More stories

  • in

    Nature-positive goals for an organization’s food consumption

    Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

    Google Scholar 
    Díaz, S., et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).Article 

    Google Scholar 
    Locke, H., et al. A Nature-Positive World: The Global Goal for Nature (Wildlife Conservation Society, 2020); https://library.wcs.org/doi/ctl/view/mid/33065/pubid/DMX3974900000.aspxOpen-ended Working Group on the Post-2020 Global Biodiversity Framework. First Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/3/3 (Convention on Biological Diversity, 2021).Open-Ended Working Group on the Post-2020 Global Biodiversity Framework. Draft Recommendation Submitted by the Co-Chairs CBD/WG2020/4/L.2-ANNEX (Convention on Biological Diversity, 2022).Environment Act 2021 (UK) (HM Government, 2021); https://www.legislation.gov.uk/ukpga/2021/30/contents/enactedBull, J. W. & Strange, N. The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798 (2018).Article 

    Google Scholar 
    Prendeville, S., Cherim, E. & Bocken, N. Circular cities: mapping six cities in transition. Environ. Innov. Soc. Transit. 26, 171–194 (2018).de Silva, G. C., Regan, E. C., Pollard, E. H. B. & Addison, P. F. E. The evolution of corporate no net loss and net positive impact biodiversity commitments: understanding appetite and addressing challenges. Bus. Strategy Environ. 28, 1481–1495 (2019).Article 

    Google Scholar 
    zu Ermgassen, S. O. S. E. et al. Exploring the ecological outcomes of mandatory biodiversity net gain using evidence from early‐adopter jurisdictions in England. Conserv. Lett. 14, e12820 (2021).Article 

    Google Scholar 
    McGlyn, J., et al. Science-Based Targets for Nature: Initial Guidance for Business (Science Based Targets Network, 2020); https://sciencebasedtargetsnetwork.org/resource-repository/zu Ermgassen, S. O. S. E. et al. Are corporate biodiversity commitments consistent with delivering ‘nature-positive’ outcomes? A review of ‘nature-positive’ definitions, company progress and challenges. J. Clean. Prod. 379, 134798 (2022).Article 

    Google Scholar 
    Addison, P. F. E., Bull, J. W. & Milner‐Gulland, E. J. Using conservation science to advance corporate biodiversity accountability. Conserv. Biol. 33, 307–318 (2019).Article 

    Google Scholar 
    Smith, T. et al. Biodiversity means business: reframing global biodiversity goals for the private sector. Conserv. Lett. 13, e12690 (2020).Article 

    Google Scholar 
    Maron, M. et al. Setting robust biodiversity goals. Conserv. Lett. https://doi.org/10.1111/conl.12816 (2021).Newing, H. & Perram, A. What do you know about conservation and human rights? Oryx 53, 595–596 (2019).Article 

    Google Scholar 
    Standard on Biodiversity Offsets (The Business and Biodiversity Offsets Programme, 2012).Arlidge, W. N. S., et al. A mitigation hierarchy approach for managing sea turtle captures in small-scale fisheries. Front. Mar. Sci. 7, 49 (2020).Squires, D. & Garcia, S. The least-cost biodiversity impact mitigation hierarchy with a focus on marine fisheries and bycatch issues. Conserv. Biol. 32, 989–997 (2018).Article 

    Google Scholar 
    Booth, H., Squires, D. & Milner-Gulland, E. J. The mitigation hierarchy for sharks: a risk-based framework for reconciling trade-offs between shark conservation and fisheries objectives. Fish Fish. 21, 269–289 (2020).Article 

    Google Scholar 
    Gupta, T. et al. Mitigation of elasmobranch bycatch in trawlers: a case study in Indian fisheries. Front. Mari. Sci. 7, 571 (2020).Budiharta, S. et al. Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices. Global Environ. Change 52, 152–161 (2018).Article 

    Google Scholar 
    Bull, J. W. et al. Net positive outcomes for nature. Nat. Ecol. Evol. 4, 4–7 (2020).Article 

    Google Scholar 
    Arlidge, W. N. S. et al. A global mitigation hierarchy for nature conservation. BioScience 68, 336–347 (2018).Article 

    Google Scholar 
    Milner-Gulland, E. J. et al. Four steps for the Earth: mainstreaming the post-2020 global biodiversity framework. One Earth 4, 75–87 (2021).Article 
    ADS 

    Google Scholar 
    Wolff, A., Gondran, N. & Brodhag, C. Detecting unsustainable pressures exerted on biodiversity by a company. Application to the food portfolio of a retailer. J. Clean. Prod. 166, 784–797 (2017).Article 

    Google Scholar 
    FAOSTAT Analytical Brief 15 Land Use and Land Cover Statistics: Global, Regional and Country Trends, 1990–2018 (FAO, 2020).Williams, D. R. et al. Proactive conservation to prevent habitat losses to agricultural expansion. Nat. Sustain. 4, 314–322 (2021).Article 

    Google Scholar 
    Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
    ADS 

    Google Scholar 
    Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

    Google Scholar 
    Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).Article 

    Google Scholar 
    Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Benton, T. G. et al. A ‘net zero’ equivalent target is needed to transform food systems. Nat. Food 2, 905–906 (2021). 2021.Article 

    Google Scholar 
    Crenna, E., Sinkko, T. & Sala, S. Biodiversity impacts due to food consumption in Europe. J. Clean. Prod. 227, 378–391 (2019).Article 
    CAS 

    Google Scholar 
    Bull, J. W., et al. Analysis: the biodiversity footprint of the University of Oxford. Nature 604, 420–424 (2022).Harrington, R. A., Adhikari, V., Rayner, M. & Scarborough, P. Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure. BMJ Open 9, e026652 (2019).Article 

    Google Scholar 
    Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Winter, L., Lehmann, A., Finogenova, N. & Finkbeiner, M. Including biodiversity in life cycle assessment—state of the art, gaps and research needs. Environ. Impact Assess. Rev. 67, 88–100 (2017).Article 

    Google Scholar 
    Chaudhary, A. & Kastner, T. Land use biodiversity impacts embodied in international food trade. Global Environ. Change 38, 195–204 (2016).Article 

    Google Scholar 
    Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Bates, B., et al. National Diet and Nutrition Survey Years 1 to 9 of the Rolling Programme (2008/2009–2016/2017): Time Trend and Income Analyses (Public Health England & Food Standards Agency, 2019).Stewart, C., Piernas, C., Cook, B. & Jebb, S. A. Trends in UK meat consumption: analysis of data from years 1–11 (2008–09 to 2018–19) of the National Diet and Nutrition Survey rolling programme. Lancet Planet. Health 5, e699–e708 (2021).Article 

    Google Scholar 
    Nielsen, K. S. et al. Improving climate change mitigation analysis: a framework for examining feasibility. One Earth 3, 325–336 (2020).Article 
    ADS 

    Google Scholar 
    Selinske, M. J. et al. We have a steak in it: eliciting interventions to reduce beef consumption and its impact on biodiversity. Conserv. Lett. 13, e12721 (2020).Article 

    Google Scholar 
    Hollands, G. J. et al. The TIPPME intervention typology for changing environments to change behaviour. Nat. Hum. Behav. 1, 1–9 (2017).Article 

    Google Scholar 
    Marteau, T. M., Hollands, G. J. & Fletcher, P. C. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science 337, 1492–1495 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Michie, S., van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).Article 

    Google Scholar 
    Moran, D., Giljum, S., Kanemoto, K. & Godar, J. From satellite to supply chain: new approaches connect earth observation to economic decisions. One Earth 3, 5–8 (2020).Article 
    ADS 

    Google Scholar 
    Godar, J., Suavet, C., Gardner, T. A., Dawkins, E. & Meyfroidt, P. Balancing detail and scale in assessing transparency to improve the governance of agricultural commodity supply chains. Environ. Res. Lett. 11, 035015 (2016).Article 
    ADS 

    Google Scholar 
    DeFries, R. S., Fanzo, J., Mondal, P., Remans, R. & Wood, S. A. Is voluntary certification of tropical agricultural commodities achieving sustainability goals for small-scale producers? A review of the evidence. Environ. Res. Lett. 12, 033001 (2017).Article 
    ADS 

    Google Scholar 
    Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J. & Milner-Gulland, E. J. Biodiversity offsets in theory and practice. Oryx 47, 369–380 (2013).Article 

    Google Scholar 
    zu Ermgassen, S. O. S. E. et al. The ecological outcomes of biodiversity offsets under “no net loss” policies: a global review. Conserv. Lett. 12, e12664 (2019).Article 

    Google Scholar 
    Waddock, S. Achieving sustainability requires systemic business transformation. Glob. Sustain. 3, e12 (2020).Travers, H., Walsh, J., Vogt, S., Clements, T. & Milner-Gulland, E. J. Delivering behavioural change at scale: what conservation can learn from other fields. Biol. Conserv. 257, 109092 (2021).Article 

    Google Scholar 
    Gaupp, F. et al. Food system development pathways for healthy, nature-positive and inclusive food systems. Nat. Food 2, 928–934 (2021).Article 

    Google Scholar 
    Astill, J. et al. Transparency in food supply chains: a review of enabling technology solutions. Trends Food Sci. Technol. 91, 240–247 (2019).Article 
    CAS 

    Google Scholar 
    Poore, J & Nemecek, T. Full Excel model: life-cycle environmental impacts of food drink products. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:a63fb28c-98f8-4313-add6-e9eca99320a5 (2018).Clark, M., et al. Estimating the environmental impacts of 57,000 food products. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).Clark, M., et al. Supplemental Data for ‘Estimating the environmental impacts of 57,000 food products’. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:4ad0b594-3e81-4e61-aefc-5d869c799a87 (2022).Bianchi, F., Dorsel, C., Garnett, E., Aveyard, P. & Jebb, S. A. Interventions targeting conscious determinants of human behaviour to reduce the demand for meat: a systematic review with qualitative comparative analysis. IJBNPA 15, 102 (2018).
    Google Scholar 
    Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P. & Jebb, S. A. Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. Lancet Planet. Health 2, e384–e397 (2018).Article 

    Google Scholar 
    Hillier-Brown, F. C. et al. The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review. Obes. Rev. 18, 227–246 (2017).Article 
    CAS 

    Google Scholar 
    von Philipsborn, P. et al. Environmental interventions to reduce the consumption of sugar-sweetened beverages and their effects on health. Cochrane Database Syst. Rev. 6, Cd012292 (2019).
    Google Scholar 
    Attwood, S., Voorheis, P., Mercer, C., Davies, K. & Vennard, D. Playbook for Guiding Diners toward Plant-Rich Dishes in Food Service (World Resources Institute, 2020); https://www.wri.org/research/playbook-guiding-diners-toward-plant-rich-dishes-food-serviceGarnett, E. E., Balmford, A., Sandbrook, C., Pilling, M. A. & Marteau, T. M. Impact of increasing vegetarian availability on meal selection and sales in cafeterias. Proc. Natl Acad. Sci. USA 116, 20923 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Reinders, M. J., Huitink, M., Dijkstra, S. C., Maaskant, A. J. & Heijnen, J. Menu-engineering in restaurants—adapting portion sizes on plates to enhance vegetable consumption: a real-life experiment. IJBNPA 14, 41 (2017).
    Google Scholar 
    Brunner, F., Kurz, V., Bryngelsson, D. & Hedenus, F. Carbon label at a university restaurant—label implementation and evaluation. Ecol. Econ. 146, 658–667 (2018).Article 

    Google Scholar 
    McClain, A. D., Hekler, E. B. & Gardner, C. D. Incorporating prototyping and iteration into intervention development: a case study of a dining hall-based intervention. J. Am. Coll. Health 61, 122–131 (2013).Article 

    Google Scholar 
    de Vaan, J. Eating Less Meat: How to Stimulate the Choice for a Vegetarian Option without Inducing Reactance. MSc thesis, Radboud Univ. (2018). More

  • in

    Anaerobic methanotroph ‘Candidatus Methanoperedens nitroreducens’ has a pleomorphic life cycle

    Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
    CAS 

    Google Scholar 
    Chadwick, G. L. et al. Comparative genomics reveals electron transfer and syntrophic mechanisms differentiating methanotrophic and methanogenic archaea. PLoS Biol. 20, e3001508 (2022).Article 

    Google Scholar 
    Haroon, M. F. et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature 500, 567–570 (2013).Article 
    CAS 

    Google Scholar 
    Hallam, S. J. et al. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science 305, 1457–1462 (2004).Article 
    CAS 

    Google Scholar 
    McGlynn, S. E. Energy metabolism during anaerobic methane oxidation in ANME Archaea. Microbes Environ. 32, 5–13 (2017).Article 

    Google Scholar 
    Beal, E. J., House, C. H. & Orphan, V. J. Manganese- and iron-dependent marine methane oxidation. Science 325, 184–187 (2009).Article 
    CAS 

    Google Scholar 
    McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).Article 
    CAS 

    Google Scholar 
    Wegener, G., Krukenberg, V., Riedel, D., Tegetmeyer, H. E. & Boetius, A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature 526, 587–590 (2015).Article 
    CAS 

    Google Scholar 
    Cai, C. et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 12, 1929–1939 (2018).Article 
    CAS 

    Google Scholar 
    Ettwig, K. F. et al. Archaea catalyze iron-dependent anaerobic oxidation of methane. Proc. Natl Acad. Sci. USA 113, 12792–12796 (2016).Article 
    CAS 

    Google Scholar 
    Leu, A. O. et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 14, 1030–1041 (2020).Article 
    CAS 

    Google Scholar 
    Leu, A. O. et al. Lateral gene transfer drives metabolic flexibility in the anaerobic methane-oxidizing archaeal family Methanoperedenaceae. mBio 11, e01325-20 (2020).Cai, C. et al. Response of the anaerobic methanotrophic archaeon Candidatus ‘Methanoperedens nitroreducens’ to the long-term ferrihydrite amendment. Front. Microbiol. 13, 799859 (2022).Arshad, A. et al. A metagenomics-based metabolic model of nitrate-dependent anaerobic oxidation of methane by Methanoperedens-like Archaea. Front. Microbiol. 6, 1423 (2015).Article 

    Google Scholar 
    Raghoebarsing, A. A. et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature 440, 918–921 (2006).Article 
    CAS 

    Google Scholar 
    Walker, D. J. F. et al. The archaellum of Methanospirillum hungatei is electrically conductive. mBio 10, e00579-19 (2019).Article 
    CAS 

    Google Scholar 
    Krukenberg, V. et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ. Microbiol. 20, 1651–1666 (2018).Article 
    CAS 

    Google Scholar 
    Schubert, C. J. et al. Evidence for anaerobic oxidation of methane in sediments of a freshwater system (Lago di Cadagno). FEMS Microbiol. Ecol. 76, 26–38 (2011).Article 
    CAS 

    Google Scholar 
    Stahl, D. A. & Amann, R. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 205–248 (Wiley, 1991).Wallner, G., Amann, R. & Beisker, W. Optimizing fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 14, 136–143 (1993).Article 
    CAS 

    Google Scholar 
    Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome- assembled genome (MIMAG) of Bacteria and Archaea. Nat. Biotechnol. 36, 660 (2018).Article 
    CAS 

    Google Scholar 
    Vo, C. H., Goyal, N., Karimi, I. A. & Kraft, M. First observation of an acetate switch in a methanogenic autotroph (Methanococcus maripaludis S2). Microbiol. Insights 13, 1178636120945300 (2020).Article 

    Google Scholar 
    Cai, C. et al. Acetate production from anaerobic oxidation of methane via intracellular storage compounds. Environ. Sci. Technol. 53, 7371–7379 (2019).Article 
    CAS 

    Google Scholar 
    Ratcliff, W. C. & Denison, R. F. Bacterial persistence and bet hedging in Sinorhizobium meliloti. Commun. Integr. Biol. 4, 98–100 (2011).Article 
    CAS 

    Google Scholar 
    Ma, K., Schicho, R. N., Kelly, R. M. & Adams, M. W. Hydrogenase of the hyperthermophile Pyrococcus furiosus is an elemental sulfur reductase or sulfhydrogenase: evidence for a sulfur-reducing hydrogenase ancestor. Proc. Natl Acad. Sci. USA 90, 5341–5344 (1993).Article 
    CAS 

    Google Scholar 
    Simon, G.-C. et al. Response of the anaerobic methanotroph “Candidatus Methanoperedens nitroreducens” to oxygen stress. Appl. Environ. Microbiol. 84, e01832-18 (2018).
    Google Scholar 
    van der Star, W. R. L. et al. The membrane bioreactor: a novel tool to grow anammox bacteria as free cells. Biotechnol. Bioeng. 101, 286–294 (2008).Article 

    Google Scholar 
    Duggin, I. G. et al. CetZ tubulin-like proteins control archaeal cell shape. Nature 519, 362–365 (2015).Article 
    CAS 

    Google Scholar 
    Schwarzer, S., Rodriguez-Franco, M., Oksanen, H. M. & Quax, T. E. F. Growth phase dependent cell shape of Haloarcula. Microorganisms 9, 231 (2021).Article 
    CAS 

    Google Scholar 
    Dang, H. Y. & Lovell, C. R. Microbial surface colonization and biofilm development in marine environments. Microbiol. Mol. Biol. Rev. 80, 91–138 (2016).Article 
    CAS 

    Google Scholar 
    Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 11, 1511–1520 (2017).Article 

    Google Scholar 
    Pires, D. P., Melo, L. D. R. & Azeredo, J. Understanding the complex phage–host interactions in biofilm communities. Annu. Rev. Virol. 8, 73–94 (2021).Canchaya, C., Proux, C., Fournous, G., Bruttin, A. & Brüssow, H. Prophage genomics. Microbiol. Mol. Biol. Rev. 67, 238–276 (2003).Article 
    CAS 

    Google Scholar 
    Zhang, X. et al. Polyhydroxyalkanoate-driven current generation via acetate by an anaerobic methanotrophic consortium. Water Res. 221, 118743 (2022).Article 
    CAS 

    Google Scholar 
    Knittel, K., Lösekann, T., Boetius, A., Kort, R. & Amann, R. Diversity and distribution of methanotrophic Archaea at cold seeps. Appl. Environ. Microbiol. 71, 467–479 (2005).Article 
    CAS 

    Google Scholar 
    Orphan, V. J., House, C. H., Hinrichs, K.-U., McKeegan, K. D. & DeLong, E. F. Multiple archaeal groups mediate methane oxidation in anoxic cold seep sediments. Proc. Natl Acad. Sci. USA 99, 7663–7668 (2002).Article 
    CAS 

    Google Scholar 
    Orphan, V. J. et al. Geological, geochemical, and microbiological heterogeneity of the seafloor around methane vents in the Eel River Basin, offshore California. Chem. Geol. 205, 265–289 (2004).Article 
    CAS 

    Google Scholar 
    Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508 (2015).Article 
    CAS 

    Google Scholar 
    Robinson, R. W. Life cycles in the methanogenic archaebacterium Methanosarcina mazei. Appl. Environ. Microbiol. 52, 17–27 (1986).Article 
    CAS 

    Google Scholar 
    Daims, H., Stoecker, K. & Wagner, M. in Molecular Microbial Ecology (eds Osborn, A. M. & Smith, C. J.) 213–239 (Taylor & Francis, 2005).Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).Article 
    CAS 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
    CAS 

    Google Scholar 
    Yilmaz, L. S., Parnerkar, S. & Noguera, D. R. mathFISH, a web tool that uses thermodynamics-based mathematical models for in silico evaluation of oligonucleotide probes for fluorescence in situ hybridization. Appl. Environ. Microbiol. 77, 1118–1122 (2011).Article 
    CAS 

    Google Scholar 
    Stoecker, K., Dorninger, C., Daims, H. & Wagner, M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl. Environ. Microbiol. 76, 922–926 (2010).Article 
    CAS 

    Google Scholar 
    Fuchs, B. M., Glockner, F. O., Wulf, J. & Amann, R. Unlabeled helper oligonucleotides increase the in situ accessibility to 16S rRNA of fluorescently labeled oligonucleotide probes. Appl. Environ. Microbiol. 66, 3603–3607 (2000).Article 
    CAS 

    Google Scholar 
    Manz, W., Amann, R., Ludwig, W., Wagner, M. & Schleifer, K.-H. Phylogenetic oligodeoxynucleotide probes for the major subclasses of Proteobacteria: problems and solutions. Syst. Appl. Microbiol. 15, 593–600 (1992).Article 

    Google Scholar 
    Ostle, A. G. & Holt, J. G. Nile blue A as a fluorescent stain for poly-beta-hydroxybutyrate. Appl. Environ. Microbiol. 44, 238–241 (1982).Article 
    CAS 

    Google Scholar 
    Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).Article 
    CAS 

    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
    CAS 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 1091 (2019).Article 
    CAS 

    Google Scholar 
    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
    CAS 

    Google Scholar 
    Eren, A. M., Vineis, J. H., Morrison, H. G. & Sogin, M. L. A filtering method to generate high quality short reads using Illumina paired-end technology. PLoS ONE 8, e66643 (2013).Article 

    Google Scholar 
    Minoche, A. E., Dohm, J. C. & Himmelbauer, H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome analyzer systems. Genome Biol. 12, R112 (2011).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).Article 
    CAS 

    Google Scholar 
    Warren, R. L. et al. LINKS: scalable, alignment-free scaffolding of draft genomes with long reads. GigaScience 4, 35 (2015).Article 

    Google Scholar 
    Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).Article 

    Google Scholar 
    Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).Article 
    CAS 

    Google Scholar 
    Wick, R. R. et al. Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biol. 22, 266 (2021).Article 
    CAS 

    Google Scholar 
    Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).Article 
    CAS 

    Google Scholar 
    Wick, R. R. & Holt, K. E. Benchmarking of long-read assemblers for prokaryote whole genome sequencing. F1000Res. 8, 2138 (2021).Vaser, R. & Šikić, M. Time- and memory-efficient genome assembly with Raven. Nat. Comput. Sci. 1, 332–336 (2021).Article 

    Google Scholar 
    Wick, R. R. & Holt, K. E. Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Comput. Biol. 18, e1009802 (2022).Article 
    CAS 

    Google Scholar 
    Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
    Google Scholar 
    Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).Article 
    CAS 

    Google Scholar 
    Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2017).Article 

    Google Scholar 
    Heller, D. & Vingron, M. SVIM: structural variant identification using mapped long reads. Bioinformatics 35, 2907–2915 (2019).Article 
    CAS 

    Google Scholar 
    Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
    CAS 

    Google Scholar 
    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).Article 
    CAS 

    Google Scholar 
    Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).Article 
    CAS 

    Google Scholar 
    Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41 (2003).Article 

    Google Scholar 
    Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).Article 
    CAS 

    Google Scholar 
    Haft, D. H. et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 41, D387–D395 (2013).Article 
    CAS 

    Google Scholar 
    Zhou, Z. et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome 10, 33 (2022).Article 
    CAS 

    Google Scholar 
    Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
    CAS 

    Google Scholar 
    Bateman, A. et al. The Pfam protein families database. Nucleic Acids Res. 32, D138–D141 (2004).Article 
    CAS 

    Google Scholar 
    Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).Article 
    CAS 

    Google Scholar 
    Daims, H., Brühl, A., Amann, R., Schleifer, K. H. & Wagner, M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: development and evaluation of a more comprehensive probe set. Syst. Appl. Microbiol. 22, 434–444 (1999).Article 
    CAS 

    Google Scholar 
    Schmid, M. C. et al. Biomarkers for in situ detection of anaerobic ammonium-oxidizing (anammox) bacteria. Appl. Environ. Microbiol. 71, 1677–1684 (2005).Article 
    CAS 

    Google Scholar 
    Yu, N. Y. et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).Article 
    CAS 

    Google Scholar 
    Bendtsen, J. D., Nielsen, H., von Heijne, G. & Brunak, S. Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–795 (2004).Article 

    Google Scholar  More

  • in

    Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology

    Edwards, R. B., Naylor, R. L., Higgins, M. M. & Falcon, W. P. Causes of Indonesia’s forest fires. World Dev. 127, 104717 (2020).Article 

    Google Scholar 
    Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Chang. Biol. 17, 798–818 (2011).Article 
    ADS 

    Google Scholar 
    Page, S., et al. Tropical Fire Ecology Ch. 9 (Springer, 2009).Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 371, 20150176 (2016).Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 1–8 (2016).Article 

    Google Scholar 
    Kusumaningtyas, S. D. A. & Aldrian, E. Impact of the June 2013 Riau province Sumatera smoke haze event on regional air pollution. Environ. Res. Lett. 11, 075007 (2016).Article 
    ADS 

    Google Scholar 
    Gaveau, D. L. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: Evidence from the 2013 Sumatran fires. Sci. Rep. 4, 1–7 (2014).Article 

    Google Scholar 
    Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640–643 (2016).Article 
    ADS 

    Google Scholar 
    Posa, M. R. C., Wijedasa, L. S. & Corlett, R. T. Biodiversity and conservation of tropical peat swamp forests. Bioscience 61, 49–57 (2011).Article 

    Google Scholar 
    Harrison, M. E. & Rieley, J. O. Tropical peatland biodiversity and conservation in Southeast Asia. Mires Peat 22, 1–7 (2018).
    Google Scholar 
    Purnomo, H. et al. Fire economy and actor network of forest and land fires in Indonesia. For. Policy Econ. 78, 21–31 (2017).Article 

    Google Scholar 
    Wösten, J. H. M., Clymans, E., Page, S. E., Rieley, J. O. & Limin, S. H. Peat–water interrelationships in a tropical peatland ecosystem in Southeast Asia. CATENA 73, 212–224 (2008).Article 

    Google Scholar 
    Taufik, M., Setiawan, B. I. & Van Lanen, H. A. Increased fire hazard in human-modified wetlands in Southeast Asia. Ambio 48, 363–373 (2019).Article 

    Google Scholar 
    Taufik, M. et al. Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nat. Clim. Chang. 7, 428–431 (2017).Article 
    ADS 

    Google Scholar 
    Fanin, T. & Werf, G. R. Precipitation–fire linkages in Indonesia (1997–2015). Biogeosciences 14, 3995–4008 (2017).Article 
    ADS 

    Google Scholar 
    Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl. Acad. Sci. U.S.A. 113, 9204–9209 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Hirano, T. et al. Effects of disturbances on the carbon balance of tropical peat swamp forests. Glob. Chang. Biol. 18, 3410–3422 (2012).Article 
    ADS 

    Google Scholar 
    Ohkubo, S., Hirano, T. & Kusin, K. Influence of fire and drainage on evapotranspiration in a degraded peat swamp forest in Central Kalimantan Indonesia. J. Hydrol. 603, 126906 (2021).Article 

    Google Scholar 
    Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. D. & Mezbahuddin, S. Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun. Earth Environ. 1, 1–8 (2020).Article 

    Google Scholar 
    Lin, Y., Wijedasa, L. S. & Chisholm, R. A. Singapore’s willingness to pay for mitigation of transboundary forest-fire haze from Indonesia. Environ. Res. Lett. 12, 024017 (2017).Article 
    ADS 

    Google Scholar 
    Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. & Mezbahuddin, S. ProbFire: A probabilistic fire early warning system for Indonesia. Nat. Hazards Earth Syst. Sci. 22, 303–322 (2022).Article 
    ADS 

    Google Scholar 
    Taufik, M., Veldhuizen, A. A., Wösten, J. H. M. & van Lanen, H. A. J. Exploration of the importance of physical properties of Indonesian peatlands to assess critical groundwater table depths, associated drought and fire hazard. Geoderma 347, 160–169 (2019).Article 
    ADS 

    Google Scholar 
    Sloan, S., Tacconi, L. & Cattau, M. E. Fire prevention in managed landscapes: Recent success and challenges in Indonesia. Mitig. Adapt. Strateg. Glob. Chang. 26, 1–30 (2021).Article 

    Google Scholar 
    Lestari, I., Murdiyarso, D. & Taufik, M. Rewetting tropical peatlands reduced net greenhouse gas emissions in Riau Province Indonesia. Forests 13, 505 (2022).Article 

    Google Scholar 
    Spessa, A. C. et al. Seasonal forecasting of fire over Kalimantan Indonesia. Nat. Hazards Earth Syst. Sci. 15, 429–442 (2015).Article 
    ADS 

    Google Scholar 
    Mezbahuddin, M., Grant, R. F. & Hirano, T. How hydrology determines seasonal and interannual variations in water table depth, surface energy exchange, and water stress in a tropical peatland: Modeling versus measurements. J. Geophys. Res. Biogeosci. 120, 2132–2157 (2015).Article 

    Google Scholar 
    Mezbahuddin, M., Grant, R. F. & Hirano, T. Modelling effects of seasonal variation in water table depth on net ecosystem CO2 exchange of a tropical peatland. Biogeosciences 11, 577–599 (2014).Article 
    ADS 

    Google Scholar 
    Cobb, A. R. & Harvey, C. F. Scalar simulation and parameterization of water table dynamics in tropical peatlands. Water Resour. Res. 55, 9351–9377 (2019).Article 
    ADS 

    Google Scholar 
    Dadap, N. C., Cobb, A. R., Hoyt, A. M., Harvey, C. F. & Konings, A. G. Satellite soil moisture observations predict burned area in Southeast Asian peatlands. Environ. Res. Lett. 14, 094014 (2019).Article 
    ADS 

    Google Scholar 
    Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra Indonesia. Geoderma 338, 410–421 (2019).Article 
    ADS 

    Google Scholar 
    Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Couwenberg, J. & Hooijer, A. Towards robust subsidence-based soil carbon emission factors for peat soils in south-east Asia, with special reference to oil palm plantations. Mires Peat 12, 1–13 (2013).
    Google Scholar 
    Khasanah, N. M. & van Noordwijk, M. Subsidence and carbon dioxide emissions in a smallholder peatland mosaic in Sumatra Indonesia. Mitig. Adapt. Strateg. Glob. Chang. 24, 147 (2019).Article 

    Google Scholar 
    Marwanto, S., Watanabe, T., Iskandar, W., Sabiham, S. & Funakawa, S. Effects of seasonal rainfall and water table movement on the soil solution composition of tropical peatland. Soil Sci. Plant Nutr. 64, 386–395 (2018).Article 
    CAS 

    Google Scholar 
    Lubis, M. E. S. et al. Changes in water table depth in an oil palm plantation and its surrounding regions in Sumatra Indonesia. J. Agron. 13, 140–146 (2014).Article 

    Google Scholar 
    Page, S. E., Rieley, J. O. & Wüst, R. Developments in Earth Surface Processes (Volume 9) Ch. 3 (Elsevier, 2006).Haffiez, N. et al. Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion. Sci. Total Environ. 839, 156211 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Grant, R. F., Desai, A. R. & Sulman, B. N. Modelling contrasting responses of wetland productivity to changes in water table depth. Biogeosciences 9, 4215–4231 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Mezbahuddin, M., Grant, R. F. & Flanagan, L. B. Modeling hydrological controls on variations in peat water content, water table depth, and surface energy exchange of a boreal western Canadian fen peatland. J. Geophys. Res. Biogeosci. 121, 2216–2242 (2016).Article 

    Google Scholar 
    Dimitrov, D. D., Grant, R. F., Lafleur, P. M. & Humphreys, E. R. Modeling the effects of hydrology on gross primary productivity and net ecosystem productivity at Mer Bleue bog. J. Geophys. Res. Biogeosci. 116, G04010 (2011).Article 
    ADS 

    Google Scholar 
    Dimitrov, D. D., Bhatti, J. S. & Grant, R. F. The transition zones (ecotone) between boreal forests and peatlands: Modelling water table along a transition zone between upland black spruce forest and poor forested fen in central Saskatchewan. Ecol. Modell. 274, 57–70 (2014).Article 

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

    Google Scholar 
    Hodnett, M. G. & Tomasella, J. Marked differences between van Genuchten soil water-retention parameters for temperate and tropical soils: A new water-retention pedo-transfer functions developed for tropical soils. Geoderma 108, 155–180 (2002).Article 
    ADS 
    CAS 

    Google Scholar 
    Funk, C. et al. The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).Article 

    Google Scholar 
    Osaki, M., Hirose, K., Segah, H. & Helmy, F. Tropical Peatland Ecosystems Ch. 9 (Springer, 2016).Razavi, S. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling. Environ. Modell. Softw. 144, 105159 (2021).Article 

    Google Scholar  More

  • in

    Similar adaptative mechanism but divergent demographic history of four sympatric desert rodents in Eurasian inland

    Species distribution modeling, spatial climate segregation and niche widthTo explore the selective regimes of the four species on external environmental factors, we first constructed species distribution modeling (SDM). We obtained a dataset including 22 environmental factors represented by climate, relief, and vegetation variables from 620 localities for DS, 1028 localities for OS, 581 localities for MM and 332 localities for PR, covering most of the species’ distribution ranges (Supplementary Fig. 1). The distribution areas of the four species overlapped widely. The contributions of environmental factors to SDMs showed similarities among the four species. The summer NDVI made important contributions for DS (41.0), OS (44.8), MM (32.5) and PR (8.1), and sand cover contributed significantly to PR (72.7) and DS (16.0) (Fig. 1c). Then, we assessed which set of environmental variables was most closely associated with species distribution via principal component analysis. The bioclimatic space occupied by the four species revealed a large overlap (Fig. 1d), which was consistent with SDM (Supplementary Fig. 1). The distribution of OS was more closely associated with higher-precipitation areas, whereas MM seemed to prefer areas with higher temperatures. Finally, we evaluated the macrohabitat niche breadth of each species. The breadths of environmental space occupation were similar for DS (0.527), MM (0.571), and PR (0.548) and slightly higher for OS (0.622), which suggests that niche selection among the four species is partially overlapping. In total, the four species are mostly similar in the selection of external environmental factors.High-quality genomic landscapes of the four desert rodentsTo investigate the genetic mechanism for desert adaptation of the four sympatric desert rodents, we generated four high-quality de novo genomes (Supplementary Fig. 2). The DS was sequenced using a combined strategy and generated 377.67 Gb of data from Illumina reads, 261.01 Gb from PacBio long reads, 299.51 Gb from 10X Genomics reads, and 389.13 Gb from Hi-C reads (Supplementary Table 1). The final genome size was 2.81 Gb with contig N50 of 31.41 Mb and ~472X mean coverage (Table 1, Supplementary Fig. 3, and Supplementary Tables 2, 3). The contigs for DS were further assembled into pseudochromosomes with lengths on the order of full chromosomes and a scaffold N50 size of 147.24 Mb (Fig. 2a, b, Table 1, and Supplementary Fig. 4). The OS, MM and PR were sequenced using the same hybrid strategy and generated 162.58 Gb, 172.22 Gb, and 214.34 Gb Illumina reads and 183.09 Gb, 161.34 Gb, and 186.45 Gb Oxford Nanopore Technologies long reads, respectively (Supplementary Table 1). The final assembly of OS, MM and PR was 2.83 Gb, 2.43 Gb, and 2.16 Gb with contig N50 of 25.87 Mb, 24.08 Mb, and 42.68 Mb, respectively (Table 1, Supplementary Fig. 4, and Supplementary Tables 2, 3).Table 1 Genome assembly statistics of the four desert rodents.Full size tableFig. 2: High-quality assembly of Dipus sagitta genome and genomic elements of the four sequenced desert rodents.a Hi-C heat map of Dipus sagitta genome assembly. b CIRCOS plot showing the distribution of GC content, transposable elements (TE), and coding sequences (CDS) in the D. sagitta genome. c Orthologous coding sequences composition inferred for thirteen rodents’ genomes. Mcar Mus caroli, Mmus Mus musculus, Mpah Mus Pahari, Mmer Meriones meridianus, Mung Meriones unguiculatus, Cgri Cricetulus griseus, Prob Phodopus roborovskii, Sgal Spalax galili, Osib Orientallactaga sibirica, Dsag Dipus sagitta, Jjac Jaculus jaculus, Hgla Heterocephalus glaber, Cpor Cavia porcellus. d Proportion of transposable elements (TEs). The barplots show the proportions of different types of TEs in corresponding species on the phylogenetic tree.Full size imageAnalyses of the four draft genomes showed that 92.9–95.9% of mammalian BUSCOs were complete, and the GC content was 41.38–42.16% (Table 1 and Supplementary Table 3). Whole-genome annotation was performed via three complementary methods: ab initio prediction, homology-based prediction and RNA-seq based prediction. A total of 23,482, 22,859, 22,533, and 22,314 protein-coding genes were annotated for DS, OS, MM, and PR, respectively (Fig. 2c, Supplementary Fig. 5, Supplementary Table 4). Approximately 98.8–99.1% of genes were functionally annotated for the four species (Supplementary Table 4). Transposable elements (TEs) accounted for 31.38–53.02% of genome assemblies, which predominantly consisted of long-terminal repeats (LTRs), long interspersed nuclear elements (LINEs) and other unknown TEs (Fig. 2d). DS and OS displayed significant LTR expansion of 47.39% and 50.88% in four sequenced genomes, while MM showed an unexpectedly high LINE expansion of 28.99% and sharp LTR contraction to 9.38% (Supplementary Table 5).Phylogenetic relationship and evolutionary historyUsing 5,102 single-copy orthologous groups, we constructed a high-confidence phylogenetic tree using the maximum-likelihood algorithm, including time calibrations based on fossil records and previous studies (Figs. 1b, 2c)22. The phylogenetic tree strongly supported nodes uniting the subfamilies Murinae and Gerbillinae, which together represented the family Muridae (Supplementary Fig. 6). This group was sister to a clade containing cricetids. Spalacidae was recovered as the earliest divergent lineage from Muridae and Cricetidae in the superfamily Muroidea. The split of the most recent common ancestor of Dipodoidea and Muroidea dated to ~56.5 Mya (Fig. 1b, Supplementary Fig. 7). In the Miocene epoch (23 Mya–5.3 Mya), accelerated global geotectonic movement aggravated global climate drying and cooling23. Geological disruptions that modified landscapes and offered new habitats favored the early adaptive radiation of extant desert rodents. The ancestors of four sequenced species emerged separately during this period (Supplementary Note 1). Our phylogenetic tree is consistent with previous evolutionary research on rodents22 and supports the independent evolution of desert adaptations in Jerboas, Gerbils and Hamsters.Expanded and contracted gene familiesComparative genomic analysis revealed 23/32, 4/22, 39/73, and 22/83 gene families exhibiting significant expansion/contraction in the genomes of DS, OS, MM, and PR, respectively (Fig. 1b and Supplementary Fig. 8). Genes belonging to the expanded/contracted families were functionally enriched (Fisher Exact  More

  • in

    Enhanced regional connectivity between western North American national parks will increase persistence of mammal species diversity

    Newmark, W. D. A land-bridge island perspective on mammalian extinctions in western North American parks. Nature 325, 430–432 (1987).Article 
    ADS 
    CAS 

    Google Scholar 
    Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).Article 

    Google Scholar 
    Radeloff, V. C. et al. Housing growth in and near United States protected areas limits their conservation value. Proc. Natl. Acad. Sci. U. S. A. 107, 940–945 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).Article 
    CAS 

    Google Scholar 
    Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. https://doi.org/10.1126/sciadv.aay0814 (2020).Article 

    Google Scholar 
    Wasser, S. K. et al. Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science 349, 84–87 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Davis, C. R. & Hansen, A. J. Trajectories in land use change around U,S. national parks and challenges and opportunities for management. Ecol. Appl. 21, 3299–3316 (2011).Article 

    Google Scholar 
    Newmark, W. D. Extinction of mammal populations in western North American national parks. Conserv. Biol. 9, 512–526 (1995).Article 

    Google Scholar 
    Newmark, W. D. Insularization of Tanzanian parks and the local extinction of large mammals. Conserv. Biol. 10, 1549–1556 (1996).Article 

    Google Scholar 
    Brashares, J. S., Arcese, P. & Sam, M. K. Human demography and reserve size predict wildlife extinction in West Africa. Proc. R. Soc. B Biol. Sci. 268, 2473–2478 (2001).Article 
    CAS 

    Google Scholar 
    Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
    ADS 
    CAS 

    Google Scholar 
    Turner, M. G. & Dale, V. H. Comparing large, infrequent disturbances: What have we learned?. Ecosystems 1, 493–496 (1998).Article 

    Google Scholar 
    Berger, J. The last mile: How to sustain long-distance migration in mammals. Conserv. Biol. 18, 320–331 (2004).Article 

    Google Scholar 
    Bolger, D. T., Newmark, W. D., Morrison, T. A. & Doak, D. F. The need for integrative approaches to understand and conserve migratory ungulates. Ecol. Lett. 11, 63–77 (2008).
    Google Scholar 
    Sawyer, H., Kauffman, M. J., Nielson, R. M. & Horne, J. S. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecol. Appl. 19, 2016–2025 (2009).Article 

    Google Scholar 
    Tucker, M. A. et al. Moving in the anthropocene: Global reductions in terrestrial mammalian movements. Science 469, 466–469 (2018).Article 
    ADS 

    Google Scholar 
    Soulé, M. E. & Terborgh, J. Conserving nature at regional and continental scales-a scientific program for North America. Bioscience 49, 809–817 (1999).Article 

    Google Scholar 
    Hilty, J. et al. Guidelines for conserving connectivity through ecological networks and corridors. Best Pract. Prot. Area Guidel. Ser. 30, 122 (2020).
    Google Scholar 
    Haddad, N. & Tewksbury, J. Impacts of corridors on populations and communities. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 390–415 (Cambridge University Press, 2010).
    Google Scholar 
    Ramiadantsoa, T., Ovaskainen, O., Rybicki, J. & Hanski, I. Large-scale habitat corridors for biodiversity conservation: A forest corridor in Madagascar. PLoS One 10, 1–18 (2015).Article 
    CAS 

    Google Scholar 
    Newmark, W. D., Jenkins, C. N., Pimm, S. L., McNeally, P. B. & Halley, J. M. Targeted habitat restoration can reduce extinction rates in fragmented forests. Proc. Natl. Acad. Sci. USA. 114, 9635–9640 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Diamond, J. M. Biogeographic kinetics: Estimation of relaxation times for avifaunas of southwest Pacific islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).Article 
    ADS 
    CAS 

    Google Scholar 
    Terborgh, J. Preservation of natural diversity: The problem of extinction prone species. Bioscience 24, 715–722 (1974).Article 

    Google Scholar 
    Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt revisited. Nature 371, 65–66 (1994).Article 
    ADS 

    Google Scholar 
    Halley, J. M., Monokrousos, N., Mazaris, A. D., Newmark, W. D. & Vokou, D. Dynamics of extinction debt across five taxonomic groups. Nat. Commun. 7, 1–6 (2016).Article 

    Google Scholar 
    Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian amazon. Science 337, 228–232 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Hanski, I. Extinction debt and species credit in boreal forests: Modelling the consequences of different approaches to conservation. Ann. Zool. Fennici 37, 271–280 (2000).
    Google Scholar 
    LaBarbera, M. Analyzing body size as a factor in ecology and evolution. Annu. Rev. Ecol. Syst. 20, 97–117 (1989).Article 

    Google Scholar 
    Oakleaf, J. K. et al. Habitat selection by recolonizing wolves in the northern Rocky mountains of the United States. J. Wildl. Manage. 70, 554–563 (2006).Article 

    Google Scholar 
    Cushman, S. A., McKelvey, K. S. & Schwartz, M. K. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23, 368–376 (2009).Article 

    Google Scholar 
    Schwartz, M. K. et al. Wolverine gene flow across a narrow climatic niche. Ecology 90, 3222–3232 (2014).Article 

    Google Scholar 
    McKelvey, K. S. et al. Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors. Ecol. Appl. 21, 2882–2897 (2011).Article 

    Google Scholar 
    Carroll, C., Mcrae, B. H. & Brookes, A. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conserv. Biol. 26, 78–87 (2012).Article 

    Google Scholar 
    Parks, S. A., McKelvey, K. S. & Schwartz, M. K. Effects of weighting schemes on the identification of wildlife corridors generated with least-cost methods. Conserv. Biol. 27, 145–154 (2013).Article 

    Google Scholar 
    Peck, C. P. et al. Potential paths for male-mediated gene flow to and from an isolated grizzly bear population. Ecosphere 8, e01969 (2017).Article 

    Google Scholar 
    Wild Migrations: Atlas of Wyoming’s Ungulates. (Oregon State University, 2018).Singleton, P. H., Gaines, W. L. & Lehmkuhl, J. F. Landscape permeability for large carnivores in Washington: A geographic information system weighted-distance and least-cost corridor assessment. (2002).Long, R. A. et al. The Cascades carnivore connectivity project: A landscape genetic assessment of connectivity in Washington’s north Cascades ecosystem. Final report for the Seattle City Light Wildlife Research Program (2013).Diamond, J. M. The island dilemma: Lessons of modern biogeographic studies for the design of natural reserves. Biol. Conserv. 7, 129–146 (1975).Article 

    Google Scholar 
    Wilson, E. O. & Willis, E. O. Applied biogeography. In Ecological structure of ecological communities (eds. Cody, M. L, & Diamond, J. M.) 522–534 (Harvard University Press, 1975)
    Google Scholar 
    Halley, J. M. & Iwasa, Y. Neutral theory as a predictor of avifaunal extinctions after habitat loss. Proc. Natl. Acad. Sci. USA 108, 2316–2321 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Cushman, S. A., Lewis, J. S. & Landguth, E. L. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov. Ecol. 1, 1–11 (2013).Article 

    Google Scholar 
    Singleton, P. H. & Lehmkuhl, J. F. I-90 Snoqualmie pass wildlife habitat linkage assessment. Final Report. USDA, Pacific Northwest Research Station. (2000).Craighead, L., Craighead, A., Oeschslia, L. & Kociolek, A. Bozeman pass post-fencing wildlife monitoring. Final Report. FHWA/MT-10-006/8173 (2011).Andis, A. Z., Huijser, M. P. & Broberg, L. Performance of arch-style road crossing structures from relative movement rates of large mammals. Front. Ecol. Evol. 5, 1–13 (2017).Article 

    Google Scholar 
    Millward, L. Small mammal microhabitat use and species composition at a wildlife crossing structure compared with nearby forest (Central Washington University, 2018).
    Google Scholar 
    Bischof, R., Steyaert, S. M. J. G. & Kindberg, J. Caught in the mesh: Roads and their network-scale impediment to animal movement. Ecography 40, 1369–1380 (2017).Article 

    Google Scholar 
    Balkenhol, N. & Waits, L. P. Molecular road ecology: Exploring the potential of genetics for investigating transportation impacts on wildlife. Mol. Ecol. 18, 4151–4164 (2009).Article 

    Google Scholar 
    Clevenger, A. P. & Wierzchowski, J. Maintaining and restoring connectivity in landscapes fragmented by roads. In Connectivity Conservation, (eds. Crooks, K. R. & Sanjayan, M.) 502–535 (Cambridge University Press, 2010.)
    Google Scholar 
    Sawaya, M. A., Kalinowski, S. T. & Clevenger, A. P. Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proc. R. Soc. B Biol. Sci. 281, 20131705 (2014).Article 

    Google Scholar 
    Sawaya, M. A., Clevenger, A. P. & Schwartz, M. K. Demographic fragmentation of a protected wolverine population bisected by a major transportation corridor. Biol. Conserv. 236, 616–625 (2019).Article 

    Google Scholar 
    Kamal, S., Grodzińska-Jurczak, M. & Brown, G. Conservation on private land: A review of global strategies with a proposed classification system. J. Environ. Plan. Manag. 58, 576–597 (2015).Article 

    Google Scholar 
    Wasserman, T. N., Cushman, S. A., Littell, J. S., Shirk, A. J. & Landguth, E. L. Population connectivity and genetic diversity of American marten (Martes americana) in the United States northern Rocky Mountains in a climate change context. Conserv. Genet. 14, 529–541 (2013).Article 

    Google Scholar 
    Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).Article 

    Google Scholar 
    Cushman, S. A., Landguth, E. L. & Flather, C. H. Evaluating the sufficiency of protected lands for maintaining wildlife population connectivity in the U.S. northern Rocky Mountains. Divers. Distrib. 18, 873–884 (2012).Article 

    Google Scholar 
    Beier, P., Spencer, W., Baldwin, R. F. & Mcrae, B. H. Toward best practices for developing regional connectivity maps. Conserv. Biol. 25, 879–892 (2011).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. (2020). More

  • in

    Migration direction in a songbird explained by two loci

    Ethics statementAnimals’ care was in accordance with institutional guidelines. Ethical permit was issued by Malmö-Lund djurförsöksetiska nämnd 5.8.18-00848/2018.Field workWe carried out the field work in Sweden during four breeding seasons (2018–2021). Adult male willow warblers were captured in their breeding territories using mist nets and playback of a song. From each bird, we collected the innermost primary feather from the right wing. From the birds that returned with a logger we also collected ~20 μl of blood from the brachial wing vein. The blood was stored in SET buffer (0.015 M NaCl, 0.05 M Tris, 0.001 M of EDTA, pH 8.0) at room temperature until deposited for permanent storage at −20 °C. We deployed Migrate Technology Ltd geolocators (Intigeo-W30Z11-DIP 12 × 5 × 4 mm, 0.32 g) and used a nylon string to mount them on birds with the “leg-loop” harness method as outlined in our previous work24. The mass of the logger relative to that of the bird was on average 3.3% (range 2.7–3.8%).The tagged birds were ringed with a numbered aluminum ring, and two, colored plastic rings for later identification in the field. In total, we tagged 466 males (349 in 2018 and 117 in 2020) at breeding territories. During the first tagging season (2018), birds were trapped at 17 locations (average 22 birds per site; range 7–30) distributed across Sweden (Fig. S1). Three of the sites were in southern Sweden to document migration routes of allopatric trochilus and three sites were located above the Arctic circle to record migratory routes of allopatric acredula, whereas the remaining (239) loggers were spread over 11 sites located in the migratory divide. Given the observed densities and distribution of hybrids after analyzing returning birds in 2019, we deployed 117 more loggers at one single site (63.439°N, 14.831°E) in 2020. We successfully retrieved tracks from 57 birds tagged in 2019 and 16 from birds tagged in 2021. In search for birds with loggers, we checked circa 3000 willow warbler males and covered an area of at least 0.5 km radius around each site the year after tagging.Geolocator data treatmentThe R package GeoLight (version 2.0)25 was used to extract and analyze locations from raw geolocator data. All twilight events were obtained with light threshold of 3 lux. The most extreme outliers were trimmed with “loessFilter” function and a K value of 3. We used GeoLight’s function “getElevation” for estimating the sun elevation angle for the breeding period: these sets of locations were used to infer the positions for autumn departure direction. In addition, we carried out a “Hill-Ekström” calibration for the longest stationary winter site during the period before the spring equinox. Winter calibration produced location sets that better reflected the winter coordinates of the main winter site in sub-Saharan Africa26. We reduced some of the inherent geolocation “noise” by applying cantered 5-day rolling means to the coordinates. The equinox periods were visually identified by inspecting standard deviations in latitude. Latitudes from equinox periods were omitted (on average autumn equinox obscured data for 45 days (range 25–68). For the main winter site, we used the longest period at which bird stayed stationary and from which in all cases begun the spring migration (mean = 118, SD = 23 days). Timing of autumn departure was estimated by manual inspection of longitudes and latitudes plotted in time series. To estimate at which longitude the birds crossed the Mediterranean, we extracted the longitude when birds crossed latitude 35 N° (Mediterranean crossing longitude). For 29 birds, it was possible to directly extract the longitude at crossing latitude 35 N°. For the rest of the cases, the birds had not reached latitude 35 N° before the latitude was obscured by the equinox, we calculated the mean longitude of 10 days from the onset of fall equinox as a measure of the Mediterranean crossing. This measurement correlated highly with the winter longitude (r = 0.78, p = 2.8 × 10−16). To control for the birds relative breeding site longitude, we extracted the departure direction (1°–360°) relative from the tagging site to the location where the birds crossed the Mediterranean (departure direction). The departure data was of circular type (measured in 360°), however the variance did not span more than 180° degrees (range 151°–224°). Therefore, we proceeded with analyses using linear statistics. Geographic distances and departure direction were calculated using R package “geosphere” (version 1.5-10). Complete set of positions of each individual bird with equinoxes excluded is presented in Supplementary Data 1.Laboratory work and molecular data extractionWe extracted DNA from blood samples following the ammonium acetate protocol16. Genotyping for divergent regions on chromosome 1 (InvP-Ch1) and chromosome 5 (InvP-Ch5) was done using a qPCR SNP assay16, which is based on one informative SNP per region (SNP 65 for chromosome 1 and SNP 285 for chromosome 5). Probes and primers were produced by Thermo Fisher Scientific and were designed using the online Custom TaqMan® Assay Design tool (Table S4). We used Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) and the universal Fast-two-steps protocol: 95 °C, 15 min—40*(95 °C, 10 s–60 °C, 30 s, plate read. Both regions contain inversion polymorphisms that restrict recombination between subspecies-specific haplotypes and contain nearly all the SNPs separating the two subspecies13. For each region, we scored genotypes as either “Tro” (homozygous for trochilus haplotypes), “Acr” (homozygous for acredula haplotypes) or “Het” (heterozygous). The method that we used to assess the presence of MARB-a is based on a qPCR assay that quantifies the copy number of a novel TE (previously known as AFLP-WW212) that has expanded in acredula. The quantification of repeats by this method has been shown to be highly repeatable (R2 = 0.88) when comparing estimates obtained from DNA in blood and feathers15. We used the forward (5′-CCTTGCATACTTCTATTTCTCCC-3′) and reverse (5′-CATAGGACAGACATTGTTGAGG-3′) primers developed by Caballero-López et al.15 to amplify the TE motif. For reference of a single copy region we used the primers SFRS3F and SFRS3R27. We diluted DNA to 1 ng/μl−1 and used a Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) with SYBR-green-based detection. Total reaction volume was 25 μl of which 4 μl of DNA, 12.5 μl of SuperMix, 0.1 μl ROX, 1 μl of primer (forward and reverse), and 6.4 μl of double distilled H2O. We ran quantifications of the single copy gene and the TE variant found on MARB-a on separate plates with the following settings: 50 °C for 2 min as initial incubation, 95 °C for 2 min X 43 (94 °C for 30 s [55.3 °C SFRS3 and 55.5 °C for TE, 30 s] and 72 °C for 45 s). Each sample was run in duplicate and together with a two-fold serial standard dilution (2.5–7.8 × 10−2 ng). Allopatric trochilus have 0–6 copies whereas allopatric acredula have 8–45 copies15; a bimodal distribution was also confirmed in this new data set (Fig. S2). Accordingly, for the present analyses, we split the data in two groups: birds with ≤6 TE copies and birds with >7, translating into absence or presence of MARB-a, with the former assumed to be homozygous for the absence of MARB-a and the latter heterozygous or homozygous for the presence of MARB-a. Data from two investigated willow warbler families suggest a Mendelian inheritance pattern and provide support for our interpretation of how TE copy numbers reflect the three genotypes (Table S5). Moreover, the TE copy numbers within the hybrid swarm have a distribution similar to a combination of allopatric trochilus and acredula, further supporting that the copies are inherited as intact blocks (haplotypes). However, a precise distinction between heterozygotes and homozygotes on MARB-a is still not possible15.Statistical analysisWe used linear models with departure direction, winter longitude, migration distance and departure timing as response variables and the three genetic markers: MARB-a (a factor with two levels), InvP-Ch1 (a factor with three levels) and InvP-Ch5 (a factor with three levels) as explanatory variables. Models were constructed with R base package “stats”. We reported Type II ANOVA for models with more than one explanatory variable and no interactions and type III ANOVA results for models with interaction term by using R package “Car” (version 3.0-12)28. We initially constructed mixed effect models with timing of departure and tagging year as random factors however, this delivered singular fits due to insufficient sample sizes across categories. Normality of residuals was checked with a Shapiro–Wilk test. For carrying out circular statistics on autumn migration direction we used the R package “circular” (version 0.4-93). Watson’s U2 pairwise comparisons of different groups delivered the same results as linear models (Table S2 and Fig. S5). Circular means were identical to conventional linear means in our data set, which we take as another evidence that linear models are appropriate for the analysis of our data (Table S3 and Fig. S5). Maps in Figs. 1 and 2b and S1, S3 and S4 were created with R package “ggplot2” (version 3.3.6) using continent contours from Natural Earth, naturalearthdata.com/. Heat gradient over the maps in Fig. 1a–d were created with R package “gstat” (version 2.0-8) and the inverse distance weighting power of 3.0. Circular plots were created with ORIANA (version 4.02). All analyses were carried out with R version 4.1.1 (R Core Team 2021).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

  • in

    Predator-mediated diversity of stream fish assemblages in a boreal river basin, China

    Chase, J. M. et al. The interaction between predation and competition: A review and synthesis. Ecol. Lett. 5, 302–315. https://doi.org/10.1046/j.1461-0248.2002.00315.x (2002).Article 

    Google Scholar 
    Droge, E., Creel, S., Becker, M. S. & M’Soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128. https://doi.org/10.1038/s41559-017-0220-9 (2017).Article 

    Google Scholar 
    Allesina, S. & Levine Jonathan, M. A competitive network theory of species diversity. Proc. Natl. Acad. Sci. U.S.A. 108, 5638–5642. https://doi.org/10.1073/pnas.1014428108 (2011).Article 
    ADS 

    Google Scholar 
    Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285. https://doi.org/10.1038/ncomms12285 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Letten, A. D. & Stouffer, D. B. The mechanistic basis for higher-order interactions and non-additivity in competitive communities. Ecol. Lett. 22, 423–436. https://doi.org/10.1111/ele.13211 (2019).Article 

    Google Scholar 
    Lotka, A. J. Elements of physical biology. Sci. Prog. Twent. Century (1919–1933) 21, 341–343 (1926).
    Google Scholar 
    Volterra, V. Variazioni e Fluttuazioni del Numero d’Individui in Specie Animali Conviventi. (Società Anonima Tipografica “Leonardo da Vinci”, 1926).Schmitz, O. J. Top predator control of plant biodiversity and productivity in an old-field ecosystem. Ecol. Lett. 6, 156–163. https://doi.org/10.1046/j.1461-0248.2003.00412.x (2003).Article 

    Google Scholar 
    Fey, K., Banks, P. B., Oksanen, L. & Korpimäki, E. Does removal of an alien predator from small islands in the Baltic Sea induce a trophic cascade?. Ecography 32, 546–552. https://doi.org/10.1111/j.1600-0587.2008.05637.x (2009).Article 

    Google Scholar 
    Terborgh John, W. Toward a trophic theory of species diversity. Proc. Natl. Acad. Sci. U.S.A. 112, 11415–11422. https://doi.org/10.1073/pnas.1501070112 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Pringle, R. M. et al. Predator-induced collapse of niche structure and species coexistence. Nature 570, 58–64. https://doi.org/10.1038/s41586-019-1264-6 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Sandom, C. et al. Mammal predator and prey species richness are strongly linked at macroscales. Ecology 94, 1112–1122. https://doi.org/10.1890/12-1342.1 (2013).Article 

    Google Scholar 
    Louette, G. & De Meester, L. Predation and priority effects in experimental zooplankton communities. Oikos 116, 419–426. https://doi.org/10.1111/j.2006.0030-1299.15381.x (2007).Article 

    Google Scholar 
    Johnston, N. K., Pu, Z. & Jiang, L. Predator identity influences metacommunity assembly. J. Anim. Ecol. 85, 1161–1170. https://doi.org/10.1111/1365-2656.12551 (2016).Article 

    Google Scholar 
    Karakoc, C., Radchuk, V., Harms, H. & Chatzinotas, A. Interactions between predation and disturbances shape prey communities. Sci. Rep. 8, 2968. https://doi.org/10.1038/s41598-018-21219-x (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) (Princeton University Press, 2011).Book 

    Google Scholar 
    MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 2001).Book 

    Google Scholar 
    Daniel, J., Gleason, J. E., Cottenie, K. & Rooney, R. C. Stochastic and deterministic processes drive wetland community assembly across a gradient of environmental filtering. Oikos 128, 1158–1169. https://doi.org/10.1111/oik.05987 (2019).Article 

    Google Scholar 
    Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028 (2004).Article 
    ADS 

    Google Scholar 
    Chase, J. M., Biro, E. G., Ryberg, W. A. & Smith, K. G. Predators temper the relative importance of stochastic processes in the assembly of prey metacommunities. Ecol. Lett. 12, 1210–1218. https://doi.org/10.1111/j.1461-0248.2009.01362.x (2009).Article 

    Google Scholar 
    Werner, E. E. & Peacor, S. D. A review of trait-mediated indirect interactions in ecological communities. Ecology 84, 1083–1100. https://doi.org/10.1890/0012-9658(2003)084[1083:AROTII]2.0.CO;2 (2003).Article 

    Google Scholar 
    Pearson, D. E., Ortega, Y. K., Eren, Ö. & Hierro, J. L. Community assembly theory as a framework for biological invasions. Trends Ecol. Evol. 33, 313–325. https://doi.org/10.1016/j.tree.2018.03.002 (2018).Article 

    Google Scholar 
    Duchesne, É. et al. Variable strength of predator-mediated effects on species occurrence in an arctic terrestrial vertebrate community. Ecography 44, 1236–1248. https://doi.org/10.1111/ecog.05760 (2021).Article 

    Google Scholar 
    Ryberg, W. A., Smith, K. G. & Chase, J. M. Predators alter the scaling of diversity in prey metacommunities. Oikos 121, 1995–2000. https://doi.org/10.1111/j.1600-0706.2012.19620.x (2012).Article 

    Google Scholar 
    Carrete Vega, G. & Wiens, J. J. Why are there so few fish in the sea?. Proc. R. Soc. B 279, 2323–2329. https://doi.org/10.1098/rspb.2012.0075 (2012).Article 

    Google Scholar 
    Barrett, M. et al. Living planet report 2018: Aiming higher. (2018).Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873. https://doi.org/10.1111/brv.12480 (2019).Article 

    Google Scholar 
    Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42. https://doi.org/10.1016/j.gecco.2017.01.008 (2017).Article 

    Google Scholar 
    Hammerschlag, N. et al. Ecosystem function and services of aquatic predators in the anthropocene. Trends Ecol. Evol. 34, 369–383. https://doi.org/10.1016/j.tree.2019.01.005 (2019).Article 

    Google Scholar 
    Wang, T. et al. Amur tigers and leopards returning to China: direct evidence and a landscape conservation plan. Landsc Ecol 31, 491–503. https://doi.org/10.1007/s10980-015-0278-1 (2016).Article 

    Google Scholar 
    Hong, S. et al. Stream health, topography, and land use influences on the distribution of the Eurasian otter Lutra lutra in the Nakdong River basin, South Korea. Ecol. Indic. 88, 241–249. https://doi.org/10.1016/j.ecolind.2018.01.004 (2018).Article 

    Google Scholar 
    Guter, A., Dolev, A., Saltz, D. & Kronfeld-Schor, N. Using videotaping to validate the use of spraints as an index of Eurasian otter (Lutra lutra) activity. Ecol. Indic. 8, 462–465. https://doi.org/10.1016/j.ecolind.2007.04.009 (2008).Article 

    Google Scholar 
    Sittenthaler, M., Bayerl, H., Unfer, G., Kuehn, R. & Parz-Gollner, R. Impact of fish stocking on Eurasian otter (Lutra lutra) densities: A case study on two salmonid streams. Mamm. Biol. 80, 106–113. https://doi.org/10.1016/j.mambio.2015.01.004 (2015).Article 

    Google Scholar 
    Zheng, B., Huang, H., Zhang, Y. & Dai, D. The Fishes of Tumen River (Jilin People’s Publishing House, 1980).
    Google Scholar 
    Fleishman, E., Murphy, D. D. & Brussard, P. F. A new method for selection of umbrella species for conservation planning. Ecol Appl 10, 569–579. https://doi.org/10.1890/1051-0761(2000)010[0569:ANMFSO]2.0.CO;2 (2000).Article 

    Google Scholar 
    Roberge, J.-M. & Angelstam, P. E. R. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18, 76–85. https://doi.org/10.1111/j.1523-1739.2004.00450.x (2004).Article 

    Google Scholar 
    McGowan, J. et al. Conservation prioritization can resolve the flagship species conundrum. Nat. Commun. 11, 994. https://doi.org/10.1038/s41467-020-14554-z (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Katano, I., Doi, H., Eriksson, B. K. & Hillebrand, H. A cross-system meta-analysis reveals coupled predation effects on prey biomass and diversity. Oikos 124, 1427–1435. https://doi.org/10.1111/oik.02430 (2015).Article 

    Google Scholar 
    Leibold, M. A. A graphical model of keystone predators in food webs: Trophic regulation of abundance, incidence, and diversity patterns in communities. Am. Nat. 147, 784–812. https://doi.org/10.1086/285879 (1996).Article 

    Google Scholar 
    McPeek, M. A. The consequences of changing the top predator in a food web: A comparative experimental approach. Ecol. Monogr. 68, 1–23. https://doi.org/10.1890/0012-9615(1998)068[0001:TCOCTT]2.0.CO;2 (1998).Article 

    Google Scholar 
    Chase, J. M. & Leibold, M. A. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, 2003).Book 

    Google Scholar 
    Gravel, D., Canham, C. D., Beaudet, M. & Messier, C. Reconciling niche and neutrality: The continuum hypothesis. Ecol. Lett. 9, 399–409. https://doi.org/10.1111/j.1461-0248.2006.00884.x (2006).Article 

    Google Scholar 
    Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306. https://doi.org/10.1038/nature01767 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Yin, X., Wang, J., Yin, H. & Ruan, Y. Does inducible defense mitigate physiological stress responses of prey to predation risk?. Hydrobiologia 843, 173–181. https://doi.org/10.1007/s10750-019-04046-7 (2019).Article 

    Google Scholar 
    Chalcraft, D. R. & Resetarits, W. J. Jr. Predator identity and ecological impacts: Functional redundancy or functional diversity?. Ecology 84, 2407–2418. https://doi.org/10.1890/02-0550 (2003).Article 

    Google Scholar 
    Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758. https://doi.org/10.1111/j.1461-0248.2006.00924.x (2006).Article 

    Google Scholar 
    Burner, R. C. et al. Functional structure of European forest beetle communities is enhanced by rare species. Biol. Conserv. 267, 109491. https://doi.org/10.1016/j.biocon.2022.109491 (2022).Article 

    Google Scholar  More

  • in

    Publisher Correction: Seasonal peak photosynthesis is hindered by late canopy development in northern ecosystems

    Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, ChinaQian Zhao, Yao Zhang & Shilong PiaoSchool of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengKey Laboratory of Earth Surface System and Human—Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengDepartment of Earth and Environment, Boston University, Boston, MA, USARanga B. MyneniCSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Catalonia, SpainJosep PeñuelasCREAF, Barcelona, Catalonia, SpainJosep PeñuelasState Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaShilong Piao More