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    Emerging satellite observations for diurnal cycling of ecosystem processes

    1.Hennessey, T. L., Freeden, A. L. & Field, C. B. Environmental effects on circadian rhythms in photosynthesis and stomatal opening. Planta 189, 369–376 (1993).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Steed, G., Ramirez, D. C., Hannah, M. A. & Webb, A. A. R. Chronoculture, harnessing the circadian clock to improve crop yield and sustainability. Science 372, eabc9141 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Zhao, T. B. & Dai, A. G. The magnitude and causes of global drought changes in the twenty-first century under a low–moderate emissions scenario. J. Clim. 28, 4490–4512 (2015).Article 

    Google Scholar 
    4.Perkins-Kirkpatrick, S. E. & Gibson, P. B. Changes in regional heatwave characteristics as a function of increasing global temperature. Sci. Rep. 7, 12256 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Bates, L. M. & Hall, A. E. Stomatal closure with soil-water depletion not associated with changes in bulk leaf water status. Oecologia 50, 62–65 (1981).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Roessler, P. G. & Monson, R. K. Midday depression in net photosynthesis and stomatal conductance in Yucca-Glauca—relative contributions of leaf temperature and leaf-to-air water-vapor concentration difference. Oecologia 67, 380–387 (1985).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Tenhunen J. D., Pearcy R. W. & Lange O. L. in Stomatal Function (eds Zeiger, E. et al.) Ch. 15 (Stanford Univ. Press, 1987).8.Tucci, M. L. S., Erismann, N. M., Machado, E. C. & Ribeiro, R. V. Diurnal and seasonal variation in photosynthesis of peach palms grown under subtropical conditions. Photosynthetica 48, 421–429 (2010).CAS 
    Article 

    Google Scholar 
    9.Kosugi, Y. & Matsuo, N. Seasonal fluctuations and temperature dependence of leaf gas exchange parameters of co-occurring evergreen and deciduous trees in a temperate broad-leaved forest. Tree Physiol. 26, 1173–1184 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Koch, G. W., Amthor, J. S. & Goulden, M. L. Diurnal patterns of leaf photosynthesis, conductance and water potential at the top of a lowland rain-forest canopy in Cameroon—measurements from the Radeau-Des-Cimes. Tree Physiol. 14, 347–360 (1994).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Olioso, A., Carlson, T. N. & Brisson, N. Simulation of diurnal transpiration and photosynthesis of a water stressed soybean crop. Agric. For. Meteorol. 81, 41–59 (1996).Article 

    Google Scholar 
    12.Cowan, I. R. & Farquhar, G. D. Stomatal function in relation to leaf metabolism and environment: stomatal function in the regulation of gas exchange. Symposia Soc. Exp. Biol. 31, 471–505 (1977).CAS 

    Google Scholar 
    13.Bollig, C. & Feller, U. Impacts of drought stress on water relations and carbon assimilation in grassland species at different altitudes. Agric. Ecosyst. Environ. 188, 212–220 (2014).Article 

    Google Scholar 
    14.Koyama, K. & Takemoto, S. Morning reduction of photosynthetic capacity before midday depression. Sci. Rep. 4, 4389 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Nelson, J. A., Carvalhais, N., Migliavacca, M., Reichstein, M. & Jung, M. Water-stress-induced breakdown of carbon–water relations: indicators from diurnal FLUXNET patterns. Biogeosciences 15, 2433–2447 (2018).CAS 
    Article 

    Google Scholar 
    16.Xu, H., Xiao, J. F. & Zhang, Z. Q. Heatwave effects on gross primary production of northern mid-latitude ecosystems. Environ. Res. Lett. 15, 074027 (2020).Article 

    Google Scholar 
    17.Baldocchi, D. et al. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434 (2001).Article 

    Google Scholar 
    18.Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

    Google Scholar 
    19.Xiao, J. F. et al. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data. Remote Sens. Environ. 114, 576–591 (2010).Article 

    Google Scholar 
    20.Anderson, M. C., Allen, R. G., Morse, A. & Kustas, W. P. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ. 122, 50–65 (2012).Article 

    Google Scholar 
    21.Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    22.Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111, 519–536 (2007).Article 

    Google Scholar 
    23.Fisher, J. B. et al. ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the International Space Station. Water Resour. Res. 56, 1–20 (2020).Article 

    Google Scholar 
    24.Hook, S. J. et al. In-flight validation of ECOSTRESS, Landsat 7 and 8 thermal infrared spectral channels using the Lake Tahoe CA/NV and Salton Sea CA automated validation sites. IEEE Trans. Geosci. Remote Sens. 58, 1294–1302 (2019).Article 

    Google Scholar 
    25.Hulley, G., Shivers, S., Wetherley, E. & Cudd, R. New ECOSTRESS and MODIS land surface temperature data reveal fine-scale heat vulnerability in cities: a case study for Los Angeles County, California. Remote Sens. 11, 2136 (2019).Article 

    Google Scholar 
    26.Anderson, M. C. et al. Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales. Remote Sens. Environ. 252, 112189 (2021).Article 

    Google Scholar 
    27.Anderson, M. C. et al. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with US drought monitor classifications. J. Hydrometeorol. 14, 1035–1056 (2013).Article 

    Google Scholar 
    28.Li, X., Xiao, J., Fisher, J. B. & Baldocchi, D. D. ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station. Remote Sens. Environ. 258, 112360 (2021).Article 

    Google Scholar 
    29.Hulley, G. C. et al. Validation and quality assessment of the ECOSTRESS level-2 land surface temperature and emissivity product. IEEE Trans. Geosci. Remote Sens. https://doi.org/10.1109/TGRS.2021.3079879 (2021).30.Aragon, B., Houborg, R., Tu, K., Fisher, J. B. & McCabe, M. CubeSats enable high spatiotemporal retrievals of crop-water use for precision agriculture. Remote Sens. 10, 1867 (2018).Article 

    Google Scholar 
    31.Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).Article 

    Google Scholar 
    32.Turner, N. C., Schulze, E.-D. & Gollan, T. The responses of stomata and leaf gas exchange to vapour pressure deficits and soil water content. Oecologia 65, 348–355 (1985).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Moore, G. W. & Heilman, J. L. Proposed principles governing how vegetation changes affect transpiration. Ecohydrology 4, 351–358 (2011).Article 

    Google Scholar 
    34.Hulley, G. C. & Hook, S. J. Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for earth science research. IEEE Trans. Geosci. Remote Sens. 49, 1304–1315 (2011).Article 

    Google Scholar 
    35.Fisher, J. B., Whittaker, R. H. & Malhi, Y. ET Come Home: a critical evaluation of the use of evapotranspiration in geographical ecology. Glob. Ecol. Biogeogr. 20, 1–18 (2011).Article 

    Google Scholar 
    36.Talsma, C. J. et al. Partitioning of evapotranspiration in remote sensing-based models. Agric. For. Meteorol. 260, 131–143 (2018).Article 

    Google Scholar 
    37.Otkin, J. A. et al. Examining rapid onset drought development using the thermal infrared-based evaporative stress index. J. Hydrometeorol. 14, 1057–1074 (2013).Article 

    Google Scholar 
    38.Stavros, E. N. et al. ISS observations offer insights into plant function. Nat. Ecol. Evolution 1, 0194 (2017).Article 

    Google Scholar 
    39.Taylor, T. E. et al. OCO-3 early mission operations and initial (vEarly) XCO2 and SIF retrievals. Remote Sens. Environ. 251, 112032 (2020).Article 

    Google Scholar 
    40.Frankenberg, C. et al. The Orbiting Carbon Observatory (OCO-2): spectrometer performance evaluation using pre-launch direct sun measurements. Atmos. Meas. Tech. 8, 301–313 (2015).CAS 
    Article 

    Google Scholar 
    41.Bilger, W., Schreiber, U. & Bock, M. Determination of the quantum efficiency of photosystem-II and of nonphotochemical quenching of chlorophyll fluorescence in the field. Oecologia 102, 425–432 (1995).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Maguire, A. J. et al. On the functional relationship between fluorescence and photochemical yields in complex evergreen needleleaf canopies. Geophys. Res. Lett. 47, e2020GL087858 (2020).Article 

    Google Scholar 
    43.Marrs, J. K. et al. Solar-induced fluorescence does not track photosynthetic carbon assimilation following induced stomatal closure. Geophys. Res. Lett. 47, e2020GL087956 (2020).CAS 
    Article 

    Google Scholar 
    44.Li, X. et al. Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: first global analysis based on OCO-2 and flux tower observations. Glob. Change Biol. 24, 3990–4008 (2018).Article 

    Google Scholar 
    45.Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).Article 
    CAS 

    Google Scholar 
    46.Li, X. & Xiao, J. F. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: a global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sens. 11, 2563 (2019).Article 

    Google Scholar 
    47.Liu, J. J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Nino. Science 358, eaam5690 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    48.Parazoo, N. C. et al. Towards a harmonized long-term spaceborne record of far-red solar-induced fluorescence. J. Geophys. Res. 124, 2518–2539 (2019).Article 

    Google Scholar 
    49.He, L. Y. et al. Tracking seasonal and interannual variability in photosynthetic downregulation in response to water stress at a temperate deciduous forest. J. Geophys. Res. 125, e2018JG005002 (2020).
    Google Scholar 
    50.Lin, C. J. et al. Evaluation and mechanism exploration of the diurnal hysteresis of ecosystem fluxes. Agric. For. Meteorol. 278, 107642 (2019).Article 

    Google Scholar 
    51.Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA 116, 11640–11645 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Yang, X. et al. FluoSpec 2—an automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors (Basel) 18, 2063 (2018).Article 
    CAS 

    Google Scholar 
    53.Miura, T., Nagai, S., Takeuchi, M., Ichii, K. & Yoshioka, H. Improved characterisation of vegetation and land surface seasonal dynamics in central Japan with Himawari-8 hypertemporal data. Sci. Rep. 9, 15692 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    54.Bessho, K. et al. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteorological Soc. Jpn 94, 151–183 (2016).Article 

    Google Scholar 
    55.Schmit, T. J. et al. A closer look at the ABI on the GOES-R series. Bull. Am. Meteorol. Soc. 98, 681–698 (2017).Article 

    Google Scholar 
    56.Oh, S. M., Borde, R., Carranza, M. & Shin, I. C. Development and intercomparison study of an atmospheric motion vector retrieval algorithm for GEO-KOMPSAT-2A. Remote Sens. 11, 2054 (2019).Article 

    Google Scholar 
    57.Yang, J., Zhang, Z. Q., Wei, C. Y., Lu, F. & Guo, Q. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Am. Meteorol. Soc. 98, 1637–1658 (2017).Article 

    Google Scholar 
    58.Ouaknine, J. et al. The FCI on Board MTG: optical design and performances. In International Conference on Space Optics—ICSO 2014 (eds Sodnik, Z. et al.) 1056323 (SPIE, 2014).59.Wang, W. et al. An introduction to the Geostationary-NASA Earth Exchange (GeoNEX) products: 1. Top-of-atmosphere reflectance and brightness temperature. Remote Sens. 12, 1267 (2020).Article 

    Google Scholar 
    60.Yamamoto, Y., Ishikawa, H., Oku, Y. & Hu, Z. Y. An algorithm for land surface temperature retrieval using three thermal infrared bands of Himawari-8. J. Meteorological Soc. Jpn. 96B, 59–76 (2018).Article 

    Google Scholar 
    61.Yu, Y. & Yu, P. in The GOES-R Series. A New Generation of Geostationary Environmental Satellites (eds Goodman, S. J. et al.) Ch. 12 (2020).62.Takenaka, H. et al. Estimation of solar radiation using a neural network based on radiative transfer. J. Geophys. Res. 116, D08215 (2011).
    Google Scholar 
    63.Hashimoto, H. et al. Hourly GPP estimation in Australia using Himawari-8 AHI products. In IGARSS 2020—2020 IEEE International Geoscience and Remote Sensing Symposium 4513–4515 (IEEE, 2020).64.Yan, K. et al. Evaluation of MODIS LAI/FPAR product collection 6. Part 1: consistency and improvements. Remote Sens. 8, 359 (2016).CAS 
    Article 

    Google Scholar 
    65.Moore, B. et al. The potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to provide multi-scale constraints on the carbon cycle in the Americas. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2018.00109 (2018).66.Zoogman, P. et al. Tropospheric emissions: monitoring of pollution (TEMPO). J. Quant. Spectrosc. Radiat. Transf. 186, 17–39 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Courrèges-Lacoste, G. B. et al. Knowing what we Breathe: Sentinel 4: a Geostationary Imaging UVN Spectrometer for Air Quality Monitoring. In International Conference on Space Optics—ICSO 2016 (eds Karafolas, N. et al.) 105621J (SPIE, 2017).68.Wekerle, T., Pessoa, J. B., da Costa, L. & Trabasso, L. G. Status and trends of smallsats and their launch vehicles—an up-to-date review. J. Aerosp. Technol. Manag. 9, 269–286 (2017).Article 

    Google Scholar 
    69.Ryswyk, M. V. Planet announces 50 cm SkySat imagery, tasking dashboard and up to 12× revisit. Planet (9 June 2020); https://www.planet.com/pulse/tasking-dashboard-50cm-12x-revisit-announcement/70.Blackwell, W. J. et al. An overview of the TROPICS NASA Earth Venture mission. Q. J. R. Meteorol. Soc. 144, 16–26 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Gao, F., Masek, J., Schwaller, M. & Hall, F. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44, 2207–2218 (2006).Article 

    Google Scholar 
    72.Franco, A. C. & Luttge, U. Midday depression in savanna trees: coordinated adjustments in photochemical efficiency, photorespiration, CO2 assimilation and water use efficiency. Oecologia 131, 356–365 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    73.Keller, M., Schimel, D. S., Hargrove, W. W. & Hoffman, F. M. A continental strategy for the National Ecological Observatory Network. Front. Ecol. Environ. 6, 282–284 (2008).Article 

    Google Scholar  More

  • in

    Sexual competition and kin recognition co-shape the traits of neighboring dioecious Diospyros morrisiana seedlings

    1.Karban, R. Plant behaviour and communication. Ecol. Lett. 11, 727–739 (2008).PubMed 
    Article 

    Google Scholar 
    2.Chen, B. J. W., During, H. J. & Anten, N. P. R. Detect the neighbor: Identity recognition at the root level in plants. Plant Sci. 195, 157–167 (2012).PubMed 
    Article 
    CAS 

    Google Scholar 
    3.Inderjit, Seastedt, T. R. et al. Allelopathy and plant invasions: traditional, congeneric, and bio-geographical approaches. Biol. Invasions 10, 875–890 (2008).Article 

    Google Scholar 
    4.Yang, X., Li, L., Xu, Y. & Kong, C. Kin recognition in rice (Oryza sativa) lines. New Phytol 220, 567–578 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    5.Kasperbauer, M. J. & Hunt, P. G. Shoot/root assimilate allocation and nodulation of vigna unguiculata seedlings as influenced by shoot light environment. Plant Soil 161, 97–101 (1994).Article 

    Google Scholar 
    6.Yu, P., Hochholdinger, F. & Li, C. Plasticity of lateral root branching in maize. Front. Plant Sci. 10, 363 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Fang, S. et al. Genotypic recognition and spatial responses by rice roots. Proc. Natl Acad. Sci. USA 110, 2670–2675 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Biedrzycki, M. L., Jilany, T. A., Dudley, S. A. & Bais, H. P. Root exudates mediate kin recognition in plants. Commun. Integr. Biol. 3, 28–35 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Wilczek, A. M. et al. Effects of genetic perturbation on seasonal life history plasticity. Science 323, 930–934 (2009).PubMed 
    Article 
    CAS 

    Google Scholar 
    10.Bhatt, M. V., Khandelwal, A. & Dudley, S. A. Kin recognition, not competitive interactions, predicts root allocation in young Cakile edentula seedling pairs. N. Phytol. 189, 1135–1142 (2011).Article 

    Google Scholar 
    11.Mercer, C. A. & Eppley, S. M. Kin and sex recognition in a dioecious grass. Plant Ecol. 215, 845–852 (2014).Article 

    Google Scholar 
    12.Dong, T., Li, J., Liao, Y., Chen, B. J. W. & Xu, X. Root-mediated sex recognition in a dioecious tree. Sci. Rep. 7, 1–7 (2017).Article 
    CAS 

    Google Scholar 
    13.Renner. The relative and absolute frequencies of angiosperm sexual systems: dioecy, monoecy, gynodioecy, and an updated online database. Am. J. Bot. 101, 1588–1596 (2014).PubMed 
    Article 

    Google Scholar 
    14.Lovett Doust, J., O’Brien, G. & Lovett Doust, L. Effect of density on secondary sex characteristics and sex ratio in Silene alba (Caryophyllaceae). Am. J. Bot. 74, 40–46 (1987).Article 

    Google Scholar 
    15.Eppley, S. M. Females make tough neighbors: sex-specific competitive effects in seedlings of a dioecious grass. Oecologia 146, 549–554 (2006).PubMed 
    Article 

    Google Scholar 
    16.Graff, P., Rositano, F. & Aguiar, M. R. Changes in sex ratios of a dioecious grass with grazing intensity: the interplay between gender traits, neighbour interactions and spatial patterns. J. Ecol. 101, 1146–1157 (2013).Article 

    Google Scholar 
    17.Dudley, S. A. & File, A. L. Kin recognition in an annual plant. Biol. Lett. 3, 435–438 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Semchenko, M., Saar, S. & Lepik, A. Plant root exudates mediate neighbour recognition and trigger complex behavioural changes. N. Phytol. 204, 631–637 (2014).Article 

    Google Scholar 
    19.Li, J., Xu, X. L. & Liu, Y. R. Kin recognition in plants with distinct lifestyles: implications of biomass and nutrient niches. Plant Growth Regul. 84, 333–339 (2018).Article 
    CAS 

    Google Scholar 
    20.Lepik, A., Abakumova, M., Zobel, K. & Semchenko, M. Kin recognition is density-dependent and uncommon among temperate grassland plants. Funct. Ecol. 26, 1214–1220 (2012).Article 

    Google Scholar 
    21.Murphy, G. P. & Dudley, S. A. Kin recognition: Competition and cooperation in Impatiens (Balsaminaceae). Am. J. Bot. 96, 1990–1996 (2009).PubMed 
    Article 

    Google Scholar 
    22.Rogers, S. R. & Eppley, S. M. Testing the interaction between inter-sexual competition and phosphorus availability in a dioecious gras. Botany 710, 704–710 (2012).Article 
    CAS 

    Google Scholar 
    23.Bierzychudek, P. & Eckhart, V. Spatial segregation of the sexes of dioecious plants. Am. Nat. 132, 34–43 (1988).Article 

    Google Scholar 
    24.Mercer, C. A. Spatial segregation of the sexes in a salt marsh grass Distichlis spicata (Poaceae). Master Thesis, Portland State University, Portland, Oregon, USA. https://doi.org/10.15760/etd.173 (Portland State University, 2010).25.Hamilton, W. D. The genetical evolution of social behavior, I & II. J. Theor. Biol. 7, 1–52 (1964).PubMed 
    Article 
    CAS 

    Google Scholar 
    26.Haichar, Z. & Bernard, C. Root exudates mediated interactions belowground. Soil Biol. Biochem. 77, 69–80 (2014).Article 
    CAS 

    Google Scholar 
    27.Biedrzycki, M. L., Venkatachalam, L. & Bais, H. P. Transcriptome analysis of Arabidopsis thaliana plants in response to kin and stranger recognition. Plant Signal. Behav. 6, 1515–1524 (2011).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    28.Zhu, S. et al. Effects of root exudates on the growth and development of male and female Morus alba seedlings. Plant Physiol. J. 52, 134–140 (2016).
    Google Scholar 
    29.Mercer, C. A. & Eppley, S. M. Inter-sexual competition in a dioecious grass. Oecologia 164, 657–664 (2010).PubMed 
    Article 

    Google Scholar 
    30.Herrera, C. M. Plant size, spacing patterns, and host-plant selection in Osyris Quadripartita, a hemiparasitic dioecious shrub. J. Ecol. 76, 995–1006 (1988).Article 

    Google Scholar 
    31.Yangxia, Z., Fengyun, L. E. I., Shuang, Q. I. U. & Shanmei, Z. Effects of fatty acid ester compounds on growth and physiological characteristics of water melon seedlings. J. Hunan Agric. Univ. Sci. 46, 297–302 (2020).
    Google Scholar 
    32.Huimin, L. I. et al. The special bacterial metabolites and allelopathic potentials in Casuarina equisetifolia woodland of different stand ages. Chin. J. Appl. Environ. Biol. 22, 808–814 (2016).
    Google Scholar 
    33.Zhang, Jhong, Sun, Hlong, Chen, Syang, Zeng, L. I. & Wang, Ttao Anti-fungal activity, mechanism studies on α-Phellandrene and Nonanal against Penicillium cyclopium. Bot. Stud. 58, 1–9 (2017).Article 
    CAS 

    Google Scholar 
    34.Zhou, T. et al. Effects of essential oil decanal on growth and transcriptome of the postharvest fungal pathogen Penicillium expansum. Postharvest Biol. Technol. 145, 203–212 (2018).Article 
    CAS 

    Google Scholar 
    35.Varga, S. Effects of arbuscular mycorrhizas on reproductive traits in sexually dimorphic plants. J. Agric. Res. 8, 11–24 (2010).
    Google Scholar 
    36.Varga, S. Transgenerational effects of plant sex and arbuscular mycorrhizal symbiosis. N. Phytol. 199, 812–821 (2013).Article 

    Google Scholar 
    37.Varga, S., Vega-Frutis, R. & Kytöviita, M.-M. Competitive interactions are mediated in a sex-specific manner by arbuscular mycorrhiza in Antennaria dioica. Plant Biol. 19, 217–226 (2017).PubMed 
    Article 
    CAS 

    Google Scholar 
    38.Varga, S. Effects of arbuscular mycorrhizal fungi and maternal plant sex on seed germination and early plant establishment. Am. J. Bot. 102, 358–366 (2015).PubMed 
    Article 

    Google Scholar 
    39.Bawa, K. S. Evolution of dioecy in flowering plants. Annu. Rev. Ecol. Syst. 11, 15–39 (1980).Article 

    Google Scholar 
    40.Zheng, D.-S., Liu, X. & Li, Y. Cultivated plants originated in China. J. Plant Genet. Resour. 13, 1–10 (2012).
    Google Scholar 
    41.Fang, S., Yan, X. & Liao, H. 3D reconstruction and dynamic modeling of root architecture in situ and its application to crop phosphorus research. Plant J. 60, 1096–1108 (2009).PubMed 
    Article 
    CAS 

    Google Scholar 
    42.Akagi, T. et al. Development of molecular markers associated with sexuality in Diospyros lotus L. and their application in D. kaki Thunb. J. Jpn. Soc. Hortic. Sci. 83, 214–221 (2014).Article 
    CAS 

    Google Scholar 
    43.Akagi, T., Henry, I. M., Tao, R. & Comai, L. A Y-chromosome-encoded small RNA acts as a sex determinant in persimmons. Science 346, 646–650 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    44.Badri, D. V. et al. Application of natural blends of phytochemicals derived from the root exudates of arabidopsis to the soil reveal that phenolic-related compounds predominantly modulate the soil microbiome. J. Biol. Chem. 288, 4502–4512 (2013).45.Fiehn, O. et al. Quality control for plant metabolomics: reporting MSI-compliant studies. Plant J. 53, 691–704 (2008).Article 
    CAS 

    Google Scholar 
    46.Lenth, R. V. Least-squares means: the {R} package {lsmeans}. J. Stat. Softw. 69, 1–33 (2016).Article 

    Google Scholar 
    47.Paine, C. E. T. et al. How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists. Methods Ecol. Evol. 3, 245–256 (2012).Article 

    Google Scholar 
    48.R Core Team. R: A Language and Environment for Statistical Computing (2017). More

  • in

    Local solutions to global phosphorus imbalances

    1.Elser, J. J. & Haygarth, P. M. Phosphorus: Past and Future (Oxford Univ. Press, 2020).2.Mogollón, J. M. et al. Nat. Food https://doi.org/10.1038/s43016-021-00303-y (2021).3.Sanchez, P. A. Nat. Plants 1, 14014 (2015).Article 

    Google Scholar 
    4.Roobroeck, D., Palm, C. A., Nziguheba, G., Weil, R. & Vanlauwe, B. Agric. Ecosyst. Environ. 305, 107165 (2021).CAS 
    Article 

    Google Scholar 
    5.Gram, G. et al. PLoS ONE 15, e0239552 (2020).CAS 
    Article 

    Google Scholar 
    6.Haygarth, P. M. et al. Environ. Sci. Technol. 48, 8417–8419 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Menezes-Blackburn, D. et al. Plant Soil 427, 5–16 (2018).CAS 
    Article 

    Google Scholar 
    8.Powers, S. M. et al. Nat. Geosci. 9, 353–356 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    9.Ockenden, M. C. et al. Nat. Commun. 8, 161 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    10.Thompson, E. L. & Smith, L. A. Economics http://www.economics-ejournal.org/economics/discussionpapers/2019-23 (2019). More

  • in

    Conversion of marginal land into switchgrass conditionally accrues soil carbon but reduces methane consumption

    1.Schubert SD, Suarez MJ, Pegion PJ, Koster RD, Bacmeister JT. On the cause of the 1930s Dust Bowl. Science. 2004;303:1855–9.2.Worster D. Dust bowl: the Southern plains in the 1930s (Oklahoma and Kansas). Dust bowl South Plains 1930s (Oklahoma Kansas). Oxford University Press; 1982; p. 15–50.3.Baumhardt LR. Dust Bowl Era. In: Encyclopedia of water science. New York: Marcel Dekker; 2003.4.Gelfand I, Sahajpal R, Zhang X, Izaurralde RC, Gross KL, Robertson GP. Sustainable bioenergy production from marginal lands in the US Midwest. Nature. 2013:493;514–7.5.Bouton JH. Molecular breeding of switchgrass for use as a biofuel crop. Curr Opin Genet Dev. 2007;6:553–8.6.Bouton, J. Genetic improvement of bioenergy crops. In: Vermerris W editors. Springer Science and Business Media; 2008. p. 295–308.7.Milbrandt AR, Heimiller DM, Perry AD, Field CB. Renewable energy potential on marginal lands in the United States. Renew Sustain Energy Rev. 2014;29:473–81.8.Stoof CR, Richards BK, Woodbury PB, Fabio ES, Brumbach AR, Cherney J, et al. Untapped potential: opportunities and challenges for sustainable bioenergy production from marginal lands in the northeast USA. Bioenergy Res. 2015;8:482–501.9.McLaughlin SB, Kszos LA. Development of switchgrass (Panicum virgatum) as a bioenergy feedstock in the United States. Biomass Bioenergy. 2005;28:515–35.10.Ditomaso JM, Barney JN, Mann JJ, Kyser G. For switchgrass cultivated as biofuel in California, invasiveness limited by several steps. Calif. Agric. 2013;67:96–103.11.Tilman D, Hill J, Lehman C. Carbon-negative biofuels from low-input high-diversity grassland biomass. Science. 2006;314:1598–1600.12.Ma Z, Wood CW, Bransby DI. Soil management impacts on soil carbon sequestration by switchgrass. Biomass Bioenergy. 2000;18:469–77.13.Slessarev EW, Nuccio EE, McFarlane KJ, Ramon CE, Saha M, Firestone MK, et al. Quantifying the effects of switchgrass (Panicum virgatum) on deep organic C stocks using natural abundance 14C in three marginal soils. GCB Bioenergy. 2020;12:834–47.14.Anderson-Teixeria KJ, Davis SC, Masters MD, Delucia EH. Changes in soil organic carbon under biofuel crops. GCB Bioenergy. 2009;1:75–96.15.Barney JN, Mann JJ, Kyser GB, Blumwald E, Van Deynze A, DiTomaso JM Tolerance of switchgrass to extreme soil moisture stress: Ecological implications. Plant Sci. 2009;177:724–32.16.Tiemann LK, Grandy AS. Mechanisms of soil carbon accrual and storage in bioenergy cropping systems. GCB Bioenergy. 2015;7:161–74.17.Sher Y, Baker NR, Herman D, Fossum C, Hale L, Zhang X, et al. Microbial extracellular polysaccharide production and aggregate stability controlled by switchgrass (Panicum virgatum) root biomass and soil water potential. Soil Biol Biochem. 2020;143:107907.18.Liebig MA, Schmer MR, Vogel KP, Mitchell RB. Soil carbon storage by switchgrass grown for bioenergy. Bioenergy Res. 2008;1:215–22.19.Zan CS, Fyles JW, Girouard P, Samson RA. Carbon sequestration in perennial bioenergy, annual corn and uncultivated systems in southern Quebec. Agric Ecosyst Environ. 2001;86:135–44.20.Frank AB, Berdahl JD, Hanson JD, Liebig MA, Johnson HA. Biomass and carbon partitioning in switchgrass. Crop Sci. 2004;44:1391–6.21.Dabney SM, Shields FD, Temple DM, Langendoen EJ. Erosion processes in gullies modified by establishing grass hedges. Trans Am Soc Agric Eng. 2004;47:1561–71.22.Cheng W, Parton WJ, Gonzalez-Meler MA, Phillips R, Asao S, Mcnickle GG, et al. Synthesis and modeling perspectives of rhizosphere priming. New Phytol. 2014;201:31–44.23.Ashiq MW, Bazrgar AB, Fei H, Coleman B, Vessey K, Gordon A, et al. A nutrient-based sustainability assessment of purpose-grown poplar and switchgrass biomass production systems established on marginal lands in Canada. Can J Plant Sci. 2017;98:255–66.24.Fontaine S, Barot S, Barré P, Bdioui N, Mary B, Rumpel C. Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature. 2007;450:277–80.25.Shahzad T, Rashid MI, Maire V, Barot S, Perveen N, Alvarez G, et al. Root penetration in deep soil layers stimulates mineralization of millennia-old organic carbon. Soil Biol Biochem. 2018;124:150–60.26.Torn MS, Trumbore SE, Chadwick OA, Vitousek PM, Hendricks DM Mineral control of soil organic carbon storage and turnover. Nature. 1997;389:170–3.27.Poeplau C, Helfrich M, Dechow R, Szoboszlay M, Tebbe CC, Don A, et al. Increased microbial anabolism contributes to soil carbon sequestration by mineral fertilization in temperate grasslands. Soil Biol Biochem. 2019;130:167–76.28.Hestrin R, Lee MR, Whitaker BK, Pett-Ridge J. The switchgrass microbiome: a review of structure, function, and taxonomic distribution. Phytobiomes J. 2020;5:e-ISSN:2471-2906.29.Lange M, Eisenhauer N, Sierra CA, Bessler H, Engels C, Griffiths RI, et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat Commun. 2015:6;6707.30.Ker K, Seguin P, Driscoll BT, Fyles JW, Smith DL Evidence for enhanced N availability during switchgrass establishment and seeding year production following inoculation with rhizosphere endophytes. Arch Agron Soil Sci. 2014;60:1553–63.31.Clark RB, Baligar VC, Zobel RW. Response of mycorrhizal switchgrass to phosphorus fractions in acidic soil. Commun Soil Sci Plant Anal. 2005;36:1337–59.32.Bahulikar RA, Torres-Jerez I, Worley E, Craven K, Udvardi MK. Diversity of nitrogen-fixing bacteria associated with switchgrass in the native tallgrass prairie of Northern Oklahoma. Appl Environ Microbiol. 2014;80:5636–4333.Ghimire SR, Charlton ND, Craven KD. The mycorrhizal fungus, sebacina vermifera, enhances seed germination and biomass production in switchgrass (Panicum virgatum l). Bioenergy Res. 2009;2:51–8.34.Kim S, Lowman S, Hou G, Nowak J, Flinn B, Mei C. Growth promotion and colonization of switchgrass (Panicum virgatum) cv. alamo by bacterial endophyte burkholderia phytofirmans strain PsJN. Biotechnol Biofuels. 2012;5:37.35.Ghimire SR, Craven KD. Enhancement of switchgrass (Panicum virgatum L.) biomass production under drought conditions by the ectomycorrhizal fungus Sebacina vermifera. Appl Environ Microbiol. 2011;77:19.36.Mulkey VR, Owens VN, Lee DK. Management of switchgrass-dominated conservation reserve program lands for biomass production in South Dakota. Crop Sci. 2006;46:712–20.37.Lee DK, Doolittle JJ, Owens VN. Soil carbon dioxide fluxes in established switchgrass land managed for biomass production. Soil Biol Biochem. 2007;39:178–86.38.Monti A, Barbanti L, Zatta A, Zegada-Lizarazu W. The contribution of switchgrass in reducing GHG emissions. GCB Bioenergy. 2012;4:420–34.39.Robertson GP, Grace PR. Greenhouse gas fluxes in tropical and temperate agriculture: the need for a full-cost accounting of global warming potentials. Environ Dev Sustain. 2004;6:51–63.40.Fritsche UR, Sims REH, Monti A. Direct and indirect land-use competition issues for energy crops and their sustainable production—an overview. Biofuels Bioprod Biorefining. 2010;4:692–704.41.Lange M, Habekost M, Eisenhauer N, Roscher C, Bessler H, Engels C, et al. Biotic and abiotic properties mediating plant diversity effects on soil microbial communities in an experimental grassland. PLOS ONE. 2014;9:e96182.42.Thakur MP, Milcu A, Manning P, Niklaus PA, Roscher C, Power S, et al. Plant diversity drives soil microbial biomass carbon in grasslands irrespective of global environmental change factors. Glob Chang Biol. 2015;21:4076–85.43.Chen C, Chen HYH, Chen X, Huang Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat Commun. 2019;10:1332.44.Prober SM, Leff JW, Bates ST, Borer ET, Firn J, Harpole WS, et al. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol Lett. 2015;18:85–95.45.Mao Y, Yannarell AC, Davis SC, Mackie RI. Impact of different bioenergy crops on N-cycling bacterial and archaeal communities in soil. Environ Microbiol. 2013;15:928–42.46.Liang T, Yang G, Ma Y, Yao Q, Ma Y, Ma H, et al. Seasonal dynamics of microbial diversity in the rhizosphere of Ulmus pumila L. var. sabulosa in a steppe desert area of Northern China. PeerJ. 2019;7:e7526.47.Jesus E da C, Liang C, Quensen JF, Susilawati E, Jackson RD, et al. Influence of corn, switchgrass, and prairie cropping systems on soil microbial communities in the upper Midwest of the United States. GCB Bioenergy. 2016;8:481–94.48.Frasier I, Noellemeyer E, Fernández R, Quiroga A. Direct field method for root biomass quantification in agroecosystems. MethodsX. 2016;3:513–9.49.Carter MR, Gregorich EG (Eds.) Soil Sampling and Methods of Analysis. CRC Press; 2007.50.Sheldrick BH, Wang C. Particle-size distribution. In: Carter, MR editor. Soil sampling and methods of analysis, Canadian society of soil science; 1993. p. 499–511.51.McLean, E. Soil pH and lime requirement. Methods of soil analysis. Part 2. Chemical and microbiological properties, American Society of Agronomy, Soil Science Society of America. 1982;52.AOAC Official Method 972.43, Microchemical Determination of Carbon, Hydrogen, and Nitrogen, Automated Method, in Official Methods of Analysis of AOAC International, 16th ed. Chapter 12, pp. 5–6, AOAC International, Arlington, VA; 1997.53.Nelson DW, Sommers LE. Total Carbon, Organic Carbon, and Organic Matter. Chapter 34, p 1001-6. JM Bigham et al. editors. Soil Science Society of America and America Society of Agronomy. Methods of Soil Analysis. Part 3. Chemical Methods-SSA Book Series no. 5. Madison, WI. 1996.54.Sah RN, Miller RO. Spontaneous reaction for acid dissolution of biological tissues in closed vessels. Anal Chem. 1996;64:230–3.Article 

    Google Scholar 
    55.Diamond D. Phosphorus in soil extracts. QuikChem Method 10-115-01-1-A. Lachat instruments, Milwaukee, WI. 1995.56.Olsen SR, Sommers LE. Phosphorus. In: AL Page, et al. (eds.) Methods of soil analysis: Part 2. Chemical and microbiological properties p. 403–30. Agron. Mongr. 9. 2nd edition. ASA and SSA, Madison, WI; 1982.57.Prokopy WR. Phopshorus in 0.5 M sodium bicarbonate soil extracts. Milwaukee, WI: QuikChem Method 12-115-01-1-B. Lachat Instruments; 1995.
    Google Scholar 
    58.Bowman RA, Moir JO. Basics EDTA as an extractant for soil organic phosphorus. Soil Sci Soc Am J. 1993;57:1516–8.CAS 
    Article 

    Google Scholar 
    59.McKeague J, Day J. Dithionite-and oxalate-extractable Fe and AL as aids in 577 differentiating various classes of soils. Can. J. Soil Sci. 1966;60.Mehra OP and Jackson ML. Iron oxide remobal from soils and clays by a dithionite-citrate system buffered with sodium bicarbonate. In Clays and clay materials (pp. 317–27). Pergamon; 2013.61.Christiansen JR, Outhwaite J, Smukler SM. Comparison of CO2, CH4 and N2O soil-atmosphere exchange measured in static chambers with cavity ring-down spectroscopy and gas chromatography. Agric For Meteorol. 2015;62.Zhou J, Bruns MA, Tiedje JM. DNA recovery from soils of diverse composition. Appl Environ Microbiol. 1996;62:316–22.63.Wu L, Wen C, Qin Y, Yin H, Tu Q, Van Nostrand JD, et al. Phasing amplicon sequencing on Illumina Miseq for robust environmental microbial community analysis. BMC Microbiol. 2015;15:125.64.Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20.65.Kuczynski J, Stombaugh J, Walters WA, González A, Caporaso JG, Knight R. Using QIIME to analyze 16s rRNA gene sequences from microbial communities. Curr Protoc Microbiol. 2012, Chapter 10:Unit 10.7.66.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.67.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.68.Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.69.Katoh K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.70.Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000;17:540–52.71.Price MN, Dehal PS, Arkin AP. Fasttree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641–50.72.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.73.R Core Team. R Core Team (2014). R: a language and environment for statistical computing. R Found Stat Comput Vienna, Austria 2014. http://wwwR-project.org/.74.Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2009.Book 

    Google Scholar 
    75.Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.76.Anderson MJ. Distance-based tests for homogeneity of multivariate dispersions. Biometrics. 2006;62:245–53.77.Rosseel Y. Lavaan: an R package for structural equation modeling. J Stat Softw. 2012;48:1–36.78.Dise NB. Methane emission from Minnesota peatlands: spatial and seasonal variability. Glob Biogeochem Cycles. 1993;7:123–42.79.Bartlett KB, Harriss RC. Review and assessment of methane emissions from wetlands. Chemosphere. 1993;26:261–20.80.Abraha M, Gelfand I, Hamilton SK, Chen J, Robertson GP. Carbon debt of field-scale conservation reserve program grasslands converted to annual and perennial bioenergy crops. Environ Res Lett. 2019;14:024019.81.Roley SS, Xue C, Hamilton SK, Tiedje JM, Robertson GP. Isotopic evidence for episodic nitrogen fixation in switchgrass (Panicum virgatum L.). Soil Biol Biochem. 2019;129:90–8.82.Cline LC, Zak DR. Soil microbial communities are shaped by plant-driven changes in resource availability during secondary succession. Ecology. 2015;96:3374–85.83.Leff JW, Jones SE, Prober SM, Barberán A, Borer ET, Firn JL, et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc Natl Acad Sci U S A. 2015;112:10967–72.84.Kang H, Fahey TJ, Bae K, Fisk M, Sherman RE, Yanai RD, et al. Response of forest soil respiration to nutrient addition depends on site fertility. Biogeochemistry. 2016;127:113–24.85.Wagai R, Brye KR, Gower ST, Norman JM, Bundy LG. Land use and environmental factors influencing soil surface CO2 flux and microbial biomass in natural and managed ecosystems in southern Wisconsin. Soil Biol Biochem. 1998;30:1501–9.86.Saunois M, Stavert AR, Poulter B, Bousquet P, Canadell JG, Jackson RB, et al. The global methane budget 2000–2017. Earth Syst Sci Data Discuss. 2019;12:1561–23.87.Jackson RB, Saunois M, Bousquet P, Canadell JG, Poulter B, Stavert AR, et al. Increasing anthropogenic methane emissions arise equally from agricultural and fossil fuel sources. Environ Res Lett. 2020;15:071002.88.Stocker TF, Qin D, Plattner GK, Tignor MMB, Allen SK, Boschung J, et al. Climate change 2013 the physical science basis: working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. Climate change 2013 the physical science basis: working group i contribution to the fifth assessment report of the intergovernmental panel on climate change. 2013. More

  • in

    Bat aggregational response to pest caterpillar emergence

    1.Solomon, M. E. The natural control of animal populations. J. Anim. Ecol. 18(1), 1–35 (1949).Article 

    Google Scholar 
    2.Sinclair, A. R. E. & Krebs, C. J. Complex numerical responses to top–down and bottom–up processes in vertebrate populations. Philos. Trans. R. Soc. B 357(1425), 1221–1231 (2002).CAS 
    Article 

    Google Scholar 
    3.Readshaw, J. L. The numerical response of predators to prey density. J. Appl. Biol. 10, 342–351 (1973).
    Google Scholar 
    4.Boyles, J. G., Cryan, P. M., McCracken, G. F. & Kunz, T. H. Economic importance of bats in agriculture. Science 332(6025), 41–42 (2011).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Taylor, P. J., Grass, I., Alberts, A. J., Joubert, E. & Tscharntke, T. Economic value of bat predation services—a review and new estimates from macadamia orchards. Ecosyst. Serv. 30, 372–381 (2018).Article 

    Google Scholar 
    6.Kunz, T. H., BraundeTorrez, E., Bauer, D., Lobova, T. & Fleming, T. H. Ecosystem services provided by bats. Ann. N. Y. Acad. Sci. 1223, 1–38 (2011).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Russo, D., Bosso, L. & Ancillotto, L. Novel perspectives on bat insectivory highlight the value of this ecosystem service in farmland: Research frontiers and management implications. Agric. Ecosyst. Environ. 266, 31–38 (2018).Article 

    Google Scholar 
    8.Boyles, J. G., Sole, C. L., Cryan, P. M. & McCracken, G. F. On estimating the economic value of insectivorous bats: prospects and priorities for biologists. In Bat Evolution, Ecology, and Conservation (eds Adams, R. A. & Pedersen, S. C.) 501–515 (Springer, 2013).Chapter 

    Google Scholar 
    9.Kemp, J. et al. Bats as potential suppressors of multiple agricultural pests: a case study from Madagascar. Agric. Ecosyst. Environ. 269, 88–96 (2019).Article 

    Google Scholar 
    10.Kolkert, H., Andrew, R., Smith, R., Rader, R. & Reid, N. Insectivorous bats selectively source moths and eat mostly pest insects on dryland and irrigated cotton farms. Ecol. Evol. 10(1), 371–388 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Weier, S. M. et al. Insect pest consumption by bats in macadamia orchards established by molecular diet analyses. Glob. Ecol. Conserv. 18, e00626 (2019).Article 

    Google Scholar 
    12.Bohmann, K. et al. Molecular diet analysis of two African free-tailed bats (Molossidae) using high throughput sequencing. PLoS ONE 6(6), e21441 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Razgour, O. et al. High-throughput sequencing offers insight into mechanisms of resource partitioning in cryptic bat species. Ecol. Evol. 1(4), 556–570 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Cleveland, C. J. et al. Economic value of the pest control service provided by Brazilian free-tailed bats in south-central Texas. Front. Ecol. Environ. 4(5), 238–243 (2006).Article 

    Google Scholar 
    15.McCracken, G. F. et al. Bats track and exploit changes in insect pest populations. PLoS ONE 7(8), e43839 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Maas, B. et al. Bird and bat predation services in tropical forests and agroforestry landscapes. Biol. Rev. 91(4), 1081–1101 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Maine, J. J. & Boyles, J. G. Bats initiate vital agroecological interactions in corn. Proc. Natl. Acad. Sci. USA 112(40), 12438–12443 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Hill, D. S. Pests of Crops in Warmer Climates and Their Control (Springer, 2008).Book 

    Google Scholar 
    19.Zhang, B. C. Index of Economically Important Lepidoptera (CAB International, Wallingford, 1994).
    Google Scholar 
    20.Riccucci, M. & Lanza, B. Bats and insect pest control: a review. Vespertilio 17, 161–169 (2014).
    Google Scholar 
    21.Andreas, M., Reiter, A. & Benda, P. Dietary composition, resource partitioning and trophic niche overlap in three forest foliage-gleaning bats in Central Europe. Acta Chiropterol. 14(2), 335–345 (2012).Article 

    Google Scholar 
    22.Vesterinen, E. J., Puisto, A. I. E., Blomberg, A. S. & Lilley, T. M. Table for five, please: dietary partitioning in boreal bats. Ecol. Evol. 8, 10914–10937 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Hope, R. P. et al. Second generation sequencing and morphological faecal analysis reveal unexpected foraging behaviour by Myotis nattereri (Chiroptera, Vespertilionidae) in winter. Front. Zool. 11, 39 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Costa, A. et al. Structural simplification compromises the potential of common insectivorous bats to provide biocontrol services against the major olive pest Pray oleae. Agric. Ecosyst. Environ. 287, 106708 (2020).Article 

    Google Scholar 
    25.Garin, I. et al. Bats from different foraging guilds prey upon the pine processionary moth. PeerJ 7, e7169 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Puig-Montserrat, X. et al. Pest control service provided by bats in Mediterranean rice paddies: linking agroecosystems structure to ecological functions. Mamm. Biol. 80, 237–245 (2015).Article 

    Google Scholar 
    27.Elgar, M. A. Predator vigilance and group size in mammals and birds: a critical review of the evidence. Biol. Rev. 64, 13–33 (1989).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Fukui, D., Murakami, M., Nakano, S. & Aoi, T. Effect of emergent aquatic insects on bat foraging in a riparian forest. J. Anim. Ecol. 75(6), 1252–1258 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Partridge, D. R., Parkins, K. L., Elbin, S. B. & Clark, J. A. Bat activity correlates with moth abundance on an urban green roof. Northeast Nat. 27(1), 77–89 (2020).Article 

    Google Scholar 
    30.Charbonnier, Y., Barbaro, L., Theillout, A. & Jactel, H. Numerical and functional responses of forest bats to a major insect pest in pine plantations. PLoS ONE 9(10), e109488 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    31.Krauel, J. J., Ratcliffe, J. M., Westbrook, J. K. & McCracken, G. F. Brazilian free-tailed bats (Tadarida brasiliensis) adjust foraging behaviour in response to migratory moths. Can. J. Zool. 96(6), 513–520 (2018).Article 

    Google Scholar 
    32.Gregor, F. & Bauerová, Z. The role of Diptera in the diet of Natterer’s bat, Myotis nattereri. Folia. Zool. 36(1), 13–19 (1987).
    Google Scholar 
    33.Swift, S. & Racey, P. Gleaning as a foraging strategy in Natterer’s bat Myotis nattereri. Behav. Ecol. Sociobiol. 52(5), 408–416 (2002).Article 

    Google Scholar 
    34.Taake, K. H. Resource utilization strategies of vespertilionid bats hunting over water in forests. Myotis 30, 7–74 (1992).
    Google Scholar 
    35.Vaughan, N. The diets of British bats (Chiroptera). Mammal. Rev. 27(2), 77–94 (1997).Article 

    Google Scholar 
    36.Siemers, B. & Swift, S. M. Differences in sensory ecology contribute to resource partitioning in the bats Myotis bechsteinii and Myotis nattereri (Chiroptera: Vespertilionidae). Behav. Ecol. Sociobiol. 59, 373–380 (2006).Article 

    Google Scholar 
    37.Norberg, U. M. & Rayner, J. M. V. Ecological morphology and flight in bats (Mammalia; Chiroptera): wing adaptations, flight Performance, foraging strategy and echolocation. Philos. Trans. R. Soc. B 316(1179), 335–427 (1987).ADS 

    Google Scholar 
    38.Entwistle, A. C., Racey, P. A. & Speakman, J. R. Habitat exploitation by a gleaning bat, Plecotus auritus. Philos. Trans. R. Soc. B 351(1342), 921–931 (1996).ADS 
    Article 

    Google Scholar 
    39.Kerth, G., Wagner, M. & König, B. Roosting together, foraging apart: information transfer about food is unlikely to explain sociality in female Bechstein’s bats (Myotis bechsteinii). Behav. Ecol. Sociobiol. 50, 283–291 (2001).Article 

    Google Scholar 
    40.Rydell, J. Food habits of northern (Eptesicus nilssoni) and brown long-eared (Plecotus auritus) bats in Sweden. Holarct. Ecol. 12(1), 16–20 (1989).
    Google Scholar 
    41.Anderson, M. E. & Racey, P. A. Feeding behaviour of captive brown long-eared bats, Plecotus auritus. Anim. Behav. 42(3), 489–493 (1991).Article 

    Google Scholar 
    42.Andreas, M. Feeding ecology of a bat community. Ph.D. Thesis, Czech Agriculture University, Prague (2002).43.Dobbertin, M. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur. J. Forest. Res. 124, 319–333 (2005).Article 

    Google Scholar 
    44.Keena, M. A., Côté, M. J., Grinberg, P. S. & Wallner, W. E. World distribution of female flight and genetic variation in Lymantria dispar (Lepidoptera: Lymantriidae). Environ. Entomol. 37(3), 636–649 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Melin, M., Viiri, H., Tikkanen, O. P., Elfving, R. & Neuvonen, S. From a rare inhabitant into a potential pest—status of the nun moth in Finland based on pheromone trapping. Silva. Fenn. 54(1), 1–9 (2020).Article 

    Google Scholar 
    46.Kuhlman, H. M. Effects of insect defoliation on growth and mortality of trees. Annu. Rev. Entomol. 16, 289–324 (1971).Article 

    Google Scholar 
    47.Bogacheva, I. A. Repeated damage of leaves by phyllophagous insects: is it influenced by rapid inducible resistance? In Forest Insect Guilds: Patterns of Interaction with Host Trees. (eds. Baranchikov, Y.N., Mattson, W.J., Hain, F.P. & Payne, T.L.) 113–122 (U.S. Dep. Agric. For. Serv. Gen. Tech. Rep. NE-153, 1991).48.Zvereva, E. L. & Kozlov, M. V. Effects of herbivory on leaf life span in woody plants: a meta-analysis. J. Ecol. 102(4), 873–881 (2014).Article 

    Google Scholar 
    49.Bréda, N., Huc, R., Granier, A. & Dreyer, E. Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann. For. Sci. 63, 625–644 (2006).Article 

    Google Scholar 
    50.Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Change Biol. 22, 2329–2352 (2016).ADS 
    Article 

    Google Scholar 
    51.Delb, H. Eichenschädlinge im Klimawandel in Südwestdeutschland. FVA-einblick. 2/2012, 11–14 (2012).52.Hittenbeck, A., Bialozyt, R. & Schmidt, M. Modelling the population fluctuation of winter moth and mottled umber moth in central and northern Germany. For. Ecosyst. 6, 4 (2019).Article 

    Google Scholar 
    53.Ims, R. A., Yoccoz, N. G. & Hagen, S. B. Do sub-Arctic winter moth populations in coastal birch forest exhibit spatially synchronous dynamics?. J. Anim. Ecol. 73, 1129–1136 (2004).Article 

    Google Scholar 
    54.Böhm, S. M., Wells, K. & Kalko, E. K. V. Top-down control of herbivory by birds and bats in the canopy of temperate broad-leaved oaks (Quercus robur). PLoS ONE 6(4), e17857 (2011).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    55.Patočka, J. Caterpillars on oaks in Czechoslovakia. (Štátne pôdohospodárske nakladateľstvo: 262, 1954).56.Hausmann, A. The geometrid moths of Europe, Volume 1: Introduction, Archiearinae, Orthostixinae, Desmobathrinae, Alsophilinae, Geometrinae, (Apollo Books, 2001).57.Zahradník, P. Calamities in Czech forests—past and present. In: Facts and myths about Czech agricultural forestry. Proceedings of papers (ed Stonawski, J.) 31–51 (Česká zemědělská univerzita, 2008).58.Macek, J., Procházka, J. & Traxler, L. Butterflies and caterpillars of Central Europe: Moths III. – Geometrids. (Academia, 2012).59.Liška, J. Winter moth, Operophtera brumata L. Lesnická Práce, 11: I–IV (2002).60.Basset, Y., Springate, N. D., Aberlenc, H. P. & Delvare, G. A review of methods for sampling arthropods in tree canopies. In Canopy Arthropods (eds Stork, N. E. et al.) 567 (Chapman & Hall, 1997).
    Google Scholar 
    61.Kimber, I. UKMOTHS. https://ukmoths.org.uk (2015).62.Bartonička, T., Miketová, N. & Hulva, P. High throughput bioacoustic monitoring and phenology of the greater noctule bat (Nyctalus lasiopterus) compared to other migratory species. Acta Chiropterol. 21(1), 75–85 (2019).Article 

    Google Scholar 
    63.Lemen, C., Freeman, P. W., White, J. A. & Andersen, B. R. The problem of low agreement among automated identification programs for acoustical surveys of bats. West. N. Am. Naturalist. 75(2), 218–225 (2015).Article 

    Google Scholar 
    64.Barataud, M. Acoustic Ecology of European Bats. Species Identification and Studies of Their Habitats and Foraging Behaviour (Biotope & National Museum of Natural History, 2015).65.McAney, C., Shiel, C., Sullivan, C. & Fairley, J. The analysis of bat droppings (An occasional publication of the Mammal society; no. 14, 1991).66.Zeale, M. R., Butlin, R. K., Barker, G. L., Lees, D. C. & Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces. Mol. Ecol. Resour. 11(2), 23–44 (2011).Article 
    CAS 

    Google Scholar 
    67.Clarke, L. J., Soubrier, J., Weyrich, L. S. & Cooper, A. Environmental metabarcodes for insects: in silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14, 1160–1170 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal 17, 10–12 (2011).
    Google Scholar 
    69.Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Wheeler, D. L. GenBank. Nucleic Acids Res. 35, 21–25 (2007).Article 

    Google Scholar 
    70.R Core Team. R: language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.r-project.org/ (2019). More

  • in

    Ecological and evolutionary approaches to improving crop variety mixtures

    1.Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W. & Mortensen, D. A. Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience 67, 386–391 (2017).Article 

    Google Scholar 
    2.Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Bommarco, R., Kleijn, D. & Potts, S. G. Ecological intensification: harnessing ecosystem services for food security. Trends Ecol. Evol. 28, 230–238 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Weiner, J. Applying plant ecological knowledge to increase agricultural sustainability. J. Ecol. 105, 865–870 (2017).Article 

    Google Scholar 
    5.Sadras, V. et al. Making science more effective for agriculture. Adv. Agron. 163, 153–177 (2020).Article 

    Google Scholar 
    6.Kremen, C. Ecological intensification and diversification approaches to maintain biodiversity, ecosystem services and food production in a changing world. Emerg. Top. Life Sci. 4, 229–240 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Tamburini, G. et al. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Brooker, R. W. et al. Improving intercropping: a synthesis of research in agronomy, plant physiology and ecology. N. Phytol. 206, 107–117 (2015).Article 

    Google Scholar 
    9.Bullock, D. G. Crop rotation. Crit. Rev. Plant Sci. 11, 309–326 (1992).Article 

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

    Google Scholar 
    11.Hector, A. et al. Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Hector, A. et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding. Ecology 91, 2213–2220 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).CAS 
    Article 

    Google Scholar 
    15.Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Prieto, I. et al. Complementary effects of species and genetic diversity on productivity and stability of sown grasslands. Nat. Plants 1, 15033 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Blüthgen, N. et al. Land use imperils plant and animal community stability through changes in asynchrony rather than diversity. Nat. Commun. 7, 10697 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    18.Voss-Fels, K. P. et al. Breeding improves wheat productivity under contrasting agrochemical input levels. Nat. Plants 5, 706–714 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Zuppinger-Dingley, D. et al. Selection for niche differentiation in plant communities increases biodiversity effects. Nature 515, 108–111 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Chacón-Labella, J., García Palacios, P., Matesanz, S., Schöb, C. & Milla, R. Plant domestication disrupts biodiversity effects across major crop types. Ecol. Lett. 22, 1472–1482 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Finckh, M. R. et al. Cereal variety and species mixtures in practice, with emphasis on disease resistance. Agronomie 20, 813–837 (2000).Article 

    Google Scholar 
    22.Newton, A. C. Exploitation of diversity within crops—the key to disease tolerance? Front. Plant Sci. 7, 665 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Newton, A. C., Begg, G. S. & Swanston, J. S. Deployment of diversity for enhanced crop function. Ann. Appl. Biol. 154, 309–322 (2009).Article 

    Google Scholar 
    24.Frankel, O. H. Analytical yield investigations on New Zealand wheat: IV. Blending varieties of wheat. J. Agric. Sci. 29, 249–261 (1939).Article 

    Google Scholar 
    25.Kristoffersen, R., Jørgensen, L. N., Eriksen, L. B., Nielsen, G. C. & Kiær, L. P. Control of Septoria tritici blotch by winter wheat cultivar mixtures: meta-analysis of 19 years of cultivar trials. Field Crops Res. 249, 107696 (2020).Article 

    Google Scholar 
    26.Mundt, C. Use of multiline cultivars and cultivar mixtures for disease management. Annu. Rev. Phytopathol. 40, 381–410 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Wolfe, M. S. The current status and prospects of multiline cultivars and variety mixtures for disease resistance. Annu. Rev. Phytopathol. 23, 251–273 (1985).Article 

    Google Scholar 
    28.Finckh, M. R. Integration of breeding and technology into diversification strategies for disease control in modern agriculture. Eur. J. Plant Pathol. 121, 399–409 (2008).Article 

    Google Scholar 
    29.Reiss, E. R. & Drinkwater, L. E. Cultivar mixtures: a meta-analysis of the effect of intraspecific diversity on crop yield. Ecol. Appl. 28, 62–77 (2018).PubMed 
    Article 

    Google Scholar 
    30.Tooker, J. F. & Frank, S. D. Genotypically diverse cultivar mixtures for insect pest management and increased crop yields. J. Appl. Ecol. 49, 974–985 (2012).Article 

    Google Scholar 
    31.McDonald, B. A., Allard, R. W. & Webster, R. K. Responses of two-, three-, and four-component barley mixtures to a variable pathogen population. Crop Sci. 28, 447–452 (1988).Article 

    Google Scholar 
    32.Zhan, J. & McDonald, B. A. Experimental measures of pathogen competition and relative fitness. Annu. Rev. Phytopathol. 51, 131–153 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Kiær, L. P., Skovgaard, I. M. & Østergård, H. Effects of inter-varietal diversity, biotic stresses and environmental productivity on grain yield of spring barley variety mixtures. Euphytica 185, 123–138 (2012).Article 

    Google Scholar 
    34.Creissen, H. E., Jorgensen, T. H. & Brown, J. K. M. Increased yield stability of field-grown winter barley (Hordeum vulgare L.) varietal mixtures through ecological processes. Crop Prot. 85, 1–8 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Borg, J. et al. Unfolding the potential of wheat cultivar mixtures: a meta-analysis perspective and identification of knowledge gaps. Field Crops Res. 221, 298–313 (2018).Article 

    Google Scholar 
    36.Kiær, L. P., Skovgaard, I. M. & Østergård, H. Grain yield increase in cereal variety mixtures: a meta-analysis of field trials. Field Crops Res. 114, 361–373 (2009).Article 

    Google Scholar 
    37.Barot, S. et al. Designing mixtures of varieties for multifunctional agriculture with the help of ecology. A review. Agron. Sustain. Dev. 37, 13 (2017).Article 

    Google Scholar 
    38.Chateil, C. et al. Crop genetic diversity benefits farmland biodiversity in cultivated fields. Agric. Ecosyst. Environ. 171, 25–32 (2013).Article 

    Google Scholar 
    39.Litrico, I. & Violle, C. Diversity in plant breeding: a new conceptual framework. Trends Plant Sci. 20, 604–613 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Van Der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Montazeaud, G. et al. Crop mixtures: does niche complementarity hold for belowground resources? An experimental test using rice genotypic pairs. Plant Soil 424, 87–202 (2018).Article 
    CAS 

    Google Scholar 
    42.Montazeaud, G. et al. Multifaceted functional diversity for multifaceted crop yield: towards ecological assembly rules for varietal mixtures. J. Appl. Ecol. 57, 2285–2295 (2020).Article 

    Google Scholar 
    43.Von Felten, S., Niklaus, P. A., Scherer-Lorenzen, M., Hector, A. & Buchmann, N. Do grassland plant communities profit from N partitioning by soil depth? Ecology 93, 2386–2396 (2012).Article 

    Google Scholar 
    44.Zhang, W. P. et al. Temporal dynamics of nutrient uptake by neighbouring plant species: evidence from intercropping. Funct. Ecol. 31, 469–479 (2017).Article 

    Google Scholar 
    45.Spehn, E. M. et al. The role of legumes as a component of biodiversity in a cross-European study of grassland biomass nitrogen. Oikos 98, 205–218 (2002).Article 

    Google Scholar 
    46.Griffiths, M. & York, L. M. Targeting root ion uptake kinetics to increase plant productivity and nutrient use efficiency. Plant Physiol. 182, 1854–1868 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Maron, J. L., Marler, M., Klironomos, J. N. & Cleveland, C. C. Soil fungal pathogens and the relationship between plant diversity and productivity. Ecol. Lett. 14, 36–41 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Mikaberidze, A., Mcdonald, B. A. & Bonhoeffer, S. Developing smarter host mixtures to control plant disease. Plant Pathol. 64, 996–1004 (2015).Article 

    Google Scholar 
    49.Wright, A. J., Wardle, D. A., Callaway, R. & Gaxiola, A. The overlooked role of facilitation in biodiversity experiments. Trends Ecol. Evol. 32, 383–390 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Petchey, O. L., Hector, A. & Gaston, K. J. How do different measures of functional diversity perform? Ecology 85, 847–857 (2004).Article 

    Google Scholar 
    51.Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

    Google Scholar 
    52.Zhang, C., Postma, J. A., York, L. M. & Lynch, J. P. Root foraging elicits niche complementarity-dependent yield advantage in the ancient ‘three sisters’ (maize/bean/squash) polyculture. Ann. Bot. 110, 521–534 (2014).
    Google Scholar 
    53.Erktan, A., McCormack, M. L. & Roumet, C. Frontiers in root ecology: recent advances and future challenges. Plant Soil 424, 1–9 (2018).CAS 
    Article 

    Google Scholar 
    54.Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

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

    Google Scholar 
    56.Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002).Article 

    Google Scholar 
    57.Morris, G. P. et al. Genotypic diversity effects on biomass production in native perennial bioenergy cropping systems. Glob. Change Biol. Bioenergy 8, 1000–1014 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Wuest, S. E. & Niklaus, P. A. A plant biodiversity effect resolved to a single chromosomal region. Nat. Ecol. Evol. 2, 1933–1939 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Chen, K., Wang, Y., Zhang, R., Zhang, H. & Gao, C. CRISPR/Cas genome editing and precision plant breeding in agriculture. Annu. Rev. Plant Biol. 70, 667–697 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Griffing, B. Concept of general and specific combining ability in relation to diallel crossing systems. Aust. J. Biol. Sci. 9, 463–493 (1956).Article 

    Google Scholar 
    61.Lopez, C. G. & Mundt, C. C. Using mixing ability analysis from two-way cultivar mixtures to predict the performance of cultivars in complex mixtures. Field Crops Res. 68, 121–132 (2000).Article 

    Google Scholar 
    62.Forst, E. et al. A generalized statistical framework to assess mixing ability from incomplete mixing designs using binary or higher order variety mixtures and application to wheat. Field Crops Res. 242, 107571 (2019).Article 

    Google Scholar 
    63.Harlan, H. V. & Martini, M. L. A composite hybrid mixture. Agron. J. 21, 487–490 (1929).Article 

    Google Scholar 
    64.Suneson, C. A. Evolutionary plant breeding. Crop Sci. 9, 119–121 (1969).Article 

    Google Scholar 
    65.Allard, R. W. & Adams, J. Populations studies in predominantly self-pollinating species. XIII. Intergenotypic competition and population structure in barley and wheat. Am. Nat. 103, 621–645 (1969).Article 

    Google Scholar 
    66.Allard, R. W. & Jain, S. K. Population studies in predominantly self-pollinated species. II. Analysis of quantitative genetic changes in a bulk-hybrid population of barley. Evolution 16, 90–101 (1962).
    Google Scholar 
    67.Döring, T. F., Knapp, S., Kovacs, G., Murphy, K. & Wolfe, M. S. Evolutionary plant breeding in cereals—into a new era. Sustainability 3, 1944–1971 (2011).Article 

    Google Scholar 
    68.Dawson, J. C. & Goldringer, I. in Organic Crop Breeding (eds Lammerts van Bueren, E. T. & Myers, J. R.) 77–98 (Wiley, 2011).69.Goldringer, I. et al. Agronomic evaluation of bread wheat varieties from participatory breeding: a combination of performance and robustness. Sustainability 12, 128 (2020).Article 

    Google Scholar 
    70.Andrew, I. K. S., Storkey, J. & Sparkes, D. L. A review of the potential for competitive cereal cultivars as a tool in integrated weed management. Weed Res. 55, 239–248 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Bertholdsson, N. O., Weedon, O., Brumlop, S. & Finckh, M. R. Evolutionary changes of weed competitive traits in winter wheat composite cross populations in organic and conventional farming systems. Eur. J. Agron. 79, 23–30 (2016).Article 

    Google Scholar 
    72.Weiner, J., Du, Y. L., Zhang, C., Qin, X. L. & Li, F. M. Evolutionary agroecology: individual fitness and population yield in wheat (Triticum aestivum). Ecology 98, 2261–2266 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    73.Weiner, J. Looking in the wrong direction for higher-yielding crop genotypes. Trends Plant Sci. 19, S1360–S1385 (2019).
    Google Scholar 
    74.Denison, R. F., Kiers, E. T. & West, S. A. Darwinian agriculture: When can humans find solutions beyond the reach of natural selection? Q. Rev. Biol. 78, 145–168 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Donald, C. M. The breeding of crop ideotypes. Euphytica 17, 385–403 (1968).Article 

    Google Scholar 
    76.Donald, C. M. in Wheat Science—Today and Tomorrow (eds Evans, L. T. & Peacock, W. J.) 223–247 (Cambridge Univ. Press, 1981).77.Knapp, S. et al. Natural selection towards wild-type in composite cross populations of winter wheat. Front. Plant Sci. 10, 1757 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Gersani, M., Brown, J. S., O’Brien, E. E., Maina, G. M. & Abramsky, Z. Tragedy of the commons as a result of root competition. J. Ecol. 89, 660–669 (2001).Article 

    Google Scholar 
    79.Rankin, D. J., Bargum, K. & Kokko, H. The tragedy of the commons in evolutionary biology. Trends Ecol. Evol. 22, 643–651 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Zhang, D. Y., Sun, G. J. & Jiang, X. H. Donald’s ideotype and growth redundancy: a game theoretical analysis. Field Crops Res. 61, 179–187 (1999).Article 

    Google Scholar 
    81.Duvick, D. N., Smith, J. S. C. & Cooper, M. in Plant Breeding Reviews. Part 2. Long Term Selection: Crops, Animals and Bacteria Vol. 24 (ed. Janick, J.) 109–151 (Wiley, 2004); https://doi.org/10.1002/9780470650288.ch482.Tian, J. et al. Teosinte ligule allele narrows plant architecture and enhances high-density maize yields. Science 365, 658–664 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Zhu, Y. H., Weiner, J., Yu, M. X. & Li, F. M. Evolutionary agroecology: trends in root architecture during wheat breeding. Evol. Appl. 12, 733–743 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    84.Tsunoda, S. A developmental aanlysis of yielding ability in varieties of field crops: II. The assimilation-system of plants as affected by the form, direction and arrangement of single leaves. Jpn. J. Breed. 9, 237–244 (1959).Article 

    Google Scholar 
    85.Jennings, P. R. Plant type as a rice breeding objective. Crop Sci. 4, 13–15 (1964).Article 

    Google Scholar 
    86.Zhu, L. & Zhang, D. Y. Donald’s ideotype and growth redundancy: a pot experimental test using an old and a modern spring wheat cultivar. PLoS ONE 8, e70006 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Jennings, P. R. & De Jesus, J. J. Studies on competition in rice I. Competition in mixtures of varieties. Evolution 22, 119–124 (1968).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.Jennings, P. R. & Herrera, R. M. Studies on competition in rice II. Competition in segregating populations. Evolution 22, 332–336 (1968).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    89.Borlaug, N. E. Wheat breeding and its impact on world food supply. In Third International Wheat Genetics Symposium 1–36 (1968).90.Vogel, O. A., Craddock, J. C., Muir, C. E., Everson, E. H. & Rohde, C. R. Semidwarf growth habit in winter wheat improvement for the Pacific Northwest. Agron. J. 48, 76–78 (1956).Article 

    Google Scholar 
    91.Reynolds, M. P., Acevedo, E., Sayre, K. D. & Fischer, R. A. Yield potential in modern wheat varieties: its association with a less competitive ideotype. Field Crops Res. 37, 149–160 (1994).Article 

    Google Scholar 
    92.Murphy, G. P., Swanton, C. J., Van Acker, R. C. & Dudley, S. A. Kin recognition, multilevel selection and altruism in crop sustainability. J. Ecol. 105, 930–934 (2017).Article 

    Google Scholar 
    93.Ohtsuki, H., Hauert, C., Lieberman, E. & Nowak, M. A. A simple rule for the evolution of cooperation on graphs and social networks. Nature 441, 502–505 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    94.Nowak, M. A. Five rules for the evolution of cooperation. Science 314, 1560–1563 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    95.Maynard Smith, J. Group selection and kin selection. Nature 201, 1145–1147 (1964).Article 

    Google Scholar 
    96.Montazeaud, G. et al. Farming plant cooperation in crops. Proc. Biol. Sci. 287, 20191290 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    97.Brown, J. K. M. Durable resistance of crops to disease: a Darwinian perspective. Annu. Rev. Phytopathol. 53, 513–539 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    98.Laine, A. L., Burdon, J. J., Dodds, P. N. & Thrall, P. H. Spatial variation in disease resistance: from molecules to metapopulations. J. Ecol. 99, 96–112 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    99.Karasov, T. L., Shirsekar, G., Schwab, R. & Weigel, D. What natural variation can teach us about resistance durability. Curr. Opin. Plant Biol. 56, 89–98 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    100.Zhan, J., Thrall, P. H., Papaïx, J., Xie, L. & Burdon, J. J. Playing on a pathogen’s weakness: using evolution to guide sustainable plant disease control strategies. Annu. Rev. Phytopathol. 53, 19–43 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    101.Smithson, J. B. & Lenné, J. M. Varietal mixtures: a viable strategy for sustainable productivity in subsistence agriculture. Ann. Appl. Biol. 128, 127–158 (1996).Article 

    Google Scholar 
    102.Huang, C., Sun, Z., Wang, H., Luo, Y. & Ma, Z. Effects of wheat cultivar mixtures on stripe rust: a meta-analysis on field trials. Crop Prot. 33, 52–58 (2012).Article 

    Google Scholar 
    103.Zhu, Y. et al. Genetic diversity and disease control in rice. Nature 406, 718–722 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    104.Mundt, C. C. Durable resistance: a key to sustainable management of pathogens and pests. Infect. Genet. Evol. 27, 446–455 (2014).PubMed 
    Article 

    Google Scholar 
    105.Finckh, M. R. Stripe rust, yield, and plant competition in wheat cultivar mixtures. Phytopathology 85, 905–913 (1992).Article 

    Google Scholar 
    106.McGrann, G. R. D. et al. A trade off between mlo resistance to powdery mildew and increased susceptibility of barley to a newly important disease, Ramularia leaf spot. J. Exp. Bot. 65, 1025–1037 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    107.Rimbaud, L., Papaïx, J., Barrett, L. G., Burdon, J. J. & Thrall, P. H. Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance? Evol. Appl. 11, 1791–1810 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    108.Zeller, S. L., Kalinina, O., Flynn, D. F. B. & Schmid, B. Mixtures of genetically modified wheat lines outperform monocultures. Ecol. Appl. 22, 1817–1826 (2012).PubMed 
    Article 

    Google Scholar 
    109.Kellerhals, M., Mouron, P., Graf, B., Bousset, L. & Gessler, C. Mischpflanzung von Apfelsorten: Einfluss auf krankheiten, schädlinge und wirtschaftlichkeit. Schweiz. Z. Obs. 13, 10–13 (2003).
    Google Scholar 
    110.Burdon, J. J., Barrett, L. G., Rebetzke, G. & Thrall, P. H. Guiding deployment of resistance in cereals using evolutionary principles. Evol. Appl. 7, 609–624 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    111.Mundt, C. C. Pyramiding for resistance durability: theory and practice. Phytopathology 108, 792–802 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    112.Newton, A. C., Johnson, S. N. & Gregory, P. J. Implications of climate change for diseases, crop yields and food security. Euphytica 179, 3–18 (2011).Article 

    Google Scholar 
    113.Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    114.Friedli, C. N., Abiven, S., Fossati, D. & Hund, A. Modern wheat semi-dwarfs root deep on demand: response of rooting depth to drought in a set of Swiss era wheats covering 100 years of breeding. Euphytica 215, 85 (2019).Article 
    CAS 

    Google Scholar 
    115.DeWitt, T. J., Sih, A. & Wilson, D. S. Costs and limits of phenotypic plasticity. Trends Ecol. Evol. 13, 77–81 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    116.Tilman, D. & Downing, J. A. Biodiversity and stability in grasslands. Nature 367, 363–365 (1994).Article 

    Google Scholar 
    117.Schweiger, A. K. et al. Spectral niches reveal taxonomic identity and complementarity in plant communities. Preprint at bioRxiv https://doi.org/10.1101/2020.04.24.060483 (2020).118.Pianka, E. R. The structure of lizard communities. Annu. Rev. Ecol. Syst. 4, 53–74 (1973).Article 

    Google Scholar 
    119.MacArthur, R. H. Population ecology of some warblers of northeastern coniferous forests. Ecology 39, 599–619 (1958).Article 

    Google Scholar 
    120.Colwell, R. K. & Futuyma, D. J. On the measurement of niche breadth and overlap. Ecology 52, 567–576 (1971).PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

  • in

    Phylogeography of Prunus armeniaca L. revealed by chloroplast DNA and nuclear ribosomal sequences

    1.Meng, H. H. & Zhang, M. L. Diversification of plant species in arid Northwest China: species-level phylogeographical history of Lagochilus Bunge ex Bentham (Lamiaceae). Mol. Phylogenet. Evol. 68, 398–409. https://doi.org/10.1111/jse.12088 (2015).Article 

    Google Scholar 
    2.Pennington, R. T. et al. Contrasting plant diversification histories within the Andean biodiversity hotspot. Proc. Natl. Acad. Sci. USA 107, 13783–13787. https://doi.org/10.1073/pnas.1001317107 (2010).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Hughes, C. & Eastwood, R. Island radiation on a continental scale: exceptional rates of plant diversification after uplift of the Andes. Proc. Natl. Acad. Sci. USA 103, 10334–10339. https://doi.org/10.1073/pnas.0601928103 (2006).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Johansson, U. S. et al. Build-up of the Himalayan avifauna through immigration: a biogeographical analysis of the Phylloscopus and Seicercus warblers. Evolution 61, 324–333. https://doi.org/10.1111/j.1558-5646.2007.00024.x (2007).Article 
    PubMed 

    Google Scholar 
    5.Hughes, C. E. & Atchison, G. W. The ubiquity of alpine plant radiations: from the Andes to the Hengduan Mountains. New Phytol. 207, 275–282. https://doi.org/10.1111/nph.13230 (2015).Article 
    PubMed 

    Google Scholar 
    6.Lagomarsino, L. P., Condamine, F. L., Antonelli, A., Mulch, A. & Davis, C. C. The abiotic and biotic drivers of rapid diversification in Andean bellflowers (Campanulaceae). New Phytol. 210, 1430–1442. https://doi.org/10.1111/nph.13920 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Ebersbach, J. et al. In and out of the Qinghai-Tibet Plateau: divergence time estimation and historical biogeography of the large arctic-alpine genus Saxifraga L. J. Biogeogr. 44, 900–910. https://doi.org/10.1111/jbi.12899 (2017).Article 

    Google Scholar 
    8.Zhang, J. Y. & Zhang, Z. In Flora of Chinese Fruit Trees 61–62 (China Forestry Press, 2003).9.Su, Z., Zhang, M. & Sanderson, S. C. Chloroplast phylogeography of Helianthemum songaricum (Cistaceae) from northwestern China: implications for preservation of genetic diversity. Conserv. Genet. 12, 1525–1537. https://doi.org/10.1007/s10592-011-0250-9 (2011).Article 

    Google Scholar 
    10.Xie, K. Q. & Zhang, M. L. The effect of Quaternary climatic oscillations on Ribes meyeri (Saxifragaceae) in northwestern China. Biochem. Syst. Ecol. 50, 39–47. https://doi.org/10.1016/j.bse.2013.03.031 (2013).CAS 
    Article 

    Google Scholar 
    11.Salvi, D., Schembri, P., Sciberras, A. & Harris, D. Evolutionary history of the maltese wall lizard Podarcis filfolensis: insights on the ‘Expansion–Contraction’ model of Pleistocene biogeography. Mol. Ecol. 23, 1167–1187. https://doi.org/10.1111/mec.12668 (2014).Article 
    PubMed 

    Google Scholar 
    12.Liu, J. Q., Sun, Y. S., Ge, X. J., Gao, L. M. & Qiu, Y. X. Phylogeographic studies of plants in China: advances in the past and directions in the future. J. Syst. Evol. 50, 267–275. https://doi.org/10.1111/j.1759-6831.2012.00214.x (2012).Article 

    Google Scholar 
    13.Hewitt, G. The genetic legacy of the quaternary ice ages. Nature 405, 907–913. https://doi.org/10.1038/35016000 (2000).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Hewitt, G. M. The structure of biodiversity-insights from molecular phylogeography. Front. Zool. 1, 1–16. https://doi.org/10.1186/1742-9994-1-4 (2004).Article 

    Google Scholar 
    15.Willis, K. J. & Niklas, K. J. The role of quaternary environmental change in plant macroevolution: the exception or the rule?. Philos. Trans. R. Soc. Lond. B 359, 159–172. https://doi.org/10.1098/rstb.2003.1387 (2004).Article 

    Google Scholar 
    16.Schmitt, T. Molecular biogeography of Europe: pleistocene cycles and postglacial trends. Front. Zool. 4, 11. https://doi.org/10.1186/1742-9994-4-11 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Shen, L., Chen, X. Y. & Li, Y. Y. Glacial refugia and postglacial recolonization patterns of organisms. Acta Ecol. Sin. 22, 1983–1990. https://doi.org/10.1088/1009-1963/11/5/313 (2002).Article 

    Google Scholar 
    18.Schonswetter, P., Popp, M. & Brochmann, C. Rare arctic-alpine plants of the European Alps have different immigration histories: the snow bed species Minuartia biflora and Ranunculus pygmaeus. Mol. Ecol. 15, 709–720. https://doi.org/10.1111/j.1365-294X.2006.02821.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    19.Guo, Y. P., Zhang, R., Chen, C. Y., Zhou, D. W. & Liu, J. Q. Allopatric divergence and regional range expansion of Juniperus sabina in China. J. Syst. Evol. 48, 153–160. https://doi.org/10.1111/j.1759-6831.2010.00073.x (2010).Article 

    Google Scholar 
    20.Jaramillo-Correa, J. P., Beaulieu, J. & Bousquet, J. Variation in mitochondrial DNA reveals multiple distant glacial refugia in black spruce (Picea mariana), a transcontinental North American conifer. Mol. Ecol. 13, 2735–2747. https://doi.org/10.1111/j.1365-294X.2004.02258.x (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    21.Afzal-Rafii, Z. & Dodd, R. S. Chloroplast DNA supports a hypothesis of glacial refugia over postglacial recolonization in disjunct populations of black pine (Pinus nigra) in western Europe. Mol. Ecol. 16, 723–736. https://doi.org/10.1111/j.1365-294X.2006.03183.x (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    22.Anderson, L., Hu, F., Nelson, D., Petit, R. & Paige, K. Ice-age endurance: DNA evidence of a white spruce refugium in Alaska. Proc. Natl. Acad. Sci. USA 103, 12447–12450. https://doi.org/10.1073/pnas.0605310103 (2006).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Volkova, P. A., Burlakov, Y. A. & Schanzer, I. A. Genetic variability of Prunus padus (Rosaceae) elaborates “a new Eurasian phylogeographical paradigm”. Plant Syst. Evol. 306, 1–9. https://doi.org/10.1007/s00606-020-01644-0 (2020).CAS 
    Article 

    Google Scholar 
    24.Xu, Z. & Zhang, M. L. Phylogeography of the arid shrub Atraphaxis frutescens (Polygonaceae) in northwestern China: evidence from cpDNA sequences. J. Hered. 106, 184–195. https://doi.org/10.1093/jhered/esu078 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    25.Rehder, A. Manual of Cultivated Trees and Shrubs Hardy in North America, Exclusive of the Subtropical and Warmer Temperate Regions 345–346 (Macmillan, 1927).26.Zhebentyayeva, T. N., Ledbetter, C., Burgos, L., & Llácer, G. Fruit Breeding 415–458 (Springer, 2012).27.Zhebentyayeva, T. N., Reighard, G. L., Gorina, V. M. & Abbott, A. G. Simple sequence repeat (SSR) analysis for assessment of genetic variability in apricot germplasm. Theor. Appl. Genet. 106, 435–444. https://doi.org/10.1007/s00122-002-1069-z (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    28.Schaal, B. A., Hayworth, D. A., Olsen, K. M., Rauscher, J. T. & Smith, W. A. Phylogeographic studies in plants: problems and prospects. Mol. Ecol. 7, 465–474. https://doi.org/10.1046/j.1365-294x.1998.00318.x (1998).Article 

    Google Scholar 
    29.Avise, J. C. Phylogeography: retrospect and prospect. J. Biogeogr. 36, 3–15. https://doi.org/10.1111/j.1365-2699.2008.02032.x (2009).Article 

    Google Scholar 
    30.Poudel, R. C., Möller, M., Li, D. Z., Shah, A. & Gao, L. M. Genetic diversity, demographical history and conservation aspects of the endangered yew tree Taxus contorta (syn. Taxus fuana) in Pakistan. Tree Genet. Genom. 10, 653–665. https://doi.org/10.1007/s11295-014-0711-7 (2014).Article 

    Google Scholar 
    31.Dutech, C., Maggia, L. & Joly, H. Chloroplast diversity in Vouacapoua americana (Caesalpiniaceae), a neotropical forest tree. Mol. Ecol. 9, 1427–1432. https://doi.org/10.1046/j.1365-294x.2000.01027.x (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    32.Li, Y. et al. Rapid intraspecific diversification of the Alpine species Saxifraga sinomontana (Saxifragaceae) in the Qinghai-Tibetan Plateau and Himalayas. Front. Genet. 9, 381. https://doi.org/10.3389/fgene.2018.00381 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Zhang, Q. P. & Liu, W. S. Advances of the apricot resources collection, evaluation and germplasm enhancement. Acta Hortic. Sin. 45, 1642–1660. https://doi.org/10.16420/j.issn.0513-353x.2017-0654 (2018).Article 

    Google Scholar 
    34.Hu, Z. B. et al. Population genomics of pearl millet (Pennisetum glaucum (L). R. Br.): comparative analysis of global accessions and Senegalese landraces. BMC Genomics 16, 1048. https://doi.org/10.1186/s12864-015-2255-0 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.White, T. J., Bruns, T., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR Protoc. Guide Methods Appl. 18, 315–322 (1990).
    Google Scholar 
    36.Dong, W. et al. ycf1, the most promising plastid DNA barcode of land plants. Sci. Rep. 5, 8348. https://doi.org/10.1038/srep08348 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Bortiri, E. et al. Phylogeny and systematics of Prunus (Rosaceae) as determined by sequence analysis of ITS and the chloroplast trnL-trnF spacer DNA. Syst. Bot. 26, 797–807. https://doi.org/10.1043/0363-6445-26.4.797 (2001).Article 

    Google Scholar 
    38.Zhang, Q. Y. et al. Latitudinal adaptation and genetic insights into the origins of Cannabis sativa L. Front Plant Sci. 9, 1876. https://doi.org/10.3389/fpls.2018.01876 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Sumo. Ser. 41, 95–98. https://doi.org/10.1021/bk-1999-0734.ch008 (1999).CAS 
    Article 

    Google Scholar 
    40.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680. https://doi.org/10.1093/nar/22.22.4673 (1994).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Simmons, M. P. & Ochoterena, H. Gaps as characters in sequence-based phylogenetic analyses. Syst. Biol. 49, 369–381. https://doi.org/10.1080/10635159950173889 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    42.Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874. https://doi.org/10.1093/molbev/msw054 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Clement, M., Posada, D. & Crandall, K. A. TCS: a computer program to estimate gene genealogies. Mol. Ecol. 9, 1657–1659. https://doi.org/10.1046/j.1365-294x.2000.01020.x (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    44.Librado, P. & Rozas, J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452. https://doi.org/10.1093/bioinformatics/btp187 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Pons, O. & Petit, R. J. Measwring and testing genetic differentiation with ordered versus unordered alleles. Genetics 144, 1237–1245. https://doi.org/10.1016/S1050-3862(96)00162-3 (1996).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 

    Google Scholar 
    47.Rogers, A. R. & Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992).CAS 
    PubMed 

    Google Scholar 
    48.Wolfe, K. H., Li, W. H. & Sharp, P. M. Rates of nucleotide substitution vary greatly among plant mitochondrial, chloroplast, and nuclear DNAs. Proc. Natl. Acad. Sci. USA 84, 9054–9058. https://doi.org/10.1073/pnas.84.24.9054 (1987).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Wang, Z. et al. Phylogeography study of the Siberian apricot (Prunus sibirica L.) in Northern China assessed by chloroplast microsatellite and DNA makers. Front. Plant Sci. 8, 1989. https://doi.org/10.3389/fpls.2017.01989 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Chin, S. W., Shaw, J., Haberle, R., Wen, J. & Potter, D. Diversification of almonds, peaches, plums and cherries-Molecular systematics and biogeographic history of Prunus (Rosaceae). Mol. Phylogenet. Evol. 76, 34–48. https://doi.org/10.1016/j.ympev.2014.02.024 (2014).Article 
    PubMed 

    Google Scholar 
    51.Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973. https://doi.org/10.1093/molbev/mss075 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Yang, J., Vazquez, L., Feng, L., Liu, Z. & Zhao, G. Climatic and soil factors shape the demographical history and genetic diversity of a deciduous oak (Quercus liaotungensis) in Northern China. Front. Plant Sci. 9, 1534. https://doi.org/10.3389/fpls.2018.01534 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Zhang, X., Shen, S., Wu, F. & Wang, Y. Inferring genetic variation and demographic history of Michelia yunnanensis Franch (Magnoliaceae) from chloroplast DNA sequences and microsatellite markers. Front. Plant Sci. 8, 583. https://doi.org/10.3389/fpls.2017.00583 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Li, M., Zhao, Z. & Miao, X. J. Genetic variability of wild apricot (Prunus armeniaca L.) populations in the Ili Valley as revealed by ISSR markers. Genet. Resour. Crop Evol. 60, 2293–2302. https://doi.org/10.1007/s10722-013-9996-x (2013).CAS 
    Article 

    Google Scholar 
    55.Li, M., Hu, X., Miao, X. J., Xu, Z. & Zhao, Z. Genetic diversity analysis of wild apricot (Prunus armeniaca) populations in the lli Valley as revealed by SRAP markers. Acta Hortic. Sin. 43, 1980–1988. https://doi.org/10.16420/j.issn.0513-353x.2016-0156 (2016).Article 

    Google Scholar 
    56.Hu, X., Zheng, P., Ni, B., Miao, X. & Li, M. Population genetic diversity and structure analysis of wild apricot (Prunus armeniaca L.) revealed by SSR markers in the Tien-Shan mountains of China. Pak. J. Bot. 50, 609–615 (2018).
    Google Scholar 
    57.Decroocq, S. et al. New insights into the history of domesticated and wild apricots and its contribution to Plum pox virus resistance. Mol. Ecol. 25, 4712–4729. https://doi.org/10.1111/mec.13772 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Liu, S. et al. The complex evolutionary history of apricots: species divergence, gene flow and multiple domestication events. Mol. Ecol. Notes 28, 5299–5314. https://doi.org/10.1111/mec.15296 (2019).Article 

    Google Scholar 
    59.Posada, D. & Crandall, K. A. Intraspecific gene genealogies: trees grafting into networks. Trends Ecol. Evol. 16, 37–45. https://doi.org/10.1016/S0169-5347(00)02026-7 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Boulnois, L. Silk Road: Monks, Warriors & Merchants on the Silk Road 115–165 (WW Norton & Co Inc, 2004).61.Zhao, C., Wang, C. B., Ma, X. G., Liang, Q. L. & He, X. J. Phylogeographic analysis of a temperate-deciduous forest restricted plant (Bupleurum longiradiatum Turcz.) reveals two refuge areas in China with subsequent refugial isolation promoting speciation. Mol. Phylogen. Evol. 68, 628–643. https://doi.org/10.1016/j.ympev.2013.04.007 (2013).Article 

    Google Scholar 
    62.Ebersbach, J., Schnitzler, J., Favre, A. & Muellner-Riehl, A. N. Evolutionary radiations in the species-rich mountain genus Saxifraga L. BMC Evol. Biol. https://doi.org/10.1186/s12862-017-0967-2 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Favre, A. et al. The role of the uplift of the Qinghai-Tibetan Plateau for the evolution of Tibetan biotas. Biol. Rev. 90, 236–253. https://doi.org/10.1111/brv.12107 (2015).Article 
    PubMed 

    Google Scholar  More

  • in

    Genomic analysis of Shiga toxin-producing Escherichia coli O157:H7 from cattle and pork-production related environments

    1.Gill, A. et al. Review of the state of knowledge on verotoxigenic Escherichia coli and the role of whole genome sequencing as an emerging technology supporting regulatory food safety in Canada. (2020).2.Thorpe, C. M. Shiga toxin-producing Escherichia coli infection. Clin. Infect. Dis. 38, 1298–1303 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Valilis, E., Ramsey, A., Sidiq, S. & DuPont, H. L. Non-O157 Shiga toxin-producing Escherichia coli-A poorly appreciated enteric pathogen: systematic review. Int. J. Infect. Dis. 76, 82–87 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Karmali, M. A., Steele, B. T., Petric, M. & Lim, C. Sporadic cases of haemolytic-uraemic syndrome associated with faecal cytotoxin and cytotoxin-producing Escherichia coli in stools. Lancet 1, 619–620 (1983).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.O’Brien, A. O., Lively, T. A., Chen, M. E., Rothman, S. W. & Formal, S. B. Escherichia coli O157:H7 strains associated with haemorrhagic colitis in the United States produce a Shigella dysenteriae 1 (SHIGA) like cytotoxin. Lancet 1, 702 (1983).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Gill, A. & Gill, C. O. Non-O157 verotoxigenic Escherichia coli and beef: a Canadian perspective. Can. J. Vet. Res 74, 161–169 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    7.Heiman, K. E., Mody, R. K., Johnson, S. D., Griffin, P. M. & Gould, L. H. Escherichia coli O157 outbreaks in the United States, 2003–2012. Emerg. Infect. Dis. 21, 1293–1301 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Callaway, T. R., Carr, M. A., Edrington, T. S., Anderson, R. C. & Nisbet, D. J. Diet, Escherichia coli O157:H7, and cattle: a review after 10 years. Curr. Issues Mol. Biol. 11, 67–79 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Tseng, M., Fratamico, P. M., Manning, S. D. & Funk, J. A. Shiga toxin-producing Escherichia coli in swine: the public health perspective. Anim. Health Res. Rev. 15, 63–75 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Waddell, T. E., Coomber, B. L. & Gyles, C. L. Localization of potential binding sites for the edema disease verotoxin (VT2e) in pigs. Can. J. Vet. Res. 62, 81–86 (1998).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Omer, M. K. et al. A systematic review of bacterial foodborne outbreaks related to red meat and meat products. Foodborne Pathog. Dis. 15, 598–611 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Honish, L. et al. Escherichia coli O157:H7 infections associated with contaminated pork products – Alberta, Canada, July–October 2014. Mmwr. Morbidity Mortal. Wkly. Rep. 65, 1477–1481 (2017).Article 

    Google Scholar 
    13.AHS. E. coli outbreak linked to certain pork products in Alberta declared over, https://www.albertahealthservices.ca/news/releases/2018/Page14457.aspx (2018).14.News, F. S. Alberta outbreak prompts raw pork and pork organ recall, https://www.foodsafetynews.com/2016/02/alberta-e-coli-outbreak-prompts-raw-pork-and-pork-organ-recall/ (2016).15.Essendoubi, S. et al. Prevalence and characterization of Escherichia coli O157:H7 on pork carcasses and in swine colon content from provincially-licensed abattoirs in Alberta, Canada. J Food Prot, (2020).16.Colello, R. et al. From farm to table: follow-up of Shiga toxin-producing Escherichia coli throughout the pork production chain in Argentina. Front Microbiol. 7, 93 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Tseng, M., Fratamico, P. M., Bagi, L., Manzinger, D. & Funk, J. A. Shiga toxin-producing E. coli (STEC) in swine: prevalence over the finishing period and characteristics of the STEC isolates. Epidemiol. Infect. 143, 505–514 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Rajkhowa, S. & Sarma, D. K. Prevalence and antimicrobial resistance of porcine O157 and non-O157 Shiga toxin-producing Escherichia coli from India. Trop. Anim. Health Prod. 46, 931–937 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Meng, Q. et al. Characterization of Shiga toxin-producing Escherichia coli isolated from healthy pigs in China. BMC Microbiol 14, 5 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Ho, W. S., Tan, L. K., Ooi, P. T., Yeo, C. C. & Thong, K. L. Prevalence and characterization of verotoxigenic-Escherichia coli isolates from pigs in Malaysia. BMC Vet. Res. 9, 109 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Choi, Y. M. et al. Changes in microbial contamination levels of porcine carcasses and fresh pork in slaughterhouses, processing lines, retail outlets, and local markets by commercial distribution. Res. Vet. Sci. 94, 413–418 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Farzan, A., Friendship, R. M., Cook, A. & Pollari, F. Occurrence of Salmonella, Campylobacter, Yersinia enterocolitica, Escherichia coli O157 and Listeria monocytogenes in swine. Zoonoses Public Health 57, 388–396 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Lenahan, M. et al. The potential use of chilling to control the growth of Enterobacteriaceae on porcine carcasses and the incidence of E. coli O157:H7 in pigs. J. Appl. Microbiol. 106, 1512–1520 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Milnes, A. S. et al. Factors related to the carriage of Verocytotoxigenic E. coli, Salmonella, thermophilic Campylobacter and Yersinia enterocolitica in cattle, sheep and pigs at slaughter. Epidemiol. Infect. 137, 1135–1148 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Kaufmann, M. et al. Escherichia coli O157 and non-O157 Shiga toxin-producing Escherichia coli in fecal samples of finished pigs at slaughter in Switzerland. J. Food Prot. 69, 260–266 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Fratamico, P. M., Bagi, L. K., Bush, E. J. & Solow, B. T. Prevalence and characterization of Shiga toxin-producing Escherichia coli in swine feces recovered in the National Animal Health Monitoring System’s Swine 2000 study. Appl Environ. Microbiol 70, 7173–7178 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Bonardi, S. et al. Detection of Salmonella spp., Yersinia enterocolitica and verocytotoxin-producing Escherichia coli O157 in pigs at slaughter in Italy. Int J. Food Microbiol 85, 101–110 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Eriksson, E., Nerbrink, E., Borch, E., Aspan, A. & Gunnarsson, A. Verocytotoxin-producing Escherichia coli O157:H7 in the Swedish pig population. Vet. Rec. 152, 712–717 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Feder, I. et al. Isolation of Escherichia coli O157:H7 from intact colon fecal samples of swine. Emerg. Infect. Dis. 9, 380–383 (2003).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Johnsen, G., Wasteson, Y., Heir, E., Berget, O. I. & Herikstad, H. Escherichia coli O157:H7 in faeces from cattle, sheep and pigs in the southwest part of Norway during 1998 and 1999. Int J. Food Microbiol 65, 193–200 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Leung, P. H., Yam, W. C., Ng, W. W. & Peiris, J. S. The prevalence and characterization of verotoxin-producing Escherichia coli isolated from cattle and pigs in an abattoir in Hong Kong. Epidemiol. Infect. 126, 173–179 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Nakazawa, M. & Akiba, M. Swine as a potential reservoir of Shiga toxin-producing Escherichia coli O157:H7 in Japan. Emerg. Infect. Dis. 5, 833–834 (1999).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Chapman, P. A., Siddons, C. A., Gerdan Malo, A. T. & Harkin, M. A. A 1-year study of Escherichia coli O157 in cattle, sheep, pigs and poultry. Epidemiol. Infect. 119, 245–250 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Tang, S. et al. Assessment and comparison of molecular subtyping and characterization methods for Salmonella. Front Microbiol. 10, 1591 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Schurch, A. C., Arredondo-Alonso, S., Willems, R. J. L. & Goering, R. V. Whole genome sequencing options for bacterial strain typing and epidemiologic analysis based on single nucleotide polymorphism versus gene-by-gene-based approaches. Clin. Microbiol Infect. 24, 350–354 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.McNally, A. et al. Combined analysis of variation in core, accessory and regulatory genome regions provides a super-resolution view into the evolution of bacterial populations. PLoS Genet. 12, e1006280 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    37.Kaas, R. S., Friis, C., Ussery, D. W. & Aarestrup, F. M. Estimating variation within the genes and inferring the phylogeny of 186 sequenced diverse Escherichia coli genomes. BMC Genomics 13, 577 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Rusconi, B. et al. Whole genome sequencing for genomics-guided investigations of Escherichia coli O157:H7 outbreaks. Front Microbiol 7, 985 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Rumore, J. et al. Evaluation of whole-genome sequencing for outbreak detection of Verotoxigenic Escherichia coli O157:H7 from the Canadian perspective. BMC Genomics 19, 870 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Manning, S. D. et al. Variation in virulence among clades of Escherichia coli O157:H7 associated with disease outbreaks. Proc. Natl Acad. Sci. USA 105, 4868–4873 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Yang, Z. et al. Identification of common subpopulations of non-sorbitol-fermenting, beta-glucuronidase-negative Escherichia coli O157:H7 from bovine production environments and human clinical samples. Appl Environ. Microbiol. 70, 6846–6854 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Clermont, O., Christenson, J. K., Denamur, E. & Gordon, D. M. The Clermont Escherichia coli phylo-typing method revisited: improvement of specificity and detection of new phylo-groups. Environ. Microbiol Rep. 5, 58–65 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Latif, H., Li, H. J., Charusanti, P., Palsson, B. O. & Aziz, R. K. A gapless, unambiguous genome sequence of the enterohemorrhagic Escherichia coli O157:H7 strain EDL933. Genome Announc. 2, e00821-14 (2014).44.Sokal, R. R. & Rohlf, F. J. The comparison of dendrograms by objective methods. Taxon 11, 33–40, (1962).45.Pightling, A. W. et al. Interpreting whole-genome sequence analyses of foodborne bacteria for regulatory applications and outbreak investigations. Front Microbiol. 9, 1482 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic acids Res. 43, D261–269 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Batisson, I. et al. Characterization of the novel factor paa involved in the early steps of the adhesion mechanism of attaching and effacing Escherichia coli. Infect. Immun. 71, 4516–4525 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Tatsuno, I. et al. toxB gene on pO157 of enterohemorrhagic Escherichia coli O157:H7 is required for full epithelial cell adherence phenotype. Infect. Immun. 69, 6660–6669 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Wells, T. J. et al. EhaA is a novel autotransporter protein of enterohemorrhagic Escherichia coli O157:H7 that contributes to adhesion and biofilm formation. Environ. Microbiol. 10, 589–604 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Paton, A. W., Srimanote, P., Woodrow, M. C. & Paton, J. C. Characterization of Saa, a novel autoagglutinating adhesin produced by locus of enterocyte effacement-negative Shiga-toxigenic Escherichia coli strains that are virulent for humans. Infect. Immun. 69, 6999–7009 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Galli, L., Torres, A. G. & Rivas, M. Identification of the long polar fimbriae gene variants in the locus of enterocyte effacement-negative Shiga toxin-producing Escherichia coli strains isolated from humans and cattle in Argentina. FEMS Microbiol Lett. 308, 123–129 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Tarr, P. I. et al. Iha: a novel Escherichia coli O157:H7 adherence-conferring molecule encoded on a recently acquired chromosomal island of conserved structure. Infect. Immun. 68, 1400–1407 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Stanley, P., Koronakis, V. & Hughes, C. Acylation of Escherichia coli hemolysin: a unique protein lipidation mechanism underlying toxin function. Microbiol Mol. Biol. Rev. 62, 309–333 (1998).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Veilleux, S. & Dubreuil, J. D. Presence of Escherichia coli carrying the EAST1 toxin gene in farm animals. Vet. Res 37, 3–13 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Savarino, S. J. et al. Enteroaggregative Escherichia coli heat-stable enterotoxin 1 represents another subfamily of E. coli heat-stable toxin. Proc. Natl Acad. Sci. USA 90, 3093–3097 (1993).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Paton, A. W., Srimanote, P., Talbot, U. M., Wang, H. & Paton, J. C. A new family of potent AB(5) cytotoxins produced by Shiga toxigenic Escherichia coli. J. Exp. Med 200, 35–46 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.Thomas, C. M. & Summers, D. Encyclopedia of life sciences. (John Wiley & Sons, Ltd, 2008).58.Carattoli, A. et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob. Agents Chemother. 58, 3895–3903 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    59.Lim, J. Y., Yoon, J. & Hovde, C. J. A brief overview of Escherichia coli O157:H7 and its plasmid O157. J. Microbiol Biotechnol. 20, 5–14 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Kim, J. Y. et al. Isolation and identification of Escherichia coli O157:H7 using different detection methods and molecular determination by multiplex PCR and RAPD. J. Vet. Sci. 6, 7–19 (2005).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Jaros, P. et al. Geographic divergence of bovine and human Shiga toxin–producing Escherichia coli O157: H7 genotypes. NZ 20, 1980 (2014).CAS 

    Google Scholar 
    62.Mellor, G. E. et al. Geographically distinct Escherichia coli O157 isolates differ by lineage, Shiga toxin genotype, and total shiga toxin production. J. Clin. Micro. 53, 579–586 (2015).CAS 
    Article 

    Google Scholar 
    63.Pianciola, L. & Rivas, M. Genotypic features of clinical and bovine Escherichia coli O157 strains isolated in countries with different associated-disease incidences. Microorganisms 6, 36 (2018).64.Strachan, N. J. et al. Whole genome sequencing demonstrates that geographic variation of Escherichia coli O157 genotypes dominates host association. Sci. Rep. 5, 14145 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Touchon, M. et al. Organised genome dynamics in the Escherichia coli species results in highly diverse adaptive paths. PLoS Genet. 5, e1000344 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    66.Wochtl, B. et al. Comparison of clinical and immunological findings in gnotobiotic piglets infected with Escherichia coli O104:H4 outbreak strain and EHEC O157:H7. Gut Pathog. 9, 30 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    67.Booher, S. L., Cornick, N. A. & Moon, H. W. Persistence of Escherichia coli O157:H7 in experimentally infected swine. Vet. Microbiol. 89, 69–81 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Moxley, R. A. Edema disease. Vet. Clin. North Am. Food Anim. Pr. 16, 175–185 (2000).CAS 
    Article 

    Google Scholar 
    69.Melton-Celsa, A. R. Shiga toxin (Stx) classification, structure, and function. Microbiol Spectr. 2, EHEC-0024-2013 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    70.Fuller, C. A., Pellino, C. A., Flagler, M. J., Strasser, J. E. & Weiss, A. A. Shiga toxin subtypes display dramatic differences in potency. Infect. Immun. 79, 1329–1337 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Tesh, V. L. et al. Comparison of the relative toxicities of Shiga-like toxins type I and type II for mice. Infect. Immun. 61, 3392–3402 (1993).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Tarr, G. A. M. et al. Contribution and interaction of Shiga toxin genes to Escherichia coli O157:H7 virulence. Toxins (Basel) 11, 607 (2019).CAS 
    Article 

    Google Scholar 
    73.Chui, L. et al. Molecular profiling of Escherichia coli O157:H7 and non-O157 strains isolated from humans and cattle in Alberta, Canada. J. Clin. Microbiol. 53, 986–990 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    74.Goma, M. K. E., Indraswari, A., Haryanto, A. & Widiasih, D. A. Detection of Escherichia coli O157:H7 and Shiga toxin 2a gene in pork, pig feces, and clean water at Jagalan slaughterhouse in Surakarta, Central Java Province, Indonesia. Vet. World 12, 1584–1590 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    75.Baranzoni, G. M. et al. Characterization of Shiga toxin subtypes and virulence genes in porcine Shiga toxin-producing Escherichia coli. Front Microbiol. 7, 574 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    76.Mohlatlole, R. P. et al. Virulence profiles of enterotoxigenic, Shiga toxin and enteroaggregative Escherichia coli in South African pigs. Trop. Anim. Health Prod. 45, 1399–1405 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Blanco, M. et al. Serotypes, virulence genes, and intimin types of Shiga toxin (verotoxin)-producing Escherichia coli isolates from cattle in Spain and identification of a new intimin variant gene (eae-xi). J. Clin. Microbiol. 42, 645–651 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Kobayashi, N. et al. Virulence gene profiles and population genetic analysis for exploration of pathogenic serogroups of Shiga toxin-producing Escherichia coli. J. Clin. Microbiol. 51, 4022–4028 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    79.Meng, J., Zhao, S. & Doyle, M. P. Virulence genes of Shiga toxin-producing Escherichia coli isolated from food, animals and humans. Int J. Food Microbiol 45, 229–235 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Mora, A. et al. Phage types, virulence genes and PFGE profiles of Shiga toxin-producing Escherichia coli O157:H7 isolated from raw beef, soft cheese and vegetables in Lima (Peru). Int J. Food Microbiol. 114, 204–210 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Sallam, K. I., Mohammed, M. A., Ahdy, A. M. & Tamura, T. Prevalence, genetic characterization and virulence genes of sorbitol-fermenting Escherichia coli O157:H- and E. coli O157:H7 isolated from retail beef. Int J. Food Microbiol 165, 295–301 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    82.Solomakos, N. et al. Occurrence, virulence genes and antibiotic resistance of Escherichia coli O157 isolated from raw bovine, caprine and ovine milk in Greece. Food Microbiol. 26, 865–871 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Tóth, I. et al. Virulence genes and molecular typing of different groups of Escherichia coli O157 strains in cattle. Appl. Environ. Microbiol. 75, 6282 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    84.Rao, S. et al. Antimicrobial drug use and antimicrobial resistance in enteric bacteria among cattle from Alberta feedlots. Foodborne Pathog. Dis. 7, 449–457 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    85.Benedict, K. M. et al. Antimicrobial resistance in Escherichia coli recovered from feedlot fattle and associations with antimicrobial use. PLoS ONE 10, e0143995 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    86.Stanford, K., Johnson, R. P., Alexander, T. W., McAllister, T. A. & Reuter, T. Influence of season and feedlot location on prevalence and virulence factors of seven serogroups of Escherichia coli in feces of western-Canadian slaughter cattle. PLoS ONE 11, e0159866 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    87.Mercer, R. G. et al. Genetic determinants of heat resistance in Escherichia coli. Front Microbiol. 6, 932 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    88.Stanford, K. et al. Monitoring Escherichia coli O157:H7 in inoculated and naturally colonized feedlot cattle and their environment. J. Food Prot. 68, 26–33 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    89.Munns, K. D. et al. Comparative genomic analysis of Escherichia coli O157:H7 isolated from super-shedder and low-shedder cattle. PLoS ONE 11, e0151673 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    90.Bach, S. J. et al. Electrolyzed oxidizing anode water as a sanitizer for use in abattoirs. J. Food Prot. 69, 1616–1622 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Stanford, K., Gibb, D. & McAllister, T. A. Evaluation of a shelf-stable direct-fed microbial for control of Escherichia coli O157 in commercial feedlot cattle. Can. J. Anim. Sci. 93, 535–542 (2013).Article 

    Google Scholar 
    92.Stanford, K., Hannon, S., Booker, C. W. & Jim, G. K. Variable efficacy of a vaccine and direct-fed microbial for controlling Escherichia coli O157:H7 in feces and on hides of feedlot cattle. Foodborne Pathog. Dis. 11, 379–387 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Berenger, B. M. et al. The utility of multiple molecular methods including whole genome sequencing as tools to differentiate Escherichia coli O157:H7 outbreaks. Euro Surveill. 20, 30073 (2015).94.Stephens, T. P., McAllister, T. A. & Stanford, K. Perineal swabs reveal effect of super shedders on the transmission of Escherichia coli O157:H7 in commercial feedlots. J. Anim. Sci. 87, 4151–4160 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    95.Zhang, P. et al. Genome sequences of 104 Escherichia coli O157:H7 isolates from pigs, cattle, and pork production environments in Alberta, Canada. Microbiol. Resour. Announc. 10, (2021).96.Riordan, J. T., Viswanath, S. B., Manning, S. D. & Whittam, T. S. Genetic differentiation of Escherichia coli O157:H7 clades associated with human disease by real-time PCR. J. Clin. Microbiol. 46, 2070–2073 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    97.Croucher, N. J. et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 43, e15–e15 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    98.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinforma. (Oxf., Engl.) 30, 1312–1313 (2014).CAS 
    Article 

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

    Google Scholar 
    100.Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinform. 69, e96 (2020).Article 

    Google Scholar 
    101.Silva, M. et al. chewBBACA: A complete suite for gene-by-gene schema creation and strain identification. Micro. Genom. 4, e000166 (2018).
    Google Scholar 
    102.Zhou, Z. et al. GrapeTree: visualization of core genomic relationships among 100,000 bacterial pathogens. Genome Res. 28, 1395–1404 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    103.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics (Oxf., Engl.) 30, 2068–2069 (2014).CAS 
    Article 

    Google Scholar 
    104.Page, A. J. et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics (Oxf., Engl.) 31, 3691–3693 (2015).CAS 
    Article 

    Google Scholar 
    105.Zhang, P., Gänzle, M. & Yang, X. Complementary antibacterial effects of bacteriocins and organic acids as revealed by comparative analysis of Carnobacterium spp. from meat. Appl. Environ. Microbiol. 85, e01227-19 (2019).106.Zheng, J., Zhao, X., Lin, X. B. & Ganzle, M. Comparative genomics Lactobacillus reuteri from sourdough reveals adaptation of an intestinal symbiont to food fermentations. Sci. Rep. 5, 18234 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    107.Schliep, K., Potts, A. J., Morrison, D. A. & Grimm, G. W. Intertwining phylogenetic trees and networks. Methods Ecol. Evol. 8, 1212–1220 (2017).Article 

    Google Scholar 
    108.Joensen, K. G. et al. Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic Escherichia coli. J. Clin. Microbiol. 52, 1501–1510 (2014).109.Bortolaia, V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 75, 3491–3500 (2020). More