More stories

  • in

    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

  • in

    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

    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

    Download PDF

    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

    Related Articles

    Stem cells

    Stem cells: highlights from research

    The promise and potential of stem cells in Parkinson’s disease

    Stem cells and spinal-cord injuries: an intricate issue

    The chimaera challenge

    The next frontier for human embryo research

    Stem-cell start-ups seek to crack the mass-production problem

    The rise of the assembloid

    Reversing blindness with stem cells

    Subjects

    Conservation biology

    Stem cells

    Zoology

    Latest on:

    Stem cells

    Stem cells
    Outlook 29 SEP 21

    Stem cells and spinal-cord injuries: an intricate issue
    Outlook 29 SEP 21

    Stem cells: highlights from research
    Outlook 29 SEP 21

    Zoology

    Flies sense the world while sleeping
    News & Views 29 SEP 21

    Solar-powered slugs have a bright reproductive future
    Research Highlight 28 SEP 21

    Bright lights at night scramble the sweet song of crickets
    Research Highlight 21 SEP 21

    Jobs

    Associate Professor

    Northwestern University (NU)
    Chicago, IL, United States

    Post Doctoral Training Fellow – Functional Genomics team

    Institute of Cancer Research (ICR)
    London, United Kingdom

    Assistant/Associate Professor in Cancer Disparities

    The University of Texas at El Paso (UTEP)
    El Paso, TX, United States

    Director of the Institute at Brown for Environment and Society (IBES)

    Brown University
    Providence, RI, United States More

  • in

    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

    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

  • in

    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

  • in

    Fine-root traits in the global spectrum of plant form and function

    1.Grime, J. P. Plant Strategies, Vegetation Processes, and Ecosystem Properties (John Wiley and Sons, 2001).2.Reich, P. B. et al. The evolution of plant functional variation: traits, spectra, and strategies. Int. J. Plant Sci. 164, S143–S164 (2003).Article 

    Google Scholar 
    3.Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    4.Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Kattge, J. et al. TRY plant trait database — enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).ADS 
    PubMed 
    Article 

    Google Scholar 
    7.Iversen, C. M. et al. A global Fine-Root Ecology Database to address below-ground challenges in plant ecology. New Phytol. 215, 15–26 (2017).PubMed 
    Article 

    Google Scholar 
    8.Guerrero-Ramírez, N. R. et al. Global root traits (GRooT) database. Glob. Ecol. Biogeogr. 30, 25–37 (2021).Article 

    Google Scholar 
    9.McCormack, M. L. et al. Redefining fine roots improves understanding of below-ground contributions to terrestrial biosphere processes. New Phytol. 207, 505–518 (2015).PubMed 
    Article 

    Google Scholar 
    10.Rasse, D. P., Rumpel, C. & Dignac, M. F. Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant Soil 269, 341–356 (2005).CAS 
    Article 

    Google Scholar 
    11.Eissenstat, D. M. Costs and benefits of constructing roots of small diameter. J. Plant Nutr. 15, 763–782 (1992).Article 

    Google Scholar 
    12.Freschet, G. T., Cornelissen, J. H. C., van Logtestijn, R. S. P. & Aerts, R. Evidence of the ‘plant economics spectrum’ in a subarctic flora. J. Ecol. 98, 362–373 (2010).Article 

    Google Scholar 
    13.Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).Article 

    Google Scholar 
    14.Shen, Y. et al. Linking aboveground traits to root traits and local environment: implications of the plant economics spectrum. Front. Plant Sci. 10, 1412 (2019).Article 

    Google Scholar 
    15.Kramer-Walter, K. R. et al. Root traits are multidimensional: specific root length is independent from root tissue density and the plant economic spectrum. J. Ecol. 104, 1299–1310 (2016).Article 

    Google Scholar 
    16.Bergmann, J., Ryo, M., Prati, D., Hempel, S. & Rillig, M. C. Root traits are more than analogues of leaf traits: the case for diaspore mass. New Phytol. 216, 1130–1139 (2017).PubMed 
    Article 

    Google Scholar 
    17.Weemstra, M. et al. Towards a multidimensional root trait framework: a tree root review. New Phytol. 211, 1159–1169 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    18.Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    19.de la Riva, E. G. et al. Root traits across environmental gradients in Mediterranean woody communities: are they aligned along the root economics spectrum? Plant Soil 424, 35–48 (2018).Article 
    CAS 

    Google Scholar 
    20.Craine, J. M., Lee, W. G., Bond, W. J., Williams, R. J. & Johnson, L. C. Environmental constraints on a global relationship among leaf and root traits of grasses. Ecology 86, 12–19 (2005).Article 

    Google Scholar 
    21.Liese, R., Alings, K. & Meier, I. C. Root branching is a leading root trait of the plant economics spectrum in temperate trees. Front. Plant Sci. 8, 315 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Carmona, C. P. et al. Erosion of global functional diversity across the tree of life. Sci. Adv. 7, eabf2675 (2021).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Niklas, K. J. Modelling below- and above-ground biomass for non-woody and woody plants. Ann. Bot. 95, 315–321 (2005).PubMed 
    Article 

    Google Scholar 
    24.Liu, G. et al. Coordinated variation in leaf and root traits across multiple spatial scales in Chinese semi-arid and arid ecosystems. New Phytol. 188, 543–553 (2010).PubMed 
    Article 

    Google Scholar 
    25.Galland, T., Carmona, C. P., Götzenberger, L., Valencia, E. & de Bello, F. Are redundancy indices redundant? An evaluation based on parameterized simulations. Ecol. Indic. 116, 106488 (2020).Article 

    Google Scholar 
    26.Valverde‐Barrantes, O. J., Maherali, H., Baraloto, C. & Blackwood, C. B. Independent evolutionary changes in fine‐root traits among main clades during the diversification of seed plants. New Phytol. 228, 541–553 (2020).PubMed 
    Article 

    Google Scholar 
    27.Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).PubMed 
    Article 

    Google Scholar 
    28.Freschet, G. T. et al. Climate, soil and plant functional types as drivers of global fine-root trait variation. J. Ecol. 105, 1182–1196 (2017).Article 

    Google Scholar 
    29.De Deyn, G. B. & Van der Putten, W. H. Linking aboveground and belowground diversity. Trends Ecol. Evol. 20, 625–633 (2005).PubMed 
    Article 

    Google Scholar 
    30.Pausas, J. G. & Bond, W. J. Humboldt and the reinvention of nature. J. Ecol. 107, 1031–1037 (2019).Article 

    Google Scholar 
    31.Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Moora, M. Mycorrhizal traits and plant communities: perspectives for integration. J. Veg. Sci. 25, 1126–1132 (2014).Article 

    Google Scholar 
    33.Freschet, G. T. et al. Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs. New Phytol. https://doi.org/10.1111/nph.17072 (2021).34.McCormack, M. L. & Iversen, C. M. Physical and functional constraints on viable belowground acquisition strategies. Front. Plant Sci. 10, 1215 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Wells, C. E. & Eissenstat, D. M. Beyond the roots of young seedlings: the influence of age and order on fine root physiology. J. Plant Growth Regul. 21, 324–334 (2002).CAS 
    Article 

    Google Scholar 
    36.Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    37.USDA. USDA PLANTS Database (accessed 3rd July 2020); https://plants.sc.egov.usda.gov38.Engemann, K. et al. A plant growth form dataset for the New World. Ecology 97, 3243 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    39.BGCI. GlobalTreeSearch online database (accessed 3rd July 2020); https://www.bgci.org/globaltree_search.php40.The Plant List. The Plant List (accessed 17th February 2020); http://www.theplantlist.org41.Cayuela, L., Macarro, I., Stein, A. & Oksanen, J. Taxonstand: Taxonomic Standardization of Plant Species Names. R package version 2.2. https://CRAN.R-project.org/package=Taxonstand (2019).42.Stekhoven, D. J. & Buhlmann, P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Oliveira, B. F., Sheffers, B. R. & Costa, G. C. Decoupled erosion of amphibians’ phylogenetic and functional diversity due to extinction. Glob. Ecol. Biogeogr. 29, 309–319 (2020).Article 

    Google Scholar 
    44.Penone, C. et al. Imputation of missing data in life-history trait datasets: which approach performs the best? Methods Ecol. Evol. 5, 961–970 (2014).Article 

    Google Scholar 
    45.Jin, Y. & Qian, H. V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).Article 

    Google Scholar 
    46.Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).PubMed 
    Article 

    Google Scholar 
    47.Whittakker, R. H. Communities and Ecosystems (Macmillan, 1975).48.Stefan, V. & Levin, S. plotbiomes: Plot Whittaker biomes with ggplot2. R package version 0.0.0.9001 https://github.com/valentinitnelav/plotbiomes (2021).49.Ricklefs, R. E. The Economy of Nature (W. H. Freeman and Company, 2008).50.GBIF. GBIF Occurrence Download (accessed 15 December 2019); https://doi.org/10.15468/dl.thlxph51.South, A. rworldmap: a new R package for mapping global data. R J. 3, 35–43 (2011).Article 

    Google Scholar 
    52.Dinno, A. paran: Horn’s Test of Principal Components/Factors. R package version 1.5.2. https://CRAN.R-project.org/package=paran (2018).53.Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. https://doi.org/10.18637/jss.v022.i04 (2007).54.Duong, T. ks: kernel density estimation and kernel discriminant analysis for multivariate data in R. J. Stat. Softw. https://doi.org/10.18637/jss.v021.i07 (2015).55.Duong, T. ks: Kernel smoothing. R package version 1.11.5 https://CRAN.R-project.org/package=ks (2019).56.Carmona, C. P., Bello, F., Mason, N. W. H. & Lepš, J. Trait probability density (TPD): measuring functional diversity across scales based on TPD with R. Ecology 100, e02876 (2019).PubMed 
    Article 

    Google Scholar 
    57.Carmona, C. P. TPD: methods for measuring functional diversity based on Trait Probability Density. R package version 1.1.0. https://CRAN.R-project.org/package=TPD (2019).58.Duong, T. & Hazelton, M. L. Plug-in bandwidth matrices for bivariate kernel density estimation. J. Nonparametr. Stat. 15, 17–30 (2003).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    59.Carmona, C. P., de Bello, F., Mason, N. W. H. & Lepš, J. Traits without borders: integrating functional diversity across scales. Trends Ecol. Evol. 31, 382–394 (2016).PubMed 
    Article 

    Google Scholar 
    60.Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111, 112–118 (2005).Article 

    Google Scholar 
    61.Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 
    Article 

    Google Scholar 
    62.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-5 https://CRAN.R-project.org/package=vegan (2019).63.Carmona, C. P. et al. Taxonomical and functional diversity turnover in Mediterranean grasslands: interactions between grazing, habitat type and rainfall. J. Appl. Ecol. 49, 1084–1093 (2012).Article 

    Google Scholar 
    64.Micó, E. et al. Contrasting functional structure of saproxylic beetle assemblages associated to different microhabitats. Sci. Rep. 10, 1520 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    65.Blonder, B. et al. New approaches for delineating n-dimensional hypervolumes. Methods Ecol. Evol. 9, 305–319 (2018).Article 

    Google Scholar 
    66.Carmona, C. P., de Bello, F., Mason, N. W. H. & Lepš, J. The density awakens: a reply to Blonder. Trends Ecol. Evol. 31, 667–669 (2016).PubMed 
    Article 

    Google Scholar 
    67.Mouillot, D. et al. Niche overlap estimates based on quantitative functional traits: a new family of non-parametric indices. Oecologia 145, 345–353 (2005).ADS 
    PubMed 
    Article 

    Google Scholar 
    68.de Bello, F., Carmona, C. P., Mason, N. W. H., Sebastià, M.-T. & Lepš, J. Which trait dissimilarity for functional diversity: trait means or trait overlap? J. Veg. Sci. 24, 807–819 (2013).Article 

    Google Scholar 
    69.Traba, J., Iranzo, E. C., Carmona, C. P. & Malo, J. E. Realised niche changes in a native herbivore assemblage associated with the presence of livestock. Oikos 126, 1400–1409 (2017).Article 

    Google Scholar 
    70.Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: Convex Hull Volume. Ecology 87, 1465–1471 (2006).PubMed 
    Article 

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

    Google Scholar 
    72.Blonder, B. Hypervolume concepts in niche- and trait-based ecology. Ecography 41, 1441–1455 (2018).Article 

    Google Scholar 
    73.Ricotta, C. et al. Measuring the functional redundancy of biological communities: a quantitative guide. Methods Ecol. Evol. 7, 1386–1395 (2016).Article 

    Google Scholar 
    74.Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. Proc. Natl. Acad. Sci. USA 111, 13757–13762 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    75.Carmona, C. P., de Bello, F., Sasaki, T., Uchida, K. & Pärtel, M. Towards a common toolbox for rarity: a response to Violle et al. Trends Ecol. Evol. 32, 889–891 (2017).PubMed 
    Article 

    Google Scholar 
    76.Violle, C. et al. Functional rarity: the ecology of outliers. Trends Ecol. Evol. 32, 356–367 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    77.Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).Article 

    Google Scholar 
    78.Gower, J. C. General coefficient of similarity and some of its properties. Biometrics 27, 857–871 (1971).Article 

    Google Scholar 
    79.Carmona, C. P. et al. Agriculture intensification reduces plant taxonomic and functional diversity across European arable systems. Funct. Ecol. 34, 1448–1460 (2020).Article 

    Google Scholar 
    80.Gherardi, L. A. & Sala, O. E. Global patterns and climatic controls of belowground net carbon fixation. Proc. Natl Acad. Sci. USA 117, 20038–20043 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Genetic purging in captive endangered ungulates with extremely low effective population sizes

    We have analyzed the inbreeding-purging process in four captive populations of different ungulate species with effective sizes ranging 4–40 and with available pedigrees as well as survival and productivity records. This allows us to explore the role of inbreeding and purging in determining the evolution of fitness traits in a range of scenarios relevant in the context of conservation.In A. lervia (Ne ≈ 4), purging is expected only for the most severely deleterious alleles (those giving dNe  > 1, which implies d  > 0.25 as, for example, in completely recessive alleles with deleterious homozygous disadvantage s  > 0.5). Thus, it could be that purging has not been detected for this species because such severely deleterious alleles had been purged during the demographic decline in the wild, before the foundation of the captive population. This would be consistent with the low and non-significant inbreeding load estimated in this species. It is also possible that these estimates are non-significant due to the relatively small number of individuals available.G. cuvieri and N. dama have significant initial inbreeding loads that, adding up the direct and maternal components, is about 1.25 in both cases, which is on the order of other estimates published for captive populations (Ralls et al. 1988). Since in both species Ne  > 10, purging should be efficient against less severely deleterious alleles than in A. lervia (d  > 0.1). Purging is detected for both species with very low P values. This result is in agreement with Moreno et al. (2015), who suggested that purging had occurred in G. cuvieri as they found an increased juvenile survival parallel to an increased inbreeding coefficient. The relative contribution of severe and mild deleterious effects to the inbreeding load of populations is under a scientific debate with direct implications in conservation biology (Ralls et al. 2020, Kyriazis et al. 2021, Pérez-Pereira et al. 2021). The large d estimates obtained in our analysis indicate that a substantial fraction of the initial inbreeding load is being purged under modest effective population sizes, implying that such substantial fraction is due to relatively severe deleterious mutations in these two populations. As far as we are aware, these are the first estimates of this purging parameter obtained in managed, non-experimental populations. Previous estimates of d were obtained in D. melanogaster bottlenecked populations, first for egg-to-pupae viability in lines with Ne = 6 or 12 under noncompetitive conditions (d = 0.09, Bersabé and García-Dorado 2013), and second in lines with higher Ne ≈ 40–50 under more competitive conditions, giving a larger estimate of d, of the order of that estimated in these two ungulate endangered species (d ≈ 0.3, López-Cortegano et al. 2016).Regarding G. dorcas, given its larger population size, purging is expected even against alleles with mild recessive component of the deleterious effect (d  > 0.025). However, although a significant (if modest) inbreeding load was estimated, no significant purging was detected. Nevertheless, the number of equivalent complete generations by the end of the pedigree (EqG = 7) was smaller than our proposed minimum number of generations required to detect purging (tm = 10). This suggests that, due to the large size of this population, more generations are needed to detect purging.The results above support the use of tm to get an approximate idea about when a pedigree is too shallow for purging to be detected. Should the number of generations available be larger than tm, IP predictions could additionally be computed to search the d values that can be expected to produce detectable purging. Supplementary Fig. S3 shows that the true number of generations required to detect purging becomes increasingly larger than tm for alleles with smaller d values, as they suffer weaker purging each time they are exposed in homozygosis. The tm approach helps to understand the failure of many studies to detect purging. Such is the case of the extensive meta-analyses on 119 zoo populations by Boakes et al. (2007), where the median Ne value was 22.6 while the median number of generations was t = 3 meaning that, for most species, at least 5 more generations were needed before purging could be detectable. On the contrary, and in agreement with this tm approach, purging was experimentally detected in lines of D. melanogaster with Ne = 43 (i.e., tm ≈ 10) where, after an initial period of inbreeding depression, fitness experienced a substantial recovery beginning between generations 10 and 20 (López-Cortegano et al. 2016).A reason why detecting purging in captive populations is challenging is that a fitness rebound can also be due to adaptation to captive conditions or to environmental effects, such as those derived from improved husbandry (Clifford et al. 2007). In fact, this might have been the case in Speke’s gazelle breeding program, where the observed rebound of fitness was first ascribed to purging (Templeton and Read 1984, 1998), while Kalinowski et al. (2000) suggested that husbandry improvements could also be responsible for these findings. Our estimates of d and δ, however, are based on the association between the fitness trait and purged inbreeding at the individual level (Wi, gi) which, in our data, is mainly expressed within cohorts while average survival showed little variation through time. In addition, the analyses included temporal factors (YOB or POM) that should have removed confounding effects from adaptation to captivity or improved husbandry. Therefore, adaptive processes or time-dependent environmental factors are not expected to have biased our IP estimates.For productivity, the estimates of inbreeding load were high (overall inbreeding load ~5, P value  More

  • in

    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