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    Lion and spotted hyena distributions within a buffer area of the Serengeti-Mara ecosystem

    1.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 

    Google Scholar 
    2.Riggio, J. et al. The size of savannah Africa: A lion’s (Panthera leo) view. Biodivers. Conserv. 22, 17–35 (2013).
    Google Scholar 
    3.Carbone, C. & Gittleman, J. L. A common rule for the scaling of carnivore density. Science 295, 2273–2276 (2002).ADS 
    CAS 
    PubMed 

    Google Scholar 
    4.Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science 363, 1424–1428 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    5.Palomares, F. & Caro, T. M. Interspecific killing among mammalian carnivores. Am. Nat. 153, 492–508 (1999).CAS 
    PubMed 

    Google Scholar 
    6.Tanner, E. et al. Wolves contribute to disease control in a multi-host system. Sci. Rep. 9, 1–12 (2019).ADS 

    Google Scholar 
    7.O’Bryan, C. J. et al. The contribution of predators and scavengers to human well-being. Nat. Ecol. Evol. 2, 229–236 (2018).PubMed 

    Google Scholar 
    8.Prugh, L. R. & Sivy, K. J. Enemies with benefits: integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. 23, 902–918 (2020).PubMed 

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

    Google Scholar 
    10.Wilfred, P. Towards sustainable wildlife management areas in Tanzania. Trop. Conserv. Sci. 3, 103–116 (2010).
    Google Scholar 
    11.Sinclair, A. R., Metzger, K. L., Mduma, S. A. & Fryxell, J. M. Serengeti IV: Sustaining Biodiversity in a Coupled Human-Natural System (University of Chicago Press, 2015).
    Google Scholar 
    12.Crooks, K. R. & Sanjayan, M. Connectivity Conservation Vol. 14 (Cambridge University Press, 2006).
    Google Scholar 
    13.Balme, G. A., Slotow, R. & Hunter, L. T. Edge effects and the impact of non-protected areas in carnivore conservation: Leopards in the Phinda-Mkhuze Complex, South Africa. Anim. Conserv. 13, 315–323 (2010).
    Google Scholar 
    14.Lindsey, P. et al. The performance of African protected areas for lions and their prey. Biol. Conserv. 209, 137–149 (2017).
    Google Scholar 
    15.Elliot, N. B. & Gopalaswamy, A. M. Toward accurate and precise estimates of lion density. Conserv. Biol. 31, 934–943 (2017).PubMed 

    Google Scholar 
    16.Masenga, E. et al. Strychnine poisoning in African wild dogs (Lycaon pictus) in the Loliondo game controlled area, Tanzania. Int. J. Biodivers. Conserv. 5, 367–370 (2013).
    Google Scholar 
    17.Metzger, K., Sinclair, A., Hilborn, R., Hopcraft, J. G. C. & Mduma, S. A. Evaluating the protection of wildlife in parks: The case of African buffalo in Serengeti. Biodivers. Conserv. 19, 3431–3444 (2010).
    Google Scholar 
    18.Mogensen, N. L., Ogutu, J. O. & Dabelsteen, T. The effects of pastoralism and protection on lion behaviour, demography and space use in the Mara Region of Kenya. Afr. Zool. 46, 78–87 (2011).
    Google Scholar 
    19.Kiffner, C., Meyer, B., Mühlenberg, M. & Waltert, M. Plenty of prey, few predators: what limits lions Panthera leo in Katavi National Park, western Tanzania?. Oryx 43, 52–59 (2009).
    Google Scholar 
    20.Kiffner, C., Stoner, C. & Caro, T. Edge effects and large mammal distributions in a national park. Anim. Conserv. 16, 97–107 (2013).
    Google Scholar 
    21.Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).
    Google Scholar 
    22.Hofer, H. & East, M. Population dynamics, population size, and the commuting system of Serengeti spotted hyenas. Serengeti II Dyn. Manag. Conserv. Ecosyst. 2, 332 (1995).
    Google Scholar 
    23.Holekamp, K. E. & Dloniak, S. M. Intraspecific variation in the behavioral ecology of a tropical carnivore, the spotted hyena. Adv. Study Behav. 42, 189–229 (2010).
    Google Scholar 
    24.Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. biol. 16, 488–502 (2002).
    Google Scholar 
    25.Martin, J. et al. Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach. Methods Ecol. Evol. 2, 595–601 (2011).ADS 

    Google Scholar 
    26.Prins, H. H., Grootenhuis, J. G. & Dolan, T. T. Wildlife Conservation by Sustainable Use Vol. 12 (Springer Science & Business Media, 2012).
    Google Scholar 
    27.Knapp, E. J. Why poaching pays: a summary of risks and benefits illegal hunters face in Western Serengeti, Tanzania. Trop. Conserv. Sci. 5, 434–445 (2012).ADS 

    Google Scholar 
    28.Revilla, E., Palomares, F. & Delibes, M. Edge-core effects and the effectiveness of traditional reserves in conservation: Eurasian badgers in Doñana National Park. Conserv. Biol. 15, 148–158 (2001).
    Google Scholar 
    29.Lindsey, P. A. et al. The bushmeat trade in African savannas: Impacts, drivers, and possible solutions. Biol. Conserv. 160, 80–96 (2013).
    Google Scholar 
    30.Ikanda, D. & Packer, C. Ritual vs. retaliatory killing of African lions in the Ngorongoro Conservation Area, Tanzania. Endanger. Species Res. 6, 67–74 (2008).
    Google Scholar 
    31.Belant, J. L. et al. Estimating lion abundance using N-mixture models for social species. Sci. Rep. 6, 1–9 (2016).
    Google Scholar 
    32.Hofer, H. & East, M. L. The commuting system of Serengeti spotted hyaenas: how a predator copes with migratory prey I. Social organization. Anim. Behav. 46, 547–557 (1993).
    Google Scholar 
    33.Durant, S. M. et al. Long-term trends in carnivore abundance using distance sampling in Serengeti National Park, Tanzania. J. Appl. Ecol. 48, 1490–1500 (2011).
    Google Scholar 
    34.Swanson, A. B. Living with Lions: Spatiotemporal Aspects of Coexistence in Savanna Carnivores (University of Minnesota, 2014).
    Google Scholar 
    35.Masenga, E. H., Lyamuya, R. D., Mjingo, E. E., Fyumagwa, R. D. & Røskaft, E. Communal knowledge and perceptions of African wild dog (Lycaon pictus) reintroduction in the western part of Serengeti National Park, Tanzania. Int. J. Biodivers. Conserv. 9, 122–129 (2017).
    Google Scholar 
    36.Hopcraft, J. G. C., Sinclair, A. & Packer, C. Planning for success: Serengeti lions seek prey accessibility rather than abundance. J. Anim. Ecol. 74, 559–566 (2005).
    Google Scholar 
    37.Packer, C. & Pusey, A. E. Adaptations of female lions to infanticide by incoming males. Am. Nat. 121, 716–728 (1983).
    Google Scholar 
    38.Kruuk, H. & Turner, M. Comparative notes on predation by lion, leopard, cheetah and wild dog in the Serengeti area, East Africa. Mammalia 31, 1–27 (1967).
    Google Scholar 
    39.Green, D. S., Johnson-Ulrich, L., Couraud, H. E. & Holekamp, K. E. Anthropogenic disturbance induces opposing population trends in spotted hyenas and African lions. Biodiver. Conserv. 27, 871–889. https://doi.org/10.1007/s10531-017-1469-7 (2018).Article 

    Google Scholar 
    40.Kolowski, J. M., Katan, D., Theis, K. R. & Holekamp, K. E. Daily patterns of activity in the spotted hyena. J. Mamm. 88, 1017–1028 (2007).
    Google Scholar 
    41.Šálek, M., Kreisinger, J., Sedláček, F. & Albrecht, T. Do prey densities determine preferences of mammalian predators for habitat edges in an agricultural landscape?. Landsc. Urban Plan. 98, 86–91 (2010).
    Google Scholar 
    42.Mosser, A., Fryxell, J. M., Eberly, L. & Packer, C. Serengeti real estate: density vs. fitness-based indicators of lion habitat quality. Ecol. Lett. 12, 1050–1060 (2009).PubMed 

    Google Scholar 
    43.Schmitt, J. A. Improving Conservation Efforts in the Serengeti Ecosystem, Tanzania: An Examination of Knowledge, Benefits, Costs, and Attitudes (University of Minnesota, 2010).
    Google Scholar 
    44.Makacha, S., Msingwa, M. J. & Frame, G. W. Threats to the Serengeti herds. Oryx 16, 437–444 (1982).
    Google Scholar 
    45.Crosmary, W.-G. et al. Lion densities in selous game reserve, Tanzania. Afr. J. Wildl. Res. 48, 1–6 (2018).
    Google Scholar 
    46.Belant, J. L. et al. Track surveys do not provide accurate or precise lion density estimates in serengeti. Glob. Ecol. 19, e00651 (2019).
    Google Scholar 
    47.Midlane, N., O’Riain, M. J., Balme, G. A. & Hunter, L. T. B. To track or to call: comparing methods for estimating population abundance of African lions Panthera leo in Kafue National Park. Biodiver. Conserv. 24, 1311–1327. https://doi.org/10.1007/s10531-015-0858-z (2015).Article 

    Google Scholar 
    48.Ogutu, J. O. & Dublin, H. T. The response of lions and spotted hyaenas to sound playbacks as a technique for estimating population size. Afr. J. Ecol. 36, 83–95. https://doi.org/10.1046/j.1365-2028.1998.113-89113.x (1998).Article 

    Google Scholar 
    49.Belant, J. L. et al. Temporal and spatial variation of broadcasted vocalizations does not reduce lion Panthera leo habituation. Wildl. Biol. wlb. 00287 (2017).50.Cozzi, G., Broekhuis, F., McNutt, J. & Schmid, B. Density and habitat use of lions and spotted hyenas in northern Botswana and the influence of survey and ecological variables on call-in survey estimation. Biodiver. Conserv. 22, 2937–2956 (2013).
    Google Scholar 
    51.M’soka, J., Creel, S., Becker, M. S. & Droge, E. Spotted hyaena survival and density in a lion depleted ecosystem: The effects of prey availability, humans and competition between large carnivores in African savannahs. Biol. Conserv. 201, 348–355 (2016).
    Google Scholar 
    52.Croes, B. et al. The impact of trophy hunting on lions (Panthera leo) and other large carnivores in the Bénoué Complex, northern Cameroon. Biol. Conserv. 144, 3064–3072 (2011).
    Google Scholar 
    53.Whitman, K., Starfield, A. M., Quadling, H. S. & Packer, C. Sustainable trophy hunting of African lions. Nature 428, 175–178 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    54.National Bureau of Statistics. Tanzania in Figures 2012 (The United Republic of Tanzania, 2013).55.McNaughton, S. Serengeti grassland ecology: The role of composite environmental factors and contingency in community organization. Ecol. Monograph. 53, 291–320 (1983).
    Google Scholar 
    56.Reed, D., Anderson, T., Dempewolf, J., Metzger, K. & Serneels, S. The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics. J. Biogeogr. 36, 770–782 (2009).
    Google Scholar 
    57.Sollmann, R., Gardner, B., Belant, J. L., Wilton, C. M. & Beringer, J. Habitat associations in a recolonizing, low‐density black bear population. Ecosphere 7 (2016).58.Royle, J. A. & Dorazio, R. M. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities (Elsevier, 2008).
    Google Scholar 
    59.Chandler, R. B., Royle, J. A. & King, D. I. Inference about density and temporary emigration in unmarked populations. Ecology 92, 1429–1435 (2011).PubMed 

    Google Scholar 
    60.Royle, J. A. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108–115 (2004).MathSciNet 
    PubMed 
    MATH 

    Google Scholar 
    61.Kellner, K. & Meredith, M. Package ‘jagsUI’. (2021).62.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).63.Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).MathSciNet 
    MATH 

    Google Scholar 
    64.Kuo, L. & Mallick, B. Variable selection for regression models. Indian J. Stat. 65–81 (1998).65.Congdon, P. Bayesian Models for Categorical Data (John Wiley and Sons, 2005).MATH 

    Google Scholar  More

  • in

    Investing wisely in land restoration

    1.Mirzabaev, A., Sacande, M., Motlagh, F., Shyrokaya, A. & Martucci, A. Nat. Sustain. https://doi.org/10.1038/s41893-021-00801-8 (2021).2.Barbier, E. B. & Hochard, J. P. Nat. Sustain. 1, 623–631 (2018).Article 

    Google Scholar 
    3.Deininger, K. & Jin, S. Eur. Econ. Rev. 50, 1245–1277 (2006).Article 

    Google Scholar 
    4.Barbier, E. B. Environ. Dev. Econ. 15, 635–660 (2010).Article 

    Google Scholar 
    5.Nkonya, E., Mirzabaev, A. & von Braun, J. in Economics of Land Degradation and Improvement—A Global Assessment for Sustainable Development (eds. Nkonya, E., Mirzabaev, A. & von Braun, J.) 1–14 (Springer, Cham, 2016).6.Barbier, E. B. & Hochard, J. P. Rev. Environ. Econ. Policy 12, 26–47 (2018).Article 

    Google Scholar  More

  • in

    Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean

    1.IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (in the press).2.Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Meltofte, H. (ed.) Arctic Biodiversity Assessment: Status and Trends in Arctic Biodiversity (CAFF International Secretariat, 2013).4.Wassmann, P. & Reigstad, M. Future Arctic Ocean seasonal ice zones and implications for pelagic-benthic coupling. Oceanography 24, 220–231 (2011).
    Google Scholar 
    5.Bunse, C. & Pinhassi, J. Marine bacterioplankton seasonal succession dynamics. Trends Microbiol. 25, 494–505 (2017).CAS 
    PubMed 

    Google Scholar 
    6.Olli, K. et al. Seasonal variation in vertical flux of biogenic matter in the marginal ice zone and the central Barents Sea. J. Mar. Syst. 38, 189–204 (2002).
    Google Scholar 
    7.Riedel, A., Michel, C., Gosselin, M. & LeBlanc, B. Winter–spring dynamics in sea-ice carbon cycling in the coastal Arctic Ocean. J. Mar. Syst. 74, 918–932 (2008).
    Google Scholar 
    8.Joli, N., Monier, A., Logares, R. & Lovejoy, C. Seasonal patterns in Arctic prasinophytes and inferred ecology of Bathycoccus unveiled in an Arctic winter metagenome. ISME J. 11, 1372–1385 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    9.Alonso-Sáez, L., Sánchez, O., Gasol, J. M., Balagué, V. & Pedrós-Alio, C. Winter-to-summer changes in the composition and single-cell activity of near-surface Arctic prokaryotes. Environ. Microbiol. 10, 2444–2454 (2008).PubMed 

    Google Scholar 
    10.Alonso-Sáez, L. et al. Role for urea in nitrification by polar marine Archaea. Proc. Natl Acad. Sci. USA 109, 17989–17994 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    11.Boetius, A., Anesio, A. M., Deming, J. W., Mikucki, J. A. & Rapp, J. Z. Microbial ecology of the cryosphere: sea ice and glacial habitats. Nat. Rev. Microbiol. 13, 677–690 (2015).CAS 
    PubMed 

    Google Scholar 
    12.Circumpolar Biodiversity Monitoring Program, Conservation of Arctic Flora and Fauna. State of the Arctic Marine Biodiversity Report (Conservation of Arctic Flora and Fauna International Secretariat, 2017).13.Kirchman, D. L., Cottrell, M. T. & Lovejoy, C. The structure of bacterial communities in the western Arctic Ocean as revealed by pyrosequencing of 16S rRNA genes. Environ. Microbiol. 12, 1132–1143 (2010).CAS 
    PubMed 

    Google Scholar 
    14.Galand, P. E., Casamayor, E. O., Kirchman, D. L., Potvin, M. & Lovejoy, C. Unique archaeal assemblages in the Arctic Ocean unveiled by massively parallel tag sequencing. ISME J. 3, 860–869 (2009).CAS 
    PubMed 

    Google Scholar 
    15.Pedrós-Alió, C., Potvin, M. & Lovejoy, C. Diversity of planktonic microorganisms in the Arctic Ocean. Prog. Oceanogr. 139, 233–243 (2015).
    Google Scholar 
    16.Amaral-Zettler, L. et al. in Life in the World’s Oceans: Diversity, Distribution, and Abundance (ed. McIntyre, A. D.) 221–245 (Blackwell Publishing Ltd, 2010).17.Christman, G. D., Cottrell, M. T., Popp, B. N., Gier, E. & Kirchman, D. L. Abundance, diversity, and activity of ammonia-oxidizing prokaryotes in the coastal Arctic Ocean in summer and winter. Appl. Environ. Microbiol. 77, 2026–2034 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Alonso-Sáez, L., Galand, P. E., Casamayor, E. O., Pedrós-Alió, C. & Bertilsson, S. High bicarbonate assimilation in the dark by Arctic bacteria. ISME J. 4, 1581–1590 (2010).PubMed 

    Google Scholar 
    19.Galand, P. E., Lovejoy, C., Pouliot, J., Garneau, M.-È. & Vincent, W. F. Microbial community diversity and heterotrophic production in a coastal Arctic ecosystem: a stamukhi lake and its source waters. Limnol. Oceanogr. 53, 813–823 (2008).
    Google Scholar 
    20.Nguyen, D. et al. Winter diversity and expression of proteorhodopsin genes in a polar ocean. ISME J. 9, 1835–1845 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    21.Cifuentes-Anticevic, J. et al. Proteorhodopsin phototrophy in Antarctic coastal waters. mSphere 6, e00525–21 (2021).CAS 
    PubMed Central 

    Google Scholar 
    22.Ghiglione, J.-F. et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc. Natl Acad. Sci. USA 109, 17633–17638 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083.e21 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Kraemer, S., Ramachandran, A., Colatriano, D., Lovejoy, C. & Walsh, D. A. Diversity and biogeography of SAR11 bacteria from the Arctic Ocean. ISME J. 14, 79–90 (2020).PubMed 

    Google Scholar 
    25.Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome 8, 47 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    26.Sunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. 18, 428–445 (2020).CAS 
    PubMed 

    Google Scholar 
    27.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. 35, 725–731 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).CAS 
    PubMed 

    Google Scholar 
    29.Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Aagaard, K., Swift, J. H. & Carmack, E. C. Thermohaline circulation in the Arctic Mediterranean Seas. J. Geophys. Res. Oceans 90, 4833–4846 (1985).
    Google Scholar 
    32.Dupont, C. L. et al. Genomes and gene expression across light and productivity gradients in eastern subtropical Pacific microbial communities. ISME J. 9, 1076–1092 (2015).CAS 
    PubMed 

    Google Scholar 
    33.Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Jones, S. E. & Lennon, J. T. Dormancy contributes to the maintenance of microbial diversity. Proc. Natl Acad. Sci. USA 107, 5881–5886 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Mestre, M. & Höfer, J. The microbial conveyor belt: connecting the globe through dispersion and dormancy. Trends Microbiol. 29, 482–492 (2021).CAS 
    PubMed 

    Google Scholar 
    36.Ciufo, S. et al. Using average nucleotide identity to improve taxonomic assignments in prokaryotic genomes at the NCBI. Int. J. Syst. Evol. Microbiol. 68, 2386–2392 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    37.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).PubMed Central 

    Google Scholar 
    38.Nelson, W. C., Tully, B. J. & Mobberley, J. M. Biases in genome reconstruction from metagenomic data. PeerJ 8, e10119 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    39.Alneberg, J. et al. Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Commun. Biol. 3, 119 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    40.Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Christensen, M. & Nilsson, A. E. Arctic sea ice and the communication of climate change. Pop. Commun. 15, 249–268 (2017).
    Google Scholar 
    42.Jaffe, A. L., Castelle, C. J., Dupont, C. L. & Banfield, J. F. Lateral gene transfer shapes the distribution of RuBisCO among candidate phyla radiation bacteria and DPANN Archaea. Mol. Biol. Evol. 36, 435–446 (2019).CAS 
    PubMed 

    Google Scholar 
    43.Kono, T. et al. A RuBisCO-mediated carbon metabolic pathway in methanogenic archaea. Nat. Commun. 8, 14007 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Sato, T., Atomi, H. & Imanaka, T. Archaeal type III RuBisCOs function in a pathway for AMP metabolism. Science 315, 1003–1006 (2007).CAS 
    PubMed 

    Google Scholar 
    45.Tabita, F. R., Satagopan, S., Hanson, T. E., Kreel, N. E. & Scott, S. S. Distinct form I, II, III, and IV Rubisco proteins from the three kingdoms of life provide clues about Rubisco evolution and structure/function relationships. J. Exp. Bot. 59, 1515–1524 (2008).CAS 
    PubMed 

    Google Scholar 
    46.Yelton, A. P. et al. Global genetic capacity for mixotrophy in marine picocyanobacteria. ISME J. 10, 2946–2957 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Cordero, P. R. F. et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 13, 2868–2881 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.King, G. M. & Weber, C. F. Distribution, diversity and ecology of aerobic CO-oxidizing bacteria. Nat. Rev. Microbiol. 5, 107–118 (2007).CAS 
    PubMed 

    Google Scholar 
    49.Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 

    Google Scholar 
    50.Sul, W. J., Oliver, T. A., Ducklow, H. W., Amaral-Zettler, L. A. & Sogin, M. L. Marine bacteria exhibit a bipolar distribution. Proc. Natl Acad. Sci. USA 110, 2342–2347 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Levins, R. Evolution in Changing Environments: Some Theoretical Explorations (Princeton Univ. Press, 1968).
    Google Scholar 
    53.Colwell, R. K. & Futuyma, D. J. On the measurement of niche breadth and overlap. Ecology 52, 567–576 (1971).PubMed 

    Google Scholar 
    54.Massana, R. & Logares, R. Eukaryotic versus prokaryotic marine picoplankton ecology. Environ. Microbiol. 15, 1254–1261 (2013).PubMed 

    Google Scholar 
    55.Székely, A. J., Berga, M. & Langenheder, S. Mechanisms determining the fate of dispersed bacterial communities in new environments. ISME J. 7, 61–71 (2013).PubMed 

    Google Scholar 
    56.Brooks, J. P. et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol. 15, 66 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    57.Logares, R. et al. Biogeography of bacterial communities exposed to progressive long-term environmental change. ISME J. 7, 937–948 (2013).CAS 
    PubMed 

    Google Scholar 
    58.Ruiz-González, C. et al. Higher contribution of globally rare bacterial taxa reflects environmental transitions across the surface ocean. Mol. Ecol. 28, 1930–1945 (2019).PubMed 

    Google Scholar 
    59.Staley, J. T. & Gosink, J. J. Poles apart: biodiversity and biogeography of sea ice bacteria. Annu. Rev. Microbiol. 53, 189–215 (1999).CAS 
    PubMed 

    Google Scholar 
    60.Chaffron, S. et al. Environmental vulnerability of the global ocean epipelagic plankton community interactome. Sci. Adv. 7, eabg1921 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Estrada, E. Characterization of topological keystone species: local, global and “meso-scale” centralities in food webs. Ecol. Complex. 4, 48–57 (2007).
    Google Scholar 
    62.Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).CAS 
    PubMed 

    Google Scholar 
    63.Tully, B. J., Sachdeva, R., Graham, E. D. & Heidelberg, J. F. 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ 2017, e3558 (2017).
    Google Scholar 
    64.Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun. Biol. 4, 604 (2021).65.Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data 4, 170093 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    71.Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    72.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).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Huang, X. & Madan, A. CAP3: a DNA sequence assembly program. Genome Res. 9, 868–877 (1999).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    75.Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    77.Wheeler, T. J. & Eddy, S. R. nhmmer: DNA homology search with profile HMMs. Bioinformatics 29, 2487–2489 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Jain, C., Rodriguez-R, L. M., Phillipy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    79.Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    81.Pertea, G. & Pertea, M. GFF utilities: GffRead and GffCompare. F1000Res. 9, ISCB Comm J-304 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    82.Aylward, F. O. & Santoro, A. E. Heterotrophic Thaumarchaeota with ultrasmall genomes are widespread in the ocean. mSystems 5, e00415–20 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    84.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2––approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    85.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    86.Louca, S., Doebeli, M. & Parfrey, L. W. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome 6, 41 (2018).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Forest fires and climate-induced tree range shifts in the western US

    1.von Humboldt, A. & Bonpland, A. Essay on the Geography of Plants (Univ. of Chicago Press, 1807).2.Woodward, F. I. Climate and Plant Distribution (Cambridge Univ. Press, 1987).3.Pausas, J. G. & Bond, W. J. Alternative biome states in terrestrial ecosystems. Trends Plant Sci. 25, 250–263 (2020).CAS 
    PubMed 

    Google Scholar 
    4.Kelly, A. E. & Goulden, M. L. Rapid shifts in plant distribution with recent climate change. Proc. Natl Acad. Sci. 105, 11823–11826 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Koide, D., Yoshida, K., Daehler, C. C. & Mueller-Dombois, D. An upward elevation shift of native and non-native vascular plants over 40 years on the island of Hawai’i. J. Veg. Sci. 28, 939–950 (2017).
    Google Scholar 
    6.Thomas, C. D. Climate, climate change and range boundaries: climate and range boundaries. Divers. Distrib. 16, 488–495 (2010).
    Google Scholar 
    7.Lenoir, J. & Svenning, J.-C. Climate-related range shifts—a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).
    Google Scholar 
    8.Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    9.Grabherr, G., Gottfried, M. & Pauli, H. Climate change impacts in alpine environments: climate change impacts in alpine environments. Geogr. Compass 4, 1133–1153 (2010).
    Google Scholar 
    10.Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).ADS 

    Google Scholar 
    11.Im, S. T., Kharuk, V. I., Sukachev Institute of Forest SB RAS – subdivision of FSC KSC SB RAS; Siberian Federal University & Lee, V. G. Migration of the northern evergreen needleleaf timberline in Siberia in the 21st century. Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Iz Kosm. 17, 176–187 (2020).
    Google Scholar 
    12.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
    CAS 
    PubMed 

    Google Scholar 
    13.Murphy, H. T., VanDerWal, J. & Lovett-Doust, J. Signatures of range expansion and erosion in eastern North American trees: signatures of range expansion and erosion. Ecol. Lett. 13, 1233–1244 (2010).PubMed 

    Google Scholar 
    14.Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T. & Curtis-McLane, S. Adaptation, migration or extirpation: climate change outcomes for tree populations: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).PubMed 
    PubMed Central 

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

    Google Scholar 
    16.Williams, M. I. & Dumroese, R. K. Preparing for climate change: forestry and assisted migration. J. For. 111, 287–297 (2013).
    Google Scholar 
    17.Anderson, J. T. & Wadgymar, S. M. Climate change disrupts local adaptation and favours upslope migration. Ecol. Lett. 23, 181–192 (2020).PubMed 

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

    Google Scholar 
    19.Anderson, R. P. When and how should biotic interactions be considered in models of species niches and distributions? J. Biogeogr. 44, 8–17 (2017).
    Google Scholar 
    20.Wilkinson, D. M. Mycorrhizal fungi and quaternary plant migrations. Glob. Ecol. Biogeogr. Lett. 7, 137 (1998).
    Google Scholar 
    21.Wilkinson, D. M. Plant colonization: are wind dispersed seeds really dispersed by birds at larger spatial and temporal scales? J. Biogeogr. 24, 61–65 (1997).
    Google Scholar 
    22.MacArthur, R. H. Geographical Ecology: Patterns in the Distribution of Species (Princeton Univ. Press, 1984).23.Pigot, A. L. & Tobias, J. A. Species interactions constrain geographic range expansion over evolutionary time. Ecol. Lett. 16, 330–338 (2013).PubMed 

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

    Google Scholar 
    25.Liang, Y., Duveneck, M. J., Gustafson, E. J., Serra-Diaz, J. M. & Thompson, J. R. How disturbance, competition, and dispersal interact to prevent tree range boundaries from keeping pace with climate change. Glob. Chang. Biol. 24, e335–e351 (2018).ADS 
    PubMed 

    Google Scholar 
    26.Moorcroft, P. R., Pacala, S. W. & Lewis, M. A. Potential role of natural enemies during tree range expansions following climate change. J. Theor. Biol. 241, 601–616 (2006).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 
    27.Moran, E. V. & Ormond, R. A. Simulating the interacting effects of intraspecific variation, disturbance, and competition on climate-driven range shifts in trees. PLoS ONE 10, e0142369 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    28.Stralberg, D. et al. Wildfire-mediated vegetation change in boreal forests of Alberta. Can. Ecosphere 9, e02156 (2018).
    Google Scholar 
    29.Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    30.Ettinger, A. & HilleRisLambers, J. Competition and facilitation may lead to asymmetric range shift dynamics with climate change. Glob. Chang. Biol. 23, 3921–3933 (2017).ADS 
    PubMed 

    Google Scholar 
    31.Caplat, P., Anand, M. & Bauch, C. Interactions between climate change, competition, dispersal, and disturbances in a tree migration model. Theor. Ecol. 1, 209–220 (2008).
    Google Scholar 
    32.Serra-Diaz, J. M., Scheller, R. M., Syphard, A. D. & Franklin, J. Disturbance and climate microrefugia mediate tree range shifts during climate change. Landsc. Ecol. 30, 1039–1053 (2015).
    Google Scholar 
    33.Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B Biol. Sci. 279, 2072–2080 (2012).
    Google Scholar 
    34.Pausas, J. G. & Keeley, J. E. Wildfires as an ecosystem service. Front. Ecol. Environ. 17, 289–295 (2019).
    Google Scholar 
    35.Harvey, B. J., Donato, D. C. & Turner, M. G. High and dry: post-fire tree seedling establishment in subalpine forests decreases with post-fire drought and large stand-replacing burn patches: Drought and post-fire tree seedlings. Glob. Ecol. Biogeogr. 25, 655–669 (2016).
    Google Scholar 
    36.Coop, J. D. et al. Wildfire-driven forest conversion in western north American landscapes. BioScience 70, 659–673 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    37.Turner, M. G., Braziunas, K. H., Hansen, W. D. & Harvey, B. J. Short-interval severe fire erodes the resilience of subalpine lodgepole pine forests. Proc. Natl Acad. Sci. 116, 11319–11328 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Stevens‐Rumann, C. S. et al. Evidence for declining forest resilience to wildfires under climate change. Ecol. Lett. 21, 243–252 (2018).PubMed 

    Google Scholar 
    39.Hanes, T. L. Succession after fire in the Chaparral of southern California. Ecol. Monogr. 41, 27–52 (1971).
    Google Scholar 
    40.McKenzie, D. A. & Tinker, D. B. Fire-induced shifts in overstory tree species composition and associated understory plant composition in Glacier National Park, Montana. Plant Ecol. 213, 207–224 (2012).
    Google Scholar 
    41.Walker, X. J., Mack, M. C. & Johnstone, J. F. Predicting ecosystem resilience to fire from tree ring analysis in black spruce forests. Ecosystems 20, 1137–1150 (2017).
    Google Scholar 
    42.Hart, S. J. et al. Examining forest resilience to changing fire frequency in a fire-prone region of boreal forest. Glob. Change Biol. 25, 869–884 (2019).ADS 

    Google Scholar 
    43.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. 116, 6193–6198 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).ADS 

    Google Scholar 
    45.Enright, N. J., Fontaine, J. B., Bowman, D. M., Bradstock, R. A. & Williams, R. J. Interval squeeze: altered fire regimes and demographic responses interact to threaten woody species persistence as climate changes. Front. Ecol. Environ. 13, 265–272 (2015).
    Google Scholar 
    46.Dobrowski, S. Z. et al. Forest structure and species traits mediate projected recruitment declines in western US tree species: tree recruitment patterns in the western US. Glob. Ecol. Biogeogr. 24, 917–927 (2015).
    Google Scholar 
    47.Anderson, T. W. An Introduction to Multivariate Statistical Analysis (Wiley-Interscience, 2003).48.Keeley, J. E. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int. J. Wildland Fire 18, 116 (2009).
    Google Scholar 
    49.Tollefson, J. Quercus chrysolepis. https://www.fs.fed.us/database/feis/plants/tree/quechr/all.html (2008).50.Fryer, J. Quercus kelloggii. https://www.fs.fed.us/database/feis/plants/tree/quekel/all.html (2007).51.Meyer, R. Chrysolepis chrysophylla. https://www.fs.fed.us/database/feis/plants/tree/quekel/all.html (2012).52.Michelle, A. Pinus contorta var. latifolia. https://www.fs.fed.us/database/feis/plants/tree/pinconl/all.html (2003).53.Cope, A. Pinus contorta var. murrayana. https://www.fs.fed.us/database/feis/plants/tree/pinconm/all.html (1993).54.Cope, A. Pinus contorta var. contorta. https://www.fs.fed.us/database/feis/plants/tree/pinconc/all.html (1993).55.Rodman, K. C. et al. A trait‐based approach to assessing resistance and resilience to wildfire in two iconic North American conifers. J. Ecol. https://doi.org/10.1111/1365-2745.13480 (2020).56.Davis, K. T., Higuera, P. E. & Sala, A. Anticipating fire‐mediated impacts of climate change using a demographic framework. Funct. Ecol. 32, 1729–1745 (2018).
    Google Scholar 
    57.Gutzler, D. S. & Robbins, T. O. Climate variability and projected change in the western United States: regional downscaling and drought statistics. Clim. Dyn. 37, 835–849 (2011).
    Google Scholar 
    58.Leung, L. R. et al. Mid-century ensemble regional climate change scenarios for the western United States. Clim. Chang. 62, 75–113 (2004).
    Google Scholar 
    59.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Ecol. Manag. 259, 660–684 (2010).
    Google Scholar 
    60.Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2013).ADS 

    Google Scholar 
    61.Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 
    PubMed 

    Google Scholar 
    62.Lenoir, J., Gegout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    63.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).64.RStudio Team. RStudio: Integrated Development Environment for R. (RStudio, PBC, 2020).65.U.S. Forest Service. Forest Inventory and Analysis National Core Field Guide. https://www.fia.fs.fed.us/library/field-guides-methods-proc/docs/2017/core_ver7-2_10_2017_final.pdf (2017).66.U.S. EPA. Level I Ecoregions of North America Shapefile. (2010).67.Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for north America. PLoS ONE 11, e0156720 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    68.Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling? Ecography 37, 191–203 (2014).
    Google Scholar 
    69.Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data: measuring niche overlap. Glob. Ecol. Biogeogr. 21, 481–497 (2012).
    Google Scholar 
    70.Hill, A. avephill/wildfire-plant_RS: Forest fires and climate-induced tree range shifts in the western US. https://doi.org/10.5281/ZENODO.5555390 (2021). More

  • in

    Raised seasonal temperatures reinforce autumn Varroa destructor infestation in honey bee colonies

    1.IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC (IPCC, 2014).2.Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).CAS 
    PubMed 
    ADS 

    Google Scholar 
    3.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
    PubMed 
    ADS 

    Google Scholar 
    4.Peñuelas, J. & Filella, I. Responses to a warming world. Science (80-). 294, 793–795 (2001).
    Google Scholar 
    5.Ockendon, N. et al. Mechanisms underpinning climatic impacts on natural populations: Altered species interactions are more important than direct effects. Glob. Change Biol. 20, 2221–2229 (2014).ADS 

    Google Scholar 
    6.Walther, G.-R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 2019–2024 (2010).
    Google Scholar 
    7.Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).CAS 
    PubMed 
    ADS 

    Google Scholar 
    8.Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).
    Google Scholar 
    9.Vanbergen, A. J. et al. Threats to an ecosystem service: Pressures on pollinators. Front. Ecol. Environ. 11, 251–259 (2013).
    Google Scholar 
    10.Hung, K. L. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. B Biol. Sci. 285, 20172140 (2018).
    Google Scholar 
    11.Watanabe, M. E. Pollination worries rise as honey bees decline. Science (80-). 265, 1170 (1994).CAS 
    ADS 

    Google Scholar 
    12.Chauzat, M.-P. et al. Demographics of the European apicultural industry. PLoS ONE 8, e79018 (2013).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    13.Conte, Y. L. & Navajas, M. Climate change: Impact on honey bee populations and diseases. OIE Rev. Sci. Tech. 27, 485–510 (2008).
    Google Scholar 
    14.Le Conte, Y., Ellis, M. & Ritter, W. Varroa mites and honey bee health: Can Varroa explain part of the colony losses?. Apidologie 41, 353–363 (2010).
    Google Scholar 
    15.Nürnberger, F., Härtel, S. & Steffan-Dewenter, I. Seasonal timing in honey bee colonies: Phenology shifts affect honey stores and Varroa infestation levels. Oecologia 189, 1121–1131 (2019).PubMed 
    ADS 

    Google Scholar 
    16.Traynor, K. S. et al. Multiyear survey targeting disease incidence in US honey bees. Apidologie https://doi.org/10.1007/s13592-016-0431-0 (2016).Article 

    Google Scholar 
    17.Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. U. S. A. 116, 1792–1801 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).PubMed 

    Google Scholar 
    19.Switanek, M., Crailsheim, K., Truhetz, H. & Brodschneider, R. Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate. Sci. Total Environ. 579, 1581–1587 (2017).CAS 
    PubMed 
    ADS 

    Google Scholar 
    20.Genersch, E. et al. The German bee monitoring project: A long term study to understand periodically high winter losses of honey bee colonies. Apidologie 41, 332–352 (2010).CAS 

    Google Scholar 
    21.van Dooremalen, C. et al. Winter survival of individual honey bees and honey bee colonies depends on level of Varroa destructor infestation. PLoS One 7, e36285 (2012).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    22.Morawetz, L. et al. Health status of honey bee colonies (Apis mellifera) and disease-related risk factors for colony losses in Austria. PLoS One 14, e0219293 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Fries, I., Imdorf, A. & Rosenkranz, P. Survival of mite infested (Varroa destructor) honey bee (Apis mellifera) colonies in a Nordic climate. Apidologie 37, 564–570 (2006).
    Google Scholar 
    24.Guzmán-Novoa, E. et al. Varroa destructor is the main culprit for the death and reduced populations of overwintered honey bee (Apis mellifera) colonies in Ontario, Canada. Apidologie 41, 443–450 (2010).
    Google Scholar 
    25.Giacobino, A. et al. Environment or beekeeping management: What explains better the prevalence of honey bee colonies with high levels of Varroa destructor?. Res. Vet. Sci. 112, 1–6 (2017).PubMed 

    Google Scholar 
    26.van de Pol, M. et al. Identifying the best climatic predictors in ecology and evolution. Methods Ecol. Evol. 7, 1246–1257 (2016).
    Google Scholar 
    27.Leza, M. M., Miranda-Chueca, M. A. & Purse, B. V. Patterns in Varroa destructor depend on bee host abundance, availability of natural resources, and climate in Mediterranean apiaries. Ecol. Entomol. 41, 542–553 (2016).
    Google Scholar 
    28.Dietemann, V. et al. Standard methods for Varroa research. J. Apic. Res. 52, 1–54 (2013).
    Google Scholar 
    29.Branco, M. R., Kidd, N. A. C. & Pickard, R. S. A comparative evaluation of sampling methods for Varroa destructor (Acari: Varroidae) population estimation. Apidologie 37, 452–461 (2006).
    Google Scholar 
    30.Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. Atmos. 113, D20119 (2008).ADS 

    Google Scholar 
    31.Bailey, L. D. & van de Pol, M. climwin: An R toolbox for climate window analysis. PLoS One 11, 1–27 (2016).
    Google Scholar 
    32.Hartig, F. Residual Diagnostics for Hierachical (Multi-Level/Mixed) Regression Models. (2021).33.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 51 (2014).
    Google Scholar 
    34.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
    Google Scholar 
    35.R Core Team. R: A Language and Environment for Statistical Computing. (2021).36.Seeley, T. D. & Morse, R. A. The nest of the honey bee (Apis mellifera L.). Insectes Soc. 23, 495–512 (1976).
    Google Scholar 
    37.Calis, J. N. M., Fries, I. & Ryrie, S. C. Population modelling of Varroa jacobsoni Oud. Apidologie 30, 111–124 (1999).
    Google Scholar 
    38.Fries, I., Hansen, H., Imdorf, A. & Rosenkranz, P. Swarming in honey bees (Apis mellifera) and Varroa destructor population development in Sweden. Apidologie 34, 389–397 (2003).
    Google Scholar 
    39.Wilde, J., Fuchs, S., Bratkowski, J. & Siuda, M. Distribution of Varroa destructor between swarms and colonies. J. Apic. Res. 44, 190–194 (2005).
    Google Scholar 
    40.Loftus, J. C., Smith, M. L. & Seeley, T. D. How honey bee colonies survive in the wild: Testing the importance of small nests and frequent swarming. PLoS One 11, 1–11 (2016).
    Google Scholar 
    41.Moretto, G., Goncalves, L. S., De Jong, D. & Bichuette, M. Z. The effects of climate and bee race on Varroa jacobsoni Oud infestations in Brazil. Apidologie 22, 197–203 (1991).
    Google Scholar 
    42.Guzmán-Novoa, E., Vandame, R. & Arechavaleta, M. E. Susceptibility of European and Africanized honey bees (Apis mellifera L.) to Varroa jacobsoni Oud. in Mexico. Apidologie 30, 173–182 (1999).
    Google Scholar 
    43.Ruttner, F. Biogeography and Taxonomy of Honeybees (Springer, 1988). https://doi.org/10.1007/978-3-642-72649-1.Book 

    Google Scholar 
    44.Adam, B. Breeding the Honeybee: A Contribution to the Science of Bee Breeding (Northern Bee Books, 2013).
    Google Scholar 
    45.Tarpy, D. R., Hatch, S. & Fletcher, D. J. C. The influence of queen age and quality during queen replacement in honeybee colonies. Anim. Behav. 59, 97–101 (2000).CAS 
    PubMed 

    Google Scholar 
    46.Simeunovic, P. et al. Nosema ceranae and queen age influence the reproduction and productivity of the honey bee colony. J. Apic. Res. 53, 545–554 (2014).
    Google Scholar 
    47.Akyol, E., Yeninar, H., Karatepe, M., Karatepe, B. & Özkök, D. Effects of queen ages on Varroa (Varroa destructor) infestation level in honey bee (Apis mellifera caucasica) colonies and colony performance. Ital. J. Anim. Sci. 6, 143–149 (2007).
    Google Scholar 
    48.Harris, J. W., Harbo, J. R., Villa, J. D. & Danka, R. G. Variable population growth of Varroa destructor (Mesostigmata: Varroidae) in colonies of honey bees (Hymenoptera: Apidae) during a 10-year period. Environ. Entomol. 32, 1305–1312 (2003).
    Google Scholar 
    49.Kruuk, L. E. B., Osmond, H. L. & Cockburn, A. Contrasting effects of climate on juvenile body size in a Southern Hemisphere passerine bird. Glob. Change Biol. 21, 2929–2941 (2015).ADS 

    Google Scholar 
    50.Dainat, B., Evans, J. D., Chen, Y. P., Gauthier, L. & Neumann, P. Predictive markers of honey bee colony collapse. PLoS One 7, e32151 (2012).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    51.Peck, D. T., Smith, M. L. & Seeley, T. D. Varroa destructor mites can nimbly climb from flowers onto foraging honey bees. PLoS One 11, e0167798 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    52.Peck, D. T. & Seeley, T. D. Mite bombs or robber lures? The roles of drifting and robbing in Varroa destructor transmission from collapsing honey bee colonies to their neighbors. PLoS One 14, e0218392 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Seeley, T. D. & Smith, M. L. Crowding honeybee colonies in apiaries can increase their vulnerability to the deadly ectoparasite Varroa destructor. Apidologie 46, 716–727 (2015).
    Google Scholar 
    54.Vetharaniam, I. Predicting reproduction rate of Varroa. Ecol. Model. 224, 11–17 (2012).
    Google Scholar 
    55.Nürnberger, F., Härtel, S. & Steffan-Dewenter, I. The influence of temperature and photoperiod on the timing of brood onset in hibernating honey bee colonies. PeerJ 6, e4801. https://doi.org/10.7717/peerj.4801 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Seeley, T. D. & Visscher, P. K. Survival of honeybees in cold climates: The critical timing of colony growth and reproduction. Ecol. Entomol. 10, 81–88 (1985).
    Google Scholar 
    57.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in worker brood of the honeybee Apis mellifera L. under natural conditions. Exp. Appl. Acarol. https://doi.org/10.1007/BF00055033 (1994).Article 

    Google Scholar 
    58.Martin, S. J. Reproduction of Varroa jacobsoni in cells of Apis mellifera containing one or more mother mites and the distribution of these cells. J. Apic. Res. 34, 187–196 (1995).
    Google Scholar 
    59.Sparks, T. H. et al. Advances in the timing of spring cleaning by the honeybee Apis mellifera in Poland. Ecol. Entomol. 35, 788–791 (2010).
    Google Scholar 
    60.Langowska, A. et al. Long-term effect of temperature on honey yield and honeybee phenology. Int. J. Biometeorol. 61, 1125–1132 (2017).PubMed 
    ADS 

    Google Scholar 
    61.Bordier, C. et al. Colony adaptive response to simulated heat waves and consequences at the individual level in honeybees (Apis mellifera). Sci. Rep. 7, 1–11 (2017).CAS 

    Google Scholar 
    62.Fahrenholz, L., Lamprecht, I. & Schricker, B. Thermal investigations of a honey bee colony: Thermoregulation of the hive during summer and winter and heat production of members of different bee castes. J. Comp. Physiol. B 159, 551–560 (1989).
    Google Scholar 
    63.Villa, J. D., Gentry, C. & Taylor, O. R. Jr. Preliminary observations on thermoregulation, clustering, and energy utilization in African and European Honey Bees. J. Kansas Entomol. Soc. 60, 4–14 (1987).
    Google Scholar 
    64.Anderson, D. L. & Trueman, J. W. H. Varroa jacobsoni (Acari: Varroidae) is more than one species. Exp. Appl. Acarol. 24, 165–189 (2000).CAS 
    PubMed 

    Google Scholar 
    65.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen–Geiger climate classification updated. Meteorol. Zeitschrift 15, 259–263 (2006).ADS 

    Google Scholar 
    66.Schmickl, T. & Crailsheim, K. Cannibalism and early capping: Strategy of honeybee colonies in times of experimental pollen shortages. J. Comp. Physiol. A Sens. Neural Behav. Physiol. 187, 541–547 (2001).CAS 

    Google Scholar 
    67.Requier, F., Odoux, J. F., Henry, M. & Bretagnolle, V. The carry-over effects of pollen shortage decrease the survival of honeybee colonies in farmlands. J. Appl. Ecol. 54, 1161–1170 (2017).
    Google Scholar 
    68.Seeley, T. D. Honeybee Ecology. A Study of Adaptation in Social Life (Princeton University Press, 1985).
    Google Scholar 
    69.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in drone brood of the honeybee Apis mellifera L. under natural conditions. Exp. Appl. Acarol. 19, 199–210 (1995).ADS 

    Google Scholar 
    70.Amiri, E., Strand, M. K., Rueppell, O. & Tarpy, D. R. Queen quality and the impact of honey bee diseases on queen health: Potential for interactions between two major threats to colony health. Insects 8, 48 (2017).PubMed Central 

    Google Scholar 
    71.Giacobino, A. et al. Risk factors associated with failures of Varroa treatments in honey bee colonies without broodless period. Apidologie 46, 573–582 (2015).
    Google Scholar 
    72.Locke, B. Natural Varroa mite-surviving Apis mellifera honeybee populations. Apidologie 47, 467–482 (2016).
    Google Scholar 
    73.FAO. Good beekeeping practices: Practical manual on how to identify and control the main diseases of the honeybee (Apis mellifera). TECA—Technologies and practices for small agricultural producers. (2020).74.Harbo, J. R. Effect of population size on brood production, worker survival and honey gain in colonies of honeybees. J. Apic. Res. 25, 22–29 (1986).
    Google Scholar 
    75.Döke, M. A., McGrady, C. M., Otieno, M., Grozinger, C. M. & Frazier, M. Colony size, rather than geographic origin of stocks, predicts overwintering success in honey bees (Hymenoptera: Apidae) in the Northeastern United States. J. Econ. Entomol. 112, 525–533 (2019).PubMed 

    Google Scholar 
    76.Martin, S. J. The role of Varroa and viral pathogens in the collapse of honeybee colonies: A modelling approach. J. Appl. Ecol. 38, 1082–1093 (2001).
    Google Scholar  More

  • in

    Unexpected myriad of co-occurring viral strains and species in one of the most abundant and microdiverse viruses on Earth

    1.Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum information about an uncultivated virus genome (MIUVIG). Nat Biotechnol 2019;37:29–37.PubMed 

    Google Scholar 
    2.Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.PubMed 

    Google Scholar 
    3.Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1109–23.PubMed 
    PubMed Central 

    Google Scholar 
    4.Kavagutti VS, Andrei AŞ, Mehrshad M, Salcher MM, Ghai R. Phage-centric ecological interactions in aquatic ecosystems revealed through ultra-deep metagenomics. Microbiome. 2019;7:1–15.
    Google Scholar 
    5.Schulz F, Alteio L, Goudeau D, Ryan EM, Yu FB, Malmstrom RR, et al. Hidden diversity of soil giant viruses. Nat Commun 2018;9:1–9.
    Google Scholar 
    6.Trubl G, Jang H Bin, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 2018;3:e00076–18.PubMed 
    PubMed Central 

    Google Scholar 
    7.Guerin E, Shkoporov A, Stockdale SR, Clooney AG, Ryan FJ, Sutton TDS, et al. Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut. Cell Host Microbe. 2018;24:653–664.e6.PubMed 

    Google Scholar 
    8.Martinez-Hernandez F, Fornas O, Lluesma Gomez M, Bolduc B, de la Cruz Peña MJ, Martínez JM, et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat Commun 2017;8:1–13.
    Google Scholar 
    9.Aguirre de Cárcer D, Angly FE, Alcamí A. Evaluation of viral genome assembly and diversity estimation in deep metagenomes. BMC Genomics. 2014;15:1–12.
    Google Scholar 
    10.Roux S, Emerson JB, Eloe-Fadrosh EA, Sullivan MB. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ. 2017;5:e3817.PubMed 
    PubMed Central 

    Google Scholar 
    11.Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D. Genomic island variability facilitates Prochlorococcus-virus coexistence. Nature. 2011;474:604–8.PubMed 

    Google Scholar 
    12.Rodriguez-Valera F, Martin-Cuadrado A-B, Rodriguez-Brito B, Pasic L, Thingstad TF, Rohwer F, et al. Explaining microbial population genomics through phage predation. Nat Rev Microbiol 2009;7:828–36.PubMed 

    Google Scholar 
    13.Marston MF, Pierciey FJ, Shepard A, Gearin G, Qi J, Yandava C, et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc Natl Acad Sci USA 2012;109:4544–9.PubMed 
    PubMed Central 

    Google Scholar 
    14.Enav H, Kirzner S, Lindell D, Mandel-Gutfreund Y, Béjà O. Adapt sub-Optim hosts is a Driv viral Diversif ocean Nat Comm 2018;9:1–11.
    Google Scholar 
    15.Boon M, Holtappels D, Lood C, van Noort V, Lavigne R. Host range expansion of pseudomonas virus LUZ7 is driven by a conserved tail fiber mutation. PHAGE. 2020;1:87–90.
    Google Scholar 
    16.Bernheim A, Sorek R. The pan-immune system of bacteria: antiviral defence as a community resource. Nat Rev Microbiol 2020;18:113–9.PubMed 

    Google Scholar 
    17.Sørensen MA, Kurland CG, Pedersen S. Codon usage determines translation rate in Escherichia coli. J Mol Biol 1989;207:365–77.PubMed 

    Google Scholar 
    18.Varenne S, Buc J, Lloubes R, Lazdunski C. Translation is a non-uniform process. Effect of tRNA availability on the rate of elongation of nascent polypeptide chains. J Mol Biol 1984;180:549–76.PubMed 

    Google Scholar 
    19.Yu CH, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS, et al. Codon Usage Influences the Local Rate of Translation Elongation to Regulate Co-translational Protein Folding. Mol Cell. 2015;59:744–54.PubMed 
    PubMed Central 

    Google Scholar 
    20.Plotkin JB, Kudla G. Synonymous but not the same: The causes and consequences of codon bias. Nat Rev Genet 2011;12:32–42.PubMed 

    Google Scholar 
    21.Chu D, Wei L. Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation. BMC Cancer. 2019;19:359.PubMed 
    PubMed Central 

    Google Scholar 
    22.Deng L, Ignacio-Espinoza JC, Gregory AC, Poulos BT, Weitz JS, Hugenholtz P, et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature. 2014;513:242–5.PubMed 

    Google Scholar 
    23.Edwards RA, Vega AA, Norman HM, Ohaeri M, Levi K, Dinsdale EA, et al. Global phylogeography and ancient evolution of the widespread human gut virus crAssphage. Nat Microbiol 2019;4:1727–36.PubMed 
    PubMed Central 

    Google Scholar 
    24.Ignacio-Espinoza JC, Ahlgren NA, Fuhrman JA. Long-term stability and Red Queen-like strain dynamics in marine viruses. Nat. Microbiol. 2019;5:1–7.25.Coutinho FH, Rosselli R, Rodríguez-Valera F. Trends of microdiversity reveal depth-dependent evolutionary strategies of viruses in the Mediterranean. mSystems. 2019;4:1–17.
    Google Scholar 
    26.Needham DM, Sachdeva R, Fuhrman JA. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 2017;11:1614–29.PubMed 
    PubMed Central 

    Google Scholar 
    27.Martinez-Hernandez F, Fornas Ò, Lluesma Gomez M, Garcia-Heredia I, Maestre-Carballa L, López-Pérez M, et al. Single-cell genomics uncover Pelagibacter as the putative host of the extremely abundant uncultured 37-F6 viral population in the ocean. ISME J. 2019;13:232–6.PubMed 

    Google Scholar 
    28.McMullen A, Martinez‐Hernandez F, Martinez‐Garcia M. Absolute quantification of infecting viral particles by chip‐based digital polymerase chain reaction. Environ Microbiol Rep. 2019;11:855–60.PubMed 

    Google Scholar 
    29.Marston MF, Amrich CG. Recombination and microdiversity in coastal marine cyanophages. Environ Microbiol. 2009;11:2893–903.PubMed 

    Google Scholar 
    30.Marston MF, Martiny JBH. Genomic diversification of marine cyanophages into stable ecotypes. Environ Microbiol 2016;18:4240–53.PubMed 

    Google Scholar 
    31.Cordero OX. Endemic cyanophages and the puzzle of phage-bacteria coevolution. Environ Microbiol 2017;19:420–2.PubMed 

    Google Scholar 
    32.Shannon CE. The mathematical theory of communication. 1963. MD Comput. 1997;14:306–17.PubMed 

    Google Scholar 
    33.Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.PubMed 

    Google Scholar 
    34.Bobay L-M, Ochman H. Biological species in the viral world. Proc Natl Acad Sci USA 2018;115:6040–5.PubMed 
    PubMed Central 

    Google Scholar 
    35.Henson MW, Lanclos VC, Faircloth BC, Thrash JC. Cultivation and genomics of the first freshwater SAR11 (LD12) isolate. ISME J. 2018;12:1846–60.PubMed 
    PubMed Central 

    Google Scholar 
    36.Paez-Espino D, Roux S, Chen I-MA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes. Nucleic Acids Res. 2019;47:D678–D686.PubMed 

    Google Scholar 
    37.Brum JR, Ignacio-Espinoza JC, Kim E-H, Trubl G, Jones RM, Roux S, et al. Illuminating structural proteins in viral ‘dark matter’ with metaproteomics. Proc Natl Acad Sci USA 2016;113:2436–41.PubMed 
    PubMed Central 

    Google Scholar 
    38.Sakowski EG, Arora-Williams K, Tian F, Zayed AA, Zablocki O, Sullivan MB, et al. Interaction dynamics and virus–host range for estuarine actinophages captured by epicPCR. Nat. Microbiol. 2021;6:1–13.39.Alonso-Sáez L, Morán XAG, Clokie MR. Low activity of lytic pelagiphages in coastal marine waters. ISME J. 2018;12:2100–2.PubMed 
    PubMed Central 

    Google Scholar 
    40.Martinez‐Hernandez F, Luo E, Tominaga K, Ogata H, Yoshida T, DeLong EF, et al. Diel cycling of the cosmopolitan abundant Pelagibacter virus 37‐F6: one of the most abundant viruses in Earth. Environ Microbiol Rep. 2020;12:214–21941.Mruwat N, Carlson MCG, Goldin S, Ribalet F, Kirzner S, Hulata Y, et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 2021;15:41–54.PubMed 

    Google Scholar 
    42.de Avila e Silva S, Echeverrigaray S, Gerhardt GJL. BacPP: bacterial promoter prediction-A tool for accurate sigma-factor specific assignment in enterobacteria. J Theor Biol 2011;287:92–99.PubMed 

    Google Scholar 
    43.Sampaio M, Rocha M, Oliveira H, Dias O. Predicting promoters in phage genomes using PhagePromoter. Bioinformatics. 2019;35:5301–2.PubMed 

    Google Scholar 
    44.Allert M, Cox JC, Hellinga HW. Multifactorial determinants of protein expression in prokaryotic open reading frames. J Mol Biol. 2010;402:905–18.PubMed 
    PubMed Central 

    Google Scholar 
    45.Dressaire C, Picard F, Redon E, Loubière P, Queinnec I, Girbal L, et al. Role of mRNA stability during bacterial adaptation. PLoS ONE 2013;8:e59059.PubMed 
    PubMed Central 

    Google Scholar 
    46.Deana A, Belasco JG. Lost in translation: The influence of ribosomes on bacterial mRNA decay. Genes Dev. 2005;19:2526–33.PubMed 

    Google Scholar 
    47.Zhao Y, Temperton B, Thrash JC, Schwalbach MS, Vergin KL, Landry ZC, et al. Abundant SAR11 viruses in the ocean. Nature. 2013;494:357–60.PubMed 

    Google Scholar 
    48.Zhang Z, Qin F, Chen F, Chu X, Luo H, Zhang R, et al. Culturing novel and abundant pelagiphages in the ocean. Environ Microbiol 2020;1462-2920:15272.
    Google Scholar 
    49.Zhao Y, Qin F, Zhang R, Giovannoni SJ, Zhang Z, Sun J, et al. Pelagiphages in the Podoviridae family integrate into host genomes. Environ Microbiol. 2018;21:1989–2001.50.Morris RM, Cain KR, Hvorecny KL, Kollman JM. Lysogenic host–virus interactions in SAR11 marine bacteria. Nat Microbiol 2020;5:1011–5.PubMed 
    PubMed Central 

    Google Scholar 
    51.Konstantinidis KT, Ramette A, Tiedje JM. The bacterial species definition in the genomic era. Philos Trans R Soc Lond, B, Biol Sci 2006;361:1929–40.
    Google Scholar 
    52.Rosselló-Mora R. Updating prokaryotic taxonomy. J Bacteriol. 2005;187:6255–7.PubMed 
    PubMed Central 

    Google Scholar 
    53.Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2017;2:1533–42.PubMed 

    Google Scholar 
    54.Richter M, Rossello-Mora R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci 2009;106:19126–31.PubMed 
    PubMed Central 

    Google Scholar 
    55.Pope WH, Bowman CA, Russell DA, Jacobs-Sera D, Asai DJ, Cresawn SG, et al. Whole genome comparison of a large collection of mycobacteriophages reveals a continuum of phage genetic diversity. eLife 2015;4:e06416.PubMed 
    PubMed Central 

    Google Scholar 
    56.Gregory AC, Solonenko SA, Ignacio-Espinoza JC, LaButti K, Copeland A, Sudek S, et al. Genomic differentiation among wild cyanophages despite widespread horizontal gene transfer. BMC genomics. 2016;17:930.PubMed 
    PubMed Central 

    Google Scholar 
    57.Martinez-Hernandez F, Garcia-Heredia I, Lluesma Gomez M, Maestre-Carballa L, Martínez Martínez J, Martinez-Garcia M. Droplet digital PCR for estimating absolute abundances of widespread Pelagibacter viruses. Front Microbiol 2019;10:1226.PubMed 
    PubMed Central 

    Google Scholar 
    58.Warwick-Dugdale J, Solonenko N, Moore K, Chittick L, Gregory AC, Allen MJ, et al. Long-read viral metagenomics captures abundant and microdiverse viral populations and their niche-defining genomic islands. PeerJ. 2019;7:e6800.PubMed 
    PubMed Central 

    Google Scholar 
    59.Beaulaurier J, Luo E, Eppley JM, Uyl P Den, Dai X, Burger A, et al. Assembly-free single-molecule sequencing recovers complete virus genomes from natural microbial communities. Genome Res. 2020;30:437–46.PubMed 
    PubMed Central 

    Google Scholar 
    60.Murigneux V, Rai SK, Furtado A, Bruxner TJC, Tian W, Harliwong I, et al. Comparison of long-read methods for sequencing and assembly of a plant genome. GigaScience 2020;9:giaa146.61.Wenger AM, Peluso P, Rowell WJ, Chang PC, Hall RJ, Concepcion GT, et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat Biotechnol 2019;37:1155–62.PubMed 
    PubMed Central 

    Google Scholar 
    62.Martínez Martínez J, Martinez-Hernandez F, Martinez-Garcia M. Single-virus genomics and beyond. Nat Rev Microbiol. 2020;18:705–16.PubMed 

    Google Scholar 
    63.Labonté JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 2015;9:2386–99.PubMed 
    PubMed Central 

    Google Scholar 
    64.Mizuno CM, Rodriguez-Valera F, Kimes NE, Ghai R. Expanding the marine virosphere using metagenomics. PLoS Genet. 2013;9:e1003987.PubMed 
    PubMed Central 

    Google Scholar 
    65.Mizuno CM, Ghai R, Saghaï A, López-García P, Rodriguez-Valera F. Genomes of abundant and widespread viruses from the deep ocean. mBio. 2016;7:e00805–16.PubMed 
    PubMed Central 

    Google Scholar 
    66.Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinforma. 2012;13:134.
    Google Scholar 
    67.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.PubMed 
    PubMed Central 

    Google Scholar 
    68.Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.PubMed 

    Google Scholar 
    69.Philosof A, Yutin N, Flores-Uribe J, Sharon I, Koonin EV, Béjà O. Novel abundant oceanic viruses of uncultured marine group II Euryarchaeota. Curr Biol. 2017;27:1362–8.PubMed 
    PubMed Central 

    Google Scholar 
    70.Vik DR, Roux S, Brum JR, Bolduc B, Emerson JB, Padilla CC, et al. Putative archaeal viruses from the mesopelagic ocean. PeerJ. 2017;5:e3428.PubMed 
    PubMed Central 

    Google Scholar 
    71.Bin Jang H, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol 2019;37:632–9.
    Google Scholar 
    72.Bobay L-M, Ellis BS-H, Ochman H. ConSpeciFix: classifying prokaryotic species based on gene flow. Bioinformatics. 2018;34:3738–40.PubMed 
    PubMed Central 

    Google Scholar 
    73.Bobay L-M, Ochman H. Biological species are universal across life’s domains. Genome Biol Evol. 2017;9:491–501.PubMed Central 

    Google Scholar 
    74.Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.
    Google Scholar 
    75.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.PubMed 
    PubMed Central 

    Google Scholar 
    76.Harris CD, Torrance EL, Raymann K, Bobay L-M. CoreCruncher: Fast and robust construction of core genomes in large prokaryotic data sets. Mol. Biol. Evol. 2020;38:727–734.77.Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.PubMed 
    PubMed Central 

    Google Scholar 
    78.Rice P, Longden L, Bleasby A EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 2000. Elsevier Ltd., 16: 276–779.Džunková M, Low SJ, Daly JN, Deng L, Rinke C, Hugenholtz P. Defining the human gut host–phage network through single-cell viral tagging. Nat Microbiol 2019;4:2192–203.PubMed 

    Google Scholar 
    80.Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
    PubMed Central 

    Google Scholar 
    81.Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.PubMed 
    PubMed Central 

    Google Scholar 
    82.Swan BK, Ehrhardt CJ, Reifel KM, Moreno LI, Valentine DL. Archaeal and bacterial communities respond differently to environmental gradients in anoxic sediments of a california hypersaline lake, the Salton Sea. Appl Environ Microbiol 2010;76:757–68.PubMed 

    Google Scholar 
    83.Baran N, Goldin S, Maidanik I, Lindell D. Quantification of diverse virus populations in the environment using the polony method. Nat Microbiol 2018;3:62–72.PubMed 

    Google Scholar  More

  • in

    Horizontal gene transfer and adaptive evolution in bacteria

    1.Maynard Smith, J., Feil, E. J. & Smith, N. H. Population structure and evolutionary dynamics of pathogenic bacteria. Bioessays 22, 1115–1122 (2000).
    Google Scholar 
    2.Garud, N. R., Good, B. H., Hallatschek, O. & Pollard, K. S. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol. 17, e3000102 (2019). Using metagenomic samples form the human gut microbiome, the authors infer lineage structure from within-host polymorphisms in more than 40 species to show adaptation on short timescales can be seeded by HGT.PubMed 
    PubMed Central 

    Google Scholar 
    3.Frazão, N., Sousa, A., Lässig, M. & Gordo, I. Horizontal gene transfer overrides mutation in Escherichia coli colonizing the mammalian gut. Proc. Natl Acad. Sci. USA 116, 17906–17915 (2019). Using the mouse microbiome as a study system, the authors show that rapid, phage-mediated HGT can transfer beneficial genes — already present in a resident strain — to an invading strain.PubMed 
    PubMed Central 

    Google Scholar 
    4.Smith, J. M., Smith, N. H., O’Rourke, M. & Spratt, B. G. How clonal are bacteria? Proc. Natl Acad. Sci. USA 90, 4384–4388 (1993).PubMed 
    PubMed Central 

    Google Scholar 
    5.Dykhuizen, D. E. & Green, L. Recombination in Escherichia coli and the definition of biological species. J. Bacteriol. 173, 7257–7268 (1991).PubMed 
    PubMed Central 

    Google Scholar 
    6.Feil, E. J. et al. Recombination within natural populations of pathogenic bacteria: short-term empirical estimates and long-term phylogenetic consequences. Proc. Natl Acad. Sci. USA 98, 182–187 (2001).PubMed 
    PubMed Central 

    Google Scholar 
    7.Suerbaum, S. et al. Free recombination within Helicobacter pylori. PNAS 95, 12619–12624 (1998).PubMed 
    PubMed Central 

    Google Scholar 
    8.Smillie, C. S. et al. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241–244 (2011).PubMed 

    Google Scholar 
    9.Lozupone, C. A. et al. The convergence of carbohydrate active gene repertoires in human gut microbes. Proc. Natl Acad. Sci. USA 105, 15076–15081 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    10.Bradley, P. H., Nayfach, S. & Pollard, K. S. Phylogeny-corrected identification of microbial gene families relevant to human gut colonization. PLoS Computational Biol. 14, e1006242 (2018). The authors use phylogenetic linear regression to control for important confounders and identify genes potentially involved in adaptation to the human gut.
    Google Scholar 
    11.Andreani, N. A., Hesse, E. & Vos, M. Prokaryote genome fluidity is dependent on effective population size. ISME J. 11, 1719–1721 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    12.Mcinerney, J. O., Mcnally, A. & Connell, M. J. O. Why prokaryotes have pangenomes. Nat. Publ. Gr. 2, 1–5 (2017).
    Google Scholar 
    13.Shapiro, B. J. The population genetics of pangenomes. Nat. Microbiol. 2, 1005860 (2017).
    Google Scholar 
    14.Vos, M. & Eyre-walker, A. Are pangenomes adaptive or not? Nat. Microbiol. https://doi.org/10.1038/s41564-017-0067-5 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Johnsborg, O., Eldholm, V. & Håvarstein, L. S. Natural genetic transformation: prevalence, mechanisms and function. Res. Microbiol. 158, 767–778 (2007).PubMed 

    Google Scholar 
    16.Johnston, C., Martin, B., Fichant, G., Polard, P. & Claverys, J. P. Bacterial transformation: distribution, shared mechanisms and divergent control. Nat. Rev. Microbiol. 12, 181–196 (2014).PubMed 

    Google Scholar 
    17.Pimentel, Z. T. & Zhang, Y. Evolution of the natural transformation protein, ComEC, in Bacteria. Front. Microbiol. 9, 1–10 (2018).
    Google Scholar 
    18.Roux, S., Hallam, S. J., Woyke, T. & Sullivan, M. B. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife 4, 1–20 (2015).
    Google Scholar 
    19.Camarillo-Guerrero, L. F. et al. Massive expansion of human gut bacteriophage diversity. Cell 184, 1098–1109.e9 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    20.Guglielmini, J., Quintais, L., Garcillán-Barcia, M. P., de la Cruz, F. & Rocha, E. P. C. The repertoire of ice in prokaryotes underscores the unity, diversity, and ubiquity of conjugation. PLoS Genet. 7, e1002222 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    21.Dubey, G. P. & Ben-Yehuda, S. Intercellular nanotubes mediate bacterial communication. Cell 144, 590–600 (2011).PubMed 

    Google Scholar 
    22.Abe, K., Nomura, N. & Suzuki, S. Biofilms: hot spots of horizontal gene transfer (HGT) in aquatic environments, with a focus on a new HGT mechanism. FEMS Microbiol. Ecol. 96, 1–12 (2020).
    Google Scholar 
    23.Bárdy, P. et al. Structure and mechanism of DNA delivery of a gene transfer agent. Nat. Commun. 11, 3034 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    24.Hasegawa, H., Suzuki, E. & Maeda, S. Horizontal plasmid transfer by transformation in Escherichia coli: environmental factors and possible mechanisms. Front. Microbiol. 9, 1–6 (2018).
    Google Scholar 
    25.Seitz, P. & Blokesch, M. Cues and regulatory pathways involved in natural competence and transformation in pathogenic and environmental Gram-negative bacteria. FEMS Microbiol. Rev. 37, 336–363 (2013).PubMed 

    Google Scholar 
    26.Wall, D. Kin recognition in bacteria. Annu. Rev. Microbiol. 70, 143–160 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    27.Frye, S. A., Nilsen, M., Tønjum, T. & Ambur, O. H. Dialects of the DNA uptake sequence in Neisseriaceae. PLoS Genet. https://doi.org/10.1371/journal.pgen.1003458 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Redfield, R. J. et al. Evolution of competence and DNA uptake specificity in the Pasteurellaceae. BMC Evol. Biol. 6, 1–15 (2006).
    Google Scholar 
    29.Dion, M. B., Oechslin, F. & Moineau, S. Phage diversity, genomics and phylogeny. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-019-0311-5 (2020).Article 
    PubMed 

    Google Scholar 
    30.Siguier, P., Gourbeyre, E. & Chandler, M. Bacterial insertion sequences: their genomic impact and diversity. FEMS Microbiol. Rev. 38, 865–891 (2014).PubMed 

    Google Scholar 
    31.Vulić, M., Dionisio, F., Taddei, F. & Radman, M. Molecular keys to speciation: DNA polymorphism and the control of genetic exchange in enterobacteria. Proc. Natl Acad. Sci. USA 94, 9763–9767 (1997).PubMed 
    PubMed Central 

    Google Scholar 
    32.Majewski, J. et al. Barriers to genetic exchange between bacterial species: Streptococcus pneumoniae transformation. J. Bacteriol. 182, 1016–1023 (2000).PubMed 
    PubMed Central 

    Google Scholar 
    33.Wyres, K. L. et al. Pneumococcal capsular switching: a historical perspective. J. Infect. Dis. 207, 439–449 (2013).PubMed 

    Google Scholar 
    34.Hallet, B. & Sherratt, D. J. Transposition and site-specific recombination: adapting DNA cut-and-paste mechanisms to a variety of genetic rearrangements. FEMS Microbiol. Rev. 21, 157–178 (1997).PubMed 

    Google Scholar 
    35.Durrant, M. G., Li, M. M., Siranosian, B. A., Montgomery, S. B. & Bhatt, A. S. A bioinformatic analysis of integrative mobile genetic elements highlights their role in bacterial adaptation. Cell Host Microbe 27, 140–153.e9 (2020).PubMed 

    Google Scholar 
    36.Rajeev, L., Malanowska, K. & Gardner, J. F. Challenging a paradigm: the role of DNA homology in tyrosine recombinase reactions. Microbiol. Mol. Biol. Rev. 73, 300–309 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    37.Hickman, A. B., Chandler, M. & Dyda, F. Integrating prokaryotes and eukaryotes: DNA transposases in light of structure. Crit. Rev. Biochem. Mol. Biol. 45, 50–69 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    38.Oliveira, P. H., Touchon, M., Cury, J. & Rocha, E. P. C. The chromosomal organization of horizontal gene transfer in bacteria. Nat. Commun. 8, 1–10 (2017).
    Google Scholar 
    39.Wadsworth, C. B., Arnold, B. J., Sater, M. R. A. & Grad, Y. Azithromycin resistance through interspecific acquisition of an epistasis-dependent efflux pump component and transcriptional regulator in Neisseria gonorrhoeae. mBio 9, 1–17 (2018).
    Google Scholar 
    40.Arevalo, P., VanInsberghe, D., Elsherbini, J., Gore, J. & Polz, M. F. A reverse ecology approach based on a biological definition of microbial populations. Cell 178, 820–834.e14 (2019). The authors create a metric of recent gene flow to define ecological populations and discover genes that have experienced positive selection across populations.PubMed 

    Google Scholar 
    41.Croucher, N. J. et al. Horizontal DNA transfer mechanisms of bacteria as weapons of intragenomic conflict. PLoS Biol. 14, 1–42 (2016). A model of transformation with known bias towards the acquisition of shorter alleles suggests HGT may effectively purge bacterial genomes of parasitic MGEs.
    Google Scholar 
    42.Apagyi, K. J., Fraser, C. & Croucher, N. J. Transformation asymmetry and the evolution of the bacterial accessory genome. Mol. Biol. Evol. 35, 575–581 (2018).PubMed 

    Google Scholar 
    43.Mira, A., Ochman, H. & Moran, N. A. Deletional bias and the evolution of bacterial genomes. Trends Genet. 17, 589–596 (2001).PubMed 

    Google Scholar 
    44.Kuo, C.-H. & Ochman, H. Deletional bias across the three domains of life. Genome Biol. Evol. 1, 145–152 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    45.Lawrence, J. G. & Roth, J. R. Selfish operons: horizontal transfer may drive the evolution of gene clusters. Genetics 143, 1843–1860 (1996).PubMed 
    PubMed Central 

    Google Scholar 
    46.Hehemann, J. H. et al. Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota. Nature 464, 908–912 (2010).PubMed 

    Google Scholar 
    47.Campbell, A. Prophage insertion sites. Res. Microbiol. 154, 277–282 (2003).PubMed 

    Google Scholar 
    48.Chu, N. D. et al. A mobile element in mutS drives hypermutation in a marine Vibrio. mBio 8, 1–13 (2017).
    Google Scholar 
    49.Bobay, L. M., Rocha, E. P. C. & Touchon, M. The adaptation of temperate bacteriophages to their host genomes. Mol. Biol. Evol. 30, 737–751 (2013).PubMed 

    Google Scholar 
    50.Lee, H., Doak, T. G., Popodi, E., Foster, P. L. & Tang, H. Insertion sequence-caused large-scale rearrangements in the genome of Escherichia coli. Nucleic Acids Res. 44, 7109–7119 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    51.Parkhill, J. et al. Comparative analysis of the genome sequences of Bordetella pertussis, Bordetella parapertussis and Bordetella bronchiseptica. Nat. Genet. 35, 32–40 (2003).PubMed 

    Google Scholar 
    52.Moran, N. A. & Plague, G. R. Genomic changes following host restriction in bacteria. Curr. Opin. Genet. Dev. 14, 627–633 (2004).PubMed 

    Google Scholar 
    53.Hendry, T. et al. Ongoing transposon-mediated genome reduction in the luminous bacterial symbionts of deep-sea ceratioid anglerfishes. mBio https://doi.org/10.1128/mBio.01033-18 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Waterworth, S. C. et al. Horizontal gene transfer to a defensive symbiont with a reduced genome in a multipartite beetle microbiome. mBio https://doi.org/10.1128/mBio.02430-19 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Vos, M. et al. Rates of lateral gene transfer in prokaryotes: high but why? Trends Microbiol. 23, 598–605 (2015).PubMed 

    Google Scholar 
    56.Cohen, E., Kessler, D. A. & Levine, H. Recombination dramatically speeds up evolution of finite populations. Phys. Rev. Lett. 94, 1–4 (2005).
    Google Scholar 
    57.Levin, B. R. & Cornejo, O. E. The population and evolutionary dynamics of homologous gene recombination in bacteria. PLoS Genet. https://doi.org/10.1371/journal.pgen.1000601 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Arnold, B. J. et al. Weak epistasis may drive adaptation in recombining bacteria. Genetics 208, 1247–1260 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    59.Moradigaravand, D. & Engelstädter, J. The effect of bacterial recombination on adaptation on fitness landscapes with limited peak accessibility. PLoS Comput. Biol. 8, 35–37 (2012).
    Google Scholar 
    60.Cooper, T. F. Recombination speeds adaptation by reducing competition between beneficial mutations in populations of Escherichia coli. PLoS Biol. 5, 1899–1905 (2007).
    Google Scholar 
    61.Winkler, J. & Kao, K. C. Harnessing recombination to speed adaptive evolution in Escherichia coli. Metab. Eng. 14, 487–495 (2012).PubMed 

    Google Scholar 
    62.Chu, H. Y., Sprouffske, K. & Wagner, A. The role of recombination in evolutionary adaptation of Escherichia coli to a novel nutrient. J. Evol. Biol. 30, 1692–1711 (2017).PubMed 

    Google Scholar 
    63.Arnold, B. et al. Fine-scale haplotype structure reveals strong signatures of positive selection in a recombining bacterial pathogen. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msz225 (2019).Article 
    PubMed Central 

    Google Scholar 
    64.Yahara, K. et al. The landscape of realized homologous recombination in pathogenic bacteria. Mol. Biol. Evol. 33, 456–471 (2016).PubMed 

    Google Scholar 
    65.Engelstädter, J. & Moradigaravand, D. Adaptation through genetic time travel? Fluctuating selection can drive the evolution of bacterial transformation. Proc. R. Soc. B Biol. Sci. 281, 20132609 (2014).
    Google Scholar 
    66.Cohan, F. M. Periodic selection and ecological diversity in bacteria. Selective Sweep https://doi.org/10.1007/0-387-27651-3_7 (2007).Article 

    Google Scholar 
    67.Shapiro, B. J., David, L. A., Friedman, J. & Alm, E. J. Looking for Darwin’s footprints in the microbial world. Trends Microbiol. 17, 196–204 (2009).PubMed 

    Google Scholar 
    68.Shapiro, B. J. et al. Population genomics of early events in the ecological differentiation of bacteria. Science 336, 48–51 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    69.Rosen, M., Davison, M., Bhaya, D. & Fisher, D. S. Fine-scale diversity and extensive recombination in a quasisexual bacterial population occupying a broad niche. Science 348, 1019–1024 (2015).PubMed 

    Google Scholar 
    70.Bendall, M. L. et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 10, 1589–1601 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    71.Porter, S. S., Chang, P. L., Conow, C. A., Dunham, J. P. & Friesen, M. L. Association mapping reveals novel serpentine adaptation gene clusters in a population of symbiotic Mesorhizobium. ISME J. 11, 248–262 (2017).PubMed 

    Google Scholar 
    72.Crits-Christoph, A., Olm, M. R., Diamond, S., Bouma-Gregson, K. & Banfield, J. F. Soil bacterial populations are shaped by recombination and gene-specific selection across a grassland meadow. ISME J. https://doi.org/10.1038/s41396-020-0655-x (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Woods, L. C. et al. Horizontal gene transfer potentiates adaptation by reducing selective constraints on the spread of genetic variation. Proc. Natl Acad. Sci. USA 117, 26868–26875 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    74.Miralles, R., Gerrish, P. J., Moya, A. & Elena, S. F. Clonal interference and the evolution of RNA viruses. Science 285, 1745–1747 (1999).PubMed 

    Google Scholar 
    75.De Visser, J. A. G. M., Zeyl, C. W., Gerrish, P. J., Blanchard, J. L. & Lenski, R. E. Diminishing returns from mutation supply rate in asexual populations. Science 283, 404–406 (1999).PubMed 

    Google Scholar 
    76.Good, B. H., Rouzine, I. M., Balick, D. J., Hallatschek, O. & Desai, M. M. Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc. Natl Acad. Sci. USA 109, 4950–4955 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    77.Takeuchi, N., Cordero, O. X., Koonin, E. V. & Kaneko, K. Gene-specific selective sweeps in bacteria and archaea caused by negative frequency-dependent selection. BMC Biol. 13, 1–11 (2015). The authors show that in the presence of NFDS, genes or mutations that are unconditionally beneficial can spread through populations only via HGT, giving rise to gene-specific sweeps.
    Google Scholar 
    78.Corander, J. et al. Frequency-dependent selection in vaccine-associated pneumococcal population dynamics. Nat. Ecol. Evol. 2017, 1950–1960 (2018).
    Google Scholar 
    79.Rodriguez-Valera, F. et al. Explaining microbial population genomics through phage predation. Nat. Rev. Microbiol. 7, 828–836 (2009).PubMed 

    Google Scholar 
    80.Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    81.Ramiro, R. S., Durão, P., Bank, C. & Gordo, I. Low mutational load allows for high mutation rate variation in gut commensal bacteria. PLoS Biol. https://doi.org/10.1101/568709 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    82.Holt, R. D. Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proc. Natl Acad. Sci. USA 106, 19659–19665 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    83.Cohan, F. M. Transmission in the origins of bacterial diversity, from ecotypes to phyla. Microbiol. Spectr. https://doi.org/10.1128/9781555819743.ch18 (2017).Article 
    PubMed 

    Google Scholar 
    84.Fondi, M. et al. “Every gene is everywhere but the environment selects”: global geolocalization of gene sharing in environmental samples through network analysis. Genome Biol. Evol. 8, 1388–1400 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    85.Cohan, F. M. The effects of rare but promiscuous genetic exchange on evolutionary divergence in prokaryotes. Am. Nat. 143, 965–986 (1994).
    Google Scholar 
    86.Majewski, J. & Cohan, F. M. Adapt globally, act locally: the effect of selective sweeps on bacterial sequence diversity. Genetics 152, 1459–1474 (1999).PubMed 
    PubMed Central 

    Google Scholar 
    87.Messer, P. W. & Petrov, D. A. Population genomics of rapid adaptation by soft selective sweeps. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2013.08.003 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    88.Cui, Y. et al. Epidemic clones, oceanic gene pools, and Eco-LD in the free living marine pathogen Vibrio parahaemolyticus. Mol. Biol. Evol. 32, 1396–1410 (2015).PubMed 

    Google Scholar 
    89.Skwark, M. et al. Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis. PLoS Genet. https://doi.org/10.1371/journal.pgen.1006508 (2016).Article 

    Google Scholar 
    90.Pensar, J. et al. Genome-wide epistasis and co-selection study using mutual information. Nucleic Acids Res. 47, e112–e112 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    91.Puranen, S. et al. SuperDCA for genome-wide epistasis analysis. Microb. Genomics 4, e000184 (2018).
    Google Scholar 
    92.Whelan, F. J., Rusilowicz, M. & McInerney, J. O. Coinfinder: detecting significant associations and dissociations in pangenomes. Microb. Genomics 6, e000338 (2020).
    Google Scholar 
    93.Slomka, S. et al. Experimental evolution of bacillus subtilis reveals the evolutionary dynamics of horizontal gene transfer and suggests adaptive and neutral effects. Genetics 216, 543–558 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    94.Maddamsetti, R. & Lenski, R. E. Analysis of bacterial genomes from an evolution experiment with horizontal gene transfer shows that recombination can sometimes overwhelm selection. PLoS Genet. 14, 1–30 (2018).
    Google Scholar 
    95.Knöppel, A., Lind, P. A., Lustig, U., Näsvall, J. & Andersson, D. I. Minor fitness costs in an experimental model of horizontal gene transfer in bacteria. Mol. Biol. Evol. 31, 1220–1227 (2014).PubMed 

    Google Scholar 
    96.Collins, R. E. & Higgs, P. G. Testing the infinitely many genes model for the evolution of the bacterial core genome and pangenome. Mol. Biol. Evol. 29, 3413–3425 (2012).PubMed 

    Google Scholar 
    97.Baumdicker, F., Hess, W. R. & Pfaffelhuber, P. The infinitely many genes model for the distributed genome of bacteria. Genome Biol. Evol. 4, 443–456 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    98.Haegeman, B. & Weitz, J. S. A neutral theory of genome evolution and the frequency distribution of genes. BMC Genomics 13, 196 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    99.Hughes, A. L. Evidence for abundant slightly deleterious polymorphisms in bacterial populations. Genetics 169, 533–538 (2005).PubMed 
    PubMed Central 

    Google Scholar 
    100.Van Passel, M. W. J., Marri, P. R. & Ochman, H. The emergence and fate of horizontally acquired genes in Escherichia coli. PLoS Comput. Biol. 4, e1000059 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    101.Hao, W. & Golding, G. B. The fate of laterally transferred genes: life in the fast lane to adaptation or death. Genome Res. 16, 636–643 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    102.Lerat, E., Daubin, V., Ochman, H. & Moran, N. A. Evolutionary origins of genomic repertoires in bacteria. 3, e130 (2005).103.Lobkovsky, A. E., Wolf, Y. I. & Koonin, E. V. Gene frequency distributions reject a neutral model of genome evolution. Genome Biol. Evol. 5, 233–242 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    104.Sela, I., Wolf, Y. I. & Koonin, E. V. Theory of prokaryotic genome evolution. Proc. Natl Acad. Sci. USA 113, 11399–11407 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    105.Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. https://doi.org/10.1038/nrg2526 (2009).Article 
    PubMed 

    Google Scholar 
    106.Cohan, F. M. & Perry, E. B. A systematics for discovering the fundamental units of bacterial diversity. Curr. Biol. 17, 373–386 (2007).
    Google Scholar 
    107.Domingo-Sananes, M. R. & McInerney, J. O. Selection-based model of prokaryote pangenomes. bioRxiv https://doi.org/10.1101/782573 (2019).Article 

    Google Scholar 
    108.Azarian, T. et al. Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae. PLoS Biol. 18, e3000878 (2020). The authors provide evidence that NFDS is a pervasive evolutionary force that shapes the accessory genome of S. pneumoniae.PubMed 
    PubMed Central 

    Google Scholar 
    109.Bobay, L. M., Touchon, M. & Rocha, E. P. C. Pervasive domestication of defective prophages by bacteria. Proc. Natl Acad. Sci. USA 111, 12127–12132 (2014). Although prophages can be considered parasitic, the authors show evidence of purifying selection within prophage genes, suggesting that they serve a beneficial purpose within their bacterial hosts.PubMed 
    PubMed Central 

    Google Scholar 
    110.Puigbò, P., Lobkovsky, A. E., Kristensen, D. M., Wolf, Y. I. & Koonin, E. V. Genomes in turmoil: quantification of genome dynamics in prokaryote supergenomes. BMC Med. 12, 1–19 (2014).
    Google Scholar 
    111.Lynch, M. Streamlining and simplification of microbial genome architecture. Annu.Rev.Microbiol. 60, 327–349 (2006).PubMed 

    Google Scholar 
    112.Bobay, L. & Ochman, H. Factors driving effective population size and pan-genome evolution in bacteria. BMC Evol. Biol. 18, 15 (2018).
    Google Scholar 
    113.Brito, I. L. et al. Mobile genes in the human microbiome are structured from global to individual scales. Nature 535, 435–439 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    114.Evans, T. G. Considerations for the use of transcriptomics in identifying the ‘genes that matter’ for environmental adaptation. J. Exp. Biol. 218, 1925–1935 (2015).PubMed 

    Google Scholar 
    115.Cain, A. K. et al. A decade of advances in transposon-insertion sequencing. Nat. Rev. Genet. 21, 526–540 (2020).PubMed 

    Google Scholar 
    116.Wu, M. et al. Genetic determinants of in vivo fitness and diet responsiveness in multiple human gut Bacteroides. Science (80-.) 350, aac5992 (2015).
    Google Scholar 
    117.Poulsen, B. E. et al. Defining the core essential genome of Pseudomonas aeruginosa. Proc. Natl Acad. Sci. USA 116, 10072–10080 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    118.Pál, C., Papp, B. & Lercher, M. J. Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat. Genet. 37, 1372–1375 (2005).PubMed 

    Google Scholar 
    119.Ansari, A. & Didelot, X. Inference of the properties of the recombination process from whole bacterial genomes. Genetics 196, 253–265 (2014).PubMed 

    Google Scholar 
    120.Lin, M. & Kussell, E. Inferring bacterial recombination rates from large-scale sequencing datasets. Nat. Methods 16, 199–204 (2019). The authors develop a fast and clever method that uses linkage information to estimate recombination rates and the diversity of the gene pool that has contributed alleles to the sample via HGT.PubMed 

    Google Scholar 
    121.Marttinen, P. et al. Detection of recombination events in bacterial genomes from large population samples. Nucleic Acids Res. 40, 1–12 (2012).
    Google Scholar 
    122.Didelot, X. & Wilson, D. J. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput. Biol. 11, 1–18 (2015).
    Google Scholar 
    123.Croucher, N. J. et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. https://doi.org/10.1371/journal.pcbi.1004041 (2015).124.Mostowy, R. et al. Efficient inference of recent and ancestral recombination within bacterial populations. Mol. Biol. Evol. 34, 1167–1182 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    125.Yahara, K., Didelot, X., Ansari, M. A., Sheppard, S. K. & Falush, D. Efficient inference of recombination hot regions in bacterial genomes. Mol. Biol. Evol. 31, 1593–1605 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    126.Daubin, V., Moran, N. A. & Ochman, H. Phylogenetics and the cohesion of bacterial genomes. Science 301, 829–832 (2003).PubMed 

    Google Scholar 
    127.Daubin, V. & Szollosi, G. Horizontal gene transfer and the tree of life. Cold Spring Harb. Perspect. Biol. https://doi.org/10.1007/978-94-007-2941-4_37 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    128.Bertelli, C., Tilley, K. E. & Brinkman, F. S. L. Microbial genomic island discovery, visualization and analysis. Brief. Bioinform. 20, 1685–1698 (2019).PubMed 

    Google Scholar 
    129.Rocha, E. P. C. et al. Comparisons of dN/dS are time dependent for closely related bacterial genomes. J. Theor. Biol. 239, 226–235 (2006).PubMed 

    Google Scholar 
    130.Kryazhimskiy, S. & Plotkin, J. B. The population genetics of dN/dS. PLoS Genet. https://doi.org/10.1371/journal.pgen.1000304 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    131.Charlesworth, B. & Charlesworth, D. Elements of Evolutionary Genetics (Roberts and Company Publishers, 2010).132.Castillo-Ramírez, S. et al. The impact of recombination on dN/dS within recently emerged bacterial clones. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1002129 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    133.David, S. et al. Dynamics and impact of homologous recombination on the evolution of Legionella pneumophila. PLoS Genet. 13, 1–21 (2017).
    Google Scholar 
    134.Dillon, M., Thakur, S., Almeida, R. & Guttman, D. Recombination of ecologically and evolutionarily significant loci maintains genetic cohesion in the Pseudomonas syringae species complex. Genome Biol. https://doi.org/10.1101/227413 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    1.Navarra, A. & Tubiana, L. (eds) Regional Assessment of Climate Change in the Mediterranean, Advances in Global Change Research (Springer Netherlands, 2013). https://doi.org/10.1007/978-94-007-5772-1.Book 

    Google Scholar 
    2.Solomon, S. S. IPCC (2007): Climate Change the Physical Science Basis. AGUFM 2007, U43D-01 (2007).3.Seneviratne, S. et al. Changes in Climate Extremes and Their Impacts on the Natural Physical Environment: An Overview of the IPCC SREX report, Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) (2012).4.Bates, B., Kundzewicz, Z. & Wu, S. Climate Change and Water. Intergovernmental Panel on Climate Change Secretariat (2008).5.Neve, P., Vila-Aiub, M. & Phytologist, F.R.-N. Evolutionary-thinking in agricultural weed management. New Phytol. 184(4), 783–793 (2009).Article 

    Google Scholar 
    6.Harrison, M. T., Cullen, B. R. & Rawnsley, R. P. Modelling the sensitivity of agricultural systems to climate change and extreme climatic events. Agric. Syst. https://doi.org/10.1016/j.agsy.2016.07.006 (2016).Article 

    Google Scholar 
    7.Moret, D., Arrúe, J. L., López, M. V. & Gracia, R. Winter barley performance under different cropping and tillage systems in semiarid Aragon (NE Spain). Eur. J. Agron. 26, 54–63. https://doi.org/10.1016/j.eja.2006.08.007 (2007).Article 

    Google Scholar 
    8.FAO (Food and Agriculture Organization). Rome: Introduction to Conservation Agriculture (Its Principles and Benefits). http://teca.fao.org/technology/introduction-conservationagriculture-its-principles-benefits (2013).9.Kertész, À. & Madarász, B. Conservation agriculture in Europe. Int. Soil Water Conserv. Res. 2(1), 91–96 (2014).Article 

    Google Scholar 
    10.Álvaro-Fuentes, J., López, M. V., Cantero-Martínez, C. & Arrúe, J. L. Tillage effects on soil organic carbon fractions in Mediterranean dryland agroecosystems. Soil Sci. Soc. Am. J. 72, 541–547 (2008).ADS 
    Article 

    Google Scholar 
    11.Bouchery, Y., Ghaffari, A., Jemai, Z. & Dallery, Y. Including sustainability criteria into inventory models. Eur. J. Oper. Res. 222, 229–240 (2012).MathSciNet 
    Article 

    Google Scholar 
    12.Soane, B. D. et al. No-till in northern, western and south-western Europe: A review of problems and opportunities for crop production and the environment. Soil Tillage Res. 118, 66–87 (2012).Article 

    Google Scholar 
    13.Madejón, E. et al. Effect of long-term conservation tillage on soil biochemical properties in Mediterranean Spanish areas. Soil Tillage Res. 105, 55–62 (2009).Article 

    Google Scholar 
    14.De Vita, P., Di Paolo, E., Fecondo, G., Di Fonzo, N. & Pisante, M. No-tillage and conventional tillage effects on durum wheat yield, grain quality and soil moisture content in southern Italy. Soil Tillage Res. 92, 69–78. https://doi.org/10.1016/j.still.2006.01.012 (2007).Article 

    Google Scholar 
    15.Giambalvo, D. et al. Faba bean grain yield, N2 fixation, and weed infestation in a long-term tillage experiment under rainfed Mediterranean conditions. Plant Soil 360, 215–227. https://doi.org/10.1007/s11104-012-1224-5 (2012).CAS 
    Article 

    Google Scholar 
    16.Ruisi, P. et al. Conservation tillage in a semiarid Mediterranean environment: Results of 20 years of research. Ital. J. Agron. 9(560), 1–7. https://doi.org/10.4081/ija.2014.560 (2014).Article 

    Google Scholar 
    17.Plaza-Bonilla, D., Cantero-Martínez, C., Viñas, P. & Álvaro-Fuentes, J. Soil aggregation and organic carbon protection in a no-tillage chronosequence under Mediterranean conditions. Geoderma 193–194, 76–82 (2013).ADS 
    Article 

    Google Scholar 
    18.Barberi, P. & Lo Cascio, B. Long-term tillage and crop rotation effects on weed seed bank size and composition. Weed Res. 41(4), 325–340. https://doi.org/10.1046/j.1365-3180.2001.00241.x (2001).Article 

    Google Scholar 
    19.Batey, T. & McKenzie, D. C. Soil compaction: Identification directly in the field. Soil Use Manag. 22, 123–131. https://doi.org/10.1111/j.1475-2743.2006.00017.x (2006).Article 

    Google Scholar 
    20.Lampurlanés, J., Plaza-Bonilla, D., Álvaro-Fuentes, J. & Cantero-Martínez, C. Long-term analysis of soil water conservation and crop yield under different tillage systems in Mediterranean rainfed conditions. Field Crops Res. 198, 59–67. https://doi.org/10.1016/j.fcr.2016.02.010 (2016).Article 

    Google Scholar 
    21.Ruisi, P. et al. Weed seedbank size and composition in a long-term tillage and crop sequence experiment. Weed Res. 55, 320–328. https://doi.org/10.1111/wre.12142 (2015).Article 

    Google Scholar 
    22.Mahli, S. S. & Lemke, R. Tillage, crop residue and N fertilizer effects on crop yield, nutrient uptake, soil quality and nitrous oxide gasemissions in a second 4-yr rotation cycle. Soil Tillage Res. 96, 269–283. https://doi.org/10.1016/j.still.2007.06.011 (2007).Article 

    Google Scholar 
    23.Santín-Montanyá, M. I., Gandía, M. L., Zambrana, E. & Tenorio, J. L. Effects of tillage systems on wheat and weed water relationships over time when growing together, in semiarid conditions. Ann. Appl. Biol. 177, 256–265. https://doi.org/10.1111/aab.12620 (2020).Article 

    Google Scholar 
    24.Chaghazardi, H. R., Jahansouz, M. R., Ahmadi, A. & Gorji, M. Effects of tillage management on productivity of wheat and chickpea under cold, rainfed conditions in western Iran. Soil Tillage Res. 162, 26–33. https://doi.org/10.1016/j.still.2016.04.010 (2016).Article 

    Google Scholar 
    25.López-Bellido, L., Fuentes, M., Castillo, J. E., López-Garrido, F. J. & Fernández, E. J. Long-term tillage, crop rotation, and nitrogen fertiliser effects on wheat yield under rainfed Mediterranean conditions. Agron. J. 88, 783–791 (1996).Article 

    Google Scholar 
    26.Cantero-Martínez, C., Angás, P. & Lampurlanés, J. Long-term yield and water use efficiency under various tillage systems in Mediterranean rainfed conditions. Ann. Appl. Biol. 150, 293–305. https://doi.org/10.1111/j.1744-7348.2007.00142.x (2007).Article 

    Google Scholar 
    27.Campiglia, E., Mancinelli, R., De Stefanis, E., Pucciarmati, S. & Radicetti, E. The long-term effects of conventional and organic ropping systems, tillage managements and weather conditions on yield and grain quality of durum wheat (Triticum durum Desf.) in the Mediterranean environment of central Italy. Field Crops Res. 176, 34–44. https://doi.org/10.1016/j.fcr.2015.02.021 (2015).Article 

    Google Scholar 
    28.Bennett, A. J., Bending, G. D., Chandler, D., Hilton, S. & Mills, P. Meeting the demand for crop production: The challenge of yield decline in crops grown in short rotations. Biol. Rev. 87, 52–71 (2012).Article 

    Google Scholar 
    29.Plourde, J. D., Pijanowski, B. C. & Pekin, B. K. Evidence for increased monoculture cropping in the Central United States. Agric. Ecosyst. Environ. 165, 50–59 (2013).Article 

    Google Scholar 
    30.Seymour, M., Kirkegaard, J. A., Peoples, M. B., White, P. F. & French, R. J. Break-crop benefits to wheat in Western Australia—Insights from over three decades of research. Crop Pasture Sci. 63, 1 (2012).Article 

    Google Scholar 
    31.Wang, H. & Ortiz-Bobea, A. Market-driven corn monocropping in the U.S. Midwest. Agric. Resour. Econ. Rev. 48, 274–296 (2019).Article 

    Google Scholar 
    32.Tekin, S., Yazar, A. & Barut, H. Comparison of wheat-based rotation systems vs monocropping under dryland Mediterranean conditions. Int. J. Agric. Biol. Eng. 10, 203–213. https://doi.org/10.25165/j.ijabe.20171005.3443 (2017).Article 

    Google Scholar 
    33.Ryan, J., Singh, M. & Pala, M. Long-term cereal-based rotation trials in the Mediterranean region: Implications for cropping sustainability. Adv. Agron. 97, 273–319. https://doi.org/10.1016/S0065-2113(07)00007-7 (2008).CAS 
    Article 

    Google Scholar 
    34.Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2, 284–293 (2020).Article 

    Google Scholar 
    35.Marini, L. et al. Crop rotations sustain cereal yields under a changing climate. Environ. Res. Lett. 15(12), 124011 (2020).Article 

    Google Scholar 
    36.Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    37.Amato, G. et al. Long-term tillage and crop sequence effects on wheat grain yield and quality. Agron. J. 105, 1317–1327 (2013).Article 

    Google Scholar 
    38.Loke, P. F., Kotzé, E. & Du Preez, C. C. Impact of long-term wheat production management practices on soil acidity, phosphorus and some micronutrients in a semi-arid Plinthosol. Soil Res. 51, 415–426. https://doi.org/10.1071/SR12359 (2013).CAS 
    Article 

    Google Scholar 
    39.Martin-Rueda, I. et al. Tillage and crop rotation effects on barley yield and soil nutrients on a Calciortidic Haploxeralf. Soil Tillage Res. 92, 1–9 (2007).Article 

    Google Scholar 
    40.Hadjichristodoulou, A. The relationship of grain yield with harvest index and total biological yield of barley in drylands. Tech. Bull. 126, 1–10 (1991).
    Google Scholar 
    41.Zimdahl, R. L. Weed-Crop Competition: A Review 49–50, 109–145 (Blackwell Publishing, 2004).42.Nkoa, R., Owen, M. D. K. & Swanton, C. J. Weed abundance, distribution, diversity, and community analyses. Weed Sci. 63, 64–90. https://doi.org/10.1614/ws-d-13-00075.1 (2015).Article 

    Google Scholar 
    43.Ter Braak, C. J. F. Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

    Google Scholar 
    44.Fried, G., Petit, S. & Reboud, X. A specialist-generalist classification of the arable flora and its response to changes in agricultural practices. BMC Ecol. 10, 20 (2010).Article 

    Google Scholar 
    45.Korres, N. E. et al. Cultivars to face climate change effects on crops and weeds: A review. Agron. Sustain. Dev. 36, 1–22. https://doi.org/10.1007/s13593-016-0350-5 (2016).Article 

    Google Scholar 
    46.Acevedo, E. H., Silva, P. C., Silva, H. R. & Solar, B. R. Wheat production in Mediterranean environments. In Wheat: Ecology and Physiology of Yield Determination 295–331 (1999).47.Ramesh, K., Matloob, A., Aslam, F., Florentine, S. K. & Chauhan, B. S. Weeds in a changing climate: Vulnerabilities, consequences, and implications for future weed management. Front. Plant Sci. 8, 1–12. https://doi.org/10.3389/fpls.2017.00095 (2017).CAS 
    Article 

    Google Scholar 
    48.Calzarano, F. et al. Durum wheat quality, yield and sanitary status under conservation agriculture. Agriculture https://doi.org/10.3390/agriculture8090140 (2018).Article 

    Google Scholar 
    49.Santín-Montanyá, M. I., Fernández-Getino, A. P., Zambrana, E. & Tenorio, J. L. Effects of tillage on winter wheat production in Mediterranean dryland fields. Arid Land Res. Manag. 31(3), 269–282. https://doi.org/10.1080/15324982.2017.1307289 (2017).Article 

    Google Scholar 
    50.Shimshi, D., Bielorai, H. & Mantell, A. Irrigation of field crops. In Arid Zone Irrigation 369–381 (Springer, 1973).51.Schultz, J. E. Crop production in a rotation trial at Tarlee, South Australia. Aust. J. Exp. Agric. 35, 865–876. https://doi.org/10.1071/EA9950865 (1995).Article 

    Google Scholar 
    52.Alarcón, R. et al. Effects of no-tillage and non-inversion tillage on weed community diversity and crop yield over nine years in a Mediterranean cereal-legume cropland. Soil Tillage Res. 179, 54–62. https://doi.org/10.1016/j.still.2018.01.014 (2018).Article 

    Google Scholar 
    53.Šíp, V., Vavera, R., Chrpová, J., Kusá, H. & Růžek, P. Winter wheat yield and quality related to tillage practice, input level and environmental conditions. Soil Tillage Res. 132, 77–85. https://doi.org/10.1016/j.still.2013.05.002 (2013).Article 

    Google Scholar 
    54.Woźniak, A. Effect of cereal monoculture and tillage systems on grain yield and weed infestation of winter durum wheat. Int. J. Plant Prod. 14, 1–8. https://doi.org/10.1007/s42106-019-00062-8 (2020).Article 

    Google Scholar 
    55.Schulte, B. J., Tomasek, B. J., Davis, A. S., Andersson, L. & Benoit, D. L. An investigation to enhance understanding of the stimulation of weed seedling emergence by soil disturbance. Weed Res. 54, 1–12. https://doi.org/10.1111/wre.12054 (2014).Article 

    Google Scholar 
    56.Calado, J. M. G., Basch, G. & de Carvalho, M. Weed emergence as influenced by soil moisture and air temperature. J. Pest Sci. 82, 81–88. https://doi.org/10.1007/s10340-008-0225-x (2009).Article 

    Google Scholar 
    57.Siddique, K. H. M. et al. Innovations in agronomy for food legumes. A review. Agron. Sustain. Dev. 32, 45–64 (2012).Article 

    Google Scholar 
    58.Payne, W. A., Rasmussen, P. E., Chen, C. & Ramig, R. E. Assessing simple wheat and pea models using data from a long-term tillage experiment. Agron. J. 93, 250–260. https://doi.org/10.2134/agronj2001.931250x (2001).Article 

    Google Scholar 
    59.Machado, S., Petrie, S., Rhinhart, K. & Ramig, R. E. Tillage effects on water use and grain yield of winter wheat and green pea in rotation. Agron. J. 100, 154–162. https://doi.org/10.2134/agrojnl2006.0218 (2008).Article 

    Google Scholar 
    60.Copec, K., Filipovic, D., Husnjak, S., Kovacev, I. & Kosustic, S. Effects of tillage systems on soil water content and yield in maize and winter wheat production. Plant Soil Environ. 61(5), 213–219. https://doi.org/10.17221/156/2015-pse (2015).Article 

    Google Scholar 
    61.López-Bellido, L., López-Bellido, R. J., Redondo, R. & Benítez, J. Faba bean nitrogen fixation in a wheat-based rotation under rainfed Mediterranean conditions: Effect of tillage system. Field Crop Res. 98, 253–260 (2006).Article 

    Google Scholar 
    62.López-Bellido, R. J., López-Bellido, L., Benítez-Vega, J. & López-Bellido, F. J. Tillage system, preceding crop, and nitrogen fertilizer in wheat crop: I. Soil water content. Agron. J. 99, 59–65. https://doi.org/10.2134/agronj2006.0025 (2007).Article 

    Google Scholar 
    63.López-Bellido, L., Muñoz-Romero, V., Fernández-García, P. & López-Bellido, R. J. Ammonium accumulation in soil: The long-term effects of tillage, rotation and N rate in a Mediterranean vertisol. Soil Use Manag. 30(4), 471–479 (2014).Article 

    Google Scholar 
    64.Bilalis, D., Efthimiadis, P. & Sidiras, N. Effect of three tillage systems on weed flora in a 3-year rotation with four crops. J. Agron. Crop Sci. 186, 135–141. https://doi.org/10.1046/j.1439-037X.2001.00458.x (2001).Article 

    Google Scholar 
    65.Feledyn-Szewczyk, B., Smagacz, J., Kwiatkowski, C. A., Harasim, E. & Woźniak, A. Weed flora and soil seed bank composition as affected by tillage system in three-year crop rotation. Agriculture https://doi.org/10.3390/agriculture10050186 (2020).Article 

    Google Scholar 
    66.Pala, M., Ryan, J., Zhang, H., Singh, M. & Harris, H. C. Water-use efficiency of wheat-based rotation systems in a Mediterranean environment. Agric. Water Manag. 93, 136–144. https://doi.org/10.1016/j.agwat.2007.07.001 (2007).Article 

    Google Scholar 
    67.Légère, A., Stevenson, F. C. & Benoit, D. L. Diversity and assembly of weed communities: Contrasting responses across cropping systems. Weed Res. 45, 303–315. https://doi.org/10.1111/j.1365-3180.2005.00459.x (2005).Article 

    Google Scholar 
    68.Sans, F. X., Berner, A., Armengot, L. & Mäder, P. Tillage effects on weed communities in an organic winter wheat-sunflower-spelt cropping sequence. Weed Res. 51, 413–421. https://doi.org/10.1111/j.1365-3180.2011.00859.x (2011).Article 

    Google Scholar 
    69.Sarani, M., Oveisi, M., Mashhadi, H. R., Alizade, H. & Gonzalez-Andujar, J. L. Interactions between the tillage system and crop rotation on the crop yield and weed populations under arid conditions. Weed Biol. Manag. 14, 198–208. https://doi.org/10.1111/wbm.12047 (2014).Article 

    Google Scholar 
    70.Pardo, G. et al. Effects of reduced and conventional tillage on weed communities: Results of a long-term experiment in Southwestern Spain. Planta Daninha https://doi.org/10.1590/s0100-83582019370100152 (2019).Article 

    Google Scholar 
    71.Fennimore, S. A. & Jackson, L. E. Organic amendment and tillage effects on vegetable field weed emergence and seedbanks 1. Weed Technol. 17, 42–50. https://doi.org/10.1614/0890-037x(2003)017[0042:oaateo]2.0.co;2 (2003).Article 

    Google Scholar 
    72.Francis, A. & Warwick, S. I. The biology of Canadian weeds. 3. Lepidium draba L., L. chalepense L., L. appelianum Al-Shehbaz (updated). Can. J. Plant Sci. 88, 379–401. https://doi.org/10.4141/CJPS07100 (2008).Article 

    Google Scholar  More