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    Living in mixed species groups promotes predator learning in degraded habitats

    1.Turner, W. R. et al. Global conservation of biodiversity and ecosystem services. Bioscience 57, 868–873. https://doi.org/10.1641/B571009 (2007).Article 

    Google Scholar 
    2.O’Connor, B., Bojinski, S., Roosli, C. & Schaepman, M. E. Monitoring global changes in biodiversity and climate essential as ecological crisis intensifies. Ecol. Inform. https://doi.org/10.1016/j.ecoinf.2019.101033 (2020).Article 

    Google Scholar 
    3.Driscoll, D. A. et al. A biodiversity-crisis hierarchy to evaluate and refine conservation indicators. Nat. Ecol. Evolut. 2, 775–781. https://doi.org/10.1038/s41559-018-0504-8 (2018).Article 

    Google Scholar 
    4.Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS. Biol. 11, 11. https://doi.org/10.1371/journal.pbio.1001569 (2013).CAS 
    Article 

    Google Scholar 
    5.Hughes, T. P., Graham, N. A. J., Jackson, J. B. C., Mumby, P. J. & Steneck, R. S. Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol. 25, 633–642. https://doi.org/10.1016/j.tree.2010.07.011 (2010).Article 
    PubMed 

    Google Scholar 
    6.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492 (2018).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Säterberg, T., Sellman, S. & Ebenman, B. High frequency of functional extinctions in ecological networks. Nature 499, 468–470 (2013).ADS 
    Article 
    PubMed 

    Google Scholar 
    8.Valiente-Banuet, A. et al. Beyond species loss: The extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).Article 

    Google Scholar 
    9.Fontoura, L. et al. Climate-driven shift in coral morphological structure predicts decline of juvenile reef fishes. Glob. Change Biol. 26, 557–567. https://doi.org/10.1111/gcb.14911 (2020).ADS 
    Article 

    Google Scholar 
    10.Chivers, D. P., McCormick, M. I., Allan, B. J. & Ferrari, M. C. O. Risk assessment and predator learning in a changing world: Understanding the impacts of coral reef degradation. Sci. Rep. 6, 32542 (2016).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    11.Downie, A. T. et al. Exposure to degraded coral habitat depresses oxygen uptake rate during exercise of a juvenile reef fish. Coral Reefs https://doi.org/10.1007/s00338-021-02113-x (2021).Article 

    Google Scholar 
    12.Ferrari, M. C. O., McCormick, M. I., Allan, B. J. & Chivers, D. P. Not equal in the face of habitat change: Closely related fishes differ in their ability to use predation-related information in degraded coral. Proc. R. Soc. B 284, 20162758 (2017).Article 
    PubMed 

    Google Scholar 
    13.McCormick, M. I., Barry, R. P. & Allan, B. J. M. Algae associated with coral degradation affects risk assessment in coral reef fishes. Sci. Rep. 7, 12. https://doi.org/10.1038/s41598-017-17197-1 (2017).CAS 
    Article 

    Google Scholar 
    14.Brown, G. E. & Chivers, D. P. in Fish cognition and behaviour (eds C. Brown, K. Laland, & J. Krause) 49–69 (Blackwell Scientific Publisher, 2006).15.Meuthen, D., Baldauf, S. A., Bakker, T. C. M. & Thunken, T. Neglected patterns of variation in phenotypic plasticity: Age- and sex-specific antipredator plasticity in a cichlid fish. Am. Nat. 191, 475–490. https://doi.org/10.1086/696264 (2018).Article 

    Google Scholar 
    16.Lonnstedt, O. M., McCormick, M. I., Meekan, M. G., Ferrari, M. C. O. & Chivers, D. P. Learn and live: Predator experience and feeding history determines prey behaviour and survival. Proc. R. Soc. B-Biol. Sci. 279, 2091–2098. https://doi.org/10.1098/rspb.2011.2516 (2012).Article 

    Google Scholar 
    17.Ferrari, M. C. O. et al. School is out on noisy reefs: The effect of boat noise on predator learning and survival of juvenile coral reef fishes. Proc. R. Soc. B-Biol. Sci. 285, 8. https://doi.org/10.1098/rspb.2018.0033 (2018).Article 

    Google Scholar 
    18.Chivers, D. P., McCormick, M. I., Mitchell, M. D., Ramasamy, R. A. & Ferrari, M. C. O. Background level of risk determines how prey categorize predators and non-predators. Proc. R. Soc. B 281, 20140355 (2014).Article 
    PubMed 

    Google Scholar 
    19.Crane, A. L. & Ferrari, M. C. O. in Social learning theory: Phylogenetic considerations across animal, plant, and microbial taxa (ed K. B. Clark) 53–82 (Nova Science Publishers, 2013).20.Ferrari, M. C. O., Wisenden, B. D. & Chivers, D. P. Chemical ecology of predator–prey interactions in aquatic ecosystems: A review and prospectus. Can. J. Zool. 88, 698–724 (2010).Article 

    Google Scholar 
    21.Mirza, R. S. & Chivers, D. P. Are chemical alarm cues conserved within salmonid fishes?. J. Chem. Ecol. 27, 1641–1655 (2001).CAS 
    Article 

    Google Scholar 
    22.Brown, G. E., Adrian, J. C., Naderi, N. T., Harvey, M. C. & Kelly, J. M. Nitrogen oxides elicit antipredator responses in juvenile channel catfish, but not in convict cichlids or rainbow trout: Conservation of the ostariophysan alarm pheromone. J. Chem. Ecol. 29, 1781–1796 (2003).CAS 
    Article 

    Google Scholar 
    23.Pollock, M. S., Chivers, D. P., Mirza, R. S. & Wisenden, B. D. Fathead minnows, Pimephales promelas, learn to recognize chemical alarm cues of introduced brook stickleback, Culaea inconstans. Environ. Biol. Fishes 66, 313–319 (2003).Article 

    Google Scholar 
    24.Chivers, D. P., Brown, G. E. & Smith, R. J. F. Acquired recognition of chemical stimuli from pike, Esox lucius, by brook sticklebacks, Culaea inconstans (Osteichthyes, Gasterosteidae). Ethology 99, 234–242 (1995).Article 

    Google Scholar 
    25.Mitchell, M. D., Cowman, P. F. & McCormick, M. I. Chemical alarm cues are conserved within the coral reef fish family Pomacentridae. Plos One 7, e47428 (2012).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    26.Ferrari, M. C. O. et al. Intrageneric variation in antipredator responses of coral reef fishes affected by ocean acidification: implications for climate change projections on marine communities. Glob. Change Biol. 17, 2980–2986 (2011).ADS 
    Article 

    Google Scholar 
    27.Chivers, D. et al. Coral degradation alters predator odour signatures and influences prey learning and survival. Proc. R. Soc. B 286, 20190562 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    28.Ferrari, M. C. O., McCormick, M. I., Meekan, M. G. & Chivers, D. P. Background level of risk and the survival of predator-naive prey: Can neophobia compensate for predator naivety in juvenile coral reef fishes?. Proc. R. Soc. Lond. B Biol. Sci. 282, 20142197 (2015).
    Google Scholar 
    29.Stewart, B. D. & Beukers, J. S. Baited technique improves censuses of cryptic fish in complex habitats. Mar. Ecol. Prog. Ser. 197, 259–272 (2000).ADS 
    Article 

    Google Scholar 
    30.Hoey, A. S. & McCormick, M. I. in Proceedings of the 10th international coral reef symposium Vol. 1. 420–424 (2006).31.McCormick, M. I., Chivers, D. P., Allan, B. J. & Ferrari, M. C. O. Habitat degradation disrupts neophobia in juvenile coral reef fish. Glob. Change Biol. 23, 719–727 (2017).ADS 
    Article 

    Google Scholar 
    32.McCormick, M. I., Moore, J. A. Y. & Munday, P. L. Influence of habitat degradation on fish replenishment. Coral Reefs 29, 537–546. https://doi.org/10.1007/s00338-010-0620-7 (2010).ADS 
    Article 

    Google Scholar 
    33.McCormick, M. I. Behaviourally mediated phenotypic selection in a disturbed coral reef environment. Plos One https://doi.org/10.1371/journal.pone.0007096 (2009).Article 
    PubMed Central 
    PubMed 

    Google Scholar 
    34.White, J. R., Meekan, M. G. & McCormick, M. I. Individual consistency in the behaviors of newly-settled reef fish. PeerJ 3, e961 (2015).Article 
    PubMed 

    Google Scholar 
    35.McCormick, M. I. & Weaver, C. J. It pays to be pushy: Intracohort interference competition between two reef fishes. Plos One 7, e42590 (2012).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Wolf, N. G. Odd fish abandon mixed-species groups when threatened. Behav. Ecol. Sociobiol. 17, 47–52 (1985).Article 

    Google Scholar 
    37.Usio, N., Konishi, M. & Nakano, S. Species displacement between an introduced and a ‘vulnerable’ crayfish: The role of aggressive interactions and shelter competition. Biol. Invasions 3, 179–185 (2001).Article 

    Google Scholar 
    38.Dargent, F., Torres-Dowdall, J., Scott, M. E., Ramnarine, I. & Fussmann, G. F. Can mixed-species groups reduce individual parasite load? A field test with two closely related poeciliid fishes (Poecilia reticulata and Poecilia picta). PloS One 8, e56789 (2013).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Uetz, G. W. & Hieber, C. S. Group size and predation risk in colonial web-building spiders: Analysis of attack abatement mechanisms. Behav. Ecol. 5, 326–333 (1994).Article 

    Google Scholar 
    40.McCormick, M. I., Barry, R. P. & Allan, B. J. Algae associated with coral degradation affects risk assessment in coral reef fishes. Sci. Rep. 7, 16937 (2017).ADS 
    Article 
    PubMed 

    Google Scholar 
    41.Lecchini, D., Planes, S. & Galzin, R. Experimental assessment of sensory modalities of coral-reef fish larvae in the recognition of their settlement habitat. Behav. Ecol. Sociobiol. 58, 18–26. https://doi.org/10.1007/s00265-004-0905-3 (2005).Article 

    Google Scholar 
    42.Lecchini, D., Planes, S. & Galzin, R. The influence of habitat characteristics and conspecifics on attraction and survival of coral reef fish juveniles. J. Exp. Mar. Biol. Ecol. 341, 85–90. https://doi.org/10.1016/j.jembe.2006.10.006 (2007).Article 

    Google Scholar 
    43.Lecchini, D., Waqalevu, V. P., Parmentier, E., Radford, C. A. & Banaigs, B. Fish larvae prefer coral over algal water cues: Implications of coral reef degradation. Mar. Ecol. Prog. Ser. 475, 303–307. https://doi.org/10.3354/meps10094 (2013).ADS 
    Article 

    Google Scholar 
    44.O’Connor, J. J. et al. Sediment pollution impacts sensory ability and performance of settling coral-reef fish. Oecologia 180, 11–21. https://doi.org/10.1007/s00442-015-3367-6 (2016).ADS 
    Article 

    Google Scholar 
    45.Chivers, D. P. & Smith, R. J. F. Chemical alarm signalling in aquatic predator–prey systems: A review and prospectus. Ecoscience 5, 338–352 (1998).Article 

    Google Scholar 
    46.Wisenden, B. D. Olfactory assessment of predation risk in the aquatic environment. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 355, 1205–1208 (2000).CAS 
    Article 

    Google Scholar 
    47.Brown, G. E., Adrian, J. C., Smyth, E., Leet, H. & Brennan, S. Ostariophysan alarm pheromones: Laboratory and field tests of the functional significance of nitrogen oxides. J. Chem. Ecol. 26, 139–154 (2000).CAS 
    Article 

    Google Scholar 
    48.Bertucci, F. et al. Decreased retention of olfactory predator recognition in juvenile surgeon fish exposed to pesticide. Chemosphere 208, 469–475 (2018).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    49.Mitchell, M. D., McCormick, M. I., Ferrari, M. C. O. & Chivers, D. P. Coral reef fishes rapidly learn to identify multiple unknown predators upon recruitment to the reefs. Plos One 6, e15764 (2011).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    50.Palacios, M., Malerba, M. & McCormick, M. Multiple predator effects on juvenile prey survival. Oecologia 188, 417–427 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    51.Auster, P. J., Cortés, J., Alvarado, J. J. & Beita-Jiménez, A. Coordinated hunting behaviors of mixed-species groups of piscivores and associated species at Isla del Coco National Park (Eastern Tropical Pacific). Neotrop. Ichthyol. 17, e180165 (2019).Article 

    Google Scholar 
    52.Pandolfi, J. M. et al. Global trajectories of the long-term decline of coral reef ecosystems. Science 301, 955–958 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    53.Cheng, L. et al. 2018 Continues record global ocean warming. Adv. Atmos. Sci. 36, 249–252. https://doi.org/10.1007/s00376-019-8276-x (2019).Article 

    Google Scholar 
    54.Lawton, J. H. & Brown, V. K. Redundancy in ecosystems Vol. 99 (Springer, 1993).
    Google Scholar  More

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    Why stem cells might save the northern white rhino

    OUTLOOK
    29 September 2021

    Why stem cells might save the northern white rhino

    Biologist Jeanne Loring explains how her work could bring endangered animal species back from the brink.

    Julianna Photopoulos

    Julianna Photopoulos

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    Stem-cell researcher Jeanne Loring in her laboratory at Scripps Research.Credit: Nelvin C. Cepeda/SDU-T/Zuma/eyevine

    Up to one million plant and animal species face extinction, many within decades, because of human activities. One of these is the northern white rhinoceros (Ceratotherium simum cottoni). Only two individuals remain, both of them female, making the subspecies functionally extinct. Jeanne Loring, a stem-cell biologist and founding director of the Center for Regenerative Medicine at Scripps Research in La Jolla, California, spoke to Nature about how collecting and reprogramming stem cells could save this species and others from extinction.What does stem-cell research have to do with saving endangered animals?Induced pluripotent stem (iPS) cells, which closely resemble embryonic stem cells, can develop into any tissue in the body, including sperm and eggs. The hope is to generate these reproductive cells from the reprogrammed stem cells of endangered animals, and use them in assisted captive-breeding programmes to rescue the species.How did you get involved in this work?My laboratory set out to make iPS cells from endangered animals in 2008, after we visited the San Diego Zoo Safari Park in California. The previous year, a team led by Shinya Yamanaka, who won a Nobel prize for the work, had become the first to make human iPS cells from skin cells called fibroblasts1, and we had immediately started making them too, to treat neurological diseases. The San Diego Zoo’s Institute for Conservation Research had been collecting and freezing fibroblasts from animals since the 1970s. The institute’s director of conservation genetics, Oliver Ryder, was thinking of using stem cells to try to treat musculoskeletal disorders, but nobody had created iPS cells from endangered species before.
    Part of Nature Outlook: Stem cells
    In 2011, my postdoctoral fellow Inbar Friedrich Ben-Nun was the first to reprogramme stem cells in two animals from endangered species: the northern white rhino and the drill monkey (Mandrillus leucophaeus)2. We’re now focused on saving the northern white rhino — Ryder’s favourite animal — but the techniques we are working on are going to become a standard way of rescuing species from extinction.When did this become a serious venture?Our endangered-species project mostly remained a hobby until 2015, when scientists and conservationists from around the world met in Vienna to explore how cell technologies might aid conservation. We seriously discussed the idea of using stem cells to rescue endangered species, and later published a rescue plan for the northern white rhino3. To begin with, embryos will be created from sperm and egg cells that were collected and stored. They’ll then be implanted into a surrogate mother, a southern white rhino (Ceratotherium simum simum). But we want to be able to create more sperm and eggs from iPS cells and implant them, too — and that’s where our team comes in.After the Vienna meeting, the San Diego Zoo invested in this idea. Staff there built a stem-cell lab and the Rhino Rescue Center, where they brought in six southern white rhinos from Africa, specifically to serve as surrogate mothers for embryos made from northern white rhinos’ cells. The animals should be compatible because southern white and northern white rhinos are closely related, and so have similar reproductive physiologies. A team of reproductive biologists led by Barbara Durrant is now working to perfect the techniques to fertilize eggs in vitro and transfer viable embryos into the southern white rhinos.What progress have you made in creating northern white rhinoceros iPS cells?When we first set out to make the cells from endangered animals, we assumed that human versions of the reprogramming genes would not work in a rhino. So we tried reprogramming the rhino’s fibroblasts with horse genes — the horse is one of the closest relatives of the rhino — but this failed. Surprisingly, the corresponding human genes did work, and we were able to generate pluripotent cells. However, we had used viral vectors to reprogramme the cells, and this has been shown to lead to tumours in mice, so it could not be used for reproduction purposes.After three years of tweaking the technique, we were able to perform the reprogramming without any genetic modification. It’s all trial and error — you just have to keep testing different combinations of variables. Earlier this year, we celebrated a milestone in our efforts to rescue the rhino: Marisa Korody’s lab at the San Diego Zoo was able to reprogramme frozen cells from nine northern white rhinos and two southern white females to become iPS cells4.

    Najin (right) and her daughter Fatu are the world’s only remaining northern white rhinos.Credit: Tony Karumba/AFP via Getty

    How do you hope to create gametes from iPS cells?The major effort now is to make eggs that can be fertilized with sperm collected from adult males. We’re following in the footsteps of other researchers who have had success, mainly with mice so far. For example, in 2016, Katsuhiko Hayashi and his team at Kyushu University in Fukuoka, Japan, artificially engineered egg cells from reprogrammed mouse skin cells, entirely in a dish, and these were used to birth pups that were healthy and fertile5.That technique required ovarian tissue to be co-cultured with the developing eggs to get them to mature, and it’s impossible to get that kind of tissue from rhinos without putting them at risk. But in July, the same team showed that it could make both egg cells and ovarian tissue from iPS cells, which was a huge improvement6.We are now trying to find an efficient way to make the precursors of gametes, known as primordial germ cells, from the iPS cells of northern white rhinos. We know it’s possible — we’ve seen it happen spontaneously in cultures of these iPS cells — but we need to learn how to generate more of them. And then we have to turn those germ cells into eggs and sperm — or at least, something like sperm. Typically, the process of in vitro fertilization (IVF) involves knocking the tail off a sperm cell and injecting the small head directly into the egg, so we might not need to make sperm with tails. The IVF process itself will need to be adapted, however, to the southern white rhino surrogates — we don’t know for sure that it will work as it does in humans, because it’s never been done before.What advantage is there to using stem-cell technology over other approaches, such as cloning?The San Diego Zoo has frozen fibroblasts from 12 northern white rhinos. We didn’t want to clone those animals, because we would still have only the same 12 individuals. But if we make gametes from them instead — sperm from males, eggs from females and, in theory, sperm from females — then we could make various combinations through IVF to get a new, genetically diverse pool of animals that will help the species to survive. We have found that there is sufficient diversity in combining that group of 12 to exceed the diversity of the current population of southern white rhinos.
    More from Nature Outlooks
    Another group, at the Leibniz Institute for Zoo and Wildlife Research in Berlin, is instead harvesting eggs from the two living animals in the hope that they can fertilize them and get new animals that way. I’m perfectly happy if that works, but the challenge is getting enough diversity in the population if you have eggs from only one or two animals.Have you encountered opposition to your iPS-cell-mediated approach?If I were doing this with humans there’d be a lot of debate, but with animals there is less. One criticism is that resources for conservation should be invested differently, for example in restoring natural habitats and educating people. One argument we hear is that there’s no purpose in rescuing a species that will be confined to zoos because of poaching. I don’t know how to stop people from hunting rhinos for their horns, but I will do what I can to try to save an animal that humans have forced into extinction.Are you confident that your work will help to save the northern white rhino?It saddens me that as we’ve made progress in the lab, these animals have been dying out. When we started this project there were 8 of them alive, and now there are only 2: Najin, aged 32, and her daughter Fatu, aged 21, who live in a protected park in Kenya. It’s possible that these last two survivors will be gone by the time we succeed. I hope that’s not the case, but we’re working with cells that have been harvested and frozen, so we can try to bring the species back to life if necessary.I can’t predict how long it will take to get there — things have happened much more slowly than I’d like. But I do hope that our efforts will pay off over the next 10 to 20 years. I want to see a new northern white rhino in my lifetime — before I become ‘extinct’!

    Nature 597, S18-S19 (2021)
    doi: https://doi.org/10.1038/d41586-021-02626-zThis interview has been edited for length and clarity.This article is part of Nature Outlook: Stem cells, an editorially independent supplement produced with the financial support of third parties. About this content.

    References1.Takahashi, K. et al. Cell 131, 861–872 (2007).PubMed 
    Article 

    Google Scholar 
    2.Ben-Nun, I. F. et al. Nature Methods 8, 829–831 (2011).PubMed 
    Article 

    Google Scholar 
    3.Saragusty, J. et al. Zoo Biol. 35, 280–292 (2016).PubMed 
    Article 

    Google Scholar 
    4.Korody, M. L. et al. Stem Cells Dev. 30, 177–189 (2021).PubMed 
    Article 

    Google Scholar 
    5.Hikabe, O. et al. Nature 539, 299–303 (2016).PubMed 
    Article 

    Google Scholar 
    6.Yoshino, T. et al. Science 373, eabe0237 (2021).PubMed 
    Article 

    Google Scholar 
    Download references

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    Protected-area targets could be undermined by climate change-driven shifts in ecoregions and biomes

    1.Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).Article 
    CAS 

    Google Scholar 
    2.Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).Article 
    CAS 

    Google Scholar 
    3.IPCC. Intergovernmental Panel on Climate Change). 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group I to IPCC AR5. (Cambridge University Press, 2014).4.Bruner, A. G., Gullison, R. E., Rice, R. E. & da Fonseca, G. A. B. Effectiveness of parks in protecting tropical biodiversity. Science 291, 125 LP–125128 (2001).Article 

    Google Scholar 
    5.Gray, C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7, 12306 (2016).CAS 
    Article 

    Google Scholar 
    6.Watson, J. E. M. et al. Set a global target for ecosystems. Nature 578, 360–362 (2020).7.Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).8.Griscom, B. W. et al. Natural climate solutions. Proc. Natl. Acad. Sci. 114, 11645 LP–11611650 (2017).Article 
    CAS 

    Google Scholar 
    9.Keith, D. A. et al. The IUCN red list of ecosystems: motivations, challenges, and applications. Conserv. Lett. 8, 214–226 (2015).Article 

    Google Scholar 
    10.Beyer, H. L., Venter, O., Grantham, H. S. & Watson, J. E. M. Substantial losses in ecoregion intactness highlight urgency of globally coordinated action. Conserv. Lett. 13, 1–9 (2020).Article 

    Google Scholar 
    11.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).Article 

    Google Scholar 
    12.Chauvenet, A. L. M. et al. To achieve big wins for terrestrial conservation, prioritize protection of ecoregions closest to meeting targets. One Earth 2, 479–486 (2020).Article 

    Google Scholar 
    13.Wilson, E. O. Half Earth: Our Planets Fight for Life (W.W. Norton and Company, 2016).14.Polak, T. et al. Efficient expansion of global protected areas requires simultaneous planning for species and ecosystems. R. Soc. Open Sci. 2, 150107 (2015).Article 

    Google Scholar 
    15.Visconti, B. P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).CAS 

    Google Scholar 
    16.Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 4787 (2019).Article 
    CAS 

    Google Scholar 
    17.Finsinger, W., Giesecke, T., Brewer, S. & Leydet, M. Emergence patterns of novelty in European vegetation assemblages over the past 15 000 years. Ecol. Lett. 20, 336–346 (2017).Article 

    Google Scholar 
    18.Fordham, D. A. et al. Using paleo-archives to safeguard biodiversity under climate change. Science 369 (2020).19.Jackson, S. T. Vegetation, environment, and time: the origination and termination of ecosystems. J. Veg. Sci. 17, 549–557 (2006).Article 

    Google Scholar 
    20.Hoffmann, S. & Beierkuhnlein, C. Climate change exposure and vulnerability of the global protected area estate from an international perspective. Divers. Distrib. 26, 1496–1509 (2020).Article 

    Google Scholar 
    21.Garcia, R. A., Cabeza, M., Rahbek, C. & Araujo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579–1247579 (2014).Article 
    CAS 

    Google Scholar 
    22.Abatzoglou, J. T., Dobrowski, S. Z. & Parks, S. A. Multivariate climate departures have outpaced univariate changes across global lands. Sci. Rep. 10 (2020).23.Heubes, J. et al. Modelling biome shifts and tree cover change for 2050 in West Africa: Biome shifts and tree cover change in West Africa. J. Biogeogr. 38, 2248–2258 (2011).Article 

    Google Scholar 
    24.Scholze, M., Knorr, W., Arnell, N. W. & Prentice, I. C. A climate-change risk analysis for world ecosystems. Proc. Natl. Acad. Sci. 103, 13116–13120 (2006).CAS 
    Article 

    Google Scholar 
    25.Salazar, L. F. & Nobre, C. A. Climate change and thresholds of biome shifts in Amazonia: CLIMATE CHANGE AND AMAZON BIOME SHIFTS. Geophys. Res. Lett. 37, n/a–n/a (2010).Article 

    Google Scholar 
    26.Yu, D., Liu, Y., Shi, P. & Wu, J. Projecting impacts of climate change on global terrestrial ecoregions. Ecol. Indic. 103, 114–123 (2019).Article 

    Google Scholar 
    27.Iwamura, T., Guisan, A., Wilson, K. A. & Possingham, H. P. How robust are global conservation priorities to climate change? Glob. Environ. Change 23, 1277–1284 (2013).Article 

    Google Scholar 
    28.Littlefield, C. E., Krosby, M., Michalak, J. L. & Lawler, J. J. Connectivity for species on the move: supporting climate-driven range shifts. Front. Ecol. Environ. 17, 270–278 (2019).Article 

    Google Scholar 
    29.McGuire, J. L., Lawler, J. J., McRae, B. H., Nuñez, T. A. & Theobald, D. M. Achieving climate connectivity in a fragmented landscape. Proc. Natl. Acad. Sci. 113, 7195 LP–7197200 (2016).Article 
    CAS 

    Google Scholar 
    30.CBD. Zero Draft of post-2020 biodiversity framework. Secr. Conv. Biol. Divers. 1–14 (2020).31.Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. 6 (2020).32.Batllori, E., Parisien, M. A., Parks, S. A., Moritz, M. A. & Miller, C. Potential relocation of climatic environments suggests high rates of climate displacement within the North American protection network. Glob. Change Biol. 23, 3219–3230 (2017).Article 

    Google Scholar 
    33.Hole, D. G. et al. Projected impacts of climate change on a continent-wide protected area network. Ecol. Lett. 12, 420–431 (2009).Article 

    Google Scholar 
    34.Corlett, R. T. & Tomlinson, K. W. Climate change and edaphic specialists: irresistible force meets immovable object? Trends Ecol. Evol. 35, 367–376 (2020).Article 

    Google Scholar 
    35.Svenning, J. C. et al. The influence of interspecific interactions on species range expansion rates. Ecography 37, 1198–1209 (2014).Article 

    Google Scholar 
    36.Urban, M. C., Zarnetske, P. L. & Skelly, D. K. Moving forward: dispersal and species interactions determine biotic responses to climate change. Ann. N. Y. Acad. Sci. 1297, 44–60 (2013).
    Google Scholar 
    37.Alagador, D., Cerdeira, J. O. & Araújo, M. B. Shifting protected areas: scheduling spatial priorities under climate change. J. Appl. Ecol. 51, 703–713 (2014).Article 

    Google Scholar 
    38.Araujo. Climate Change and Spatial Conservation Planning. Spatial Conservation Prioritization: Quantitative Methods and Computational Tools (Oxford Univ. Press, 2009).39.Woodward, F. I. Climate and Plant Distribution (Cambridge Univ. Press, 1987).40.Stephenson, N. L. Climatic control of vegetation distribution: the role of the water balance. Am. Nat. 135, 649–670 (1990).Article 

    Google Scholar 
    41.Burke, K. D. et al. Differing climatic mechanisms control transient and accumulated vegetation novelty in Europe and eastern North America. Philos. Trans. R. Soc. B Biol. Sci. 374, 20190218 (2019).42.Williams, J. W., Jackson, S. T. & Kutzbach, J. E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl. Acad. Sci. 104, 5738 LP–5735742 (2007).Article 
    CAS 

    Google Scholar 
    43.OECD. The post-2020 biodiversity framework: targets, indicators and measurability implications at global and national level. (2019).44.Carroll, C. & Noss, R. F. Rewilding in the face of climate change. Conserv. Biol. 00, 1–13 (2020).
    Google Scholar 
    45.Lovejoy, T. E. & Hannah, L. Avoiding the climate failsafe point. Sci. Adv. 4 (2018).46.Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch‐Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. Glob. Change Biol. 25, 811–826 (2019).Article 

    Google Scholar 
    47.Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl. Acad. Sci. 106, 9322–9327 (2009).CAS 
    Article 

    Google Scholar 
    48.Franklin, J. F. & Lindenmayer, D. B. Importance of matrix habitats in maintaining biological diversity. Proc. Natl. Acad. Sci. 106, 349–350 (2009).CAS 
    Article 

    Google Scholar 
    49.Galán-Acedo, C. et al. The conservation value of human-modified landscapes for the world’s primates. Nat. Commun. 10, 152 (2019).Article 
    CAS 

    Google Scholar 
    50.Boesing, A. L., Nichols, E. & Metzger, J. P. Biodiversity extinction thresholds are modulated by matrix type. Ecography 41, 1520–1533 (2018).Article 

    Google Scholar 
    51.Carroll, C., Lawler, J. J., Roberts, D. R. & Hamann, A. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10, e0140486 (2015).52.Hamann, A., Roberts, D. R., Barber, Q. E., Carroll, C. & Nielsen, S. E. Velocity of climate change algorithms for guiding conservation and management. Glob. Change Biol. 21, 997–1004 (2015).Article 

    Google Scholar 
    53.Dobrowski, S. Z. & Parks, S. A. Climate change velocity underestimates climate change exposure in mountainous regions. Nat. Commun. 7 (2016).54.Parks, S. A., Carroll, C., Dobrowski, S. Z. & Allred, B. W. Human land uses reduce climate connectivity across North America. Glob. Change Biol. 26 (2020).55.Carroll, C., Parks, S. A., Dobrowski, S. Z. & Roberts, D. R. Climatic, topographic, and anthropogenic factors determine connectivity between current and future climate analogs in North America. Glob. Change Biol. 24 (2018).56.Vos, C. C. et al. Adapting landscapes to climate change: examples of climate-proof ecosystem networks and priority adaptation zones. J. Appl. Ecol. 45, 1722–1731 (2008).Article 

    Google Scholar 
    57.Hannah, L. et al. Fine-grain modeling of species’ response to climate change: holdouts, stepping-stones, and microrefugia. Trends Ecol. Evol. 29, 390–397 (2014).Article 

    Google Scholar 
    58.Fitzpatrick, M. C. & Dunn, R. R. Contemporary climatic analogs for 540 North American urban areas in the late 21st century. Nat. Commun. 10, 614 (2019).CAS 
    Article 

    Google Scholar 
    59.Beale, C. M., Lennon, J. J., Yearsley, J. M., Brewer, M. J. & Elston, D. A. Regression analysis of spatial data. Ecol. Lett. 13, 246–264 (2010).Article 

    Google Scholar 
    60.Dormann, C. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).Article 

    Google Scholar 
    61.Mahony, C. R., Cannon, A. J., Wang, T. & Aitken, S. N. A closer look at novel climates: new methods and insights at continental to landscape scales. Glob. Change Biol. 23, 3934–3955 (2017).Article 

    Google Scholar 
    62.Fitzpatrick, M. C. et al. How will climate novelty influence ecological forecasts? Using the quaternary to assess future reliability. Glob. Change Biol. 24, 3575–3586 (2018).Article 

    Google Scholar 
    63.Mahony, C. R., MacKenzie, W. H. & Aitken, S. N. Novel climates: trajectories of climate change beyond the boundaries of British Columbia’s forest management knowledge system. For. Ecol. Manag. 410, 35–47 (2018).Article 

    Google Scholar 
    64.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (1998).Article 

    Google Scholar 
    65.Smith, J. R. et al. A global test of ecoregions. Nat. Ecol. Evol. 2, 1889–1896 (2018).Article 

    Google Scholar 
    66.Stephenson, N. L. Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales. J. Biogeogr. 25, 855–870 (1998).Article 

    Google Scholar 
    67.Corlett, R. T. & Westcott, D. A. Will plant movements keep up with climate change? Trends Ecol. Evol. 28, 482–488 (2013).Article 

    Google Scholar 
    68.Svenning, J. C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).Article 

    Google Scholar 
    69.Davis, K. T. et al. Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration. Proc. Natl. Acad. Sci. U.S.A. 116, 6193–6198 (2019).70.Rodriguez Mega, E. Apocalypic fires are ravaging the worlds largest tropical wetland. Nature 586, 20–21 (2020).71.van Oldenborgh, G. J. et al. Attribution of the Australian bushfire risk to anthropogenic climate change. Nat. Hazards Earth Syst. Sci. https://doi.org/10.5194/nhess-2020-69 (2020).72.Wintle, B. A. et al. Global synthesis of conservation studies reveals the importance of small habitat patches for biodiversity. Proc. Natl. Acad. Sci. 116, 909 LP–909914 (2019).Article 
    CAS 

    Google Scholar 
    73.Taylor, P. G. et al. Temperature and rainfall interact to control carbon cycling in tropical forests. Ecol. Lett. 20, 779–788 (2017).74.Parks, S. A. et al. How will climate change affect wildland fire severity in the western US? Environ. Res. Lett. 11, 035002 (2016).75.Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5 (2018).76.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).Article 

    Google Scholar 
    77.Mitchell, T. D. Pattern scaling: an examination of the accuracy of the technique for describing future climates. Clim. Change 60, 217–242 (2003).CAS 
    Article 

    Google Scholar 
    78.Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).Article 

    Google Scholar 
    79.Bowman, J., Jaeger, J. A. G. & Fahrig, L. Dispersal distance of mammal is proportional to home range size. Ecology 83, 2049–2055 (2002).Article 

    Google Scholar 
    80.Smith, A. M. & Green, D. Dispersal and the metapopulation paradigm in amphibian ecology and conservation: are all amphibian populations metapopulations? Ecography 28, 110–128 (2005).Article 

    Google Scholar 
    81.Sutherland, G., Harestad, A. S., Price, K. & Lertzman, K. Scaling of natal dispersal distances in terrestrial birds and mammals. Conserv. Ecol. 4 (2000).82.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

    Google Scholar 
    83.Michalak, J. L., Lawler, J. J., Roberts, D. R. & Carroll, C. Distribution and protection of climatic refugia in North America. Conserv. Biol. 32, 1414–1425 (2018).Article 

    Google Scholar  More

  • in

    Population genetic structure of raccoons as a consequence of multiple introductions and range expansion in the Boso Peninsula, Japan

    1.Millennium Ecosystem Assessment. Ecosystems and Human Well-Being Vol. 5, 563 (Island Press, 2005).
    Google Scholar 
    2.Parker, I. M. et al. Impact: Toward a framework for understanding the ecological effects of invaders. Biol. Invas. 1(1), 3–19 (1999).Article 

    Google Scholar 
    3.Crowl, T. A., Crist, T. O., Parmenter, R. R., Belovsky, G. & Lugo, A. E. The spread of invasive species and infectious disease as drivers of ecosystem change. Front. Ecol. Environ. 6(5), 238–246 (2008).Article 

    Google Scholar 
    4.Mazza, G., Tricarico, E., Genovesi, P. & Gherardi, F. Biological invaders are threats to human health: An overview. Ethol. Ecol. Evol. 26, 112–129 (2014).Article 

    Google Scholar 
    5.Pimentel, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien invasive species in the United States. Ecol. Econ 52, 273–288 (2005).Article 

    Google Scholar 
    6.Vilà, M. et al. How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front. Ecol. Environ. 8(3), 135–144 (2010).Article 

    Google Scholar 
    7.Lindenmayer, D. B. & Likens, G. E. Adaptive monitoring: A new paradigm for long-term research and monitoring. Trends Ecol. Evol. 24(9), 482–486 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Lawson Handley, L.-J. et al. Ecological genetics of invasive alien species. Biocontrol 56, 409–428 (2011).Article 

    Google Scholar 
    9.Fischer, M. L. et al. Multiple founder effects are followed by range expansion and admixture during the invasion process of the raccoon (Procyon lotor) in Europe. Divers. Distrib. 23(4), 409–420 (2017).Article 

    Google Scholar 
    10.Salgado, I. Is the raccoon (Procyon lotor) out of control in Europe?. Biodivers. Conserv. 27, 2243–2256 (2018).Article 

    Google Scholar 
    11.Asada, M. “Lag-phase management” as a population management method in low density areas in sika deer (Cervus nippon) and racoon (Procyon lotor). Honyurui Kagaku (Mamm. Sci.) 53(2), 243–255 (2013).
    Google Scholar 
    12.Gehrt, S. D. & Fritzell, E. K. Duration of familial bonds and dispersal patterns for raccoons in south Texas. J. Mammal. 79(3), 859–872. https://doi.org/10.2307/1383094 (1998).Article 

    Google Scholar 
    13.Gascoigne, J., Berec, L., Gregory, S. & Courchamp, F. Dangerously few liaisons: A review of mate-finding Allee effects. Popul. Ecol. 51(3), 355–372 (2009).Article 

    Google Scholar 
    14.Porter, W. F., Mathews, N. E., Underwood, H. B., Sage, R. W. & Behrend, D. F. Social organization in deer: Implications for localized management. Environ. Manage. 15(6), 809–814 (1991).Article 
    ADS 

    Google Scholar 
    15.Long, J. Introduced Mammals of the World: Their History Distribution and Influence (CSIRO Publishing, 2003).Book 

    Google Scholar 
    16.Gehrt, S. D. Wild mammals of North America. In Raccoons and Allies (eds Feldhamer, G. A. et al.) 611–634 (CABI, 2003).
    Google Scholar 
    17.Ikeda, T., Asano, M., Matoba, Y. & Abe, G. Present status of invasive alien raccoon and its impact in Japan. Glob. Environ. Res. 8, 125–131 (2004).
    Google Scholar 
    18.Ministry of the Environment Government of Japan. The Birds and Beasts (Bears) Need Care Habitation Distribution Survey in 2017. Survey Report, Raccoon, Palm civet, Nutria (2018).19.Okuyama, M. W. et al. Genetic population structure of invasive raccoons (Procyon lotor) in Hokkaido, Japan: Unique phenomenon caused by pet escape or abandonment. Sci. Rep. 10(1), 1–10 (2020).Article 
    CAS 

    Google Scholar 
    20.Ochiai, K., Ishii, M. & Furukawa, T. Invasion and distribution of the raccoon, Procyon lotor, in Chiba Prefecture, Central Japan. J. Nat. Hist. Museum Inst. 7, 21–27 (2002).
    Google Scholar 
    21.Asada, M. Bayesian estimation of population size in raccoon (Procyon lotor) using state-space model based on removal sampling. Honyurui Kagaku (Mamm. Sci.) 54, 207–218 (2014).
    Google Scholar 
    22.Sugai, T., Matsushima, H. & Mizuno, K. Last 400 ka landform evolution of the Kanto Plain: Under the influence of concurrent glacio-eustatic sea level changes and tectonic activity. J. Geogr. Chigaku Zasshi 122(6), 921–948 (2013).CAS 
    Article 

    Google Scholar 
    23.Bagan, H. & Yamagata, Y. Landsat analysis of urban growth: How Tokyo became the world’s largest megacity during the last 40 years. Remote Sens. Environ. 127, 210–222 (2012).Article 
    ADS 

    Google Scholar 
    24.Japan Meteorology Agency. Search Past Weather Data (2021). http://www.data.jma.go.jp/obd/stats/etrn/view/monthly_h1.php?prec_no=45&block_no=00&year=2020&month=&day=&view=p1. Accessed 28 Feb 2021.25.Geospatial Information Authority of Japan. https://maps.gsi.go.jp/. Accessed 4 Apr 2021.26.Frantz, A. C. et al. Limited mitochondrial DNA diversity is indicative of a small number of founders of the German raccoon (Procyon lotor) population. Eur. J. Wildl. Res. 59, 665–674 (2013).Article 

    Google Scholar 
    27.Cullingham, C. I., Kyle, C. J., Pond, B. A. & White, B. N. Genetic structure of raccoons in eastern North America based on mtDNA: Implications for subspecies designation and rabies disease dynamics. Can. J. Zool. 86, 947–958 (2008).CAS 
    Article 

    Google Scholar 
    28.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35(6), 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Cullingham, C. I., Kyle, C. J. & White, B. N. Isolation, characterization and multiplex genotyping of raccoon tetranucleotide microsatellite loci. Mol. Ecol. Notes 6(4), 1030–1032 (2006).CAS 
    Article 

    Google Scholar 
    30.Fike, J. A., Drauch, A. M., Beasley, J. C., Dharmarajan, G. & Rhodes, O. E. Development of 14 multiplexed microsatellite loci for raccoons Procyon lotor: Primer note. Mol. Ecol. Notes 7(3), 525–527 (2007).CAS 
    Article 

    Google Scholar 
    31.Siripunkaw, C. et al. Isolation and characterization of polymorphic microsatellite loci in the raccoon (Procyon lotor). Mol. Ecol. Resour. 8(1), 199–201 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Koressaar, T. & Remm, M. Enhancements and modifications of primer design program Primer3. Bioinformatics 23(10), 1289–1291 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    33.Untergasser, A. et al. Primer3-new capabilities and interfaces. Nucleic Acids Res. 40(15), 1–12 (2012).Article 
    CAS 

    Google Scholar 
    34.Brownstein, M. J., Carpten, J. D. & Smith, J. R. Modulation of non-templated nucleotide addition by Taq DNA polymerase: Primer modifications that facilitate genotyping. Biotechniques 20(6), 1004–1010 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Pompanon, F., Bonin, A., Bellemain, E. & Taberlet, P. Genotyping errors: Causes, consequences and solutions. Nat. Rev. Genet. 6(11), 847–859 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    36.Holleley, C. E. & Geerts, P. G. Multiplex Manager 1.0: A cross-platform computer program that plans and optimizes multiplex PCR. Biotechniques 46(7), 511–517 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Yoshida, K., Hirose, M., Hasegawa, M. & Inoue, E. Mitochondrial DNA analyses of invasive raccoons (Procyon lotor) in the Boso Peninsula, Japan. Mamm. Study 45(1), 1–6 (2020).Article 

    Google Scholar 
    38.Bandelt, H. J., Forster, P. & Rohl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Cullingham, A., Zalewski, A., Bartoszewicz, M., Okarma, H. & Jędrzejewska, E. The genetic structure of raccoon introduced in Central Europe reflects multiple invasion pathways. Biol. Invas. 16, 1611–1625 (2014).Article 

    Google Scholar 
    40.Fischer, M. L. et al. Historical invasion records can be misleading: genetic evidence for multiple introductions of invasive raccoons (Procyon lotor) in Germany. PLoS ONE 10(5), e0125441 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    41.Alda, F. et al. Genetic evidence for multiple introduction events of raccoons (Procyon lotor) in Spain. Biol. Invas. 15, 687–698 (2013).Article 

    Google Scholar 
    42.Biedrzycka, A., Zalewski, A., Bartoszewicz, M., Okarma, H. & Jędrzejewska, E. The genetic structure of raccoon introduced in Central Europe reflects multiple invasion pathways. Biol. Invas. 16(8), 1611–1625 (2014).Article 

    Google Scholar 
    43.Santonastaso, T. T., Dubach, J., Hauver, S. A., Graser, W. H. & Gehrt, S. D. Microsatellite analysis of raccoon (Procyon lotor) population structure across an extensive metropolitan landscape. J. Mammal. 93(2), 447–455 (2012).Article 

    Google Scholar 
    44.Peakall, R. O. D. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6(1), 288–295 (2006).Article 

    Google Scholar 
    45.Peakall, R. & Smouse, P. E. GenALEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28(19), 2537–2539 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Goudet, J. FSTAT, a Program to Estimate and Test Gene Diversity and Fixation Indices (Version 2.9. 3). http://www2.unil.ch/popgen/softwares/fstat.htm (2001).47.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155(2), 945–959 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Earl, D. A. & von Holdt, B. M. Structure harvester: A website and program for visualizing structure output and implementing the Evanno method. Conserv. Genet. Resour. 4(2), 359–361 (2012).Article 

    Google Scholar 
    49.Jakobsson, M. & Rosenberg, N. A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14), 1801–1806. https://doi.org/10.1093/bioinformatics/btm233 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Rosenberg, N. A. Distruct: A program for the graphical display of population structure. Mol. Ecol. Notes 4(1), 137–138 (2004).Article 

    Google Scholar 
    51.Ratnayeke, S., Tuskan, G. A. & Pelton, M. R. Genetic relatedness and female spatial organization in a solitary carnivore, the raccoon, Procyon lotor. Mol. Ecol. 11(6), 1115–1124 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Abdelkrim, J., Pascal, M., Calmet, C. & Samadi, S. Importance of assessing population genetic structure before eradication of invasive species: Examples from insular Norway rat populations. Conserv. Biol. 19(5), 1509–1518 (2005).Article 

    Google Scholar  More

  • in

    Changes in microbial community phylogeny and metabolic activity along the water column uncouple at near sediment aphotic layers in fjords

    The present study was carried out in six fjords within New Zealand’s Fiordland system, specifically Breaksea Sound, Chalky Inlet, Doubtful Sound, Dusky Sound, Long Sound, and Wet Jacket Arm, as described in Tobias-Hünefeldt et al.15. Analyses were divided into three categories: (1) a multi-fjord analysis comprising five of the tested fjords (excluding Long Sound), (2) a high-resolution study along Long Sound’s horizontal axis, and (3) a depth profile of Long Sound’s deepest location (at 421 m). These categories were established to identify trends across multiple fjords, and then test the trends using a fjord analysed at a higher resolution. Total community composition (via 16S and 18S rRNA gene sequencing) and metabolic potential did not significantly covary across the five studied fjords (Mantel, r  More

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    The effect of estuarine system on the meiofauna and nematodes in the East Siberian Sea

    1.Stein, R. & Macdonald, R. W. Organic carbon budget: Arctic Ocean vs. global ocean. In The Organic Carbon Cycle in the Arctic Ocean (eds Stein, R. & Macdonald, R. W.) (Springer, 2004).Chapter 

    Google Scholar 
    2.Barber, D. G. & Massom, R. A. The role of sea ice in Arctic and Antarctic polynyas. Oceanogr. Ser. 74, 1–54. https://doi.org/10.1016/S0422-9894(06)74001-6 (2007).Article 

    Google Scholar 
    3.Sheremetevskiy, A. M. Role of meiobenthos of the South Sakhalin shelf, Eastern Kamchatka, and Novosibirsk shallow water area. Issledovaniya Fauny Morei 35, 43 (1987).
    Google Scholar 
    4.Golikov, A. N. Ecosystems of the New Siberian shoals and fauna of the Laptev Sea and adjacent waters of the Arctic Ocean (in Russian). Explor. Fauna Seas 37, 4 (1990).
    Google Scholar 
    5.Golikov, A. N. Fauna of the East Siberian Sea. Part III. Explor. Fauna Seas 49, 57 (1994).
    Google Scholar 
    6.Sirenko, B. I. & Denisenko, S. G. Fauna of the East Siberian Sea, distribution patterns and structure of bottom communities. Explor. Fauna Seas 66, 74 (2010).
    Google Scholar 
    7.Sirenko, B. I. List of species of free-living invertebrates of Eurasian Arctic seas and adjacent deep waters. Explor. Fauna Seas 51(59), 1–76 (2001).
    Google Scholar 
    8.Schmidt-Rhaesa, A. Handbook of Zoology: Gastrotricha, Cycloneuralia, Gnathifera Vol. 2, 608 (De Gruyter, 2020).
    Google Scholar 
    9.Udalov, A. et al. Integrity of benthic assemblages along the arctic estuarine-coastal system. Ecol. Indic. 121, 107115. https://doi.org/10.1016/j.ecolind.2020.107115 (2021).Article 

    Google Scholar 
    10.Portnova, D., Fedyaeva, M., Udalov, A. & Tchesunov, A. Community structure of nematodes in the Laptev Sea shelf with notes on the lives of ice nematodes. Reg. Stud. Mar. Sci. 31, 100757. https://doi.org/10.1016/j.rsma.2019.100757 (2019).Article 

    Google Scholar 
    11.Gallucci, F., Moens, T. & Fonseca, G. Small-scale spatial patterns of meiobenthos in the Arctic deep sea. Mar. Biodivers. 39(1), 9–25. https://doi.org/10.1007/s12526-009-0003-x (2009).Article 

    Google Scholar 
    12.Lei, Y., Stumm, K., Volkenborn, N., Wickham, S. A. & Berninger, U. G. Impact of Arenicola marina (Polychaeta) on the microbial assemblages and meiobenthos in a marine intertidal flat. Mar. Biol. 157(6), 1271–1282. https://doi.org/10.1007/s00227-010-1407-7 (2010).Article 

    Google Scholar 
    13.Flint, M. V., Poyarkov, S. G. & Rymsky-Korsakov, N. A. Ecosystems of the Siberian Arctic Seas-2017 (Cruise 69 of the R/V Akademik Mstislav Keldysh). Oceanology 58(2), 315–318. https://doi.org/10.1134/S0001437018020042 (2018).ADS 
    Article 

    Google Scholar 
    14.Garlitska, L. A. & Azovsky, A. I. Benthic harpacticoid copepods of the Yenisei Gulf and the adjacent shallow waters of the Kara Sea. J. Nat. Hist. 50, 2941–2959. https://doi.org/10.1080/00222933.2016.1219410 (2016).Article 

    Google Scholar 
    15.Portnova, D., Garlitska, L., Udalov, A. & Kondar, D. Meiobenthos and nematode community in the Yenisei Bay and adjacent parts of the Kara Sea shelf. Oceanology 57(1), 1–15. https://doi.org/10.1134/S0001437017010155 (2017).Article 

    Google Scholar 
    16.Carmack, E. et al. Toward quantifying the increasing role of oceanic heat in sea ice loss in the new Arctic. Bull. Am. Meteorol. Soc. 96(12), 2079–2105. https://doi.org/10.1175/BAMS-D-13-00177.1 (2005).ADS 
    Article 

    Google Scholar 
    17.Peterson, B. J. et al. Increasing river discharge to the Arctic Ocean. Science 298(5601), 2171–2173. https://doi.org/10.1126/science.1077445 (2002).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    18.Polukhin, A. The role of river runoff in the Kara Sea surface layer acidification and carbonate system changes. ERL 14(10), 105007. https://doi.org/10.1088/1748-9326/ab421e (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Lisitzin, A. P. Marginal filter of the oceans. Oceanology 34(5), 735–743 (1994).CAS 

    Google Scholar 
    20.Moens, T., Braeckman, U., Derycke, S., Fonseca, G., Gallucci, F., Gingold, R., Guilini, Katja, Ingles, J., Leduc, D., Vanaverbeke, J., Van Colen, C., Vanreusel, A, & Vincx, M. Ecology of free-living marine nematodes. In Volume 2 Nematoda, 109–152. De Gruyter (2013)21.Aller, J. Y. & Aller, R. C. General characteristics of benthic faunas on the Amazon inner continental shelf with comparison to the shelf off the Changjiang River, East China Sea. Cont. Shelf Res. 6(1–2), 291–310. https://doi.org/10.1016/0278-4343(86)90065-8 (1986).ADS 
    Article 

    Google Scholar 
    22.Soetaert, K., Vincx, M., Wittoeck, J. & Tulkens, M. Meiobenthic distribution and nematode community structure in five European estuaries. Hydrobiologia 311(1), 185–206. https://doi.org/10.1007/BF00008580 (1995).Article 

    Google Scholar 
    23.Tank, S. E. et al. The processing and impact of dissolved riverine nitrogen in the Arctic Ocean. Estuaries Coast 35, 401–415. https://doi.org/10.1007/s12237-011-9417-3 (2012).CAS 
    Article 

    Google Scholar 
    24.Galtsova, V. V., Lukina, T. G. & Vladimirov, M. V. Meiobenthos of Chaunskaya Bay, East Siberian Sea. Issledovaniya Fauny Morei 48(56), 67–97 (1994).
    Google Scholar 
    25.Coull, B. C. Role of meiofauna in estuarine soft‐bottom habitats. Austral Ecol. 24(4), 327–343 (1999).Article 

    Google Scholar 
    26.Vincx, M., Meire, P., & Heip, C. The distribution of nematodes communities in the Southern Bight of the North Sea. Cah Biol Mar. 31(1), 107–129 (1990).27.Vanaverbeke, J., Gheskiere, T., Steyaert, M., & Vincx, M. Nematode assemblages from subtidal sandbanks in the Southern Bight of the North Sea: effect of small sedimentological differences. J. Sea Res. 48(3), 197–207. https://doi.org/10.1016/S1385-1101(02)00165-X (2002)ADS 
    Article 

    Google Scholar 
    28.Steyaert, M., et al. The importance of fine-scale, vertical profiles in characterising nematode community structure. Estuar Coast Shelf Sci. 58(2), 353–366 (2003).ADS 
    Article 

    Google Scholar 
    29.Alves, A. S., Adão, H., Patrício, J., Neto, J. M., Costa, M. J., & Marques, J. C. Spatial distribution of subtidal meiobenthos along estuarine gradients in two southern European estuaries (Portugal). J. Mar. Biol. Assoc. U. K. 89(8), 1529–1540 (2009).CAS 
    Article 

    Google Scholar 
    30.Garlitska, L. A., Chertoprud, E. S., Portnova, D. A. & Azovsky, A. I. Benthic harpacticoida of the Kara Sea: Species composition and bathymetrically related distribution. Oceanology 59(4), 541–551. https://doi.org/10.1134/S0001437019040064 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    31.Huang, D. et al. Preliminary study on community structures of meiofauna in the middle and eastern Chukchi Sea. Acta Oceanol. Sin. 40(6), 83–91. https://doi.org/10.1007/s13131-021-1777-3 (2021).ADS 
    Article 

    Google Scholar 
    32.Giere, O. Meiobenthology: The Microscopic Motile Fauna in Aquatic Sediments 2nd edn. (Springer, 2009).
    Google Scholar 
    33.Semiletov, I. et al. The East Siberian Sea as a transition zone between Pacific-derived waters and Arctic shelf waters. Geophys. Res. Lett. https://doi.org/10.1029/2005GL022490 (2005).Article 

    Google Scholar 
    34.Miroshnikov, A. Y. et al. Ecological state and mineral-geochemical characteristics of the bottom sediments of the East Siberian Sea. Oceanology 60(4), 595–610. https://doi.org/10.31857/S0030157420040152 (2020).Article 

    Google Scholar 
    35.Frontalini, F. et al. The response of cultured meiofaunal and benthic foraminiferal communities to lead exposure: Results from mesocosm experiments. Environ. Toxicol. Chem. 37(9), 2439–2447. https://doi.org/10.1002/etc.4207 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Fonseca, G. & Soltwedel, T. Deep-sea meiobenthic communities underneath the marginal ice zone off Eastern Greenland. Polar Biol. 30, 607–618. https://doi.org/10.1007/s00300-006-0220-8 (2007).Article 

    Google Scholar 
    37.Portnova, D. & Polukhin, A. Meiobenthos of the eastern shelf of the Kara Sea compared with the meiobenthos of other parts of the sea. Reg. Stud. Mar. Sci. 24, 370–378. https://doi.org/10.1016/j.rsma.2018.10.002 (2018).Article 

    Google Scholar 
    38.Alexeev, D. K., & Galtsova, V. V. Effect of radioactive pollution on the biodiversity of marine benthic ecosystems of the Russian Arctic shelf. Polar Sci. 6(2), 183–195 (2012).ADS 
    Article 

    Google Scholar 
    39.Grzelak, K. & Sørensen, M. V. Diversity and community structure of kinorhynchs around Svalbard: First insights into spatial patterns and environmental drivers. Zool. Anz. 282, 31–43. https://doi.org/10.1016/j.jcz.2019.05.009 (2019).Article 

    Google Scholar 
    40.Landers, S. C. et al. Kinorhynch communities from Alabama coastal waters. Mar. Biol. Res. 16(6–7), 494–504. https://doi.org/10.1080/17451000.2020.1789660 (2020).Article 

    Google Scholar 
    41.Holovachov, O. New and known species of the genus Campylaimus Cobb, 1920 (Nematoda: Araeolaimida: Diplopeltidae) from North European marine habitats. Biodivers. Data J. https://doi.org/10.3897/BDJ.7.e46545 (2007).Article 

    Google Scholar 
    42.Sharma, J. & Bluhm, B. A. Diversity of larger free-living nematodes from macrobenthos ( > 250 μm) in the Arctic deep-sea Canada Basin. Mar. Biodivers. 41(3), 455–465. https://doi.org/10.1007/s12526-010-0060-1 (2010).Article 

    Google Scholar 
    43.Kotwicki, L., Grzelak, K. & Bełdowski, J. Benthic communities in chemical munitions dumping site areas within the Baltic deeps with special focus on nematodes. Deep Sea Res. II 128, 123–130. https://doi.org/10.1016/j.dsr2.2015.12.012 (2016).CAS 
    Article 

    Google Scholar 
    44.Netto, S. A., Pagliosa, P. R., Colling, A., Fonseca, A. L. & Brauk, K. M. Benthic estuarine assemblages from the Southern Brazilian marine ecoregion. Braz. Estuaries. https://doi.org/10.1007/978-3-319-77779-5_6 (2018).Article 

    Google Scholar 
    45.Broman, E., et al. Uncovering diversity and metabolic spectrum of animals in dead zone sediments. Commun. Biol. 3(1), 1–12 (2020).46.Zeppilli, D., et al. Characteristics of meiofauna in extreme marine ecosystems: a review. Mar. Biodiver. 48(1), 35–71 (2018).47.Pérez-García, J. A. et al. Nematode diversity of freshwater and anchialine caves of Western Cuba. PBSW 131(1), 144–155. https://doi.org/10.2988/17-00024 (2018).Article 

    Google Scholar 
    48.Bezzubova, E. M., Seliverstova, A. M., Zamyatin, I. A. & Romanova, N. D. Heterotrophic bacterioplankton of the Laptev and East Siberian Sea shelf affected by freshwater inflow areas. Oceanology 60, 62–73. https://doi.org/10.1134/S0001437020010026 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    49.Vanreusel, A. et al. Meiobenthos of the central Arctic Ocean with special emphasis on the nematode community structure. Deep Sea Res. I 47, 1855–1879. https://doi.org/10.1016/S0967-063728002900007-8 (2000).Article 

    Google Scholar 
    50.Tahseen, Q. Nematodes in aquatic environments: Adaptations and survival strategies. Biodivers. J. 3(1), 13–40 (2012).
    Google Scholar 
    51.Williams, W. J. & Carmack, E. C. The ‘interior’ shelves of the Arctic Ocean: Physical oceanographic setting, climatology and effects of sea-ice retreat on cross-shelf exchange. Prog. Ocean 139, 24–41. https://doi.org/10.1016/j.pocean.2015.07.008 (2015).Article 

    Google Scholar 
    52.Magritsky, D. V. et al. Long-term changes of river water inflow into the seas of the Russian Arctic sector. Polarforschung 87(2), 177–194. https://doi.org/10.2312/polarforschung.87.2.177 (2018).Article 

    Google Scholar 
    53.Anderson, L. G. et al. East Siberian Sea, an Arctic region of very high biogeochemical activity. Biogeosciences 4, 6. https://doi.org/10.5194/bg-8-1745-2011 (2011).CAS 
    Article 

    Google Scholar 
    54.Dmitrienko, I. A. et al. Impact of the Arctic Ocean Atlantic water layer on Siberian shelf hydrography. J. Geophys. Res. Oceans. https://doi.org/10.1029/2009JC006020 (2010).Article 

    Google Scholar 
    55.Stein, R. Arctic Ocean Sediments: Processes, PROXIES, and Paleoenvironment (Elsevier, 2008).
    Google Scholar 
    56.Petrova, V. I., Batova, G. I., Kursheva, A. V. & Litvinenko, I. V. Geochemistry of organic matter of bottom sediments in the rises of the central Arctic Ocean. Russ. Geol. Geophys. 51(1), 88–97. https://doi.org/10.1016/j.rgg.2009.12.008 (2010).ADS 
    Article 

    Google Scholar 
    57.Millero, F. J. Thermodynamics of the carbon dioxide system in oceans. GCA 59(4), 661–677. https://doi.org/10.12691/wjce-3-6-1 (1995).ADS 
    CAS 
    Article 

    Google Scholar 
    58.Pavlova, G. Y. et al. Intercalibration of Bruevich’s method to determine the total alkalinity in seawater. Oceanology 48, 438. https://doi.org/10.1134/S0001437008030168 (2008).ADS 
    Article 

    Google Scholar 
    59.Dickson, A. G. & Goyet, C. Handbook of Methods for the Analysis of the Various Parameters of the Carbon Dioxide System in Sea Water. Version 2 (No. ORNL/CDIAC-74) (1994).60.Dickson, A. G., Afghan, J. D. & Anderson, G. C. Reference materials for oceanic CO2 analysis: A method for the certification of total alkalinity. Mar. Chem. 80, 185–197. https://doi.org/10.1016/S0304-4203(02)00133-0 (2003).CAS 
    Article 

    Google Scholar 
    61.Lewis, E. & Wallace, D. W. R. Program Developed for CO2 System Calculations. ORNL/CDIAC-105 (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, 1998).Book 

    Google Scholar 
    62.Shiklomanov, A. I., Holmes, J. W., McClelland, S. E., Tank, R. & Spencer, G.M. Arctic Great Rivers Observatory. Discharge Dataset, Version 20200801 (2020).63.Niemistö, L. A gravity corer for studies of soft sediments. Merentutkimuslait. Julk./Havsforskningsinst. Skr. 238, 33–38 (1974).
    Google Scholar 
    64.Eleftheriou, A. Methods for the Study of Marine Benthos (Wiley, 2013).Book 

    Google Scholar 
    65.Wieser, W. Beziehungen zwischen Mundhöhlengestalt, Ernährungsweise und Vorkommen bei freilebenden, marinen Nematoden. Ark. Zool. 2, 439–484 (1953).
    Google Scholar 
    66.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001).
    Google Scholar 
    67.Heip, C. & Herman, P. Indices of diversity and evenness. Oceanis 24(4), 61–88 (2001).
    Google Scholar  More

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    Nutritional resources of the yeast symbiont cultivated by the lizard beetle Doubledaya bucculenta in bamboos

    Insects and bamboosFive internodes (length: mean ± SD = 44.8 ± 1.1 cm, n = 5; diameter in the middle part of internodes: 21.4 ± 0.8 mm, n = 5) of five living mature culms of P. simonii bamboo were sampled at Kawaminami, Miyazaki Prefecture, Japan [32°9′ N, 131°29′ E] on 6 June, 2019. Per internode, four semi-cylindrical strips (ca. 15 × 2 cm) were made and stored at − 25 °C until use.To obtain fungus-free larvae of D. bucculenta, we sampled five beetle eggs from P. simonii bamboo obtained at Toyota, Aichi Prefecture, Japan [35°9′ N, 137°13′ E] on 9 May, 2019 in the laboratory from ovipositing females collected at Kawaminami on 10 and 11 April, 2019. The eggs were immersed in 99.5% ethanol for 10 s followed by 70% ethanol for 10 s for surface sterilization and then individually placed on potato dextrose agar (PDA) (Difco, Detroit, MI, USA) plates. The plates were incubated at 25 °C in the dark until 30 days after larval hatching to confirm the absence of the formation of yeast or other microbial colonies. Consequently, all five larvae hatched successfully and aseptically.The bamboo used in this study was morphologically identified using the literature29. This is native to the study areas and no other host bamboo species are distributed there29. Therefore, no voucher specimen of this bamboo has been deposited in a publicly available herbarium. No specific permits were required for the described field studies. The location is not privately-owned or protected in any way. The field studies did not involve endangered or protected species. All applicable international, national, and/or institutional guidelines for the care and use of animals and plants were followed. This study is reported in accordance with ARRIVE guidelines.Component analyses of bamboo tissuesFor YP and LP, the yeast W. anomalus originating from D. bucculenta in Kawaminami (strain: DBL05Kawaminami) was cultured on a 9-cm PDA plate to obtain enough biomass for further experiments. Afterwards, yeast cells were suspended in ca. 10 mL of sterilized water, and were inoculated on the inner surface of the autoclaved internode strips using an autoclaved tissue paper immersed with the yeast suspension. For LP, additionally, the fungus-free 2nd instar larvae (weight: mean ± SD = 2.4 ± 0.4 mg, n = 5) were individually placed on the yeast-inoculated strips. Each of these yeast-inoculated and yeast-and-larva-inoculated strips was then put in a sterilized test tube (3.0 cm in diameter and 20 cm tall) with moistened cotton placed at the bottom. Each of the test tubes was covered with a sterilized polypropylene cap, sealed with Parafilm Sealing Film (Pechiney Plastic Packaging, Chicago, IL, USA) on which three small holes were made using a fire-sterilized insect pin to avoid oxygen shortage, and individually put in a plastic zipper bag. These yeasts and insects were incubated at 25 °C in the dark for 47 days for YP (n = 5), and 47 (n = 4) and 73 (n = 1) days until these larvae reached adulthood for LP (adult elytral length: mean ± SD = 9.2 ± 0.4 mm, n = 5). Microbial contamination was invisible to the naked eye.For FP, YP and LP, the inner surface (up to 0.3 mm in thickness, dry weight: 336 to 935 mg) of a strip was sampled using a small U-shaped gouge. In the case of FX, first, the pith of a strip was completely removed, and then xylem tissue (up to 0.5 mm in thickness, dry weight: 729 to 872 mg) was sampled using a small U-shaped gouge. These tissues were individually sampled from five strips derived from five different internodes for each tissue type.Samples were extracted by aqueous ethanol and hydrolyzed by sulfuric acid with reference to the literature30,31,32 as follows. Four types of samples were freeze-dried and pulverized using a rotor-speed mill (Fritsch, PULVERISETTE 14, 0.2 mm mesh). About 80 mg of powdered sample was extracted using 5-mL 80% ethanol aqueous solution (aq.) at 63 °C three times. The volume of the extracts was adjusted to 25 mL, filtered, and analyzed using ion exchange chromatography measurements (extractable sugar analysis). Their extracted residues were hydrolyzed using sulfuric acid as follows: 50-mg samples were immersed in 1.64-g 72% sulfuric acid aq. at 30 °C for 2 h, boiled in 39.4-g 3% sulfuric acid aq. for 4 h, and filtered to collect sulfuric acid residues as sulfuric acid lignin fractions. The volumes of the filtrates were fixed to 100 mL, passed through a sulfuric acid-removing filter (DIONEX OnGuard IIA), and submitted to ion exchange chromatography measurements (structural sugar analysis). For the uronic acid measurements, the sulfuric acid-removing filter was not used.Ion exchange chromatography measurements were conducted using a DIONEX ICS-3000 apparatus. The measurement conditions were as follows: column, CarboPac PA-1 (2.0 mm I.D. × 250 mm L, Dionex corp.); flow rate, 0.3 mL min−1; column temperature, 30 °C; injection volume, 25 µL; eluent, H2O (solvent A), 100 mM NaOHaq. (solvent B), aqueous solution containing 100 mM NaOH and 1.0 M CH3COONa (solvent C), and aqueous solution containing 100 mM NaOH and 150 mM CH3COONa (solvent D). The gradient conditions for monomers, dimers, and uronic acids were as follows: for monomers, with a gradient of B 0.5% C 0% 45 min, C 100% 10 min, B 100% 10 min, B 0.5% C 0% 20 min; for dimers, with a gradient of B 50% C 0% 50 min, C 100% 10 min, B 100% 10 min, B 50% C 0% 15 min; for uronic acids, with a gradient of D 100% 10 min. These extraction, hydrolysis, and measurement procedures were conducted using n = 5 samples. For the structural sugars, their yield was calculated as the dehydrated state. The values of other extractives % were calculated by the subtraction of total extractable sugars % from total extractives %.Elemental analysis (carbon, hydrogen, nitrogen) was conducted by 2400 CHNS Organic Elemental Analyzer (PerkinElmer Japan, Yokohama, Japan). About 1-mg dried samples were burned completely and the produced CO2, H2O, and N2 (after reduction of NOx species) gasses were quantified by a thermal conductivity detector.Means of components of bamboo tissues were compared among tissue types using the Steel–Dwass test after the Kruskal–Wallis test. Calculations were performed using R 3.5.133.Carbon assimilation testThe yeast W. anomalus (DBL05Kawaminami) was cultured aerobically in 20 mL of yeast nitrogen base (YNB) (Difco) containing 0.5% glucose at 25 °C in the dark for 2 days with shaking at 85 rpm. The culture media were centrifuged and cell pellets were suspended in sterile water, in which the OD600 was adjusted to 0.10. Fifty μL of the cell suspension was added into a tube (2 mL) with 1 mL of each of 14 different media containing YNB and one of the following carbon sources: d-glucose, d-galactose, d-mannose, d-xylose, l-arabinose, d-fructose, d-galacturonic acid, d-glucuronic acid, sucrose, cellobiose, starch from corn, xylan from corn, carboxymethyl cellulose, and no carbon source (n = 5 to 6). The concentration of each carbon source was 0.5 g L−1, except for xylan at 1.5 g L−1. The tubes were shaken at 85 rpm and incubated at 25 °C in the dark for 7 days. Afterwards, the presence of visible pellets of yeasts and OD600 were recorded to determine the growth of the strain. The degree of assimilation was scored according to the presence of the pellets and the difference in the turbidity increase (ΔOD600) between culture media containing no and a given carbon source as follows: no growth (without a pellet, ΔOD600  More

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    A doubling of stony coral cover on shallow forereefs at Carrie Bow Cay, Belize from 2014 to 2019

    1.Hughes, T. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).Article 

    Google Scholar 
    3.Brander, L. M., Van Beukering, P. & Cesar, H. S. The recreational value of coral reefs: A meta-analysis. Ecol. Econ. 63, 209–218 (2007).Article 

    Google Scholar 
    4.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 
    5.Bruno, J. F., Sweatman, H., Precht, W. F., Selig, E. R. & Schutte, V. G. Assessing evidence of phase shifts from coral to macroalgal dominance on coral reefs. Ecology 90, 1478–1484 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Hughes, T. Catastrophes, phase shifts, and large-scale degradation of a Caribbean Coral Reef. Science 265, 1547–1551 (1994).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Hughes, T. P., Bellwood, D. R., Folke, C. S., McCook, L. J. & Pandolfi, J. M. No-take areas, herbivory and coral reef resilience. Trends Ecol. Evol. 22, 1–3 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Edgar, G. J. et al. Reef Life Survey: Establishing the ecological basis for conservation of shallow marine life. Biol. Conserv. 252, 108855 (2020).Article 

    Google Scholar 
    10.Chabanet, P., Bigot, L., Garnier, R., Tessier, E. & Moyne-Picard, M. Coral reef monitoring at Reunion island (Western Indian Ocean) using the GCRMN method. Proc. 9th Int. Coral Reef Symp. 2, 873–878 (2000).
    Google Scholar 
    11.Lang, J. C., Marks, K. W., Kramer, P. A., Kramer, P. R. & Ginsburg, R. N. AGRRA Protocols Version 5.4. (2010).12.Cortés, J. et al. The CARICOMP network of Caribbean Marine Laboratories (1985–2007): History, key findings, and lessons learned. Front. Mar. Sci. 5, 519 (2019).Article 

    Google Scholar 
    13.Dethier, M. N., Graham, E. S., Cohen, S. & Tear, L. M. Visual versus random-point percent cover estimations: ‘objective’ is not always better. Mar. Ecol. Prog. Ser. 96, 93–100 (1993).Article 

    Google Scholar 
    14.Beijbom, O., Edmunds, P. J., Kline, D. I., Mitchell, B. G. & Kriegman, D. Automated annotation of coral reef survey images. In 2012 IEEE Conference on Computer Vision and Pattern Recognition 1170–1177 (IEEE, 2012).15.Beijbom, O. et al. Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation. PloS One 10, e0130312 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Williams, I. D. et al. Leveraging automated image analysis tools to transform our capacity to assess status and trends of coral reefs. Front. Mar. Sci. 6, 222 (2019).Article 

    Google Scholar 
    17.Roelfsema, C. et al. Benthic and coral reef community field data for Heron Reef, Southern Great Barrier Reef, Australia, 2002–2018. Sci. Data 8, 1–7 (2021).MathSciNet 
    Article 

    Google Scholar 
    18.Gonzalez-Rivero, M. et al. Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach. Remote Sens. 12, 489 (2020).Article 

    Google Scholar 
    19.Cairns, S. D. Stony corals (Cnidaria: Hydrozoa, Scleractinia) of Carrie Bow Cay, Belize. Smithson. Contrib. Mar. Sci. 21, 271–302 (1982).
    Google Scholar 
    20.Rutzler, K. & Macintyre, I. G. The Atlantic Barrier Reef Ecosystem at Carrie Bow Cay, Belize, 1: Structure and Communities (Smithsonian Institution Press, 1982). https://doi.org/10.5479/si.01960768.12.539.Book 

    Google Scholar 
    21.Rutzler, K. Caribbean coral reef ecosystems: Thirty-five years of smithsonian marine science in Belize. In Proceedings of the Smithsonian Marine Science Symposium (2009).22.McField, M. et al. Mesoamerican Reef Report Card. (2020).23.Cox, C. E. et al. Genetic testing reveals some mislabeling but general compliance with a ban on herbivorous fish harvesting in Belize. Conserv. Lett. 6, 132–140 (2013).Article 

    Google Scholar 
    24.Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5, 9–13 (2005).
    Google Scholar 
    25.Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446 (2018).Article 

    Google Scholar 
    26.R Core Team. A language and environment for statistical computing. (2020).27.Althaus, F. et al. A standardised vocabulary for identifying benthic biota and substrata from underwater imagery: The CATAMI classification scheme. PLoS One 10, e0141039 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    28.Goatley, C. H., Bonaldo, R. M., Fox, R. J. & Bellwood, D. R. Sediments and herbivory as sensitive indicators of coral reef degradation. Ecol. Soc. 21(1), 29 (2016).Article 

    Google Scholar 
    29.Connell, S., Foster, M. & Airoldi, L. What are algal turfs? Towards a better description of turfs. Mar. Ecol. Prog. Ser. 495, 299–307 (2014).Article 

    Google Scholar 
    30.Lozada-Misa, P., Schumacher, B. D. & Vargas-Angel, B. Analysis of benthic survey images via CoralNet: A summary of standard operating procedures and guidelines. Pacific
    Islands Fish. Sci. Cent. Natl. Mar. Fish. Serv. https://doi.org/10.7289/V5%2FAR-PIFSC-H-17-02 (2017).Article 

    Google Scholar 
    31.Obura, D. & Grimsditch, G. Resilience Assessment of Coral Reefs: Assessment Protocol for Coral Reefs, Focusing on Coral Bleaching and Thermal Stress (Citeseer, 2009).
    Google Scholar 
    32.Broeke, J., Pérez, J. M. M., & Pascau, J. Image processing with ImageJ. (Packt Publishing Ltd, 2015).
    Google Scholar 
    33.Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press, 2017).MATH 
    Book 

    Google Scholar 
    34.Fasiolo, M., Nedellec, R., Goude, Y. & Wood, S. N. Scalable visualization methods for modern generalized additive models. J. Comput. Graph. Stat. 29, 78–86 (2020).MathSciNet 
    Article 

    Google Scholar 
    35.Oksanen, J. et al. Community ecology package. R Package Version 2, (2013).36.Arnold, S. N. & Steneck, R. S. Settling into an increasingly hostile world: The rapidly closing ‘“Recruitment Window”’ for corals. PLoS One. https://doi.org/10.1371/journal.pone.0028681 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Mumby, P. J. & Harborne, A. R. Marine reserves enhance the recovery of corals on Caribbean reefs. PLoS One 5, e8657 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    38.Adam, T. C., Burkepile, D. E., Ruttenberg, B. I. & Paddack, M. J. Herbivory and the resilience of Caribbean coral reefs: Knowledge gaps and implications for management. Mar. Ecol. Prog. Ser. 520, 1–20 (2015).Article 

    Google Scholar 
    39.Suchley, A., McField, M. D. & Alvarez-Filip, L. Rapidly increasing macroalgal cover not related to herbivorous fishes on Mesoamerican reefs. PeerJ 4, e2084 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Arnold, S. N., Steneck, R. S. & Mumby, P. J. Running the gauntlet: Inhibitory effects of algal turfs on the processes of coral recruitment. Mar. Ecol. Prog. Ser. 414, 91–105 (2010).Article 

    Google Scholar 
    41.Box, S. J. & Mumby, P. J. Effect of macroalgal competition on growth and survival of juvenile Caribbean corals. Mar. Ecol. Prog. Ser. 342, 139–149 (2007).Article 

    Google Scholar 
    42.Williams, I. & Polunin, N. Large-scale associations between macroalgal cover and grazer biomass on mid-depth reefs in the Caribbean. Coral Reefs 19, 358–366 (2001).Article 

    Google Scholar 
    43.Newman, M. J., Paredes, G. A., Sala, E. & Jackson, J. B. Structure of Caribbean coral reef communities across a large gradient of fish biomass. Ecol. Lett. 9, 1216–1227 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Mumby, P. J., Steneck, R. S., Roff, G. & Paul, V. J. Marine reserves, fisheries ban, and 20 years of positive change in a coral reef ecosystem. Conserv. Biol. https://doi.org/10.1111/cobi.13738 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Aronson, R., Precht, W., Toscano, M. & Koltes, K. The 1998 bleaching event and its aftermath on a coral reef in Belize. Mar. Biol. 141, 435–447 (2002).Article 

    Google Scholar 
    46.Green, D. H., Edmunds, P. J. & Carpenter, R. C. Increasing relative abundance of Porites astreoides on Caribbean reefs mediated by an overall decline in coral cover. Mar. Ecol. Prog. Ser. 359, 1–10 (2008).Article 

    Google Scholar 
    47.Roff, G., Joseph, J. & Mumby, P. J. Multi-decadal changes in structural complexity following mass coral mortality on a Caribbean reef. Biogeosciences 17, 5909–5918 (2020).Article 

    Google Scholar 
    48.Graham, N. & Nash, K. The importance of structural complexity in coral reef ecosystems. Coral Reefs 32, 315–326 (2013).Article 

    Google Scholar 
    49.Aronson, R. B. & Precht, W. F. White-band disease and the changing face of Caribbean coral reefs. Ecol. Etiol. New. Emerg. Mar. Dis. 159, 25–38 (2001).
    Google Scholar 
    50.Aronson, R. B., Macintyre, I. G., Precht, W. F., Murdoch, T. J. & Wapnick, C. M. The expanding scale of species turnover events on coral reefs in Belize. Ecol. Monogr. 72, 233–249 (2002).Article 

    Google Scholar 
    51.McField, M. et al. Status of the Mesoamerican Reef after the 2005 coral bleaching event. Status Caribb. Coral Reefs Bleach. Hurric. In 45–60 (2005).52.Arias-González, J. E. et al. A coral-algal phase shift in Mesoamerica not driven by changes in herbivorous fish abundance. PLoS One 12, e0174855 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    53.Miller, S. Automatically Annotating 175,000+ Images with the CoralNet API. CoralNet (Accessed 23 August 2021); https://coralnet.ucsd.edu/blog/automatically-annotating-175000-images-with-the-coralnet-api/ (2020).54.Muller, E. M., Sartor, C., Alcaraz, N. I. & van Woesik, R. Spatial epidemiology of the stony-coral-tissue-loss disease in Florida. Front. Mar. Sci. 7, 163 (2020).Article 

    Google Scholar 
    55.Alvarez-Filip, L., Estrada-Saldívar, N., Pérez-Cervantes, E., Molina-Hernández, A. & González-Barrios, F. J. A rapid spread of the stony coral tissue loss disease outbreak in the Mexican Caribbean. PeerJ 7, e8069 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Weil, E. et al. Spread of the new coral disease “SCTLD” into the Caribbean: implications for Puerto Rico. Reef Encount. 34, 38–43 (2019).
    Google Scholar 
    57.Heres, M. M., Farmer, B. H., Elmer, F. & Hertler, H. Ecological consequences of Stony Coral Tissue Loss Disease in the Turks and Caicos Islands. Coral Reefs 40, 609–624 (2021).Article 

    Google Scholar 
    58.Walton, C. J., Hayes, N. K. & Gilliam, D. S. Impacts of a regional, multi-year, multi-species coral disease outbreak in Southeast Florida. Front. Mar. Sci. 5, 323 (2018).Article 

    Google Scholar  More