More stories

  • in

    Albedo changes caused by future urbanization contribute to global warming

    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

  • in

    Rapid Eocene diversification of spiny plants in subtropical woodlands of central Tibet

    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

  • in

    Leaf bacterial microbiota response to flooding is controlled by plant phenology in wheat (Triticum aestivum L.)

    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

  • in

    Microbiota mediated plasticity promotes thermal adaptation in the sea anemone Nematostella vectensis

    Huxley, J. Evolution. The Modern Synthesis (Allen & Unwin, 1942).Bay, R. A. & Palumbi, S. R. Rapid acclimation ability mediated by transcriptome changes in reef-building corals. Genome Biol. Evol. 7, 1602–1612 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).CAS 
    PubMed 

    Google Scholar 
    Bang, C. et al. Metaorganisms in extreme environments: do microbes play a role in organismal adaptation? Zoology 127, 1–19 (2018).PubMed 

    Google Scholar 
    Fraune, S., Forêt, S. & Reitzel, A. M. Using Nematostella vectensis to study the interactions between genome, epigenome, and bacteria in a changing environment. Front. Mar. Sci. 3, 1–8 (2016).
    Google Scholar 
    Kolodny, O. & Schulenburg, H. Opinion piece Microbiome-mediated plasticity directs host evolution along several distinct time scales. Phil. Trans. R. Soc. B 375, 20190589 (2020).Reshef, L., Koren, O., Loya, Y., Zilber-Rosenberg, I. & Rosenberg, E. The coral probiotic hypothesis. Environ. Microbiol. 8, 2068–2073 (2006).CAS 
    PubMed 

    Google Scholar 
    Webster, N. S. & Reusch, T. B. H. Microbial contributions to the persistence of coral reefs. ISME J. 11, 2167–2174 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Totton, A. K. The British sea anemones. Nature 135, 977–978 (1935).
    Google Scholar 
    Hand, C. & Uhlinger, K. R. The unique, widely distributed, estuarine sea anemone, Nematostella vectensis Stephenson: a review, new facts, and questions. Estuaries 17, 501–501 (1994).
    Google Scholar 
    Darling, J. A., Reitzel, A. M. & Finnerty, J. R. Regional population structure of a widely introduced estuarine invertebrate: Nematostella vectensis Stephenson in New England. Mol. Ecol. 13, 2969–2981 (2004).CAS 
    PubMed 

    Google Scholar 
    Darling, J. A. et al. Rising starlet: the starlet sea anemone, Nematostella vectensis. BioEssays 27, 211–221 (2005).CAS 
    PubMed 

    Google Scholar 
    Hand, C. & Uhlinger, K. R. The culture, sexual and asexual reproduction, and growth of the sea anemone Nematostella vectensis. Biol. Bull. 182, 169–176 (1992).CAS 
    PubMed 

    Google Scholar 
    Pearson, C. V. M., Rogers, A. D. & Sheader, M. The genetic structure of the rare lagoonal sea anemone, Nematostella vectensis Stephenson (Cnidaria; Anthozoa) in the United Kingdom based on RAPD analysis. Mol. Ecol. 11, 2285–2293 (2002).CAS 
    PubMed 

    Google Scholar 
    Reitzel, A. M., Darling, J. A., Sullivan, J. C. & Finnerty, J. R. Global population genetic structure of the starlet anemone Nematostella vectensis: multiple introductions and implications for conservation policy. Biol. Invasions 10, 1197–1213 (2008).
    Google Scholar 
    Stefanik, D. J., Friedman, L. E. & Finnerty, J. R. Collecting, rearing, spawning and inducing regeneration of the starlet sea anemone, Nematostella vectensis. Nat. Protoc. 8, 916–923 (2013).PubMed 

    Google Scholar 
    Fritzenwanker, J. H. & Technau, U. Induction of gametogenesis in the basal cnidarian Nematostella vectensis (Anthozoa). Dev. Genes Evol. 212, 99–103 (2002).PubMed 

    Google Scholar 
    Mortzfeld, B. M. et al. Response of bacterial colonization in Nematostella vectensis to development, environment, and biogeography. Environ. Microbiol. 18, 1764–1781 (2016).PubMed 

    Google Scholar 
    Baldassarre, L. et al. Contribution of maternal and paternal transmission to bacterial colonization in Nematostella vectensis. Front. Microbiol. 12, 2892 (2021).
    Google Scholar 
    Domin, H. et al. Predicted bacterial interactions affect in vivo microbial colonization dynamics in Nematostella. Front. Microbiol. 9, 728 (2018).Guest, J. J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, e33353–e33353 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Puisay, A., Pilon, R., Goiran, C. & Hédouin, L. Thermal resistances and acclimation potential during coral larval ontogeny in Acropora pulchra. Mar. Environ. Res. 135, 1–10 (2018).CAS 
    PubMed 

    Google Scholar 
    Van Oppen, M. J. H., Oliver, J. K., Putnam, H. M. & Gates, R. D. Building coral reef resilience through assisted evolution. Proc. Natl Acad. Sci. USA 112, 2313 (2015).
    Google Scholar 
    Torda, G. et al. Rapid adaptive responses to climate change in corals. Nat. Clim. Change 7, 627–636 (2017).
    Google Scholar 
    Yu, Xiaopeng et al. Thermal acclimation increases heat tolerance of the scleractinian coral Acropora pruinosa,. Sci. Total Environ. 733, 139319–139319 (2020).CAS 
    PubMed 

    Google Scholar 
    Jury, C. P. & Toonen, R. J. Adaptive responses and local stressor mitigation drive coral resilience in warmer, more acidic oceans. Proc. R. Soc. B Biol. Sci. 286, 20190614–20190614 (2019).
    Google Scholar 
    Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 5 (2019).
    Google Scholar 
    Thomas, L. et al. Mechanisms of thermal tolerance in reef-building corals across a fine-grained environmental mosaic: lessons from Ofu,. Am. Samoa. Front. Mar. Sci. 4, 434 (2018).
    Google Scholar 
    Oliver, T. A. & Palumbi, S. R. Many corals host thermally resistant symbionts in high-temperature habitat. Coral Reefs 30, 241–250 (2011).
    Google Scholar 
    Kenkel, C. D. & Matz, M. V. Gene expression plasticity as a mechanism of coral adaptation to a variable environment. Nat. Ecol. Evol. 1, 14 (2017).Barker, V. Exceptional thermal tolerance of coral reefs in American Samoa a review. Curr. Clim. Change Rep. 4, 427 (2018).
    Google Scholar 
    Bourne, D., Iida, Y., Uthicke, S. & Smith-Keune, C. Changes in coral-associated microbial communities during a bleaching event. ISME J. 2, 350–63 (2008).CAS 
    PubMed 

    Google Scholar 
    Carrier, T. J. & Reitzel, A. M. The hologenome across environments and the implications of a host-associated microbial repertoire. Front. Microbiol. 8, 802 (2017).Koren, O. & Rosenberg, E. Bacteria associated with mucus and tissues of the coral Oculina patagonica in summer and winter. Appl. Environ. Microbiol. 72, 5254–5259 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Littman, R., Willis, B. L. & Bourne, D. G. Metagenomic analysis of the coral holobiont during a natural bleaching event on the Great Barrier Reef. Environ. Microbiol. Rep. 3, 651–60 (2011).CAS 
    PubMed 

    Google Scholar 
    Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213–14213 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thurber, R. V. et al. Metagenomic analysis of stressed coral holobionts. Environ. Microbiol. 11, 2148–2163 (2009).CAS 

    Google Scholar 
    van Oppen, M. J. H. & Blackall, L. L. Coral microbiome dynamics, functions and design in a changing world. Nat. Rev. Microbiol. 17, 557–567 (2019).PubMed 

    Google Scholar 
    Moran, N. A. & Yun, Y. Experimental replacement of an obligate insect symbiont. Proc. Natl Acad. Sci. USA 112, 2093–2096 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ainsworth, T. D. T. et al. The coral core microbiome identifies rare bacterial taxa as ubiquitous endosymbionts. ISME J. 9, 2261–2274 (2015).CAS 

    Google Scholar 
    Hester, E. R., Barott, K. L., Nulton, J., Vermeij, M. J. A. & Rohwer, F. L. Stable and sporadic symbiotic communities of coral and algal holobionts. ISME J. 10, 1157–1169 (2016).CAS 
    PubMed 

    Google Scholar 
    Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 340 (2016).
    Google Scholar 
    Pollock, F. J. et al. Reduced diversity and stability of coral-associated bacterial communities and suppressed immune function precedes disease onset in corals. R. Soc. Open Sci. 6, 31312497 (2019).Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol. Rev. 32, 723–735 (2008).CAS 
    PubMed 

    Google Scholar 
    Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).CAS 
    PubMed 

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

    Google Scholar 
    Bourne, D. G. Microbiological assessment of a disease outbreak on corals from Magnetic Island (Great Barrier Reef, Australia). Coral Reefs 24, 304–312 (2005).
    Google Scholar 
    Leach, W. B., Carrier, T. J. & Reitzel, A. M. Diel patterning in the bacterial community associated with the sea anemone Nematostella vectensis. Ecol. Evol. 9, 9935–9947 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Pootakham, W. et al. Heat-induced shift in coral microbiome reveals several members of the Rhodobacteraceae family as indicator species for thermal stress in Porites lutea. MicrobiologyOpen 8, e935 (2019).Webster, N. Host-associated coral reef microbes respond to the cumulative pressures of ocean warming and ocean acidification. Sci. Rep. 6, 19324 (2016).Van, K. L., Ae, A., Schupp, P. & Slattery, M. The distribution of dimethylsulfoniopropionate in tropical Pacific coral reef invertebrates. Coral Reefs 25, 321–327 (2006).
    Google Scholar 
    Rypien, K. L., Ward, J. R. & Azam, F. Antagonistic interactions among coral-associated bacteria. Environ. Microbiol. 12, 28–39 (2010).CAS 
    PubMed 

    Google Scholar 
    Blazejak, A., Erséus, C., Amann, R. & Dubilier, N. Coexistence of bacterial sulfide oxidizers, sulfate reducers, and spirochetes in a gutless worm (oligochaeta) from the Peru margin. Appl. Environ. Microbiol. 71, 1553–1561 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dubilier, N. et al. Phylogenetic diversity of bacterial endosymbionts in the gutless marine oligochete Olavius loisae (Annelida). Mar. Ecol. Prog. Ser. 178, 271–280 (1999).
    Google Scholar 
    Rincón-Rosales, R., Lloret, L., Ponce, E. & Martínez-Romero, E. Erratum: Rhizobia with different symbiotic efficiencies nodulate Acaciella angustissima in Mexico, including Sinorhizobium chiapanecum sp. nov. which has common symbiotic genes with Sinorhizobium mexicanum (FEMS Microbiology Ecology (2009) 67 (103-117)). FEMS Microbiol. Ecol. 68, 255–255 (2009).
    Google Scholar 
    Rosenberg, E. & DeLong, E. F., Stackebrandt, E., Lory, S., Thompson, F. The Prokaryotes—Prokaryotic Biology and Symbiotic Associations. (Springer, 2013).Kimura, H., Higashide, Y. & Naganuma, T. Endosymbiotic microflora of the Vestimentiferan Tubeworm (Lamellibrachia sp.) from a Bathyal Cold Seep. Mar. Biotechnol. 5, 593–603 (2003).CAS 

    Google Scholar 
    Melillo, A. A., Bakshi, C. S. & Melendez, J. A. Francisella tularensis antioxidants harness reactive oxygen species to restrict macrophage signaling and cytokine production. J. Biol. Chem. 285, 27553–27560 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rabadi, S. M. et al. Antioxidant defenses of Francisella tularensis modulate macrophage function and production of proinflammatory cytokines. J. Biol. Chem. 291, 5009–5021 (2016).CAS 
    PubMed 

    Google Scholar 
    McBride, M. J. in The Prokaryotes: Other Major Lineages of Bacteria and The Archaea. Vol. 9783642389542, 643–676 (Springer-Verlag Berlin Heidelberg, 2014).Augustin, R., Fraune, S. & Bosch, T. C. G. How Hydra senses and destroys microbes. Semin. Immunol. 22, 54–58 (2010).CAS 
    PubMed 

    Google Scholar 
    Augustin, R. et al. A secreted antibacterial neuropeptide shapes the microbiome of Hydra. Nat. Commun. 8, 698 (2017).Franzenburg, S. et al. Distinct antimicrobial peptide expression determines host species-specific bacterial associations. Proc. Natl Acad. Sci. USA 110, E3730–E3738 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fraune, S., Abe, Y. & Bosch, T. C. G. G. Disturbing epithelial homeostasis in the metazoan Hydra leads to drastic changes in associated microbiota. Environ. Microbiol. 11, 2361–9 (2009).CAS 
    PubMed 

    Google Scholar 
    Brennan, J. J. et al. Sea anemone model has a single Toll-like receptor that can function in pathogen detection, NF-κB signal transduction, and development. Proc. Natl Acad. Sci. USA 114, E10122–E10131 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sullivan, J. C. et al. Two alleles of NF-κB in the sea anemone Nematostella vectensis are widely dispersed in nature and encode proteins with distinct activities. PLoS ONE 4, e7311 (2009).Wolenski, F. S. et al. Characterization of the core elements of the NF-B signaling pathway of the sea anemone Nematostella vectensis. Mol. Cell. Biol. 31, 1076–1087 (2011).CAS 
    PubMed 

    Google Scholar 
    Gáliková, M., Klepsatel, P., Senti, G. & Flatt, T. Steroid hormone regulation of C. elegans and Drosophila aging and life history. Exp. Gerontol. 46, 141–147 (2011).PubMed 

    Google Scholar 
    Taubenheim, J., Kortmann, C. & Fraune, S. Function and evolution of nuclear receptors in environmental-dependent postembryonic development. Front. Cell Dev. Biol. 9, 653792 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Becker, P. B. & Workman, J. L. Nucleosome remodeling and epigenetics. Cold Spring Harb. Perspect. Biol. 5, a017905–a017905 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Barno, A. R., Villela, H. D. M., Aranda, M., Thomas, T. & Peixoto, R. S. Host under epigenetic control: a novel perspective on the interaction between microorganisms and corals. BioEssays 43, 2100068.Reitzel, A. M. et al. Physiological and developmental responses to temperature by the sea anemone Nematostella vectensis. Mar. Ecol. Prog. Ser. 484, 115–130 (2013).
    Google Scholar 
    Chua, C. M., Leggat, W., Moya, A. & Baird, A. H. Temperature affects the early life history stages of corals more than near future ocean acidification. Mar. Ecol. Prog. Ser. 475, 85–92 (2013).
    Google Scholar 
    Ericson, J. A. et al. Combined effects of two ocean change stressors, warming and acidification, on fertilization and early development of the Antarctic echinoid Sterechinus neumayeri. Polar Biol. 35, 1027–1034 (2012).
    Google Scholar 
    Sheppard Brennand, H., Soars, N., Dworjanyn, S. A., Davis, A. R. & Byrne, M. Impact of ocean warming and ocean acidification on larval development and calcification in the sea urchin Tripneustes gratilla. PLoS ONE 5, e11372 (2010).Bernal, M. A. et al. Phenotypic and molecular consequences of stepwise temperature increase across generations in a coral reef fish. Mol. Ecol. 27, 4516–4528 (2018).CAS 
    PubMed 

    Google Scholar 
    Clark, M. S. et al. Molecular mechanisms underpinning transgenerational plasticity in the green sea urchin Psammechinus miliaris. Sci. Rep. 9, 1–12 (2019).
    Google Scholar 
    Donelson, J. et al. Rapid transgenerational acclimation of a tropical reef fish to climate change. Nat. Clim. Change 2, 30–32 (2012).
    Google Scholar 
    Miller, G. M., Watson, S. A., Donelson, J. M., McCormick, M. I. & Munday, P. L. Parental environment mediates impacts of increased carbon dioxide on a coral reef fish. Nat. Clim. Change 2, 858–861 (2012).CAS 

    Google Scholar 
    Munday, P. L. Transgenerational acclimation of fishes to climate change and ocean acidification. F1000Prime Rep. 6, 99–99 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Ryu, T. et al. An epigenetic signature for within-generational plasticity of a reef fish to ocean warming. Front. Mar. Sci. 7, 284 (2020).Veilleux, H. et al. Molecular processes of transgenerational acclimation to a warming ocean. Nat. Clim. Change 5, 1074–1078 (2015).CAS 

    Google Scholar 
    Zhao, C. et al. Transgenerational effects of ocean warming on the sea urchin Strongylocentrotus intermedius. Ecotoxicol. Environ. Saf. 151, 212–219 (2018).CAS 
    PubMed 

    Google Scholar 
    Eirin-Lopez, J. M. & Putnam, H. M. Marine Environmental Epigenetics. Annu. Rev. Mar. Sci. 11, 335–368 (2019).
    Google Scholar 
    Fallet, M., Luquet, E., David, P. & Cosseau, C. Epigenetic inheritance and intergenerational effects in mollusks. Gene 729, 144166–144166 (2020).CAS 
    PubMed 

    Google Scholar 
    Putnam, H. M. & Gates, R. D. Preconditioning in the reef-building coral Pocillopora damicornis and the potential for trans-generational acclimatization in coral larvae under future climate change conditions. J. Exp. Biol. 218, 2365–2372 (2015).PubMed 

    Google Scholar 
    Daxinger, L. & Whitelaw, E. Transgenerational epigenetic inheritance: more questions than answers. Genome Res. 20, 1623–1628 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ptashne, M. Epigenetics: core misconcept. Proc. Natl Acad. Sci. USA 110, 7101–7103 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rivera, H. E., Chen, C.-Y., Gibson, M. C. & Tarrant, A. M. Plasticity in parental effects confers rapid larval thermal tolerance in the estuarine anemone Nematostella vectensis. J. Exp. Biol. 224, jeb236745 (2021).Hirose, E. & Fukuda, T. Vertical transmission of photosymbionts in the colonial ascidian Didemnum molle: The larval tunic prevents symbionts from attaching to the anterior part of larvae. Zool. Sci. 23, 669–674 (2006).
    Google Scholar 
    Padilla-Gamiño, J. L., Pochon, X., Bird, C., Concepcion, G. T. & Gates, R. D. From parent to gamete: vertical transmission of Symbiodinium (Dinophyceae) ITS2 sequence assemblages in the reef building coral Montipora capitata. PLoS ONE 7, e38440–e38440 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Sharp, K. H., Eam, B., John Faulkner, D. & Haygood, M. G. Vertical transmission of diverse microbes in the tropical sponge Corticium sp. Appl. Environ. Microbiol. 73, 622–629 (2007).CAS 
    PubMed 

    Google Scholar 
    Sipkema, D. et al. Similar sponge-associated bacteria can be acquired via both vertical and horizontal transmission. Environ. Microbiol. 17, 3807–3821 (2015).CAS 
    PubMed 

    Google Scholar 
    Apprill, A., Marlow, H. Q., Martindale, M. Q. & Rappé, M. S. The onset of microbial associations in the coral Pocillopora meandrina. ISME J. 3, 685–699 (2009).PubMed 

    Google Scholar 
    Sharp, K. H., Distel, D. & Paul, V. J. Diversity and dynamics of bacterial communities in early life stages of the Caribbean coral Porites astreoides. ISME J. 6, 790–801 (2012).CAS 
    PubMed 

    Google Scholar 
    Lesser, M. P., Stat, M. & Gates, R. D. The endosymbiotic dinoflagellates (Symbiodinium sp.) of corals are parasites and mutualists. Coral Reefs 32, 603–611 (2013).
    Google Scholar 
    Ceh, J., Raina, J. B., Soo, R. M., van Keulen, M. & Bourne, D. G. Coral-bacterial communities before and after a coral mass spawning event on Ningaloo Reef. PLoS ONE 7, e36920 (2012).Ricardo, G. F., Jones, R. J., Negri, A. P. & Stocker, R. That sinking feeling: suspended sediments can prevent the ascent of coral egg bundles. Sci. Rep. 6, 21567 (2016).Leite, D. C. A. D. et al. Broadcast spawning coral Mussismilia Hispida can vertically transfer its associated bacterial core. Front. Microbiol. 8, 176–176 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Epstein, H. E. et al. Microbiome engineering: enhancing climate resilience in corals. Front. Ecol. Environ. 17, 108 (2019).
    Google Scholar 
    Peixoto, R. S. et al. Beneficial microorganisms for corals (BMC) Proposed mechanisms for coral health and resilience. Front. Microbiol. 8, 341 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Chakravarti, L. J., Beltran, V. H. & van Oppen, M. J. H. Rapid thermal adaptation in photosymbionts of reef-building corals. Glob. Change Biol. 23, 4675–4688 (2017).
    Google Scholar 
    Damjanovic, K., Blackall, L. L., Webster, N. S. & van Oppen, M. J. H. H. The contribution of microbial biotechnology to mitigating coral reef degradation. Microb. Biotechnol. 10, 1236–1243 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Damjanovic, K., Van Oppen, M. J. H., Menéndez, P. & Blackall, L. L. Experimental inoculation of coral recruits with marine bacteria indicates scope for microbiome manipulation in Acropora tenuis and Platygyra daedalea. Front. Microbiol. 10, 1702 (2019).Rosado, P. M. et al. Marine probiotics: increasing coral resistance to bleaching through microbiome manipulation. ISME J. 13, 921–936 (2019).CAS 
    PubMed 

    Google Scholar 
    Fraune, S. et al. Bacteria-bacteria interactions within the microbiota of the ancestral metazoan Hydra contribute to fungal resistance. ISME J. 9, 1543–1556 (2015).CAS 
    PubMed 

    Google Scholar 
    Fadrosh, D. W. et al. An improved dual-indexing approach for multiplexed 16 S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2, 6 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Rausch, P. et al. Analysis of factors contributing to variation in the C57BL/6 J fecal microbiota across German animal facilities. Int. J. Med. Microbiol. 306, 343–355 (2016).PubMed 

    Google Scholar 
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439–1237439 (2013).PubMed 
    PubMed Central 

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

    Google Scholar 
    Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shao, M. & Kingsford, C. accurate assembly of transcripts through phase-preserving graph decomposition. Nat. Biotechnol. 35, 1167–1169 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Niknafs, Y. S., Pandian, B., Iyer, H. K., Chinnaiyan, A. M. & Iyer, M. K. TACO produces robust multisample transcriptome assemblies from RNA-seq. Nat. Methods 14, 68–70 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Pertea, M. & Pertea, G. GFF Utilities: GffRead and GffCompare. F1000Research 9, 304–304 (2020).
    Google Scholar 
    Manni, M., Berkeley, M. R., Seppey, M., Simão, F. A. & Zdobnov, E. M. BUSCO update: novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol. Biol. Evol. 38, 4647–4654 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Liao, Y., Smyth, G. K. & Shi, W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).CAS 
    PubMed 

    Google Scholar 
    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550–550 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29–R29 (2014).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    eDNA metabarcoding as a promising conservation tool to monitor fish diversity in Beijing water systems compared with ground cages

    Zou, K. et al. eDNA metabarcoding as a promising conservation tool for monitoring fish diversity in a coastal wetland of the Pearl River Estuary compared to bottom trawling. Sci. Total Environ. 702, 134704 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Almond, R., Grooten, M. & Peterson, T. Living Planet Report 2020-Bending the Curve of Biodiversity Loss (World Wildlife Fund, 2020).
    Google Scholar 
    Beverton, R. Fish resources; threats and protection. Neth. J. Zool. 42, 139–175 (1991).Article 

    Google Scholar 
    Jackson, S. & Head, L. Australia’s mass fish kills as a crisis of modern water: Understanding hydrosocial change in the Murray-Darling Basin. Geoforum 109, 44–56 (2020).Article 

    Google Scholar 
    Rees, H. C. et al. REVIEW: The detection of aquatic animal species using environmental DNA—a review of eDNA as a survey tool in ecology. J. Appl. Ecol. 51, 1450–1459 (2014).CAS 
    Article 

    Google Scholar 
    Rees, H. C. et al. The application of eDNA for monitoring of the Great Crested Newt in the UK. Ecol. Evol. 4, 4023–4032 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wang, C. et al. Research on the biodiversity of Qinhuai River based on environmental DNA metabacroding. Acta Ecol. Sin. 42, 611–624 (2022).Article 

    Google Scholar 
    Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Cons. 183, 53–63 (2015).Article 

    Google Scholar 
    Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Miralles, L., Parrondo, M., Hernandez de Rojas, A., Garcia-Vazquez, E. & Borrell, Y. J. Development and validation of eDNA markers for the detection of Crepidula fornicata in environmental samples. Mar. Pollut. Bull. 146, 827–830 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Takahara, T., Minamoto, T., Yamanaka, H., Doi, H. & Kawabata, Z. Estimation of fish biomass using environmental DNA. PLoS ONE 7, e35868 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Aglieri, G. et al. Environmental DNA effectively captures functional diversity of coastal fish communities. Mol. Ecol. 30, 3127–3139 (2020).PubMed 
    Article 

    Google Scholar 
    Yang, H. et al. Effectiveness assessment of using riverine water eDNA to simultaneously monitor the riverine and riparian biodiversity information. Sci. Rep. 11, 24241 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Altermatt, F. et al. Uncovering the complete biodiversity structure in spatial networks: the example of riverine systems. Oikos 129, 607–618 (2020).Article 

    Google Scholar 
    Stat, M. et al. Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv. Biol. 33, 196–205 (2019).PubMed 
    Article 

    Google Scholar 
    Hallam, J., Clare, E. L., Jones, J. I. & Day, J. J. Biodiversity assessment across a dynamic riverine system: A comparison of eDNA metabarcoding versus traditional fish surveying methods. Environ. DNA 3, 1247–1266 (2021).Article 

    Google Scholar 
    Gao, W. Beijing Vertebrate Key (Beijing Publishing House, 1994).
    Google Scholar 
    Wang, H. Beijing Fish and Amphibians and Reptiles (Beijing Publishing House, 1994).
    Google Scholar 
    Chen, W., Hu, D. & Fu, B. Research on Biodiversity of Beijing Wetland (Science Press, 2007).
    Google Scholar 
    Zhang, C. et al. Fish species diversity and conservation in Beijing and adjacent areas. Biodivers. Sci. 19, 597–604 (2011).Article 

    Google Scholar 
    Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).Article 

    Google Scholar 
    Fu, M., Xiao, N., Zhao, Z., Gao, X. & Li, J. Effects of Urbanization on Ecosystem Services in Beijing. Res. Soil Water Conserv. 23, 235–239 (2016).
    Google Scholar 
    Hao, L. & Sun, G. Impacts of urbanization on watershed ecohydrological processes: progresses and perspectives. Acta Ecol. Sin. 41, 13–26 (2021).
    Google Scholar 
    Su, G. et al. Human impacts on global freshwater fish biodiversity. Science 371, 835–838 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Yan, B. et al. Effects of urban development on soil microbial functional diversity in Beijing. Res. Environ. Sci. 29, 1325–1335 (2016).CAS 

    Google Scholar 
    Xiao, N., Gao, X., Li, J. & Bai, J. Evaluation and Conservation Measures of Beijing Biodiversity (China Forestry Publishing House, 2018).
    Google Scholar 
    Xu, S., Wang, Z., Liang, J. & Zhang, S. Use of different sampling tools for comparison of fish-aggregating effects along horizontal transect at two artificial reef sites in Shengsi. J. Fish. China 40, 820–831 (2016).
    Google Scholar 
    Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics (Oxford, England) 30, 614–620 (2014).CAS 
    Article 

    Google Scholar 
    Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics (Oxford, England) 34, 884–890 (2018).Article 
    CAS 

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics (Oxford, England) 26, 2460–2461 (2010).CAS 
    Article 

    Google Scholar 
    Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Iwasaki, W. et al. MitoFish and MitoAnnotator: A mitochondrial genome database of fish with an accurate and automatic annotation pipeline. Mol. Biol. Evol. 30, 2531–2540 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wang, H. Beijing Fish Records (Beijing Publishing House, 1984).
    Google Scholar 
    Du, L. et al. Fish community characteristics and spatial pattern in major rivers of Beijing City. Res. Environ. Sci. 32, 447–457 (2019).
    Google Scholar 
    Shen, W. & Ren, H. TaxonKit: A practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics 48, 844–850 (2021).PubMed 
    Article 

    Google Scholar 
    Karr, J. R. Assessment of biotic integrity using fish communities. Fisheries 6, 21–27 (1981).Article 

    Google Scholar 
    Zhang, C. & Zhao, Y. Fishes in Beijing and Adjacent Areas (China. Science Press, 2013).
    Google Scholar 
    Wu, H. & Zhong, J. Fauna Sinica, Osteichthyes, Perciformess(Five),Gobioidei (Science Press, 2008).
    Google Scholar 
    Di, Y. et al. Distribution of fish communities and its influencing factors in the Nansha and Beijing sub-center reaches of the Beiyun River. Acta Sci. Circumst. 41, 156–163 (2020).
    Google Scholar 
    Walters, D. M., Freeman, M. C., Leigh, D. S., Freeman, B. J. & Pringle, C. M. in Effects of Urbanization on Stream Ecosystems Vol. 47 American Fisheries Society Symposium 69–85 (2005).Hu, X., Zuo, D., Liu, B., Huang, Z. & Xu, Z. Quantitative analysis of the correlation between macrobenthos community and water environmental factors and aquatic ecosystem health assessment in the North Canal River Basin of Beijing. Environ. Sci. 43, 247–255 (2022).
    Google Scholar 
    Kadye, W. T., Magadza, C. H. D., Moyo, N. A. G. & Kativu, S. Stream fish assemblages in relation to environmental factors on a montane plateau (Nyika Plateau, Malawi). Environ. Biol. Fishes 83, 417–428 (2008).Article 

    Google Scholar 
    Smith, T. A. & Kraft, C. E. Stream fish assemblages in relation to landscape position and local habitat variables. Trans. Am. Fish. Soc. 134, 430–440 (2005).Article 

    Google Scholar 
    Blabolil, P. et al. Environmental DNA metabarcoding uncovers environmental correlates of fish communities in spatially heterogeneous freshwater habitats. Ecol. Ind. 126, 107698 (2021).CAS 
    Article 

    Google Scholar 
    Xie, R. et al. eDNA metabarcoding revealed differential structures of aquatic communities in a dynamic freshwater ecosystem shaped by habitat heterogeneity. Environ. Res. 201, 111602 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Qu, C. et al. Comparing fish prey diversity for a critically endangered aquatic mammal in a reserve and the wild using eDNA metabarcoding. Sci. Rep. 10, 16715 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pont, D. et al. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci. Rep. 8, 10361 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Doble, C. J. et al. Testing the performance of environmental DNA metabarcoding for surveying highly diverse tropical fish communities: A case study from Lake Tanganyika. Environ. DNA 2, 24–41 (2020).Article 

    Google Scholar 
    Xu, N. et al. Monitoring seasonal distribution of an endangered anadromous sturgeon in a large river using environmental DNA. Sci. Nat. 105, 62 (2018).Article 
    CAS 

    Google Scholar 
    Laramie, M. B., Pilliod, D. S. & Goldberg, C. S. Characterizing the distribution of an endangered salmonid using environmental DNA analysis. Biol. Cons. 183, 29–37 (2015).Article 

    Google Scholar 
    Harper, L. R. et al. Development and application of environmental DNA surveillance for the threatened crucian carp (Carassius carassius). Freshw. Biol. 64, 93–107 (2019).CAS 
    Article 

    Google Scholar 
    Ushio, M. et al. Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding Metagenomics 2, e2329 (2018).
    Google Scholar 
    Evans, N. T. et al. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour. 16, 29–41 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Harrison, J. B., Sunday, J. M. & Rogers, S. M. Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proc. Biol. Sci. 286, 20191409 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kelly, R. P., Shelton, A. O. & Gallego, R. Understanding PCR processes to draw meaningful conclusions from environmental DNA studies. Sci. Rep. 9, 12133 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Civade, R. et al. Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PLoS ONE 11, e0157366 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, 1819–1827 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Shogren, A. J. et al. Water flow and biofilm cover influence environmental DNA detection in recirculating streams. Environ. Sci. Technol. 52, 8530–8537 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhao, B., van Bodegom, P. M. & Trimbos, K. The particle size distribution of environmental DNA varies with species and degradation. Sci. Total Environ. 797, 149175 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Vision and vocal communication guide three-dimensional spatial coordination of zebra finches during wind-tunnel flights

    Dynamic in-flight flock organizationIt is commonly assumed that during flocking, flock members follow three basic interaction rules: Attraction, Repulsion and Alignment, to coordinate spatial positions between each other18. To study the spatial organization of our zebra finch flock during flight, the spatial positions of all birds in the flight section were tracked in every fifth frame (sample rate: 24 Hz (that is, frames per second)) of the synchronized footage recorded by two high-speed digital video cameras (Camera 1: centred upwind view, Fig. 1a,b; Camera 2: upturned vertical view, Fig. 1a,c) for the entire duration (51.7, 58.3, 69.2 and 127 s) of four (session 2, 5, 8 and 13) out of 13 flight sessions. Flight paths were reconstructed from the tracking data for each bird in the flock, with horizontal and vertical coordinates delivered by Camera 1 and coordinates in wind direction delivered by Camera 2. The data show that each bird mainly occupied a particular area in the flight section, and that this spatial preference was stable over different flight sessions. Bird Green, for example, was preferentially flying very low above the flight section’s floor, and bird Lilac preferred to fly at upwind positions in front of the flock (Fig. 1d, Extended Data Figs. 1 and 3 and Supplementary Information).Despite their preference in flight area, all birds constantly changed their spatial positions fast and rhythmically along the horizontal dimension of the flight section (Fig. 1e–g, Extended Data Figs. 2 and 4, Supplementary Video 1 and Supplementary Information). This behaviour is reminiscent of the flight behaviour of wild zebra finches: when being surprised in flight by a predator, zebra finches fly in a rapid zig-zag course low above the ground, heading for nearby vegetation16. Whether the sideways oscillating flight manoeuvres, which are performed by both wild birds in open space and domesticated birds in the wind tunnel’s flight section, are caused by the close proximity to the ground or are part of an escape reaction is yet unknown.From the tracking data, we further calculated the spatial distances in all three dimensions between all pairwise combinations of birds throughout the four flight sessions (sample rate: 24 Hz). When normalized to the maximum distance detected for each bird pairing, each dimension and each flight session, mean distances of bird pairings in all dimensions were narrowly distributed within a range of 27.7–38.0% of maximum distance (Fig. 1h and Supplementary Table 1). This may indicate that during flocking flight, zebra finches actively balance Attraction and Repulsion to maintain a stable 3D distance towards all other members of the flock. Owing to the spatial limitations in the wind tunnel’s flight section, we did not expect the zebra finches to perform large-scale flight manoeuvres with movements aligned between all flock members (Extended Data Fig. 5 and Supplementary Information), as can be observed, for example, in freely flying flocks of homing pigeons (Columba livia domestica)19 and white storks (Ciconia Ciconia)20.Visually guided horizontal repositioningWhen observing the dynamic spatial organization of our zebra finch flock, a question immediately arises: how do the birds prevent collisions during their frequent horizontal position changes? When considering the spatial limitation experienced by the flock of six birds during flight in the flight section and their highly dynamic flight style, collision rates seemed to be astonishingly low (median: 0.02 Hz; interquartile range (IQR): 0–0.03 Hz; n = 13 sessions) during flocking flight (in total 16 collisions in 13 min of analysed flight time). In birds, the visual system represents the main input channel for environmental information. To tackle the above question, we therefore first investigated the role of vision during flocking flight, and tested whether a bird’s viewing direction was correlated with the direction of horizontal position change. As gaze changes are governed by head movements in birds21, we used a bird’s head direction as an indicator for the orientation of its visual axis. We tracked (sample rate: 120 Hz) the position of a bird’s beak tip and neck in each frame of the footage during ten horizontal position changes (Fig. 2a and Supplementary Video 2) per bird, and found a strong interaction between a bird’s head angle relative to the wind direction and its direction of horizontal position change. During horizontal position changes, the birds always turned their heads in the direction of the position change (Fig. 2b). While the population’s median absolute angle of position change was 84.0° (IQR: 78.6–87.2°; n = 60) relative to 0° in wind direction, the population’s median absolute head turning angle was 36.0° (IQR: 26.4–42.5°; n = 60; see Supplementary Information for results on head movements during solo flight). The eyes of zebra finches are positioned laterally on their heads22 and each retina features a small region of highest ganglion cell density (fovea, that is, region of highest visual spatial resolution) at an area that receives visual input from horizontal positions at 60° relative to the midsagittal plane23. By turning their heads by about 36° during horizontal position changes, the zebra finches roughly align the foveal area in the retina of one eye with their direction of position change, and in the retina of the other eye with the wind direction (Fig. 2c,d). Thus, head turns in the direction of position change may indicate that the birds use visual cues while repositioning themselves within the flock. This hypothesis is supported by a study on zebra finch head movements performed during an obstacle avoidance task. In this study, instead of fixating on the obstacle, zebra finches turned their head in the direction of movement while navigating around the obstacle24.Fig. 2: Horizontal position changes are accompanied by head turns.a, Head and body orientation of bird Orange (ventral view) during one example of position changes to the right, tracked (sample rate: 120 Hz) in the footage of Camera 2. Circles: beak tip positions; plus signs: neck positions; upward pointing triangles: tail base positions. Cutouts of freeze frames of the footage taken with Camera 2 show the bird’s head and body posture for 11 time points during the position change. b, In all birds, the median angle of head turn during horizontal position change in flocking flight is positively correlated (linear mixed effects model (LMM), estimates ± s.e.m.: 2.05 ± 0.1, P  More

  • in

    Decision-making of citizen scientists when recording species observations

    Fink, D. et al. Crowdsourcing meets ecology: he misphere wide spatiotemporal species distribution models. AI Mag. 35, 19–30. https://doi.org/10.1609/aimag.v35i2.2533 (2014).Article 

    Google Scholar 
    Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Cons. 213, 280–294. https://doi.org/10.1016/j.biocon.2016.09.004 (2017).Article 

    Google Scholar 
    Schmeller, D. S. et al. Advantages of volunteer-based biodiversity monitoring in Europe. Conserv. Biol. 23, 307–316. https://doi.org/10.1111/j.1523-1739.2008.01125.x (2009).Article 
    PubMed 

    Google Scholar 
    Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol. https://doi.org/10.1371/journal.pbio.1000385 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Follett, R. & Strezov, V. An analysis of citizen science based research: Usage and publication patterns. PLoS ONE https://doi.org/10.1371/journal.pone.0143687 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zattara, E. E. & Aizen, M. A. Worldwide occurrence records suggest a global decline in bee species richness. One Earth 4, 114–123. https://doi.org/10.1016/j.oneear.2020.12.005 (2021).ADS 
    Article 

    Google Scholar 
    Dickinson, J. L. et al. The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10, 291–297. https://doi.org/10.1890/110236 (2012).Article 

    Google Scholar 
    Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560. https://doi.org/10.1002/fee.1436 (2016).Article 

    Google Scholar 
    Bayraktarov, E. et al. Do big unstructured biodiversity data mean more knowledge?. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00239 (2019).Article 

    Google Scholar 
    Burgess, H. K. et al. The science of citizen science: Exploring barriers to use as a primary research tool. Biol. Cons. 208, 113–120. https://doi.org/10.1016/j.biocon.2016.05.014 (2017).Article 

    Google Scholar 
    Isaac, N. J. B. & Pocock, M. J. O. Bias and information in biological records. Biol. J. Lin. Soc. 115, 522–531. https://doi.org/10.1111/bij.12532 (2015).Article 

    Google Scholar 
    August, T., Fox, R., Roy, D. B. & Pocock, M. J. O. Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias. Sci. Rep. https://doi.org/10.1038/s41598-020-67658-3 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour. Sci. Rep. https://doi.org/10.1038/srep33051 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Di Cecco, G. J. et al. Observing the observers: How participants contribute data to iNaturalist and implications for biodiversity science. Bioscience 71, 1179–1188. https://doi.org/10.1093/biosci/biab093 (2021).Article 

    Google Scholar 
    Kamp, J., Oppel, S., Heldbjerg, H., Nyegaard, T. & Donald, P. F. Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark. Divers. Distrib. 22, 1024–1035. https://doi.org/10.1111/ddi.12463 (2016).Article 

    Google Scholar 
    Altwegg, R. & Nichols, J. D. Occupancy models for citizen-science data. Methods Ecol. Evol. 10, 8–21. https://doi.org/10.1111/2041-210x.13090 (2019).Article 

    Google Scholar 
    Courter, J. R., Johnson, R. J., Stuyck, C. M., Lang, B. A. & Kaiser, E. W. Weekend bias in citizen science data reporting: Implications for phenology studies. Int. J. Biometeorol. 57, 715–720. https://doi.org/10.1007/s00484-012-0598-7 (2013).ADS 
    Article 
    PubMed 

    Google Scholar 
    Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400. https://doi.org/10.1093/biosci/biw022 (2016).Article 

    Google Scholar 
    Geldmann, J. et al. What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. Divers. Distrib. 22, 1139–1149. https://doi.org/10.1111/ddi.12477 (2016).Article 

    Google Scholar 
    Girardello, M. et al. Gaps in butterfly inventory data: A global analysis. Biol. Cons. 236, 289–295. https://doi.org/10.1016/j.biocon.2019.05.053 (2019).Article 

    Google Scholar 
    Husby, M., Hoset, K. S. & Butler, S. Non-random sampling along rural-urban gradients may reduce reliability of multi-species farmland bird indicators and their trends. Ibis https://doi.org/10.1111/ibi.12896 (2021).Article 

    Google Scholar 
    Petersen, T. K., Speed, J. D. M., Grøtan, V. & Austrheim, G. Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection. Ecol. Solut. Evid. https://doi.org/10.1002/2688-8319.12048 (2021).Article 

    Google Scholar 
    Egerer, M., Lin, B. B. & Kendal, D. Towards better species identification processes between scientists and community participants. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.133738 (2019).Article 
    PubMed 

    Google Scholar 
    Jimenez, M. F., Pejchar, L. & Reed, S. E. Tradeoffs of using place-based community science for urban biodiversity monitoring. Conserv. Sci. Pract. https://doi.org/10.1111/csp2.338 (2021).Article 

    Google Scholar 
    Branchini, S. et al. Using a citizen science program to monitor coral reef biodiversity through space and time. Biodivers. Conserv. 24, 319–336. https://doi.org/10.1007/s10531-014-0810-7 (2015).Article 

    Google Scholar 
    Snall, T., Kindvall, O., Nilsson, J. & Part, T. Evaluating citizen-based presence data for bird monitoring. Biol. Cons. 144, 804–810. https://doi.org/10.1016/j.biocon.2010.11.010 (2011).Article 

    Google Scholar 
    Gardiner, M. M. et al. Lessons from lady beetles: Accuracy of monitoring data from US and UK citizen-science programs. Front. Ecol. Environ. 10, 471–476. https://doi.org/10.1890/110185 (2012).Article 

    Google Scholar 
    Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. https://doi.org/10.1038/s41598-017-09084-6 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Johansson, F. et al. Can information from citizen science data be used to predict biodiversity in stormwater ponds?. Sci. Rep. https://doi.org/10.1038/s41598-020-66306-0 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Everett, G. & Geoghegan, H. Initiating and continuing participation in citizen science for natural history. BMC Ecol. https://doi.org/10.1186/s12898-016-0062-3 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Richter, A. et al. The social fabric of citizen science drivers for long-term engagement in the German butterfly monitoring scheme. J. Insect Conserv. 22, 731–743. https://doi.org/10.1007/s10841-018-0097-1 (2018).Article 

    Google Scholar 
    MacPhail, V. J., Gibson, S. D. & Colla, S. R. Community science participants gain environmental awareness and contribute high quality data but improvements are needed: Insights from Bumble Bee Watch. PeerJ https://doi.org/10.7717/peerj.9141 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maund, P. R. et al. What motivates the masses: Understanding why people contribute to conservation citizen science projects. Biol. Conserv. https://doi.org/10.1016/j.biocon.2020.108587 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moczek, N., Nuss, M. & Kohler, J. K. Volunteering in the citizen science project “Insects of Saxony”—The larger the island of knowledge, the longer the bank of questions. Insects https://doi.org/10.3390/insects12030262 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Branchini, S. et al. Participating in a citizen science monitoring program: Implications for environmental education. PLoS ONE https://doi.org/10.1371/journal.pone.0131812 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kelemen-Finan, J., Scheuch, M. & Winter, S. Contributions from citizen science to science education: An examination of a biodiversity citizen science project with schools in Central Europe. Int. J. Sci. Educ. 40, 2078–2098. https://doi.org/10.1080/09500693.2018.1520405 (2018).Article 

    Google Scholar 
    Deguines, N., Prince, K., Prevot, A. C. & Fontaine, B. Assessing the emergence of pro-biodiversity practices in citizen scientists of a backyard butterfly survey. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.136842 (2020).Article 
    PubMed 

    Google Scholar 
    Peter, M., Diekötter, T., Höffler, T. & Kremer, K. Biodiversity citizen science: Outcomes for the participating citizens. People Nat. 3, 294–311. https://doi.org/10.1002/pan3.10193 (2021).Article 

    Google Scholar 
    Phillips, T. B., Bailey, R. L., Martin, V., Faulkner-Grant, H. & Bonter, D. N. The role of citizen science in management of invasive avian species: What people think, know, and do. J. Environ. Manage. https://doi.org/10.1016/j.jenvman.2020.111709 (2021).Article 
    PubMed 

    Google Scholar 
    Parrish, J. K. et al. Hoping for optimality or designing for inclusion: Persistence, learning, and the social network of citizen science. Proc. Natl. Acad. Sci. U.S.A. 116, 1894–1901. https://doi.org/10.1073/pnas.1807186115 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mac Domhnaill, C., Lyons, S. & Nolan, A. The citizens in citizen science: Demographic, socioeconomic, and health characteristics of biodiversity recorders in Ireland. Citiz. Sci.: Theory Pract. 5, 16. https://doi.org/10.5334/cstp.283 (2020).Article 

    Google Scholar 
    van der Wal, R., Sharma, N., Mellish, C., Robinson, A. & Siddharthan, A. The role of automated feedback in training and retaining biological recorders for citizen science. Conserv. Biol. 30, 550–561. https://doi.org/10.1111/cobi.12705 (2016).Article 
    PubMed 

    Google Scholar 
    Bloom, E. H. & Crowder, D. W. Promoting data collection in pollinator citizen science projects. Citiz. Sci.: Theory Pract. 5, 3. https://doi.org/10.5334/cstp.217 (2020).Article 

    Google Scholar 
    Johnston, A., Fink, D., Hochachka, W. M. & Kelling, S. Estimates of observer expertise improve species distributions from citizen science data. Methods Ecol. Evol. 9, 88–97. https://doi.org/10.1111/2041-210x.12838 (2018).Article 

    Google Scholar 
    Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179. https://doi.org/10.1093/biosci/biz010 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Koen, B., Loosveldt, G., Vandenplas, C. & Stoop, I. Response rates in the european social survey: Increasing, decreasing, or a matter of fieldwork efforts?. Surv. Methods: Insights Field https://doi.org/10.13094/SMIF-2018-00003 (2018).Article 

    Google Scholar 
    Gideon, L. Handbook of Survey Methodology for the Social Sciences (Springer, 2012).Book 

    Google Scholar 
    Wolf, C., Joye, D., Smith, T. W. & Fu, Y. C. The SAGE Handbook of Survey Methodology (SAGE Publications Ltd, 2016).Book 

    Google Scholar 
    Richter, A. et al. Motivation and support services in citizen science insect monitoring: A cross-country study. Biol. Conserv. 263, 109325. https://doi.org/10.1016/j.biocon.2021.109325 (2021).Article 

    Google Scholar 
    Johnston, A., Moran, N., Musgrove, A., Fink, D. & Baillie, S. R. Estimating species distributions from spatially biased citizen science data. Ecol. Model. https://doi.org/10.1016/j.ecolmodel.2019.108927 (2020).Article 

    Google Scholar 
    Isaac, N. J. B., van Strien, A. J., August, T. A., de Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: Extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060. https://doi.org/10.1111/2041-210x.12254 (2014).Article 

    Google Scholar 
    Liao, H.-I., Yeh, S.-L. & Shimojo, S. Novelty vs. familiarity principles in preference decisions: Task context of past experience matters. Front. Psychol. https://doi.org/10.3389/fpsyg.2011.00043 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Park, J., Shimojo, E. & Shimojo, S. Roles of familiarity and novelty in visual preference judgments are segregated across object categories. Proc. Natl. Acad. Sci. U.S.A. 107, 14552–14555. https://doi.org/10.1073/pnas.1004374107 (2010).ADS 
    Article 
    PubMed 

    Google Scholar 
    Tiago, P., Gouveia, M. J., Capinha, C., Santos-Reis, M. & Pereira, H. M. The influence of motivational factors on the frequency of participation in citizen science activities. Nat. Conserv.-Bulg. https://doi.org/10.3897/natureconservation.18.13429 (2017).Article 

    Google Scholar 
    Davis, A., Taylor, C. E. & Martin, J. M. Are pro-ecological values enough? Determining the drivers and extent of participation in citizen science programs. Hum. Dimens. Wildl. 24, 501–514. https://doi.org/10.1080/10871209.2019.1641857 (2019).Article 

    Google Scholar 
    Bell, S. et al. What counts? Volunteers and their organisations in the recording and monitoring of biodiversity. Biodivers. Conserv. 17, 3443–3454. https://doi.org/10.1007/s10531-008-9357-9 (2008).Article 

    Google Scholar 
    Toomey, A. H. & Domroese, M. C. Can citizen science lead to positive conservation attitudes and behaviors?. Hum. Ecol. Rev. 20, 50–62 (2013).Article 

    Google Scholar 
    Dennis, E. B., Morgan, B. J. T., Brereton, T. M., Roy, D. B. & Fox, R. Using citizen science butterfly counts to predict species population trends. Conserv. Biol. 31, 1350–1361. https://doi.org/10.1111/cobi.12956 (2017).Article 
    PubMed 

    Google Scholar 
    Callaghan, C. T., Poore, A. G. B., Major, R. E., Rowley, J. J. L. & Cornwell, W. K. Optimizing future biodiversity sampling by citizen scientists. Proc. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rspb.2019.1487 (2019).Article 

    Google Scholar 
    Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384. https://doi.org/10.1038/s41559-020-1111-z (2020).Article 
    PubMed 

    Google Scholar 
    Bowler, D. E. et al. Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany. Divers. Distrib. https://doi.org/10.1111/ddi.13274 (2021).Article 

    Google Scholar  More

  • in

    Carbon fixation rates in groundwater similar to those in oligotrophic marine systems

    Falkowski, P. et al. The global carbon cycle: a test of our knowledge of Earth as a system. Science 290, 291–296 (2000).Article 

    Google Scholar 
    McMahon, S. & Parnell, J. Weighing the deep continental biosphere. FEMS Microbiol. Ecol. 87, 113–120 (2014).Article 

    Google Scholar 
    Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11, 707–717 (2018).Article 

    Google Scholar 
    Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E. & Cardenas, M. B. The global volume and distribution of modern groundwater. Nat. Geosci. 9, 161–167 (2016).Article 

    Google Scholar 
    Stevanović, Z. Karst waters in potable water supply: a global scale overview. Environ. Earth Sci. 78, 662 (2019).Article 

    Google Scholar 
    Poulson, T. L. & White, W. B. The cave environment. Science 165, 971–981 (1969).Article 

    Google Scholar 
    Rusterholtz, K. J. & Mallory, L. M. Density, activity, and diversity of bacteria indigenous to a karstic aquifer. Microb. Ecol. 28, 79–99 (1994).Article 

    Google Scholar 
    Smith, H. J. et al. Impact of hydrologic boundaries on microbial planktonic and biofilm communities in shallow terrestrial subsurface environments. FEMS Microbiol. Ecol. 94, fiy191 (2018).
    Google Scholar 
    Alexander, M. Introduction to Soil Microbiology (Wiley, 1977).Griebler, C. & Lueders, T. Microbial biodiversity in groundwater ecosystems. Freshw. Biol. 54, 649–677 (2009).Article 

    Google Scholar 
    Krumholz, L. R., McKinley, J. P., Ulrich, G. A. & Suflita, J. M. Confined subsurface microbial communities in Cretaceous rock. Nature 386, 64–66 (1997).Article 

    Google Scholar 
    Probst, A. J. et al. Differential depth distribution of microbial function and putative symbionts through sediment-hosted aquifers in the deep terrestrial subsurface. Nat. Microbiol. 3, 328–336 (2018).Article 

    Google Scholar 
    Magnabosco, C. et al. A metagenomic window into carbon metabolism at 3 km depth in Precambrian continental crust. ISME J. 10, 730–741 (2016).Article 

    Google Scholar 
    Stevens, T. O. & McKinley, J. P. Lithoautotrophic microbial ecosystems in deep basalt aquifers. Science 270, 450–455 (1995).Article 

    Google Scholar 
    Tiago, I. & Veríssimo, A. Microbial and functional diversity of a subterrestrial high pH groundwater associated to serpentinization. Environ. Microbiol. 15, 1687–1706 (2013).Article 

    Google Scholar 
    Mccollom, T. M. & Amend, J. P. A thermodynamic assessment of energy requirements for biomass synthesis by chemolithoautotrophic micro-organisms in oxic and anoxic environments. Geobiology 3, 135–144 (2005).Article 

    Google Scholar 
    Momper, L., Jungbluth, S. P., Lee, M. D. & Amend, J. P. Energy and carbon metabolisms in a deep terrestrial subsurface fluid microbial community. ISME J. 11, 2319–2333 (2017).Article 

    Google Scholar 
    Jewell, T. N. M., Karaoz, U., Brodie, E. L., Williams, K. H. & Beller, H. R. Metatranscriptomic evidence of pervasive and diverse chemolithoautotrophy relevant to C, S, N and Fe cycling in a shallow alluvial aquifer. ISME J. 10, 2106–2117 (2016).Article 

    Google Scholar 
    Herrmann, M., Rusznyák, A. & Akob, D. M. Large fractions of CO2-fixing microorganisms in pristine limestone aquifers appear to be involved in the oxidation of reduced sulfur and nitrogen compounds. Appl. Environ. Microbiol. 81, 2384–2394 (2015).Peterson, B. J. Aquatic primary productivity and the 14C–CO2 method: a history of the productivity problem. Annu. Rev. Ecol. Syst. 11, 359–385 (1980).Article 

    Google Scholar 
    Viviani, D. A., Karl, D. M. & Church, M. J. Variability in photosynthetic production of dissolved and particulate organic carbon in the North Pacific Subtropical Gyre. Front. Mar. Sci. 2, 73 (2015).Article 

    Google Scholar 
    Kohlhepp, B. et al. Aquifer configuration and geostructural links control the groundwater quality in thin-bedded carbonate–siliciclastic alternations of the Hainich CZE, central Germany. Hydrol. Earth Syst. Sci. 21, 6091–6116 (2017).Article 

    Google Scholar 
    Pedersen, K. & Ekendahl, S. Assimilation of CO2 and introduced organic compounds by bacterial communities in groundwater from southeastern Sweden deep crystalline bedrock. Microb. Ecol. 23, 1–14 (1992).Article 

    Google Scholar 
    Partensky, F. & Garczarek, L. Prochlorococcus: advantages and limits of minimalism. Ann. Rev. Mar. Sci. 2, 305–331 (2010).Article 

    Google Scholar 
    Karl, D. M., Hebel, D. V., Björkman, K. & Letelier, R. M. The role of dissolved organic matter release in the productivity of the oligotrophic North Pacific Ocean. Limnol. Oceanogr. 43, 1270–1286 (1998).Article 

    Google Scholar 
    Liang, Y. et al. Estimating primary production of picophytoplankton using the carbon-based ocean productivity model: a preliminary study. Front. Microbiol. 8, 1926 (2017).Article 

    Google Scholar 
    Steinberg, D. K. et al. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res. 2 48, 1405–1447 (2001).Article 

    Google Scholar 
    Gundersen, K., Orcutt, K. M., Purdie, D. A., Michaels, A. F. & Knap, A. H. Particulate organic carbon mass distribution at the Bermuda Atlantic Time-series Study (BATS) site. Deep Sea Res. 2 48, 1697–1718 (2001).Article 

    Google Scholar 
    Karl, D. M. & Lukas, R. The Hawaii Ocean Time-series (HOT) program: background, rationale and field implementation. Deep Sea Res. 2 43, 129–156 (1996).Article 

    Google Scholar 
    Martiny, A. C., Vrugt, J. A. & Lomas, M. W. Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Sci. Data 1, 140048 (2014).Article 

    Google Scholar 
    Martiny, A. C., Vrugt, J. A. & Lomas, M. W. Data from: Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Dryad https://doi.org/10.5061/dryad.d702p (2015).Schwab, V. F. et al. 14C-free carbon Is a major contributor to cellular biomass in geochemically distinct groundwater of shallow sedimentary bedrock aquifers. Water Resour. Res. 55, 2104–2121 (2019).Article 

    Google Scholar 
    Taubert, M. et al. Bolstering fitness via CO2 fixation and organic carbon uptake: mixotrophs in modern groundwater. ISME J 16, 1153–1162 (2022).Article 

    Google Scholar 
    Rimstidt, J. D. & Vaughan, D. J. Pyrite oxidation: a state-of-the-art assessment of the reaction mechanism. Geochim. Cosmochim. Acta 67, 873–880 (2003).Article 

    Google Scholar 
    Lin, W. et al. Genomic insights into the uncultured genus “Candidatus Magnetobacterium” in the phylum Nitrospirae. ISME J. 8, 2463–2477 (2014).Article 

    Google Scholar 
    Kato, S. et al. Genome-enabled metabolic reconstruction of dominant chemosynthetic colonizers in deep-sea massive sulfide deposits. Environ. Microbiol. 20, 862–877 (2018).Article 

    Google Scholar 
    Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).Article 

    Google Scholar 
    Kojima, H., Watanabe, T. & Fukui, M. Sulfuricaulis limicola gen. nov., sp. nov., a sulfur oxidizer isolated from a lake. Int. J. Syst. Evol. Microbiol. 66, 266–270 (2016).Article 

    Google Scholar 
    Strous, M., Van Gerven, E., Kuenen, J. G. & Jetten, M. Effects of aerobic and microaerobic conditions on anaerobic ammonium-oxidizing (anammox) sludge. Appl. Environ. Microbiol. 63, 2446–2448 (1997).Article 

    Google Scholar 
    Ji, X., Wu, Z., Sung, S. & Lee, P.-H. Metagenomics and metatranscriptomics analyses reveal oxygen detoxification and mixotrophic potentials of an enriched anammox culture in a continuous stirred-tank reactor. Water Res. 166, 115039 (2019).Article 

    Google Scholar 
    Dalsgaard, T. et al. Oxygen at nanomolar levels reversibly suppresses process rates and gene expression in anammox and denitrification in the oxygen minimum zone off northern Chile. mBio 5, e01966 (2014).Article 

    Google Scholar 
    Smith, R. L., Böhlke, J. K., Song, B. & Tobias, C. R. Role of anaerobic ammonium oxidation (anammox) in nitrogen removal from a freshwater aquifer. Environ. Sci. Technol. 49, 12169–12177 (2015).Article 

    Google Scholar 
    Strous, M., Heijnen, J. J., Kuenen, J. G. & Jetten, M. S. M. The sequencing batch reactor as a powerful tool for the study of slowly growing anaerobic ammonium-oxidizing microorganisms. Appl. Microbiol. Biotechnol. 50, 589–596 (1998).Article 

    Google Scholar 
    Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature 549, 269–272 (2017).Article 

    Google Scholar 
    Rittmann, B. E. & McCarty, P. L. Environmental Biotechnology: Principles and Applications (McGraw-Hill Education, 2001).Zhang, Y. et al. Nitrifier adaptation to low energy flux controls inventory of reduced nitrogen in the dark ocean. Proc. Natl. Acad. Sci. USA 117, 4823–4830 (2020).Article 

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

    Google Scholar 
    Lehmann, R. & Totsche, K. U. Multi-directional flow dynamics shape groundwater quality in sloping bedrock strata. J. Hydrol. 580, 124291 (2020).Article 

    Google Scholar 
    Küsel, K. et al. How deep can surface signals be traced in the Critical Zone? Merging biodiversity with biogeochemistry research in a central German Muschelkalk landscape. Front. Earth Sci. 4, 32 (2016).Article 

    Google Scholar 
    Yan, L. et al. Environmental selection shapes the formation of near-surface groundwater microbiomes. Water Res. 170, 115341 (2019).Article 

    Google Scholar 
    Pack, M. A. et al. A method for measuring methane oxidation rates using low levels of 14C-labeled methane and accelerator mass spectrometry: methane oxidation rates by AMS. Limnol. Oceanogr. Methods 9, 245–260 (2011).Article 

    Google Scholar 
    Nielsen, E. S. The use of radio-active carbon (C14) for measuring organic production in the sea. ICES J. Mar. Sci. 18, 117–140 (1952).Article 

    Google Scholar 
    Xu, X. et al. Modifying a sealed tube zinc reduction method for preparation of AMS graphite targets: reducing background and attaining high precision. Nucl. Instrum. Methods Phys. Res. B 259, 320–329 (2007).Article 

    Google Scholar 
    Merser, S. Acetabulum online interactive statistical calculators. Accessed Feb, 2021. https://acetabulum.dk/anova.htmlBermuda Oceanographic Timeseries, accessed 21 Oct 2020, http://batsftp.bios.edu/BATS/production/bats_primary_production.txtHawaiian Oceanographic Timeseries, accessed 21 Oct 2020, ftp://ftp.soest.hawaii.edu/hot/primary_productionHawaiian Oceanographic Timeseries, accessed 21 Oct 2020, https://hahana.soest.hawaii.edu/FTP/hot/microscopy/EPIslides.txtKumar, S. et al. Nitrogen loss from pristine carbonate-rock aquifers of the Hainich Critical Zone Exploratory (Germany) is primarily driven by chemolithoautotrophic anammox processes. Front. Microbiol. 8, 1951 (2017).Article 

    Google Scholar 
    Füssel, J. et al. Nitrite oxidation in the Namibian oxygen minimum zone. ISME J. 6, 1200–1209 (2012).Article 

    Google Scholar 
    McIlvin, M. R. & Altabet, M. A. Chemical conversion of nitrate and nitrite to nitrous oxide for nitrogen and oxygen isotopic analysis in freshwater and seawater. Anal. Chem. 77, 5589–5595 (2005).Article 

    Google Scholar 
    Dalsgaard, T., Thamdrup, B., Farías, L. & Revsbech, N. P. Anammox and denitrification in the oxygen minimum zone of the eastern South Pacific. Limnol. Oceanogr. 57, 1331–1346 (2012).Article 

    Google Scholar 
    Thamdrup, B. et al. Anaerobic ammonium oxidation in the oxygen-deficient waters off northern Chile. Limnol. Oceanogr. 51, 2145–2156 (2006).Article 

    Google Scholar 
    Taubert, M. et al. Tracking active groundwater microbes with D2O labelling to understand their ecosystem function. Environ. Microbiol. 20, 369–384 (2018).Article 

    Google Scholar 
    Bushnell, B. BBMap (SourceForge, 2014); http://sourceforge.net/projects/bbmapBornemann, T. L. V. et al. Geological degassing enhances microbial metabolism in the continental subsurface. Preprint at bioRxiv https://doi.org/10.1101/2020.03.07.980714 (2020).Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).Article 

    Google Scholar 
    Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).Article 

    Google Scholar 
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).Article 

    Google Scholar 
    Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).Article 

    Google Scholar 
    Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain bacteria. Nature 523, 208–211 (2015).Article 

    Google Scholar 
    Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).Article 

    Google Scholar 
    Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).Article 

    Google Scholar 
    Murat Eren, A. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).Article 

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

    Google Scholar 
    Graham, E. D., Heidelberg, J. F. & Tully, B. J. Potential for primary productivity in a globally-distributed bacterial phototroph. ISME J. 12, 1861–1866 (2018).Article 

    Google Scholar 
    Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).Article 

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

    Google Scholar 
    Pelikan, C. et al. Diversity analysis of sulfite- and sulfate-reducing microorganisms by multiplex dsrA and dsrB amplicon sequencing using new primers and mock community-optimized bioinformatics. Environ. Microbiol. 18, 2994–3009 (2016).Article 

    Google Scholar 
    Lücker, S., Nowka, B., Rattei, T., Spieck, E. & Daims, H. The genome of Nitrospina gracilis Illuminates the metabolism and evolution of the major marine nitrite oxidizer. Front. Microbiol. 4, 27 (2013).Article 

    Google Scholar 
    Orellana, L. H., Rodriguez-R, L. M. & Konstantinidis, K. T. ROCker: accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores. Nucleic Acids Res. 45, e14 (2017).
    Google Scholar 
    Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).
    Google Scholar 
    Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 

    Google Scholar 
    Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0501-8 (2020).Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11, 538 (2010).Article 

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

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

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

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

    Google Scholar 
    Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).Article 

    Google Scholar 
    Emiola, A. & Oh, J. High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage. Nat. Commun. 9, 4956 (2018).Article 

    Google Scholar 
    Wegner, C.-E. et al. Biogeochemical regimes in shallow aquifers reflect the metabolic coupling of the elements nitrogen, sulfur, and carbon. Appl. Environ. Microbiol. 85, e02346-18 (2019).Article 

    Google Scholar 
    R: A Language and Environment for Statistical Computing (R Core Team, 2018).RStudio: Integrated Development Environment for R (RStudio Team, 2016).Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 

    Google Scholar 
    Neuwirth, E. RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer (2014). More