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    Compound heat and moisture extreme impacts on global crop yields under climate change

    Ray, D. K., Gerber, J. S., Macdonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).Article 

    Google Scholar 
    Frieler, K. et al. Understanding the weather signal in national crop-yield variability. Earths Future 5, 605–616 (2017).Article 

    Google Scholar 
    Vogel, E. et al. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010 (2019).Article 

    Google Scholar 
    Zscheischler, J. et al. A typology of compound weather and climate events. Nat. Rev. Earth Environ. 1, 333–347 (2020).Article 

    Google Scholar 
    Ridder, N. N., Ukkola, A. M., Pitman, A. J. & Perkins-Kirkpatrick, S. E. Increased occurrence of high impact compound events under climate change. npj Clim. Atmos. Sci. 5, 3 (2022).Article 

    Google Scholar 
    Lesk, C. & Anderson, W. Decadal variability modulates trends in concurrent heat and drought over global croplands. Environ. Res. Lett. 16, 055024 (2021).Article 

    Google Scholar 
    Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D. & Diffenbaugh, N. S. Multidimensional risk in a nonstationary climate: joint probability of increasingly severe warm and dry conditions. Sci. Adv. 4, eaau3487 (2018).Article 

    Google Scholar 
    Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).Article 

    Google Scholar 
    Lobell, D. B., Bänziger, M., Magorokosho, C. & Vivek, B. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1, 42–45 (2011).Article 

    Google Scholar 
    Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).Article 

    Google Scholar 
    Buckley, T. N. How do stomata respond to water status? New Phytol. 224, 21–36 (2019).Article 

    Google Scholar 
    Miralles, D. G., Gentine, P., Seneviratne, S. I. & Teuling, A. J. Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges. Ann. NY Acad. Sci. 1436, 19–35 (2019).Article 

    Google Scholar 
    Mueller, B. & Seneviratne, S. I. Hot days induced by precipitation deficits at the global scale. Proc. Natl Acad. Sci. USA 109, 12398–12403 (2012).Article 

    Google Scholar 
    Cohen, I., Zandalinas, S. I., Huck, C., Fritschi, F. B. & Mittler, R. Meta-analysis of drought and heat stress combination impact on crop yield and yield components. Physiol. Plant 171, 66–76 (2021).Article 

    Google Scholar 
    Ostmeyer, T. et al. Impacts of heat, drought, and their interaction with nutrients on physiology, grain yield, and quality in field crops. Plant Physiol. Rep. 25, 549–568 (2020).Article 

    Google Scholar 
    Matiu, M., Ankerst, D. P. & Menzel, A. Interactions between temperature and drought in global and regional crop yield variability during 1961–2014. PLoS ONE 12, e0178339 (2017).Article 

    Google Scholar 
    Scheff, J., Mankin, J. S., Coats, S. & Liu, H. CO2-plant effects do not account for the gap between dryness indices and projected dryness impacts in CMIP6 or CMIP5. Environ. Res. Lett. 16, 034018 (2021).Article 

    Google Scholar 
    Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G. & Pitman, A. J. Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophys. Res. Lett. 47, e2020GL087820 (2020).Article 

    Google Scholar 
    Allan, R. P. et al. Advances in understanding large-scale responses of the water cycle to climate change. Ann. NY Acad. Sci. 1472, 49–75 (2020).Article 

    Google Scholar 
    Ault, T. R. On the essentials of drought in a changing climate. Science 368, 256–260 (2020).Article 

    Google Scholar 
    Fowler, H. J. et al. Anthropogenic intensification of short-duration rainfall extremes. Nat. Rev. Earth Environ. 2, 107–122 (2021).Article 

    Google Scholar 
    Raymond, C. et al. Understanding and managing connected extreme events. Nat. Clim. Change 10, 611–621 (2020).Article 

    Google Scholar 
    Mills, G. et al. Closing the global ozone yield gap: quantification and cobenefits for multistress tolerance. Glob. Chang. Biol. 24, 4869–4893 (2018).Article 

    Google Scholar 
    Pandey, P., Irulappan, V., Bagavathiannan, M. V. & Senthil-Kumar, M. Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front. Plant Sci. 8, 537 (2017).Article 

    Google Scholar 
    Couasnon, A. et al. Measuring compound flood potential from river discharge and storm surge extremes at the global scale. Nat. Hazards Earth Syst. Sci. 20, 489–504 (2020).Article 

    Google Scholar 
    Nguyen, L. T. T. et al. Flooding and prolonged drought have differential legacy impacts on soil nitrogen cycling, microbial communities and plant productivity. Plant Soil 431, 371–387 (2018).Article 

    Google Scholar 
    Medrano, H., Escalona, J. M., Bota, J., Gulías, J. & Flexas, J. Regulation of photosynthesis of C3 plants in response to progressive drought: stomatal conductance as a reference parameter. Ann. Bot. 89, 895–905 (2002).Article 

    Google Scholar 
    Scafaro, A. P. et al. Responses of leaf respiration to heatwaves. Plant Cell Environ. 44, 2090–2101 (2021).Article 

    Google Scholar 
    Atkin, O. K. & Tjoelker, M. G. Thermal acclimation and the dynamic response of plant respiration to temperature. Trends Plant Sci. 8, 343–351 (2003).Article 

    Google Scholar 
    Lukac, M., Gooding, M. J., Griffiths, S. & Jones, H. E. Asynchronous flowering and within-plant flowering diversity in wheat and the implications for crop resilience to heat. Ann. Bot. 109, 843–850 (2012).Article 

    Google Scholar 
    Coast, O., Murdoch, A. J., Ellis, R. H., Hay, F. R. & Jagadish, K. S. V. Resilience of rice (Oryza spp.) pollen germination and tube growth to temperature stress. Plant. Cell Environ. 39, 26–37 (2016).Article 

    Google Scholar 
    Li, Y., Guan, K., Schnitkey, G. D., Delucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Chang. Biol. https://doi.org/10.1111/gcb.14628 (2019).Article 

    Google Scholar 
    Tian, L. X. et al. How does the waterlogging regime affect crop yield? A global meta-analysis. Front. Plant Sci. 12, 634898 (2021).Article 

    Google Scholar 
    Langan, P. et al. Phenotyping for waterlogging tolerance in crops: current trends and future prospects. J. Exp. Bot. https://doi.org/10.1093/jxb/erac243 (2022).Article 

    Google Scholar 
    Tong, C. et al. Opportunities for improving waterlogging tolerance in cereal crops — physiological traits and genetic mechanisms. Plants 10, 1560 (2021).Article 

    Google Scholar 
    Colmer, T. D., Cox, M. C. H. & Voesenek, L. A. C. J. Root aeration in rice (Oryza sativa): evaluation of oxygen, carbon dioxide, and ethylene as possible regulators of root acclimatizations. New Phytol. 170, 767–778 (2006).Article 

    Google Scholar 
    Hattori, Y. et al. The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water. Nature 460, 1026–1030 (2009).Article 

    Google Scholar 
    Prasad, P. V. V., Pisipati, S. R., Momčilović, I. & Ristic, Z. Independent and combined effects of high temperature and drought stress during grain filling on plant yield and chloroplast EF-Tu expression in spring wheat. J. Agron. Crop Sci. 197, 430–441 (2011).Article 

    Google Scholar 
    Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E. & Mittler, R. Abiotic and biotic stress combinations. New Phytol. 203, 32–43 (2014).Article 

    Google Scholar 
    Hussain, H. A. et al. Interactive effects of drought and heat stresses on morpho-physiological attributes, yield, nutrient uptake and oxidative status in maize hybrids. Sci. Rep. 9, 3890 (2019).Article 

    Google Scholar 
    Mittler, R. Abiotic stress, the field environment and stress combination. Trends Plant Sci. 11, 15–19 (2006).Article 

    Google Scholar 
    Choudhury, F. K., Rivero, R. M., Blumwald, E. & Mittler, R. Reactive oxygen species, abiotic stress and stress combination. Plant J. 90, 856–867 (2017).Article 

    Google Scholar 
    Van Der Wiel, K., Selten, F. M., Bintanja, R., Blackport, R. & Screen, J. A. Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions. Environ. Res. Lett. 15, 034050 (2020).Article 

    Google Scholar 
    Moore, C. E. et al. The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems. J. Exp. Bot. 72, 2822–2844 (2021).Article 

    Google Scholar 
    Fahad, S. et al. Crop production under drought and heat stress: plant responses and management options. Front. Plant Sci. 8, 1147 (2017).Article 

    Google Scholar 
    Zandalinas, S. I., Fritschi, F. B. & Mittler, R. Signal transduction networks during stress combination. J. Exp. Bot. 71, 1734–1741 (2020).Article 

    Google Scholar 
    Zhang, H. & Sonnewald, U. Differences and commonalities of plant responses to single and combined stresses. Plant J. 90, 839–855 (2017).Article 

    Google Scholar 
    Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017).Article 

    Google Scholar 
    Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E. & Raymond, C. A review of recent advances in research on extreme heat events. Curr. Clim. Change Rep. 2, 242–259 (2016).Article 

    Google Scholar 
    Trenberth, K. E. & Shea, D. J. Relationships between precipitation and surface temperature. Geophys. Res. Lett. 32, L14703 (2005).Article 

    Google Scholar 
    Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & De Arellano, J. V. G. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).Article 

    Google Scholar 
    Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).Article 

    Google Scholar 
    Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).Article 

    Google Scholar 
    Koster, R. D., Chang, Y., Wang, H. & Schubert, S. D. Impacts of local soil moisture anomalies on the atmospheric circulation and on remote surface meteorological fields during boreal summer: a comprehensive analysis over North America. J. Clim. 29, 7345–7364 (2016).Article 

    Google Scholar 
    Zhou, S. et al. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Change 11, 38–44 (2021).Article 

    Google Scholar 
    Berg, A., Lintner, B., Findell, K. & Giannini, A. Soil moisture influence on seasonality and large-scale circulation in simulations of the West African monsoon. J. Clim. 30, 2295–2317 (2017).Article 

    Google Scholar 
    Lesk, C. et al. Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields. Nat. Food 2, 683–691 (2021).Article 

    Google Scholar 
    Wei, Z. et al. Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophys. Res. Lett. 44, 2792–2801 (2017).Article 

    Google Scholar 
    Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

    Google Scholar 
    Lian, X. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nat. Clim. Change 8, 640–646 (2018).Article 

    Google Scholar 
    Teuling, A. J. et al. Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci. 3, 722–727 (2010).Article 

    Google Scholar 
    Raymond, C. et al. Increasing spatiotemporal proximity of heat and precipitation extremes in a warming world quantified by a large model ensemble. Environ. Res. Lett. 17, 035005 (2022).Article 

    Google Scholar 
    Raymond, C. et al. On the controlling factors for globally extreme humid heat. Geophys. Res. Lett. 48, e2021GL096082 (2021).Article 

    Google Scholar 
    Speizer, S., Raymond, C., Ivanovich, C. & Horton, R. M. Concentrated and intensifying humid heat extremes in the IPCC AR6 regions. Geophys. Res. Lett. 49, e2021GL097261 (2022).Article 

    Google Scholar 
    Ning, G. et al. Rising risks of compound extreme heat‐precipitation events in China. Int. J. Climatol. https://doi.org/10.1002/joc.7561 (2022).Article 

    Google Scholar 
    Thiery, W. et al. Warming of hot extremes alleviated by expanding irrigation. Nat. Commun. 11, 290 (2020).Article 

    Google Scholar 
    Mueller, N. D. et al. Global relationships between cropland intensification and summer temperature extremes over the last 50 years. J. Clim. 30, 7505–7528 (2017).Article 

    Google Scholar 
    Siebert, S., Ewert, F., Eyshi Rezaei, E., Kage, H. & Graß, R. Impact of heat stress on crop yield — on the importance of considering canopy temperature. Environ. Res. Lett. 9, 044012 (2014).Article 

    Google Scholar 
    Singh, D. et al. Distinct influences of land cover and land management on seasonal climate. J. Geophys. Res. Atmos. 123, 12017–12039 (2018).Article 

    Google Scholar 
    Luan, X. & Vico, G. Canopy temperature and heat stress are increased by compound high air temperature and water stress and reduced by irrigation — a modeling analysis. Hydrol. Earth Syst. Sci. 25, 1411–1423 (2021).Article 

    Google Scholar 
    Siebert, S., Webber, H., Zhao, G. & Ewert, F. Heat stress is overestimated in climate impact studies for irrigated agriculture. Environ. Res. Lett. 12, 054023 (2017).Article 

    Google Scholar 
    Sinha, R. et al. Differential regulation of flower transpiration during abiotic stress in annual plants. New Phytol. https://doi.org/10.1111/nph.18162 (2022).Article 

    Google Scholar 
    He, Y., Lee, E. & Mankin, J. S. Seasonal tropospheric cooling in Northeast China associated with cropland expansion. Environ. Res. Lett. 15, 034032 (2020).Article 

    Google Scholar 
    Alter, R. E., Douglas, H. C., Winter, J. M. & Eltahir, E. A. B. Twentieth century regional climate change during the summer in the Central United States attributed to agricultural intensification. Geophys. Res. Lett. 45, 1586–1594 (2018).Article 

    Google Scholar 
    Sánchez, B., Rasmussen, A. & Porter, J. R. Temperatures and the growth and development of maize and rice: a review. Glob. Chang. Biol. 20, 408–417 (2014).Article 

    Google Scholar 
    Prasad, P. V. V., Bheemanahalli, R. & Jagadish, S. V. K. Field crops and the fear of heat stress — opportunities, challenges and future directions. Field Crops Res. 200, 114–121 (2017).Article 

    Google Scholar 
    Schauberger, B. et al. Consistent negative response of US crops to high temperatures in observations and crop models. Nat. Commun. 8, 13931 (2017).Article 

    Google Scholar 
    Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).Article 

    Google Scholar 
    Sadok, W. & Jagadish, S. V. K. The hidden costs of nighttime warming on yields. Trends Plant Sci. 25, 644–651 (2020).Article 

    Google Scholar 
    Troy, T. J., Kipgen, C. & Pal, I. The impact of climate extremes and irrigation on US crop yields. Environ. Res. Lett. 10, 054013 (2015).Article 

    Google Scholar 
    Cook, B. I., Shukla, S. P., Puma, M. J. & Nazarenko, L. S. Irrigation as an historical climate forcing. Clim. Dyn. 44, 1715–1730 (2015).Article 

    Google Scholar 
    Li, Y. et al. Quantifying irrigation cooling benefits to maize yield in the US Midwest. Glob. Chang. Biol. 26, 3065–3078 (2020).Article 

    Google Scholar 
    Entekhabi, B. D. et al. The Soil Moisture Active Passive (SMAP). IEEE Proc. 98, 704–716 (2010).Article 

    Google Scholar 
    Ortiz-Bobea, A., Wang, H., Carrillo, C. M. & Ault, T. R. Unpacking the climatic drivers of US agricultural yields. Environ. Res. Lett. 14, 064003 (2019).Article 

    Google Scholar 
    Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).Article 

    Google Scholar 
    Proctor, J., Rigden, A., Chan, D. & Huybers, P. Accurate specification of water availability shows its importance for global crop production. Preprint at EarthArXiv https://doi.org/10.31223/X5ZS7P (2021).Article 

    Google Scholar 
    Carter, E. K., Melkonian, J., Riha, S. J. & Shaw, S. B. Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize. Environ. Res. Lett. 11, 094012 (2016).Article 

    Google Scholar 
    Hamed, R., Van Loon, A. F., Aerts, J. & Coumou, D. Impacts of compound hot-dry extremes on US soybean yields. Earth Syst. Dyn. 12, 1371–1391 (2021).Article 

    Google Scholar 
    Feng, S., Hao, Z., Zhang, X. & Hao, F. Probabilistic evaluation of the impact of compound dry-hot events on global maize yields. Sci. Total Environ. 689, 1228–1234 (2019).Article 

    Google Scholar 
    Haqiqi, I., Grogan, D. S., Hertel, T. W. & Schlenker, W. Quantifying the impacts of compound extremes on agriculture. Hydrol. Earth Syst. Sci. 25, 551–564 (2021).Article 

    Google Scholar 
    Zhu, P., Zhuang, Q., Archontoulis, S. V., Bernacchi, C. & Müller, C. Dissecting the nonlinear response of maize yield to high temperature stress with model-data integration. Glob. Chang. Biol. 25, 2470–2484 (2019).Article 

    Google Scholar 
    Jin, Z. et al. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Glob. Chang. Biol. 22, 3112–3126 (2016).Article 

    Google Scholar 
    Filipa Silva Ribeiro, A., Russo, A., Gouveia, C. M., Páscoa, P. & Zscheischler, J. Risk of crop failure due to compound dry and hot extremes estimated with nested copulas. Biogeosciences 17, 4815–4830 (2020).Article 

    Google Scholar 
    Hsiao, J., Swann, A. L. S. & Kim, S. H. Maize yield under a changing climate: the hidden role of vapor pressure deficit. Agric. For. Meteorol. 279, 107692 (2019).Article 

    Google Scholar 
    Heinicke, S., Frieler, K., Jägermeyr, J. & Mengel, M. Global gridded crop models underestimate yield responses to droughts and heatwaves. Environ. Res. Lett. 17, 044026 (2022).Article 

    Google Scholar 
    Cook, B. I. et al. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earths Future 8, e2019EF001461 (2020).Article 

    Google Scholar 
    He, Y., Hu, X., Xu, W., Fang, J. & Shi, P. Increased probability and severity of compound dry and hot growing seasons over world’s major croplands. Sci. Total Environ. 824, 153885 (2022).Article 

    Google Scholar 
    Wu, Y. et al. Global observations and CMIP6 simulations of compound extremes of monthly temperature and precipitation. GeoHealth 5, e2021GH000390 (2021).Article 

    Google Scholar 
    Zhang, Y., Hao, Z., Zhang, X. & Hao, F. Anthropogenically forced increases in compound dry and hot events at the global and continental scales. Environ. Res. Lett. 17, 024018 (2022).Article 

    Google Scholar 
    Chen, Y., Liao, Z., Shi, Y., Tian, Y. & Zhai, P. Detectable increases in sequential flood-heatwave events across China during 1961–2018. Geophys. Res. Lett. 48, e2021GL092549 (2021).
    Google Scholar 
    Raymond, C., Matthews, T. & Horton, R. M. The emergence of heat and humidity too severe for human tolerance. Sci. Adv. 6, eaaw1838 (2020).Article 

    Google Scholar 
    Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture–temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).Article 

    Google Scholar 
    Garcia-Herrera, R., Díaz, J., Trigo, R. M., Luterbacher, J. & Fischer, E. M. A review of the European summer heat wave of 2003. Crit. Rev. Environ. Sci. Technol. 40, 267–306 (2010).Article 

    Google Scholar 
    Wegren, S. Food security and Russia’s 2010 drought. Eurasian Geogr. Econ. 52, 140–156 (2011).Article 

    Google Scholar 
    Christian, J. I., Basara, J. B., Hunt, E. D., Otkin, J. A. & Xiao, X. Flash drought development and cascading impacts associated with the 2010 Russian heatwave. Environ. Res. Lett. 15, 094078 (2020).Article 

    Google Scholar 
    Glotter, M. & Elliott, J. Simulating US agriculture in a modern Dust Bowl drought. Nat. Plants 3, 16193 (2016).Article 

    Google Scholar 
    Yuan, X., Wang, L. & Wood, E. F. Anthropogenic intensification of southern African flash droughts as exemplified by the 2015/16 season. Bull. Am. Meteorol. Soc. 99, S86–S90 (2018).Article 

    Google Scholar 
    Ben-Ari, T. et al. Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France. Nat. Commun. 9, 1627 (2018).Article 

    Google Scholar 
    Gampe, D. et al. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Change 11, 772–779 (2021).Article 

    Google Scholar 
    Iizumi, T. & Ramankutty, N. Changes in yield variability of major crops for 1981–2010 explained by climate change. Environ. Res. Lett. 11, 034003 (2016).Article 

    Google Scholar 
    Brás, T. A., Seixas, J., Carvalhais, N. & Jagermeyr, J. Severity of drought and heatwave crop losses tripled over the last five decades in Europe. Environ. Res. Lett. 16, 065012 (2021).Article 

    Google Scholar 
    Lobell, D. B., Deines, J. M. & Di Tommaso, S. Changes in the drought sensitivity of US maize yields. Nat. Food 1, 729–735 (2020).Article 

    Google Scholar 
    Seneviratne, S. I. et al. Climate extremes, land–climate feedbacks and land-use forcing at 1.5 °C. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20160450 (2018).Article 

    Google Scholar 
    Pfleiderer, P., Schleussner, C. F., Kornhuber, K. & Coumou, D. Summer weather becomes more persistent in a 2 °C world. Nat. Clim. Change 9, 666–671 (2019).Article 

    Google Scholar 
    Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019).Article 

    Google Scholar 
    Dai, A., Zhao, T. & Chen, J. Climate change and drought: a precipitation and evaporation perspective. Curr. Clim. Chang. Rep. 4, 301–312 (2018).Article 

    Google Scholar 
    Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).Article 

    Google Scholar 
    Lesk, C., Coffel, E. & Horton, R. Net benefits to US soy and maize yields from intensifying hourly rainfall. Nat. Clim. Change 10, 819–822 (2020).Article 

    Google Scholar 
    Goulart, H. M. D., Van Der Wiel, K., Folberth, C., Balkovic, J. & Van Den Hurk, B. Weather-induced crop failure events under climate change: a storyline approach. Earth Syst. Dyn. https://doi.org/10.5194/esd-2021-40 (2021).Article 

    Google Scholar 
    Franke, J. A. et al. Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change. Glob. Chang. Biol. 28, 167–181 (2022).Article 

    Google Scholar 
    Jägermeyr, J. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2, 873–885 (2021).Article 

    Google Scholar 
    Waha, K. et al. Multiple cropping systems of the world and the potential for increasing cropping intensity. Glob. Environ. Chang. 64, 102131 (2020).Article 

    Google Scholar 
    Zhu, T., Fonseca De Lima, C. F. & De Smet, I. The heat is on: how crop growth, development, and yield respond to high temperature. J. Exp. Bot. 72, 7359–7373 (2021).
    Google Scholar 
    Lizaso, J. I. et al. Impact of high temperatures in maize: phenology and yield components. Field Crops Res. 216, 129–140 (2018).Article 

    Google Scholar 
    Rezaei, E. E., Siebert, S. & Ewert, F. Intensity of heat stress in winter wheat — phenology compensates for the adverse effect of global warming. Environ. Res. Lett. 10, 024012 (2015).Article 

    Google Scholar 
    Liu, K. et al. Climate change shifts forward flowering and reduces crop waterlogging stress. Environ. Res. Lett. 16, 094017 (2021).Article 

    Google Scholar 
    Bagley, J. et al. The influence of photosynthetic acclimation to rising CO2 and warmer temperatures on leaf and canopy photosynthesis models. Global Biogeochem. Cycles https://doi.org/10.1002/2014GB004848 (2015).Article 

    Google Scholar 
    Hossain, M. A. et al. Heat or cold priming-induced cross-tolerance to abiotic stresses in plants: key regulators and possible mechanisms. Protoplasma 255, 399–412 (2018).Article 

    Google Scholar 
    Wolz, K. J., Wertin, T. M., Abordo, M., Wang, D. & Leakey, A. D. B. Diversity in stomatal function is integral to modelling plant carbon and water fluxes. Nat. Ecol. Evol. 1, 1292–1298 (2017).Article 

    Google Scholar 
    Ainsworth, E. A. & Long, S. P. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation? Glob. Chang. Biol. 27, 27–49 (2021).Article 

    Google Scholar 
    Toreti, A. et al. Narrowing uncertainties in the effects of elevated CO2 on crops. Nat. Food 1, 775–782 (2020).Article 

    Google Scholar 
    Myers, S. S. et al. Climate change and global food systems: potential impacts on food security and undernutrition. Annu. Rev. Public Health 38, 259–277 (2017).Article 

    Google Scholar 
    Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).Article 

    Google Scholar 
    Houshmandfar, A., Fitzgerald, G. J., Armstrong, R., Macabuhay, A. A. & Tausz, M. Modelling stomatal conductance of wheat: an assessment of response relationships under elevated CO2. Agric. For. Meteorol. 214–215, 117–123 (2015).Article 

    Google Scholar 
    Chavan, S. G., Duursma, R. A., Tausz, M. & Ghannoum, O. Elevated CO2 alleviates the negative impact of heat stress on wheat physiology but not on grain yield. J. Exp. Bot. 70, 6447–6459 (2019).Article 

    Google Scholar 
    Gray, S. B. et al. Intensifying drought eliminates the expected benefits of elevated carbon dioxide for soybean. Nat. Plants 2, 16132 (2016).Article 

    Google Scholar 
    Coffel, E. D. et al. Future hot and dry years worsen Nile basin water scarcity despite projected precipitation increases. Earths Future 7, 967–977 (2019).Article 

    Google Scholar 
    Mishra, V., Thirumalai, K., Singh, D. & Aadhar, S. Future exacerbation of hot and dry summer monsoon extremes in India. npj Clim. Atmos. Sci. 3, 10 (2020).Article 

    Google Scholar 
    Bevacqua, E., Zappa, G., Lehner, F. & Zscheischler, J. Precipitation trends determine future occurrences of compound hot–dry events. Nat. Clim. Change 12, 350–355 (2022).Article 

    Google Scholar 
    Seager, R. et al. Climate variability and change of Mediterranean-type climates. J. Clim. 32, 2887–2915 (2019).Article 

    Google Scholar 
    Vogel, M. M., Hauser, M. & Seneviratne, S. I. Projected changes in hot, dry and wet extreme events’ clusters in CMIP6 multi-model ensemble. Environ. Res. Lett. 15, 094021 (2020).Article 

    Google Scholar 
    Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).Article 

    Google Scholar 
    Byrne, M. P. Amplified warming of extreme temperatures over tropical land. Nat. Geosci. 14, 837–841 (2021).Article 

    Google Scholar 
    McDermid, S. S. et al. Disentangling the regional climate impacts of competing vegetation responses to elevated atmospheric CO2. J. Geophys. Res. Atmos. 126, e2020JD034108 (2021).Article 

    Google Scholar 
    Swann, A. L. S., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).Article 

    Google Scholar 
    Ali, H., Fowler, H. J., Lenderink, G., Lewis, E. & Pritchard, D. Consistent large-scale response of hourly extreme precipitation to temperature variation over land. Geophys. Res. Lett. https://doi.org/10.1029/2020GL090317 (2021).Article 

    Google Scholar 
    Dai, A., Rasmussen, R. M., Liu, C., Ikeda, K. & Prein, A. F. A new mechanism for warm-season precipitation response to global warming based on convection-permitting simulations. Clim. Dyn. 55, 343–368 (2020).Article 

    Google Scholar 
    Fishman, R. More uneven distributions overturn benefits of higher precipitation for crop yields. Environ. Res. Lett. 11, 024004 (2016).Article 

    Google Scholar 
    Shortridge, J. Observed trends in daily rainfall variability result in more severe climate change impacts to agriculture. Clim. Chang. 157, 429–444 (2019).Article 

    Google Scholar 
    Guan, K., Sultan, B., Biasutti, M., Baron, C. & Lobell, D. B. What aspects of future rainfall changes matter for crop yields in West Africa? Geophys. Res. Lett. 42, 8001–8010 (2015).Article 

    Google Scholar 
    Byrne, M. P. & O’Gorman, P. A. Trends in continental temperature and humidity directly linked to ocean warming. Proc. Natl Acad. Sci. USA 115, 4863–4868 (2018).Article 

    Google Scholar 
    Coffel, E. D., Horton, R. M. & De Sherbinin, A. Temperature and humidity based projections of a rapid rise in global heat stress exposure during the 21st century. Environ. Res. Lett. 13, 014001 (2018).Article 

    Google Scholar 
    Matthews, T. Humid heat and climate change. Prog. Phys. Geogr. 42, 391–405 (2018).Article 

    Google Scholar 
    McKinnon, K. A. & Poppick, A. Estimating changes in the observed relationship between humidity and temperature using noncrossing quantile smoothing splines. J. Agric. Biol. Environ. Stat. 25, 292–314 (2020).Article 

    Google Scholar 
    Parsons, L. A. et al. Global labor loss due to humid heat exposure underestimated for outdoor workers. Environ. Res. Lett. 17, 014050 (2022).Article 

    Google Scholar 
    Ridder, N. N., Pitman, A. J. & Ukkola, A. M. Do CMIP6 climate models simulate global or regional compound events skillfully? Geophys. Res. Lett. 48, e2020GL091152 (2021).Article 

    Google Scholar 
    Hao, Z., Aghakouchak, A. & Phillips, T. J. Changes in concurrent monthly precipitation and temperature extremes. Environ. Res. Lett. 8, 034014 (2013).Article 

    Google Scholar 
    Zhang, B. & Soden, B. J. Constraining climate model projections of regional precipitation change. Geophys. Res. Lett. 46, 10522–10531 (2019).Article 

    Google Scholar 
    Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).Article 

    Google Scholar 
    Butler, E. E., Mueller, N. D. & Huybers, P. Peculiarly pleasant weather for US maize. Proc. Natl Acad. Sci. USA 115, 11935–11940 (2018).Article 

    Google Scholar 
    Lombardozzi, D. L. et al. Simulating agriculture in the Community Land Model Version 5. J. Geophys. Res. Biogeosci. 125, e2019JG005529 (2020).Article 

    Google Scholar 
    Puma, M. J. & Cook, B. I. Effects of irrigation on global climate during the 20th century. J. Geophys. Res. Atmos. 115, D16120 (2010).Article 

    Google Scholar 
    Coffel, E. D., Lesk, C., Winter, J. M., Osterberg, E. C. & Mankin, J. S. Crop–climate feedbacks boost US maize and soy yields. Environ. Res. Lett. 17, 024012 (2022).Article 

    Google Scholar 
    Mueller, N. D. et al. Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Change 6, 317–322 (2016).Article 

    Google Scholar 
    Zaveri, E. & B. Lobell, D. The role of irrigation in changing wheat yields and heat sensitivity in India. Nat. Commun. 10, 4144 (2019).Article 

    Google Scholar 
    DeLucia, E. H. et al. Are we approaching a water ceiling to maize yields in the United States? Ecosphere 10, e02773 (2019).Article 

    Google Scholar 
    Cook, B. I. et al. Divergent regional climate consequences of maintaining current irrigation rates in the 21st century. J. Geophys. Res. Atmos. 125, e2019JD031814 (2020).Article 

    Google Scholar 
    Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl Acad. Sci. USA 115, 6644–6649 (2018).Article 

    Google Scholar 
    Liu, W. et al. Future climate change significantly alters interannual wheat yield variability over half of harvested areas. Environ. Res. Lett. 16, 094045 (2021).Article 

    Google Scholar 
    Wang, X. et al. Global irrigation contribution to wheat and maize yield. Nat. Commun. 12, 1235 (2021).Article 

    Google Scholar 
    Rosa, L., Chiarelli, D. D., Rulli, M. C., Dell’Angelo, J. & D’Odorico, P. Global agricultural economic water scarcity. Sci. Adv. 6, eaaz6031 (2020).Article 

    Google Scholar 
    Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).Article 

    Google Scholar 
    Livneh, B. & Badger, A. M. Drought less predictable under declining future snowpack. Nat. Clim. Change 10, 452–458 (2020).Article 

    Google Scholar 
    Elliott, J. et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl Acad. Sci. USA 111, 3239–3244 (2014).Article 

    Google Scholar 
    Jägermeyr, J. et al. Integrated crop water management might sustainably halve the global food gap. Environ. Res. Lett. 11, 025002 (2016).Article 

    Google Scholar 
    Rosa, L. et al. Potential for sustainable irrigation expansion in a 3 °C warmer climate. Proc. Natl Acad. Sci. USA 117, 29526–29534 (2020).Article 

    Google Scholar 
    Gleeson, T., Wada, Y., Bierkens, M. F. P. & Van Beek, L. P. H. Water balance of global aquifers revealed by groundwater footprint. Nature 488, 197–200 (2012).Article 

    Google Scholar 
    Bhattarai, N. et al. The impact of groundwater depletion on agricultural production in India. Environ. Res. Lett. 16, 085003 (2021).Article 

    Google Scholar 
    Nie, W. et al. Irrigation water demand sensitivity to climate variability across the contiguous United States. Water Resour. Res. 57, e2020WR027738 (2021).Article 

    Google Scholar 
    Wu, W.-Y. et al. Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers. Nat. Commun. 11, 3710 (2020).Article 

    Google Scholar 
    Jain, M. et al. Groundwater depletion will reduce cropping intensity in India. Sci. Adv. 7, eabd2849 (2021).Article 

    Google Scholar 
    Kerr, R. B., Hasegawa, T. & Lasco, R. Food, fibre and other ecosystem products. In IPCC WGII Sixth Assessment Report 11–13 Ch. 5 (IPCC, 2022).Zandalinas, S. I. & Mittler, R. Plant responses to multifactorial stress combination. New Phytol. 234, 1161–1167 (2022).Article 

    Google Scholar 
    Barrett, C. B. et al. Bundling innovations to transform agri-food systems. Nat. Sustain. 3, 974–976 (2020).Article 

    Google Scholar 
    Peng, B. & Guan, K. Harmonizing climate-smart and sustainable agriculture. Nat. Food 2, 853–854 (2021).Article 

    Google Scholar 
    Zabel, F. et al. Large potential for crop production adaptation depends on available future varieties. Glob. Chang. Biol. 27, 3870–3882 (2021).Article 

    Google Scholar 
    Challinor, A. J., Koehler, A.-K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).Article 

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

    Google Scholar 
    Vogel, E. & Meyer, R. Climate Change, Climate Extremes, and Global Food Production — Adaptation in the Agricultural Sector. Resilience: The Science of Adaptation to Climate Change (Elsevier Inc., 2018).Lal, R. Soil health and carbon management. Food Energy Secur. 5, 212–222 (2016).Article 

    Google Scholar 
    Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food 2, 54–65 (2021).Article 

    Google Scholar 
    Baldos, U. L. C. & Hertel, T. W. The role of international trade in managing food security risks from climate change. Food Secur. 7, 275–290 (2015).Article 

    Google Scholar 
    Deguines, N. et al. Large-scale trade-off between agricultural intensification and crop pollination services. Front. Ecol. Environ. 12, 212–217 (2014).Article 

    Google Scholar 
    Vyas, S., Dalhaus, T., Kropff, M., Aggarwal, P. & Meuwissen, M. P. M. Mapping global research on agricultural insurance. Environ. Res. Lett. 16, 103003 (2021).Article 

    Google Scholar 
    Hazell, P. & Varangis, P. Best practices for subsidizing agricultural insurance. Glob. Food Sec. 25, 100326 (2020).Article 

    Google Scholar 
    Funk, C. et al. Recognizing the famine early warning systems network over 30 years of drought early warning science advances and partnerships promoting global food security. Bull. Am. Meteorol. Soc. 100, 1011–1027 (2019).Article 

    Google Scholar 
    Reichstein, M., Riede, F. & Frank, D. More floods, fires and cyclones — plan for domino effects on sustainability goals. Nature 592, 347–349 (2021).Article 

    Google Scholar 
    Müller, C. et al. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environ. Res. Lett. 16, 034040 (2021).Article 

    Google Scholar 
    Hao, Z., Hao, F., Xia, Y., Singh, V. P. & Zhang, X. A monitoring and prediction system for compound dry and hot events. Environ. Res. Lett. 14, 114034 (2019).Article 

    Google Scholar 
    Benami, E. et al. Uniting remote sensing, crop modelling and economics for agricultural risk management. Nat. Rev. Earth Environ. 2, 140–159 (2021).Article 

    Google Scholar 
    Famine Early Warning System Network. East Africa seasonal monitor. FEWS https://fews.net/sites/default/files/documents/reports/EAST_AFRICA_Seasonal_Monitor_20_May_2022_1.pdf (2022).Becker-Reshef, I. et al. The GEOGLAM crop monitor for AMIS: assessing crop conditions in the context of global markets. Glob. Food Sec. 23, 173–181 (2019).Article 

    Google Scholar 
    GEOGLAM Crop Monitor. Special report: unprecedented 4th consecutive poor rainfall season for the Horn of Africa. Crop Monitor https://cropmonitor.org/documents/SPECIAL/reports/Special_Report_20220523_East_Africa.pdf (2022).Geange, S. R. et al. The thermal tolerance of photosynthetic tissues: a global systematic review and agenda for future research. New Phytol. 229, 2497–2513 (2021).Article 

    Google Scholar 
    Reynolds, M. P. et al. Harnessing translational research in wheat for climate resilience. J. Exp. Bot. 72, 5134–5157 (2021).Article 

    Google Scholar 
    Makondo, C. C. & Thomas, D. S. G. Climate change adaptation: linking indigenous knowledge with western science for effective adaptation. Environ. Sci. Policy 88, 83–91 (2018).Article 

    Google Scholar 
    Sharafi, L., Zarafshani, K., Keshavarz, M., Azadi, H. & Van Passel, S. Farmers’ decision to use drought early warning system in developing countries. Sci. Total Environ. 758, 142761 (2021).Article 

    Google Scholar 
    Fischer, K. Why new crop technology is not scale-neutral — A critique of the expectations for a crop-based African Green Revolution. Res. Policy 45, 1185–1194 (2016).Article 

    Google Scholar 
    Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).Article 

    Google Scholar 
    Glauber, J., Baldwin, K., Antón, J. & Ziebinska, U. Design principles for agricultural risk management policies. OECD Food Agric. Fish. Pap. https://doi.org/10.1787/1048819f-en (2021).Article 

    Google Scholar 
    Annan, F. & Schlenker, W. Federal crop insurance and the disincentive to adapt to extreme heat. Am. Econ. Rev. 105, 262–266 (2015).Article 

    Google Scholar 
    Deryugina, T. & Konar, M. Impacts of crop insurance on water withdrawals for irrigation. Adv. Water Resour. 110, 437–444 (2017).Article 

    Google Scholar 
    Agrimonti, C., Lauro, M. & Visioli, G. Smart agriculture for food quality: facing climate change in the 21st century. Crit. Rev. Food Sci. Nutr. 61, 971–981 (2021).Article 

    Google Scholar 
    Sloat, L. L. et al. Climate adaptation by crop migration. Nat. Commun. 11, 1243 (2020).Article 

    Google Scholar 
    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).Article 

    Google Scholar 
    Willmott, C. J. & Matsuura, K. Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1950–1999). University of Delaware http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts.html (2000).Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations — the CRU TS3.10 dataset. Int. J. Clim. 34, 623–642 (2014).Article 

    Google Scholar 
    Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).Article 

    Google Scholar 
    Beyer, R. M., Hua, F., Martin, P. A., Manica, A. & Rademacher, T. Relocating croplands could drastically reduce the environmental impacts of global food production. Commun. Earth Environ. 3, 49 (2022).Article 

    Google Scholar  More

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    Multiple drivers and lineage-specific insect extinctions during the Permo–Triassic

    Fossil record of insectsWe compiled all species-level fossil occurrences of insects using https://paleobiodb.org/ (PBDB) as a starting point (downloaded October 12, 2021). The dataset obtained from PBDB contained initially 5808 occurrences for a period ranging from the Asselian to the Rhaetian. The dataset was cleaned of synonyms, outdated combinations, nomina dubia, and other erroneous and doubtful records, based on revisions provided in the literature and/or on the expertise of the authors. After correction, including data addition from the literature, our dataset was composed of 3636 species (1784 genera, and 418 families) for 17,250 occurrences resulting from an in-depth study and curation of the entire bibliography of fossil insects, spanning from the Asselian (lowermost Permian) to the Rhaetian (uppermost Triassic). Although most of the taxa included in the datasets are nominal taxa (published and named), a few unnamed taxa (genera or species) that are considered separate from others were also included, although not formally named in the literature or not published yet. These unpublished taxa are identifiable by the notation ‘fam. nov.’ or ‘gen. nov.’ following their names.Occurrences used here are specimens originating from a given stratigraphic horizon assigned to a given taxon. The age of each occurrence is based on data from PBDB, corrected with a more precise age (generally stage, sometimes substage), and the age of each time bin boundaries relies on the stratigraphic framework proposed in the International Chronostratigraphic Chart (updated to correspond with the ICS 2022/0295). Similarly, the ages of some species assigned to the wrong stage were corrected. In fact, some species from the French Permian deposit of Lodève were initially considered to be of Artinskian age in PBDB but most species from this deposit originate from the Merifons member, which is of Kungurian age96.Our data compilation allows a robust integration of data before and after our period of interest (i.e. the lower Permian and all geologic stages after the Carnian) to encompass occurrences of genera that may survive until the Late Triassic and to generate a sufficient background for the model to correctly estimate the extinction events around the P/T boundary. Since we used different datasets, the differences between genus-level or family-level occurrence numbers are explained by the systematic placement of some specimens that can only be placed confidently in a family but not in a genus (Supplementary Table 1). Tentative species identifications originally placed with uncertainty (reported as ‘aff.’ or ‘?’) were always included at a higher taxonomic level. Uncertain generic attributions were integrated as occurrences at the family level (e.g. a fossil initially considered Tupus? is recorded as an occurrence of Meganeuridae). Our total dataset was subdivided into smaller datasets, which represent orders or other subclades of insects (e.g. Mecoptera, Holometabola and Polyneoptera). Note that all the ichnospecies—a species name assigned to trace fossils (e.g. resting trace, nest and leaf damage)— and insect eggs (e.g. Clavapartus latus, Furcapartus exilis and Monilipartus tenuis) were not included in the analyses97. To prevent potential issues regarding the diversification estimates for clades with poor delineation, we refrained from analysing several orders that serve as taxonomic ‘wastebaskets’ (e.g. Grylloblattodea). These groups are poorly defined, likely polyphyletic or paraphyletic, and not supported by apomorphic characters—e.g. the monophyly of the ‘Grylloblattodea’ (Grylloblattida Walker, 1914 plus numerous fossil families and genera of uncertain affinities) is not supported by any synapomorphy, nor the relationships within this group. The occurrences assigned to these orders were rather included in analyses conducted at a higher taxonomic level (at the Polyneoptera level in the case of the ‘Grylloblattodea’). The detail of the composition of all the datasets is given in Supplementary Table 14, and each dataset is available in Supplementary Data 1.Studying extinction should, when possible, rely on species-level diversity to better circumscribe extinction events at this taxonomic rank, which is primarily affected by extinction98,99,100. However, in palaeoentomology, species-level occurrence data may contain less information than genus-level data, mainly because species are most of the time only known from one deposit, resulting in reduced life span, and are also sometimes poorly defined. Insects are also less prone to long-lasting genera or species than other lineages, maybe because of the relatively short time between generations (allowing for rapid evolution) or because morphological characters are better preserved or more diagnostic than in other lineages (i.e. wing venation), allowing easier differentiation. Another argument for the use of genus-level datasets is the possibility to add occurrences represented by fossils that cannot be assigned at the species level because of poor preservation or an insufficient number of specimens/available characters. By extension, the genus life span provides clues as to survivor taxa and times of origination during periods of post-extinction or recovery. A genus encompassing extinction events indicates that at least one species of this genus crossed the extinction. To get the best signal and infer a robust pattern of insect dynamics around the P/T events, we have chosen to analyse our dataset at different taxonomic ranks (e.g. genus, family and order levels) to extract as much evidence as possible.To further support our choice to work at these different levels, most recent works aiming to decipher the diversification and extinction in insect lineages have worked using a combination of analyses21,22,26; this also applies to non-insect clades51,101,102. This multi-level approach should maximise our understanding of the Permo–Triassic events.Assessing optimal parameters and preliminary testsPrior to choosing the settings for the final analyses (see detail in Dynamics of origination and extinction), a series of tests were carried out to better evaluate the convergence of our analyses. First, we analysed our genus-level dataset with PyRate36 running for 10 million generations and sampling every 10,000 generations, on ten randomly replicated datasets using the reversible-jump Markov Chain Monte Carlo (RJMCMC) model37 and the parameters of PyRate set by default. As the convergence was too low, new settings were used, notably increasing the number of generations to 50 million generations and monitoring the MCMC mixing and effective sample size (ESS) each 10 million generations. We modified the minimal interval between two shifts (-min_dt option, testing 0.5, 1.5 and 2), and found no major difference in diversification patterns between our tests. We have opted for 50 million generations with a predefined time frame set for bins corresponding to the Permian and Triassic stages, and a minimum interval between two shifts of two Ma. These parameters allow for maintaining a short bin frame and high convergence values while correctly identifying periods of diversification and extinction. For each analysis, ten datasets were generated using the extract.ages function to randomly resample the age of fossil occurrences within their respective temporal ranges (i.e. resampled ages are randomly drawn between the minimum and the maximum ages of the geological stratum). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 after excluding the first 10% of the samples as a burn-in period. The parameters are considered convergent when their ESS are greater than 200.Dynamics of origination and extinctionWe carried out the analyses of the fossil datasets based on the Bayesian framework implemented in the programme PyRate36. We analysed the fossil datasets under two models: the birth–death model with constrained shifts (BDCS38) and the RJMCMC (-A 4 option37). These models allow for a simultaneous estimate for each taxon: (1) the parameters of the preservation process (Supplementary Fig. 17), (2) the times of origination (Ts) and extinction (Te) of each taxon, (3) the origination and extinction rates and their variation through time for each stage and (4) the number and magnitude of shifts in origination and extinction rates.All analyses were set with the best-fit preservation process after comparing (-PPmodeltest option) the homogeneous Poisson process (-mHPP option), the non-homogeneous Poisson process (default option), and the time-variable Poisson process (-qShift option). The preservation process infers the individual origination and extinction times of each taxon based on all fossil occurrences and on an estimated preservation rate, denoted q, expressed as expected occurrences per taxon per Ma. The time-variable Poisson process assumes that preservation rates are constant within a predefined time frame but may vary over time (here, set for bins corresponding to stages). This model is thus appropriate when rates over time are heterogeneous.We ran PyRate for 50 million MCMC generations and a sampling every 50,000 generations for the BDCS and RJMCMC models with time bins corresponding to Permian and Triassic stages (-fixShift option). All analyses were set with a time-variable Poisson process (-qShift option) of preservation and accounted for varying preservation rates across taxa using the Gamma model (-mG option), that is, with gamma-distributed rate heterogeneity with four rate categories36. As explained above, the minimal interval between two shifts (-min_dt option) was modified and a value of 2 was used. The default prior to the vector of preservation rates is a single gamma distribution with shape = 1.5 and rate = 1.5. We reduced the subjectivity of this parameter, and favoured a better adequation to the data, allowing PyRate to estimate the rate parameter of the prior from the data by setting the rate parameter to 0 (-pP option). Therefore, PyRate assigns a vague exponential hyper-prior to the rate and samples the rate along with all other model parameters. Similarly, because our dataset does not encompass the entire fossil record of insects, we assumed that a possible edge effect may interfere with our analyses, with a strong diversification during the lowermost Permian and, conversely a strong extinction during the uppermost Triassic. Because the RJMCMC and BDCS algorithms look for rate shifts, we constrained the algorithm to only search for shifts (-edgeShift option) within the following time range 295.0 to 204.5 Ma. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 after excluding the first 10% of the samples as a burn-in period. The parameters are considered convergent when their ESS are greater than 200.We then combined the posterior estimates of the origination and extinction rates across all replicates to generate rates through-time plots (origination, extinction, and net diversification). Shifts of diversification were considered significant when log Bayes factors were >6 in the RJMCMC model, while we considered shifts to be significant in the BDCS model when mean rates in a time bin did not overlap with the 95% credibility interval (CI) of the rates of adjacent time bins.We replicated all the analyses on ten randomly generated datasets of each clade and calculated estimates of the Ts and the Te as the average of the posterior samples from each replicate. Thus, we obtained ten posterior estimates of the Ts and Te for all taxa and we used these values to estimate the past diversity dynamics by calculating the number of living taxa at each time point. For all the subsequent analyses, we used the estimated Ts and Te of all taxa to test whether or not the origination and the extinction rate dynamics were correlated with particular abiotic factors, as suggested by the drastic changes in environmental conditions known during the Permo–Triassic. We used proxies for abiotic factors, such as global continental fragmentation or the dynamic of major clades of plants, and for biotic factors via species interaction within and between ecological guilds. This approach avoids re-modelling preservation and re-estimating times of origination and extinction, which reduces drastically the computational burden, while still allowing to account for the preservation process and the uncertainties associated with fossil ages. Similarly, the times of origination and extinction used in all the subsequent analyses were obtained while accounting for the heterogeneity of preservation, origination and extinction rates. To discuss the magnitude of the periods of extinction and diversification, we compared the magnitude of these events to the background origination and extinction rates (i.e. not during extinction or diversification peaks).The PyRate approach has proven to be robust following a series of tests and simulations that reflect commonly observed biases when modelling past diversity dynamics31,38. These simulations were based on datasets simulated under a range of potential biases (i.e. violations of the sampling assumptions, variable preservation rates, and incomplete taxon sampling) and reflecting the limitations of the fossil record. Simulation results showed that PyRate is able to correctly estimate the dynamics of origination and extinction rates, including sudden rate changes and mass extinction, even if the preservation levels are low (down to 1–3 fossil occurrences per species on average), the taxon sampling is partial (up to 80% missing) or if the datasets have a high proportion of singletons (exceeding 30% of the taxa in some cases). The strongest bias in birth–death rate estimates is caused by incomplete data (i.e. missing lineages) altering the distribution of taxa; a pervasive effect often mentioned for phylogeny-based models104,105,106. However, in the case of PyRate, the simulations confirm the absence of consistent biases due to an incomplete fossil record36. Finally, the recently implemented RJMCMC model was shown to be very accurate for estimating origination and extinction rates (i.e. more accurate than the BDCS model, the boundary-crossing and three-time methods) and is able to recover sudden extinction events regardless of the biases in the fossil dataset37.The severity of extinctions and survivorsFor each event—the Roadian–Wordian, the LPME, and the Ladinian–Carnian—we quantified the percentage of extinctions and survivors at the genus level. We used the Te and Ts from our RJMCMC analysis and computed the mean for the Te (Tem) and for the Ts (Tsm) of each genus. We then filtered our dataset to keep only the genera with a Tsm older than the upper boundary of the focal event, i.e., we only kept the genera that appeared before the end of the event. Then, we discarded the genera that have disappeared before the lower boundary of the focal event, i.e. Tem older that the lower boundary of the event. The remaining genera, which corresponds to all the genera (total) present during the crisis (Ttgen), can be classified into two categories, ‘survivor genera’ (Sgen), i.e. those that survived the crisis, and those that died: ‘extinct genera’ (Egen). The survivors have a Tem younger than the upper boundary of the focal event, while the ‘extinct genera’ died out during the event and have a Tem between the lower and upper boundaries of the event of interest. To obtain the percentage of survivors, we used the following formula: (Sgen/Ttgen) × 100. Similarly, the percentage of extinction is calculated as: (Egen/Ttgen) × 100.Age-dependent extinction modelWe assessed the effect of taxon age on the extinction probability by fitting the age-dependent extinction (ADE; -ADE 1 option) model50. This model estimates the probability for a lineage to become extinct as a function of its age, also named longevity, which is the elapsed time since its origination. It is recommended to run the ADE model over time windows with roughly constant origination and extinction rates, as convergence is difficult—but not impossible—to reach in extinction or diversification contexts50. We ran PyRate for 50 million MCMC generations with a sampling every 50,000 generations, with a time-variable Poisson process of preservation (-qShift option), while accounting for varying preservation rates across taxa using the Gamma model (-mG option). We replicated the analyses on ten randomised datasets and combined the posterior estimates across all replicates. We estimated the shape (Φ) and scale (Ψ) parameters of the Weibull distribution, and the taxon longevity in a million years. According to ref. 50, there is no evidence of age-dependent extinction rates if Φ = 1. However, the extinction rate is higher for young species and decreases with species age if Φ  1. Although ADE models are prone to high error rates when origination and extinction rates increase or decrease through time, simulations with PyRate have shown that fossil-based inferences are robust50. We investigated the effect of ADE during three different periods (-filter option) as follows: (1) between 264.28 Ma and 255 Ma (pre-decline), (2) between 254.5 Ma and 251.5 Ma (decline) and (3) between 234 Ma and 212 Ma (post-crisis). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Selection of abiotic and biotic variablesTo test correlations of insect diversification with environmental changes, we examined the link between a series of environmental variables and origination/extinction rates over a period encompassing the GEE, the LPME and the CPE but also for each extinction event. We focused on the role of nine variables, also called proxies, which have been demonstrated or assumed to be linked to extinctions and changes in insect diversity26,67.The variations in the atmospheric CO2 and O2 concentrations are thought to be correlated with the diversification of several insect lineages, including the charismatic giant Meganeuridae65,66,67. Because the increase of O2 concentration has likely driven the diversification of some insects, its diminution may have resulted in the extinction or decline of some lineages. Therefore, we investigated the potential correlation of the variations of this variable with insect dynamics using data from ref. 55. We extracted the data, with 1-million-year time intervals, spanning the Permo–Triassic.Similarly, the modification of CO2 concentration, notably its increase, is known to promote speciation in some modern insect groups107. Therefore, a similar effect may have occurred during the Permian and Triassic but remains to be tested. We based our analyses on the dataset of ref. 108. We used their cleaned dataset and extracted all verified values for the Permo–Triassic interval. Because the initial data (i.e. independent estimates) were made in various locations for the same age, different values of the CO2 concentration are provided. We incorporated all these values in our analysis, allowing PyRate to search for a correlation for each value of the CO2 concentration. We obtain a final correlation independent of the sampling location, in line with our large-scale analysis.The continental fragmentation, as approximated by plate tectonic change over time, has recently been proposed as a driver of Plecoptera dynamics26. Because the period studied encompasses a major geological event, the fragmentation of the supercontinent Pangea, we investigated the effect of continental fragmentation on insect diversification dynamics. We retrieved the index of continental fragmentation developed by ref. 69 using paleogeographic reconstructions for 1-million-year time intervals. This index approaches 1 when all plates are disjoined (complete plate fragmentation) and approaches 0 when the continental aggregation is maximal.Climate change (variations in warming and cooling periods) is a probable driver of diversification changes over the history of insects21,109. Temperature is likely directly linked with insect dynamics109 but also with their food sources, notably plants110. Because it was demonstrated that modification of temperature impacted floral assemblages110, we tested the correlation between temperature variations and the diversification dynamic of insects. Major trends in global climate change through time are typically estimated from relative proportions of different oxygen isotopes (δ18O) in samples of benthic foraminiferan shells111. We used the data from ref. 112, converted to absolute temperatures following the methodology described in Condamine et al.113 (see their section Global temperature variations through time). The resulting temperature data reflects planetary-scale climatic trends, with time intervals inferior to 1-million-year, which can be expected to have led to temporally coordinated diversification changes in several clades rather than local or seasonal fluctuations.The fluctuation in relative diversity of gymnosperms, non-Polypodiales ferns, Polypodiales ferns, spore-plants, and later the rise of angiosperms has likely driven the diversification of numerous insects57,60,61,114. Close interactions between insects and plants are well-recorded during the Permian and Triassic57,60,61. In fact, herbivorous insects are known to experience high selection pressure from bottom-up forces, resulting from interactions with their hosts or feeding plants30,72. Therefore, it appears crucial to investigate the effect of these modifications on the insects’ past dynamics. We used the data from ref. 38 for the different plant lineages (all with 1-million-year time intervals). All the datasets for these variables are available in the publications cited aside from each variable or in Supplementary Data 1.Multivariate birth–death modelWe used the multivariate birth–death (MBD) model to assess to what extent biotic and abiotic factors can explain temporal variation in origination and extinction rates55. The model is described in ref. 55, where origination and extinction rates can change through time in relation to environmental variables so that origination and extinction rates depend on the temporal variations of each factor. The strength and sign (positive or negative) of the correlations are jointly estimated for each variable. The sign of the correlation parameters indicates the sign of the resulting correlation. When their value is estimated around zero, no correlation is estimated. An MCMC algorithm combined with a horseshoe prior, controlling for over-parameterisation and for the potential effects of multiple testing, jointly estimates the baseline origination (λ0) and extinction (µ0) rates and all correlation parameters (Gλ and Gµ)55. The horseshoe prior is used to discriminate which correlation parameters should be treated as noise (shrunk around 0) and which represent a true signal (i.e. significantly different from 0). In the MBD model, a correlation parameter is estimated to quantify independently the role of each variable on the origination and the extinction.We ran the MBD model using 20 (for short intervals) or 50 million MCMC generations and sampling every 20,000 or 50,000 to approximate the posterior distribution of all parameters (λ0, µ0, nine Gλ, nine Gµ and the shrinkage weights of each correlation parameter, ωG). The MBD analyses used the Ts and the Te derived from our previous analyses under the RJMCMC model. The results of the MBD analyses were summarised by calculating the posterior mean and 95% CI of all correlation parameters and the mean of the respective shrinkage weights (across ten replicates), as well as the mean and 95% CI of the baseline origination and extinction rates. We carried out six analyses, over: (1) the Permo–Triassic (between 298.9 and 201.3 Ma); (2) the Roadian–Wordian (R/W) boundary (between 270 and 265 Ma), (3) the LPME (between 254.5 and 250 Ma), (4) the Ladinian–Carnian (L/C) boundary (between 240 to 234 Ma), (5) the Permian period (between 298.9 and 251.902 Ma) and (6) the Triassic period (between 251.902 and 201.3 Ma). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Multiple clade diversity-dependence modelTo assess the potential effect of diversity-dependence on the diversity dynamics of three or four insect guilds, we used the multiple clade diversity-dependence (MCDD) model in which origination and extinction rates are correlated with the diversity trajectory of other clades31. This model postulates that competitive interactions linked with an increase in diversity results in decreasing origination rates and/or increasing extinction rates. The MCDD model allows for testing diversity-dependence between genera of a given clade or between genera of distinct clades sharing a similar ecology.We estimated the past diversity dynamics for three (i.e. herbivores, predators, and a guild composed of generalists + detritivores/fungivores dubbed ‘others’) or four insect groups or guilds (i.e. herbivores, predators, generalists and detritivores/fungivores) by calculating the number of living species at every point in time based on the times of origination (Ts) and extinction (Te) estimated under the RJMCMC model (see above) (Supplementary Figs. 19–24). We defined our four insect groups with a cautious approach i.e. insect genera, families or orders for which nothing is known about the ecology or about the ecology of their close relatives were not considered for the analysis. For example, no diet was assigned to Diptera, Mecoptera or Glosselytrodea. The ecology of the Triassic Diptera and Permo–Triassic Mecoptera is difficult to establish because extant Diptera and Mecoptera have a wide diversity of ecology. Fossil Mecoptera are also putatively involved in numerous interactions with plants (species with elongated mouthparts), suggesting a placement in the herbivore group, while other species were likely predators. Therefore, we cannot decide to which group each species belongs. Similarly, nothing is known about the body and mouthparts of the Glosselytrodea, most of the time described based on isolated wings; we did not assign the order to any group. The definition and delineation of insect clades have also challenged the placement of several orders (e.g. ‘Grylloblattodea’) in one of our four groups. The order ‘Grylloblattodea’ is poorly delineated and mostly serves as a taxonomic ‘wastebasket’ to which it is impossible to assign a particular ecology. Finally, genera, species, or families not placed in a higher clade (e.g. Meshemipteron, Perielytridae) were not included in the analysis. Oppositely, the guilds ‘herbivores’ and ‘predators’ are well defined, and their ecology is evidenced by the morphology of their representatives and the principle of actualism. For example, the ecology of Meganeurites gracilipes (Meganeuridae) has been deeply studied, and its enlarged compound eyes, its sturdy mandibles with acute teeth, its tarsi and tibiae bearing strong spines, and the presence of a pronounced thoracic skewness are specialisations today found in dragonflies that capture their prey while in flight115. All Odonatoptera are well-known predator insects. The raptorial forelegs of the representatives of the order Titanoptera and their mouthparts with strong mandibles are linked with predatory habits81. The Palaeodictyopteroidea were herbivorous insects with long, beak-like, piercing mouthparts, and probably a sucking organ81,82. Most Hemiptera are confidently considered herbivorous insects by comparison with their extant representatives. For example, the Cicadomorpha or Sternorrhyncha are known to feed on plants and their fossil representatives likely possessed the same ecology because of similar morphologies116. Some hemipteran families (e.g. Nabidae) are predators and we cautiously distinguished herbivorous and carnivorous taxa among Hemiptera. The detail of the ecological assignations for the 1009 genera included in our analyses can be found in Supplementary Data 1 (Table MCCD).We calculated ten diversity trajectories from the ten replicated analyses under the RJMCMC model. The estimation of past species diversity might be biased by low preservation rates or taxonomic uncertainties. However, such trajectory curves are likely to provide a reasonably accurate representation of the past diversity changes in the studied clades, notably because the preservation during the Permian and Triassic period is relatively good for insects (i.e. no gaps).Our MCDD analyses comprise all the insect genera spanning from the lowermost Permian to the uppermost Triassic and were run and repeated on ten replicates (using the Te and Ts estimated under the RJMCMC model) with 50 million MCMC generations and a sampling frequency of 50,000. For each of the four insect groups, we computed the median and the 95% CI of the baseline origination and extinction rates (λi and µi), the within-group diversity-dependence parameters gλi and gµi, and the between-groups diversity dependence parameters gλij and gµij. The mean of the sampled diversity dependence parameters (e.g. gλij) was used as a measure of the intensity of the negative (if positive) or positive interactions (if negative) between each pair of groups. The interactions were considered significant when their median was different from 0 and the 95% CI did not overlap with 0. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.We cross-validated the result of the MCDD model using the MBD model. The MBD model can be used to run a multiple clade diversity-dependence analysis by providing the diversity trajectories of insect guilds as a continuous variable. These data are directly generated by PyRate using the lineages-through-time generated by the RJMCMC analyses (-ltt option). We ran the MBD model using 50 million MCMC generations and sampling every 50,000 to approximate the posterior distribution of all parameters (λ0, µ0, four Gλ, four Gµ and the shrinkage weights of each correlation parameter, ωG). We carried out three analyses, over the period encompassing the three extinction events (between 275 and 230 Ma): (1) for herbivores; (2) for predators; and (3) for ‘others’. For each analysis, the lineages-through-time data of the two other guilds are used as continuous variables to investigate a diversity dependence effect. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

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    New globally distributed bacterial phyla within the FCB superphylum

    Identification, phylogeny, and distribution of five phylaTo advance our understanding of marine sediment microbial diversity, we obtained over 30 billion paired DNA sequences from 42 marine sediment samples (coastal and deep sea) (Supplementary Data 1). From this, we reconstructed over 8000 ( >50% complete, 95%) to genes from coastal waters (Venezuela), a hypersaline pond in Carpinteria (US), sediments in Garolim Bay (Korea), and others (Supplementary Data 6 and 7). The worldwide distribution of these five phyla suggests that they have potentially overlooked ecological roles across many environments.Detection of novel protein familiesTo explore novel metabolic capabilities of these bacteria, we employed a recently described approach to identify and characterize unknown genes exclusive to uncultivated taxa17. Using this computational method, we identified 1,934 novel protein families (NPFs) and 6,893 novel singletons (NSs) in the 55 MAGs. The former can be define as families that do not show any homology in broadly used databases (including eggNOG, pfamA, pfamB, and RefSeq, see “Methods”) while the latter (NSs) are NPFs that are detected only once in each given genome or group of genomes. To determine if this novelty was specific to the five phyla or distributed across other uncultivated prokaryotic taxa, we mapped these NPFs and NSs against a comprehensive dataset of 169,642 bacterial and archaeal genomes covered in Rodriguez del Río et al.17. Using an in-house pipeline (Supplementary Fig. 4), we found that 44.6% of these NPFs and NSs are present in other uncultured taxa, highlighting the novel and undescribed metabolic repertoire that these five phyla share with other uncultured prokaryotic lineages17. Specifically, we found that these proteins are also present in Marinisomatota, Bacteroidota, and WOR-3 from publicly available genomes obtained from both marine and terrestrial environments17. When comparing the total number of NPFs per genome in the novel bacterial phyla against the genomic dataset (approximately 170,000 genomes), we found that the novel taxa described in this study have a higher than average percentage of novel proteins per genome (5.68 ± 4.89%) (p  0.7) and widespread (coverage > 0.7) within each phylum are shown in dark purple bars. The number of novel protein families with conserved neighboring genes are shown in light gray bars. c, d, Selected examples of phylogenetic trees and novel protein family genomic context marked in gray with a black outline) in Blakebacterota and Arandabacterota. The protein families are similar between these two phyla and have conserved neighboring genes, including translation initiation factor IF-3 gene (infC), large subunit ribosomal protein L20 gene (rplT), phenylalanyl-tRNA synthetase genes (pheST), cell division protein gene (zapA), phosphodiesterase gene (ymdB), methenyltetrahydrofolate cyclohydrolase gene (folD), and exodeoxyribonuclease genes (xseAB). e Phylogenetic tree and genomic context of a novel protein family uniquely distributed in Joyebacterota. The novel protein family has conserved genomic neighbors related to energy conservation (Rnf complex genes, rnfABCDEG). The phylogeny was generated using FastTree2 and numbers on the top and bottom of the branch represent the bootstrap and branch length, respectively. Source data are provided as a Source Data file.Full size imageMetabolic pathways are often encoded by ‘genome neighborhoods’ (gene clusters and/or operons)18. Therefore, we calculated the genomic context conservation of the NPFs containing three or more sequences (3773 NPFs in total) and examined the annotation of genes found in genomic proximity of the NPFs to determine their potential function. Of the inspected families, 513 (14%) had a conservation score ≥ 0.9 (see “Methods”) indicating a high degree of conserved neighboring proteins. Manual annotation of these neighboring proteins indicated they are potentially involved in sulfur reduction, energy conservation, as well as the degradation of organics such as starch, fatty acids, and amino acids (highlighted in red in Supplementary Fig. 5). For example, a NPF predominantly found in Blakebacterota is neighbored by putative menaquinone reductases (QrcABCD), a conserved complex related to energy conservation in sulfate reducing bacteria19,20,21,22. However, metabolic annotations of Blakebacterota genomes that encode QrcABCD indicate that they largely lack the key enzymes for sulfate reduction, dissimilatory sulfite reductases (DsrABC), suggesting this QrcABCD complex may be involved in other bioenergetic contexts such as linking periplasmic hydrogen and formate oxidation to the menaquinone pool22.In some instances, we found NPFs coded near genes predicted to produce key proteins in nitrogen cycling. Two of the Joyebacterota MAGs code NPF neighboring proteins with homology to hydroxylamine dehydrogenases (HAO). HAO is a key enzyme in marine nitrogen cycling that has traditionally been thought to catalyze the oxidation of hydroxylamine (NH2OH) to nitrite (NO2−) in ammonia oxidizing bacteria. Recently, it has been suggested that HAO may also convert hydroxylamine to nitric oxide (NO) as an intermediate, which is then further oxidized to nitrite by an unknown mechanism. Hydroxylamine is also known to be an intermediate in the nitrogen cycle. It is a potential precursor of nitrous oxide (N2O), a potent greenhouse gas that is a byproduct of denitrification, nitrification23,24, and anaerobic ammonium oxidation25. The presence of HAO within the genomic context of these NPFs suggests they may be involved in mediating hydroxylamine metabolism, and thus may play an important role in nitrogen cycling.A number of NPFs are colocalized with genes predicted to be involved in the utilization of organic carbon. For example, one NPF found in Blakebacterota genomes is adjacent to a peptidase (PepQ; K01271) for dipeptide degradation. Another NPF, only detected in Blakebacterota, is neighbored by long-chain acyl-CoA synthetase (FadD; K01897), a key enzyme in fatty acid degradation (Supplementary Fig. 6). In Joyebacterota, as well as in publicly available Bacteroidetes and Latescibacteria we identified an NPF that is colocalized with amylo-alpha-1,6-glucosidase (Glycoside Hydrolase Family 57), suggesting a potential role in starch degradation.We also identified NPFs that are specific and very conserved in AABM5, Blakebacterota, Orphanbacterota, Arandabacterota, and Joyebacterota (2, 39, 3, 16, and 26 respectively). These NPFs were found in at least 70% of the MAGs belonging to each phylum, and rarely present in other genomes across the tree of life. Due to their unique nature, the 86 unique NPFs could be used as marker genes for future characterizations of the novel bacteria described in this study. When examining the genomic context of the phyla-specific NPFs, we found that more than half of the NPFs (49 of 86) shared the same gene order and are next to genes predicted to be involved in various catabolic and anabolic processes. For example, an NPF in Joyebacterota MAGs is adjacent to an Rnf complex26, which is important for energy conservation in numerous organisms21 (Fig. 2e). Also, two different NPFs in Blakebacterota and Arandabacterota MAGs were located next to tRNA synthesis genes (Fig. 2c, d). Additional phyla-specific NPFs were colocalized with genes predicted to be involved in other important processes, including peptidoglycan biosynthesis (Supplementary Fig. 6a), F-type ATPase (Supplementary Fig. 6b), acyl-CoA dehydrogenase, elements for transportation, sulfur assimilation (Supplementary Fig. 6c), and others (Supplementary Fig. 6d).Metabolic potential of the novel bacterial phylaIn addition to NPF-based analyses, we compared the predicted proteins in the novel lineages to a variety of databases and gene phylogenies to understand their metabolism (see “Methods”). The distribution of key metabolic proteins based on presence/absence of protein families (using MEBS: see methods) in the 61 MAGs is largely consistent with their phylogeny (Fig. 1a). Below, we detail the predicted metabolism of each novel bacterial phyla based on these analyses (Supplementary Fig. 5 and Supplementary Data 8 and 9, see details in Supplementary Information).JoyebacterotaJoyebacterota is composed of 20 MAGs predominantly reconstructed from hydrothermal vent sediments (blue, lower right side in the phylogeny shown in Fig. 1a). Metabolic inference suggests that these bacteria are obligate anaerobes encoding extracellular carbohydrate-active enzymes (CAZymes) with the potential to degrade pectate or pectin, photosynthetically fixed carbon in marine diatoms, macrophytes27, and terrestrial plants28. Furthermore, Joyebacterota seems to be involved in the sulfur cycle. Seven Joyebacterota MAGs encode sulfide:quinone oxidoreductases (SQR). Phylogenetic analysis indicate these SQR belong to the membrane-bound type I and III29. Interestingly, these SQR type I sequences are closely related to those sequences mostly found in terrestrial environments, e.g., freshwater, soil, and hot spring, while SQR-III  have been previously suggested to play a key role maintaining the sulfide homeostasis or bioenergetics in deep-sea sediments30. The presence of these pathways highlight the potential adaptation of Joyebacterota to several environments, contributing to recycling of carbon and sulfur.BlakebacterotaThe Blakebacterota phylum is composed of 11 MAGs predominantly reconstructed from the surface layer of GB sediments (0–6 cm). In this environment, temperatures range from 25 to 29 °C, CH4 measures 0.4–0.8 mM, CO2 reaches up to 10 mM, and SO42− concentrations are high (up to 28 mM)30. Metabolic inference using MEBS31 suggests Blakebacterota play an important role in N and S cycles. These findings were supported by the presence of key enzymes in these cycles. For example, we identified a nitrous oxide reductase in Blakebacterota, the only known enzyme to catalyze the reduction of nitrous oxide to nitrogen gas. This reaction acts as a sink for nitrous oxide, and thus is an important removal mechanism for this potent greenhouse gas. In addition to nitrogen cycling, we identified key genes involved in sulfur cycling in Blakebacterota. Six of the MAGs possess genes that code for SQR with sulfate or nitrous oxide as the final electron accepter. In addition, seven of the MAGs contain genes for thiosulfate dehydrogenase (doxD), which may convert thiosulfate to tetrathionate. Finally, one MAG is predicted to produce dimethyl sulfide (DMS) under oxic conditions via methanethiol S-methyltransferase (MddA) from methylate L-methionine or methanethiol (MeSH). Thus, these bacteria may play important roles in a variety of intermediate steps in nitrogen and sulfur cycling.ArandabacterotaLike Joyebacterota, Arandabacterota were largely recovered from shallow (2–14 cm) GB and deep (26–38 cm) BS sediments. This phylum contains 11 MAGs that are predicted to be anaerobic polysulfide and elemental sulfur reducers. They may mediate sulfur reduction via sulfhydrogenases (HydGB), which results in the production of sulfide32,33. Thus, Arandabacterota may contribute to sulfur cycling in marine sediments. Arandabacterota also code distinct hydrogenases, [NiFe] 3c and 4g types, (Fig. 3) for H2 oxidation. In addition, Arandabacterota may reduce nitrite via periplasmic dissimilatory nitrite reductases (NrfAH) present in Meg22_24_Bin_129, BHB10-38_Bin_9, and SY70-4-3_Bin_59. This mechanism for energy conservation is more efficient than polysulfide and elemental sulfur reduction. Therefore, they are likely to use sulfur species as electron donors in the absence of nitrite.Fig. 3: Maximum likelihood phylogenetic tree of NiFe hydrogenases from the novel phyla.The majority of NiFe hydrogenases identified from the five phyla in this study are highlighted in the gray background. Most hydrogenases are types 4g and 3c. Starred branches denote the minor NiFe hydrogenases identified in this study. Bootstrap values ≥ 80 are shown in circles. Source data are provided as a Source Data file.Full size imageOrphanbacterotaOrphanbacterota is composed of seven MAGs that were mostly obtained from the BS, and appear to be metabolically versatile, facultative aerobes. The BS has an average water depth of 18 m and is strongly influenced by anthropogenic activities in China, mainly the terrestrial input of nutrients and organic matter34. Orphanbacterota code a diversity of CAZymes for the degradation of complex carbohydrates. We identified genes coding for extracellular glycoside hydrolase family 16 (GH16), which may be involved in the degradation of laminarin, releasing glucose and oligosaccharides35. Six Orphanbacterota genomes also contain genes predicted to produce extracellular peptidases belonging to family M28 and S8, which are nonspecific peptidases (Supplementary Fig. 7 and Supplementary Data 10–14). The released amino acids could be taken up via ABC transporters coded by these bacteria.Consistent with their recovery from shallow sediment habitats (Supplementary Data 1), Orphanbacterota have a diverse repertoire of terminal cytochrome oxidase genes (Supplementary Data 9) suggesting they are capable of surviving in a range of oxygen concentrations. Based on the presence of isocitrate lyase and malate synthase, they may use the glyoxylate cycle for carbohydrate synthesis when sugar is not available, or use simple two-carbon compounds for energy conservation36,37. They also appear capable of reducing nitrate to nitrite via periplasmic nitrate reductases (NapAB)38. Moreover, they could reduce nitrate via the membrane-bound nitrate reductase for energy conservation and reducing nitrous oxide.One Orphanbacterota genome (M3-44_Bin_119) has genes predicted to mediate sulfate/sulfite reduction, including DsrABC, QmoABC, and membrane bound Rnf complexes (Supplementary Fig. 8a, b and Supplementary Data 8 and 9). Another Orphanbacterota (LQ108M_Bin_12) is predicted to contain diverse metabolic pathways, including MmdA for DMS production, SQR for sulfide oxidation, the Rnf complex for energy conservation21 or detoxification (Supplementary Fig. 8c), and sulfhydrogenases (HydABDG) for H2 oxidation. In addition to energy conservation and detoxification, sulfide oxidation is important for preventing the loss of sulfur through H2S volatilization. This is predicted to be an important process in sulfur-rich sediments, where large quantities of the self-produced H2S are produced during heterotrophic growth29.AABM5AABM5 (12 genomes, 7 obtained in this study) is an understudied bacterial group that has largely been recovered from shallow (4–12 cm) sediments in GB and deep (44–62 cm) sediments in BS. Despite the distinct environments where they have been found, genomes within this phylum have several shared metabolic abilities. In contrast to the strict anaerobic lifestyle that was previously reported in a subgroup within AABM5 (candidate division LCP–89)12, we predict they are facultative anaerobes. In support of this, we identified cytochrome c oxidase (CtaDCEF) and cytochrome bd ubiquinol oxidase (CydAB) for aerobic respiration39. In addition, we identified DsrABC in nine genomes (Supplementary Fig. 8 and Supplementary Data 15), indicating these organisms can potentially reduce sulfate/sulfite for energy conservation. Several AABM5 genomes are predicted to use H2 as an electron donor due to the presence of type 3c [NiFe] hydrogenase (MvhADG) (Fig. 3, Supplementary Fig. 9, and Supplementary Data 8 and 9). The metabolic versatility in this phylum better explains their global distribution.Ecological significance of the new phylaThese previously overlooked bacterial phyla appear to be involved in key biogeochemical processes in marine sediments, namely sulfur and nitrogen cycling, and the degradation of organic carbon. However, we did not find any evidence for complete autotrophic metabolisms (Wood-Ljungdahl pathway, Calvin–Benson–Bassham, reductive tricarboxylic acid, 3-hydroxypropionate bicycle, 3-hydroxypropionate-4-hydroxybutyrate, and dicarboxylate-4-hydroxybutyrate cycles) in any of these bacteria. Instead, they have a variety of pathways for the utilization of organic compounds as detailed above. These novel bacteria phyla (all except Blakebacterota) have the potential to degrade the algal glycan laminarin, one of the most important complex carbon compounds in the ocean40. These novel phyla encode extracellular laminarinases that specifically cleave the laminarin into more readily degradable sugars, e.g., glucose and oligosaccharide (Supplementary Fig. 7 and Supplementary Data 10–12). Laminarin glycan is produced in the surface ocean by microalgae that sequester CO2 as an important carbon sink in the oceans41. This is a key process of the global carbon cycle, and most studies have focused on understanding aerobic laminarin-degrading bacteria in the surface oceans41,42. Recently, it has been shown that laminarin plays a prominent role in oceanic carbon export and energy flow to higher trophic levels and the deep ocean40, yet the organisms responsible for laminarin degradation under anoxic conditions are unknown. The discovery of  these novel bacterial phyla opens new doors for future studies exploring laminarin degradation in the deep sea. In addition, most of them contain genes predicted to code for sulfatases. Blakebacterota, Orphanbacterota, Arandabacterota, and Joyebacterota code for arylsulfatase, mainly arylsulfatase A, for desulfation of galactosyl moiety of sulfatide. They also code choline sulfatase, iduronate 2-sulfatase and some uncharacterized sulfatases for different types of substrates43. This suggests they are capable of cleaving organic sulfate ester bonds as a source of sulfur and organic carbon on the ocean floor.Many metabolic processes identified here, including pathways for polysaccharide degradation, sulfur, and nitrogen metabolism are often incomplete (Fig. 4). This may be due to the incompleteness of these genomes, or it suggests that these processes occur via metabolic handoffs within the community. Some of the phyla are capable of mediating a variety of sulfur and nitrogen redox reactions (Fig. 4a, b). For example, four phyla code DsrABC, suggesting they play an overlooked role in inorganic matter degradation in marine sediments through sulfate reduction. The resultant sulfide may be reoxidized to sulfur intermediates and organic sulfur compounds by these newly described bacteria. Four phyla (Blakebacterota, Orphanbacterota, Arandabacterota, and Joyebacterota) code an SQR for producing elemental sulfur from sulfide. Methanethiol S-methyltransferase (MddA) is predicted to be produced by individual MAGs Blakebacterota (M3-38_Bin_215) and Orphanbacterota (LQ108M_Bin_12) for the production of DMS from methionine44. DMS is important in climate regulation and sulfur cycling in marine environments45,46, though little is known about the fate or production of DMS in anoxic environments like marine sediments. As detailed above, Blakebacterota contains genes for the conversion of thiosulfate to tetrathionate. Four phyla (AABM5, Orphanbacterota, Arandabacterota, and Joyebacterota) are predicted to disproportionate thiosulfate to sulfite via thiosulfate/3-mercaptopyruvate sulfurtransferase. Thus, we suspect these bacteria may be capable of mediating intermediate sulfur species in anoxic environments. These results provide a predictive framework for future physiological studiesto confirm our genomic-based predictions.Fig. 4: Genomic-based predictions of the potential metabolic role of the novel bacterial phyla.Key steps in the (a) sulfur and (b) nitrogen cycles predicted in the five bacterial phyla. Compounds (in gray triangle frames) were arranged according to the standard Gibbs free energy of formation of each sulfur or nitrogen compound (values next to the compound taken from Caspi et al.93). Star, square, triangle, pentagon, and diamond shapes correspond to AABM5, Blakebacterota, Orphanbacterota, Arandabacterota and Joyebacterota, respectively. Colored shapes represent the presence of genes in a given pathway. Fully colored shapes indicate the presence of genes in over 50% of the phyla. Half colored shapes signify that less than 50% of the phyla code for those genes. Uncolored shapes indicate presence of genes in only one MAG. Note that only pathways encoded in at least one MAG are shown. The red dotted line indicates the assimilatory process. The blue soild line indicates the confirmed pathway with phylogeny of key genes. c Phylogenetic tree and genomic context of a novel protein family (NPF) next to putative menaquinone reductase complex genes (qrcABCD) found in Blakebacterota and Orphanbacterota. d Phylogenetic tree and genomic context of a NPF next to hydroxylamine oxidoreductase genes (hao) in Joyebacterota.Full size imageIn addition to potential roles in sulfur cycling, the phyla described here may play key roles in nitrogen processes, for example several MAGs contain genes that code predicted hydroxylamine dehydrogenase proteins (HAO, confirmed by different databases)47,48. HAO is a precursor of nitrous oxide (N2O), a potent greenhouse gas and ozone destructing agent in the atmosphere. Marine N2O stems from nitrification and denitrification processes which depend on organic matter cycling and dissolved oxygen. Since hydroxylamine is a precursor of N2O, deciphering the organisms that can mediate the formation of N2O has important implications for Earth’s climate49. In addition, three phyla (AABM5, Blakebacterota, and Orphanbacterota) code for periplasmic and/or transmembrane nitrate reductase, and two phyla (AABM5 and Arandabacterota) are predicted to reduce nitrite via dissimilatory nitrite reductase.In recent years, there have been large advances in the exploration of novel microbial diversity. Genomic data has provided crucial insights into the ecological roles and biology of these new microbes. The recovery of bacterial genomes belonging to five overlooked, globally distributed phyla with considerably novel protein composition reminds us there is much to be learned about the microbial world. The identification of NPFs provides targets for future studies to elucidate the ecophysiology of these organisms. The presence of genes for organic carbon degradation and sulfur and nitrogen cycling in these new bacteria suggests they contribute to a variety of key processes in marine sediments. Thus, the addition of these bacterial genomes to ecosystem models will likely transform our understanding of how microbial communities drive carbon degradation and nutrient cycling in the oceans. More

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    Prioritize gender equality to meet global biodiversity goals

    Parties to the Convention on Biological Diversity will meet this month to finalize the post-2020 Global Biodiversity Framework and the text for the stand-alone target on gender equality (Target 22). This target aims to reshape conservation policy and practice to make them more inclusive, equitable and effective.
    Competing Interests
    The authors declare no competing interests. More

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    Ruminant inner ear shape records 35 million years of neutral evolution

    Zachos, J. C., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Erwin, D. H. Climate as a driver of evolutionary change. Curr. Biol. 19, R575–R583 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mayhew, P. J., Jenkins, G. B. & Benton, T. G. A long-term association between global temperature and biodiversity, origination and extinction in the fossil record. Proc. R. Soc. Lond. B 275, 47–53 (2008).
    Google Scholar 
    Raia, P. et al. Past extinctions of Homo species coincided with increased vulnerability to climatic change. One Earth 3, 480–490 (2020).Article 
    ADS 

    Google Scholar 
    deMencoal, P. Climate and human evolution. Science 331, 540–542 (2011).Article 
    ADS 

    Google Scholar 
    Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47, 507–532 (2016).Article 

    Google Scholar 
    Potts, R. & Faith, J. T. Alternating high and low climate variability: The context of natural selection and speciation in Plio-Pleistocene hominin evolution. J. Hum. Evol. 87, 5–20 (2015).Article 
    PubMed 

    Google Scholar 
    Clavel, J. & Morlon, H. Accelerated body size evolution during cold climatic periods in the Cenozoic. Proc. Natl Acad. Sci. USA 114, 4183–4188 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Mihlbachler, M. C., Rivals, F., Solounias, N. & Semprebon, G. M. Dietary change and evolution of horses in North America. Science 331, 1178–1181 (2011).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Mennecart, B. et al. Bony labyrinth morphology clarifies the origin and evolution of deer. Sci. Rep. 7, 13176 (2017).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Ponce, deLeón et al. Human bony labyrinth is an indicator of population history and dispersal from Africa. Proc. Natl Acad. Sci. USA 115, 4128–4133 (2018).Article 
    ADS 

    Google Scholar 
    Luo, Z.-X. The inner ear and its bony housing in tritylodontids and implications for the evolution of the mammalian ear. Bull. Mus. Comp. Zool. 156, 81–97 (2001).
    Google Scholar 
    Ekdale, E. G. Comparative anatomy of the bony labyrinth (inner ear) of placental mammals. PLoS ONE 8, e66624 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    O’Leary, M. A. An anatomical and phylogenetic study of the osteology of the petrosal of extant and extinct artiodactylans (Mammalia) and relatives. Bull. Am. Mus. Nat. Hist. 335, 1–206 (2010).Article 

    Google Scholar 
    Costeur, L. et al. The bony labyrinth of toothed whales reflects both phylogeny and habitat preferences. Sci. Rep. 8, 7841 (2018).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Spoor, F., Bajpai, S., Hussain, S. T., Kumar, K. & Thewissen, J. G. M. Vestibular evidence for the evolution of aquatic behavior in early cetaceans. Nature 417, 163–166 (2002).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Davies, K. T. J., Bates, P. J. J., Maryanto, I., Cotton, J. A. & Rossiter, S. J. The evolution of bat vestibular systems in the face of potential antagonistic selection pressures for flight and echolocation. PLoS ONE 8, e61998 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Park, T., Mennecart, B., Costeur, L., Grohé, C. & Cooper, N. Convergent evolution in toothed whale cochleae. BMC Evol. Biol. 1, 195 (2019).Article 

    Google Scholar 
    Benoit, J. et al. A test of the lateral semicircular canal correlation to head posture, diet and other biological traits in “ungulate” mammals. Sci. Rep. 10, 19602 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Morimoto, N. et al. Variation of bony labyrinthine morphology in Mio-Plio-Pleistocene and modern anthropoids. Am. J. Phys. Anthropol. 2020, 1–17 (2020).
    Google Scholar 
    DeMiguel, D., Azanza, B. & Morales, J. Key innovations in ruminant evolution: A paleontological perspective. Int. Zool. 9, 412–433 (2014).Article 

    Google Scholar 
    Gunz, P., Ramsier, M., Kuhrig, M., Hublin, J. & Spoor, F. The mammalian bony labyrinth reconsidered, introducing a comprehensive geometric morphometric approach. J. Anat. 220, 529–543 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grohe, C., Tseng, Z. J., Lebrun, R., Boistel, R. & Flynn, J. J. Bony labyrinth shape variation in extant Carnivora: a case study of Musteloidea. J. Anat. 228, 366–383 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Urciuoli, A. et al. A comparative analysis of the vestibular apparatus in Epipliopithecus vindobonensis: Phylogenetic implications. J. Hum. Evol. 151, 102930 (2021).Article 
    PubMed 

    Google Scholar 
    IUCN. The IUCN red list of threatened species. Version 2021-1. https://www.iucnredlist.org. Accessed 17 June 2021.Kingdon, J. & Hoffmann. M. Mammals of Africa. Volume VI pigs, hippopotamuses, chevrotains, Giraffes, deer and bovids 704 (Bloomsbury Publishing, 2013).Chen, L. et al. Large-scale ruminant genome sequencing provides insights into their evolution and distinct traits. Science 364 eaav6202 (2019).Hassanin, A. et al. Pattern and timing of diversification of Cetartiodactyla (Mammalia, Laurasiatheria), as revealed by a comprehensive analysis of mitochondrial genomes. C. R. Biol. 335, 32–50 (2012).Article 
    PubMed 

    Google Scholar 
    Wang, Y. et al. Genetic basis of ruminant headgear and rapid antler regeneration. Science 364, 1153 (2019).Article 

    Google Scholar 
    Myers, E. A. & Bubrink, F. T. Ecological opportunity: Trigger of adaptative radiation. Nat. Educ. Knowl. 3, 23 (2012).
    Google Scholar 
    Gentry, A. W. Bovidae. In Cenozoic mammals of Africa (eds Werdelin, L. & Sanders, W. J.) 741–796 (University of California Press, 2010).Harris, J. M., Solounias, N. & Geraads, D. Giraffoidea. In Werdelin, L. & Sanders, W. J. Cenozoic mammals of Africa. 797–812 (University of California Press, 2010).Clauss, M. & Rössner, G. E. Old world ruminant morphophysiology, life history, and fossil record: exploring key innovations of a diversification sequence. Ann. Zool. Fenn. 51, 80–94 (2014).Article 

    Google Scholar 
    Johnston, A. R. & Anthony, N. M. A multi-locus species phylogeny of African forest duikers in the subfamily Cephalophinae: evidence for a recent radiation in the Pleistocene. BMC Evol. Biol. 12, 120 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cooney, C. R. & Thomas, G. H. Heterogeneous relationships between rates of speciation and body size evolution across vertebrate clades. Nat. Ecol. Evol. 5, 101–110 (2020).Article 
    PubMed 

    Google Scholar 
    Köhler, M. & Moyà-Solà, S. Physiological and life history strategies of a fossil large mammal in a resource-limited environment. Proc. Natl Acad. Sci. USA 106, 20354–22035 (2009).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Bibi, F. A multi-calibrated mitochondrial phylogeny of extant Bovidae (Artiodactyla, Ruminantia) and the importance of the fossil record to systematics. BMC Evol. Biol. 13, 1–15 (2013).Article 

    Google Scholar 
    Geraads, D. A reassessment of the Bovidae (Mammalia) from the Nawata Formation of Lothagam, Kenya, and the late Miocene diversification of the family in Africa. J. Syst. Palaeontol. 17, 1–14 (2017).
    Google Scholar 
    Mennecart, B., Aiglstorfer, M., Li, Y., Li, C. & Wang, S. Ruminants reveal Eocene Asiatic palaeobiogeographical provinces as the origin of diachronous mammalian Oligocene dispersals into Europe. Sci. Rep. 11, 17710 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Rössner, G. E. Family tragulidae. In: The evolution of artiodactyls (eds Prothero, D. R. & Foss S. C.) (The Johns Hopkins University Press, Baltimore, 2007).Sánchez, I. M., Quiralte, V., Morales, J. & Pickford, M. A new genus of tragulid ruminant from the early Miocene of Kenya. Acta Palaeontol. Pol. 55, 177–187 (2010).Article 

    Google Scholar 
    Sánchez, I. M., Quiralte, V., Ríos, M., Morales, J. & Pickford, M. First African record of the Miocene Asian mouse-deer Siamotragulus (Mammalia, Ruminantia, Tragulidae): implications for the phylogeny and evolutionary history of the advanced selenodont tragulids. J. Syst. Palaeontol. 13, 543–556 (2015).Article 

    Google Scholar 
    Mennecart, B. et al. The first French tragulid skull (Mammalia, Ruminantia, Tragulidae) and associated tragulid remains from the Middle Miocene of Contres (Loir-et-Cher, France). C. R. Palevol 17, 189–200 (2018).Article 

    Google Scholar 
    Bobe, R. & Eck, G. C. Responses of African bovids to Pliocene climatic change. Paleobiology 27, 1–47 (2001).Article 

    Google Scholar 
    Strömberg, C. A. E. Decoupled taxonomic radiation and ecological expansion of open-habitat grasses in the Cenozoic of North America. Proc. Natl Acad. Sci. USA 102, 11980–11984 (2005).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Kaya, F. et al. The rise and fall of the Old World savannah fauna and the origins of the African savannah biome. Nat. Ecol. Evol. 2, 241–246 (2017).Article 

    Google Scholar 
    Gravilets, S. & Losos, J. B. Adaptive radiation: contrasting theory with data. Science 323, 732–737 (2009).Article 
    ADS 

    Google Scholar 
    Moen, D. & Morlon, H. Why does diversification slow down? Trends Ecol. Evol. 29, 190–197 (2014).Article 
    PubMed 

    Google Scholar 
    Couvreur, T. L. P. et al. Tectonics, climate and the diversification of the tropical African terrestrial flora and fauna. Biol. Rev. 96, 16–51 (2020).Article 
    PubMed 

    Google Scholar 
    Fontoura, E., Darival Ferreira, J., Bubadué, J., Ribeiro, A. M. & Kerber, L. Virtual brain endocast of Antifer (Mammalia: Cervidae), an extinct large cervid from South America. J. Morphol. 281, 1–18 (2020).Article 

    Google Scholar 
    Trauth M. A. et al. Recurring types of variability and transitions in the ∼620 kyr record of climate change from the Chew Bahir basin, southern Ethiopia Quaternary. Sci. Rev. https://doi.org/10.1016/j.quascirev.2020.106777 (2021).Janis, C. M. & Manning, E. Antilocapridae. In Evolution of tertiary mammals of North America (eds Janis, C. M., Scott, K. M. & Jacobs, L. L.) 491–507 (Cambridge University Press, 1998).Klimova, A., Munguia-Vega, A., Hoffman, J. I. & Culver, M. Genetic diversity and demography of two endangered captive pronghorn subspecies from the Sonoran Desert. J. Mammal. 95, 1263–1277 (2014).Article 

    Google Scholar 
    Evin, A., et al. Size and shape of the semicircular canal of the inner ear: A new marker of pig domestication? J. Exp. Zool. B Mol. Dev. Evol. https://doi.org/10.1002/jez.b.23127 (2022).Sánchez, I. M., Cantalapiedra, J. L., Ríos, M., Quiralte, V. & Morales, J. Systematics and evolution of the Miocene three-horned Palaeomerycid ruminants (Mammalia, Cetartiodactyla). PLoS ONE 10, e0143034 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wiley, D. Landmark Editor 3.6 (Institute for Data Analysis and Visualization, Davis, CA, University of California, 2006).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2022). https://www.R-project.org/.Gunz, P. & Mitteroecker, P. Semilandmarks: a method for quantifying curves and surfaces. Hystrix 24, 103–109 (2013).
    Google Scholar 
    Adams, D. C. & Otárola-Castillo, E. geomorph: an R package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).Article 

    Google Scholar 
    Adams, D. C., Collyer, M. L., Kaliontzopoulou, A. geomorph: software for geometric morphometric analyses. R package version 3.2.1 software (2020).Gunz, P., Mitteroecker, P., Bookstein, F. L. Semilandmarks in three dimensions. In Modern morphometrics in physical anthropology. Springer, pp. 73–98 (2005).Maddison, W. P., Maddison, D. R. Mesquite: a modular system for evolutionary analysis. Version 3.04. (2010).Gromolard, C. & Guérin, C. Mise au point sur Parabos cordieri (de Christol), un Bovidé (Mammalia, Artiodactyla) du Pliocène d’Europe occidentale. Géobios 13, 741–755 (1980).Article 

    Google Scholar 
    Duvernois, M.-P. Mise au point sur le genre Leptobos (Mammalia, Artiodactyla, Bovidae); implications biostratigraphiques et phylogénétiques. Géobios 25, 155–166 (1992).Article 

    Google Scholar 
    Janis, C. M., Manning, E. Dromomerycidae. In Evolution of Tertiary mammals of North America Volume1: Terrestrial carnivores, ungulates, and ungulatelike mammals (eds. Janis, C. M., Scott, K. M., Jacobs L. L.) 477–490 (Cambridge University Press, 1998).Birungi, J. & Arctander, P. Molecular systematics and phylogeny of the reduncini (artiodactyla: bovidae) inferred from the analysis of mitochondrial cytochrome b gene sequences. J. Mamm. Evol. 8, 125–147 (2001).Article 

    Google Scholar 
    Lalueza-Fox, C. et al. Molecular dating of caprines using ancient DNA sequences of Myotragus balearicus, an extinct endemic Balear mammal. BMC Evol. Biol. 5, 1–11 (2005).Article 

    Google Scholar 
    Marot, J. D. Molecular phylogeny of terrestrial artiodactyls, conflict and resolution. In The evolution of artiodactyls (eds Prothero, D. R., Foss, S. C.) 4–18 (The Johns Hopkins University Press, 2007).Webb, D. S. Hornless ruminants. In Evolution of Tertiary mammals of North America Volume1: Terrestrial carnivores, ungulates, and ungulatelike mammals (eds Janis, C. M., Scott, K. M., Jacobs, L. L.) 463–476 (Cambridge University Press, 1998).Mennecart, B. & Métais, G. Mosaicomeryx gen. nov., a ruminant mammal from the Oligocene of Europe and the significance of ‘gelocids’. J. Syst. Palaeontol. 13, 581–600 (2015).Article 

    Google Scholar 
    Sánchez, I. M., DeMiguel, D., Quiralte, V. & Morales, J. The first known Asian Hispanomeryx (Mammalia, Ruminantia, Moschidae.). J. Vert. Paleontolo. 31, 1397–1403 (2011).Heckeberg, N. S., Erpenbeck, D., Wörheide, G. & Rössner, G. Systematic relationships of five newly sequenced cervid species. PeerJ 4, e2307 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ríos, M., Sánchez, I. M. & Morales, J. A new giraffid (Mammalia, Ruminantia, Pecora) from the late Miocene of Spain, and the evolution of the sivathere-samothere lineage. PLoS ONE 12, e0185378 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vislobokova, I. New data on late Miocene mammals of Kohfidisch, Austria. Paleontol. J. 41, 451–460 (2007).Article 

    Google Scholar 
    Aiglstorfer, M., Rössner, G. E. & Böhme, M. Dorcatherium naui and pecoran ruminants from the late Middle Miocene Gratkorn locality (Austria). Palaeobiodivers. Palaeoenviron. 94, 83–123 (2014).Article 

    Google Scholar 
    Janis, C. M. & Scott, K. M. The interrelationships of higher ruminant families with special emphasis on the members of the Cervoidea. Am. Mus. Novit. 2893, 1–85 (1987).
    Google Scholar 
    Leinders, J. Hoplitomerycidae fam. nov. (Ruminantia, Mammalia) from Neogene fissure fillings in Gargano (Italy). Scr. Geol. 70, 1–68 (1984).
    Google Scholar 
    Hassanin, A. & Douzery, E. Molecular and morphological phylogenies of Ruminantia, and the alternative position of the Moschidae. Syst. Biol. 52, 206–228 (2003).Article 
    PubMed 

    Google Scholar 
    Métais, G. & Vislobokova, I. Basal ruminants. In The evolution of artiodactyls (eds Prothero, D. R. & Foss, S. C.) 189–212 (The Johns Hopkins University Press, 2007).Mennecart, B., Zoboli, D., Costeur, L. & Pillola, G. L. On the systematic position of the oldest insular ruminant Sardomeryx oschiriensis (Mammalia, Ruminantia) and the early evolution of the Giraffomorpha. J. Syst. Palaeontol. 17, 691–704 (2019).Article 

    Google Scholar 
    Aiglstorfer, M. et al. Musk Deer on the Run – Dispersal of Miocene Moschidae in the Context of Environmental Changes. In Evolution of Cenozoic land mammal faunas and ecosystems: 25 years of the NOW database of fossil mammals. (eds Casanovas-Vilar, I., van den Hoek Ostende, L. W., Janis, C. M. & Saarinen J.) (Cham: Springer, in press).Klingenberg, C. P. MorphoJ: an integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).Article 
    PubMed 

    Google Scholar 
    Schlager, S. Morpho and Rvcg – Shape analysis in R. In Zheng, G., Li, S., Szekely, G. Statistical shape and deformation analysis, 217–256 (MA: Academic Press, 2017).Klingenberg, C. P. & Gidaszewski, N. A. Testing and quantifying phylogenetic signals and homoplasy in morphometric data. Syst. Biol. 59, 245–261 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Marriott, F. H. C. Barnard’s monte carlo tests: how many simulations? Appl. Stat. 28, 75–77 (1979).Article 

    Google Scholar 
    Edgington, E. S. Randomization tests (Marcel Dekker, 1987).Tzeng, T. D. & Yeh, S. Y. Permutation tests for difference between two multivariate allometric patterns. Zool. Stud. 38, 10–18 (1999).
    Google Scholar 
    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

    Google Scholar 
    Renaud, S., Dufour, A.-B., Hardouin, E. A., Ledevin, R. & Auffray, C. Once upon multivariate analyses: when they tell several stories about biological evolution. PLoS ONE 10, e0132801 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mitteroecker, P. & Bookstein, F. Linear discrimination, ordination, and the visualization of selection gradients in modern morphometrics. Evol. Biol. 38, 100–114 (2011).Article 

    Google Scholar 
    Raia, P., Castiglione, S., Serio, C., Mondanaro, A. & Raia, M. P. Package ‘RRphylo’. CRAN Repos. 4, 1–31 (2018).
    Google Scholar 
    Castiglione, S. et al. A new method for testing evolutionary rate variation and shifts in phenotypic evolution. Methods Ecol. Evol. 9, 974–983 (2018).Article 

    Google Scholar 
    Morlon, H. et al. “RPANDA: an R package for macroevolutionary analyses on phylogenetic trees.”. Methods Ecol. Evol. 7, 589–597 (2016).Article 

    Google Scholar 
    Costeur, L., Mennecart, B., Müller, B., Schulz, G. Observations on the scaling relationship between bony labyrinth, skull size and body mass in ruminants. Proc. SPIE 11113, https://doi.org/10.1117/12.2530702 (2019).Costeur, L., Mennecart, B., Müller, B. & Schulz, G. Prenatal growth stages show the development of the ruminant bony labyrinth and petrosal bone. J. Anat. 230, 347–353 (2017).Article 
    PubMed 

    Google Scholar 
    Mennecart, B. & Costeur, L. Shape variation and ontogeny of the ruminant bony labyrinth, an example in Tragulidae. J. Anat. 229, 422–435 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clauss, M., Steuer, P., Müller, D. W. H., Codron, D. & Hummel, J. Herbivory and body size: allometries of diet quality and gastrointestinal physiology, and implications for herbivore ecology and dinosaur gigantism. PLoS One 8, e68714 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    du Toit, J. T. & Owen-Smith, N. Body size, population metabolism, and habitat specialization among large African herbivores. Am. Nat. 133, 736–740 (1989).Article 

    Google Scholar 
    Mennecart B., Becker D., & Berger J. -P. Mandible shape of ruminants: between phylogeny and feeding habits. In: Ruminants: Anatomy, behavior, and diseases, (ed. Mendes R. E.) 205–226 (Nova Science Publishers, 2012).Bokma, F. et al. Testing for Depéret’s rule (body size increase) in mammals using combined extinct and extant data. Syst. Biol. 65, 98–108 (2016).Article 
    PubMed 

    Google Scholar 
    Besiou, E., Choupa, M. N., Lyras, G. & van der Geer, A. Body mass divergence in sympatric deer species of Pleistocene Crete (Greece). Palaeontol. Electron. 25, a23 (2022).
    Google Scholar 
    Mennecart B., Métais G., Tissier J., Rössner G. E., & Costeur L. 3D models related to the publication: Reassessment of the enigmatic ruminant Miocene genus Amphimoschus Bourgeois, 1873 (Mammalia, Artiodactyla, Ruminantia, Pecora). MorphoMuseuM 7, e131 (2021).Mennecart, B., Perthuis de, A. D. & Costeur, L. 3D models related to the publication: The first French tragulid skull (Mammalia, Ruminantia, Tragulidae) and associated tragulid remains from the Middle Miocene of Contres (Loir-et-Cher, France). MorphoMuseuM 3, e4 (2018).Article 

    Google Scholar 
    Aiglstorfer, M., Costeur, L., Mennecart, B. & Heizmann, E. P. J. Micromeryx? eiselei – a new moschid species from Steinheim am Albuch, Germany, and the first comprehensive description of moschid cranial material from the Miocene of Central Europe. MorphoMuseuM 3, e4 (2107).Article 

    Google Scholar 
    Costeur, L. & Mennecart, B. 3D models related to the publication: Prenatal growth stages show the development of the ruminant bony labyrinth and petrosal bone. MorphoMuseuM 2, e3 (2016).Article 

    Google Scholar 
    Mennecart, B. & Costeur, L. 3D models related to the publication: a Dorcatherium (Mammalia, Ruminantia, Middle Miocene) petrosal bone and the tragulid ear region. MorphoMuseuM 2, e2 (2016).Article 

    Google Scholar 
    Mennecart, B. et al. Allometric and phylogenetic aspects of stapes morphology in ruminantia (Mammalia, Artiodactyla). Front. Earth Sci. 8, 176 (2020). More

  • in

    Soil qualities and change rules of Eucalyptus grandis × Eucalyptus urophylla plantation with different slash disposals

    Jiao, N., Liu, J., Shi, T., Zhang, C. & Pan, D. Implement negative ocean carbon emissions and perform the carbon neutral strategy. Sci. Sinica 51, 632–643. https://doi.org/10.1360/SSTe-2020-0358 (2021).Article 

    Google Scholar 
    Arnold, R. J., Xie, Y. J., Luo, J. Z., Wang, H. & Midgley, S. J. A tale of two genera: Exotic Eucalyptus and Acacia species in China. 1. Domestication and research. Int. For. Rev. 22, 1–18. https://doi.org/10.1505/146554820828671571 (2020).Article 

    Google Scholar 
    Zhu, L., Wang, X., Chen, F., Li, C. & Wu, L. Effects of the successive planting of Eucalyptus urophylla on soil bacterial and fungal community structure, diversity, microbial biomass, and enzyme activity. Land Degrad. Dev. 30, 636–646. https://doi.org/10.1002/ldr.3249 (2019).Article 

    Google Scholar 
    Weixin, L. Eucalyptus robusta planting status and sustainable development countermeasrues based on ecological concept. For. Sci. Technol. Inform. 52, 23–25 (2020).
    Google Scholar 
    Masyagina, O. V. Carbon dioxide emissions and vegetation recovery in fire-affected forest ecosystems of Siberia: recent local estimations. Current Opinion in Environmental Science & Health 23, https://www.sciencedirect.com/science/article/abs/pii/S2468584421000556. Accessed 17 March 2021.xDajun, D. et al. Short-term effects of black carbon on soil extractable nutrient elements in a Pinus massoniana plantation subjected to slash burning. J. Soil Water Conserv. 33, 157–162 (2019).
    Google Scholar 
    Huanhuan, W. et al. Research and application of biochar in soil CO2 emission, fertility, and microorganisms: A sustainable solution to solve China’s agricultural straw burning problem. Sustainability 12, 1–17. https://doi.org/10.3390/su12051922 (2020).Article 

    Google Scholar 
    McIntosh, P. D., Laffan, M. D. & Hewitt, A. E. The role of fire and nutrient loss in the genesis of the forest soils of Tasmania and southern New Zealand. For. Ecol. Manage. 220, 185–215 (2005).Article 

    Google Scholar 
    Arocena, J. M. & Opio, C. Prescribed fire-induced changes in properties of sub-boreal forest soils. Geoderma 113, 1–16 (2003).Article 

    Google Scholar 
    Hart, S. C., DeLuca, T. H., Newman, G. S., MacKenzie, M. D. & Boyle, S. I. Post-fire vegetative dynamics as drivers of microbial community structure and function in forest soils. For. Ecol. Manage. 220, 166–184 (2005).Article 

    Google Scholar 
    Long, S., Yuan, L., Binqing, Z., Fei, L. & Tongxin, H. Effects of moderate fire disturbance on soil respiration components and soil microbial biomass in secondary forest of Maoer mountains China. J. Northeast For. Univ. 47, 90–98. https://doi.org/10.13759/j.cnki.dlxb.2019.07.016 (2019).Article 

    Google Scholar 
    Suping, Z., Falin, L., Meifang, Z., Guangjun, W. & Xiaowei, C. Effects of fire disturbance intensities on soil physiochemical properties of pour subtropical forest types. Acta Ecol. Sin. 40, 233–246. https://doi.org/10.5846/stxb201812052665 (2020).Article 

    Google Scholar 
    Nan, W., Yuetai, W., Guang, Y., Xueying, D. & Xiankui, Q. Effects of fire disturbanceon soil microbial community of larix gmelinii forset. J. Northeast For. Univ. 48, 21–28 (2020).
    Google Scholar 
    Bushra, M. & Tom, L. Temporal variations in litterfall biomass input and nutrient return under long-term prescribed burning in a wet sclerophyll forest, Queensland, Australia. Sci. Total Environ. 706, 36–45. https://doi.org/10.1016/j.scitotenv.2019 (2019).Article 

    Google Scholar 
    Mengya, Z., Xinjie, W., Le, L., Peng, Z. & Yao, F. Effect of burning disposal method on undergrouwth vegetation diversity and soil properties of Cunningham ialanceolata. J. Northeast For. Univ. 45, 63–67+76. https://doi.org/10.13759/j.cnki.dlxb.2017.03.013 (2017).Article 

    Google Scholar 
    Hernández, J., Pino, A. D., Hitta, M. & Lorenzo, M. Management of forest harvest residues affects soil nutrient availability during reforestation of Eucalyptus grandis. Nutr. Cycl. Agroecosyst. 105, 1385–1314. https://doi.org/10.1007/s10705-016-9781-2 (2016).Article 

    Google Scholar 
    Jiang, L., Kou, L. & Li, S. Alterations of early-stage decomposition of leaves and absorptive roots by deposition of nitrogen and phosphorus have contrasting mechanisms. Soil Biol. Biochem. 127, 213–222. https://doi.org/10.1016/j.soilbio.2018.09.037 (2018).Article 

    Google Scholar 
    Ma, X. Temperature and Humidity Effects on Dendrolimus Superans Butler Grow and Develop (Northeast Forestry University, USA, 2017).
    Google Scholar 
    Weng, Y. Decomposition and Nutrient Release Characteristics of Harvest Residues in Eucalyptus Plantation (Central South University of Forestry and Technology, USA, 2019).
    Google Scholar 
    Huanyu, Y. et al. Effects of residue composting treatemt on soil quality of Larix principies-rupprechtii plantation. J. Cent. South Univ. For. Technol. 36, 22–27. https://doi.org/10.14067/j.cnki.1673-923x.2016.11.004 (2016).Article 

    Google Scholar 
    Qiyue, S. et al. Optimizing the process of logging residue of Larix principis-ruppechtii based on orthogonal experiment. J. Fujian Agric. For. Univ. (Nat. Sci. Ed.) 48, 633–639 (2019).
    Google Scholar 
    Mengdi, C., Qibo, C., Jianqiang, L., Jiaxuan, L. & Ruizhang, W. Evaluation of the effects of litter input managements on the soil quality in Pinus yunnanensis forest. J. Yunnan Agric. Univ. (Nat. Sci.) 35, 149–155. https://doi.org/10.12101/j.issn.1004-390X(n).20180535 (2020).Article 

    Google Scholar 
    Kennard, D. K. & Gholz, H. L. Effects of high- and low-intensity fires on soil properties and plant growth in a Bolivian dry forest. Plant Soil 234, 119–129 (2001).Article 

    Google Scholar 
    Yangyang, Y. et al. Effects of ground clearance on the growth of Eucalyptus plantation. J. Fujian Agric. For. Univ. (Nat. Sci. Ed.) 48, 41–47 (2019).
    Google Scholar 
    Changzhun, L. et al. Effects of litter treatment on soil organic carbon, total nitrogen and total phosphorus in different forset types. Sci. Soil Water Conserv. 18, 100–109 (2020).
    Google Scholar 
    Gude, A., Kandeler, E. & Gleixner, G. Input related microbial carbon dynamic of soil organic matter in particle size fractions. Soil Biol. Biochem. 47, 209–219. https://doi.org/10.1016/j.soilbio.2012.01.003 (2012).Article 

    Google Scholar 
    Kang, T., Biao, H., Zhe, X. & Wenyou, H. Geochemical baseline establishment and ecological risk evaluation of heavy metals in greenhouse soils from Dongtai China. Ecol. Indic. 72, 510–520. https://doi.org/10.1016/j.ecolind.2016.08.037 (2017).Article 

    Google Scholar 
    Vidal-Legaz, B., Souza, D. M. D., Teixeira, R. F., Anton, A. & Sala, S. Soil quality, properties, and functions in life cycle assessment: An evaluation of models. J. Clean. Prod. 140, 502–515. https://doi.org/10.1016/j.jclepro.2016.05.077 (2017).Article 

    Google Scholar 
    Emmet-Booth, J. P. et al. Grass VESS: A modification of the visual evaluation of soil structure method for grasslands. Soil Use Manag. 34, 37–47. https://doi.org/10.1111/sum.12396 (2018).Article 

    Google Scholar 
    Thoumazeau, A. et al. A new framework to assess the impact of land management on soil quality. Part A: Concept and validation of the set of indicators. Ecol. Indic. 97, 100–110. https://doi.org/10.1016/j.ecolind.2018.09.023 (2019).Article 

    Google Scholar 
    Santos-Francés, F., Martínez-Graña, A., Ávila-Zarza, C., Criado, M. & Sánchez, Y. Comparison of methods for evaluating soil quality of semiarid ecosystem and evaluation of the effects of physico-chemical properties and factor soil erodibility (Northern Plateau, Spain). Geoderma 354, 113872–113872. https://doi.org/10.1016/j.geoderma.2019.07.030 (2019).Article 

    Google Scholar 
    Jihong, P., Xiaojing, L. & Qinghua, H. A new quality evaluation system of soil and water conservation for sustainable agricultural development. Agric. Water Manag. 240, 106235. https://doi.org/10.1016/j.agwat.2020.106235 (2020).Article 

    Google Scholar 
    Kang, G. S., Beri, V., Sidhu, B. S. & Rupela, O. P. A new index to assess soil quality and sustainability of wheat-based cropping systems. Biol. Fertil. Soils 41, 389–398. https://doi.org/10.1007/s00374-005-0857-4 (2005).Article 

    Google Scholar 
    Gordillo-Rivero, Á. J., García-Moreno, J., Jordán, A., Zavala, L. M. & Granja-Martins, F. M. Fire severity and surface rock fragments cause patchy distribution of soil water repellency and infiltration rates after burning. Hydrol. Process. 28, 5832–5843. https://doi.org/10.1002/hyp.10072 (2014).Article 

    Google Scholar 
    Moody, J. A., Kinner, D. A. & Úbeda, X. Linking hydraulic properties of fire-affected soils to infiltration and water repellency. J. Hydrol. 379, 291–303. https://doi.org/10.1016/j.jhydrol.2009.10.015 (2009).Article 

    Google Scholar 
    Xiaoguang, W. et al. Litter water-holding capacity and soil physical properties of main afforestation tree species in sandstone area. J. Soil Water Conserv. 34, 137–144. https://doi.org/10.13870/j.cnki.stbcxb.2020.04.021 (2020).Article 

    Google Scholar 
    Guoshuang, G. Study on the determination of soil bulk density. Journal of Irrigation and Dranage Engineering. 4, 38–40 (1983).
    Google Scholar 
    Zhu, L., Wang, J., Weng, Y., Chen, X. & Wu, L. Soil characteristics of Eucalyptus urophylla × Eucalyptus grandis plantations under different management measures for harvest residues with soil depth gradient across time. Ecol. Ind. 117, 106530. https://doi.org/10.1016/j.ecolind.2020.106530 (2020).Article 

    Google Scholar 
    Xiao, K. Carbon and Nitrogen Mineralization and Alkalinity Release Following Application of Plant Materials to Acid Soils Differing in Initial pH (Zhejiang University, 2014).
    Google Scholar 
    Tu, J., Qiao, J., Zhu, Z., Li, P. & Wu, L. Soil bacterial community responses to long-term fertilizer treatments in Paulownia plantations in subtropical China. Appl. Soil. Ecol. 124, 317–326. https://doi.org/10.1016/j.apsoil.2017.09.036 (2018).Article 

    Google Scholar 
    Chuihua, K. Research on plant allelopathy in China for the recent 16 years. Chin. J. Appl. Ecol. 31, 2139–2140 (2020).
    Google Scholar 
    Ying, X., Yaru, L., Haiyan, Z. & Qizhi, L. Effect of polyphenols on camellia oil fatty acid and triglyceride under heating conditions. J. Cent. South Univ. For. Technol. 40, 127–134 (2020).
    Google Scholar 
    Xu, Y. et al. Effects of different rotation periods of Eucalyptus plantations on soil physiochemical properties, enzyme activities, microbial biomass and microbial community structure and diversity. For. Ecol. Manage. 456, 148–153. https://doi.org/10.1016/j.foreco.2019.117683 (2020).Article 

    Google Scholar 
    Sollins, P. & Gregg, J. W. Soil organic matter accumulation in relation to changing soil volume, mass, and structure: Concepts and calculations. Geoderma 301, 60–71. https://doi.org/10.1016/j.geoderma.2017.04.013 (2017).Article 

    Google Scholar 
    Bobo, W. et al. Effects of logging residues on surface soil biochemical properties and enzymatic activity. Acta Ecol. Sin. 34, 1645–1653. https://doi.org/10.5846/stxb201310162495 (2014).Article 

    Google Scholar 
    Ruiyong, J. et al. Correlation bwtween soil enzyme activity and physicochemical characteristics in agricultural black soils in Northeast China. Res. Soil Water Conserv. 22, 132–137+142 (2015).
    Google Scholar 
    Bing, L. et al. Activity and influencing factors of soils CAT in different utilization types oflLand in Shenbei area. J. Shenyang Univ. (Nat. Sci.) 31, 465–473. https://doi.org/10.14108/j.cnki.1008-8873.2019.04.008 (2019).Article 

    Google Scholar 
    Song, Y. et al. Short-term response of the soil microbial abundances and enzyme activities to experimental warming in a boreal peatland in Northeast China. Sustainability 11, 1–16. https://doi.org/10.3390/su11030590 (2019).Article 

    Google Scholar 
    Giacomo, C. Fire as a soil-forming factor. Ambio 43, 191–195 (2014).Article 

    Google Scholar 
    Liu, J., Wu, L., Chen, D., Li, M. & Wei, C. Soil quality assessment of different Camellia oleifera stands in mid-subtropical China. Appl. Soil. Ecol. 113, 29–35. https://doi.org/10.1016/j.apsoil.2017.01.010 (2017).Article 

    Google Scholar 
    Zhili, Z., Liwei, Z., Qian, C., Xuehua, X. & Yuling, L. Water-holding capacity of three typical forest litter and soil in Mulan-weichang. J. Soil Water Conserv. 29, 207–213. https://doi.org/10.13870/j.cnki.stbcxb.2015.01.040 (2015).Article 

    Google Scholar 
    Zhao, J. Study on the Effect of Refining Treatment on Soil Properties and Growth of Eucalyptus Urophylla Plantation (Central South University of Forestry and Technology, 2019).
    Google Scholar 
    Moro, M. A. J. & Domingo, F. Litter decomposition in four woody species in a mediterranean climate: Weight loss, N and P dynamics. Ann. Bot. 86, 1065–1071. https://doi.org/10.1006/anbo.2000.1269 (2000).Article 

    Google Scholar 
    Sharma, B. D., Arora, H., Kumar, R. & Nayyar, V. K. Relationships between soil characteristics and total and DTPA-extractable micronutrients in inceptisols of Punjab. Commun. Soil Sci. Plant Anal. 35, 799–818. https://doi.org/10.1081/CSS-120030359 (2004).Article 

    Google Scholar 
    Yonghong, L. et al. Spatial variability and impacting factors of trace elements in hilly region of cropland in northwestern Zhejiang Province. J. Plant Nutr. Fertil. 22, 1710–1718 (2016).
    Google Scholar 
    Lipeng, W. et al. Seasonal variations of growth and photosynthetic characteristice of Eucalyptus plantation. Guangdong For. Sci. Technol. 27, 63–66. https://doi.org/10.3969/j.issn.1006-4427.2011.05.012 (2011).Article 

    Google Scholar 
    Xinmin, D., Zhonghong, W., Yongqin, Z. & Xuexia, P. Study on changes of soil salt and nutrient in greenhouse of different planting years. J. Soil Water Conserv. 21, 78–80 (2007).
    Google Scholar 
    Linying, M., Yuelan, L., Guojun, W. & Yun, L. Studies of relations between soil organic matter content and soil bulk density in different soil level in Donglan county. Hubei Agric. Sci. 53, 59–62. https://doi.org/10.3969/j.issn.0439-8114.2014.01.016 (2014).Article 

    Google Scholar 
    Mohammed, K., Lamb, D. T., Ray, C., Mallavarapu, M. & Ravi, N. Pore-water chemistry explains zinc phytotoxicity in soil. Ecotoxicol. Environ. Saf. 122, 252–259. https://doi.org/10.1016/j.ecoenv.2015.08.004 (2015).Article 

    Google Scholar 
    Tsiknia, M., Tzanakakis, V. A., Oikonomidis, D., Paranychianakis, N. V. & Nikolaidis, N. P. Effects of olive mill wastewater on soil carbon and nitrogen cycling. Appl. Microbiol. Biotechnol. 98, 2739–2749. https://doi.org/10.1007/s13762-013-0285-1 (2014).Article 

    Google Scholar 
    Ouyang, W., Wei, X. & Hao, F. Long-term soil nutrient dynamics comparison under smallholding land and farmland policy in northeast of China. Sci. Total Environ. 450–451, 129–139. https://doi.org/10.1016/j.scitotenv.2013.02.016 (2013).Article 

    Google Scholar 
    Daniels, M. B. et al. Soil phosphorus variability in pastures: implications for sampling and environmental management strategies. J. Environ. Qual. 30, 2157–2165. https://doi.org/10.1006/jema.2001.0501 (2001).Article 

    Google Scholar 
    Yanu, P. & Jakmunee, J. Flow injection with in-line reduction column and conductometric detection for determination of total inorganic nitrogen in soil. Talanta 144, 263–267. https://doi.org/10.1016/j.talanta.2015.06.002 (2015).Article 

    Google Scholar 
    Ryan, B. C., Maguire, R. O. & Havlin, J. L. Change in soluble phosphorus in soils following fertilization is dependent on initial Mehlich-3 phosphorus. J. Environ. Qual. 35, 1818–1824. https://doi.org/10.2134/jeq2005.0404 (2006).Article 

    Google Scholar 
    Guan, S. Y., Zhang, D. & Zhang, Z. Soil enzyme and its reserach methods. Agric. Beijing. 1, 274–297 (1986).

    Google Scholar 
    Bailey, M. J. A note on the use of dinitrosalicylic acid for determining the products of enzymatic reactions. Appl. Microbiol. Biotechnol. 29, 494–496. https://doi.org/10.1007/BF00269074 (1988).Article 

    Google Scholar 
    Murali, G., Alka, G., Arunachalam, V. & Magu, P. S. Impact of azadirachtin, an insecticidal allelochemical from neem on soil microflora, enzyme and respiratory activities. Biores. Technol. 98, 3154–3158. https://doi.org/10.1016/j.biortech.2006.10.010 (2007).Article 

    Google Scholar 
    Mahajan, G. et al. Soil quality assessment of coastal salt-affected acid soils of India. Environ. Sci. Pollut. Res. 27, 26221–26238. https://doi.org/10.1007/s11356-020-09010-w (2020).Article 

    Google Scholar 
    Guishun, X. Ji Chu Tu Rang Xue (China Agriculture Press Co., 2001).
    Google Scholar 
    Qiao, J., Zhu, Y., Jia, X., Huang, L. & Shao, M. A. Development of pedotransfer functions for soil hydraulic properties in the critical zone on the Loess Plateau, China. Hydrol. Process. 32, 2915–2921. https://doi.org/10.1002/hyp.13216 (2018).Article 

    Google Scholar 
    Liu, Y. et al. New insights into the role of microbial community composition in driving soil respiration rates. Soil Biol. Biochem. 118, 35–41. https://doi.org/10.1016/j.soilbio.2017.12.003 (2018).Article 

    Google Scholar  More

  • in

    Dominant phytoplankton groups as the major source of polyunsaturated fatty acids for hilsa (Tenualosa ilisha) in the Meghna estuary Bangladesh

    Valle-Levinson, A. Contemporary Issues in Estuarine Physics (Cambridge University Press, 2010).Book 

    Google Scholar 
    Singh, S. Analysis of plankton diversity and density with physico-chemical parameters of open pond in town Deeg (Bhratpur) Rajasthan, India. Int. Res. J. Biol. Sci 4, 61–69 (2015).
    Google Scholar 
    Roussel, M., Pontier, D., Cohen, J.-M., Lina, B. & Fouchet, D. Quantifying the role of weather on seasonal influenza. BMC Public Health 16, 1–14 (2016).Article 

    Google Scholar 
    Davies, O., Abowei, J. & Tawari, C. Phytoplankton community of Elechi creek, Niger Delta, Nigeria-a nutrient-polluted tropical creek. Am. J. Appl. Sci. 6, 1143–1152 (2009).Article 
    CAS 

    Google Scholar 
    Choudhury, S. & Panigrahy, R. Seasonal distribution and behavior of nutrients in the Greek and coastal waters of Gopalpur, East coast of India: Mahasagar. Bull. Natl. Inst. Oeanogr 24, 91–88 (1991).
    Google Scholar 
    Ratheesh, K., Krishnan, A., Das, R. & Vimexen, V. Seasonal phytoplankton succession in Netravathi-Gurupura estuary, Karnataka, India: Study on a three tier hydrographic platform. Estuar. Coast. Shelf Sci. 242, 106830 (2020).Article 

    Google Scholar 
    Deng, Y., Tang, X., Huang, B. & Ding, L. Effect of temperature and irradiance on the growth and reproduction of the green macroalga, Chaetomorpha valida (Cladophoraceae, Chlorophyta). J. Appl. Phycol. 24, 927–933 (2012).Article 
    CAS 

    Google Scholar 
    Gamier, J., Billen, G. & Coste, M. Seasonal succession of diatoms and Chlorophyceae in the drainage network of the Seine River: Observation and modeling. Limnol. Oceanogr. 40, 750–765 (1995).Article 

    Google Scholar 
    Meng, F. et al. Phytoplankton alpha diversity indices response the trophic state variation in hydrologically connected aquatic habitats in the Harbin Section of the Songhua River. Sci. Rep. 10, 1–13 (2020).Article 

    Google Scholar 
    Köhler, J. Growth, production and losses of phytoplankton in the lowland River Spree. I. Population dynamics. J. Plankton Res. 15, 335–349 (1993).Article 

    Google Scholar 
    Murrell, M. C. & Caffrey, J. M. High cyanobacterial abundance in three northeastern Gulf of Mexico estuaries. Gulf Caribbean Res. 17, 95–106 (2005).Article 

    Google Scholar 
    Haldar, G., Rahman, M. & Haroon, A. Hilsa, Tenualosa ilisha (Ham.) fishery of the Feni River with reference to the impacts of the flood control structure. J. Zool. 7, 51–56 (1992).
    Google Scholar 
    Hossain, M. S., Sarker, S., Chowdhury, S. R. & Sharifuzzaman, S. Discovering spawning ground of Hilsa shad (Tenualosa ilisha) in the coastal waters of Bangladesh. Ecol. Model. 282, 59–68 (2014).Article 

    Google Scholar 
    Bhaumik, U. & Sharma, A. The fishery of Indian Shad (Tenualosa ilisha) in the Bhagirathi-Hooghly river system. Fishing Chimes 31, 21–27 (2011).
    Google Scholar 
    Mitra, G. & Devsundaram, M. P. On the hilsa of Chilka Lake with note on the Hilsa in Orissa. J. Asiatic Soc. Sci. 20, 33–40 (1954).
    Google Scholar 
    Abdul, W., Phillips, M. & Beveridge, M. (WorldFish (WF), 2020).Hasan, K. M. M., Wahab, M. A., Ahmed, Z. F. & Mohammed, E. Y. The biophysical assessments of the hilsa fish (Tenualosa ilisha) habitat in the lower Meghna, Bangladesh (International Institute for Environment and Development, 2015).Begum, M. et al. Fatty acid composition of Hilsa (Tenualosa ilisha) fish muscle from different locations in Bangladesh. Thai J. Agric. Sci. 52, 172–179 (2019).
    Google Scholar 
    Jónasdóttir, S. H. Fatty acid profiles and production in marine phytoplankton. Mar. Drugs 17, 151 (2019).Article 

    Google Scholar 
    Otero, P., Ruiz-Villarreal, M., Peliz, Á. & Cabanas, J. M. Climatology and reconstruction of runoff time series in northwest Iberia: Influence in the shelf buoyancy budget off Ría de Vigo. Sci. Mar. 74, 247–266 (2010).Article 

    Google Scholar 
    Grasshoff, K., Kremling, K. & Ehrhardt, M. Methods of Seawater Analysis (Wiley, 2009).
    Google Scholar 
    Parsons, T., Maita, Y. & Lalli, C. A manual of chemical and biological methods for seawater analysis. Pergamon, Oxford sized algae and natural seston size fractions. Mar. Ecol. Prog. Ser. 199, 43–53 (1984).
    Google Scholar 
    Scor-Unesco, W. Determination of photosynthetic pigments. Determination of Photosynthetic Pigments in Sea-water, 9–18 (1966).Snow, G., Bate, G. & Adams, J. The effects of a single freshwater release into the Kromme Estuary. 2: Microalgal response. Water SA-Pretoria 26, 301–310 (2000).CAS 

    Google Scholar 
    Ward, H. B. & Whipple, G. C. Freshwater Biology Vol. 2, 12–48 (Willey, London, 1959).
    Google Scholar 
    Prescott, G. W. Algae of the western Great Lakes area. (1962).Bellinger, E. G. A Key to Common Algae: Freshwater, Estuarine and Some Coastal Species (Institution of Water and Environmental Management London, 1992).
    Google Scholar 
    Kimmerer, W. J. & Slaughter, A. M. A new electivity index for diet studies that use count data. Limnol. Oceanogr. Methods 19, 552–565 (2021).Article 

    Google Scholar 
    Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. R Development Core Team. nlme: Linear and nonlinear mixed effects models, 2012. http://CRAN.R-project.org/package=nlme. R package version, 3.1–103 (2020).Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

    Google Scholar 
    Galili, T., O’Callaghan, A., Sidi, J. & Sievert, C. heatmaply: an R package for creating interactive cluster heatmaps for online publishing. Bioinformatics 34, 1600–1602 (2018).Article 
    CAS 

    Google Scholar 
    Wickham, H., Chang, W. & Wickham, M. H. Package ‘ggplot2’. Create Elegant Data Visualisations Using the Grammar of Graphics. Version 2, 1–189 (2016).Peterson, B. G. et al. Package ‘PerformanceAnalytics’. R Team Cooperation (2018).Lewis, R. E. & Uncles, R. J. Factors affecting longitudinal dispersion in estuaries of different scale. Ocean Dyn. 53, 197–207 (2003).Article 

    Google Scholar 
    Shaha, D., Cho, Y.-K., Seo, G.-H., Kim, C.-S. & Jung, K. Using flushing rate to investigate spring-neap and spatial variations of gravitational circulation and tidal exchanges in an estuary. Hydrol. Earth Syst. Sci. 14, 1465–1476 (2010).Article 

    Google Scholar 
    Shaha, D. C., Cho, Y.-K., Kim, T.-W. & Valle-Levinson, A. Spatio-temporal variation of flushing time in the Sumjin River Estuary. Terrestr. Atmos. Ocean. Sci. 23, 119 (2012).Article 

    Google Scholar 
    Shivaprasad, A. et al. Seasonal stratification and property distributions in a tropical estuary (Cochin estuary, west coast, India). Hydrol. Earth Syst. Sci. 17, 187–199 (2013).Article 

    Google Scholar 
    Haralambidou, K., Sylaios, G. & Tsihrintzis, V. A. Salt-wedge propagation in a Mediterranean micro-tidal river mouth. Estuar. Coast. Shelf Sci. 90, 174–184 (2010).Article 
    CAS 

    Google Scholar 
    Dyer, K. R. Estuaries: A physical introduction (1973).Rahman, M. et al. Impact assessment of twenty-two days fishing ban in the major spawning grounds of Tenualosa ilisha (Hamilton, 1822) on its spawning success in Bangladesh. J. Aquac. Res. Dev. 8, 489 (2017).Article 

    Google Scholar 
    Alves, A. S. et al. Spatial distribution of subtidal meiobenthos along estuarine gradients in two southern European estuaries (Portugal). J. Mar. Biol. Assoc. U.K. 89, 1529–1540 (2009).Article 
    CAS 

    Google Scholar 
    Teixeira, H., Salas, F., Borja, A., Neto, J. & Marques, J. A benthic perspective in assessing the ecological status of estuaries: The case of the Mondego estuary (Portugal). Ecol. Ind. 8, 404–416 (2008).Article 

    Google Scholar 
    Garmendia, M. et al. Eutrophication assessment in Basque estuaries: Comparing a North American and a European method. Estuar. Coasts 35, 991–1006 (2012).Article 

    Google Scholar 
    Istvánovics, V. Eutrophication of Lakes and Reservoirs. Lake Ecosystem Ecology 47–55 (Elsevier, 2010).
    Google Scholar 
    Dodds, W. K. Eutrophication and trophic state in rivers and streams. Limnol. Oceanogr. 51, 671–680 (2006).Article 
    CAS 

    Google Scholar 
    Bricker, S., Ferreira, J. & Simas, T. An integrated methodology for assessment of estuarine trophic status. Ecol. Model. 169, 39–60 (2003).Article 
    CAS 

    Google Scholar 
    Vega, M., Pardo, R., Barrado, E. & Debán, L. Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res. 32, 3581–3592 (1998).Article 
    CAS 

    Google Scholar 
    Huang, Y., Yang, C., Wen, C. & Wen, G. S-type dissolved oxygen distribution along water depth in a canyon-shaped and algae blooming water source reservoir: Reasons and control. Int. J. Environ. Res. Public Health 16, 987 (2019).Article 
    CAS 

    Google Scholar 
    Rahman, M. & Cowx, I. Lunar periodicity in growth increment formation in otoliths of hilsa shad (Tenualosa ilisha, Clupeidae) in Bangladesh waters. Fish. Res. 81, 342–344 (2006).Article 

    Google Scholar 
    Rahman, M. J. Population Biology and Management of hilsa shad (Tenualosa ilisha) in Bangladesh (University of Hull, 2001).Milton, D. A. & Chenery, S. R. Movement patterns of the tropical shad hilsa (Tenualosa ilisha) inferred from transects of 87Sr/86Sr isotope ratios in their otoliths. Can. J. Fish. Aquat. Sci. 60, 1376–1385 (2003).Article 

    Google Scholar 
    Rahman, S., Sarker, M. R. H. & Mia, M. Y. Spatial and temporal variation of soil and water salinity in the South-Western and South-Central Coastal Region of Bangladesh. Irrig. Drain. 66, 854–871 (2017).Article 

    Google Scholar 
    Kida, S. & Yamazaki, D. The mechanism of the freshwater outflow through the Ganges–Brahmaputra–Meghna delta. Water Resour. Res. 56, e2019WR026412 (2020).Article 

    Google Scholar 
    Sarma, V. et al. Intra-annual variability in nutrients in the Godavari estuary, India. Contin. Shelf Res. 30, 2005–2014 (2010).Article 

    Google Scholar 
    Burford, M. et al. Controls on phytoplankton productivity in a wet–dry tropical estuary. Estuar. Coast. Shelf Sci. 113, 141–151 (2012).Article 
    CAS 

    Google Scholar 
    Vitousek, P. M. et al. Towards an ecological understanding of biological nitrogen fixation. Biogeochemistry 57, 1–45 (2002).Article 

    Google Scholar 
    Galloway, J. N. & Cowling, E. B. Reactive nitrogen and the world: 200 years of change. Ambio 31, 64–71 (2002).Article 

    Google Scholar 
    Kennish, M. & De Jonge, V. in Human-Induced Problems (Uses and Abuses) 113–148 (Elsevier Inc., 2012).Alongi, D., Boto, K. & Robertson, A. Nitrogen and phosphorus cycles. Coastal and Estuarine Studies, 251–251 (1993).Wolanski, E., McLusky, D., Laane, R. & Middleburg, J. (Academic Press, 2011).Suthers, I., Rissik, D. & Richardson, A. Plankton: A Guide to Their Ecology and Monitoring for Water Quality (CSIRO Publishing, 2019).Book 

    Google Scholar 
    Mackay, D. W. & Fleming, G. Correlation of dissolved oxygen levels, fresh-water flows and temperatures in a polluted estuary. Water Res. 3, 121–128 (1969).Article 

    Google Scholar 
    Lomas, M. W. & Glibert, P. M. Temperature regulation of nitrate uptake: A novel hypothesis about nitrate uptake and reduction in cool-water diatoms. Limnol. Oceanogr. 44, 556–572 (1999).Article 
    CAS 

    Google Scholar 
    Dortch, Q. The interaction between ammonium and nitrate uptake in phytoplankton. Mar. Ecol. Prog. Ser. Oldendorf 61, 183–201 (1990).Article 
    CAS 

    Google Scholar 
    Admiraal, W., Riaux-Gobin, C. & Laane, R. W. Interactions of ammonium, nitrate, and D-and L-amino acids in the nitrogen assimilation of two species of estuarine benthic diatoms. Mar. Ecol. Prog. Ser. 40, 267–273 (1987).Article 
    CAS 

    Google Scholar 
    Rabalais, N., Turner, R., Dortch, Q., Wiseman, W. Jr. & Sen Gupta, B. Nutrient changes in the Mississippi River and system responses on the adjacent continental shelf. Estuaries 19, 386 (1996).Article 
    CAS 

    Google Scholar 
    Gholizadeh, M. H., Melesse, A. M. & Reddi, L. Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida. Sci. Total Environ. 566, 1552–1567 (2016).Article 

    Google Scholar 
    Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).Article 

    Google Scholar 
    Teichberg, M. et al. Eutrophication and macroalgal blooms in temperate and tropical coastal waters: Nutrient enrichment experiments with Ulva spp. Glob. Change Biol. 16, 2624–2637 (2010).Article 

    Google Scholar 
    Valiela, I. & Bowen, J. Nitrogen sources to watersheds and estuaries: Role of land cover mosaics and losses within watersheds. Environ. Pollut. 118, 239–248 (2002).Article 
    CAS 

    Google Scholar 
    Woodland, R. J. et al. Nitrogen loads explain primary productivity in estuaries at the ecosystem scale. Limnol. Oceanogr. 60, 1751–1762 (2015).Article 

    Google Scholar 
    Howarth, R. et al. Coupled biogeochemical cycles: Eutrophication and hypoxia in temperate estuaries and coastal marine ecosystems. Front. Ecol. Environ. 9, 18–26 (2011).Article 

    Google Scholar 
    Winder, J. A. & Cheng, D. M. Quantification of Factors Controlling the Development of Anabaena Circinalis Blooms (Urban Water Research Association of Australia, 1995).
    Google Scholar 
    Descy, J.-P. Phytoplankton composition and dynamics in the River Meuse (Belgium). Arch. Hydrobiol. Supplementband. Monographische Beiträge 78, 225–245 (1987).
    Google Scholar 
    Robarts, R. D. & Zohary, T. Temperature effects on photosynthetic capacity, respiration, and growth rates of bloom-forming cyanobacteria. NZ J. Mar. Freshw. Res. 21, 391–399 (1987).Article 
    CAS 

    Google Scholar 
    Visser, P. M., Ibelings, B. W., Bormans, M. & Huisman, J. Artificial mixing to control cyanobacterial blooms: A review. Aquat. Ecol. 50, 423–441 (2016).Article 
    CAS 

    Google Scholar 
    Krishnan, A., Das, R. & Vimexen, V. Seasonal phytoplankton succession in Netravathi-Gurupura estuary, Karnataka, India: Study on a three tier hydrographic platform. Estuar. Coast. Shelf Sci. 242, 106830 (2020).Article 

    Google Scholar 
    Srinivas, L., Seeta, Y. & Reddy, M. Bacillariophyceae as ecological indicators of water quality in Manair Dam, Karimnagar, India. Int. J. Sci. Res. Sci. Tech 4, 468–474 (2018).
    Google Scholar 
    Mohanty, B. P. et al. Fatty acid profile of Indian shad Tenualosa ilisha oil and its dietary significance. Natl. Acad. Sci. Lett. 35, 263–269 (2012).Article 
    CAS 

    Google Scholar 
    De, D. et al. Nutritional profiling of hilsa (Tenualosa ilisha) of different size groups and sensory evaluation of their adults from different riverine systems. Sci. Rep. 9, 1–11 (2019).Article 
    CAS 

    Google Scholar 
    Hasan, K. M. M., Ahmed, Z. F., Wahab, M. A. & Mohammed, E. Y. Food and Feeding Ecology of hilsa (Tenualosa ilisha) in Bangladesh’s Meghna River Basin. (International Institute for Environment and Development, 2016). More

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    Carbon turnover gets wet

    Whether land acts as a carbon sink or source depends largely on two opposite fluxes: carbon uptake through photosynthesis and carbon release through turnover. Turnover occurs through multiple processes, including but not limited to, leaf senescence, tree mortality, and respiration by plants, microbes, and animals. Each of these processes is sensitive to climate, and ecologists and climatologists have been working to figure out how temperature regulates biological activities and to what extent the carbon cycle responds to global warming. Previous theoretical and experimental studies have yielded conflicting relationships between temperature and carbon turnover, with large variations across ecosystems, climate and time-scale1,2,3,4. Writing in Nature Geoscience, Fan et al.5 find that hydrometeorological factors have an important influence on how the turnover time of land carbon responds to changes in temperature. More