Philippa Kaur

Krause, M. & Robins, K. Charismatic species and beyond: How cultural schemas and organisational routines shape conservation. Conserv. Soc. 15, 313–321 (2017).
Google Scholar 
Marshall, K. N., Stier, A. C., Samhouri, J. F., Kelly, R. P. & Ward, E. J. Conservation challenges of predator recovery. Conserv. Lett. 9, 70–78 (2016).
Google Scholar 
Bearzi, G., Holcer, D. & Di Sciara, G. N. The role of historical dolphin takes and habitat degradation in shaping the present status of northern Adriatic cetaceans. Aquat. Conserv. Mar. Freshw. Ecosyst. 14, 363–379 (2004).
Google Scholar 
Lavigne, D. M. Marine mammals and fisheries: The role of science in the culling debate. In Marine Mammals: Fisheries Tourism and Management Issues (eds Gales, N. et al.) 31–47 (CSIRO Publishing, 2003).
Google Scholar 
Bowen, W. D. & Lidgard, D. Marine mammal culling programs: Review of effects on predator and prey populations. Mamm. Rev. 43, 207–220 (2013).
Google Scholar 
Svanbäck, R. & Persson, L. Individual diet specialization, niche width and population dynamics: Implications for trophic polymorphisms. J. Anim. Ecol. 73, 973–982 (2004).
Google Scholar 
Butler, J. R. A. et al. The Moray Firth Seal Management Plan: An adaptive framework for balancing the conservation of seals, salmon, fisheries and wildlife tourism in the UK. Aquat. Conserv. Mar. Freshw. Ecosyst. 18, 1025–1038 (2008).
Google Scholar 
Graham, I. M., Harris, R. N., Matejusová, I. & Middlemas, S. J. Do ‘rogue’ seals exist? Implications for seal conservation in the UK. Anim. Conserv. 14, 587–598 (2011).
Google Scholar 
Linnell, J. D. C., Aanes, R., Swenson, J. E., Odden, J. & Smith, M. E. Large carnivores that kill livestock: Do ‘problem individuals’ really exist?. Wildl. Soc. Bull. 27, 698–705 (1999).
Google Scholar 
Tidwell, K. S., van der Leeuw, B. K., Magill, L. N., Carrothers, B. A. & Wertheimer, R. H. Evaluation of pinniped predation on adult salmonids and other fish in the Bonneville Dam tailrace (2017).Guillemette, M. & Brousseau, P. Does culling predatory gulls enhance the productivity of breeding common terns?. J. Appl. Ecol. 38, 1–8 (2001).
Google Scholar 
Rudolf, V. H. W. & Rasmussen, N. L. Population structure determines functional differences among species and ecosystem processes. Nat. Commun. 4, 2318 (2013).ADS 
PubMed 

Google Scholar 
Harmon, L. J. et al. Evolutionary diversification in stickleback affects ecosystem functioning. Nature 458, 1167–1170 (2009).ADS 
CAS 
PubMed 

Google Scholar 
Adams, J. et al. A century of Chinook salmon consumption by marine mammal predators in the Northeast Pacific Ocean. Ecol. Inform. 34, 44–51 (2016).
Google Scholar 
Chasco, B. et al. Competing tradeoffs between increasing marine mammal predation and fisheries harvest of Chinook salmon. Sci. Rep. 7, 1–14 (2017).CAS 

Google Scholar 
Bearhop, S. et al. Stable isotopes indicate sex-specific and long-term individual foraging specialisation in diving seabirds. Mar. Ecol. Prog. Ser. 311, 157–164 (2006).ADS 

Google Scholar 
Thiemann, G. W., Iverson, S. J., Stirling, I. & Obbard, M. E. Individual patterns of prey selection and dietary specialization in an Arctic marine carnivore. Oikos 120, 1469–1478 (2011).
Google Scholar 
Königson, S., Fjälling, A., Berglind, M. & Lunneryd, S. G. Male gray seals specialize in raiding salmon traps. Fish. Res. 148, 117–123 (2013).
Google Scholar 
Sih, A., Sinn, D. L. & Patricelli, G. L. On the importance of individual differences in behavioural skill. Anim. Behav. 155, 307–317 (2019).
Google Scholar 
Bjorkland, R. H. et al. Stable isotope mixing models elucidate sex and size effects on the diet of a generalist marine predator. Mar. Ecol. Prog. Ser. 526, 213–225 (2015).ADS 

Google Scholar 
Schwarz, D. et al. Large-scale molecular diet analysis in a generalist marine mammal reveals male preference for prey of conservation concern. Ecol. Evol. 8, 9889–9905 (2018).PubMed 
PubMed Central 

Google Scholar 
Tinker, M. T., Costa, D. P., Estes, J. A. & Wieringa, N. Individual dietary specialization and dive behaviour in the California sea otter: Using archival time-depth data to detect alternative foraging strategies. Deep. Res. Part II Top. Stud. Oceanogr. 54, 330–342 (2007).ADS 

Google Scholar 
Voelker, M. R., Schwarz, D., Thomas, A., Nelson, B. W. & Acevedo-Gutiérrez, A. Large-scale molecular barcoding of prey DNA reveals predictors of intrapopulation feeding diversity in a marine predator. Ecol. Evol. 10, 9867–9885 (2020).PubMed 
PubMed Central 

Google Scholar 
Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).MathSciNet 
PubMed 

Google Scholar 
Harcourt, R. Individual variation in predation on fur seals by southern sea lions (Otaria byronia) in Peru. Can. J. Zool. 71, 1908–1911 (1993).
Google Scholar 
Marine Mammal Commission. Marine Mammal Protection Act. Marine Mammal Protection Act Amendment 1–56 (U.S. Fish and Wildlife Service, 2004). https://doi.org/10.1002/tcr.201190008.Book 

Google Scholar 
National Marine Fisheries Service. Willamette Falls Pinniped-Fishery Interaction Task Force Marine Mammal Protection Act, Section 120 (National Marine Fisheries Service, 2018).
Google Scholar 
Jefferson, T. A., Smultea, M. A., Ward, E. J. & Berejikian, B. Estimating the stock size of harbor seals (Phoca vitulina richardii) in the inland waters of Washington State using line-transect methods. PLoS ONE 16, e0241254 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jeffries, S., Huber, H., Calambokidis, J. & Laake, J. Trends and status of harbor seals in Washington State: 1978–1999. J. Wildl. Manag. 67, 208–219 (2003).
Google Scholar 
Thomas, A. C., Lance, M. M., Jeffries, S. J., Miner, B. G. & Acevedo-Gutiérrez, A. Harbor seal foraging response to a seasonal resource pulse, spawning Pacific herring. Mar. Ecol. Prog. Ser. 441, 225–239 (2011).ADS 

Google Scholar 
Chasco, B. et al. Estimates of chinook salmon consumption in Washington State inland waters by four marine mammal predators from 1970 to 2015. Can. J. Fish. Aquat. Sci. 74, 1173–1194 (2017).
Google Scholar 
Farrer, J. & Acevedo-Gutiérrez, A. Use of haul-out sites by harbor seals (Phoca vitulina) in Bellingham: Implications for future development. Northwest. Nat. 91, 74–79 (2010).
Google Scholar 
Steingass, S., Jeffries, S., Hatch, D. & Dupont, J. Field report: 2020 pinniped research and management activities at Bonneville Dam (2020).Tidwell, K. S., Carrothers, B. A., Blumstein, D. T. & Schakner, Z. A. Steller sea lion (Eumetopias jubatus) response to non-lethal hazing at Bonneville Dam. Front. Conserv. Sci. 2, 1–9 (2021).
Google Scholar 
Hiruki, L. M., Schwartz, M. K. & Boveng, P. L. Hunting and social behaviour of leopard seals (Hydrurga leptonyx) at Seal Island, South Shetland Islands, Antarctica. J. Zool. 249, 97–109 (1999).
Google Scholar 
Ainley, D. G., Ballard, G., Karl, B. J. & Dugger, K. M. Leopard seal predation rates at penguin colonies of different size. Antarct. Sci. 17, 335–340 (2005).ADS 

Google Scholar 
Páez-Rosas, D. et al. Hunting and cooperative foraging behavior of Galapagos sea lion: An attack to large pelagics. Mar. Mammal Sci. 36, 386–391 (2020).
Google Scholar 
Macneale, K. H., Kiffney, P. M. & Scholz, N. L. Pesticides, aquatic food webs, and the conservation of Pacific salmon. Front. Ecol. Environ. 8, 475–482 (2010).
Google Scholar 
Roni, P., Anders, P. J., Beechie, T. J. & Kaplowe, D. J. Review of tools for identifying, planning, and implementing habitat restoration for Pacific salmon and steelhead. North Am. J. Fish. Manag. 38, 355–376 (2018).
Google Scholar 
Morissette, L., Christensen, V. & Pauly, D. Marine mammal impacts in exploited ecosystems: Would large scale culling benefit fisheries?. PLoS ONE 7, 1–18 (2012).
Google Scholar 
Thompson, D., Coram, A. J., Harris, R. N. & Sparling, C. E. Review of non-lethal seal control options to limit seal predation on salmonids in rivers and at finfish farms. Scott. Mar. Freshw. Sci. 12, 137 (2021).
Google Scholar 
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Google Scholar 
Fairbanks, C. & Penttila, D. Bellingham Bay Forage Fish Spawning Assessment (2016).Madsen, S. W. & Nightengale, T. Whatcom Creek Ten-Years After: Summary Report (Department of Public Works, 2009). https://doi.org/10.2307/j.ctt20krzd7.7.Book 

Google Scholar 
Martin, P. & Bateson, P. Measuring Behaviour: An Introductory Guide (Cambridge University Press, 2007).
Google Scholar 
Bolger, D. T., Morrison, T. A., Vance, B., Lee, D. & Farid, H. A computer-assisted system for photographic mark-recapture analysis. Methods Ecol. Evol. 3, 813–822 (2012).
Google Scholar 
Harrison, P. J. et al. Incorporating movement into models of grey seal population dynamics. J. Anim. Ecol. 75, 634–645 (2006).PubMed 

Google Scholar 
Thompson, P. M. & Wheeler, H. Photo-ID-based estimates of reproductive patterns in female harbor seals. Mar. Mammal Sci. 24, 138–146 (2008).
Google Scholar 
Washington Department of Fish and Wildlife. Whatcom Creek Hatchery (WDFW, 2019).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Core Team, 2020).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Lloyd-Smith, J. O. Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases. PLoS ONE 2, 1–8 (2007).
Google Scholar 
Zhang, D. rsq: R-Squared and Related Measures. R package version 2.1 (2020).Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).ADS 

Google Scholar 
Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6.Book 
MATH 

Google Scholar  More

Land coverUrban landscapes are characterized by small clusters of patches, forming land mosaics that are distinct from natural landscapes. An accurate estimation of climate forcing requires a land cover dataset at high resolutions that does not omit small urban patches. In this study, the RF estimates are based on 500-m and 1-km land cover datasets. This fine resolution is necessary to preserve spatial details of small urban patches while avoiding the large underestimation of urban land areas at coarse resolution (e.g., ~19% underestimation at 10 km compared to that at 1 km)3. We used 500-m resolution MODIS Land Cover product (MCD12Q1v006) for historical land cover changes. For future urban land cover distributions, we used the global urban land expansion products simulated under the SSPs for 2030–2100 (i.e., Chen-2020)4. The simulation performance was tested using historical urban expansion from 2000 to 2015 based on Global Human Settlement Layer51, where the agreement between simulated and observed urban land was evaluated using the Figure of Merit (FoM) indicator52 that has showed similar or better values than those reported in other existing land simulation applications4. The high-resolution Chen-2020 also shows very high spatial consistency with the prominent coarse resolution global urban land projection LUH2 that is recommended in CMIP64. Considering different scenarios is also necessary to account for the uncertainties of future socioeconomic and environmental conditions, so we included simulated urban lands under three scenarios (Supplementary Table 1): Sustainability -SSP1, Middle of the Road – SSP2, and Fossil-fueled Development – SSP553. Within each SSP scenario, the product provides a likelihood map of each grid becoming urban, based on 100 urbanization simulations. We used the likelihood map to account for spatial uncertainties of urban expansion by deriving 90% confidence intervals of projected urban land demand within a SSP scenario. We used the MODIS IGBP Land Cover classes (Supplementary Table 2) and resampled the original 500-m resolution MODIS products in 2018 to 1-km resolution to match the future simulations when it was used as a baseline year. To isolate the independent effect of urbanization (vs other types of land uses) in future estimates, land covers that are not converted to urban are assumed to have the same cover types as in 2018 (i.e., the baseline year). Though there are other global land cover products for current periods, we choose the MODIS IGBP land cover products because the albedo look-up maps (LUMs) were based on IGBP land cover types (see Albedo Look-Up Maps).To further evaluate the uncertainties caused by different projections of future urbanization, we also included the other two SSPs from Chen-2020, and another two 1-km resolution urban land cover products projected for the future for the purpose of comparison. The other two products include four projections of SRES scenarios (i.e., A1, B1, A1B, and B2) (i.e., Li-2017 mentioned above)3 and one without scenario description but assumed historical development would continue (i.e., Zhou-2019 mentioned above)2. These projections of future urban land expansion were calibrated with different historical urban land products and can be regarded as independent.Albedo look-up maps (LUMs)Albedo Look-Up Maps (LUMs)31 were derived from the intersection of MODIS land cover54 and surface albedo55 products, which are used to determine the albedo values for each IGBP land cover type by month and by location. Monthly means of white-sky (i.e., diffuse surface illumination condition) and black sky (i.e., direct surface illumination condition) during 2001–2011 were processed for snow-free and snow-covered periods for each of the 17 IGBP land cover classes at spatial resolutions of 0.05°−1°31. The LUMs have been verified by comparing the reconstructed albedo using the LUMs with the original MODIS albedo, which shows very similar values31. We used the LUMs at a resolution of 1° due to the significantly fewer missing values, to assure the spatial continuity of albedo changes at a global scale while keeping the matches with the 1° resolution of radiation data and RF kernels. The underlying assumption is that albedo of the same land cover type varies insignificantly within a 1° grid.Snow and radiation productSnow cover can significantly change the albedo of land regardless of cover types (Supplementary Fig. 4). In this study, we tally monthly albedo using snow-free and snow-covered categories in estimating RF. Past and present snow-free and snow-covered conditions were derived from level 3 MODIS/Terra Snow Cover (MOD10CM.006)56 at 0.05° spatial resolution and resampled to a 1° spatial resolution. Monthly means of 2001–2005 vs 2015–2019 were used for 2001 and 2018 respectively. For future periods, ensemble mean snow cover for each year and month, projected under the CMIP5 framework for three Representative Concentration Pathway (RCP) scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) were used (for more details see Supplementary Note 2B). By comparing the model outputs with MODIS observations for a recent decade (2006–2015), we found that the multi-model mean snow cover was systematically biased compared to MODIS observations. Consequently, we calibrated the ensemble mean projections by subtracting the biases for the grids. In each 10th year of the future (e.g., 2030, 2040, etc.), the decadal monthly mean snow cover (e.g., 2026–2035 for 2030, and 2036–2045 for 2040, etc.) was used for the year.We used the long-term monthly averages (1981–2010) of diffuse and direct incoming surface solar radiation reanalysis Gaussian grid product from National Centers for Environmental Prediction (NCEP)57. Visible and near infrared beam downward radiation and diffuse downward radiation from NCEP were used to compute the white-sky and black-sky fractions. As for snow cover, ensemble mean shortwave radiation at surface (SWSF) and at top-of-atmosphere (SWTOA) projected from CMIP5 models (Supplementary Note 3C) for RCP2.6, RCP4.5, and RCP8.5 were collected for empirically computing future albedo kernels (see section 3.4 below).Radiative kernelsRadiative kernels were used to compute top-of-atmosphere RF due to small perturbations of temperature, water vapor, and albedo. We used the latest state-of-the-art albedo kernels calculated with CESM v1.1.258 to compute RF in 2018 relative to 2001. In brief, the albedo kernel is the change in top-of-atmosphere radiative flux for a 0.01 change in surface albedo. The CESM1.1.2 kernels are separated into clear- and all-sky illumination conditions. We used the all-sky kernels because we include both black-sky and white-sky albedos. For future periods, because there are no available radiative kernels produced from general circulation models, we approximated the future kernels using an empirical parameterization following Bright et al.59:$${K}_{m}left(iright)={{SW}}^{{SF}}(i)times {sqrt}left(frac{{{SW}}^{{SF}}(i)}{{{SW}}^{{TOA}}(i)}right)/(-100)$$
(1)
where m is the month, i is the location, and SWSF and SWTOA are the surface and top-of-atmosphere shortwave radiation; dividing by −100 is for matching the CESM1.1.2 kernel definition of a 0.01 change in surface albedo.Estimation of albedo change and RFWe analyzed the RF in 2018 due to albedo changes caused by urbanization since 2001 (2018–2001), and in the future from 2030 to 2100 at decadal intervals (i.e., 2030, 2040, 2050, …, and 2100) since 2018 under three illustrative scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, which combine SSP-based urbanization projections and RCP-based climate projections. The three illustrative scenarios were selected following the scenario designation of the latest IPCC report50 and represent low greenhouse gas (GHG) emissions with CO2 emissions declining to net zero around or after 2050, intermediate GHG emissions with CO2 emissions remaining around current levels until the mid-century, and very high CO2 emissions that roughly double from current levels by 2050, respectively. We selected 2018 as the baseline year to divide the past from the future because 2018 was the latest year with available MODIS land cover products at the time of this study. We used ArcGIS 10.6 to produce spatial maps of all variables, including area of each land cover type within a 1° × 1°-grid, snow cover, albedo, radiation, and kernels, and R 3.6.1 to compute the RF.We focused only on albedo changes induced by urbanization, including the conversions from all other 16 IGBP land cover types to urban land. The changes of albedo for each grid (x, y) of a month (m) were obtained by computing the difference between albedo of that grid in the baseline year (t = t0) and in a later year (t = t1) with urban expansion:$${triangle alpha }_{m,t1-t0}(x,y)={alpha }_{m,t=t1}(x,y)-{alpha }_{m,t=t0}(x,y)$$
(2)
where αm, t = t1 (x, y) and αm, t = t0) (x, y) is the albedo for each grid (x,y) of a month (m) at the base year and later year respectively; the grid-scale albedo is computed as the weighted sum of albedo by land cover types with the weighing factor corresponding to areal percentage of a land cover within the grid. The albedo for each land cover type of a grid was then obtained by applying the albedo LUMs that provide spatially continuous black-sky, white-sky, snow-covered, and snow-free albedo maps for a given month for each land cover. Firstly, monthly mean albedo is computed as:$${alpha }_{m,t}(x,y)=mathop{sum }limits_{l=1}^{17}mathop{sum }limits_{s=0}^{1}mathop{sum }limits_{r=0}^{1}{{f}_{l,t}(x,y){f}_{s,m,t}(x,y)f}_{r,m,t}(x,y)left({alpha }_{l,s,r,m}(x,y)right)$$
(3)
where m is the month, t is the year, l is the land cover type, fl is the proportion of a cover type within the grid, fs,m,t is the fraction for snow-covered (s = 0) and snow-free (s = 1) conditions of the time (m, t), fr,m,t (x, y) is the fraction for white-sky (r = 0) or black-sky (r = 1) conditions of the time, and αl,s,r,m (x, y) is the albedo for land cover type l in month m that is extracted from the albedo LUMs corresponding to snow condition (s) and radiation condition (r). The annual mean albedo change is reported as the mean of monthly albedo change:$${triangle alpha }_{t1-t0}(x,y)=frac{1}{12}mathop{sum }limits_{m=1}^{m=12}({alpha }_{m,t=t1}(x,y)-{alpha }_{m,t=t0}(x,y))$$
(4)
The conversion of other land covers to urban land can contribute differently to the global RF, as the total area that is converted into urban land is different among non-urban land covers and the albedo differences between urban land and non-urban land cover types vary. To estimate the proportional contributions of different land conversions, we first decomposed the total albedo of each grid into the proportion of each land cover type:$${alpha }_{l,m,t}(x,y)={f}_{l,m,t}(x,y)mathop{sum }limits_{s=0}^{1}mathop{sum }limits_{r=0}^{1}{f}_{s,m,t}(x,y){f}_{r,m,t}(x,y)left({alpha }_{l,s,r,m}(x,y)right)$$
(5)
The global RF due to albedo change caused by conversion from each non-urban land cover type (l ≠ 13) to urban land (l = 13) (see Supplementary Table 2 land cover labels) was calculated as:$${{RF}}_{triangle alpha ,l(lne 13),{global}}=frac{1}{{A}_{{Earth}}}mathop{sum }limits_{i=1}^{n}mathop{sum }limits_{m=1}^{12}{({alpha }_{13,m,t=t1}left(iright)-{alpha }_{l,m,t=t0}left(iright))Delta p}_{lto 13}left(iright){Area}left(iright){K}_{m}(i)$$
(6)
where i refers to a grid, n is the total number of pixels on global lands, AEarth is the global surface area (5.1  ×  108 km2), α13,m,t = t1) (i) is the albedo of urban land in month m in the later year with urban expansion, αl,m,t = t0 (i) is the albedo of a targeted non-urban land cover type in the base year t0, Δpl→13 is the percentage of the non-urban land cover type that is converted to urban land in the year t1 compared to year t0, Area(i) is the area of the pixel, and Km (i) is the radiative kernel at the grid.The global RF due to urbanization-induced albedo changes was then calculated as:$${{RF}}_{triangle alpha ,{global}}=mathop{sum }limits_{l=1}^{17}{{RF}}_{triangle alpha ,l,{global}}(l,ne, 13)$$
(7)
GWP: CO2-equivalentWe followed GWP calculations by explicitly accounting for the lifetime and dynamic behavior of CO2 to convert RF to CO2 equivalent60,61:$${GWP}[{kg},{of},{{CO}}_{2}-{eq}]=frac{{int }_{t=0}^{{TH}}{{RF}}_{triangle alpha ,{global}}(t)}{{k}_{{CO}_2}{int }_{t=0}^{{TH}}{y}_{{{CO}}_{2}}(t)}$$
(8)
where kCO2 is radiative efficiency of CO2 in the atmosphere (W/m2/kg) at a constant background concentration of 389 ppmv, which is taken as 1.76  ×  1015 W/m2/kg62, and RF∆α,global is the global RF caused by albedo changes (W/m2). ({y}_{{{CO}}_{2}}) is the impulse-response function (IRF) for CO2 that ranges from 1 at the time of the emission pulse (t = 0) to 0.41 after 100 years, and here it is set to a mean value of 0.52 over 100 years60. The time horizon (TH) of our GWP calculations was fixed at 100 years following IPCC standards and previous studies60,63,64.Global mean surface air temperature changeWe estimated the 100-year global mean surface temperature change for the estimated RF by adopting an equilibrium climate sensitivity (ECS), defined as the global mean surface air temperature increase that follows a doubling of pre-industrial atmospheric carbon dioxide (RF = 3.7 W/m2). Given a value of RF induced by a forcing agent, the temperature change is estimated as RF/3.7 × ECS. To consider the uncertainties of ECS, we adopted a mean value of 3 °C and a very likely (90% confidence interval) range of 2–5 °C following IPCC AR650. Without knowing the exact distribution shape of ECS and future albedo-change-induced RF, we created a log-normal distribution (Supplementary Note 4) to approximate their asymmetric distribution through numerical simulation. We then conducted Monte Carlo simulations that draw 5000 random samples from each distribution to jointly estimate the uncertainties of global mean surface air temperature changes. We report the mean and 90% interval ranges of the change in temperature. More

Ronce O. How does it feel to be like a rolling stone? Ten questions about dispersal evolution. Annu Rev Ecol Evol Syst. 2007;38:231–53.Article 

Google Scholar 
Shmida A, Wilson MV. Biological determinants of species diversity. J Biogeogr. 1985;12:1–20.Article 

Google Scholar 
Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.PubMed 
Article 

Google Scholar 
Slatkin M. Gene flow and the geographic structure of natural populations. Science. 1987;236:787–92.CAS 
PubMed 
Article 

Google Scholar 
Baas-Becking, LGM. Geobiology or introduction to environmental science (Translated from Dutch). The Hague: W.P. Van Stockum & Zoon; 1934.Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
PubMed 
Article 

Google Scholar 
Peay KG, Schubert MG, Nguyen NH, Bruns TD. Measuring ectomycorrhizal fungal dispersal: macroecological patterns driven by microscopic propagules. Mol Ecol. 2012;21:4122–36.PubMed 
Article 

Google Scholar 
Andam CP, Doroghazi JR, Campbell AN, Kelly PJ, Choudoir MJ, Buckley DH. A latitudinal diversity gradient in terrestrial bacteria of the genus Streptomyces. mBio. 2016;7:e02200–15.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Choudoir MJ, Barberán A, Menninger HL, Dunn RR, Fierer N. Variation in range size and dispersal capabilities of microbial taxa. Ecology. 2018;99:322–34.PubMed 
Article 

Google Scholar 
Hanson CA, Müller AL, Loy A, Dona C, Appel R, Jørgensen BB, et al. Historical factors associated with past environments influence the biogeography of thermophilic endospores in Arctic marine sediments. Front Microbiol. 2019;10:245.PubMed 
PubMed Central 
Article 

Google Scholar 
Albright MBN, Martiny JBH. Dispersal alters bacterial diversity and composition in a natural community. ISME J. 2018;12:296–9.PubMed 
Article 

Google Scholar 
Evans SE, Bell-Dereske LP, Dougherty KM, Kittredge HA. Dispersal alters soil microbial community response to drought. Environ Microbiol. 2020;22:905–16.CAS 
PubMed 
Article 

Google Scholar 
Svoboda P, Lindström ES, Ahmed Osman O, Langenheder S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 2018;12:644–6.PubMed 
Article 

Google Scholar 
Cevallos-Cevallos JM, Danyluk MD, Gu G, Vallad GE, van Bruggen AHC. Dispersal of Salmonella typhimurium by rain splash onto tomato plants. J Food Prot. 2012;75:472–9.PubMed 
Article 

Google Scholar 
Lindström ES, Langenheder S. Local and regional factors influencing bacterial community assembly. Environ Microbiol Rep. 2012;4:1–9.PubMed 
Article 

Google Scholar 
Rime T, Hartmann M, Frey B. Potential sources of microbial colonizers in an initial soil ecosystem after retreat of an alpine glacier. ISME J. 2016;10:1625–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lindström ES, Östman Ö. The importance of dispersal for bacterial community composition and functioning. PLoS ONE. 2011;6:e25883.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Declerck SAJ, Winter C, Shurin JB, Suttle CA, Matthews B. Effects of patch connectivity and heterogeneity on metacommunity structure of planktonic bacteria and viruses. ISME J. 2013;7:533–42.PubMed 
Article 

Google Scholar 
Souffreau C, Pecceu B, Denis C, Rummens K, De Meester L. An experimental analysis of species sorting and mass effects in freshwater bacterioplankton. Freshw Biol. 2014;59:2081–95.Article 

Google Scholar 
Comte J, Langenheder S, Berga M, Lindström ES. Contribution of different dispersal sources to the metabolic response of lake bacterioplankton following a salinity change. Environ Microbiol. 2017;19:251–60.CAS 
PubMed 
Article 

Google Scholar 
Albright MBN, Sevanto S, Gallegos-Graves LV, Dunbar J. Biotic interactions are more important than propagule pressure in microbial community invasions. mBio. 2020;11:e02089–20.PubMed 
PubMed Central 
Article 

Google Scholar 
Galès A, Latrille E, Wéry N, Steyer JP, Godon JJ. Needles of Pinus halepensis as biomonitors of bioaerosol emissions. PLoS ONE. 2014;9:e112182.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bell E, Blake LI, Sherry A, Head IM, Hubert CRJ. Distribution of thermophilic endospores in a temperate estuary indicate that dispersal history structures sediment microbial communities. Environ Microbiol. 2018;20:1134–47.PubMed 
PubMed Central 
Article 

Google Scholar 
Leung MHY, Wilkins D, Li EKT, Kong FKF, Lee PKH. Indoor-air microbiome in an urban subway network: diversity and dynamics. Appl Environ Microbiol. 2014;80:6760–70.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Maignien L, DeForce EA, Chafee ME, Murat Eren A, Simmons SL. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. mBio. 2014;5:e00682–13.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bell T. Experimental tests of the bacterial distance-decay relationship. ISME J. 2010;4:1357–65.PubMed 
Article 

Google Scholar 
Kaneko R, Kaneko S. The effect of bagging branches on levels of endophytic fungal infection in Japanese beech leaves. For Pathol. 2004;34:65–78.Article 

Google Scholar 
Vannette RL, Fukami T. Dispersal enhances beta diversity in nectar microbes. Ecol Lett. 2017;20:901–10.PubMed 
Article 

Google Scholar 
Satou M, Kubota M, Nishi K. Measurement of horizontal and vertical movement of Ralstonia solanacearum in soil. J Phytopathol. 2006;154:592–7.CAS 
Article 

Google Scholar 
Veen GF, Snoek BL, Bakx-Schotman T, Wardle DA, van der Putten WH. Relationships between fungal community composition in decomposing leaf litter and home-field advantage effects. Funct Ecol. 2019;33:1524–35.Article 

Google Scholar 
Liu G, Cornwell WK, Pan X, Ye D, Liu F, Huang Z, et al. Decomposition of 51 semidesert species from wide-ranging phylogeny is faster in standing and sand-buried than in surface leaf litters: implications for carbon and nutrient dynamics. Plant Soil. 2015;396:175–87.CAS 
Article 

Google Scholar 
Kimball S, Goulden ML, Suding KN, Parker S. Altered water and nitrogen input shifts succession in a southern California coastal sage community. Ecol Appl. 2014;24:1390–404.PubMed 
Article 

Google Scholar 
Finks SS, Weihe C, Kimball S, Allison SD, Martiny AC, Treseder KK, et al. Microbial community response to a decade of simulated global changes depends on the plant community. Elementa. 2021;9:124.
Google Scholar 
Khalili B, Weihe C, Kimball S, Schmidt KT, Martiny JBH. Optimization of a method to quantify soil bacterial abundance by flow cytometry. mSphere. 2019;4:e00435–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci USA. 1985;82:6955–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Looby CI, Maltz MR, Treseder KK. Belowground responses to elevation in a changing cloud forest. Ecol Evol. 2016;6:1996–2009.PubMed 
PubMed Central 
Article 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2019;47:D259–D264.CAS 
PubMed 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.CAS 
PubMed 
Article 

Google Scholar 
Smith DJ, Ravichandar JD, Jain S, Griffin DW, Yu H, Tan Q, et al. Airborne bacteria in Earth’s lower stratosphere resemble taxa detected in the troposphere: results from a new NASA Aircraft Bioaerosol Collector (ABC). Front Microbiol. 2018;9:1752.PubMed 
PubMed Central 
Article 

Google Scholar 
Bryan NC, Christner BC, Guzik TG, Granger DJ, Stewart MF. Abundance and survival of microbial aerosols in the troposphere and stratosphere. ISME J. 2019;13:2789–99.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Matulich KL, Weihe C, Allison SD, Amend AS, Berlemont R, Goulden ML, et al. Temporal variation overshadows the response of leaf litter microbial communities to simulated global change. ISME J. 2015;9:2477–89.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kim N, Zabaloy MC, Villamil MB, Riggins CW, Rodríguez-Zas S. Microbial shifts following five years of cover cropping and tillage practices in fertile agroecosystems. Microorganisms. 2020;8:1773.CAS 
PubMed Central 
Article 

Google Scholar 
Gurfield N, Grewal S, Cua LS, Torres PJ, Kelley ST. Endosymbiont interference and microbial diversity of the Pacific coast tick, Dermacentor occidentalis, in San Diego County, California. PeerJ. 2017;5:e3202.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Knights D, Kuczynski J, Charlson ES, Zaneveld J, Mozer MC, Collman RG, et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods. 2011;8:761–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bertolini V, Gandolfi I, Ambrosini R, Bestetti G, Innocente E, Rampazzo G, et al. Temporal variability and effect of environmental variables on airborne bacterial communities in an urban area of Northern Italy. Appl Microbiol Biotechnol. 2013;97:6561–70.CAS 
PubMed 
Article 

Google Scholar 
Voříšková J, Baldrian P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 2013;7:477–86.PubMed 
Article 
CAS 

Google Scholar 
Rastogi G, Coaker GL, Leveau JHJ. New insights into the structure and function of phyllosphere microbiota through high-throughput molecular approaches. FEMS Microbiol Lett. 2013;348:1–10.CAS 
PubMed 
Article 

Google Scholar 
Lindow SE, Leveau JHJ. Phyllosphere microbiology. Curr Opin Biotechnol. 2002;13:238–43.CAS 
PubMed 
Article 

Google Scholar 
Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Kapturska D, et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol Ecol. 2016;25:4059–74.CAS 
PubMed 
Article 

Google Scholar 
Austin AT, Vivanco L. Plant litter decomposition in a semi-arid ecosystem controlled by photodegradation. Nature. 2006;442:555–8.CAS 
PubMed 
Article 

Google Scholar 
Glassman SI, Weihe C, Li J, Albright MBN, Looby CI, Martiny AC, et al. Decomposition responses to climate depend on microbial community composition. Proc Natl Acad Sci USA. 2018;115:11994–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Punnapayak H, Sudhadham M, Prasongsuk S, Pichayangkura S. Characterization of Aureobasidium pullulans isolated from airborne spores in Thailand. J Ind Microbiol Biotechnol. 2003;30:89–94.CAS 
PubMed 
Article 

Google Scholar 
Elmassry MM, Ray N, Sorge S, Webster J, Merry K, Caserio A, et al. Investigating the culturable atmospheric fungal and bacterial microbiome in West Texas: implication of dust storms and origins of the air parcels. FEMS Microbes. 2020;1:xtaa009.Article 

Google Scholar 
Van Diepen LTA, Frey SD, Landis EA, Morrison EW, Pringle A. Fungi exposed to chronic nitrogen enrichment are less able to decay leaf litter. Ecology. 2017;98:5–11.PubMed 
Article 

Google Scholar 
Du X, Guo Q, Gao X, Ma K. Seed rain, soil seed bank, seed loss and regeneration of Castanopsis fargesii (Fagaceae) in a subtropical evergreen broad-leaved forest. Ecol Manag. 2007;238:212–9.Article 

Google Scholar 
Work TT, Buddle CM, Korinus LM, Spence JR. Pitfall trap size and capture of three taxa of litter-dwelling arthropods: implications for biodiversity studies. Environ Entomol. 2002;31:438–48.Article 

Google Scholar 
Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol Lett. 2004;7:601–13.Article 

Google Scholar 
Evans S, Martiny JBH, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.PubMed 
Article 

Google Scholar 
Cadotte MW. Dispersal and species diversity: a meta-analysis. Am Nat. 2006;167:913–24.PubMed 
Article 

Google Scholar 
Schmidt SK, Nemergut DR, Darcy JL, Lynch R. Do bacterial and fungal communities assemble differently during primary succession? Mol Ecol. 2014;23:254–8.CAS 
PubMed 
Article 

Google Scholar 
Baker NR, Khalili B, Martiny JBH, Allison SD. Microbial decomposers not constrained by climate history along a Mediterranean climate gradient in southern California. Ecology. 2018;99:1441–52.PubMed 
Article 

Google Scholar 
Martiny JBH, Martiny AC, Weihe C, Lu Y, Berlemont R, Brodie EL, et al. Microbial legacies alter decomposition in response to simulated global change. ISME J. 2017;11:490–9.PubMed 
Article 

Google Scholar 
Santander MV, Mitts BA, Pendergraft MA, Dinasquet J, Lee C, Moore AN, et al. Tandem fluorescence measurements of organic matter and bacteria released in sea spray aerosols. Environ Sci Technol. 2021;55:5171–9.CAS 
PubMed 
Article 

Google Scholar 
Hobbie SE. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol Evol. 2015;30:357–63.PubMed 
Article 

Google Scholar  More

Szathmáry E. Toward major evolutionary transitions theory 2.0. Proc Natl Acad Sci USA. 2015;112:10104–11. https://doi.org/10.1073/pnas.1421398112CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Niklas KJ, Newman SA. The origins of multicellular organisms. Evol Dev. 2013;15:41–52. https://doi.org/10.1111/ede.12013Article 
PubMed 

Google Scholar 
Pfeiffer T, Bonhoeffer S. An evolutionary scenario for the transition to undifferentiated multicellularity. Proc Natl Acad Sci USA. 2003;100:1095–8. https://doi.org/10.1073/pnas.0335420100CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fisher RM, Regenberg B, Multicellular group formation in Saccharomyces cerevisiae. Proc Royal Soc B: Biol Sci. 2019;286. https://doi.org/10.1098/rspb.2019.1098Umen JG. Green algae and the origins of multicellularity in the plant kingdom. Cold Spring Harb Perspect Biol. 2014;6:a016170 https://doi.org/10.1101/cshperspect.a016170Article 
PubMed 
PubMed Central 

Google Scholar 
Knoll AH. The multiple origins of complex multicellularity. Annu Rev Earth Planet Sci. 2011;39:217–39. https://doi.org/10.1146/annurev.earth.031208.100209CAS 
Article 

Google Scholar 
Bonner JT. The origins of multicellularity. Integr Biol Issues N. Rev. 1998;1:27–36.Article 

Google Scholar 
Tarnita CE, Taubes CH, Nowak MA. Evolutionary construction by staying together and coming together. J Theor Biol. 2013;320:10–22. https://doi.org/10.1016/j.jtbi.2012.11.022Article 
PubMed 

Google Scholar 
Ratcliff WC, Denison RF, Borrello M, Travisano M. Experimental evolution of multicellularity. Proc Natl Acad Sci USA. 2012;109:1595–1600. https://doi.org/10.1073/pnas.1115323109Article 
PubMed 
PubMed Central 

Google Scholar 
Koschwanez JH, Foster KR, Murray AW. Sucrose utilization in budding yeast as a model for the origin of undifferentiated multicellularity. PLoS Biol. 2011;9:e1001122 https://doi.org/10.1371/journal.pbio.1001122CAS 
Article 
PubMed 

Google Scholar 
Kuzdzal-Fick JJ, Chen L, Balázsi G. Disadvantages and benefits of evolved unicellularity versus multicellularity in budding yeast. Ecol Evol. 2019;9:8509–23. https://doi.org/10.1002/ece3.5322Article 
PubMed 
PubMed Central 

Google Scholar 
Brückner S, Schubert R, Kraushaar T, Hartmann R, Hoffmann D, Jelli E, et al. Kin discrimination in social yeast is mediated by cell surface receptors of the flo11 adhesin family. eLife 2020;9. https://doi.org/10.7554/eLife.55587Smukalla S, Caldara M, Pochet N, Beauvais A, Guadagnini S, Yan C, et al. FLO1 is a variable green beard gene that drives biofilm-like cooperation in budding yeast. Cell. 2008;135:726–37. https://doi.org/10.1016/j.cell.2008.09.037CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Driscoll WW, Travisano M, Synergistic cooperation promotes multicellular performance and unicellular free-rider persistence. Nat Commun. 2017;8. https://doi.org/10.1038/ncomms15707Pentz JT, Márquez-Zacarías P, Bozdag GO, Burnetti A, Yunker PJ, Libby E, et al. Ecological advantages and evolutionary limitations of aggregative multicellular development. Curr Biol. 2020;30:4155–.e6. https://doi.org/10.1016/j.cub.2020.08.006.CAS 
Article 
PubMed 

Google Scholar 
Goossens K, Willaert R. Flocculation protein structure and cell-cell adhesion mechanism in Saccharomyces cerevisiae. Biotechnol Lett. 2010;32:1571–85. https://doi.org/10.1007/s10529-010-0352-3CAS 
Article 
PubMed 

Google Scholar 
Di Gianvito P, Tesnière C, Suzzi G, Blondin B, Tofalo R. FLO5 gene controls flocculation phenotype and adhesive properties in a Saccharomyces cerevisiae sparkling wine strain. Sci Rep. 2017;7:1–12. https://doi.org/10.1038/s41598-017-09990-9CAS 
Article 

Google Scholar 
Veelders M, Brückner S, Ott D, Unverzagt C, Mösch HU, Essen LO. Structural basis of flocculin-mediated social behavior in yeast. Proc Natl Acad Sci USA. 2010;107:22511–6. https://doi.org/10.1073/pnas.1013210108Article 
PubMed 
PubMed Central 

Google Scholar 
Verstrepen KJ, Jansen A, Lewitter F, Fink GR. Intragenic tandem repeats generate functional variability. Nat Genet. 2005;37:986–90. https://doi.org/10.1038/ng1618CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Verstrepen KJ, Klis FM. Flocculation, adhesion and biofilm formation in yeasts. Mol Microbiol. 2006;60:5–15. https://doi.org/10.1111/j.1365-2958.2006.05072.xCAS 
Article 
PubMed 

Google Scholar 
Verstrepen KJ, Reynolds TB, Fink GR. Origins of variation in the fungal cell surface. Nat Rev Microbiol. 2004;2:533–40. https://doi.org/10.1038/nrmicro927CAS 
Article 
PubMed 

Google Scholar 
Kraushaar T, Brückner S, Veelders M, Rhinow D, Schreiner F, Birke R, et al. Interactions by the fungal Flo11 adhesin depend on a fibronectin type III-like adhesin domain girdled by aromatic bands. Structure. 2015;23:1005–17. https://doi.org/10.1016/j.str.2015.03.021CAS 
Article 
PubMed 

Google Scholar 
Chen L, Noorbakhsh J, Adams RM, Samaniego-Evans J, Agollah G, Nevozhay D, et al. Two-dimensionality of yeast colony expansion accompanied by pattern formation. PLoS Comput Biol. 2014;10. https://doi.org/10.1371/journal.pcbi.1003979Oppler ZJ, Parrish ME, Murphy HA, Variation at an adhesin locus suggests sociality in natural populations of the yeast saccharomyces cerevisiae. Proc Royal Soc B: Biol Sci. 2019;286. https://doi.org/10.1098/rspb.2019.1948Lo WS, Dranginis AM. The cell surface flocculin Flo11 is required for pseudohyphae formation and invasion by Saccharomyces cerevisiae. Mol Biol Cell. 1998;9:161–71. https://doi.org/10.1091/mbc.9.1.161CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
El-Kirat-Chatel S, Beaussart A, Vincent SP, Abellán Flos M, Hols P, Lipke PN, et al. Forces in yeast flocculation. Nanoscale. 2015;7:1760–7. https://doi.org/10.1039/c4nr06315eCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kobayashi O, Hayashi N, Kuroki R, Sone H. Region of Flo1 proteins responsible for sugar recognition. J Bacteriol. 1998;180:6503–10. https://doi.org/10.1128/jb.180.24.6503-6510.1998CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kapsetaki SE, West SA. The costs and benefits of multicellular group formation in algae. Evolution. 2019;73:1296–308. https://doi.org/10.1111/evo.13712Article 
PubMed 

Google Scholar 
Quintero-Galvis JF, Paleo-López R, Solano-Iguaran JJ, Poupin MJ, Ledger T, Gaitan-Espitia JD, et al. Exploring the evolution of multicellularity in Saccharomyces cerevisiae under bacteria environment: An experimental phylogenetics approach. Ecol Evol. 2018;8:4619–30. https://doi.org/10.1002/ece3.3979Article 
PubMed 
PubMed Central 

Google Scholar 
Goossens KV, Ielasi FS, Nookaew I, Stals I, Alonso-Sarduy L, Daenen L, et al. Molecular mechanism of flocculation self-recognition in yeast and its role in mating and survival. mBio. 2015;6:1–16. https://doi.org/10.1128/mBio.00427-15CAS 
Article 

Google Scholar 
Hamilton WD. The genetical evolution of social behaviour. I. J Theor Biol. 1964;7:1–16. https://doi.org/10.1016/0022-5193(64)90038-4CAS 
Article 
PubMed 

Google Scholar 
Queller DC, Ponte E, Bozzaro S, Strassmann JE. Single-gene greenbeard effects in the social amoeba Dictyostelium discoideum. Science. 2003;299:105–6. https://doi.org/10.1126/science.1077742CAS 
Article 
PubMed 

Google Scholar 
Foty RA, Steinberg MS. The differential adhesion hypothesis: A direct evaluation. Dev Biol. 2005;278:255–63. https://doi.org/10.1016/j.ydbio.2004.11.012CAS 
Article 
PubMed 

Google Scholar 
Nowak MA. Five rules for the evolution of cooperation. Science. 2006;314:1560–3. https://doi.org/10.1126/science.1133755Article 
PubMed 
PubMed Central 

Google Scholar 
Nadell CD, Foster KR, Xavier JB. Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comput Biol. 2010;6:e1000716 https://doi.org/10.1371/journal.pcbi.1000716CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Drescher K, Nadell CD, Stone HA, Wingreen NS, Bassler BL. Solutions to the public goods dilemma in bacterial biofilms. Curr Biol. 2014;24:50–55. https://doi.org/10.1016/j.cub.2013.10.030CAS 
Article 
PubMed 

Google Scholar 
Liu CG, Li ZY, Hao Y, Xia J, Bai FW, Mehmood MA, Computer simulation elucidates yeast flocculation and sedimentation for efficient industrial fermentation. Biotechnol J. 2018;13. https://doi.org/10.1002/biot.201700697Boraas ME, Seale DB, Boxhorn JE. Phagotrophy by flagellate selects for colonial prey: A possible origin of multicellularity. Evol Ecol. 1998;12:153–64. https://doi.org/10.1023/A:1006527528063Article 

Google Scholar 
Staps M, van Gestel J, Tarnita CE. Emergence of diverse life cycles and life histories at the origin of multicellularity. Nat Ecol Evol. 2019;3:1197–205. https://doi.org/10.1038/s41559-019-0940-0Article 
PubMed 

Google Scholar 
De Vargas Roditi L, Boyle KE, Xavier JB. Multilevel selection analysis of a microbial social trait. Mol Syst Biol. 2013;9:684 https://doi.org/10.1038/msb.2013.42Article 
PubMed 
PubMed Central 

Google Scholar 
Damore JA, Gore J. Understanding microbial cooperation. J Theor Biol. 2012;299:31–41. https://doi.org/10.1016/j.jtbi.2011.03.008Article 
PubMed 

Google Scholar 
Denoth Lippuner A, Julou T, Barral Y. Budding yeast as a model organism to study the effects of age. FEMS Microbiol Rev. 2014;38:300–25. https://doi.org/10.1111/1574-6976.12060CAS 
Article 
PubMed 

Google Scholar 
Janssens GE, Veenhoff LM. The natural variation in lifespans of single yeast cells is related to variation in cell size, ribosomal protein, and division time. PLoS ONE. 2016;11:e0167394 https://doi.org/10.1371/journal.pone.0167394CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ross-Gillespie A, Gardner A, West SA, Griffin AS. Frequency dependence and cooperation: Theory and a test with bacteria. Am Nat. 2007;170:331–42. https://doi.org/10.1086/519860Article 
PubMed 

Google Scholar 
Healey D, Axelrod K, Gore J. Negative frequency-dependent interactions can underlie phenotypic heterogeneity in a clonal microbial population. Mol Syst Biol. 2016;12:877 https://doi.org/10.15252/msb.20167033CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Harrow GL, Lees JA, Hanage WP, Lipsitch M, Corander J, Colijn C, et al. Negative frequency-dependent selection and asymmetrical transformation stabilise multi-strain bacterial population structures. ISME J. 2021;15:1523–38. https://doi.org/10.1038/s41396-020-00867-wCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Avilés L. Solving the freeloaders paradox: Genetic associations and frequency-dependent selection in the evolution of cooperation among nonrelatives. Proc Natl Acad Sci USA. 2002;99:14268–73. https://doi.org/10.1073/pnas.212408299CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fisher RM, Cornwallis CK, West SA. Group formation, relatedness, and the evolution of multicellularity. Curr Biol. 2013;23:1120–5. https://doi.org/10.1016/j.cub.2013.05.004CAS 
Article 
PubMed 

Google Scholar 
Pentz JT, Travisano M, Ratcliff WC, Clonal development is evolutionarily superior to aggregation in wild-collected Saccharomyces cerevisiae. In Artificial Life 14 – Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014, 2014;550–4. 10.7551/978-0-262-32621-6-ch088.Melbinger A, Cremer J, Frey E, The emergence of cooperation from a single mutant during microbial life cycles. J Royal Soc Interface. 2015;12. https://doi.org/10.1098/rsif.2015.0171 More

The copy numbers for 16S and ITS1 rRNA, and the sequencing depth for all samples are presented in Supplementary File 3 (qPCR data, Sequencing Rarefaction Curves). An average of 14,265.25 reads per housefly sample for the V4 16SrRNA and 16,149.4 reads per housefly sample for the ITS1 were retained after quality filtering. After quality filtering of the egg-laying substrate samples, an average of 10,371.75 reads were retained per sample for the V4 16SrRNA, and an average of 25,479.75 reads were retained per sample for the ITS1 region. The extracted DNA from newly emerged adult houseflies of the Spanish laboratory strain (12 samples in total, newly emerged adults, three replicates from four generations, strain SP100) returned a low copy number for the fungal ITS1 (qPCR data, Supplementary File 3) and a low number of acquired sequencing reads; they were therefore omitted from any further analysis of the fungal microbiota. In addition, the mitochondrial COI phylogeny showed that the Dutch wild-caught strain and the Dutch laboratory strain, which were sampled from the same locality at different times, are in close proximity and form a separate clade from the Spanish lab strain phylotypes (Supplementary File 2).The housefly microbiota alpha-diversity is determined by sampling environmentAbsolute richness (number of ASVs), Shannon index, and Phylogenetic diversity for all housefly strains and developmental stages are shown in Fig. 1. The highest bacterial alpha diversity was observed for the wild-caught housefly population GK0. Strain was an important factor for separating Shannon biodiversity levels both for newly emerged (F = 4.37, P  More

Reich, P. B. et al. The evolution of plant functional variation: traits, spectra, and strategies. Int. J. Plant Sci. 164, S143–S164 (2003).
Google Scholar 
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).
Google Scholar 
Liu, X. J. & Ma, K. P. Plant functional traits concepts, applications and future directions. Sci. Sin. Vitae 45, 325–339 (2015).
Google Scholar 
Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).
Google Scholar 
Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl Acad. Sci. USA 112, 797–802 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Barton, K. E. Tougher and thornier: general patterns in the induction of physical defence traits. Func. Ecol. 30, 181–187 (2016).
Google Scholar 
Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).PubMed 

Google Scholar 
Wright, S. J. et al. Functional traits and the growth–mortality trade-off in tropical trees. Ecology 91, 3664–3674 (2010).PubMed 

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

Google Scholar 
Ruiz-Jaen, M. C. & Potvin, C. Can we predict carbon stocks in tropical ecosystems from tree diversity? Comparing species and functional diversity in a plantation and a natural forest. New Phytol. 189, 978–987 (2011).PubMed 

Google Scholar 
Grubb, P. J. A positive distrust in simplicity-lessons from plant defences and from competition among plants and among animals. J. Ecol. 80, 585–610 (1992).
Google Scholar 
Hanley, M. E., Lamont, B. B., Fairbanks, M. M. & Rafferty, C. M. Plant structural traits and their role in anti-herbivore defence. Perspect. Plant Ecol. 8, 157–178 (2007).
Google Scholar 
Burns, K. C. Spinescence in the New Zealand flora: parallels with Australia. N. Z. J. Bot. 54, 273–289 (2016).
Google Scholar 
Goheen, J. R., Young, T. P., Keesing, F. & Palmer, T. M. Consequences of herbivory by native ungulates for the reproduction of a savanna tree. J. Ecol. 95, 129–138 (2007).
Google Scholar 
Goldel, B., Kissling, W. D. & Svenning, J.-C. Geographical variation and environmental correlates of functional trait distributions in palms (Arecaceae) across the New World. Bot. J. Linn. Soc. 179, 602–617 (2015).
Google Scholar 
Alves-Silva, E. & Del-Claro, K. Herbivory causes increases in leaf spinescence and fluctuating asymmetry as a mechanism of delayed induced resistance in a tropical savanna tree. Plant Ecol. Evol. 149, 73–80 (2016).
Google Scholar 
Cooper, S. M. & Ginnett, T. F. Spines protect plants against browsing by small climbing mammals. Oecologia 113, 219–221 (1998).ADS 
PubMed 

Google Scholar 
Charles-Dominique, T. et al. Spiny plants, mammal browsers, and the origin of African savannas. Proc. Natl Acad. Sci. USA 113, E5572–E5579 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ratnam, J., Tomlinson, K. W., Rasquinha, D. N. & Sankaran, M. Savannahs of Asia: antiquity, biogeography, and an uncertain future. Philos. Trans. R. Soc. B. 371, 20150305 (2016).
Google Scholar 
Scholes, R. & Archer, S. Tree-grass interactions in savannas. Annu. Rev. Ecol. Syst. 28, 517–544 (1997).
Google Scholar 
Cerling, T. E. Development of grasslands and savannas in East Africa during the Neogene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 97, 241–247 (1992).
Google Scholar 
Brown, R. W. Additions to the flora of the Green River formation. U. S. Geol. Surv. Prof. Paper, U. S. Gov. Print. Off. 154-J, 279–292 (1929).Manchester, S. Oligocene fossil plants of the John Day Formation, Oregon. Or. Geol. 49, 115d–127d (1987).
Google Scholar 
Meyer, H. W. & Manchester, S. R. Oligocene Bridge Creek flora of the John Day Formation, Oregon (Univ. California Press, 1997).Lancucka-Srodoniowa, M. Tortonian flora from the “Gdow Bay” in the south of Poland. Acta Palaeobot. 7, 1–134 (1966).
Google Scholar 
Yuan, J. et al. Rapid drift of the Tethyan Himalaya terrane before two-stage India-Asia collision. Natl Sci. Rev. 8, nwaa173 (2021).PubMed 

Google Scholar 
Spicer, R. A. et al. Why the ‘Uplift of the Tibetan Plateau’is a myth. Natl Sci. Rev. 8, nwaa091 (2021).PubMed 

Google Scholar 
Spicer, R. A. Tibet, the Himalaya, Asian monsoons and biodiversity–In what ways are they related? Plant Divers. 39, 233–244 (2017).PubMed 
PubMed Central 

Google Scholar 
DeCelles, P. G., Kapp, P., Gehrels, G. E. & Ding, L. Paleocene-Eocene foreland basin evolution in the Himalaya of southern Tibet and Nepal: implications for the age of initial India-Asia collision. Tectonics 33, 824–849 (2014).ADS 

Google Scholar 
Royden, L. H., Burchfiel, B. C. & van der Hilst, R. D. The geological evolution of the Tibetan Plateau. Science 321, 1054–1058 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Deng, T., Wu, F. X., Zhou, Z. K. & Su, T. Tibetan Plateau: an evolutionary junction for the history of modern biodiversity. Sci. China Earth Sci. 63, 172–187 (2020).ADS 

Google Scholar 
Favre, A. et al. The role of the uplift of the Qinghai‐Tibetan Plateau for the evolution of Tibetan biotas. Biol. Rev. 90, 236–253 (2015).PubMed 

Google Scholar 
Su, T. et al. A Middle Eocene lowland humid subtropical “Shangri-La” ecosystem in central Tibet. Proc. Natl Acad. Sci. USA 117, 32989–32995 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scientific Expedition Team to the Qinghai-Xizang Plateau. Vegetation of Xizang (Tibet) (Sci. Press, 1988).Liu. X. H. Paleoelevation History and Evolution of the Cenozoic Lunpola basin, Central Tibet. Doctoral thesis (Institute of Tibetan Plateau Research, Chinese Academy of Sciences, 2018).Xiong, Z. Y. et al. The rise and demise of the Paleogene Central Tibetan Valley. Sci. Adv. 8, eabj0944 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Reichgelt, T., West, C. K. & Greenwood, D. R. The relation between global palm distribution and climate. Sci. Rep. 8, 4721 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Farnsworth, A. et al. Paleoclimate model-derived thermal lapse rates: towards increasing precision in paleoaltimetry studies. Earth Planet. Sci. Lett. 564, 116903 (2021).CAS 

Google Scholar 
Spicer, R. A. et al. Why do foliar physiognomic climate estimates sometimes differ from those observed? Insights from taphonomic information loss and a CLAMP case study from the Ganges Delta. Palaeogeogr. Palaeoclimatol. Palaeoecol. 302, 381–395 (2011).
Google Scholar 
Walter, H. Vegetation of the Earth and Ecological Systems of the Geo-biosphere (Springer Berlin Heidelb., 1973).Burley, J. Encyclopedia of Forest Sciences (Acad. Press, 2004).Deng, T. et al. A mammalian fossil from the Dingqing Formation in the Lunpola Basin, northern Tibet, and its relevance to age and paleo-altimetry. Sci. Bull. 57, 261–269 (2012).CAS 

Google Scholar 
Ma, P. F. et al. Late Oligocene-early Miocene evolution of the Lunpola Basin, central Tibetan Plateau, evidences from successive lacustrine records. Gondwana Res. 48, 224–236 (2017).ADS 

Google Scholar 
Hempson, G. P., Archibald, S. & Bond, W. J. A continent-wide assessment of the form and intensity of large mammal herbivory in Africa. Science 350, 1056–1061 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Spicer, R. A. The formation and interpretation of plant fossil assemblages. Adv. Bot. Res. 16, 95–191 (1989).
Google Scholar 
Gibson, D. J. Grasses and Grassland Ecology (Oxford Univ. Press, 2009).Eltringham, S. K. The Hippos: Natural History and Conservation (Princeton Univ. Press, 1999).Jiang, H. et al. Oligocene Koelreuteria (Sapindaceae) from the Lunpola Basin in central Tibet and its implication for early diversification of the genus. J. Asian Earth Sci. 175, 99–108 (2019).ADS 

Google Scholar 
Liu, J. et al. Biotic interchange through lowlands of Tibetan Plateau suture zones during Paleogene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 524, 33–40 (2019).
Google Scholar 
Jia, L. B. et al. First fossil record of Cedrelospermum (Ulmaceae) from the Qinghai-Tibetan Plateau: implications for morphological evolution and biogeography. J. Syst. Evol. 57, 94–104 (2019).
Google Scholar 
Su, T. et al. No high Tibetan Plateau until the Neogene. Sci. Adv. 5, eaav2189 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, Y. L., Li, B. Y. & Zheng, D. A discussion on the boundary and area of the Tibetan Plateau in China. Geol. Res. 21, 1–8 (2002).
Google Scholar 
Yao, T. D. et al. From Tibetan Plateau to Third Pole and Pan-Third Pole. Bull. Chin. Acad. Sci. 32, 924–931 (2017).
Google Scholar 
Spicer, R. A., Farnsworth, A. & Su, T. Cenozoic topography, monsoons and biodiversity conservation within the Tibetan Region: an evolving story. Plant Divers. 42, 229–254 (2020).PubMed 
PubMed Central 

Google Scholar 
Liu, X. H., Xu, Q. & Ding, L. Differential surface uplift: Cenozoic paleoelevation history of the Tibetan Plateau. Sci. China Earth Sci. 59, 2105–2120 (2016).ADS 
CAS 

Google Scholar 
Ding, L., Li, Z. Y. & Song, P. P. Core fragments of Tibetan Plateau from Gondwanaland united in Northern Hemisphere. Bull. Chin. Acad. Sci. 32, 945–950 (2017).
Google Scholar 
Deng, T. & Ding, L. Paleoaltimetry reconstructions of the Tibetan Plateau: progress and contradictions. Natl Sci. Rev. 2, 417–437 (2015).CAS 

Google Scholar 
Li, S. F. et al. Orographic evolution of northern Tibet shaped vegetation and plant diversity in eastern Asia. Sci. Adv. 7, eabc7741 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Ding, L. et al. The Andean-type Gangdese Mountains: Paleoelevation record from the Paleocene–Eocene Linzhou Basin. Earth Planet. Sci. Lett. 392, 250–264 (2014).ADS 
CAS 

Google Scholar 
Deng, T. et al. Review: implications of vertebrate fossils for paleo-elevations of the Tibetan Plateau. Glob. Planet. Change 174, 58–69 (2019).ADS 

Google Scholar 
Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Hopkins, W. G. Introduction to Plant Physiology (John Wiley & Sons, 1999).Sun, J. M., Liu, W. G., Liu, Z. H. & Fu, B. H. Effects of the uplift of the Tibetan Plateau and retreat of Neotethys ocean on the stepwise aridification of mid-latitude Asian interior. Bull. Chin. Acad. Sci. 32, 951–958 (2017).
Google Scholar 
Zong, G. F. Cenezoic Mammals and Environment of Hengduan Mountains Region (China Ocean Press, 1996).Deng, T. et al. An Oligocene giant rhino provides insights into Paraceratherium evolution. Commun. Biol. 4, 639 (2021).PubMed 
PubMed Central 

Google Scholar 
Young, T. P., Stanton, M. L. & Christian, C. E. Effects of natural and simulated herbivory on spine lengths of Acacia drepanolobium in Kenya. Oikos 101, 171–179 (2003).
Google Scholar 
Karban, R. & Myers, J. H. Induced plant responses to herbivory. Annu. Rev. Ecol. Syst. 20, 331–348 (1989).
Google Scholar 
Huntly, N. Herbivores and the dynamics of communities and ecosystems. Annu. Rev. Ecol. Syst. 22, 477–503 (1991).
Google Scholar 
Asner, G. P. et al. Large-scale impacts of herbivores on the structural diversity of African savannas. Proc. Natl Acad. Sci. USA 106, 4947–4952 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sankaran, M., Augustine, D. J. & Ratnam, J. Native ungulates of diverse body sizes collectively regulate long‐term woody plant demography and structure of a semi‐arid savanna. J. Ecol. 101, 1389–1399 (2013).
Google Scholar 
Staver, A. C. & Bond, W. J. Is there a ‘browse trap’? Dynamics of herbivore impacts on trees and grasses in an African savanna. J. Ecol. 102, 595–602 (2014).
Google Scholar 
Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Spicer, R. A. et al. The topographic evolution of the Tibetan Region as revealed by palaeontology. Palaeobio. Palaeoenv. 101, 213–243 (2021).
Google Scholar 
Rowley, D. B. & Currie, B. S. Palaeo-altimetry of the late Eocene to Miocene Lunpola basin, central Tibet. Nature 439, 677–681 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Sun, J. M. et al. Palynological evidence for the latest Oligocene-early Miocene paleoelevation estimate in the Lunpola Basin, central Tibet. Palaeogeogr. Palaeoclimatol. Palaeoecol. 399, 21–30 (2014).
Google Scholar 
DeCelles, P. G., Kapp, P., Ding, L. & Gehrels, G. E. Late Cretaceous to middle Tertiary basin evolution in the central Tibetan Plateau: Changing environments in response to tectonic partitioning, aridification, and regional elevation gain. Geol. Soc. Am. Bull. 119, 654–680 (2007).ADS 

Google Scholar 
Tang, H. et al. Extinct genus Lagokarpos reveals a biogeographic connection between Tibet and other regions in the Northern Hemisphere during the Paleogene. J. Syst. Evol. 57, 670–677 (2019).
Google Scholar 
Wang, T. X. et al. Fossil fruits of Illigera (Hernandiaceae) from the Eocene of central Tibetan Plateau. J. Syst. Evol. 59, 1276–1286 (2021).
Google Scholar 
Del Rio, C. et al. Asclepiadospermum gen. nov., the earliest fossil record of Asclepiadoideae (Apocynaceae) from the early Eocene of central Qinghai-Tibetan Plateau, and its biogeographic implications. Am. J. Bot. 107, 126–138 (2020).PubMed 

Google Scholar 
Xu, Z. Y. The Tertiary and its petroleum potential in the Lunpola Basin, Tibet. Oil Gas. Geol. 1, 153–158 (1980).
Google Scholar 
Zhang, K. X. et al. Paleogene-Neogene stratigraphic realm and sedimentary sequence of the Qinghai-Tibet Plateau and their response to uplift of the plateau. Sci. China Earth Sci. 53, 1271–1294 (2010).ADS 

Google Scholar 
Wu, Y. F. & Chen, Y. Y. Fossil cyprinid fishes from the late Tertiary of north Xizang, China. Vertebrata Palasiat. 18, 15–20 (1980).
Google Scholar 
Wu, F. X., Miao, D. S., Chang, M. M., Shi, G. L. & Wang, N. Fossil climbing perch and associated plant megafossils indicate a warm and wet central Tibet during the late Oligocene. Sci. Rep. 7, 878 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cai, C. Y., Huang, D. Y., Wu, F. X., Zhao, M. & Wang, N. Tertiary water striders (Hemiptera, Gerromorpha, Gerridae) from the central Tibetan Plateau and their palaeobiogeographic implications. J. Asian Earth Sci. 175, 121–127 (2017).ADS 

Google Scholar 
Low, S. L. et al. Oligocene Limnobiophyllum (Araceae) from the central Tibetan Plateau and its evolutionary and palaeoenvironmental implications. J. Syst. Palaeontol. 18, 415–431 (2020).
Google Scholar 
Bell, A. D. & Bryan, A. Plant Form: An Illustrated Guide to Flowering Plant Morphology (Timber Press, 2008).Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 35, 526–528 (2019).CAS 
PubMed 

Google Scholar 
Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics. 24, 129–131 (2008).CAS 
PubMed 

Google Scholar 
Maddison, W. P. Confounding asymmetries in evolutionary diversification and character change. Evolution 60, 1743–1746 (2006).PubMed 

Google Scholar 
Forest, C. E., Molnar, P. & Emanuel, K. A. Palaeoaltimetry from energy conservation principles. Nature 374, 347–350 (1995).ADS 
CAS 

Google Scholar 
Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models: HadCM3@ Bristol v1.0. Geosci. Model Dev. 10, 3715–3743 (2017).ADS 
CAS 

Google Scholar 
Gough, D. O. Solar interior structure and luminosity variations. Sol. Phys. 74, 21–34 (1981).ADS 
CAS 

Google Scholar 
Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 14845 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cox, P. M. Description of the “TRIFFID” Dynamic Global Vegetation Model. 1–16 (Met Office Hadley Centre, 2001).Cox, P., Huntingford, C. & Harding, R. A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J. Hydrol. 212, 79–94 (1998).ADS 

Google Scholar 
McInerney, F. A., Strömberg, C. A. E. & White, J. W. C. The Neogene transition from C3 to C4 grasslands in North America stable carbon isotope ratios of fossil phytoliths. Paleobiology 37, 23–49 (2011).
Google Scholar 
Lu, H. Y. et al. Phytoliths as quantitative indicators for the reconstruction of past environmental conditions in China II: palaeoenvironmental reconstruction in the Loess Plateau. Quat. Sci. Rev. 25, 945–959 (2006).ADS 

Google Scholar  More

RESEARCH HIGHLIGHT
01 July 2022

Rock doves on some Scottish islands show almost no sign of having interbred with domestic pigeons.

The relatively long, slender bill of this rock dove from the Outer Hebridean islands of Scotland are characteristic of feral pigeons’ ancestors. Credit: W. J. Smith et al./iScience

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Charles Darwin developed his theory of natural selection in part by studying a form of artificial selection: the nineteenth-century rage for pigeon breeding, which created a wealth of fantastical varieties of pigeon (Columba livia). So widespread was pigeon fancying that it seeded the world with escaped domestic birds and their feral descendants, which then hybridized with their wild ancestors, the rock doves.

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doi: https://doi.org/10.1038/d41586-022-01780-2

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Conservation biology

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Conservation biology More

Hassani, M. A., Durán, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6(1), 58. https://doi.org/10.1186/s40168-018-0445-0 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Sapp, M., Ploch, S., Fiore-Donno, A. M., Bonkowski, M. & Rose, L. E. Protists are an integral part of the Arabidopsis thaliana microbiome. Environ Microbiol 20(1), 30–43. https://doi.org/10.1111/1462-2920.13941 (2018).CAS 
Article 
PubMed 

Google Scholar 
Herrera Paredes, S. & Lebeis, S. L. Giving back to the community: Microbial mechanisms of plant–soil interactions. Funct. Ecol. 30(7), 1043–1052. https://doi.org/10.1111/1365-2435.12684 (2016).Article 

Google Scholar 
Nath, A. & Sundaram, S. Microbiome community interactions with social forestry and agroforestry. In Microbial services in restoration ecology (eds Singh, J. S. & Vimal, S. R.) 71–82 (Elsevier, 2020).Chapter 

Google Scholar 
Rodriguez, P. A. et al. Systems biology of plant–microbiome interactions. Mol. Plant 12(6), 804–821. https://doi.org/10.1016/j.molp.2019.05.006 (2019).CAS 
Article 
PubMed 

Google Scholar 
Guttman, D. S., McHardy, A. C. & Schulze-Lefert, P. Microbial genome-enabled insights into plant–microorganism interactions. Nat. Rev. Genet. 15(12), 797–813. https://doi.org/10.1038/nrg3748 (2014).CAS 
Article 
PubMed 

Google Scholar 
Lewin, S., Francioli, D., Ulrich, A. & Kolb, S. Crop host signatures reflected by co-association patterns of keystone bacteria in the rhizosphere microbiota. Environ. Microb. 16(1), 18. https://doi.org/10.1186/s40793-021-00387-w (2021).CAS 
Article 

Google Scholar 
Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: From community assembly to plant health. Nat. Rev. Microbiol. 18(11), 607–621. https://doi.org/10.1038/s41579-020-0412-1 (2020).CAS 
Article 
PubMed 

Google Scholar 
Bardelli, T. et al. Effects of slope exposure on soil physico-chemical and microbiological properties along an altitudinal climosequence in the Italian Alps. Sci. Total Environ. 575, 1041–1055. https://doi.org/10.1016/j.scitotenv.2016.09.176 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Francioli, D., van Ruijven, J., Bakker, L. & Mommer, L. Drivers of total and pathogenic soil-borne fungal communities in grassland plant species. Fungal Ecol. 48, 100987. https://doi.org/10.1016/j.funeco.2020.100987 (2020).Article 

Google Scholar 
Hamonts, K. et al. Field study reveals core plant microbiota and relative importance of their drivers. Environ. Microbiol. 20(1), 124–140. https://doi.org/10.1111/1462-2920.14031 (2018).CAS 
Article 
PubMed 

Google Scholar 
Trivedi, P., Batista, B. D., Bazany, K. E. & Singh, B. K. Plant–microbiome interactions under a changing world: Responses, consequences and perspectives. New Phytol. 234(6), 1951–1959. https://doi.org/10.1111/nph.18016 (2022).Article 
PubMed 

Google Scholar 
Hawkes, C. V. et al. Extension of plant phenotypes by the foliar microbiome. Annu. Rev. Plant Biol. 72(1), 823–846. https://doi.org/10.1146/annurev-arplant-080620-114342 (2021).CAS 
Article 
PubMed 

Google Scholar 
Hunter, P. The revival of the extended phenotype: After more than 30 years, Dawkins’ extended phenotype hypothesis is enriching evolutionary biology and inspiring potential applications. EMBO Rep. 19(7), e46477. https://doi.org/10.15252/embr.201846477 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Thapa, S. & Prasanna, R. Prospecting the characteristics and significance of the phyllosphere microbiome. Ann. Microbiol. 68(5), 229–245. https://doi.org/10.1007/s13213-018-1331-5 (2018).CAS 
Article 

Google Scholar 
Vacher, C. et al. The phyllosphere: Microbial jungle at the plant-climate interface. Annu. Rev. Ecol. Evol. Syst. 47(1), 1–24. https://doi.org/10.1146/annurev-ecolsys-121415-032238 (2016).Article 

Google Scholar 
Copeland, J. K., Yuan, L., Layeghifard, M., Wang, P. W. & Guttman, D. S. Seasonal community succession of the phyllosphere microbiome. Mol. Plant Microbe Interact. 28(3), 274–285. https://doi.org/10.1094/mpmi-10-14-0331-fi (2015).CAS 
Article 
PubMed 

Google Scholar 
Pérez-Bueno, M. L., Pineda, M., Díaz-Casado, E. & Barón, M. Spatial and temporal dynamics of primary and secondary metabolism in Phaseolus vulgaris challenged by Pseudomonas syringae. Physiol. Plant. 153(1), 161–174. https://doi.org/10.1111/ppl.12237 (2015).CAS 
Article 
PubMed 

Google Scholar 
Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M. & Vorholt, J. A. A Synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10(4), e1004283. https://doi.org/10.1371/journal.pgen.1004283 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Giauque, H. & Hawkes, C. V. Climate affects symbiotic fungal endophyte diversity and performance. Am. J. Bot. 100(7), 1435–1444. https://doi.org/10.3732/ajb.1200568 (2013).Article 
PubMed 

Google Scholar 
Rodriguez, R. J. et al. Stress tolerance in plants via habitat-adapted symbiosis. ISME J. 2(4), 404–416. https://doi.org/10.1038/ismej.2007.106 (2008).Article 
PubMed 

Google Scholar 
Trivedi, P., Mattupalli, C., Eversole, K. & Leach, J. E. Enabling sustainable agriculture through understanding and enhancement of microbiomes. New Phytol. 230(6), 2129–2147. https://doi.org/10.1111/nph.17319 (2021).Article 
PubMed 

Google Scholar 
Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl. Acad. Sci. 106(38), 16428–16433. https://doi.org/10.1073/pnas.0905240106%JProceedingsoftheNationalAcademyofSciences (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10(12), 828–840. https://doi.org/10.1038/nrmicro2910 (2012).CAS 
Article 
PubMed 

Google Scholar 
Kembel, S. W. et al. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proc. Natl. Acad. Sci. 111(38), 13715–13720. https://doi.org/10.1073/pnas.1216057111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Whipps, J. M., Hand, P., Pink, D. & Bending, G. D. Phyllosphere microbiology with special reference to diversity and plant genotype. J. Appl. Microbiol. 105(6), 1744–1755. https://doi.org/10.1111/j.1365-2672.2008.03906.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528(7582), 364–369. https://doi.org/10.1038/nature16192 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Laforest-Lapointe, I., Messier, C. & Kembel, S. W. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. Microbiome 4(1), 27. https://doi.org/10.1186/s40168-016-0174-1 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Sapkota, R., Knorr, K., Jørgensen, L. N., O’Hanlon, K. A. & Nicolaisen, M. Host genotype is an important determinant of the cereal phyllosphere mycobiome. New Phytol. 207(4), 1134–1144. https://doi.org/10.1111/nph.13418 (2015).CAS 
Article 
PubMed 

Google Scholar 
Grady, K. L., Sorensen, J. W., Stopnisek, N., Guittar, J. & Shade, A. Assembly and seasonality of core phyllosphere microbiota on perennial biofuel crops. Nat. Commun. 10(1), 4135. https://doi.org/10.1038/s41467-019-11974-4 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Latz, M. A. C. et al. Succession of the fungal endophytic microbiome of wheat is dependent on tissue-specific interactions between host genotype and environment. Sci. Total Environ. 759, 143804. https://doi.org/10.1016/j.scitotenv.2020.143804 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rastogi, G. et al. Leaf microbiota in an agroecosystem: Spatiotemporal variation in bacterial community composition on field-grown lettuce. ISME J. 6(10), 1812–1822. https://doi.org/10.1038/ismej.2012.32 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bao, L. et al. Seasonal variation of epiphytic bacteria in the phyllosphere of Gingko biloba, Pinus bungeana and Sabina chinensis. FEMS Microbiol. Ecol. 96, 3. https://doi.org/10.1093/femsec/fiaa017 (2020).CAS 
Article 

Google Scholar 
Ding, T. & Melcher, U. Influences of plant species, season and location on leaf endophytic bacterial communities of non-cultivated plants. PLoS ONE 11(3), e0150895. https://doi.org/10.1371/journal.pone.0150895 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Perreault, R. & Laforest-Lapointe, I. Plant-microbe interactions in the phyllosphere: Facing challenges of the anthropocene. ISME J. https://doi.org/10.1038/s41396-021-01109-3 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Redford, A. J. & Fierer, N. Bacterial succession on the leaf surface: A novel system for studying successional dynamics. Microb. Ecol. 58(1), 189–198. https://doi.org/10.1007/s00248-009-9495-y (2009).Article 
PubMed 

Google Scholar 
Campisano, A. et al. Temperature drives the assembly of endophytic communities’ seasonal succession. Environ. Microbiol. 19(8), 3353–3364. https://doi.org/10.1111/1462-2920.13843 (2017).Article 
PubMed 

Google Scholar 
Ren, G. et al. Response of soil, leaf endosphere and phyllosphere bacterial communities to elevated CO2 and soil temperature in a rice paddy. Plant Soil 392(1), 27–44. https://doi.org/10.1007/s11104-015-2503-8 (2015).CAS 
Article 

Google Scholar 
Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11(1), 3044. https://doi.org/10.1038/s41467-020-16757-w (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421(6918), 37–42. https://doi.org/10.1038/nature01286 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Donn, S., Kirkegaard, J. A., Perera, G., Richardson, A. E. & Watt, M. Evolution of bacterial communities in the wheat crop rhizosphere. Environ. Microbiol. 17(3), 610–621. https://doi.org/10.1111/1462-2920.12452 (2015).Article 
PubMed 

Google Scholar 
Francioli, D., Schulz, E., Buscot, F. & Reitz, T. Dynamics of soil bacterial communities over a vegetation season relate to both soil nutrient status and plant growth phenology. Microb. Ecol. 75(1), 216–227. https://doi.org/10.1007/s00248-017-1012-0 (2018).CAS 
Article 
PubMed 

Google Scholar 
Breitkreuz, C., Buscot, F., Tarkka, M. & Reitz, T. Shifts between and among populations of wheat rhizosphere Pseudomonas, Streptomyces and Phyllobacterium suggest consistent phosphate mobilization at different wheat growth stages under abiotic stress. Front. Microbiol. 10, 3109–3109. https://doi.org/10.3389/fmicb.2019.03109 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Na, X. et al. Plant stage, not drought stress, determines the effect of cultivars on bacterial community diversity in the rhizosphere of broomcorn millet (Panicum miliaceum L.). Front. Microbiol. 10, 828. https://doi.org/10.3389/fmicb.2019.00828 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Ad-hoc-AG-Boden. Bodenkundliche Kartieranleitung 438 (Schweizerbart, 2005).
Google Scholar 
Zadoks, J. C., Chang, T. T. & Konzak, C. F. A decimal code for the growth stages of cereals. Weed Res. 14(6), 415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x (1974).Article 

Google Scholar 
Cannell, R. Q., Belford, R. K., Gales, K., Dennis, C. W. & Prew, R. D. Effects of waterlogging at different stages of development on the growth and yield of winter wheat. J. Sci. Food Agric. 31(2), 117–132. https://doi.org/10.1002/jsfa.2740310203 (1980).Article 

Google Scholar 
Drew, M. C. Soil aeration and plant root metabolism. Soil Sci. 154(4), 259–268 (1992).ADS 
Article 

Google Scholar 
Meyer, W. et al. Effect of irrigation on soil oxygen status and root and shoot growth of wheat in a clay soil. Aust. J. Agric. Res. https://doi.org/10.1071/AR9850171 (1985).Article 

Google Scholar 
Riehm, H. Bestimmung der laktatlöslichen Phosphorsäure in karbonathaltigen Böden. Phosphorsäure 1, 167–178. https://doi.org/10.1002/jpln.19420260107 (1943).Article 

Google Scholar 
Murphy, J., & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36. https://doi.org/10.1016/S0003-2670(00)88444-5 (1962).CAS 
Article 

Google Scholar 
Francioli, D., Lentendu, G., Lewin, S. & Kolb, S. DNA metabarcoding for the characterization of terrestrial microbiota—pitfalls and solutions. Microorganisms 9(2), 361 (2021).CAS 
Article 

Google Scholar 
Chelius, M. K. & Triplett, E. W. The diversity of archaea and bacteria in association with the roots of Zea mays L. Microb. Ecol. 41(3), 252–263. https://doi.org/10.1007/s002480000087 (2001).CAS 
Article 
PubMed 

Google Scholar 
Redford, A. J., Bowers, R. M., Knight, R., Linhart, Y. & Fierer, N. The ecology of the phyllosphere: Geographic and phylogenetic variability in the distribution of bacteria on tree leaves. Environ. Microbiol. 12(11), 2885–2893. https://doi.org/10.1111/j.1462-2920.2010.02258.x (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 1. https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581. https://doi.org/10.1038/Nmeth.3869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Francioli, D. et al. Flooding causes dramatic compositional shifts and depletion of putative beneficial bacteria on the spring wheat microbiota. Front. Microbiol. 12, 3371. https://doi.org/10.3389/fmicb.2021.773116 (2021).Article 

Google Scholar 
Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). In Wiley StatsRef: Statistics Reference Online 1–15 (Wiley, 2017).
Google Scholar 
Dray, S., Legendre, P. & Blanchet, G. Packfor: Forward Selection with Permutation. R package version 0.0‐8/r100 ed. (2011).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-2. ed. (2018).Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12(6), R60. https://doi.org/10.1186/gb-2011-12-6-r60 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Lahti, L. & Sudarshan, S. Tools for Microbiome Analysis in R. Version 2.1.28. ed. (2020).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
Chen, S. et al. Root-associated microbiomes of wheat under the combined effect of plant development and nitrogen fertilization. Microbiome 7(1), 136. https://doi.org/10.1186/s40168-019-0750-2 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, J. et al. Wheat and rice growth stages and fertilization regimes alter soil bacterial community structure, but not diversity. Front. Microbiol. 7, 1207. https://doi.org/10.3389/fmicb.2016.01207 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Comby, M., Lacoste, S., Baillieul, F., Profizi, C. & Dupont, J. Spatial and temporal variation of cultivable communities of co-occurring endophytes and pathogens in wheat. Front. Microbiol. 7, 403. https://doi.org/10.3389/fmicb.2016.00403 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Robinson, R. J. et al. Endophytic bacterial community composition in wheat (Triticum aestivum) is determined by plant tissue type, developmental stage and soil nutrient availability. Plant Soil 405(1), 381–396. https://doi.org/10.1007/s11104-015-2495-4 (2016).CAS 
Article 

Google Scholar 
Sapkota, R., Jørgensen, L. N. & Nicolaisen, M. Spatiotemporal variation and networks in the mycobiome of the wheat canopy. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.01357 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Chaudhry, V. et al. Shaping the leaf microbiota: Plant–microbe–microbe interactions. J. Exp. Bot. 72(1), 36–56. https://doi.org/10.1093/jxb/eraa417 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Liu, Z., Cheng, R., Xiao, W., Guo, Q. & Wang, N. Effect of off-season flooding on growth, photosynthesis, carbohydrate partitioning, and nutrient uptake in Distylium chinense. PLoS ONE 9(9), e107636. https://doi.org/10.1371/journal.pone.0107636 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rosa, M. et al. Soluble sugars. Plant Signal. Behav. 4(5), 388–393. https://doi.org/10.4161/psb.4.5.8294 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, H., Qualls, R. G. & Blank, R. R. Effect of soil flooding on photosynthesis, carbohydrate partitioning and nutrient uptake in the invasive exotic Lepidium latifolium. Aquat. Bot. 82(4), 250–268. https://doi.org/10.1016/j.aquabot.2005.02.013 (2005).CAS 
Article 

Google Scholar 
Bacanamwo, M. & Purcell, L. C. Soybean dry matter and N accumulation responses to flooding stress, N sources and hypoxia. J. Exp. Bot. 50(334), 689–696. https://doi.org/10.1093/jxb/50.334.689 (1999).CAS 
Article 

Google Scholar 
Boem, F. H. G., Lavado, R. S. & Porcelli, C. A. Note on the effects of winter and spring waterlogging on growth, chemical composition and yield of rapeseed. Field Crop. Res. 47(2), 175–179. https://doi.org/10.1016/0378-4290(96)00025-1 (1996).Article 

Google Scholar 
Kozlowski, T. T. Plant responses to flooding of soil. Bioscience 34(3), 162–167. https://doi.org/10.2307/1309751 (1984).Article 

Google Scholar 
Topa, M. A. & Cheeseman, J. M. 32P uptake and transport to shoots in Pinuus serotina seedlings under aerobic and hypoxic growth conditions. Physiol. Plant. 87(2), 125–133. https://doi.org/10.1111/j.1399-3054.1993.tb00134.x (1993).CAS 
Article 

Google Scholar 
Colmer, T. D. & Flowers, T. J. Flooding tolerance in halophytes. New Phytol. 179(4), 964–974. https://doi.org/10.1111/j.1469-8137.2008.02483.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Gibbs, J. & Greenway, H. Mechanisms of anoxia tolerance in plants. I. Growth, survival and anaerobic catabolism. Funct. Plant Biol. 30(1), 1–47. https://doi.org/10.1071/PP98095 (2003).CAS 
Article 
PubMed 

Google Scholar 
Board, J. E. Waterlogging effects on plant nutrient concentrations in soybean. J. Plant Nutr. 31(5), 828–838. https://doi.org/10.1080/01904160802043122 (2008).CAS 
Article 

Google Scholar 
Smethurst, C. F., Garnett, T. & Shabala, S. Nutritional and chlorophyll fluorescence responses of lucerne (Medicago sativa) to waterlogging and subsequent recovery. Plant Soil 270(1), 31–45. https://doi.org/10.1007/s11104-004-1082-x (2005).CAS 
Article 

Google Scholar 
Thomson, C. J., Atwell, B. J. & Greenway, H. Response of wheat seedlings to low O2 concentrations in nutrient solution: II. K+/Na+ selectivity of root tissues. J. Exp. Bot. 40(9), 993–999. https://doi.org/10.1093/jxb/40.9.993 (1989).Article 

Google Scholar 
Barrett-Lennard, E. G. The interaction between waterlogging and salinity in higher plants: Causes, consequences and implications. Plant Soil 253(1), 35–54. https://doi.org/10.1023/A:1024574622669 (2003).CAS 
Article 

Google Scholar 
Granzow, S. et al. The effects of cropping regimes on fungal and bacterial communities of wheat and faba bean in a greenhouse pot experiment differ between plant species and compartment. Front. Microbiol. 8, 902. https://doi.org/10.3389/fmicb.2017.00902 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Gdanetz, K. & Trail, F. The wheat microbiome under four management strategies, and potential for endophytes in disease protection. Phytobiomes J. 1(3), 158–168. https://doi.org/10.1094/PBIOMES-05-17-0023-R (2017).Article 

Google Scholar 
Shade, A., McManus, P. S., Handelsman, J. & Zhou, J. Unexpected diversity during community succession in the apple flower microbiome. MBio 4(2), e00602-00612. https://doi.org/10.1128/mBio.00602-12 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Guo, J. et al. Seed-borne, endospheric and rhizospheric core microbiota as predictors of plant functional traits across rice cultivars are dominated by deterministic processes. New. Phytol. 230(5), 2047–2060. https://doi.org/10.1111/nph.17297 (2021).Article 
PubMed 

Google Scholar 
Allwood, J. W. et al. Profiling of spatial metabolite distributions in wheat leaves under normal and nitrate limiting conditions. Phytochemistry 115, 99–111. https://doi.org/10.1016/j.phytochem.2015.01.007 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. et al. Plant phenotypic traits eventually shape its microbiota: A common garden test. Front. Microbiol. 9, 2479. https://doi.org/10.3389/fmicb.2018.02479 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Xiong, C. et al. Plant developmental stage drives the differentiation in ecological role of the maize microbiome. Microbiome 9(1), 171. https://doi.org/10.1186/s40168-021-01118-6 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Schlechter, R. O., Miebach, M. & Remus-Emsermann, M. N. P. Driving factors of epiphytic bacterial communities: A review. J. Adv. Res. 19, 57–65. https://doi.org/10.1016/j.jare.2019.03.003 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mathur, P., Mehtani, P. & Sharma, C. (2021). Leaf Endophytes and Their Bioactive Compounds. In Symbiotic Soil Microorganisms: Biology and Applications, (eds Shrivastava, N. et al.) 147–159 (Cham, Springer International Publishing, 2021).Aquino, J., Junior, F. L. A., Figueiredo, M., De Alcântara Neto, F. & Araujo, A. Plant growth-promoting endophytic bacteria on maize and sorghum1. Pesq. Agrop. Trop. https://doi.org/10.1590/1983-40632019v4956241 (2019).Article 

Google Scholar 
Gamalero, E. et al. Screening of bacterial endophytes able to promote plant growth and increase salinity tolerance. Appl. Sci. 10(17), 5767 (2020).CAS 
Article 

Google Scholar 
Borah, A. & Thakur, D. Phylogenetic and functional characterization of culturable endophytic actinobacteria associated with Camellia spp. for growth promotion in commercial tea cultivars. Front. Microbiol. 11, 318. https://doi.org/10.3389/fmicb.2020.00318 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Haidar, B. et al. Population diversity of bacterial endophytes from jute (Corchorus olitorius) and evaluation of their potential role as bioinoculants. Microbiol. Res. 208, 43–53. https://doi.org/10.1016/j.micres.2018.01.008 (2018).Article 
PubMed 

Google Scholar 
Bind, M. & Nema, S. Isolation and molecular characterization of endophytic bacteria from pigeon pea along with antimicrobial evaluation against Fusarium udum. J. Appl. Microbiol. Open Access 5, 163 (2019).
Google Scholar 
de Almeida Lopes, K. B. et al. Screening of bacterial endophytes as potential biocontrol agents against soybean diseases. J. Appl. Microbiol. 125(5), 1466–1481. https://doi.org/10.1111/jam.14041 (2018).CAS 
Article 
PubMed 

Google Scholar 
Müller, T. & Behrendt, U. Exploiting the biocontrol potential of plant-associated pseudomonads: A step towards pesticide-free agriculture?. Biol. Control 155, 104538. https://doi.org/10.1016/j.biocontrol.2021.104538 (2021).CAS 
Article 

Google Scholar 
Safin, R. I. et al. Features of seeds microbiome for spring wheat varieties from different regions of Eurasia. In: International Scientific and Practical Conference “AgroSMART: Smart Solutions for Agriculture”, 766–770 (Atlantis Press).Adler, P. B. & Drake, J. Environmental variation, stochastic extinction, and competitive coexistence. Am. Nat. 172(5), E186–E195. https://doi.org/10.1086/591678 (2008).Article 

Google Scholar 
Gilbert, B. & Levine, J. M. Ecological drift and the distribution of species diversity. Proc. R. Soc. B 284(1855), 20170507. https://doi.org/10.1098/rspb.2017.0507 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Fitzpatrick, C. R. et al. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl. Acad. Sci. 115(6), E1157–E1165. https://doi.org/10.1073/pnas.1717617115 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Freschet, G. T. et al. Root traits as drivers of plant and ecosystem functioning: Current understanding, pitfalls and future research needs. New Phytol. 232(3), 1123–1158. https://doi.org/10.1111/nph.17072 (2021).Article 
PubMed 

Google Scholar 
Kembel, S. W. & Mueller, R. C. Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities. Botany 92(4), 303–311. https://doi.org/10.1139/cjb-2013-0194 (2014).Article 

Google Scholar 
Leff, J. W. et al. Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits. ISME J. 12(7), 1794–1805. https://doi.org/10.1038/s41396-018-0089-x (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Ulbrich, T. C., Friesen, M. L., Roley, S. S., Tiemann, L. K. & Evans, S. E. Intraspecific variability in root traits and edaphic conditions influence soil microbiomes across 12 switchgrass cultivars. Phytobiom. J. 5(1), 108–120. https://doi.org/10.1094/pbiomes-12-19-0069-fi (2021).Article 

Google Scholar 
Arduini, I., Orlandi, C., Pampana, S. & Masoni, A. Waterlogging at tillering affects spike and spikelet formation in wheat. Crop Pasture Sci. 67(7), 703–711. https://doi.org/10.1071/CP15417 (2016).CAS 
Article 

Google Scholar 
Ding, J. et al. Effects of waterlogging on grain yield and associated traits of historic wheat cultivars in the middle and lower reaches of the Yangtze River, China. Field Crops Res. 246, 107695. https://doi.org/10.1016/j.fcr.2019.107695 (2020).Article 

Google Scholar 
Malik, I., Colmer, T., Lambers, H. & Schortemeyer, M. Changes in physiological and morphological traits of roots and shoots of wheat in response to different depths of waterlogging. Austral. J. Plant Physiol. 28, 1121–1131. https://doi.org/10.1071/PP01089 (2001).Article 

Google Scholar 
Pampana, S., Masoni, A. & Arduini, I. Grain yield of durum wheat as affected by waterlogging at tillering. Cereal Res. Commun. 44(4), 706–716. https://doi.org/10.1556/0806.44.2016.026 (2016).Article 

Google Scholar 
Xu, L. et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc. Natl. Acad. Sci. 115(18), E4284–E4293. https://doi.org/10.1073/pnas.1717308115%JProceedingsoftheNationalAcademyofSciences (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Angel, R. et al. The root-associated microbial community of the world’s highest growing vascular plants. Microb. Ecol. 72(2), 394–406. https://doi.org/10.1007/s00248-016-0779-8 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Edwards, J. A. et al. Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLoS Biol. 16(2), e2003862. https://doi.org/10.1371/journal.pbio.2003862 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kuźniar, A. et al. Culture-independent analysis of an endophytic core microbiome in two species of wheat: Triticum aestivum L. (cv. ‘Hondia’) and the first report of microbiota in Triticum spelta L. (cv. ‘Rokosz’). Syst. Appl. Microbiol. 43(1), 126025. https://doi.org/10.1016/j.syapm.2019.126025 (2020).CAS 
Article 
PubMed 

Google Scholar 
Soldan, R. et al. Bacterial endophytes of mangrove propagules elicit early establishment of the natural host and promote growth of cereal crops under salt stress. Microbiol. Res. 223–225, 33–43. https://doi.org/10.1016/j.micres.2019.03.008 (2019).CAS 
Article 
PubMed 

Google Scholar 
Truyens, S., Weyens, N., Cuypers, A. & Vangronsveld, J. Bacterial seed endophytes: Genera, vertical transmission and interaction with plants. Environ. Microbiol. Rep. 7(1), 40–50. https://doi.org/10.1111/1758-2229.12181 (2015).Article 

Google Scholar 
Chimwamurombe, P. M., Grönemeyer, J. L. & Reinhold-Hurek, B. Isolation and characterization of culturable seed-associated bacterial endophytes from gnotobiotically grown Marama bean seedlings. FEMS Microbiol. Ecol. 92, 6. https://doi.org/10.1093/femsec/fiw083 (2016).CAS 
Article 

Google Scholar 
Eid, A. M. et al. Harnessing bacterial endophytes for promotion of plant growth and biotechnological applications: An overview. Plants 10(5), 935 (2021).CAS 
Article 

Google Scholar 
Mareque, C. et al. The endophytic bacterial microbiota associated with sweet sorghum (Sorghum bicolor) is modulated by the application of chemical N fertilizer to the field. Int. J. Genom. 2018, 7403670. https://doi.org/10.1155/2018/7403670 (2018).CAS 
Article 

Google Scholar 
Francioli, D. et al. Mineral vs organic amendments: Microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol. 7, 1446. https://doi.org/10.3389/fmicb.2016.01446 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Schrey, S. D. & Tarkka, M. T. Friends and foes: Streptomycetes as modulators of plant disease and symbiosis. Antonie Van Leeuwenhoek 94(1), 11–19. https://doi.org/10.1007/s10482-008-9241-3 (2008).Article 
PubMed 

Google Scholar 
Patel, J. K., Madaan, S. & Archana, G. Antibiotic producing endophytic Streptomyces spp. colonize above-ground plant parts and promote shoot growth in multiple healthy and pathogen-challenged cereal crops. Microbiol. Res. 215, 36–45. https://doi.org/10.1016/j.micres.2018.06.003 (2018).CAS 
Article 
PubMed 

Google Scholar 
Yi, Y.-S. et al. Antifungal activity of Streptomyces sp. against Puccinia recondita causing wheat leaf rust. J. Microbiol. Biotechnol. 14(2), 422–425 (2004).CAS 

Google Scholar 
Sperdouli, I. & Moustakas, M. Leaf developmental stage modulates metabolite accumulation and photosynthesis contributing to acclimation of Arabidopsis thaliana to water deficit. J. Plant. Res. 127(4), 481–489. https://doi.org/10.1007/s10265-014-0635-1 (2014).CAS 
Article 
PubMed 

Google Scholar  More