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    Ecology directs host–parasite coevolutionary trajectories across Daphnia–microparasite populations

    1.
    Paterson, S. et al. Antagonistic coevolution accelerates molecular evolution. Nature 464, 275–278 (2010).
    CAS  PubMed  PubMed Central  Article  Google Scholar 
    2.
    Schulte, R. D., Makus, C., Hasert, B., Michiels, N. K. & Schulenburg, H. Multiple reciprocal adaptations and rapid genetic change upon experimental coevolution of an animal host and its microbial parasite. Proc. Natl Acad. Sci. USA 107, 7359–7364 (2010).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    3.
    Koskella, B. & Lively, C. M. Evidence for negative frequency-dependent selection during experimental coevolution of a freshwater snail and a sterilizing trematode. Evolution 63, 2213–2221 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    4.
    Decaestecker, E. et al. Host–parasite ‘Red Queen’ dynamics archived in pond sediment. Nature 450, 870–873 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Gómez, P. & Buckling, A. Bacteria–phage antagonistic coevolution in soil. Science 332, 106–109 (2011).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    6.
    Refardt, D. & Ebert, D. Inference of parasite local adaptation using two different fitness components. J. Evol. Biol. 20, 921–929 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Duffy, M. A., Hall, S. R., Cáceres, C. E. & Ives, A. R. Rapid evolution, seasonality, and the termination of parasite epidemics. Ecology 90, 1441–1448 (2009).
    PubMed  Article  Google Scholar 

    8.
    Springer, Y. P. Clinical resistance structure and pathogen local adaptation in a serpentine flax–flax rust interaction. Evolution 61, 1812–1822 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    9.
    Tack, A. J. M., Laine, A.-L., Burdon, J. J., Bissett, A. & Thrall, P. H. Below-ground abiotic and biotic heterogeneity shapes above-ground infection outcomes and spatial divergence in a host–parasite interaction. New Phytol. 207, 1159–1169 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    10.
    Wolinska, J. & King, K. C. Environment can alter selection in host–parasite interactions. Trends Parasitol. 25, 236–244 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    11.
    Auld, S. K. J. R., Hall, S. R., Ochs, J. H., Sebastian, M. & Duffy, M. A. Predators and patterns of within-host growth can mediate both among-host competition and evolution of transmission potential of parasites. Am. Nat. 184, S77–S90 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    12.
    Wright, R. C. T., Brockhurst, M. A. & Harrison, E. Ecological conditions determine extinction risk in co-evolving bacteria–phage populations. BMC Evol. Biol. 16, 227 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    13.
    Duffy, M. A. et al. Ecological context influences epidemic size and parasite-driven evolution. Science 335, 1636–1638 (2012).
    CAS  PubMed  Article  Google Scholar 

    14.
    Auld, S. K. J. R. & Brand, J. Environmental variation causes different (co) evolutionary routes to the same adaptive destination across parasite populations. Evol. Lett. 1, 245–254 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    15.
    Su, M. & Boots, M. The impact of resource quality on the evolution of virulence in spatially heterogeneous environments. J. Theor. Biol. 416, 1–7 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Auld, S. K. J. R. & Tinsley, M. C. The evolutionary ecology of complex lifecycle parasites: linking phenomena with mechanisms. Heredity 114, 125–132 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Cardon, M., Loot, G., Grenouillet, G. & Blanchet, S. Host characteristics and environmental factors differentially drive the burden and pathogenicity of an ectoparasite: a multilevel causal analysis. J. Anim. Ecol. 80, 657–667 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    18.
    Mahmud, M. A., Bradley, J. E. & MacColl, A. D. C. Abiotic environmental variation drives virulence evolution in a fish host–parasite geographic mosaic. Funct. Ecol. 31, 2138–2146 (2017).
    Article  Google Scholar 

    19.
    Arruda, J. A., Marzolf, G. R. & Faulk, R. T. The role of suspended sediments in the nutrition of zooplankton in turbid reservoirs. Ecology 64, 1225–1235 (1983).
    Article  Google Scholar 

    20.
    Mostowy, R. & Engelstädter, J. The impact of environmental change on host–parasite coevolutionary dynamics. Proc. R. Soc. B 278, 2283–2292 (2011).
    PubMed  Article  Google Scholar 

    21.
    Thompson, J. N. The Geographic Mosaic of Coevolution (Univ. Chicago Press, 2005).

    22.
    Brett, M. T. Chaoborus and fish-mediated influences on Daphnia longispina population structure, dynamics and life history strategies. Oecologia 89, 69–77 (1992).
    PubMed  Article  Google Scholar 

    23.
    Goss, L. B. & Bunting, D. L. Daphnia development and reproduction: responses to temperature. J. Therm. Biol. 8, 375–380 (1983).
    Article  Google Scholar 

    24.
    Luijckx, P., Fienberg, H., Duneau, D. & Ebert, D. A matching-allele model explains host resistance to parasites. Curr. Biol. 23, 1085–1088 (2013).
    CAS  PubMed  Article  Google Scholar 

    25.
    Bento, G. et al. The genetic basis of resistance and matching-allele interactions of a host–parasite system: the Daphnia magna–Pasteuria ramosa model. PLoS Genet. 13, e1006596 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    26.
    Grosberg, R. K. Mate selection and the evolution of highly polymorphic self/nonself recognition genes. Science 289, 2111–2114 (2000).
    CAS  PubMed  Article  Google Scholar 

    27.
    Hutchinson, G. E. The Ecological Theater and the Evolutionary Play (Yale Univ. Press, 1965).

    28.
    Stuart, Y. E. et al. Contrasting effects of environment and genetics generate a continuum of parallel evolution. Nat. Ecol. Evol. 1, 0158 (2017).
    Article  Google Scholar 

    29.
    Klüttgen, B., Dülmer, U., Engels, M. & Ratte, H. ADaM, an artificial freshwater for the culture of zooplankton. Water Res. 28, 743–746 (1994).
    Article  Google Scholar 

    30.
    Ebert, D., Zschokke-Rohringer, C. D. & Carius, H. J. Within- and between-population variation for resistance of Daphnia magna to the bacterial endoparasite Pasteuria ramosa. Proc. R. Soc. B 265, 2127–2134 (1998).
    Article  Google Scholar 

    31.
    Auld, S. K. J. R. & Brand, J. Simulated climate change, epidemic size, and host evolution across host–parasite populations. Glob. Change Biol. 23, 5045–5053 (2017).
    Article  Google Scholar 

    32.
    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
    Google Scholar 

    33.
    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

    34.
    Brereton, R. G. & Lloyd, G. R. Re-evaluating the role of the Mahalanobis distance measure. J. Chemom. 30, 134–143 (2016).
    CAS  Article  Google Scholar 

    35.
    D’Orazio, M. StatMatch: Statistical Matching or Data Fusion. R package version 1.4.0 (2019).

    36.
    Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22, 1–22 (2007).
    Article  Google Scholar 

    37.
    Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
    Article  Google Scholar 

    38.
    Auld, S. K. J. R., Wilson, P. J. & Little, T. J. Rapid change in parasite infection traits over the course of an epidemic in a wild host–parasite population. Oikos 123, 232–238 (2014).
    Article  Google Scholar 

    39.
    Shocket, M. S. et al. Parasite rearing and infection temperatures jointly influence disease transmission and shape seasonality of epidemics. Ecology 99, 1975–1987 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    40.
    Duncan, A. B., Mitchell, S. E. & Little, T. J. Parasite-mediated selection and the role of sex and diapause in Daphnia. J. Evol. Biol. 19, 1183–1189 (2006).
    CAS  PubMed  Article  Google Scholar 

    41.
    Auld, S. K. J. R. et al. Variation in costs of parasite resistance among natural host populations. J. Evol. Biol. 26, 2479–2486 (2013).
    CAS  PubMed  Article  Google Scholar 

    42.
    Laine, A.-L. Evolution of host resistance: looking for coevolutionary hotspots at small spatial scales. Proc. R. Soc. B 273, 267–273 (2006).
    PubMed  Article  Google Scholar 

    43.
    Lohse, K., Gutierrez, A. & Kaltz, O. Experimental evolution of resistance in Paramecium caudatum against the bacterial parasite Holospora undulata. Evolution 60, 1177–1186 (2006).
    Article  Google Scholar 

    44.
    Duffy, M. A. & Sivars-Becker, L. Rapid evolution and ecological host–parasite dynamics. Ecol. Lett. 10, 44–53 (2007).
    PubMed  Article  Google Scholar 

    45.
    Brewer, M. J., Butler, A. & Cooksley, S. L. The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods Ecol. Evol. 7, 679–692 (2016).
    Article  Google Scholar 

    46.
    Shipley, B. A new inferential test for path models based on directed acyclic graphs. Struct. Equ. Model. 7, 206–218 (2000).
    Article  Google Scholar  More

  • in

    Resolving cryptic species complexes in marine protists: phylogenetic haplotype networks meet global DNA metabarcoding datasets

    1.
    Mayr E. Populations, species, and evolution: an abridgment of animal species and evolution. Cambridge: Belknap Press of Harvard University Press; 1970.
    2.
    Bickford D, Lohman DJ, Sodhi NS, Ng PKL, Meier R, Winker K, et al. Cryptic species as a window on diversity and conservation. Trends Ecol Evol. 2007;22:148–55.
    PubMed  Article  Google Scholar 

    3.
    Fišer C, Robinson CT, Malard F. Cryptic species as a window into the paradigm shift of the species concept. Mol Ecol. 2018;27:613–35.
    PubMed  Article  Google Scholar 

    4.
    Struck TH, Feder JL, Bendiksby M, Birkeland S, Cerca J, Gusarov VI, et al. Finding evolutionary processes hidden in cryptic species. Trends Ecol Evol. 2018;33:153–63.
    PubMed  Article  Google Scholar 

    5.
    Sarno D, Kooistra WHCF, Medlin LK, Percopo I, Zingone A. Diversity in the genus Skeletonema (Bacillariophyceae). II. An assessment of the taxonomy of S. costatum-like species with the description of four new species. J Phycol. 2005;41:151–76.
    Article  Google Scholar 

    6.
    Gaonkar CC, Kooistra WHCF, Lange CB, Montresor M, Sarno D. Two new species in the Chaetoceros socialis complex (Bacillariophyta): C. sporotruncatus and C. dichatoensis, and characterization of its relatives. J Phycol. 2017;53:889–907.
    CAS  PubMed  Article  Google Scholar 

    7.
    Li Y, Boonprakob A, Gaonkar CC, Kooistra WHCF, Lange CB, Hernández-Becerril D, et al. Diversity in the globally distributed diatom genus Chaetoceros (Bacillariophyceae): three new species from warm-temperate waters. PLoS ONE. 2017;12:e0168887.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    8.
    Finlay BJ, Clarke KJ. Ubiquitous dispersal of microbial species. Nature. 1999;400:828.
    CAS  Article  Google Scholar 

    9.
    Finlay BJ, Fenchel T. Divergent perspectives on protist species richness. Protist. 1999;150:229–33.
    CAS  PubMed  Article  Google Scholar 

    10.
    Fenchel T, Finlay BJ. The ubiquity of small species: patterns of local and global diversity. Bioscience. 2004;54:777.
    Article  Google Scholar 

    11.
    Fenchel T. Cosmopolitan microbes and their ‘cryptic’ species. Aquat Microb Ecol. 2005;41:49–54.
    Article  Google Scholar 

    12.
    Miglietta MP, Faucci A, Santini F. Speciation in the sea: overview of the symposium and discussion of future directions. Integr Comp Biol. 2011;51:449–55.
    PubMed  Article  Google Scholar 

    13.
    Kooistra WHCF, Sarno D, Balzano S, Gu H, Andersen RA, Zingone A. Global diversity and biogeography of Skeletonema species (Bacillariophyta). Protist. 2008;159:177–93.
    CAS  PubMed  Article  Google Scholar 

    14.
    Nanjappa D, Audic S, Romac S, Kooistra WHCF, Zingone A. Assessment of species diversity and distribution of an ancient diatom lineage using a DNA metabarcoding approach. PLoS ONE. 2014;9:e103810.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    15.
    Kaczmarska I, Mather L, Luddington IA, Muise F, Ehrman JM. Cryptic diversity in a cosmopolitan diatom known as Asterionellopsis glacialis (Fragilariaceae): Implications for ecology, biogeography, and taxonomy. Am J Bot. 2014;101:267–86.
    PubMed  Article  Google Scholar 

    16.
    Zhao Y, Yi Z, Gentekaki E, Zhan A, Al-Farraj SA, Song W. Utility of combining morphological characters, nuclear and mitochondrial genes: An attempt to resolve the conflicts of species identification for ciliated protists. Mol Phylogenet Evol. 2016;94:718–29.
    PubMed  Article  Google Scholar 

    17.
    Weiner A, Aurahs R, Kurasawa A, Kitazato H, Kucera M. Vertical niche partitioning between cryptic sibling species of a cosmopolitan marine planktonic protist. Mol Ecol. 2012;21:4063–73.
    PubMed  Article  Google Scholar 

    18.
    Lamari N, Ruggiero MV, d’Ippolito G, Kooistra WHCF, Fontana A, Montresor M. Specificity of lipoxygenase pathways supports species delineation in the marine diatom genus Pseudo-nitzschia. PLoS ONE. 2013;8:e73281.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    19.
    Škaloud P, Friedl T, Hallmann C, Beck A, Dal Grande F. Taxonomic revision and species delimitation of coccoid green algae currently assigned to the genus Dictyochloropsis (Trebouxiophyceae, Chlorophyta). J Phycol. 2016;52:599–617.
    PubMed  Article  CAS  Google Scholar 

    20.
    de Jesus PB, Costa AL, de Castro Nunes JM, Manghisi A, Genovese G, Morabito M, et al. Species delimitation methods reveal cryptic diversity in the Hypnea cornuta complex (Cystocloniaceae, Rhodophyta). Eur J Phycol. 2019;54:135–53.
    Article  CAS  Google Scholar 

    21.
    Díaz-Tapia P, Ly M, Verbruggen H. Extensive cryptic diversity in the widely distributed Polysiphonia scopulorum (Rhodomelaceae, Rhodophyta): molecular species delimitation and morphometric analyses. Mol Phylogenet Evol. 2020;152:106909.
    PubMed  Article  Google Scholar 

    22.
    Huson DH, Rupp R, Scornavacca C. Phylogenetic networks. Cambridge: Cambridge University Press; 2009.

    23.
    Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006;23:254–67.
    CAS  PubMed  Article  Google Scholar 

    24.
    Solís-Lemus C, Yang M, Ané C. Inconsistency of species tree methods under gene flow. Syst Biol. 2016;65:843–51.
    PubMed  Article  Google Scholar 

    25.
    Deiner K, Bik HM, Mächler E, Seymour M, Lacoursière-Roussel A, Altermatt F, et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol Ecol. 2017;26:5872–95.
    PubMed  Article  Google Scholar 

    26.
    Pawlowski J, Audic S, Adl S, Bass D, Belbahri L, Berney C, et al. CBOL protist working group: barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLoS Biol. 2012;10:e1001419.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    27.
    Trobajo R, Mann DG, Clavero E, Evans KM, Vanormelingen P, McGregor RC. The use of partial cox1, rbcL and LSU rDNA sequences for phylogenetics and species identification within the Nitzschia palea species complex (Bacillariophyceae). Eur J Phycol. 2010;45:413–25.
    CAS  Article  Google Scholar 

    28.
    Decelle J, Suzuki N, Mahé F, De Vargas C, Not F. Molecular phylogeny and morphological evolution of the acantharia (Radiolaria). Protist. 2012;163:435–50.
    PubMed  Article  Google Scholar 

    29.
    Stoeck T, Przybos E, Dunthorn M. The D1-D2 region of the large subunit ribosomal DNA as barcode for ciliates. Mol Ecol Resour. 2014;14:458–68.
    CAS  PubMed  Article  Google Scholar 

    30.
    Moniz MBJ, Kaczmarska I. Barcoding of diatoms: nuclear encoded ITS revisited. Protist. 2010;161:7–34.
    CAS  PubMed  Article  Google Scholar 

    31.
    Gile GH, Stern RF, James ER, Keeling PJ. DNA barcoding of chlorarachniophytes using nucleomorph ITS sequences. J Phycol. 2010;46:743–50.
    CAS  Article  Google Scholar 

    32.
    Stern RF, Andersen RA, Jameson I, Küpper FC, Coffroth M-A, Vaulot D, et al. Evaluating the ribosomal internal transcribed spacer (ITS) as a candidate dinoflagellate barcode marker. PLoS ONE. 2012;7:e42780.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Saunders GW. Applying DNA barcoding to red macroalgae: a preliminary appraisal holds promise for future applications. Philos Trans R Soc B Biol Sci. 2005;360:1879–88.

    34.
    MacGillivary ML, Kaczmarska I. Survey of the efficacy of a short fragment of the rbcL gene as a supplemental DNA barcode for diatoms. J Eukaryot Microbiol. 2011;58:529–36.
    CAS  PubMed  Article  Google Scholar 

    35.
    Zimmermann J, Jahn R, Gemeinholzer B. Barcoding diatoms: evaluation of the V4 subregion on the 18S rRNA gene, including new primers and protocols. Org Divers Evol. 2011;11:173–92.
    Article  Google Scholar 

    36.
    Piredda R, Tomasino MP, D’Erchia AM, Manzari C, Pesole G, Montresor M, et al. Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiol Ecol. 2016;93:fiw200.
    PubMed  Article  CAS  Google Scholar 

    37.
    Pawlowski J, Lecroq B. Short rDNA barcodes for species identification in foraminifera. J Eukaryot Microbiol. 2010;57:197–205.
    CAS  PubMed  Article  Google Scholar 

    38.
    Mordret S, Piredda R, Vaulot D, Montresor M. Kooistra WHCF, Sarno D. dinoref: a curated dinoflagellate (Dinophyceae) reference database for the 18S rRNA gene. Mol Ecol Resour. 2018;18:974–87.
    CAS  Article  Google Scholar 

    39.
    Gaonkar CC, Piredda R, Minucci C, Mann DG, Montresor M, Sarno D, et al. Annotated 18S and 28S rDNA reference sequences of taxa in the planktonic diatom family Chaetocerotaceae. PLoS ONE. 2018;13:e0208929.
    PubMed  PubMed Central  Article  Google Scholar 

    40.
    Balzano S, Percopo I, Siano R, Gourvil P, Chanoine M, Marie D, et al. Morphological and genetic diversity of Beaufort Sea diatoms with high contributions from the Chaetoceros neogracilis species complex. J Phycol. 2017;53:161–87.
    CAS  PubMed  Article  Google Scholar 

    41.
    Kopf A, Bicak M, Kottmann R, Schnetzer J, Kostadinov I, Lehmann K, et al. The ocean sampling day consortium. Gigascience. 2015;4. https://doi.org/10.1186/s13742-015-0066-5.

    42.
    Pesant S, Not F, Picheral M, Kandels-Lewis S, Le Bescot N, Gorsky G, et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci Data. 2015;2:150023.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Yau S, Lopes dos Santos A, Eikrem W, Gérikas Ribeiro C, Gourvil P, Balzano S, et al. Mantoniella beaufortii and Mantoniella baffinensis sp. nov. (Mamiellales, Mamiellophyceae), two new green algal species from the high arctic. J Phycol. 2020;56:37–51.
    PubMed  Article  Google Scholar 

    44.
    Lopes Dos Santos A, Gourvil P, Tragin M, Noël M-H, Decelle J, Romac S, et al. Diversity and oceanic distribution of prasinophytes clade VII, the dominant group of green algae in oceanic waters. ISME J. 2017;11:512–28.
    PubMed  Article  Google Scholar 

    45.
    Kuwata A, Yamada K, Ichinomiya M, Yoshikawa S, Tragin M, Vaulot D, et al. Bolidophyceae, a sister picoplanktonic group of diatoms—a review. Front Mar Sci. 2018;5:370.
    Article  Google Scholar 

    46.
    Segawa T, Matsuzaki R, Takeuchi N, Akiyoshi A, Navarro F, Sugiyama S, et al. Bipolar dispersal of red-snow algae. Nat Commun. 2018;9:1–8.
    CAS  Article  Google Scholar 

    47.
    Ichinomiya M, Dos Santos AL, Gourvil P, Yoshikawa S, Kamiya M, Ohki K, et al. Diversity and oceanic distribution of the Parmales (Bolidophyceae), a picoplanktonic group closely related to diatoms. ISME J. 2016;10:2419–34.
    PubMed  PubMed Central  Article  Google Scholar 

    48.
    Tragin M, Vaulot D. Novel diversity within marine Mamiellophyceae (Chlorophyta) unveiled by metabarcoding. Sci Rep. 2019;9:1–14.
    CAS  Article  Google Scholar 

    49.
    Morard R, Vollmar NM, Greco M, Kucera M. Unassigned diversity of planktonic foraminifera from environmental sequencing revealed as known but neglected species. PLoS ONE. 2019;14:e0213936.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    50.
    Pinseel E, Janssens SB, Verleyen E, Vanormelingen P, Kohler TJ, Biersma EM, et al. Global radiation in a rare biosphere soil diatom. Nat Commun. 2020;11:1–12.
    Article  CAS  Google Scholar 

    51.
    Hasle GR, Syvertsen EE. Marine diatoms. In: Tomas CR, editor. Identifying marine phytoplankton. San Diego: Academic Press; 1997. pp 5–385.

    52.
    Kooistra WHCF, Sarno D, Hernández-Becerril DU, Assmy P, Di Prisco C, Montresor M. Comparative molecular and morphological phylogenetic analyses of taxa in the Chaetocerotaceae (Bacillariophyta). Phycologia. 2010;49:471–500.
    Article  Google Scholar 

    53.
    De Luca D, Sarno D, Piredda R, Kooistra WHCF. A multigene phylogeny to infer the evolutionary history of Chaetocerotaceae (Bacillariophyta). Mol Phylogenet Evol. 2019;140:106575.
    PubMed  Article  Google Scholar 

    54.
    Longhurst AR. Toward and ecological geography of the sea. In: Longhurst AR, editor. Ecological geography of the sea. 2nd ed. Cambridge: Academic Press; 2007. pp 1–17.

    55.
    Stoeck T, Bass D, Nebel M, Christen R, Jones MDM, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.
    CAS  PubMed  Article  Google Scholar 

    56.
    Amaral-Zettler LA, McCliment EA, Ducklow HW, Huse SM. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS ONE. 2009;4:e6372.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    57.
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    58.
    De Vargas C, Audic S, Tara Oceans Consortium C, Tara Oceans Expedition P. Total V9 rDNA information organized at the metabarcode level for the Tara Oceans Expedition (2009–12). 2017. PANGAEA. https://doi.org/10.1594/PANGAEA.873277.

    59.
    Ibarbalz FM, Henry N, Brandão MC, Martini S, Busseni G, Byrne H, et al. Global trends in marine plankton diversity across kingdoms of life. Cell. 2019;179:1084–97.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.
    CAS  Article  Google Scholar 

    61.
    Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 2019;20:1160–6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    62.
    Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    63.
    Han MV, Zmasek CM. phyloXML: XML for evolutionary biology and comparative genomics. BMC Bioinform. 2009;10:356.
    Article  CAS  Google Scholar 

    64.
    Templeton AR, Crandall KA, Sing CF. A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation. Genetics. 1992;132:619–33.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    Clement M, Posada D, Crandall KA. TCS: a computer program to estimate gene genealogies. Mol Ecol. 2000;9:1657–9.
    CAS  PubMed  Article  Google Scholar 

    66.
    Leigh JW, Bryant D. popart: full‐feature software for haplotype network construction. Methods Ecol Evol. 2015;6:1110–6.
    Article  Google Scholar 

    67.
    Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.
    CAS  Article  Google Scholar 

    68.
    Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Jukes TH, Cantor CR. Evolution of protein molecules. Mamm Protein Metab. 1969;3:21–132.
    CAS  Article  Google Scholar 

    71.
    Meyer CP, Paulay G. DNA barcoding: error rates based on comprehensive sampling. PLoS Biol. 2005;3:e422.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    72.
    R Core Team. R: a language and environment for statistical computing. 2019. Vienna, Austria: R Foundation for Statistical Computing; 2019.

    73.
    McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016. https://ggplot2.tidyverse.org.

    75.
    Becker A, Wilks AR. Maps: draw geographical maps. 2018. https://CRAN.R-project.org/package=maps.

    76.
    Markmann M, Tautz D. Reverse taxonomy: an approach towards determining the diversity of meiobenthic organisms based on ribosomal RNA signature sequences. Philos Trans R Soc Lond B Biol Sci. 2005;360:1917–24.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    77.
    López-Escardó D, Paps J, de Vargas C, Massana R, Ruiz-Trillo I, Del Campo J. Metabarcoding analysis on European coastal samples reveals new molecular metazoan diversity. Sci Rep. 2018;8:9106.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    78.
    Álvarez I, Wendel JF. Ribosomal ITS sequences and plant phylogenetic inference. Mol Phylogenet Evol. 2003;29:417–34.

    79.
    Alverson AJ, Kolnick L. Intragenomic nucleotide polymorphism among small subunit (18S) rDNA paralogs in the diatom genus Skeletonema (Bacillariophyta). J Phycol. 2005;41:1248–57.
    CAS  Article  Google Scholar 

    80.
    Gaonkar CC, Piredda R, Sarno D, Zingone A, Montresor M, Kooistra WHCF. Species detection and delineation in the marine planktonic diatoms Chaetoceros and Bacteriastrum through metabarcoding: making biological sense of haplotype diversity. Environ Microbiol. 2020;22:1917–29.
    CAS  PubMed  Article  Google Scholar 

    81.
    Cleve PT. Pelagisk Diatomeer från Kattegat. In: Petersen CGJ, editor. Det Videnskabelige Udbytte af Kanonbaaden ‘Hauchs’ Togter i de Danske Have Indefor Skagen, I. Aarene 1883–86. Kjøbenhavn: Andr. Fred. Høst & Sons Forlag; 1889. pp 53–56.

    82.
    Gran HH. Den Norske Nordhaus-Expedition 1876-1878. Botanik, Protophyta: Diatomaceae, Silicoflagellata og Cilioflagellata. Christiania: Grøndal & Søns; 1897.

    83.
    De Luca D, Kooistra WHCF, Sarno D, Gaonkar CC, Piredda R. Global distribution and diversity of Chaetoceros (Bacillariophyta, Mediophyceae): integration of classical and novel strategies. PeerJ. 2019;7:e7410.
    PubMed  PubMed Central  Article  Google Scholar 

    84.
    Wang J, Wu J. Occurrence and potential risks of harmful algal blooms in the East China Sea. Sci Total Environ. 2009;407:4012–21.
    CAS  PubMed  Article  Google Scholar 

    85.
    Zhen Y, Mi T, Yu Z. Detection of several harmful algal species by sandwich hybridization integrated with a nuclease protection assay. Harmful Algae. 2009;8:651–7.
    CAS  Article  Google Scholar 

    86.
    Richter DJ, Watteaux R, Vannier T, Leconte J, Frémont P, Reygondeau G, et al. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems. bioRxiv. 2019. https://doi.org/10.1101/867739.

    87.
    Sarno D, Kooistra WHCF, Balzano S, Hargraves PE, Zingone A. Diversity in the genus Skeletonema (Bacillariophyceae). III. Phylogenetic position and morphological variability of Skeletonema costatum and Skeletonema grevillei, with the description of Skeletonema ardens sp. nov. J Phycol. 2007;43:156–70.
    CAS  Article  Google Scholar 

    88.
    Hasle GR. The biogeography of some marine planktonic diatoms. Deep Sea Res Oceanogr Abstr. 1976;23:319–338, IN1-IN6.

    89.
    Pargana A. Functional and molecular diversity of the diatom family Leptocylindraceae. 2017. PhD Thesis, The Open University, Milton Keynes, UK.

    90.
    Novis PM. Taxonomy of Klebsormidium (Klebsormidiales, Charophyceae) in New Zealand streams and the significance of low-pH habitats. Phycologia. 2006;45:293–301.
    Article  Google Scholar 

    91.
    Rindi F, Guiry MD, López-Bautista JM. Distribution, morphology, and phylogeny of Klebsormidium (Klebsormidiales, Charophyceae) in urban environments in Europe. J Phycol. 2008;44:1529–40.
    PubMed  Article  Google Scholar 

    92.
    Rindi F, Mikhailyuk TI, Sluiman HJ, Friedl T, López-Bautista JM. Phylogenetic relationships in Interfilum and Klebsormidium (Klebsormidiophyceae, Streptophyta). Mol Phylogenet Evol. 2011;58:218–31.
    PubMed  Article  Google Scholar 

    93.
    Škaloud P, Rindi F. Ecological differentiation of cryptic species within an asexual protist morphospecies: a case study of filamentous green alga Klebsormidium (Streptophyta). J Eukaryot Microbiol. 2013;60:350–62.
    PubMed  Article  CAS  Google Scholar 

    94.
    Baas Becking LGM. Geobiologie of Inleiding tot de Milieukunde. The Hague: Van Stockum & Zoon; 1934).

    95.
    Shapiro BJ, Leducq J-B, Mallet J. What is speciation? PLoS Genet. 2016;12:e1005860.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    96.
    Godhe A, Rynearson T. The role of intraspecific variation in the ecological and evolutionary success of diatoms in changing environments. Philos Trans R Soc Lond B Biol Sci. 2017;372:20160399.
    PubMed  PubMed Central  Article  Google Scholar 

    97.
    de Vargas C, Norris R, Zaninetti L, Gibb SW, Pawlowski J. Molecular evidence of cryptic speciation in planktonic foraminifers and their relation to oceanic provinces. Proc Natl Acad Sci USA. 1999;96:2864–8.
    PubMed  Article  Google Scholar 

    98.
    Amato A, Kooistra WHCF, Levialdi Ghiron JH, Mann DG, Pröschold T, Montresor M. Reproductive isolation among sympatric cryptic species in marine diatoms. Protist. 2007;158:193–207.
    CAS  PubMed  Article  Google Scholar 

    99.
    Weisse T. Distribution and diversity of aquatic protists: an evolutionary and ecological perspective. Biodivers Conserv. 2007;17:243–59.
    Article  Google Scholar 

    100.
    Vanelslander B, Créach V, Vanormelingen P, Ernst A, Chepurnov VA, Sahan E, et al. Ecological differentiation between sympatric pseudocryptic species in the estuarine benthic diatom Navicula phyllepta (Bacillariophyceae). J Phycol. 2009;45:1278–89.
    CAS  PubMed  Article  Google Scholar  More

  • in

    Impacts of wildlife trade on terrestrial biodiversity

    1.
    Haken, J. Transnational Crime in the Developing World (Global Financial Integrity, 2011).
    2.
    Patel, N. G. et al. Quantitative methods of identifying the key nodes in the illegal wildlife trade network. Proc. Natl Acad. Sci. USA 112, 7948–7953 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    3.
    Bager Olsen, M. T. et al. Thirty-six years of legal and illegal wildlife trade entering the USA. Oryx https://doi.org/10.1017/S0030605319000541 (2019).

    4.
    Tittensor, D. P. et al. Evaluating the relationships between the legal and illegal international wildlife trades. Conserv. Lett. https://doi.org/10.1111/conl.12724 (2020).

    5.
    Harfoot, M. et al. Unveiling the patterns and trends in 40 years of global trade in CITES-listed wildlife. Biol. Conserv. 223, 47–57 (2018).
    Article  Google Scholar 

    6.
    Scheffers, B. R., Oliveira, B. F., Lamb, I. & Edwards, D. P. Global wildlife trade across the tree of life. Science 76, 71–76 (2019).
    Article  CAS  Google Scholar 

    7.
    Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Nielsen, M. R., Meilby, H., Smith-Hall, C., Pouliot, M. & Treue, T. The importance of wild meat in the global south. Ecol. Econ. 146, 696–705 (2018).
    Article  Google Scholar 

    9.
    ’t Sas-Rolfes, M., Challender, D. W. S., Hinsley, A., Veríssimo, D. & Milner-Gulland, E. J. Illegal wildlife trade: scale, processes, and governance. Annu. Rev. Environ. Resour. 44, 201–228 (2019).
    Article  Google Scholar 

    10.
    Cooney, R. et al. From poachers to protectors: engaging local communities in solutions to illegal wildlife trade. Conserv. Lett. 10, 367–374 (2017).
    Article  Google Scholar 

    11.
    Bodmer, R. E. & Lozano, E. P. Rural development and sustainable wildlife use in Peru. Conserv. Biol. 15, 1163–1170 (2001).
    Article  Google Scholar 

    12.
    McClenachan, L., Cooper, A. B. & Dulvy, N. K. Rethinking trade-driven extinction risk in marine and terrestrial megafauna. Curr. Biol. 26, 1640–1646 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Wittemyer, G. et al. Illegal killing for ivory drives global decline in African elephants. Proc. Natl Acad. Sci. USA 111, 13117–13121 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Brook, S., Van Coeverden De Groot, P. J., Mahood, S. & Long, B. Extinction of the Javan Rhinoceros (Rhinoceros sondaicus) from Vietnam (WWF, 2011).

    15.
    Heinrich, S. et al. Where did all the pangolins go? International CITES trade in pangolin species. Glob. Ecol. Conserv. 8, 241–253 (2016).
    Article  Google Scholar 

    16.
    Cowlishaw, G., Mendelson, S. & Rowcliffe, J. M. Evidence for post-depletion sustainability in a mature bushmeat market. J. Appl. Ecol. 42, 460–468 (2005).
    Article  Google Scholar 

    17.
    Hutton, J. M. & Webb, G. in The Trade in Wildlife: Regulation for Conservation (ed. Oldfield, S.) Ch. 11 (Earthscan, 2003).

    18.
    Harris, J. B. C. et al. Using market data and expert opinion to identify overexploited species in the wild bird trade. Biol. Conserv. 187, 51–60 (2015).
    Article  Google Scholar 

    19.
    Milner-Gulland, E. J. & Clayton, L. The trade in babirusas and wild pigs in North Sulawesi, Indonesia. Ecol. Econ. 42, 165–183 (2002).
    Article  Google Scholar 

    20.
    Thuiller, W. et al. Vulnerability of African mammals to anthropogenic climate change under conservative land transformation assumptions. Glob. Change Biol. 12, 424–440 (2006).
    Article  Google Scholar 

    21.
    Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    22.
    Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    23.
    CITES Trade Statistics Derived from the CITES Trade Database (CITES, 2020).

    24.
    Phelps, J. & Webb, E. L. ‘Invisible’ wildlife trades: Southeast Asia’s undocumented illegal trade in wild ornamental plants. Biol. Conserv. 186, 296–305 (2015).
    Article  Google Scholar 

    25.
    Davies, G., Schulte-Herbrüggen, B., Kümpel, N. F. & Mendelson, S. Hunting and trapping in Gola Forests, south-eastern Sierra Leone: bushmeat from farm, fallow and forest. Bushmeat Livelihoods Wildl. Manage. Poverty Reduct. https://doi.org/10.1002/9780470692592.ch1 (2008).

    26.
    Linder, J. M. & Oates, J. F. Differential impact of bushmeat hunting on monkey species and implications for primate conservation in Korup National Park, Cameroon. Biol. Conserv. 144, 738–745 (2011).
    Article  Google Scholar 

    27.
    Gilroy, J. J. & Edwards, D. P. Source–sink dynamics: a neglected problem for landscape-scale biodiversity conservation in the tropics. Curr. Landsc. Ecol. Rep. 2, 51–60 (2017).
    Article  Google Scholar 

    28.
    Watson, J. E. M. et al. Catastrophic declines in wilderness areas undermine global environment targets. Curr. Biol. 26, 2929–2934 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Marshall, H. et al. Spatio-temporal dynamics of consumer demand driving the Asian songbird crisis. Biol. Conserv. 241, 108237 (2020).
    Article  Google Scholar 

    30.
    Harris, J. B. C. et al. Measuring the impact of the pet trade on Indonesian birds. Conserv. Biol. 31, 394–405 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    31.
    Carrasco, L. R., Chan, J., Mcgrath, F. L. & Nghiem, L. T. P. Biodiversity conservation in a telecoupled world. Ecol. Soc. 22, 24 (2017).
    Article  Google Scholar 

    32.
    Blundell, A. G. & Mascia, M. B. Discrepancies in reported levels of international wildlife trade. Conserv. Biol. 19, 2020–2025 (2005).
    Article  Google Scholar 

    33.
    Nelson, A. et al. A suite of global accessibility indicators. Sci. Data 6, 266 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    34.
    Rao, M., Zaw, T., Htun, S. & Myint, T. Hunting for a living: wildlife trade, rural livelihoods and declining wildlife in the Hkakaborazi National Park, North Myanmar. Environ. Manage. 48, 158–167 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    35.
    Symes, W. S., Edwards, D. P., Miettinen, J., Rheindt, F. E. & Carrasco, L. R. Combined impacts of deforestation and wildlife trade on tropical biodiversity are severely underestimated. Nat. Commun. 9, 4052 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    36.
    Hinsley, A. et al. Building sustainability into the Belt and Road Initiative’s traditional Chinese medicine trade. Nat. Sustain. 3, 96–100 (2020).
    Article  Google Scholar 

    37.
    Lechner, A. M., Chan, F. K. S. & Campos-Arceiz, A. Biodiversity conservation should be a core value of China’s Belt and Road Initiative. Nat. Ecol. Evol. 2, 408–409 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    38.
    Farhadinia, M. S. et al. Belt and Road Initiative may create new supplies for illegal wildlife trade in large carnivores. Nat. Ecol. Evol. 3, 1267–1268 (2019).
    PubMed  Article  Google Scholar 

    39.
    Courchamp, F. et al. Rarity value and species extinction: the anthropogenic Allee effect. PLoS Biol. 4, 2405–2410 (2006).
    CAS  Article  Google Scholar 

    40.
    Jetz, W. & Freckleton, R. P. Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information. Phil. Trans. R. Soc. B 370, 20140016 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    41.
    Dulac, J. Global Land Transport Infrastructure Requirements: Estimating Road and Railway Infrastructure Capacity and Costs to 2050 (IEA, 2013).

    42.
    Vilela, T. et al. A better Amazon road network for people and the environment. Proc. Natl Acad. Sci. USA 117, 7095–7102 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    43.
    Challender, D. W. S., Harrop, S. R. & MacMillan, D. C. Towards informed and multi-faceted wildlife trade interventions. Glob. Ecol. Conserv. 3, 129–148 (2015).
    Article  Google Scholar 

    44.
    Papworth, S., Milner-Gulland, E. J. & Slocombe, K. Hunted woolly monkeys (Lagothrix poeppigii) show threat-sensitive responses to human presence. PLoS ONE 8, e62000 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    Toledo-Aceves, T., Garcia-Franco, J. G. & Lopez-Barrera, F. Bromeliad rain: an opportunity for cloud forest management. Ecol. Manage. 329, 129–136 (2014).
    Article  Google Scholar 

    46.
    Challender, D. W. S. et al. Mischaracterization of wildlife trade threat. Science (30 October 2019).

    47.
    Leung, B. et al. Clustered versus catastrophic global vertebrate declines. Nature https://doi.org/10.1038/s41586-020-2920-6 (2020).

    48.
    Tierney, M. et al. Use it or lose it: measuring trends in wild species subject to substantial use. Oryx 48, 420–429 (2014).
    Article  Google Scholar 

    49.
    Jachmann, H. Monitoring law-enforcement performance in nine protected areas in Ghana. Biol. Conserv. 141, 89–99 (2008).
    Article  Google Scholar 

    50.
    Cardador, L., Tella, J. L., Anadón, J. D., Abellán, P. & Carrete, M. The European trade ban on wild birds reduced invasion risks. Conserv. Lett. https://doi.org/10.1111/conl.12631 (2019).

    51.
    Grames, E. M., Stillman, A. N., Tingley, M. W. & Elphick, C. S. An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks. Methods Ecol. Evol. 10, 1645–1654 (2019).
    Article  Google Scholar 

    52.
    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

    53.
    Espinosa-Andrade, S. R. Road development, bushmeat extraction and jaguar conservation in Yasuni Biosphere Reserve Ecuador PhD Thesis, Univ. Florida (2012); https://ufdc.ufl.edu/UFE0044108/00001/citation

    54.
    Schoppe, S., Matillano, J., Cervancia, M. & Acosta, D. Conservation needs of the critically endangered Philippine forest turtle, Siebenrockiella leytensis, in Palawan, Philippines. Chelonian Conserv. Biol. 9, 145–153 (2010).
    Article  Google Scholar 

    55.
    Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).
    Article  Google Scholar 

    56.
    Slavenko, A., Tallowin, O. J. S., Itescu, Y., Raia, P. & Meiri, S. Late Quaternary reptile extinctions: size matters, insularity dominates. Glob. Ecol. Biogeogr. 25, 1308–1320 (2016).
    Article  Google Scholar 

    57.
    Aquino, R. & Calle, A. Evaluation of the conservation status of the game mammals: a comparative model in communities of the Pacaya Samira National Reserve (Loreto, Peru). Rev. Peru. Biol. 10, 163–174 (2003).
    Google Scholar 

    58.
    Aquino, R., Lopez, L., Garcia, G., Charpentier, E. & Arevalo, I. Conservation status and threats to atelids in the northeastern Peruvian Amazon. Primate Conserv. 30, 21–29 (2016).
    Google Scholar 

    59.
    Carrillo, E., Wong, G. & Cuarón, A. D. Monitoring mammal populations in Costa Rican protected areas under different hunting restrictions. Conserv. Biol. 14, 1580–1591 (2000).
    Article  Google Scholar 

    60.
    Cronin, D. T. The Impact of Bushmeat Hunting on the Primates of Bioko Island, Equatorial Guinea (Drexel Univ., 2013).

    61.
    Dasgupta, S. & Hilaluddin Differential effects of hunting on populations of hornbills and imperial pigeons in the rainforests of the eastern Indian Himalaya. Indian Forester 138, 902–909 (2012).
    Google Scholar 

    62.
    De Thoisy, B., Renoux, F. & Julliot, C. Hunting in northern French Guiana and its impact on primate communities. Oryx 39, 149–157 (2005).
    Article  Google Scholar 

    63.
    Fay, J. M. An elephant (Loxodonta africana) survey using dung counts in the forests of the Central African Republic. J. Trop. Ecol. 7, 25–36 (1991).
    Article  Google Scholar 

    64.
    Gamble, T. & Simons, A. M. Comparison of harvested and nonharvested painted turtle populations. Wildl. Soc. Bull. 32, 1269–1277 (2004).
    Article  Google Scholar 

    65.
    Gonzalez, J. A. Harvesting, local trade, and conservation of parrots in the northeastern Peruvian Amazon. Biol. Conserv. 114, 437–446 (2003).
    Article  Google Scholar 

    66.
    Gray, T. N. E. & Phan, C. Habitat preferences and activity patterns of the larger mammal community in Phnom Prich Wildlife Sanctuary, Cambodia. Raffles Bull. Zool. 59, 311–318 (2011).
    Google Scholar 

    67.
    Hall, J. S. et al. A survey of elephants (Loxodonta africana) in the Kahuzi-Biega National Park lowland sector and adjacent forest in eastern Zaire. Afr. J. Ecol. 35, 213–223 (1997).
    Article  Google Scholar 

    68.
    Klemens, M. W. & Moll, D. An assessment of the effects of commercial exploitation on the pancake tortoise, Malacochersus tornieri, in Tanzania. Chelonian Conserv. Biol. 1, 197–206 (1995).
    Google Scholar 

    69.
    Kümpel, N. F., Milner-Gulland, E. J., Rowcliffe, J. M. & Cowlishaw, G. Impact of gun-hunting on diurnal primates in continental Equatorial Guinea. Int. J. Primatol. 29, 1065–1082 (2008).
    Article  Google Scholar 

    70.
    Magige, F. J., Holmern, T., Stokke, S., Mlingwa, C. & Røskaft, E. Does illegal hunting affect density and behaviour of African grassland birds? A case study on ostrich (Struthio camelus). Biodivers. Conserv. 18, 1361–1373 (2009).
    Article  Google Scholar 

    71.
    Maldonado, A. M. & Peck, M. R. Research and in situ conservation of owl monkeys enhances environmental law enforcement at the Colombian–Peruvian border. Am. J. Primatol. 76, 658–669 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    72.
    Maldonado, A. M., Nijman, V. & Bearder, S. K. Trade in night monkeys Aotus spp. in the Brazil–Colombia–Peru tri-border area: international wildlife trade regulations are ineffectively enforced. Endanger. Species Res. 9, 143–149 (2009).
    Article  Google Scholar 

    73.
    Muchaal, P. K. & Ngandjui, G. Impact of village hunting on wildlife populations in the Western Dja Reserve, Cameroon. Conserv. Biol. 13, 385–396 (1999).
    Article  Google Scholar 

    74.
    Nuñez-iturril, G. & Howe, H. F. Bushmeat and the fate of trees with seeds dispersed by large primates in a lowland rain forest in western Amazonia. Biotropica 39, 348–354 (2007).
    Article  Google Scholar 

    75.
    O’Brien, S. et al. Decline of the Madagascar radiated tortoise Geochelone radiata due to overexploitation. Oryx 37, 338–343 (2003).
    Google Scholar 

    76.
    Patrick, D. A., Shirk, P., Vonesh, J. R., Harper, E. B. & Howell, K. M. Abundance and roosting ecology of chameleons in the Eastern Arc Mountains of Tanzania and potential effects of harvesting. Herpetol. Conserv. Biol. 6, 422–431 (2011).
    Google Scholar 

    77.
    Poulsen, J. R., Clark, C. J. & Bolker, B. M. Decoupling the effect of logging and hunting on an Afrotropical animal community. Ecol. Appl. 21, 1819–1836 (2011).
    CAS  PubMed  Article  Google Scholar 

    78.
    Remis, M. J. & Kpanou, J. B. Primate and ungulate abundance in response to multi-use zoning and human extractive activities in a central African reserve. Afr. J. Ecol. 49, 70–80 (2010).
    Article  Google Scholar 

    79.
    Rist, J., Milner-Gulland, E. J., Cowlishaw, G. & Rowcliffe, J. M. The importance of hunting and habitat in determining the abundance of tropical forest species in Equatorial Guinea. Biotropica 41, 700–710 (2009).
    Article  Google Scholar 

    80.
    Rovero, F., Mtui, A. S., Kitegile, A. S. & Nielsen, M. R. Hunting or habitat degradation? Decline of primate populations in Udzungwa Mountains, Tanzania: an analysis of threats. Biol. Conserv. 146, 89–96 (2012).
    Article  Google Scholar 

    81.
    Segura, A. & Acevedo, P. The importance of protected and unprotected areas for the Mediterranean spur-thighed tortoise demography in northwest Morocco. Amphib. Reptilia https://doi.org/10.6084/m9.figshare.7751783.v1 (2019).

    82.
    Sung, Y.-H., Karraker, N. E. & Hau, B. C. H. Demographic evidence of illegal harvesting of an endangered Asian turtle. Conserv. Biol. 27, 1421–1428 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    83.
    Topp-Jorgensen, E., Nielsen, M. R., Marshall, A. R. & Pedersen, U. Relative densities of mammals in response to different levels of bushmeat hunting in the Udzungwa Mountains, Tanzania. Trop. Conserv. Sci. 2, 70–87 (2009).
    Article  Google Scholar 

    84.
    Yasuoka, H. The sustainability of duiker (Cephalophus spp.) hunting for the Baka hunter–gatherers in southeastern Cameroon. Afr. Study Monogr. 33, 95–120 (2006).
    Google Scholar 

    85.
    Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, 2020).

    86.
    QGIS Development Team QGIS Geographic Information System v.3.12.0 (Open Source Geospatial Foundation Project, 2020).

    87.
    User Manual for the World Database on Protected Areas and World Database on Other Effective Area-Based Conservation Measures: 1.6 (UNEP-WCMC, 2019).

    88.
    Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
    Article  Google Scholar 

    89.
    Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    90.
    Lajeunesse, M. J. in Handbook of Meta-analysis in Ecology and Evolution (eds Koricheva, J. et al.) Ch. 13 (Princeton Univ. Press, 2013); https://doi.org/10.23943/princeton/9780691137285.003.0013

    91.
    Rubin, D. B. & Schenker, N. Multiple imputation in health-care databases: an overview and some applications. Stat. Med. 10, 585–598 (1991).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    92.
    Sweeting, M. J., Sutton, A. J. & Lambert, P. C. What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Stat. Med. 23, 1351–1375 (2004).
    PubMed  Article  PubMed Central  Google Scholar 

    93.
    Viechtbauer, W. Conducting meta-analyses in R with the metafor. J. Stat. Softw. 36, 1–48 (2010).
    Article  Google Scholar 

    94.
    Hothorn, T. et al. multcomp: Simultaneous inference in general parametric models. R package version 1.4-15 (2016).

    95.
    Cochran, W. G. The combination of estimates from different experiments. Biometrics 10, 101–129 (1954).
    Article  Google Scholar 

    96.
    Hoaglin, D. C. Misunderstandings about Q and ‘Cochran’s Q test’ in meta-analysis. Stat. Med. 35, 485–495 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    97.
    McFadden, D. Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments Discussion Paper No. 474 (Cowles Foundation, 1977).

    98.
    Geary, R. C. The frequency distribution of the quotient of two normal variates. J. R. Stat. Soc. 93, 442–446 (1930).
    Article  Google Scholar 

    99.
    Lajeunesse, M. J. Bias and correction for the log response ratio in ecological meta-analysis. Ecol. Soc. Am. 96, 2056–2063 (2015).
    Google Scholar 

    100.
    Rosenthal, R. The file drawer problem and tolerance for null results. Psychol. Bull. 86, 638–641 (1979).
    Article  Google Scholar 

    101.
    Rosenberg, M. S. The file-drawer problem revisited: a general weighted method for calculating fail-safe number in meta-analysis. Evolution 59, 464–468 (2005).
    PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Reduced nest development of reared Bombus terrestris within apiary dense human-modified landscapes

    1.
    Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120, 321–326. https://doi.org/10.1111/j.1600-0706.2010.18644.x (2011).
    Article  Google Scholar 
    2.
    Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313. https://doi.org/10.1098/rspb.2006.3721 (2007).
    Article  Google Scholar 

    3.
    Kremen, C., Williams, N. M. & Thorp, R. W. Crop pollination from native bees at risk from agricultural intensification. Proc. Natl. Acad. Sci. U.S.A. 99, 16812–16816. https://doi.org/10.1073/pnas.262413599 (2002).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    4.
    Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353. https://doi.org/10.1016/j.tree.2010.01.007 (2010).
    Article  PubMed  Google Scholar 

    5.
    Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes—Eight hypotheses. Biol. Rev. 87, 661–685. https://doi.org/10.1111/j.1469-185X.2011.00216.x (2012).
    Article  PubMed  Google Scholar 

    6.
    Winfree, R., Aguilar, R., Vazquez, D. P., LeBuhn, G. & Aizen, M. A. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90, 2068–2076. https://doi.org/10.1890/08-1245.1 (2009).
    Article  PubMed  Google Scholar 

    7.
    Isaacs, R. et al. Integrated crop pollination: Combining strategies to ensure stable and sustainable yields of pollination-dependent crops. Basic Appl. Ecol. 22, 44–60. https://doi.org/10.1016/j.baae.2017.07.003 (2017).
    Article  Google Scholar 

    8.
    Steffan-Dewenter, I. & Tscharntke, T. Resource overlap and possible competition between honey bees and wild bees in central Europe. Oecologia 122, 288–296. https://doi.org/10.1007/s004420050034 (2000).
    ADS  CAS  Article  Google Scholar 

    9.
    Paini, D. R. & Roberts, J. D. Commercial honey bees (Apis mellifera) reduce the fecundity of an Australian native bee (Hylaeus alcyoneus). Biol. Cons. 123, 103–112. https://doi.org/10.1016/j.biocon.2004.11.001 (2005).
    Article  Google Scholar 

    10.
    Schaffer, W. M. et al. Competition for nectar between introduced honey bees and native North American bees and ants. Ecology 64, 564–577. https://doi.org/10.2307/1939976 (1983).
    Article  Google Scholar 

    11.
    Dupont, Y. L., Hansen, D. M., Valido, A. & Olesen, J. M. Impact of introduced honey bees on native pollination interactions of the endemic Echium wildpretii (Boraginaceae) on Tenerife, Canary Islands. Biol. Cons. 118, 301–311. https://doi.org/10.1016/j.biocon.2003.09.010 (2004).
    Article  Google Scholar 

    12.
    Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611. https://doi.org/10.1126/science.1230200 (2013).
    ADS  CAS  Article  PubMed  Google Scholar 

    13.
    Thomson, D. M. Local bumble bee decline linked to recovery of honey bees, drought effects on floral resources. Ecol. Lett. 19, 1247–1255. https://doi.org/10.1111/ele.12659 (2016).
    Article  PubMed  Google Scholar 

    14.
    Thomson, D. Competitive interactions between the invasive European honey bee and native bumble bees. Ecology 85, 458–470. https://doi.org/10.1890/02-0626 (2004).
    Article  Google Scholar 

    15.
    Goulson, D. & Sparrow, K. Evidence for competition between honeybees and bumblebees; effects on bumblebee worker size. J. Insect. Conserv. 13, 177–181. https://doi.org/10.1007/s10841-008-9140-y (2009).
    Article  Google Scholar 

    16.
    Paini, D. R. Impact of the introduced honey bee (Apis mellifera) (Hymenoptera : Apidae) on native bees: A review. Austral. Ecol. 29, 399–407. https://doi.org/10.1111/j.1442-9993.2004.01376.x (2004).
    Article  Google Scholar 

    17.
    Gross, C. L. The effect of introduced honeybees on native bee visitation and fruit-set in Dillwynia juniperina (Fabaceae) in a fragmented ecosystem. Biol. Cons. 102, 89–95. https://doi.org/10.1016/s0006-3207(01)00088-x (2001).
    Article  Google Scholar 

    18.
    Nielsen, A., Reitan, T., Rinvoll, A. W. & Brysting, A. K. Effects of competition and climate on a crop pollinator community. Agric. Ecosyst. Environ. 246, 253–260. https://doi.org/10.1016/j.agee.2017.06.006 (2017).
    Article  Google Scholar 

    19.
    Lindström, S. A. M., Herbertssön, L., Rundlof, M., Bommarco, R. & Smith, H. G. Experimental evidence that honeybees depress wild insect densities in a flowering crop. Proc. R. Soc. B Biol. Sci. 283, 8. https://doi.org/10.1098/rspb.2016.1641 (2016).
    Article  Google Scholar 

    20.
    Magrach, A., González-Varo, J. P., Boiffier, M., Vilà, M. & Bartomeus, I. Honeybee spillover reshuffles pollinator diets and affects plant reproductive success. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-017-0249-9 (2017).
    Article  PubMed  Google Scholar 

    21.
    González-Varo, J. P. & Vilà, M. Spillover of managed honeybees from mass-flowering crops into natural habitats. Biol. Conserv. 212, 376–382. https://doi.org/10.1016/j.biocon.2017.06.018 (2017).
    Article  Google Scholar 

    22.
    Begon, M., Harper, J. L. & Townsend, C. R. Ecology: Individuals, Populations, and Communities 3rd edn. (Blackwell Science Ltd, Hoboken, 1996).
    Google Scholar 

    23.
    United Nations. (United Nations, Department of Economic and Social Affairs, Population Division, New York, 2012).

    24.
    Goulson, D., Nicholls, E., Botias, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1435. https://doi.org/10.1126/science.1255957 (2015).
    CAS  Article  Google Scholar 

    25.
    Scheper, J. et al. Local and landscape-level floral resources explain effects of wildflower strips on wild bees across four European countries. J. Appl. Ecol. 52, 1165–1175. https://doi.org/10.1111/1365-2664.12479 (2015).
    Article  Google Scholar 

    26.
    McCune, F., Normandin, E., Mazerolle, M. J. & Fournier, V. Response of wild bee communities to beekeeping, urbanization, and flower availability. Urban Ecosyst. https://doi.org/10.1007/s11252-019-00909-y (2019).
    Article  Google Scholar 

    27.
    Samuelson, A. E., Gill, R. J., Brown, M. J. F. & Leadbeater, E. Lower bumblebee colony reproductive success in agricultural compared with urban environments. Proc. R. Soc. B Biol. Sci. 285, 9. https://doi.org/10.1098/rspb.2018.0807 (2018).
    Article  Google Scholar 

    28.
    Steffan-Dewenter, I. & Kuhn, A. Honeybee foraging in differentially structured landscapes. Proc. R. Soc. B Biol. Sci. 270, 569–575. https://doi.org/10.1098/rspb.2002.2292 (2003).
    Article  Google Scholar 

    29.
    Couvillon, M. J., Schurch, R. & Ratnieks, F. L. W. Dancing bees communicate a foraging preference for rural lands in high-level agri-environment schemes. Curr. Biol. 24, 1212–1215. https://doi.org/10.1016/j.cub.2014.03.072 (2014).
    CAS  Article  PubMed  Google Scholar 

    30.
    Bänsch, S., Tscharntke, T., Ratnieks, F. L. W., Härtel, S. & Westphal, C. Foraging of honey bees in agricultural landscapes with changing patterns of flower resources. Agric. Ecosyst. Environ. 291, 106792. https://doi.org/10.1016/j.agee.2019.106792 (2020).
    Article  Google Scholar 

    31.
    Walther-Hellwig, K. & Frankl, R. Foraging distances of Bombus muscorum, Bombus lapidarius, and Bombus terrestris (Hymenoptera, Apidae). J. Insect Behav. 13, 239–246. https://doi.org/10.1023/A:1007740315207 (2000).
    Article  Google Scholar 

    32.
    Chauzat, M. P. et al. Demographics of the European apicultural industry. PLoS ONE 8, e79018. https://doi.org/10.1371/journal.pone.0079018 (2013).
    ADS  Article  PubMed  PubMed Central  Google Scholar 

    33.
    Stanley, D. A., Gunning, D. & Stout, J. C. Pollinators and pollination of oilseed rape crops (Brassica napus L.) in Ireland: ecological and economic incentives for pollinator conservation. J. Insect Conserv. 17, 1181–1189. https://doi.org/10.1007/s10841-013-9599-z (2013).
    Article  Google Scholar 

    34.
    Westphal, C. et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 78, 653–671. https://doi.org/10.1890/07-1292.1 (2008).
    Article  Google Scholar 

    35.
    Lebuhn, G., Droege, S., Connor, E., Gemmill-Herren, B. & Azzu, N. in Guidance for practioners 64 pp. (FAO, Rome, 2016).

    36.
    De Saeger, S. et al. (ed Rapporten van het Instituut voor Natuur- en Bosonderzoek 2016) (Instituut voor Natuur- en Bosonderzoek, Brussel, 2016).

    37.
    3QGIS_Development_Team. QGIS Geographic Information System, 2018).

    38.
    Oksanen, J. et al. Community Ecology Package ‘Vegan’. (2016). https://github.com/vegandevs/vegan.

    39.
    Meeus, I., de Graaf, D. C., Jans, K. & Smagghe, G. Multiplex PCR detection of slowly-evolving trypanosomatids and neogregarines in bumblebees using broad-range primers. J. Appl. Microbiol. 109, 107–115. https://doi.org/10.1111/j.1365-2672.2009.04635.x (2010).
    CAS  Article  PubMed  Google Scholar 

    40.
    Ravoet, J. et al. Widespread occurrence of honey bee pathogens in solitary bees. J. Invertebr. Pathol. 122, 55–58. https://doi.org/10.1016/j.jip.2014.08.007 (2014).
    Article  PubMed  Google Scholar 

    41.
    De Smet, L. et al. BeeDoctor, a versatile MLPA-based diagnostic tool for screening bee viruses. PLoS ONE 7, e47953. https://doi.org/10.1371/journal.pone.0047953 (2012).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    42.
    Parmentier, L. et al. Commercial bumblebee hives to assess an anthropogenic environment for pollinator support: A case study in the region of Ghent (Belgium). Environ. Monit. Assess. 186, 2357–2367. https://doi.org/10.1007/s10661-013-3543-2 (2014).
    CAS  Article  PubMed  Google Scholar 

    43.
    Rundlöf, M. et al. Seed coating with a neonicotinoid insecticide negatively affects wild bees. Nature 521, 77–80. https://doi.org/10.1038/nature14420 (2015).
    ADS  CAS  Article  PubMed  Google Scholar 

    44.
    Goulson, D. Bumblebees: Their Behaviour and Ecology (Oxford University Press, Oxford, 2003).
    Google Scholar 

    45.
    Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Article  Google Scholar 

    46.
    Hedges, L. & Olkin, I. Statistical Methods for Meta-Analysis (Academic Press, Cambridge, 1985).
    Google Scholar 

    47.
    DeBach, P. The competitive displacement and coexistence principles. Annu. Rev. Entomol. 11, 183–212. https://doi.org/10.1146/annurev.en.11.010166.001151 (1966).
    Article  Google Scholar 

    48.
    Balfour, N. J., Gandy, S. & Ratnieks, F. L. W. Exploitative competition alters bee foraging and flower choice. Behav. Ecol. Sociobiol. 69, 1731–1738. https://doi.org/10.1007/s00265-015-1985-y (2015).
    Article  Google Scholar 

    49.
    Herbertssön, L., Lindström, S. A. M., Rundlof, M., Bornmarco, R. & Smith, H. G. Competition between managed honeybees and wild bumblebees depends on landscape context. Basic Appl. Ecol. 17, 609–616. https://doi.org/10.1016/j.baae.2016.05.001 (2016).
    Article  Google Scholar 

    50.
    Ropars, L., Dajoz, I., Fontaine, C., Muratet, A. & Geslin, B. Wild pollinator activity negatively related to honey bee colony densities in urban context. PLoS ONE 14, 16. https://doi.org/10.1371/journal.pone.0222316 (2019).
    CAS  Article  Google Scholar 

    51.
    Ellis, C., Park, K. J., Whitehorn, P., David, A. & Goulson, D. The neonicotinoid insecticide Thiacloprid impacts upon bumblebee colony development under field conditions. Environ. Sci. Technol. 51, 1727–1732. https://doi.org/10.1021/acs.est.6b04791 (2017).
    ADS  CAS  Article  PubMed  Google Scholar 

    52.
    Geslin, B., Gauzens, B., Thebault, E. & Dajoz, I. Plant pollinator networks along a gradient of urbanisation. PLoS ONE 8, e63421 (2013).
    ADS  Article  Google Scholar 

    53.
    Neame, L. A., Griswold, T. & Elle, E. Pollinator nesting guilds respond differently to urban habitat fragmentation in an oak-savannah ecosystem. Insect Conserv. Divers. 6, 57–66 (2013).
    Article  Google Scholar 

    54.
    Glaum, P., Simao, M.-C., Vaidya, C., Fitch, G. & Iulinao, B. Big city Bombus: Using natural history and land-use history to find significant environmental drivers in bumble-bee declines in urban development. R. Soc. Open Sci. 4, 170156 (2017).
    ADS  Article  Google Scholar 

    55.
    Normandin, E., Vereecken, N. J., Buddle, C. M. & Fournier, V. Taxonomic and functional trait diversity of wild bees in two urban settings. PeerJ 5, e3051 (2017).
    Article  Google Scholar 

    56.
    Moerman, R., Vanderplanck, M., Fournier, D., Jacquemart, A. L. & Michez, D. Pollen nutrients better explain bumblebee colony development than pollen diversity. Insect Conserv. Divers. 10, 171–179. https://doi.org/10.1111/icad.12213 (2017).
    Article  Google Scholar  More

  • in

    Bridgehead effect and multiple introductions shape the global invasion history of a termite

    1.
    Lewis, S. L. & Maslin, M. A. Defining the Anthropocene. Nature 519, 171–180 (2015).
    CAS  PubMed  Article  Google Scholar 
    2.
    Capinha, C., Essl, F., Seebens, H., Moser, D. & Miguel Pereira, H. The dispersal of alien species redefines biogeography in the Anthropocene. Science 348, 1248–1251 (2015).
    CAS  PubMed  Article  Google Scholar 

    3.
    Hulme, P. E. Trade, transport and trouble: managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18 (2009).
    Article  Google Scholar 

    4.
    Meyerson, L. A. & Mooney, H. A. Invasive alien species in an era of globalization. Front. Ecol. Environ. 5, 199–208 (2007).
    Article  Google Scholar 

    5.
    Banks, N. C., Paini, D. R., Bayliss, K. L. & Hodda, M. The role of global trade and transport network topology in the human-mediated dispersal of alien species. Ecol. Lett. 18, 188–199 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    6.
    Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    7.
    Simberloff, D. et al. Impacts of biological invasions: what’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    8.
    Bellard, C., Cassey, P. & Blackburn, T. M. Alien species as a driver of recent extinctions. Biol. Lett. 12, 20150623 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    9.
    Schrieber, K. & Lachmuth, S. The genetic paradox of invasions revisited: the potential role of inbreeding  environment interactions in invasion success. Biol. Rev. 92, 939–952 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    10.
    Allendorf, F. W. & Lundquist, L. L. Introduction: population biology, evolution, and control of invasive species. Conserv. Biol. 17, 24–30 (2003).
    Article  Google Scholar 

    11.
    Estoup, A. et al. Is there a genetic paradox of biological invasion? Annu. Rev. Ecol. Evol. Syst. 47, 51–72 (2016).
    Article  Google Scholar 

    12.
    Roman, J. & Darling, J. A. Paradox lost: genetic diversity and the success of aquatic invasions. Trends Ecol. Evol. 22, 454–464 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    13.
    Uller, T. & Leimu, R. Founder events predict changes in genetic diversity during human-mediated range expansions. Glob. Change Biol. 17, 3478–3485 (2011).
    Article  Google Scholar 

    14.
    Bossdorf, O. et al. Phenotypic and genetic differentiation between native and introduced plant populations. Oecologia 144, 1–11 (2005).
    PubMed  Article  PubMed Central  Google Scholar 

    15.
    Dlugosch, K. M. & Parker, I. M. Founding events in species invasions: genetic variation, adaptive evolution, and the role of multiple introductions. Mol. Ecol. 17, 431–449 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Hufbauer, R. A. et al. Anthropogenically induced adaptation to invade (AIAI): contemporary adaptation to human-altered habitats within the native range can promote invasions. Evol. Appl. 5, 89–101 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Facon, B. et al. A general eco-evolutionary framework for understanding bioinvasions. Trends Ecol. Evol. 21, 130–135 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    18.
    Facon, B., Pointier, J.-P., Jarne, P., Sarda, V. & David, P. High genetic variance in life-history strategies within invasive populations by way of multiple introductions. Curr. Biol. 18, 363–367 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Lombaert, E. et al. Bridgehead effect in the worldwide invasion of the biocontrol Harlequin ladybird. PLoS ONE 5, e9743 (2010).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    20.
    Ascunce, M. S. et al. Global invasion history of the fire ant Solenopsis invicta. Science 331, 1066–1068 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    Bertelsmeier, C. et al. Recurrent bridgehead effects accelerate global alien ant spread. Proc. Natl Acad. Sci. USA 115, 5486 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Bertelsmeier, C. & Keller, L. Bridgehead effects and role of adaptive evolution in invasive populations. Trends Ecol. Evol. 33, 527–534 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    23.
    Cristescu, M. E. Genetic reconstructions of invasion history. Mol. Ecol. 24, 2212–2225 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    24.
    Estoup, A. & Guillemaud, T. Reconstructing routes of invasion using genetic data: why, how and so what? Mol. Ecol. 19, 4113–4130 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    25.
    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 Vol. 12 (Invasive Species Specialist Group, 2000).

    26.
    Wang, J. & Grace, J. K. Current status of Coptotermes Wasmann (Isoptera: Rhinotermitidae) in China, Japan, Australia and the American Pacific. Sociobiology 33, 295–305 (1999).
    Google Scholar 

    27.
    Evans, T. A., Forschler, B. T. & Grace, J. K. Biology of invasive termites: a worldwide review. Annu. Rev. Entomol. 58, 455–474 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Shiraki, T. On the Japanese termites. Transcr. Entomol., Jpn. 2, 229–242 (1909).
    Google Scholar 

    29.
    Kistner, D. H. A new genus and species of termitophilous Aleocharinae from mainland China associated with Coptotermes formosanus and its zoogeographical significance (Coleoptera: Staphylinidae). Sociobiology 10, 93–104 (1985).
    Google Scholar 

    30.
    Maruyama, M. & Iwata, R. Two new termitophiles of the tribe Termitohospitini (Coleoptera: Staphylinidae: Aleocharinae) associated with Coptotermes formosanus (Isoptera: Rhinotermitidae). Can. Entomologist 134, 419–432 (2002).
    Article  Google Scholar 

    31.
    Maruyama, M., Kanao, T. & Iwata, R. Discovery of two Aleocharine Staphylinid species (Coleoptera) associated with Coptotermes formosanus (Isoptera: Rhinotermitidae) from Central Japan, with a review of the possible natural distribution of C. formosanus in Japan and surrounding countries. Sociobiology 59, 605–616 (2014).
    Google Scholar 

    32.
    Li, G. in Fauna Sinica: Insecta (eds Huang, F. et al.) 299–341 (Science Press, 2000).

    33.
    Chouvenc, T. et al. Revisiting Coptotermes (Isoptera: Rhinotermitidae): a global taxonomic road map for species validity and distribution of an economically important subterranean termite genus. Syst. Entomol. 41, 299–306 (2016).
    Article  Google Scholar 

    34.
    Yeap, B.-K., Othman, A. S. & Lee, C.-Y. Molecular systematics of Coptotermes (Isoptera: Rhinotermitidae) from East Asia and Australia. Ann. Entomol. Soc. Am. 102, 1077–1090 (2009).
    Article  Google Scholar 

    35.
    Lee, T. R. C., Cameron, S. L., Evans, T. A., Ho, S. Y. W. & Lo, N. The origins and radiation of Australian Coptotermes termites: from rainforest to desert dwellers. Mol. Phylogen. Evol. 82, 234–244 (2015).
    Article  Google Scholar 

    36.
    Austin, J. W. et al. Genetic evidence for two introductions of the Formosan subterranean termite, Coptotermes Formosanus (Isoptera: Rhinotermitidae), to the United States. Fla. Entomol. 89, 183–193 (2006).
    CAS  Article  Google Scholar 

    37.
    Li, H.-F., Ye, W., Su, N.-Y. & Kanzaki, N. Phylogeography of Coptotermes Gestroi and Coptotermes Formosanus (Isoptera: Rhinotermitidae) in Taiwan. Ann. Entomol. Soc. Am. 102, 684–693 (2009).
    Article  Google Scholar 

    38.
    Fang, R., Huang, L. & Zhong, J. H. Surprising low levels of genetic diversity of Formosan subterranean termites in South China as revealed by the COII gene (Isoptera: Rhinotermitidae). Sociobiology 51, 1–20 (2008).
    Google Scholar 

    39.
    Tokuda, G., Isagawa, H. & Sugio, K. The complete mitogenome of the Formosan termite, Coptotermes formosanus Shiraki. Insectes Soc. 59, 17–24 (2012).
    Article  Google Scholar 

    40.
    Vargo, E. L., Husseneder, C. & Grace, J. K. Colony and population genetic structure of the Formosan subterranean termite, Coptotermes formosanus, in Japan. Mol. Ecol. 12, 2599–2608 (2003).
    CAS  PubMed  Article  Google Scholar 

    41.
    Broughton, R. E. & Grace, J. K. Lack of mitochondrial DNA variation in an introduced population of the Formosan subterranean termite (Isoptera: Rhinotermitidae). Sociobiology 24, 121–126 (1994).
    Google Scholar 

    42.
    Korman, A. K. & Pashley, D. P. Genetic comparisons among U.S. populations of Formosan subterranean termites. Sociobiology 19, 41–50 (1991).
    Google Scholar 

    43.
    Wang, J. & Grace, J. K. Genetic relationship of Coptotermes formosanus (Isoptera: Rhinotermitidae) populations from the United States and China. Sociobiology 36, 7–19 (2000).
    Google Scholar 

    44.
    Vargo, E. L., Husseneder, C., Woodson, D., Waldvogel, M. G. & Grace, J. K. Genetic analysis of colony and population structure of three introduced populations of the Formosan subterranean termite (Isoptera: Rhinotermitidae) in the continental United States. Environ. Entomol. 35, 151–166 (2006).
    Article  Google Scholar 

    45.
    Gentz, M. C., Rubinoff, D. & Grace, J. K. Phylogenetic analysis of subterranean termites (Coptotermes spp., Isoptera: Rhinotermitidae) indicates the origins of Hawaiian and North American invasions: potential implications for invasion biology. Proc. Hawaii. Entomol. Soc. 40, 1–9 (2008).
    Google Scholar 

    46.
    Husseneder, C. et al. Genetic diversity and colony breeding structure in native and introduced ranges of the Formosan subterranean termite, Coptotermes formosanus. Biol. Invasions 14, 419–437 (2012).
    Article  Google Scholar 

    47.
    Haverty, M. I., Nelson, L. J. & Page, M. Cuticular hydrocarbons of four populations of Coptotermes formosanus Shiraki in the United States. J. Chem. Ecol. 16, 1635–1647 (1990).
    CAS  PubMed  Article  Google Scholar 

    48.
    Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    49.
    Swezey, O. H. Notes and exhibitions. Proc. Hawaii. Entomol. Soc. 3 (1914).

    50.
    Swezey, O. H. Entomological notes. Proc. Hawaii. Entomol. Soc. 3 (1915).

    51.
    Su, N.-Y. & Tamashiro, M. An Overview of the Formosan Subterranean Termite (Isoptera: Rhinotermitidae) in the World 3–15 (University of Hawaii, College of Tropical Agriculture and Human Resources research extension series, 1987).

    52.
    Chambers, D. M., Zungoli, P. A. & Hill, H. S. J. Distribution and habitats of the Formosan subterranean termite (Isoptera: Rhinotermitidae) in South Carolina. J. Econ. Entomol. 81, 1611–1619 (1988).
    Article  Google Scholar 

    53.
    Beal, R. H. Formosan invader. Pest Control 35, 13–17 (1967).
    Google Scholar 

    54.
    Spink, W. The Formosan subterranean termite in Louisiana. La. State Univeristy Circ. 89, 12 (1967).
    Google Scholar 

    55.
    Shi, M.-M., Michalski, S. G., Welk, E., Chen, X.-Y. & Durka, W. Phylogeography of a widespread Asian subtropical tree: genetic east–west differentiation and climate envelope modelling suggest multiple glacial refugia. J. Biogeogr. 41, 1710–1720 (2014).
    Article  Google Scholar 

    56.
    Ye, Z. et al. Phylogeography of a semi-aquatic bug, Microvelia horvathi (Hemiptera: Veliidae): an evaluation of historical, geographical and ecological factors. Sci. Rep. 6, 21932 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    57.
    Qiu, Y.-X., Fu, C.-X. & Comes, H. P. Plant molecular phylogeography in China and adjacent regions: tracing the genetic imprints of Quaternary climate and environmental change in the world’s most diverse temperate flora. Mol. Phylogen. Evol. 59, 225–244 (2011).
    Article  Google Scholar 

    58.
    Lemopoulos, A. et al. Comparing RADseq and microsatellites for estimating genetic diversity and relatedness – Implications for brown trout conservation. Ecol. Evol. 9, 2106–2120 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    59.
    Fischer, M. C. et al. Estimating genomic diversity and population differentiation—an empirical comparison of microsatellite and SNP variation in Arabidopsis halleri. BMC Genom. 18, 69 (2017).
    Article  Google Scholar 

    60.
    Mori, H. The Formosan Subterranean Termite in Japan: its Distribution, Damage, and Current and Potential Control Measures 23–26 (University of Hawaii, College of Tropical Agriculture and Human Resources research extension series, 1987).

    61.
    Westphal, M. I., Browne, M., MacKinnon, K. & Noble, I. The link between international trade and the global distribution of invasive alien species. Biol. Invasions 10, 391–398 (2008).
    Article  Google Scholar 

    62.
    Floerl, O., Inglis, G. J., Dey, K. & Smith, A. The importance of transport hubs in stepping-stone invasions. J. Appl. Ecol. 46, 37–45 (2009).
    Article  Google Scholar 

    63.
    Nordyke, E. C. & Lee, R. K. C. Chinese in Hawai’i: a historical and demographic perspective. Hawaii. J. Hist. 23, 196–216 (1989).
    Google Scholar 

    64.
    Gay, F. J. A World Review of Introduced Species of Termites (CSIRO, 1967).

    65.
    Boyd, M. Oriental immigration: the experience of the Chinese, Japanese, and Filipino populations in the United States. Int. Migr. Rev. 5, 48–61 (1971).
    Article  Google Scholar 

    66.
    Matsumoto, Y. S. Okinawa migrants to Hawaii. Hawaii. J. Hist. 16, 125–133 (1982).
    Google Scholar 

    67.
    Javal, M. et al. Deciphering the worldwide invasion of the Asian long-horned beetle: a recurrent invasion process from the native area together with a bridgehead effect. Mol. Ecol. 28, 951–967 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    68.
    van Boheemen, L. A. et al. Multiple introductions, admixture and bridgehead invasion characterize the introduction history of Ambrosia artemisiifolia in Europe and Australia. Mol. Ecol. 26, 5421–5434 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    69.
    Lesieur, V. et al. The rapid spread of Leptoglossus occidentalis in Europe: a bridgehead invasion. J. Pest Sci. 92, 189–200 (2019).
    Article  Google Scholar 

    70.
    Correa, M. C. G. et al. European bridgehead effect in the worldwide invasion of the obscure mealybug. Biol. Invasions 21, 123–136 (2019).
    Article  Google Scholar 

    71.
    Sherpa, S. et al. Unravelling the invasion history of the Asian tiger mosquito in Europe. Mol. Ecol. 28, 2360–2377 (2019).
    PubMed  Article  Google Scholar 

    72.
    Yang, C.-C. et al. Propagule pressure and colony social organization are associated with the successful invasion and rapid range expansion of fire ants in China. Mol. Ecol. 21, 817–833 (2012).
    PubMed  Article  Google Scholar 

    73.
    Blumenfeld, A. J. & Vargo, E. L. Geography, opportunity and bridgeheads facilitate termite invasions to the United States. Biol. Invasions 22, 3269–3282 (2020).
    Article  Google Scholar 

    74.
    Barrett, S. C. H. & Charlesworth, D. Effects of a change in the level of inbreeding on the genetic load. Nature 352, 522–524 (1991).
    CAS  PubMed  Article  Google Scholar 

    75.
    Crnokrak, P. & Barrett, S. C. H. Perspective: purging the genetic load: a review of the experimental evidence. Evolution 56, 2347–2358 (2002).
    PubMed  Article  Google Scholar 

    76.
    Eyer, P. A. et al. Inbreeding tolerance as a pre-adapted trait for invasion success in the invasive ant Brachyponera chinensis. Mol. Ecol. 27, 4711–4724 (2018).
    PubMed  Google Scholar 

    77.
    Facon, B. et al. Inbreeding depression is purged in the invasive insect Harmonia axyridis. Curr. Biol. 21, 424–427 (2011).
    CAS  PubMed  Article  Google Scholar 

    78.
    Charlesworth, J. & Eyre-Walker, A. The other side of the nearly neutral theory, evidence of slightly advantageous back-mutations. Proc. Natl Acad. Sci. USA 104, 16992 (2007).
    CAS  PubMed  Article  Google Scholar 

    79.
    Lanfear, R., Calcott, B., Kainer, D., Mayer, C. & Stamatakis, A. Selecting optimal partitioning schemes for phylogenomic datasets. BMC Evol. Biol. 14, 82 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    80.
    Zepeda‐Paulo, F. et al. The invasion route for an insect pest species: the tobacco aphid in the New World. Mol. Ecol. 19, 4738–4752 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    81.
    Miller, N. et al. Multiple transatlantic introductions of the western corn rootworm. Science 310, 992 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    82.
    Kolbe, J. J. et al. Multiple sources, admixture, and genetic variation in introduced Anolis lizard populations. Conserv. Biol. 21, 1612–1625 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    83.
    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 

    84.
    Tsutsui, N. D., Suarez, A. V., Holway, D. A. & Case, T. J. Reduced genetic variation and the success of an invasive species. Proc. Natl Acad. Sci. USA 97, 5948–5953 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    85.
    Pearcy, M., Goodisman, M. A. & Keller, L. Sib mating without inbreeding in the longhorn crazy ant. Proc. R. Soc. B: Biol. Sci. 278, 2677–2681 (2011).
    Article  Google Scholar 

    86.
    Eyer, P.-A., Blumenfeld, A. J. & Vargo, E. L. Sexually antagonistic selection promotes genetic divergence between males and females in an ant. Proc. Natl Acad. Sci. USA 116, 24157–24163 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    87.
    Su, N.-Y., Scheffrahn, R. H. & Weissling, T. A new introduction of a subterranean termite, Coptotermes havilandi Holmgren (Isoptera: Rhinotermitidae) in Miami, Florida. Fla. Entomol. 80, 408–411 (1997).
    Article  Google Scholar 

    88.
    Chouvenc, T., Scheffrahn, R. H., Mullins, A. J. & Su, N.-Y. Flight phenology of two Coptotermes species (Isoptera: Rhinotermitidae) in southeastern Florida. J. Econ. Entomol. 110, 1693–1704 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    89.
    Chouvenc, T., Helmick, E. E. & Su, N.-Y. Hybridization of two major termite invaders as a consequence of human activity. PLoS ONE 10, https://doi.org/10.1371/journal.pone.0120745 (2015).

    90.
    Chouvenc, T., Sillam-Dussès, D. & Robert, A. Courtship behavior confusion in two subterranean termite species that evolved in allopatry (Blattodea, Rhinotermitidae, Coptotermes). J. Chem. Ecol. https://doi.org/10.1007/s10886-020-01178-2 (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    91.
    Perdereau, E. et al. Global genetic analysis reveals the putative native source of the invasive termite, Reticulitermes flavipes, in France. Mol. Ecol. 22, 1105–1119 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    92.
    Perdereau, E. et al. Relationship between invasion success and colony breeding structure in a subterranean termite. Mol. Ecol. 24, 2125–2142 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    93.
    Vargo, E. L. Diversity of termite breeding systems. Insects 10, 52 (2019).
    PubMed Central  Article  Google Scholar 

    94.
    Clement, J. L. & Bagneres, A. G. in Pheromone Communication in Social Insects. Ants, Wasps, Bees, and Termites (eds Vander Meer, R. K., Breed, M. D., Espelie, K. E. & Winston, M. L.) 126–155 (Westview Press, 1998).

    95.
    Perdereau, E., Dedeine, F., Christidès, J.-P. & Bagnères, A.-G. Variations in worker cuticular hydrocarbons and soldier isoprenoid defensive secretions within and among introduced and native populations of the subterranean termite, Reticulitermes flavipes. J. Chem. Ecol. 36, 1189–1198 (2010).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    96.
    Perdereau, E., Dedeine, F., Christidès, J. P., Dupont, S. & Bagnères, A. G. Competition between invasive and indigenous species: an insular case study of subterranean termites. Biol. Invasions 13, 1457–1470 (2010).
    Article  Google Scholar 

    97.
    Perdereau, E., Bagnères, A. G., Dupont, S. & Dedeine, F. High occurrence of colony fusion in a European population of the American termite Reticulitermes flavipes. Insectes Soc. 57, 393–402 (2010).
    Article  Google Scholar 

    98.
    Fournier, D. et al. Clonal reproduction by males and females in the little fire ant. Nature 435, 1230–1234 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    99.
    Thoms, E. M. et al. Bugs, baits, and bureaucracy: completing the first termite bait efficacy trials (quarterly replenishment of noviflumuron) initiated after adoption of Florida Rule, Chapter 5E-2.0311. Am. Entomol. 55, 29–39 (2009).
    Article  Google Scholar 

    100.
    Vargo, E. & Husseneder, C. in Biology of Termites: A Modern Synthesis (eds Bignell, D. E., Roisin, Y. & Lo, N.) 321–348 (Springer, 2011).

    101.
    FastQC v0.11.8 (Babraham Bioinformatics, Babraham Institute, 2018).

    102.
    Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    103.
    Paris, J. R., Stevens, J. R. & Catchen, J. M. Lost in parameter space: a road map for stacks. Methods Ecol. Evol. 8, 1360–1373 (2017).
    Article  Google Scholar 

    104.
    Benestan, L. M. et al. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol. Ecol. 25, 2967–2977 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    105.
    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    106.
    Lischer, H. E. L. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299 (2011).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    107.
    Raj, A., Stephens, M. & Pritchard, J. K. fastSTRUCTURE: variational Inference of Population Structure in Large SNP Data Sets. Genetics 197, 573 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    108.
    Pina-Martins, F., Silva, D. N., Fino, J. & Paulo, O. S. Structure_threader: an improved method for automation and parallelization of programs structure, fastStructure and MavericK on multicore CPU systems. Mol. Ecol. Resour. 17, e268–e274 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    109.
    Chhatre, V. E. Distruct v2.3, A modified cluster membership plotting script. http://distruct2.popgen.org (2018).

    110.
    Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).
    PubMed  PubMed Central  Article  Google Scholar 

    111.
    R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).

    112.
    Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    113.
    Malinsky, M., Trucchi, E., Lawson, D. J. & Falush, D. RADpainter and fineRADstructure: population inference from RADseq data. Mol. Biol. Evol. 35, 1284–1290 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    114.
    Lawson, D. J., Hellenthal, G., Myers, S. & Falush, D. Inference of population structure using dense haplotype data. PLoS Genet. 8, e1002453 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    115.
    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 

    116.
    Leaché, A. D., Banbury, B. L., Felsenstein, J., de Oca, An-M. & Stamatakis, A. Short tree, long tree, right tree, wrong tree: new acquisition bias corrections for inferring SNP phylogenies. Syst. Biol. 64, 1032–1047 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    117.
    Pattengale, N. D., Masoud, A., Bininda-Emonds, O. R. P., Moret, B. M. E. & Stamatkis, A. How many bootstrap replicates are necessary? J. Comput. Biol. 17, 337–354 (2010).
    CAS  PubMed  Article  Google Scholar 

    118.
    Beaumont, M. A., Zhang, W. & Balding, D. J. Approximate Bayesian computation in population genetics. Genetics 162, 2025 (2002).
    PubMed  PubMed Central  Google Scholar 

    119.
    Pudlo, P. et al. Reliable ABC model choice via random forests. Bioinformatics 32, 859–866 (2016).
    CAS  PubMed  Article  Google Scholar 

    120.
    Ryan, S. F. et al. Global invasion history of the agricultural pest butterfly Pieris rapae revealed with genomics and citizen science. Proc. Natl Acad. Sci. USA 116, 20015 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    121.
    Fraimout, A. et al. Deciphering the routes of invasion of Drosophila suzukii by means of ABC random forest. Mol. Biol. Evol. 34, 980–996 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    122.
    Cornuet, J.-M. et al. DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    123.
    Raynal, L. et al. ABC random forests for Bayesian parameter inference. Bioinformatics 35, 1720–1728 (2018).
    Article  CAS  Google Scholar 

    124.
    Liu, X. & Fu, Y.-X. Stairway Plot 2: demographic history inference with folded SNP frequency spectra. Genome Biol. 21, 280 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    125.
    Drummond, A. J., Rambaut, A., Shapiro, B. & Pybus, O. G. Bayesian coalescent inference of past population dynamics from molecular sequences. Mol. Biol. Evol. 22, 1185–1192 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    126.
    Liu, X., Fu, Y.-X., Maxwell, T. J. & Boerwinkle, E. Estimating population genetic parameters and comparing model goodness-of-fit using DNA sequences with error. Genome Res. 20, 101–109 (2010).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    127.
    Nielsen, R. Estimation of population parameters and recombination rates from single nucleotide polymorphisms. Genetics 154, 931 (2000).
    CAS  PubMed  PubMed Central  Google Scholar 

    128.
    Liu, S., Ferchaud, A.-L., Grønkjær, P., Nygaard, R. & Hansen, M. M. Genomic parallelism and lack thereof in contrasting systems of three-spined sticklebacks. Mol. Ecol. 27, 4725–4743 (2018).
    PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Patterns and processes of pathogen exposure in gray wolves across North America

    1.
    Ferrari, M. J. et al. The dynamics of measles in sub-Saharan Africa. Nature 451, 679–684 (2008).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Hudson, P. J. et al. Trophic interactions and population growth rates: Describing patterns and identifying mechanisms. Philos. Trans. R. Soc. B Biol. Sci. 357, 1259–1271 (2002).
    Article  Google Scholar 

    3.
    Thieltges, D. W., Ferguson, M. A. D., Jones, C. S., Leslie, R. & Poulin, R. Biogeographical patterns of marine larval trematode parasites in two intermediate snail hosts in Europe. J. Biogeogr. 36, 1493–1501 (2009).
    Article  Google Scholar 

    4.
    Bryan, H. M. et al. Seasonal and biogeographical patterns of gastrointestinal parasites in large carnivores: Wolves in a coastal archipelago. Parasitology 139, 781–790 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    5.
    Hosseini, P. R., Dhondt, A. A. & Dobson, A. Seasonality and wildlife disease: how seasonal birth, aggregation and variation in immunity affect the dynamics of Mycoplasma gallisepticum in house finches. Proc. R Soc. London Ser. B Biol. Sci. 271, 2569–2577 (2004).
    Article  Google Scholar 

    6.
    Guernier, V., Hochberg, M. E. & Guégan, J. F. Ecology drives the worldwide distribution of human diseases. PLoS Biol. 2, 740–746 (2004).
    CAS  Article  Google Scholar 

    7.
    Nunn, C. L., Altizer, S. M., Sechrest, W. & Cunningham, A. A. Latitudinal gradients of parasite species richness in primates. Divers. Distrib. 11, 249–256 (2005).
    Article  Google Scholar 

    8.
    Merino, S. et al. Haematozoa in forest birds from southern Chile: Latitudinal gradients in prevalence and parasite lineage richness. Austral. Ecol. 33, 329–340 (2008).
    Article  Google Scholar 

    9.
    Benejam, L., Alcaraz, C., Sasal, P., Simon-Levert, G. & García-Berthou, E. Life history and parasites of the invasive mosquitofish (Gambusia holbrooki) along a latitudinal gradient. Biol. Invasions 11, 2265–2277 (2009).
    Article  Google Scholar 

    10.
    Seabloom, E. W., Borer, E. T., Mitchell, C. E. & Power, A. G. Viral diversity and prevalence gradients in North American Pacific Coast grasslands. Ecology 91, 721–732 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    11.
    Bonds, M. H., Dobson, A. P. & Keenan, D. C. Disease ecology, biodiversity, and the latitudinal gradient in income. PLoS Biol. 10, e1001456 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    12.
    Kriger, K. M., Pereoglou, F. & Hero, J. M. Latitudinal variation in the prevalence and intensity of chytrid (Batrachochytrium dendrobatidis) infection in eastern Australia. Conserv. Biol. 21, 1280–1290 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    13.
    Peterson, R. O., Thomas, N. J., Thurber, J. M., Vucetich, J. A. & Waite, T. A. Population limitations and the wolves of Isle Royale. J. Mammal. 97, 828–841 (1998).
    Article  Google Scholar 

    14.
    Almberg, E. S., Mech, L. D., Smith, D. W., Sheldon, J. W. & Crabtree, R. L. A serological survey of infectious disease in Yellowstone National Park’s canid community. PLoS ONE 4, e7042 (2009).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    15.
    Almberg, E. S. et al. Social living mitigates the costs of a chronic illness in a cooperative carnivore. Ecol. Lett. 18, 660–667 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    16.
    Brandell, E. E. et al. Infectious diseases in Yellowstone’s Wolves. In Yellowstone Wolves: Science and Discovery in the World’s First National Park (eds. Smith, D. W., Stahler, D. R. & MacNulty, D. R.) 121–133 (The University of Chicago Press, 2020).

    17.
    Watts, D. E. & Benson, A. M. Prevalence of antibodies for selected canine pathogens among wolves (Canis lupus) from the Alaska Peninsula, USA. J. Wildl. Dis. 52, 506–515 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    18.
    Carstensen, M. et al. A serosurvey of diseases of free-ranging gray wolves (Canis lupus) in Minnesota, USA. J. Wildl. Dis. 53, 459–471 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Anderson, R. M. & May, R. M. Regulation and stability of host-parasite population interactions: I. Regulatory processes. J. Anim. Ecol. 47, 219–247 (1978).
    Article  Google Scholar 

    20.
    Silbernagel, E. R., Skelton, N. K., Waldner, C. L. & Bollinger, T. K. Interaction among deer in a chronic wasting disease endemic zone. J. Wildl. Manag. 75, 1453–1461 (2011).
    Article  Google Scholar 

    21.
    Gehrt, S. D. Raccoons and allies. In Wild Mammals of North America: Biology, Management, and Conservation (eds. Feldhamer, G., Thompson, B. & Chapman, J.) 611–633 (2003).

    22.
    McFarlane, R., Sleigh, A. & McMichael, T. Synanthropy of wild mammals as a determinant of emerging infectious diseases in the Asian-Australasian region. EcoHealth 9, 24–35 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    23.
    Woodroffe, R. et al. Contact with domestic dogs increases pathogen exposure in endangered African wild dogs (Lycaon pictus). PLoS ONE 7, e30099 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Knobel, D. L., Butler, J. R., Lembo, T., Critchlow, R. & Gompper, M. E. Dogs, disease, and wildlife. In Free-Ranging Dogs and Wildlife Conservation (ed. Gompper, M. E.) (Oxford University Press, Oxford, 2014).
    Google Scholar 

    25.
    Viana, M. et al. Dynamics of a morbillivirus at the domestic–wildlife interface: Canine distemper virus in domestic dogs and lions. Proc. Natl. Acad. Sci. 112, 1464–1469 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    26.
    Bianco, A. et al. Two waves of canine distemper virus showing different spatio-temporal dynamics in Alpine wildlife (2006–2018). Infect. Genet. Evol. 84, 104359 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Dubey, J. P., Schares, G. & Ortega-Mora, L. M. Epidemiology and control of neosporosis and Neospora caninum. Clin. Microbiol. Rev. 20, 323–367 (2007).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    28.
    Anderson, T. M. et al. Molecular and evolutionary history of melanism in North American gray wolves. Science 323, 1339–1343 (2009).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    Candille, S. I. et al. A β-defensin mutation causes black coat color in domestic dogs. Science 318, 1418–1423 (2007).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    30.
    Coulson, T., Macnulty, D. R., Stahler, D. R., Wayne, R. K. & Smith, D. W. Modeling effects of environmental change on wolf population dynamics, trait evolution, and life history. Science 334, 1275–1278 (2011).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Hedrick, P. W., Stahler, D. R. & Dekker, D. Heterozygote advantage in a finite population: Black color in wolves. J. Hered. 105, 457–465 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    32.
    Altizer, S., Davis, A. K., Cook, K. C. & Cherry, J. J. Age, sex, and season affect the risk of mycoplasmal conjunctivitis in a southeastern house finch population. Can. J. Zool. 82, 755–763 (2004).
    Article  Google Scholar 

    33.
    Biek, R. et al. Factors associated with pathogen seroprevalence and infection in Rocky Mountains cougars. J. Wildl. Dis. 42, 606–615 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    34.
    Härkönen, T., Harding, K., Rasmussen, T. D., Teilmann, J. & Dietz, R. Age- and sex-specific mortality patterns in an emerging wildlife epidemic: The phocine distemper in European harbour seals. PLoS ONE 2, e887 (2007).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    35.
    Guerra-Silveira, F. & Abad-Franch, F. Sex bias in infectious disease epidemiology: Patterns and processes. PLoS ONE 8, e62390 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    McDonald, J. L., Smith, G. C., McDonald, R. A., Delahay, R. J. & Hodgson, D. Mortality trajectory analysis reveals the drivers of sex-specific epidemiology in natural wildlife–disease interactions. Proc. R. Soc. B Biol. Sci. 281, 20140526 (2014).
    Article  Google Scholar 

    37.
    Williams, E. S. & Barker, I. K. (eds) Infectious Diseases of Wild Mammals (Wiley, New York, 2001).
    Google Scholar 

    38.
    USGS. North America Political Boundaries. (2006). Available at: https://www.sciencebase.gov/catalog/item/4fb555ebe4b04cb937751db9.

    39.
    Justice-Allen, A. & Clement, M. J. Effect of canine parvovirus and canine distemper virus on the Mexican wolf (Canis lupus baileyi) population in the USA. J. Wildl. Dis. 55, 682–688 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    40.
    Nelson, B. et al. Prevalence of antibodies to canine parvovirus and distemper virus in wolves in the Canadian Rocky Mountains. J. Wildl. Dis. 48, 68–76 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    41.
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing). (2019 v3.6.3). Available at: https://www.R-project.org.

    42.
    Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Article  Google Scholar 

    43.
    Gelman, A. & Loken, E. The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Psychol. Bull. 140, 1272–1280 (2013).
    Google Scholar 

    44.
    Fuller, T. K. & Murray, D. L. Biological and logistical explanations of variation in wolf population density. Anim. Conserv. 1, 153–157 (1998).
    Article  Google Scholar 

    45.
    Fuller, T. K. & Sievert, P. R. Carnivore demography and the consequences of changes in prey availability. In Conservation biology series – Cambridge 163–178 (2001).

    46.
    MacNulty, D. R., Tallian, A., Stahler, D. R. & Smith, D. W. Influence of group size on the success of wolves hunting bison. PLoS ONE 9, 1–8 (2014).
    Article  CAS  Google Scholar 

    47.
    Barber-Meyer, S. M., Mech, L. D., Newton, W. E. & Borg, B. L. Differential wolf-pack-size persistence and the role of risk when hunting dangerous prey. Behaviour 153, 1473–1487 (2016).
    Article  Google Scholar 

    48.
    Gipson, P. S., Ballard, W. B., Nowak, R. M. & Mech, L. D. Accuracy and precision of estimating age of gray wolves by tooth wear. J. Wildl. Manag. 64, 752 (2000).
    Article  Google Scholar 

    49.
    Fuller, T. K., Mech, L. D. & Cochrane, J. F. Wolf population dynamics. In Wolves: Behavior, Ecology, and Conservation (eds Mech, L. D. & Boitani, L.) 161–191 (University of Chicago Press, Chicago, 2003).
    Google Scholar 

    50.
    Jimenez, M. D. et al. Wolf dispersal in the Rocky Mountains, Western United States: 1993–2008. J. Wildl. Manag. 81, 581–592 (2017).
    Article  Google Scholar 

    51.
    NASA Socioeconomic Data and Applications Center. Gridded Population of the World (GPW), v4. EarthData (2015). Available at: https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/data-download.

    52.
    Millán, J. et al. Patterns of exposure of Iberian wolves (Canis lupus) to canine viruses in human-dominated landscapes. EcoHealth 13, 123–134 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    53.
    North American Land Change Monitoring System 30m, 2010–2015 (Landsat). Commission of Environmental Cooperation (2015). Available at: http://www.cec.org/north-american-land-change-monitoring-system/.

    54.
    USGS EROS Archive—Digital Elevation—Global 30 Arc-Second Elevation (GTOPO30). Earth Resources Observation and Science (EROS) Center (1996).

    55.
    Hesselbarth, M. H. K., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).
    Article  Google Scholar 

    56.
    Poole, K. G., Wakelyn, L. A. & Nicklen, P. N. Habitat selection by lynx in the Northwest Territories. Can. J. Zool. 74, 845–850 (1996).
    Article  Google Scholar 

    57.
    Nielsen, S. E., Boyce, M. S., Stenhouse, G. B. & Munro, R. H. M. Modeling grizzly bear habitats in the yellowhead ecosystem of Alberta: Taking autocorrelation seriously. Ursus 13, 45–56 (2001).
    Google Scholar 

    58.
    Arjo, W. M. & Pletscher, D. H. Coyote and wolf habitat use in northwestern Montana. Northwest Sci. 78, 24–32 (2004).
    Google Scholar 

    59.
    Oakleaf, J. K. et al. Habitat selection by recolonizing wolves in the northern Rocky Mountains of the United States. J. Wildl. Manag. 70, 554–563 (2006).
    Article  Google Scholar 

    60.
    Hebblewhite, M. & Merrill, E. Modelling wildlife-human relationships for social species with mixed-effects resource selection models. J. Appl. Ecol. 45, 834–844 (2008).
    Article  Google Scholar 

    61.
    Roever, C. L., Boyce, M. S. & Stenhouse, G. B. Grizzly bears and forestry II: Grizzly bear habitat selection and conflicts with road placement. For. Ecol. Manag. 256, 1262–1269 (2008).
    Article  Google Scholar 

    62.
    Houle, M., Fortin, D., Dussault, C., Courtois, R. & Ouellet, J. P. Cumulative effects of forestry on habitat use by gray wolf (Canis lupus) in the boreal forest. Landsc. Ecol. 25, 419–433 (2010).
    Article  Google Scholar 

    63.
    Mayor, S. J., Schneider, D. C., Schaefer, J. A. & Mahoney, S. P. Habitat selection at multiple scales. Ecoscience 16, 238–247 (2009).
    Article  Google Scholar 

    64.
    Milakovic, B. et al. Habitat selection by a focal predator (Canis lupus) in a multiprey ecosystem of the northern Rockies. J. Mammal. 92, 568–582 (2011).
    Article  Google Scholar 

    65.
    Kittle, A. M. et al. Wolves adapt territory size, not pack size to local habitat quality. J. Anim. Ecol. 84, 1177–1186 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    66.
    Kittle, A. M. et al. Landscape-level wolf space use is correlated with prey abundance, ease of mobility, and the distribution of prey habitat. Ecosphere 8, e01783 (2017).
    Article  Google Scholar 

    67.
    Morin, S. J., Bowman, J., Marrotte, R. R. & Fortin, M. J. Fine-scale habitat selection by sympatric Canada lynx and bobcat. Ecol. Evol. 10, 9396–9409 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    68.
    O’Neil, S. T., Vucetich, J. A., Beyer, D. E., Hoy, S. R. & Bump, J. K. Territoriality drives preemptive habitat selection in recovering wolves: Implications for carnivore conservation. J. Anim. Ecol. 89, 1433–1447 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    69.
    Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models (Cambridge University Press, 2007).

    70.
    Menard, S. Standards for standardized logistic regression coefficients. Soc. Forces 89, 1409–1428 (2011).

    71.
    Hosmer, D. W. & Lemeshow, S. Applied Logistic Regression (Wiley, New York, 2000).
    Google Scholar 

    72.
    Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, Berlin, 2009).
    Google Scholar 

    73.
    Finkelman, B. S. et al. Global patterns in seasonal activity of influenza A/H3N2, A/H1N1, and B from 1997 to 2005: Viral coexistence and latitudinal gradients. PLoS ONE 2, e1296 (2007).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Nguyen, D. et al. Fungal disease incidence along tree diversity gradients depends on latitude in European forests. Ecol. Evol. 6, 2426–2438 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    75.
    Rhodes, C. J., Atkinson, R. P. D., Anderson, R. M. & Macdonald, D. W. Rabies in Zimbabwe: reservoir dogs and the implications for disease control. Philos. Trans. R Soc. Lond. Ser. B Biol. Sci. 353, 999–1010 (1998).
    CAS  Article  Google Scholar 

    76.
    Lembo, T. et al. Exploring reservoir dynamics: A case study of rabies in the Serengeti ecosystem. J. Appl. Ecol. 45, 1246–1257 (2008).
    PubMed  PubMed Central  Article  Google Scholar 

    77.
    Nunn, C. L., Altizer, S., Jones, K. E. & Sechrest, W. Comparative tests of parasite species richness in primates. Am. Nat. 162, 597–614 (2003).
    PubMed  Article  PubMed Central  Google Scholar 

    78.
    Nunn, C. L. & Heymann, E. W. Malaria infection and host behavior: A comparative study of Neotropical primates Malaria infection and host behavior. Behav. Ecol. Sociobiol. 59, 30–37 (2005).
    Article  Google Scholar 

    79.
    Begon, M., Bowers, R. G., Kadianakis, N. & Hodgkinson, D. E. Disease and community structure: the importance of host self-regulation in a host-host-pathogen model. Am. Nat. 139, 1131–1150 (1992).
    Article  Google Scholar 

    80.
    Power, A. G. & Mitchell, C. E. Pathogen spillover in disease epidemics. Am. Nat. 164, S79–S89 (2004).
    PubMed  Article  PubMed Central  Google Scholar 

    81.
    Keesing, F., Holt, R. D. & Ostfeld, R. S. Effects of species diversity on disease risk. Ecol. Lett. 9, 485–498 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    82.
    Schweizer, R. M. et al. Natural selection and origin of a melanistic allele in North American gray wolves. Mol. Biol. Evol. 35, 1190–1209 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    83.
    Wilson, P. J., Grewal, S. K., Mallory, F. F. & White, B. N. Genetic characterization of hybrid wolves across Ontario. J. Hered. 100, S80–S89 (2009).
    CAS  Article  Google Scholar 

    84.
    Gondim, L. F. P. et al. Transmission of Neospora caninum between wild and domestic animals. J. Parasitol. 90, 1361–1365 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    85.
    Dubey, J. P. et al. Seroprevalence of Neospora caninum and Toxoplasma gondii antibodies in white-tailed deer (Odocoileus virginianus) from Iowa and Minnesota using four serologic tests. Vet. Parasitol. 161, 330–334 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    86.
    Stieve, E., Beckmen, K., Kania, S. A., Widner, A. & Patton, S. Neospora caninum and Toxoplasma gondii antibody prevalence in Alaska wildlife. J. Wildl. Dis. 46, 348–355 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    87.
    Pruvot, M., Hutchins, W. & Orsel, K. Statistical evaluation of a commercial Neospora caninum competitive ELISA in the absence of a gold standard: Application to wild elk (Cervus elaphus) in Alberta. Parasitol. Res. 113, 2899–2905 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    88.
    Bondo, K. J. et al. Health survey of boreal caribou (Rangifer tarandus caribou) in northeastern British Columbia, Canada. J. Wildl. Dis. 55, 544–562 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    89.
    Donahoe, S. L., Lindsay, S. A., Krockenberger, M., Phalen, D. & Šlapeta, J. A review of neosporosis and pathologic findings of Neospora caninum infection in wildlife. Int. J. Parasitol. Parasites Wildl. 4, 216–238 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    90.
    Huggard, D. J. Prey selectivity of wolves in Banff National Park I. Prey species. Can. J. Zool. 71, 130–139 (1993).
    Article  Google Scholar 

    91.
    Hebblewhite, M., Paquet, P. C., Pletscher, D. H., Lessard, R. B. & Callaghan, C. J. Development and application of a ratio estimator to estimate wolf kill rates and variance in a multiple-prey system. Wildl. Soc. Bull. 31, 933–946 (2003).
    Google Scholar 

    92.
    Adams, L. G. et al. Are inland wolf-ungulate systems influenced by marine subsidies of Pacific salmon?. Ecol. Appl. 20, 251–262 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    93.
    Latham, A. D. M., Latham, M. C., McCutchen, N. A. & Boutin, S. Invading white-tailed deer change wolf-caribou dynamics in northeastern Alberta. J. Wildl. Manag. 75, 204–212 (2011).
    Article  Google Scholar 

    94.
    Metz, M. C., Smith, D. W., Vucetich, J. A., Stahler, D. R. & Peterson, R. O. Seasonal patterns of predation for gray wolves in the multi-prey system of Yellowstone National Park. J. Anim. Ecol. 81, 553–563 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    95.
    Merkle, J. A., Polfus, J. L., Derbridge, J. J. & Heinemeyer, K. S. Dietary niche partitioning among black bears, grizzly bears and wolves in a multi-prey ecosystem. Can. J. Zool. 95, 663–671 (2017).
    Article  Google Scholar 

    96.
    Gable, T. D., Windels, S. K., Bruggink, J. G. & Barber-Meyer, S. M. Weekly summer diet of gray wolves (Canis lupus) in northeastern Minnesota. Am. Midl. Nat. 179, 15–27 (2018).
    Article  Google Scholar 

    97.
    O’Donovan, S. A., Budge, S. M., Hobson, K. A., Kelly, A. P. & Derocher, A. E. Intrapopulation variability in wolf diet revealed using a combined stable isotope and fatty acid approach. Ecosphere 9, e02420 (2018).
    Article  Google Scholar 

    98.
    Whittington, J., St. Clair, C. C. & Mercer, G. Spatial responses of wolves to roads and trails in mountain valleys. Ecol. Appl. 15, 543–553 (2005).
    Article  Google Scholar  More

  • in

    Capturing yeast associated with grapes and spontaneous fermentations of the Negro Saurí minority variety from an experimental vineyard near León

    1.
    Csoma, H., Zakany, N., Capece, A., Romano, P. & Sipiczki, M. Biological diversity of Saccharomyces yeasts of spontaneously fermenting wines in four wine regions: Comparative genotypic and phenotypic analysis. Int. J. Food Microbiol. 140, 239–248. https://doi.org/10.1016/j.ijfoodmicro.2010.03.024 (2010).
    CAS  Article  PubMed  Google Scholar 
    2.
    Di Maio, S. et al. Biodiversity of indigenous Saccharomyces populations from old wineries of South-Eastern Sicily (Italy): Preservation and economic potential. PLoS ONE 7, e30428. https://doi.org/10.1371/journal.pone.0030428 (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    3.
    Bokulich, N. A., Ohta, M., Richardson, P. M. & Mills, D. A. Monitoring seasonal changes in winery-resident microbiota. PLoS ONE 8, e66437. https://doi.org/10.1371/journal.pone.0066437 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    4.
    Mas, A., Padilla, B., Esteve-Zarzoso, B. & Beltran, G. Utilización de inóculos mixtos de levaduras autóctonas como herramienta para reproducir la huella microbiológica de la zona. Acenologica. http://www.acenologia.com/cienciaytecnologia/inoculos_mixtos_levaduras_autoctonas_cienc0715.htm (2013).

    5.
    Varela, C. & Borneman, A. R. Yeasts found in vineyards and wineries. Yeast 34, 111–128. https://doi.org/10.1002/yea.3219 (2017).
    CAS  Article  PubMed  Google Scholar 

    6.
    Fleet, G. H. Yeast interactions and wine flavour. Int. J. Food Microbiol. 86, 11–22. https://doi.org/10.1016/S0168-1605(03)00245-9 (2003).
    CAS  Article  PubMed  Google Scholar 

    7.
    Mannazzu, I., Clementi, F. & Ciani, M. In Biodiversity and Biotechnology of Wine Yeasts 19–34 (2002).

    8.
    Martini, A., Ciani, M. & Scorzetti, G. Direct enumeration and isolation of wine yeasts from grape surfaces. Am. J. Enol. Vit. 47, 435 (1996).
    Google Scholar 

    9.
    Mortimer, R. & Polsinelli, M. On the origins of wine yeast. Res. Microbiol. 150, 199–204. https://doi.org/10.1016/S0923-2508(99)80036-9 (1999).
    CAS  Article  PubMed  Google Scholar 

    10.
    Ciani, M., Comitini, F., Mannazzu, I. & Domizio, P. Controlled mixed culture fermentation: A new perspective on the use of non-Saccharomyces yeasts in winemaking. FEMS Yeast Res. 10, 123–133. https://doi.org/10.1111/j.1567-1364.2009.00579.x (2010).
    CAS  Article  PubMed  Google Scholar 

    11.
    Ribéreau-Gayon, P., Dubourdieu, D., Donéche, B. & Lonvaud, A. The Microbiology of Wine and Vinifications 2nd edn, Vol. 1, 512 (2006).

    12.
    Charoenchai, C., Fleet, G. H., Henschke, P. A. & Todd, B. E. N. T. Screening of non-Saccharomyces wine yeasts for the presence of extracellular hydrolytic enzymes. Aus. J. Grape Wine Res. 3, 2–8. https://doi.org/10.1111/j.1755-0238.1997.tb00109.x (1997).
    CAS  Article  Google Scholar 

    13.
    Fernández, M. T., Ubeda, J. F. & Briones, A. I. Comparative study of non-Saccharomyces microflora of musts in fermentation, by physiological and molecular methods. FEMS Microbiol. Lett. 173, 223–229. https://doi.org/10.1111/j.1574-6968.1999.tb13506.x (1999).
    Article  Google Scholar 

    14.
    Zott, K., Miot-Sertier, C., Claisse, O., Lonvaud-Funel, A. & Masneuf-Pomarede, I. Dynamics and diversity of non-Saccharomyces yeasts during the early stages in winemaking. Int. J. Food Microbiol. 125, 197–203. https://doi.org/10.1016/j.ijfoodmicro.2008.04.001 (2008).
    CAS  Article  PubMed  Google Scholar 

    15.
    Grangeteau, C. et al. Diversity of yeast strains of the genus Hanseniaspora in the winery environment: What is their involvement in grape must fermentation?. Food Microbiol. 50, 70–77. https://doi.org/10.1016/j.fm.2015.03.009 (2015).
    ADS  CAS  Article  PubMed  Google Scholar 

    16.
    Fleet, G. H. Wine yeasts for the future. FEMS Yeast Res. 8, 979–995. https://doi.org/10.1111/j.1567-1364.2008.00427.x (2008).
    CAS  Article  PubMed  Google Scholar 

    17.
    Canonico, L., Comitini, F., Oro, L. & Ciani, M. Sequential fermentation with selected immobilized non-Saccharomyces yeast for reduction of ethanol content in wine. Front. Microbiol. 7, 278–278. https://doi.org/10.3389/fmicb.2016.00278 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    18.
    Padilla, B., Gil, J. V. & Manzanares, P. Past and future of non-Saccharomyces yeasts: From spoilage microorganisms to biotechnological tools for improving wine aroma complexity. Front. Microbiol. 7, 411–411. https://doi.org/10.3389/fmicb.2016.00411 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    19.
    Esteve-Zarzoso, B., Manzanares, P., Ramön, D. & Quero, A. The role of non-Saccharomyces yeasts in industrial winemaking. Int. Microbiol. 1, 143–148 (1998).
    CAS  PubMed  Google Scholar 

    20.
    Gonzalez, R., Quirós, M. & Morales, P. Yeast respiration of sugars by non-Saccharomyces yeast species: A promising and barely explored approach to lowering alcohol content of wines. Trends Food Sci. Techol. 29, 55–61. https://doi.org/10.1016/j.tifs.2012.06.015 (2013).
    CAS  Article  Google Scholar 

    21.
    Quirós, M., Rojas, V., Gonzalez, R. & Morales, P. Selection of non-Saccharomyces yeast strains for reducing alcohol levels in wine by sugar respiration. Int. J. Food Microbiol. 181, 85–91. https://doi.org/10.1016/j.ijfoodmicro.2014.04.024 (2014).
    CAS  Article  PubMed  Google Scholar 

    22.
    Morales, P., Rojas, V., Quirós, M. & Gonzalez, R. The impact of oxygen on the final alcohol content of wine fermented by a mixed starter culture. Appl. Microbiol. Biotechnol. 99, 3993–4003. https://doi.org/10.1007/s00253-014-6321-3 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    23.
    Varela, C. et al. Strategies for reducing alcohol concentration in wine. Aus. J. Grape Wine Res. 21, 670–679. https://doi.org/10.1111/ajgw.12187 (2015).
    Article  Google Scholar 

    24.
    Roudil, L. et al. Non-Saccharomyces commercial starter cultures: Scientific trends, recent patents and innovation in the wine sector. Recent Patents Food Nutr. Agric. https://doi.org/10.2174/2212798410666190131103713 (2019).
    Article  Google Scholar 

    25.
    Le Jeune, C., Erny, C., Demuyter, C. & Lollier, M. Evolution of the population of Saccharomyces cerevisiae from grape to wine in a spontaneous fermentation. Food Microbiol. 23, 709–716. https://doi.org/10.1016/j.fm.2006.02.007 (2006).
    CAS  Article  PubMed  Google Scholar 

    26.
    Versavaud, A., Courcoux, P., Roulland, C., Dulau, L. & Hallet, J. N. Genetic diversity and geographical distribution of wild Saccharomyces cerevisiae strains from the wine-producing area of Charentes, France. Appl. Environ. Microbiol. 61, 3521 (1995).
    CAS  Article  Google Scholar 

    27.
    Pérez-Coello, M. S., Briones Pérez, A. I., Ubeda Iranzo, J. F. & Martin Alvarez, P. J. Characteristics of wines fermented with different Saccharomyces cerevisiae strains isolated from the La Mancha region. Food Microbiol. 16, 563–573. https://doi.org/10.1006/fmic.1999.0272 (1999).
    CAS  Article  Google Scholar 

    28.
    Torriani, S., Zapparoli, G. & Suzzi, G. Genetic and phenotypic diversity of Saccharomyces sensu stricto strains isolated from Amarone wine. Antonie Van Leeuwenhoek 75, 207–215. https://doi.org/10.1023/A:1001773916407 (1999).
    CAS  Article  PubMed  Google Scholar 

    29.
    Naumov, G. I., Masneuf, I., Naumova, E. S., Aigle, M. & Dubourdieu, D. Association of Saccharomyces bayanus var. uvarum with some French wines: Genetic analysis of yeast populations. Res. Microbiol. 151, 683–691. https://doi.org/10.1016/s0923-2508(00)90131-1 (2000).
    CAS  Article  PubMed  Google Scholar 

    30.
    Redžepović, S., Orlić, S., Sikora, S., Majdak, A. & Pretorius, I. S. Identification and characterization of Saccharomyces cerevisiae and Saccharomyces paradoxus strains isolated from Croatian vineyards. Letts. Appl. Microbiol. 35, 305–310. https://doi.org/10.1046/j.1472-765X.2002.01181.x (2002).
    Article  Google Scholar 

    31.
    Rementeria, A. et al. Yeast associated with spontaneous fermentations of white wines from the “Txakoli de Bizkaia” region (Basque Country, North Spain). Int. J. Food Microbiol. 86, 201–207. https://doi.org/10.1016/S0168-1605(03)00289-7 (2003).
    CAS  Article  PubMed  Google Scholar 

    32.
    Cappello, M. S., Bleve, G., Grieco, F., Dellaglio, F. & Zacheo, G. Characterization of Saccharomyces cerevisiae strains isolated from must of grape grown in experimental vineyard. J. Appl. Microbiol. 97, 1274–1280. https://doi.org/10.1111/j.1365-2672.2004.02412.x (2004).
    CAS  Article  PubMed  Google Scholar 

    33.
    Fay, J. C. & Benavides, J. A. Evidence for domesticated and wild populations of Saccharomyces cerevisiae. PLoS Genet. 1, e5. https://doi.org/10.1371/journal.pgen.0010005 (2005).
    CAS  Article  PubMed Central  Google Scholar 

    34.
    Schuller, D., Alves, H., Dequin, S. & Casal, M. Ecological survey of Saccharomyces cerevisiae strains from vineyards in the Vinho Verde Region of Portugal. FEMS Microbiol. Ecol. 51, 167–177. https://doi.org/10.1016/j.femsec.2004.08.003 (2005).
    CAS  Article  PubMed  Google Scholar 

    35.
    Viel, A. et al. The geographic distribution of Saccharomyces cerevisiae isolates within three Italian neighboring winemaking regions reveals strong differences in yeast abundance, genetic diversity and industrial strain dissemination. Front. Microbiol. 8, 1595–1595. https://doi.org/10.3389/fmicb.2017.01595 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    36.
    Sun, Y. et al. Evaluation of Chinese Saccharomyces cerevisiae wine strains from different geographical origins. Am. J. Enol. Vit. 68, 73. https://doi.org/10.5344/ajev.2016.16059 (2017).
    Article  Google Scholar 

    37.
    da Silva, G. A. D., Agustini, B. C., de Mello, L. M. R. & Tonietto, J. Autochthonous yeast populations from different Brazilian geographic indications. BIO Web Conf. 7 (2016).

    38.
    Crosato, G. et al. Genetic variability and physiological traits of Saccharomyces cerevisiae strains isolated from “Vale dos Vinhedos” vineyards reflect agricultural practices and history of this Brazilian wet subtropical area. World J. Microbiol. Biotechnol. 34, 105. https://doi.org/10.1007/s11274-018-2490-z (2018).
    CAS  Article  PubMed  Google Scholar 

    39.
    Chavan, P. et al. Natural yeast flora of different varieties of grapes used for wine making in India. Food Microbiol. 26, 801–808. https://doi.org/10.1016/j.fm.2009.05.005 (2009).
    CAS  Article  PubMed  Google Scholar 

    40.
    Kachalkin, A. V., Abdullabekova, D. A., Magomedova, E. S., Magomedov, G. G. & Chernov, I. Y. Yeasts of the vineyards in Dagestan and other regions. Microbiology 84, 425–432. https://doi.org/10.1134/S002626171503008X (2015).
    CAS  Article  Google Scholar 

    41.
    Cordero-Bueso, G., Arroyo, T., Serrano, A. & Valero, E. Remanence and survival of commercial yeast in different ecological niches of the vineyard. FEMS Microbiol. Ecol. 77, 429–437. https://doi.org/10.1111/j.1574-6941.2011.01124.x (2011).
    CAS  Article  PubMed  Google Scholar 

    42.
    Valero, E., Schuller, D., Cambon, B., Casal, M. & Dequin, S. Dissemination and survival of commercial wine yeast in the vineyard: A large-scale, three-years study. FEMS Yeast Res. 5, 959–969. https://doi.org/10.1016/j.femsyr.2005.04.007 (2005).
    CAS  Article  PubMed  Google Scholar 

    43.
    Valero, E., Cambon, B., Schuller, D., Casal, M. & Dequin, S. Biodiversity of Saccharomyces yeast strains from grape berries of wine-producing areas using starter commercial yeasts. FEMS Yeast Res. 7, 317–329. https://doi.org/10.1111/j.1567-1364.2006.00161.x (2007).
    CAS  Article  PubMed  Google Scholar 

    44.
    Blanco, P., Mirás-Avalos, J. M. & Orriols, I. Effect of must characteristics on the diversity of Saccharomyces strains and their prevalence in spontaneous fermentations. J. Appl. Microbiol. 112, 936–944. https://doi.org/10.1111/j.1365-2672.2012.05278.x (2012).
    CAS  Article  PubMed  Google Scholar 

    45.
    Garofalo, C., Tristezza, M., Grieco, F., Spano, G. & Capozzi, V. From grape berries to wine: Population dynamics of cultivable yeasts associated to “Nero di Troia” autochthonous grape cultivar. World J. Microbiol. Biotechnol. 32, 59. https://doi.org/10.1007/s11274-016-2017-4 (2016).
    CAS  Article  PubMed  Google Scholar 

    46.
    Schuller, D. et al. Genetic diversity and population structure of Saccharomyces cerevisiae strains isolated from different grape varieties and winemaking regions. PLoS ONE 7, e32507. https://doi.org/10.1371/journal.pone.00325 (2012).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    47.
    Martinez, M. C. & Perez, J. E. The forgotten vineyard of the Asturias Princedom (north of Spain) and ampelographic description of its grapevine cultivars (Vitis vinifera L.). Am. J. Enol. Vit. 51, 370–378 (2000).
    Google Scholar 

    48.
    Yuste, J. et al. Identification of autochthonous grapevine varieties in the germplasm collection at the ITA of “Castilla y León” in Zamadueñas Station, Valladolid. Spain. Spanish J. Agric. Res. https://doi.org/10.5424/sjar/2006041-175 (2006).
    Article  Google Scholar 

    49.
    Cabello, F., Saiz, R. & Muñoz, G. Estudio de variedades españolas minoritarias de vid. Acenologica. http://www.acenologia.com/cienciaytecnologia/variedades_minoritarias_cienc0213.htm (2013).

    50.
    Balda, P. & de Toda, F. M. Variedades minoritarias de vid en La Rioja. Consejería de Agricultura, Ganadería y Medio Ambiente. (2017).

    51.
    Martínez de Toda, F. Veinte nuevas variedades de vid, rescatadas de la desaparición, en la viticultura española y nuevos vinos. Acenologica. http://www.acenologia.com/dossier/dossier135.htm (2013).

    52.
    Arranz, C. et al. Variedades de vid cultivadas en la Sierra de Francia. Importancia, identificación, sinonimias y homonimias. La Semana Vitivinícola 3223, 1414–1420 (2008).
    Google Scholar 

    53.
    Ibáñez, J., Carreño, J., Yuste, J. & Martínez-Zapater, J. M. In Grapevine Breeding Programs for the Wine Industry (ed Reynolds, A.) 183–209 (Woodhead Publishing, 2015).

    54.
    Arranz Hernández, C., Barajas Tola, E., Yuste Bombín, J. & Rubio Cano, J. A. 45–58 (Comunidad de Madrid (España): Ministerio de Agricultura, Alimentación y Medio Ambiente, 2016).

    55.
    Esteve-Zarzoso, B., Belloch, C., Uruburu, F. & Querol, A. Identification of yeasts by RFLP analysis of the 5.8S rRNA gene and the two ribosomal internal transcribed spacers. Int. J. Syst. Bact. 49, 329–337. https://doi.org/10.1099/00207713-49-1-329 (1999).
    CAS  Article  Google Scholar 

    56.
    Madden, T. L., Tatusov, R. L. & Zhang, J. Methods in Enzymology Vol. 266, 131–141 (Academic Press, London, 1996).
    Google Scholar 

    57.
    Legras, J.-L. & Karst, F. Optimisation of interdelta analysis for Saccharomyces cerevisiae strain characterisation. FEMS Microbiol. Lett. 221, 249–255. https://doi.org/10.1016/S0378-1097(03)00205-2 (2003).
    CAS  Article  PubMed  Google Scholar 

    58.
    Ness, F., Lavallée, F., Dubourdieu, D., Aigle, M. & Dulau, L. Identification of yeast strains using the polymerase chain reaction. J. Sci. Food Agric. 62, 89–94. https://doi.org/10.1002/jsfa.2740620113 (1993).
    CAS  Article  Google Scholar 

    59.
    Lebart, L., Morineau, A. & Piron, M. Statistique Exploratoire Multidimensionnelle (Dunod Publishers, Paris, 1995).
    Google Scholar 

    60.
    Granato, D., Santos, J. S., Escher, G. B., Ferreira, B. L. & Maggio, R. M. Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends Food Sci. Technol. 72, 83–90. https://doi.org/10.1016/j.tifs.2017.12.006 (2018).
    CAS  Article  Google Scholar 

    61.
    Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J. M. & Perona, I. An extensive comparative study of cluster validity indices. Pattern Recogn. 46, 243–256. https://doi.org/10.1016/j.patcog.2012.07.021 (2013).
    Article  Google Scholar 

    62.
    Orlić, S. et al. Diversity and oenological characterization of indigenous Saccharomyces cerevisiae associated with Žilavka grapes. World J. Microbiol. Biotechnol. 26, 1483–1489. https://doi.org/10.1007/s11274-010-0323-9 (2010).
    Article  Google Scholar 

    63.
    Tristezza, M. et al. Molecular and technological characterization of Saccharomyces cerevisiae strains isolated from natural fermentation of Susumaniello grape must in Apulia, Southern Italy. Int. J. Microbiol. 897428–897428, 2014. https://doi.org/10.1155/2014/897428 (2014).
    CAS  Article  Google Scholar 

    64.
    SchvarczovÁ, E. V. A., ŠtefáNiková, J., Jankura, E. & Kolek, E. Selection of autochthonous Saccharomyces cerevisiae strains for production of typical Pinot Gris wines. J. Food Nutr. Res. 56, 389–397 (2017).
    Google Scholar 

    65.
    Tristezza, M. et al. Biodiversity and safety aspects of yeast strains characterized from vineyards and spontaneous fermentations in the Apulia Region, Italy. Food Microbiol. 36, 335–342. https://doi.org/10.1016/j.fm.2013.07.001 (2013).
    CAS  Article  PubMed  Google Scholar 

    66.
    Sabate, J., Cano, J., Querol, A. & Guillamon, J. M. Diversity of Saccharomyces strains in wine fermentations: Analysis for two consecutive years. Lett. Appl. Microbiol. 26, 452–455. https://doi.org/10.1046/j.1472-765X.1998.00369.x (1998).
    CAS  Article  PubMed  Google Scholar 

    67.
    Bougreau, M., Ascencio, K., Bugarel, M., Nightingale, K. & Loneragan, G. Yeast species isolated from Texas High Plains vineyards and dynamics during spontaneous fermentations of Tempranillo grapes. PLoS ONE 14, e0216246–e0216246. https://doi.org/10.1371/journal.pone.0216246 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    68.
    Martiniuk, J. T. et al. Impact of commercial strain use on Saccharomyces cerevisiae population structure and dynamics in Pinot Noir vineyards and spontaneous fermentations of a Canadian winery. PLoS ONE 11, e0160259. https://doi.org/10.1371/journal.pone.0160259 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    69.
    Mercado, L., Jubany, S., Gaggero, C., Masuelli, R. W. & Combina, M. Molecular relationships between Saccharomyces cerevisiae strains involved in winemaking from Mendoza, Argentina. Curr. Microbiol. 61, 506–514. https://doi.org/10.1007/s00284-010-9645-y (2010).
    CAS  Article  PubMed  Google Scholar 

    70.
    de Celis, M. et al. Diversity of Saccharomyces cerevisiae yeasts associated to spontaneous and inoculated fermenting grapes from Spanish vineyards. Lett. Appl. Microbiol. 68, 580–588. https://doi.org/10.1111/lam.13155 (2019).
    Article  PubMed  Google Scholar 

    71.
    Knight, S., Klaere, S., Fedrizzi, B. & Goddard, M. R. Regional microbial signatures positively correlate with differential wine phenotypes: Evidence for a microbial aspect to terroir. Sci. Rep. 5, 14233. https://doi.org/10.1038/srep14233 (2015).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    72.
    Álvarez-Pérez, J. M., Garzón-Jimeno, E. & Coque, J. J. R. Population of indigenous yeast strains from Prieto Picudo grapes in different growing areas of Denomination of Origin “Tierra de León”. Bull. Univ. Agric. Sci. Vet. Med. Cluj-Napoca Hortic. 72, 17–26. https://doi.org/10.15835/buasvmcn-hort:11013 (2015).
    Article  Google Scholar 

    73.
    Sabate, J., Cano, J., Esteve-Zarzoso, B. & Guillamón, J. M. Isolation and identification of yeasts associated with vineyard and winery by RFLP analysis of ribosomal genes and mitochondrial DNA. Microbiol. Res. 157, 267–274. https://doi.org/10.1078/0944-5013-00163 (2002).
    CAS  Article  PubMed  Google Scholar 

    74.
    Barata, A., Malfeito-Ferreira, M. & Loureiro, V. The microbial ecology of wine grape berries. Int. J. Food Microbiol. 153, 243–259. https://doi.org/10.1016/j.ijfoodmicro.2011.11.025 (2012).
    CAS  Article  PubMed  Google Scholar 

    75.
    Bokulich, N. A., Thorngate, J. H., Richardson, P. M. & Mills, D. A. Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate. PNAS 111, E139–E148. https://doi.org/10.1073/pnas.1317377110 (2014).
    ADS  CAS  Article  PubMed  Google Scholar 

    76.
    Russo, P. et al. Pesticide residues and stuck fermentation in Wine: New evidences indicate the urgent need of tailored regulations. Fermentation 5, 23. https://doi.org/10.3390/fermentation5010023 (2019).
    CAS  Article  Google Scholar 

    77.
    Agarbati, A., Canonico, L., Ciani, M. & Comitini, F. The impact of fungicide treatments on yeast biota of Verdicchio and Montepulciano grape varieties. PLoS ONE 14, e0217385. https://doi.org/10.1371/journal.pone.0217385 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    78.
    Kosel, J., Raspor, P. & Čadež, N. Maximum residue limit of fungicides inhibits the viability and growth of desirable non-Saccharomyces wine yeasts. Aust. J. Grape Wine Res. 25, 43–52. https://doi.org/10.1111/ajgw.12364 (2019).
    CAS  Article  Google Scholar 

    79.
    Čadež, N., Zupan, J. & Raspor, P. The effect of fungicides on yeast communities associated with grape berries. FEMS Yeast Res. 10, 619–630. https://doi.org/10.1111/j.1567-1364.2010.00635.x (2010).
    CAS  Article  PubMed  Google Scholar 

    80.
    Lewis, K. A., Tzilivakis, J., Warner, D. J. & Green, A. An international database for pesticide risk assessments and management. Hum. Ecol. Risk Assess. Int. J. 22, 1050–1064. https://doi.org/10.1080/10807039.2015.1133242 (2016).
    CAS  Article  Google Scholar 

    81.
    Killham, K., Lindley, N. D. & Wainwright, M. Inorganic sulfur oxidation by Aureobasidium pullulans. Appl. Environ. Microbiol. 42, 629–631 (1981).
    CAS  Article  Google Scholar 

    82.
    Gadd, G. M. & de Rome, L. Biosorption of copper by fungal melanin. Appl. Microbiol. Biotechnol. 29, 610–617. https://doi.org/10.1007/BF00260993 (1988).
    CAS  Article  Google Scholar 

    83.
    Belda, I. et al. Unraveling the enzymatic basis of wine “flavorome”: A phylo-functional study of wine related yeast species. Front. Microbiol. 7, 12–12. https://doi.org/10.3389/fmicb.2016.00012 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    84.
    Lin, M.M.-H. et al. Evaluation of indigenous non-Saccharomyces yeasts isolated from a South Australian vineyard for their potential as wine starter cultures. Int. J. Food Microbiol. 312, 108373. https://doi.org/10.1016/j.ijfoodmicro.2019.108373 (2020).
    CAS  Article  PubMed  Google Scholar 

    85.
    Hranilovic, A., Bely, M., Masneuf-Pomarede, I., Jiranek, V. & Albertin, W. The evolution of Lachancea thermotolerans is driven by geographical determination, anthropisation and flux between different ecosystems. PLoS ONE 12, e0184652. https://doi.org/10.1371/journal.pone.0184652 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    86.
    Hranilovic, A. et al. Oenological traits of Lachancea thermotolerans show signs of domestication and allopatric differentiation. Sci. Rep. 8, 14812. https://doi.org/10.1038/s41598-018-33105-7 (2018).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    87.
    Hu, K., Jin, G.-J., Mei, W.-C., Li, T. & Tao, Y.-S. Increase of medium-chain fatty acid ethyl ester content in mixed H. uvarum/S. cerevisiae fermentation leads to wine fruity aroma enhancement. Food Chem. 239, 495–501. https://doi.org/10.1016/j.foodchem.2017.06.151 (2018).
    CAS  Article  PubMed  Google Scholar 

    88.
    Oro, L., Ciani, M. & Comitini, F. Antimicrobial activity of Metschnikowia pulcherrima on wine yeasts. J. Appl. Microbiol. 116, 1209–1217. https://doi.org/10.1111/jam.12446 (2014).
    CAS  Article  PubMed  Google Scholar 

    89.
    Contreras, A., Curtin, C. & Varela, C. Yeast population dynamics reveal a potential ‘collaboration’ between Metschnikowia pulcherrima and Saccharomyces uvarum for the production of reduced alcohol wines during Shiraz fermentation. Appl. Microbiol. Biotechnol. 99, 1885–1895. https://doi.org/10.1007/s00253-014-6193-6 (2015).
    CAS  Article  PubMed  Google Scholar 

    90.
    Benito, S. The impacts of Lachancea thermotolerans yeast strains on winemaking. Appl. Microbiol. Biotechnol. 102, 6775–6790. https://doi.org/10.1007/s00253-018-9117-z (2018).
    CAS  Article  PubMed  Google Scholar 

    91.
    Morata, A. et al. Lachancea thermotolerans applications in wine technology. Fermentation https://doi.org/10.3390/fermentation4030053 (2018).
    Article  Google Scholar 

    92.
    Belda, I. et al. Selection and use of pectinolytic yeasts for improving clarification and phenolic extraction in winemaking. Int. J. Food Microbiol. 223, 1–8. https://doi.org/10.1016/j.ijfoodmicro.2016.02.003 (2016).
    CAS  Article  PubMed  Google Scholar 

    93.
    Jolly, N., Augustyn, O. & Pretorius, I. The role and use of non-Saccharomyces yeasts in wine production. J. Enol. Vitic. 27. https://doi.org/10.21548/27-1-1475 (2006).

    94.
    Capozzi, V., Fragasso, M. & Russo, P. Microbiological safety and the management of microbial resources in artisanal foods and beverages: The need for a transdisciplinary assessment to conciliate actual trends and risks avoidance. Microorganisms 8, 306. https://doi.org/10.3390/microorganisms (2020).
    Article  PubMed Central  Google Scholar 

    95.
    Benito, S. The impact of Torulaspora delbrueckii yeast in winemaking. Appl. Microbiol. Biotechnol. 102, 3081–3094. https://doi.org/10.1007/s00253-018-8849-0 (2018).
    CAS  Article  PubMed  Google Scholar 

    96.
    Attila, K., Ján, M., Eva, I., Margarita, T. & Miroslava, K. Microorganisms of grape berries. In Proc. Latvian Acad. Sciences. Section B. Natural, Exact & Appl. Sci. Vol. 71, 502–508, https://doi.org/10.1515/prolas-2017-0087 (2017).

    97.
    Pretorius, I. S. Tailoring wine yeast for the new millennium: Novel approaches to the ancient art of winemaking. Yeast 16, 675–729. https://doi.org/10.1002/1097-0061(20000615)16:8%3c675::AID-YEA585%3e3.0.CO;2-B (2000).
    CAS  Article  PubMed  Google Scholar 

    98.
    Clavijo, A., Calderón, I. L. & Paneque, P. Diversity of Saccharomyces and non-Saccharomyces yeasts in three red grape varieties cultured in the Serranía de Ronda (Spain) vine-growing region. Int. J. Food Microbiol. 143, 241–245. https://doi.org/10.1016/j.ijfoodmicro.2010.08.010 (2010).
    CAS  Article  PubMed  Google Scholar 

    99.
    Capece, A. et al. Diversity of Saccharomyces cerevisiae strains isolated from two Italian wine-producing regions. Front Microbiol. 7, 1018. https://doi.org/10.3389/fmicb (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    100.
    Santamaría, P. et al. Biodiversity of Saccharomyces cerevisiae yeasts in spontaneous alcoholic fermentations: Typical cellar or zone strains? Advances in Grape and Wine Biotechnology. (ed. Morata, A. & Loira, I.) 1–15 (Intech Open, 2019). https://doi.org/10.5772/intechopen.84870

    101.
    Kurtzman, C., P., & Fell, J. W. The Yeasts, A Taxonomic Study. 4th edn, (Elsevier Science Publishers, 1998).

    102.
    Lõoke, M., Kristjuhan, K. & Kristjuhan, A. Extraction of genomic DNA from yeasts for PCR-based applications. Biotechniques 50, 325–328. https://doi.org/10.2144/000113672 (2011).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    103.
    Liu, Y., Wang, C., Joseph, C. M. L. & Bisson, L. F. Comparison of two PCR-based genetic fingerprinting methods for assessment of genetic diversity in Saccharomyces strains. Am. J. Enol. Vit. 65, 109. https://doi.org/10.5344/ajev.2013.13056 (2014).
    Article  Google Scholar 

    104.
    Dazy, F. & Le Barzic, J.-F. L’analyse des donnees evolutives: Methodes et applications (Technip Publishers, 1996). More

  • in

    Bacterial microbiota similarity between predators and prey in a blue tit trophic network

    1.
    Hooper LV, Bry L, Falk PG, Gordon JI. Host-microbial symbiosis in the mammalian intestine: exploring an internal ecosystem. BioEssays. 1998;20:336–43.
    CAS  PubMed  Article  Google Scholar 
    2.
    Mazmanian SK, Liu CH, Tzianabos AO, Kasper DL. An immunomodulatory molecule of symbiotic bacteria directs maturation of the host immune system. Cell. 2005;122:107–18.
    CAS  Article  Google Scholar 

    3.
    Chung H, Pamp SJ, Hill JA, Surana NK, Edelman SM, Troy EB, et al. Gut immune maturation depends on colonization with a host-specific microbiota. Cell. 2012;149:1578–93.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    4.
    Heijtz RD, Wang S, Anuar F, Qian Y, Bjorkholm B, Samuelsson A, et al. Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci USA. 2011;108:3047–52.
    CAS  Article  Google Scholar 

    5.
    Erny D, de Angelis ALH, Jaitin D, Wieghofer P, Staszewski O, David E, et al. Host microbiota constantly control maturation and function of microglia in the CNS. Nat Neurosci. 2015;18:965–77.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    6.
    van der Waaij D. The ecology of the human intestine and its consequences for overgrowth by pathogens such as clostridium difficile. Annu Rev Microbiol. 1989;43:69–87.
    PubMed  Article  Google Scholar 

    7.
    Dinan TG, Stilling RM, Stanton C, Cryan JF. Collective unconscious: how gut microbes shape human behavior. J Psychiatr Res. 2015;63:1–9.
    PubMed  Article  Google Scholar 

    8.
    Hird SM. Evolutionary biology needs wild microbiomes. Front Microbiol. 2017;8:1–10.
    Article  Google Scholar 

    9.
    Scupham AJ, Patton TG, Bent E, Bayles DO. Comparison of the cecal microbiota of domestic and wild turkeys. Micro Ecol. 2008;56:322–31.
    Article  Google Scholar 

    10.
    Goodrich JK, Davenport ER, Waters JL, Clark AG, Ley RE. Cross-species comparisons of host genetic associations with the microbiome. Science. 2016;352:532–5.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    11.
    Hird SM, Carstens BC, Cardiff SW, Dittmann DL, Brumfield RT. Sampling locality is more detectable than taxonomy or ecology in the gut microbiota of the brood-parasitic brown-headed cowbird (Molothrus ater). PeerJ. 2014;2:1–21.
    Article  Google Scholar 

    12.
    Benson AK, Kelly SA, Legge R, Ma F, Low SJ, Kim J, et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc Natl Acad Sci. 2019;107:18933–8.
    Article  Google Scholar 

    13.
    Musitelli F, Ambrosini R, Rubolini D, Saino N, Franzetti A, Gandolfi I. Cloacal microbiota of barn swallows from Northern Italy. Ethol Ecol Evol. 2018;30:362–72.
    Article  Google Scholar 

    14.
    Muegge BD, Kuczynski J, Knights D, Clemente JC, González A, Fontana L, et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science. 2011;332:970–4.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    15.
    Hird SM, Sánchez C, Carstens BC, Brumfield RT. Comparative gut microbiota of 59 neotropical bird species. Front Microbiol. 2015;6:1403.
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Bili M, Cortesero AM, Mougel C, Gauthier JP, Ermel G, Simon JC, et al. Bacterial community diversity harboured by interacting species. PLoS One. 2016;11:1–23.
    Article  CAS  Google Scholar 

    17.
    Sugio A, Dubreuil G, Giron D, Simon J. Plant – insect interactions under bacterial influence: ecological implications and underlying mechanisms. J Exp Bot. 2015;66:467–78.
    CAS  PubMed  Article  Google Scholar 

    18.
    Hannula SE, Zhu F, Heinen R, Bezemer TM. Foliar-feeding insects acquire microbiomes from the soil rather than the host plant. Nat Commun. 2019;10:1–9.
    CAS  Article  Google Scholar 

    19.
    White J, Mirleau P, Danchin E, Mulard H, Hatch SA, Heeb P, et al. Sexually transmitted bacteria affect female cloacal assemblages in a wild bird. Ecol Lett. 2010;13:1515–24.
    PubMed  PubMed Central  Article  Google Scholar 

    20.
    Schlechter RO, Miebach M, Remus-Emsermann MNP. Driving factors of epiphytic bacterial communities: a review. J Adv Res. 2019;19:57–65.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    21.
    Remus-Emsermann MNP, Lücker S, Müller DB, Potthoff E, Daims H, Vorholt JA. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ Microbiol. 2014;16:2329–40.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Remus-Emsermann MNP, Tecon R, Kowalchuk GA, Leveau JHJ. Variation in local carrying capacity and the individual fate of bacterial colonizers in the phyllosphere. ISME J. 2012;6:756–65.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    23.
    Rogers TJ, Leppanen C, Brown V, Fordyce JA, LeBude A, Ranney T, et al. Exploring variation in phyllosphere microbial communities across four hemlock species. Ecosphere. 2018;9:1–11.
    Article  Google Scholar 

    24.
    Redford AJ, Bowers RM, Knight R, Linhart Y, Fierer N. The ecology of the phyllosphere: geographic and phylogenetic variability in the distribution of bacteria on tree leaves. Environ Microbiol. 2010;12:2885–93.
    PubMed  PubMed Central  Article  Google Scholar 

    25.
    Laforest-Lapointe I, Messier C, Kembel SW. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. Microbiome. 2016;4:1–10.
    Article  Google Scholar 

    26.
    Kembel SW, Mueller RC. Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities. Botany. 2014;92:303–11.
    Article  Google Scholar 

    27.
    Appel MH. The chewing herbivore gut lumen: Physicochemical conditions and their impact on plant nutrients, allelochemicals, and insect pathogens. In: Bernays EA (ed.). Insect-plant interactions, 1st ed. 1994. CRC Press, Boca Raton, pp 209–23.

    28.
    Shannon AL, Attwood G, Hopcroft DH, Christeller JT. Characterization of lactic acid bacteria in the larval midgut of the keratinophagous lepidopteran, Hofmannophila pseudospretella. Lett Appl Microbiol. 2001;32:36–41.
    CAS  PubMed  Article  Google Scholar 

    29.
    Kukal O, Dawson TE, Kukal O, Dawson TE. Temperature and food quality influences feeding behavior, assimilation efficiency and growth rate of arctic woolly-bear caterpillars. Oecologia. 1989;79:526–32.
    PubMed  Article  Google Scholar 

    30.
    Vilanova C, Baixeras J, Latorre A, Porcar M. The generalist inside the specialist: gut bacterial communities of two insect species feeding on toxic plants are dominated by Enterococcus sp. Front Microbiol. 2016;7:1–8.
    Article  Google Scholar 

    31.
    Priya NG, Ojha A, Kajla MK, Raj A, Rajagopal R. Host plant induced variation in gut bacteria of Helicoverpa armigera. PLoS One. 2012;7:1–10.
    Google Scholar 

    32.
    Jones AG, Mason CJ, Felton GW, Hoover K. Host plant and population source drive diversity of microbial gut communities in two polyphagous insects. Sci Rep. 2019;9:1–11.
    Article  CAS  Google Scholar 

    33.
    Hammer TJ, Janzen DH, Hallwachs W, Jaffe SP, Fierer N. Caterpillars lack a resident gut microbiome. PNAS. 2017;114:9641–6.
    CAS  PubMed  Article  Google Scholar 

    34.
    Whitaker MRL, Salzman S, Sanders JG, Kaltenpoth M, Pierce NE. Microbial communities of lycaenid butterflies do not correlate with larval diet. Front Microbiol. 2016;7:1–13.
    Article  Google Scholar 

    35.
    Stanley D, Geier MS, Hughes RJ, Denman SE, Moore RJ. Highly variable microbiota development in the chicken gastrointestinal tract. PLoS One. 2013;8:6–13.
    Google Scholar 

    36.
    Azcárate-García M, Ruiz-Rodríguez M, Díaz-Lora S, Ruiz-Castellano C, Soler JJ. Experimentally broken faecal sacs affect nest bacterial environment, development and survival of spotless starling nestlings. J Avian Biol. 2019;50:1–10.
    Article  Google Scholar 

    37.
    Devaynes A, Antunes A, Bedford A, Ashton P. Progression in the bacterial load during the breeding season in nest boxes occupied by the Blue Tit and its potential impact on hatching or fledging success. J Ornithol. 2018;159:1009–17.
    Article  Google Scholar 

    38.
    Janczyk P, Hall B, Souffrant WB. Microbial community composition of the crop and ceca contents of laying hens fed diets supplemented with Chlorella vulgaris. Poult Sci. 2009;88:2324–32.
    CAS  PubMed  Article  Google Scholar 

    39.
    Waite DW, Taylor MW. Exploring the avian gut microbiota: current trends and future directions. Front Microbiol. 2015;6:1–12.
    Article  Google Scholar 

    40.
    Pan D, Yu Z. Intestinal microbiome of poultry and its interaction with host and diet. Gut Microbes. 2014;5:108–19.
    PubMed  Article  Google Scholar 

    41.
    Lewis WB, Moore FR, Wang S. Changes in gut microbiota of migratory passerines during stopover after crossing an ecological barrier. Auk. 2017;134:137–45.
    Article  Google Scholar 

    42.
    Kulkarni S, Heeb P. Social and sexual behaviours aid transmission of bacteria in birds. Behav Process. 2007;74:88–92.
    Article  Google Scholar 

    43.
    Dawkins R. The extended phenotype. Oxford: Oxford University Press; 1982.
    Google Scholar 

    44.
    Fisher DN, Haines JA, Boutin S, Dantzer B, Lane JE, Coltman DW, et al. Indirect effects on fitness between individuals that have never met via an extended phenotype. Ecol Lett. 2019;22:697–706.
    PubMed  Article  Google Scholar 

    45.
    Mennerat A, Perret P, Lambrechts MM. Local individual preferences for nest materials in a passerine bird. PLoS One. 2009;4:1–6.
    Article  CAS  Google Scholar 

    46.
    Blondel J, Thomas DW, Charmantier A, Perret P, Bourgault P, Lambrechts MM. A thirty-year study of phenotypic and genetic variation of blue tits in mediterranean habitat mosaics. Bioscience. 2006;56:661–73.
    Article  Google Scholar 

    47.
    Blondel J, Dias PC, Maistre M, Perret P. Habitat heterogeneity and life-history variation of mediterranean blue tits (Parus caeruleus). Auk. 1993;110:511–20.
    Article  Google Scholar 

    48.
    Visser ME, Van Noordwijk AJ, Tinbergen JM, Lessells CM. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc R Soc B Biol Sci. 1998;265:1867–70.
    Article  Google Scholar 

    49.
    Stenning M. The Blue Tit, 1st ed. (T. & A. D. Poyser, London, UK. 2018) pp 69–109.

    50.
    Blondel J, Aronson J, Bodiou J-Y, Boeuf G. The mediterranean region: biological diversity in space and time, 2nd ed. 2010. Oxford University Press, Oxford.

    51.
    Charmantier A, Doutrelant C, Dubuc-messier G, Fargevieille A, Szulkin M. Mediterranean blue tits as a case study of local adaptation. Evol Appl. 2016;9:135–52.
    PubMed  Article  Google Scholar 

    52.
    Dubuc-Messier G, Réale D, Perret P, Charmantier A. Environmental heterogeneity and population differences in blue tits personality traits. Behav Ecol. 2017;28:448–59.
    PubMed  Google Scholar 

    53.
    Bańbura J, Blondel J, de Wilde-Lambrechts H, Galan M-J, Maistre M. Nestling diet variation in an insular mediterranean population of blue tits Parus caeruleus: effects of years, territories and individuals. Oecologia. 1994;100:413–20.
    PubMed  Article  Google Scholar 

    54.
    Alda F, Rey I, Doadrio I. An improved method of extracting degraded DNA samples from birds and other species. Ardeola. 2007;54:331–4.
    Google Scholar 

    55.
    Oehm J, Juen A, Nagiller K, Neuhauser S, Traugott M. Molecular scatology: how to improve prey DNA detection success in avian faeces? Mol Ecol Resour. 2011;11:620–8.
    PubMed  Article  Google Scholar 

    56.
    Eriksson P, Mourkas E, González-Acuna D, Olsen B, Ellström P. Evaluation and optimization of microbial DNA extraction from fecal samples of wild Antarctic bird species. Infect Ecol Epidemiol. 2017;7:1–9.
    Google Scholar 

    57.
    Chelius MK, Triplett EW. The diversity of archaea and bacteria in association with the roots of Zea mays L. Micro Ecol. 2001;41:252–63.
    CAS  Article  Google Scholar 

    58.
    Callahan BJ, Mcmurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high resolution sample inference from illumina amplicon data. Nat Methods. 2016;13:581–3.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Davis NM, Proctor D, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2017;6:1–8.
    Google Scholar 

    61.
    McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:1–11.
    Article  CAS  Google Scholar 

    62.
    Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
    Google Scholar 

    63.
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community ecology package. R package version 2.5-7. 2020.

    64.
    Vorholt JA. Microbial life in the phyllosphere. Nat Rev Microbiol. 2012;10:828–40.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    Bulgarelli D, Schlaeppi K, Spaepen S, van Themaat EVL, Schulze-Lefert P. Structure and functions of the bacterial microbiota of plants. Annu Rev Plant Biol. 2013;64:807–38.
    CAS  PubMed  Article  Google Scholar 

    66.
    Müller T, Ruppel S. Progress in cultivation-independent phyllosphere microbiology. FEMS Microbiol Ecol. 2014;87:2–17.
    PubMed  Article  CAS  Google Scholar 

    67.
    Chaturvedi S, Rego A, Lucas LK, Gompert Z. Sources of variation in the gut microbial community of Lycaeides melissa caterpillars. Sci Rep. 2017;7:1–13.
    Article  CAS  Google Scholar 

    68.
    Videvall E, Strandh M, Engelbrecht A, Cloete S, Cornwallis CK. Measuring the gut microbiome in birds: comparison of faecal and cloacal sampling. Mol Ecol Resour. 2017;18:424–34.
    PubMed  Article  CAS  Google Scholar 

    69.
    Lewis WB, Moore FR, Wang S. Characterization of the gut microbiota of migratory passerines during stopover along the northern coast of the Gulf of Mexico. J Avian Biol. 2016;47:659–68.
    Article  Google Scholar 

    70.
    Sun CH, Liu H-Y, Zhang Y, Lu C-H. Comparative analysis of the gut microbiota of hornbill and toucan in captivity. Microbiologyopen. 2019;8:1–7.
    CAS  Article  Google Scholar 

    71.
    Teyssier A, Lens L, Matthysen E, White J. Dynamics of gut microbiota diversity during the early development of an avian host: evidence from a cross-foster experiment. Front Microbiol. 2018;9:1–12.
    Article  Google Scholar 

    72.
    Ambrosini R, Corti M, Franzetti A, Caprioli M, Rubolini D, Motta VM, et al. Cloacal microbiomes and ecology of individual barn swallows. FEMS Microbiol Ecol. 2019;95:1–13.
    Article  CAS  Google Scholar 

    73.
    Minard G, Tikhonov G, Ovaskainen O, Saastamoinen M. The microbiome of the Melitaea cinxia butterfly shows marked variation but is only little explained by the traits of the butterfly or its host plant. Environ Microbiol. 2019;21:4253–69.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Godoy-Vitorino F, Leal SJ, Díaz WA, Rosales J, Goldfarb KC, García-Amado MA, et al. Differences in crop bacterial community structure between hoatzins from different geographical locations. Res Microbiol. 2012;163:211–20.
    PubMed  Article  Google Scholar 

    75.
    Lucas FS, Heeb P. Environmental factors shape cloacal bacterial assemblages in great tit Parus major and blue tit P. caeruleus nestlings. J Avian Biol. 2005;36:510–6.
    Article  Google Scholar  More