Ecology

Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to Quaternary climate change. Science 292, 673–679 (2001).CAS 
PubMed 
Article 

Google Scholar 
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
PubMed 
Article 

Google Scholar 
Williams, J. E. & Blois, J. L. Range shifts in response to past and future climate change: can climate velocities and species’ dispersal capabilities explain variation in mammalian range shifts? J. Biogeogr. 45, 2175–2189 (2018).Article 

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

Google Scholar 
Thomas, C. D. Climate, climate change and range boundaries. Divers. Distrib. 16, 488–495 (2010).Article 

Google Scholar 
Bradshaw, A. D. & McNeilly, T. Evolutionary response to global climatic change. Ann. Bot. 67, 5–14 (1991).Article 

Google Scholar 
Harter, D. E. V. et al. Impacts of global climate change on the floras of oceanic islands—projections, implications and current knowledge. Perspect. Plant Ecol. Evol. Syst. 17, 160–183 (2015).Article 

Google Scholar 
Veron, S., Haevermans, T., Govaerts, R., Mouchet, M. & Pellens, R. Distribution and relative age of endemism across islands worldwide. Sci. Rep. 9, 1–12 (2019).Article 
CAS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jump, A. S. & Peñuelas, J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8, 1010–1020 (2005).PubMed 
Article 

Google Scholar 
Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gilbert, K. J. & Whitlock, M. C. The genetics of adaptation to discrete heterogeneous environments: frequent mutation or large-effect alleles can allow range expansion. J. Evol. Biol. 30, 591–602 (2017).CAS 
PubMed 
Article 

Google Scholar 
Christmas, M. J., Breed, M. F. & Lowe, A. J. Constraints to and conservation implications for climate change adaptation in plants. Conserv. Genet. 17, 305–320 (2015).Article 
CAS 

Google Scholar 
Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).PubMed 
Article 

Google Scholar 
Lai, Y. T. et al. Standing genetic variation as the predominant source for adaptation of a songbird. Proc. Natl Acad. Sci. USA 116, 2152–2157 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hoban, S. et al. Finding the genomic basis of local adaptation: Pitfalls, practical solutions, and future directions. Am. Nat. 188, 379–397 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
PubMed 
Article 

Google Scholar 
Catullo, R. A., Llewelyn, J., Phillips, B. L. & Moritz, C. C. The potential for rapid evolution under anthropogenic climate change. Curr. Biol. 29, R996–R1007 (2019).CAS 
PubMed 
Article 

Google Scholar 
Botkin, D. B. et al. Forecasting the effects of global warming on biodiversity. BioScience 57, 227–236 (2007).Article 

Google Scholar 
Wiens, J. A., Stralberg, D., Jongsomjit, D., Howell, C. A. & Snyder, M. A. Niches, models, and climate change: assessing the assumptions and uncertainties. Proc. Natl Acad. Sci. USA 106, 19729–19736 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H. H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).PubMed 
Article 

Google Scholar 
Waldvogel, A.-M. et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4, 4–18 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Razgour, O. et al. An integrated framework to identify wildlife populations under threat from climate change. Mol. Ecol. Resour. 18, 18–31 (2018).PubMed 
Article 

Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aguirre-Liguori, J. A., Ramírez-Barahona, S., Tiffin, P. & Eguiarte, L. E. Climate change is predicted to disrupt patterns of local adaptation in wild and cultivated maize. Proc. R. Soc. B 286, 20190486 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Evans, T. G., Diamond, S. E. & Kelly, M. W. Mechanistic species distribution modelling as a link between physiology and conservation. Conserv. Physiol. 3, cov056 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Hall, S. J. G. Haemoglobin polymorphism in the bank vole, Clethrionomys glareolus, in Britain. J. Zool. 187, 153–160 (1979).Article 

Google Scholar 
Kotlík, P. et al. Adaptive phylogeography: functional divergence between haemoglobins derived from different glacial refugia in the bank vole. Proc. R. Soc. B 281, 20140021 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Searle, J. B. et al. The Celtic fringe of Britain: Insights from small mammal phylogeography. Proc. R. Soc. B 276, 4287–4294 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Escalante, M. A., Horníková, M., Marková, S. & Kotlík, P. Niche differentiation in a postglacial colonizer, the bank vole Clethrionomys glareolus. Ecol. Evol. 11, 8054–8070 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Reischl, E., Dafre, A. L., Franco, J. L. & Wilhelm Filho, D. Distribution, adaptation and physiological meaning of thiols from vertebrate hemoglobins. Comp. Biochem. Physiol. Part C. Toxicol. Pharmacol. 146, 22–53 (2007).Article 
CAS 

Google Scholar 
Storz, J. F. & Wheat, C. W. Integrating evolutionary and functional approaches to infer adaptation at specific loci. Evolution 64, 2489–2509 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rossi, R. et al. Different metabolizing ability of thiol reactants in human and rat blood. Biochemical and pharmacological implications. J. Biol. Chem. 276, 7004–7010 (2001).CAS 
PubMed 
Article 

Google Scholar 
Vitturi, D. A. et al. Antioxidant functions for the hemoglobin β93 cysteine residue in erythrocytes and in the vascular compartment in vivo. Free Radic. Biol. Med. 55, 119–129 (2013).CAS 
PubMed 
Article 

Google Scholar 
Petersen, A. G. et al. Hemoglobin polymerization via disulfide bond formation in the hypoxia-tolerant turtle Trachemys scripta: Implications for antioxidant defense and O2 transport. Am. J. Physiol. Regul. Integr. Comp. Physiol. 314, R84–R93 (2018).PubMed 
Article 
CAS 

Google Scholar 
Paital, B. et al. Longevity of animals under reactive oxygen species stress and disease susceptibility due to global warming. World J. Biol. Chem. 7, 110–127 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Jacobs, P. J., Oosthuizen, M. K., Mitchell, C., Blount, J. D. & Bennett, N. C. Heat and dehydration induced oxidative damage and antioxidant defenses following incubator heat stress and a simulated heat wave in wild caught four-striped field mice Rhabdomys dilectus. PLoS One 15, e0242279 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kotlík, P., Marková, S., Horníková, M., Escalante, M. A. & Searle, J. B. The bank vole (Clethrionomys glareolus) as a model system for adaptive phylogeography in the European theater. Front. Ecol. Evol. 10, 866605 (2022).Article 

Google Scholar 
Strážnická, M., Marková, S., Searle, J. B. & Kotlík, P. Playing hide-and-seek in beta-globin genes: Gene conversion transferring a beneficial mutation between differentially expressed gene guplicates. Genes 9, 492 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
Stocker, T. Climate Change 2013: the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2013).Araújo, M. B., Pearson, R. G., Thuiller, W. & Erhard, M. Validation of species-climate impact models under climate change. Glob. Chang. Biol. 11, 1504–1513 (2005).Article 

Google Scholar 
Peterson, A. T., Papeş, M. & Soberón, J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell. 213, 63–72 (2008).Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 
Article 

Google Scholar 
Warren, D. L. et al. ENMTools 1.0: an R package for comparative ecological biogeography. Ecography 44, 504–511 (2021).Article 

Google Scholar 
Mayes, J. & Wheeler, D. Regional weather and climates of the British Isles—part 1: introduction. Weather 68, 3–8 (2013).Article 

Google Scholar 
Kotlík, P., Marková, S., Konczal, M., Babik, W. & Searle, J. B. Genomics of end-Pleistocene population replacement in a small mammal. Proc. R. Soc. B 285, 20172624 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (mal)adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).Article 

Google Scholar 
Benito Garzón, M., Robson, T. M. & Hampe, A. ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity. N. Phytol. 222, 1757–1765 (2019).Article 

Google Scholar 
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).Article 

Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. in Twenty-first International Conference on Machine Learning – ICML ’04 9, 83 (ACM Press, 2004).Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Zeng, Y., Low, B. W. & Yeo, D. C. J. Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish. Ecol. Modell. 341, 5–13 (2016).Article 

Google Scholar 
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).PubMed 
Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).Article 

Google Scholar 
Gent, P. R. et al. The community climate system model version 4. J. Clim. 24, 4973–4991 (2011).Article 

Google Scholar 
Dufresne, J. L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).Article 

Google Scholar 
Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).Article 

Google Scholar 
Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).Article 

Google Scholar 
Schoener, T. W. The anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).Article 

Google Scholar  More

Galindo, I. & Alonso, C. African swine fever virus: A review. Viruses 9, 103 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
Blome, S., Franzke, K. & Beer, M. African swine fever: A review of current knowledge. Virus Res. 2020, 198099 (2020).Article 
CAS 

Google Scholar 
Li, X. & Tian, K. African swine fever in China. Vet. Rec. 183, 300 (2018).PubMed 
Article 

Google Scholar 
Wang, T., Sun, Y. & Qiu, H. J. African swine fever: An unprecedented disaster and challenge to China. Infect. Dis. Poverty 7, 66–70 (2018).Article 

Google Scholar 
Gaudreault, N. N., Madden, D. W., Wilson, W. C., Trujillo, J. D. & Richt, J. A. African swine fever virus: An emerging DNA arbovirus. Front. Vet. Sci. 7, 215 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Ge, S. et al. Molecular characterization of African swine fever virus, China, 2018. Emerg. Infect. Dis. 24, 2131–2133 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Mason-D’Croz, D. et al. Modelling the global economic consequences of a major African swine fever outbreak in China. Nat. Food 1, 221–228 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Woonwong, Y., Do, T. D. & Thanawongnuwech, R. The future of the pig industry after the introduction of African swine fever into Asia. Anim. Front. 10, 30–37 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Mulieri, P. R. & Patitucci, L. D. Using ecological niche models to describe the geographical distribution of the myiasis-causing Cochliomyia hominivorax (Diptera: Calliphoridae) in southern South America. Parasitol. Res. 118, 1077–1086 (2019).PubMed 
Article 

Google Scholar 
Escobar, L. E. Ecological niche modeling: An introduction for veterinarians and epidemiologists. Front. Vet. Sci. 7, 519059 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Bosso, L. et al. The rise and fall of an alien: why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea. Biol. Invasions https://doi.org/10.1007/s10530-022-02838-y (2022).Article 

Google Scholar 
Wen, X. et al. Prediction of the potential distribution pattern of the great gerbil (Rhombomys opimus) under climate change based on ensemble modelling. Pest Manag. Sci. 78, 3128–3134 (2022).CAS 
PubMed 
Article 

Google Scholar 
Cheng, Y. et al. Evaluating the risk for Usutu virus circulation in Europe: Comparison of environmental niche models and epidemiological models. Int. J. Health Geogr. 17, 1–14 (2018).Article 

Google Scholar 
Naimi, B. & Araújo, M. B. Sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).Article 

Google Scholar 
Georges, D. & Thuiller, W. An example of species distribution modeling with biomod2. https://r-forge.r-project.org/…/inst/doc/Simple_species_modelling.pdf?root=biomod (2013).Thuiller, W. BIOMOD: Optimizing predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol. 9, 1353–1362 (2003).ADS 
Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD: A platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

Google Scholar 
Thuiller, W. Editorial commentary on “BIOMOD: Optimizing predictions of species distributions and projecting potential future shifts under global change”. Glob. Change Biol. 20, 3591–3592 (2014).ADS 
Article 

Google Scholar 
Navarro-Cerrillo, R. M., Duque-Lazo, J., Manzanedo, R. D., Sánchez-Salguero, R. & Palacios-Rodriguez, G. Climate change may threaten the southernmost Pinus nigra subsp. salzmannii (Dunal) Franco populations: An ensemble niche-based approach. iForest Biogeosci. For. 11, 396–405 (2018).Article 

Google Scholar 
Assefa, A., Tibebu, A., Bihon, A., Dagnachew, A. & Muktar, Y. Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060. Sci. Rep. 12, 1748 (2022).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raffini, F. et al. From nucleotides to satellite imagery: Approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).CAS 
Article 

Google Scholar 
Wani, I. A. et al. Predicting habitat suitability and niche dynamics of Dactylorhiza hatagirea and Rheum webbianum in the Himalaya under projected climate change. Sci. Rep. 12, 13205 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boulanger-Lapointe, N. et al. Herbivore species coexistence in changing rangeland ecosystems: First high resolution national open-source and open-access ensemble models for Iceland. Sci. Total Environ. 845, 157140 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sillero, N. & Barbosa, A. M. Common mistakes in ecological niche models. Int. J. Geogr. Inf. Sci. 35, 213–226 (2020).Article 

Google Scholar 
Varela, S., Anderson, R. P., García-Valdés, R. & Fernández-González, F. Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography 37, 1084–1091 (2014).
Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2010).Article 

Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
Xiao-Ge, X. et al. Introduction of BCC models and its participation in CMIP6. Clim. Change Res. 5, 533–539 (2019).
Google Scholar 
Wu, T. et al. The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev. 12, 1573–1600 (2019).ADS 
Article 

Google Scholar 
Thomson, A. M. et al. RCP4.5: A pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).ADS 
CAS 
Article 

Google Scholar 
Assefa, A., Tibebu, A., Bihon, A. & Yimana, M. Global ecological niche modelling of current and future distribution of peste des petits ruminants virus (PPRv) with an ensemble modelling algorithm. Transbound Emerg. Dis. 68, 3601–3610 (2021).PubMed 
Article 

Google Scholar 
Jori, F. & Bastos, A. D. Role of wild suids in the epidemiology of African swine fever. EcoHealth 6, 296–310 (2009).PubMed 
Article 

Google Scholar 
Teklue, T., Sun, Y., Abid, M., Luo, Y. & Qiu, H. J. Current status and evolving approaches to African swine fever vaccine development. Transbound Emerg. Dis. 67, 529–542 (2020).PubMed 
Article 

Google Scholar 
Arias, M. et al. Approaches and perspectives for development of African swine fever virus vaccines. Vaccines 5, 35 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
Chenais, E. et al. Epidemiological considerations on African swine fever in Europe 2014–2018. Porcine Health Manag. 5, 1–10 (2019).Article 

Google Scholar 
Quembo, C. J., Jori, F., Vosloo, W. & Heath, L. Genetic characterization of African swine fever virus isolates from soft ticks at the wildlife/domestic interface in Mozambique and identification of a novel genotype. Transbound Emerg. Dis. 65, 420–431 (2018).CAS 
PubMed 
Article 

Google Scholar 
Torres, J. R. et al. Chikungunya fever: Atypical and lethal cases in the Western hemisphere: A Venezuelan experience. IDCases 2, 6–10 (2015).PubMed 
Article 

Google Scholar 
Nuanualsuwan, S. et al. Persistence of African swine fever virus on porous and non-porous fomites at environmental temperatures. Porc. Health Manag. 8, 34 (2022).Article 

Google Scholar 
Davies, K. et al. Survival of African swine fever virus in excretions from pigs experimentally infected with the Georgia 2007/1 isolate. Transbound Emerg. Dis. 64, 425–431 (2017).CAS 
PubMed 
Article 

Google Scholar 
Carlson, J. et al. Stability of African swine fever virus in soil and options to mitigate the potential transmission risk. Pathogens 9, 977 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Salari, L. S., Vatandoost, H., Telmadarraiy, Z., Entezar, M. R. & Kia, E. Seasonal activity of ticks and their importance in tick-borne infectious diseases in West Azerbaijan, Iran. J. Arthropod. Borne Dis. 2, 28–34 (2008).
Google Scholar 
Vial, L. Biological and ecological characteristics of soft ticks (Ixodida: Argasidae) and their impact for predicting tick and associated disease distribution. Parasite 16, 191–202 (2009).CAS 
PubMed 
Article 

Google Scholar 
Jian, L. et al. WANG potential adaptability of soft tick vectors of African swine fever to China. Chin. J. Vect. Biol. Control 21, 317–320 (2010).
Google Scholar 
Cwynar, P., Stojkov, J. & Wlazlak, K. African swine fever status in Europe. Viruses 11, 310 (2019).PubMed Central 
Article 

Google Scholar 
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K. & Thuiller, W. Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15, 59–69 (2009).Article 

Google Scholar  More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).CAS 
PubMed 
Article 

Google Scholar 
Boyce, D. G., Lewis, M. R. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).CAS 
PubMed 
Article 

Google Scholar 
Henson, S. A., Cael, B. B., Allen, S. R. & Dutkiewicz, S. Future phytoplankton diversity in a changing climate. Nat. Commun. 12, 5372 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaulot, D., Eikrem, W., Viprey, M. & Moreau, H. The diversity of small eukaryotic phytoplankton (≤3 μm) in marine ecosystems. FEMS Microbiol. Rev. 32, 795–820 (2008).CAS 
PubMed 
Article 

Google Scholar 
Agawin, N. S. R., Duarte, C. M. & Agustí, S. Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production. Limnol. Oceanogr. 45, 591–600 (2000).CAS 
Article 

Google Scholar 
Morán, X. A. G., López-Urrutia, Á., Calvo-Díaz, A. & Li, W. K. W. Increasing importance of small phytoplankton in a warmer ocean. Glob. Change Biol. 16, 1137–1144 (2010).Article 

Google Scholar 
Li, W. K. W., McLaughlin, F. A., Lovejoy, C. & Carmack, E. C. Smallest algae thrive as the arctic ocean freshens. Science 326 https://doi.org/10.1126/science.1179798 (2009).Benner, I., Irwin, A. J. & Finkel, Z. V. Capacity of the common Arctic picoeukaryote Micromonas to adapt to a warming ocean. Limnol. Oceanography Lett. 5, 221–227 (2020).Sunda, W. G. & Huntsman, S. A. Iron uptake and growth limitation in oceanic and coastal phytoplankton. Mar. Chem. 50, 189–206 (1995).CAS 
Article 

Google Scholar 
Raven, J. A. The twelfth Tansley Lecture. Small is beautiful: the picophytoplankton. Funct. Ecol. 12, 503–513 (1998).Article 

Google Scholar 
Morel, F. M. M. & Price, N. M. The biogeochemical cycles of trace metals in the oceans. Science 300, 944–947 (2003).CAS 
PubMed 
Article 

Google Scholar 
Gao, X., Bowler, C. & Kazamia, E. Iron metabolism strategies in diatoms. J. Exp. Bot. 72, 2165–2180 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caputi, L. et al. Community-level responses to iron availability in open ocean plankton ecosystems. Glob. Biogeochemical Cycles 33, 391–419 (2019).CAS 
Article 

Google Scholar 
Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. 9, 373 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Morrissey, J. et al. A novel protein, ubiquitous in marine phytoplankton, concentrates iron at the cell surface and facilitates uptake. Curr. Biol. 25, 364–371 (2015).CAS 
PubMed 
Article 

Google Scholar 
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).CAS 
Article 

Google Scholar 
Kumar, A. & Bera, S. Revisiting nitrogen utilization in algae: a review on the process of regulation and assimilation. Bioresour. Technol. Rep. 12, 100584 (2020).Article 

Google Scholar 
Smith, S. R. et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat. Commun. 10, 4552 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Berg, G. M., Glibert, P. M., Lomas, M. W. & Burford, M. A. Organic nitrogen uptake and growth by the chrysophyte Aureococcus anophagefferens during a brown tide event. Mar. Biol. 129, 377–387 (1997).CAS 
Article 

Google Scholar 
Andersen, R. A., Saunders, G. W., Paskind, M. P. & Sexton, J. P. Ultrastructure and 18s rRNA gene sequence for Pelagomonas calceolata gen. et sp. nov. and the description of a new algal class, the pelagophyceae classis nov. J. Phycol. 29, 701–715 (1993).CAS 
Article 

Google Scholar 
Choi, C. J. et al. Seasonal and geographical transitions in eukaryotic phytoplankton community structure in the Atlantic and Pacific Oceans. Front. Microbiol. 11, 542372 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Duerschlag, J. et al. Niche partitioning by photosynthetic plankton as a driver of CO2-fixation across the oligotrophic South Pacific Subtropical Ocean. ISME J 1–12 https://doi.org/10.1038/s41396-021-01072-z (2021).Worden, A. Z. et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr. Biol. 22, R675–R677 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dimier, C. é, Brunet, C., Geider, R. & Raven, J. Growth and photoregulation dynamics of the picoeukaryote Pelagomonas calceolata in fluctuating light. Limnol. Oceanogr. 54, 823–836 (2009).CAS 
Article 

Google Scholar 
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 
Article 

Google Scholar 
Kang, Y. et al. Transcriptomic responses of four pelagophytes to nutrient (N, P) and light stress. Front. Mar. Sci. 8, 636699 (2021).Huff, J. T., Zilberman, D. & Roy, S. W. Mechanism for DNA transposons to generate introns on genomic scales. Nature 538, 533–536 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).CAS 
PubMed 
Article 

Google Scholar 
Nambiar, M. & Smith, G. R. Repression of harmful meiotic recombination in centromeric regions. Semin Cell Dev. Biol. 54, 188–197 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pessia, E. et al. Evidence for widespread GC-biased gene conversion in eukaryotes. Genome Biol. Evol. 4, 675–682 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chi, J., Mahé, F., Loidl, J., Logsdon, J. & Dunthorn, M. Meiosis gene inventory of four ciliates reveals the prevalence of a synaptonemal complex-independent crossover pathway. Mol. Biol. Evol. 31, 660–672 (2014).CAS 
PubMed 
Article 

Google Scholar 
Ramesh, M. A., Malik, S.-B. & Logsdon, J. M. A phylogenomic inventory of meiotic genes; evidence for sex in Giardia and an early eukaryotic origin of meiosis. Curr. Biol. 15, 185–191 (2005).CAS 
PubMed 

Google Scholar 
Schurko, A. M. & Logsdon, J. M. Using a meiosis detection toolkit to investigate ancient asexual ‘scandals’ and the evolution of sex. Bioessays 30, 579–589 (2008).CAS 
PubMed 
Article 

Google Scholar 
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 
Article 

Google Scholar 
Frémont, P. et al. Restructuring of plankton genomic biogeography in the surface ocean under climate change. Nat. Clim. Chang. 12, 393–401 (2022).Article 

Google Scholar 
Ward, D. M. & Kaplan, J. Ferroportin-mediated iron transport: expression and regulation. Biochim Biophys. Acta 1823, 1426–1433 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gobler, C. J., Lonsdale, D. J. & Boyer, G. L. A review of the causes, effects, and potential management of harmful brown tide blooms caused by Aureococcus anophagefferens (Hargraves et sieburth). Estuaries 28, 726–749 (2005).Article 

Google Scholar 
Agusti, S., Lubián, L. M., Moreno-Ostos, E., Estrada, M. & Duarte, C. M. Projected changes in photosynthetic picoplankton in a warmer subtropical ocean. Front. Mar. Sci. 5, 506 (2019).Article 

Google Scholar 
Anderson, S. I., Barton, A. D., Clayton, S., Dutkiewicz, S. & Rynearson, T. A. Marine phytoplankton functional types exhibit diverse responses to thermal change. Nat. Commun. 12, 6413 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, J. H. et al. Testing the iron hypothesis in ecosystems of the equatorial Pacific Ocean. Nature 371, 123–129 (1994).CAS 
Article 

Google Scholar 
Shi, D., Xu, Y., Hopkinson, B. M. & Morel, F. M. M. Effect of ocean acidification on iron availability to marine phytoplankton. Science 327, 676–679 (2010).CAS 
PubMed 
Article 

Google Scholar 
McQuaid, J. B. et al. Carbonate-sensitive phytotransferrin controls high-affinity iron uptake in diatoms. Nature 555, 534–537 (2018).CAS 
PubMed 
Article 

Google Scholar 
Turnšek, J. et al. Proximity proteomics in a marine diatom reveals a putative cell surface-to-chloroplast iron trafficking pathway. eLife 10, e52770 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Urzica, E. I. et al. Systems and trans-system level analysis identifies conserved iron deficiency responses in the plant lineage[W][OA]. Plant Cell 24, 3921–3948 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mao, X. et al. Diversity, prevalence, and expression of cyanase genes (cynS) in planktonic marine microorganisms. ISME J. 16, 602–605 (2022).CAS 
PubMed 
Article 

Google Scholar 
Ou, L., Cai, Y., Jin, W., Wang, Z. & Lu, S. Understanding the nitrogen uptake and assimilation of the Chinese strain of Aureococcus anophagefferens (Pelagophyceae). Algal Res. 34, 182–190 (2018).Article 

Google Scholar 
Shu, C. J., Ulrich, L. E. & Zhulin, I. B. The NIT domain: a predicted nitrate-responsive module in bacterial sensory receptors. Trends Biochem Sci. 28, 121–124 (2003).CAS 
PubMed 
Article 

Google Scholar 
Wu, S. Q., Chai, W., Lin, J. T. & Stewart, V. General nitrogen regulation of nitrate assimilation regulatory gene nasR expression in Klebsiella oxytoca M5al. J. Bacteriol. 181, 7274–7284 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008).CAS 
PubMed 
Article 

Google Scholar 
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 
Article 

Google Scholar 
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 
PubMed 
Article 

Google Scholar 
Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. https://doi.org/10.1101/gr.210641.116 (2016).Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaser, R. & Šikić, M. Yet another de novo genome assembler. BioRxiv. https://doi.org/10.1101/656306 (2019).Liu, H. et al. SMARTdenovo: a de novo assembler using long noisy reads. Gigabyte 2021, 1–9 (2021).Article 

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

Google Scholar 
Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).CAS 
PubMed 
Article 

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

Google Scholar 
Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res 27, 737–746 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aury, J.-M. & Istace, B. Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads. NAR Genomics Bioinform. 3, lqab034 (2021).Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morgulis, A., Gertz, E. M., Schäffer, A. A. & Agarwala, R. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J. Comput Biol. 13, 1028–1040 (2006).CAS 
PubMed 
Article 

Google Scholar 
Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker. http://repeatmasker.org/ (2013).Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).CAS 
PubMed 
Article 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pedersen, B. S. & Quinlan, A. R. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34, 867–868 (2018).CAS 
PubMed 
Article 

Google Scholar 
Schulz, M. H., Zerbino, D. R., Vingron, M. & Birney, E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 28, 1086–1092 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Marchler-Bauer, A. et al. CDD: NCBI’s conserved domain database. Nucleic Acids Res. 43, D222–D226 (2015).CAS 
PubMed 
Article 

Google Scholar 
Niang, G. et al. METdb: A genomic reference database for marine species. F1000Research, https://doi.org/10.7490/f1000research.1118000.1 (2020).Kent, W. J. BLAT–the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

Google Scholar 
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dubarry, M. et al. Gmove a tool for eukaryotic gene predictions using various evidences. F1000Research, https://doi.org/10.7490/f1000research.1111735.1 (2016).Sibbald, S. J., Lawton, M. & Archibald, J. M. Mitochondrial genome evolution in pelagophyte algae. Genome Biol. Evol. 13, evab018 (2021).Quevillon, E. et al. InterProScan: protein domains identifier. Nucleic Acids Res. 33, W116–W120 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).CAS 
PubMed 
Article 

Google Scholar 
Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages abundant in the sunlit ocean. Cell Genomics 2, 100123 (2022).CAS 
Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
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 
Article 

Google Scholar 
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geoscientific Model Dev. 8, 2465–2513 (2015).CAS 
Article 

Google Scholar 
Clayton, S. et al. Biogeochemical versus ecological consequences of modeled ocean physics. Biogeosciences 14, 2877–2889 (2017).CAS 
Article 

Google Scholar 
Ravindra, K., Rattan, P., Mor, S. & Aggarwal, A. N. Generalized additive models: building evidence of air pollution, climate change and human health. Environ. Int. 132, 104987 (2019).CAS 
PubMed 
Article 

Google Scholar 
Günther, F. & Fritsch, S. neuralnet: training of neural networks. R. J. 2, 30–38 (2010).Article 

Google Scholar 
Gobler, C. J. et al. Niche of harmful alga Aureococcus anophagefferens revealed through ecogenomics. Proc. Natl Acad. Sci. USA 108, 4352–4357 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guo, L. et al. Genome assembly of Nannochloropsis oceanica provides evidence of host nucleus overthrow by the symbiont nucleus during speciation. Commun. Biol. 2, 1–12 (2019).CAS 
Article 

Google Scholar 
Bowler, C. et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456, 239–244 (2008).CAS 
PubMed 
Article 

Google Scholar 
Armbrust, E. V. et al. The genome of the diatom thalassiosira pseudonana: ecology, evolution, and metabolism. Science 306, 79–86 (2004).CAS 
PubMed 
Article 

Google Scholar 
Worden, A. Z. et al. Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes micromonas. Science 324, 268–272 (2009).CAS 
PubMed 
Article 

Google Scholar 
Palenik, B. et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. PNAS 104, 7705–7710 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moreau, H. et al. Gene functionalities and genome structure in Bathycoccus prasinos reflect cellular specializations at the base of the green lineage. Genome Biol. 13, R74 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Read, B. A. et al. Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature 499, 209–213 (2013).CAS 
PubMed 
Article 

Google Scholar  More

We confirm that Cuba is home to the world’s largest European honeybee population that has naturally become Varroa-resistant, with an estimated 220,000 colonies being maintained without any form of chemical treatment for over two decades19 although some drone-trapping occurred during the early years of the transition period This is despite the presence of the K-haplotype of the mite20 and the widespread occurrence of DWV19 throughout Cuba. Hence, the Cuban honeybee population is the first major case of Varroa-resistant European bees occupying an entire country of a large size (109,884 km2). In Europe the proportion of varroa-resistant honeybee populations in each country is highly variable21,22, but they still consist of small, isolated populations within any country. For example, the second largest known area of European Varroa-resistant honeybees is in North Wales, UK where 104 beekeepers have managed around 500 honey bee colonies over an area of 2500 km2 without treatment for over a decade23.It has long been established that sub-Sharan African and Africanised honeybees are Varroa-resistant and both populations cover much larger areas than Cuba, but these honeybee races are not capable of thriving in temperate regions or are rejected by beekeepers in Northern hemispheres. However, previous studies on African/Africanised and European honeybees4,5,6,9 all appear to have evolved with the same resistance mechanism7 and Cuban honeybees follow this pattern showing high recapping behaviour, high mite removal behaviour and low mite reproduction (Figs. 1, 4, Table 1).The strongest evidence that increased recapping behaviour is a direct response to the presence of Varroa, is the very low recapping rates in Varroa-naïve colonies. This is evidenced by the recapping baseline data that has now been collected from four different Varroa-naïve (Varroa free) honeybee populations (Australia, UK [two populations] and Hawaii [this study]) all producing similar results (Fig. 1). Across the four populations, a total of 9542 worker cells from 15 colonies have been studied with an average recapping rate of 2.0% (+ SD 3.2). Interestingly, only two of the colonies had atypical recapping rates of 8.5% and 10.7%, from Australia and Kauai respectively. This may suggest increased sensitivity in these colonies as no obvious causes e.g., wax moth or dead pupa, were detected in either colony. The data summary in Fig. 1 indicates that even in Varroa-treated populations the workers are still able to detect mite infested cells, but the average consistently falls significantly below that found in resistant populations. That is, in non-infested worker cells recapping rates are significantly higher in resistant populations in comparison to susceptible populations (Fig. 1) t4, 5 = − 4.185, p = 0.0023 as well as for infested cells t4, 5 = − 6.905, p = 0.00007.The ability of Cuban honeybees to detect infested cells causes not only high recapping levels but also high removal rates of artificially mite-infested cells. A mean removal rate of 81% is among one of the highest recorded in Apis mellifera7. The average control rate of 45% is driven by three colonies that all removed more than 75% of the controls, while the average of the remaining seven colonies was 28%. During the mite-removal studies in March 2022 natural Varroa infestation was 23%, whereas in December 2021 it was only 13%. This is due to decreasing worker brood rearing, caused by a shortage of nectar during the annual dry season. During this time there is an increase in hygienic behaviour in the colonies24, which could help explain the higher-than-expected removal of control cells.The reproductive ability of Varroa to produce viable i.e., mated, female offspring (r) in infested worker cells in resistant colonies in South Africa4 (r = 0.9), Brazil4 (r = 0.8), Mexico18 (r = 0.73), Europe3 (r = 0.84) is similar to the 0.87 found in Cuba (this study). In Cuba ‘r’ reduces to 0.77 when both single and multiple infested cells are considered. This reduction in mite reproduction, relative to susceptible colonies that have values of r greater than one, is directly linked to the increased ability of resistant workers to both detect and remove, by cannibalisation, the infested pupa. Hence, this ensures the invading mite fails to reproduce7 or reduces mite fertility due to the recapping process4. Although, in this study no significant difference was found in the reproduction of Varroa in recapped or non-recapped cells, supporting the findings of two previous studies5,9. Therefore, recapping may be playing a minor role in resistance. However, recapping remains the best indicator or ‘proxy’ of resistance within the vast majority of honeybee populations since it’s easier, quicker, and it requires less skill to measure recapping rates than mite removal rates. However, recapping is a highly variable trait7, hence both many cells (200–300) per colony and many colonies ( > 10) per population ideally need to be studied to help reduce the variablity, also in temperate countries measuring recapping when mite-infestation rates peak in autumn maximises detecting infested cells since the recapping of cells is spatially associated with infested cells11.Despite the current focus on what is happening in worker cells, studies focusing on the role of recapping in drone brood are still in their infancy with. Currently, data is only available from South Africa9 (Fig. 1) and now Cuba (this study). Interestingly, both studies indicate no significant difference in recapping rates between infested and non-infested brood. This is caused by some colonies performing no recapping of drone brood, while some colonies do recap cells but in a non-targeted manner. Whereas there is a significant increase in the size of the recapped area between infested (3.1 mm) and non-infested (2.3 mm) worker cells (Fig. 3), this does not occur in drone brood, as it appears that the holes are entirely exploratory. However, the lack of removal of infested drone brood may be playing an important role in mite-resistance (see below).The mite infestation of worker cells currently varies between 23 and 13% in Cuba (this study), roughly 25 years after it was first detected (1996). Whereas, in Mexico and Brazil, infestation rates of worker brood have fallen from around 20% in 1996/1999 down to 4% in 2018/197. Although, Varroa was first detected in Brazil much earlier, in 197225 and the Africanised honeybees adapted to the mite and spread northward replacing the susceptible European colonies. Therefore, we predict that the worker infestation rate in Cuba will continue to fall over the next 20 years, especially if high mite-removal rates persist. Correspondingly, we would expect to see the infestation rates of the drone brood (currently at 40%) to remain high as mites potentially avoid reproduction in worker cells. This potentially is a key, but currently overlooked part, of the resistance mechanism. Since an empirical model26 indicated that negative mite population growth occurs in (resistant) Africanised honeybee colonies only when the initial drone cells are present. This is thought to arise because mites also show a tenfold preference to reproduce in drone cells (which comprises only 1–5% of all the honeybee brood) and they soon become overcrowded as the mite population increases. This leads to inter-mite competition for the limited food and space, causing an increase in mite mortality27, resulting in negative reproductive success for mites entering these overcrowded drone cells. Thus, mite population growth in drone brood cells is limited by a density-dependent mechanism. In Cuba it has been observed that strong colonies typically with drone brood do not weaken during the drought season, whereas colonies without drone brood are weak and often die during the drought (APP personal comm).Although Cuban beekeepers have been aware of their mite-resistant honeybees for 15 to 20 years’, Cuba’s situation has only recently come to light16,18. The main reason for Varroa-resistance in Cuba is due to the centralised decision to allow natural resistance to evolve, as also was done successfully in South Africa3, rather than becoming locked into using miticides, as has happened throughout the Northern hemisphere. The CIAPI and Veterinarian Services central decision to ‘not treat’ was greatly assisted by all Cuban beekeepers being professional, registered and embedded within a strong locally based beekeeping community where colony movement and exchange of queens is within each province.There is also a large feral population and due to Cuba’s sub-tropical climate, queens are replaced annually in managed colonies because of almost continuous egg-laying, similar to honeybees in Hawaii. This rapid queen turnover speeds up natural selection relative to honeybee populations in more temperate climates. Finally, Cuba’s 60-year ban on honeybee importation has helped isolate the country from been invaded by Africanised bees which has occurred in many nearby regions (eg. Mexico, Southern USA, Puerto Rico, neighbouring Dominican Republic13 and Haiti (D. Macdonald, Apiary Inspector, Min. of Agi BC, Canada, pers. Comm.). Cuba has many managed European colonies coupled with many queen rearing stations. These colonies are productive and mild mannered. Thus, Cuba is an excellent example of the power of natural selection in honeybees when they are allowed to adapt naturally to Varroa with minimal human interference. More

Coffaro, G. & Bocci, M. Resources competition between Ulva rigida and Zostera marina: A quantitative approach applied to the Lagoon of Venice. Ecol. Model. 102(1), 81–95 (1997).CAS 
Article 

Google Scholar 
Araújo, C. V. et al. Feeding niche preference of the mudsnail Peringia ulvae. Mar. Freshw. Res. 66(7), 573–581 (2015).Article 

Google Scholar 
Whitfield, A. K. The role of seagrass meadows, mangrove forests, salt marshes and reed beds as nursery areas and food sources for fishes in estuaries. Rev. Fish Biol. Fish. 27(1), 75–110 (2017).Article 

Google Scholar 
Su, L. et al. The occurrence of microplastic in specific organs in commercially caught fishes from coast and estuary area of east China. J. Hazard. Mater. 365, 716–724 (2019).CAS 
PubMed 
Article 

Google Scholar 
Benassai, G. Introduction to Coastal Dynamics and Shoreline Protection (Wit Press, 2006).
Google Scholar 
Decho, A. W. Microbial biofilms in intertidal systems: An overview. Cont. Shelf Res. 20(10–11), 1257–1273 (2000).ADS 
Article 

Google Scholar 
Thompson, C. E., Amos, C. L. & Umgiesser, G. A comparison between fluid shear stress reduction by halophytic plants in Venice Lagoon, Italy and Rustico Bay, Canada—Analyses of in situ measurements. J. Mar. Syst. 51(1–4), 293–308 (2004).Article 

Google Scholar 
Neumeier, U. & Amos, C. L. Turbulence reduction by the canopy of coastal Spartina salt-marshes. J. Coast. Res. 53, 433–439 (2006).
Google Scholar 
Black, K. S., Tolhurst, T. J., Paterson, D. M. & Hagerthey, S. E. Working with natural cohesive sediments. J. Hydraul. Eng. 128(1), 2–8 (2002).Article 

Google Scholar 
Paterson, D. M. Short-term changes in the erodibility of intertidal cohesive sediments related to the migratory behavior of epipelic diatoms. Limnol. Oceanogr. 34(1), 223–234 (1989).ADS 
Article 

Google Scholar 
Tolhurst, T.J., Jesus, B., Brotas, V. & Paterson, D.M. Diatom migration and sediment armouring—An example from the Tagus Estuary, Portugal. in Migrations and Dispersal of Marine Organisms. 183–193. (Springer, 2003).Tinoco, R. O. & Coco, G. Observations of the effect of emergent vegetation on sediment resuspension under unidirectional currents and waves. Earth Surf. Dyn. 2(1), 83 (2014).ADS 
Article 

Google Scholar 
Chen, Y. et al. Differential sediment trapping abilities of mangrove and saltmarsh vegetation in a subtropical estuary. Geomorphology 318, 270–282 (2018).ADS 
Article 

Google Scholar 
Cozzolino, L., Nicastro, K. R., Zardi, G. I. & Carmen, B. Species-specific plastic accumulation in the sediment and canopy of coastal vegetated habitats. Sci. Total Environ. 723, 138018 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Widdows, J., Pope, N. D. & Brinsley, M. D. Effect of Spartina anglica stems on near-bed hydrodynamics, sediment erodability and morphological changes on an intertidal mudflat. Mar. Ecol. Prog. Ser. 362, 45–57 (2008).ADS 
Article 

Google Scholar 
Marion, C., Anthony, E. J. & Trentesaux, A. Short-term (≤ 2 yrs) estuarine mudflat and saltmarsh sedimentation: High-resolution data from ultrasonic altimetery, rod surface-elevation table, and filter traps. Estuar. Coast. Shelf Sci. 83(4), 475–484 (2009).ADS 
Article 

Google Scholar 
Coulombier, T., Neumeier, U. & Bernatchez, P. Sediment transport in a cold climate salt marsh (St. Lawrence Estuary, Canada), the importance of vegetation and waves. Estuar. Coast. Shelf Sci. 101, 64–75 (2012).ADS 
Article 

Google Scholar 
Neumeier, U. & Ciavola, P. Flow resistance and associated sedimentary processes in a Spartina maritima salt-marsh. J. Coast. Res. 20(2), 435–447 (2002).
Google Scholar 
Yao, W. et al. Micro-and macroplastic accumulation in a newly formed Spartina alterniflora colonized estuarine saltmarsh in southeast China. Mar. Pollut. Bull. 149, 110636 (2019).CAS 
Article 

Google Scholar 
Fok, L. & Cheung, P. K. Hong Kong at the Pearl River Estuary: A hotspot of microplastic pollution. Mar. Pollut. Bull. 99(1–2), 112–118 (2015).CAS 
PubMed 
Article 

Google Scholar 
Weinstein, J. E., Crocker, B. K. & Gray, A. D. From macroplastic to microplastic: Degradation of high-density polyethylene, polypropylene, and polystyrene in a salt marsh habitat. Environ. Toxicol. Chem. 35(7), 1632–1640 (2016).CAS 
PubMed 
Article 

Google Scholar 
Willis, K. A., Eriksen, R., Wilcox, C. & Hardesty, B. D. Microplastic distribution at different sediment depths in an urban estuary. Front. Mar. Sci. 4, 419 (2017).Article 

Google Scholar 
Stead, J. L. et al. Identification of tidal trapping of microplastics in a temperate salt marsh system using sea surface microlayer sampling. Sci. Rep. 10(1), 1–10 (2020).Article 
CAS 

Google Scholar 
Friend, P. L., Ciavola, P., Cappucci, S. & Santos, R. Bio-dependent bed parameters as a proxy tool for sediment stability in mixed habitat intertidal areas. Cont. Shelf Res. 23(17–19), 1899–1917 (2003).ADS 
Article 

Google Scholar 
Hurley, R., Woodward, J. & Rothwell, J. J. Microplastic contamination of river beds significantly reduced by catchment-wide flooding. Nat. Geosci. 11(4), 251–257 (2018).ADS 
CAS 
Article 

Google Scholar 
Ockelford, A., Cundy, A. & Ebdon, J. E. Storm response of fluvial sedimentary microplastics. Sci. Rep. 10(1), 1–10 (2020).Article 
CAS 

Google Scholar 
Wang, J. Q. et al. Bioturbation of burrowing crabs promotes sediment turnover and carbon and nitrogen movements in an estuarine salt marsh. Ecosystems 13(4), 586–599 (2010).CAS 
Article 

Google Scholar 
Soulsby, R.L.. The bottom boundary layer of shelf seas. in Elsevier Oceanography Series. Vol. 35. 189–266. (Elsevier, 1983).Thompson, C. E., Amos, C. L., Lecouturier, M. & Jones, T. E. R. Flow deceleration as a method of determining drag coefficient over roughened flat beds. J. Geophys. Res. Oceans 109, C3 (2004).
Google Scholar 
Chirol, C. et al. The influence of bed roughness on turbulence: Cabras Lagoon, Sardinia, Italy. J. Mar. Sci. Eng. 3(3), 935–956 (2015).Article 

Google Scholar 
Kassem, H., Sutherland, T. F. & Amos, C. L. Hydrodynamic controls on the particle size of resuspended sediment from sandy and muddy substrates in British Columbia, Canada. J. Coast. Res. 37, 691 (2021).CAS 
Article 

Google Scholar 
Nepf, H. M. Flow and transport in regions with aquatic vegetation. Annu. Rev. Fluid Mech. 44, 123–142 (2012).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Bouma, T. J. et al. Density-dependent linkage of scale-dependent feedbacks: A flume study on the intertidal macrophyte Spartina anglica. Oikos 118(2), 260–268 (2009).Article 

Google Scholar 
Amos, C. L. et al. The stability of tidal flats in Venice Lagoon—The results of in-situ measurements using two benthic, annular flumes. J. Mar. Syst. 51(1–4), 211–241 (2004).Article 

Google Scholar 
Amos, C. L., Feeney, T., Sutherland, T. F. & Luternauer, J. L. The stability of fine-grained sediments from the Fraser River Delta. Estuar. Coast. Shelf Sci. 45(4), 507–524 (1997).ADS 
CAS 
Article 

Google Scholar 
Tolhurst, T.J., Gust, G., & Paterson, D.M. The influence of an extracellular polymeric substance (EPS) on cohesive sediment stability. in Proceedings in Marine Science. Vol. 5. 409–425. (Elsevier, 2002).Brückner, M. Z. et al. Benthic species as mud patrol-modelled effects of bioturbators and biofilms on large-scale estuarine mud and morphology. Earth Surf. Proc. Land. 46(6), 1128–1144 (2021).ADS 
Article 

Google Scholar 
Ferdowsi, B., Ortiz, C. P., Houssais, M. & Jerolmack, D. J. River-bed armouring as a granular segregation phenomenon. Nat. Commun. 8(1), 1–10 (2017).CAS 
Article 

Google Scholar 
Andersen, T. J., Jensen, K. T., Lund-Hansen, L., Mouritsen, K. N. & Pejrup, M. Enhanced erodibility of fine-grained marine sediments by Hydrobia ulvae. J. Sea Res. 48(1), 51–58 (2002).ADS 
Article 

Google Scholar 
Orvain, F., Sauriau, P. G., Sygut, A., Joassard, L. & Le Hir, P. Interacting effects of Hydrobia ulvae bioturbation and microphytobenthos on the erodibility of mudflat sediments. Mar. Ecol. Prog. Ser. 278, 205–223 (2004).ADS 
Article 

Google Scholar 
Orvain, F., Sauriau, P. G., Bacher, C. & Prineau, M. The influence of sediment cohesiveness on bioturbation effects due to Hydrobia ulvae on the initial erosion of intertidal sediments: A study combining flume and model approaches. J. Sea Res. 55(1), 54–73 (2006).ADS 
Article 

Google Scholar 
Widdows, J. et al. Inter-comparison between five devices for determining erodability of intertidal sediments. Cont. Shelf Res. 27(8), 1174–1189 (2007).ADS 
Article 

Google Scholar 
Amos, C. L. et al. The stability of a mudflat in the Humber estuary, South Yorkshire, UK. Geol. Soc. Lond. Spec. Publ. 139(1), 25–43 (1998).ADS 
Article 

Google Scholar 
Tolhurst, T. J., Black, K. S. & Paterson, D. M. Muddy sediment erosion: Insights from field studies. J. Hydraul. Eng. 135(2), 73–87 (2009).Article 

Google Scholar 
Quaresma, V. D. S., Bastos, A. C. & Amos, C. L. Sedimentary processes over an intertidal flat: A field investigation at Hythe flats, Southampton Water (UK). Mar. Geol. 241(1–4), 117–136 (2007).ADS 
Article 

Google Scholar 
Helcoski, R., Yonkos, L. T., Sanchez, A. & Baldwin, A. H. Wetland soil microplastics are negatively related to vegetation cover and stem density. Environ. Pollut. 256, 113391 (2020).CAS 
PubMed 
Article 

Google Scholar 
Rochman, C. M. et al. Classify plastic waste as hazardous. Nature 494(7436), 169–171 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Barboza, L. G. A., Vethaak, A. D., Lavorante, B. R., Lundebye, A. K. & Guilhermino, L. Marine microplastic debris: An emerging issue for food security, food safety and human health. Mar. Pollut. Bull. 133, 336–348 (2018).CAS 
PubMed 
Article 

Google Scholar 
de Barros, M. S. F., dos Santos Calado, T. C., Silva, A. S. & dos Santos, E. V. Ingestion of plastic debris affects feeding intensity in the rocky shore crab Pachygrapsus transversus Gibbes 1850 (Brachyura: Grapsidae). Int. J. Biodivers. Conserv. 12(1), 113–117 (2020).
Google Scholar 
Villagran, D. M., Truchet, D. M., Buzzi, N. S., Lopez, A. D. F. & Severini, M. D. F. A baseline study of microplastics in the burrowing crab (Neohelice granulata) from a temperate southwestern Atlantic estuary. Mar. Pollut. Bull. 150, 110686 (2020).CAS 
PubMed 
Article 

Google Scholar 
Townend, I. A Conceptual Model of Southampton Water. Vol 1. (Tech. Rep.). ABPmer report.. http://www.estuary-guide.net/pdfs/southampton_water_case_study.pdf. Accessed 21 May 2008 (ABP Marine Environmental Research Ltd., 2008).Amos, C. L., Grant, J., Daborn, G. R. & Black, K. Sea carousel—A benthic, annular flume. Estuar. Coast. Shelf Sci. 34(6), 557–577 (1992).ADS 
Article 

Google Scholar 
Thompson, C. E., Amos, C. L., Jones, T. E. R. & Chaplin, J. The manifestation of fluid-transmitted bed shear stress in a smooth annular flume-a comparison of methods. J. Coast. Res. 1, 1094–1103 (2003).
Google Scholar 
Buls, T., Anderskouv, K., Friend, P. L., Thompson, C. E. & Stemmerik, L. Physical behaviour of Cretaceous calcareous nannofossil ooze: Insight from flume studies of disaggregated chalk. Sedimentology 64(2), 478–507 (2017).Article 

Google Scholar 
Tuprakay, S., Usahanunth, N. & Tuprakay, S. R. A study bakelite plastics waste from industrial process in concrete products as aggregate. Int. J. Struct. Civ. Eng. Res. 6(4), 7 (2017).
Google Scholar 
Thompson, C. E. L., Couceiro, F., Fones, G. R. & Amos, C. L. Shipboard measurements of sediment stability using a small annular flume—Core mini flume (CMF). Limnol. Oceanogr. Methods 11(11), 604–615 (2013).Article 

Google Scholar 
Kassem, H., Thompson, C. E., Amos, C. L. & Townend, I. H. Wave-induced coherent turbulence structures and sediment resuspension in the nearshore of a prototype-scale sandy barrier beach. Cont. Shelf Res. 109, 78–94 (2015).ADS 
Article 

Google Scholar 
Kassem, H. et al. Observations of nearbed turbulence over mobile bedforms in combined, collinear wave-current flows. Water 12(12), 3515 (2020).CAS 
Article 

Google Scholar 
Elgar, S., Raubenheimer, B. & Guza, R. T. Quality control of acoustic Doppler velocimeter data in the surfzone. Meas. Sci. Technol. 16(10), 1889 (2005).ADS 
CAS 
Article 

Google Scholar 
Goring, D. G. & Nikora, V. I. Despiking acoustic Doppler velocimeter data. J. Hydraul. Eng. 128(1), 117–126 (2002).Article 

Google Scholar 
Mori, N., Suzuki, T. & Kakuno, S. Noise of acoustic Doppler velocimeter data in bubbly flows. J. Eng. Mech. 133(1), 122–125 (2007).
Google Scholar 
Stapleton, K. R. & Huntley, D. A. Seabed stress determinations using the inertial dissipation method and the turbulent kinetic energy method. Earth Surf. Proc. Land. 20(9), 807–815 (1995).ADS 
Article 

Google Scholar 
Dyer, K. Estuaries, A Physical Introduction. 2nd edn. https://doi.org/10.2307/1797104 (Wiley, 1997). More

von Wintersdorff, C. J. et al. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front. Microbiol. 7, 173 (2016).
Google Scholar 
Suay-García, B. & Pérez-Gracia, M. T. Present and future of carbapenem-resistant Enterobacteriaceae (CRE) infections. Antibiotics 8, 122 (2019).PubMed Central 
Article 
CAS 

Google Scholar 
Blair, J. M., Webber, M. A., Baylay, A. J., Ogbolu, D. O. & Piddock, L. J. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13, 42–51 (2015).CAS 
PubMed 
Article 

Google Scholar 
Codjoe, F. S. & Donkor, E. S. Carbapenem resistance: a review. Med Sci. 6, 1 (2017).
Google Scholar 
Schechner, V. et al. Asymptomatic rectal carriage of blaKPC producing carbapenem-resistant Enterobacteriaceae: who is prone to become clinically infected? Clin. Microbiol. Infect. 19, 451–456 (2013).CAS 
PubMed 
Article 

Google Scholar 
Penders, J., Stobberingh, E. E., Savelkoul, P. H. & Wolffs, P. F. The human microbiome as a reservoir of antimicrobial resistance. Front Microbiol. 4, 87 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Nordmann, P., Naas, T. & Poirel, L. Global spread of Carbapenemase-producing Enterobacteriaceae. Emerg. Infect. Dis. 17, 1791–1798 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tooke, C. L. et al. β-Lactamases and β-lactamase inhibitors in the 21st century. J. Mol. Biol. 431, 3472–3500 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sun, X. et al. Microbiota-derived metabolic factors reduce campylobacteriosis in mice. Gastroenterology 154, 1751–1763.e2 (2018).CAS 
PubMed 
Article 

Google Scholar 
Ichinohe, T. et al. Microbiota regulates immune defense against respiratory tract influenza A virus infection. Proc. Natl Acad. Sci. USA 108, 5354–5359 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).CAS 
PubMed 
Article 

Google Scholar 
Lieberman, T. D. et al. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nat. Genet. 43, 1275–1280 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
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).PubMed 
PubMed Central 
Article 

Google Scholar 
Chu, N. D., Smith, M. B., Perrotta, A. R., Kassam, Z. & Alm, E. J. Profiling living bacteria informs preparation of fecal microbiota transplantations. PLoS ONE 12, e0170922 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ferreiro, A., Crook, N., Gasparrini, A. J. & Dantas, G. Multiscale evolutionary dynamics of host-associated microbiomes. Cell 172, 1216–1227 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mo, Y. et al. Duration of carbapenemase-producing Enterobacteriaceae carriage in hospital patients. Emerg. Infect. Dis. 26, 2182–2185 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haverkate, M. R. et al. Duration of colonization with Klebsiella pneumoniae carbapenemase-producing bacteria at long-term acute care hospitals in Chicago, Illinois. Open Forum Infect. Dis. 3, ofw178 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Korach-Rechtman, H. et al. Intestinal dysbiosis in carriers of carbapenem-resistant Enterobacteriaceae. mSphere 5, e00173–20 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yoshida, N. et al. Bacteroides vulgatus and Bacteroides dorei reduce gut microbial lipopolysaccharide production and inhibit atherosclerosis. Circulation 138, 2486–2498 (2018).CAS 
PubMed 
Article 

Google Scholar 
Lenoir, M. et al. Butyrate mediates anti-inflammatory effects of. Gut Microbes 12, 1–16 (2020).PubMed 
Article 
CAS 

Google Scholar 
Riedel, C. U. et al. Anti-inflammatory effects of bifidobacteria by inhibition of LPS-induced NF-κB activation. World J. Gastroenterol. 12, 3729–3735 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
Zeng, M. Y., Inohara, N. & Nuñez, G. Mechanisms of inflammation-driven bacterial dysbiosis in the gut. Mucosal Immunol. 10, 18–26 (2017).CAS 
PubMed 
Article 

Google Scholar 
Winter, S. E. & Bäumler, A. J. A breathtaking feat: to compete with the gut microbiota, Salmonella drives its host to provide a respiratory electron acceptor. Gut Microbes 2, 58–60 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Rivera-Chávez, F., Lopez, C. A. & Bäumler, A. J. Oxygen as a driver of gut dysbiosis. Free Radic. Biol. Med. 105, 93–101 (2017).PubMed 
Article 
CAS 

Google Scholar 
Chng, K. R. et al. Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut. Nat. Ecol. Evol. 4, 1256–1267 (2020).PubMed 
Article 

Google Scholar 
Tenaillon, O., Skurnik, D., Picard, B. & Denamur, E. The population genetics of commensal Escherichia coli. Nat. Rev. Microbiol. 8, 207–217 (2010).CAS 
PubMed 
Article 

Google Scholar 
Stacy, A. et al. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell 184, 615–627.e17 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barreto, H. C., Sousa, A. & Gordo, I. The landscape of adaptive evolution of a gut commensal bacteria in aging mice. Curr. Biol. 30, 1102–1109.e5 (2020).CAS 
PubMed 
Article 

Google Scholar 
Ernst, C. M. et al. Adaptive evolution of virulence and persistence in carbapenem-resistant Klebsiella pneumoniae. Nat. Med. 26, 705–711 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao, S. et al. Adaptive evolution within gut microbiomes of healthy people. Cell Host Microbe 25, 656–667.e8 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Warsi, O. M., Andersson, D. I. & Dykhuizen, D. E. Different adaptive strategies in E. coli populations evolving under macronutrient limitation and metal ion limitation. BMC Evol. Biol. 18, 72 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hickman, R. A., Munck, C. & Sommer, M. O. A. Time-resolved tracking of mutations reveals diverse allele dynamics during Escherichia coli antimicrobial adaptive evolution to single drugs and drug pairs. Front. Microbiol. 8, 893 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Auriol, C., Bestel-Corre, G., Claude, J. B., Soucaille, P. & Meynial-Salles, I. Stress-induced evolution of Escherichia coli points to original concepts in respiratory cofactor selectivity. Proc. Natl Acad. Sci. USA 108, 1278–1283 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Juers, D. H., Matthews, B. W. & Huber, R. E. LacZ β-galactosidase: structure and function of an enzyme of historical and molecular biological importance. Protein Sci. 21, 1792–1807 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rogers, A. W. L., Tsolis, R. M. & Bäumler, A. J. Salmonella versus the microbiome. Microbiol. Mol. Biol. Rev. 85, e00027–19 (2021).CAS 
PubMed 
Article 

Google Scholar 
Hughes, E. R. et al. Microbial respiration and formate oxidation as metabolic signatures of inflammation-associated dysbiosis. Cell Host Microbe 21, 208–219 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gupta, S., Allen-Vercoe, E. & Petrof, E. O. Fecal microbiota transplantation: in perspective. Ther. Adv. Gastroenterol. 9, 229–239 (2016).Article 

Google Scholar 
Wortelboer, K., Nieuwdorp, M. & Herrema, H. Fecal microbiota transplantation beyond Clostridioides difficile infections. EBioMedicine 44, 716–729 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Lee, S. M. et al. Bacterial colonization factors control specificity and stability of the gut microbiota. Nature 501, 426–429 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martinson, J. N. V. et al. Rethinking gut microbiome residency and the Enterobacteriaceae in healthy human adults. ISME J. 13, 2306–2318 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Woyke, T., Doud, D. F. R. & Schulz, F. The trajectory of microbial single-cell sequencing. Nat. Methods 14, 1045–1054 (2017).CAS 
PubMed 
Article 

Google Scholar 
Domingo, E. & Perales, C. Viral quasispecies. PLoS Genet. 15, e1008271 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yamada, C. et al. Molecular insight into evolution of symbiosis between breast-fed infants and a member of the human gut microbiome Bifidobacterium longum. Cell Chem. Biol. 24, 515–524.e5 (2017).CAS 
PubMed 
Article 

Google Scholar 
Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gao, S., Bertrand, D., Chia, B. K. & Nagarajan, N. OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees. Genome Biol. 17, 102 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gao, S., Bertrand, D. & Nagarajan, N. FinIS: improved in silico finishing using an exact quadratic programming formulation. Lect. Notes Comput. Sci. 7534, 314–325 (2012).Article 

Google Scholar 
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv 1303.3997v2 (2013).Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Hawinkel, S., Mattiello, F., Bijnens, L. & Thas, O. A broken promise: microbiome differential abundance methods do not control the false discovery rate. Brief. Bioinformatics 20, 210–221 (2019).PubMed 
Article 

Google Scholar 
Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Inouye, M. et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 6, 90 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Alcock, B. P. et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 48, D517–D525 (2020).CAS 
PubMed 
Article 

Google Scholar 
Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
Wilm, A. et al. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res. 40, 11189–11201 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hinrichs, A. S. et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–D598 (2006).CAS 
PubMed 
Article 

Google Scholar 
Pracana, R., Priyam, A., Levantis, I., Nichols, R. A. & Wurm, Y. The fire ant social chromosome supergene variant Sb shows low diversity but high divergence from SB. Mol. Ecol. 26, 2864–2879 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Quinlan, A. R. BEDTools: the Swiss-Army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.1–34 (2014).Article 

Google Scholar 
Spedicato, G. Discrete time Markov chains with R. R J. 9.2, 84 (2017).Article 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hahsler, M., Piekenbrock, M. & Doran, D. dbscan: fast density-based clustering with R. J. Stat. Softw. 91, 1–30 (2019).Article 

Google Scholar 
Galata, V., Fehlmann, T., Backes, C. & Keller, A. PLSDB: a resource of complete bacterial plasmids. Nucleic Acids Res. 47, D195–D202 (2019).CAS 
PubMed 
Article 

Google Scholar 
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Quan, S. et al. Adaptive evolution of the lactose utilization network in experimentally evolved populations of Escherichia coli. PLoS Genet. 8, e1002444 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tsuchido, T., VanBogelen, R. A. & Neidhardt, F. C. Heat shock response in Escherichia coli influences cell division. Proc. Natl Acad. Sci. USA 83, 6959–6963 (1986).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Trubetskoy, D., Proux, F., Allemand, F., Dreyfus, M. & Iost, I. SrmB, a DEAD-box helicase involved in Escherichia coli ribosome assembly, is specifically targeted to 23S rRNA in vivo. Nucleic Acids Res. 37, 6540–6549 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Garoff, L., Huseby, D. L., Praski Alzrigat, L. & Hughes, D. Effect of aminoacyl-tRNA synthetase mutations on susceptibility to ciprofloxacin in Escherichia coli. J. Antimicrob. Chemother. 73, 3285–3292 (2018).CAS 
PubMed 
Article 

Google Scholar 
Aponte, R. A., Zimmermann, S. & Reinstein, J. Directed evolution of the DnaK chaperone: mutations in the lid domain result in enhanced chaperone activity. J. Mol. Biol. 399, 154–167 (2010).CAS 
PubMed 
Article 

Google Scholar 
Mundhada, H. et al. Increased production of l-serine in Escherichia coli through adaptive laboratory evolution. Metab. Eng. 39, 141–150 (2017).CAS 
PubMed 
Article 

Google Scholar 
Conrad, T. M. et al. RNA polymerase mutants found through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media. Proc. Natl Acad. Sci. USA 107, 20500–20505 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, Y. et al. LPS remodeling is an evolved survival strategy for bacteria. Proc. Natl Acad. Sci. USA 109, 8716–8721 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Iron and carbon dependent mutualism between Chlorella sorokiniana and Ralstonia pickettii forms a synthetic phototrophic communityThe synthetic microalgal-bacterial community based on the active exchange of iron and carbon was developed by screening multiple siderophore producer bacteria and dye decolorizer algae (Fig. 1; refer to Supplementary Data S1 for detailed results). Out of seven bacterial isolates obtained from untreated textile wastewater, five showed relatively high siderophore production in CAS agar plates and broth (Fig. S1). In broth, Serratia plymuthica PW1, Serratia liquefaciens PW71, and Ralstonia pickettii PW2 produced siderophores in decreasing order of concentration, i.e., 15.26 ± 1.3  > 13.28 ± 0.9  > 10.85 ± 0.7 µMmL−1 (Table 1). Arnow’s assay confirmed that S. plymuthica PW1 (81.10 ± 9.8 µMmL−1), R. pickettii PW2 (97.43 ± 16.8 µMmL−1), and S. liquefaciens PW71 (103.1 ± 8.3 µMmL−1) produced catecholate-type siderophores. On the other hand, Csaky’s assay confirmed that Stenotrophomonas maltophilia PW5 (37.86 ± 0.4 µMmL−1) and Stenotrophomonas maltophilia PW6 (17.73 ± 0.2 µMmL−1) produced hydroxamate-type of siderophores. Out of the five algal species, only freshwater microalgae Chlorella sorokiniana and Scenedesmus sp. showed the highest dye degradation potential; therefore, they were selected for further experiments (Data S1).Fig. 1: The study design explains different stages of experiments to develop a phototrophic community of previously non-associated algae and bacteria.The stages include (A) isolation of bacterial strains from textile wastewater collected from Panipat Industrial area, Haryana (India); B cultivation of freshwater and marine algal strains; C assessment of siderophore production in bacterial strains using Schwyn and Neilands’s universal Chrome Azurol S (CAS) assay; D assessment of dye degradation potential of algae strains using Acid Black 1 (AB1) dye; E interaction study between siderophore producing bacteria and dye degrader microalgae to identify bacterial strains that could sustain on algae-derived DOM secreted in algal exudates; F algal-bacterial co-culturability assessment to study different types of microbial interactions viz. antagonism, mutualism, or no interaction between the two organisms, and G identification of algal-bacterial model phototrophic community based on the active exchange of iron and DOM (refer to Data S1 for detailed results).Full size imageTable 1 Characterization of siderophore production in bacterial strains isolated from textile wastewater.Full size tableAfter that, the sterile exudates from C. sorokiniana and Scenedesmus sp. were used as the sole source of dissolved organic matter for bacterial growth and selection of appropriate microalgal-bacterial partners comprising the phototrophic community (Fig. 1E; Data S2). All five bacterial isolates grew well on the exudate of C. sorokiniana as a sole source of carbon. On the contrary, on exudates of Scenedesmus sp., S. plymuthica PW1 showed moderate growth in 20 h, while the growth of R. pickettii PW2 and S. liquefaciens PW71 remained insignificant. S. maltophilia PW5 and PW6 failed to grow in the exudate of Scenedesmus sp. (Fig. S2B).Finally, the compatibility between the phototrophic community of selected microalgae (C. sorokiniana/ Scenedesmus sp.) and siderophore-producer bacteria (S. plymuthica PW1/ R. pickettii PW2/ S. liquefaciens PW71) was tested by co-culturing them in iron limiting BBM media (BBM-Fe; without EDTA) (Fig. 1F). In the absence of EDTA, Fe precipitates rapidly as iron oxyhydroxides and becomes unavailable to microbes. Microalgal growth curves in co-culture assays were used to measure and compare population characteristics such as carrying capacity ‘k’, growth rate ‘r’, etc., in axenic and consortium setups. Algal growth parameters in co-culture with a bacterial partner were used to categorize their interaction as putative mutualistic, antagonistic, and neutral (Data S1, Tables S1 and S2) [42]. Under iron-limiting conditions, axenic C. sorokiniana experienced iron stress as the cell growth was 4.2 ± 0.4 × 106 cells mL−1 after 200 h incubation. On the other hand, axenic Scenedesmus sp. showed a significantly higher growth (11.3 ± 1.2 × 106 cells mL−1) than C. sorokiniana suggesting an effective iron uptake mechanism under iron-limiting conditions (k; t-test, p = 0.001) (Table S1). In contrast to the axenic microalgal culture, C. sorokiniana in co-culture with R. pickettii PW2 showed a significant increase in cell count at 200 h (6.2 ± 0.85 × 106 cells mL−1) (auc; p = 0.000). However, S. plymuthica PW1 exerted a negative effect on C. sorokiniana (Fig. 2A), as indicated by its significant increase in doubling time (p = 0.009) and reduction in auc (p = 0.001) (Fig. 3A). While S. liquefaciens PW71 remained neutral to C. sorokiniana (auc; p = 0.430) (Fig. 2A, Table 2). On the other hand, the interaction of Scenedesmus sp. with both R. pickettii PW2 and S. liquefaciens PW71 was neutral, while S. plymuthica PW1 showed a negative effect (Figs. 2A and 3A).Fig. 2: Assessment of algal and bacterial growth in co-culture experiments.A The growth curves represent the difference in the growth of C. sorokiniana when grown axenically or in co-culture with S. plymuthica PW1, R. pickettii PW2, and S. liquefaciens PW71 under iron limiting conditions. Whereas, the effect of bacteria on the growth of Scenedesmus sp. was less prominent. The difference in the CFUs of bacterial strains in axenic culture and co-culture suggests the growth-promoting effect of C. sorokiniana on S. plymuthica PW1 and R. pickettii PW2. B Anion-exchange chromatography suggests a difference in the glycosyl composition in the EPS of C. sorokiniana and Scenedesmus sp. C The area under curve (auc) of S. plymuthica PW1 and R. pickettii PW2 obtained after growth curves in different sugars. Here, ‘a’, ‘b’, etc., represent grouping after Tukey’s post hoc test.Full size imageFig. 3: Assessment of algal growth parameters in the algal-bacterial phototrophic community under iron-limiting conditions.A The confidence interval plots represent the significant difference in the growth parameters i.e., growth rate ‘r’, carrying capacity ‘k’, doubling time ‘Dt’, and area under curve ‘auc’, of C. sorokiniana (left panel) and Scenedesmus sp. (right panel) in algal-bacterial co-cultures w.r.t. to axenic culture (horizontal blue dashed line). The symbols ‘*’ and ‘**’ represent p values with statistical significance of ‘p  More

Our model results indicate that in species with a strict social dominance hierarchy where social rank is determined by nepotism, a beneficial mutation occurring in a low-ranking female is not very likely to get established. This outcome emerged despite the immense advantage of the modeled mutation, which doubled its carrier’s survival probability. Moreover, the reproductive skew in our model (see Supplementary Fig. 1) was less radical than the skew reported for the spotted hyena females21, which means that in the model, low-ranking females had a relatively higher reproductive success potential than in reality. In other words, our model may be underestimating the severity of the negative selection a low rank induces.It is reasonable to assume that a low-ranking mutant female in a female dominant society would produce very few surviving offspring due to her low rank and ensuing lack of access to resources. Thus, this female would have only a slight chance to transmit the mutation to the next generation. If this female does reproduce successfully and produces a female which also inherits the mutation, chances of this daughter to pass on the mutation are also slim, as her rank would be even lower than that of her mother. However, if the young produced is a male and has inherited the mutation, chances of transmitting the mutation may increase depending on the male’s reproduction odds. As demonstrated by the four scenarios, the reduction in mutation establishment with decreasing mutant female’s rank became more and more prominent with increasing restrictions on male reproduction. In all four scenarios, the mutation establishment rate median was zero for the lowest ranking mutants, and in all cases but Scenario I, it was 41. Although female dominance hierarchy exists in a few of these species (e.g., Peruvian squirrel monkey41, ring-tailed lemur (Lemur catta)39,42, Verreaux’s sifaka (Propithecus verreauxi))13,25, we did not find any studies indicating female reproductive skew in any of them. Holekamp and Engh25, who reviewed the more classical female dominant species, also reported no evidence for female reproductive skew.This seemingly lack of female reproductive skew among most female dominant species is quite surprising in light of the rather common correlation between social rank and female reproductive success in male dominant species. To mention a few, considerable female reproductive skew is found in baboons (Papio spp), macaques (macaca spp.), feral horses (Equus caballus) and plains zebras (Equus burchelli)8,15,19.Holekamp and Smale28 state that “reproductive skew among female spotted hyenas appears to be greater than that documented among females of male-dominated species characterized by plural breeding”. They suggest that the key determinant of reproductive success among females in this species is rank-related priority of access to food resources. This high priority is reinforced by female dominance over males and is particularly important as this species resides in an environment in which prey availability is seasonal and scarce at times21. Our study suggests that this extreme difference in reproductive success, which, unlike in male-dominated species, is determined by nepotism rather than by physical characters, may induce a handicap on the entire population preventing the establishment of beneficial mutations. This may also hinder adaptation to a changing environment. However, our study results indicate that male equal access to females may, at least partially, counter the inhibition effect on a beneficial mutation establishment. More research is necessary in order to investigate female reproductive skew in species with a social structure similar to that of the spotted hyena, which is characterized by female dominance over males, plural breeding, and a strict female linear social hierarchy determined by nepotism.One intriguing possibility for testing this model’s validity would be an empirical study, provided that the value of some adaptive trait can be measured. In the case of the spotted hyena such a trait may refer to hunting success or physical capabilities. It is well established that adult female spotted hyenas are larger and more aggressive than adult males21, but little attention has been allocated to the study of individual physical differences among females of different ranks. Smith et al.43 studied within clan aggression in the context of the fission-fusion behavior characterizing the spotted hyena clans. Their results indicate more frequent aggression and resulting fissions occurring during times of food shortage. Rank was found to be the major correlate of an aggressive incident result. If it is possible to identify low-ranking females with some beneficial trait (independent of rank), it would be interesting to follow such females’ inclusive reproductive success along time, and even more so, the reproductive success of their sons.Another possible path around the conflict this model suggests would be through the selection of male admission into new clans. Male admission into clans is often constrained by severe aggression of resident immigrant males which may prevent or delay male admission21,26. Such behavior may in fact promote mutant male chances, at least in the case of a mutation that improves physical capabilities.One last, though not very likely possible detour around this difficulty is the occurrence of dominance rank exchanges. Such rank improvements are not very common among female dominated societies, except for in the case of aging females who may clear the way for their daughters44. However, Straus and Holekamp44 found that individuals who repeatedly form coalitions with their top allies are likely to improve their position, and, according to Strauss and Holekamp44, “facilitate revolutionary social change”. It should be kept in mind that not only are such incidents rather rare, but they are unlikely to turn a very low-ranking female into a high-ranking one, especially not when group size is large.More empirical and theoretical research should shed more light on this intriguing question of possible maladaptive evolution. Our model, in line with a few other models such as that of Holman31, suggests that evolution may not always lead to the best solution. As in every process, a local optimum may get evolution trapped and prevent further advance to better optima. More