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    Ancient mitogenomics elucidates diversity of extinct West Indian tortoises

    1.
    TTWG [Turtle Taxonomy Working Group; Rhodin, A. G. J. et al.] Turtles of the World. Annotated Checklist and Atlas of Taxonomy, Synonymy, Distribution, and Conservation Status (8th Ed.) (Chelonian Research Foundation and Turtle Conservancy, Chelonian Research Monographs 7, 2017).
    2.
    TEWG [Turtle Extinctions Working Group; Rhodin, A. G. J. et al.] Turtles and Tortoises of the World During the Rise and Global Spread of Humanity: First Checklist and Review of Extinct Pleistocene and Holocene Chelonians (IUCN/SSC Tortoise and Freshwater Turtle Specialist Group, Chelonian Research Monographs 5, 2015).

    3.
    Clausen, C. J., Cohen, A. D., Emiliani, C., Holman, J. A. & Stipp, J. J. Little Salt Spring, Florida: A unique underwater site. Science 203, 609–614 (1979).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    4.
    Holman, J. A. & Clausen, C. J. Fossil vertebrates associated with Paleo-Indian artifact at Little Salt Spring, Florida. J. Vertebr. Paleontol. 4, 146–154 (1984).
    Article  Google Scholar 

    5.
    Cantalamessa, G. et al. A new vertebrate fossiliferous site from the Late Quaternary at San José on the north coast of Ecuador: Preliminary note. J. South Am. Earth Sci. 14, 331–334 (2001).
    ADS  Article  Google Scholar 

    6.
    Aguilera Socorro, O. Tesoros paleontológicos de Venezuela. El Cuaternario del Estado Falcón (Ministerio de la Cultura, Instituto del Patrimonio Cultural, Caracas, 2006).
    Google Scholar 

    7.
    Zacarías, G. G., de la Fuente, M. S., Fernández, M. S. & Zurita, A. E. Nueva especie de tortuga terrestre gigante del género Chelonoidis Fitzinger, 1835 (Cryptodira: Testudinidae), del miembro inferior de la Formación Toropí/Yupoí (Pleistoceno tardío/Lujanense), Bella Vista, Corrientes, Argentina. Ameghiniana 50, 298–318 (2013).
    Article  Google Scholar 

    8.
    Zacarías, G. G., de la Fuente, M. S. & Zurita, A. E. Testudinoidea Fitzinger (Testudines: Cryptodira) de la Formación Toropí/Yupoí (ca. 58–28 ka) en la Provincia de Corrientes, Argentina: Taxonomía y aspectos paleoambientales. Rev. Bras. Paleontol. 17, 389–404 (2014).
    Article  Google Scholar 

    9.
    Torres Chiriboga, F. J. Histología ósea de una tortuga gigante del Pleistoceno (Testudinidae) de Ecuador continental, con comentarios del origen de las tortugas de Galápagos (Disertación previa, Pontificia Universidad Católica del Ecuador, Quito, 2016).
    Google Scholar 

    10.
    Cadena, E. A. & Román-Carrión, J. L. A review of the fossil record of Ecuador, with insights about its challenges and future development. Ameghiniana 55, 571–591 (2018).
    Article  Google Scholar 

    11.
    Franz, R., Albury, N. A. & Steadman, D. W. Extinct tortoises from the Turks and Caicos Islands. Florida Mus. Nat. Hist. Bull. 58, 1–38 (2020).
    Google Scholar 

    12.
    Williams, E. E. Testudo cubensis and the evolution of Western Hemisphere tortoises. Bull. Am. Mus. Nat. Hist. 95, 1–36 (1950).
    Google Scholar 

    13.
    Williams, E. E. A new fossil tortoise from Mona Island, West Indies, and a tentative arrangement of the tortoises of the world. Bull. Am. Mus. Nat. Hist. 99, 545–560 (1952).
    Google Scholar 

    14.
    Auffenberg, W. Notes on West Indian tortoises. Herpetologica 23, 34–44 (1967).
    Google Scholar 

    15.
    Franz, R. & Woods, C. A. A fossil tortoise from Hispaniola. J. Herpetol. 17, 79–81 (1983).
    Article  Google Scholar 

    16.
    Franz, R. & Franz, S. A new fossil land tortoise in the genus Chelonoidis (Testudines: Testudinidae) from the northern Bahamas, with an osteological assessment of other Neotropical tortoises. Florida Mus. Nat. Hist. Bull. 49, 1–44 (2009).
    Google Scholar 

    17.
    Steadman, D. W. et al. Exceptionally well preserved late Quaternary plant and vertebrate fossils from a blue hole on Abaco, The Bahamas. Proc. Natl. Acad. Sci. USA 104, 19897–19902 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Hastings, A. K., Krigbaum, J., Steadman, D. W. & Albury, N. A. Domination by reptiles in a terrestrial food web of the Bahamas prior to human occupation. J. Herpetol. 48, 380–388 (2014).
    Article  Google Scholar 

    19.
    Kehlmaier, C. et al. Tropical ancient DNA reveals relationships of the extinct Bahamian giant tortoise Chelonoidis alburyorum. Proc. R. Soc. B 284, 20162235 (2017).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    20.
    Steadman, D. W. et al. The paleoecology and extinction of endemic tortoises in the Bahamian Archipelago. Holocene 30, 420–427 (2020).
    ADS  Article  Google Scholar 

    21.
    Albury, N. A., Franz, R., Rimoli, P., Lehman, P. & Rosenberger, A. L. Fossil land tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a description of a new species. Am. Mus. Novit. 3904, 1–28 (2018).
    Article  Google Scholar 

    22.
    Fulton, T. L. & Shapiro, B. Setting up an ancient DNA laboratory. In Ancient DNA: Methods and Protocols. Methods in Molecular Biology, Vol. 1963 (eds Shapiro, B. et al.), 1–13 (Humana Press, Totowa, 2019).
    Google Scholar 

    23.
    Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. USA 110, 15758–15763 (2013).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Gansauge, M.-T. & Meyer, M. Single-stranded DNA library preparation for the sequencing of ancient or damaged DNA. Nat. Protoc. 8, 737–748 (2013).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    25.
    Korlević, P. et al. Reducing microbial and human contamination in DNA extractions from ancient bones and teeth. Biotechniques 58, 87–93 (2015).
    Google Scholar 

    26.
    Maricic, T., Whitten, M. & Pääbo, S. Multiplexed DNA sequence capture of mitochondrial genomes using PCR products. PLoS One 5, e14004 (2010).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    27.
    Horn, S. Target enrichment via DNA hybridization capture. In Ancient DNA: Methods and Protocols. Methods in Molecular Biology, Vol. 840 (eds Shapiro, B. & Hofreiter, M.), 177–188 (Springer, Berlin, 2012).
    Google Scholar 

    28.
    Jiang, H., Lei, R., Ding, S. W. & Zhu, S. Skewer: A fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinform. 15, 182 (2014).
    Article  Google Scholar 

    29.
    Bushnell, B., Rood, J. & Singer, E. BBMerge—accurate paired shotgun read merging via overlap. PLoS One 12, e0185056 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    30.
    Wingett, S. W. & Andrews, S. FastQ Screen: A tool for multi-genome mapping and quality control [version 2; referees: 4 approved]. F1000Research 7, 1338 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    31.
    Hahn, C., Bachmann, L. & Chevreux, B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—a baiting and iterative mapping approach. Nucleic Acids Res. 41, 1–9 (2013).
    Article  CAS  Google Scholar 

    32.
    Milne, I. et al. Using Tablet for visual exploration of second-generation sequencing data. Brief. Bioinform. 14, 193–202 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    33.
    Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    34.
    Kehlmaier, C. et al. Ancient mitogenomics clarifies radiation of extinct Mascarene giant tortoises. Sci. Rep. 9, 17487 (2019).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    35.
    Poulakakis, N. et al. Colonization history of Galapagos giant tortoises: Insights from mitogenomes support the progression rule. J. Zool. Syst. Evol. Res. 58, 1262–1275 (2020).
    Article  Google Scholar 

    36.
    Thompson, J. D., Higgins, D. G. & Gibson, T. J. Clustal W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).
    CAS  Google Scholar 

    38.
    Bernt, M. et al. MITOS: Improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).
    PubMed  Article  Google Scholar 

    39.
    Kumar, S., Stecher, G., Knyaz, C. & Tamura, K. MEGA X: Molecular Evolutionary Genetic Analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    40.
    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 

    41.
    Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    42.
    Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2016).
    Google Scholar 

    43.
    Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 5, 901–904 (2018).
    Article  CAS  Google Scholar 

    44.
    Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    Woods, R. et al. Rapid size change associated with intra-island evolutionary radiation in extinct Caribbean “island shrews”. BMC Evol. Biol. 29, 106 (2020).
    Article  CAS  Google Scholar 

    46.
    Geist, D., Snell, H. L., Snell, H. M., Goddard, C. & Kurz, M. Paleogeography of the Galápagos Islands and biogeographical implications. In The Galápagos: A Natural Laboratory for the Earth Sciences, Vol. 204 (eds Harpp, K., Mittelstaedt, E., d’Ozouville, N. & Graham, D.) 145–166 (American Geophysical Union, New York, 2014).
    Google Scholar 

    47.
    Hearty, P. J., Kindler, P., Cheng, H. & Edwards, R. A +20 m middle Pleistocene sea-level highstand (Bermuda and the Bahamas) due to partial collapse of Antarctic ice. Geology 27, 375–378 (1999).
    ADS  Article  Google Scholar 

    48.
    Bowen, D. Sea level ∼400 000 years ago (MIS 11): Analogue for present and future sea-level? Clim. Past 6, 19–29 (2010).
    Article  Google Scholar 

    49.
    Steadman, D. W. & Franklin, J. Origin, paleoecology, and extirpation of bluebirds and crossbills in the Bahamas across the last glacial-interglacial transition. Proc. Natl. Acad. Sci. USA 114, 9924–9929 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    50.
    Fritz, U., Široký, P., Kami, H. & Wink, M. Environmentally caused dwarfism or a valid species—Is Testudo weissingeri Bour, 1996 a distinct evolutionary lineage? New evidence from mitochondrial and nuclear genomic markers. Mol. Phylogenet. Evol. 37, 389–401 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Fritz, U. et al. Phenotypic plasticity leads to incongruence between morphology-based taxonomy and genetic differentiation in western Palaearctic tortoises (Testudo graeca complex; Testudines, Testudinidae). Amphibia-Reptilia 28, 97–121 (2007).
    Article  Google Scholar 

    52.
    Fritz, U. et al. Mitochondrial phylogeography and subspecies of the wide-ranging sub-Saharan leopard tortoise Stigmochelys pardalis (Testudines: Testudinidae)—a case study for the pitfalls of pseudogenes and GenBank sequences. J. Zool. Syst. Evol. Res. 48, 348–359 (2010).
    Article  Google Scholar 

    53.
    Fritz, U. et al. Northern genetic richness and southern purity, but just one species in the Chelonoidis chilensis complex. Zool. Scr. 41, 220–232 (2012).
    Article  Google Scholar 

    54.
    Carlson, L. A. & Keegan, W. F. Resource depletion in the prehistoric northern West Indies. In Voyages of Discovery (ed. Fitzpatrick, S. M.) 85–107 (Praeger, Westport, 2004).
    Google Scholar 

    55.
    Keegan, W. F. Taino Indian Myth and Practice: The Arrival of the Stranger King (University Press of Florida, Gainesville, 2007).
    Google Scholar 

    56.
    Oswald, J. A. et al. Ancient DNA and high-resolution chronometry reveal a long-term human role in the historical diversity and biogeography of the Bahamian hutia. Sci. Rep. 10, 1373 (2020).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    57.
    Loire, E. & Galtier, N. Lacking conservation genomics in the giant Galápagos tortoise. bioRxiv 101980, 1–14 (2017).
    Google Scholar 

    58.
    Fontaine, M. C. A genomic perspective is needed for the re-evaluation of species boundaries, evolutionary trajectories, and conservation strategies of the Galápagos giant tortoises. PCI Evol. Biol. 100031, 1–3 (2017).
    Google Scholar 

    59.
    Vargas-Ramírez, M., Maran, J. & Fritz, U. Red- and yellow-footed tortoises (Chelonoidis carbonaria, C. denticulata) in South American savannahs and forests: Do their phylogeographies reflect distinct habitats? Org. Divers. Evol. 10, 161–172 (2010).
    Article  Google Scholar 

    60.
    Blake, S. et al. Seed dispersal by Galápagos tortoises. J. Biogeogr. 39, 1961–1972 (2012).
    Article  Google Scholar 

    61.
    Walton, R. et al. In the land of giants: Habitat use and selection of the Aldabra giant tortoise on Aldabra Atoll. Biodiv. Conserv. 28, 3183–3198 (2019).
    Article  Google Scholar  More

  • in

    Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika

    1.
    Alin SR, Johnson TC. Carbon cycling in large lakes of the world: a synthesis of production, burial, and lake-atmosphere exchange estimates. Glob Biogeochemical Cycles. 2007;21:GB3002.
    Google Scholar 
    2.
    Durisch-Kaiser E, Schmid M, Peeters F, Kipfer R, Dinkel C, Diem T, et al. What prevents outgassing of methane to the atmosphere in Lake Tanganyika? J Geophys Res. 2011;116:G02022.
    Google Scholar 

    3.
    Takahashi T, Koblmüller S. The adaptive radiation of Cichlid fish in Lake Tanganyika: a morphological perspective. Int J Evolut Biol. 2011;2011:1–14.
    Article  Google Scholar 

    4.
    Salzburger W. Understanding explosive diversification through Cichlid fish genomics. Nat Rev Genet. 2018;19:705–17.
    CAS  PubMed  Article  Google Scholar 

    5.
    Corman JR, McIntyre PB, Kuboja B, Mbemba W, Fink D, Wheeler CW, et al. Upwelling couples chemical and biological dynamics across the littoral and pelagic zones of Lake Tanganyika, East Africa. Limnol Oceanogr. 2010;55:214–24.
    CAS  Article  Google Scholar 

    6.
    Cabello-Yeves PJ, Zemskaya TI, Rosselli R, Coutinho FH, Zakharenko AS, Blinov VV, et al. Genomes of novel microbial lineages assembled from the sub-ice waters of Lake Baikal. Appl Environ Microbiol. 2017;84:e02132–17.
    PubMed  PubMed Central  Article  Google Scholar 

    7.
    Cabello‐Yeves PJ, Zemskaya TI, Zakharenko AS, Sakirko MV, Ivanov VG, Ghai R, et al. Microbiome of the deep Lake Baikal, a unique oxic bathypelagic habitat. Limnol Oceanogr. 2019;65:1471–88.
    Article  CAS  Google Scholar 

    8.
    De Wever A. Spatio-temporal dynamics in the microbial food web in Lake Tanganyika. University of Gent; 2006. p. 1–169.

    9.
    Pirlot S, Unrein F, Descy J-P, Servais P. Fate of heterotrophic bacteria in Lake Tanganyika (East Africa): fate of bacteria in Lake Tanganyika. FEMS Microbiol Ecol. 2007;62:354–64.
    CAS  PubMed  Article  Google Scholar 

    10.
    Schubert CJ, Durisch-Kaiser E, Wehrli B, Thamdrup B, Lam P, Kuypers MMM. Anaerobic ammonium oxidation in a tropical freshwater system (Lake Tanganyika). Environ Microbiol. 2006;8:1857–63.
    CAS  PubMed  Article  Google Scholar 

    11.
    Shade A, Kent AD, Jones SE, Newton RJ, Triplett EW, McMahon KD. Interannual dynamics and phenology of bacterial communities in a eutrophic lake. Limnol Oceanogr. 2007;52:487–94.
    CAS  Article  Google Scholar 

    12.
    Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    13.
    Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    14.
    Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.
    PubMed  PubMed Central  Article  Google Scholar 

    15.
    Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.
    CAS  PubMed  Article  Google Scholar 

    16.
    Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    17.
    Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–68.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    18.
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    19.
    The Genome Standards Consortium, Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.
    Article  CAS  Google Scholar 

    20.
    Bushnell B. BBMAP. https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/. 2014.

    21.
    Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
    Article  CAS  Google Scholar 

    22.
    Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:13219.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    23.
    Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:1–6.
    Article  CAS  Google Scholar 

    25.
    Brown AMV, Howe DK, Wasala SK, Peetz AB, Zasada IA, Denver DR. Comparative genomics of a plant-parasitic nematode endosymbiont suggest a role in nutritional symbiosis. Genome Biol Evol. 2015;7:2727–46.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    26.
    Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    27.
    Miller MA, Pfeiffer W, Schwartz Terri. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. Proceedings of the Gateway Computing Environments Workshop. New Orleans, LA; 2010. p. 1–8.

    28.
    Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    Newton RJ, Jones SE, Eiler A, McMahon KD, Bertilsson S. A guide to the natural history of freshwater lake bacteria. Microbiol Mol Biol Rev. 2011;1:14.
    Article  CAS  Google Scholar 

    30.
    Rohwer RR, Hamilton JJ, Newton RJ, McMahon KD. TaxAss: leveraging a custom freshwater database achieves fine-scale taxonomic resolution. mSphere. 2018;3:e00327–18.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    31.
    Soo RM, Hemp J, Parks DH, Fischer WW, Hugenholtz P. On the origins of oxygenic photosynthesis and aerobic respiration in Cyanobacteria. Science. 2017;355:1436–40.
    CAS  PubMed  Article  Google Scholar 

    32.
    Linz AM, He S, Stevens SLR, Anantharaman K, Rohwer RR, Malmstrom RR, et al. Freshwater carbon and nutrient cycles revealed through reconstructed population genomes. PeerJ. 2018;6:e6075.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    33.
    Bendall ML, Stevens SL, Chan L-K, Malfatti S, Schwientek P, Tremblay J, et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 2016;10:1589–601.
    PubMed  PubMed Central  Article  Google Scholar 

    34.
    Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    35.
    Soo RM, Skennerton CT, Sekiguchi Y, Imelfort M, Paech SJ, Dennis PG, et al. An expanded genomic representation of the phylum cyanobacteria. Genome Biol Evolution. 2014;6:1031–45.
    Article  Google Scholar 

    36.
    Zhou Z, Tran P, Liu Y, Kieft K, Anantharaman K. METABOLIC: a scalable high-throughput metabolic and biogeochemical functional trait profiler based on microbial genomes. bioRxiv. 2019;761643.

    37.
    Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    38.
    Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Katta HY, Mojica A, et al. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res. 2019;47:D649–59.
    CAS  PubMed  Article  Google Scholar 

    39.
    Edmond JM, Stallard RF, Craig H, Craig V, Weiss RF, Coulter GW. Nutrient chemistry of the water column of Lake Tanganyika. Limnol Oceanogr. 1993;38:725–38.
    CAS  Article  Google Scholar 

    40.
    Verburga P, Hecky RE. The physics of the warming of Lake Tanganyika by climate change. Limnol Oceanogr. 2009;54:2418–30.
    Article  Google Scholar 

    41.
    Järvinen M, Salonen K, Sarvala J, Vuorio K, Virtanen A. The stoichiometry of particulate nutrients in Lake Tanganyika—implications for nutrient limitation of phytoplankton. Hydrobiologia. 1999;407:81–8.
    Article  Google Scholar 

    42.
    Ehrenfels B, Bartosiewicz M, Mbonde AS, Baumann KBL, Dinkel C, Junker J, et al. Thermocline depth and euphotic zone thickness regulate the abundance of diazotrophic cyanobacteria in Lake Tanganyika. Preprint at https://doi.org/10.5194/bg-2020-214 (2020).

    43.
    Tran P, Ramachandran A, Khawasik O, Beisner BE, Rautio M, Huot Y, et al. Microbial life under ice: Metagenome diversity and in situ activity of Verrucomicrobia in seasonally ice‐covered Lakes. Environ Microbiol. 2018;20:2568–84.
    CAS  PubMed  Article  Google Scholar 

    44.
    Martinez-Garcia M, Brazel DM, Swan BK, Arnosti C, Chain PSG, Reitenga KG, et al. Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of verrucomicrobia. PLoS ONE. 2012;7:1–11.
    Google Scholar 

    45.
    Damrow R, Maldener I, Zilliges Y. The multiple functions of common microbial carbon polymers, glycogen and PHB, during stress responses in the non-diazotrophic Cyanobacterium Synechocystis sp. PCC 6803. Front Microbiol. 2016;7:966.
    PubMed  PubMed Central  Article  Google Scholar 

    46.
    Paerl HW, Otten TG. Duelling ‘CyanoHABs’: unravelling the environmental drivers controlling dominance and succession among diazotrophic and non-N2-fixing harmful cyanobacteria. Environ Microbiol. 2016;18:316–24.
    CAS  PubMed  Article  Google Scholar 

    47.
    Raymond J, Siefert JL, Staples CR, Blankenship RE. The natural history of nitrogen fixation. Mol Biol Evol. 2004;21:541–54.
    CAS  PubMed  Article  Google Scholar 

    48.
    Berman-Frank I, Lundgren P, Falkowski P. Nitrogen fixation and photosynthetic oxygen evolution in cyanobacteria. Res Microbiol. 2003;154:157–64.
    CAS  PubMed  Article  Google Scholar 

    49.
    Cabello-Yeves PJ, Ghai R, Mehrshad M, Picazo A, Camacho A, Rodriguez-valera F. Reconstruction of diverse verrucomicrobial genomes from metagenome datasets of freshwater reservoirs. Front Microbiol. 2017;8:2131.
    PubMed  PubMed Central  Article  Google Scholar 

    50.
    Hansel CM, Fendorf S, Jardine PM, Francis CA. Changes in bacterial and archaeal community structure and functional diversity along a geochemically variable soil profile. Appl Environ Microbiol. 2008;74:1620–33.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    51.
    Edlund A, Hårdeman F, Jansson JK, Sjöling S. Active bacterial community structure along vertical redox gradients in Baltic Sea sediment. Environ Microbiol. 2008;10:2051–63.
    PubMed  Article  CAS  Google Scholar 

    52.
    Beman JM, Carolan MT. Deoxygenation alters bacterial diversity and community composition in the ocean’s largest oxygen minimum zone. Nat Commun. 2013;4:2705.
    PubMed  Article  CAS  Google Scholar 

    53.
    Schoell M, Tietze K, Schoberth SM. Origin of methane in Lake Kivu (East-Central Africa). Chem Geol. 1988;71:257–65.
    CAS  Article  Google Scholar 

    54.
    Bogard MJ, del Giorgio PA, Boutet L, Chaves MCG, Prairie YT, Merante A, et al. Oxic water column methanogenesis as a major component of aquatic CH4 fluxes. Nat Commun. 2014;5:5350.
    CAS  PubMed  Article  Google Scholar 

    55.
    Vanwonterghem I, Evans PN, Parks DH, Jensen PD, Woodcroft BJ, Hugenholtz P, et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat Microbiol. 2016;1:16170.
    CAS  PubMed  Article  Google Scholar 

    56.
    Gao Q, Chen S, Kimirei IA, Zhang L, Mgana H, Mziray P, et al. Wet deposition of atmospheric nitrogen contributes to nitrogen loading in the surface waters of Lake Tanganyika, East Africa: a case study of the Kigoma region. Environ Sci Pollut Res. 2018;25:11646–60.
    CAS  Article  Google Scholar 

    57.
    Chale FMM. Inorganic nutrient concentrations and chlorophyll in the euphotic zone of Lake Tanganyika. Hydrobiologia. 2004;523:189–97.
    CAS  Article  Google Scholar 

    58.
    Higgins SN, Hecky RE, Taylor WD. Epilithic nitrogen fixation in the rocky littoral zones of Lake Malawi, Africa. Limnol Oceanogr. 2001;46:976–82.
    CAS  Article  Google Scholar 

    59.
    Brion N, Nzeyimana E, Goeyens L, Nahimana D, Tungaraza C, Baeyens W. Inorganic nitrogen uptake and river inputs in northern Lake Tanganyika. J Gt Lakes Res. 2006;32:553–64.
    CAS  Article  Google Scholar 

    60.
    Norici A, Hell R, Giordano M. Sulfur and primary production in aquatic environments: an ecological perspective. Photosynth Res. 2005;86:409–17.
    CAS  PubMed  Article  Google Scholar 

    61.
    Botz RW, Stoffers P. Light hydrocarbon gases in Lake Tanganyika hydrothermal fluids (East-Central Africa). Chem Geol. 1993;104:217–24.
    CAS  Article  Google Scholar 

    62.
    Tiercelin J-J, Pflumio C, Castrec M, Boulégue J, Gente P, Rolet J, et al. Hydrothermal vents in Lake Tanganyika, East African, Rift system. Geology. 1993;21:499–502.
    CAS  Article  Google Scholar 

    63.
    Elsgaard L, Prieur D. Hydrothermal vents in Lake Tanganyika harbor spore-forming thermophiles with extremely rapid growth. J Gt Lakes Res. 2011;37:203–6.
    CAS  Article  Google Scholar 

    64.
    Preisler A, de Beer D, Lichtschlag A, Lavik G, Boetius A, Jørgensen BB. Biological and chemical sulfide oxidation in a Beggiatoa inhabited marine sediment. ISME J. 2007;1:341–53.
    CAS  PubMed  Article  Google Scholar 

    65.
    McAllister SM, Moore RM, Gartman A, Luther GW, Emerson D, Chan CS. The Fe(II)-oxidizing Zetaproteobacteria: historical, ecological and genomic perspectives. FEMS Microbiol Ecol. 2019;95:fiz015.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    66.
    Carpenter SR. Phosphorus control is critical to mitigating eutrophication. Proc Natl Acad Sci. 2008;105:11039–40.
    CAS  PubMed  Article  Google Scholar 

    67.
    Lewis WM, Jr. Causes for the high frequency of nitrogen limitation in tropical lakes. SIL Proceedings. vol. 28. 2002; p. 210–3.

    68.
    De Keyzer ELR, Masilya Mulungula P, Alunga Lufungula G, Amisi Manala C, Andema Muniali A, Bashengezi Cibuhira P, et al. Local perceptions on the state of the pelagic fisheries and fisheries management in Uvira, Lake Tanganyika, DR Congo. J Great Lakes Res. 2020;46:1740–53.
    Article  Google Scholar 

    69.
    Mölsä, H. Management of fisheries on Lake Tanganyika challenges for research and the community. University of Kuopio; 2008.

    70.
    Foley B, Jones ID, Maberly SC, Rippey B. Long-term changes in oxygen depletion in a small temperate lake: effects of climate change and eutrophication. Freshw Biol. 2012;57:278–89.
    CAS  Article  Google Scholar  More

  • in

    Eye fluke infection changes diet composition in juvenile European perch (Perca fluviatilis)

    1.
    Minchella, D. J. & Scott, M. E. Parasitism: a cryptic determinant of animal community structure. Trends Ecol. Evol. 6(8), 250–254. https://doi.org/10.1016/0169-5347(91)90071-5 (1991).
    CAS  Article  PubMed  Google Scholar 
    2.
    Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: how many parasites? How many host?. Proc. Natl. Acad. Sci. USA 105, 11482–11489. https://doi.org/10.1073/pnas.0803232105 (2008).
    ADS  Article  PubMed  Google Scholar 

    3.
    Hatcher, M. J. & Dunn, A. M. Parasites in ecological communities: from interactions to ecosystems. https://doi.org/10.1017/CBO9780511987359 (Cambridge University Press, Cambridge, 2011).
    Google Scholar 

    4.
    Sures, B., Nachev, M., Pahl, M., Grabner, D. & Selbach, C. Parasites as drivers of key processes in aquatic ecosystems: facts and future directions. Exp. Parasitol. 180, 141–147. https://doi.org/10.1016/j.exppara.2017.03.011 (2017).
    CAS  Article  PubMed  Google Scholar 

    5.
    Marcogliese, D. J. & Cone, D. K. Food webs: a plea for parasites. Trends Ecol. Evol. 12, 320–325. https://doi.org/10.1016/S0169-5347(97)01080-X (1997).
    CAS  Article  PubMed  Google Scholar 

    6.
    Thompson, R. M., Mouritsen, K. N. & Poulin, R. Importance of parasites and their life cycle characteristics in determining the structure of a large marine food web. J. Anim. Ecol. 74, 77–85. https://doi.org/10.1111/j.1365-2656.2004.00899.x (2005).
    Article  Google Scholar 

    7.
    Hernandez, A. D. & Sukhdeo, M. V. K. Parasites alter the topology stream food web across seasons. Oecologia 156, 613–624. https://doi.org/10.1007/s00442-008-0999-9 (2008).
    ADS  Article  PubMed  Google Scholar 

    8.
    Dick, J. T. A. et al. Parasitism may enhance rather than reduce the predatory impact of an invader. Biol. Lett. 6, 636–638. https://doi.org/10.1098/rsbl.2010.0171 (2010).
    Article  PubMed  PubMed Central  Google Scholar 

    9.
    Buck, J. C. Indirect effects explain the role of parasites in ecosystems. Trends Parasitol. 35, 835–847. https://doi.org/10.1016/j.pt.2019.07.007 (2019).
    Article  PubMed  Google Scholar 

    10.
    Sabadel, A. J. M., Stumbo, A. D. & MacLeod, C. D. Stable-isotope analysis: a neglected tool for placing parasites in food webs. J. Helminthol. 93, 1–7. https://doi.org/10.1017/S0022149X17001201 (2019).
    CAS  Article  PubMed  Google Scholar 

    11.
    Barber, I., Hoare, D. & Krause, J. Effects of parasites on fish behaviour: an evolutionary perspective and review. Rev. Fish Biol. Fish. 10, 131–165. https://doi.org/10.1023/A:1016658224470 (2000).
    Article  Google Scholar 

    12.
    Barber, I. & Wright, H.A. Effects of parasites on fish behaviour: interactions with host physiology in Fish physiology (eds. Katherine, R.W.W., Sloman, A. & Sigal, B.) 109–149. https://doi.org/10.1016/S1546-5098(05)24004-9 (Academic Press, 2005)

    13.
    Hughes, D. P., Brodeur, J. & Thomas, F. Host Manipulation by Parasites (Oxford University Press, Oxford, 2012).
    Google Scholar 

    14.
    Moore, J. Parasites and Behaviour of Animals (Oxford University Press, Oxford, 2002).
    Google Scholar 

    15.
    Shariff, M., Richards, R. H. & Sommerville, C. The histopathology of acute and chronic infections of rainbow trout Salmo gairdneri Richardson with eye flukes, Diplostomum spp. J. Fish. Dis. 3, 455–465. https://doi.org/10.1111/j.1365-2761.1980.tb00432.x (1980).
    Article  Google Scholar 

    16.
    Stumbo, A. D. & Poulin, R. Possible mechanism of host manipulation resulting from a diel behaviour pattern of eye-dwelling parasites?. Parasitology 143, 1261–1267. https://doi.org/10.1017/S0031182016000810 (2016).
    Article  PubMed  Google Scholar 

    17.
    Poulin, R. & Cribb, T. H. Trematode life cycles: short is sweet?. Trends Parasitol. 18, 176–183. https://doi.org/10.1016/S1471-4922(02)02262-6 (2002).
    Article  PubMed  Google Scholar 

    18.
    Cribb, T. H., Bray, R. A., Olson, P. D. & Littlewood, D. T. J. Life cycle evolution in the Digenea: a new perspective from phylogeny. Adv. Parasitol. 54, 197–254. https://doi.org/10.1016/S0065-308X(03)54004-0 (2003).
    Article  PubMed  Google Scholar 

    19.
    Streilein, J. W. Oculae immune privilege: the eye takes a dim but practical view of immunity and inflammation. J. Leukoc. Biol. 74, 179–185. https://doi.org/10.1189/jlb.1102574 (2003).
    CAS  Article  PubMed  Google Scholar 

    20.
    Crowden, A. E. & Broom, D. M. Effects of the eyefluke, Diplostomum spathaceum, on the behaviour of dace (Leuciscus leuciscus). Anim. Behav. 28, 287–294. https://doi.org/10.1016/S0003-3472(80)80031-5 (1980).
    Article  Google Scholar 

    21.
    Seppälä, O., Karvonen, A. & Valtonen, E. T. Manipulation of fish host by eye flukes in relation to cataract formation and parasite infectivity. Anim. Behav. 70, 889–894. https://doi.org/10.1016/j.anbehav.2005.01.020 (2005).
    Article  Google Scholar 

    22.
    Seppälä, O., Karvonen, A. & Valtonen, E. T. Shoaling behaviour of fish under parasitism and predation risk. Anim. Behav. 75, 145–150. https://doi.org/10.1016/j.anbehav.2007.04.022 (2008).
    Article  Google Scholar 

    23.
    Vivas Muñoz, J. C., Bierbach, D. & Knopf, K. Eye fluke (Tylodelphys clavata) infection impairs visual ability and hampers foraging success in European perch. Parasitol. Res. 118, 2531–2541. https://doi.org/10.1007/s00436-019-06389-5 (2019).
    Article  PubMed  Google Scholar 

    24.
    Vivas Muñoz, J. C., Staaks, G. & Knopf, K. The eye fluke Tylodelphys clavata affects prey detection and intraspecific competition of European perch (Perca fluviatilis). Parasitol. Res. 116, 2561–2567. https://doi.org/10.1007/s00436-017-5564-1 (2017).
    Article  PubMed  Google Scholar 

    25.
    Bergman, E. Foraging abilities and niche breadths of two percids, Perca fluviatilis and Gymnocephalus cernua, under different environmental conditions. J. Anim. Ecol. 57, 443–453. https://doi.org/10.2307/4916 (1988).
    Article  Google Scholar 

    26.
    Diehl, S. Foraging efficiency of three freshwater fishes: effects of structural complexity and light. Oikos 53, 207–214. https://doi.org/10.2307/3566064 (1988).
    Article  Google Scholar 

    27.
    Craig, J. F. Percid Fishes: Systematics, Ecology and Exploitation (Blackwell Science, Hoboken, 2000). https://doi.org/10.1002/9780470696033.
    Google Scholar 

    28.
    Kennedy, C. R. & Burrough, R. Parasites of trout and perch in Malham Tarn. Fld. Stud. 4, 617–629 (1978).
    Google Scholar 

    29.
    Kennedy, C. R. Long term studies on the population biology of two species of eye fluke, Diplostomurn gasterostei and Tylodelphys clavata (Digenea: Diplostomatidae), concurrently infecting the eyes of perch, Perca fluviatilis. J. Fish Biol. 19, 221–236. https://doi.org/10.1111/j.1095-8649.1981.tb05826.x (1981).
    Article  Google Scholar 

    30.
    Kennedy, C. R. Interspecific interactions between larval digeneans in the eyes of perch, Perca fluviatilis. Parasitology 122, S13–S22. https://doi.org/10.1017/S0031182000016851 (2001).
    Article  PubMed  Google Scholar 

    31.
    Valtonen, E. T., Holmes, J. C., Aronen, J. & Rautalahti, I. Parasite communities as indicators of recovery from pollution: parasites of roach (Rutilus rutilus) and perch (Perca fluviatilis) in Central Finland. Parasitology 126, S43–S52. https://doi.org/10.1017/S0031182003003494 (2003).
    CAS  Article  PubMed  Google Scholar 

    32.
    Behrmann-Godel, J. Parasite identification, succession and infection pathways in perch fry (Perca fluviatilis): new insights through a combined morphological and genetic approach. Parasitology 140, 509–520. https://doi.org/10.1017/S0031182012001989 (2013).
    CAS  Article  PubMed  Google Scholar 

    33.
    Soylu, E. Metazoan parasites of perch Perca fluviatilis L. from Lake Sığırcı, Ipsala. Turkey. Pak. J. Zool. 45, 47–52 (2013).
    Google Scholar 

    34.
    Vivas Muñoz, J.C. Tylodelphys clavata in perch (Perca fluviatilis): spatial heterogeneity, impact on feeding behaviour and intraspecific competition. Master Thesis. Humboldt-Universität zu Berlin (2014)

    35.
    Hjelm, J., Svanbäck, R., Byström, P., Persson, L. & Wahlström, E. Diet dependent body morphology and ontogenetic reaction norms in Eurasian perch. Oikos 95, 311–323. https://doi.org/10.1034/j.1600-0706.2001.950213.x (2001).
    Article  Google Scholar 

    36.
    Svanbäck, R. & Eklöv, P. Effects of habitat and food resources on morphology and ontogenetic growth trajectories in perch. Oecologia 131, 61–70. https://doi.org/10.1007/s00442-001-0861-9 (2002).
    ADS  Article  PubMed  Google Scholar 

    37.
    Svanbäck, R. & Eklöv, P. Morphology dependent foraging efficiency in perch: a trade-off for ecological specialization?. Oikos 102, 273–284. https://doi.org/10.1034/j.1600-0706.2003.12657.x (2003).
    Article  Google Scholar 

    38.
    Svanbäck, R. & Eklöv, P. Morphology in perch affects habitat specific feeding efficiency. Funct. Ecol. 18, 503–510. https://doi.org/10.1111/j.0269-8463.2004.00858.x (2004).
    Article  Google Scholar 

    39.
    Quevedo, M. & Olsson, J. The effect of small-scale resource origin on trophic position estimates in Perca fluviatilis. J. Fish Biol. 69, 141–150. https://doi.org/10.1111/j.1095-8649.2006.01072.x (2006).
    Article  Google Scholar 

    40.
    Quevedo, M., Svanbäck, R. & Eklöv, P. Intrapopulation niche partitioning in a generalist predator limits food web connectivity. Ecology 90, 2263–2274. https://doi.org/10.1890/07-1580.1 (2009).
    Article  PubMed  Google Scholar 

    41.
    Frankiewicz, P. & Wojtal-Frankiewicz, A. Two different feeding tactics of young-of-the-year perch, Perca fluviatilis L., inhabiting the littoral zone of the lowland Sulejow Reservoir (Central Poland). Ecohydrol. Hydrobiol. 12, 35–41. https://doi.org/10.2478/v10104-012-0001-7 (2012).
    Article  Google Scholar 

    42.
    Persson, L. Effects of reduced interspecific competition on resource utilization in perch (Perca fluviatilis). Ecology 67, 355–364. https://doi.org/10.2307/1938578 (1986).
    Article  Google Scholar 

    43.
    Persson, L. & Greenberg, L. Interspecific and intraspecific size class competition affecting resource use and growth of perch, Perca fluviatilis. Oikos 59, 97–106. https://doi.org/10.2307/3545128 (1990).
    Article  Google Scholar 

    44.
    Diehl, S. Effects of habitat structure on resource availability, diet and growth of benthivorous perch, Perca fluviatilis. Oikos 67, 403–414. https://doi.org/10.2307/3545353 (1993).
    Article  Google Scholar 

    45.
    Svanbäck, R. & Persson, L. Individual diet specialization, niche width and population dynamics: implications for trophic polymorphisms. J. Anim. Ecol. 73, 973–982. https://doi.org/10.1111/j.0021-8790.2004.00868.x (2004).
    Article  Google Scholar 

    46.
    Eklöv, P. & Svanbäck, R. Predation risk influences adaptive morphological variation in fish populations. Am. Nat. 167, 440–452. https://doi.org/10.1086/499544 (2006).
    Article  PubMed  Google Scholar 

    47.
    Svanbäck, R. & Bolnick, D. I. Intraspecific competition drives increased resource use diversity within a natural population. Proc. R. Soc. B Biol. Sci. 274, 839–844. https://doi.org/10.1098/rspb.2006.0198 (2007).
    Article  Google Scholar 

    48.
    Sharma, C. M. & Borgstrøm, R. Shift in density, habitat use, and diet of perch and roach: An effect of changed predation pressure after manipulation of pike. Fish. Res. 91, 98–106. https://doi.org/10.1016/j.fishres.2007.11.011 (2008).
    Article  Google Scholar 

    49.
    Svanbäck, R., Eklöv, P., Fransson, R. & Holmgren, K. Intraspecific competition drives multiple species resource polymorphism in fish communities. Oikos 117, 114–124. https://doi.org/10.1111/j.2007.0030-1299.16267.x (2008).
    Article  Google Scholar 

    50.
    Okun, N. & Mehner, T. Distribution and feeding of juvenile fish on invertebrates in littoral reed (Phragmites) stands. Ecol. Freshw. Fish 14, 139–149. https://doi.org/10.1111/j.1600-0633.2005.00087.x (2005).
    Article  Google Scholar 

    51.
    Hyslop, E. J. Stomach content analysis: a review of methods and their application. J. Fish Biol. 17, 411–429. https://doi.org/10.1111/j.1095-8649.1980.tb02775.x (1980).
    Article  Google Scholar 

    52.
    Peterson, B. J. & Fry, B. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18, 293–320. https://doi.org/10.1146/annurev.ecolsys.18.1.293 (1987).
    Article  Google Scholar 

    53.
    Beaudoin, C. P., Tonn, W. M., Prepas, E. E. & Wassenaar, L. I. Individual specialization and trophic adaptability of northern pike (Esox lucius): an isotope and dietary analysis. Oecologia 120, 386–396. https://doi.org/10.1007/s004420050871 (1999).
    ADS  Article  PubMed  Google Scholar 

    54.
    Bolnick, D. I. et al. The ecology of individuals: incidence and implications of individual specialization. Am. Nat. 161, 1–28. https://doi.org/10.2307/3078879 (2003).
    MathSciNet  Article  PubMed  Google Scholar 

    55.
    Bearhop, S. et al. Stable isotopes indicate sex-specific and long-term individual foraging specialization in diving seabirds. Mar. Ecol. Prog. Ser. 311, 157–164. https://doi.org/10.3354/meps311157 (2006).
    ADS  Article  Google Scholar 

    56.
    Phillips, D. L. & Gregg, J. W. Source partitioning using stable isotopes: coping with too many sources. Oecologia 136, 261–269. https://doi.org/10.1007/s00442-003-1218-3 (2003).
    ADS  Article  PubMed  Google Scholar 

    57.
    Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5, e9672. https://doi.org/10.1371/journal.pone.0009672 (2010).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    58.
    Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399. https://doi.org/10.1002/env.2221 (2013).
    MathSciNet  Article  Google Scholar 

    59.
    Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology? Trends Ecol. Evol. 26, 183–192. https://doi.org/10.1016/j.tree.2011.01.009 (2011).
    Article  Google Scholar 

    60.
    Voutilainen, A., Figueiredo, K. & Huuskonen, H. Effects of the eye fluke Diplostomum spathaceum on the energetics and feeding of Arctic charr Salvelinus alpinus. J. Fish Biol. 73, 2228–2237. https://doi.org/10.1111/j.1095-8649.2008.02050.x (2008).
    Article  Google Scholar 

    61.
    Padrós, F., Knuden, R. & Blasco-Costa, I. Histopathological characterisation of retinal lesions associated to Diplostomum species (Platyhelminthes: Trematoda) infection in polymorphic Arctic charr Salvelinus alpinus. Int. J. Parasito. 7, 68–74. https://doi.org/10.1016/j.ijppaw.2018.01.007 (2018).
    Article  Google Scholar 

    62.
    Ubels, J. L. et al. Impairment of retinal function in yellow perch (Perca flavescens) by Diplostomum baeri metacercariae. Int. J. Parasitol. Parasites Wildl. 7, 171–179. https://doi.org/10.1016/j.ijppaw.2018.05.001 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    63.
    Lemly, A. D. & Esch, G. W. Effects of the trematode Uvulifer ambloplitis on juvenile bluegill sunfish, Lepomis macrochirus: ecological implications. J. Parasit. 70, 475–492. https://doi.org/10.2307/3281395 (1984).
    Article  Google Scholar 

    64.
    Santoro, M. et al. Parasitic infection by larval helminths in Antarctic fishes: pathological changes and impact on the host body condition index. Dis. Aquat. Org. 105, 139–148. https://doi.org/10.3354/dao02626 (2013).
    CAS  Article  Google Scholar 

    65.
    Owen, S. F., Barber, I. & Hart, P. J. B. Low level infection by eye fluke, Diplostomum spp., affects the vision of three-spined sticklebacks, Gasterosteus aculeatus. J. Fish Biol. 42, 803–806. https://doi.org/10.1111/j.1095-8649.1993.tb00387.x (1993).
    Article  Google Scholar 

    66.
    Pennycuick, L. Quantitative effects of three species of parasites on a population of three-spined sticklebacks, Gasterosteus aculeatus L. J. Zool. 165, 143–162. https://doi.org/10.1111/j.1469-7998.1971.tb02179.x (1971).
    Article  Google Scholar 

    67.
    Marcogliese, D. J. et al. Spatial and temporal variations in abundance of Diplostomum spp. in walleye (Stizostedion vitreum) and white sucker (Catostomus commersoni) from the St. Lawrence River: importance the importance of gulls and fish stocks. Can. J. Zool. 79, 355–369. https://doi.org/10.1139/z00-209 (2001).
    Article  Google Scholar 

    68.
    Dörücü, M., Dildiz, N. & Grabbe, M. C. J. Occurrence and effects of Diplostomum sp. infection in eyes of Acanthobrama marmid in Keban Dam Lake, Elazığ, Turkey. Turk. J. Vet. Anim. Sci. 26, 239–243 (2002).
    Google Scholar 

    69.
    Machado, P. M., Takemoto, R. M. & Pavanelli, G. C. Diplostomum (Austrodiplostomum) compactum (Lutz, 1928) (Platyhelminthes, Digenea) metacercariae in fish from the floodplain of the Upper Paraná River. Brazil. Parasitol. Res. 97, 436–444. https://doi.org/10.1007/s00436-005-1483-7 (2005).
    CAS  Article  PubMed  Google Scholar 

    70.
    Weatherley, A. H. Growth and Ecology of Fish Populations (Academic Press, London, 1972).
    Google Scholar 

    71.
    Lagrue, C. & Poulin, R. Measuring fish body condition with or without parasites: does it matter?. J. Fish Biol. 87, 836–847. https://doi.org/10.1111/jfb.12749 (2015).
    CAS  Article  PubMed  Google Scholar 

    72.
    Craig, J. F. A study of the food and feeding of perch, Perca fluviatilis L., inWindermere. Freshw Biol 8, 59–68. https://doi.org/10.1111/j.1365-2427.1978.tb01426.x (1978).
    Article  Google Scholar 

    73.
    Guma’a, S.A. The food and feeding habits of young perch, Perca fluviatilis, in Windermere. Freshw Biol 8, 177–187. https://doi.org/10.1111/j.1365-2427.1978.tb01439.x (1978).
    Article  Google Scholar 

    74.
    Wang, N. & Eckmann, R. Distribution of perch (Perca fluviatilis L.) during their first year of life in Lake Constance. Hydrobiologia 277, 135–143. https://doi.org/10.1007/BF00007295 (1994).
    Article  Google Scholar 

    75.
    Imbock, F., Appenzeller, A. & Eckmann, R. Diel and seasonal distribution of perch in Lake Constance: a hydroacoustic study and in situ observations. J. Fish Biol. 49, 1–13. https://doi.org/10.1111/j.1095-8649.1996.tb00001.x (1996).
    Article  Google Scholar 

    76.
    Hejlm, J., Persson, L. & Christensen, B. Growth, morphological variation and ontogenetic niche shifts in perch (Perca fluviatilis) in relation to resource availability. Oceologia 122, 190–199. https://doi.org/10.1007/PL00008846 (2000).
    ADS  Article  Google Scholar 

    77.
    Horppila, J. et al. Seasonal changes in the diets and relative abundances of perch and roach in the littoral and pelagic zones of a large lake. J. Fish Biol. 56, 51–72. https://doi.org/10.1111/j.1095-8649.2000.tb02086.x (1999).
    Article  Google Scholar 

    78.
    Allen, K. R. The food and migration of the perch (Perca fluviatilis) in Windermere. J Anim Ecol 4, 264–273. https://doi.org/10.2307/1016 (1935).
    Article  Google Scholar 

    79.
    Mustamäki, N., Cederberg, T. & Mattila, J. Diet, stable isotopes and morphology of Eurasian perch (Perca fluviatilis) in littoral and pelagic habitats in the northern Baltic Proper. Environ. Biol. Fish 97, 675–689. https://doi.org/10.1007/s10641-013-0169-8 (2014).
    Article  Google Scholar 

    80.
    Bootsma, H. A., Hecky, R. E., Hesslein, R. H. & Turner, G. F. Food partitioning among Lake Malawi nearshore fishes as revealed by stable isotope analyses. Ecology 77, 1286–1290. https://doi.org/10.2307/2265598 (1996).
    Article  Google Scholar 

    81.
    Jakobsen, P. J., Johnsen, G. H. & Larsson, P. Effects of predation risk and parasitism on the feeding ecology, habitat use, and abundance of lacustrine threespine stickleback (Gasterosteus aculeatus). Can. J. Fish. Aq. Sci. 45, 426–431. https://doi.org/10.1139/f88-051 (1988).
    Article  Google Scholar 

    82.
    Milinski, M. Parasites determine a predator’s optimal feeding strategy. Behav. Ecol. Sociobiol. 15, 35–37. https://doi.org/10.1007/BF00310212 (1984).
    Article  Google Scholar 

    83.
    Barber, I. & Huntingford, F. A. The effect of Schistocephalus solidus (Cestoda: Pseudophyllidea) on the foraging and shoaling behaviour of three-spined sticklebacks, Gasterosteus aculeatus. Behaviour 132, 1223–1240. https://doi.org/10.1163/156853995X00540 (1995).
    Article  Google Scholar 

    84.
    Van den Brink, F. W. B., Van der Velde, G. & Bij de Vaate, A. Amphipod invasion on the Rhine. Nature 352, 576. https://doi.org/10.1038/352576a0 (1991).
    ADS  Article  Google Scholar 

    85.
    den Hartog, C., Van den Brink, F. W. B. & Van der Velde, G. Why was the invasion of the river Rhine by Corophium curvispinum and Corbicula species so successful?. J. Nat. Hist. 26, 1121–1129. https://doi.org/10.1080/00222939200770651 (1992).
    Article  Google Scholar 

    86.
    Dick, J. T. A. & Platvoet, D. Invading predatory crustacean Dikerogammarus villosus eliminates both native and exotic species. Proc. R. Soc. Lond. B Biol. Sci. 267, 977–983. https://doi.org/10.1098/rspb.2000.1099 (2000).
    CAS  Article  Google Scholar 

    87.
    Platvoet, D., Van Der Velde, G., Dick, J. T. A. & Li, S. Q. Flexible omnivory in Dikerogammarus villosus (Sowinsky, 1894) (Amphipoda) – Amphipod Pilot Species Project (AMPIS) Report 5. Crustaceana 82, 703–720. https://doi.org/10.1163/156854009X423201 (2009).
    Article  Google Scholar 

    88.
    Richter, L. et al. The very hungry amphipod: the invasive Dikerogammarus villosus shows high consumption rates for two food sources and independent of predator cues. Biol. Invasions 20, 1321–1335. https://doi.org/10.1007/s10530-017-1629-4 (2018).
    Article  Google Scholar 

    89.
    Worischka, S. et al. Food consumption of the invasive amphipod Dikerogammarus villosus in field mesocosms and its effects on leaf decomposition and periphyton. Aquat. Invasions 13, 261–275. https://doi.org/10.3391/ai.2018.13.2.07 (2018).
    Article  Google Scholar 

    90.
    Berg, M.B. Laval food and feeding behaviour in The Chironomidae (eds. Armitage, P.D., Cranston, P.S. & Pinder, L.C.V.) 136–168. https://doi.org/10.1007/978-94-011-0715-0_7 (Springer, 1995)

    91.
    Henriques-Oliveira, A. L., Nessimian, J. L. & Dorvillé, L. F. M. Feeding habits of chironomid larvae (Insecta: Diptera) from a stream in the Floresta da Tijuca, Rio de janeiro, Brazil. Braz. J. Biol. 63, 269–281. https://doi.org/10.1590/S1519-69842003000200012 (2003).
    CAS  Article  PubMed  Google Scholar 

    92.
    Post, D. M. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83, 703–718. https://doi.org/10.2307/3071875 (2002).
    Article  Google Scholar 

    93.
    Syrovátka, V. The predatory behaviour of Monopelopia tenuicalcar (Kieffer, 1918) larvae in a laboratory experiment. J. Limnol. 77, 88–94. https://doi.org/10.4081/jlimnol.2018.1792 (2018).
    Article  Google Scholar 

    94.
    Bernot, R. J. & Lamberti, G. A. Indirect effects of a parasite on a benthic community: an experiment with trematodes, snails and periphyton. Freshw. Biol. 53, 322–329. https://doi.org/10.1111/j.1365-2427.2007.01896.x (2008).
    Article  Google Scholar 

    95.
    Seppälä, O., Karvonen, A. & Valtonen, E. T. Parasite-induced change in host behaviour and susceptibility to predation in an eye fluke-fish interaction. Anim. Behav. 68, 257–263. https://doi.org/10.1016/j.anbehav.2003.10.021 (2004).
    Article  Google Scholar 

    96.
    Gopko, M., Mikheev, V. N. & Taskinen, J. Deterioration of basic components of the anti-predator behavior in fish harboring eye fluke larvae. Behav. Ecol. Sociobiol. 71, 68. https://doi.org/10.1007/s00265-017-2300-x (2017).
    Article  Google Scholar 

    97.
    Flink, H., Behrens, J. W. & Svensson, P. A. Consequences of eye fluke infection on anti-predator behaviours in invasive round gobies in Kalmar Sound. Parasitol. Res. 116, 1653–1663. https://doi.org/10.1007/s00436-017-5439-5 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    98.
    Scheffer, M., Hosper, S. H., Meijer, M. L., Moss, B. & Jeppesen, E. Alternative equilibria in shallow lakes. Trends Evol. Ecol. 8, 275–279. https://doi.org/10.1016/0169-5347(93)90254-M (1993).
    CAS  Article  Google Scholar 

    99.
    Driescher, E., Behrendt, H., Schellenberger, G. & Stellmacher, R. Lake Müggelsee and its environment – natural conditions and anthropogenic impacts. Int. Revue. ges. Hydrobiol. 78, 327–343. https://doi.org/10.1002/iroh.19930780303 (1993).
    CAS  Article  Google Scholar 

    100.
    Kozicka, J. & Niewiadomska, K. Studies on the biology and taxonomy of trematodesof the genus Tylodelphys Diesing, 1850 (Diplostomatidae). Acta Parasitol. Pol. 8, 379–400 (1960).
    Google Scholar 

    101.
    Dönges, J. Entwicklungs- und Lebensdauer von Metacercarien. Z. Parasitenk. 31, 340–366. https://doi.org/10.1007/BF00259732 (1969).
    Article  PubMed  Google Scholar 

    102.
    Kennedy, C. R. Long-term stability in the population levels of the eyefluke Tylodelphys podicipina(Digenea: Diplostomatidae) in perch. J. Fish Biol. 31, 571–581. https://doi.org/10.1111/j.1095-8649.1987.tb05259.x (1987).
    Article  Google Scholar 

    103.
    Höglund, J. & Thulin, J. Identification of Diplostomumspp. in the retina of perch Perca fluviatilisand the lens of roach Rutilus rutilusfrom the Baltic Sea – an experimental study. Syst. Parasitol. 21, 1–19. https://doi.org/10.1007/BF00009910 (1992).
    Article  Google Scholar 

    104.
    Niewiadomska, K. Rasoẑyty ryb Polski Prywry – Digenea (Polskie Towarzystwo Parazytologiczne, Warsaw, Poland, 2003).
    Google Scholar 

    105.
    Blasco-Costa, I. et al. Fish pathogens near the Arctic Circle: molecular, morphological and ecological evidence for unexpected diversity of Diplostomum (Digenea: diplostomidae) in Iceland. Int. J. Parasitol. 44, 703–715. https://doi.org/10.1016/j.ijpara.2014.04.009 (2014).
    Article  PubMed  Google Scholar 

    106.
    Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al revisited. J. Parasitol. 83, 575–583. https://doi.org/10.2307/3284227 (1997).
    CAS  Article  PubMed  Google Scholar 

    107.
    Nash, R. D. M., Valencia, A. H. & Geffen, A. J. The origin of Fulton’s condition factor: setting the record straight. Fisheries 31, 236–238 (2006).
    Google Scholar 

    108.
    Persson, L., Andersson, J., Wahlström, E. & Eklöv, P. Size–specific interactions in lake systems: predator gape limitation and prey growth rate and mortality. Ecology 77, 900–911. https://doi.org/10.2307/2265510 (1996).
    Article  Google Scholar 

    109.
    Pinder, L. C. V. Biology of freshwater Chironomidae. Ann. Rev. Entomol. 31, 1–23. https://doi.org/10.1146/annurev.en.31.010186.000245 (1986).
    Article  Google Scholar 

    110.
    Linzmaier, S. M., Twardochleb, L. A., Olden, J. D., Mehner, T. & Arlinghaus, R. Size-dependent foraging niches of European Perch Perca fluviatilis (Linnaeus, 1758) and North American Yellow Perch Perca flavescens (Mitchill, 1814). Environ. Biol. Fish 101, 23–37. https://doi.org/10.1007/s10641-017-0678-y (2018).
    Article  Google Scholar 

    111.
    Nachev, M. et al. Understanding trophic interactions in host–parasite associations using stable isotopes of carbon and nitrogen. Parasit Vectors 10, 90. https://doi.org/10.1186/s13071-017-2030-y (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    112.
    Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid. Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).
    ADS  CAS  Article  PubMed  Google Scholar 

    113.
    DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506. https://doi.org/10.1016/0016-7037(78)90199-0 (1978).
    ADS  CAS  Article  Google Scholar 

    114.
    DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–351. https://doi.org/10.1016/0016-7037(81)90244-1 (1981).
    ADS  CAS  Article  Google Scholar 

    115.
    Fry, B. & Sherr, E. B. δ13C measurements as indicators of carbon flow in marine and freshwater ecosystems. Contrib. Mar. Sci. 27, 13–47 (1984).
    CAS  Google Scholar 

    116.
    Minagawa, M. & Wada, E. Stepwise enrichment of 15N along food chains: Further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140. https://doi.org/10.1016/0016-7037(84)90204-7 (1984).
    ADS  CAS  Article  Google Scholar 

    117.
    Vander Zanden, M. J. & Rasmussen, J. B. Variation in δ15N and δ13C trophic fractionation: Implications for aquatic food web studies. Limnol. Oceanogr. 46, 2061–2066. https://doi.org/10.4319/lo.2001.46.8.2061 (2001).
    ADS  CAS  Article  Google Scholar 

    118.
    Elsdon, T. S., Ayvazian, S., McMahon, K. W. & Thorrold, S. R. Experimental evaluation of stable isotope fractionation in fish muscle and otoliths. Mar. Ecol. Prog. Ser. 408, 195–205. https://doi.org/10.3354/meps08518 (2010).
    ADS  CAS  Article  Google Scholar 

    119.
    Parnell, A. & Jackson, A. SIAR: Stable isotope analysis in R. R package ver. 4.2. http://CRAN.R-project.org/package=siar (2013)

    120.
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2018) More

  • in

    Past landscape structure drives the functional assemblages of plants and birds

    1.
    Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes: eight hypotheses. Biol. Rev. 87, 661–685 (2012).
    PubMed  Article  Google Scholar 
    2.
    Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes: Heterogeneity and biodiversity. Ecol. Lett. 14, 101–112 (2011).
    PubMed  Article  Google Scholar 

    3.
    Rundlöf, M., Nilsson, H. & Smith, H. G. Interacting effects of farming practice and landscape context on bumble bees. Biol. Conserv. 141, 417–426 (2008).
    Article  Google Scholar 

    4.
    Wamser, S., Diekötter, T., Boldt, L., Wolters, V. & Dauber, J. Trait-specific effects of habitat isolation on carabid species richness and community composition in managed grasslands: Effects of habitat isolation on carabid beetles. Insect Conser. Divers. 5, 9–18 (2012).
    Article  Google Scholar 

    5.
    Sonnier, G., Jamoneau, A. & Decocq, G. Evidence for a direct negative effect of habitat fragmentation on forest herb functional diversity. Landsc. Ecol. 29, 857–866 (2014).
    Article  Google Scholar 

    6.
    Wilcove, D. S. & McLellan, C. H. Habitat fragmentation in the temperate zone. Conserv. Biol. 1, 237–256 (1986).
    Google Scholar 

    7.
    Wilcox, B. A. & Murphy, D. D. Conservation strategy: The effects of fragmentation on extinction. Am. Nat. 125, 879–887 (1985).
    Article  Google Scholar 

    8.
    Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology: The metacommunity concept. Ecol. Lett. 7, 601–613 (2004).
    Article  Google Scholar 

    9.
    Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).
    Article  Google Scholar 

    10.
    Fletcher, R. J. et al. Is habitat fragmentation good for biodiversity?. Biol. Conserv. 226, 9–15 (2018).
    Article  Google Scholar 

    11.
    Fahrig, L. et al. Is habitat fragmentation bad for biodiversity?. Biol. Conserv. 230, 179–186 (2019).
    Article  Google Scholar 

    12.
    Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    13.
    Perović, D. et al. Configurational landscape heterogeneity shapes functional community composition of grassland butterflies. J. Appl. Ecol. 52, 505–513 (2015).
    Article  Google Scholar 

    14.
    Concepción, E. D. et al. Contrasting trait assembly patterns in plant and bird communities along environmental and human-induced land-use gradients. Ecography 40, 753–763 (2017).
    Article  Google Scholar 

    15.
    Rocha-Santos, L. et al. The loss of functional diversity: A detrimental influence of landscape-scale deforestation on tree reproductive traits. J. Ecol. 108, 212–223 (2019).
    Article  Google Scholar 

    16.
    Provost, G. L. et al. Land-use history impacts functional diversity across multiple trophic groups. PNAS 117, 1573–1579 (2020).
    PubMed  Article  CAS  Google Scholar 

    17.
    Solé-Senan, X. O., Juárez-Escario, A., Robleño, I., Conesa, J. A. & Recasens, J. Using the response-effect trait framework to disentangle the effects of agricultural intensification on the provision of ecosystem services by Mediterranean arable plants. Agric. Ecosyst. Environ. 247, 255–264 (2017).
    Article  Google Scholar 

    18.
    Grime, J. P. Trait convergence and trait divergence in herbaceous plant communities: mechanisms and consequences. J. Veg. Sci. 17, 255–260 (2006).
    Article  Google Scholar 

    19.
    Macarthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).
    Article  Google Scholar 

    20.
    de Bello, F. et al. Evidence for scale- and disturbance-dependent trait assembly patterns in dry semi-natural grasslands. J. Ecol. 101, 1237–1244 (2013).
    Article  Google Scholar 

    21.
    Muscarella, R. & Uriarte, M. Do community-weighted mean functional traits reflect optimal strategies?. Proc. R. Soc. B 283, 20152434 (2016).
    PubMed  Article  Google Scholar 

    22.
    de Bello, F. et al. Partitioning of functional diversity reveals the scale and extent of trait convergence and divergence. J. Veg. Sci. 20, 475–486 (2009).
    Article  Google Scholar 

    23.
    Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules: Functional diversity measures. Funct. Ecol. 24, 867–876 (2010).
    Article  Google Scholar 

    24.
    Shmida, A. & Wilson, M. V. Biological determinants of species diversity. J. Biogeogr. 12, 1–20 (1985).
    Article  Google Scholar 

    25.
    Baudry, J. & Papy, F. The role of landscape heterogeneity in the sustainability of cropping systems. In Crop Science: Progress and Prospects (eds Baudry, J. & Papy, F.) 243–249 (CABI Publishing, Oxfordshire, 2001).
    Google Scholar 

    26.
    Duflot, R., Georges, R., Ernoult, A., Aviron, S. & Burel, F. Landscape heterogeneity as an ecological filter of species traits. Acta Oecol. 56, 19–26 (2014).
    ADS  Article  Google Scholar 

    27.
    Cleland, E., Chuine, I., Menzel, A., Mooney, H. & Schwartz, M. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    28.
    Hendrickx, F. et al. Pervasive effects of dispersal limitation on within- and among-community species richness in agricultural landscapes. Glob. Ecol. Biogeogr. 18, 607–616 (2009).
    Article  Google Scholar 

    29.
    Dunning, J. B., Danielson, B. J. & Pulliam, H. R. Ecological processes that affect populations in complex landscapes. Oikos 65, 169 (1992).
    Article  Google Scholar 

    30.
    Jonason, D. et al. Weak functional response to agricultural landscape homogenisation among plants, butterflies and birds. Ecography 40, 1221–1230 (2017).
    Article  Google Scholar 

    31.
    Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).
    PubMed  Article  Google Scholar 

    32.
    Diamond, J. M. Biogeographic kinetics: estimation of relaxation times for avifaunas of Southwest Pacific Islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).
    ADS  CAS  PubMed  Article  Google Scholar 

    33.
    Hanski, I. & Ovaskainen, O. Extinction debt at extinction threshold. Conserv. Biol. 16, 666–673 (2002).
    Article  Google Scholar 

    34.
    Helm, A., Hanski, I. & Partel, M. Slow response of plant species richness to habitat loss and fragmentation. Ecol. Lett. 9, 72–77 (2005).
    Google Scholar 

    35.
    Sang, A., Teder, T., Helm, A. & Pärtel, M. Indirect evidence for an extinction debt of grassland butterflies half century after habitat loss. Biol. Conserv. 143, 1405–1413 (2010).
    Article  Google Scholar 

    36.
    Lindborg, R. Evaluating the distribution of plant life-history traits in relation to current and historical landscape configurations. J. Ecol. 95, 555–564 (2007).
    Article  Google Scholar 

    37.
    Saar, L., de Bello, F., Pärtel, M. & Helm, A. Trait assembly in grasslands depends on habitat history and spatial scale. Oecologia 184, 1–12 (2017).
    ADS  PubMed  Article  Google Scholar 

    38.
    Yamanaka, S., Akasaka, T., Yamaura, Y., Kaneko, M. & Nakamura, F. Time-lagged responses of indicator taxa to temporal landscape changes in agricultural landscapes. Ecol. Ind. 48, 593–598 (2015).
    Article  Google Scholar 

    39.
    Piqueray, J. et al. Plant species extinction debt in a temperate biodiversity hotspot: Community, species and functional traits approaches. Biol. Conserv. 144, 1619–1629 (2011).
    Article  Google Scholar 

    40.
    Barbaro, L. & van Halder, I. Linking bird, carabid beetle and butterfly life-history traits to habitat fragmentation in mosaic landscapes. Ecography 32, 321–333 (2009).
    Article  Google Scholar 

    41.
    Grime, J. P. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
    Article  Google Scholar 

    42.
    Lortie, C. J. et al. Rethinking plant community theory. Oikos 107, 433–438 (2004).
    Article  Google Scholar 

    43.
    Turnbull, L. A., Rees, M. & Crawley, M. J. Seed mass and the competition/colonization trade-off: A sowing experiment. J. Ecol. 87, 899–912 (1999).
    Article  Google Scholar 

    44.
    van Kleunen, M., Fischer, M. & Schmid, B. Effects of intraspecific competition on size variation and reproductive allocation in a clonal plant. Oikos 94, 515–524 (2001).
    Article  Google Scholar 

    45.
    Zambrano, J. et al. The effects of habitat loss and fragmentation on plant functional traits and functional diversity: What do we know so far?. Oecologia 191, 505–518 (2019).
    ADS  PubMed  Article  Google Scholar 

    46.
    Atauri, J. A. & de Lucio, J. V. The role of landscape structure in species richness distribution of birds, amphibians, reptiles and lepidopterans in Mediterranean landscapes. Landsc. Ecol. 16, 147–159 (2001).
    Article  Google Scholar 

    47.
    Weibull, A.-C., Östman, Ö. & Granqvist, Å. Species richness in agroecosystems: the effect of landscape, habitat and farm management. Biodivers. Conserv. 12, 1335–1355 (2003).
    Article  Google Scholar 

    48.
    Smith, H. G., Dänhardt, J., Lindström, Å. & Rundlöf, M. Consequences of organic farming and landscape heterogeneity for species richness and abundance of farmland birds. Oecologia 162, 1071–1079 (2010).
    ADS  PubMed  Article  Google Scholar 

    49.
    Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. PNAS 116, 16442–16447 (2019).
    CAS  PubMed  Article  Google Scholar 

    50.
    Redon, M., Bergès, L., Cordonnier, T. & Luque, S. Effects of increasing landscape heterogeneity on local plant species richness: How much is enough?. Landsc. Ecol. 29, 773–787 (2014).
    Article  Google Scholar 

    51.
    Fahrig, L. Rethinking patch size and isolation effects: the habitat amount hypothesis. J. Biogeogr. 40, 1649–1663 (2013).
    Article  Google Scholar 

    52.
    MacDonald, Z. G., Anderson, I. D., Acorn, J. H. & Nielsen, S. E. The theory of island biogeography, the sample-area effect, and the habitat diversity hypothesis: Complementarity in a naturally fragmented landscape of lake islands. J. Biogeogr. 45, 2730–2743 (2018).
    Article  Google Scholar 

    53.
    Smart, S. M., Bunce, R. G. H., Firbank, L. G. & Coward, P. Do field boundaries act as refugia for grassland plant species diversity in intensively managed agricultural landscapes in Britain?. Agric. Ecosyst. Environ. 91, 73–87 (2002).
    Article  Google Scholar 

    54.
    Klimesova, J., Latzel, V., de Bello, F. & van Groenendael, J. M. Plant functional traits in studies of vegetation changes in response to grazing and mowing: Towards a use of more specific traits. Preslia 80, 245–253 (2008).
    Google Scholar 

    55.
    Fuller, R. J., Chamberlain, D. E., Burton, N. H. K. & Gough, S. J. Distributions of birds in lowland agricultural landscapes of England and Wales: How distinctive are bird communities of hedgerows and woodland?. Agric. Ecosyst. Environ. 84, 79–92 (2001).
    Article  Google Scholar 

    56.
    Hinsley, S. A. & Bellamy, P. E. The influence of hedge structure, management and landscape context on the value of hedgerows to birds: A review. J. Environ. Manage. 60, 33–49 (2000).
    Article  Google Scholar 

    57.
    Noh, J., Echeverría, C., Pauchard, A. & Cuenca, P. Extinction debt in a biodiversity hotspot: the case of the Chilean Winter Rainfall-Valdivian Forests. Landsc. Ecol. Eng. 15, 1–12 (2019).
    Article  Google Scholar 

    58.
    Saar, L., Takkis, K., Pärtel, M. & Helm, A. Which plant traits predict species loss in calcareous grasslands with extinction debt? Traits predicting extinctions in grasslands. Divers. Distrib. 18, 808–817 (2012).
    Article  Google Scholar 

    59.
    Figueiredo, L., Krauss, J., Steffan-Dewenter, I. & Cabral, J. S. Understanding extinction debts: Spatio–temporal scales, mechanisms and a roadmap for future research. Ecography 42, 1973–1990 (2019).
    Article  Google Scholar 

    60.
    Krauss, J. et al. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels: Immediate and time-delayed biodiversity loss. Ecol. Lett. 13, 597–605 (2010).
    PubMed  PubMed Central  Article  Google Scholar 

    61.
    With, K. A. How fast do migratory songbirds have to adapt to keep pace with rapidly changing landscapes?. Landsc. Ecol 30, 1351–1361 (2015).
    Article  Google Scholar 

    62.
    Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: A review. Oikos 71, 355–366 (1994).
    Article  Google Scholar 

    63.
    Kavelaars, M. M. et al. Breeding habitat loss reveals limited foraging flexibility and increases foraging effort in a colonial breeding seabird. Mov. Ecol. 8, 45 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    64.
    van Zanten, B. T. et al. European agricultural landscapes, common agricultural policy and ecosystem services: A review. Agron. Sustain. Dev. 34, 309–325 (2014).
    Article  Google Scholar 

    65.
    Ramalho, C. E., Laliberté, E., Poot, P. & Hobbs, R. Effects of fragmentation on the plant functional composition and diversity of remnant woodlands in a young and rapidly expanding city. J. Veg. Sci. 29, 285–296 (2018).
    Article  Google Scholar 

    66.
    Jackson, S. T. & Sax, D. F. Balancing biodiversity in a changing environment: Extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25, 153–160 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    67.
    Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182 (2018).
    Article  Google Scholar 

    68.
    Damien, M. & Tougeron, K. Prey–predator phenological mismatch under climate change. Curr. Opin. Insect Sci. 35, 60–68 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    69.
    Lalechère, E., Archaux, F. & Jabot, F. Relative importance of landscape and species characteristics on extinction debt, immigration credit and relaxation time after habitat turnover. Popul. Ecol. 61, 383–395 (2019).
    Article  Google Scholar 

    70.
    Ernoult, A. et al. Potential landscape drivers of biodiversity components in a flood plain: Past or present patterns?. Biol. Conserv. 127, 1–17 (2006).
    Article  Google Scholar 

    71.
    Meeus, J. H. A., Wijermans, M. P. & Vroom, M. J. Agricultural landscapes in Europe and their transformation. Landsc. Urban Plan. 18, 289–352 (1990).
    Article  Google Scholar 

    72.
    McGarigal, K., Cushman, S. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html. (2012).

    73.
    Duflot, R., Aviron, S., Ernoult, A., Fahrig, L. & Burel, F. Reconsidering the role of ‘semi-natural habitat’ in agricultural landscape biodiversity: A case study. Ecol. Res. 30, 75–83 (2015).
    Article  Google Scholar 

    74.
    Bibby, C. J., Burgess, N. D., Hill, D. A. & Mustoe, S. Bird Census Techniques (Elsevier, Amsterdam, 2000).
    Google Scholar 

    75.
    Kühn, I., Durka, W. & Klotz, S. BiolFlor: A new plant-trait database as a tool for plant invasion ecology: BiolFlor: A plant-trait database. Divers. Distrib. 10, 363–365 (2004).
    Article  Google Scholar 

    76.
    Kleyer, M. et al. The LEDA Traitbase: a database of life-history traits of the Northwest European flora. J. Ecol. 96, 1266–1274 (2008).
    Article  Google Scholar 

    77.
    Duquet, M. Tout sur les Oiseaux d’Europe (Delachaux, Colombes, 2015).
    Google Scholar 

    78.
    Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Article  Google Scholar 

    79.
    Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).
    Article  Google Scholar 

    80.
    Sonnier, G., Shipley, B. & Navas, M.-L. Quantifying relationships between traits and explicitly measured gradients of stress and disturbance in early successional plant communities. J. Veg. Sci. 21, 1014–1024 (2010).
    Article  Google Scholar 

    81.
    R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, Vienna, 2020).
    Google Scholar 

    82.
    Blomberg, S. P., Garland, T. & Ives, A. R. Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 57, 717–745 (2003).
    PubMed  Article  Google Scholar 

    83.
    Blomberg, S. P. & Garland, T. Tempo and mode in evolution: Phylogenetic inertia, adaptation and comparative methods: Phylogenetic inertia. J. Evol. Biol. 15, 899–910 (2002).
    Article  Google Scholar 

    84.
    Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).
    ADS  CAS  PubMed  Article  Google Scholar 

    85.
    Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    86.
    Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
    Article  Google Scholar 

    87.
    de Bello, F. et al. On the need for phylogenetic ‘corrections’ in functional trait-based approaches. Folia Geobot. 50, 349–357 (2015).
    Article  Google Scholar 

    88.
    Bernard-Verdier, M. et al. Community assembly along a soil depth gradient: Contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland. J. Ecol. 100, 1422–1433 (2012).
    Article  Google Scholar 

    89.
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, New York, 2002).
    Google Scholar 

    90.
    Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2019).
    Google Scholar 

    91.
    Fahrig, L. Effects of Habitat Fragmentation on Biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
    Article  Google Scholar  More

  • in

    Carbon fractions in the world’s dead wood

    1.
    Pugh, T. A. et al. Role of forest regrowth in global carbon sink dynamics. Proc. Natl Acad. Sci. USA 116, 4382–4387 (2019).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    3.
    Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

    4.
    Domke, G. M., Oswalt, S. N., Walters, B. F. & Morin, R. S. Tree planting has the potential to increase carbon sequestration capacity of forests in the United States. Proc. Natl Acad. Sci. USA 117, 24649–24651 (2020).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).
    ADS  MathSciNet  CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

    6.
    Luyssaert, S. et al. CO2 balance of boreal, temperate, and tropical forests derived from a global database. Glob. Change Biol. 13, 2509–2537 (2007).
    ADS  Article  Google Scholar 

    7.
    Harmon, M. E. et al. Ecology of coarse woody debris in temperate ecosystems. Adv. Ecol. Res. 15, 133–302 (1986).
    Article  Google Scholar 

    8.
    Weedon, J. T. et al. Global meta‐analysis of wood decomposition rates: a role for trait variation among tree species? Ecol. Lett. 12, 45–56 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    9.
    McGee, G. G. The contribution of beech bark disease-induced mortality to coarse woody debris loads in northern hardwood stands of Adirondack Park, New York, USA. Can. J. Res. 30, 1453–1462 (2000).
    Article  Google Scholar 

    10.
    Woodall, C. W. et al. Net carbon flux of dead wood in forests of the Eastern US. Oecologia 177, 861–874 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Campbell, J. L. et al. Estimating uncertainty in the volume and carbon storage of downed coarse woody debris. Ecol. Appl. 29, e01844 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    12.
    Russell, M. B. et al. Quantifying carbon stores and decomposition in dead wood: a review. Ecol. Manag. 350, 107–128 (2015).
    Article  Google Scholar 

    13.
    Campbell, J., Alberti, G., Martin, J. & Law, B. E. Carbon dynamics of a ponderosa pine plantation following a thinning treatment in the northern Sierra Nevada. Ecol. Manag. 257, 453–463 (2009).
    Article  Google Scholar 

    14.
    Chambers, J. Q. et al. Response of tree biomass and wood litter to disturbance in a Central Amazon forest. Oecologia 141, 596–611 (2004).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Domke, G. M., Woodall, C. W. & Smith, J. E. Accounting for density reduction and structural loss in standing dead trees: implications for forest biomass and carbon stock estimates in the United States. Carbon Balance Manag. 6, 14 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Janisch, J. E. & Harmon, M. E. Successional changes in live and dead wood carbon stores: implications for net ecosystem productivity. Tree Physiol. 22, 77–89 (2002).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Keith, H., Mackey, B. G. & Lindenmayer, D. B. Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proc. Natl Acad. Sci. USA 106, 11635–11640 (2009).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Martin, A. R., Doraisami, M. & Thomas, S. C. Global patterns in wood carbon concentration across the world’s trees and forests. Nat. Geosci. 11, 915–922 (2018).
    ADS  CAS  Article  Google Scholar 

    19.
    Thomas, S. C. & Martin, A. R. Carbon content of tree tissues: a synthesis. Forests 3, 332–352 (2012).
    Article  Google Scholar 

    20.
    Martin, A. R. & Thomas, S. C. A reassessment of carbon content in tropical trees. PLoS ONE 6, e23533 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    21.
    Weggler, K., Dobbertin, M., Jüngling, E., Kaufmann, E. & Thürig, E. Dead wood volume to dead wood carbon: the issue of conversion factors. Eur. J. Res. 131, 1423–1438 (2012).
    Article  Google Scholar 

    22.
    Gorgolewski, A., Rudz, P., Jones, T., Basiliko, N. & Caspersen, J. Assessing coarse woody debris nutrient dynamics in managed northern hardwood forests using a matrix transition model. Ecosystems 23, 541–554 (2019).
    Article  CAS  Google Scholar 

    23.
    Moreira, A. B., Gregoire, T. G. & do Couto, H. T. Z. Wood density and carbon concentration of coarse woody debris in native forests. Braz. Ecosyst. 6, 18 (2019).
    Article  Google Scholar 

    24.
    Sandström, F., Petersson, H., Kruys, N. & Ståhl, G. Biomass conversion factors (density and carbon concentration) by decay classes for dead wood of Pinus sylvestris, Picea abies and Betula spp. in boreal forests of Sweden. Ecol. Manag. 243, 19–27 (2007).
    Article  Google Scholar 

    25.
    Cousins, S. J., Battles, J. J., Sanders, J. E. & York, R. A. Decay patterns and carbon density of standing dead trees in California mixed conifer forests. Ecol. Manag. 353, 136–147 (2015).
    Article  Google Scholar 

    26.
    Harmon, M. E., Fasth, B., Woodall, C. W. & Sexton, J. Carbon concentration of standing and downed woody detritus: effects of tree taxa, decay class, position, and tissue type. For. Ecol. Manag. 291, 259–267 (2013).

    27.
    Köster, K., Metslaid, M., Engelhart, J. & Köster, E. Dead wood basic density, and the concentration of carbon and nitrogen for main tree species in managed hemiboreal forests. Ecol. Manag. 354, 35–42 (2015).
    Article  Google Scholar 

    28.
    Clark, D. B., Clark, D. A., Brown, S., Oberbauer, S. F. & Veldkamp, E. Stocks and flows of coarse woody debris across a tropical rain forest nutrient and topography gradient. Ecol. Manag. 164, 237–248 (2002).
    Article  Google Scholar 

    29.
    Yang, F. F. et al. Dynamics of coarse woody debris and decomposition rates in an old-growth forest in lower tropical China. Ecol. Manag. 259, 1666–1672 (2010).
    Article  Google Scholar 

    30.
    Chao, K. J. et al. Carbon concentration declines with decay class in tropical forest woody debris. Ecol. Manag. 391, 75–85 (2017).
    Article  Google Scholar 

    31.
    Guo, J., Chen, G., Xie, J., Yang, Z. & Yang, Y. Patterns of mass, carbon and nitrogen in coarse woody debris in five natural forests in southern China. Ann. Sci. 71, 585–594 (2014).
    Article  Google Scholar 

    32.
    Martin, A. R., Gezahegn, S. & Thomas, S. C. Variation in carbon and nitrogen concentration among major woody tissue types in temperate trees. Can. J. Res. 45, 744–757 (2015).
    CAS  Article  Google Scholar 

    33.
    Gao, B., Taylor, A. R., Chen, H. Y. & Wang, J. Variation in total and volatile carbon concentration among the major tree species of the boreal forest. Ecol. Manag. 375, 191–199 (2016).
    Article  Google Scholar 

    34.
    Dossa, G. G. et al. The cover uncovered: bark control over wood decomposition. J. Ecol. 106, 2147–2160 (2018).
    Article  Google Scholar 

    35.
    Jones, D. A. & O’Hara, K. L. Variation in carbon fraction, density, and carbon density in conifer tree tissues. Forests 9, 430 (2018).
    Article  Google Scholar 

    36.
    Fukasawa, Y. The geographical gradient of pine log decomposition in Japan. For. Ecol. Manag. 349, 29–35 (2015).

    37.
    IPCC. in 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Vol. 4: Agriculture, Forestry and Other Land Use (eds Blain, D., Agus, F., Alfaro, M. A. & Vreuls, H.) 68 (IPCC, 2019).

    38.
    Jones, D. A. & O’Hara, K. L. The influence of preparation method on measured carbon fractions in tree tissues. Tree Physiol. 36, 1177–1189 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Beech, E., Rivers, M., Oldfield, S. & Smith, P. GlobalTreeSearch: the first complete global database of tree species and country distributions. J. Sustain. 36, 454–489 (2017).
    Article  Google Scholar 

    40.
    Lamlom, S. H. & Savidge, R. A. A reassessment of carbon content in wood: variation within and between 41 North American species. Biomass Bioenergy 25, 381–388 (2003).
    CAS  Article  Google Scholar 

    41.
    Thomas, S. C. & Malczewski, G. Wood carbon content of tree species in Eastern China: interspecific variability and the importance of the volatile fraction. J. Environ. Manag. 85, 659–662 (2007).
    CAS  Article  Google Scholar 

    42.
    Hafner, S. D., Groffman, P. M. & Mitchell, M. J. Leaching of dissolved organic carbon, dissolved organic nitrogen, and other solutes from coarse woody debris and litter in a mixed forest in New York State. Biogeochemistry 74, 257–282 (2005).
    CAS  Article  Google Scholar 

    43.
    Hillis, W. Chemical aspects of heartwood formation. Wood Sci. Technol. 2, 241–259 (1968).
    CAS  Article  Google Scholar 

    44.
    Meerts, P. Mineral nutrient concentrations in sapwood and heartwood: a literature review. Ann. Sci. 59, 713–722 (2002).
    Article  Google Scholar 

    45.
    Bert, D. & Danjon, F. Carbon concentration variations in the roots, stem and crown of mature Pinus pinaster (Ait.). Ecol. Manag. 222, 279–295 (2006).
    Article  Google Scholar 

    46.
    Jones, D. A. & O’Hara, K. L. Carbon density in managed coast redwood stands: implications for forest carbon estimation. Forestry 85, 99–110 (2012).
    Article  Google Scholar 

    47.
    Ma, S. et al. Variations and determinants of carbon content in plants: a global synthesis. Biogeosciences 15, 693 (2018).
    ADS  CAS  Article  Google Scholar 

    48.
    Cornelissen, J. H. C. et al. Leaf digestibility and litter decomposability are related in a wide range of subarctic plant species and types. Funct. Ecol. 18, 779–786 (2004).
    Article  Google Scholar 

    49.
    Ganjegunte, G. K., Condron, L. M., Clinton, P. W., Davis, M. R. & Mahieu, N. Decomposition and nutrient release from radiata pine (Pinus radiata) coarse woody debris. Ecol. Manag. 187, 197–211 (2004).
    Article  Google Scholar 

    50.
    Pettersen, R. C. in The Chemistry of Solid Wood (ed. Rowell, R.) 57–126 (American Chemical Society, 1984).

    51.
    Berg, B., Ekbohm, G. & McClaugherty, C. Lignin and holocellulose relations during long-term decomposition of some forest litters. Long-term decomposition in a Scots pine forest. IV. Can. J. Bot. 62, 2540–2550 (1984).
    CAS  Article  Google Scholar 

    52.
    Schowalter, T. D., Zhang, Y. L. & Sabin, T. E. Decomposition and nutrient dynamics of oak Quercus spp. logs after five years of decomposition. Ecography 21, 3–10 (1998).
    Article  Google Scholar 

    53.
    Buxton, R. D. Termites and the turnover of dead wood in an arid tropical environment. Oecologia 51, 379–384 (1981).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Riley, R. et al. Extensive sampling of basidiomycete genomes demonstrates inadequacy of the white-rot/brown-rot paradigm for wood decay fungi. Proc. Natl Acad. Sci. USA 111, 9923–9928 (2014).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    55.
    Moore, T. R., Trofymow, J. A., Prescott, C. E., Titus, B. D. & Group, C. W. Can short-term litter-bag measurements predict long-term decomposition in northern forests? Plant Soil 416, 419–426 (2017).
    CAS  Article  Google Scholar 

    56.
    vandenEnden, L., Frey, S. D., Nadelhoffer, K. J., LeMoine, J. M., Lajtha, K. & Simpson, M. J. Molecular-level changes in soil organic matter composition after 10 years of litter, root and nitrogen manipulation in a temperate forest. Biogeochemistry 141, 183–197 (2018).
    CAS  Article  Google Scholar 

    57.
    Warner, D. L., Villarreal, S., McWilliams, K., Inamdar, S. & Vargas, R. Carbon dioxide and methane fluxes from tree stems, coarse woody debris, and soils in an upland temperate forest. Ecosystems 20, 1205–1216 (2017).
    CAS  Article  Google Scholar 

    58.
    Van Mantgem, P. J. et al. Widespread increase of tree mortality rates in the western United States. Science 323, 521–524 (2009).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    59.
    Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    60.
    Brad, B. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. BMC Bioinform. 14, 16 (2013).
    Article  Google Scholar 

    61.
    Krankina, O. N. & Harmon, M. E. Dynamics of the dead wood carbon pool in northwestern Russian boreal forests. Water Air Soil Pollut. 82, 227–238 (1995).
    ADS  CAS  Article  Google Scholar 

    62.
    Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).
    ADS  Article  Google Scholar 

    63.
    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 

    64.
    Lenth, R. V. Least-squares means: the R Package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
    Article  Google Scholar 

    65.
    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

    66.
    Fox, J. & Weisberg, S. An R Companion to Applied Regression 2nd edn (Sage, 2011).

    67.
    Messier, J., McGill, B. J. & Lechowicz, M. J. How do traits vary across ecological scales? A case for trait-based ecology. Ecol. Lett. 13, 838–848 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    68.
    Martin, A. R. et al. Intraspecific trait variation across multiple scales: the leaf economics spectrum in coffee. Funct. Ecol. 31, 604–612 (2017).
    Article  Google Scholar 

    69.
    Pinheiro, J. et al. nlme: linear and nonlinear mixed effects models. R package version 3.1-131. https://CRAN.R-project.org/package=nlme (2017).

    70.
    Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004). More

  • in

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

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

    Full size image

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

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

    Full size image

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

  • in

    The amphibian microbiome exhibits poor resilience following pathogen-induced disturbance

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    52.
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl Environ Microbiol. 2009;75:7537–41.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • in

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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