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    Composition and toxicity of venom produced by araneophagous white-tailed spiders (Lamponidae: Lampona sp.)

    Schendel, V., Rash, L. D., Jenner, R. A. & Undheim, E. A. The diversity of venom: The importance of behavior and venom system morphology in understanding its ecology and evolution. Toxins 11(11), 666 (2019).Article 
    CAS 

    Google Scholar 
    Casewell, N. R., Wüster, W., Vonk, F. J., Harrison, R. A. & Fry, B. G. Complex cocktails: The evolutionary novelty of venoms. Trends Ecol. Evol. 28(4), 219–229 (2013).Article 

    Google Scholar 
    Pineda, S. S. et al. Structural venomics reveals evolution of a complex venom by duplication and diversification of an ancient peptide-encoding gene. Proc. Natl. Acad. Sci. USA 117(21), 11399–11408 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Chippaux, J. P., Williams, V. & White, J. Snake venom variability: Methods of study, results and interpretation. Toxicon 29(11), 1279–1303 (1991).Article 
    CAS 

    Google Scholar 
    Lyons, K., Dugon, M. M. & Healy, K. Diet breadth mediates the prey specificity of venom potency in snakes. Toxins 12(2), 74 (2020).Article 

    Google Scholar 
    Pekár, S. et al. Venom gland size and venom complexity—essential trophic adaptations of venomous predators: A case study using spiders. Mol. Ecol. 27(21), 4257–4269 (2018).Article 

    Google Scholar 
    Phuong, M. A., Mahardika, G. N. & Alfaro, M. E. Dietary breadth is positively correlated with venom complexity in cone snails. BMC Genom. 17(1), 401 (2016).Article 

    Google Scholar 
    Holding, M. L., Biardi, J. E. & Gibbs, H. L. Coevolution of venom function and venom resistance in a rattlesnake predator and its squirrel prey. Proc. R. Soc. B. 283(1829), 20152841 (2016).Article 

    Google Scholar 
    Pekár, S., Líznarová, E., Bočánek, O. & Zdráhal, Z. Venom of prey-specialized spiders is more toxic to their preferred prey: A result of prey-specific toxins. J. Anim. Ecol. 87(6), 1639–1652 (2018).Article 

    Google Scholar 
    Pekár, S., Coddington, J. A. & Blackledge, T. A. Evolution of stenophagy in spiders (Araneae): Evidence based on the comparative analysis of spider diets. Evolution 66(3), 776–806 (2012).Article 

    Google Scholar 
    Herzig, V., King, G. F. & Undheim, E. A. Can we resolve the taxonomic bias in spider venom research?. Toxicon: X 1, 100005 (2019).Article 
    CAS 

    Google Scholar 
    Platnick, N. A relimitation and revision of the Australasian ground spider family Lamponidae (Araneae: Gnaphosoidea). Bull. Am. Mus. Nat. Hist. 2000(245), 1–328 (2000).Article 

    Google Scholar 
    World Spider Catalog. Version 22.0. Natural History Museum Bern. http://wsc.nmbe.ch. Accessed 15 Mar 2021 (2021).White, J. & Weinstein, S. A. A phoenix of clinical toxinology: White-tailed spider (Lampona spp.) bites. A case report and review of medical significance. Toxicon 87, 76–80 (2014).Article 
    CAS 

    Google Scholar 
    Rash, L. D., King, R. G. & Hodgson, W. C. Sex differences in the pharmacological activity of venom from the white-tailed spider (Lampona cylindrata). Toxicon 38, 1111–1127 (2000).Article 
    CAS 

    Google Scholar 
    Young, A. R. & Pincus, S. J. Comparison of enzymatic activity from three species of necrotising arachnids in Australia: Loxosceles rufescens, Badumna insignis and Lampona cylindrata. Toxicon 39, 391–400 (2001).Article 
    CAS 

    Google Scholar 
    Michálek, O., Petráková, L. & Pekár, S. Capture efficiency and trophic adaptations of a specialist and generalist predator: A comparison. Ecol. Evol. 7(8), 2756–2766 (2017).Article 

    Google Scholar 
    Klint, J. K. et al. Spider-venom peptides that target voltage-gated sodium channels: Pharmacological tools and potential therapeutic leads. Toxicon 60(4), 478–491 (2012).Article 
    CAS 

    Google Scholar 
    Diniz, M. R. et al. An overview of Phoneutria nigriventer spider venom using combined transcriptomic and proteomic approaches. PLoS ONE 13(8), e0200628 (2018).Article 

    Google Scholar 
    Wilson, D. et al. The aromatic head group of spider toxin polyamines influences toxicity to cancer cells. Toxins 9(11), 346 (2017).Article 

    Google Scholar 
    Herzig, V. & King, G. F. The cystine knot is responsible for the exceptional stability of the insecticidal spider toxin ω-hexatoxin-Hv1a. Toxins 7(10), 4366–4380 (2015).Article 
    CAS 

    Google Scholar 
    Wang, X. H. et al. Discovery and characterization of a family of insecticidal neurotoxins with a rare vicinal disulfide bridge. Nat. Struct. Biol. 7(6), 505–513 (2000).Article 
    CAS 

    Google Scholar 
    Yuan, C. H. et al. Discovery of a distinct superfamily of Kunitz-type toxin (KTT) from tarantulas. PLoS ONE 3(10), e3414 (2008).Article 
    ADS 

    Google Scholar 
    Luo, J. et al. Molecular diversity and evolutionary trends of cysteine-rich peptides from the venom glands of Chinese spider Heteropoda venatoria. Sci. Rep. 11, 3211 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Cole, J., Buszka, P. A., Mobley, J. A. & Hataway, R. A. Characterization of the venom proteome for the wandering spider, Ctenus hibernalis (Aranea: Ctenidae). J. Proteom. Bioinform. 9, 196–199 (2016).Article 

    Google Scholar 
    Korolkova, Y. et al. New Insectotoxin from Tibellus Oblongus Spider venom presents novel daptation of ICK Fold. Toxins 13(1), 29 (2021).Article 
    CAS 

    Google Scholar 
    Koua, D. et al. Proteotranscriptomic insights into the venom composition of the wolf spider Lycosa tarantula. Toxins 12(8), 501 (2020).Article 
    CAS 

    Google Scholar 
    Liberato, T., Troncone, L. R. P., Yamashiro, E. T., Serrano, S. M. & Zelanis, A. High-resolution proteomic profiling of spider venom: Expanding the toxin diversity of Phoneutria nigriventer venom. Amino Acids 48(3), 901–906 (2016).Article 
    CAS 

    Google Scholar 
    Oldrati, V. et al. Peptidomic and transcriptomic profiling of four distinct spider venoms. PLoS ONE 12(3), e0172966 (2017).Article 

    Google Scholar 
    King, G. F. & Hardy, M. C. Spider-venom peptides: Structure, pharmacology, and potential for control of insect pests. Annu. Rev. Entomol. 58, 475–496 (2013).Article 
    CAS 

    Google Scholar 
    Turner, A. J., Isaac, R. E. & Coates, D. The neprilysin (NEP) family of zinc metalloendopeptidases: Genomics and function. BioEssays 23(3), 261–269 (2001).Article 
    CAS 

    Google Scholar 
    Casewell, N. R., Harrison, R. A., Wüster, W. & Wagstaff, S. C. Comparative venom gland transcriptome surveys of the saw-scaled vipers (Viperidae: Echis) reveal substantial intra-family gene diversity and novel venom transcripts. BMC Genom. 10(1), 1–12 (2009).Article 

    Google Scholar 
    Tan, C. H., Tan, K. Y., Fung, S. Y. & Tan, N. H. Venom-gland transcriptome and venom proteome of the Malaysian king cobra (Ophiophagus hannah). BMC Genom. 16(1), 1–21 (2015).Article 

    Google Scholar 
    Tan, K. Y., Tan, C. H., Chanhome, L. & Tan, N. H. Comparative venom gland transcriptomics of Naja kaouthia (monocled cobra) from Malaysia and Thailand: Elucidating geographical venom variation and insights into sequence novelty. PeerJ 5, e3142 (2017).Article 

    Google Scholar 
    Undheim, E. A. et al. A proteomics and transcriptomics investigation of the venom from the barychelid spider Trittame loki (brush-foot trapdoor). Toxins. 5(12), 2488–2503 (2013).Article 
    CAS 

    Google Scholar 
    do Nascimento, S. M., de Oliveira, U. C., Nishiyama-Jr, M. Y., Tashima, A. K. & Silva Junior, P. I. D. Presence of a neprilysin on Avicularia juruensis (Mygalomorphae: Theraphosidae) venom. Toxin Rev. 41(2), 370–379 (2021).Article 

    Google Scholar 
    Zobel-Thropp, P. A. et al. Not so dangerous after all? Venom composition and potency of the Pholcid (daddy long-leg) spider Physocyclus mexicanus. Front. Ecol. Evol. 7, 256 (2019).Article 

    Google Scholar 
    Diniz, M. R. et al. An overview of Phoneutria nigriventer spider venom using combined transcriptomic and proteomic approaches. PLoS ONE 13(8), e0200628 (2018).Article 

    Google Scholar 
    He, Q. et al. The venom gland transcriptome of Latrodectus tredecimguttatus revealed by deep sequencing and cDNA library analysis. PLoS ONE 8(11), e81357 (2013).Article 
    ADS 

    Google Scholar 
    Haney, R. A., Ayoub, N. A., Clarke, T. H., Hayashi, C. Y. & Garb, J. E. Dramatic expansion of the black widow toxin arsenal uncovered by multi-tissue transcriptomics and venom proteomics. BMC Genom. 15(1), 1–18 (2014).Article 

    Google Scholar 
    Haney, R. A., Matte, T., Forsyth, F. S. & Garb, J. E. Alternative transcription at venom genes and its role as a complementary mechanism for the generation of venom complexity in the common house spider. Front. Ecol. Evol. 7, 85 (2019).Article 

    Google Scholar 
    Lüddecke, T. et al. An economic dilemma between molecular weapon systems may explain an arachno-atypical venom in wasp spiders (Argiope bruennichi). Biomolecules 10(7), 978 (2020).Article 

    Google Scholar 
    Fainzilber, M., Gordon, D., Hasson, A., Spira, M. E. & Zlotkin, E. Mollusc-specific toxins from the venom of Conus textile neovicarius. Eur. J. Biochem. 202(2), 589–595 (1991).Article 
    CAS 

    Google Scholar 
    Pawlak, J. et al. Denmotoxin, a three-finger toxin from the colubrid snake Boiga dendrophila (Mangrove Catsnake) with bird-specific activity. J. Biol. Chem. 281(39), 29030–29041 (2006).Article 
    CAS 

    Google Scholar 
    Krasnoperov, V. G., Shamotienko, O. G. & Grishin, E. V. Isolation and properties of insect and crustacean-specific neurotoxins from the venom of the black widow spider (Latrodectus mactans tredecimguttatus). J. Nat. Toxins 1, 17–23 (1992).CAS 

    Google Scholar 
    Xu, X. et al. A comparative analysis of the venom gland transcriptomes of the fishing spiders Dolomedes mizhoanus and Dolomedes sulfurous. PLoS ONE 10(10), e0139908 (2015).Article 

    Google Scholar 
    Kuzmenkov, A. I., Sachkova, M. Y., Kovalchuk, S. I., Grishin, E. V. & Vassilevski, A. A. Lachesana tarabaevi, an expert in membrane-active toxins. Biochem. J. 473(16), 2495–2506 (2016).Article 
    CAS 

    Google Scholar 
    Pekár, S. & Toft, S. Trophic specialisation in a predatory group: The case of prey-specialised spiders (Araneae). Biol. Rev. 90(3), 744–761 (2015).Article 

    Google Scholar 
    Nyffeler, M. & Pusey, B. J. Fish predation by semi-aquatic spiders: A global pattern. PLoS ONE 9(6), e99459 (2014).Article 
    ADS 

    Google Scholar 
    Pekár, S. & Lubin, Y. Prey and predatory behavior of two zodariid species (Araneae, Zodariidae). J. Arachnol. 37(1), 118–121 (2009).Article 

    Google Scholar 
    Michálek, O., Kuhn-Nentwig, L. & Pekár, S. High specific efficiency of venom of two prey-specialized spiders. Toxins 11(12), 687 (2019).Article 

    Google Scholar 
    Modahl, C. M., Mrinalini, Frietze, S. & Mackessy, S. P. Adaptive evolution of distinct prey-specific toxin genes in rear-fanged snake venom. Proc. R. Soc. B. 285(1884), 20181003 (2018).Article 

    Google Scholar 
    Harris, R. J., Zdenek, C. N., Harrich, D., Frank, N. & Fry, B. G. An appetite for destruction: Detecting prey-selective binding of α-neurotoxins in the venom of Afro-Asian elapids. Toxins 12(3), 205 (2020).Article 
    CAS 

    Google Scholar 
    Duran, L. H., Rymer, T. L. & Wilson, D. T. Variation in venom composition in the Australian funnel-web spiders Hadronyche valida. Toxicon: X 8, 100063 (2020).Article 
    CAS 

    Google Scholar 
    Kuhn-Nentwig, L., Schaller, J. & Nentwig, W. Purification of toxic peptides and the amino acid sequence of CSTX-1 from the multicomponent venom of Cupiennius salei (Araneae: Ctenidae). Toxicon 32(3), 287–302 (1994).Article 
    CAS 

    Google Scholar 
    Friedel, T. & Nentwig, W. Immobilizing and lethal effects of spider venoms on the cockroach and the common mealbeetle. Toxicon 27(3), 305–316 (1989).Article 
    CAS 

    Google Scholar 
    Eggs, B., Wolff, J. O., Kuhn-Nentwig, L., Gorb, S. N. & Nentwig, W. Hunting without a web: How lycosoid spiders subdue their prey. Ethology 121(12), 1166–1177 (2015).Article 

    Google Scholar 
    Andrews, S. FastQC: A quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2015).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120 (2014).Article 
    CAS 

    Google Scholar 
    Song, L. & Florea, L. Rcorrector: Efficient and accurate error correction for Illumina RNA-seq reads. GigaScience 4(1), s13742–s14015 (2015).Article 

    Google Scholar 
    Grabherr, M. G. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29(7), 644 (2011).Article 
    CAS 

    Google Scholar 
    Gilbert, D. EvidentialGene: Evidence directed gene predictions for eukaryotes. Available online at: http://arthropods.eugenes.org/EvidentialGene/ (2010).Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10(3), 1–10 (2009).Article 

    Google Scholar 
    Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness. In Gene Prediction (ed. Kollmar, M.) 227–245 (Humana, 2019).
    Google Scholar 
    Haas, B. TransDecoder. Available online at: https://github.com/TransDecoder/TransDecoder (2015).Petersen, T. N., Brunak, S., Von Heijne, G. & Nielsen, H. SignalP 4.0: Discriminating signal peptides from transmembrane regions. Nat. Methods 8(10), 785–786 (2011).Article 
    CAS 

    Google Scholar 
    Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997).Article 
    CAS 

    Google Scholar 
    UniProt. The universal protein knowledgebase in 2021. Nucleic Acids Res. 49(1), 480–489 (2021).
    Google Scholar 
    Eddy, S. R. A probabilistic model of local sequence alignment that simplifies statistical significance estimation. PLoS Comput. Biol. 4(5), e1000069 (2008).Article 
    ADS 
    MathSciNet 

    Google Scholar 
    Finn, R. D. et al. Pfam: The protein families database. Nucleic Acids Res. 42(1), 222–230 (2014).Article 

    Google Scholar 
    Wong, E. S., Hardy, M. C., Wood, D., Bailey, T. & King, G. F. SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula. PLoS ONE 8(7), e66279 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    King, G. F., Gentz, M. C., Escoubas, P. & Nicholson, G. M. A rational nomenclature for naming peptide toxins from spiders and other venomous animals. Toxicon 52(2), 264–276 (2008).Article 
    CAS 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at: https://www.R-project.org/ (2019).Venables, W. N. & Ripley, B. D. Random and mixed effects in Modern Applied Statistics with S 271–300 (Springer, New York, 2002).Pekár, S. & Brabec, M. Modern Analysis of Biological Data: Generalized Linear Models in R (Masaryk University Press, 2016).
    Google Scholar 
    Halekoh, U., Højsgaard, S. & Yan, J. The R package geepack for generalized estimating equations. J. Stat. Softw. 15(2), 1–11 (2006).Article 

    Google Scholar 
    Pekár, S. & Brabec, M. Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences. Ethology 124(2), 86–93 (2018).Article 

    Google Scholar  More

  • in

    Biodiversity–stability relationships strengthen over time in a long-term grassland experiment

    Doak, D. F. et al. The statistical inevitability of stability‐diversity relationships in community ecology. Am. Nat. 151, 264–276 (1998).Article 
    CAS 

    Google Scholar 
    Schläpfer, F. & Schmid, B. Ecosystem effects of biodiversity: a classification hypotheses and exploration of empirical results. Ecol. Appl. 9, 893–912 (1999).Article 

    Google Scholar 
    Lehman, C. L. & Tilman, D. Biodiversity, stability, and productivity in competitive communities. Am. Nat. 156, 534–552 (2000).Article 

    Google Scholar 
    Allan, E. et al. More diverse plant communities have higher functioning over time due to turnover in complementary dominant species. Proc. Natl Acad. Sci. USA 108, 17034–17039 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Wagg, C. et al. Plant diversity maintains long-term ecosystem productivity under frequent drought by increasing short-term variation. Ecology 98, 2952–2961 (2017).Article 

    Google Scholar 
    Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Guerrero-Ramírez, N. R. et al. Diversity-dependent temporal divergence of ecosystem functioning in experimental ecosystems. Nat. Ecol. Evol. 1, 1639–1642 (2017).Article 

    Google Scholar 
    Meyer, S. T. et al. Effects of biodiversity strengthen over time as ecosystem functioning declines at low and increases at high biodiversity. Ecosphere 7, e01619 (2016).Article 

    Google Scholar 
    Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Bongers, F. J. et al. Functional diversity effects on productivity increase with age in a forest biodiversity experiment. Nat. Ecol. Evol. 5, 1594–1603 (2021).Article 

    Google Scholar 
    Weisser, W. W. et al. Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: Patterns, mechanisms, and open questions. Basic Appl. Ecol. 23, 1–73 (2017).Article 

    Google Scholar 
    Guerrero-Ramírez, N. R., Reich, P. B., Wagg, C., Ciobanu, M. & Eisenhauer, N. Diversity-dependent plant–soil feedbacks underlie long-term plant diversity effects on primary productivity. Ecosphere 10, e02704 (2019).Article 

    Google Scholar 
    Eisenhauer, N. The shape that matters: how important is biodiversity for ecosystem functioning. Sci. China Life Sci. 65, 651–653 (2022).Article 

    Google Scholar 
    Cardinale, B. J. et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl Acad. Sci. USA 104, 18123–18128 (2007).Article 
    ADS 
    CAS 

    Google Scholar 
    Marquard, E. et al. Plant species richness and functional composition drive overyielding in a six-year grassland experiment. Ecology 90, 3290–3302 (2009).Article 

    Google Scholar 
    Zuppinger-Dingley, D. et al. Selection for niche differentiation in plant communities increases biodiversity effects. Nature 515, 108–111 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Wang, S. et al. How complementarity and selection affect the relationship between ecosystem functioning and stability. Ecology 102, e03347 (2021).Article 

    Google Scholar 
    Yan, Y. et al. Mechanistic links between biodiversity effects on ecosystem functioning and stability in a multi-site grassland experiment. J. Ecol. 109, 3370–3378 (2021).Article 

    Google Scholar 
    Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).Article 

    Google Scholar 
    Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).Article 
    ADS 
    CAS 

    Google Scholar 
    Gonzalez, A. & Loreau, M. The causes and consequences of compensatory dynamics in ecological communities. Annu. Rev. Ecol. Evol. Syst. 40, 393–414 (2009).Article 

    Google Scholar 
    Thibaut, L. M. & Connolly, S. R. Understanding diversity–stability relationships: towards a unified model of portfolio effects. Ecol. Lett. 16, 140–150 (2013).Article 

    Google Scholar 
    Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).Article 

    Google Scholar 
    Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Loreau, M. Biodiversity and Ecosystem Functioning (Princeton Univ. Press,2010).Loreau, M. & de Mazancourt, C. Biodiversity and ecosystem stability: a synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115 (2013).Article 

    Google Scholar 
    Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).Article 

    Google Scholar 
    Maron, J. L., Marler, M., Klironomos, J. N. & Cleveland, C. C. Soil fungal pathogens and the relationship between plant diversity and productivity. Ecol. Lett. 14, 36–41 (2011).Article 

    Google Scholar 
    Schnitzer, S. A. et al. Soil microbes drive the classic plant diversity–productivity pattern. Ecology 92, 296–303 (2011).Article 

    Google Scholar 
    Marquard, E. et al. Changes in the abundance of grassland species in monocultures versus mixtures and their relation to biodiversity effects. PLoS ONE 8, e75599 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Roscher, C. et al. Identifying population- and community-level mechanisms of diversity–stability relationships in experimental grasslands. J. Ecol. 99, 1460–1469 (2011).Article 

    Google Scholar 
    Civitello, D. J. et al. Biodiversity inhibits parasites: broad evidence for the dilution effect. Proc. Natl Acad. Sci. USA 112, 8667–8671 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Kulmatiski, A., Beard, K. H. & Heavilin, J. Plant–soil feedbacks provide an additional explanation for diversity–productivity relationships. Proc. R. Soc. B 279, 3020–3026 (2012).Article 

    Google Scholar 
    van Moorsel, S. J. et al. Co-occurrence history increases ecosystem stability and resilience in experimental plant communities. Ecology 102, e03205 (2021).Article 

    Google Scholar 
    Schöb, C., Brooker, R. W. & Zuppinger-Dingley, D. Evolution of facilitation requires diverse communities. Nat. Ecol. Evol. 2, 1381–1385 (2018).Article 

    Google Scholar 
    Temperton, V. M., Mwangi, P. N., Scherer-Lorenzen, M., Schmid, B. & Buchmann, N. Positive interactions between nitrogen-fixing legumes and four different neighbouring species in a biodiversity experiment. Oecologia 151, 190–205 (2007).Article 
    ADS 

    Google Scholar 
    Furey, G. N. & Tilman, D. Plant biodiversity and the regeneration of soil fertility. Proc. Natl Acad. Sci. USA 118, e2111321118 (2021).Article 
    CAS 

    Google Scholar 
    Gubsch, M. et al. Foliar and soil δ15N values reveal increased nitrogen partitioning among species in diverse grassland communities. Plant Cell Environ. 34, 895–908 (2011).Article 
    CAS 

    Google Scholar 
    Roscher, C., Schmid, B., Buchmann, N., Weigelt, A. & Schulze, E.-D. Legume species differ in the responses of their functional traits to plant diversity. Oecologia 165, 437–452 (2011).Article 
    ADS 

    Google Scholar 
    Eisenhauer, N. et al. Plant diversity effects on soil microorganisms support the singular hypothesis. Ecology 91, 485–496 (2010).Article 
    CAS 

    Google Scholar 
    Fornara, D. A. & Tilman, D. Plant functional composition influences rates of soil carbon and nitrogen accumulation. J. Ecol. 96, 314–322 (2008).Article 
    CAS 

    Google Scholar 
    Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Xu, S. et al. Species richness promotes ecosystem carbon storage: evidence from biodiversity-ecosystem functioning experiments. Proc. R. Soc. B 287, 20202063 (2020).Article 
    CAS 

    Google Scholar 
    Cong, W.-F. et al. Plant species richness promotes soil carbon and nitrogen stocks in grasslands without legumes. J. Ecol. 102, 1163–1170 (2014).Article 
    CAS 

    Google Scholar 
    Leimer, S. et al. Mechanisms behind plant diversity effects on inorganic and organic N leaching from temperate grassland. Biogeochemistry 131, 339–353 (2016).Article 
    CAS 

    Google Scholar 
    Xu, Q. et al. Consistently positive effect of species diversity on ecosystem, but not population, temporal stability. Ecol. Lett. 24, 2256–2266 (2021).Article 

    Google Scholar 
    Hector, A. et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding. Ecology 91, 2213–2220 (2010).Article 
    CAS 

    Google Scholar 
    Turnbull, L. A., Levine, J. M., Loreau, M. & Hector, A. Coexistence, niches and biodiversity effects on ecosystem functioning. Ecol. Lett. 16, 116–127 (2013).Article 

    Google Scholar 
    Wright, A. J. et al. Flooding disturbances increase resource availability and productivity but reduce stability in diverse plant communities. Nat. Commun. 6, 6092 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Fischer, F. M. et al. Plant species richness and functional traits affect community stability after a flood event. Philos. Trans. R. Soc. B 371, 20150276 (2016).Article 

    Google Scholar 
    Roscher, C. et al. A functional trait-based approach to understand community assembly and diversity–productivity relationships over 7 years in experimental grasslands. Perspect. Plant Ecol. Evol. Syst. 15, 139–149 (2013).Article 

    Google Scholar 
    Eisenhauer, N. et al. Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Res. Ideas Outcomes 5, e47042 (2019).Article 

    Google Scholar 
    van Moorsel, S. J., Schmid, M. W., Hahl, T., Zuppinger-Dingley, D. & Schmid, B. Selection in response to community diversity alters plant performance and functional traits. Perspect. Plant Ecol. Evol. Syst. 33, 51–61 (2018).Article 

    Google Scholar 
    van Moorsel, S. J. et al. Community evolution increases plant productivity at low diversity. Ecol. Lett. 21, 128–137 (2018).Article 

    Google Scholar 
    Roeder, A. et al. Plant diversity effects on plant longevity and their relationships to population stability in experimental grasslands. J. Ecol. 109, 2566–2579 (2021).Article 

    Google Scholar 
    Cadotte, M. W., Dinnage, R. & Tilman, D. Phylogenetic diversity promotes ecosystem stability. Ecology 93, S223–S233 (2012).Article 

    Google Scholar 
    Pu, Z., Daya, P., Tan, J. & Jiang, L. Phylogenetic diversity stabilizes community biomass. J. Plant Ecol. 7, 176–187 (2014).Article 

    Google Scholar 
    Carrara, F., Giometto, A., Seymour, M., Rinaldo, A. & Altermatt, F. Experimental evidence for strong stabilizing forces at high functional diversity of aquatic microbial communities. Ecology 96, 1340–1350 (2015).Article 

    Google Scholar 
    Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a conceensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

    Google Scholar 
    Ruijven, J. V. & Berendse, F. Contrasting effects of diversity on the temporal stability of plant populations. Oikos 116, 1323–1330 (2007).Article 

    Google Scholar 
    Proulx, R. et al. Diversity promotes temporal stability across levels of ecosystem organization in experimental grasslands. PLoS ONE 5, e13382 (2010).Article 
    ADS 

    Google Scholar 
    Hoaglin, D. C., Iglewicz, B. & Tukey, J. W. Performance of some resistant rules for outlier labeling. JASA 81, 991–999 (1986).Article 
    MathSciNet 

    Google Scholar 
    Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).Article 

    Google Scholar 
    Gross, K. et al. Species richness and the temporal stability of biomass production: a new analysis of recent biodiversity experiments. Am. Nat. 183, 1–12 (2014).Article 

    Google Scholar 
    Schmid, B., Baruffol, M., Wang, Z. & Niklaus, P. A. A guide to analyzing biodiversity experiments. J. Plant Ecol. 10, 91–110 (2017).Article 

    Google Scholar 
    Rosseel, Y. lavaan: An R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2012).Article 

    Google Scholar  More

  • in

    Adaptations by the coral Acropora tenuis confer resilience to future thermal stress

    Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).Article 
    CAS 

    Google Scholar 
    Frolicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).Article 
    CAS 

    Google Scholar 
    Riegl, B. & Purkis, S. Coral population dynamics across consecutive mass mortality events. Glob. Chang Biol. 21, 3995–4005 (2015).Article 

    Google Scholar 
    Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373−+ (2017).Article 

    Google Scholar 
    Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. 50, 839–866 (1999).
    Google Scholar 
    van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nat. Clim. Change 3, 508–511 (2013).Article 

    Google Scholar 
    Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).Article 
    CAS 

    Google Scholar 
    Frieler, K. et al. Limiting global warming to 2 degrees C is unlikely to save most coral reefs. Nat. Clim. Change 3, 165–170 (2013).Article 

    Google Scholar 
    Guest, J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, https://doi.org/10.1371/journal.pone.0033353 (2012).Oliver, T. A. & Palumbi, S. R. Do fluctuating temperature environments elevate coral thermal tolerance? Coral Reefs 30, 429–440 (2011).Article 

    Google Scholar 
    Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Change 2, 116–120 (2012).Article 

    Google Scholar 
    Bay, R. A., Rose, N. H., Logan, C. A. & Palumbi, S. R. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, https://doi.org/10.1126/sciadv.1701413 (2017).Loya, Y. et al. Coral bleaching: the winners and the losers. Ecol. Lett. 4, 122–131 (2001).Article 

    Google Scholar 
    Edmunds, P. J., Gates, R. D. & Gleason, D. F. The biology of larvae from the reef coral Porites astreoides, and their response to temperature disturbances. Mar. Biol. 139, 981–989 (2001).Article 

    Google Scholar 
    Lager, C. V. A., Hagedorn, M., Rodgers, K. S. & Jokiel, P. L. The impact of short-term exposure to near shore stressors on the early life stages of the reef building coral Montipora capitata. Peerj 8, https://doi.org/10.7717/peerj.9415 (2020).Ross, C., Ritson-Williams, R., Olsen, K. & Paul, V. J. Short-term and latent post-settlement effects associated with elevated temperature and oxidative stress on larvae from the coral Porites astreoides. Coral Reefs 32, 71–79 (2013).Article 

    Google Scholar 
    Putnam, H. M. & Gates, R. D. Preconditioning in the reef-building coral Pocillopora damicornis and the potential for trans-generational acclimatization in coral larvae under future climate change conditions. J. Exp. Biol. 218, 2365–2372 (2015).Article 

    Google Scholar 
    Baird, A. H. & Marshall, P. A. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 237, 133–141 (2002).Article 

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

    Google Scholar 
    Michalek-Wagner, K. & Willis, B. L. Impacts of bleaching on the soft coral Lobophytum compactum. I. Fecundity, fertilization and offspring viability. Coral Reefs 19, 231–239 (2001).Article 

    Google Scholar 
    Paxton, C. W., Baria, M. V. B., Weis, V. M. & Harii, S. Effect of elevated temperature on fecundity and reproductive timing in the coral Acropora digitifera. Zygote 24, 511–516 (2016).Article 

    Google Scholar 
    Nozawa, Y. & Harrison, P. L. Effects of elevated temperature on larval settlement and post-settlement survival in scleractinian corals, Acropora solitaryensis and Favites chinensis. Mar. Biol. 152, 1181–1185 (2007).Article 

    Google Scholar 
    Hughes, T. P. & Tanner, J. E. Recruitment failure, life histories, and long-term decline of Caribbean corals. Ecology 81, 2250–2263 (2000).Article 

    Google Scholar 
    Edmunds, P. J. Juvenile coral population dynamics track rising seawater temperature on a Caribbean reef. Mar. Ecol. Prog. Ser. 269, 111–119 (2004).Article 

    Google Scholar 
    Davis, K. & Marshall, D. J. Offspring size in a resident species affects community assembly. J. Anim. Ecol. 83, 322–331 (2014).Article 

    Google Scholar 
    Chua, C. M., Leggat, W., Moya, A. & Baird, A. H. Temperature affects the early life history stages of corals more than near future ocean acidification. Mar. Ecol. Prog. Ser. 475, 85–92 (2013).Article 

    Google Scholar 
    Schnitzler, C. E., Hollingsworth, L. L., Krupp, D. A. & Weis, V. M. Elevated temperature impairs onset of symbiosis and reduces survivorship in larvae of the Hawaiian coral, Fungia scutaria. Mar. Biol. 159, 633–642 (2012).Article 

    Google Scholar 
    Ward, S., Harrison, P. & Hoegh-Guldberg, O. in: Proceedings of the Ninth International Coral Reef Symposium, Bali, 23–27 October 2000. 1123–1128 (2000).Foster, T. & Gilmour, J. Egg size and fecundity of biannually spawning corals at Scott Reef. Sci Rep-Uk 10, https://doi.org/10.1038/s41598-020-68289-4 (2020).Negri, A. P., Marshall, P. A. & Heyward, A. J. Differing effects of thermal stress on coral fertilization and early embryogenesis in four Indo Pacific species. Coral Reefs 26, 759–763 (2007).Article 

    Google Scholar 
    Randall, C. J. & Szmant, A. M. Elevated temperature affects development, survivorship, and settlement of the Elkhorn Coral, Acropora palmata (Lamarck 1816). Biol. Bull. 217, 269–282 (2009).Article 

    Google Scholar 
    Anlauf, H., D’Croz, L. & O’Dea, A. A corrosive concoction: the combined effects of ocean warming and acidification on the early growth of a stony coral are multiplicative. J. Exp. Mar. Biol. Ecol. 397, 13–20 (2011).Article 

    Google Scholar 
    Foster, T., Gilmour, J. P., Chua, C. M., Falter, J. L. & McCulloch, M. T. Effect of ocean warming and acidification on the early life stages of subtropical Acropora spicifera. Coral Reefs 34, 1217–1226 (2015).Article 

    Google Scholar 
    Randall, C. J. & Szmant, A. M. Elevated temperature reduces survivorship and settlement of the larvae of the Caribbean scleractinian coral, Favia fragum (Esper). Coral Reefs 28, 537–545 (2009).Article 

    Google Scholar 
    Gilmour, J. P., Smith, L. D., Heyward, A. J., Baird, A. H. & Pratchett, M. S. Recovery of an isolated coral reef system following severe disturbance. Science 340, 69–71 (2013).Article 

    Google Scholar 
    Doropoulos, C., Ward, S., Roff, G., Gonzalez-Rivero, M. & Mumby, P. J. Linking Demographic Processes of Juvenile Corals to Benthic Recovery Trajectories in Two Common Reef Habitats. PLoS ONE 10, https://doi.org/10.1371/journal.pone.0128535 (2015).Donelson, J. M., Munday, P. L., McCormick, M. I. & Pitcher, C. R. Rapid transgenerational acclimation of a tropical reef fish to climate change. Nat. Clim. Change 2, 30–32 (2012).Article 

    Google Scholar 
    Yuyama, I., Nakamura, T., Higuchi, T. & Hidaka, M. Different stress tolerances of juveniles of the Coral Acropora tenuis associated with clades C1 and D symbiodinium. Zool. Stud. 55, https://doi.org/10.6620/Zs.2016.55-19 (2016).Mumby, P. J. Can Caribbean coral populations be modelled at metapopulation scales? Mar. Ecol. Prog. Ser. 180, 275–288 (1999).Article 

    Google Scholar 
    Raymundo, L. J. & Maypa, A. P. Getting bigger faster: mediation of size-specific mortality via fusion in jevenile coral transplants. Ecol. Appl. 14, 281–295 (2004).Article 

    Google Scholar 
    Heyward, A. J. & Negri, A. P. Plasticity of larval pre-competency in response to temperature: observations on multiple broadcast spawning coral species. Coral Reefs 29, 631–636 (2010).Article 

    Google Scholar 
    Figueiredo, J., Baird, A. H., Harii, S. & Connolly, S. R. Increased local retention of reef coral larvae as a result of ocean warming. Nat. Clim. Change 4, 498–502 (2014).Article 

    Google Scholar 
    Ward, S. & Harrison, P. Changes in gametogenesis and fecundity of acroporid corals that were exposed to elevated nitrogen and phosphorus during the ENCORE experiment. J. Exp. Mar. Biol. Ecol. 246, 179–221 (2000).Article 
    CAS 

    Google Scholar 
    Jones, A. M. & Berkelmans, R. Tradeoffs to thermal acclimation: energetics and reproduction of a reef coral with heat tolerant Symbiodinium type-D. J. Mar. Biol. 2011, 185890 (2011).Harriott, V. Reproductive ecology of four scleratinian species at Lizard Island, Great Barrier Reef. Coral Reefs 2, 9–18 (1983).Article 

    Google Scholar 
    Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: Comparative studies of reef-building corals. Ecology 77, 950–963 (1996).Article 

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

    Google Scholar 
    Hoegh-Guldberg, O. & Smith, G. J. Influence of the population density of zooxanthellae and supply of ammonium on the biomass and metabolic characteristics of the reef corals Seriatopora hystrix and Stylophora pistillata. Mar. Ecol. Prog. Ser. 57, 173–186 (1989).Lesser, M. P. Elevated temperatures and ultraviolet radiation cause oxidative stress and inhibit photosynthesis in ymbiotic dinoflagellates. Limnol. Oceanogr. 41, 271–283 (1996).Article 
    CAS 

    Google Scholar 
    Jones, R. J., Hoegh-Guldberg, O., Larkum, A. W. D. & Schreiber, U. Temperature-induced bleaching of corals begins with impairment of the CO2 fixation mechanism in zooxanthellae. Plant Cell Environ. 21, 1219–1230 (1998).Article 
    CAS 

    Google Scholar 
    Warner, M. E., Fitt, W. K. & Schmidt, G. W. Damage to photosystem II in symbiotic dinoflagellates: A determinant of coral bleaching. Proc. Natl Acad. Sci. USA 96, 8007–8012 (1999).Article 
    CAS 

    Google Scholar 
    McRae, C. J., Huang, W. B., Fan, T. Y. & Côté, I. M. Effects of thermal conditioning on the performance of Pocillopora acuta adult coral colonies and their offspring. Coral Reefs 40, 1491–1503 (2021).Article 

    Google Scholar 
    Howells, E. J. et al. Species-specific trends in the reproductive output of corals across environmental gradients and bleaching histories. Mar. Pollut. Bull. 105, 532–539 (2016).Article 
    CAS 

    Google Scholar 
    Galanto, N., Sartor, C., Moscato, V., Lizama, M. & Lemer, S. Effects of elevated temperature on reproduction and larval settlement in Leptastrea purpurea. Coral Reefs 41, 293–302 (2022).Article 

    Google Scholar 
    Hazraty-Kari, S., Masaya, M., Kawachi, M. & Harii, S. The early acquisition of symbiotic algae benefits larval survival and juvenile growth in the coral Acropora tenuis. J. Exp. Zool. A Ecol. Integr. Physiol. https://doi.org/10.1002/jez.2589 (2022).Moran, A. L. & Manahan, D. T. Energy metabolism during larval development of green and white abalone, Haliotis fulgens and H. sorenseni. Biol. Bull. 204, 270–277 (2003).Article 

    Google Scholar 
    Sewell, M. A. Utilization of lipids during early development of the sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 304, 133–142 (2005).Article 
    CAS 

    Google Scholar 
    Alexander, G., Hancock, J., Huffmyer, A. & Matsuda, S. Larval thermal conditioning does not improve post-settlement thermal tolerance in the dominant reef-building coral, Montipora capitata. Coral Reefs 41, 333–342 (2022).Rivest, E. B., Chen, C. S., Fan, T. Y., Li, H. H. & Hofmann, G. E. Lipid consumption in coral larvae differs among sites: a consideration of environmental history in a global ocean change scenario. Proc. Biol. Sci. 284, https://doi.org/10.1098/rspb.2016.2825 (2017).Graham, E. M., Baird, A. H., Connolly, S. R., Sewell, M. A. & Willis, B. L. Uncoupling temperature-dependent mortality from lipid depletion for scleractinian coral larvae. Coral Reefs 36, 97–104 (2017).Article 

    Google Scholar 
    Graham, E. M., Baird, A. H., Connolly, S. R., Sewell, M. A. & Willis, B. L. Rapid declines in metabolism explain extended coral larval longevity. Coral Reefs 32, 539–549 (2013).Article 

    Google Scholar 
    Reid, E. C. et al. Internal waves influence the thermal and nutrient environment on a shallow coral reef. Limnol. Oceanogr. 64, 1949–1965 (2019).Article 

    Google Scholar 
    Maynard, J. A., Anthony, K. R. N., Marshall, P. A. & Masiri, I. Major bleaching events can lead to increased thermal tolerance in corals. Mar. Biol. 155, 173–182 (2008).Article 

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

    Google Scholar 
    Veal, C. J., Holmes, G., Nunez, M., Hoegh-Guldberg, O. & Osborn, J. A comparative study of methods for surface area and three-dimensional shape measurement of coral skeletons. Limnol. Oceanogr.-Meth 8, 241–253 (2010).Article 

    Google Scholar 
    Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanz. 167, 191–194 (1975).Article 
    CAS 

    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
    CAS 

    Google Scholar 
    R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria 2021). More

  • in

    Dynamics of rumen microbiome in sika deer (Cervus nippon yakushimae) from unique subtropical ecosystem in Yakushima Island, Japan

    Gruninger, R. J., Ribeiro, G. O., Cameron, A. & McAllister, T. A. Invited review: Application of meta-omics to understand the dynamic nature of the rumen microbiome and how it responds to diet in ruminants. Animal 13, 1843–1854 (2019).CAS 

    Google Scholar 
    Morgavi, D. P., Kelly, W. J., Janssen, P. H. & Attwood, G. T. Rumen microbial (meta)genomics and its application to ruminant production. Animal 7, 184–201 (2013).CAS 

    Google Scholar 
    Bergman, E. N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567–590 (1990).CAS 

    Google Scholar 
    Flint, H. J. The rumen microbial ecosystem—Some recent developments. Trends Microbiol. 5, 483–488 (1997).CAS 

    Google Scholar 
    Hobson, P. N. & Stewart, C. S. The Rumen Microbial Ecosystem. (Springer, 2012).Moraïs, S. & Mizrahi, I. The road not taken: The rumen microbiome, functional groups, and community states. Trends Microbiol. 27, 538–549 (2019).
    Google Scholar 
    Cheng, K. J., Forsberg, C. W., Minato, H. & Costerton, J. W. in Physiological Aspects of Digestion and Metabolism in Ruminants (eds T. Tsuda, Y. Sasaki, & R. Kawashima) 595–624 (Academic Press, 1991).McSweeney, C. S., Palmer, B., McNeill, D. M. & Krause, D. O. Microbial interactions with tannins: Nutritional consequences for ruminants. Anim. Feed Sci. Technol. 91, 83–93 (2001).CAS 

    Google Scholar 
    Skene, I. K. & Brooker, J. D. Characterization of tannin acylhydrolase activity in the ruminal bacterium Selenomonas ruminantium. Anaerobe 1, 321–327 (1995).CAS 

    Google Scholar 
    Khanbabaee, K. & van Ree, T. Tannins: Classification and definition. Nat. Prod. Rep. 18, 641–649 (2001).CAS 

    Google Scholar 
    Makkar, H. P. S. & Becker, K. Isolation of tannins from leaves of some trees and shrubs and their properties. J. Agric. Food Chem. 42, 731–734 (1994).CAS 

    Google Scholar 
    Bhat, T. K., Kannan, A., Singh, B. & Sharma, O. P. Value addition of feed and fodder by alleviating the antinutritional effects of tannins. Agr. Res. 2, 189–206 (2013).CAS 

    Google Scholar 
    Shimada, T. Salivary proteins as a defense against dietary tannins. J. Chem. Ecol. 32, 1149–1163 (2006).CAS 

    Google Scholar 
    Zhu, J., Filippich, L. J. & Alsalami, M. T. Tannic acid intoxication in sheep and mice. Res. Vet. Sci. 53, 280–292 (1992).CAS 

    Google Scholar 
    Kohl, K. D., Stengel, A. & Dearing, M. D. Inoculation of tannin-degrading bacteria into novel hosts increases performance on tannin-rich diets. Environ. Microbiol. 18, 1720–1729 (2016).CAS 

    Google Scholar 
    Kumar, K., Chaudhary, L. C., Agarwal, N. & Kamra, D. N. Isolation and characterization of tannin-degrading bacteria from the rumen of goats fed oak (Quercus semicarpifolia) leaves. Agr. Res. 3, 377–385 (2014).
    Google Scholar 
    Odenyo, A. A. et al. Characterization of tannin-tolerant bacterial isolates from East African ruminants. Anaerobe 7, 5–15 (2001).CAS 

    Google Scholar 
    Grilli, D. J. et al. Analysis of the rumen bacterial diversity of goats during shift from forage to concentrate diet. Anaerobe 42, 17–26 (2016).
    Google Scholar 
    Tong, J. et al. Illumina sequencing analysis of the ruminal microbiota in high-yield and low-yield lactating dairy cows. PLoS ONE 13, e0198225 (2018).
    Google Scholar 
    Pope, P. B. et al. Metagenomics of the Svalbard reindeer rumen microbiome reveals abundance of polysaccharide utilization loci. PLoS ONE 7, e38571 (2012).ADS 
    CAS 

    Google Scholar 
    Østbye, K., Wilson, R. & Rudi, K. Rumen microbiota for wild boreal cervids living in the same habitat. FEMS Microbiol. Lett. 363, fnw233 (2016).
    Google Scholar 
    Gruninger, R. J., Sensen, C. W., McAllister, T. A. & Forster, R. J. Diversity of rumen bacteria in Canadian cervids. PLoS ONE 9, e89682 (2014).ADS 

    Google Scholar 
    Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567 (2015).CAS 

    Google Scholar 
    Reese, A. T. & Kearney, S. M. Incorporating functional trade-offs into studies of the gut microbiota. Curr. Opin. Microbiol. 50, 20–27 (2019).CAS 

    Google Scholar 
    Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).ADS 

    Google Scholar 
    Okano, T. & Matsuda, H. Biocultural diversity of Yakushima Island: Mountains, beaches, and sea. J. Mar. Isl. Cult. 2, 69–77 (2013).
    Google Scholar 
    Agetsuma, N., Agetsuma-Yanagihara, Y. & Takafumi, H. Food habits of Japanese deer in an evergreen forest: Litter-feeding deer. Mamm. Biol. 76, 201–207 (2011).
    Google Scholar 
    Higashi, Y., Hirota, S. K., Suyama, Y. & Yahara, T. Geographical and seasonal variation of plant taxa detected in faces of Cervus nippon yakushimae based on plant DNA analysis in Yakushima Island. Ecol. Res. 37, 582–597 (2022).CAS 

    Google Scholar 
    Kuroiwa, A. Nutritional ecology of the Yakushika (Cervus nippon yakushimae) population under high density Ph.D. thesis, Kyushu University, (2017).Koda, R., Agetsuma, N., Agetsuma-Yanagihara, Y., Tsujino, R. & Fujita, N. A proposal of the method of deer density estimate without fecal decomposition rate: A case study of fecal accumulation rate technique in Japan. Ecol. Res. 26, 227–231 (2011).
    Google Scholar 
    Yahara, T. in Deer eats world heritages: Ecology of deer and forets (eds T. Yumoto & H. Matsuda) 168–187 (Bunichi-Sogo-Shuppan, 2006).Onoda, Y. & Yahara, T. in Challenges for Conservation Ecology in Space and Time. (eds T. Miyashita & J. Nishihiro) 126–149 (University of Tokyo Press, 2015).Kagoshima Prefecture Nature Conservation Division. The current status of Yakusika in FY 2020, available at https://www.rinya.maff.go.jp/kyusyu/fukyu/shika/attach/pdf/yakushikaWG_R3_2-23.pdf (2020).Kuroiwa, A., Kuroe, M. & Yahara, T. Effects of density, season, and food intake on sika deer nutrition on Yakushima Island, Japan. Ecol. Res. 32, 369–378 (2017).
    Google Scholar 
    Hiura, T., Hashidoko, Y., Kobayashi, Y. & Tahara, S. Effective degradation of tannic acid by immobilized rumen microbes of a sika deer (Cervus nippon yesoensis) in winter. Anim. Feed Sci. Technol. 155, 1–8 (2010).CAS 

    Google Scholar 
    Kawarai, S. et al. Seasonal and geographical differences in the ruminal microbial and chloroplast composition of sika deer (Cervus nippon) in Japan. Sci. Rep. 12, 6356 (2022).ADS 
    CAS 

    Google Scholar 
    Li, Z. et al. Response of the rumen microbiota of sika deer Cervus nippon fed different concentrations of tannin rich plants. PLoS ONE 10, e0123481 (2015).
    Google Scholar 
    McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).CAS 

    Google Scholar 
    Kim, M., Morrison, M. & Yu, Z. Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol. Ecol. 76, 49–63 (2011).CAS 

    Google Scholar 
    Weimer, P. J. Redundancy, resilience, and host specificity of the ruminal microbiota: Implications for engineering improved ruminal fermentations. Front. Microbiol. 6, 296 (2015).
    Google Scholar 
    Scott, K. P., Gratz, S. W., Sheridan, P. O., Flint, H. J. & Duncan, S. H. The influence of diet on the gut microbiota. Pharmacol. Res. 69, 52–60 (2013).CAS 

    Google Scholar 
    Tapio, I. et al. Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows. PLoS ONE 12, e0180260 (2017).
    Google Scholar 
    Ohara, M. in Agriculture in Hokkaido v2 (ed K. Iwama, Ohara, M., Araki, H., Yamada, T., Nakatsuji, H., Kataoka, T., Yamamoto, Y.) 1–18(Faculty of Agriculture, Hokkaido Univ., 2009).Igota, H., Sakuragi, M. & Uno, H. in Sika Deer: Biology and Management of Native and Introduced Populations (eds. Dale R. McCullough, Seiki Takatsuki, & Koichi Kaji) 251–272 (Springer Japan, 2009).Fernando, S. C. et al. Rumen microbial population dynamics during adaptation to a high-grain diet. Appl. Environ. Microbiol. 76, 7482–7490 (2010).ADS 
    CAS 

    Google Scholar 
    Hu, X. et al. High-throughput analysis reveals seasonal variation of the gut microbiota composition within forest musk deer (Moschus berezovskii). Front. Microbiol. 9, (2018).Artzi, L., Morag, E., Shamshoum, M. & Bayer, E. A. Cellulosomal expansion: Functionality and incorporation into the complex. Biotechnol. Biofuels 9, 61 (2016).
    Google Scholar 
    Biddle, A., Stewart, L., Blanchard, J. & Leschine, S. Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in diverse gut communities. Diversity 5, (2013).Eisenhauer, N., Scheu, S. & Jousset, A. Bacterial diversity stabilizes community productivity. PLoS ONE 7, e34517 (2012).ADS 
    CAS 

    Google Scholar 
    Miller, A. W., Oakeson, K. F., Dale, C. & Dearing, M. D. Effect of dietary oxalate on the gut microbiota of the mammalian herbivore Neotoma albigula. Appl. Environ. Microbiol. 82, 2669–2675 (2016).ADS 
    CAS 

    Google Scholar 
    Adams, J. M., Rehill, B., Zhang, Y. & Gower, J. A test of the latitudinal defense hypothesis: Herbivory, tannins and total phenolics in four North American tree species. Ecol. Res. 24, 697–704 (2009).CAS 

    Google Scholar 
    Nabeshima, E., Murakami, M. & Hiura, T. Effects of herbivory and light conditions on induced defense in Quercus crispula. J. Plant Res. 114, 403–409 (2001).
    Google Scholar 
    Yang, C.-M., Yang, M.-M., Hsu, J.-M. & Jane, W.-N. Herbivorous insect causes deficiency of pigment–protein complexes in an oval-pointed cecidomyiid gall of Machilus thunbergii leaf. Bot. Bull. Acad. Sin. 44, 315–321 (2003).
    Google Scholar 
    Agetsuma, N., Agetsuma-Yanagihara, Y., Takafumi, H. & Nakaji, T. Plant constituents affecting food selection by sika deer. J. Wildl. Manag. 83, 669–678 (2019).
    Google Scholar 
    Couch, C. E. et al. Diet and gut microbiome enterotype are associated at the population level in African buffalo. Nat. Commun. 12, 2267 (2021).ADS 
    CAS 

    Google Scholar 
    Goel, G., Puniya, A. K. & Singh, K. Tannic acid resistance in ruminal streptococcal isolates. J. Basic Microbiol. 45, 243–245 (2005).CAS 

    Google Scholar 
    Jiménez, N. et al. Genetic and biochemical approaches towards unravelling the degradation of gallotannins by Streptococcus gallolyticus. Microb. Cell Fact. 13, 154 (2014).
    Google Scholar 
    Nelson, K. E., Thonney, M. L., Woolston, T. K., Zinder, S. H. & Pell, A. N. Phenotypic and phylogenetic characterization of ruminal tannin-tolerant bacteria. Appl. Environ. Microbiol. 64, 3824–3830 (1998).ADS 
    CAS 

    Google Scholar 
    Selwal, M. K. et al. Optimization of cultural conditions for tannase production by Pseudomonas aeruginosa IIIB 8914 under submerged fermentation. World J. Microbiol. Biotechnol. 26, 599–605 (2010).CAS 

    Google Scholar 
    Kohl, K. D., Weiss, R. B., Cox, J., Dale, C. & Denise Dearing, M. Gut microbes of mammalian herbivores facilitate intake of plant toxins. Ecol. Lett. 17, 1238–1246 (2014).
    Google Scholar 
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Method 7, 335–336 (2010).CAS 

    Google Scholar 
    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 

    Google Scholar 
    Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2009).
    Google Scholar 
    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 

    Google Scholar 
    R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).Osawa, R. Formation of a clear zone on tannin-treated brain heart infusion agar by a Streptococcus sp. isolated from feces of koalas. Appl. Environ. Microbiol. 56, 829–831 (1990).ADS 
    CAS 

    Google Scholar 
    Hamamura, N., Olson, S. H., Ward, D. M. & Inskeep, W. P. Diversity and functional analysis of bacterial communities associated with natural hydrocarbon seeps in acidic soils at Rainbow Springs, Yellowstone National Park. Appl. Environ. Microbiol. 71, 5943–5950 (2005).ADS 
    CAS 

    Google Scholar 
    Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2012).ADS 

    Google Scholar 
    Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2020)Suzuki, M. T., Taylor, L. T. & Delong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5 ’-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614 (2000).ADS 
    CAS 

    Google Scholar  More

  • in

    Gapless genome assembly of East Asian finless porpoise

    Gao, A. L. & Zhou, K. Y. Growth and reproduction of three populations of finless porpoise, Neophocaena phocaenoides, in Chinese waters. Aquat Mamm 19, 3–12 (1993).
    Google Scholar 
    Jefferson, T. A. Preliminary analysis of geographic variation in cranial morphometrics of the finless porpoise (Neophocaena phocaenoides). Raffles Bull Zool 10, 3–14 (2002).
    Google Scholar 
    Pilleri, G. & Gihr, M. Contribution to the knowledge of the cetaceans of Pakistan with particular reference to the genera Neomeris, Sousa, Delphinus and Tursiops and description of a new Chinese porpoise (Neomeris asiaeorientalis). Investig Cetacea 4, 107–162 (1972).
    Google Scholar 
    Pilleri, G. & Gihr, M. On the taxonomy and ecology of the finless black porpoise, Neophocaena (Cetacea, Delphinidae). Mammalia 39, 657–673 (1975).Article 

    Google Scholar 
    Wang, P. L. The morphological characters and the problem of subspecies identifications of the finless porpoise. Fish Sci 11, 4–8 (1992).
    Google Scholar 
    Wang, P. L. On the taxonomy of the finless porpoise in China. Fish Sci 6, 10–14 (1992).
    Google Scholar 
    Gao, A. L. & Zhou, K. Y. Geographical variation of external measurements and three subspecies of Neophocaena phocaenoides in Chinese waters. Acta Theriol Sin 15, 81–92 (1995).
    Google Scholar 
    Wang, J. Y., Frasier, T. R., Yang, S. C. & White, B. N. Detecting recent speciation events: the case of the finless porpoise (genus Neophocaena). Heredity 101, 145–155 (2008).Article 

    Google Scholar 
    Jefferson, T. A. & Wang, J. Y. Revision of the taxonomy of finless porpoises (genus Neophocaena): the existence of two species. J Mar Anim Ecol 4, 3–16 (2011).
    Google Scholar 
    Zhou, X. M. et al. Population genomics of finless porpoises reveal an incipient cetacean species adapted to freshwater. Nat Commun 9, 1276 (2018).Article 
    ADS 

    Google Scholar 
    Wang, D., Turvey, S.T., Zhao, X. & Mei, Z. Neophocaena asiaeorientalis ssp. asiaeorientalis. The IUCN Red List of Threatened Species https://www.iucnredlist.org/species/43205774/45893487 (2013).Wang, J. Y. & Reeves, R. Neophocaena Asiaeorientalis. The IUCN Red List of Threatened Species https://www.iucnredlist.org/species/41754/50381766 (2017).Kasuya, T. Japanese whaling and other cetacean fisheries. Environ Sci Pollut Res Int 14, 39–48 (2007).Article 

    Google Scholar 
    Yoshida, H., Shirakihara, K., Kishino, H. & Shirakihara, M. A population size estimate of the finless porpoise, Neophocaena phocaenoides, from aerial sighting surveys in Ariake Sound and Tachibana Bay, Japan. Popul Ecol 39, 239–247 (1997).Article 

    Google Scholar 
    Amano, M., Nakahara, F., Hayano, A. & Shirakihara, K. Abundance estimate of finless porpoises off the Pacific coast of eastern Japan based on aerial surveys. Mamm Study 28, 103–110 (2003).Article 

    Google Scholar 
    Shirakihara, K., Shirakihara, M. & Yamamoto, Y. Distribution and abundance of finless porpoise in the Inland Sea of Japan. Mar Biol 150, 1025–1032 (2007).Article 

    Google Scholar 
    Zuo, T., Sun, J. Q., Shi, Y. Q. & Wang, J. Primary survey of finless porpoise population in the Bohai Sea. Acta Theriol Sin 38, 551–561 (2018).
    Google Scholar 
    Ruan, R., Guo, A. H., Hao, Y. J., Zheng, J. S. & Wang, D. De novo assembly and characterization of narrow-ridged finless porpoise renal transcriptome and identification of candidate genes involved in osmoregulation. Int J Mol Sci 16, 2220–2238 (2015).Article 

    Google Scholar 
    Li, S. H. et al. Echolocation click sounds from wild inshore finless porpoise (Neophocaena phocaenoides sunameri) with comparisons to the sonar of riverine N. p. asiaeorientalis. J Acoust Soc Am 121, 3938–3946 (2007).Article 
    ADS 

    Google Scholar 
    Dong, J. H., Wang, G. J. & Xiao, Z. Z. Migration and population difference of the finless porpoise in China. Mar Sci 5, 42–45 (1993).
    Google Scholar 
    Lu, Z. C. et al. Analysis of the diet of finless porpoise (Neophocaena asiaeorientalis sunameri) based on prey morphological characters and DNA barcoding. Conserv Genet Resour 8, 523–531 (2016).Article 

    Google Scholar 
    Chen, B. et al. Finless porpoises (Neophocaena asiaeorientalis) in the East China Sea: insights into feeding habits using morphological, molecular, and stable isotopic techniques. Can J Fish Aquat Sci 74, 1628–1645 (2017).Article 

    Google Scholar 
    Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022).Article 
    ADS 

    Google Scholar 
    Chen, Y. X. et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience 7, 1–6 (2018).Article 
    ADS 

    Google Scholar 
    Chikhi, R. & Medvedev, P. Informed and automated k-mer size selection for genome assembly. Bioinformatics 30, 31–37 (2014).Article 

    Google Scholar 
    Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat Methods 10, 563–569 (2013).Article 

    Google Scholar 
    Cheng, H. Y., Concepcion, G. T., Feng, X. W., Zhang, H. W. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170–175 (2021).Article 

    Google Scholar 
    Roach, M. J., Schmidt, S. A. & Borneman, A. R. Purge Haplotigs: allelic contig reassignment for third-gen diploid genome assemblies. BMC Bioinformatics 19, 1–10 (2018).Article 

    Google Scholar 
    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).Article 

    Google Scholar 
    Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst 3, 95–98 (2016).Article 

    Google Scholar 
    Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).Article 
    ADS 

    Google Scholar 
    Xiong, Y., Brandley, M. C., Xu, S. X., Zhou, K. Y. & Yang, G. Seven new dolphin mitochondrial genomes and a time-calibrated phylogeny of whales. BMC Evol Biol 9, 1–13 (2009).Article 

    Google Scholar 
    Alonge, M. et al. RaGOO: fast and accurate reference-guided scaffolding of draft genomes. Genome Biol 20, 1–17 (2019).Article 

    Google Scholar 
    Mayer, A., Lahr, G., Swaab, D. F., Pilgrim, C. & Reisert, I. The Y-chromosomal genes SRY and ZFY are transcribed in adult human brain. Neurogenetics 1, 281–288 (1998).Article 

    Google Scholar 
    Sinclair, A. H. et al. A gene from the human sex-determining region encodes a protein with homology to a conserved DNA-binding motif. Nature 346, 240–244 (1990).Article 
    ADS 

    Google Scholar 
    Koopman, P., Gubbay, J., Vivian, N., Goodfellow, P. & Lovell-Badge, R. Male development of chromosomally female mice transgenic for Sry. Nature 351, 117–121 (1991).Article 
    ADS 

    Google Scholar 
    Salo, P. et al. Molecular mapping of the putative gonadoblastoma locus on the Y chromosome. Genes Chromosomes Cancer 14, 210–214 (1995).Article 

    Google Scholar 
    Tsuchiya, K., Reijo, R., Page, D. C. & Disteche, C. M. Gonadoblastoma: molecular definition of the susceptibility region on the Y chromosome. Am J Hum Genet 57, 1400–1407 (1995).
    Google Scholar 
    Gegenschatz-Schmid, K., Verkauskas, G., Stadler, M. B. & Hadziselimovic, F. Genes located in Y-chromosomal regions important for male fertility show altered transcript levels in cryptorchidism and respond to curative hormone treatment. Basic Clin Androl 29, 1–8 (2019).Article 

    Google Scholar 
    Chen, N. Using Repeat Masker to identify repetitive elements in genomic sequences. Curr protoc Bioinf 5, 4–10 (2004).Article 

    Google Scholar 
    Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res 35, W265–W268 (2007).Article 

    Google Scholar 
    Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).Article 

    Google Scholar 
    Bao, W. D., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob DNA 6, 1–6 (2015).Article 

    Google Scholar 
    Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res 27, 573–580 (1999).Article 

    Google Scholar 
    Liu, W. et al. Blood Transcriptome Analysis Reveals Gene Expression Differences between Yangtze Finless Porpoises from Two Habitats: Natural and Ex Situ Protected Waters. Fishes 7, 96 (2022).Article 

    Google Scholar 
    Yin, D. H. et al. Integrated analysis of blood mRNAs and microRNAs reveals immune changes with age in the Yangtze finless porpoise (Neophocaena asiaeorientalis). Comp Biochem Physiol B Biochem Mol Biol 256, 110635 (2021).Article 

    Google Scholar 
    Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907–915 (2019).Article 

    Google Scholar 
    Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol 20, 1–13 (2019).Article 

    Google Scholar 
    Stanke, M., Diekhans, M., Baertsch, R. & Haussler, D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics 24, 637–644 (2008).Article 

    Google Scholar 
    Keane, M. et al. Insights into the evolution of longevity from the bowhead whale genome. Cell Rep 10, 112–122 (2015).Article 

    Google Scholar 
    Yim, H. S. et al. Minke whale genome and aquatic adaptation in cetaceans. Nat Genet 46, 88–92 (2014).Article 

    Google Scholar 
    Jones, S. J. et al. The genome of the beluga whale (Delphinapterus leucas). Genes 8, 378 (2017).Article 
    ADS 

    Google Scholar 
    Zhou, X. M. et al. Baiji genomes reveal low genetic variability and new insights into secondary aquatic adaptations. Nat Commun 4, 1–6 (2013).Article 
    ADS 

    Google Scholar 
    Foote, A. D. et al. Convergent evolution of the genomes of marine mammals. Nat Genet 47, 272–275 (2015).Article 

    Google Scholar 
    Keilwagen, J., Hartung, F. & Grau, J. GeMoMa: homology-based gene prediction utilizing intron position conservation and RNA-seq data. Methods Mol Biol 1962, 161–177 (2019).Article 

    Google Scholar 
    Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44, D457–D462 (2016).Article 

    Google Scholar 
    Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28, 45–48 (2000).Article 

    Google Scholar 
    Korf, I. Gene finding in novel genomes. BMC bioinformatics 5, 1–9 (2004).Article 

    Google Scholar 
    Finn, R. D. et al. InterPro in 2017-beyond protein family and domain annotations. Nucleic Acids Res 45, D190–D199 (2017).Article 

    Google Scholar 
    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J Mol Biol 215, 403–410 (1990).Article 

    Google Scholar 
    Mulder, N. J. & Apweiler, R. InterPro and InterProScan: tools for protein sequence classification and comparison. Methods Mol Biol 396, 59–70 (2007).Article 

    Google Scholar 
    Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat Genet 25, 25–29 (2000).Article 

    Google Scholar 
    NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR21047154 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20760935 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20760936 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997931 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997932 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997933 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997934 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997935 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRP389529 (2022).Yin, D. H. et al. Neophocaena asiaeorientalis sunameri isolate NAS202207, whole genome shotgun sequencing project. GenBank https://identifiers.org/insdc.gca:GCA_026225855.1 (2022).Yin, D. H. et al. Gapless genome assembly of East Asian finless porpoise, Neophocaena asiaeorientalis sunameri. figshare https://doi.org/10.6084/m9.figshare.20381274.v2 (2022).Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).Article 

    Google Scholar 
    Marçais, G. et al. MUMmer4: A fast and versatile genome alignment system. PLoS Comput Biol 14, e1005944 (2018).Article 

    Google Scholar  More

  • in

    Half-millennium evidence suggests that extinction debts of global vertebrates started in the Second Industrial Revolution

    Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt. Nature 371, 65–66 (1994).Article 

    Google Scholar 
    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fonseca, C. R. et al. Conservation biology: four decades of problem- and solution-based research. Perspect. Ecol. Conserv. 19, 121–130 (2021).
    Google Scholar 
    Smits, P. & Finnegan, S. How predictable is extinction? Forecasting species survival at million-year timescales. Philos. Trans. R. Soc. B Biol. Sci. 374, 20190392 (2019).Article 

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

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

    Google Scholar 
    Ridding, L. E. et al. Inconsistent detection of extinction debts using different methods. Ecography 44, 33–43 (2021).Article 

    Google Scholar 
    Berglund, H. & Jonsson, B. G. Verifying an extinction debt among lichens and fungi in northern Swedish boreal forests. Conserv. Biol. 19, 338–348 (2005).Article 

    Google Scholar 
    Jones, I. L., Bunnefeld, N., Jump, A. S., Peres, C. A. & Dent, D. H. Extinction debt on reservoir land-bridge islands. Biol. Conserv. 199, 75–83 (2016).Article 

    Google Scholar 
    Triantis, K. et al. Extinction debt on oceanic islands. Ecography 33, 285–294 (2010).
    Google Scholar 
    Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian Amazon. Science 337, 228–232 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Pan, Y. et al. Spatial and temporal scales of landscape structure affect the biodiversity-landscape relationship across ecologically distinct species groups. Landsc. Ecol. 37, 2311–2325 (2022).Article 

    Google Scholar 
    Soga, M. & Koike, S. Mapping the potential extinction debt of butterflies in a modern city: Implications for conservation priorities in urban landscapes. Anim. Conserv. 16, 1–11 (2013).Article 

    Google Scholar 
    Knapp, S., Winter, M. & Klotz, S. Increasing species richness but decreasing phylogenetic richness and divergence over a 320-year period of urbanization. J. Appl. Ecol. 54, 1152–1160 (2017).Article 

    Google Scholar 
    McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the anthropocene. Trends Ecol. Evol. 30, 104–113 (2015).Article 
    PubMed 

    Google Scholar 
    Chen, Y. & Peng, S. Evidence and mapping of extinction debts for global forest-dwelling reptiles, amphibians and mammals. Sci. Rep. 7, 1–10 (2017).
    Google Scholar 
    Krauss, J. et al. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecol. Lett. 13, 597–605 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cowlishaw, G. Predicting the pattern of decline of African primate diversity: An extinction debt from historical deforestation. Conserv. Biol. 13, 1183–1193 (1999).Article 

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

    Google Scholar 
    Aerts, R. & Honnay, O. Forest restoration, biodiversity and ecosystem functioning. BMC Ecol. 11, 1–21 (2011).Article 

    Google Scholar 
    Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    IUCN. The IUCN Red List of Threatened Species, Version 2019-1. https://www.iucnredlist.org. Downloaded on 23 February 2022. (2019).Brown, J. L. et al. Spatial biodiversity patterns of Madagascar’s amphibians and reptiles. PLoS ONE 11, e0144076 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Powney, G. D., Grenyer, R., Orme, C. D. L., Owens, I. P. F. & Meiri, S. Hot, dry and different: Australian lizard richness is unlike that of mammals, amphibians and birds. Glob. Ecol. Biogeogr. 19, 386–396 (2010).Article 

    Google Scholar 
    Pianka, E. R. Desert lizard diversity: additional comments and some data. Am. Nat. 134, 344–364 (1989).Article 

    Google Scholar 
    Chen, Y. H. Combining the species-area-habitat relationship and environmental cluster analysis to set conservation priorities: A study in the Zhoushan Archipelago, China. Conserv. Biol. 23, 537–545 (2009).Article 
    PubMed 

    Google Scholar 
    Ricklefs, R. E. & Lovette, I. J. The roles of island area per se and habitat diversity in the species-area relationships of four Lesser Antillean faunal groups. J. Anim. Ecol. 68, 1142–1160 (1999).Article 

    Google Scholar 
    Souza, F. L., Martins, F. I. & Raizer, J. Habitat heterogeneity and anuran community of an agroecosystem in the Pantanal of Brazil. Phyllomedusa 13, 41–50 (2014).Article 

    Google Scholar 
    Kelt, D. A. & Van Vuren, D. H. The ecology and macroecology of mammalian home range area. Am. Nat. 157, 637–645 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    McNab, B. K. Bioenergetics and the determination of home range size. Am. Nat. 97, 133–140 (1963).Article 

    Google Scholar 
    Powell, R. A. & Mitchell, M. S. What is a home range? J. Mammal. 93, 948–958 (2012).Article 

    Google Scholar 
    Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 1–10 (2019).Article 

    Google Scholar 
    Giam, X. et al. Reservoirs of richness: least disturbed tropical forests are centres of undescribed species diversity. Proc. R. Soc. B 279, 67–76 (2012).Article 
    PubMed 

    Google Scholar 
    Pillay, R. et al. Tropical forests are home to over half of the world’s vertebrate species. Front. Ecol. Environ. 20, 10–15 (2022).Article 
    PubMed 

    Google Scholar 
    Li, H. et al. Large numbers of vertebrates began rapid population decline in the late 19th century. Proc. Natl Acad. Sci. USA 113, 14079–14084 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pringle, R. M. Upgrading protected areas to conserve wild biodiversity. Nature 546, 91–99 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Forzieri, G., Dakos, V., McDowell, N. G., Ramdane, A. & Cescatti, A. Emerging signals of declining forest resilience under climate change. Nature 608, 534–539 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Diamond, J. M. Biogeographic kinetics: estimation of relaxation times for Avifaunas of southwest Pacific islands. Proc. Natl Acad. Sci. USA 69, 3199–3203 (1972).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    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).Article 
    PubMed 

    Google Scholar 
    Foley, J. A. et al. Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front. Ecol. Environ. 5, 25–32 (2007).Article 

    Google Scholar 
    Asamoah, E. F., Beaumont, L. J. & Maina, J. M. Climate and land-use changes reduce the benefits of terrestrial protected areas. Nat. Clim. Chang. 11, 1105–1110 (2021).Article 

    Google Scholar 
    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).Article 

    Google Scholar 
    Peng, S. et al. Sensitivity of land use change emission estimates to historical land use and land cover mapping. Glob. Biogeochem. Cycles 31, 626–643 (2017).Article 
    CAS 

    Google Scholar 
    Jain, A. K., Meiyappan, P., Song, Y. & House, J. I. CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Glob. Chang. Biol. 19, 2893–2906 (2013).Article 
    PubMed 

    Google Scholar 
    Poulter, B. et al. Plant functional type classification for earth system models: results from the European Space Agency’s Land Cover Climate Change Initiative. Geosci. Model Dev. 8, 2315–2328 (2015).Article 

    Google Scholar 
    Pongratz, J., Reick, C., Raddatz, T. & Claussen, M. A reconstruction of global agricultural areas and land cover for the last millennium. Global Biogeochem. Cycles 22, (2008).Dietz, F. C. The industrial revolution. In the Hands of a Child (1970).Gütschow, J., Jeffery, L. & Gieseke, R. The PRIMAP-hist national historical emissions time series (1850-2016). V. 2.0. GFZ Data Services (2019).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Protected Planet: The World Database on Protected Areas (UNEP-WCMC and IUCN, accessed 9 January 2022); www.protectedplanet.net.Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing Version 4.0.2 (2020). More

  • in

    Quantitative environmental DNA metabarcoding shows high potential as a novel approach to quantitatively assess fish community

    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Magurran, A. E. et al. Divergent biodiversity change within ecosystems. Proc. Natl. Acad. Sci. 115, 1843–1847 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Blowes, S. A. et al. Local biodiversity change reflects interactions among changing abundance, evenness, and richness. Ecology online, e3820 (2022).Crowder, D. W., Northfield, T. D., Gomulkiewicz, R. & Snyder, W. E. Conserving and promoting evenness: Organic farming and fire-based wildland management as case studies. Ecology 93, 2001–2007 (2012).Article 

    Google Scholar 
    Hillebrand, H., Bennett, D. M. & Cadotte, M. W. Consequences of dominance: A review of evenness effects on local and regional ecosystem processes. Ecology 89, 1510–1520 (2008).Article 

    Google Scholar 
    Masuda, R. et al. Fish assemblages associated with three types of artificial reefs: density of assemblages and possible impacts on adjacent fish abundance. Fishery Bulletin, National Oceanic and Atmospheric Administration. 108, 162–173 (2010).
    Google Scholar 
    Miyazono, S., Patiño, R. & Taylor, C. M. Desertification, salinization, and biotic homogenization in a dryland river ecosystem. Sci. Total Environ. 511, 444–453 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Yonekura, R., Kita, M. & Yuma, M. Species diversity in native fish community in Japan: Comparison between non-invaded and invaded ponds by exotic fish. Ichthyol. Res. 51, 176–179 (2004).Article 

    Google Scholar 
    Evans, N. T., Shirey, P. D., Wieringa, J. G., Mahon, A. R. & Lamberti, G. A. Comparative cost and effort of fish distribution detection via environmental DNA analysis and electrofishing. Fisheries 42, 90–99 (2017).Article 

    Google Scholar 
    Miya, M., Gotoh, R. O. & Sado, T. MiFish metabarcoding: A high-throughput approach for simultaneous detection of multiple fish species from environmental DNA and other samples. Fish. Sci. 86, 939–970 (2020).Article 
    CAS 

    Google Scholar 
    Oka, S. et al. Environmental DNA metabarcoding for biodiversity monitoring of a highly diverse tropical fish community in a coral reef lagoon: Estimation of species richness and detection of habitat segregation. Environ. DNA 3, 55–69 (2021).Article 
    CAS 

    Google Scholar 
    Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).Article 
    CAS 

    Google Scholar 
    Pimm, S. L. et al. Emerging technologies to conserve biodiversity. Trends Ecol. Evol. 30, 685–696 (2015).Article 

    Google Scholar 
    Rourke, M. L. et al. Environmental DNA (eDNA) as a tool for assessing fish biomass: A review of approaches and future considerations for resource surveys. Environ. DNA 4, 9–33 (2022).Article 
    CAS 

    Google Scholar 
    Tsuji, S. et al. Real-time multiplex PCR for simultaneous detection of multiple species from environmental DNA: An application on two Japanese medaka species. Sci. Rep. 8, 1–8 (2018).Article 
    CAS 

    Google Scholar 
    Kissling, W. D. et al. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).Article 

    Google Scholar 
    Rodríguez-Ezpeleta, N. et al. Biodiversity monitoring using environmental DNA. Mol. Ecol. Resour. 21, 1405–1409 (2021).Article 

    Google Scholar 
    Boivin-Delisle, D. et al. Using environmental DNA for biomonitoring of freshwater fish communities: Comparison with established gillnet surveys in a boreal hydroelectric impoundment. Environ. DNA 3, 105–120 (2021).Article 
    CAS 

    Google Scholar 
    Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).Article 

    Google Scholar 
    Doi, H. et al. Compilation of real-time PCR conditions toward the standardization of environmental DNA methods. Ecol. Res. 36, 379–388 (2021).Article 
    CAS 

    Google Scholar 
    Kelly, R. P. Making environmental DNA count. Mol. Ecol. Resour. 16, 10–12 (2016).Article 
    CAS 

    Google Scholar 
    Kumar, G., Eble, J. E. & Gaither, M. R. A practical guide to sample preservation and pre-PCR processing of aquatic environmental DNA. Mol. Ecol. Resour. 20, 29–39 (2020).Article 

    Google Scholar 
    Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Let. 4, 423–425 (2008).Article 

    Google Scholar 
    Kuwae, M. et al. Sedimentary DNA tracks decadal-centennial changes in fish abundance. Commun. Biol. 3, 1–12 (2020).Article 

    Google Scholar 
    Lynggaard, C. et al. Airborne environmental DNA for terrestrial vertebrate community monitoring. Curr. Biol. 32, 701–707.e5 (2022).Article 
    CAS 

    Google Scholar 
    Tsuji, S., Takahara, T., Doi, H., Shibata, N. & Yamanaka, H. The detection of aquatic macroorganisms using environmental DNA analysis—A review of methods for collection, extraction, and detection. Environ. DNA 1, 99–108 (2019).Article 

    Google Scholar 
    Bylemans, J., Gleeson, D. M., Duncan, R. P., Hardy, C. M. & Furlan, E. M. A performance evaluation of targeted eDNA and eDNA metabarcoding analyses for freshwater fishes. Environ. DNA 1, 402–414 (2019).Article 

    Google Scholar 
    Wozney, K. M. & Wilson, C. C. Quantitative PCR multiplexes for simultaneous multispecies detection of Asian carp eDNA. J. Great Lakes Res. 43, 771–776 (2017).Article 
    CAS 

    Google Scholar 
    Evans, N. T. et al. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour. 16, 29–41 (2016).Article 
    CAS 

    Google Scholar 
    Fraija-Fernández, N. et al. Marine water environmental DNA metabarcoding provides a comprehensive fish diversity assessment and reveals spatial patterns in a large oceanic area. Ecol. Evol. 10, 7560–7584 (2020).Article 

    Google Scholar 
    Kelly, R. P., Port, J. A., Yamahara, K. M. & Crowder, L. B. Using environmental DNA to census marine fishes in a large mesocosm. PLoS ONE 9, e86175 (2014).Article 
    ADS 

    Google Scholar 
    Thomsen, P. F. et al. Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes. PLoS ONE 11, e0165252 (2016).Article 

    Google Scholar 
    Lamb, P. D. et al. How quantitative is metabarcoding: A meta-analytical approach. Mol. Ecol. 28, 420–430 (2019).Article 

    Google Scholar 
    Lim, N. K. M. et al. Next-generation freshwater bioassessment: eDNA metabarcoding with a conserved metazoan primer reveals species-rich and reservoir-specific communities. R. Soc. Open Sci. 3, 160635 (2016).Article 
    ADS 

    Google Scholar 
    Hoshino, T., Nakao, R., Doi, H. & Minamoto, T. Simultaneous absolute quantification and sequencing of fish environmental DNA in a mesocosm by quantitative sequencing technique. Sci. Rep. 11, 4372 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Smets, W. et al. A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol. Biochem. 96, 145–151 (2016).Article 
    CAS 

    Google Scholar 
    Ushio, M. et al. Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcod. Metagenom. 2, e23297 (2018).
    Google Scholar 
    Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Sato, M. et al. Quantitative assessment of multiple fish species around artificial reefs combining environmental DNA metabarcoding and acoustic survey. Sci. Rep. 11, 1–14 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Ushio, M. Interaction capacity as a potential driver of community diversity. Proc. R. Soc. B Biol. Sci. 289, 20212690 (2022).Article 

    Google Scholar 
    Andruszkiewicz, E. A., Sassoubre, L. M. & Boehm, A. B. Persistence of marine fish environmental DNA and the influence of sunlight. PLoS ONE 12, e0185043 (2017).Article 

    Google Scholar 
    Bylemans, J., Gleeson, D. M., Hardy, C. M. & Furlan, E. Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia). Ecol. Evol. 8, 8697–8712 (2018).Article 

    Google Scholar 
    Civade, R. et al. Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PLoS ONE 11, e0157366 (2016).Article 

    Google Scholar 
    Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 12544 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Hänfling, B. et al. Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Mol. Ecol. 25, 3101–3119 (2016).Article 

    Google Scholar 
    Nakagawa, H. et al. Comparing local-and regional-scale estimations of the diversity of stream fish using eDNA metabarcoding and conventional observation methods. Freshw. Biol. 63, 569–580 (2018).Article 
    CAS 

    Google Scholar 
    Sato, H., Sogo, Y., Doi, H. & Yamanaka, H. Usefulness and limitations of sample pooling for environmental DNA metabarcoding of freshwater fish communities. Sci. Rep. 7, 14860 (2017).Article 
    ADS 

    Google Scholar 
    Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).Article 

    Google Scholar 
    Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).Article 
    CAS 

    Google Scholar 
    Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Jane, S. F. et al. Distance, flow and PCR inhibition: eDNA dynamics in two headwater streams. Mol. Ecol. Resour. 15, 216–227 (2015).Article 
    CAS 

    Google Scholar 
    Harper, L. R. et al. Needle in a haystack? A comparison of eDNA metabarcoding and targeted qPCR for detection of the great crested newt (Triturus cristatus). Ecol. Evol. 8, 6330–6341 (2018).Article 

    Google Scholar 
    Nichols, R. V. et al. Minimizing polymerase biases in metabarcoding. Mol. Ecol. Resour. 18, 927–939 (2018).Article 
    CAS 

    Google Scholar 
    Hosoya, K. Yamakei Handy Illustrated Book 15: Freshwater fishes of Japan (Yama-Kei Publishers, 2019).
    Google Scholar 
    Nakabo, T. Fishes of Japan with Pictorial Keys to the Species (3-Volume Set). (Tokai University Press, 2013).Goutte, A., Molbert, N., Guérin, S., Richoux, R. & Rocher, V. Monitoring freshwater fish communities in large rivers using environmental DNA metabarcoding and a long-term electrofishing survey. J. Fish Biol. 97, 444–452 (2020).Article 
    CAS 

    Google Scholar 
    Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).Article 
    CAS 

    Google Scholar 
    Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).Article 

    Google Scholar 
    Tsuji, S., Ushio, M., Sakurai, S., Minamoto, T. & Yamanaka, H. Water temperature-dependent degradation of environmental DNA and its relation to bacterial abundance. PLoS ONE 12, e0176608 (2017).Article 

    Google Scholar 
    Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).Article 

    Google Scholar 
    Nester, G. M. et al. Development and evaluation of fish eDNA metabarcoding assays facilitate the detection of cryptic seahorse taxa (family: Syngnathidae). Environ. DNA 2, 614–626 (2020).Article 

    Google Scholar 
    Piñol, J., Mir, G., Gomez-Polo, P. & Agustí, N. Universal and blocking primer mismatches limit the use of high-throughput DNA sequencing for the quantitative metabarcoding of arthropods. Mol. Ecol. Resour. 15, 819–830 (2015).Article 

    Google Scholar 
    Zhang, S., Zhao, J. & Yao, M. A comprehensive and comparative evaluation of primers for metabarcoding eDNA from fish. Methods Ecol. Evol. 11, 1609–1625 (2020).Article 
    ADS 

    Google Scholar 
    Yamanaka, H. et al. A simple method for preserving environmental DNA in water samples at ambient temperature by addition of cationic surfactant. Limnology 18, 233–241 (2017).Article 
    CAS 

    Google Scholar 
    Minamoto, T. et al. An illustrated manual for environmental DNA research: Water sampling guidelines and experimental protocols. Environ. DNA 3, 8–13 (2021).Article 
    CAS 

    Google Scholar 
    Tsuji, S., Nakao, R., Saito, M., Minamoto, T. & Akamatsu, Y. Pre-centrifugation before DNA extraction mitigates extraction efficiency reduction of environmental DNA caused by the preservative solution (benzalkonium chloride) remaining in the filters. Limnology 23, 9–16 (2022).Article 
    CAS 

    Google Scholar 
    R Core Team. R. A Language and Environment for Statistical Computing. (2021).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002).Coulter, D. P. et al. Nonlinear relationship between Silver Carp density and their eDNA concentration in a large river. PLoS ONE 14, e0218823 (2019).Article 
    CAS 

    Google Scholar 
    Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).Article 
    CAS 

    Google Scholar 
    Kanno, K., Onikura, N., Kurita, Y., Koyama, A. & Nakajima, J. Morphological, distributional, and genetic characteristics of Cottus pollux in the Kyushu Island, Japan: indication of fluvial and amphidromous life histories within a single lineage. Ichthyol. Res. 65, 462–470 (2018).Article 

    Google Scholar  More

  • in

    Revenue loss due to whale entanglement mitigation and fishery closures

    Whale entanglements in fishing gear threaten whale populations, seafood production and long-term sustainability of commercial fisheries. While multiple mitigation strategies to reduce entanglements exist, there has been minimal consideration of the economic impact of these strategies. Here, we estimated retrospective losses to ex-vessel revenues for one of California’s most lucrative fisheries. Overall, we found fishery closures decreased ex-vessel revenue, with results showing some uncertainty due to large model prediction error. Regional differences in losses revealed interesting trends in the capacity for the fishery to recoup costs. For example, in the NMA, relatively small losses at the fishery level were predicted ($0.3 million in total) for the 2019 season despite an early closure to the season due to whale entanglement risk.NMA fishers collectively were able to meet predicted revenue for the season despite a shortening of the fishing 2019 season. In the 2020 season however, the NMA did not experience disturbances due to whale entanglements but larger ex-vessel losses (of $3.9 million) were predicted. This suggests that other disturbances such as a delay to the season due to crab meat quality, lost fishing opportunity related to the COVID-19 pandemic, or other unknown factors, had an influence on ex-vessel revenue during the 2020 season. While most of the 2020 season landings in the NMA occurred before COVID-19 arrived in the US, there is evidence that prices in latter part of the season may have been depressed due to loss of export markets for live crab47.In the CMA however, despite landing the majority of crab available during the 2019 season (see Fig. 2c), losses of $9.4 million were experienced across the fishery. While total fishery catch was not greatly reduced, closure to the fishery in the spring may be responsible for revenue losses through other mechanisms (e.g. price). In the 2020 season, whale entanglement risk substantially shortened the fishing season in the CMA, through a delay at the beginning of the season and an early closure in the spring. Estimated losses were largest ($14.4 million) during this season. It is likely that the COVID-19 pandemic was also responsible for some of this estimated loss in the CMA in the 2020 season47. Our model did not control for impacts of the COVID-19 pandemic. However, price trends suggest that that price of Dungeness Crab in California was not affected until mid-March 2020, at which point the fishery had caught 92% of the seasons catch (see Supplementary File S2). Prices then returned to normal levels in mid-May. If we apply extrapolated prices between mid-March and mid-May by replacing observed prices with linearly increasing prices by week, revenues would have been $753,754 higher in total across the fishery. This rough estimate suggests we can attribute 4.1% of overall estimated revenue losses during the 2020 season to COVID-19 impacts, with the caveat that we do not know what prices would have been in the absence of the pandemic. A counterfactual approach has been used to disentangle multiple stressors to infer causal impacts of management interventions elsewhere48, however as these closures, and the COVID pandemic, potentially impacted all fishers in the California Dungeness crab fishery, there are no control groups available for comparison and therefore this approach would not be appropriate.Closures and other disturbances appear to have been less impactful in the NMA and high price for Dungeness crab may have contributed to the ability of vessels operating in the NMA to withstand disturbances (Supplementary Fig. S2). Prices were particularly high during the summer portion of the season in 2020 during which time the CMA was closed to Dungeness crab fishing (Supplementary Fig. S2). The NMA did not experience closures due to whale entanglement during 2020 and was predicted to have lower than average pre-season abundance (lower catch potential) during 2020 (see Fig. 2.b), while the CMA was predicted to have high catch potential for 2020 (Fig. 2.c), therefore differences in management measures implemented, and seasons’ catch potential, also contributed to differences in losses estimated.The CMA also experienced high prices, including decadal high prices for crab during the November–December of the 2019 fishing season (Supplementary Fig. S2). However, losses observed overall across the two seasons suggest the fishery, unlike the NMA, did not get much overall benefit from the high price in 2019 or the high pre-season abundance of crab (i.e. catch potential) estimated for the 2020 season in the CMA. A number of factors may have contributed to a poor season in the CMA including catchability or biology of Dungeness crab as well as external factors such as the COVID-19 pandemic behavioral choice factors, for example deciding not to fish45. Temporally shifting or reducing the opportunity for participation through closed periods due to whale entanglement risk may have exacerbated other impacts on revenues in the CMA which were not as impactful on revenues in the NMA.The high variability in estimated economic impacts per vessel reported here demonstrates that closures did not affect all vessels equally, similarly to impacts observed following a climate related harmful algal bloom in the 2016 season which were variable by vessel size and between communities45. The estimated losses we present at the fishery level in the NMA and CMA may therefore be underestimated, or overestimated, for particular groups of vessels within those management areas. This reflects the diverse nature of the Dungeness Crab fishery in behaviour and fishing strategy and highlights the importance of capturing impacts at finer scales than the fishery level alone.Limitations to the estimation of closure impactsA limitation of the hurdle model is that there are other latent factors influencing fishery participation and revenues that our model does not incorporate, particularly those determining fisher behavior such as fuel price, shipyard backlogs and market demand. A behavioral choice model, for example one that incorporates location or fishing alternative choice given a closure50,51,52 would be a potential method to better understand how spatial management strategies affect fisher behavior and is recommended as a future analysis to assess trade-offs involving socio-economic risk. Our results, reporting losses from Dungeness crab fishing revenue only, also do not account for the ability of some fishers to mitigate revenue losses by participating in other fisheries. Dungeness crab fishing is highly connected within west coast fishery participation networks44,45. Thus, it is important to note that our results for the 2019 and 2020 seasons present only losses from Dungeness crab fishing and may overestimate total annual revenue losses by some vessels that are able to mitigate impacts with participation in other fisheries.The model, predicting out-of-sample, over-estimated revenues in recent years suggesting that our predictions of revenues may also be over-predicted. An improved estimation at the vessel level, given some over-estimation of vessels that did not fish, could be investigated through a selection model approach rather than a two-part model approach54. However, two-part models are most appropriate for estimation of conditional (actual) outcomes as was intended here rather than unconditional (potential) outcomes and they do not require separate drivers for the selection and estimation model, which we did not have available54. When the impacts of policy interventions are difficult to disentangle from other impacts, approaches such as a counterfactual synthetic control48 approach could be used to separate the impacts of the policy alone. In this context, however, it is useful to report the cumulative impact of disturbances given that these disturbances (e.g., delays due to crab quality, harmful algal blooms) happen frequently and therefore the closures will rarely happen in isolation.Whilst there are limitations to our approach, revenue predictions presented here offer more insight compared to predicting revenues based only on a 5-year average of total fishery revenues (Supplementary Table S3) as is commonly conducted to calculate disaster assistance requirement, as our analysis includes an estimation of crab abundance as well as historical vessel level data in its estimation. Accounting for the influence of crab abundance is critical in this fishery given abundance is highly variable and the majority of fishable biomass is taken each year. Estimation of revenue at the individual vessel level allows for consideration of fishery heterogeneity (e.g., by vessel size). Revenues calculated on a 5-year average would suggest total California Commercial Dungeness crab fishery revenues would have been $10.62 million higher than observed in 2019 and $12.73 million higher than observed in 2020 (Supplementary Table S3). Thus, revenues estimated on the 5-year average suggest that losses would have been $0.97 million higher than our model prediction across the fishery for 2019 and $5.56 million lower than our model prediction for 2020. Our predictions suggest that delays and closures due to whale entanglement mitigation and other disturbances in to the 2019 and 2020 seasons were similar to the impact of closures due to the HAB in the 2016 season, which were estimated at $13.6 million in losses from Dungeness Crab revenues across the fishery38.Economic cost of mitigationMany strategies that prevent fishery interactions with marine mammals exist, including gear reductions or modifications, depth limitations and dynamic or seasonal time-area closures13,14,22,23,24,25,26,55. Whilst the fishery does implement pro-active gear modification measures set out in the best practices guide34, only two management intervention options were enacted in the 2019 and 2020 seasons to mitigate against entanglements of marine life with Dungeness crab gear; delays to the start of the crab season in the winter and early closures in spring due to overlap with whale distribution in fishing grounds. These delays and closures can have differential impacts on the fishery as the fishing season is not heterogeneously prosperous. An example is that closures during the holiday season (Nov–Dec) when Dungeness crab is traditionally consumed can cause substantial lost revenue opportunity for fishers at a time when price and demand are highest35,49. The fishery operates as a derby in which the majority of revenues are made in the first month of the fishery being open. The strong seasonal dynamics of the Dungeness crab fishery, largely driven by rapid depletion of legal sized crab, mean that the timing of management actions can have important impacts on fishing revenues. Across the fishery, based on observed vessel level revenues during the 2011–2018 baseline period, vessels earned an average of 62.33% (SD 24.04) of annual ex-vessel revenue during the first month of the season (15th Nov–15th Dec for the CMA/1st Dec–31st Dec for the NMA). After April 1st, vessels on average earn 10.54% (SD 18.98) of annual ex-vessel revenue. This average, based only on vessels that historically have actively participate past April 1st, (283 vessels in the NMA, 346 vessels in the CMA) rises to 20.36% (SD 13.37) of ex-vessel revenue. Thus, while the majority of the overall fisheries revenue is taken at the start of the season, an April 1st closure could still have a substantial impact on the revenues of active fishing vessels in the spring. Determination of economic risk for the fishery, at a minimum, should consider timing of closures in addition to total revenue losses, in order to quantify losses that will be felt at the individual vessel level. We suggest further research to investigate how closures affect different groups of fishers through stakeholder participation.Socio-economic impacts from whale mitigation measures could permeate into communities further than our analysis (based on ex-vessel revenue only) conveys35,36,37,49, and further investigation into these community level impacts is necessary to understand and sustain an equitable fishery supply chain even where there is no absolute revenue loss. Some of the communities influenced by whale entanglement mitigation in California rely heavily on ocean resources for employment, through fishing occupations but also through hospitality and tourism. Managing this issue in a way that minimizes the burden on resource dependent communities is strongly in line with the objectives set out in the UN Sustainable Development Goals (SDG’s), especially SDG 14 (life below water) but also related goals such as human well-being, reducing inequality and reducing the impacts of climate change56.Management ImplicationsBalancing socio-economic impacts against whale entanglement risk is challenging given the legally protected status of whale populations. However, potential economic losses reported here should motivate the development of mitigation measures (through cooperative innovation between industry, researchers and managers) that allow fishery production to be optimized whilst ensuring successful whale protection. At present, entire management areas, which constitute large regions of the coast, are closed in response to whale entanglement risk in California. Investigating how to minimize the spatiotemporal footprint of closures, such as by defining high risk zones dynamically based on fine-scale information of whale density and fishing effort, could provide an alternative mitigation structure. This could better consider the economic and conservation trade-offs while still being sensitive to changing environmental conditions. The introduction of dynamic zone closures, often broadly referred to as dynamic ocean management, has been demonstrated to reduce risk whilst minimizing lost fishing opportunities12,26,57,58, especially when environmental variability is high or species have a dynamic distribution59. Moreover, analysis of policy instruments to reduce whale entanglements with the American lobster fishery on the US Northeast coast found that economic costs of risk reduction could be 20% lower when mitigation decisions considered fishing opportunity costs alongside non-monetary benefits (biological risk), compared to non-monetary benefits alone12. This is promising for the implementation of such strategies in the California Current System.The caveat of this strategy is that dynamic zone closures require spatially and temporally explicit information on whale density and fishing effort which can be costly to attain. The use of ropeless gear has also been suggested as an alternative whale entanglement mitigation measure that requires further research and development before being initiated as an alternative regulatory tool60. The costs of monitoring or technical advancements however may outweigh the financial and societal cost of fishery closures. Revenue losses for Dungeness crab estimated here for the 2019 and 2020 seasons are on par with losses experienced during the HAB period. During the delays to the 2016 fishing season an estimated $26.1 million was lost from ex-vessel revenues from all species that crab fishers target, including $13.6 million from Dungeness crab alone38, requiring $25 million in government aid. Whale mitigation under the RAMP regulation will potentially delay or close the fishery year after year with uncertain economic impact that cannot be sustainably resolved with government aid. Development of tools to mitigate against economic loss while achieving whale protection will be necessary to come to a sustainable solution. This can only be achieved by first including economic loss in risk assessments. Doing so may also provide balance to partnerships between fishery managers and fishers.Regulators are obligated to protect Humpback whales, blue whales and Leatherback turtles using the best available science33. In this fishery, current triggers to open and close are based on a range of factors, but thus ultimately depend on the number of whales present within a management region33. Regulators have a number of alternative regulatory options available to them, which include depth restrictions, gear restrictions or modifications and fleet advisories, if they can offer the same level of whale protection33. Yet, the RAMP process lacks the socio-economic information needed to consider the socio-economic risk of regulatory actions, and that of the alternatives, to the fishing community. Results presented here highlight that the economic effects and that risk to fishing communities should be considered when designing whale entanglement mitigation programs33. Having this economic information will facilitate the ability of managers, as set out in the RAMP regulation (subsection d4)33, to consider the socio-economic impact if deciding between management measures that equivalently reduce entanglement risk.We have used two fishing seasons as an example of the economic impacts of these new whale entanglement regulations which will be implemented each year going forward. Synthesis of ex-vessel revenues is not a complete picture of the socio-economic impacts of regulations, but it provides a starting point for protecting both whales and fishing communities. While reported whale entanglements remain higher than pre-2014 totals, reported whale entanglements in California have declined markedly in the years following the 2014–2016 large marine heatwave (Fig. 1b). This is a success for this fishery and attributed to increased awareness, development of best practices for fishing gear and the mitigation program to protect whales. We now need to be successful at protecting and mitigating the socio-economic impacts to fishery participants and the fishing communities they support. More