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    Diversity of MHC IIB genes and parasitism in hybrids of evolutionarily divergent cyprinoid species indicate heterosis advantage

    1.Arnold, M. L. Natural Hybridization and Evolution (Oxford University Press, 1997).
    Google Scholar 
    2.Stelkens, R. & Seehausen, O. Genetic distance between species predicts novel trait expression in their hybrids. Evolution 63, 884–897 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Grant, P. R. & Grant, B. R. Hybridization of bird species. Science 256, 193–197 (1992).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Saino, N. & Villa, S. Pair composition and reproductive success across a hybrid zone of carrion crows and hooded crows. Auk 109, 543–555 (1992).
    Google Scholar 
    5.Good, T. P., Ellis, J. C., Annett, C. A. & Pierotti, R. Bounded hybrid superiority in an avian hybrid zone: effects of mate, diet, and habitat choice. Evolution 54, 1774–1783 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Bartley, D. M., Rana, K. & Immink, A. J. The use of inter-specific hybrids in aquaculture and fisheries. Rev. Fish Biol. Fisher. 10, 325–337 (2001).Article 

    Google Scholar 
    7.Rosenfield, J. A., Nolasco, S., Lindauer, S., Sandoval, C. & Kodric-Brown, A. The role of hybrid vigor in the replacement of Pecos pupfish by its hybrids with sheepshead minnow. Conserv. Biol. 18, 1589–1598 (2004).Article 

    Google Scholar 
    8.Sun, Y. et al. Comparative transcriptomic study of muscle provides new insights into the growth superiority of a novel grouper hybrid. PLoS ONE 11, e0168802 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    9.Scribner, K. T., Page, K. S. & Bartron, M. L. Hybridization in freshwater fishes: A review of case studies and cytonuclear methods of biological inference. Rev. Fish Biol. Fisher. 10, 293–323 (2001).Article 

    Google Scholar 
    10.Ottová, E. et al. Evolution and trans-species polymorphism of MHC class IIB genes in cyprinid fish. Fish Shellfish Immun. 18, 199–222 (2005).Article 
    CAS 

    Google Scholar 
    11.Šimková, A. et al. Does invasive Chondrostoma nasus shift the parasite community structure of endemic Parachondrostoma toxostoma in sympatric zones?. Parasite. Vector. 5, 200 (2012).Article 

    Google Scholar 
    12.Klein, J. & OhUigin, C. MHC polymorphism and parasites. Philos. Trans. R. Soc. Lond. B Biol. Sci. 346, 351–358 (1994).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Klein, J., Klein, D., Figueroa, F., OhUigin, C. & Sato, A. Major histocompatibility complex genes in the study of fish phylogeny. In Molecular Systematic of Fishes (eds Kocher, T. D. & Stepien, C. A.) 271–283 (Academic Press, 1997).Chapter 

    Google Scholar 
    14.Hughes, A. L. & Nei, M. Nucleotide substitution at major histocompatibility complex class II loci: Evidence for overdominant selection. Proc. Nat. Acad. Sci. USA 56, 958–962 (1989).ADS 
    Article 

    Google Scholar 
    15.Klein, J. & OhUigin, C. Composite origin of major histocompatibility complex genes. Curr. Opin. Genet. Dev. 3, 923–930 (1993).CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Hughes, A. L. & Nei, M. Models of host-parasite interactions and MHC polymorphism. Genetics 132, 863–864 (1992).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Klein, J. Of HLA, tryps, and selection? An essay on coevolution of MHC and parasites. Hum. Immunol. 30, 247–258 (1991).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Hughes, A. L., Hughes, M. K., Howell, C. Y. & Nei, M. Natural selection at the class II major histocompatibility complex loci of mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 346, 359–367 (1994).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Hedrick, P. W. Pathogen resistence and genetic variation at MHC loci. Evolution 56, 1902–1908 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Nowak, M. A., Tarczy-Hornoch, K. & Austyn, J. M. The optimal number of major histocompatibility complex molecules in an individual. Proc. Nat. Acad. Sci. U.S.A. 89, 10896–10899 (1992).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Wegner, K. M., Reusch, T. B. H. & Kalbe, M. Multiple parasites are driving major histocompatibility complex polymorphism in the wild. J. Evol. Biol. 16, 224–232 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    22.Eizaguirre, C., Lenz, T. L., Traulsen, A. & Milinski, M. Speciation accelerated and stabilized by pleiotropic major histocompatibility complex immunogenes. Ecol. Lett. 12, 5–12 (2009).PubMed 
    Article 

    Google Scholar 
    23.Nadachowska-Brzyska, K., Zielinski, P., Radwan, J. & Babiks, W. Interspecific hybridization increases MHC class II diversity in two sister species of newts. Mol. Ecol. 21, 887–906 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Wegner, K. M. & Eizaguirre, C. New(t)s and views from hybridizing MHC genes: Introgression rather than trans-species polymorphism may shape allelic repertoires. Mol. Ecol. 21, 779–781 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    25.Dudek, K., Gaczorek, T. S., Zielinski, P. & Babik, W. Massive introgression of major histocompatibility complex (MHC) genes in newt hybrid zones. Mol. Ecol. 28, 4798–4810 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    26.Šimková, A., Civáňová, K., Gettová, L. & Gilles, A. Genomic porosity between invasive Chondrostoma nasus and endangered endemic Parachondrostoma toxostoma (Cyprinidae): The evolution of MHC IIB genes. PLoS ONE 8, e65883 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    27.Zhang, S., Wang, Z. & Wang, H. Maternal immunity in fish. Dev. Comp. Immunol. 39, 72–78 (2013).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    28.Šimková, A., Vojtek, L., Halačka, K., Hyršl, P. & Vetešník, L. The effect of hybridization on fish physiology, immunity and blood biochemistry: A case study in hybridizing Cyprinus carpio and Carassius gibelio (Cyprinidae). Aquaculture 435, 381–389 (2015).Article 
    CAS 

    Google Scholar 
    29.Cowx, I. G. The biology of bream, Abramis brama (L), and its natural hybrid with roach, Rutilus rutilus (L), in the River Exe. J. Fish Biol. 22, 631–646 (1983).Article 

    Google Scholar 
    30.Economidis, P. S. & Wheeler, A. Hybrids of Abramis brama with Scardinius erythrophthalmus and Rutilus rutilus from Lake Volvi, Macedonia, Greece. J. Fish Biol. 35, 295–299 (1989).Article 

    Google Scholar 
    31.Toscano, B. J. et al. An ecomorphological framework for the coexistence of two cyprinid fish and their hybrids in a novel environment. Biol. J. Linn. Soc. 99, 768–783 (2010).Article 

    Google Scholar 
    32.Hayden, B. et al. Hybridisation between two cyprinid fishes in a novel habitat: Genetics, morphology and life-history traits. BMC Evol. Biol. 10, 169 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    33.Kuparinen, A., Vinni, M., Teacher, A. G. F., Kähkönen, K. & Merilä, J. Mechanism of hybridization between bream Abramis brama and roach Rutilus rutilus in their native range. J. Fish Biol. 84, 237–242 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Konopinski, M. K. & Amirowicz, A. Genetic composition of a population of natural common bream Abramis brama x roach Rutilus rutilus hybrids and their morphological characteristics in comparison with parent species. J. Fish Biol. 92, 365–385 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Krasnovyd, V., Vetešník, L., Gettová, L., Civáňová, K. & Šimková, A. Patterns of parasite distribution in the hybrids of non-congeneric cyprinid fish species: Is asymmetry in parasite infection the result of limited coadaptation?. Int. J. Parasitol. 47, 471–483 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Hayden, B. et al. Trophic dynamics within a hybrid zone—interactions between an abundant cyprinid hybrid and sympatric parental species. Freshwater Biol. 56, 1723–1735 (2011).Article 

    Google Scholar 
    37.Nzau Matondo, B. et al. Hybridization success of three common European cyprinid species, Rutilus rutilus, Blicca bjoerkna and Abramis brama and larval resistance to stress tests. Fish. Sci. 73, 1137–1146 (2007).Article 
    CAS 

    Google Scholar 
    38.Hayden, B., McLoone, P., Coyne, J. & Caffrey, J. M. Extensive hybridization between roach, Rutilus rutilus L., and common bream, Abramis brama L. Irish lakes and rivers. Biol. Environ. 114B, 35–39 (2014).
    Google Scholar 
    39.Eizaguirre, C. et al. Parasite diversity, patterns of MHC II variation and olfactory based mate choice in diverging threespined stickleback ecotypes. Evol. Ecol. 25, 605–622 (2011).Article 

    Google Scholar 
    40.Hubbs, C. L. Hybridization between fish species in nature. Syst. Zool. 4, 1–20 (1955).Article 

    Google Scholar 
    41.Rauch, G., Kalbe, M. & Reusch, T. B. H. Relative importance of MHC and genetic background for parasite load in a field experiment. Evol. Ecol. Res. 8, 373–386 (2006).
    Google Scholar 
    42.Eizaguirre, C., Lenz, T. L., Kalbe, M. & Milinski, M. Divergent selection on locally adapted major histocompatibility complex immune genes experimentally proven in the field. Ecol. Lett. 15, 723–731 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Šimková, A., Dávidová, M., Papoušek, I. & Vetešník, L. Does interspecies hybridization affect the host specificity of parasites in cyprinid fish?. Parasite. Vector. 6, 95 (2013).Article 

    Google Scholar 
    44.Seifertová, M., Jarkovský, J. & Šimková, A. Does the parasite-mediated selection drive the MHC class IIB diversity in wild populations of European chub (Squalius cephalus)?. Parasitol. Res. 115, 1401–1415 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Nzau Matondo, B., Ovidio, M., Philippart, J. C. & Poncin, P. Reproductive behaviour and sexual production in the first-generation hybrids of roach Rutilus rutilus L. × common bream Abramis brama L. J. Appl. Ichthyol. 27, 859–867 (2011).Article 

    Google Scholar 
    46.Graser, R., OhUigin, C., Vincek, V., Meyer, A. & Klein, J. Trans-species polymorphism of class II Mhc loci in danio fishes. Immunogenetics 44, 36–48 (1996).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Figueroa, F. et al. MHC class IIB gene evolution in East African cichlid fishes. Immunogenetics 51, 556–575 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Migalska, M., Sebastian, A. & Radwan, J. Major histocompatibility complex class I diversity limits the repertoire of T cell
    receptors.Proc. Natl. Acad. Sci. USA 116, 5021–5026 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Šimková, A., Košař, M., Vetešník, L. & Vyskočilová, M. MHC genes and parasitism in Carassius gibelio, a diploid-triploid fish species with dual reproduction strategies. BMC Evol. Biol. 13, 122 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Borghans, J. A. M., Beltman, J. B. & De Boer, J. B. MHC polymorphism under host-pathogen coevolution. Immunogenetics 55, 732–739 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    51.Ejsmond, M. J. & Radwan, J. Red queen processes drive positive selection on major histocompatibility complex (MHC) genes. PLoS Comput. Biol. 11, e1004627 (2015).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    52.Phillips, K. P. et al. Immunogenetic novelty confers a selective advantage in host-pathogen coevolution. Proc. Natl. Acad. Sci. USA 115, 1552–1557 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Gaigher, A., Burri, R., San-Jose, L. M., Roulin, A. & Fumagalli, L. Lack of statistical power as a major limitation in understanding MHC-mediated immunocompetence in wild vertebrate populations. Mol. Ecol. 28, 5115–5132 (2019).PubMed 
    Article 

    Google Scholar 
    54.Šimková, A., Ottová, E. & Morand, S. MHC variability, life-traits and parasite diversity of European cyprinid fish. Evol. Ecol. 20, 465–477 (2006).Article 

    Google Scholar 
    55.Clarke, B. & Kirby, D. R. S. Maintenance of histocompatibility polymorphisms. Nature 211, 999–1000 (1966).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    56.Meglécz, E. et al. SESAME (SEquence Sorter & AMplicon Explorer): Genotyping based on high throughput multiplex amplicon sequencing. Bioinformatics 27, 277–278 (2011).PubMed 
    Article 
    CAS 

    Google Scholar 
    57.Zagalska-Neubauer, M. et al. 454 sequencing reveals extreme complexity of the class II major histocompatibility complex in the collared flycatcher. BMC Evol. Biol. 10, 395 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Van Erp, S. H. M., Egberts, E. & Stet, R. J. M. Characterization of class II A and B genes in a gynogenetic carp clone. Immunogenetics 44, 192–202 (1996).PubMed 
    Article 

    Google Scholar 
    59.Šimková, A. Major histocompatibility complex genes and parasites in cyprinid fish. Vie Milieu 67, 139–148 (2017).
    Google Scholar 
    60.Klein, J. et al. Nomenclature for the major histocompatibility complexes of different species: A proposal. Immunogenetics 31, 217–219 (1990).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Dixon, B., Nagelkerke, L. A. J., Sibbing, F. A., Egberts, E. & Stet, R. J. M. Evolution of MHC class II beta chain-encoding genes in the Lake Tana barbel species flock (Barbus intermedius complex). Immunogenetics 44, 419–431 (1996).CAS 
    PubMed 

    Google Scholar 
    62.Rakus, K. L. et al. Major histocompatibility (MH) class IIB gene polymorphism influences disease resistance of common carp (Cyprinus carpio L). Aquaculture 288, 44–50 (2009).CAS 
    Article 

    Google Scholar 
    63.Seifertová, M. & Šimková, A. Structure, diversity and evolutionary patterns of expressed MHC class IIB genes in chub (Squalius cephalus), a cyprinid fish species from Europe. Immunogenetics 63, 167–181 (2011).PubMed 
    Article 

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

    Google Scholar 
    65.Darriba, D., Taboala, G. L., Doallo, R. & Posada, D. J. ModelTest2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Yang, Z. H. PAML4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    67.Doledec, S. & Chessel, D. Co-inertia analysis—an alternative method for studying species environment relationships. Freshwater Biol. 31, 277–294 (1994).Article 

    Google Scholar 
    68.Dray, S., Chessel, D. & Thioulouse, J. Co-inertia analysis and the linking of ecological data tables. Ecology 84, 3078–3089 (2003).Article 

    Google Scholar 
    69.Deter, J. et al. Association between the DQA MHC class II gene and puumala virus infection in Myodes glareolus, the bank vole. Infect. Genet. Evol. 8, 450–458 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    70.Evans, M. L. & Neff, B. D. Major histocompatibility complex heterozygote advantage and widespread bacterial infections in populations of Chinook salmon (Oncorhynchus tshawytscha). Mol. Ecol. 18, 4716–4729 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.Zuur, A. et al. Mixed Effects Models and Extensions in Ecology With R (Springer, 2009).MATH 
    Book 

    Google Scholar 
    72.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

    Google Scholar 
    73.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/(2018).74.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    75.Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.15.1. http://CRAN.R-project.org/package=MuMIn (2018).76.Thioulouse, J. & Dray, S. Interactive multivariate data analysis in R with the ade4 and ade4tkgui packages. J. Stat. Softw. 22, 1–14 (2007).Article 

    Google Scholar  More

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    Field measurements of a massive Porites coral at Goolboodi (Orpheus Island), Great Barrier Reef

    The location, diameter, height and circumference of the coral were measured (Table 1, Fig. 2). The Porites was brown to cream in colour and hemispherical in shape (Fig. 2). It was identified as either Porites lutea (Hump or Pore coral) or P. lobata (Lobe coral)14.The primary habitat on the Porites was live coral (70%), followed by sponge, live coral rock and a small amount of macroalgae (Table 2). No recently dead coral, coral rubble or sand was recorded (Table 2). We observed competition between the Porites and other species of coral and invertebrate including encrusting sponge, plating and branching Acropora spp., Montipora, Chlorodesmis, soft coral and zoanthids (Table 2, Figs. 3, 4).Table 2 Reef Health Impact Survey (RHIS) of habitat and species categories on Porites sp.Full size tableFigure 3Detail of the sub-habitats and competitive interactions Porites sp. and boring sponge Cliona viridis (left) and live coral Porites sp. and Montipora sp. (right) along interspecific contact zones.Full size imageFigure 4Detail of Reef Health Impact Survey (RHIS) of Porites.Full size imageThe boring sponge, Cliona viridis, is abundant on the Great Barrier Reef15. It is a common bioeroding species advancing laterally at around 1 cm and to a depth of 1.2 cm per annum15. Abundance of Cliona viridis is often correlated to substrate availability and water energy with the greatest abundance often on the windward side of bommies15. This correlates to our observations as the large proportion of the substrate estimated to cover the bommie (15%) was on the windward side. The sponge’s advances will likely continue to compromise the colony size and health.We recorded marine debris at the base of the Porites. The debris was 2–3 m of rope that appeared to have been wrapped around the base of an adjacent coral. Adjacent to the bommie were three concrete blocks.How big is the Porites coral at Goolboodi compared to other big corals in the GBR, and the world? Potts et al.6 reported a very large, rounded Porites colony, 6.9 m in diameter which is 3.1 m smaller than this study. Lough et al.16 reported coral cores from colonies between 1.6–8.0 m in height with the largest corals of 6.0 m at Havannah, North Molle and Masthead Islands, 7.5 m at Abraham Reef and 8.0 m at Sanctuary Reef. Recognising the limitations of published data, the Porites coral at Goolboodi is the largest diameter coral that has been measured, and the 6th tallest in the GBR. It is unknown if the other corals are still alive or dead.Other comparatively large massive Porites have previously been located throughout the Pacific. These have included multiple bommies measuring more than 10 m4 and one exceptionally large colony observed measuring 17 m × 12 m in American Samoa17. Additionally, large Porites sp. bommies have been observed at Green Island, 30 km east of Taiwan18 as well as an 11 m diameter Porites australiensis at Sesoko Island, Okinawa, Japan19.How old is this massive Porites? In discussions with the Australian Institute of Marine Science (AIMS), there is a robust, linear relationship ( > 80% variance explained) between Porites average linear extension rate and average annual sea surface temperature (SST)20,21 that provides an estimate of colony age from its height. Using average annual SST at 18.5S, 146.5E of 26.12C (from HadiSST data set), the estimated linear extension rate is determined by (2.97 × 26.12) − 65.46 = 1.21 cm/year. Given the colony height of 5.1–5.3 m, this gives an estimated age of 421–438 years. This is well before European exploration and settlement of Australia. AIMS has investigated over 328 colonies of massive Porites corals from 69 reefs along the GBR and has aged them as being from 10–436 years21. AIMS has not investigated this coral (pers. comm Neal Cantin). Based on limitations of published data, the Porites coral at Goolboodi is one of the oldest corals on the GBR.Why is the Porites partially dead on top and living on the side? The proportion of live coral tissue on a colony reflects the cumulative, integrated effect of both beneficial and adverse environmental factors. Substantial portions of coral tissue can die from exposure to sun at low tides or warm water without lethal consequences to the colony as a whole10. Partial mortality of large bommies provides available real estate for opportunistic, fast growing sessile organisms. In this instance, multiple species of tabulate and branching Acropora sp., encrusting Montipora sp. and encrusting sponges are among the benthic organisms to have colonised 30% of the coral bommies’ surface area. Intraspecific competition is also evident from the skeletal barriers created along contact zones22 (Fig. 3). There was no observation of disease or coral bleaching.The Porites is located in a relatively remote, rarely visited and highly protected Marine National Park (green) zone. Its location had not been previously reported and there is no existing database for significant corals in Australia or globally. Cataloguing the location of massive and long-lived corals can have multiple benefits. Scientific benefits include geochemical and isotopic analyses in coral skeletal cores which can help understand century-scale changes in oceanographic events and can be used to verify climate models. Social and economic benefits can include diving tourism, citizen science23 culture and stewardship. Perhaps the Significant Trees Register, which was designed by the National Trust24 to protect and celebrate Australia’s heritage could be considered as a model. There are risks of cataloguing the location of massive corals. It could be damaged by direct and indirect human uses including anchoring, research and pollution.Indigenous languages are an integral part of Indigenous culture, spirituality, and connection to country. We consulted Manbarra Traditional Owners about protocol and an appropriate cultural name for the Porites and they considered: Big (Muga), Home (Wanga), Coral reef (Muugar), Coral (Dhambi), Old (Anki, Gurgu), Old man (Gulula) and Old person (Gurgurbu)25. The recommendation by Manbarra Traditional Owners is that the Porites is named as Muga dhambi (Big coral). The feedback from the process of consultation was very positive with acknowledgement of the respect that the scientists have demonstrated to acknowledge Traditional Owners in this way.The large Porites coral at Goolboodi (Orpheus) Island is unusually rare and resilient. It has survived coral bleaching, invasive species, cyclones, severely low tides and human activities for almost 500 years. In an attempt to contextualise the resilience of these individual Porites we have reviewed major historic disturbances such as coral bleaching which has occurred since at least 1575 and potentially 99 bleaching events in the GBR over the past 400 plus years26. Other indicators such as high-density ‘stress bands’ were recorded from 1877 and are significantly more frequent in the late twentieth and early twenty-first centuries in accordance with rising temperatures from anthropogenic global warming27. In addition there have been an average of 1–2 tropical cyclones per decade (40–80 in total) that have potentially impacted the coral adjacent to Goolboodi Island28,29; 46 tropical cyclones impacted the area between Ingham and Townsville from 1858 to 200830. The cumulative impact of almost 100 bleaching events and up to 80 major cyclones over a period of four centuries, plus declining nearshore water quality contextualise the high resilience of this Porites coral. Looking to the future there is real concern for corals in the GBR due to many impacts including climate change, declining water quality, overfishing and coastal development31,32. This field note provides important geospatial, environmental, and cultural information of a rare coral that can be monitored, appreciated, potentially restored and hopefully inspire future generations to care more for our reefs and culture. More

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    Diversity, prevalence, and expression of cyanase genes (cynS) in planktonic marine microorganisms

    1.Nowakowska M, Sterzel M, Szczubiałka K. Photosensitized oxidation of cyanide in aqueous solutions of photoactive modified hydroxyethylcellulose. J Polym Environ. 2006;14:59–64.CAS 
    Article 

    Google Scholar 
    2.Kamennaya NA, Chernihovsky M, Post AF. The cyanate utilization capacity of marine unicellular Cyanobacteria. Limnol Oceanogr. 2008;53:2485–94.CAS 
    Article 

    Google Scholar 
    3.Palatinszky M, Herbold C, Jehmlich N, Pogoda M, Han P, von Bergen M, et al. Cyanate as an energy source for nitrifiers. Nature. 2015;524:105–8.CAS 
    Article 

    Google Scholar 
    4.Mooshammer M, Wanek W, Jones SH, Richter A, Wagner M. Cyanate–a low abundant but actively cycled nitrogen compound in soil. https://www.biorxiv.org/content/10.1101/2020.07.12.199737v1.full. 2020.5.Linder T. Cyanase-independent utilization of cyanate as a nitrogen source in ascomycete yeasts. World J Micro Biot. 2019;35:1–7.CAS 
    Article 

    Google Scholar 
    6.Widner B, Fuchsman CA, Chang BX, Rocap G, Mulholland MR. Utilization of urea and cyanate in waters overlying and within the eastern tropical north Pacific oxygen deficient zone. FEMS Microbiol Ecol. 2018;94:fiy138.CAS 
    Article 

    Google Scholar 
    7.Widner B, Mulholland MR, Mopper K. Distribution, sources, and sinks of cyanate in the coastal North Atlantic Ocean. Environ Sci Tech Let. 2016;3:297–302.CAS 
    Article 

    Google Scholar 
    8.Widner B, Mulholland MR, Mopper K. Chromatographic determination of nanomolar cyanate concentrations in estuarine and sea waters by precolumn fluorescence derivatization. Anal Chem. 2013;85:6661–6.CAS 
    Article 

    Google Scholar 
    9.Widner B, Mordy CW, Mulholland MR. Cyanate distribution and uptake above and within the Eastern Tropical South Pacific oxygen deficient zone. Limnol Oceanogr. 2018;63:S177–S192.CAS 
    Article 

    Google Scholar 
    10.Kuypers MMM, Marchant HK, Kartal B. The microbial nitrogen-cycling network. Nat Rev Microbiol. 2018;16:263.CAS 
    Article 

    Google Scholar 
    11.Smith SR, Dupont CL, McCarthy JK, Broddrick JT, Oborník M, Horák A, et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat Commun. 2019;10:1–14.Article 

    Google Scholar 
    12.Allen JrCM, Jones ME. Decomposition of carbamylphosphate in aqueous solutions. Biochemistry. 1964;3:1238–47.CAS 
    Article 

    Google Scholar 
    13.Kamenaya NA, Post AF. Characterization of cyanate metabolism in marine Synechococcus and Prochlorococcus spp. Appl Enviro Micro. 2011;77:291–301.Article 

    Google Scholar 
    14.Kamennaya NA, Post AF. Distribution and expression of the cyanate acquisition potential among cyanobacterial populations in oligotrophic marine waters. Limnol Oceanogr. 2013;58:1959–71.CAS 
    Article 

    Google Scholar 
    15.Kitzinger K, Padilla CC, Marchant HK, Hach PF, Herbold CW, Kidane AT, et al. Cyanate and urea are substrates for nitrification by Thaumarchaeota in the marine environment. Nat Microbiol. 2019;4:234–43.CAS 
    Article 

    Google Scholar 
    16.Pachiadaki MG, Sintes E, Bergauer K, Brown JM, Record NR, Swan BK, et al. Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science. 2017;358:1046–51.CAS 
    Article 

    Google Scholar 
    17.Ganesh S, Bertagnolli AD, Bristow LA, Padilla CC, Blackwood N, Aldunate M, et al. Single cell genomic and transcriptomic evidence for the use of alternative nitrogen substrates by anammox bacteria. ISME J. 2018;12:2706–22.CAS 
    Article 

    Google Scholar 
    18.Johnson WV, Anderson PM. Bicarbonate is a recycling substrate for cyanase. J Biol Chem. 1987;262:9021–5.CAS 
    Article 

    Google Scholar 
    19.Miller AG, Espie GS. Photosynthetic metabolism of cyanate by the cyanobacterium Synechococcus UTEX 625. Arch Microbiol. 1994;162:151–7.CAS 
    Article 

    Google Scholar 
    20.Harano Y, Suzuki I, Maeda S, Kaneko T, Tabata S, Omata T. Identification and nitrogen regulation of the cyanase gene from the cyanobacteria Synechocystis sp. strain PCC 6803 and Synechococcus sp. strain PCC 7942. J Bacteriol. 1997;179:5744.CAS 
    Article 

    Google Scholar 
    21.Sung YC, Anderson PM, Fuchs JA. Characterization of high-level expression and sequencing of the Escherichia coli K-12 cynS gene encoding cyanase. J Bacteriol. 1987;169:5224.CAS 
    Article 

    Google Scholar 
    22.Sáez LP, Cabello P, Ibáñez MI, Luque-Almagro VM, Roldán MD, Moreno-Vivián C. Cyanate assimilation by the alkaliphilic cyanide-degrading bacterium Pseudomonas pseudoalcaligenes CECT5344: mutational analysis of the cyn gene cluster. Int J Mol Sci. 2019;20:3008.Article 

    Google Scholar 
    23.Wood AP, Kelly DP, McDonald IR, Jordan SL, Morgan TD, Khan S, et al. A novel pink-pigmented facultative methylotroph, Methylobacterium thiocyanatum sp. nov., capable of growth on thiocyanate or cyanate as sole nitrogen sources. Arch Microbiol. 1998;169:148–58.CAS 
    Article 

    Google Scholar 
    24.Elleuche S, Pöggeler S. A cyanase is transcriptionally regulated by arginine and involved in cyanate decomposition in Sordaria macrospora. Fungal Genet Biol. 2008;45:1458–69.CAS 
    Article 

    Google Scholar 
    25.Schlachter CR, Klapper V, Wybouw N, Radford T, Van Leeuwen T, Grbic M, et al. Structural characterization of a eukaryotic cyanase from Tetranychus urticae. J Agr Food Chem. 2017;65:5453–62.CAS 
    Article 

    Google Scholar 
    26.Qian D, Jiang L, Lu L, Wei C, Li Y. Biochemical and structural properties of cyanases from Arabidopsis thaliana and Oryza sativa. PLoS One. 2011;6:e18300.CAS 
    Article 

    Google Scholar 
    27.Zarlenga DS, Mitreva M, Thompson P, Tyagi R, Tuo W, Hoberg EP. A tale of three kingdoms: members of the Phylum Nematoda independently acquired the detoxifying enzyme cyanase through horizontal gene transfer from plants and bacteria. Parasitology. 2019;146:445–52.CAS 
    Article 

    Google Scholar 
    28.Ranjan B, Choi PH, Pillai S, Permaul K, Tong L, Singh S. Crystal structure of a thermophilic fungal cyanase and its implications on the catalytic mechanism for bioremediation. Sci Rep. 2021;11:1–10.Article 

    Google Scholar 
    29.Villar E, Vannier T, Vernette C, Lescot M, Cuenca M, Alexandre A, et al. The Ocean Gene Atlas: exploring the biogeography of plankton genes online. Nucleic Acids Res. 2018;46:W289–W295.CAS 
    Article 

    Google Scholar 
    30.Walsh MA, Otwinowski Z, Perrakis A, Anderson PM, Joachimiak A. Structure of cyanase reveals that a novel dimeric and decameric arrangement of subunits is required for formation of the enzyme active site. Structure. 2000;8:505–14.CAS 
    Article 

    Google Scholar 
    31.Butryn A, Stoehr G, Linke-Winnebeck C, Hopfner KP. Serendipitous crystallization and structure determination of cyanase (CynS) from Serratia proteamaculans. Acta Crystallogr F. 2015;71:471–6.CAS 
    Article 

    Google Scholar 
    32.Pederzoli R, Tarantino D, Gourlay LJ, Chaves-Sanjuan A, Bolognesi M. Detecting the nature and solving the crystal structure of a contaminant protein from an opportunistic pathogen. Acta Crystallogr F. 2020;76:392–7.CAS 
    Article 

    Google Scholar 
    33.Wybouw N, Balabanidou V, Ballhorn DJ, Dermauw W, Grbić M, Vontas J, et al. A horizontally transferred cyanase gene in the spider mite Tetranychus urticae is involved in cyanate metabolism and is differentially expressed upon host plant change. Insect Biochem Molec. 2012;42:881–9.CAS 
    Article 

    Google Scholar 
    34.Spang A, Poehlein A, Offre P, Zumbrägel S, Haider S, Rychlik N, et al. The genome of the ammonia‐oxidizing candidatus nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ Microbiol. 2012;14:3122–45.CAS 
    Article 

    Google Scholar 
    35.Palomo A, Pedersen AG, Fowler SJ, Dechesne A, Sicheritz-Pontén T, Smets BF. Comparative genomics sheds light on niche differentiation and the evolutionary history of comammox Nitrospira. ISMEJ. 2018;12:1779–93.Article 

    Google Scholar  More

  • in

    Late Pleistocene human paleoecology in the highland savanna ecosystem of mainland Southeast Asia

    1.Roberts, P. & Stewart, B. A. Defining the ‘generalist specialist’ niche for Pleistocene Homo sapiens. Nat. Hum. Behav. 2, 542–550 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Heaney, L. R. A synopsis of climatic and vegetational change in Southeast Asia. Clim. Change 19, 53–61 (1991).ADS 
    Article 

    Google Scholar 
    3.Morley, R. J. Origin and Evolution of Tropical Rain Forests (Wiley, 2000).
    Google Scholar 
    4.Bird, M. I., Taylor, D. & Hunt, C. Palaeoenvironments of insular Southeast Asia during the last Glacial Period: a savanna corridor in Sundaland?. Quat. Sci. Rev. 24, 2228–2242 (2005).ADS 
    Article 

    Google Scholar 
    5.Wurster, C. M., Rifai, H., Zhou, B., Haig, J. & Bird, M. I. Savanna in equatorial Borneo during the late Pleistocene. Sci. Rep. 9, 6392. https://doi.org/10.1038/s41598-019-42670-4 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Wurster, C. M. & Bird, M. I. Barriers and bridges: early human dispersals in equatorial SE Asia. Geol. Soc. Spec. Publ. 411, 235–250 (2016).ADS 
    Article 

    Google Scholar 
    7.Zaim, Y. Geological evidence for the earliest appearance of hominins in Indonesia. In Out of Africa I: The First Hominin Colonization of Eurasia (eds Fleagle, J. G. et al.) 97–110 (Springer, 2010).Chapter 

    Google Scholar 
    8.Cannon, C. H., Morley, R. J. & Bush, A. B. G. The current refugial rainforests of Sundaland are unrepresentative of their biogeographic past and highly vulnerable to disturbance. Proc. Natl. Acad. Sci. USA 106, 11188–11193 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Raes, N. et al. Historical distribution of Sundaland’s Dipterocarp rainforests at Quaternary glacial maxima. Proc. Natl. Acad. Sci. USA 111, 16790–16795 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Suraprasit, K., Jongautchariyakul, S., Yamee, C., Pothichaiya, C. & Bocherens, H. New fossil and isotope evidence for the Pleistocene zoogeographic transition and hypothesized savanna corridor in peninsular Thailand. Quat. Sci. Rev. 221, 1055861 (2019).Article 

    Google Scholar 
    11.Pookajorn, S. Human activities and environmental changes during the late pleistocene to middle holocene in Southern Thailand and Southeast Asia. In Humans at the End of the Ice Age: The Archaeology of the Pleistocene—Holocene Transition, Interdisciplinary Contributions to Archaeology (eds Straus, L. G. et al.) 201–213 ( Springer, 1996).Chapter 

    Google Scholar 
    12.Schepartz, L. A., Miller-Antonio, S. & Bakken, D. A. Upland resources and the early palaeolithic occupation of Southern China, Vietnam, Laos Thailand and Burma. World Archaeol. 32, 1–13 (2000).Article 

    Google Scholar 
    13.Mudar, K. & Anderson, D. New evidence for Southeast Asian pleistocene foraging economies: faunal remains from the early levels of Lang Rongrien Rockshelter, Krabi, Thailand. Asian Perspect. 46, 298–334 (2007).Article 

    Google Scholar 
    14.Shoocongdej, R. Late Pleistocene activities at the Tham Lod rockshelter in Highland Pang Mapha, Mae Hong Son province, Norhwestern Thailand. In Uncovering Southeast Asia’s Past (eds Bacus, E. et al.) 22–37 (NUS Press, 2006).
    Google Scholar 
    15.Shoocongdej, R. et al. Final report of Highland Archaeology Project in Pang Mapha District, Mae Hong Son Province Phase 2, Vol. 2 (Thailand Research Fund, 2007).16.Demeter, F. et al. Anatomically modern human in Southeast Asia (Laos) by 46 ka. Proc. Natl. Acad. Sci. USA 109, 14375–14380 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17Demeter, F. et al. Early modern humans and morphological variation in Southeast Asia: fossil evidence from Tam Pa Ling. Laos. PLoS ONE 10, e0121193. https://doi.org/10.1371/journal.pone.0121193 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Viet, N. First archaeological evidence of symbolic activities from the Pleistocene of Vietnam. In Emergence and Diversity of Human Behavior Paleolithic Asia (ed. Kaifu, Y.) 133–139 (Texas A&M University Press, 2015).
    Google Scholar 
    19.Higham, C. F. & Thosarat, R. An early hunter-gatherer site at Ban Non Wat, Northeast Thailand. J. Indo. Pacif. Archaeol. 43, 93–96 (2019).Article 

    Google Scholar 
    20.Gorman, C. F. Excavations at Spirit Cave, North Thailand: Some Interim Interpretations. Asian Perspect. 13, 79–107 (1970).
    Google Scholar 
    21.Tayles, N., Halcrow, S. E., Sayavongkhamdy, T. & Souksavatdy, V. A prehistoric flexed human burial from Pha Phen, Middle Mekong Valley, Laos: its context in Southeast Asia. Anthropol. Sci. 123, 1–12 (2015).Article 

    Google Scholar 
    22.Conrad, C., Higham, C., Eda, M. & Marwick, B. Palaeoecology and forager subsistence strategies during the Pleistocene—Holocene transition: A reinvestigation of the zooarchaeological assemblage from Spirit Cave, Mae Hong Son Province, Thailand. Asian Perspect. 5, 2–27 (2016).Article 

    Google Scholar 
    23.Zeitoun, V. D. et al. Discovery of an outstanding Hoabinhian site from the Late Pleistocene at Doi Pha Kan (Lampang province, northern Thailand). Archaeol. Res. Asia 18, 1–16 (2019).Article 

    Google Scholar 
    24.Shoocongdej, R. Forager mobility organization in seasonal tropical environments of western Thailand. World Archaeol. 32, 14–40 (2000).Article 

    Google Scholar 
    25.Forestier, H. et al. The Hoabinhian from Laang Spean Cave in its stratigraphic, chronological, typo-technological and environmental context (Cambodia, Battambang province). J. Archaeol. Sci. Rep. 3, 194–206 (2015).
    Google Scholar 
    26.Chitkament, T., Gaillard, C. & Shoocongdej, R. Tham Lod rockshelter (Pang Mapha district, north-western Thailand): Evolution of the lithic assemblages during the late Pleistocene. Quat. Int. 416, 151–161 (2016).Article 

    Google Scholar 
    27.Marwick, B. The Hoabinhian of Southeast Asia and its relationship to regional Pleistocene lithic technologies. In Lithic Technological Organization and Paleoenvironmental Change Global and Diachronic Perspectives (eds Robinson, E. & Sellet, F.) 63–78 (Springer, 2018).Chapter 

    Google Scholar 
    28.Marwick, B. & Gagan, M. K. Late Pleistocene monsoon variability in northwest Thailand: an oxygen isotope sequence from the bivalve Margaritanopsis laosensis excavated in Mae Hong Son province. Quat. Sci. Rev. 30, 3088–3098 (2011).ADS 
    Article 

    Google Scholar 
    29.Marwick, B. Multiple Optima in Hoabinhian flaked stone artefact palaeoeconomics and palaeoecology at two archaeological sites in Northwest Thailand. J. Anthropol. Archaeol. 32, 553–564 (2013).Article 

    Google Scholar 
    30.Wattanapituksakul, A., Filoux, A., Amphansri, A. & Tumpeesuwan, S. Late Pleistocene Caprinae assemblages of Tham Lod Rockshelter (Mae Hong Son Province, Northwest Thailand). Quat. Int. 493, 212–226 (2018).Article 

    Google Scholar 
    31.Shoocongdej, R. & Wattanapituksakul, A. Faunal assemblages and demography during the Late Pleistocene (MIS 2–1) to Early Holocene in Highland Pang Mapha, Northwest Thailand. Quat. Int. 563, 51–63 (2020).Article 

    Google Scholar 
    32.DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Van Der Merwe, N. J. & Vogel, J. C. 13C Content of human collagen as a measure of prehistoric diet in woodland North America. Nature 276, 815–816 (1978).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Cerling, T. E. & Harris, J. M. Carbon isotope fractionation between diet and bioapatite in ungulate mammals and implications for ecological and paleoecological studies. Oecologia 120, 347–363 (1999).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Cerling, T. E., Hart, J. A. & Hart, T. B. Stable isotope ecology in the Ituri Forest. Oecologia 138, 5–12 (2004).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Bourgon, N. et al. Zinc isotopes in Late Pleistocene fossil teeth from a Southeast Asian cave setting preserve paleodietary information. Proc. Natl. Acad. Sci. USA 117, 4675–4681 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.van Klinken, G. J. Bone Collagen quality indicators for palaeodietary and radiocarbon measurements. J. Archaeol. Sci. 26, 687–695 (1999).Article 

    Google Scholar 
    38.Pestle, W. J. & Colvard, M. Bone collagen preservation in the tropics: a case study from ancient Puerto Rico. J. Archaeol. Sci. 39, 2079–2090 (2012).CAS 
    Article 

    Google Scholar 
    39.Ecker, M. et al. Middle Pleistocene ecology and Neanderthal subsistence: Insights from stable isotope analyses in Payre (Ardèche, southeastern France). J. Hum. Evol. 65, 363–373 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40Kohn, M. & Cerling, T. E. Stable isotope compositions of biological apatite. In Phosphates—Geochemical Geobiological and Materials Importance Reviews in Mineralogy and Geochemistry Vol. 48 (eds Kohn, M. et al.) 455–488 (Mineralogical Society of America, 2002).Chapter 

    Google Scholar 
    41.Biasatti, D., Wang, Y., Gao, F., Xu, Y. & Flynn, L. Paleoecologies and paleoclimates of late cenozoic mammals from Southwest China: evidence from stable carbon and oxygen isotopes. J. Asian Earth Sci. 44, 48–61 (2012).ADS 
    Article 

    Google Scholar 
    42.Clementz, M. T., Fox-Dobbs, K., Wheatley, P.-V., Koch, P. L. & Doak, D. F. Revisiting old bones: coupled carbon isotope analysis of bioapatite and collagen as an ecological and palaeoecological tool. Geol. J. 44, 605–620 (2009).CAS 
    Article 

    Google Scholar 
    43.Domingo, M. S., Domingo, L., Badgley, C., Sanisidro, O. & Morales, J. Resource partitioning among top predators in a Miocene food web. Proc. R. Soc. B 280, 20122138. https://doi.org/10.1098/rspb.2012.2138 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Codron, D., Clauss, M., Codron, J. & Tütken, T. Within trophic level shifts in collagen–carbonate stable carbon isotope spacing are propagated by diet and digestive physiology in large mammal herbivores. Ecol. Evol. 8, 3983–3995 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45Tejada-Lara, J. V. et al. Body mass predicts isotope enrichment in herbivorous mammals. Proc. R. Soc. B 285, 20181020. https://doi.org/10.1098/rspb.2018.1020 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Cerling, T. E. et al. Stable isotope-based diet reconstructions of Turkana Basin hominins. Proc. Natl. Acad. Sci. USA 110, 10501–10506 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Ayliffe, L. K. & Chivas, A. R. Oxygen isotope composition of the bone phosphate of Australian kangaroos: potential as a palaeoenvironmental recorder. Geochim. Cosmochim. Acta 54, 2603–2609 (1990).ADS 
    CAS 
    Article 

    Google Scholar 
    48.Levin, N. E., Cerling, T. E., Passey, B. H., Harris, J. M. & Ehleringer, J. R. A stable isotope aridity index for terrestrial environments. Proc. Natl. Acad. Sci. USA 103, 11201–11205 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Bocherens, H., Koch, P., Mariotti, A., Geraads, D. & Jaeger, J.-J. Isotopic biogeochemistry (13C, 18O) of mammalian enamel from African Pleistocene hominoid sites. Palaios 11, 306–308 (1996).ADS 
    Article 

    Google Scholar 
    50.Hambali, K., Ismail, A., Md-Zain, B. M., Amir, A. & Karim, F. A. Diet of long-tailed macaques (Macaca fascicularis) at the entrance of Kuala Selangor Nature Park (anthropogenic habitat): food selection that leads to human-macaque conflict. Acta Biol. Malay. 3, 58–68 (2014).
    Google Scholar 
    51.Nila, S., Suryobroto, B. & Widayati, K. A. Dietary variation of long tailed macaques (Macaca fascicularis) in Telaga Warna, Bogor, West Java. HAYATI J. Biosci. 21, 8–14 (2014).Article 

    Google Scholar 
    52.Lekagul, B. & McNeely, J. A. Mammals of Thailand: Association for the Conservation of Wildlife (Kurusapa Ladproa Press, 1988).
    Google Scholar 
    53.Suraprasit, K., Bocherens, H., Chaimanee, Y., Panha, S. & Jaeger, J.-J. Late Middle Pleistocene ecology and climate in Northeastern Thailand inferred from the stable isotope analysis of Khok Sung herbivore tooth enamel and the land mammal cenogram. Quat. Sci. Rev. 193, 24–42 (2018).ADS 
    Article 

    Google Scholar 
    54Suraprasit, K. et al. Long-term isotope evidence on the diet and habitat breadth of pleistocene to holocene caprines in Thailand: implications for the extirpation and conservation of Himalayan Gorals. Front. Ecol. Evol. 8, 67. https://doi.org/10.3389/fevo.2020.00067 (2020).Article 

    Google Scholar 
    55.Kohn, M. J. Predicting animal δ18O: Accounting for diet and physiological adaptation. Geochim. Cosmochim. Acta 60, 4811–4829 (1996).ADS 
    CAS 
    Article 

    Google Scholar 
    56.Kohn, M. J., Schoeninger, M. J. & Valley, J. W. Herbivore tooth oxygen isotope compositions: effects of diet and physiology. Geochim. Cosmochim. Acta 60, 3889–3896 (1996).ADS 
    CAS 
    Article 

    Google Scholar 
    57.Dunbar, J. & Wilson, T. Oxygen and hydrogen isotopes in fruits and vegetable juices. Plant Physiol. 72, 725–727 (1983).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Yakir, D. Variations in the natural abundances of oxygen-18 and deuterium in plant carbohydrates. Plant Cell Environ. 15, 1005–1020 (1992).CAS 
    Article 

    Google Scholar 
    59.Fricke, H. C. & O’Neil, J. R. Inter- and intra-tooth variation in the oxygen isotope composition of mammalian tooth enamel phosphate: implications for palaeoclimatological and palaeobiological research. Palaeogeogr. Palaeoclimatol. Palaeoecol. 126, 91–99 (1996).Article 

    Google Scholar 
    60.Fricke, H. C., Clyde, W. C. & O’Neil, J. R. Intra-tooth variations in δ18O (PO4) of mammalian tooth enamel as a record of seasonal variations in continental climate variables. Geochem. Cosmochim. Acta 62, 1839–1850 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    61.Balasse, M., Ambrose, S. H., Smith, A. B. & Price, T. D. The seasonal mobility model for prehistoric herders in the south-western Cape of South Africa assessed by isotopic analysis of sheep tooth enamel. J. Archaeol. Sci. 29, 917–932 (2002).Article 

    Google Scholar 
    62Ratnam, J., Tomlinson, K. W., Rasquinha, D. N. & Sankaran, M. Savannahs of Asia: antiquity, biogeography, and an uncertain future. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 2015305. https://doi.org/10.1098/rstb.2015.0305 (2016).CAS 
    Article 

    Google Scholar 
    63.Pushkina, D., Bocherens, H., Chaimanee, Y. & Jaeger, J.-J. Stable carbon isotope reconstructions of diet and paleoenvironment from the late middle Pleistocene Snake Cave in Northeastern Thailand. Naturwissenschaften 97, 299–309 (2010).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Louys, J. & Roberts, P. Environmental drivers of megafauna and hominin extinction in Southeast Asia. Nature 586, 402–406 (2020).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Passey, B. H. et al. Carbon isotope fractionation between diet, breath CO2, and bioapatite in different mammals. J. Archaeol. Sci. 32, 1459–1470 (2005).Article 

    Google Scholar 
    66.Dutt, S. et al. Abrupt changes in Indian summer monsoon strength during 33,800 to 5500 years B.P. Geophys. Res. Lett. 42, 5526–5532 (2015).ADS 
    Article 

    Google Scholar 
    67.Ronay, E. R., Breitenbach, S. F. M. & Oster, J. L. Sensitivity of speleothem records in the Indian Summer Monsoon region to dry season infiltration. Sci. Rep. 9, 5091. https://doi.org/10.1038/s41598-019-41630-2 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    68Liu, G. et al. On the glacial-interglacial variability of the Asian monsoon in speleothem δ18O records. Sci. Adv. 6, 8eaay8189. https://doi.org/10.1126/sciadv.aay8189 (2020).CAS 
    Article 

    Google Scholar 
    69.Lambeck, K., Rouby, H., Purcell, A., Sun, Y. & Sambridge, M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proc. Natl. Acad. Sci. USA 111, 15296–15303 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70Rabett, R. J. Human Adaptation in the Asian Palaeolithic: hominin dispersal and behaviour during the late quaternary (Cambridge University Press, 2012).Book 

    Google Scholar 
    71.Bailey, R. C. et al. Hunting and gathering in tropical rain forest: Is it possible?. Am. Anthropol. 91, 59–82 (1989).Article 

    Google Scholar 
    72.Mercader, J. Forest people: the role of African rainforests in human evolution and dispersal. Evol. Anthropol. 11, 117–124 (2002).Article 

    Google Scholar 
    73.Mercader, J. Under the Canopy: The Archaeology of Tropical Rainforests (Rutgers University Press, 2002).
    Google Scholar 
    74.Mercader, J. Foragers of the Congo: the early settlement of the Ituri forest. In Under the Canopy: The Archeology of Tropical Rain Forests (ed. Mercader, J.) 93–116 (Rutgers University Press, London, 2003).
    Google Scholar 
    75.Perera, N. et al. People of the ancient rainforest: Late Pleistocene foragers at the Batadomba-lena rockshelter, Sri Lanka. J. Hum. Evol. 61, 254–269 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Roberts, P. et al. Direct evidence for human reliance on rainforest resources in late Pleistocene Sri Lanka. Science 347, 1246–1249 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Roberts, P. et al. Fruits of the forest: human stable isotope ecology and rainforest adaptations in Late Pleistocene and Holocene (~36 to 3 ka) Sri Lanka. J. Hum. Evol. 106, 102–118 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78Wedage, O. et al. Specialized rainforest hunting by Homo sapiens ~45,000 years ago. Nat. Commun. 10, 739. https://doi.org/10.1038/s41467-019-08623-1 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Ji, X. et al. The oldest Hoabinhian technocomplex in Asia (43.5 ka) at Xiaodong rockshelter, Yunnan Province, southwest China. Quat. Int. 400, 166–174 (2016).Article 

    Google Scholar 
    80.Olsen, J. W. & Ciochon, R. L. A review of evidence for postulated Middle Pleistocene occupations in Viet Nam. J. Hum. Evol. 19, 761–788 (1990).Article 

    Google Scholar 
    81.Rabett, R. et al. The Tràng An Project: Late-to-Post-Pleistocene Settlement of the Lower Song Hong Valley, North Vietnam. J. R. Asiat. Soc. 19, 83–109 (2009).Article 

    Google Scholar 
    82.Rabett, R. et al. Tropical limestone forest resilience and late Pleistocene foraging during MIS-2 in the Tràng An massif, Vietnam. Quat. Int. 448, 62–81 (2017).Article 

    Google Scholar 
    83.Barker, G. et al. The ‘Human Revolution’ in lowland tropical Southeast Asia: the antiquity and behavior of anatomically modern humans at Niah Cave (Sarawak, Borneo). J. Hum. Evol. 52, 243–261 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    84.Piper, P. & Rabett, R. Hunting in a tropical rainforest: evidence from the terminal Pleistocene at Lobang Hangus, Niah Caves, Sarawak. Int. J. Osteoarchaeol. 19, 551–565 (2009).Article 

    Google Scholar 
    85.Hunt, C. O., Gilbertson, D. D. & Rushworth, G. A 50,000-year record of late Pleistocene tropical vegetation and human impact in lowland Borneo. Quat. Sci. Rev. 37, 61–80 (2012).ADS 
    Article 

    Google Scholar 
    86.de Vos, J. The Pongo faunas from Java and Sumatra and their significance for biostratigraphical and paleoecological interpretations. Proc. K. Ned. Akad. Wet. B. 86, 417–425 (1983).
    Google Scholar 
    87.Westaway, K. E. An early modern human presence in Sumatra 73000–63000 years ago. Nature 548, 322–325 (2017).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.Storm, P. et al. Late Pleistocene Homo Sapiens in a tropical rainforest Fauna in East Java. J. Hum. Evol. 49, 536–545 (2005).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    89.Storm, P. & de Vos, J. Rediscovery of the late Pleistocene Punung Hominin Sites and the Discovery of a New Site Gunung Dawung in East Java. Senck. Leth. 86, 271–281 (2006).Article 

    Google Scholar 
    90Roberts, P. et al. Isotopic evidence for initial coastal colonization and subsequent diversification in the human occupation of Wallacea. Nat. Commun. 11, 2068. https://doi.org/10.1038/s41467-020-15969-4 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    91.Pasveer, J. M., Clarke, S. J. & Miller, G. H. Late Pleistocene human occupation of inland rainforest, Bird’s Head, Papua. Archaeol. Oceania 37, 92–95 (2002).Article 

    Google Scholar 
    92.Summerhayes, G. R. et al. Human adaptation and plant use in highland New Guinea 49,000 to 44,000 Years Ago. Science 330, 78–81 (2010).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Summerhayes, G. R., Field, J. H., Shaw, B. & Gaffney, D. The archaeology of forest exploitation and change in the tropics during the Pleistocene: the case of Northern Sahul (Pleistocene New Guinea). Quat. Int. 448, 14–30 (2017).Article 

    Google Scholar 
    94.Roberts, P., Gaffney, D., Lee-Thorp, J. A. & Summerhayes, G. R. Persistent tropical foraging in the highlands of terminal Pleistocene/Holocene New Guinea. Nature Ecol. Evol. 1, 1–6 (2017).CAS 
    Article 

    Google Scholar 
    95.Wedage, O. et al. Microliths in the South Asian rainforest ~45–4 ka: New insights from Fa-Hien Lena Cave, Sri Lanka. PLoS ONE https://doi.org/10.1371/journal.pone.0222606 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.Bettis, E. A. et al. Way out of Africa: early Pleistocene paleoenvironments inhabited by Homo erectus in Sangiran, Java. J. Hum. Evol. 56, 11–24 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    97.Brumm, A. et al. Age and context of the oldest known hominin fossils from Flores. Nature 534, 249–253 (2016).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    98.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001).
    Google Scholar  More

  • in

    Effect of salinity on the zinc(II) binding efficiency of siderophore functional groups and implications for salinity tolerance mechanisms in barley

    1.McLean, J. E., Pabst, M. W., Miller, C. D., Dimkpa, C. O. & Anderson, A. J. Effect of complexing ligands on the surface adsorption, internalization, and bioresponse of copper and cadmium in a soil bacterium, Pseudomonas Putida. Chemosphere 91(3), 374–382. https://doi.org/10.1016/j.chemosphere.2012.11.071 (2013).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    2.Clemens, S. Metal ligands in micronutrient acquisition and homeostasis. Plant. Cell Environ. 42(10), 2902–2912. https://doi.org/10.1111/pce.13627 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    3.Ma, H. et al. Elucidation of the mechanisms into effects of organic acids on soil fertility, cadmium speciation and ecotoxicity in contaminated soil. Chemosphere 239, 124706. https://doi.org/10.1016/j.chemosphere.2019.124706 (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    4.Ahmed, E. & Holmström, S. J. M. Siderophores in environmental research: Roles and applications. Microb. Biotechnol. 7(3), 196–208. https://doi.org/10.1111/1751-7915.12117 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Butler, A. & Theisen, R. M. Iron(III)-siderophore coordination chemistry: Reactivity of marine siderophores. Coord. Chem. Rev. 254(3–4), 288–296. https://doi.org/10.1016/j.ccr.2009.09.010 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Hider, R. C. & Kong, X. Chemistry and biology of siderophores. Nat. Prod. Rep. 27(5), 637. https://doi.org/10.1039/b906679a (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Kirby, M. E., Sonnenberg, J. L., Simperler, A. & Weiss, D. J. Stability series for the complexation of six key siderophore functional groups with uranyl using density functional theory. J. Phys. Chem. A 124(12), 2460–2472. https://doi.org/10.1021/acs.jpca.9b10649 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Harrington, J. et al. Structural dependence of Mn complexation by siderophores: Donor group dependence on complex stability and reactivity. GCA. 88, 106–119 (2012).ADS 
    CAS 

    Google Scholar 
    9.McRose, D. L., Seyedsayamdost, M. R. & Morel, F. M. M. Multiple siderophores: Bug or feature?. JBIC J. Biol. Inorg. Chem. 23(7), 983–993. https://doi.org/10.1007/s00775-018-1617-x (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    10.Johnstone, T. C., Nolan, E. M. Beyond iron: Non-classical biological functions of bacterial siderophores. In Dalton Transactions. Royal Society of Chemistry April 14, 2015, pp 6320–6339. https://doi.org/10.1039/c4dt03559c.11.Northover, G. H. R., Garcia-España, E. & Weiss, D. J. Unravelling the modus operandi of phytosiderophores during zinc uptake in rice: The importance of geochemical gradients and accurate stability constants. J. Exp. Bot. https://doi.org/10.1093/jxb/eraa580 (2020).Article 

    Google Scholar 
    12.Ghavami, N., Alikhani, H. A., Pourbabaee, A. A. & Besharati, H. Study the effects of siderophore-producing bacteria on zinc and phosphorous nutrition of canola and maize plants. Commun. Soil Sci. Plant Anal. 47(12), 1517–1527. https://doi.org/10.1080/00103624.2016.1194991 (2016).CAS 
    Article 

    Google Scholar 
    13.Weiss, D. et al. Isotope fractionation of zinc in the paddy rice soil-water environment and the role of 2’deoxymugineic acid (DMA) as zincophore under Zn limiting conditions. Chem. Geol. 577, 120271. https://doi.org/10.1016/j.chemgeo.2021.120271 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    14.Suzuki, M. et al. Biosynthesis and secretion of mugineic acid family phytosiderophores in zinc-deficient barley. Plant J. 48(1), 85–97. https://doi.org/10.1111/j.1365-313X.2006.02853.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Zaman, M. , Shahid, S. A., Heng, L., Shahid, S. A., Zaman, M., Heng, L. Soil salinity: Historical perspectives and a world overview of the problem. In Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques 43–53 (Springer, 2018). https://doi.org/10.1007/978-3-319-96190-3_2.16.Alfarrah, N. & Walraevens, K. Groundwater overexploitation and seawater intrusion in coastal areas of arid and semi-arid regions. Water 10(2), 143. https://doi.org/10.3390/w10020143 (2018).CAS 
    Article 

    Google Scholar 
    17.Trenberth, K. Changes in precipitation with climate change. Clim. Res. 47(1), 123–138. https://doi.org/10.3354/cr00953 (2011).Article 

    Google Scholar 
    18.Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7(1), 1–9. https://doi.org/10.1038/s41598-017-17966-y (2017).CAS 
    Article 

    Google Scholar 
    19.Errabii, T., Gandonou, C. H., Essalmani, H., Jamal; Senhaji, N. S. Effects of NaCl and mannitol induced stress on sugarcane (Saccharum Sp.) Callus Cultures. https://doi.org/10.1007/s11738-006-0006-1.20.Saboora, A., Hajihashemi, S. & Khatam, B. NaCl tolerance of wheat genotypes at germination and early seedling growth article in Pakistan. J. Biol. Sci. https://doi.org/10.3923/pjbs.2006.2009.2021 (2006).Article 

    Google Scholar 
    21.Chand, M., Randhawa, N. S. & Bhumbla, D. R. Effectiveness of zinc chelates in zinc nutrition of greenhouse rice crop in a saline-sodic soil. Plant Soil 59(2), 217–225. https://doi.org/10.1007/BF02184195 (1981).CAS 
    Article 

    Google Scholar 
    22.Lores, E. M. & Pennock, J. R. The effect of salinity on binding of Cd, Cr, Cu and Zn to dissolved organic matter. Chemosphere 37(5), 861–874. https://doi.org/10.1016/S0045-6535(98)00090-3 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    23.Cigala, R. M. et al. Zinc(II) complexes with hydroxocarboxylates and mixed metal species with Tin(II) in different salts aqueous solutions at different ionic strengths: Formation, stability, and weak interactions with supporting electrolytes. Monatshefte fur Chemie 146(4), 527–540. https://doi.org/10.1007/s00706-014-1394-3 (2015).CAS 
    Article 

    Google Scholar 
    24.Laird, D. A., Koskinen, I. W. C. Triazine Soil Interactions. In The Triazine Herbicides 275–299 (Elsevier, 2008). https://doi.org/10.1016/B978-044451167-6.50024-6.25.Cigala, R. M. et al. Speciation of Tin(II) in aqueous solution: Thermodynamic and spectroscopic study of simple and mixed hydroxocarboxylate complexes. Monatshefte fur Chemie 144(6), 761–772. https://doi.org/10.1007/s00706-013-0961-3 (2013).CAS 
    Article 

    Google Scholar 
    26.Daniele, P. G., Rigano, C. & Sammartano, S. Ionic strength dependence of formation constants-I protonation constants of organic and inorganic acids. Talanta 30(2), 81–87. https://doi.org/10.1016/0039-9140(83)80023-X (1983).CAS 
    Article 
    PubMed 

    Google Scholar 
    27.Bretti, C., Foti, C. & Sammartano, S. A new approach in the use of sit in determining the dependence on ionic strength of activity coefficients. Application to Some Chloride Salts Of Interest In The Speciation Of Natural Fluids. Chem. Speciat. Bioavailab. 16(3), 105–110. https://doi.org/10.3184/095422904782775036 (2004).CAS 
    Article 

    Google Scholar 
    28.Bretti, C., De Stefano, C., Foti, C. & Sammartano, S. Critical evaluation of protonation constants. Literature analysis and experimental potentiometric and calorimetric data for the thermodynamics of phthalate protonation in different ionic media. J. Solution Chem. 35(9), 1227–1244. https://doi.org/10.1007/s10953-006-9057-6 (2006).CAS 
    Article 

    Google Scholar 
    29.Cigala, R. M. et al. Quantitative study on the interaction of Sn2+ and Zn2+ with some phosphate ligands, in aqueous solution at different ionic strengths. J. Mol. Liq. 165, 143–153. https://doi.org/10.1016/j.molliq.2011.11.002 (2012).CAS 
    Article 

    Google Scholar 
    30.Northover, G. H. R., Mao, Y., Hanif M. D., Blasco, S., Vilar, R., Garcia-Espana, E. & Weiss, D. J. The control of pH and ionic strength gradients on the interaction of low-molecular-weight organic acids and siderophores. ChemRxiv. Preprint (2021). https://doi.org/10.26434/chemrxiv.14706036.v1.31.Domenico, P. A., Harris, D. R., Schwartz, F. W., Wiley, J., Chichester, N. Y., Brisbane, W. & Singapore, T. Physical and Chemical Hydrogeology 2nd edn.32.Pankow, J.; Taylor & Francis Group. Aquatic Chemistry Concepts 2nd edn.33.Graziano, G. Role of salts on the strength of pairwise hydrophobic interaction. Chem. Phys. Lett. 483(1–3), 67–71. https://doi.org/10.1016/j.cplett.2009.10.040 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Mancera, R. L. Does salt increase the magnitude of the hydrophobic effect? A computer simulation study. Chem. Phys. Lett. 296(5–6), 459–465. https://doi.org/10.1016/S0009-2614(98)01080-X (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Mancera, R. L. Computer simulation of the effect of salt on the hydrophobic effect. J. Chem. Soc. Faraday Trans. 94(24), 3549–3559. https://doi.org/10.1039/a806899b (1998).CAS 
    Article 

    Google Scholar 
    36.Ghosh, T., Kalra, A. & Garde, S. On the salt-induced stabilization of pair and many-body hydrophobic interactions. J. Phys. Chem. B 109(1), 642–651. https://doi.org/10.1021/jp0475638 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    37.Papaneophytou, C. P., Grigoroudis, A. I., McInnes, C. & Kontopidis, G. Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity. ACS Med. Chem. Lett. 5(8), 931–936. https://doi.org/10.1021/ml500204e (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Ghafoor, K., AL-Juhaimi, F., Ozcan, M. M. & Jahurul, M. H. A. Some nutritional characteristics and mineral contents in Barley (Hordeum Vulgare L.) seeds cultivated under salt stress. Qual. Assur. Saf. Crop. Foods 7(3), 363–368. https://doi.org/10.3920/QAS2013.0380 (2015).CAS 
    Article 

    Google Scholar 
    39.Akman, Z. Effects of plant growth regulators on nutrient content of young wheat and barley plants under
    saline conditions. J. Anim. Vet. Adv. 8(10), 2018–2021 (2009).CAS 

    Google Scholar 
    40.Yousfi, S., Houmani, H., Zribi, F., Abdelly, C. & Gharsalli, M. Physiological responses of wild and cultivated barley to the interactive effect of salinity and iron deficiency. (2012). https://doi.org/10.5402/2012/121983.41.Alderighi, L. et al. Hyperquad simulation and speciation (HySS): A utility program for the investigation of equilibria involving soluble and partially soluble species. Coord. Chem. Rev. 184(1), 311–318. https://doi.org/10.1016/S0010-8545(98)00260-4 (1999).CAS 
    Article 

    Google Scholar 
    42.Gans, P. & O’Sullivan, B. GLEE: A new computer program for glass electrode calibration. Talanta 51(1), 33–37. https://doi.org/10.1016/s0039-9140(99)00245-3 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Gans, P., Sabatini, A. & Vacca, A. Investigation of equilibria in solution. Determination of equilibrium constants with the HYPERQUAD suite of programs. Talanta 43(10), 1739–1753. https://doi.org/10.1016/0039-9140(96)01958-3 (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    44.Hu, W., Xie, J., Chau, H. W. & Si, B. C. Evaluation of parameter uncertainties in nonlinear regression using Microsoft excel spreadsheet. Environ. Syst. Res. 4(1), 1–12. https://doi.org/10.1186/s40068-015-0031-4 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    45.Harris, W. R., Raymond, K. N. & Weitl, F. L. Ferric ion sequestering agents. 6. The spectrophotometric and potentiometric evaluation of sulfonated tricatecholate ligands. J. Am. Chem. Soc. 103(10), 2667–2675. https://doi.org/10.1021/ja00400a030 (1981).CAS 
    Article 

    Google Scholar 
    46.Bravin, M. N., Tentscher, P., Rose, J. & Hinsinger, P. Rhizosphere PH Gradient Controls Copper Availability in a Strongly Acidic Soil. Environ. Sci. Technol. 43(15), 5686–5691. https://doi.org/10.1021/es900055k (2009).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    47.Gollany, H. T. & Schumacher, T. E. Combined use of colorimetric and microelectrode methods for evaluating rhizosphere PH. Plant Soil 154(2), 151–159. https://doi.org/10.1007/BF00012520 (1993).CAS 
    Article 

    Google Scholar 
    48.Kirk, G. J. D. Root ventilation, rhizosphere modification, and nutrient uptake by rice. In Systems Approaches for Agricultural Development 221–232 (Springer, Netherlands, 1993). https://doi.org/10.1007/978-94-011-2842-1_13.49.Li, J. & Heap, A. D. Spatial interpolation methods applied in the environmental sciences: A review. In Environmental Modelling and Software 173–189 (Elsevier, 2014). https://doi.org/10.1016/j.envsoft.2013.12.008.50.Gergely, A., Kiss, T. & Deák, G. Complexes of 3,4-dihydroxyphenyl derivatives. II. Complex formation processes in the Nickel(II)-L-DOPA and Zinc(II)-L-DOPA systems. Inorganica Chim. Acta 36(1), 113–120. https://doi.org/10.1016/S0020-1693(00)89379-2 (1979).CAS 
    Article 

    Google Scholar 
    51.Griesser, R. & Sigel, H. Ternary complexes in solution. XI. complex formation between the cobalt(h)-, nickel(ii)-, copper(ii)-, and zinc(II)-2,2′-bipyridyl 1:1 complexes and ethylenediamine, glycinate, or pyrocatecholate. Inorg. Chem. 10(10), 2229–2232. https://doi.org/10.1021/ic50104a028 (1971).CAS 
    Article 

    Google Scholar 
    52.Das, A. K. Studies on mixed ligand complexes of cobalt(II), nickel(II), copper(II) and zinc(II) involving 8-hydroxyquinoline-5-sulphonic acid as a primary ligand and substituted catechols as secondary ligands. Transition Met. Chem. 14, 200–209 (1989).CAS 
    Article 

    Google Scholar 
    53.Das, A. K. Astatistical aspects of the stabilities of ternary complexes of cobalt(II), nickel(II), copper(II) and zinc(II) involving amino-polycarboxylic acids and heteroaromatic N-bases as primary ligands and acetohydroxamic acid as a secondary ligand. Transition Met. Chem. 14, 66–68 (1989).CAS 
    Article 

    Google Scholar 
    54.Cannan, R. K. & Kibrick, A. Complex formation between carboxylic acids and divalent metal cations. J. Am. Chem. Soc. 60(10), 2314–2320. https://doi.org/10.1021/ja01277a012 (1938).CAS 
    Article 

    Google Scholar 
    55.Farkas, E., Brown, D. A., Cittaro, R. & Glass, W. K. Metal complexes of glutamic acid-γ-hydroxamic acid (Glu-γ-Ha) (N-hydroxyglutamine) in aqueous solution. J. Chem. Soc. Dalt. Trans. 18, 2803–2807. https://doi.org/10.1039/DT9930002803 (1993).Article 

    Google Scholar 
    56.Farkas, E., Enyedy, É. A. & Csóka, H. Some factors affecting metal ion-monohydroxamate interactions in aqueous solution. J. Inorg. Biochem. 79(1–4), 205–211. https://doi.org/10.1016/S0162-0134(99)00158-0 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    57.Warnke, Z. Investigation on divalent metal complexes with oxyacids in aqueous solutions. 6. Potentiometric investigation on copper(II), zinc(II), and cadmium(II) complexes with glycolic acd. Rocz. Chem. 43, 1939 (1969).CAS 

    Google Scholar 
    58.Lengyel, T. Investigations on ion exchange equilibria with radioactive tracer method. 15. Liquid ion exchange technique for investigating mixed complex species of zinc with glycolic and alpha-hydroxyisobutyric acid. Acta Chim. Acad. Sci. Hung. 60, 373 (1969).CAS 

    Google Scholar 
    59.Athavale, V. T., Prabhu, L. H. & Vartak, D. G. Solution stability constants of some metal complexes of derivatives of catechol. J. Inorg. Nucl. Chem. 28(5), 1237–1249. https://doi.org/10.1016/0022-1902(66)80450-5 (1966).CAS 
    Article 

    Google Scholar 
    60.Portanova, R., Lajunen, L. H. J., Tolazzi, M. & Piispanen, J. Critical evaluation of stability constants for α-hydroxycarboxylic acid complexes with protons and metal ions and the accompanying enthalpy changes: Part II. Aliphatic 2-hydroxycarboxylic acids (IUPAC technical report). Pure Appl. Chem. 75(4), 495–540. https://doi.org/10.1351/pac200375040495 (2003).CAS 
    Article 

    Google Scholar 
    61.Krężel, A. & Maret, W. The biological inorganic chemistry of zinc ions. Arch. Biochem. Biophys. 611, 3–19. https://doi.org/10.1016/j.abb.2016.04.010 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Al-Sogair, F. M.; Operschall, B. P.; Sigel, A.; Sigel, H.; Schnabl, J.; Sigel, R. K. O. Probing the Metal-Ion-Binding Strength of the Hydroxyl Group. In Chemical Reviews. American Chemical Society August 10, 964–5003 (2011). https://doi.org/10.1021/cr100415s.63.Gries, D., Brunn, S., Crowley, D. E. & Parker, D. R. Phytosiderophore release in relation to micronutrient metal deficiencies in Barley. Plant Soil 172(2), 299–308. https://doi.org/10.1007/BF00011332 (1995).CAS 
    Article 

    Google Scholar 
    64.Welch, R. M. & Shuman, L. Micronutrient nutrition of plants. CRC Crit. Rev. Plant Sci. 14(1), 49–82. https://doi.org/10.1080/07352689509701922 (1995).CAS 
    Article 

    Google Scholar 
    65.Arnold, T. et al. Evidence for the mechanisms of zinc uptake by rice using isotope fractionation. Plant. Cell Environ. 33(3), 370–381. https://doi.org/10.1111/j.1365-3040.2009.02085.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    66.Haas, H. Fungal siderophore metabolism with a focus on Aspergillus fumigatus. Nat. Prod. Rep. 31(10), 1266–1276. https://doi.org/10.1039/c4np00071d (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Griffin, A. S., West, S. A. & Buckling, A. Cooperation and competition in pathogenic bacteria. Nature 430(7003), 1024–1027. https://doi.org/10.1038/nature02744 (2004).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Wu, D. et al. Tissue metabolic responses to salt stress in wild and cultivated barley. PLoS ONE 8(1), e55431. https://doi.org/10.1371/journal.pone.0055431 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Widodo, Patterson, J. H.; Newbigin, E. et al.. Metabolic responses to salt stress of Barley (Hordeum Vulgare L.) cultivars, sahara and clipper, which differ in salinity tolerance. J. Exp. Bot. 60(14), 4089–4103 (2009). https://doi.org/10.1093/jxb/erp243CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Yang, C.-W. et al. Comparative effects of salt-stress and alkali-stress on the growth, photosynthesis, solute accumulation, and ion balance of Barley plants. Phytosynthetica 47, 79–86 (2009).CAS 
    Article 

    Google Scholar  More

  • in

    Highly restricted dispersal in habitat-forming seaweed may impede natural recovery of disturbed populations

    1.Wernberg, T. & Filbee-Dexter, K. Missing the marine forest for the trees. Mar. Ecol. Prog. Ser. 612, 209–215 (2019).ADS 
    Article 

    Google Scholar 
    2.Thompson, R. C., Wilson, B. J., Tobin, M. L., Hill, A. S. & Hawkins, S. J. Biologically generated habitat provision and diversity of rocky shore organisms at a hierarchy of spatial scales. J. Exp. Mar. Biol. Ecol. 202, 73–84 (1996).Article 

    Google Scholar 
    3.Christie, H., Jørgensen, N. M. & Norderhaug, K. M. Bushy or smooth, high or low; importance of habitat architecture and vertical position for distribution of fauna on kelp. J. Sea Res. 58, 198–208 (2007).ADS 
    Article 

    Google Scholar 
    4.Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).Article 

    Google Scholar 
    5.Strain, E. M. A., Thomson, R. J., Micheli, F., Mancuso, F. P. & Airoldi, L. Identifying the interacting roles of stressors in driving the global loss of canopy-forming to mat-forming algae in marine ecosystems. Glob. Change Biol. 20, 3300–3312 (2014).ADS 
    Article 

    Google Scholar 
    6.Mineur, F. et al. European seaweeds under pressure: Consequences for communities and ecosystem functioning. J. Sea Res. 98, 91–108 (2015).ADS 
    Article 

    Google Scholar 
    7.Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. PNAS 113, 13785–13790 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Straub, S. C. et al. Resistance, extinction, and everything in between—The diverse responses of seaweeds to marine heatwaves. Front. Mar. Sci. 6, 763 (2019).Article 

    Google Scholar 
    9.Cheminée, A. et al. Nursery value of Cystoseira forests for Mediterranean rocky reef fishes. J. Exp. Mar. Biol. Ecol. 442, 70–79 (2013).Article 

    Google Scholar 
    10.Piazzi, L. et al. Biodiversity in canopy-forming algae: Structure and spatial variability of the Mediterranean Cystoseira assemblages. Estuar. Coast. Shelf Sci. 207, 132–141 (2018).ADS 
    Article 

    Google Scholar 
    11.Thibaut, T., Pinedo, S., Torras, X. & Ballesteros, E. Long-term decline of the populations of Fucales (Cystoseira spp. and Sargassum spp.) in the Albères coast (France, North-western Mediterranean). Mar. Pollut. Bull. 50, 1472–1489 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Gianni, F. et al. Conservation and restoration of marine forests in the Mediterranean Sea and the potential role of marine protected areas. Adv. Oceanogr. Limnol. 4, 83–101 (2013).Article 

    Google Scholar 
    13.Blanfuné, A., Boudouresque, C. F., Verlaque, M. & Thibaut, T. The fate of Cystoseira crinita, a forest-forming Fucale (Phaeophyceae, Stramenopiles), in France (North Western Mediterranean Sea). Estuar. Coast. Shelf Sci. 181, 196–208 (2016).ADS 
    Article 

    Google Scholar 
    14.Gubbay, S. et al. European Red List of Habitats. Part 1. Marine habitats. Luxembourg: Publications Office of the European Union (2016).15.Perkol-Finkel, S., Ferrario, F., Nicotera, V. & Airoldi, L. Conservation challenges in urban seascapes: Promoting the growth of threatened species on coastal infrastructures. J. Appl. Ecol. 49, 1457–1466 (2012).Article 

    Google Scholar 
    16.Falace, A., Kaleb, S., Fuente, G. D. L., Asnaghi, V. & Chiantore, M. Ex situ cultivation protocol for Cystoseira amentacea var. stricta (Fucales, Phaeophyceae) from a restoration perspective. PLoS ONE 13, e0193011 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Gianni, F., Bartolini, F., Airoldi, L. & Mangialajo, L. Reduction of herbivorous fish pressure can facilitate focal algal species forestation on artificial structures. Mar. Environ. Res. 138, 102–109 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Gianni, F. et al. Optimizing canopy-forming algae conservation and restoration with a new herbivorous fish deterrent device. Restor. Ecol. 28, 750–756 (2020).Article 

    Google Scholar 
    19.Verdura, J., Sales, M., Ballesteros, E., Cefalì, M. E. & Cebrian, E. Restoration of a canopy-forming alga based on recruitment enhancement: Methods and long-term success assessment. Front. Plant Sci. 9, 1832 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Fuente, G. D. L., Chiantore, M., Asnaghi, V., Kaleb, S. & Falace, A. First ex situ outplanting of the habitat-forming seaweed Cystoseira amentacea var. stricta from a restoration perspective. PeerJ 7, e7290 (2019).Article 

    Google Scholar 
    21.Tamburello, L. et al. Are we ready for scaling up restoration actions? An insight from Mediterranean macroalgal canopies. PLoS ONE 14, e0224477 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Medrano, A. et al. From marine deserts to algal beds: Treptacantha elegans revegetation to reverse stable degraded ecosystems inside and outside a No-Take marine reserve. Restor. Ecol. 28, 632–644 (2020).Article 

    Google Scholar 
    23.Chryssovergis, F. & Panayotidis, P. Évolution des peuplements macrophytobenthiques le long d’un gradient d’eutrophisation. Oceanol. Acta 18, 649–658 (1995).
    Google Scholar 
    24.Sales, M., Cebrian, E., Tomas, F. & Ballesteros, E. Pollution impacts and recovery potential in three species of the genus Cystoseira (Fucales, Heterokontophyta). Estuar. Coast. Shelf Sci. 92, 347–357 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    25.Díez, I., Santolaria, A., Secilla, A. & Gorostiaga, J. M. Recovery stages over long-term monitoring of the intertidal vegetation in the ‘Abra de Bilbao’ area and on the adjacent coast (N. Spain). Eur. J. Phycol. 44, 1–14 (2009).Article 

    Google Scholar 
    26.Bringloe, T. T. et al. Phylogeny and evolution of the brown algae. Crit. Rev. Plant Sci. 39, 281–321 (2020).CAS 
    Article 

    Google Scholar 
    27.Guern, M. Embryologie de quelques espèces du genre Cystoseira Agardh 1821 (FUCALES). Vie et Milieu 649–680 (1962).28.Dudgeon, S., Kübler, J. E., Wright, W. A., Vadas, R. L. & Petraitis, P. S. Natural variability in zygote dispersal of Ascophyllum nodosum at small spatial scales. Funct. Ecol. 15, 595–604 (2001).Article 

    Google Scholar 
    29.Mangialajo, L. et al. Zonation patterns and interspecific relationships of fucoids in microtidal environments. J. Exp. Mar. Biol. Ecol. 412, 72–80 (2012).Article 

    Google Scholar 
    30.Capdevila, P. et al. Recruitment patterns in the Mediterranean deep-water alga Cystoseira zosteroides. Mar. Biol. 162, 1165–1174 (2015).CAS 
    Article 

    Google Scholar 
    31.Assis, J. et al. A fine-tuned global distribution dataset of marine forests. Sci. Data 7, 119 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Fabbrizzi, E. et al. Modeling macroalgal forest distribution at Mediterranean scale: Present status, drivers of changes and insights for conservation and management. Front. Mar. Sci. 7, 20 (2020).Article 

    Google Scholar 
    33.Benedetti-Cecchi, L., Tamburello, L., Maggi, E. & Bulleri, F. Experimental perturbations modify the performance of early warning indicators of regime shift. Curr. Biol. 25, 1867–1872 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Bulleri, F., Benedetti-Cecchi, L., Ceccherelli, G. & Tamburello, L. A few is enough: A low cover of a non-native seaweed reduces the resilience of Mediterranean macroalgal stands to disturbances of varying extent. Biolical Invasions 19, 2291–2305 (2017).Article 

    Google Scholar 
    35.Rindi, L., Bello, M. D., Dai, L., Gore, J. & Benedetti-Cecchi, L. Direct observation of increasing recovery length before collapse of a marine benthic ecosystem. Nat. Ecol. Evol. 1, 1–7 (2017).Article 

    Google Scholar 
    36.Draisma, S. G. A., Ballesteros, E., Rousseau, F. & Thibaut, T. DNA sequence data demonstrate the polyphyly of the genus Cystoseira and other Sargassaceae genera (Phaeophyceae). J. Phycol. 46, 1329–1345 (2010).Article 

    Google Scholar 
    37.Bruno de Sousa, C. et al. Improved phylogeny of brown algae Cystoseira (Fucales) from the Atlantic-Mediterranean region based on mitochondrial sequences. PLoS ONE 14, e0210143 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Jódar-Pérez, A. B., Terradas-Fernández, M., López-Moya, F., Asensio-Berbegal, L. & López-Llorca, L. V. Multidisciplinary analysis of Cystoseira sensu lato (SE Spain) suggest a complex colonization of the Mediterranean. J. Mar. Sci. Eng. 8, 961 (2020).Article 

    Google Scholar 
    39.Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. PNAS 101, 8998–9002 (2004).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Reusch, T. B. H. & Hughes, A. R. The emerging role of genetic diversity for ecosystem functioning: Estuarine macrophytes as models. Estuaries and Coasts J ERF 29, 159–164 (2006).Article 

    Google Scholar 
    41.Reusch, T. B. H., Ehlers, A., Hämmerli, A. & Worm, B. Ecosystem recovery after climatic extremes enhanced by genotypic diversity. PNAS 102, 2826–2831 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Ehlers, A., Worm, B. & Reusch, T. B. H. Importance of genetic diversity in eelgrass Zostera marina for its resilience to global warming. Mar. Ecol. Prog. Ser. 355, 1–7 (2008).ADS 
    Article 

    Google Scholar 
    43.Hughes, A. R., Inouye, B. D., Johnson, M. T. J., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).PubMed 
    Article 

    Google Scholar 
    44.Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics (Cambridge University Press, 2002) https://doi.org/10.1017/CBO9780511808999
    .Book 

    Google Scholar 
    45.Cowen, R., Gawarkiewicz, G., Pineda, J., Thorrold, S. & Werner, F. Population connectivity in marine systems: An overview. Oceanography 20, 14–21 (2007).Article 

    Google Scholar 
    46.Mayr, E. Animal Species and Evolution. Animal Species and Evolution (Harvard University Press, 2013).
    Google Scholar 
    47.Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge University Press, 1983) https://doi.org/10.1017/CBO9780511623486
    .Book 

    Google Scholar 
    48.Frankham, R. Conservation genetics. Annu. Rev. Genet. 29, 305–327 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    49.Lacy, R. C. Loss of genetic diversity from managed populations: Interacting effects of drift, mutation, immigration, selection, and population subdivision. Conserv. Biol. 1, 143–158 (1987).Article 

    Google Scholar 
    50.Frankham, R. et al. Genetic Management of Fragmented Animal and Plant Populations (Oxford University Press, 2017).Book 

    Google Scholar 
    51.Planes, S., Jones, G. P. & Thorrold, S. R. Larval dispersal connects fish populations in a network of marine protected areas. PNAS https://doi.org/10.1073/pnas.0808007106 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Pineda, J., Hare, J. A. & Sponaugle, S. Larval transport and dispersal in the coastal ocean and consequences for population connectivity. Oceanography 20, 22–39 (2007).Article 

    Google Scholar 
    53.Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).Article 

    Google Scholar 
    54.Buonomo, R. et al. Predicted extinction of unique genetic diversity in marine forests of Cystoseira spp. Mar. Environ. Res. 138, 119–128 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    55.Buonomo, R. et al. Habitat continuity and stepping-stone oceanographic distances explain population genetic connectivity of the brown alga Cystoseira amentacea. Mol. Ecol. 26, 766–780 (2017).PubMed 
    Article 

    Google Scholar 
    56.Bermejo, R. et al. Marine forests of the Mediterranean-Atlantic Cystoseira tamariscifolia complex show a southern Iberian genetic hotspot and no reproductive isolation in parapatry. Sci. Rep. 8, 10427 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    57.Engelen, A. H. et al. A population genetics toolbox for the threatened canopy-forming brown seaweeds Cystoseira tamariscifolia and C. amentacea (Fucales, Sargassaceae). J. Appl. Phycol. 29, 627–629 (2017).Article 

    Google Scholar 
    58.Thibaut, T. et al. Connectivity of populations of the seaweed Cystoseira amentacea within the Bay of Marseille (Mediterranean Sea): Genetic structure and hydrodynamic connections. crya 37, 233–255 (2016).Article 

    Google Scholar 
    59.Guiry, M.D. & Guiry, G.M. AlgaeBase. World-wide electronic publication (National University of Ireland, 2021) http://www.algaebase.org (Accessed 21 Jan 2021).60.Sales, M. & Ballesteros, E. Shallow Cystoseira (Fucales: Ochrophyta) assemblages thriving in sheltered areas from Menorca (NW Mediterranean): Relationships with environmental factors and anthropogenic pressures. Estuar. Coast. Shelf Sci. 84, 476–482 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    61.Robvieux, P. et al. First characterization of eight polymorphic microsatellites for Cystoseira amentacea var. stricta (Fucales, Sargassaceae). Conserv. Genet. Resour. 4, 923–925 (2012).Article 

    Google Scholar 
    62.Sadogurska, S. S., Neiva, J., Falace, A., Serrão, E. A. & Israel, Á. The genus Cystoseira s.l. (Ochrophyta, Fucales, Sargassaceae) in the Black Sea: Morphological variability and molecular taxonomy of Gongolaria barbata and endemic Ericaria crinita f. bosphorica comb. nov. Phytotaxa 480, 1–21 (2021).Article 

    Google Scholar 
    63.Bologa, A. S. & Sava, D. Progressive decline and present trend of Romanian Black Sea macroalgal flora. Cercetari Mar. 36, 31–60 (2006).
    Google Scholar 
    64.Irving, A. D., Balata, D., Colosio, F., Ferrando, G. A. & Airoldi, L. Light, sediment, temperature, and the early life-history of the habitat-forming alga Cystoseira barbata. Mar. Biol. 156, 1223–1231 (2009).Article 

    Google Scholar 
    65.Allendorf, F. W. Genetics and the conservation of natural populations: Allozymes to genomes. Mol. Ecol. 26, 420–430 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Ellegren, H. Microsatellites: Simple sequences with complex evolution. Nat. Rev. Genet. 5, 435–445 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.De Meeûs, T. et al. Deceptive combined effects of short allele dominance and stuttering: An example with Ixodes scapularis, the main vector of Lyme disease in the USA. bioRxiv https://doi.org/10.1101/622373 (2019).Article 

    Google Scholar 
    68.De Meeûs, T. Revisiting FIS, FST, Wahlund effects, and null alleles. J. Hered. 109, 446–456 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.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).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.Manangwa, O. et al. Detecting Wahlund effects together with amplification problems: Cryptic species, null alleles and short allele dominance in Glossina pallidipes populations from Tanzania. Mol. Ecol. Resour. 19, 757–772 (2019).PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    73.Engel, C. R., Brawley, S. H., Edwards, K. J. & Serrão, E. Isolation and cross-species amplification of microsatellite loci from the fucoid seaweeds Fucus vesiculosus, F. serratus and Ascophyllum nodosum (Heterokontophyta, Fucaceae). Mol. Ecol. Notes 3, 180–182 (2003).CAS 
    Article 

    Google Scholar 
    74.Paulino, C. et al. Characterization of 12 polymorphic microsatellite markers in the sugar kelp Saccharina latissima. J. Appl. Phycol. 28, 3071–3074 (2016).Article 

    Google Scholar 
    75.Coleman, M. A., Dolman, G., Kelaher, B. P. & Steinberg, P. D. Characterisation of microsatellite loci in the subtidal habitat-forming alga, Phyllospora comosa (Phaeophyceae, Fucales). Conserv. Genet. 9, 1015–1017 (2008).CAS 
    Article 

    Google Scholar 
    76.Coleman, M. A. & Brawley, S. H. Are life history characteristics good predictors of genetic diversity and structure? A case study of the intertidal alga Fucus spiralis (heterokontophyta; Phaeophyceae). J. Phycol. 41, 753–762 (2005).Article 

    Google Scholar 
    77.Coleman, M. A. & Brawley, S. H. Spatial and temporal variability in dispersal and population genetic structure of a rockpool alga. Mar. Ecol. Prog. Ser. 300, 63–77 (2005).ADS 
    Article 

    Google Scholar 
    78.Engel, C. R., Daguin, C. & Serrão, E. A. Genetic entities and mating system in hermaphroditic Fucus spiralis and its close dioecious relative F. vesiculosus (Fucaceae, Phaeophyceae). Mol. Ecol. 14, 2033–2046 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Medrano, A. et al. Ecological traits, genetic diversity and regional distribution of the macroalga Treptacantha elegans along the Catalan coast (NW Mediterranean Sea). Sci. Rep. 10, 19219 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    80.Engelen, A. H. et al. Periodicity of propagule expulsion and settlement in the competing native and invasive brown seaweeds, Cystoseira humilis and Sargassum muticum (Phaeophyta). Eur. J. Phycol. 43, 275–282 (2008).Article 

    Google Scholar 
    81.Assis, J., Serrão, E. A., Claro, B., Perrin, C. & Pearson, G. A. Climate-driven range shifts explain the distribution of extant gene pools and predict future loss of unique lineages in a marine brown alga. Mol. Ecol. 23, 2797–2810 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    82.Neiva, J. et al. Genes left behind: Climate change threatens cryptic genetic diversity in the canopy-forming seaweed Bifurcaria bifurcata. PLoS ONE 10, e0131530 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    83.Coleman, M. A. & Kelaher, B. P. Connectivity among fragmented populations of a habitat-forming alga, Phyllospora comosa (Phaeophyceae, Fucales) on an urbanised coast. Mar. Ecol. Prog. Ser. 381, 63–70 (2009).ADS 
    Article 

    Google Scholar 
    84.Boissin, E. et al. Chaotic genetic structure and past demographic expansion of the invasive gastropod Tritia neritea in its native range, the Mediterranean Sea. Sci. Rep. 10, 21624 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Olsen, J. L. et al. North Atlantic phylogeography and large-scale population differentiation of the seagrass Zostera marina L. Mol. Ecol. 13, 1923–1941 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    86.Peijnenburg, K. T. C. A., Breeuwer, J. A. J., Pierrot-Bults, A. C. & Menken, S. B. J. Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European Seas. Evolution 58, 1472–1487 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    87.Luttikhuizen, P. C., Campos, J., van Bleijswijk, J., Peijnenburg, K. T. C. A. & van der Veer, H. W. Phylogeography of the common shrimp, Crangon crangon (L.) across its distribution range. Mol. Phylogenet. Evol. 46, 1015–1030 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.Wilson, A. B. & Eigenmann Veraguth, I. The impact of Pleistocene glaciation across the range of a widespread European coastal species. Mol. Ecol. 19, 4535–4553 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    89.Riquet, F. et al. Parallel pattern of differentiation at a genomic island shared between clinal and mosaic hybrid zones in a complex of cryptic seahorse lineages. Evolution 73, 817–835 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    90.Hewitt, G. M. Hybrid zones-natural laboratories for evolutionary studies. Trends Ecol. Evol. 3, 158–167 (1988).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Johannesson, K., Le Moan, A., Perini, S. & André, C. A Darwinian laboratory of multiple contact zones. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2020.07.015 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.McCoy, S. J., Krueger-Hadfield, S. A. & Mieszkowska, N. Evolutionary phycology: Toward a macroalgal species conceptual framework. J. Phycol. 56, 1404–1413 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    93.Neiva, J., Pearson, G. A., Valero, M. & Serrão, E. A. Fine-scale genetic breaks driven by historical range dynamics and ongoing density-barrier effects in the estuarine seaweed Fucus ceranoides L. BMC Evol. Biol. 12, 78 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    94.Whitlock, M. C. & McCauley, D. E. Indirect measures of gene flow and migration: FST ≠1/(4 Nm + 1). Heredity 82, 117–125 (1999).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    95.Lowe, W. H. & Allendorf, F. W. What can genetics tell us about population connectivity?. Mol. Ecol. 19, 3038–3051 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    96.Durrant, H. M. S. et al. Implications of macroalgal isolation by distance for networks of marine protected areas. Conserv. Biol. 28, 438–445 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    97.Engelen, A., Olsen, J., Breeman, A. & Stam, W. Genetic differentiation in Sargassum polyceratium (Fucales: Phaeophyceae) around the island of Curaçao (Netherlands Antilles). Mar. Biol. 139, 267–277 (2001).CAS 
    Article 

    Google Scholar 
    98.Billot, C., Engel, C. R., Rousvoal, S., Kloareg, B. & Valero, M. Current patterns, habitat discontinuities and population genetic structure: The case of the kelp Laminaria digitata in the English Channel. Mar. Ecol. Prog. Ser. 253, 111–121 (2003).ADS 
    Article 

    Google Scholar 
    99.Tatarenkov, A., Jönsson, R. B., Kautsky, L. & Johannesson, K. Genetic structure in populations of Fucus vesiculosus (phaeophyceae) over spatial scales from 10 m to 800 km. J. Phycol. 43, 675–685 (2007).CAS 
    Article 

    Google Scholar 
    100.Susini, M.-L., Thibaut, T., Meinesz, A. & Forcioli, D. A preliminary study of genetic diversity in Cystoseira amentacea (C. Agardh) Bory var. stricta Montagne (Fucales, Phaeophyceae) using random amplified polymorphic DNA. Phycologia 46, 605–611 (2007).Article 

    Google Scholar 
    101.Korotenko, K., Bowman, M. & Dietrich, D. High-resolution numerical model for predicting the transport and dispersal of oil spilled in the Black Sea. Terrest. Atmos. Oceanic Sci. J. 21, 123–136 (2010).Article 

    Google Scholar 
    102.Barale, V., Schiller, C., Tacchi, R. & Marechal, C. Trends and interactions of physical and bio-geo-chemical features in the Adriatic Sea as derived from satellite observations. Sci. Total Environ. 353, 68–81 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    103.Hauser, L. & Carvalho, G. R. Paradigm shifts in marine fisheries genetics: Ugly hypotheses slain by beautiful facts. Fish Fish. 9, 333–362 (2008).Article 

    Google Scholar 
    104.Orellana, S., Hernández, M. & Sansón, M. Diversity of Cystoseira sensu lato (Fucales, Phaeophyceae) in the eastern Atlantic and Mediterranean based on morphological and DNA evidence, including Carpodesmia gen. emend. and Treptacantha gen. emend. Eur. J. Phycol. 54, 447–465 (2019).CAS 
    Article 

    Google Scholar 
    105.Richard, B., A. & Wilks, A., R. Maps in S. AT&T Bell Laboratories Statistics Research Report [93.2] (1993).106.Richard, B., A. & Wilks, A., R. Constructing a Geographical Database. AT&T Bell Lab-oratories Statistics Research Report [95.2] (1995).107.R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2017).108.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).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    109.Peakall, R. & Smouse, P. E. genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

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

    Google Scholar 
    111.De Meeûs, T., Guégan, J.-F. & Teriokhin, A. T. MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods on proportional data. BMC Bioinform. 10, 443 (2009).Article 
    CAS 

    Google Scholar 
    112.Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    113.Oosterhout, C. V., Weetman, D. & Hutchinson, W. F. Estimation and adjustment of microsatellite null alleles in nonequilibrium populations. Mol. Ecol. Notes 6, 255–256 (2006).Article 

    Google Scholar 
    114.Petit, R. J., Mousadik, A. E. & Pons, O. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 12, 844–855 (1998).Article 

    Google Scholar 
    115.El Mousadik, A. & Petit, R. J. High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor. Appl. Genet. 92, 832–839 (1996).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    116.Raymond, M. & Rousset, F. GENEPOP (Version 1.2): Population genetics Software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).Article 

    Google Scholar 
    117.Szulkin, M., Bierne, N. & David, P. Heterozygosity-fitness correlations: A time for reappraisal. Evolution 64, 1202–1217 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    118.David, P., Pujol, B., Viard, F., Castella, V. & Goudet, J. Reliable selfing rate estimates from imperfect population genetic data. Mol. Ecol. 16, 2474–2487 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    119.Wright, S. The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19, 395–420 (1965).Article 

    Google Scholar 
    120.Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    122.Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    124.Jakobsson, M. & Rosenberg, N. A. Clumpp: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    126.Séré, M., Thévenon, S., Belem, A. M. G. & De Meeûs, T. Comparison of different genetic distances to test isolation by distance between populations. Heredity 119, 55–63 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    127.Rousset, F. & Raymond, M. Statistical analyses of population genetic data: New tools, old concepts. Trends Ecol. Evol. 12, 313–317 (1997).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    128.Hijmans, R. J. Geosphere: Spherical Trigonometry. https://CRAN.R-project.org/package=geosphere. R package version 1.5–5. (2016).129.Korotenko, K. A. Effects of mesoscale eddies on behavior of an oil spill resulting from an accidental deepwater blowout in the Black Sea: An assessment of the environmental impacts. PeerJ 6, e5448 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    130.López-Márquez, V. et al. Seascape genetics and connectivity modelling for an endangered Mediterranean coral in the northern Ionian and Adriatic seas. Landsc. Ecol. 34, 2649–2668 (2019).Article 

    Google Scholar 
    131.Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    132.Watts, P. C. et al. Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: Analysis of ‘neighbourhood size’ using a more precise estimator. Mol. Ecol. 16, 737–751 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    133.Hill, W. G. Estimation of effective population size from data on linkage disequilibrium. Genet. Res. 38, 209–216 (1981).Article 

    Google Scholar 
    134.Waples, R. S. Seed banks, salmon, and sleeping genes: Effective population size in semelparous, age-structured species with fluctuating abundance. Am. Nat. 167, 118–135 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    135.Waples, R. S. & Do, C. ldne: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756 (2008).PubMed 
    Article 

    Google Scholar 
    136.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).CAS 
    PubMed 
    Article 
    PubMed Central 

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

    Google Scholar  More

  • in

    Grazing intensity drives plant diversity but does not affect forage production in a natural grassland dominated by the tussock-forming grass Andropogon lateralis Nees

    1.IBGE. Instituto Brasileiro de Geografia e Estatística – Censo Agro 2017. IBGE | Censo Agro 2017, Dados preliminares https://censos.ibge.gov.br/agro/2017/ (2017).2.Boldrini, I. I. et al. Flora. In Biodiversidade dos Campos do Planalto das Araucárias 39–94 (2009).3.Iganci, J. R. V., Heiden, G., Miotto, S. T. S. & Pennington, R. T. Campos de Cima da Serra: The Brazilian subtropical highland Grasslands show an unexpected level of plant endemism. Bot. J. Linn. Soc. 167, 378–393 (2011).Article 

    Google Scholar 
    4.Borer, E. T. et al. Herbivores and nutrients control grassland plant diversity via light limitation. Nature 508, 517–520 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    5.Alhamad, M. N. & Alrababah, M. A. Defoliation and competition effects in a productivity gradient for a semiarid Mediterranean annual grassland community. Basic Appl. Ecol. 9, 224–232 (2008).Article 

    Google Scholar 
    6.Fedrigo, J. K. et al. Temporary grazing exclusion promotes rapid recovery of species richness and productivity in a long-term overgrazed Campos grassland. Restor. Ecol. https://doi.org/10.1111/rec.12635 (2017).Article 

    Google Scholar 
    7.Mavromihalis, J. A., Dorrough, J., Clark, S. G., Turner, V. & Moxham, C. Manipulating livestock grazing to enhance native plant diversity and cover in native grasslands. Rangel. J. 35, 95–108 (2013).Article 

    Google Scholar 
    8.Bircham, J. S. & Hodgson, J. The influence of sward condition on rates of herbage growth and senescence in mixed swards under continuous stocking management. Grass Forage Sci. 38, 323–331 (1983). Article 

    Google Scholar 
    9.Sbrissia, A. F. et al. Defoliation strategies in pastures submitted to intermittent stocking method: Underlying mechanisms buffering forage accumulation over a range of grazing heights. Crop Sci. 58, 945–954 (2018).Article 

    Google Scholar 
    10.Jaurena, M. et al. Native grasslands at the core: A new paradigm of intensification for the Campos of Southern South America to increase economic and environmental sustainability. Front. Sustain. Food Syst. 5, 11 (2021).Article 

    Google Scholar 
    11.Cruz, P. et al. Leaf traits as functional descriptors of the intensity of continuous grazing in native grasslands in the South of Brazil. Rangel. Ecol. Manag. 63, 350–358 (2010).Article 

    Google Scholar 
    12.Benitez, C. A. & Fernandez, J. G. Espécies forrageiras de la pradera natural: Fenologia y respuesta a la frequência e severidad de corte (1970).13.Herve, A. M. B. & Valls, J. F. M. Genêro Andropogon L. (Gramineae) no Rio Grande do Sul. Anuario tecnico do Instituto de Pesquisas Zootecnicas Francisco Osorio (1980).14.Zanin, A. & Longhi-Wagner, H. M. Revisão de Andropogon (Poaceae – Andropogoneae) para o Brasil. Rodriguesia 62, 171–202 (2011).Article 

    Google Scholar 
    15.Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: Herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165 (1998).Article 

    Google Scholar 
    16.Fraser, L. H. et al. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science 350, 1177b (2015).ADS 
    Article 

    Google Scholar 
    17.Connell, J. H. Diversity in tropical rain forests and coral reefs: High diversity of trees and corals is maintained only in a nonequilibrium state. Science 199, 1302–1310 (1978).ADS 
    CAS 
    Article 

    Google Scholar 
    18.Milchunas, D. G., Sala, O. E. & Lauenroth, W. K. A generalized model of the effects of grazing by large herbivores on grassland community structure. Am. Nat. 132, 87–106 (1988).Article 

    Google Scholar 
    19.Liu, J. et al. Impacts of grazing by different large herbivores in grassland depend on plant species diversity. J. Appl. Ecol. 52(4), 1053–1062 (2015).Article 

    Google Scholar 
    20.Ren, H., Schönbach, P., Wan, H., Gierus, M. & Taube, F. Effects of grazing intensity and environmental factors on species composition and diversity in typical Steppe of Inner Mongolia, China. PLoS ONE 7(12), e52180 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Sbrissia, A. F., Silva, S. C., Schmitt, D. & Duchini, P. G. Unravelling the relationship between a seasonal environment and the dynamics of forage growth in grazed swards. J. Agron. Crop Sci. 206, 630–639 (2020).Article 

    Google Scholar 
    22.Hernández-Lambraño, R. E., González-Moreno, P. & Sánchez-Agudo, J. Á. Towards the top: Niche expansion of Taraxacum officinale and Ulex europaeus in mountain regions of South America. Austral. Ecol. 42, 577–589 (2017).Article 

    Google Scholar 
    23.Pinto, L. F. M. et al. Dinâmica do acúmulo de matéria seca em pastagens de Tifton 85 sob pastejo. Sci. Agric. 58, 439–447 (2001).Article 

    Google Scholar 
    24.Duchini, P. G., Guzatti, G. C., Ribeiro Filho, H. M. N. & Sbrissia, A. F. Tiller size/density compensation in temperate climate grasses grown in monoculture or in intercropping systems under intermittent grazing. Grass Forage Sci. 69, 655–665 (2014).CAS 
    Article 

    Google Scholar 
    25.Briske, D. D. & Anderson, V. J. Competitive ability of the bunchgrass Schizachyrium scoparium as affected by grazing history and defoliation. Vegetatio 103, 41–49 (1992).
    Google Scholar 
    26.Altesor, A., Oesterheld, M., Leoni, E., Lezama, F. & Rodriguez, C. Effect of grazing on community structure and productivity of a Uruguayan grassland. Plant Ecol. 179, 83–91 (2005).Article 

    Google Scholar 
    27.Lezama, F. et al. Variation of grazing-induced vegetation changes across a large-scale productivity gradient. J. Veg. Sci. 25, 8–21 (2014).Article 

    Google Scholar 
    28.Lattanzi, F. A. et al. 13C-labeling shows the effect of hierarchy on the carbon gain of individuals and functional groups in dense field stands. Ecology 93, 169–179 (2012).Article 

    Google Scholar 
    29.Roscher, C. et al. Functional composition has stronger impact than species richness on carbon gain and allocation in experimental grasslands. PLoS ONE 14(1), e0204715 (2019).CAS 
    Article 

    Google Scholar 
    30.Wan, C. & Sosebee, R. E. Central dieback of the dryland bunchgrass Eragrostis curvula (weeping lovegrass) re-examined: The experimental clearance of tussock centres. J. Arid Environ. 46, 69–78 (2000).ADS 
    Article 

    Google Scholar 
    31.Angassa, A. Effects of grazing intensity and bush encroachment on herbaceous species and rangeland condition in Southern Ethiopia. L. Degrad. Dev. 25, 438–451 (2014).Article 

    Google Scholar 
    32.Schultz, N. L., Morgan, J. W. & Lunt, I. D. Effects of grazing exclusion on plant species richness and phytomass accumulation vary across a regional productivity gradient. J. Veg. Sci. 22, 130–142 (2011).Article 

    Google Scholar 
    33.Chaneton, E. J. & Facelli, J. M. Disturbance effects on plant community diversity: Spatial scales and dominance hierarchies. Vegetatio 93, 143–155 (1991).Article 

    Google Scholar 
    34.Tow, P. G. & Lazenby, A. Competition and Succession in Pastures (CAB International, 2001). doi:https://doi.org/10.1079/9780851994413.0000.35.Briske, D. D. & Hendrickson, J. R. Does selective defoliation mediate competitive interactions in a semiarid savannah? A demographic evaluation. J. Veg. Sci. 9, 611–622 (1998).Article 

    Google Scholar 
    36.Baer, S. G., Blair, J. M. & Collins, S. L. Environmental heterogeneity has a weak effect on diversity during community assembly in tallgrass prairie. Ecol. Monogr. 86, 94–106 (2016).Article 

    Google Scholar 
    37.Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. M. & Sparovek, G. Köppen’s climate classification map for Brazil. Meteorol. Zeitschrift 22, 711–728 (2013).ADS 
    Article 

    Google Scholar 
    38.Pallarés, O. R., Berretta, E. J. & Maraschin, G. The South American Campos ecosystem BT—Grasslands of the World. Grasslands of the World 1–49 (2005). 39.Allen, V. G. et al. An international terminology for grazing lands and grazing animals. Grass Forage Sci. 66, 2–28 (2011).Article 

    Google Scholar 
    40.Zanini, G. D., Santos, G. T., Schmitt, D. & Padilha, D. A. Distribuição de colmo na estrutura vertical de pastos de capim Aruana e azevém anual submetidos a pastejo intermitente por ovinos. Ciênc. Rural 42, 882–887 (2012).Article 

    Google Scholar 
    41.Carvalho, P. C. F. Harry Stobbs Memorial Lecture: Can grazing behaviour support innovations in grassland management?. Trop. Grassl. Forrajes Trop. 1, 137–155 (2013).Article 

    Google Scholar 
    42.Barthram, G. T. Experimental techniques: The HFRO sward stick. In The Hill Farming Research Organization Biennial Report 1984/1985 29–30 (HFRO, 1985).43.Haydock, K. P. & Shaw, N. H. The comparative yield method for estimating dry matter yield of pasture. Aust. J. Exp. Agric. 15, 663–670 (1975).
    Google Scholar 
    44.Williams, R. J. Gap dynamics in subalpine heathland and grassland vegetation in south-eastern Australia. J. Ecol. 80, 343–352 (1992).Article 

    Google Scholar 
    45.Derner, J. D., Briske, D. D. & Polley, H. W. Tiller organization within the tussock grass Schizachyrium scoparium: A field assessment of competition–cooperation tradeoffs. Botany 90, 669–677 (2012).Article 

    Google Scholar 
    46.Mueller-Dombois, D. & Ellenberg, D. Aims and methods of vegetation ecology. In Community Sampling: The Relevé Method 45–66 (1974).47.Tothill, J. C., Hargreaves, J. N. G., Jones, R. M. & McDonald, C. K. Botanal—A comprehensive sampling and computing procedure for estimating pasture yield and composition. 1. Field sampling. Trop. Agron. Tech. Mem. 78, 1–24 (1992).
    Google Scholar 
    48.’t Mannetje, L. Measuring biomass of grassland vegetation. In Field and Laboratory Methods for Grassland and Animal Production Research 151–177 (CABI, 2000). doi:https://doi.org/10.1079/9780851993515.0151.49.Oksanen, F.J., Blanchet, G., Friendly, M., Kindt, R., Legendre, P. et al. vegan: Community Ecology Package. R package version 2.5-7. (2020). https://CRAN.R-project.org/package=vegan.50.Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre (ICRAF), Nairobi. ISBN: 92-9059-179-X (2005).51.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2021). https://www.R-project.org/.52.Watkins, A. J. & Wilson, J. B. Plant community structure, and its relation to the vertical complexity of communities: dominance/diversity and spatial rank consistency. Oikos 70, 91–98 (1994).Article 

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

    Google Scholar 
    54.Sbrissia, A. F., Zanella, P. G., Pinto, C. E., Baldissera, T. C. & Garagorry, F. C. Natural grasslands experiment – 2015 – 2017 – Pablo. figshare. https://doi.org/10.6084/m9.figshare.14055419.v1 (2021). More

  • in

    Opportunities and challenges of macrogenetic studies

    1.Brown, J. H. & Maurer, B. A. Macroecology: the division of food and space among species on continents. Science 243, 1145–1150 (1989).CAS 
    Article 

    Google Scholar 
    2.Gaston, K. J., Robinson, D. & Chown, S. L. Macrophysiology: large-scale patterns in physiological traits and their ecological implications. Funct. Ecol. 18, 159–167 (2004).Article 

    Google Scholar 
    3.Chown, S. L. & Gaston, K. J. Macrophysiology–progress and prospects. Funct. Ecol. 30, 330–344 (2016).Article 

    Google Scholar 
    4.Avise, J. C. Phylogeography: the History and Formation of Species (Harvard University Press, 2000).5.Ebach, M. C. Origins of Biogeography. Vol. 13 (Springer, 2015).6.Brundin, L. On the real nature of transantarctic relationships. Evolution 19, 496–505 (1965).
    Google Scholar 
    7.Beheregaray, L. B. Twenty years of phylogeography: the state of the field and the challenges for the Southern Hemisphere. Mol. Ecol. 17, 3754–3774 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    8.Hickerson, M. J. et al. Phylogeography’s past, present, and future: 10 years after Avise, 2000. Mol. Phylogenet. Evol. 54, 291–301 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Gaston, K. J. & Blackburn, T. M. A critique for macroecology. Oikos 84, 353–368 (1999).Article 

    Google Scholar 
    10.Lovegrove, B. G. The zoogeography of mammalian basal metabolic rate. Am. Nat. 156, 201–219 (2000).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: Global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Chown, S. L. & Gaston, K. J. Macrophysiology for a changing world. Proc. Biol. Sci. 275, 1469–1478 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    13.Kerr, J. T., Kharouba, H. M. & Currie, D. J. The macroecological contribution to global change solutions. Science 316, 1581–1584 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Blanchet, S., Prunier, J. G. & De Kort, H. Time to go bigger: Emerging patterns in macrogenetics. Trends Genet. 33, 579–580 (2017). This study coined the term ‘macrogenetics’ and illustrated, through three study examples, how shifting toward macrogenetics should generate new perspectives and theories concerning genetic diversity patterns.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Blanchet, S. et al. A river runs through it: the causes, consequences, and management of intraspecific diversity in river networks. Evol. Appl. 13, 1195–1213 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Frankham, R. Resolving conceptual issues in conservation genetics: the roles of laboratory species and meta-analyses. Hereditas 130, 195–201 (2004).Article 

    Google Scholar 
    17.Arnqvist, G. & Wooster, D. Meta-analysis: synthesizing research findings in ecology and evolution. Trends Ecol. Evol. 10, 236–240 (1995).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Paz-Vinas, I. et al. Systematic conservation planning for intraspecific genetic diversity. Proc. Biol. Sci. 285, 20172746 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    19.Pelletier, T. A. & Carstens, B. C. Geographical range size and latitude predict population genetic structure in a global survey. Biol. Lett. 14, 20170566 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Miraldo, A. et al. An anthropocene map of genetic diversity. Science 353, 1532–1535 (2016). This paper is thought to be the first published study to massively repurpose public mtDNA sequences to explore global genetic patterns (100,791 sequences from >4,500 terrestrial mammal and amphibian species).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Yiming, L. et al. Latitudinal gradients in genetic diversity and natural selection at a highly adaptive gene in terrestrial mammals. Ecography 44, 206–218 (2021). This study found that adaptive IGV is higher at low latitudes and in smaller mammal species using repurposed MHC gene data from 93 mammal species.Article 

    Google Scholar 
    22.Manel, S. et al. Global determinants of freshwater and marine fish genetic diversity. Nat. Commun. 11, 692 (2020). This study repurposed 58,565 public mtDNA sequences from 5,912 freshwater and marine fish to explore the effects of environmental drivers (temperature, species diversity) on intraspecific genetic diversity.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Theodoridis, S. et al. Evolutionary history and past climate change shape the distribution of genetic diversity in terrestrial mammals. Nat. Commun. 11, 2557 (2020). This study revealed a negative effect of past rapid climate change and a positive effect of interannual precipitation variability in shaping the genetic diversity of terrestrial mammals using 46,965 mtDNA sequences.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Barrow, L. N., da Fonseca, E. M., Thompson, C. E. P. & Carstens, B. C. Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13303 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.De Kort, H. et al. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 12, 516 (2021). This study found weak support for latitudinal IGV gradients, taxonomic-specific effects of temperature stability and life-history traits, and higher IGV in animals compared to plants using microsatellite and amplified fragment length polymorphism data from 8,386 local populations from 727 animal and plant species.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    26.Schmidt, C., Domaratzki, M., Kinnunen, R. P., Bowman, J. & Garroway, C. J. Continent-wide effects of urbanization on bird and mammal genetic diversity. Proc. Biol. Sci. 287, 20192497 (2020). This study used archived microsatellite data from 85 studies (66 species) to explore the effects of urbanization in mammals and birds.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Millette, K. L. et al. No consistent effects of humans on animal genetic diversity worldwide. Ecol. Lett. 23, 55–67 (2020). The authors of this article conducted spatial and temporal analysis of the effects of humans on animal genetic diversity worldwide, by repurposing 175,247 mtDNA sequences from >17,000 animal species.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Taberlet, P. et al. Genetic diversity in widespread species is not congruent with species richness in alpine plant communities. Ecol. Lett. 15, 1439–1448 (2012). This paper reports a Class I macrogenetic study based on amplified fragment length polymorphism genetic data from 27 alpine plant species that tested whether genetic and species diversities co-vary.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Manel, S. et al. Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation. Mol. Ecol. 21, 3729–3738 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Gugerli, F. et al. Relationships among levels of biodiversity and the relevance of intraspecific diversity in conservation – a project synopsis. Perspect. Plant. Ecol. Evol. Syst. 10, 259–281 (2008).Article 

    Google Scholar 
    31.Schlaepfer, D. R., Braschler, B., Rusterholz, H.-P. & Baur, B. Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta-analysis. Ecosphere 9, e02488 (2018).Article 

    Google Scholar 
    32.González, A. V., Gómez-Silva, V., Ramírez, M. J. & Fontúrbel, F. E. Meta-analysis of the differential effects of habitat fragmentation and degradation on plant genetic diversity. Conserv. Biol. 34, 711–720 (2020).PubMed 
    Article 

    Google Scholar 
    33.Ratnasingham, S. & Hebert, P. D. N. Bold: the barcode of life data system. Mol. Ecol. Notes 7, 355–364 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

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

    Google Scholar 
    36.Theodoridis, S., Rahbek, C. & Nogues-Bravo, D. Exposure of mammal genetic diversity to mid-21st century global change. Ecography 44, 817–831 (2021).Article 

    Google Scholar 
    37.Rissler, L. J. Union of phylogeography and landscape genetics. Proc. Natl Acad. Sci. USA 113, 8079–8086 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Hubbell, S. P. The unified neutral theory of biodiversity and biogeography (Princeton University Press, 2001).39.Haldane, J. B. S. A mathematical theory of natural and artificial selection, Part V: selection and mutation. Math. Proc. Camb. Philos. Soc. 23, 838–844 (1927).Article 

    Google Scholar 
    40.Wright, S. Evolution in Mendelian populations. Genetics 16, 97–159 (1931).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Fisher, R. A. On the dominance ratio. Proc. R. Soc. Edinburgh 42, 321–341 (1922).Article 

    Google Scholar 
    42.Kimura, M. & Weiss, G. H. The stepping stone model of population structure and the decrease of genetic correlation with distance. Genetics 49, 561–576 (1964).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Kingman, J. F. C. The coalescent. Stoch. Process. Their Appl. 13, 235–248 (1982).Article 

    Google Scholar 
    44.Kimura, M. Evolutionary rate at the molecular level. Nature 217, 624–626 (1968).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Soulé, M. E. in Molecular Evolution (ed. Ayala, F. J.) 60–77 (Sinauer Associates, 1976).46.Brown, A. H. Isozymes, plant population genetic structure and genetic conservation. Tag. Theor. Appl. Genet. Theor. Angew. Genet. 52, 145–157 (1978).CAS 
    Article 

    Google Scholar 
    47.Mullis, K. et al. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harb. Symp. Quant. Biol. 51, 263–273 (1986).CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5467 (1977).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Miller, M. R., Dunham, J. P., Amores, A., Cresko, W. A. & Johnson, E. A. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res. 17, 240–248 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Carroll, E. L. et al. Genetic and genomic monitoring with minimally invasive sampling methods. Evol. Appl. 11, 1094–1119 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. Biol. Sci. 270, 313–321 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Gauthier, J. et al. Museomics identifies genetic erosion in two butterfly species across the 20th century in Finland. Mol. Ecol. Resour. 20, 1191–1205 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Wandeler, P., Hoeck, P. E. A. & Keller, L. F. Back to the future: museum specimens in population genetics. Trends Ecol. Evol. 22, 634–642 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Strasser, B. J. The experimenter’s museum: GenBank, natural history, and the moral economies of biomedicine. Isis 102, 60–96 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Whitlock, M. C. Data archiving in ecology and evolution: best practices. Trends Ecol. Evol. 26, 61–65 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Deck, J. et al. The Genomic Observatories Metadatabase (GeOMe): A new repository for field and sampling event metadata associated with genetic samples. PLoS Biol. 15, e2002925 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    59.R Core Team. R: a language and environment for statistical computing, R Foundation for Statistical Computing http://www.r-project.org/index.html (2021).60.Manel, S. & Holderegger, R. Ten years of landscape genetics. Trends Ecol. Evol. 28, 614–621 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Prunier, J. G., Colyn, M., Legendre, X., Nimon, K. F. & Flamand, M. C. Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses. Mol. Ecol. 24, 263–283 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Stanley, R. R. E. et al. A climate-associated multispecies cryptic cline in the northwest Atlantic. Sci. Adv. 4, eaaq0929 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Fenderson, L. E., Kovach, A. I. & Llamas, B. Spatiotemporal landscape genetics: investigating ecology and evolution through space and time. Mol. Ecol. 29, 218–246 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Daza, J. M., Castoe, T. A. & Parkinson, C. L. Using regional comparative phylogeographic data from snake lineages to infer historical processes in middle America. Ecography 33, 343–354 (2010).
    Google Scholar 
    65.Riddle, B. R. Comparative phylogeography clarifies the complexity and problems of continental distribution that drove A. R. Wallace to favor islands. Proc. Natl Acad. Sci. USA 113, 7970–7977 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Carstens, B. C., Morales, A. E., Field, K. & Pelletier, T. A. A global analysis of bats using automated comparative phylogeography uncovers a surprising impact of Pleistocene glaciation. J. Biogeogr. 45, 1795–1805 (2018).Article 

    Google Scholar 
    67.Smith, B. T., Seeholzer, G. F., Harvey, M. G., Cuervo, A. M. & Brumfield, R. T. A latitudinal phylogeographic diversity gradient in birds. PLoS Biol. 15, e2001073 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    68.Smith, B. T. et al. The drivers of tropical speciation. Nature 515, 406–409 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Ballin, M., Barcaroli, G., Masselli, M. & Scarnó, M. Redesign Sample for Land Use/Cover Area Frame Survey (LUCAS) 2018 (EU Publications, 2018).70.Buchhorn, M. et al. Copernicus global land cover layers — Collection 2. Remote. Sens. 12, 1044 (2020).Article 

    Google Scholar 
    71.Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals: Ecological Archives E090-184. Ecology 90, 2648–2648 (2009).Article 

    Google Scholar 
    72.Tedesco, P. A. et al. A global database on freshwater fish species occurrence in drainage basins. Sci. Data 4, 170141 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Vellend, M. & Geber, M. A. Connections between species diversity and genetic diversity: species diversity and genetic diversity. Ecol. Lett. 8, 767–781 (2005).Article 

    Google Scholar 
    74.Fourtune, L., Paz-Vinas, I., Loot, G., Prunier, J. G. & Blanchet, S. Lessons from the fish: a multi-species analysis reveals common processes underlying similar species-genetic diversity correlations. Freshw. Biol. 61, 1830–1845 (2016).Article 

    Google Scholar 
    75.Bertin, A. et al. Genetic variation of loci potentially under selection confounds species-genetic diversity correlations in a fragmented habitat. Mol. Ecol. 26, 431–443 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Lawrence, E. R. & Fraser, D. J. Latitudinal biodiversity gradients at three levels: linking species richness, population richness and genetic diversity. Glob. Ecol. Biogeogr. 29, 770–788 (2020).Article 

    Google Scholar 
    77.Schmidt, C., Dray, S. & Garroway, C. J. Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity. bioRxiv https://doi.org/10.1101/2020.06.03.132092 (2021).Article 
    PubMed 
    PubMed Central 

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

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

    Google Scholar 
    80.Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Schmidt, C. & Garroway, C. J. The conservation utility of mitochondrial genetic diversity in macrogenetic research. Conserv. Genet. 22, 323–327 (2021).Article 

    Google Scholar 
    82.Gratton, P. et al. Which latitudinal gradients for genetic diversity? Trends Ecol. Evol. 32, 724–726 (2017). This response to Miraldo et al.20 identified a limitation of that article in that it did not account for the decay of genetic similarity with distance and represents the first critique of the downsides of the macrogenetic approach and the need for rigorous statistics.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Loveless, M. D. & Hamrick, J. L. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Syst. 15, 65–95 (1984).Article 

    Google Scholar 
    84.Hu, Y. et al. Spatial patterns and conservation of genetic and phylogenetic diversity of wildlife in China. Sci. Adv. 7, eabd5725 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    85.Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    86.Aguilar, R., Quesada, M., Ashworth, L., Herrerias-Diego, Y. & Lobo, J. Genetic consequences of habitat fragmentation in plant populations: susceptible signals in plant traits and methodological approaches. Mol. Ecol. 17, 5177–5188 (2008).PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    88.Leigh, D. M., Hendry, A. P., Vázquez-Domínguez, E. & Friesen, V. L. Estimated six per cent loss of genetic variation in wild populations since the industrial revolution. Evol. Appl. 12, 1505–1512 (2019). This study estimated the magnitude of the loss of genetic variation over a century-scale using microsatellite data from 91 species.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Schmidt, C. & Garroway, C. J. The population genetics of urban and rural amphibians in north America. Mol. Ecol. https://doi.org/10.1111/mec.16005 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Bazin, E., Glémin, S. & Galtier, N. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570–572 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Galtier, N., Nabholz, B., Glémin, S. & Hurst, G. D. D. Mitochondrial DNA as a marker of molecular diversity: a reappraisal. Mol. Ecol. 18, 4541–4550 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    92.Allio, R., Donega, S., Galtier, N. & Nabholz, B. Large variation in the ratio of mitochondrial to nuclear mutation rate across animals: implications for genetic diversity and the use of mitochondrial DNA as a molecular marker. Mol. Biol. Evol. 34, 2762–2772 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Almeida-Rocha, J. M., Soares, L. A. S. S., Andrade, E. R., Gaiotto, F. A. & Cazetta, E. The impact of anthropogenic disturbances on the genetic diversity of terrestrial species: a global meta-analysis. Mol. Ecol. 29, 4812–4822 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    94.Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19, 4179–4191 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    95.Paz-Vinas, I. et al. Macrogenetic studies must not ignore limitations of genetic markers and scale. Ecol. Lett. 24, 1282–1284 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    96.Crandall, E. D. et al. The molecular biogeography of the Indo-Pacific: testing hypotheses with multispecies genetic patterns. Glob. Ecol. Biogeogr. 28, 943–960 (2019).Article 

    Google Scholar 
    97.Excoffier, L. & Foll, M. fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios. Bioinformatics 27, 1332–1334 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    98.Guillaume, F. & Rougemont, J. Nemo: an evolutionary and population genetics programming framework. Bioinformatics 22, 2556–2557 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    99.Phillips, J. D., French, S. H., Hanner, R. H. & Gillis, D. J. HACSim: an R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves. PeerJ Comput. Sci. 6, e243 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    100.Gratton, P. et al. A world of sequences: can we use georeferenced nucleotide databases for a robust automated phylogeography? J. Biogeogr. 44, 475–486 (2017).Article 

    Google Scholar 
    101.Kimura, M. On the probability of fixation of mutant genes in a population. Genetics 47, 713–719 (1962).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    102.Baguette, M. & Van Dyck, H. Landscape connectivity and animal behavior: functional grain as a key determinant for dispersal. Landsc. Ecol. 22, 1117–1129 (2007).Article 

    Google Scholar 
    103.Crow, J. F. & Aoki, K. Group selection for a polygenic behavioral trait: estimating the degree of population subdivision. Proc. Natl Acad. Sci. USA 81, 6073–6077 (1984).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    104.Lanner, R. Why do trees live so long? Ageing Res. Rev. 1, 653–671 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    105.Nabholz, B., Mauffrey, J.-F., Bazin, E., Galtier, N. & Glemin, S. Determination of mitochondrial genetic diversity in mammals. Genetics 178, 351–361 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    106.Lasne, C., Heerwaarden, B., Sgrò, C. M. & Connallon, T. Quantifying the relative contributions of the X chromosome, autosomes, and mitochondrial genome to local adaptation. Evolution 73, 262–277 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    107.Phillips, J. D., Gillis, D. J. & Hanner, R. H. Incomplete estimates of genetic diversity within species: implications for DNA barcoding. Ecol. Evol. 9, 2996–3010 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    108.Humphries, P. & Winemiller, K. O. Historical impacts on river fauna, shifting baselines, and challenges for restoration. BioScience 59, 673–684 (2009).Article 

    Google Scholar 
    109.Stoffel, M. A. et al. Demographic histories and genetic diversity across pinnipeds are shaped by human exploitation, ecology and life-history. Nat. Commun. 9, 4836 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    110.Collier-Robinson, L., Rayne, A., Rupene, M., Thoms, C. & Steeves, T. Embedding indigenous principles in genomic research of culturally significant species: a conservation genomics case study. N. Z. J. Ecol. 43, 3389 (2019).
    Google Scholar 
    111.Des Roches, S., Pendleton, L. H., Shapiro, B. & Palkovacs, E. P. Conserving intraspecific variation for nature’s contributions to people. Nat. Ecol. Evol. 5, 574–582 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    112.Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    113.Pope, L. C., Liggins, L., Keyse, J., Carvalho, S. B. & Riginos, C. Not the time or the place: the missing spatio-temporal link in publicly available genetic data. Mol. Ecol. 24, 3802–3809 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    114.Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    115.Sibbett, B., Rieseberg, L. H. & Narum, S. The genomic observatories metadatabase. Mol. Ecol. Resour. 20, 1453–1454 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    116.Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2020).Article 

    Google Scholar 
    117.Cornwell, W. K., Pearse, W. D., Dalrymple, R. L. & Zanne, A. E. What we (don’t) know about global plant diversity. Ecography 42, 1819–1831 (2019).Article 

    Google Scholar 
    118.Li, X. et al. Plant DNA barcoding: from gene to genome. Biol. Rev. 90, 157–166 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    119.Vasquez-Gross, H. A. et al. CartograTree: connecting tree genomes, phenotypes and environment. Mol. Ecol. Resour. 13, 528–537 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    120.Lawrence, E. R. et al. Geo-referenced population-specific microsatellite data across American continents, the MacroPopGen Database. Sci. Data 6, 14 (2019). This paper reports a compilation of georeferenced vertebrate microsatellite data, summary statistics and meta-data across the Americas for 897 species and 9,090 genetically distinct populations.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    121.Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    122.Barber, P. H. et al. Advancing biodiversity research in developing countries: the need for changing paradigms. Bull. Mar. Sci. 90, 187–210 (2014).Article 

    Google Scholar 
    123.Bork, P. et al. Tara Oceans. Tara Oceans studies plankton at planetary scale. Introduction. Science 348, 873–873 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    124.Lotterhos, K. E. & Whitlock, M. C. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol. Ecol. 24, 1031–1046 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    125.Hoban, S. et al. Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol. Conserv. 248, 108654 (2020).Article 

    Google Scholar 
    126.Holmes, M. W. et al. Natural history collections as windows on evolutionary processes. Mol. Ecol. 25, 864–881 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    127.Boukhdoud, L. et al. First DNA sequence reference library for mammals and plants of the Eastern Mediterranean Region. Genome 64, 39–49 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    128.Colella, J. P. et al. The Open-Specimen movement. BioScience 71, 405–414 (2020).Article 

    Google Scholar 
    129.Wright, S. Correlation and causation. J. Agric. Res. 20, 557–585 (1921).
    Google Scholar 
    130.Fourtune, L. et al. Inferring causalities in landscape genetics: an extension of Wright’s causal modeling to distance matrices. Am. Nat. 191, 491–508 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    131.Paz-Vinas, I., Loot, G., Stevens, V. M. & Blanchet, S. Evolutionary processes driving spatial patterns of intraspecific genetic diversity in river ecosystems. Mol. Ecol. 24, 4586–4604 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    132.Beaumont, M. A., Zhang, W. & Balding, D. J. Approximate Bayesian computation in population genetics. Genetics 162, 2025–2035 (2002).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    133.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

    Google Scholar 
    134.Proença, V. et al. Global biodiversity monitoring: From data sources to Essential Biodiversity Variables. Biol. Conserv. 213, 256–263 (2017).Article 

    Google Scholar 
    135.Ve˅trovský, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 10, 5142 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    136.Hanson, J. O. et al. Conservation planning for adaptive and neutral evolutionary processes. J. Appl. Ecol. 57, 2159–2169 (2020).Article 

    Google Scholar 
    137.Xuereb, A., D’Aloia, C. C., Andrello, M., Bernatchez, L. & Fortin, M. Incorporating putatively neutral and adaptive genomic data into marine conservation planning. Conserv. Biol. 35, 909–920 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    138.Carvalho, S. B., Torres, J., Tarroso, P. & Velo-Antón, G. Genes on the edge: a framework to detect genetic diversity imperiled by climate change. Glob. Change Biol. 25, 4034–4047 (2019).Article 

    Google Scholar 
    139.Adams, W. M. & Sandbrook, C. Conservation, evidence and policy. Oryx 47, 329–335 (2013).Article 

    Google Scholar 
    140.Laikre, L. et al. Post-2020 goals overlook genetic diversity. Science 367, 1083.2–1085 (2020).Article 
    CAS 

    Google Scholar 
    141.Thomson, A. I. et al. Charting a course for genetic diversity in the UN Decade of Ocean Science. Evol. Appl. 14, 1497–1518 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    142.Hoban, S. M. et al. Bringing genetic diversity to the forefront of conservation policy and management. Conserv. Genet. Resour. 5, 593–598 (2013).Article 

    Google Scholar 
    143.Carroll, S. R. et al. The CARE principles for indigenous data governance. Data Sci. J. 19, 43 (2020).Article 

    Google Scholar 
    144.Fargeot, L. et al. Patterns of epigenetic diversity in two sympatric fish species: genetic vs. environmental determinants. Genes 12, 107 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    145.Gaggiotti, O. E. et al. Diversity from genes to ecosystems: a unifying framework to study variation across biological metrics and scales. Evol. Appl. 11, 1176–1193 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    146.Waples, R. S., Antao, T. & Luikart, G. Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197, 769–780 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    147.Waples, R. S. & Yokota, M. Temporal estimates of effective population size in species with overlapping generations. Genetics 175, 219–233 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    148.Antao, T., Pérez-Figueroa, A. & Luikart, G. Early detection of population declines: high power of genetic monitoring using effective population size estimators. Evol. Appl. 4, 144–154 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    149.Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1996).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    150.Phillips, J. D., Gwiazdowski, R. A., Ashlock, D. & Hanner, R. An exploration of sufficient sampling effort to describe intraspecific DNA barcode haplotype diversity: examples from the ray-finned fishes (Chordata: Actinopterygii). DNA Barcodes 3, 66–73 (2015).Article 

    Google Scholar 
    151.Tajima, F. The effect of change in population size on DNA polymorphism. Genetics 123, 597–601 (1989).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    152.Jordan, R., Breed, M. F., Prober, S. M., Miller, A. D. & Hoffmann, A. A. How well do revegetation plantings capture genetic diversity? Biol. Lett. 15, 20190460 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    153.Holderegger, R. & Di Giulio, M. The genetic effects of roads: a review of empirical evidence. Basic. Appl. Ecol. 11, 522–531 (2010).Article 

    Google Scholar 
    154.Hale, M. L., Burg, T. M. & Steeves, T. E. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS One 7, e45170 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    155.Jackson, T. M., Roegner, G. C. & O’Malley, K. G. Evidence for interannual variation in genetic structure of Dungeness crab (Cancer magister) along the California Current System. Mol. Ecol. 27, 352–368 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    156.Hoban, S. et al. Comparative evaluation of potential indicators and temporal sampling protocols for monitoring genetic erosion. Evol. Appl. 7, 984–998 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    157.Anderson, C. N. K., Ramakrishnan, U., Chan, Y. L. & Hadly, E. A. Serial SimCoal: a population genetics model for data from multiple populations and points in time. Bioinformatics 21, 1733–1734 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    158.Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

    Google Scholar 
    159.Elbrecht, V., Vamos, E. E., Steinke, D. & Leese, F. Estimating intraspecific genetic diversity from community DNA metabarcoding data. PeerJ 6, e4644 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    160.Shum, P. & Palumbi, S. R. Testing small-scale ecological gradients and intraspecific differentiation for hundreds of kelp forest species using haplotypes from metabarcoding. Mol. Ecol. https://doi.org/10.1111/mec.15851 (2021).Article 
    PubMed 

    Google Scholar 
    161.Yamahara, K. M. et al. In situ autonomous acquisition and preservation of marine environmental DNA using an autonomous underwater vehicle. Front. Mar. Sci. 6, 373 (2019).Article 

    Google Scholar 
    162.Breed, M. F. et al. Mating patterns and pollinator mobility are critical traits in forest fragmentation genetics. Heredity 115, 108–114 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    163.Hoban, S., Gaggiotti, O. & Bertorelle, G. Sample Planning Optimization Tool for conservation and population Genetics (SPOTG): a software for choosing the appropriate number of markers and samples. Methods Ecol. Evol. 4, 299–303 (2013).Article 

    Google Scholar 
    164.Peck, S. L. Simulation as experiment: a philosophical reassessment for biological modeling. Trends Ecol. Evol. 19, 530–534 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    165.Reid, B. N., Naro-Maciel, E., Hahn, A. T., FitzSimmons, N. N. & Gehara, M. Geography best explains global patterns of genetic diversity and postglacial co-expansion in marine turtles. Mol. Ecol. 28, 3358–3370 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    166.Kardos, M., Luikart, G. & Allendorf, F. W. Measuring individual inbreeding in the age of genomics: marker-based measures are better than pedigrees. Heredity 115, 63–72 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    167.Willing, E.-M., Dreyer, C. & van Oosterhout, C. Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers. PLoS One 7, e42649 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    168.Shafer, A. B. A. et al. Bioinformatic processing of RAD-seq data dramatically impacts downstream population genetic inference. Methods Ecol. Evol. 8, 907–917 (2017).Article 

    Google Scholar 
    169.Cariou, M., Duret, L. & Charlat, S. How and how much does RAD-seq bias genetic diversity estimates? BMC Evol. Biol. 16, 240 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    170.De-Kayne, R. et al. Sequencing platform shifts provide opportunities but pose challenges for combining genomic data sets. Mol. Ecol. Resour. 21, 653–660 (2021).PubMed 
    Article 
    CAS 

    Google Scholar 
    171.Leigh, D. M., Lischer, H. E. L., Grossen, C. & Keller, L. F. Batch effects in a multiyear sequencing study: false biological trends due to changes in read lengths. Mol. Ecol. Resour. 18, 778–788 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    172.Linck, E. & Battey, C. J. Minor allele frequency thresholds strongly affect population structure inference with genomic data sets. Mol. Ecol. Resour. 19, 639–647 (2019).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    174.Feng, S. et al. Dense sampling of bird diversity increases power of comparative genomics. Nature 587, 252–257 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    175.Brandies, P., Peel, E., Hogg, C. J. & Belov, K. The value of reference genomes in the conservation of threatened species. Genes 10, 846 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar  More