Pollinator-mediated reproductive isolation among dioecious fig species (Ficus, Moraceae). Evolution 66, 3710–3721 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    60.
    Wang, G., Compton, S. G. & Chen, J. The mechanism of pollinator specificity between two sympatric fig varieties: a combination of olfactory signals and contact cues. Ann. Bot. 111, 173–181 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    61.
    Bronstein, J. L. Maintenance of species-specificity in a neotropical fig – pollinator wasp mutualism. Oikos 48, 39–46 (1987).
    Article  Google Scholar 

    62.
    Ware, A., Kaye, P., Compton, S. & Noort, S. Fig volatiles: their role in attracting pollinators and maintaining pollinator specificity. Plant Syst. Evol. 186, 147–156 (1993).
    Article  Google Scholar 

    63.
    Soler, C. C. L., Proffit, M., Bessière, J. M., Hossaert-McKey, M. & Schatz, B. Evidence for intersexual chemical mimicry in a dioecious plant. Ecol. Lett. 15, 978–985 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    64.
    Hossaert-McKey, M., Soler, C., Schatz, B. & Proffit, M. Floral scents: their roles in nursery pollination mutualisms. Chemoecology 20, 75–88 (2010).
    Article  Google Scholar 

    65.
    Knudsen, J. T., Eriksson, R., Gershenzon, J. & Stahl, B. Diversity and distribution of floral scent. Bot. Rev. 72, 1–120 (2006).
    Article  Google Scholar 

    66.
    Herre, E. A., Jander, K. C. & Machado, C. A. Evolutionary ecology of figs and their associates: Recent progress and outstanding puzzles. Annu. Rev. Ecol. Evol. Syst. 39, 439–458 (2008).
    Article  Google Scholar 

    67.
    Kiester, A. R., Lande, R. & Schemske, D. W. Models of coevolution and speciation in plants and their pollinators. Am. Nat. 124, 220–243 (1984).
    Article  Google Scholar 

    68.
    Vereecken, N. J., Cozzolino, S. & Schiestl, F. P. Hybrid floral scent novelty drives pollinator shift in sexually deceptive orchids. BMC Evol. Biol. 10, 103 (2010).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    69.
    Rønsted, N. et al. 60 million years of co-divergence in the fig-wasp symbiosis. Proc. R. Soc. B Biol. Sci. 272, 0962–8452 (2005). 2593-2599.
    Google Scholar 

    70.
    Wiebes, J. T. Co-evolution of figs and their insect pollinators. Annu. Rev. Ecol. Syst. 10, 1–12 (1979).
    Article  Google Scholar 

    71.
    Zhu, H. et al. Native Seed Plants in Xishuangbanna of Yunnan (eds Zhu, H. & Yan, L.) 1–565 (Science Press, 2012).

    72.
    Yang, J. B., Li, D. Z. & Li, H. T. Highly effective sequencing whole chloroplast genomes of angiosperms by nine novel universal primer pairs. Mol. Ecol. Resour. 14, 1024–1031 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    73.
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Andrews, S. FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

    75.
    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    76.
    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    77.
    McKenna, A. et al. The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    78.
    Jin, J.-J. et al. GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes. Genome Biol. 21, 31 (2020).
    Article  Google Scholar 

    79.
    Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    80.
    Weiß, C. L., Pais, M., Cano, L. M., Kamoun, S. & Burbano, H. A. nQuire: a statistical framework for ploidy estimation using next generation sequencing. BMC Bioinformatics 19, 122 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    81.
    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    82.
    Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    83.
    Yang, Z. H. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
    CAS  PubMed  Article  Google Scholar 

    84.
    Zhu, T. Q., Dos Reis, M. & Yang, Z. H. Characterization of the uncertainty of divergence time estimation under relaxed molecular clock models using multiple loci. Syst. Biol. 64, 267–280 (2015).
    CAS  PubMed  Article  Google Scholar 

    85.
    Gardner, E. M., Sarraf, P., Williams, E. W. & Zerega, N. J. C. Phylogeny and biogeography of Maclura (Moraceae) and the origin of an anachronistic fruit. Mol. Phylogenet. Evol. 117, 49–59 (2017).
    PubMed  Article  Google Scholar 

    86.
    dos Reis, M. & Yang, Z. Approximate likelihood calculation on a phylogeny for bayesian estimation of divergence times. Mol. Biol. Evol. 28, 2161–2172 (2011).
    PubMed  Article  CAS  Google Scholar 

    87.
    Yang, Z. & Rannala, B. Bayesian estimation of species divergence times under a molecular clock using multiple fossil calibrations with soft bounds. Mol. Biol. Evol. 23, 212–226 (2006).
    CAS  PubMed  Article  Google Scholar 

    88.
    Matzke, N. J. Model selection in historical biogeography reveals that founder-event speciation is a crucial process in Island Clades. Syst. Biol. 63, 951–970 (2014).
    PubMed  Article  Google Scholar 

    89.
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    90.
    Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772–772 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    91.
    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    92.
    Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15, 1–13 (2014).
    Article  Google Scholar 

    93.
    Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    94.
    Wang, G. et al. Data from: Genomic evidence of prevalent hybridization throughout the evolutionary history of the fig-wasp pollination mutualism. Dryad, Dataset https://doi.org/10.5061/dryad.zcrjdfn7m (2020).

    95.
    Zhang, T. & Zhang, S. C. Code from: Genomic evidence of prevalent hybridization throughout the evolutionary history of the fig-wasp pollination mutualism. Github https://doi.org/10.5281/zenodo.4308886 (2020). More

  • in

    Implications of monsoon season and UVB radiation for COVID-19 in India

    1.
    Dong, E., Du, H. & Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20, 533–534 (2020).
    CAS  PubMed  PubMed Central  Article  Google Scholar 
    2.
    Chadha, M. S. et al. Dynamics of influenza seasonality at sub-regional levels in India and implications for vaccination timing. PLoS ONE 10, e0124122 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    3.
    Dash, N., Rose, W. & Nallasamy, K. India’s lockdown exit: Are we prepared to lock horns with COVID-19 and dengue in the rainy season?. Pediatr. Res. https://doi.org/10.1038/s41390-020-1063-7 (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    4.
    Moozhipurath, R. K. & Kulkarni, P. Monsoon, Vitamin-D, COVID-19: Implications for India. Postgraduate Medical Journal Blog (accessed 20 November 2020). https://blogs.bmj.com/pmj/2020/07/08/monsoon-vitamin-d-covid-19-implications-for-india/ (2020).

    5.
    D’Avolio, A. et al. 25-Hydroxyvitamin D concentrations are lower in patients with positive PCR for SARS-CoV-2. Nutrients 12, 1359 (2020).
    PubMed Central  Article  CAS  Google Scholar 

    6.
    Meltzer, D. O. et al. Association of vitamin D status and other clinical characteristics with COVID-19 test results. JAMA Netw. Open 3, e2019722 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    7.
    Merzon, E. et al. Low plasma 25 (OH) vitamin D level is associated with increased risk of COVID-19 infection: An Israeli population-based study. FEBS J. 287, 3693–3702 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Kaufman, H. W., Niles, J. K., Kroll, M. H., Bi, C. & Holick, M. F. SARS-CoV-2 positivity rates associated with circulating 25-hydroxyvitamin D levels. PLoS ONE 15, e0239252 (2020).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    9.
    Honardoost, M., Ghavideldarestani, M. & Khamseh, M. E. Role of vitamin D in pathogenesis and severity of COVID-19 infection. Arch. Physiol. Biochem. https://doi.org/10.1080/13813455.2020.1792505 (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    10.
    Ilie, P. C., Stefanescu, S. & Smith, L. The role of vitamin D in the prevention of coronavirus disease 2019 infection and mortality. Aging Clin. Exp. Res. 32, 1195–1198 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    11.
    Maghbooli, Z. et al. Vitamin D sufficiency, a serum 25-hydroxyvitamin D at least 30 ng/mL reduced risk for adverse clinical outcomes in patients with COVID-19 infection. PLoS ONE 15, e0239799 (2020).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    12.
    Castillo, M. E. et al. Effect of calcifediol treatment and best available therapy versus best available therapy on intensive care unit admission and mortality among patients hospitalized for COVID-19: A pilot randomized clinical study. J. Steroid Biochem. Mol. Biol. 203, 105751 (2020).
    Article  CAS  Google Scholar 

    13.
    Benskin, L. L. A basic review of the preliminary evidence that COVID-19 risk and severity is increased in vitamin D deficiency. Front. Public Health 8, 513 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    Moozhipurath, R. K., Kraft, L. & Skiera, B. Evidence of protective role of ultraviolet-B (UVB) radiation in reducing COVID-19 deaths. Sci. Rep. 10, 17705 (2020).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    15.
    Engelsen, O., Brustad, M., Aksnes, L. & Lund, E. Daily duration of vitamin D synthesis in human skin with relation to latitude, total ozone, altitude, ground cover, aerosols and cloud thickness. Photochem. Photobiol. 81, 1287–1290 (2005).
    CAS  PubMed  Article  Google Scholar 

    16.
    Li, Y. et al. Global patterns in monthly activity of influenza virus, respiratory syncytial virus, parainfluenza virus, and metapneumovirus: A systematic analysis. Lancet Glob. Health 7, e1031–e1045 (2019).
    PubMed  Article  Google Scholar 

    17.
    Li, Y., Wang, X. & Nair, H. Global seasonality of human seasonal coronaviruses: A clue for postpandemic circulating season of severe acute respiratory syndrome coronavirus 2?. J. Infect. Dis. 222, 1090–1097 (2020).
    CAS  PubMed  Article  Google Scholar 

    18.
    Gupta, E., Dar, L., Kapoor, G. & Broor, S. The changing epidemiology of dengue in Delhi, India. Virol. J. 3, 1–5 (2006).
    Article  Google Scholar 

    19.
    Laneri, K. et al. Forcing versus feedback: Epidemic malaria and monsoon rains in Northwest India. PLoS Comput. Biol. 6, e1000898 (2010).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    20.
    Shaman, J., Jeon, C. Y., Giovannucci, E. & Lipsitch, M. Shortcomings of vitamin D-based model simulations of seasonal influenza. PLoS ONE 6, e20743 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    21.
    Ianevski, A. et al. Low temperature and low UV indexes correlated with peaks of influenza virus activity in Northern Europe during 2010–2018. Viruses 11, 207 (2019).
    CAS  PubMed Central  Article  Google Scholar 

    22.
    Yang, W. et al. Dynamics of influenza in tropical Africa: Temperature, humidity, and co-circulating (sub)types. Influenza Other Respir. Viruses 12, 446–456 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    23.
    Sajadi, M. M. et al. Temperature, humidity, and latitude analysis to estimate potential spread and seasonality of coronavirus disease 2019 (COVID-19). JAMA Netw. Open 3, e2011834 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    24.
    Dhara, V. R., Schramm, P. J. & Luber, G. Climate change & infectious diseases in India: Implications for health care providers. Indian J. Med. Res. 138, 847–852 (2013).
    PubMed  PubMed Central  Google Scholar 

    25.
    Nimitphong, H., Chanprasertyothin, S., Jongjaroenprasert, W. & Ongphiphadhanakul, B. The association between vitamin D status and circulating adiponectin independent of adiposity in subjects with abnormal glucose tolerance. Endocrine 36, 205–210 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    26.
    Sagripanti, J.-L. & Lytle, C. D. Inactivation of influenza virus by solar radiation. Photochem. Photobiol. 83, 1278–1282 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Hart, P. H., Gorman, S. & Finlay-Jones, J. J. Modulation of the immune system by UV radiation: More than just the effects of vitamin D?. Nat. Rev. Immunol. 11, 584–596 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Bodiwala, D. et al. Prostate cancer risk and exposure to ultraviolet radiation: Further support for the protective effect of sunlight. Cancer Lett. 192, 145–149 (2003).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Grant, W. B. An estimate of premature cancer mortality in the US due to inadequate doses of solar ultraviolet-B radiation. Cancer 94, 1867–1875 (2002).
    PubMed  Article  PubMed Central  Google Scholar 

    30.
    Grant, W. B. An ecologic study of the role of solar UV-B radiation in reducing the risk of cancer using cancer mortality data, dietary supply data, and latitude for European countries. In Biologic Effects of Light 2001 (ed. Holick, M. F.) 267–276 (Springer, Berlin, 2002).
    Google Scholar 

    31.
    Rostand, S. G. Ultraviolet light may contribute to geographic and racial blood pressure differences. Hypertension 30, 150–156 (1997).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    32.
    Holick, M. F. Vitamin D deficiency. N. Engl. J. Med. 357, 266–281 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    33.
    Ritu, G. & Gupta, A. Vitamin D deficiency in India: Prevalence, causalities and interventions. Nutrients 6, 729–775 (2014).
    MathSciNet  Article  CAS  Google Scholar 

    34.
    Zittermann, A. Vitamin D in preventive medicine: Are we ignoring the evidence?. Br. J. Nutr. 89, 552–572 (2003).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    35.
    Tangpricha, V., Pearce, E. N., Chen, T. C. & Holick, M. F. Vitamin D insufficiency among free-living healthy young adults. Am. J. Med. 112, 659–662 (2002).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    Crowe, F. L. et al. Plasma concentrations of 25-hydroxyvitamin D in meat eaters, fish eaters, vegetarians and vegans: Results from the EPIC–Oxford study. Public Health Nutr. 14, 340–346 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    37.
    Harinarayan, C. V., Holick, M. F., Prasad, U. V., Vani, P. S. & Himabindu, G. Vitamin D status and sun exposure in India. Dermato-endocrinology 5, 130–141 (2013).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    38.
    Grant, W. B. et al. Evidence that vitamin D supplementation could reduce risk of influenza and COVID-19 infections and deaths. Nutrients 12, 988 (2020).
    CAS  PubMed Central  Article  PubMed  Google Scholar 

    39.
    Charoenngam, N. & Holick, M. F. Immunologic effects of vitamin D on human health and disease. Nutrients 12, 2097 (2020).
    CAS  PubMed Central  Article  PubMed  Google Scholar 

    40.
    Cui, C. et al. Vitamin D receptor activation regulates microglia polarization and oxidative stress in spontaneously hypertensive rats and angiotensin II-exposed microglial cells: Role of renin-angiotensin system. Redox Biol. 26, 101295 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    41.
    Xu, J. et al. Vitamin D alleviates lipopolysaccharide-induced acute lung injury via regulation of the renin–angiotensin system. Mol. Med. Rep. 16, 7432–7438 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    42.
    Adams, J. S. et al. Vitamin D-directed rheostatic regulation of monocyte antibacterial responses. J. Immunol. 182, 4289–4295 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Herr, C., Shaykhiev, R. & Bals, R. The role of cathelicidin and defensins in pulmonary inflammatory diseases. Expert Opin. Biol. Ther. 7, 1449–1461 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    44.
    Zhou, Y. et al. Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: A modelling study using mobile phone data. Lancet Digit. Health 2, e417–e424 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    45.
    Lytle, C. D. & Sagripanti, J.-L. Predicted inactivation of viruses of relevance to biodefense by solar radiation. J. Virol. 79, 14244–14252 (2005).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    46.
    Deliconstantinos, G., Villiotou, V. & Stravrides, J. C. Release by ultraviolet B (u.v.B.) radiation of nitric oxide (NO) from human keratinocytes: A potential role for nitric oxide in erythema production. Br. J. Pharmacol. 114, 1257–1265 (1995).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    47.
    D’Orazio, J., Jarrett, S., Amaro-Ortiz, A. & Scott, T. UV radiation and the skin. Int. J. Mol. Sci. 14, 12222–12248 (2013).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    48.
    Grant, W. B. The effect of solar UVB doses and vitamin D production, skin cancer action spectra, and smoking in explaining links between skin cancers and solid tumours. Eur. J. Cancer 44, 12–15 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Estrogen induces shift in abundances of specific groups of the coral microbiome

    1.
    Ghiselli, G. & Jardim, W. F. Interferentes endócrinos no meio ambiente. Quím. Nova 30, 695–706 (2007).
    Article  Google Scholar 
    2.
    Vilela, C. L. S., Bassin, J. P. & Peixoto, R. S. Water contamination by endocrine disruptors: Impacts, microbiological aspects and trends for environmental protection. Environ. Poll. 235, 546–559 (2018).
    CAS  Article  Google Scholar 

    3.
    Muller, M. et al. Occurrence of estrogens in sewage sludge and their fate during plant-scale anaerobic digestion. Chemosphere 81, 65–71 (2010).
    ADS  CAS  PubMed  Article  Google Scholar 

    4.
    Mills, M. R. et al. Removal of ecotoxicity of 17α-ethinylestradiol using TAML/peroxide water treatment. Sci. Rep. 5, 1–10 (2015).
    Article  CAS  Google Scholar 

    5.
    Laurenson, J. P., Bloom, R. A., Page, S. & Sadrieh, N. Ethinylestradiol and other human pharmaceutical estrogens in the aquatic environment: A review of recent risk assessment data. AAPS J. 16, 299–310 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    6.
    Luzio, A., Santos, D., Fontaínhas-Fernandes, A. A., Monteiro, S. M. & Coimbra, A. M. Effects of 17α-ethinylestradiol at different water temperatures on zebrafish sex differentiation and gonad development. Aquat. Toxicol. 174, 22–35 (2016).
    CAS  PubMed  Article  Google Scholar 

    7.
    Blewett, T., MacLatchy, D. L. & Wood, C. M. The effects of temperature and salinity on 17-α-ethynylestradiol uptake and its relationship to oxygen consumption in the model euryhaline teleost (Fundulus heteroclitus). Aquat. Toxicol. 127, 61–71 (2013).
    CAS  PubMed  Article  Google Scholar 

    8.
    Atkinson, S., Atkinson, M. J. & Tarrant, A. M. Estrogens from sewage in coastal marine environments. Environ. Health Persp. 111, 531–535 (2003).
    CAS  Article  Google Scholar 

    9.
    Segner, H. et al. Identification of endocrine-disrupting effects in aquatic vertebrates and invertebrates: Report from the European IDEA project. Ecotoxicol. Environ. Safe. 54, 302–314 (2003).
    CAS  Article  Google Scholar 

    10.
    Tyler, C. R., Jobling, S. & Sumpter, J. P. Endocrine disruption in wildlife: A critical review of the evidence. Crit. Rev. Toxicol. 28, 319–361 (1998).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Johnson, A. C., Belfroid, A. & Di Corcia, A. Estimating steroid oestrogen inputs into activated sludge treatment works and observations on their removal from the effluent. Sci. Total Environ. 256, 163–173 (2000).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Cadwell, D. J. et al. Derivation of an aquatic predicted no-effect concentration for the synthetic hormone, 17α-ethinyl estradiol. Environ. Sci. Technol. 10, 272–283 (2008).
    Google Scholar 

    13.
    Ternes, T. A. et al. Behavior and occurrence of estrogens in municipal sewage treatment plants—I. Investigations in Germany, Canada and Brazil. Sci. Total Environ. 225, 81–90 (1999).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Kolpin, D. W. et al. Pharmaceuticals, hormones, and other organic wastewater contaminants in U.S. streams, 1999–2000: A national reconnaissance. Environ. Sci. Technol. 36, 1202–1211 (2002).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Huang, Y., Wang, X. L., Zhang, J. W. & Wu, K. S. Impact of endocrine-disrupting chemicals on reproductive function in zebrafish (Danio rerio). Reprod. Domest. Anim. 50, 1–6 (2009).
    Article  CAS  Google Scholar 

    16.
    Länge, R. et al. Effects of the synthetic estrogen 17α-ethinylestradiol on the life-cycle of the fathead minnow (Pimephales promelas). Environ. Toxicol. Chem. 20, 1216–1227 (2001).
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Parrott, J. L. & Blunt, B. R. Life-cycle exposure of fathead minnows (Pimephales promelas) to an ethinylestradiol concentration below 1 ng/L reduces egg fertilization success and demasculinizes males. Environ. Toxicol. 20, 131–141 (2005).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Bloom, M. S., Micu, R. & Neamtiu, I. Female infertility and “emerging” organic pollutants of concern. Curr. Epidemiol. Rep. 3, 39–50. https://doi.org/10.1007/s40471-016-0060-1 (2016).
    Article  Google Scholar 

    19.
    Nash, J. P. et al. Long-term exposure to environmental concentrations of the pharmaceutical ethynylestradiol causes reproductive failure in fish. Environ. Health Persp. 112, 1725–1733 (2004).
    CAS  Article  Google Scholar 

    20.
    Wu, C., Huang, X., Lin, J. & Liu, J. Occurrence and fate of selected endocrine-disrupting chemicals in water and sediment from an urban lake. Arch. Environ. Contam. Toxicol. 68, 225–236 (2014).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    21.
    Pennington, M. J. et al. Effects of contaminants of emerging concern on Megaselia scalaris (Lowe, Diptera: Phoridae) and its microbial community. Sci. Rep. 7, 1–12 (2017).
    CAS  Article  Google Scholar 

    22.
    Pennington, M. J., Prager, S. M., Walton, W. E. & Trumble, J. T. Culex quinquefasciatus larval microbiomes vary with instar and exposure to common wastewater contaminants. Sci. Rep. 6, 1–9 (2016).
    Article  CAS  Google Scholar 

    23.
    Pennington, M. J., Rivas, N. G., Prager, S. M., Walton, W. E. & Trumble, J. T. Pharmaceuticals and personal care products alter the holobiome and development of a medically important mosquito. Environ. Pollut. 203, 199–207 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Fosch, S. E. et al. Contraception: Influence on vaginal microbiota and identification of vaginal lactobacilli using MALDI-TOF MS and 16S rDNA sequencing. Open Microbiol. J. 12, 218–229 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    25.
    Tarrant, A. M., Blomquist, C. H., Lima, P. H., Atkinson, M. J. & Atkinson, S. Metabolism of estrogens and androgens by scleractinian corals. Comp. Biochem. Phys. B. 136, 473–485 (2003).
    Article  CAS  Google Scholar 

    26.
    Tarrant, A. M., Atkinson, M. J. & Atkinson, S. Effects of steroidal estrogens on coral growth and reproduction. Mar. Ecol. Prog. Ser. 269, 121–129 (2004).
    ADS  CAS  Article  Google Scholar 

    27.
    Atkinson, S. & Atkinson, M. J. Detection of estradiol-17β during a mass coral spawn. Coral Reefs 11, 33–35 (1992).
    ADS  Article  Google Scholar 

    28.
    Atkinson, S. Uptake of estrone from the water column by a coral community. Mar. Biol. 139, 321–325 (2001).
    Article  Google Scholar 

    29.
    Rougée, L. R., Richmond, R. H. & Collier, A. C. Molecular reproductive characteristics of the reef coral Pocillopora damicornis. Comp. Biochem. Phys. A 189, 38–44 (2015).
    Article  CAS  Google Scholar 

    30.
    Blomquist, C. H., Lima, P. H., Tarrant, A. M., Atkinson, M. J. & Atkinson, S. 17β-Hydroxysteroid dehydrogenase (17β-HSD) in scleractinian corals and zooxanthellae. Comp. Biochem. Phys. B 143, 397–403 (2006).
    Article  CAS  Google Scholar 

    31.
    Tarrant, A. M., Atkinson, S. & Atkinson, M. J. Estrone and estradiol-17β concentration in tissue of the scleractinian coral, Montipora verrucosa. Comp. Biochem. Phys. A 122, 85–92 (1999).
    CAS  Article  Google Scholar 

    32.
    Twan, W. et al. Hormones and reproduction in scleractinian corals. Comp. Biochem. Phys. A 144, 247–253 (2006).
    Article  CAS  Google Scholar 

    33.
    Tarrant, A., Atkinson, M. & Atkinson, S. Uptake of estrone from the water column by a coral community. Mar. Biol. 139, 321–325 (2001).
    CAS  Article  Google Scholar 

    34.
    Bosch, T. C. G. & McFall-Ngai, M. J. Metaorganisms as the new frontier. Zoology 114, 185–190 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    35.
    Rosenberg, E., Koren, O., Reshef, L. & Efrony, R. The role of microorganisms in coral health, disease and evolution. Nat. Rev. Microbiol. 5, 355–362 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    36.
    Rosenberg, E. Coral microbiology. Microb. Biotechnol. 2, 147 (2009).
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Peixoto, R. S., Rosado, P. M., Leite, D. C., Rosado, A. S. & Bourne, D. G. Beneficial microorganisms for corals (BMC): Proposed mechanisms for coral health and resilience. Front. Mar. Sci. 8, 1–16 (2017).
    CAS  Google Scholar 

    38.
    Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Patterns of coral heat tolerance. Nature Comm. 1, 1–8 (2017).
    Google Scholar 

    39.
    Peixoto, R. S., Sweet, M. & Bourne, D. G. Customized medicine for corals. Front. Mar. Sci. 6, 686 (2019).
    Article  Google Scholar 

    40.
    Rosado, P. M. et al. Marine probiotics: Increasing coral resistance to bleaching through microbiome manipulation. ISME J. 13, 921–936 (2019).
    CAS  PubMed  Article  Google Scholar 

    41.
    Lesser, M. P., Mazel, C. H., Gorbunov, M. Y. & Falkowski, P. G. Discovery of symbiotic nitrogen-fixing cyanobacteria in corals. Science 305, 997–1000 (2004).
    ADS  CAS  PubMed  Article  Google Scholar 

    42.
    Wegley, L., Edwards, R., Rodriguez-Brito, B., Liu, H. & Rohwer, F. Metagenomic analysis of the microbial community associated with the coral Porites astreoides. Environ. Microbiol. 9, 2707–2719 (2007).
    CAS  PubMed  Article  Google Scholar 

    43.
    Reshef, L., Koren, O., Loya, Y., Zilber-Rosenberg, I. & Rosenberg, E. The coral probiotic hypothesis. Environ. Microbiol. 8, 2068–2073 (2006).
    CAS  PubMed  Article  Google Scholar 

    44.
    Ritchie, K. B. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar. Ecol. Prog. Ser. 322, 1–14 (2006).
    ADS  CAS  Article  Google Scholar 

    45.
    Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: Underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).
    CAS  PubMed  Article  Google Scholar 

    46.
    Santos, H. F. et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 8, 2272–2279 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    47.
    Santos, H. F. et al. Impact of oil spills on coral reefs can be reduced by bioremediation using probiotic microbiota. Sci. Rep. 5, 1–11 (2015).
    Google Scholar 

    48.
    Röthig, T., Yum, L. K., Kremb, S. G., Roik, A. & Voolstra, C. R. Microbial community composition of deep-sea corals from the Red Sea provides insight into functional adaption to a unique environment. Sci. Rep. 7, 44714 (2017).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    49.
    Meyer, J. L., Paul, V. J. & Teplitski, M. Community shifts in the surface microbiomes of the coral Porites astreoides with unusual lesions. PLoS ONE 9, e100316 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    50.
    Sweet, M. J. & Bulling, M. T. On the importance of the microbiome and pathobiome in coral health and disease. Front. Mar. Sci. 4, 1–11 (2017).
    Article  Google Scholar 

    51.
    Grottoli, A. G. et al. Coral physiology and microbiome dynamics under combined warming and ocean acidification. PLoS ONE 13, e0191156 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    52.
    Webster, N. S. et al. Host-associated coral reef microbes respond to the cumulative pressures of ocean warming and ocean acidification. Sci. Rep. 6, 19324 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    53.
    Ainsworth, T. D., Thurber, R. V. & Gates, R. D. The future of coral reefs: A microbial perspective. Trends Ecol. Evol. 25, 233–240 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    54.
    Gissi, F. et al. The effect of dissolved nickel and copper on the adult coral Acropora muricata and its microbiome. Environ. Pollut. 250, 792–806 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    55.
    Leite, D. C. et al. Coral bacterial-core abundance and network complexity as proxies for anthropogenic pollution. Front. Microbiol. 9, 833 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    56.
    Vega Thurber, R. et al. Metagenomic analysis of stressed coral holobionts. Environ. Microbiol. 11, 2148–2163 (2009).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    57.
    Meron, D. et al. The impact of reduced pH on the microbial community of the coral Acropora eurystoma. ISME J. 5, 51–60 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    58.
    McDevitt-Irwin, J. M., Baum, J. K., Garren, M. & Vega Thurber, R. L. Responses of coral-associated bacterial communities to local and global stressors. Front. Mar. Sci. 4, 262 (2017).
    Article  Google Scholar 

    59.
    Al-Dahash, L. M. & Mahmoud, H. M. Harboring oil-degrading bacteria: A potential mechanism of adaptation and survival in corals inhabiting oil-contaminated reefs. Mar. Pollut. Bull. 72, 364–374 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    60.
    Wenger, A. S., Fabricius, K. E., Jones, G. P. & Brodie, J. E. Effects of sedimentation, eutrophication, and chemical pollution on coral reef fishes. In Ecology of Fishes on Coral Reefs (ed. Mora, C.) 145–153 (Cambridge University Press, Cambridge, 2015).
    Google Scholar 

    61.
    Zaneveld, J. R. et al. Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat. Commun. 7, 1–12 (2016).
    Article  CAS  Google Scholar 

    62.
    Marangoni, L. F. et al. Copper effects on biomarkers associated with photosynthesis, oxidative status and calcification in the Brazilian coral Mussismilia harttii (Scleractinia, Mussidae). Mar. Environ. Res. 130, 248–257 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    63.
    Ferrigno, F. et al. Corals in high diversity reefs resist human impact. Ecol. Indic. 70, 106–113 (2016).
    Article  Google Scholar 

    64.
    Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 80, 929–933 (2003).
    ADS  Article  CAS  Google Scholar 

    65.
    Pastorok, R. & Bilyard, G. Effects of sewage pollution on coral-reef communities. Mar. Ecol. Prog. Ser. 21, 175–189 (1985).
    ADS  Article  Google Scholar 

    66.
    Tarrant, A. M. Hormonal signaling in cnidarians: Do we understand the pathways well enough to know whether they are being disrupted?. Ecotoxicology 16, 5–13 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    67.
    Peters, E. C., Gassman, N. J., Firman, J. C., Richmond, R. H. & Power, E. A. Ecotoxicology of tropical marine ecosystems. Environ. Toxicol. Chem. 16, 12–40 (1997).
    CAS  Article  Google Scholar 

    68.
    Holert, J. et al. Metagenomes reveal global distribution of bacterial steroid catabolism in natural, engineered, and host environments. MBio 9, e02345-e2417 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Winter, A. P. M., Chaloub, R. M. & Duarte, G. A. S. Photosynthetic responses of corals Mussismilia harttii (Verrill, 1867) from turbid waters to changes in temperature and presence/absence of light. Braz. J. Oceanogr. 64, 203–216 (2016).
    Article  Google Scholar 

    70.
    Fonseca, J. S., Marangoni, L. F. B., Marques, J. A. & Bianchini, A. Effects of increasing temperature alone and combined with copper exposure on biochemical and physiological parameters in the zooxanthellate scleractinian coral Mussismilia harttii. Aquat. Toxicol. 190, 121–132 (2017).
    CAS  PubMed  Article  Google Scholar 

    71.
    Ralph, P. J., Schreiber, U., Gademann, R., Kühl, M. & Larkum, A. W. D. Coral photobiology studied with a new imaging pulse amplitude modulated fluorometer. J. Phycol. 41, 335–342 (2005).
    Article  Google Scholar 

    72.
    Sato, Y., Bourne, D. G. & Willis, B. L. Effects of temperature and light on the progression of black band disease on the reef coral, Montiporahispida. Coral Reefs 30, 753–761 (2011).
    ADS  Article  Google Scholar 

    73.
    Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 160–164 (2013).
    ADS  CAS  Article  Google Scholar 

    74.
    Fernando, S. C. et al. Microbiota of the major south atlantic reef building. Microb. Ecol. 69, 267–280 (2015).
    PubMed  Article  Google Scholar 

    75.
    De Castro, A. P., Dias, S. A. & Reis, A. M. M. Bacterial community associated with healthy and diseased reef coral Mussismilia hispida from eastern Brazil. Microb. Ecol. 59, 658–667 (2010).
    PubMed  Article  Google Scholar 

    76.
    Santos, H. F. et al. Mangrove bacterial diversity and the impact of oil contamination revealed by pyrosequencing: Bacterial proxies for oil pollution. PLoS ONE 6, e14693 (2011).
    Article  CAS  Google Scholar 

    77.
    Santos, H. F., Cury, J. C., Carmo, F. L., Rosado, A. S. & Peixoto, R. S. 18S rDNA sequences from microeukaryotes reveal oil indicators in mangrove sediment. PLoS ONE 5, e12437 (2010).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    78.
    Oleynik, G. N., Yurishinets, V. I. & Starosila, Y. V. Bacterioplankton and bacteriobenthos as biological indicators of the aquatic ecosystems’ state (a review). Hydrobiol. J. 47, 37–48 (2011).
    Article  Google Scholar 

    79.
    Jain, A., Singh, B. N., Singh, S. P., Singh, H. B. & Singh, S. Exploring biodiversity as bioindicators for water pollution. Natl. Conf. Biodivers. Dev. Poverty Alleviation, 50–56 (2010).

    80.
    Bloem, J. & Breure, A. M. Microbial indicators. In Bioindicators and Biomonitors (eds Markert, B. A. et al.) 257–282 (Elsevier, Amsterdam, 2003).
    Google Scholar 

    81.
    Parmar, T. K., Rawtani, D. & Agrawal, Y. K. Bioindicators: The natural indicator of environmental pollution. Front. Life Sci. 9, 110–118 (2016).
    CAS  Article  Google Scholar 

    82.
    Wilkins, L. G. E. et al. Host-associated microbiomes drive structure and function of marine ecosystems. PLoS Biol. 17, e3000533 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    83.
    Kurisu, F., Ogura, M., Saitoh, S., Yamazoe, A. & Yagi, O. Degradation of natural estrogen and identification of the metabolites produced by soil isolates of Rhodococcus sp. and Sphingomonas sp.. J. Biosci. Bioeng. 109, 576–582 (2010).
    CAS  PubMed  Article  Google Scholar 

    84.
    Wang, Y. et al. Degradation of 17 β-estradiol and products by a mixed culture of Rhodococcus equi DSSKP-R-001 and Comamonas testosteroni QYY20150409. Biotechnol. Biotechnol. Equip. 33, 268–277 (2019).
    CAS  Article  Google Scholar 

    85.
    Yoshimoto, T. et al. Isolates from activated sludge in wastewater treatment plants. Appl. Environ. Microbiol. 70, 5283–5289 (2004).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    86.
    Zhao, H. et al. Genome analysis of Rhodococcus sp. DSSKP-R-001: A highly effective β-estradiol-degrading bacterium. Int. J. Genomics 2018, 3505428 (2018).
    PubMed  PubMed Central  Google Scholar 

    87.
    Edet, U. O. & Antai, S. P. Correlation and distribution of xenobiotics genes and metabolic activities with level of total petroleum hydrocarbon in soil, sediment and estuary water in the Niger Delta Region of Nigeria. Asian J. Biotechnol. Genet. Eng. 1(1), 1–11 (2018).
    Google Scholar 

    88.
    Parida, S. & Sharma, D. The microbiome-estrogen connection and breast cancer risk. Cells 8, 1642 (2019).
    CAS  PubMed Central  Article  PubMed  Google Scholar 

    89.
    Schreiber, U. Pulse-amplitude-modulation (PAM) fluorometry and saturation pulse method: An overview. In Chlorophyll a Fluorescence: A Signature of Photosynthesis (eds Papageorgiou, G. C. & Govindjee, C.) 279–319 (Springer, Berlin, 2004).
    Google Scholar 

    90.
    Siebeck, U. E., Marshall, N. J., Klüter, A. & Hoegh-Guldberg, O. Monitoring coral bleaching using a colour reference card. Coral Reefs 25, 453–460 (2006).
    ADS  Article  Google Scholar 

    91.
    Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package. Palaeontol. Electron. 4, 1–9 (2001).
    Google Scholar 

    92.
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    93.
    Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    94.
    McCune, B. & Mefford, M. J. PC-ORD v. 6.0. MjM Software, Gleneden Beach (2010). More

  • in

    Hydro-climatic changes of wetlandscapes across the world

    1.
    Mitsch, W. J. & Gosselink, J. G. Wetlands [Elektronisk Resurs] (Wiley, Hoboken, 2015).
    Google Scholar 
    2.
    Sieben, E. J. J., Khubeka, S. P., Sithole, S., Job, N. M. & Kotze, D. C. The classification of wetlands: Integration of top-down and bottom-up approaches and their significance for ecosystem service determination. Wetl. Ecol. Manage. 26, 441–458 (2018).
    Article  Google Scholar 

    3.
    Seifollahi-Aghmiuni, S., Nockrach, M. & Kalantari, Z. The potential of wetlands in achieving the sustainable development goals of the 2030 Agenda. Water 11, 609 (2019).
    Article  Google Scholar 

    4.
    Jaramillo, F. et al. Priorities and interactions of sustainable development goals (SDGs) with focus on wetlands. Water 11, 619 (2019).
    Article  Google Scholar 

    5.
    Thorslund, J. et al. Solute evidence for hydrological connectivity of geographically isolated wetlands. Land Degrad. Dev. 29, 3954–3962 (2018).
    Article  Google Scholar 

    6.
    Quin, A., Jaramillo, F. & Destouni, G. Dissecting the ecosystem service of large-scale pollutant retention: The role of wetlands and other landscape features. Ambio 44, 127–137 (2015).
    Article  Google Scholar 

    7.
    Thorslund, J. et al. Wetlands as large-scale nature-based solutions: Status and challenges for research, engineering and management. Ecol. Eng. 108, 489–497 (2017).
    Article  Google Scholar 

    8.
    Åhlén, I. et al. Wetlandscape size thresholds for ecosystem service delivery: Evidence from the Norrström drainage basin, Sweden. Sci. Total Environ. 704, 135452 (2020).
    ADS  Article  Google Scholar 

    9.
    Moomaw, W. R. et al. Wetlands in a changing climate: Science, policy and management. Wetlands 38, 183–205 (2018).
    Article  Google Scholar 

    10.
    Erwin, K. L. Wetlands and global climate change: The role of wetland restoration in a changing world. Wetl. Ecol. Manage. 17, 71 (2008).
    Article  Google Scholar 

    11.
    Jaramillo, F., Prieto, C., Lyon, S. W. & Destouni, G. Multimethod assessment of evapotranspiration shifts due to non-irrigated agricultural development in Sweden. J. Hydrol. 484, 55–62 (2013).
    ADS  Article  Google Scholar 

    12.
    Bring, A. et al. Implications of freshwater flux data from the CMIP5 multimodel output across a set of Northern Hemisphere drainage basins: Implications of freshwater flux data from the CMIP5 multimodel output across. Earths Future 3, 206–217 (2015).
    ADS  Article  Google Scholar 

    13.
    Jarsjö, J., Asokan, S. M., Prieto, C., Bring, A. & Destouni, G. Hydrological responses to climate change conditioned by historic alterations of land-use and water-use. Hydrol. Earth Syst. Sci. 16, 1335–1347 (2012).
    ADS  Article  Google Scholar 

    14.
    Moor, H., Hylander, K. & Norberg, J. Predicting climate change effects on wetland ecosystem services using species distribution modeling and plant functional traits. Ambio 44, 113–126 (2015).
    Article  Google Scholar 

    15.
    Jaramillo, F. et al. Effects of hydroclimatic change and rehabilitation activities on salinity and mangroves in the Ciénaga Grande de Santa Marta, Colombia. Wetlands 38, 755–767 (2018).
    Article  Google Scholar 

    16.
    Jarsjö, J. et al. Projecting impacts of climate change on metal mobilization at contaminated sites: Controls by the groundwater level. Sci. Total Environ. 712, 135560 (2020).
    ADS  Article  Google Scholar 

    17.
    Ghajarnia, N. et al. Wetlandscape change information database (WetCID). Earth Syst. Sci. Data 12(2), 1083–1083. https://doi.org/10.1594/PANGAEA.907398 (2019).
    ADS  Article  Google Scholar 

    18.
    Karlsson, J. M., Jaramillo, F. & Destouni, G. Hydro-climatic and lake change patterns in Arctic permafrost and non-permafrost areas. J. Hydrol. 529, 134–145 (2015).
    ADS  Article  Google Scholar 

    19.
    Hofstede, R. G. M. Effects of livestock farming and recommendations for management and conservation of páramo grasslands (Colombia). Land Degrad. Dev. 6, 133–147 (1995).
    Article  Google Scholar 

    20.
    Agudelo, C. & Fernanda, M. Ecohydrology of Paramos in Colombia: Vulnerability to Climate Change and Land Use (Universidad Nacional de Colombia, Medellín, 2019).
    Google Scholar 

    21.
    Fallah, M. & Zamani-Ahmadmahmoodi, R. Assessment of water quality in Iran’s Anzali Wetland, using qualitative indices from 1985, 2007, and 2014. Wetl. Ecol. Manage. 25, 597–605 (2017).
    CAS  Article  Google Scholar 

    22.
    Khazaei, B. et al. Climatic or regionally induced by humans? Tracing hydro-climatic and land-use changes to better understand the Lake Urmia tragedy. J. Hydrol. 569, 203–217 (2019).
    ADS  Article  Google Scholar 

    23.
    Shibuo, Y., Jarsjö, J. & Destouni, G. Hydrological responses to climate change and irrigation in the Aral Sea drainage basin. Geophys. Res. Lett. https://doi.org/10.1029/2007GL031465 (2007).
    Article  Google Scholar 

    24.
    Jarsjö, J., Törnqvist, R. & Su, Y. Climate-driven change of nitrogen retention–attenuation near irrigated fields: Multi-model projections for Central Asia. Environ. Earth Sci. 76, 117 (2017).
    Article  Google Scholar 

    25.
    Marjani, A. & Jamali, M. Role of exchange flow in salt water balance of Urmia Lake. Dyn. Atmos. Oceans 65, 1–16 (2014).
    ADS  Article  Google Scholar 

    26.
    Törnqvist, R. et al. Evolution of the hydro-climate system in the Lake Baikal basin. J. Hydrol. 519, 1953–1962 (2014).
    ADS  Article  Google Scholar 

    27.
    Pietroń, J. et al. Sedimentation patterns in the Selenga River delta under changing hydroclimatic conditions. Hydrol. Process. 32, 278–292 (2018).
    ADS  Article  Google Scholar 

    28.
    Foufoula-Georgiou, E., Takbiri, Z., Czuba, J. A. & Schwenk, J. The change of nature and the nature of change in agricultural landscapes: Hydrologic regime shifts modulate ecological transitions. Water Resour. Res. 51, 6649–6671 (2015).
    ADS  Article  Google Scholar 

    29.
    Meter, K. J. V. & Basu, N. B. Signatures of human impact: Size distributions and spatial organization of wetlands in the Prairie Pothole landscape. Ecol. Appl. 25, 451–465 (2015).
    Article  Google Scholar 

    30.
    McCartney, M., Morardet, S., Rebelo, L.-M., Finlayson, C. M. & Masiyandima, M. A study of wetland hydrology and ecosystem service provision: GaMampa wetland, South Africa. Hydrol. Sci. J. 56, 1452–1466 (2011).
    Article  Google Scholar 

    31.
    Wolf, K. L., Noe, G. B. & Ahn, C. Hydrologic connectivity to streams increases nitrogen and phosphorus inputs and cycling in soils of created and natural floodplain wetlands. J. Environ. Qual. 42, 1245–1255 (2013).
    CAS  Article  Google Scholar 

    32.
    Kasimov, N., Karthe, D. & Chalov, S. Environmental change in the Selenga River—Lake Baikal Basin. Reg. Environ. Change 17, 1945–1949 (2017).
    Article  Google Scholar 

    33.
    Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen-Geiger climate classification updated. Meteorol. Z. 15, 259–263. https://doi.org/10.1127/0941-2948/2006/0130 (2006).
    Article  Google Scholar 

    34.
    Ghajarnia, N. et al. Data for wetlandscapes and their changes around the world. Earth Syst. Sci. Data 12, 1083–1100 (2020).
    ADS  Article  Google Scholar 

    35.
    Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
    Article  Google Scholar 

    36.
    Budyko, M. I. & Miller, D. H. Climate and Life (Academic Press, New York, 1974).
    Google Scholar 

    37.
    Roderick, M. L. & Farquhar, G. D. A simple framework for relating variations in runoff to variations in climatic conditions and catchment properties. Water Resour. Res. https://doi.org/10.1029/2010WR009826 (2011).
    Article  Google Scholar 

    38.
    Yang, H., Yang, D., Lei, Z. & Sun, F. New analytical derivation of the mean annual water-energy balance equation. Water Resour. Res. https://doi.org/10.1029/2007WR006135 (2008).
    Article  Google Scholar 

    39.
    Zhang, D., Cong, Z., Ni, G., Yang, D. & Hu, S. Effects of snow ratio on annual runoff within the Budyko framework. Hydrol. Earth Syst. Sci. 19, 1977–1992 (2015).
    ADS  Article  Google Scholar 

    40.
    Wen, X., Tang, G., Wang, S. & Huang, J. Comparison of global mean temperature series. Adv. Clim. Change Res. 2, 187–192 (2011).
    Article  Google Scholar  More

  • in

    Impact of root-associated strains of three Paraburkholderia species on primary and secondary metabolism of Brassica oleracea

    Paraburkholderia species promote Broccoli growth in a cultivar-dependent manner
    Root tip inoculation of the two Broccoli cultivars with strains of three different Paraburkholderia species led to changes in leaf color (deep green leaves), shoot biomass, root biomass and root architecture (Fig. 1a). Percent change in biomass was used as a measure to assess the growth-promoting effects of the Paraburkholderia species in the two Broccoli cultivars. Two-way analysis of variance (ANOVA) was conducted to assess the influence of the two independent variables (strains of Paraburkholderia species and Broccoli cultivars) on both shoot and root biomass. The Paraburkholderia species included three levels (Pbg, Pbh, Pbt) and the Broccoli cultivars consisted of two levels (Coronado, Malibu). For shoots, all interactions, except Pbt-Malibu, resulted in significant increases in biomass relative to the non-treated control plants, while for roots all three Paraburkholderia species significantly increased the biomass in both Broccoli cultivars (Fig. 1b). In general, the relative impact of Paraburkholderia species was up to 3 times higher for root biomass than for shoot biomass (Fig. 1b). Two-way ANOVA showed highly significant interactions between the strains of Paraburkholderia species and Broccoli cultivars regarding the percent changes in shoot and root biomass (Supplementary Table S1). Overall, for cultivar Coronado the percent change in shoot biomass was about 40% compared to the control, and not significantly different between the different strains of Paraburkholderia species, whereas in cultivar Malibu the percent change in shoot biomass was significantly higher for Pbg (~ 70%) and Pbh (~ 90%) as compared to Pbt. Furthermore, inoculation with Pbh led to a significantly higher increase in shoot biomass in cultivar Malibu than in Coronado. Regarding the percent change in root biomass, only inoculation of Pbt showed significant differences between the two Broccoli cultivars. As indicated above, the shoot biomass of cultivar Malibu inoculated with Pbt was not significantly different from the control plants (Fig. 1b). Over a period of 11 days, both Pbg and Pbh-treated Broccoli cultivars showed significantly higher shoot and root biomass from 7 days post inoculation (dpi) onwards, while Pbt-treated plants showed higher shoot biomass in Coronado from 9 dpi onwards (Fig. 1c).
    Figure 1

    Biomass and phenotypic changes in Broccoli cultivars in response to root tip inoculation with strains of three Paraburkholderia species. (a) Pictures of MS agar plate with two Broccoli cultivars (Coronado and Malibu) at 11 days post inoculation with strains of three Paraburkholderia species (Pbg: Paraburkholderia graminis PHS1, Pbh: P. hospita mHSR1, and Pbt: P. terricola mHS1). (b) Percent changes in shoot and root biomass (mean ± standard error, n = 4 (shoot) and n = 6 (root)) of two Broccoli cultivars inoculated with the strains of the Paraburkholderia species. Treatments sharing the same letters are not significantly different (Two-way ANOVA, Tukey’s HSD post hoc test, P  More