More stories

  • in

    Bird population declines and species turnover are changing the acoustic properties of spring soundscapes

    Bird dataNorth America: we used annual bird count data collated under the North American Breeding Bird Survey (NA-BBS: https://www.pwrc.usgs.gov/bbs/) from 1996 to 2017. NA-BBS survey routes, consisting of 50 survey points (hereafter sites) evenly distributed over ~24.5 miles, are distributed across the United States and Canada and are usually surveyed in June. At each site, skilled volunteers conduct a three-minute point count, recording all birds seen or heard within a 400-m radius59.Europe: we used annual bird count data from 23 survey schemes across 22 countries collated under the Pan-European Common Bird Monitoring Scheme (PECBMS: https://pecbms.info) from 1998 to 2018. In each scheme, skilled volunteers carry out either line transects, point counts or territory mapping at survey sites during the breeding season and record all birds encountered60 (Supplementary Table 5); while methods vary between survey schemes, they are consistent within schemes across the time period included here.Where count data were reported for subspecies, these were aggregated to species level and any records of hybrid species or specifying genus only were removed. The longitude and latitude of each survey site (just the first site of each NA-BBS survey route) were also provided by NA-BBS and PECBMS. Not all sites were surveyed in every year and only sites surveyed at least three times during the defined time period were included in analyses. Note that similar results were found when restricting data to sites surveyed in at least 10 years during the defined period.Sound recordingsSound files for all species detected on NA-BBS and PECBMS surveys were downloaded from Xeno Canto, an online database of sound recordings of wild birds from around the world (http://www.xeno-canto.org). Specifically, we identified all files longer than 30 s, with associated metadata categorising them as high quality (category “A”) and as either “song”, “call” or “drumming” types; sound files whose type category including the term “wingbeat”, “flap”, “begging”, “alarm” or “night” types were excluded. Sound files downloaded for NA-BBS species were restricted to those recorded in North America and those from PECBMS to recordings made in Europe. If no sound files met these requirements for a given species, we downloaded all files of shorter duration for that species that met the quality and type criteria and stitched repeats of these together to produce files longer than 30 s. Where more than 50 sound files for a given species met our criteria for inclusion, a random selection of 50 was taken for use in subsequent analyses. We used multiple sound files for each species to capture, where possible, between-individual variation in song and call structure, with the sound file(s) for inclusion in specific soundscapes randomly subsampled from this set. If no sound files for a species were available, the sites where that species was detected were removed from subsequent analyses; this represented More

  • in

    Scientists say Australian plan to cull up to 10,000 wild horses doesn’t go far enough

    NEWS
    01 November 2021

    Scientists say Australian plan to cull up to 10,000 wild horses doesn’t go far enough

    A fast-growing population of feral horses in an alpine national park needs to be substantially reduced in number, researchers argue.

    Bianca Nogrady

    0

    Bianca Nogrady

    Bianca Nogrady is a freelance science journalist based in Sydney, Australia.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Share on Twitter
    Share on Twitter

    Share on Facebook
    Share on Facebook

    Share via E-Mail
    Share via E-Mail

    Brumbies roam a wintry landscape near Yarangobilly in Australia’s Kosciuszko National Park.Credit: Perry Duffin/EPA-EFE/Shutterstock

    Up to 10,000 feral horses might be killed or removed from Australia’s largest alpine national park under a draft plan to control the rapidly growing population of non-native animals. Scientists have welcomed the idea of removing them, but are alarmed that the plan still allows for thousands to remain, threatening endangered species and habitats.The proposed cull, in Kosciuszko National Park, New South Wales (NSW), contrasts with a ban on lethal control measures in the United States, where large populations of wild horses known as mustangs also cause problems.
    Australian scientists call for ‘feral horse’ culls in alpine national park
    The draft plan, released last month, recommends reducing the park’s population of wild horses, known in Australia as brumbies, from an estimated 14,000 to about 3,000 through a combination of mostly ground-based shooting, as well as rounding up and rehoming.But the Australian Academy of Science argues that the number of horses should be rapidly reduced below 3,000. In an open letter with 69 signatories including scientists and institutions sent to the NSW environment minister on Friday, they note that “alpine wetlands continue to degrade even with very small numbers of feral horses. Kosciusko cannot begin to recover from drought, extensive bushfires and overgrazing if, as currently proposed, 3,000 feral horses remain.”Capitulating to lobby groupsResearchers say the draft plan capitulates to a small but vocal group that has lobbied the government to protect horses because of the animals’ heritage value. The plan would allow the remaining brumbies to roam over one-third of the park. That would include threatened alpine sphagnum bogs and the habitats of endangered and vulnerable species such as a fish called the stocky galaxias (Galaxias tantangara), the alpine tree frog (Litoria verreauxii alpina) and the broad-toothed rat (Mastacomys fuscus).Australia has no native mammals with hard hooves, and so horses do more damage to delicate vegetation and soils than soft-footed species, such as kangaroos and wallabies, as well as creating problems through over-grazing.
    Ancient DNA points to origins of modern domestic horses
    David Watson, an ecologist at Charles Sturt University in Albury–Wodonga — which straddles NSW and the neighbouring state of Victoria — says the NSW government “couldn’t have picked a worse place” to allow feral horses to roam. He makes the point that Australia’s alpine environment covers just 1% of the continent and has many endemic and threatened species that are found nowhere else.“These areas are just too fragile to have large herbivores trampling around in them,” adds Don Driscoll, an ecologist at Deakin University in Melbourne.Management of feral horses has been a long-running issue in Australia’s mountainous alpine region, which extends across three states. The Australian Capital Territory, which shares a border with Kosciuszko National Park, has a zero-tolerance approach to feral horses and uses methods including aerial shooting.Victoria also shares an alpine border with New South Wales, but its latest management plan, released on 1 November, recommends using culling and other measures to remove all feral horses in the most delicate alpine environments, and the steady reduction of numbers elsewhere.Brumbies and mustangsThe NSW state government had previously tried to control the brumbies by rehoming them on private land, but was never able to find a place for more than a few hundred horses a year, rehoming only about 1,000 since 2002. Jamie Pittock, an environmental scientist at the Australian National University in Canberra, says that the government’s acknowledgement that the exponentially growing population cannot be managed with rehoming alone is at least “a step forward”.But Watson says that 3,000 horses would breed rapidly enough that 1,000 would still need to be removed or killed every few years, meaning that even a small population will create a continuing headache in terms of damage to the park and removal requirements.
    Ancient horses went dark to hide in forests
    A spokesperson for the NSW National Parks and Wildlife Service said the proposed target of 3,000 horses would maintain the “environmental values of the park” and that removing horses from two thirds of the park would provide “effective protection” for threatened species. They did not respond to Nature’s specific questions about scientists’ criticisms of the draft plan.The United States is grappling with similar issues with mustangs in national parks, says ecosystem scientist John Derek Scasta at the University of Wyoming in Laramie. “The goal is to get within an agreed-upon number of horses that are sustainable,” he says, but not everybody agrees on what that number is.Because legislation bans culling, the US Bureau of Land Management instead relies on rounding up, sterilization, rehoming or paying to keep the horses on either private or federal holdings. But Scasta says rising numbers, and the costs of looking after them, might mean the United States has to face its own reckoning with wild horses in the not-too-distant future.

    doi: https://doi.org/10.1038/d41586-021-02977-7

    Related Articles

    Australian scientists call for ‘feral horse’ culls in alpine national park

    Ancient DNA points to origins of modern domestic horses

    Ancient horses went dark to hide in forests

    Subjects

    Government

    Policy

    Environmental sciences

    Conservation biology

    Latest on:

    Government

    Scientists’ fears of racial bias surge amid US crackdown on China ties
    News 29 OCT 21

    Why hundreds of scientists are weighing in on a high-stakes US abortion case
    News 26 OCT 21

    Brazil’s scientists face 90% budget cut
    Correspondence 25 OCT 21

    Policy

    UK research funding to grow slower than hoped
    News 28 OCT 21

    The advocacy frontier
    Outlook 27 OCT 21

    The fluoride wars rage on
    Outlook 27 OCT 21

    Environmental sciences

    Machine learning enables global solar-panel detection
    News & Views 27 OCT 21

    Air quality: WHO guidelines could deepen inequities
    Correspondence 26 OCT 21

    Marine urban sprawl is gobbling up Earth’s coastlines
    Research Highlight 26 OCT 21

    Jobs

    Epidemiologist / Postdoc

    German Cancer Research Center in the Helmholtz Association (DKFZ)
    Heidelberg, Germany

    PhD Student in Liquid Biopsy

    German Cancer Research Center in the Helmholtz Association (DKFZ)
    Germany

    PhD (f/m/d) Data-driven Continuum Modelling of Infected Cell Dynamics on the Tissue Scale / Master’s degree in physics, computer science, bioinformatics, computational biology, data science, machine learning or relevant discipline / …

    Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
    Görlitz, Germany

    PhD (f/m/d) Generative Machine Learning of Time-Lapse Virological Imaging Data / Master’s degree in Computer science, Bioinformatics, Computational biology, Data science, Machine learning or relevant discipline / Engage with our …

    Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
    Görlitz, Germany More

  • in

    Venatorbacter cucullus gen. nov sp. nov a novel bacterial predator

    1.Pérez, J., Moraleda-Muñoz, A., Marcos-Torres, F. J. & Muñoz-Dorado, J. Bacterial predation: 75 years and counting!. Environ. Microbiol. 18, 766–779 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Linares-Otoya, L. et al. Diversity and antimicrobial potential of predatory bacteria from the Peruvian coastline. Mar. Drugs. 15, E308. https://doi.org/10.3390/md15100308 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Pasternak, Z. et al. By their genes ye shall know them: Genomic signatures of predatory bacteria. ISME J. 7, 756–769 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Sockett, R. E. Predatory lifestyle of Bdellovibrio bacteriovorus. Ann. Rev. Microbiol. 63, 523–539 (2009).CAS 
    Article 

    Google Scholar 
    5.Korp, J., Vela Gurovic, M. S. & Nett, M. Antibiotics from predatory bacteria. Beilstein J. Org. Chem. 12, 594–607 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Johnke, J., Fraune, S., Bosch, T. C. G., Hentschel, U. & Schulenburg, H. Bdellovibrio and like organisms are predictors of microbiome diversity in distinct host groups. Microb. Ecol. 79, 252–257 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Vila, J., Moreno-Morales, J. & Ballesté-Delpierre, C. Current landscape in the discovery of novel antibacterial agents. Clin. Microbiol. Infect. https://doi.org/10.1016/j.cmi.2019.09.015 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Hobley, L. et al. Dual predation by bacteriophage and Bdellovibrio bacteriovorus can eradicate Escherichia coli prey in situations where single predation cannot. J. Bacteriol. 202, e00629-19. https://doi.org/10.1128/JB.00629-19 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.El-Shibiny, A., Connerton, P. L. & Connerton, I. F. Enumeration and diversity of campylobacters and bacteriophages isolated during the rearing cycles of free-range and organic chickens. Appl. Environ. Microbiol. 71, 1259–1266 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Wilkinson, D. A. et al. Updating the genomic taxonomy and epidemiology of Campylobacter hyointestinalis. Sci. Rep. 8, 2393. https://doi.org/10.1038/s41598-018-20889-x (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Lee, M. D. GToTree: A user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 10, e1002195 (2011).MathSciNet 
    Article 
    CAS 

    Google Scholar 
    13.Edgar, R. C. MUSCLE: A multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004).Article 
    CAS 

    Google Scholar 
    14.Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. TrimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Hyatt, D., LoCascio, P. F., Hauser, L. J. & Uberbacher, E. C. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics 28, 2223–2230 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Shen, W. & Xiong, J. TaxonKit: A cross-platform and efficient NCBI taxonomy toolkit. bioRxiv. (Accessed 1 June 2021); https://www.biorxiv.org/content/10.1101/513523v1 (2019).
    17.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    18.Tange, O. GNU Parallel. (Accessed 1 June 2021); https://zenodo.org/record/1146014#.YOHaiJhKiUk (2018).19.Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Czech, L. et al. Role of the extremolytes ectoine and hydroxyectoine as stress protectants and nutrients: Genetics, phylogenomics, biochemistry, and structural Analysis. Genes (Basel). 9, E177. https://doi.org/10.3390/genes9040177 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Gregson, B. H., Metodieva, G., Metodiev, M. V., Golyshin, P. N. & McKew, B. A. Differential protein expression during growth on medium versus long-chain alkanes in the obligate marine hydrocarbon-degrading bacterium Thalassolituus oleivorans MIL-1. Front. Microbiol. 9, 3130 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Pasternak, Z., Ben Sasson, T., Cohen, Y., Segev, E. & Jurkevitch, E. A new comparative-genomics approach for defining phenotype-specific indicators reveals specific genetic markers in predatory bacteria. PLoS One. 10, e0142933. https://doi.org/10.1371/journal.pone.0142933 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Yakimov, M. M. et al. Thalassolituus oleivorans gen. nov., sp. nov., a novel marine bacterium that obligately utilizes hydrocarbons. Int. J. Syst. Evol. Microbiol. 54, 141–148 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Wang, Y., Yu, M., Liu, Y., Yang, X. & Zhang, X. H. Bacterioplanoides pacificum gen. nov., sp. nov., isolated from seawater of South Pacific Gyre. Int. J. Syst. Evol. Microbiol. 66, 5010–5015 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Bowditch, R. D., Baumann, L. & Baumann, P. Description of Oceanospirillum kriegii sp. nov. and O. jannaschii sp. nov. and assignment of two species of Alteromonas to this genus as O. commune comb. nov. and O. vagum comb. nov. Curr. Microbiol. 10, 221–229 (1984).CAS 
    Article 

    Google Scholar 
    26.Dong, C., Chen, X., Xie, Y., Lai, Q. & Shao, Z. Complete genome sequence of Thalassolituus oleivorans R6-15, an obligate hydrocarbonoclastic marine bacterium from the Arctic Ocean. Stand Genom. Sci. 9, 893–901 (2014).Article 

    Google Scholar 
    27.Choi, A. & Cho, J.-C. Thalassolituus marinus sp. nov., a hydrocarbon utilizing marine bacterium. Int. J. Syst. Evol. Microbiol. 63, 2234–2238 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Alain, K., Harder, J., Widdel, F. & Zengler, K. Anaerobic utilization of toluene by marine alpha- and gammaproteobacteria reducing nitrate. Microbiology 158, 2946–2957 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Liu, J., Wu, W., Chen, C., Sun, F. & Chen, Y. Prokaryotic diversity, composition structure, and phylogenetic analysis of microbial communities in leachate sediment ecosystems. Appl. Microbiol. Biotechnol. 91, 1659–1675 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Yakimov, M. M., Timmis, K. N. & Golyshin, P. N. Obligate oil-degrading marine bacteria. Curr. Opin. Biotechnol. 18, 257–266 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.McKew, B. A. et al. Efficacy of intervention strategies for bioremediation of crude oil in marine systems and effects on indigenous hydrocarbonoclastic bacteria. Environ. Microbiol. 9, 1562–1571 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Satomi, M., Kimura, B., Hamada, T., Harayama, S. & Fujii, T. Phylogenetic study of the genus Oceanospirillum based on 16S rRNA and gyrB genes: emended description of the genus Oceanospirillum, description of Pseudospirillum gen. nov., Oceanobacter gen. nov. and Terasakiella gen. nov. and transfer of Oceanospirillum jannaschii and Pseudomonas stanieri to Marinobacterium as Marinobacterium jannaschii comb. nov. and Marinobacterium stanieri comb. no. Int. J. Syst. Evol. Microbiol. 52, 739–747 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Qin, Q. L. et al. A proposed genus boundary for the prokaryotes based on genomic insights. J. Bacteriol. 196, 2210–2215 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    34.Nicholson, A. C. et al. Division of the genus Chryseobacterium: Observation of discontinuities in amino acid identity values, a possible consequence of major extinction events, guides transfer of nine species to the genus Epilithonimonas, eleven species to the genus Kaistella, and three species to the genus Halpernia gen. nov., with description of Kaistella daneshvariae sp. nov. and Epilithonimonas vandammei sp. nov. derived from clinical specimens. Int. J. Syst. Evol. Microbiol. 70, 4432–4450 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Barco, R. A. et al. A genus definition for Bacteria and Archaea based on a standard genome relatedness index. MBio 11, e02475-192020. https://doi.org/10.1128/mBio.02475-19 (2020).Article 

    Google Scholar 
    37.Andersson, J. O. & Andersson, S. G. Insights into the evolutionary process of genome degradation. Curr. Opin. Genet. Dev. 9, 664–671 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Wall, D. & Kaiser, D. Type IV pili and cell motility. Mol. Microbiol. 32, 1–10 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Jenal, U. & Malone, J. Mechanisms of cyclic-di-GMP signaling in bacteria. Ann. Rev. Genet. 40, 385–407 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Dow, J. M., Fouhy, Y., Lucey, J. F. & Ryan, R. P. The HD-GYP domain, cyclic di-GMP signaling, and bacterial virulence to plants. Mol. Plant Microbe Interact. 19, 1378–1384 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Hobley, L. et al. Discrete cyclic di-GMP-dependent control of bacterial predation versus axenic growth in Bdellovibrio bacteriovorus. PLoS Pathog. 8, e1002493. https://doi.org/10.1371/journal.ppat.1002493 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Seccareccia, I., Kovács, Á. T., Gallegos-Monterrosa, R. & Nett, M. Unraveling the predator-prey relationship of Cupriavidus necator and Bacillus subtilis. Microbiol. Res. 192, 231–238 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Mu, D. S. et al. Bradymonabacteria, a novel bacterial predator group with versatile survival strategies in saline environments. Microbiome 8, 1262020 (2020).Article 

    Google Scholar 
    44.Zepeda, V. K. et al. Terasakiispira papahanaumokuakeensis gen. nov., sp. nov., a gammaproteobacterium from Pearl and Hermes Atoll, Northwestern Hawaiian Islands. Int. J. Syst. Evol. Microbiol. 65, 3609–3617 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    45.Terasaki, Y. Transfer of five species and two subspecies of Spirillum to other genera (Aquaspirillum and Oceanospirillum), with emended descriptions of the species and subspecies. Int. J. Syst. Evol. Microbiol. 29, 130–144 (1979).
    Google Scholar 
    46.Baker, D. A. & Park, R. W. Changes in morphology and cell wall structure that occur during growth of Vibrio sp. NCTC4716 in batch culture. J. Gen. Microbiol. 86, 12–28 (1975).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Ng, L. K., Sherburne, R., Taylor, D. E. & Stiles, M. E. Morphological forms and viability of Campylobacter species studied by electron microscopy. J. Bacteriol. 164, 338–343 (1985).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Reshetnyak, V. I. & Reshetnyak, T. M. Significance of dormant forms of Helicobacter pylori in ulcerogenesis. World J. Gastroenterol. 23, 4867–4878 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Loc Carrillo, C. et al. Bacteriophage therapy to reduce Campylobacter jejuni colonization of broiler chickens. Appl. Environ. Microbiol. 71, 6554–6563 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Clinical and Laboratory Standards Institute. Methods for determining bactericidal activity of antimicrobial agents; approved guideline M26-A. Clin. Lab. Stand. Inst. 19, 7 (1999).
    Google Scholar 
    51.Legat, A., Gruber, C., Zangger, K., Wanner, G. & Stan-Lotter, H. Identification of polyhydroxyalkanoates in Halococcus and other haloarchaeal species. Appl. Microbiol. Biotechnol. 87, 1119–1127 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Tamura, K. & Nei, M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10, 512–526 (1993).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Felsenstein, J. Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39, 783–791 (1985).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Rodriguez-R, L. M. & Konstantinidis, K. T. Bypassing cultivation to identify bacterial species. Microbe 9, 111–118 (2014).
    Google Scholar 
    56.Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Blue carbon as a natural climate solution

    1.Nesshöver, C. et al. The science, policy and practice of nature-based solutions: an interdisciplinary perspective. Sci. Total Environ. 579, 1215–1227 (2017).Article 

    Google Scholar 
    2.Chausson, A. et al. Mapping the effectiveness of nature-based solutions for climate change adaptation. Glob. Chang. Biol. 26, 6134–6155 (2020).Article 

    Google Scholar 
    3.Pires, J. C. M. Negative emissions technologies: a complementary solution for climate change mitigation. Sci. Total Environ. 672, 502–514 (2019).Article 

    Google Scholar 
    4.McLaren, D. A comparative global assessment of potential negative emissions technologies. Process Saf. Environ. Prot. 90, 489–500 (2012).Article 

    Google Scholar 
    5.Anderson, K. & Peters, G. The trouble with negative emissions. Science 354, 182–183 (2016).Article 

    Google Scholar 
    6.Nellemann, C. et al. Blue Carbon — The Role of Healthy Oceans in Binding Carbon (UN Environment, 2009).7.Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).Article 

    Google Scholar 
    8.Himes-Cornell, A., Grose, S. O. & Pendleton, L. Mangrove ecosystem service values and methodological approaches to valuation: where do we stand? Front. Mar. Sci. 5, 376 (2018).Article 

    Google Scholar 
    9.Friess, D. A. et al. in Oceanography and Marine Biology Vol. 58 Ch. 3 (CRC, 2020).10.Lovelock, C. E. & Duarte, C. M. Dimensions of blue carbon and emerging perspectives. Biol. Lett. 15 https://doi.org/10.1098/rsbl.2018.0781 (2019).11.Duarte, C. M., Losada, I. J., Hendriks, I. E., Mazarrasa, I. & Marbà, N. The role of coastal plant communities for climate change mitigation and adaptation. Nat. Clim. Chang. 3, 961–968 (2013).Article 

    Google Scholar 
    12.Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8 (2005).Article 

    Google Scholar 
    13.Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

    Google Scholar 
    14.Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742 (2016).Article 

    Google Scholar 
    15.Macreadie, P. I. et al. Vulnerability of seagrass blue carbon to microbial attack following exposure to warming and oxygen. Sci. Total Environ. 686, 264–275 (2019).Article 

    Google Scholar 
    16.Sippo, J. Z., Lovelock, C. E., Santos, I. R., Sanders, C. J. & Maher, D. T. Mangrove mortality in a changing climate: an overview. Estuar. Coast. Shelf Sci. 215, 241–249 (2018).Article 

    Google Scholar 
    17.Lovelock, C. E. et al. Assessing the risk of carbon dioxide emissions from blue carbon ecosystems. Front. Ecol. Environ. 15, 257–265 (2017).Article 

    Google Scholar 
    18.Zhao, Q. et al. Where marine protected areas would best represent 30% of ocean biodiversity. Biol. Conserv. 244, 108536 (2020).Article 

    Google Scholar 
    19.Duarte, C. M. et al. Rebuilding marine life. Nature 580, 39–51 (2020).Article 

    Google Scholar 
    20.Bayraktarov, E. et al. The cost and feasibility of marine coastal restoration. Ecol. Appl. 26, 1055–1074 (2016).Article 

    Google Scholar 
    21.Van, T. T. et al. Changes in mangrove vegetation area and character in a war and land use change affected region of Vietnam (Mui Ca Mau) over six decades. Acta Oecol. 63, 71–81 (2015).Article 

    Google Scholar 
    22.Dung, L. V., Tue, N. T., Nhuan, M. T. & Omori, K. Carbon storage in a restored mangrove forest in Can Gio Mangrove Forest Park, Mekong Delta, Vietnam. For. Ecol. Manage. 380, 31–40 (2016).Article 

    Google Scholar 
    23.Nam, V. N., Sasmito, S. D., Murdiyarso, D., Purbopuspito, J. & MacKenzie, R. A. Carbon stocks in artificially and naturally regenerated mangrove ecosystems in the Mekong Delta. Wetl. Ecol. Manag. 24, 231–244 (2016).Article 

    Google Scholar 
    24.Reynolds, L. K., Waycott, M., McGlathery, K. J. & Orth, R. J. Ecosystem services returned through seagrass restoration. Restor. Ecol. 24, 583–588 (2016).Article 

    Google Scholar 
    25.Das, S. Ecological restoration and livelihood: contribution of planted mangroves as nursery and habitat for artisanal and commercial fishery. World Dev. 94, 492–502 (2017).Article 

    Google Scholar 
    26.Kiesel, J. et al. Effective design of managed realignment schemes can reduce coastal flood risks. Estuar. Coast. Shelf Sci. 242, 106844 (2020).Article 

    Google Scholar 
    27.McNally, C. G., Uchida, E. & Gold, A. J. The effect of a protected area on the tradeoffs between short-run and long-run benefits from mangrove ecosystems. Proc. Natl Acad. Sci. USA 108, 13945–13950 (2011).Article 

    Google Scholar 
    28.Chow, J. Mangrove management for climate change adaptation and sustainable development in coastal zones. J. Sustain. For. 37, 139–156 (2018).Article 

    Google Scholar 
    29.Dasgupta, S., Islam, M. S., Huq, M., Huque Khan, Z. & Hasib, M. R. Quantifying the protective capacity of mangroves from storm surges in coastal Bangladesh. PLoS ONE 14, e0214079 (2019).Article 

    Google Scholar 
    30.Sutton-Grier, A. E. & Moore, A. Leveraging carbon services of coastal ecosystems for habitat protection and restoration. Coast. Manag. 44, 259–277 (2016).Article 

    Google Scholar 
    31.Owuor, M. A., Mulwa, R., Otieno, P., Icely, J. & Newton, A. Valuing mangrove biodiversity and ecosystem services: a deliberative choice experiment in Mida Creek, Kenya. Ecosyst. Serv. 40, 101040 (2019).Article 

    Google Scholar 
    32.Mcowen, C. J. et al. A global map of saltmarshes. Biodivers. Data J. 5, e11764 (2018).Article 

    Google Scholar 
    33.Bunting, P. et al. The global mangrove watch — a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669 (2018).Article 

    Google Scholar 
    34.Jayathilake, D. R. M. & Costello, M. J. A modelled global distribution of the seagrass biome. Biol. Conserv. 226, 120–126 (2018).Article 

    Google Scholar 
    35.McKenzie, L. J. et al. The global distribution of seagrass meadows. Environ. Res. Lett. 15, 74041 (2020).Article 

    Google Scholar 
    36.Trumbore, S. E. Potential responses of soil organic carbon to global environmental change. Proc. Natl Acad. Sci. USA 94, 8284–8291 (1997).Article 

    Google Scholar 
    37.Hamilton, S. E. & Friess, D. A. Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012. Nat. Clim. Chang. 8, 240–244 (2018).Article 

    Google Scholar 
    38.Ouyang, X. & Lee, S. Y. Improved estimates on global carbon stock and carbon pools in tidal wetlands. Nat. Commun. 11, 317 (2020).Article 

    Google Scholar 
    39.Kauffman, J. B. et al. Total ecosystem carbon stocks of mangroves across broad global environmental and physical gradients. Ecol. Monogr. 90, e01405 (2020).Article 

    Google Scholar 
    40.Simard, M. et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat. Geosci. 12, 40–45 (2019).Article 

    Google Scholar 
    41.Hutchison, J., Manica, A., Swetnam, R., Balmford, A. & Spalding, M. Predicting global patterns in mangrove forest biomass. Conserv. Lett. 7, 233–240 (2014).Article 

    Google Scholar 
    42.Atwood, T. B. et al. Global patterns in mangrove soil carbon stocks and losses. Nat. Clim. Chang. 7, 523–528 (2017).Article 

    Google Scholar 
    43.Sanderman, J. et al. A global map of mangrove forest soil carbon at 30 m spatial resolution. Environ. Res. Lett. 13, 55002 (2018).Article 

    Google Scholar 
    44.Traganos, D. et al. Towards global-scale seagrass mapping and monitoring using Sentinel-2 on Google Earth Engine: the case study of the Aegean and Ionian Seas. Remote Sens. 10, 1227 (2018).Article 

    Google Scholar 
    45.Hossain, M. S. & Hashim, M. Potential of Earth Observation (EO) technologies for seagrass ecosystem service assessments. Int. J. Appl. Earth Obs. Geoinf. 77, 15–29 (2019).Article 

    Google Scholar 
    46.Atwood, T. B., Witt, A., Mayorga, J., Hammill, E. & Sala, E. Global patterns in marine sediment carbon stocks. Front. Mar. Sci. 7, 165 (2020).Article 

    Google Scholar 
    47.Coastal carbon atlas. Coastal Carbon Research Coordination Network. CCRCN https://ccrcn.shinyapps.io/CoastalCarbonAtlas/_w_8595a9b5/#tab-6425-6 (2019).48.UNEP-WCMC. Ocean data viewer: global distribution of seagrasses. UNEP https://doi.org/10.34892/x6r3-d211 (2018).49.Hammerstrom, K. K., Kenworthy, W. J., Fonseca, M. S. & Whitfield, P. E. Seed bank, biomass, and productivity of Halophila decipiens, a deep water seagrass on the west Florida continental shelf. Aquat. Bot. 84, 110–120 (2006).Article 

    Google Scholar 
    50.Pergent-Martini, C. et al. Descriptors of Posidonia oceanica meadows: use and application. Ecol. Indic. 5, 213–230 (2005).Article 

    Google Scholar 
    51.Esteban, N., Unsworth, R. K. F., Gourlay, J. B. Q. & Hays, G. C. The discovery of deep-water seagrass meadows in a pristine Indian Ocean wilderness revealed by tracking green turtles. Mar. Pollut. Bull. 134, 99–105 (2018).Article 

    Google Scholar 
    52.York, P. H. et al. Dynamics of a deep-water seagrass population on the Great Barrier Reef: annual occurrence and response to a major dredging program. Sci. Rep. 5, 13167 (2015).Article 

    Google Scholar 
    53.Serrano, O. et al. Australian vegetated coastal ecosystems as global hotspots for climate change mitigation. Nat. Commun. 10, 4313 (2019).Article 

    Google Scholar 
    54.Chmura, G. L., Anisfeld, S. C., Cahoon, D. R. & Lynch, J. C. Global carbon sequestration in tidal, saline wetland soils. Glob. Biogeochem. Cycles 17, 1111 (2003).Article 

    Google Scholar 
    55.Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 

    Google Scholar 
    56.Rogers, K. et al. Wetland carbon storage controlled by millennial-scale variation in relative sea-level rise. Nature 567, 91–95 (2019).Article 

    Google Scholar 
    57.Rovai, A. S. et al. Global controls on carbon storage in mangrove soils. Nat. Clim. Chang. 8, 534–538 (2018).Article 

    Google Scholar 
    58.Worthington, T. A. et al. A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Sci. Rep. 10, 14652 (2020).Article 

    Google Scholar 
    59.Maher, D. T., Call, M., Santos, I. R. & Sanders, C. J. Beyond burial: lateral exchange is a significant atmospheric carbon sink in mangrove forests. Biol. Lett. 14, 20180200 (2018).Article 

    Google Scholar 
    60.Santos, I. R., Maher, D. T., Larkin, R., Webb, J. R. & Sanders, C. J. Carbon outwelling and outgassing vs. burial in an estuarine tidal creek surrounded by mangrove and saltmarsh wetlands. Limnol. Ocean 64, 996–1013 (2019).Article 

    Google Scholar 
    61.Kelleway, J. J. et al. A national approach to greenhouse gas abatement through blue carbon management. Glob. Environ. Chang. 63, 102083 (2020).Article 

    Google Scholar 
    62.Goldberg, L., Lagomasino, D., Thomas, N. & Fatoyinbo, T. Global declines in human-driven mangrove loss. Glob. Chang. Biol. 68, 5844–5855 (2020).Article 

    Google Scholar 
    63.Richards, D. R. & Friess, D. A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. 113, 344–349 (2016).Article 

    Google Scholar 
    64.Thomas, N. et al. Distribution and drivers of global mangrove forest change, 1996–2010. PLoS ONE 12, e0179302 (2017).Article 

    Google Scholar 
    65.Worthington, T. & Spalding, M. Mangrove restoration potential: a global map highlighting a critical opportunity (OECD, 2018).66.Kearney, M. S., Riter, J. C. A. & Turner, R. E. Freshwater river diversions for marsh restoration in Louisiana: twenty-six years of changing vegetative cover and marsh area. Geophys. Res. Lett. 38, 16405 (2011).Article 

    Google Scholar 
    67.Lee, S. Y., Hamilton, S., Barbier, E. B., Primavera, J. & Lewis, R. R. Better restoration policies are needed to conserve mangrove ecosystems. Nat. Ecol. Evol. 3, 870–872 (2019).Article 

    Google Scholar 
    68.Lovelock, C. E. & Brown, B. M. Land tenure considerations are key to successful mangrove restoration. Nat. Ecol. Evol. 3, 1135 (2019).Article 

    Google Scholar 
    69.Herr, D., Blum, J., Himes-Cornell, A. & Sutton-Grier, A. An analysis of the potential positive and negative livelihood impacts of coastal carbon offset projects. J. Environ. Manag. 235, 463–479 (2019).Article 

    Google Scholar 
    70.Mojica Vélez, J. M., Barrasa García, S. & Espinoza Tenorio, A. Policies in coastal wetlands: key challenges. Environ. Sci. Policy 88, 72–82 (2018).Article 

    Google Scholar 
    71.Zeng, Y. et al. Economic and social constraints on reforestation for climate mitigation in Southeast Asia. Nat. Clim. Chang. 10, 842–844 (2020).Article 

    Google Scholar 
    72.van Katwijk, M. M. et al. Global analysis of seagrass restoration: the importance of large-scale planting. J. Appl. Ecol. 53, 567–578 (2016).Article 

    Google Scholar 
    73.Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl Acad. Sci. USA 106, 12377–12381 (2009).Article 

    Google Scholar 
    74.Orth, R. J. et al. A global crisis for seagrass ecosystems. Bioscience 56, 987–996 (2006).Article 

    Google Scholar 
    75.Tan, Y. M. et al. Seagrass restoration is possible: insights and lessons from Australia and New Zealand. Front. Mar. Sci. 7, 617 (2020).Article 

    Google Scholar 
    76.Greiner, J. T., McGlathery, K. J., Gunnell, J. & McKee, B. A. Seagrass restoration enhances ‘blue carbon’ sequestration in coastal waters. PLoS ONE 8, e72469 (2013).Article 

    Google Scholar 
    77.Orth, R. J. et al. Restoration of seagrass habitat leads to rapid recovery of coastal ecosystem services. Sci. Adv. 6, eabc6434 (2020).Article 

    Google Scholar 
    78.Cunha, A. H. et al. Changing paradigms in seagrass restoration. Restor. Ecol. 20, 427–430 (2012).Article 

    Google Scholar 
    79.Rezek, R. J., Furman, B. T., Jung, R. P., Hall, M. O. & Bell, S. S. Long-term performance of seagrass restoration projects in Florida, USA. Sci. Rep. 9, 15514 (2019).Article 

    Google Scholar 
    80.Worthington, T. A. et al. Harnessing big data to support the conservation and rehabilitation of mangrove forests globally. One Earth 2, 429–443 (2020).Article 

    Google Scholar 
    81.Kandus, P. et al. Remote sensing of wetlands in South America: status and challenges. Int. J. Remote Sens. 39, 993–1016 (2018).Article 

    Google Scholar 
    82.Gallant, A. L. The challenges of remote monitoring of wetlands. Remote Sens. 7, 10938–10950 (2015).Article 

    Google Scholar 
    83.Unsworth, R. K. F. et al. Sowing the seeds of seagrass recovery using hessian bags. Front. Ecol. Evol. 7, 311 (2019).Article 

    Google Scholar 
    84.Duarte, C. M., Dennison, W. C., Orth, R. J. W. & Carruthers, T. J. B. The charisma of coastal ecosystems: addressing the imbalance. Estuaries Coasts 31, 233–238 (2008).Article 

    Google Scholar 
    85.de los Santos, C. B. et al. Recent trend reversal for declining European seagrass meadows. Nat. Commun. 10, 3356 (2019).Article 

    Google Scholar 
    86.Hamilton, S. E. & Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob. Ecol. Biogeogr. 25, 729–738 (2016).Article 

    Google Scholar 
    87.Pendleton, L. et al. Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS ONE 7, e43542 (2012).Article 

    Google Scholar 
    88.Deegan, L. A. et al. Coastal eutrophication as a driver of salt marsh loss. Nature 490, 388–392 (2012).Article 

    Google Scholar 
    89.Cardoso, P. G., Raffaelli, D. & Pardal, M. A. The impact of extreme weather events on the seagrass Zostera noltii and related Hydrobia ulvae population. Mar. Pollut. Bull. 56, 483–492 (2008).Article 

    Google Scholar 
    90.Rogers, K. Accommodation space as a framework for assessing the response of mangroves to relative sea-level rise. Singap. J. Trop. Geogr. 42, 163–183 (2021).Article 

    Google Scholar 
    91.Marbà, N. & Duarte, C. M. Mediterranean warming triggers seagrass (Posidonia oceanica) shoot mortality. Glob. Chang. Biol. 16, 2366–2375 (2010).Article 

    Google Scholar 
    92.Lefcheck, J. S., Wilcox, D. J., Murphy, R. R., Marion, S. R. & Orth, R. J. Multiple stressors threaten the imperiled coastal foundation species eelgrass (Zostera marina) in Chesapeake Bay, USA. Glob. Chang. Biol. 23, 3474–3483 (2017).Article 

    Google Scholar 
    93.Arias-Ortiz, A. et al. A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Chang. 8, 338–344 (2018).Article 

    Google Scholar 
    94.Kendrick, G. A. et al. A systematic review of how multiple stressors from an extreme event drove ecosystem-wide loss of resilience in an iconic seagrass community. Front. Mar. Sci. 6, 455 (2019).Article 

    Google Scholar 
    95.Duke, N. C. et al. Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: a severe ecosystem response, coincidental with an unusually extreme weather event. Mar. Freshw. Res. 68, 1816–1829 (2017).Article 

    Google Scholar 
    96.Taillie, P. J. et al. Widespread mangrove damage resulting from the 2017 Atlantic mega hurricane season. Environ. Res. Lett. 15, 64010 (2020).Article 

    Google Scholar 
    97.Asbridge, E., Lucas, R., Rogers, K. & Accad, A. The extent of mangrove change and potential for recovery following severe Tropical Cyclone Yasi, Hinchinbrook Island, Queensland, Australia. Ecol. Evol. 8, 10416–10434 (2018).Article 

    Google Scholar 
    98.Hickey, S. M. et al. Is climate change shifting the poleward limit of mangroves? Estuaries Coasts 40, 1215–1226 (2017).Article 

    Google Scholar 
    99.Saintilan, N., Wilson, N. C., Rogers, K., Rajkaran, A. & Krauss, K. W. Mangrove expansion and salt marsh decline at mangrove poleward limits. Glob. Chang. Biol. 20, 147–157 (2014).Article 

    Google Scholar 
    100.Whitt, A. A. et al. March of the mangroves: drivers of encroachment into southern temperate saltmarsh. Estuar. Coast. Shelf Sci. 240, 106776 (2020).Article 

    Google Scholar 
    101.Cavanaugh, K. C. et al. Sensitivity of mangrove range limits to climate variability. Glob. Ecol. Biogeogr. 27, 925–935 (2018).Article 

    Google Scholar 
    102.Cavanaugh, K. C. et al. Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proc. Natl Acad. Sci. USA 111, 723–727 (2014).Article 

    Google Scholar 
    103.Coldren, G. A., Langley, J. A., Feller, I. C. & Chapman, S. K. Warming accelerates mangrove expansion and surface elevation gain in a subtropical wetland. J. Ecol. 107, 79–90 (2019).Article 

    Google Scholar 
    104.Yando, E. S. et al. Salt marsh–mangrove ecotones: using structural gradients to investigate the effects of woody plant encroachment on plant–soil interactions and ecosystem carbon pools. J. Ecol. 104, 1020–1031 (2016).Article 

    Google Scholar 
    105.Doughty, C. L. et al. Mangrove range expansion rapidly increases coastal wetland carbon storage. Estuaries Coasts 39, 385–396 (2016).Article 

    Google Scholar 
    106.Lovelock, C. E. et al. Sea level and turbidity controls on mangrove soil surface elevation change. Estuar. Coast. Shelf Sci. 153, 1–9 (2015).Article 

    Google Scholar 
    107.Woodroffe, C. D. et al. Mangrove sedimentation and response to relative sea-level rise. Ann. Rev. Mar. Sci. 8, 243–266 (2016).Article 

    Google Scholar 
    108.Lovelock, C. E. & Reef, R. Variable impacts of climate change on blue carbon. One Earth 3, 195–211 (2020).Article 

    Google Scholar 
    109.Saintilan, N. et al. Thresholds of mangrove survival under rapid sea level rise. Science 368, 1118–1121 (2020).Article 

    Google Scholar 
    110.Nicholls, R. J. Coastal flooding and wetland loss in the 21st century: changes under the SRES climate and socio-economic scenarios. Glob. Environ. Chang. 14, 69–86 (2004).Article 

    Google Scholar 
    111.Schuerch, M. et al. Future response of global coastal wetlands to sea-level rise. Nature 561, 231–234 (2018).Article 

    Google Scholar 
    112.Adame, M. F. et al. Future carbon emissions from global mangrove forest loss. Glob. Chang. Biol. 27, 2856–2866 (2021).Article 

    Google Scholar 
    113.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).Article 

    Google Scholar 
    114.Friedlingstein, P. et al. Global Carbon Budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).Article 

    Google Scholar 
    115.Morris, R. L., Boxshall, A. & Swearer, S. E. Climate-resilient coasts require diverse defence solutions. Nat. Clim. Chang. 10, 485–487 (2020).Article 

    Google Scholar 
    116.Macreadie, P. I. et al. The future of blue carbon science. Nat. Commun. 10, 3998 (2019).Article 

    Google Scholar 
    117.Wylie, L., Sutton-Grier, A. E. & Moore, A. Keys to successful blue carbon projects: lessons learned from global case studies. Mar. Policy 65, 76–84 (2016).Article 

    Google Scholar 
    118.Howard, J. F. et al. Clarifying the role of coastal and marine systems in climate mitigation. Front. Ecol. Environ. 15, 42–50 (2017).Article 

    Google Scholar 
    119.Lenihan, H. S. & Peterson, C. H. How habitat degradation through fishery disturbance enhances impacts of hypoxia on oysters reefs. Ecol. Appl. 8, 128–140 (1998).Article 

    Google Scholar 
    120.Ellison, A. M., Felson, A. J. & Friess, D. A. Mangrove rehabilitation and restoration as experimental adaptive management. Front. Mar. Sci. 7, 327 (2020).Article 

    Google Scholar 
    121.Lester, S. E., Dubel, A. K., Hernan, G., McHenry, J. & Rassweiler, A. Spatial planning principles for marine ecosystem restoration. Front. Mar. Sci. 7, 328 (2020).Article 

    Google Scholar 
    122.Herr, D. & Landis, E. Coastal blue carbon ecosystems: opportunities for nationally determined contributions. Policy brief (IUCN, 2016).123.Apple Newsroom. Conserving mangroves, a lifeline for the world. Apple (22 April 2019) https://www.apple.com/newsroom/2019/04/conserving-mangroves-a-lifeline-for-the-world124.Hochard, J. P., Hamilton, S. & Barbier, E. B. Mangroves shelter coastal economic activity from cyclones. Proc. Natl Acad. Sci. USA 116, 12232–12237 (2019).Article 

    Google Scholar 
    125.Herr, D., von Unger, M., Laffoley, D. & McGivern, A. Pathways for implementation of blue carbon initiatives. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 116–129 (2017).Article 

    Google Scholar 
    126.Friess, D. A. et al. in Sustainable Development Goals: Their Impacts on Forests and People Ch. 14 (eds Katila, P. et al.) 445–481 (Cambridge Univ. Press, 2019).127.Waltham, N. J. et al. UN Decade on Ecosystem Restoration 2021–2030 — what chance for success in restoring coastal ecosystems? Front. Mar. Sci. 7, 71 (2020).Article 

    Google Scholar 
    128.Convention on Biological Diversity. Conference of the Parties Decision X/2: strategic plan for biodiversity 2011–2020. CBD https://www.cbd.int/decision/cop/?id=12268 (2011).129.United Nations. Transforming our world: the 2030 Agenda for Sustainable Development (UN, 2015).130.Brander, L. M. et al. The global costs and benefits of expanding marine protected areas. Mar. Policy 116, 103953 (2020).Article 

    Google Scholar 
    131.Howard, J. F. et al. The potential to integrate blue carbon into MPA design and management. Aquat. Conserv. 27, 100–115 (2017).Article 

    Google Scholar 
    132.Needelman, B. A. et al. The science and policy of the Verified Carbon Standard methodology for tidal wetland and seagrass restoration. Estuaries Coasts 41, 2159–2171 (2018).Article 

    Google Scholar 
    133.Michaelowa, A., Hermwille, L., Obergassel, W. & Butzengeiger, S. Additionality revisited: guarding the integrity of market mechanisms under the Paris Agreement. Clim. Policy 19, 1211–1224 (2019).Article 

    Google Scholar 
    134.Intergovernmental Panel on Climate Change. 2013 Supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: wetlands (IPCC, 2014).135.United Nations Environment Programme. Out of the blue: the value of seagrasses to the environment and to people (UNEP, 2020).136.Murdiyarso, D. et al. The potential of Indonesian mangrove forests for global climate change mitigation. Nat. Clim. Chang. 5, 1089–1092 (2015).Article 

    Google Scholar 
    137.Jones, T. et al. Madagascar’s mangroves: quantifying nation-wide and ecosystem specific dynamics, and detailed contemporary mapping of distinct ecosystems. Remote Sens. 8, 106 (2016).Article 

    Google Scholar 
    138.Holmquist, J. R. et al. Uncertainty in United States coastal wetland greenhouse gas inventorying. Environ. Res. Lett. 13, 115005 (2018).Article 

    Google Scholar 
    139.Maher, D. T., Drexl, M., Tait, D. R., Johnston, S. G. & Jeffrey, L. C. iAMES: an inexpensive, automated methane ebullition sensor. Environ. Sci. Technol. 53, 6420–6426 (2019).Article 

    Google Scholar 
    140.Primavera, J. H. & Esteban, J. M. A. A review of mangrove rehabilitation in the Philippines: successes, failures and future prospects. Wetl. Ecol. Manag. 16, 345–358 (2008).Article 

    Google Scholar 
    141.Silliman, B. R. et al. Facilitation shifts paradigms and can amplify coastal restoration efforts. Proc. Natl Acad. Sci. USA 112, 14295–14300 (2015).Article 

    Google Scholar 
    142.Enwright, N. M., Griffith, K. T. & Osland, M. J. Barriers to and opportunities for landward migration of coastal wetlands with sea-level rise. Front. Ecol. Environ. 14, 307–316 (2016).Article 

    Google Scholar 
    143.Burkholz, C., Garcias-Bonet, N. & Duarte, C. M. Warming enhances carbon dioxide and methane fluxes from Red Sea seagrass (Halophila stipulacea) sediments. Biogeosciences 17, 1717–1730 (2020).Article 

    Google Scholar 
    144.Bianchi, T. S. et al. Historical reconstruction of mangrove expansion in the Gulf of Mexico: linking climate change with carbon sequestration in coastal wetlands. Estuar. Coast. Shelf Sci. 119, 7–16 (2013).Article 

    Google Scholar 
    145.Apostolaki, E. T. et al. Exotic Halophila stipulacea is an introduced carbon sink for the eastern Mediterranean Sea. Sci. Rep. 9, 9643 (2019).Article 

    Google Scholar 
    146.Bell, J. & Lovelock, C. E. Insuring mangrove forests for their role in mitigating coastal erosion and storm-surge: an Australian case study. Wetlands 33, 279–289 (2013).Article 

    Google Scholar 
    147.Reguero, B. G. et al. Financing coastal resilience by combining nature-based risk reduction with insurance. Ecol. Econ. 169, 106487 (2020).Article 

    Google Scholar 
    148.Thomas, S. Blue carbon: knowledge gaps, critical issues, and novel approaches. Ecol. Econ. 107, 22–38 (2014).Article 

    Google Scholar 
    149.International Partnership for Blue Carbon. Blue carbon partnership. IPBC https://bluecarbonpartnership.org (2017).150.Boon, P. I. & Prahalad, V. Ecologists, economics and politics: problems and contradictions in applying neoliberal ideology to nature conservation in Australia. Pac. Conserv. Biol. 23, 115–132 (2017).Article 

    Google Scholar 
    151.Adame, M. F. et al. The undervalued contribution of mangrove protection in Mexico to carbon emission targets. Conserv. Lett. 11, e12445 (2018).Article 

    Google Scholar 
    152.Bell-James, J. & Lovelock, C. E. Legal barriers and enablers for reintroducing tides: an Australian case study in reconverting ponded pasture for climate change mitigation. Land Use Policy 88, 104192 (2019).Article 

    Google Scholar 
    153.Gattuso, J.-P. et al. Ocean solutions to address climate change and its effects on marine ecosystems. Front. Mar. Sci. 5, 337 (2018).Article 

    Google Scholar 
    154.Saderne, V. et al. Role of carbonate burial in blue carbon budgets. Nat. Commun. 10, 1106 (2019).Article 

    Google Scholar 
    155.Duarte, C. M., Wu, J., Xiao, X., Bruhn, A. & Krause-Jensen, D. Can seaweed farming play a role in climate change mitigation and adaptation? Front. Mar. Sci. 4, 100 (2017).
    Google Scholar 
    156.Froehlich, H. E., Afflerbach, J. C., Frazier, M. & Halpern, B. S. Blue growth potential to mitigate climate change through seaweed offsetting. Curr. Biol. 29, 3087–3093.e3 (2019).Article 

    Google Scholar 
    157.Ritchie, H. & Roser, M. CO2 and greenhouse gas emissions. Our World in Data https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions (2017).158.Smith, S. V. Marine macrophytes as a global carbon sink. Science 211, 838–840 (1981).Article 

    Google Scholar 
    159.Intergovernmental Panel on Climate Change. Special report on the ocean and cryosphere in a changing climate (IPCC, 2019).160.Verified Carbon Standard. VM0007 REDD+ methodology framework (REDD+MF) (VCS, 2020).161.Carnell, P. E. et al. Mapping ocean wealth Australia: the value of coastal wetlands to people and nature. The Nature Conservancy https://doi.org/10.21153/carnell2019mapping (2019).162.Jänes, H. et al. Stable isotopes infer the value of Australia’s coastal vegetated ecosystems from fisheries. Fish Fish. 21, 80–90 (2020).Article 

    Google Scholar 
    163.Jänes, H. et al. Quantifying fisheries enhancement from coastal vegetated ecosystems. Ecosyst. Serv. 43, 101105 (2020).Article 

    Google Scholar 
    164.Huang, B. et al. Quantifying welfare gains of coastal and estuarine ecosystem rehabilitation for recreational fisheries. Sci. Total Environ. 710, 134680 (2020).Article 

    Google Scholar  More

  • in

    A large invasive consumer reduces coastal ecosystem resilience by disabling positive species interactions

    1.Vilà, M. et al. Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities and ecosystems. Ecol. Lett. 14, 702–708 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Vitousek, P. M., DAntonio, C. M., Loope, L. L., Westbrooks, R. & D’Antonio, C. M. Biological invasions as global environmental change. Am. Sci. 84, 468–478 (1996).ADS 

    Google Scholar 
    3.Pejchar, L. & Mooney, H. A. Invasive species, ecosystem services and human well-being. Trends Ecol. Evol. 24, 497–504 (2009).PubMed 
    Article 

    Google Scholar 
    4.Ehrenfeld, J. G. Ecosystem consequences of biological invasions. Annu. Rev. Ecol. Evol. Syst. 41, 59–80 (2010).Article 

    Google Scholar 
    5.Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl Acad. Sci. USA 113, 11261–11265 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Gallardo, B., Clavero, M., Sánchez, M. I. & Vilà, M. Global ecological impacts of invasive species in aquatic ecosystems. Glob. Change Biol. 22, 151–163 (2016).ADS 
    Article 

    Google Scholar 
    7.Didham, R. K., Tylianakis, J. M., Hutchison, M. A., Ewers, R. M. & Gemmell, N. J. Are invasive species the drivers of ecological change? Trends Ecol. Evol. 20, 470–474 (2005).PubMed 
    Article 

    Google Scholar 
    8.Simberloff, D. How common are invasion-induced ecosystem impacts? Biol. Invasions 13, 1255–1268 (2011).Article 

    Google Scholar 
    9.Guy-Haim, T. et al. Diverse effects of invasive ecosystem engineers on marine biodiversity and ecosystem functions: a global review and meta-analysis. Glob. Change Biol. https://doi.org/10.1111/gcb.14007 (2018).10.Vander Zanden, M. J., Casselman, J. M. & Rasmussen, J. B. Stable isotope evidence for the food web consequences of species invasions in lakes. Nature 401, 464–467 (1999).ADS 
    Article 
    CAS 

    Google Scholar 
    11.Bartomeus, I., Vilà, M. & Santamaría, L. Contrasting effects of invasive plants in plant-pollinator networks. Oecologia 155, 761–770 (2008).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Aizen, M. A., Morales, C. L. & Morales, J. M. Invasive mutualists erode native pollination webs. PLoS Biol. 6, 0396–0403 (2008).CAS 
    Article 

    Google Scholar 
    13.Olesen, J. M., Eskildsen, L. I. & Venkatasamy, S. Invasion of pollination networks on oceanic islands: importance of invader complexes and endemic super generalists. Divers. Distrib. 8, 181–192 (2002).Article 

    Google Scholar 
    14.Carvalheiro, L. G., Barbosa, E. R. M. & Memmott, J. Pollinator networks, alien species and the conservation of rare plants: Trinia glauca as a case study. J. Appl. Ecol. 45, 1419–1427 (2008).Article 

    Google Scholar 
    15.Anderson, C. B., Griffith, C. R., Rosemond, A. D., Rozzi, R. & Dollenz, O. The effects of invasive North American beavers on riparian plant communities in Cape Horn, Chile. Biol. Conserv. 128, 467–474 (2006).Article 

    Google Scholar 
    16.Walsh, J. R., Carpenter, S. R. & Vander Zanden, M. J. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proc. Natl Acad. Sci. USA 113, 201600366 (2016).
    Google Scholar 
    17.Wiles, G. J., Bart, J., Beck, R. E. & Aguon, C. F. Impacts of the Brown Tree Snake: patterns of decline and species persistence in Guam’s Avifauna. Conserv. Biol. 17, 1350–1360 (2003).Article 

    Google Scholar 
    18.Ludyanskiy, M., McDonald, D. & MacNeill, D. Impact of the Zebra Mussei, a Bivalve Invader. BioScience 43, 533–544 (1993).Article 

    Google Scholar 
    19.Byrnes, J. E., Reynolds, P. L. & Stachowicz, J. J. Invasions and extinctions reshape coastal marine food webs. PLoS ONE 2, 1–7 (2007).Article 

    Google Scholar 
    20.Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

    Google Scholar 
    21.Stachowicz, J. J. Mutualism, facilitation, and the structure of ecological communities. BioScience 51, 235 (2001).Article 

    Google Scholar 
    22.Berkelmans, R. & van Oppen, M. J. H. The role of zooxanthellae in the thermal tolerance of corals: a ‘nugget of hope’ for coral reefs in an era of climate change. Proc. R. Soc. B Biol. Sci. 273, 2305–2312 (2006).Article 

    Google Scholar 
    23.Bulleri, F., Bruno, J. F., Silliman, B. R. & Stachowicz, J. J. Facilitation and the niche: implications for coexistence, range shifts and ecosystem functioning. Funct. Ecol. 30, 70–78 (2016).Article 

    Google Scholar 
    24.Angelini, C. et al. Foundation species’ overlap enhances biodiversity and multifunctionality from the patch to landscape scale in southeastern United States salt marshes. Proc. R. Soc. B Biol. Sci. 282, 20150421 (2015).Article 

    Google Scholar 
    25.Anthelme, F., Cavieres, L. A. & Dangles, O. Facilitation among plants in alpine environments in the face of climate change. Front. Plant Sci. 5 (2014).26.Angelini, C. & Silliman, B. R. Secondary foundation species as drivers of trophic and functional diversity: evidence from a tree-epiphyte system. Ecology 95, 185–196 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.van der Heide, T. et al. A three-stage symbiosis forms the foundation of seagrass ecosystems. Science 336, 1432–1434 (2012).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    28.Nummi, P. & Holopainen, S. Whole-community facilitation by beaver: ecosystem engineer increases waterbird diversity: ecosystem engineer increases waterbird diversity. Aquat. Conserv. Mar. Freshw. Ecosyst. 24, 623–633 (2014).Article 

    Google Scholar 
    29.Rosell, F., Bozser, O., Collen, P. & Parker, H. Ecological impact of beavers Castor fiber and Castor canadensis and their ability to modify ecosystems. Mammal. Rev. 35, 248–276 (2005).Article 

    Google Scholar 
    30.He, Q., Bertness, M. D. & Altieri, A. H. Global shifts towards positive species interactions with increasing environmental stress. Ecol. Lett. 16, 695–706 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Schuerch, M. et al. Future response of global coastal wetlands to sea-level rise. Nature 561, 231–234 (2018).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Jackson, J. B. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Lotze, H. K. et al. Depletion, degredation, and recovery potential of estuaries and coastal seas. Science 312, 1806–1809 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Grosholz, E. Ecological and evolutionary consequences of coastal invasions. Trends Ecol. Evol. 17, 22–27 (2002).Article 

    Google Scholar 
    36.Syvitski, J. P. M. et al. Sinking deltas due to human activities. Nat. Geosci. 2, 681–686 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    37.He, Q. & Silliman, B. R. Climate change, human impacts, and coastal ecosystems in the anthropocene. Curr. Biol. 29, R1021–R1035 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).Article 

    Google Scholar 
    39.Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    40.Angelini, C. et al. A keystone mutualism underpins resilience of a coastal ecosystem to drought. Nat. Commun. 7, 12473 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Bruno, J. F. & Bertness, M. D. Habitat modification and facilitation in benthic marine communities. in Marine Community Ecology (eds Bertness, M. D., Gaines, S. & Hay, M.) 201–216 (Sinauer, 2001).42.De Fouw, J. et al. Drought, mutualism breakdown, and landscape-scale degradation of seagrass beds. Curr. Biol. 26, 1051–1056 (2016).PubMed 
    Article 
    CAS 

    Google Scholar 
    43.Ellison, A. M., Farnsworth, E. J. & Twilley, R. R. Facultative mutualism between red mangroves and root‐fouling sponges in belizean mangal. Ecology https://doi.org/10.2307/2265744 (1996).44.Arkema, K. K. et al. Coastal habitats shield people and property from sea-level rise and storms. Nat. Clim. Change 3, 913–918 (2013).ADS 
    Article 

    Google Scholar 
    45.McKee, K. L., Mendelssohn, I. A. & Materne, M. D. Acute salt marsh dieback in the Mississippi River deltaic plain: a drought-induced phenomenon? Glob. Ecol. Biogeogr. 13, 65–73 (2004).Article 

    Google Scholar 
    46.Alber, M., Swenson, E. M., Adamowicz, S. C. & Mendelssohn, I. A. Salt Marsh Dieback: an overview of recent events in the US. Estuar. Coast. Shelf Sci. 80, 1–11 (2008).ADS 
    Article 

    Google Scholar 
    47.Wang, H., Fu, R., Kumar, A. & Li, W. Intensification of summer rainfall variability in the southeastern United States during recent decades. J. Hydrometeorol. 11, 1007–1018 (2010).ADS 
    Article 

    Google Scholar 
    48.Stiven, A. E. & Gardner, S. A. Population processes in the ribbed mussel Geukensia demissa (Dillwyn) in a North Carolina salt marsh tidal gradient: spatial pattern, predation, growth and mortality. J. Exp. Mar. Biol. Ecol. 160, 81–102 (1992).Article 

    Google Scholar 
    49.Angelini, C. & Silliman, B. R. Patch size-dependent community recovery after massive disturbance. Ecology 93, 101–110 (2012).PubMed 
    Article 

    Google Scholar 
    50.Mendelssohn, I. & Morris, J. Ecophysiological controls on the productivity of Spartina alterniflora. in Concepts and Controversies in Tidal Marsh Ecology (eds Weinstein, M. & Kreeger, D.) 59–80 (Kluwer Academic Publishers, 1999).51.Bertness, M. D. Ribbed mussels and Spartina alterniflora production in a New England marsh. Ecology 65, 1794–1807 (1984).Article 

    Google Scholar 
    52.Siemann, E., Carrillo, J. A., Gabler, C. A., Zipp, R. & Rogers, W. E. Experimental test of the impacts of feral hogs on forest dynamics and processes in the southeastern US. Ecol. Manag. 258, 546–553 (2009).Article 

    Google Scholar 
    53.Campbell, T. A. & Long, D. B. Feral swine damage and damage management in forested ecosystems. Ecol. Manag. 257, 2319–2326 (2009).Article 

    Google Scholar 
    54.Barrios-Garcia, M. N. & Ballari, S. A. Impact of wild boar (Sus scrofa) in its introduced and native range: a review. Biol. Invasions 14, 2283–2300 (2012).Article 

    Google Scholar 
    55.Graves, H. B. Behavior and ecology of wild and feral swine (Sus-Scrofa). J. Anim. Sci. 58, 482–492 (1984).Article 

    Google Scholar 
    56.Wood, G. W. & Roark, N. D. Food habits of feral hogs in coastal South Carolina. J. Wildl. Manag. 44, 506–511 (1980).Article 

    Google Scholar 
    57.Sharp, S. J. & Angelini, C. The role of landscape composition and disturbance type in mediating salt marsh resilience to feral hog invasion. Biol. Invasions https://doi.org/10.1007/s10530-019-02018-5 (2019).58.Crotty, S. M. et al. Foundation species patch configuration mediates salt marsh biodiversity, stability and multifunctionality. Ecol. Lett. 21, 1681–1692 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Zhu, Z. et al. Historic storms and the hidden value of coastal wetlands for nature-based flood defence. Nat. Sustain. https://doi.org/10.1038/s41893-020-0556-z (2020).60.Thomsen, M. S. et al. Habitat cascades: the conceptual context and global relevance of facilitation cascades via habitat formation and modification. Integr. Comp. Biol. 50, 158–175 (2010).PubMed 
    Article 

    Google Scholar 
    61.Silliman, B. R. et al. Facilitation shifts paradigms and can amplify coastal restoration efforts. Proc. Natl Acad. Sci. USA 112, 14295–14300 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Silliman, B. R. et al. Field experiments and meta-analysis reveal wetland vegetation as a crucial element in the coastal protection paradigm. Curr. Biol. 29, 1800–1806 (2019). e3.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Neumann, B., Vafeidis, A. T., Zimmermann, J. & Nicholls, R. J. Future coastal population growth and exposure to sea-level rise and coastal flooding—a global assessment. PLoS ONE 10, e0118571 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    64.Rogers, K. et al. Wetland carbon storage controlled by millennial-scale variation in relative sea-level rise. Nature 567, 91–95 (2019).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Rogers, H. S. et al. Effects of an invasive predator cascade to plants via mutualism disruption. Nat. Commun. 8, 14557 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Albins, M. & Hixon, M. Invasive Indo-Pacific lionfish Pterois volitans reduce recruitment of Atlantic coral-reef fishes. Mar. Ecol. Prog. Ser. 367, 233–238 (2008).ADS 
    Article 

    Google Scholar 
    67.Albins, M. Invasive Pacific lionfish Pterois volitans reduce abundance and species richness of native Bahamian coral-reef fishes. Mar. Ecol. Prog. Ser. 522, 231–243 (2015).ADS 
    Article 

    Google Scholar 
    68.Ling, S. D. Range expansion of a habitat-modifying species leads to loss of taxonomic diversity: a new and impoverished reef state. Oecologia 156, 883–894 (2008).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Johnson, C. R. et al. Climate change cascades: shifts in oceanography, species’ ranges and subtidal marine community dynamics in eastern Tasmania. J. Exp. Mar. Biol. Ecol. 400, 17–32 (2011).Article 

    Google Scholar 
    70.Ling, S. D., Johnson, C. R., Frusher, S. D. & Ridgway, K. R. Overfishing reduces resilience of kelp beds to climate-driven catastrophic phase shift. Proc. Natl Acad. Sci. USA 106, 22341–22345 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Persico, E. P., Sharp, S. J. & Angelini, C. Feral hog disturbance alters carbon dynamics in southeastern US salt marshes. Mar. Ecol. Prog. Ser. 580, 57–68 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    72.Shaffer, G. P. et al. System response, nutria herbivory, and vegetation recovery of a wetland receiving secondarily-treated effluent in coastal Louisiana. Ecol. Eng. 79, 120–131 (2015).Article 

    Google Scholar 
    73.Fleming, P. A. et al. Is the loss of Australian digging mammals contributing to a deterioration in ecosystem function?: loss of Australian digging mammals and ecosystem function. Mammal. Rev. 44, 94–108 (2014).Article 

    Google Scholar 
    74.Woinarski, J. C. Z., Burbidge, A. A. & Harrison, P. L. Ongoing unraveling of a continental fauna: decline and extinction of Australian mammals since European settlement. Proc. Natl Acad. Sci. USA 112, 4531–4540 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    75.Croll, D. A. Introduced predators transform subarctic islands from grassland to tundra. Science 307, 1959–1961 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Siero, E. et al. Grazing away the resilience of patterned ecosystems. Am. Nat. 193, 472–480 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Crotty, S. M. & Angelini, C. Geomorphology and species interactions control facilitation cascades in a salt marsh ecosystem. Curr. Biol. 30, 1562–1571.e4 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78.Geisser, H. & Reyer, H.-U. Efficacy of hunting, feeding, and fencing to reduce crop damage by wild boars. J. Wildl. Manag. 68, 939–946 (2004).Article 

    Google Scholar 
    79.Engeman, R. M. et al. Feral swine management for conservation of an imperiled wetland habitat: Florida’s vanishing seepage slopes. Biol. Conserv. 134, 440–446 (2007).Article 

    Google Scholar 
    80.Bevins, S. N., Pedersen, K., Lutman, M. W., Gidlewski, T. & Deliberto, T. J. Consequences associated with the recent range expansion of nonnative feral swine. BioScience 64, 291–299 (2014).Article 

    Google Scholar 
    81.McClure, M. L. et al. Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States. PLoS ONE 10, 1–17 (2015).
    Google Scholar 
    82.Oldfield, C. A. & Evans, J. P. Twelve years of repeated wild hog activity promotes population maintenance of an invasive clonal plant in a coastal dune ecosystem. Ecol. Evol. 6, 2569–2578 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    83.Ford, M. A. & Grace, J. B. Effects of vertebrate herbivores on soil processes, plant biomass, litter accumulation and soil elevation changes in a coastal marsh. J. Ecol. 86, 974–982 (1998).Article 

    Google Scholar 
    84.Hensel, M. J. S. & Silliman, B. R. Consumer diversity across kingdoms supports multiple functions in a coastal ecosystem. Proc. Natl Acad. Sci. USA 110, 20621–20626 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Silliman, B. R. et al. Are the ghosts of nature’s past haunting ecology today? Curr. Biol. 28, R532–R537 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    86.Morse, N. B. et al. Novel ecosystems in the Anthropocene: a revision of the novel ecosystem concept for pragmatic applications. Ecol. Soc. 19, art12 (2014).Article 

    Google Scholar 
    87.Goigel Turner, M. Effects of grazing by feral horses, clipping, trampling, and burning on a Georgia salt marsh. Estuaries. 10, 54–60 (2014).Article 

    Google Scholar 
    88.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.4.4. https://CRAN.R-project.org/package=DHARMa (2021).89.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2017).90.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    91.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

    Google Scholar 
    92.AgiSoft PhotoScan Professional. (AgiSoft, 2016).93.Rasband, W. S. ImageJ. (U.S. National Institutes of Health, 1997).94.Kuenzler, E. J. Structure and energy flow of a mussel population in a Georgia salt marsh. Limnol. Oceanogr. 6, 191–204 (1961).ADS 
    Article 

    Google Scholar 
    95.Length, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.7.0. https://CRAN.R-project.org/package=emmeans (2021).96.Guichard, F., Halpin, P. M., Allison, G. W., Lubchenco, J. & Menge, B. A. Mussel disturbance dynamics: signatures of oceanographic forcing from local interactions. Am. Nat. 161, 889–904 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    97.Silliman, B. R., van de Koppel, J., Bertness, M. D., Stanton, L. E. & Mendelssohn, I. A. Drought, snails, and large-scale die-off of southern U.S. salt marshes. Science 310, 1803–1806 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

  • in

    Microevolutionary dynamics show tropical valleys are deeper for montane birds of the Atlantic Forest

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

    Google Scholar 
    2.Pyron, R. A., Alexander Pyron, R. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. B Biol. Sci. 280, 20131622 (2013).Article 

    Google Scholar 
    3.Pyron, R. A. Temperate extinction in squamate reptiles and the roots of latitudinal diversity gradients. Glob. Ecol. Biogeogr. 23, 1126–1134 (2014).Article 

    Google Scholar 
    4.Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Stevens, G. C. The latitudinal gradient in geographical range: how so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).Article 

    Google Scholar 
    6.Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. Biol. Sci. 278, 1823–1830 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    7.Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).Article 

    Google Scholar 
    8.Cadena, C. D. et al. Latitude, elevational climatic zonation and speciation in New World vertebrates. Proc. R. Soc. B Biol. Sci. 279, 194–201 (2012).Article 

    Google Scholar 
    9.Eo, S. H., Wares, J. P. & Carroll, J. P. Population divergence in plant species reflects latitudinal biodiversity gradients. Biol. Lett. 4, 382–384 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Polato, N. R. et al. Narrow thermal tolerance and low dispersal drive higher speciation in tropical mountains. Proc. Natl Acad. Sci. USA 115, 12471–12476 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.McCain, C. M. Vertebrate range sizes indicate that mountains may be ‘higher’ in the tropics. Ecol. Lett. 12, 550–560 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Chan, W.-P. et al. Seasonal and daily climate variation have opposite effects on species elevational range size. Science 351, 1437–1439 (2016).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Shah, A. A. et al. Climate variability predicts thermal limits of aquatic insects across elevation and latitude. Funct. Ecol. 31, 2118–2127 (2018).14.Kozak, K. H. & Wiens, J. J. Climatic zonation drives latitudinal variation in speciation mechanisms. Proc. Biol. Sci. 274, 2995–3003 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    15.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 
    16.Hewitt, G. The genetic legacy of the quaternary ice ages. Nature 405, 907–913 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    17.Smith, B. T., Bryson, R. W. Jr, Houston, D. D. & Klicka, J. An asymmetry in niche conservatism contributes to the latitudinal species diversity gradient in New World vertebrates. Ecol. Lett. 15, 1318–1325 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Bull, R. A. S. et al. Why replication is important in landscape genetics: American black bear in the Rocky Mountains. Mol. Ecol. 20, 1092–1107 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Peterman, W. E. ResistanceGA: an R package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol. Evol. 9, 1638–1647 (2018).Article 

    Google Scholar 
    20.Burney, C. W. & Brumfield, R. T. Ecology predicts levels of genetic differentiation in neotropical birds. Am. Nat. 174, 358–368 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Kipp, F. A. Der Handflügel-Index als flugbiologisches Maß. Vogelwarte 20, 77–86 (1959).
    Google Scholar 
    22.Stotz, D. F., Fitzpatrick, J. W., Parker, T. A., III & Moskovits, D. K. Neotropical Birds: Ecology and Conservation (Univ. Chicago Press, 1996).23.Weir, J. T. & Schluter, D. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315, 1574–1576 (2007).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    25.Bradburd, G. S., Coop, G. M. & Ralph, P. L. Inferring continuous and discrete population genetic structure across space. Genetics 210, 33–52 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Batalha-Filho, H., Cabanne, G. S. & Miyaki, C. Y. Phylogeography of an Atlantic forest passerine reveals demographic stability through the last glacial maximum. Mol. Phylogenet. Evol. 65, 892–902 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Raposo do Amaral, F. et al. Rugged relief and climate promote isolation and divergence between two neotropical cold-associated birds. Evolution 75, 2371–2387 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Dunbar, M. B. & Brigham, R. M. Thermoregulatory variation among populations of bats along a latitudinal gradient. J. Comp. Physiol. B 180, 885–893 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Gaitán-Espitia, J. D. et al. Geographic variation in thermal physiological performance of the intertidal crab Petrolisthes violaceus along a latitudinal gradient. J. Exp. Biol. 217, 4379–4386 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    30.Molina-Montenegro, M. A. & Naya, D. E. Latitudinal patterns in phenotypic plasticity and fitness-related traits: assessing the climatic variability hypothesis (CVH) with an invasive plant species. PLoS ONE 7, e47620 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Louthan, A. M., Doak, D. F. & Angert, A. L. Where and when do species interactions set range limits? Trends Ecol. Evol. 30, 780–792 (2015).PubMed 
    Article 

    Google Scholar 
    32.Macedo, G., Silva, M., do Amaral, F. R. & Maldonado-Coelho, M. Symmetrical discrimination despite weak song differentiation in 2 suboscine bird sister species. Behav. Ecol. 30, 1205–1215 (2019).Article 

    Google Scholar 
    33.Dhondt, A. A. Interspecific Competition in Birds (OUP, 2012).34.Freeman, B. G. Competitive interactions upon secondary contact drive elevational divergence in tropical birds. Am. Nat. 186, 470–479 (2015).PubMed 
    Article 

    Google Scholar 
    35.Zuloaga, J. & Kerr, J. T. Over the top: do thermal barriers along elevation gradients limit biotic similarity? Ecography 40, 478–486 (2017).Article 

    Google Scholar 
    36.Botero, C. A., Dor, R., McCain, C. M. & Safran, R. J. Environmental harshness is positively correlated with intraspecific divergence in mammals and birds. Mol. Ecol. 23, 259–268 (2014).PubMed 
    Article 

    Google Scholar 
    37.Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Harvey, M. G. et al. The evolution of a tropical biodiversity hotspot. Science 370, 1343–1348 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Thom, G. et al. Climatic dynamics and topography control genetic variation in Atlantic Forest montane birds. Mol. Phylogenet. Evol. 148, 106812 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Rabosky, D. L. & Glor, R. E. Equilibrium speciation dynamics in a model adaptive radiation of island lizards. Proc. Natl Acad. Sci. USA 107, 22178–22183 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Weir, J. T. & Price, T. D. Limits to speciation inferred from times to secondary sympatry and ages of hybridizing species along a latitudinal gradient. Am. Nat. 177, 462–469 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Harvey, M. G. et al. Positive association between population genetic differentiation and speciation rates in New World birds. Proc. Natl Acad. Sci. USA 114, 6328–6333 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Eaton, D. A. R. & Overcast, I. ipyrad: interactive assembly and analysis of RADseq datasets. Bioinformatics 36, 2592–2594 (2016).44.Harvey, M. G., Smith, B. T., Glenn, T. C., Faircloth, B. C. & Brumfield, R. T. Sequence capture versus restriction site associated DNA sequencing for shallow systematics. Syst. Biol. 65, 910–924 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Cumer, T. et al. Double-digest RAD-sequencing: do pre- and post-sequencing protocol parameters impact biological results? Mol. Genet. Genomics 296, 457–471 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590 (1978).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Jombart, T. & Ahmed, I. adegenet 1.3–1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Gehara, M. et al. Estimating synchronous demographic changes across populations using hABC and its application for a herpetological community from northeastern Brazil. Mol. Ecol. 26, 4756–4771 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).Article 

    Google Scholar 
    51.Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n-dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).Article 

    Google Scholar 
    52.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67, 1–48 (2015).53.Clarke, R. T., Rothery, P. & Raybould, A. F. Confidence limits for regression relationships between distance matrices: estimating gene flow with distance. J. Agric. Biol. Environ. Stat. 7, 361 (2002).Article 

    Google Scholar 
    54.Pavlidis, P., Laurent, S. & Stephan, W. msABC: a modification of Hudson’s ms to facilitate multi-locus ABC analysis. Mol. Ecol. Resour. 10, 723–727 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Csilléry, K., François, O. & Blum, M. G. B. abc: an R package for approximate Bayesian computation (ABC). Methods Ecol. Evol. 3, 475–479 (2012).Article 

    Google Scholar 
    56.Orme, D. et al. The caper package: comparative analysis of phylogenetics and evolution in R. R. Package Version 5, 1–36 (2013).
    Google Scholar 
    57.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
    CAS 
    Article 

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

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

    Google Scholar 
    60.Pavoine, S. adiv: An r package to analyse biodiversity in ecology. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13430 (2020).Article 

    Google Scholar  More

  • in

    Local adaptations of Mediterranean sheep and goats through an integrative approach

    1.Vigne, J.-D. Early domestication and farming: What should we know or do for a better understanding?. Anthropozoologica 50(2), 123–150. https://doi.org/10.5252/az2015n2a5 (2015).Article 

    Google Scholar 
    2.Zeder, M. A. Animal domestication in the Zagros: An update and directions for future research. MOM Édit. 49(1), 243–277 (2008).
    Google Scholar 
    3.Sponenberg, D. P. & Bixby, D. E. Managing Breeds for a Secure Future: Strategies for Breeders and Breed Associations (ALBC, 2007).
    Google Scholar 
    4.Taberlet, P. et al. Are cattle, sheep, and goats endangered species?. Mol. Ecol. 17(1), 275–284. https://doi.org/10.1111/j.1365-294X.2007.03475.x (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    5.Berihulay, H., Abied, A., He, X., Jiang, L. & Ma, Y. Adaptation mechanisms of small ruminants to environmental heat stress. Anim. Open Access J. MDPI 9(3), 75. https://doi.org/10.3390/ani9030075 (2019).Article 

    Google Scholar 
    6.Leroy, G., Baumung, R., Boettcher, P., Scherf, B. & Hoffmann, I. Review: Sustainability of crossbreeding in developing countries; definitely not like crossing a meadow…. Animal 10(2), 262–273. https://doi.org/10.1017/S175173111500213X (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Edea, Z., Dadi, H., Dessie, T. & Kim, K.-S. Genomic signatures of high-altitude adaptation in Ethiopian sheep populations. Genes Genomics 41(8), 973–981. https://doi.org/10.1007/s13258-019-00820-y (2019).Article 
    PubMed 

    Google Scholar 
    8.Wei, C. et al. Genome-wide analysis reveals adaptation to high altitudes in Tibetan sheep. Sci. Rep. 6(1), 26770. https://doi.org/10.1038/srep26770 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Yang, J. et al. Whole-genome sequencing of native sheep provides insights into rapid adaptations to extreme environments. Mol. Biol. Evol. 33(10), 2576–2592. https://doi.org/10.1093/molbev/msw129 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Kim, E. S. et al. Multiple genomic signatures of selection in goats and sheep indigenous to a hot arid environment. Heredity 116(3), 255–264. https://doi.org/10.1038/hdy.2015.94 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    11.Ciani, E. et al. On the origin of European sheep as revealed by the diversity of the Balkan breeds and by optimizing population-genetic analysis tools. Genet. Sel. Evol. GSE 52, 1–14. https://doi.org/10.1186/s12711-020-00545-7 (2020).Article 

    Google Scholar 
    12.Colli, L. et al. Genome-wide SNP profiling of worldwide goat populations reveals strong partitioning of diversity and highlights post-domestication migration routes. Genet. Sel. Evol. GSE 50, 1–20. https://doi.org/10.1186/s12711-018-0422-x (2018).Article 

    Google Scholar 
    13.Kijas, J. W. et al. A genome wide survey of SNP variation reveals the genetic structure of sheep breeds. PLoS ONE 4(3), e4668. https://doi.org/10.1371/journal.pone.0004668 (2009).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Brisebarre, A. Races ovines, systèmes d’élevage et représentations des éleveurs. in Développement rural, environnement et enjeux territoriaux. Regards croisés Oriental marocain et Sud-Est tunisien (dir. Bonte, P., Elloumi, M., Guillaume, H. & Mahdi, M.) 63–78 (Cérès Ed., 2009).15.Hall, S. J. G. Livestock biodiversity as interface between people, landscapes and nature. People Nat. 1(3), 284–290. https://doi.org/10.1002/pan3.23 (2019).Article 

    Google Scholar 
    16.Caballero, R. et al. Grazing Systems and Biodiversity in Mediterranean Areas: Spain, Italy and Greece (Pastos, 2011).
    Google Scholar 
    17.Collantes, F. The demise of European Mountain Pastoralism: Spain 1500–2000. Nomadic People 13(2), 124–145 (2009).Article 

    Google Scholar 
    18.Luu, K., Bazin, E. & Blum, M. G. B. pcadapt: An R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17(1), 67–77. https://doi.org/10.1111/1755-0998.12592 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    19.Frichot, E., Schoville, S. D., Bouchard, G. & François, O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30(7), 1687–1699. https://doi.org/10.1093/molbev/mst063 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.FAO. The State of the World’s Animal Genetic Resources for Food and Agriculture, edited by B. Rischkowsky & D. Pilling. Rome. (2007).21.François, O. Running Structure-Like Population Genetic Analyses with R. R Tutorials in Population Genetics 1–9 (U. Grenoble-Alpes, 2016).
    Google Scholar 
    22.Dalongeville, A., Benestan, L., Mouillot, D., Lobreaux, S. & Manel, S. Combining six genome scan methods to detect candidate genes to salinity in the Mediterranean striped red mullet (Mullus surmuletus). BMC Genomics 19, 1–13. https://doi.org/10.1186/s12864-018-4579-z (2018).CAS 
    Article 

    Google Scholar 
    23.De Kort, H., Vandepitte, K., Mergeay, J., Mijnsbrugge, K. V. & Honnay, O. The population genomic signature of environmental selection in the widespread insect-pollinated tree species Frangula alnus at different geographical scales. Heredity 115(5), 415–425. https://doi.org/10.1038/hdy.2015.41 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Capblancq, T., Luu, K., Blum, M. G. B. & Bazin, E. Evaluation of redundancy analysis to identify signatures of local adaptation. Mol. Ecol. Resour. 18(6), 1223–1233. https://doi.org/10.1111/1755-0998.12906 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    25.Bertolini, F. et al. Signatures of selection and environmental adaptation across the goat genome post-domestication. Genet. Sel. Evol. 50(1), 57. https://doi.org/10.1186/s12711-018-0421-y (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Fariello, M.-I. et al. Selection signatures in worldwide sheep populations. PLoS ONE 9(8), e103813. https://doi.org/10.1371/journal.pone.0103813 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Manunza, A. et al. Population structure of eleven Spanish ovine breeds and detection of selective sweeps with BayeScan and hapFLK. Sci. Rep. 6(1), 1–10. https://doi.org/10.1038/srep27296 (2016).CAS 
    Article 

    Google Scholar 
    28.Oget, C., Servin, B. & Palhière, I. Genetic diversity analysis of French goat populations reveals selective sweeps involved in their differentiation. Anim. Genet. 50(1), 54–63. https://doi.org/10.1111/age.12752 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Rochus, C. M. et al. Revealing the selection history of adaptive loci using genome-wide scans for selection: An example from domestic sheep. BMC Genomics 19(1), 71. https://doi.org/10.1186/s12864-018-4447-x (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Ruiz-Larrañaga, O. et al. Genomic selection signatures in sheep from the Western Pyrenees. Genet. Sel. Evol. GSE 50, 1–12. https://doi.org/10.1186/s12711-018-0378-x (2018).CAS 
    Article 

    Google Scholar 
    31.Wang, Q., Wang, D., Yan, G., Sun, L. & Tang, C. TRPC6 is required for hypoxia-induced basal intracellular calcium concentration elevation, and for the proliferation and migration of rat distal pulmonary venous smooth muscle cells. Mol. Med. Rep. 13(2), 1577–1585. https://doi.org/10.3892/mmr.2015.4750 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    32.Wang, X. et al. Whole-genome sequencing of eight goat populations for the detection of selection signatures underlying production and adaptive traits. Sci. Rep. 6, 38932. https://doi.org/10.1038/srep38932 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Graae, B. et al. On the use of weather data in ecological studies along altitudinal and latitudinal gradients. Oikos 121, 3–19. https://doi.org/10.1111/j.1600-0706.2011.19694.x (2011).Article 

    Google Scholar 
    34.Rellstab, C., Gugerli, F., Eckert, A. J., Hancock, A. M. & Holderegger, R. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol. 24(17), 4348–4370. https://doi.org/10.1111/mec.13322 (2015).Article 
    PubMed 

    Google Scholar 
    35.Qi, X. et al. The transcriptomic landscape of yaks reveals molecular pathways for high altitude adaptation. Genome Biol. Evol. 11(1), 72–85. https://doi.org/10.1093/gbe/evy264 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Yang, F., Wang, Q., Wang, M., He, K. & Pan, Y. Associations between gene polymorphisms in two crucial metabolic pathways and growth traits in pigs. Chin. Sci. Bull. 57(21), 2733–2740. https://doi.org/10.1007/s11434-012-5328-3 (2012).CAS 
    Article 

    Google Scholar 
    37.Schmidt, H. et al. Hypoxia tolerance, longevity and cancer-resistance in the mole rat Spalax—A liver transcriptomics approach. Sci. Rep. https://doi.org/10.1038/s41598-017-13905-z (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Tian, R. et al. Adaptive evolution of energy metabolism-related genes in hypoxia-tolerant mammals. Front. Genet. 8, 205. https://doi.org/10.3389/fgene.2017.00205 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Cheng, A. H. et al. SOX2-dependent transcription in clock neurons promotes the robustness of the central circadian pacemaker. Cell Rep. 26(12), 3191-3202.e8. https://doi.org/10.1016/j.celrep.2019.02.068 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    40.Bai, L. et al. Hypoxic and cold adaptation insights from the Himalayan Marmot Genome. IScience 11, 519–530. https://doi.org/10.1016/j.isci.2018.11.034 (2019).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    41.Stronen, A. V., Pertoldi, C., Iacolina, L., Kadarmideen, H. N. & Kristensen, T. N. Genomic analyses suggest adaptive differentiation of northern European native cattle breeds. Evol. Appl. https://doi.org/10.1111/eva.12783 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Lan, D. et al. Genetic diversity, molecular phylogeny, and selection evidence of Jinchuan Yak revealed by whole-genome resequencing. G3 (Bethesda, Md.) 8(3), 945–952. https://doi.org/10.1534/g3.118.300572 (2018).CAS 
    Article 

    Google Scholar 
    43.Chen, J. et al. Deletion of TRPC6 attenuates NMDA receptor-mediated Ca2+ entry and Ca2+-induced neurotoxicity following cerebral ischemia and oxygen-glucose deprivation. Front. Neurosci. 11, 138. https://doi.org/10.3389/fnins.2017.00138 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Munsch, T., Freichel, M., Flockerzi, V. & Pape, H.-C. Contribution of transient receptor potential channels to the control of GABA release from dendrites. Proc. Natl. Acad. Sci. U. S. A. 100(26), 16065–16070. https://doi.org/10.1073/pnas.2535311100 (2003).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Duan, J. et al. Structure of the mouse TRPC4 ion channel. Nat. Commun. 9, 1–10. https://doi.org/10.1101/282715 (2018).CAS 
    Article 

    Google Scholar 
    46.Malczyk, M. et al. The role of transient receptor potential channel 6 channels in the pulmonary vasculature. Front. Immunol. 8, 707. https://doi.org/10.3389/fimmu.2017.00707 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Li, S. et al. Crucial role of TRPC6 in maintaining the stability of HIF-1α in glioma cells under hypoxia. J. Cell Sci. 128(17), 3317–3329. https://doi.org/10.1242/jcs.173161 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    48.Xu, L. et al. Chronic hypoxia increases TRPC6 expression and basal intracellular Ca2+ concentration in rat distal pulmonary venous smooth muscle. PLoS ONE 9(11), e112007. https://doi.org/10.1371/journal.pone.0112007 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Deng, L. et al. Prioritizing natural-selection signals from the deep-sequencing genomic data suggests multi-variant adaptation in Tibetan highlanders. Natl. Sci. Rev. 6(6), 1201–1222. https://doi.org/10.1093/nsr/nwz108 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Howard, J. T. et al. Beef cattle body temperature during climatic stress: A genome-wide association study. Int. J. Biometeorol. 58, 1665–1672. https://doi.org/10.1007/s00484-013-0773-5 (2013).Article 
    PubMed 

    Google Scholar 
    51.Kijas, J. W. et al. Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biol. 10(2), e1001258. https://doi.org/10.1371/journal.pbio.1001258 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Wei, C. et al. Genome-wide analysis reveals population structure and selection in Chinese indigenous sheep breeds. BMC Genomics 16(1), 1–12. https://doi.org/10.1186/s12864-015-1384-9 (2015).Article 

    Google Scholar 
    53.Chen, M. et al. Genome-wide detection of selection signatures in Chinese indigenous Laiwu pigs revealed candidate genes regulating fat deposition in muscle. BMC Genet. 19, 1–9. https://doi.org/10.1186/s12863-018-0622-y (2018).CAS 
    Article 

    Google Scholar 
    54.Chen, C. et al. Copy number variation in the MSRB3 gene enlarges porcine ear size through a mechanism involving miR-584-5p. Genet. Sel. Evol. GSE 50, 1–18. https://doi.org/10.1186/s12711-018-0442-6 (2018).CAS 
    Article 

    Google Scholar 
    55.Webster, M. T. et al. Linked genetic variants on chromosome 10 control ear morphology and body mass among dog breeds. BMC Genomics 16, 474. https://doi.org/10.1186/s12864-015-1702-2 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Mastrangelo, S. et al. Novel and known signals of selection for fat deposition in domestic sheep breeds from Africa and Eurasia. PLoS ONE 14(6), e0209632. https://doi.org/10.1371/journal.pone.0209632 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Xi, Y. et al. HMGA2 promotes adipogenesis by activating C/EBPβ-mediated expression of PPARγ. Biochem. Biophys. Res. Commun. 472(4), 617–623. https://doi.org/10.1016/j.bbrc.2016.03.015 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Gou, X. et al. Whole-genome sequencing of six dog breeds from continuous altitudes reveals adaptation to high-altitude hypoxia. Genome Res. 24(8), 1308–1315. https://doi.org/10.1101/gr.171876.113 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Yuan, Z. et al. Selection signature analysis reveals genes associated with tail type in Chinese indigenous sheep. Anim. Genet. 48(1), 55–66. https://doi.org/10.1111/age.12477 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Zhu, C. et al. GWAS and Post-GWAS to Identification of Genes Associated with Sheep Tail Fat Deposition. Retrieved from https://www.preprints.org/manuscript/201906.0093/v1 (2019).61.Allais-Bonnet, A. et al. Novel insights into the bovine polled phenotype and horn ontogenesis in Bovidae. PLoS ONE 8(5), e63512. https://doi.org/10.1371/journal.pone.0063512 (2013).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Johnston, S. E. et al. Genome-wide association mapping identifies the genetic basis of discrete and quantitative variation in sexual weaponry in a wild sheep population. Mol. Ecol. 20(12), 2555–2566. https://doi.org/10.1111/j.1365-294X.2011.05076.x (2011).Article 
    PubMed 

    Google Scholar 
    63.Oksenberg, N., Stevison, L., Wall, J. D. & Ahituv, N. Function and regulation of AUTS2, a gene implicated in autism and human evolution. PLoS Genet. 9(1), e1003221. https://doi.org/10.1371/journal.pgen.1003221 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Hayashi, S. & Takeichi, M. Emerging roles of protocadherins: From self-avoidance to enhancement of motility. J. Cell Sci. 128(8), 1455–1464. https://doi.org/10.1242/jcs.166306 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    65.Seong, E., Yuan, L. & Arikkath, J. Cadherins and catenins in dendrite and synapse morphogenesis. Cell Adhes. Migr. 9(3), 202–213. https://doi.org/10.4161/19336918.2014.994919 (2015).CAS 
    Article 

    Google Scholar 
    66.Shin, D.-H. et al. Deleted copy number variation of Hanwoo and Holstein using next generation sequencing at the population level. BMC Genomics 15(1), 240. https://doi.org/10.1186/1471-2164-15-240 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Zeng, X. Angus Cattle at High Altitude: Pulmonary Arterial Pressure, Estimated Breeding Value and Genome-Wide Association Study (PhD thesis). (Colorado State University, 2017).68.Benjelloun, B. Diversité des génomes et adaptation locale des petits ruminants d’un pays méditerranéen : le Maroc (PhD thesis) (Université Grenoble Alpes, France, 2015).69.Onzima, R. B. et al. Genome-wide characterization of selection signatures and runs of homozygosity in Ugandan Goat Breeds. Front. Genet. 9, 318. https://doi.org/10.3389/fgene.2018.00318 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Farzana, F. et al. Neurobeachin regulates glutamate- and GABA-receptor targeting to synapses via distinct pathways. Mol. Neurobiol. 53(4), 2112–2123. https://doi.org/10.1007/s12035-015-9164-8 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    71.Nair, R. et al. Neurobeachin regulates neurotransmitter receptor trafficking to synapses. J. Cell Biol. 200(1), 61–80. https://doi.org/10.1083/jcb.201207113 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Alberto, F. J. et al. Convergent genomic signatures of domestication in sheep and goats. Nat. Commun. 9, 1–9. https://doi.org/10.1038/s41467-018-03206-y (2018).CAS 
    Article 

    Google Scholar 
    73.Iranmehr, A. et al. Novel insight into the genetic basis of high-altitude pulmonary hypertension in Kyrgyz highlanders. Eur. J. Hum. Genet. EJHG 27(1), 150–159. https://doi.org/10.1038/s41431-018-0270-8 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    74.Newman, J. H. et al. High-altitude pulmonary hypertension in cattle (Brisket disease): Candidate genes and gene expression profiling of peripheral blood mononuclear cells. Pulmon. Circ. 1(4), 462–469. https://doi.org/10.4103/2045-8932.93545 (2011).CAS 
    Article 

    Google Scholar 
    75.Yang, X., Kong, Q., Zhao, C., Cai, Z., & Wang, M. New pathogenic variant of BMPR2 in pulmonary arterial hypertension. Cardiology in the Young, 29(4), 462–466. https://doi.org/10.1017/S1047951119000015 (2019).76.Anderson, L. et al. Bmp2 and Bmp4 exert opposing effects in hypoxic pulmonary hypertension. Am. J. Physiol. Regul. Integr. Comp. Physiol. 298(3), R833–R842. https://doi.org/10.1152/ajpregu.00534.2009 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    77.Ciani, E. et al. Genome-wide analysis of Italian sheep diversity reveals a strong geographic pattern and cryptic relationships between breeds. Anim. Genet. 45(2), 256–266. https://doi.org/10.1111/age.12106 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    78.ESRI. ArcGIS Desktop: Release 10 (Environmental Systems Research Institute, 2011).
    Google Scholar 
    79.Ruiz, M. & Ruiz, J. P. Ecological history of transhumance in Spain. Biol. Conserv. 37, 73–86 (1986).Article 

    Google Scholar 
    80.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 
    81.Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled seamless SRTM dataV4, International Centre for Tropical Agriculture (CIAT). Available from https://srtm.csi.cgiar.org (2008).82.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018). https://www.R-project.org.83.Brenning, A. Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models. In Hamburger Beitraege zur Physischen Geographie und Landschaftsoekologie (eds Böhner, J. et al.) 23–32 (SAGA, 2008).
    Google Scholar 
    84.Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn (Springer, 2013). http://www.asdar-book.org/.85.Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5(2), 9–13. https://CRAN.R-project.org/doc/Rnews/ (2005).86.Keitt, T. H., Bivand, R., Pebesma, E. & Rowlingson, B. rgdal: Bindings for the geospatial data abstraction library. Copy at http://www.tinyurl.com/h8w8n29 (2010).87.Le, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01 (2008).Article 

    Google Scholar 
    88.Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27(15), 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    89.Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81(3), 559–575. https://doi.org/10.1086/519795 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Frichot, E. & François, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6(8), 925–929. https://doi.org/10.1111/2041-210X.12382 (2015).Article 

    Google Scholar 
    91.Cattell, R. B. The Scree plot test for the number of factors. Multivar. Behav. Res. 1, 140–161 (1966).
    Google Scholar 
    92.Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U. S. A. 100, 9440–9445. https://doi.org/10.1073/pnas.1530509100 (2003).ADS 
    MathSciNet 
    CAS 
    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 
    93.Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    94.Ablondi, M., Viklund, Å., Lindgren, G., Eriksson, S. & Mikko, S. Signatures of selection in the genome of Swedish warmblood horses selected for sport performance. BMC Genomics 20(1), 717. https://doi.org/10.1186/s12864-019-6079-1 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    95.Avila, F., Mickelson, J. R., Schaefer, R. J. & McCue, M. E. Genome-wide signatures of selection reveal genes associated with performance in American Quarter Horse subpopulations. Front. Genet. 9, 249. https://doi.org/10.3389/fgene.2018.00249 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.Chen, M. et al. Identification of selective sweeps reveals divergent selection between Chinese Holstein and Simmental cattle populations. Genet. Sel. Evol. 48(1), 76. https://doi.org/10.1186/s12711-016-0254-5 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    97.Cheruiyot, E. K. et al. Signatures of selection in admixed dairy cattle in Tanzania. Front. Genet. 9, 607. https://doi.org/10.3389/fgene.2018.00607 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    98.López, M. E. et al. Multiple selection signatures in farmed Atlantic Salmon adapted to different environments across hemispheres. Front. Genet. 10, 901. https://doi.org/10.3389/fgene.2019.00901 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19(9), 1655–1664. https://doi.org/10.1101/gr.094052.109 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Alexander, D. H. & Lange, K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinform. 12, 246 (2011).Article 

    Google Scholar 
    101.Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G. & Francois, O. Fast and efficient estimation of individual ancestry coefficients. Genetics 196, 973–983 (2014).Article 

    Google Scholar 
    102.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15(5), 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

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

    Google Scholar  More

  • in

    High variability in SSU rDNA gene copy number among planktonic foraminifera revealed by single-cell qPCR

    1.Pyron M. Characterizing communities. Nat Educ Knowl. 2010;3:39.
    Google Scholar 
    2.Hatton IA, Heneghan RF, Bar-On YM, Galbraith ED The global ocean size-spectrum from bacteria to whales. bioRxiv 2021; 2021.04.03.438320.3.Karsenti E, Acinas SG, Bork P, Bowler C, De Vargas C, Raes J, et al. A holistic approach to marine Eco-systems biology. PLoS Biol. 2011;9:e1001177.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    4.Mora C, Tittensor DP, Adl S, Simpson AGB, Worm B. How many species are there on Earth and in the ocean? PLoS Biol. 2011;9:e1001127.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    5.de Vargas C, Audic S, Henry N, Decelle J, Mahé F, Logares R, et al. Eukaryotic plankton diversity in the sunlit ocean. Science. 2015;348:1–12.Article 
    CAS 

    Google Scholar 
    6.Lejzerowicz F, Gooday AJ, Barrenechea Angeles I, Cordier, T, Morard, R, Apothéloz-Perret-Gentil L, et al. Eukaryotic biodiversity and spatial patterns in the Clarion-Clipperton zone and other abyssal regions: insights from sediment DNA and RNA metabarcoding. Front Mar Sci. 2021;8:1–23.Article 

    Google Scholar 
    7.Mahé F, de Vargas C, Bass D, Czech L, Stamatakis A, Lara E, et al. Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests. Nat Ecol Evol. 2017;1:1–8.Article 

    Google Scholar 
    8.Pawlowski J, Kelly-Quinn M, Altermatt F, Apothéloz-Perret-Gentil L, Beja P, Boggero A, et al. The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems. Sci Total Environ. 2018;637–638:1295–310.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    9.Ruppert KM, Kline RJ, Rahman MS. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob Ecol Conserv. 2019;17:e00547.Article 

    Google Scholar 
    10.Zinger L, Bonin A, Alsos IG, Bálint M, Bik H, Boyer F, et al. DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions. Mol Ecol. 2019;28:1857–62.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Lamb PD, Hunter E, Pinnegar JK, Creer S, Davies RG, Taylor MI. How quantitative is metabarcoding: A meta-analytical approach. Mol Ecol. 2019;28:420–30.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.del Campo J, Kolisko M, Boscaro V, Santoferrara LF, Nenarokov S, Massana R, et al. EukRef: Phylogenetic curation of ribosomal RNA to enhance understanding of eukaryotic diversity and distribution. PLoS Biol. 2018;16:e2005849.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    13.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–D604.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    14.Godhe A, Asplund ME, Härnström K, Saravanan V, Tyagi A, Karunasagar I. Quantification of diatom and dinoflagellate biomasses in coastal marine seawater samples by real-time PCR. Appl Environ Microbiol. 2008;74:7174–82.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Gong J, Dong J, Liu X, Massana R. Extremely high copy numbers and polymorphisms of the rDNA operon estimated from single cell analysis of oligotrich and peritrich ciliates. Protist. 2013;164:369–79.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    16.Rodríguez-Martínez R, Labrenz M, del Campo J, Forn I, Jürgens K, Massana R. Distribution of the uncultured protist MAST-4 in the Indian Ocean, Drake Passage and Mediterranean Sea assessed by real-time quantitative PCR. Environ Microbiol. 2009;11:397–408.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    17.Zhu F, Massana R, Not F, Marie D, Vaulot D. Mapping of picoeucaryotes in marine ecosystems with quantitative PCR of the 18S rRNA gene. FEMS Microbiol Ecol. 2005;52:79–92.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    18.Wang Y, Jiang Y, Liu Y, Li Y, Katz LA, Gao F, et al. Comparative studies on the polymorphism and copy number variation of mtSSU rDNA in ciliates (Protista, ciliophora): Implications for phylogenetic, environmental, and ecological research. Microorganisms. 2020;8:316.PubMed Central 
    Article 
    CAS 

    Google Scholar 
    19.Dunthorn M, Stoeck T, Clamp J, Warren A, Mahé F. Ciliates and the rare biosphere: A review. J Eukaryot Microbiol. 2014;61:404–9.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Gong W, Marchetti A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front Mar Sci. 2019;6:1–5.Article 

    Google Scholar 
    21.Kucera M. Chapter six planktonic foraminifera as tracers of past oceanic environments. Dev Mar Geol. 2007;1:213–62.
    Google Scholar 
    22.Morard R, Escarguel G, Weiner AK, André A, Douady CJ, Wade CM, et al. Nomenclature for the nameless: a proposal for an integrative molecular taxonomy of cryptic diversity exemplified by planktonic foraminifera. Syst Biol. 2016;65:925–40.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Darling KF, Wade CM. The genetic diversity of planktic foraminifera and the global distribution of ribosomal RNA genotypes. Mar Micropaleontol. 2008;67:216–38.Article 

    Google Scholar 
    24.André A, Quillévéré F, Morard R, Ujiié Y, Escarguel G, de Vargas C, et al. SSU rDNA divergence in planktonic foraminifera: molecular taxonomy and biogeographic implications. PLoS ONE. 2014;9:e104641.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    25.Morard R, Garet-Delmas MJ, Mahé F, Romac S, Poulain J, Kucera M, et al. Surface ocean metabarcoding confirms limited diversity in planktonic foraminifera but reveals unknown hyper-abundant lineages. Sci Rep. 2018;8:1–10. 2539Article 
    CAS 

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

    Google Scholar 
    27.Lecroq B, Lejzerowicz F, Bachar D, Christen R, Esling P, Baerlocher L, et al. Ultra-deep sequencing of foraminiferal microbarcodes unveils hidden richness of early monothalamous lineages in deep-sea sediments. Proc Natl Acad Sci U S A. 2011;108:13177–82.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Lejzerowicz F, Esling P, Pawlowski J. Patchiness of deep-sea benthic Foraminifera across the Southern Ocean: Insights from high-throughput DNA sequencing. Deep Sea Res Part II Top Stud Oceanogr. 2014;108:17–26.Article 
    CAS 

    Google Scholar 
    29.Shi J, Lei Y, Li Q, Lyu M, Li T. Molecular diversity and spatial distribution of benthic foraminifera of the seamounts and adjacent abyssal plains in the tropical Western Pacific Ocean. Mar Micropaleontol. 2020;156:101850.Article 

    Google Scholar 
    30.Li Q, Lei Y, Morard R, Li T, Wang B. Diversity hotspot and unique community structure of foraminifera in the world’s deepest marine blue hole—Sansha Yongle Blue Hole. Sci Rep. 2020;10:10257.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    31.Cordier T, Barrenechea I, Lejzerowicz F, Reo E, Pawlowski J. Benthic foraminiferal DNA metabarcodes significantly vary along a gradient from abyssal to hadal depths and between each side of the Kuril-Kamchatka trench. Prog Oceanogr. 2019;178:102175.Article 

    Google Scholar 
    32.Weber AA-T, Pawlowski J. Can Abundance of Protists Be Inferred from Sequence Data: A Case Study of Foraminifera. PLoS One. 2013;8:e56739.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    33.Jonkers L, Meilland J, Rillo MC, de Garidel-Thoron T, Kitchener JA, Kucera M. Linking zooplankton time series to the fossil record. ICES J Mar Sci. 2021:1–8.34.Weiner AKM, Morard R, Weinkauf MFG, Darling KF, André A, Quillévéré F, et al. Methodology for single-cell genetic analysis of planktonic foraminifera for studies of protist diversity and evolution. Front Mar Sci. 2016;3:1–15.Article 

    Google Scholar 
    35.Arya M, Shergill IS, Williamson M, Gommersall L, Arya N, Patel HR. Basic principles of real-time quantitative PCR. Expert Rev Mol Diagn. 2005;5:209–19.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    36.Barrenechea Angeles I, Lejzerowicz F, Cordier T, Scheplitz J, Kucera M, Ariztegui D, et al. Planktonic foraminifera eDNA signature deposited on the seafloor remains preserved after burial in marine sediments. Sci Rep. 2020;10:1–12.Article 
    CAS 

    Google Scholar 
    37.Morard R, Darling KF, Mahé F, Audic S, Ujiié Y, Weiner AK, et al. PFR2: a curated database of planktonic foraminifera 18S ribosomal DNA as a resource for studies of plankton ecology, biogeography and evolution. Mol Ecol Resour. 2015;15:1472–85.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    38.R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.r-project.org/.39.Wickham H ggplot2: Elegant graphics for data analysis. 2009. Springer-Verlag, New York.40.Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.Article 

    Google Scholar 
    41.Debode F, Marien A, Janssen É, Bragard C, Berben G. The influence of amplicon length on real-time PCR results. Biotechnol Agron Soc Environ. 2017;21:3–11.CAS 

    Google Scholar 
    42.Bé AWH, Lott L. Shell growth and structure of planktonic foraminifera. Science. 1964;145:2–3.Article 

    Google Scholar 
    43.Caromel AGM, Schmidt DN, Fletcher I, Rayfield EJ Morphological change during the ontogeny of the planktic foraminifera. J Micropalaeontology. 2015; 2014–017.44.Darling KF, Kucera M, Kroon D, Wade CM. A resolution for the coiling direction paradox in Neogloboquadrina pachyderma. Paleoceanography. 2006;21:PA2011–n/a.Article 

    Google Scholar 
    45.Parfrey LW, Lahr DJG, Katz LA. The dynamic nature of eukaryotic genomes. Mol Biol Evol. 2008;25:787–94.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    46.Parfrey LW, Katz LA. Dynamic genomes of eukaryotes and the maintenance of genomic integrity. Microbe. 2010;5:156–63.
    Google Scholar 
    47.Parfrey LW, Katz LA. Genome dynamics are influenced by food source in Allogromia laticollaris strain CSH (Foraminifera). Genome Biol Evol. 2010;2:678–85.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Finkel ZV, Beardall J, Flynn KJ, Quigg A, Rees TAV, Raven JA. Phytoplankton in a changing world: Cell size and elemental stoichiometry. J Plankton Res. 2010;32:119–37.Article 
    CAS 

    Google Scholar 
    49.Goldstein ST. Gametogenesis and the antiquity of reproductive pattern in the Foraminiferida. J Foraminifer Res. 1997;27:319–28.Article 

    Google Scholar 
    50.Davis CV, Livsey CM, Palmer HM, Hull PM, Thomas E, Hill TM, et al. Extensive morphological variability in asexually produced planktic foraminifera. Sci Adv. 2020;6:1–8.Article 
    CAS 

    Google Scholar 
    51.Takagi H, Kurasawa A, Kimoto K. Observation of asexual reproduction with symbiont transmission in planktonic foraminifera. J Plankton Res. 2020;42:403–10.Article 
    CAS 

    Google Scholar 
    52.Lessa D, Morard R, Jonkers L, Venancio IM, Reuter R, Baumeister A, et al. Distribution of planktonic foraminifera in the subtropical South Atlantic: Depth hierarchy of controlling factors. Biogeosciences. 2020;17:4313–42.Article 
    CAS 

    Google Scholar 
    53.Kucera M, Michael S, Raphaël M, Lukas J, Christiane S, Philipp M, et al. Scales of Population Dynamics, Ecology and Diversity of Planktonic Foraminifera and their Relationship to Particle Flux in the Eastern Tropical Atlantic: Cruise No. M140, 11.8.2017-5.9.2017, Mindelo (Cabo Verde)-Las Palmas (Spain) – FORAMFLUX. 2019. Bonn.54.Schiebel R. Planktic foraminiferal sedimentation and the marine calcite budget. Global Biogeochem Cycles. 2002;16:3-1–3–21.Article 
    CAS 

    Google Scholar 
    55.Meilland J, Schiebel R, Lo Monaco C, Sanchez S, Howa H. Abundances and test weights of living planktic foraminifers across the Southwest Indian Ocean: implications for carbon fluxes. Deep Sea Res Part I Oceanogr Res Pap. 2018;131:27–40.Article 
    CAS 

    Google Scholar 
    56.Pierella Karlusich JJ, Pelletier E, Lombard F, Carsique M, Dvorak E, Colin S, et al. Global distribution patterns of marine nitrogen-fixers by imaging and molecular methods. Nat Commun. 2021;12:4160.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar  More