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    Genetic monitoring on the world’s first MSC eco-labeled common octopus (O. vulgaris) fishery in western Asturias, Spain

    FAO. El estado mundial de la pesca y la acuicultura 2020 (FAO, 2020).
    Google Scholar 
    Jackson, J. B. C. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Scheffer, M., Carpenter, S. & de Young, B. Cascading effects of overfishing marine systems. Trends Ecol. Evol. 20, 579–581 (2005).Article 
    PubMed 

    Google Scholar 
    Coll, M., Libralato, S., Tudela, S., Palomera, I. & Pranovi, F. Ecosystem overfishing in the ocean. PLoS ONE 3, e3881 (2008).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Peterson, M. S. & Lowe, M. R. Implications of cumulative impacts to estuarine and marine habitat quality for fish and invertebrate resources. Rev. Fish. Sci. 17, 505–523 (2009).Article 

    Google Scholar 
    Claudet, J. & Fraschetti, S. Human-driven impacts on marine habitats: A regional meta-analysis in the Mediterranean Sea. Biol. Cons. 143, 2195–2206 (2010).Article 

    Google Scholar 
    Smith, V. H., Tilman, G. D. & Nekola, J. C. Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ. Pollut. 100, 179–196 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Derraik, J. G. B. The pollution of the marine environment by plastic debris: A review. Mar. Pollut. Bull. 44, 842–852 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    Doney, S. C. et al. Climate change impacts on marine ecosystems. Ann. Rev. Mar. Sci. 4, 11–37 (2012).Article 
    PubMed 

    Google Scholar 
    Molnar, J. L., Gamboa, R. L., Revenga, C. & Spalding, M. D. Assessing the global threat of invasive species to marine biodiversity. Front. Ecol. Environ. 6, 485–492 (2008).Article 

    Google Scholar 
    Wojnarowska, M., Sołtysik, M. & Prusak, A. Impact of eco-labelling on the implementation of sustainable production and consumption. Environ. Impact Assess. Rev. 86, 106505 (2021).Article 

    Google Scholar 
    Yan, H. F. et al. Overfishing and habitat loss drive range contraction of iconic marine fishes to near extinction. Sci. Adv. 7, 6026 (2021).Article 
    ADS 

    Google Scholar 
    Bastardie, F. et al. Spatial planning for fisheries in the Northern Adriatic: Working toward viable and sustainable fishing. Ecosphere 8, e01696 (2017).Article 

    Google Scholar 
    Arkema, K. K. et al. Integrating fisheries management into sustainable development planning. Ecol. Soc. 24, 0201 (2019).Article 

    Google Scholar 
    Aguión, A. et al. Establishing a governance threshold in small-scale fisheries to achieve sustainability. Ambio. https://doi.org/10.1007/s13280-021-01606-x (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gudmundsson, E. & Wessells, C. R. Ecolabeling seafood for sustainable production: Implications for fisheries management. Mar. Resour. Econ. 15, 97–113 (2000).Article 

    Google Scholar 
    FAO. Guidelines for the Ecolabelling of Fish and Fishery Products from Marine Capture Fisheries. Revision 1 (FAO, 2009).
    Google Scholar 
    Hilborn, R. & Ovando, D. Reflections on the success of traditional fisheries management. ICES J. Mar. Sci. 71, 1040–1046 (2014).Article 

    Google Scholar 
    Casey, J., Jardim, E. & Martinsohn, J. T. H. The role of genetics in fisheries management under the E.U. common fisheries policy. J. Fish Biol. 89, 2755–2767 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    MSC. MSC Fisheries Standard v2.01. https://www.msc.org/docs/default-source/default-document-library/for-business/program-documents/fisheries-program-documents/msc-fisheries-standard-v2-01.pdf?sfvrsn=8ecb3272_9 (2018).Costello, C. et al. Status and solutions for the world’s unassessed fisheries. Science 338, 517–520 (2012).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hilborn, R. et al. Effective fisheries management instrumental in improving fish stock status. PNAS 117, 2218–2224 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Worm, B. & Branch, T. A. The future of fish. Trends Ecol. Evol. 27, 594–599 (2012).Article 
    PubMed 

    Google Scholar 
    Palomares, M. L. D. et al. Fishery biomass trends of exploited fish populations in marine ecoregions, climatic zones and ocean basins. Estuar. Coast. Shelf Sci. 243, 106896 (2020).Article 

    Google Scholar 
    Ihssen, P. E. et al. Stock identification: Materials and methods. Can. J. Fish. Aquat. Sci. 38, 1838–1855 (1981).Article 

    Google Scholar 
    Carvalho, G. R. & Hauser, L. Molecular genetics and the stock concept in fisheries. In Molecular Genetics in Fisheries (eds Carvalho, G. R. & Pitcher, T. J.) 55–79 (Springer, 1995).Chapter 

    Google Scholar 
    Worm, B. et al. Rebuilding global fisheries. Science 325, 578–585 (2009).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gough, C. L. A., Dewar, K. M., Godley, B. J., Zafindranosy, E. & Broderick, A. C. Evidence of overfishing in small-scale fisheries in Madagascar. Front. Mar. Sci. 7, 317 (2020).Article 

    Google Scholar 
    Widjaja, S. et al. Illegal, Unreported and Unregulated Fishing and Associated Drivers 60 (2020).Walters, C. & Martell, S. J. D. Stock assessment needs for sustainable fisheries management. Bull. Mar. Sci. 70, 629–638 (2002).
    Google Scholar 
    Moreira, A. A., Tomás, A. R. G. & Hilsdorf, A. W. S. Evidence for genetic differentiation of Octopus vulgaris (Mollusca, Cephalopoda) fishery populations from the southern coast of Brazil as revealed by microsatellites. J. Exp. Mar. Biol. Ecol. 407, 34–40 (2011).Article 

    Google Scholar 
    Allendorf, F. W., Ryman, N. & Utter, F. M. Genetics and fishery management. In Population Genetics and Fishery Management 1–19 (1987).Oosthuizen, A., Jiwaji, M. & Shaw, P. Genetic analysis of the Octopus vulgaris population on the coast of South Africa. S. Afr. J. Sci. 100, 603–607 (2004).CAS 

    Google Scholar 
    Botsford, L. W., Castilla, J. C. & Peterson, C. H. The management of fisheries and marine ecosystems. Science 277, 509–515 (1997).Article 
    CAS 

    Google Scholar 
    Hilborn, R., Orensanz, J. M. & Parma, A. M. Institutions, incentives and the future of fisheries. Philos. Trans. R. Soc. B Biol. Sci. 360, 47. https://doi.org/10.1098/rstb.2004.1569 (2005).Article 

    Google Scholar 
    Ovenden, J. R., Berry, O., Welch, D. J., Buckworth, R. C. & Dichmont, C. M. Ocean’s eleven: A critical evaluation of the role of population, evolutionary and molecular genetics in the management of wild fisheries. Fish Fish. 16, 125–159 (2015).Article 

    Google Scholar 
    Aguirre-Sarabia, I. et al. Evidence of stock connectivity, hybridization, and misidentification in white anglerfish supports the need of a genetics-informed fisheries management framework. Evol. Appl. 14, 2221 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grover, A. & Sharma, P. C. Development and use of molecular markers: Past and present. Crit. Rev. Biotechnol. 36, 290 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Valenzuela-Quiñonez, F. How fisheries management can benefit from genomics? Brief. Funct. Genom. 15, 352–357 (2016).Article 

    Google Scholar 
    Khoufi, W., Jabeur, C. & Bakhrouf, A. Stock assessment of the common octopus (Octopus vulgaris) in Monastir; the Mid-eastern Coast of Tunisia. Int. J. Mar. Sci. 2, 1 (2012).
    Google Scholar 
    Pita, C. et al. Fisheries for common octopus in Europe: Socioeconomic importance and management. Fish. Res. 235, 105820 (2021).Article 

    Google Scholar 
    Melis, R. et al. Genetic population structure and phylogeny of the common octopus Octopus vulgaris Cuvier, 1797 in the western Mediterranean Sea through nuclear and mitochondrial markers. Hydrobiologia 807, 277–296 (2018).Article 
    CAS 

    Google Scholar 
    De Luca, D., Catanese, G., Procaccini, G. & Fiorito, G. Octopus vulgaris (Cuvier, 1797) in the Mediterranean Sea: Genetic diversity and population structure. PLoS ONE 11, e0149496 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fernández-Rueda, P. & García-Flórez, L. Octopus vulgaris (Mollusca: Cephalopoda) fishery management assessment in Asturias (north-west Spain). Fish. Res. 83, 351–354 (2007).Article 

    Google Scholar 
    Gobierno del Principado de Asturias. BOPA núm. 233 de 03-XII-2021, Vol. 233 (2021).Roa-Ureta, R. H. et al. Estimation of the spawning stock and recruitment relationship of Octopus vulgaris in Asturias (Bay of Biscay) with generalized depletion models: Implications for the applicability of MSY. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsab113 (2021).Article 

    Google Scholar 
    González, A. F., Macho, G., de Novoa, J. & García, M. Western Asturias Octopus Traps Fishery of Artisanal Cofradías 181 (2015).Sánchez, J. L. F., Fernández Polanco, J. M. & Llorente García, I. Evidence of price premium for MSC-certified products at fishers’ level: The case of the artisanal fleet of common octopus from Asturias (Spain). Mar. Policy 119, 104098 (2020).Article 

    Google Scholar 
    Murphy, J. M., Balguerías, E., Key, L. N. & Boyle, P. R. Microsatellite DNA markers discriminate between two Octopus vulgaris (Cephalopoda: Octopoda) fisheries along the northwest African coast. Bull. Mar. Sci. 71, 545–553 (2002).
    Google Scholar 
    Cabranes, C., Fernandez-Rueda, P. & Martínez, J. L. Genetic structure of Octopus vulgaris around the Iberian Peninsula and Canary Islands as indicated by microsatellite DNA variation. ICES J. Mar. Sci. 65, 12–16 (2008).Article 

    Google Scholar 
    Quinteiro, J., Rodríguez-Castro, J., Rey-Méndez, M. & González-Henríquez, N. Phylogeography of the insular populations of common octopus, Octopus vulgaris Cuvier, 1797, in the Atlantic Macaronesia. PLoS ONE 15, e0230294 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Greatorex, E. C. et al. Microsatellite markers for investigating population structure in Octopus vulgaris (Mollusca: Cephalopoda). Mol. Ecol. 9, 641–642 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    De Luca, D., Catanese, G., Fiorito, G. & Procaccini, G. A new set of pure microsatellite loci in the common octopus Octopus vulgaris Cuvier, 1797 for multiplex PCR assay and their cross-amplification in O. maya Voss & Solís Ramírez, 1966. Conserv. Genet. Resour. 7, 299–301 (2015).Article 

    Google Scholar 
    Zuo, Z., Zheng, X., Liu, C. & Li, Q. Development and characterization of 17 polymorphic microsatellite loci in Octopus vulgaris Cuvier, 1797. Conserv. Genet. Resour. 4, 367–369 (2012).Article 

    Google Scholar 
    Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358 (1984).CAS 
    PubMed 

    Google Scholar 
    Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nei, M. & Takezaki, N. Estimation of Genetic Distances and Phylogenetic Trees from DNA Analysis 8 (1983).Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Waples, R. S. Separating the wheat from the chaff: Patterns of genetic differentiation in high gene flow species. J. Hered. 89, 438–450 (1998).Article 

    Google Scholar 
    Taboada, F. G. & Anadón, R. Patterns of change in sea surface temperature in the North Atlantic during the last three decades: Beyond mean trends. Clim. Change 115, 419–431 (2012).Article 
    ADS 

    Google Scholar 
    Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sinclair, M. & Valdimarsson, G. Responsible Fisheries in the Marine Ecosystem (CABI, 2003).Book 

    Google Scholar 
    Pinsky, M. L. & Palumbi, S. R. Meta-analysis reveals lower genetic diversity in overfished populations. Mol. Ecol. 23, 29–39 (2014).Article 
    PubMed 

    Google Scholar 
    Bradbury, I. R., Laurel, B., Snelgrove, P. V. R., Bentzen, P. & Campana, S. E. Global patterns in marine dispersal estimates: The influence of geography, taxonomic category and life history. Proc. R. Soc. B Biol. Sci. 275, 1803–1809 (2008).Article 

    Google Scholar 
    Waples, R. S. Testing for Hardy-Weinberg proportions: Have we lost the plot? J. Hered. 106, 1–19 (2015).Article 
    PubMed 

    Google Scholar 
    Casu, M. et al. Genetic structure of Octopus vulgaris (Mollusca, Cephalopoda) from the Mediterranean Sea as revealed by a microsatellite locus. Ital. J. Zool. 69, 295–300 (2002).Article 

    Google Scholar 
    Fadhlaoui-Zid, K. et al. Genetic structure of Octopus vulgaris (Cephalopoda, Octopodidae) in the central Mediterranean Sea inferred from the mitochondrial COIII gene. C.R. Biol. 335, 625–636 (2012).Article 
    PubMed 

    Google Scholar 
    Queiroga, H. et al. Oceanographic and behavioural processes affecting invertebrate larval dispersal and supply in the western Iberia upwelling ecosystem. Prog. Oceanogr. 74, 174–191 (2007).Article 
    ADS 

    Google Scholar 
    Mereu, M. et al. Mark–recapture investigation on Octopus vulgaris specimens in an area of the central western Mediterranean Sea. J. Mar. Biol. Assoc. U.K. 95, 131–138 (2015).Article 
    ADS 

    Google Scholar 
    Mereu, M. et al. Movement estimation of Octopus vulgaris Cuvier, 1797 from mark recapture experiment. J. Exp. Mar. Biol. Ecol. 470, 64–69 (2015).Article 

    Google Scholar 
    Roura, Á. et al. Life strategies of cephalopod paralarvae in a coastal upwelling system (NW Iberian Peninsula): Insights from zooplankton community and spatio-temporal analyses. Fish. Oceanogr. 25, 241–258 (2016).Article 

    Google Scholar 
    Moreno, A. et al. Essential habitats for pre-recruit Octopus vulgaris along the Portuguese coast. Fish. Res. 152, 74–85 (2014).Article 
    ADS 

    Google Scholar 
    Chédia, J., Widien, K. & Amina, B. Role of sea surface temperature and rainfall in determining the stock and fishery of the common octopus (Octopus vulgaris, Mollusca, Cephalopoda) in Tunisia. Mar. Ecol. 31, 431–438 (2010).Article 
    ADS 

    Google Scholar 
    Otero, J. et al. Bottom-up control of common octopus Octopus vulgaris in the Galician upwelling system, northeast Atlantic Ocean. Mar. Ecol. Prog. Ser. 362, 181–192 (2008).Article 
    ADS 

    Google Scholar 
    Hedgecock, D. & Pudovkin, A. I. A. I. Sweepstakes reproductive success in highly fecund marine fish and shellfish: A review and commentary. Bull. Mar. Sci. 87, 971–1002 (2011).Article 

    Google Scholar 
    Kalinowski, S. T. & Waples, R. S. Relationship of effective to census size in fluctuating populations. Conserv. Biol. 16, 129–136 (2002).Article 
    PubMed 

    Google Scholar 
    Sonderblohm, C. P., Pereira, J. & Erzini, K. Environmental and fishery-driven dynamics of the common octopus (Octopus vulgaris) based on time-series analyses from leeward Algarve, southern Portugal. ICES J. Mar. Sci. 71, 2231–2241 (2014).Article 

    Google Scholar 
    Sonderblohm, C. P. et al. Participatory assessment of management measures for Octopus vulgaris pot and trap fishery from southern Portugal. Mar. Policy 75, 133–142 (2017).Article 

    Google Scholar 
    Arkhipkin, A. I. et al. Stock assessment and management of cephalopods: Advances and challenges for short-lived fishery resources. ICES J. Mar. Sci. 78, 714–730 (2021).Article 

    Google Scholar 
    Franklin, I. R. Evolutionary change in small populations. In Conservation Biology: An Evolutionary-Ecological Perspective (eds Soulé, M. E. & Wilcox, B. A.) 395 (Sinauer Associates, 1980).
    Google Scholar 
    Slatkin, M. Rare alleles as indicators of gene flow. Evolution 39, 53–65 (1985).Article 
    PubMed 

    Google Scholar 
    Holleley, C. E. & Geerts, P. G. Multiplex manager 1.0: A cross-platform computer program that plans and optimizes multiplex PCR. Biotechniques 46, 511–517 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 

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

    Google Scholar 
    Paradis, E. Pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Goudet, J. HIERFSTAT, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

    Google Scholar 
    Adamack, A. T. & Gruber, B. PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).Article 

    Google Scholar 
    Goudet, J. FSTAT (Version 1.2): A computer program to calculate F-STATISTICS. J. Hered. 86, 485–486 (1995).Article 

    Google Scholar 
    Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223 (1989).Article 
    PubMed 

    Google Scholar 
    Piry, S., Luikart, G. & Cornuet, J. M. M. Bottleneck: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).Article 

    Google Scholar 
    Luikart, G., Allendorf, F. W., Cornuet, J.-M.M. & Sherwin, W. B. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. https://doi.org/10.1093/jhered/89.3.238 (1998).Article 
    PubMed 

    Google Scholar 
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Besnier, F. & Glover, K. A. ParallelStructure: A R package to distribute parallel runs of the population genetics program STRUCTURE on multi-core computers. PLoS ONE 8, e70651 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gilbert, K. J. et al. Recommendations for utilizing and reporting population genetic analyses: The reproducibility of genetic clustering using the program structure. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2012.05754.x (2012).Article 
    PubMed 

    Google Scholar 
    Earl, D. A. & VonHoldt, B. M. Structure harvester: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

    Google Scholar 
    Takezaki, N., Nei, M. & Tamura, K. POPTREEW: Web version of POPTREE for constructing population trees from allele frequency data and computing some other quantities. Mol. Biol. Evol. 31, 1622–1624 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Letunic, I. & Bork, P. Interactive tree of life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dray, S. & Dufour, A.-B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

    Google Scholar 
    Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).Article 
    PubMed 

    Google Scholar 
    Cavalli-Sforza, L. L. & Edwards, A. W. F. Phylogenetic analysis. Models and estimation procedures. Am. J. Hum. Genet. 19, 233–257 (1967).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Waples, R. S. A generalized approach for estimating effective population size from temporal changes in allele frequency. Genetics 121, 379–391 (1989).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Katsanevakis, S. & Verriopoulos, G. Seasonal population dynamics of Octopus vulgaris in the eastern Mediterranean. ICES J. Mar. Sci. 63, 151–160 (2006).Article 

    Google Scholar 
    Jereb, P. et al. Cephalopod Biology and Fisheries in Europe: II Species Accounts 360 (ICES, 2015).
    Google Scholar  More

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    Origination of the modern-style diversity gradient 15 million years ago

    Fine, P. V. Ecological and evolutionary drivers of geographic variation in species diversity. Annu. Rev. Ecol. Evol. Syst. 46, 369–392 (2015).Article 

    Google Scholar 
    Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).Article 
    PubMed 

    Google Scholar 
    Mittelbach, G. G. et al. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecol. Lett. 10, 315–331 (2007).Article 
    PubMed 

    Google Scholar 
    Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst. 34, 273–309 (2003).Article 

    Google Scholar 
    Pontarp, M. et al. The latitudinal diversity gradient: novel understanding through mechanistic eco-evolutionary models. Trends Ecol. Evol. 34, 211–223 (2019).Article 
    PubMed 

    Google Scholar 
    Crame, J. A. Taxonomic diversity gradients through geological time. Divers Distrib. 7, 175–189 (2011).
    Google Scholar 
    Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).Article 
    PubMed 

    Google Scholar 
    Powell, M. G. Latitudinal diversity gradients for brachiopod genera during late Palaeozoic time: links between climate, biogeography and evolutionary rates. Glob. Ecol. Biogeogr. 16, 519–528 (2007).Article 

    Google Scholar 
    Powell, M. G., Beresford, V. P. & Colaianne, B. A. The latitudinal position of peak marine diversity in living and fossil biotas. J. Biogeogr. 39, 1687–1694 (2012).Article 

    Google Scholar 
    Hillebrand, H. Strength, slope and variability of marine latitudinal gradients. Mar. Ecol. Prog. Ser. 273, 251–267 (2004).Article 
    ADS 

    Google Scholar 
    Beaugrand, G., Rombouts, I. & Kirby, R. R. Towards an understanding of the pattern of biodiversity in the oceans. Glob. Ecol. Biogeogr. 22, 440–449 (2013).Article 

    Google Scholar 
    Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Pianka, E. R. Latitudinal gradients in species diversity: a review of concepts. Am. Nat. 100, 33–46 (1966).Article 

    Google Scholar 
    Saupe, E. E. et al. Spatio-temporal climate change contributes to latitudinal diversity gradients. Nat. Ecol. Evol. 3, 1419–1429 (2019).Article 
    PubMed 

    Google Scholar 
    Stehli, F. G., Douglas, R. G. & Newell, N. D. Generation and maintenance of gradients in taxonomic diversity. Science 164, 947–949 (1969).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Rutherford, S., D’Hondt, S. & Prell, W. Environmental controls on the geographic distribution of zooplankton diversity. Nature 4000, 749–752 (1999).Article 
    ADS 

    Google Scholar 
    Klopfer, P. H. Environmental determinants of faunal diversity. Am. Nat. 93, 337–342 (1959).Article 

    Google Scholar 
    Haffer, J. & Prance, G. T. Climatic forcing of evolution in Amazonia during the Cenozoic: on the refuge theory of biotic differentiation. Amazoniana 16, 579–607 (2001).
    Google Scholar 
    Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115–9120 (2000).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dobzhansky, T. Evolution in the tropics. Am. Sci. 38, 209–221 (1950).
    Google Scholar 
    Williams, C. B. Patterns in the Balance of Nature (Academic Press, 1964).Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).Article 

    Google Scholar 
    Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).Article 

    Google Scholar 
    Currie, D. J. Energy and large-scale patterns of animal and plant species richness. Am. Nat. 137, 27–49 (1991).Article 

    Google Scholar 
    Connell, J. H. & Orias, E. The ecological regulation of species diversity. Am. Nat. 98, 399–414 (1964).Article 

    Google Scholar 
    Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge Univ. Press, 1995).Fenton, I. S. et al. The impact of Cenozoic cooling on assemblage diversity in planktonic foraminifera. Phil. Trans. R. Soc. B 371, 20150224 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yasuhara, M., Hunt, G., Dowsett, H. J., Robinson, M. M. & Stoll, D. K. Latitudinal species diversity gradient of marine zooplankton for the last three million years. Ecol. Lett. 15, 1174–1179 (2012).Article 
    PubMed 

    Google Scholar 
    Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Combining marine macroecology and palaeoecology in understanding biodiversity: microfossils as a model. Biol. Rev. 92, 199–215 (2017).Article 
    PubMed 

    Google Scholar 
    Fenton, I. S. et al. Triton, a new species-level database of Cenozoic planktonic foraminiferal occurrences. Sci. Data 8, 160 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yasuhara, M. & Deutsch, C. A. Paleobiology provides glimpses of future ocean. Science 375, 25–26 (2022).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Yasuhara, M. et al. Time machine biology cross-timescale integration of ecology, evolution, and oceanography. Oceanography 33, 16–28 (2020).Article 

    Google Scholar 
    Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Al-Sabouni, N., Kucera, M. & Schmidt, D. N. Vertical niche separation control of diversity and size disparity in planktonic foraminifera. Mar. Micropaleontol. 63, 75–90 (2007).Article 
    ADS 

    Google Scholar 
    Lowery, C. M., Bown, P. R., Fraass, A. J. & Hull, P. M. Ecological response of plankton to environmental change: thresholds for extinction. Annu. Rev. Earth Planet. Sci. 48, 403–429 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Lear, C. H., Elderfield, H. & Wilson, P. A. Cenozoic deep-sea temperatures and global ice volumes from Mg/Ca in benthic foraminiferal calcite. Science 287, 269–272 (2000).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Weiner, A., Aurahs, R., Kurasawa, A., Kitazato, H. & Kučera, M. Vertical niche partitioning between cryptic sibling species of a cosmopolitan marine planktonic protist. Mol. Ecol. 21, 4063–4073 (2012).Article 
    PubMed 

    Google Scholar 
    Schneider, E. & Kennett, J. P. Segregation and speciation in the Neogene planktonic foraminiferal clade Globoconella. Paleobiology 25, 383–395 (1999).Article 

    Google Scholar 
    Raja, N. B. & Kiessling, W. Out of the extratropics: the evolution of the latitudinal diversity gradient of Cenozoic marine plankton. Proc. Biol. Sci. 288, 20210545 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).Article 
    PubMed 

    Google Scholar 
    Irigoien, X., Huisman, J. & Harris, R. P. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429, 863–886 (2004).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Schiebel, R. & Hemleben, C. Planktic Foraminifers in the Modern Ocean (Springer-Verlag, 2017).Ruddimann, W. F. Recent planktonic foraminifera: dominance and diversity in North Atlantic surface sediments. Science 164, 1164–1167 (1969).Article 
    ADS 

    Google Scholar 
    Bé, A. W. H. & Tolderlund, D. S. in Micropaleontology of Marine Bottom Sediments (eds Funnell, B. M. & Riedel, W. K.) 105–149 (Cambridge Univ. Press, 1971).Sibert, E., Norris, R., Cuevas, J. & Graves, L. Eighty-five million years of Pacific Ocean gyre ecosystem structure: long-term stability marked by punctuated change. Proc. Biol. Sci. 283, 20160189 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Worm, B. & Tittensor, D. P. A Theory of Global Biodiversity (Princeton Univ. Press, 2018).Boscolo-Galazzo, F. et al. Temperature controls carbon cycling and biological evolution in the ocean twilight zone. Science 371, 1148–1152 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Boscolo-Galazzo, F. et al. Late Neogene evolution of modern deep-dwelling plankton. Biogeosciences 19, 743–762 (2022).Article 
    ADS 

    Google Scholar 
    Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).Article 
    PubMed 

    Google Scholar 
    Matthews, K. J. et al. Global plate boundary evolution and kinematics since the late Paleozoic. Glob. Planet. Change 146, 226–250 (2016).Article 
    ADS 

    Google Scholar 
    Gyldenfeldt, A.-B. V., Carstens, J. & Meincke, J. Estimation of the catchment area of a sediment trap by means of current meters and foraminiferal tests. Deep Sea Res. Part II 47, 1701–1717 (2000).Article 
    ADS 

    Google Scholar 
    Qiu, Z., Doglioli, A. M. & Carlotti, F. Using a Lagrangian model to estimate source regions of particles in sediment traps. Sci. China Earth Sci. 57, 2447–2456 (2014).Article 
    ADS 

    Google Scholar 
    Siegel, D. A. & Deuser, W. G. Trajectories of sinking particles in the Sargasso Sea: modeling of statistical funnels above deep-ocean sediment traps. Deep Sea Res. Part I 44, 1519–1541 (1997).Article 

    Google Scholar 
    Waniek, J., Koeve, W. & Prien, R. D. Trajectories of sinking particles and the catchment areas above sediment traps in the Northeast Atlantic. J. Mar. Res. 58, 983–1006 (2000).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org (R Foundation for Statistical Computing, 2019).Alroy, J. The fossil record of North American mammals: evidence for a Paleocene evolutionary radiation. Syst. Biol. 48, 107–118 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Marcot, J. D. The fossil record and macroevolutionary history of North American ungulate mammals: standardizing variation in intensity and geography of sampling. Paleobiology 40, 238–255 (2014).Article 

    Google Scholar 
    Gaston, K. J., Williams, P. H., Eggleton, P. & Humphries, C. J. Large scale patterns of biodiversity: spatial variation in family richness. Proc. R. Soc. Lond. B 260, 149–154 (1995).Article 
    ADS 

    Google Scholar 
    Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0. Geosci. Model Dev. 10, 3715–3743 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Cox, P. M. et al. The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Clim. Dyn. 15, 183–203 (1999).Article 

    Google Scholar 
    Sagoo, N., Valdes, P., Flecker, R. & Gregoire, L. J. The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions? Phil. Trans. R. Soc. A 371, 20130123 (2013).Article 
    ADS 
    PubMed 

    Google Scholar 
    Kiehl, J. T. & Shields, C. A. Sensitivity of the Palaeocene–Eocene thermal maximum climate to cloud properties. Phil. Trans. R. Soc. A 371, 20130093 (2013).Article 
    ADS 
    PubMed 

    Google Scholar 
    Cox, M. D. A Primitive Equation, 3-Dimensional Model of the Ocean. GFDL Ocean Group Technical Report No. 1 (GFDL Princeton Univ., 1984).Collins, M., Tett, S. F. B. & Cooper, C. The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Clim. Dyn. 17, 61–81 (2001).Article 

    Google Scholar 
    Farnsworth, A. et al. Climate sensitivity on geological timescales controlled by nonlinear feedbacks and ocean circulation. Geophys. Res. Lett. 46, 9880–9889 (2019).Article 
    ADS 

    Google Scholar 
    Valdes, P. J., Scotese, C. R. & Lunt, D. J. Deep ocean temperatures through time. Clim. Past 17, 1483–1506 (2021).Article 

    Google Scholar 
    Farnsworth, A. et al. Past East Asian monsoon evolution controlled by paleogeography, not CO2. Sci. Adv. 5, eaax1697 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jones, L. A., Mannion, P. D., Farnsworth, A., Bragg, F. & Lunt, D. J. Climatic and tectonic drivers shaped the tropical distribution of coral reefs. Nat. Commun. 13, 3120 (2022).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Scotese, C. R. & Wright, N. PALEOMAP paleodigital elevation models (PaleoDEMS) for the Phanerozoic. Zenodo https://doi.org/10.5281/zenodo.5460860 (2018).Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 14845 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gough, D. O. Solar interior structure and luminosity variations. Sol. Phys. 74, 21–34 (1981).Article 
    ADS 
    CAS 

    Google Scholar 
    Farnsworth, A. et al. Paleoclimate model-derived thermal lapse rates: towards increasing precision in paleoaltimetry studies. Earth Planet. Sci. Lett. 564, 116903 (2021).Article 
    CAS 

    Google Scholar 
    Bahcall, J. N., Pinsonneault, M. H. & Basu, S. Solar models: current epoch and time dependences, neutrinos, and helioseismological properties. Astrophys. J. 555, 990–1012 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1108 (2009).Article 
    ADS 

    Google Scholar 
    Kraus, E. B. & Turner, J. S. A one-dimensional model of the seasonal thermocline II. The general theory and its consequences. Tellus 19, 98–105 (1967).ADS 

    Google Scholar 
    Foreman, S. J. The Ocean Model Report. Unified Model Documentaiton Paper Number 40 (The Met Office, 2005).HH: Statistical Analysis and Data Display: Heiberger and Holland. R package version 3.1-47 (2022).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    Bivand, R., Millo, G. & Piras, G. A review of software for spatial econometrics in R. Mathematics 9, 1276 (2021).Article 

    Google Scholar 
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    Cooper, N. & Purvis, A. Body size evolution in mammals: complexity in tempo and mode. Am. Nat. 175, 727–738 (2010).Article 
    PubMed 

    Google Scholar 
    geosphere: Spherical Trigonometry. R package version 1.5-14 (2021).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).Wade, B. S., Pearson, P. N., Berggren, W. A. & Pälike, H. Review and revision of Cenozoic tropical planktonic foraminiferal biostratigraphy and calibration to the geomagnetic polarity and astronomical time scale. Earth Sci. Rev. 104, 111–142 (2011).Article 
    ADS 

    Google Scholar  More

  • in

    Tropical biodiversity linked to polar climate

    Wallace, A. R. Tropical Nature and Other Essays (Macmillan, 1878).
    Google Scholar 
    von Humboldt, A. Ansichten der Natur: mit wissenschaftlichen Erläuterungen (Cotta, 1808).
    Google Scholar 
    Brown, J. H. J. Biogeogr. 41, 8–22 (2014).Article 
    PubMed 

    Google Scholar 
    Fenton, I. S., Aze, T., Farnsworth, A., Valdes, P. & Saupe, E. E. Nature https://doi.org/10.1038/s41586-023-05712-6 (2023).Article 

    Google Scholar 
    Woodhouse, A., Swain, A., Fagan, W. F., Fraass, A. J. & Lowery, C. M. Nature https://doi.org/10.1038/s41586-023-05694-5 (2023).Article 

    Google Scholar 
    Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Biol. Rev. 92, 199–215 (2017).Article 
    PubMed 

    Google Scholar 
    Yasuhara, M. et al. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
    PubMed 

    Google Scholar 
    Song, H. et al. Proc. Natl Acad. Sci. USA 117, 17578–17583 (2020).Article 
    PubMed 

    Google Scholar 
    Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Science 362, eaat1327 (2018).Article 
    PubMed 

    Google Scholar 
    Janzen, D. H. Am. Nat. 101, 233–249 (1967).Article 

    Google Scholar 
    Hahn, L. C., Armour, K. C., Zelinka, M. D., Bitz, C. M. & Donohoe, A. Front. Earth Sci. 9, 710036 (2021).Article 

    Google Scholar 
    Penn, J. L. & Deutsch, C. Science 376, 524–526 (2022).Article 
    PubMed 

    Google Scholar  More

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    A molecular atlas reveals the tri-sectional spinning mechanism of spider dragline silk

    Chromosomal-scale genome assembly and full spidroin gene set of T. clavata
    To explore dragline silk production in T. clavata, we sought to assemble a high-quality genome of this species. Thus, we first performed a cytogenetic analysis of T. clavata captured from the wild in Dali City, Yunnan Province, China, and found a chromosomal complement of 2n = 26 in females and 2n = 24 in males, comprising eleven pairs of autosomal elements and unpaired sex chromosomes (X1X1X2X2 in females and X1X2 in males) (Fig. 1a). Then, DNA from adult T. clavata was used to generate long-read (Oxford Nanopore Technologies (ONT)), short-read (Illumina), and Hi-C data (Supplementary Data 1). A total of 349.95 Gb of Nanopore reads, 199.55 Gb of Illumina reads, and ~438.41 Gb of Hi-C raw data were generated. Our sequential assembly approach (Supplementary Fig. 1c) resulted in a 2.63 Gb genome with a scaffold N50 of 202.09 Mb and a Benchmarking Universal Single-Copy Ortholog (BUSCO) genome completeness score of 93.70% (Table 1; Supplementary Data 3). Finally, the genome was assembled into 13 pseudochromosomes. Sex-specific Pool-Seq analysis of spiders indicated that Chr12 and Chr13 were sex chromosomes (Fig. 1b; Supplementary Fig. 2). Based on the MAKER2 pipeline34 (Supplementary Fig. 1e), we annotated 37,607 protein-encoding gene models and predicted repetitive elements with a collective length of 1.42 Gb, accounting for 53.94% of the genome.Table 1 Characteristics of the T. clavata genome assemblyFull size tableTo identify T. clavata spidroin genes, we searched the annotated gene models for sequences similar to 443 published spidroins (Supplementary Data 6) and performed a phylogenetic analysis of the putative spidroin sequences for classification (Supplementary Fig. 12a). Based on the knowledge that a typical spidroin gene consists of a long repeat domain sandwiched between the nonrepetitive N/C-terminal domains16, 128 nonrepetitive hits were primarily identified. These candidates were further validated and reconstructed using full-length transcript isoform sequencing (Iso-seq) and transcriptome sequencing (RNA-seq) data. We thus identified 28 spidroin genes, among which 26 were full-length (Supplementary Fig. 11a), including 9 MaSps, 5 minor ampullate spidroins (MiSps), 2 flagelliform spidroins (FlSps), 1 tubuliform spidroin (TuSp), 2 aggregate spidroins (AgSp), 1 aciniform spidroin (AcSp), 1 pyriform spidroin (PySp), and 5 other spidroins. This full set of spidroin genes was located across nine of the 13 T. clavata chromosomes. Interestingly, we found that the MaSp1a–c & MaSp2e, MaSp2a–d, and MiSp-a–e genes were distributed in three independent groups on chromosomes 4, 7, and 6, respectively (Fig. 1c). Notably, using the genomic data of another orb-weaving spider species, Trichonephila antipodiana35, we identified homologous group distributions of spidroin genes on T. antipodiana chromosomes (Fig. 1d), which indicated the reliability of the grouping results of our study. When we compared the spidroin gene catalog of T. clavata and those of five other orb-web spider species with genomic data28,29,36,37, we found that T. clavata and Trichonephila clavipes possessed the largest number of spidroin genes (28 genes in both species; Fig. 1e).To further explore the expression of spidroin genes in different glands, all morphologically distinct glands (major and minor ampullate- (Ma and Mi), flagelliform- (Fl), tubuliform- (Tu), and aggregate (Ag) glands) were cleanly and separately dissected from adult female T. clavata spiders except for the aciniform and pyriform glands, which could not be cleanly separated because of their proximal anatomical locations and were therefore treated as a combined sample (aciniform & pyriform gland (Ac & Py)). After RNA sequencing of these silk glands, we performed expression clustering analysis of transcriptomic data and found that the Ma and Mi glands showed the closest relationship in terms of both morphological structure (Fig. 1g) and gene expression (Fig. 1f, h). We noted that the expression profiles of spidroin genes were largely consistent with their putative roles in the corresponding morphologically distinct silk glands; for example, MaSp expression was found in the Ma gland (Fig. 1h). However, some spidroin transcripts, such as MiSps and TuSp, were expressed in several silk glands (Fig. 1h). Unclassified spidroin genes, such as Sp-GP-rich, did not appear to show gland-specific expression (Fig. 1h).In summary, the chromosomal-scale genome of T. clavata allowed us to obtain detailed structural and location information for all spidroin genes of this species. We also found a relatively diverse set of spidroin genes and a grouped distribution of MaSps and MiSps in T. clavata.Dragline silk origin and the functional character of the Ma gland segmentsTo further evaluate the detailed molecular characteristics of the Ma gland-mediated secretion of dragline silk, we performed integrated analyses of the transcriptomes of the three T. clavata Ma gland segments and the proteome and metabolome of T. clavata dragline silk (Fig. 2a). Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) analysis of dragline silk mainly showed a thick band above 240 kDa, suggesting a relatively small variety of total proteins (Fig. 2b). Subsequent liquid chromatography–mass spectrometry (LC–MS) analysis identified 28 proteins, including ten spidroins (nine MaSps and one MiSp) and 18 nonspidroin proteins (one glucose dehydrogenase (GDH), one mucin-19, one venom protein, and 15 SpiCEs of dragline silk (SpiCE-DS)) (Fig. 2b; Supplementary Data 10). Among these proteins, we found that the core protein components of dragline silk in order of intensity-based absolute quantification (iBAQ) percentages were MaSp1c (37.7%), MaSp1b (12.2%), SpiCE-DS1 (11.9%, also referred to as SpiCE-NMa1 in a previous study28), MaSp1a (10.4%), and MaSp-like (7.2%), accounting for approximately 80% of the total protein abundance in dragline silk (Fig. 2b). These results revealed potential protein components that might be highly correlated with the excellent strength and toughness of dragline silk.Fig. 2: Dragline silk origin and the functional character of the Ma gland segments.a Schematic illustration of Ma gland segmentation. b Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) (left) and LC–MS (right) analyses of dragline silk protein. iBAQ, intensity-based absolute quantification. Similar results were obtained in three independent experiments and summarized in Source data. c Classification of the identified metabolites in dragline silk. d LC–MS analyses of the metabolites. e LC–MS analyses of the golden extract from T. clavata dragline silk. The golden pigment was extracted with 80% methanol. The extracted ion chromatograms (EICs) showed a peak at m/z 206 [M + H]+ for xanthurenic acid. f Pearson correlation of different Ma gland segments (Tail, Sac, and Duct). g Expression clustering of the Tail, Sac, and Duct. The transcriptomic data were clustered according to the hierarchical clustering (HC) method. h Combinational analysis of the transcriptome and proteome showing the expression profile of the dragline silk genes in the Tail, Sac, and Duct. i Concise biosynthetic pathway of xanthurenic acid (tryptophan metabolism) in the T. clavata Ma gland. Gene expression levels mapped to tryptophan metabolism are shown in three segments of the Ma gland. Enzymes involved in the pathway are indicated in red, and the genes encoding the enzymes are shown beside them. j Gene Ontology (GO) enrichment analysis of Ma gland segment-specific genes indicating the biological functions of the Tail, Sac, and Duct. The top 12 significantly enriched GO terms are shown for each segment of the Ma gland. A P-value  2) were identified in the 2 kb regions upstream and downstream of genes, and 10,501,151 (Tail), 11,356,55 (Sac), and 9,778,368 (Duct) significant ATAC peaks (RPKM  > 2) were identified at the whole-genome level. The Tail (mean RPKM: 1.78) and Sac (mean RPKM: 2.04) plots showed genes with more accessible chromatin than the Duct (mean RPKM: 1.59) plots (Fig. 3a). We then analyzed the genome-wide DNA methylation level in the Tail, Sac, and Duct. We found the highest levels of DNA methylation in the CG context (beta value: 0.12 in Tail, 0.13 in Sac, and 0.10 in Duct) and only a small amount in the CHH (beta value: 0.04 in Tail, 0.05 in Sac, and 0.03 in Duct) and CHG (beta value: 0.04 in Tail, 0.05 in Sac, and 0.04 in Duct) contexts (Fig. 3b). Overall, there was no significant difference in methylation levels among the Tail, Sac, and Duct. Taken together, our results suggest a potential regulatory role of CA rather than DNA methylation in the transcription of dragline silk genes.Fig. 3: Comprehensive epigenetic features and ceRNA network of the tri-sectional Ma gland.a Metagene plot of ATAC-seq signals and heatmap of the ATAC-seq read densities in the Tail, Sac, and Duct. The chromatin accessibility was indicated by the mean RPKM value (upper) and the blue region (bottom). b Metagene plot of DNA methylation levels in CG/CHG/CHH contexts in the Tail, Sac, and Duct. (c, d) Screenshots of the methylation and ATAC-seq tracks of the MaSp1b (c) and MaSp2b (d) genes within the Tail, Sac, and Duct. The potential TF motifs (E-value More

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    Balancing the bloom

    Algal blooms that form because of phytoplankton proliferation have key roles in marine ecology and carbon fixation. When the blooms die, most of the fixed carbon is transferred to higher trophic levels, and a small fraction sinks into the deep sea. Viral infection is one of the causes of bloom termination, but its effect on the fate and flow of carbon in the ocean is unknown. In this study, Vincent et al. perform a mesocosm experiment to analyse the bloom dynamics of the coccolithophore microalga Emiliania huxleyi and the impact of viral infection on surrounding bacterial communities and the carbon cycle. The authors observed that viral infection was not only the main cause of phytoplankton mortality, but it also shaped the composition of free-living bacterial and eukaryotic species in the blooms. On viral infection of E. huxleyi, the authors found a comparable biomass of eukaryotic and bacterial heterotrophic recyclers, as well as increased organic and inorganic carbon release that contributed to carbon sinking into the deep ocean. Altogether, these results highlight the impact of viruses on the microbial communities of blooms and the consequences on carbon cycling. More

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    Two odorant receptors regulate 1-octen-3-ol induced oviposition behavior in the oriental fruit fly

    Insect rearingWT B. dorsalis were collected from Haikou, Hainan province, China, in 2008. They were maintained at the Key Laboratory of Entomology and Pest Control Engineering in Chongqing at 27 ± 1 °C, 70 ± 5% relative humidity, with a 14-h photoperiod. Adult flies were reared on an artificial diet containing honey, sugar, yeast powder, and vitamin C. Newly hatched larvae were transferred to an artificial diet containing corn and wheat germ flour, yeast powder, agar, sugar, sorbic acid, linoleic acid, and filter paper.Behavioral assaysDouble trap lure assays were set up to compare the olfactory preferences of gravid and virgin females in a 20 × 20 × 20 cm transparent cage with evenly distributed holes (diameter = 1.5 mm) on the side walls. The traps were refitted from inverted 50-mL centrifuge tubes and were placed along the diagonal of the cage. The top of each trap was pierced with a 1-mL pipette tip, which was shortened to ensure flies could access the trap from the pipette. For the olfactory preference assay with mango, one trap was loaded with 60 mg mango flesh and the other trap with 20 μL MO in the cap of a 200-μL PCR tube. For the olfactory preference assay with 1-octen-3-ol (≥98%, sigma, USA), one trap was loaded with 20 μL 10% (v/v) 1-octen-3-ol diluted in MO, and the other with 20 μL MO. A cotton ball soaked in water was placed at the center of the cage to provide water for the flies. Groups of 30 female flies were introduced into the cage for each experiment, and each experiment was repeated to provide eight biological replicates. All experiments commenced at 10 am to ensure circadian consistency. The number of flies in each trap was counted every 2 h for 24 h. We compared the preferences of 3-day-old immature females, 15-day-old virgin females, and 15-day-old mated females. The olfactory preference index was calculated using the following formula41: (number of flies in mango/odorant trap – number of flies in control trap)/total number of flies.Oviposition behavior was monitored in a 10 × 10 × 10 cm transparent cage with evenly distributed holes on the side walls as above. A 9-cm Petri dish filled with 1% agar was served as an oviposition substrate, and the mango flesh, 10% (v/v) 1-octen-3-ol or MO were added at opposite edges of the dish. We tested the preference of flies for different substrates: (1) ~60 mg of mango flesh on one edge and 20 μL of MO on the other; (2) 20 μL of 1-octen-3-ol on one edge and 20 μL of MO on the other; (3) ~60 mg mango flesh on one edge and 20 μL of 1-octen-3-ol on the other; and (4) ~60 mg mango flesh plus 20 μL 1-octen-3-ol on one side and ~60 mg of mango flesh plus 20 μL MO on the other. The agar disc was covered in a pierced plastic wrap to mimic fruit skin, encouraging flies extend their ovipositor into the plastic wrap to lay eggs. The agar disc was placed at the center of the cage, and we introduced eight 15-day-old gravid females. Two Sony FDR-AX40 cameras recorded the behavior of the flies for 24 h, one fixed above the cage to record the tracks and the other placed in front of the cage to record the oviposition behavior. Based on the results from double traps luring assays, a 3 h duration (6–9 h) of the videos was selected to analyze the tracks and spent time of all flies in observed area (the surface of Petri dish). The videos were analyzed using EthoVision XT v16 (Noldus Information Technology) to determine the total time of all flies spent on each side in seconds and the total distance of movement in centimeters, and the tracks were visualized in the form of heat maps17. The number of eggs laid by the eight flies in each experiment was counted under a CNOPTEC stereomicroscope, and each experimental group comprised 7–16 replicates.Annotation of B. dorsalis OR genesD. melanogaster amino acid sequences downloaded from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/) were used as BLASTP queries against the B. dorsalis amino acid database with an identity cut-off of 30%. The candidate OR genes were compared with deep transcriptome data from B. dorsalis antennae42, maxillary palps and proboscis, and other tissues.Cloning of candidate B. dorsalis OR genesHigh-fidelity PrimerSTAR Max DNA polymerase (TaKaRa, Dalian, China) was used to amplify the full open reading frame of BdorOR genes by nested PCR using primers (Supplementary Table 2) designed according to B. dorsalis genome data. Each 25-μL reaction comprised 12.5 μL 2 × PrimerSTAR Max Premix (TaKaRa), 10.5 μL ultrapure water, 1 μL of each primer (10 μM), and 1 μL of the cDNA template. An initial denaturation step at 98 °C for 2 min was followed by 35 cycles of 10 s at 98 °C, 15 s at 55 °C and 90 s at 72 °C, and a final extension step of 10 min at 72 °C. Purified PCR products were transferred to the vector pGEM-T Easy (Promega, Madison, WI) for sequencing (BGI, Beijing, China).Transcriptional profilingTotal RNA was extracted from (i) male and female antennae, maxillary palps, head cuticle (without antenna, maxillary palps, and proboscis), proboscis, legs, wings and ovipositors, and (ii) from the heads of 15-day-old virgin and mated females using TRIzol reagent (Invitrogen, Carlsbad, CA). Genomic DNA was eliminated with RNase-free DNase I (Promega) and first-strand cDNA was synthesized from 1 µg total RNA using the PrimeScript RT reagent kit (TaKaRa). Standard curves were used to evaluate primer efficiency (Supplementary Table 3) with fivefold serial dilutions of cDNA. Quantitative real-time PCR (qRT-PCR) was carried out using a CFX Connect Real-Time System (Bio-Rad, Hercules, CA) in a total reaction volume of 10 µL containing 5 μL SYBR Supermix (Novoprotein, Shanghai, China), 3.9 μL nuclease-free water, 0.5 μL cDNA (~200 ng/μL) and 0.3 μL of the forward and reverse primers (10 μM). We used α-tubulin (GenBank: GU269902) and ribosomal protein S3 (GenBank: XM_011212815) as internal reference genes. Four biological replicates were prepared for each experiment. Relative expression levels were determined using the 2−∆∆Ct method43, and data were analyzed using SPSS v20.0 (IBM).Two-electrode voltage clamp electrophysiological recordingsVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pT7Ts for expression in oocytes. The plasmids were linearized for the synthesis of cRNAs using the mMESSAGE mMACHINE T7 Kit (Invitrogen, Lithuania). The purified cRNA was diluted to 2 µg/µL and ~60 ng cRNA was injected into X. laevis oocytes. The oocytes were pre-treated with 1.5 mg/mL collagenase I (GIBCO, Carlsbad, CA) in washing buffer (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, pH 7.6) for 30–40 min at room temperature before injection. After incubation for 2 days at 18 °C in Ringer’s solution (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, 0.8 mM CaCl2), the oocytes were exposed to different concentrations of 1-octen-3-ol diluted in Ringer’s solution from a 1 M stock in DMSO. Odorant-induced whole-cell inward currents were recorded from injected oocytes using a two-electrode voltage clamp and an OC-725C amplifier (Warner Instruments, Hamden, CT) at a holding potential of –80 mV. The signal was processed using a low-pass filter at 50 Hz and digitized at 1 kHz. Oocytes injected with nuclease-free water served as a negative control. Data were acquired using a Digidata 1550 A device (Warner Instruments, Hamden, CT) and analyzed using pCLAMP10.5 software (Axon Instruments Inc., Union City, CA).Calcium imaging assayVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pcDNA3.1(+) along with an mCherry tag that confers red fluorescence to confirm transfection. High-quality plasmid DNA was prepared using the Qiagen plasmid MIDIprep kit (QIAgen, Düsseldorf, Germany). The B. dorsalis OR and BdorOrco plasmids were co-transfected into HEK 293 cell using TransIT-LT1 transfection reagent (Mirus Bio LLC, Japan) in 96-well plates. The fluorescent dye Fluo-4 AM (Invitrogen) was prepared as a 1 mM stock in DMSO and diluted to 2.5 μM in Hanks’ balanced salt solution (HBSS, Invitrogen, Lithuania) to serve as a calcium indicator. The cell culture medium was removed 24–30 h after transfection and cells were rinsed three times with HBSS before adding Fluo 4-AM and incubating the cells for 1 h in the dark. After three rinses in HBSS, 99 μL of fresh HBSS was added to each well before testing in the dark with 1 μL of diluted 1-octen-3-ol. Fluorescent images were acquired on a laser scanning confocal microscope (Zeiss, Germany). Fluo 4-AM was excited at 488 nm and mCherry at 555 nm. The relative change in fluorescence (ΔF/F0) was used to represent the change in Ca2+, where F0 is the baseline fluorescence and ΔF is the difference between the peak fluorescence induced by 1-octen-3-ol stimulation and the baseline. The healthy and successfully transfected cells (red when excited at 555 nm) were used for analysis. The final concentration of 10−4 M was initially used to screen corresponding ORs, and then to determine the response of screened ORs to stimulation with different concentrations of 1-octen-3-ol. Each concentration of 1-octen-3-ol was tested in triplicate. Concentration–response curves were prepared using GraphPad Prism v8.0 (GraphPad Software).Genome editingThe exon sequences of BdorOR7a-6 and BdorOR13a were predicted using the high-quality B. dorsalis genome assembly. Each gRNA sequence was 20 nucleotides in length plus NGG as the protospacer adjacent motif (PAM). The potential for off-target mutations was evaluated by using CasOT to screen the B. dorsalis genome sequence. Each gRNA was synthesized using the GeneArt Precision gRNA Synthesis Kit (Invitrogen) and purified using the GeneArt gRNA Clean-up Kit (Invitrogen). Embryos were microinjected as previously described20. Purified gRNA and Cas9 protein from the GeneArt Platinum Cas9 Nuclease Kit (Invitrogen) were mixed and diluted to final concentrations of 600 and 500 ng/µL, respectively. Fresh eggs (laid within 20 min) were collected and exposed to 1% sodium hypochlorite for 90 s to soften the chorion. The eggs were fixed on glass slides and injected with the mix of gRNA and Cas9 protein at the posterior pole using an IM-300 device (Narishige, Tokyo, Japan) and needles prepared using a Model P-97 micropipette puller (Sutter Instrument Co, Novato, CA). Eggs were injected with nuclease-free water as a negative control. Injection was completed within 2 h. The injected embryos were cultured in a 27 °C incubator and mortality was recorded during subsequent development.G0 mutants were screened as previously described20. G0 adult survivors were individually backcrossed to WT flies (single pair) to collect G1 offspring. Genomic DNA was extracted from G0 individuals after oviposition using the DNeasy Blood & Tissue Kit (Qiagen). The region surrounding each gRNA target was amplified by PCR using the extracted DNA as a template, the specific primers listed in Supplementary Table 2, and 2 × Taq PCR MasterMix (Biomed, Beijing, China). PCR products were analyzed by capillary electrophoresis using the QIAxcel DNA High Resolution Kit (Qiagen). PCR products differing from the WT alleles were purified and transferred to the vector pGEM-T Easy for sequencing. To confirm the mutation was inherited, genomic DNA was also extracted from one mesothoracic leg of G1 flies using InstaGene Matrix (Bio-Rad, Hercules, CA) and was analyzed as above. To avoid potential off-target mutations, heterozygous G1 mutants were backcrossed to WT flies more than 10 generations before self-crossing to generate homozygous mutant flies.Electroantennogram (EAG) recordingThe antennal responses of 15-day-old B. dorsalis adults to 1-octen-3-ol were determined by EAG recording (Syntech, the Netherlands) as previously reported20. Briefly, antennae were fixed to two electrodes using Spectra 360 electrode gel (Parker, Fairfield, NJ, USA). The signal response was amplified using an IDAC4 device and collected using EAG-2000 software (Syntech). Before each experiment, 1-octen-3-ol and other three volatiles (ethyl tiglate, ethyl acetate, ethyl butyrate) were diluted to 10%, 1% and 0.1% (v/v) with MO to serve as the electrophysiological stimulus, and MO was used as a negative control. A constant air flow (100 mL/min) was produced using a controller (Syntech) to stimulate the antenna. We then placed 10 µL of each dilution or MO onto a piece of filter paper (5 × 1 cm), and the negative control (MO) was applied before and after the diluted odorants to calibrate the response signal. The EAG responses at each concentration were recorded for 15–20 antennae, and each concentration was recorded twice. Each test lasted 1 s, and the interval between tests was 30 s. EAG response data from WT and mutant flies for the diluted odorants were analyzed using Student’s t test with SPSS v20.0.Molecular docking and site-directed mutagenesisThe three dimensional-structures of BdorOR7a-6 and BdorOR13a were modeled using AlphaFold 2.044. The quality and rationality of each protein structure was evaluated online using a PROCHECK Ramachandran plot in SAVES 6.0 (https://saves.mbi.ucla.edu/). AutoDock Vina 1.1.2 was used for docking analysis, and the receptor protein structure and ligand molecular structure were pre-treated using AutoDock 4.2.6. The docking parameters were set according to the protein structure and active sites, and the optimal docking model was selected based on affinity (kcal/mol). Docking models were imported into Pymol and Discovery Studio 2016 Client for analysis and image processing. Based on the molecular docking data, three residues (Asn86 in OR7a-6, Asp320, and Lys323 in OR13a) were replaced with alanine by site-directed mutagenesis45 using the primers listed in Supplementary Table 2. Calcium imaging assays and molecular docking of mutated proteins were then carried out as described above.Statistics reproducibilityAll of the olfactory preference assays, oviposition bioassays, expression profiles analysis, EAG recording assays were analyzed using Student’s t-test (*p  More

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    An ankylosaur larynx provides insights for bird-like vocalization in non-avian dinosaurs

    Reilly, S. M. & Lauder, G. V. The evolution of tetrapod feeding behavior: kinematic homologies in prey transport. Evolution 44, 1542–1557 (1990).Article 

    Google Scholar 
    Iwasaki, S. Evolution of the structure and function of the vertebrate tongue. J. Anat. 201, 1–13 (2002).Article 

    Google Scholar 
    Fitch, W. T. & Suthers, R. A. In Vertebrate Sound Production and Acoustic Communication (eds Suthers, R. A., Fitch, W. T., Fay, R. R., & Popper, A. N.) 1–18 (Springer, 2016).Carroll, R. L. The Palaeozoic ancestry of salamanders, frogs and caecilians. Zool. J. Linn. Soc. 150, 1–140 (2007).Article 

    Google Scholar 
    Schwenk, K. in Feeding: Form, Function and Evolution in Tetrapod Vertebrates (ed. Schwenk, K.) 175–291 (Academic Press, 2000).Schwenk, K. & Rubega, M. In Physiological and ecological adaptations to feeding in vertebrates, (eds. Starck, M. & Wang, T.) 1–41 (Science Pub. Inc., 2005).Schumacher, G. H. In Biology of the Reptilia, 4 (ed Gans, C.) 101–200 (Academic Press, 1973).Reese, A. M. The laryngeal region of Alligator mississippiensis. Anat. Rec. 92, 273–277 (1945).Article 

    Google Scholar 
    Riede, T., Li, Z., Tokuda, I. & Farmer, C. G. Functional morphology of the Alligator mississippiensis larynx with implications for vocal production. J. Exp. Biol. 218, 991–998 (2015).Article 

    Google Scholar 
    McLelland, J. In Form and Function in Birds, 4 (eds King, A. S. & McLelland, J.) 69–103 (Academic Press, 1989).Homberger, D. G. In The Biology of the Avian Respiratory System (ed Maina, J. N.) 27–97 (Springer, 2017).Fitch, W. T. In Encyclopedia of Language & Linguistics (ed Brown, K.) 115–121 (Elsevier, 2006).Clarke, J. A. et al. Fossil evidence of the avian vocal organ from the Mesozoic. Nature 538, 502–505 (2016).Article 

    Google Scholar 
    Kingsley, E. P. et al. Identity and novelty in the avian syrinx. Proc. Natl Acad. Sci. USA 115, 10209–10217 (2018).Article 
    CAS 

    Google Scholar 
    Riede, T., Thomson, S. L., Titze, I. R. & Goller, F. The evolution of the syrinx: an acoustic theory. PLoS Biol. 17, e2006507 (2019).Nowicki, S. Vocal tract resonances in oscine bird sound production: evidence from birdsongs in a helium atmosphere. Nature 325, 53–55 (1987).Article 
    CAS 

    Google Scholar 
    Hill, R. V. et al. A complex hyobranchial apparatus in a Cretaceous dinosaur and the antiquity of avian paraglossalia. Zool. J. Linn. Soc. 175, 892–909 (2015).Article 

    Google Scholar 
    Li, Z. H., Zhou, Z. H. & Clarke, J. A. Convergent evolution of a mobile bony tongue in flighted dinosaurs and pterosaurs. PLoS One 13, e0198078 (2018).Article 

    Google Scholar 
    Bonaparte, J. F., Novas, F. E. & Coria, R. A. Carnotaurus sastrei Bonaparte, the horned, lightly built carnosaur from the Middle Cretaceous of Patagonia. Contrib. in Sci. Nat. Hist. Mus. L. A. 416, 1–42 (1990).Maryanska, T. Ankylosauridae (Dinosauria) from Mongolia. Palaeontol. Pol. 37, 85–151 (1977).
    Google Scholar 
    Mori, C. A comparative anatomical study on the laryngeal cartilages and laryngeal muscles of birds, and a developmental study on the larynx of the domestic fowl. Acta Med. 27, 2629–2678 (1957).
    Google Scholar 
    Siebenrock, F. Über den Kehlkopf und die Luftröhre der Schildkröten. Sitzungsberichte Der Kais. 108, 581–595 (1899).
    Google Scholar 
    Soley, J. T., Tivane, C. & Crole, M. R. Gross morphology and topographical relationships of the hyobranchial apparatus and laryngeal cartilages in the ostrich (Struthio camelus). Acta Zool. 96, 442–451 (2015).Article 

    Google Scholar 
    Olson, S. L. & Feduccia, A. Presbyornis and the origin of the Anseriformes (Aves: Charadriomorphae). Smithson. Contrib. Zool. 323, 1–24 (1980).Soley, J. T., Tivane, C. & Crole, M. R. A Gross morphology and topographical relationships of the hyobranchial apparatus and laryngeal cartilages in the ostrich (Struthio camelus). Acta Zool. 94, 442–451 (2015).Article 

    Google Scholar 
    Hogg, D. A. Ossification of the laryngeal, tracheal and syringeal cartilages in the domestic fowl. J. Anat. 134, 57–71 (1982).CAS 

    Google Scholar 
    Gaunt, A. S., Stein, R. C. & Gaunt, S. L. Pressure and air flow during distress calls of the starling, Sturnus vulgaris (Aves; Passeriformes). J. Exp. Zool. 183, 241–261 (1973).Article 

    Google Scholar 
    Sacchi, R., Galeotti, P., Fasola, M. & Gerzeli, G. Larynx morphology and sound production in three species of Testudinidae. J. Morphol. 261, 175–183 (2004).Article 

    Google Scholar 
    Titze, I. R. The physics of small-amplitude oscillation of the vocal folds. J. Acoust. Soc. Am. 83, 1536–1552 (1988).Article 
    CAS 

    Google Scholar 
    Russell, A. P., Hood, H. A. & Bauer, A. M. Laryngotracheal and cervical muscular anatomy in the genus Uroplatus (Gekkota: Gekkonidae) in relation to distress call emission. Afr. J. Herpetol. 63, 127–151 (2014).Article 

    Google Scholar 
    Russell, A. P., Rittenhouse, D. R. & Bauer, A. M. Laryngotracheal morphology of Afro‐Madagascan Geckos: a comparative survey. J. Morphol. 245, 241–268 (2000).Article 
    CAS 

    Google Scholar 
    Gans, C. & Maderson, P. F. Sound producing mechanisms in recent reptiles: review and comment. Am. Zool. 13, 1195–1203 (1973).Article 

    Google Scholar 
    Galeotti, P., Sacchi, R., Fasola, M. & Ballasina, D. Do mounting vocalisations in tortoises have a communication function? A comparative analysis. Herpetol. J. 15, 61–71 (2005).
    Google Scholar 
    Fletcher, N. H. Bird song—a quantitative acoustic model. J. Theor. Biol. 135, 455–481 (1988).Article 

    Google Scholar 
    Vergne, A. L., Pritz, M. B. & Mathevon, N. Acoustic communication in crocodilians: from behaviour to brain. Biol. Rev. 84, 391–411 (2009).Article 
    CAS 

    Google Scholar 
    Marler, P. R. & Slabbekoorn, H. Nature’s music: The science of birdsong (Academic Press, San Diego, USA, 2004).White, S. S. In Sisson and Grossman’s The Anatomy of the Domestic Animals. 2 (ed Getty, R.) 1891–1897 (Saunders, Philadelphia, USA 975).Kirchner, J. A. The vertebrate larynx: adaptations and aberrations. Laryngoscope 103, 1197–1201 (1993).Article 
    CAS 

    Google Scholar 
    Mackelprang, R. & Goller, F. Ventilation patterns of the songbird lung/air sac system during different behaviors. J. Exp. Biol. 216, 3611–3619 (2013).
    Google Scholar 
    Brocklehurst, R. J., Schachner, E. R. & Sellers, W. I. Vertebral morphometrics and lung structure in non-avian dinosaurs. R. Soc. Open Sci. 5, 180983 (2018).Article 

    Google Scholar 
    Cerda, I. A., Salgado, L. & Powell, J. E. Extreme postcranial pneumaticity in sauropod dinosaurs from South America. Paläontol. Z. 86, 441–449 (2012).Article 

    Google Scholar 
    Sereno, P. C. et al. Evidence for avian intrathoracic air sacs in a new predatory dinosaur from Argentina. PLoS One 3, e3303 (2008).Chiari, Y., Cahais, V., Galtier, N. & Delsuc, F. Phylogenomic analyses support the position of turtles as the sister group of birds and crocodiles (Archosauria). BMC Biol. 10, 65 (2012).Article 

    Google Scholar  More

  • in

    Playing “hide and seek” with the Mediterranean monk seal: a citizen science dataset reveals its distribution from molecular traces (eDNA)

    Shaw, J., Weyrich, L. & Cooper, A. Using environmental (e)DNA sequencing for aquatic biodiversity surveys: A beginner’s guide. Mar. Freshw. Res. 68, 68 (2016).
    Google Scholar 
    Smith, K. J. et al. Stable isotope analysis of specimens of opportunity reveals ocean-scale site fidelity in an elusive whale species. Front. Conserv. Sci. 2, 1–11 (2021).Article 

    Google Scholar 
    Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS One 5, (2010).Cavanagh, R. D. & Gibson, C. Overview of the conservation status of cartilaginous fishes (Chondrichthyans) in the Mediterranean Sea. https://doi.org/10.2305/iucn.ch.2007.mra.3.en (2007).Pace, D. S., Tizzi, R. & Mussi, B. Cetaceans value and conservation in the Mediterranean Sea. Journal Biodivers. Endanger. Species S1:
    S1.004 (2015).Carlucci, R. et al. Modeling the spatial distribution of the striped dolphin (Stenella coeruleoalba) and common bottlenose dolphin (Tursiops truncatus) in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). Ecol. Indic. 69, 707–721 (2016).Article 

    Google Scholar 
    Boldrocchi, G. et al. Distribution, ecology, and status of the white shark, Carcharodon carcharias, in the Mediterranean Sea. Rev. Fish Biol. Fish. 27, 515–534 (2017).Article 

    Google Scholar 
    Karamanlidis, A. A. et al. The Mediterranean monk seal Monachus monachus: Status, biology, threats, and conservation priorities. Mammal Review 46, 92–105. https://doi.org/10.1111/mam.12053 (2016).Article 

    Google Scholar 
    Johnson, W. M. The role of the Mediterranean monk seal (Monachus monachus) in European history and culture, from the fall of Rome to the 20th century Monk Seals in Post-Classical History. (2004).Johnson, W. M. & Lavigne, D. M. The Mediterranean Monk Seal (Monachus monachus) in Ancient History and Literature Monk Seals in Antiquity. (1999).Israëls, l. D. Thirty Years of Mediterranean Monk Seal Protection – A Review. Netherlands Com- Mission Int. Nat. Prot. Inst. voor Taxon. Zoölogie/Zoölogische Museum, Univ. van Amsterdam, Amsterdam, Netherlands. Meded. No. 281–65. (1992).Stringer, C. B. et al. Neanderthal exploitation of marine mammals in Gibraltar. Proc. Natl. Acad. Sci. U. S. A. 105, 14319–14324 (2008).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    La Mesa, G., Lauriano, G., Mo, G., Paglialonga, A. & Tunesi, L. Assessment of the conservation status of marine species of the Habitats Directive (92/43/EEC) in Italy: results, drawbacks and perspectives of the fourth national report (2013–2018). Biodivers Conserv (2021).Adamantopoulou, S., Karamanlidis, A. A., Dendrinos, P. & Gimenez, O. Citizen science indicates significant range recovery and defines new conservation priorities for Earth’s most endangered pinniped in Greece. Anim. Conserv. https://doi.org/10.1111/acv.12806 (2022).Article 

    Google Scholar 
    Nicolaou, H., Dendrinos, P., Marcou, M., Michaelides, S. & Karamanlidis, A. A. Re-establishment of the Mediterranean monk seal Monachus monachus in Cyprus: Priorities for conservation. Oryx 55, 526–528 (2021).Article 

    Google Scholar 
    Tenan, S. et al. Evaluating mortality rates with a novel integrated framework for nonmonogamous species. Conserv. Biol. 30, 1307–1319 (2016).Article 
    PubMed 

    Google Scholar 
    Vanpe, C. et al. Estimating abundance of a recovering transboundary brown bear population with capture- recapture models. Peer Community Journal, 2, e71. (2022).Lecaudey, L. A., Schletterer, M., Kuzovlev, V. V., Hahn, C. & Weiss, S. J. Fish diversity assessment in the headwaters of the Volga River using environmental DNA metabarcoding. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 1785–1800 (2019).Article 

    Google Scholar 
    Itakura, H. et al. Environmental DNA analysis reveals the spatial distribution, abundance, and biomass of Japanese eels at the river-basin scale. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 361–373 (2019).Article 

    Google Scholar 
    Closek, C. J. et al. Marine vertebrate biodiversity and distribution within the central California current using environmental DNA (eDNA) metabarcoding and ecosystem surveys. Front. Mar. Sci. Vol. 6. (2019).Boldrocchi, G. & Storai, T. Data-mining social media platforms highlights conservation action for the Mediterranean Critically Endangered blue shark Prionace glauca. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 3087–3099 (2021).Article 

    Google Scholar 
    Thiel, M. et al. Citizen scientists and marine research: Volunteer participants, their contributions, and projection for the future. Oceanogr. Mar. Biol. An Annu. Rev. 52, 257–314 (2014).
    Google Scholar 
    Araujo, G. et al. Citizen science sheds light on the cryptic ornate eagle ray Aetomylaeus vespertilio. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 2012–2018 (2020).Article 

    Google Scholar 
    Silvertown, J. A new dawn for citizen science. Trends Ecol. Evol. 24, 467–471 (2009).Article 
    PubMed 

    Google Scholar 
    Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).Article 

    Google Scholar 
    Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, (2014).Strickler, K. M., Fremier, A. K. & Goldberg, C. S. Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol. Conserv. 183, 85–92 (2015).Article 

    Google Scholar 
    Eichmiller, J., Best, S. E. & Sorensen, P. W. Effects of temperature and trophic state on degradation of environmental DNA in lake water. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.5b05672 (2016).Article 
    PubMed 

    Google Scholar 
    Mächler, E., Osathanunkul, M. & Altermatt, F. Shedding light on eDNA: neither natural levels of UV radiation nor the presence of a filter feeder affect eDNA-based detection of aquatic organisms. PLoS ONE 13, 1–15 (2018).Article 

    Google Scholar 
    Jo, T., Murakami, H., Yamamoto, S., Masuda, R. & Minamoto, T. Effect of water temperature and fish biomass on environmental DNA shedding, degradation, and size distribution. Ecol. Evol. 9, 1135–1146 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mauvisseau, Q. et al. The multiple states of environmental DNA and what is known about their persistence in aquatic environments. Environ. Sci. Technol. 56, 5322–5333 (2022).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Valsecchi, E. et al. A species – specific qPCR assay provides novel insight into range expansion of the Mediterranean monk seal (Monachus monachus ) by means of eDNA analysis. Biodivers. Conserv. 31, 1175–1196 (2022).Article 

    Google Scholar 
    Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. https://doi.org/10.1038/s42003-018-0192-6 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhao, B., P.M., B. & Timbros, K. The particle size distribution of environmental DNA varies with species and degradation. Sci. Total Environ. 797, 149175 (2021).Würtz, M. Mediterranean submarine canyons. in Ecology and Governance (ed. IUCN) 192 (2012).Valsecchi, E. et al. Ferries and environmental DNA: Underway sampling from commercial vessels provides new opportunities for systematic genetic surveys of marine biodiversity. Front. Mar. Sci. 8, 1–17 (2021).Article 

    Google Scholar 
    Bustin, S. A. et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 622, 611–622 (2009).Article 

    Google Scholar 
    Klymus, K. E. et al. Reporting the limits of detection and quantification for environmental DNA assays. Environ. DNA 1–12. https://doi.org/10.1002/edn3.29 (2019).Goldberg, G. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 1299–1307. https://doi.org/10.1111/2041-210X.12595 (2016).Farrell, J. A. et al. Detection and population genomics of sea turtle species via noninvasive environmental DNA analysis of nesting beach sand tracks and oceanic water. Mol. Ecol. Resour. (2022).Shamblin, B. M. et al. Loggerhead turtle eggshells as a source of maternal nuclear genomic DNA for population genetic studies. Mol. Ecol. Resour. 11, 110–115 (2011).Article 
    PubMed 

    Google Scholar 
    MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).Article 

    Google Scholar 
    White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 37–41 (1999).Akaike, H. Information theory and an extension of the maximum likelihood principle in Breakthroughs in Statistics, Vol.I, Foundations and Basic Theory, (eds. Kotz, S. and Johnson, N.L.) 610–624 (Springer-Verlag, New York, 1992).Adamantopoulou, S. et al. Movements of Mediterranean Monk Seals (Monachus monachus) in the Eastern Mediterranean Sea. Aquat. Mamm. 37, 256–261 (2011).Article 

    Google Scholar  More