Philippa Kaur

Genome’s collection and phylogenetic analysisThe study examined the genomes of 139 isolates, 61 of environmental samples (ENV) and 78 clinical (CLI) (Supplementary Table 1, Supplementary Fig. 1), with origin in 21 countries: USA (23/139, 17%), UK, Portugal and Spain (each 15/139, 33%), China (14/139, 10%), Germany (13/139, 9%), Thailand (11/139, 8%) and other countries (each  More

Chai et al. Stable water isotope and surface heat flux simulation using ISOLSM: Evaluation against in-situ measurements. J. Hydrol. 523, 67–78 (2015).Article 

Google Scholar 
Brooks et al. Stable isotope estimates of evaporation: Inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnol. Oceanogr. 59, 2150–2165 (2014).Article 

Google Scholar 
Good, S. P., Noone, D. & Bowen, G. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349, 175–177 (2015).CAS 
Article 

Google Scholar 
Gupta, A., Gerber, E. P. & Lauritzen, P. H. Numerical impacts on tracer transport: A proposed intercomparison test of atmospheric general circulation models. Quart. J. Roy. Meteor. Soc. 146, 3937–3964 (2020).Article 

Google Scholar 
Kanner, L. C., Buenning, N. H., Stott, L. D., Timmermann, A. & Noone, D. The role of soil processes in d18O. Global Biogeochem. Cycles 28, 239–252 (2014).CAS 
Article 

Google Scholar 
Remondi, F., Kircher, J. W., Burlando, P. & Fatichi, S. Water flux tracking with a distributed hydrologic model to quantify controls on the spatio-temporal variability of transit time distributions. Water Resour. Res. 54, 3081–3099 (2018).Article 

Google Scholar 
Abbott, B. W. et al. Using multi-tracer inference to move beyond single catchment ecohydrology. Earth-Sci. Rev. 160, 19–42 (2016).Article 

Google Scholar 
Krause, P., Boyle, D. P. & Bäse, F. Comparison of different efficiency criteria for hydrological model assessment. Adv. in Geosci. 5, 89–97 (2005).Article 

Google Scholar 
Bowen, G. J. & Good, S. P. Incorporating water isotopes in hydrological and water resource investigations. Wiley Interdiscip. Rev.: Water 2, 107–119 (2015).Article 

Google Scholar 
McGuire, K. J. & McDonnell, J. J. A review and evaluation of catchment transit time modeling. J. Hydrol. 330, 543–563 (2006).Article 

Google Scholar 
Sprenger, M. et al. The demographics of water: A review of water ages in the critical zone. Rev. Geophys. 57, 800–834 (2019).Article 

Google Scholar 
Turnadge, C. & Smerdon, B. D. A review of methods for modelling environmental tracers in groundwater: Advantages of tracer concentration simulation. J. Hydrol. 519, 3674–3689 (2014).CAS 
Article 

Google Scholar 
Fiorella, R. et al. Calibration Strategies for Detecting Macroscale Patterns in NEON Atmospheric Carbon Isotope Observations. J. Geophys. Res. Biogeosci. 126 (2021).Xiao, W., Wei, Z. & Wen, X. Evapotranspiration partitioning at the ecosystem scale using the stable isotope method—A review. Agric For Meteorol. 263, 346–361 (2018).Article 

Google Scholar 
Wu, Y. et al. Stable isotope measurements show increases in corn water use efficiency under deficit irrigation. Sci Rep 8, 14113 (2018).Article 

Google Scholar 
Al-Oqaili, F., Good, S. P., Frost, K. & Higgins, C. W. Differences in soil evaporation between row and interrow positions in furrowed agricultural fields. Vadose Zone J. 19, e20086 (2020).CAS 
Article 

Google Scholar 
Bowen, G. J., Cai, Z., Fiorella, R. P. & Putman, A. L. Isotopes in the water cycle: Regional- to global-scale patterns and applications. Annu. Rev. Earth Planet. Sci. 47, 453–479 (2019).CAS 
Article 

Google Scholar 
Lu, X. et al. Partitioning of evapotranspiration using a stable isotope technique in an arid and high temperature agricultural production system. Agric. Water Manag. 179, 103–109 (2017).Article 

Google Scholar 
Wieser, G. et al. Stable water use efficiency under climate change of three sympatric conifer species at the alpine treeline. Front. Plant Sci. 7, 799 (2016).Article 

Google Scholar 
Pataki, D. E. et al. The application and interpretation of Keeling plots in terrestrial carbon cycle research. Global Biogeochem. Cycles, 17 (2003).Miller, J. B., & Tans, P. P., Calculating isotopic fractionation from atmospheric measurements at various scales. Tellus, 55 (2003).Finkenbiner, C. E., Good, S. P., Allen, S. T., Fiorella, R. P. & Bowen, G. J. A statistical method for generating temporally downscaled geochemical tracers in precipitation. J. Hydrometeorol. 22 (2021).NEON (National Ecological Observatory Network). Precipitation (DP1.00006.001), RELEASE-2022. https://doi.org/10.48443/6wkc-1p05. Dataset accessed from https://data.neonscience.org on May 12, 2022.Lunch, C. K. & Laney, C. M. NEON (National Ecological Observatory Network). neonUtilities: Utilities for working with NEON data. R package version 1.3.4. https://github.com/NEONScience/NEON-utilities (2020).Lee, R. and S. Weintraub. NEON User Guide to Stable Isotopes in Precipitation (NEON.DPI.00038) Version B. NEON (National Ecological Observatory Network). (2021).IAEA: Global network of isotopes in precipitation. https://www.iaea.org/services/networks/gnip 2020.Allen, S. T., Kirchner, J. W. & Goldsmith, G. R. Predicting spatial patterns in precipitation isotope (δ2H and δ18O) seasonality using sinusoidal isoscapes. Geophys. Res. 45, 4859–4868 (2018).
Google Scholar 
Craig, H. Isotopic variations in meteoric waters. Science 133, 1702–1703 (1961).CAS 
Article 

Google Scholar 
Dansgaard, W. Stable isotopes in precipitation. Tellus 16, 436–468 (1964).Article 

Google Scholar 
Sklar, A. Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Stat. Univ. Paris. 8, 229–231 (1959).MATH 

Google Scholar 
NEON (National Ecological Observatory Network). Bundled data products – eddy covariance (DP4.00200.001). https://data.neonscience.org (2021).Good, S. P., Soderberg, K., Wang, L., & Caylor, K. K. Uncertainties in the assessment of the isotopic composition of surface fluxes: A direct comparison of techniques using laser‐based water vapor isotope analyzers. J. Geophys. Res. Atmos. 177 (2012).Wutzler, T. et al. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosci. 15, 5015–5030 (2018).CAS 
Article 

Google Scholar 
Zobitz, J. M., Keener, J. P., Schnyder, H. & Bowling, D. R. Sensitivity analysis and quantification of uncertainty for isotopic mixing relationships in carbon cycle research. Agric For Meteorol. 136, 56–75 (2006).Article 

Google Scholar 
Wehr, R. & Saleska, S. R. An improved isotopic method for partitioning net ecosystem-atmosphere CO2 exchange. Agric For Meteorol. 214, 515–531 (2015).Article 

Google Scholar 
Bailey, A., Noone, D., Berkelhammer, M., Steen-Larsen, H. C. & Sato, P. The stability and calibration of water vapor isotope ratio measurements during long-term deployments. Atmos. Meas. Tech. 8, 4521–4538 (2015).CAS 
Article 

Google Scholar 
Rambo, J., Lai, C., Farlin, J., Schroeder, M. & Bible, K. Vapor isotope ratios using off-axis cavity-enhanced absorption spectroscopy. J Atmos. Ocean Technol. 28, 1448–1457 (2011).Article 

Google Scholar 
Finkenbiner, C. The National Ecological Observation Network Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) Dataset, HydroShare, https://doi.org/10.4211/hs.e74edc35d45441579d51286ea01b519f (2022). More

Haque, M. N. Dietary manipulation: A sustainable way to mitigate methane emissions from ruminants. J. Anim. Sci. Technol. 60, 1–10. https://doi.org/10.1186/s40781-018-0175-7(2018) (2018).Article 

Google Scholar 
IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press (in press).Lauder, A. R. et al. Offsetting methane emissions—An alternative to emission equivalence metrics. Int. J. Greenh. 12, 419–429. https://doi.org/10.1016/j.ijggc.2012.11.028 (2013).CAS 
Article 

Google Scholar 
Hill, J., McSweeney, C., Wright, A. G., Bishop-Hurley, G. & Kalantar-Zadeh, K. Measuring methane production from ruminants. Trends Biotechnol. 34, 26–35. https://doi.org/10.1016/j.tibtech.2015.10.004 (2016).CAS 
Article 
PubMed 

Google Scholar 
Van Zanten, H. H. E. et al. Defining a land boundary for sustainable livestock consumption. Glob Change Biol. 24, 4185–4194. https://doi.org/10.1111/gcb.14321 (2018).ADS 
Article 

Google Scholar 
Naumann, H. D., Tedeschi, L. O., Zeller, W. E. & Huntley, N. F. The role of condensed tannins in ruminant animal production: Advances, limitations and future directions. Rev. Bras. de Zootec. 46, 929–949. https://doi.org/10.1590/S1806-92902017001200009 (2017).Article 

Google Scholar 
Mueller-Harvey, I. Unravelling the conundrum of tannins in animal nutrition and health. J. Sci. Food Agric. 86, 2010–2037. https://doi.org/10.1002/jsfa.2577 (2006).CAS 
Article 

Google Scholar 
Burggraaf, V. T. et al. Morphology and agronomic performance of white clover with increased flowering and condensed tannin concentration. N. Z. J. Agric. Res. 49, 147–155. https://doi.org/10.1080/00288233.2006.9513704 (2006).CAS 
Article 

Google Scholar 
Einarsson, R. et al. Crop production and nitrogen use in European cropland and grassland 1961–2019. Sci. Data 8, 288. https://doi.org/10.1038/s41597-021-01061-z (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Salminen, J.-P. & Karonen, M. Chemical ecology of tannins and other phenolics: we need a change in approach. Funct. Ecol. 25, 325–338. https://doi.org/10.1111/j.1365-2435.2010.01826.x (2011).Article 

Google Scholar 
Zeller, W. E. Activity, purification, and analysis of condensed tannins: current state of affairs and future endeavors. Crop Sci. 59, 886–904. https://doi.org/10.2135/cropsci2018.05.0323 (2019).CAS 
Article 

Google Scholar 
Barbehenn, R. V. & Peter Constabel, C. Tannins in plant–herbivore interactions. Phytochemistry 72, 1551–1565. https://doi.org/10.1016/j.phytochem.2011.01.040 (2011).CAS 
Article 
PubMed 

Google Scholar 
Chung, Y. H. et al. Enteric methane emission, diet digestibility, and nitrogen excretion from beef heifers fed sainfoin or alfalfa1. J. Anim. Sci. 91, 4861–4874. https://doi.org/10.2527/jas.2013-6498 (2013).CAS 
Article 
PubMed 

Google Scholar 
Christensen, R. G. et al. Effects of feeding birdsfoot trefoil hay on neutral detergent fiber digestion, nitrogen utilization efficiency, and lactational performance by dairy cows1. J. Dairy Sci. 98, 7982–7992. https://doi.org/10.3168/jds.2015-9348 (2015).CAS 
Article 
PubMed 

Google Scholar 
Jonker, A. & Yu, P. The occurrence, biosynthesis, and molecular structure of proanthocyanidins and their effects on legume forage protein precipitation, digestion and absorption in the ruminant digestive tract. Int. J. Mol. Sci. 18, 1105. https://doi.org/10.3390/ijms18051105 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
Barry, T. N. & McNabb, W. C. The implications of condensed tannins on the nutritive value of temperate forages fed to ruminants. Br. J. Nutr. 81, 263–272. https://doi.org/10.1017/S0007114599000501 (1999).CAS 
Article 
PubMed 

Google Scholar 
Verma, S., Taube, F. & Malisch, C. S. Examining the variables leading to apparent incongruity between antimethanogenic potential of tannins and their observed effects in ruminants—A review. Sustainability 13, 2743. https://doi.org/10.3390/su13052743 (2021).CAS 
Article 

Google Scholar 
Malisch, C. S. et al. Large variability of proanthocyanidin content and composition in Sainfoin (Onobrychis viciifolia). J. Agric. Food Chem. 63, 10234–10242. https://doi.org/10.1021/acs.jafc.5b04946 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Verma, S., Salminen, J.-P., Taube, F. & Malisch, C. S. Large inter- and intraspecies variability of polyphenols and proanthocyanidins in eight temperate forage species indicates potential for their exploitation as nutraceuticals. J. Agric. Food Chem. 69, 12445–12455. https://doi.org/10.1021/acs.jafc.1c03898 (2021).CAS 
Article 
PubMed 

Google Scholar 
Lorenz, H., Reinsch, T., Kluß, C., Taube, F. & Loges, R. Does the admixture of forage herbs affect the yield performance, yield stability and forage quality of a grass clover ley?. Sustainability 12, 5842. https://doi.org/10.3390/su12145842 (2020).Article 

Google Scholar 
Hofer, D. et al. Yield of temperate forage grassland species is either largely resistant or resilient to experimental summer drought. J. Appl. Ecol. 53, 1023–1034. https://doi.org/10.1111/1365-2664.12694 (2016).Article 

Google Scholar 
Mueller-Harvey, I. et al. Benefits of condensed tannins in forage legumes fed to ruminants : Importance of structure, concentration and diet compsition. Crop Sci. 59, 861–885. https://doi.org/10.2135/cropsci2017.06.0369 (2017).CAS 
Article 

Google Scholar 
Loza, C. et al. Assessing the potential of diverse forage mixtures to reduce enteric methane emissions in vitro. Animals 11, 1126. https://doi.org/10.3390/ani11041126 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Min, B. R. et al. Dietary mitigation of enteric methane emissions from ruminants: A review of plant tannin mitigation options. Anim. Nutr. 6, 231–236. https://doi.org/10.1016/j.aninu.2020.05.002 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
van Gastelen, S., Dijkstra, J. & Bannink, A. Are dietary strategies to mitigate enteric methane emission equally effective across dairy cattle, beef cattle, and sheep?. J. Dairy Sci. 102, 6109–6130. https://doi.org/10.3168/jds.2018-15785 (2019).CAS 
Article 
PubMed 

Google Scholar 
Hatew, B. et al. Relationship between in vitro and in vivo methane production measured simultaneously with different dietary starch sources and starch levels in dairy cattle. Anim. Feed Sci. Technol. 202, 20–31. https://doi.org/10.1016/j.anifeedsci.2015.01.012 (2015).CAS 
Article 

Google Scholar 
Storm, I. M. L. D., Hellwing, A. L. F., Nielsen, N. I. & Madsen, J. Methods for measuring and estimating methane emission from ruminants. Animals 2, 160–183. https://doi.org/10.3390/ani2020160 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Dewhurst, R. J., Delaby, L., Moloney, A., Boland, T. & Lewis, E. Nutritive value of forage legumes used for grazing and silage. Irish J. Agric. Food Res. 48, 167–187 (2009).CAS 

Google Scholar 
Hakl, J., Fuksa, P., Konečná, J. & Šantrůček, J. Differences in the crude protein fractions of lucerne leaves and stems under different stand structures. Grass Forage Sci. 71, 413–423. https://doi.org/10.1111/gfs.12192 (2016).CAS 
Article 

Google Scholar 
Jayanegara, A., Makkar, H. & Becker, K. The use of principal component analysis in identifying and integrating variables related to forage quality and methane production. J. Indones. Trop. Anim. 34, 241–247. https://doi.org/10.14710/jitaa.34.4.241-247 (2009).Article 

Google Scholar 
Maccarana, L. et al. Methodological factors affecting gas and methane production during in vitro rumen fermentation evaluated by meta-analysis approach. J. Anim. Sci. Biotechnol. 7, 35–35. https://doi.org/10.1186/s40104-016-0094-8 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Baruah, L., Malik, P. K., Kolte, A. P., Dhali, A. & Bhatta, R. Methane mitigation potential of phyto-sources from Northeast India and their effect on rumen fermentation characteristics and protozoa in vitro. Vet. World 11, 809–818. https://doi.org/10.14202/vetworld.2018.809-818 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hassanat, F. & Benchaar, C. Assessment of the effect of condensed (acacia and quebracho) and hydrolysable (chestnut and valonea) tannins on rumen fermentation and methane production in vitro. J. Sci. Food Agric. 93, 332–339. https://doi.org/10.1002/jsfa.5763 (2013).CAS 
Article 
PubMed 

Google Scholar 
Naumann, H. et al. Relationships between structures of condensed tannins from texas legumes and methane production during in vitro rumen digestion. Molecules 23, 2123. https://doi.org/10.3390/molecules23092123 (2018).CAS 
Article 
PubMed Central 

Google Scholar 
Jayanegara, A., Makkar, H. P. S. & Becker, K. Addition of purified tannin sources and polyethylene glycol treatment on methane emission and rumen fermentation in vitro. Media Peternakan 38, 57–63. https://doi.org/10.5398/medpet.2015.38.1.57 (2015).Article 

Google Scholar 
Jayanegara, A., Goel, G., Makkar, H. P. S. & Becker, K. Divergence between purified hydrolysable and condensed tannin effects on methane emission, rumen fermentation and microbial population in vitro. Anim. Feed Sci. Technol. 209, 60–68. https://doi.org/10.1016/j.anifeedsci.2015.08.002 (2015).CAS 
Article 

Google Scholar 
Hatew, B. et al. Diversity of condensed tannin structures affects rumen in vitro methane production in sainfoin (Onobrychis viciifolia) accessions. Grass Forage Sci. 70, 474–490. https://doi.org/10.1111/gfs.12125 (2015).CAS 
Article 

Google Scholar 
Huyen, N. T. et al. Structural features of condensed tannins affect in vitro ruminal methane production and fermentation characteristics. J. Agric. Sci. 154, 1474–1487. https://doi.org/10.1017/S0021859616000393 (2016).CAS 
Article 

Google Scholar 
Salami, S. A. et al. Characterisation of the ruminal fermentation and microbiome in lambs supplemented with hydrolysable and condensed tannins. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiy061 (2018).Article 
PubMed 

Google Scholar 
Salminen, J. P., Karonen, M. & Sinkkonen, J. Chemical ecology of tannins: Recent developments in tannin chemistry reveal new structures and structure-activity patterns. Chem.-Eur. J. 17, 2806–2816. https://doi.org/10.1002/chem.201002662 (2011).CAS 
Article 
PubMed 

Google Scholar 
Bezabih, M., Pellikaan, W. F., Tolera, A., Khan, N. A. & Hendriks, W. Chemical composition and in vitro total gas and methane production of forage species from the Mid Rift Valley grasslands of Ethiopia. Grass Forage Sci. 69, 635–643. https://doi.org/10.1111/gfs.12091 (2013).CAS 
Article 

Google Scholar 
Navarrete, S., Kemp, P. D., Pain, S. J. & Back, P. J. Bioactive compounds, aucubin and acteoside, in plantain (Plantago lanceolata L.) and their effect on in vitro rumen fermentation. Anim. Feed Sci. Technol. 222, 158–167. https://doi.org/10.1016/j.anifeedsci.2016.10.008 (2016).CAS 
Article 

Google Scholar 
Basha, N. A., Scogings, P. F. & Nsahlai, I. V. Effects of season, browse species and polyethylene glycol addition on gas production kinetics of forages in the subhumid subtropical savannah, South Africa. J. Sci. Food Agric. 93, 1338–1348. https://doi.org/10.1002/jsfa.5895 (2013).CAS 
Article 
PubMed 

Google Scholar 
O’Donovan, L. & Brooker, J. D. Effect of hydrolysable and condensed tannins on growth, morphology and metabolism of Streptococcus gallolyticus (S. caprinus) and Streptococcus bovis. Microbiology 147, 1025–1033. https://doi.org/10.1099/00221287-147-4-1025 (2001).CAS 
Article 
PubMed 

Google Scholar 
Bhatta, R. et al. Difference in the nature of tannins on in vitro ruminal methane and volatile fatty acid production and on methanogenic archaea and protozoal populations. J. Dairy Sci. 92, 5512–5522. https://doi.org/10.3168/jds.2008-1441 (2009).CAS 
Article 
PubMed 

Google Scholar 
Naumann, H. D. et al. Effect of molecular weight and concentration of legume condensed tannins on in vitro larval migration inhibition of Haemonchus contortus. Vet. Parasitol. 199, 93–98. https://doi.org/10.1016/j.vetpar.2013.09.025 (2014).CAS 
Article 
PubMed 

Google Scholar 
Jayanegara, A., Goel, G., Makkar, H.P.S., & Becker, K. Reduction in
methane emissions from ruminants by plant secondary metabolites: Effects of polyphenols and saponins. Food and Agriculture Organization of the United Nations (FAO) Rome, Italy, 151–157. ISBN 978-92-5-106697-3 (2010).Hatew, B. et al. Impact of variation in structure of condensed tannins from sainfoin (Onobrychis viciifolia) on in vitro ruminal methane production and fermentation characteristics. J. Anim. Physiol. Anim. Nutr. 100, 348–360. https://doi.org/10.1111/jpn.12336 (2016).CAS 
Article 

Google Scholar 
Waghorn, G. C., Douglas, G. B., Niezen, J. H., McNabb, W. C. & Foote, A. G. Forages with condensed tannins-their management and nutritive value for ruminants. Proc. N. Z. Grassl. Assoc., 60, 89−98 (1998).Woodward, S. L., Waghorn, G. C. & Lassey, K. Early indications that feeding Lotus will reduce methane emissions from ruminants. Proc. N. Z. Soc. Anim. Prod. 61, 23–26 (2001).
Google Scholar 
Molle, G. et al. Responses to condensed tannins of flowering sulla (Hedysarum coronarium L.) grazed by dairy sheep: Part 1: Effects on feeding behaviour, intake, diet digestibility and performance. Livest. Sci. 123, 138–146. https://doi.org/10.1016/j.livsci.2008.11.018 (2009).Article 

Google Scholar 
Orlandi, T., Kozloski, G. V., Alves, T. P., Mesquita, F. R. & Ávila, S. C. Digestibility, ruminal fermentation and duodenal flux of amino acids in steers fed grass forage plus concentrate containing increasing levels of Acacia mearnsii tannin extract. Anim. Feed Sci. Technol. 210, 37–45. https://doi.org/10.1016/j.anifeedsci.2015.09.012 (2015).CAS 
Article 

Google Scholar 
Patra, A. K. & Yu, Z. Effects of adaptation of in vitro rumen culture to garlic oil, nitrate, and saponin and their combinations on methanogenesis, fermentation, and abundances and diversity of microbial populations. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01434 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Niderkorn, V. et al. Effect of increasing the proportion of chicory in forage-based diets on intake and digestion by sheep. Animal 13, 718–726. https://doi.org/10.1017/S1751731118002185 (2019).CAS 
Article 
PubMed 

Google Scholar 
Lee, J., Hemmingson, N., Minneé, E. & Clark, C. Management strategies for chicory (Cichorium intybus) and plantain (Plantago lanceolata): Impact on dry matter yield, nutritive characteristics, and plant density. Crop Pasture Sci. 66, 168. https://doi.org/10.1071/CP14181 (2015).CAS 
Article 

Google Scholar 
Cong, W.-F., Jing, J., Rasmussen, J., Søegaard, K. & Eriksen, J. Forbs enhance productivity of unfertilised grass-clover leys and support low-carbon bioenergy. Sci. Rep. 7, 1422. https://doi.org/10.1038/s41598-017-01632-4 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sanderson, M. A., Labreveux, M., Hall, M. H. & Elwinger, G. F. Nutritive value of chicory and English plantain forage. Crop Sci. 43, 1797. https://doi.org/10.2135/cropsci2003.1797 (2003).CAS 
Article 

Google Scholar 
Van Soest, P. J., Robertson, J. B. & Lewis, B. A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74, 3583–3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2 (1991).Article 
PubMed 

Google Scholar 
Engström, M. T. et al. Rapid qualitative and quantitative analyses of proanthocyanidin oligomers and polymers by UPLC-MS/MS. J. Agric. Food Chem. 62, 3390–3399. https://doi.org/10.1021/jf500745y (2014).CAS 
Article 
PubMed 

Google Scholar 
Menke, K. & Steingass, H. Estimation of the energetic feed value obtained from chemical analysis and in vitro gas production using rumen fluid. Anim. Res. Dev. 28, 7–55 (1988).
Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Venables, B. & Ripley, B. Generalised linear models. In Modern Applied Statistics With S.(4th edition) 183–208 (Springer, 2013). More

Harbach, C. J. et al. Delayed senescence in soybean: Terminology, research update, and survey results from growers. Plant Health Progress 17, 76–83 (2016).Article 

Google Scholar 
Hobbs, H. A. et al. Green stem disorder of soybean. Plant Dis. 90, 513–518 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hill, C. B., Hartman, G. L., Esgar, R. & Hobbs, H. A. Field evaluation of green stem disorder in soybean cultivars. Crop Sci. 46, 879–885 (2006).Article 

Google Scholar 
Morita, K. et al. (2006) Effect of green stem on soiled bean index at harvest of soybean by combine harvester. Hokuriku Crop Sci. 41, 107–109 (2006) (in Japanese).
Google Scholar 
Ogiwara, H. Delayed leaf senescence. In: Agriculture, Forestry and Fisheries Research Council of Japan, ed. Soybean-technical development for improving national food self-sufficiency ratio. Annotated bibliography of Agriculture, Forestry, and Fisheries Research, vol. 27, 291–294 (2002). (in Japanese).Crafts-Brandner, S. J. & Egli, D. B. Sink removal and leaf senescence in soybean. Plant Physiol. 85, 662–666 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crafts-Brandner, S. J., Below, F. E., Harper, J. E. & Hageman, R. H. Effects of pod removal on metabolism and senescence of nodulating and nonnodulating soybean isolines. Plant Physiol. 75, 311–317 (1984).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Egli, D. B. & Bruening, W. P. Depodding causes green-stem syndrome in soybean. Crop Manag. 5(1), 1–9. https://doi.org/10.1094/CM-2006-0104-01-RS (2006).Article 

Google Scholar 
Htwe, N. M. P. S. et al. Leaf senescence of soybean at reproductive stage is associated with induction of autophagy-related genes, GmATG8c, GmATG8i and GmATG4. Plant Prod. Sci. 14, 141–147 (2011).CAS 
Article 

Google Scholar 
Leopold, A. C., Niedergang-Kamien, E. & Janick, J. Experimental modification of plant senescence. Plant Physiol. 34, 570–573 (1959).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mondal, M. H., Brun, W. A. & Brenner, M. L. Effects of sink removal on photosynthesis and senescence in leaves of soybean (Glycine max L.) plants. Plant Physiol. 61, 394–397 (1978).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Effect of pod removal on leaf senescence in soybean. Plant Physiol. 70, 1544–1548 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Effect of pod removal on leaf photosynthesis and soluble protein composition of field-grown soybeans. Plant Physiol. 73, 121–124 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Purification and characterization of a soybean leaf storage glycoprotein. Plant Physiol. 73, 125–129 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Staswick, P. E. Developmental regulation and the influence of plant sinks on vegetative storage protein gene expression in soybean leaves. Plant Physiol. 89, 309–315 (1989).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sato, J., Shiraiwa, T., Sakashita, M., Tsujimoto, Y. & Yoshida, R. The occurrence of delayed stem senescence in relation to trans-zeatin riboside level in the xylem exudate in soybeans grown under excess-wet and drought soil conditions. Plant Prod. Sci. 10, 460–467 (2007).Article 

Google Scholar 
Takehara, T. et al. Occurrence of delayed leaf senescence of soybean caused by Rhizoctonia aerial blight in Japan. Jpn. Agric. Res. Q. 50, 201–208 (2016).Article 

Google Scholar 
Boethel, D. J. et al. Delayed maturity associated with southern green stink bug (Heteroptera: Pentatomidae) injury at various soybean phenological stages. J. Econ. Entomol. 93, 707–712 (2000).CAS 
PubMed 
Article 

Google Scholar 
Islam, M. M. et al. Nitrogen manipulation affects leaf senescence during late seed filling in soybean. Acta Physiol. Plant. 39, 42 (2017).Article 
CAS 

Google Scholar 
Yamazaki, R., Katsube-Tanaka, T. & Shiraiwa, T. Effect of thinning and shade removal on green stem disorder in soybean. Plant Prod. Sci. 21, 83–92 (2018).CAS 
Article 

Google Scholar 
Yamazaki, R., Katsube-Tanaka, T., Kawasaki, Y., Katayama, K. & Shiraiwa, T. Effect of thinning on cultivar differences of green stem disorder in soybean. Plant Prod. Sci. 22, 311–318 (2019).CAS 
Article 

Google Scholar 
Board, J. E. & Tan, Q. Assimilatory capacity effects on soybean yield components and pod number. Crop Sci. 35, 846–851 (1995).Article 

Google Scholar 
Egli, D. B. Soybean reproductive sink size and short-term reductions in photosynthesis during flowering and pod set. Crop Sci. 50, 1971–1977 (2010).Article 

Google Scholar 
Wells, R., Schulze, L. L., Ashley, D. A., Boerma, H. R. & Brown, R. H. Cultivar differences in canopy apparent photosynthesis and their relationship to seed yield in soybean. Crop Sci. 22, 886–890 (1982).Article 

Google Scholar 
Islam, M. M. et al. Nitrogen redistribution and its relationship with the expression of GmATG8c during seed filling in soybean. J. Plant Physiol. 192, 71–74 (2016).CAS 
PubMed 
Article 

Google Scholar 
Zhao, X., Zheng, S. H. & Arima, S. Influence of nitrogen enrichment during reproductive growth stage on leaf nitrogen accumulation and seed yield in soybean. Plant Prod. Sci. 17, 209–217 (2014).CAS 
Article 

Google Scholar 
Brown, A. W. & Hudson, K. A. Transcriptional profiling of mechanically and genetically sink-limited soybeans. Plant Cell Environ. 40, 2307–2318 (2017).CAS 
PubMed 
Article 

Google Scholar 
Tranbarger, T. J., Franceschi, V. R., Hildebrand, D. F. & Grimes, H. D. The soybean 94-kilodalton vegetative storage protein is a lipoxygenase that is localized in paraveinal mesophyll cell vacuoles. Plant Cell 3, 973–987 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
Melo, B. P. et al. Revisiting the soybean GmNAC superfamily. Front. Plant Sci. 9, 1864 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, H. J. et al. Gene regulatory cascade of senescence-associated NAC transcription factors activated by ETHYLENE-INSENSITIVE2-mediated leaf senescence signaling in Arabidopsis. J. Exp. Bot. 65, 4023–4036 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Tucker, M. L., Burke, A., Murphy, C. A., Thai, V. K. & Ehrenfried, M. L. Gene expression profiles for cell wall-modifying proteins associated with soybean cyst nematode infection, petiole abscission, root tips, flowers, apical buds, and leaves. J. Exp. Bot. 58, 3395–3406 (2007).CAS 
PubMed 
Article 

Google Scholar 
Turner, G. W. et al. Experimental sink removal induces stress responses, including shifts in amino acid and phenylpropanoid metabolism, in soybean leaves. Planta 235, 939–954 (2012).CAS 
PubMed 
Article 

Google Scholar 
Roach, T. & Krieger-Liszkay, A. The role of the PsbS protein in the protection of photosystems I and II against high light in Arabidopsis thaliana. Biochim. Biophys. Acta Bioenerg. 1817, 2158–2165 (2012).CAS 
Article 

Google Scholar 
Horton, P., Ruban, A. V. & Walters, R. G. Regulation of light harvesting in green plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 47, 655–684 (1996).CAS 
PubMed 
Article 

Google Scholar 
Hutin, C. et al. Early light-induced proteins protect Arabidopsis from photooxidative stress. Proc. Natl. Acad. Sci. U.S.A. 100, 4921–4926 (2003).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Wang, H. et al. Functional characterization of dihydroflavonol-4-reductase in anthocyanin biosynthesis of purple sweet potato underlies the direct evidence of anthocyanins function against abiotic stresses. PLoS ONE 8, e78484 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Saravitz, D. M. & Siedow, J. N. The differential expression of wound-inducible lipoxygenase genes in soybean leaves. Plant Physiol. 110, 287–299 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pimenta, M. R. et al. The stress-induced soybean NAC transcription factor GmNAC81 plays a positive role in developmentally programmed leaf senescence. Plant Cell Physiol. 57, 1098–1114 (2016).CAS 
PubMed 
Article 

Google Scholar 
Fujimoto, M. et al. Transcriptional switch for programmed cell death in pith parenchyma of sorghum stems. Proc. Natl. Acad. Sci. U.S.A. 115, 8783–8792 (2018).Article 
CAS 

Google Scholar 
Egli, D. B. Variation in leaf starch and sink limitations during seed filling in soybean. Crop Sci. 39, 1361–1368 (1999).CAS 
Article 

Google Scholar 
Board, J. E. & Harville, B. G. Late-planted soybean yield response to reproductive source/sink stress. Crop Sci. 38, 763–771 (1998).Article 

Google Scholar 
Fatichin, Zheng, S. H., Narasaki, K. & Arima, S. Genotypic adaptation of soybean to late sowing in southwestern Japan. Plant Prod. Sci. 16, 123–130 (2013).CAS 
Article 

Google Scholar 
Wakasugi, K. & Fujimori, S. Subsurface Water Level Control System “FOEAS” that promotes the full use of paddy fields. J. Jpn. Soc. Irrig. Drain. Rural Eng. 77, 705–708 (2009) (in Japanese).
Google Scholar 
Fehr, W. R. & Caviness, C. E. Stages of soybean development. Spec. Rep. 80. Iowa Agric. Home Econ. Exp. Stn. Iowa State Univ., Ames. (1977).Furuya, T. & Umezaki, T. Simplified distinction method of degree of delayed stem maturation of soybean plants. Jpn. J. Crop Sci. 62, 126–127 (1993) (in Japanese with English abstract).Article 

Google Scholar 
Kim, D. et al. TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

Aissi A, Beghami Y, Heuertz M (2019) Le chêne faginé (Quercus faginea, Fagaceae) en Algérie: potentiel germinatif et variabilité morphologique des glands et des semis. Plant Ecol Evol 152:437–449Article 

Google Scholar 
Aissi A, Beghami Y, Lepais O, Véla E (2021) Morphological and taxonomic analysis of Quercus faginea (Fagaceae) complex in Algeria. Botany 99:99–113Article 

Google Scholar 
Alberto F, Niort J, Derory J, Lepais O, Vitalis R, Galop D et al. (2010) Population differentiation of sessile oak at the altitudinal front of migration in the French Pyrenees. Mol Ecol 19:2626–2639CAS 
PubMed 
Article 

Google Scholar 
Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell PubDe Barba M, Miquel C, Lobréaux S, Quenette PY, Swenson JE, Taberlet P (2016) High-throughput microsatellite genotyping in ecology: Improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Mol Ecol Resour 17:492–507PubMed 
Article 
CAS 

Google Scholar 
Barthe S, Gugerli F, Barkley NA, Maggia L, Cardi C, Scotti I (2012) Always look on both sides: phylogenetic information conveyed by simple sequence repeat allele sequences. PLoS One 7:e40699CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162:2025–35PubMed 
PubMed Central 
Article 

Google Scholar 
Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2004) GENETIX 4.05, logiciel sous Windows TM pour la génétique des populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5171, Université de Montpellier II, Montpellier (France)
Google Scholar 
Bradbury IR, Wringe BF, Watson B, Paterson I, Horne J, Beiko R et al. (2018) Genotyping-by-sequencing of genome-wide microsatellite loci reveals fine-scale harvest composition in a coastal Atlantic salmon fishery. Evol Appl 11:918–930CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buschbom J, Yanbaev Y, Degen B (2011) Efficient long-distance gene flow into an isolated relict oak stand. J Hered 102:464–472PubMed 
Article 

Google Scholar 
Chapuis M, Raynal L, Plantamp C, Meynard CN, Blondin L, Marin J et al. (2020) A young age of subspecific divergence in the desert locust inferred by ABC Random Forest. Mol Ecol 29:4542–4558PubMed 
Article 

Google Scholar 
Cornuet J-M, Ravigné V, Estoup A (2010) Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0). BMC Bioinforma 11:401Article 
CAS 

Google Scholar 
Crow JF, Aoki K (1984) Group selection for a polygenic behavioral trait: Estimating the degree of population subdivision. Proc Natl Acad Sci USA 81:6073–6077CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Curto M, Winter S, Seiter A, Schmid L, Scheicher K, Barthel LMF et al. (2019) Application of a SSR-GBS marker system on investigation of European Hedgehog species and their hybrid zone dynamics. Ecol Evol 9:2814–2832PubMed 
PubMed Central 
Article 

Google Scholar 
Darby BJ, Erickson SF, Hervey SD, Ellis-Felege SN (2016) Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing. Ecol Evol 6:4502–4512PubMed 
PubMed Central 
Article 

Google Scholar 
Dickey AM, Hall PM, Shatters RG, Mckenzie CL (2013) Evolution and homoplasy at the Bem6 microsatellite locus in three sweetpotato whitefly (Bemisia tabaci) cryptic species. BMC Res Notes 6:249PubMed 
PubMed Central 
Article 

Google Scholar 
Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2013) NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214PubMed 
Article 

Google Scholar 
Durand J, Bodenes C, Chancerel E, Frigerio JM, Vendramin G, Sebastiani F et al. (2010) A fast and cost-effective approach to develop and map EST-SSR markers: oak as a case study. BMC Genomics 11:570PubMed 
PubMed Central 
Article 

Google Scholar 
Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361Article 

Google Scholar 
Estoup A, Jarne P, Cornuet J-M (2002) Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Mol Ecol 11:1591–1604CAS 
PubMed 
Article 

Google Scholar 
Estoup A, Raynal L, Verdu P, Marin J-M (2018) Model choice using Approximate Bayesian Computation and Random Forests: analyses based on model grouping to make inferences about the genetic history of Pygmy human populations. J la Société Fr Stat 159:167–190
Google Scholar 
Excoffier L, Dupanloup I, Huerta-Sánchez E, Sousa VC, Foll M (2013) Robust demographic inference from genomic and SNP data. PLoS Genet 9:e1003905PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567PubMed 
Article 

Google Scholar 
Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–87CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Feliner GN (2014) Patterns and processes in plant phylogeography in the Mediterranean Basin. A review. Perspect Plant Ecol, Evol Syst 16:265–278Article 

Google Scholar 
Flagel L, Brandvain Y, Schrider DR (2019) The unreasonable effectiveness of convolutional neural networks in population genetic inference. Mol Biol Evol 36:220–238CAS 
PubMed 
Article 

Google Scholar 
Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180:977–993PubMed 
PubMed Central 
Article 

Google Scholar 
Gaggiotti OE, Chao A, Peres-Neto P, Chiu CH, Edwards C, Fortin MJ et al. (2018) Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales. Evol Appl 11:1176–1193PubMed 
PubMed Central 
Article 

Google Scholar 
García Murillo P., Harvey-Brown Y (2017) Quercus canariensis. In: The IUCN Red List of Threatened Species,, p e.T78809256A80570536GBIF Secratariat (2021a) Quercus faginea Lam. In: GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omeiaccessed via GBIF.org on 2022-05-10GBIF Secratariat (2021b). Quercus canariensis Willd. In: GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omeiaccessed via GBIF.org on 2022-05-10Gómez A, Lunt DH (2007) Refugia within refugia: Patterns of phylogeographic concordance in the Iberian peninsula. In: Weiss S, Ferrand N (eds) Phylogeography of Southern European Refugia. Springer Netherlands, Dordrecht, p 155–188Chapter 

Google Scholar 
Gorener V, Harvey-Brown Y, Barstow M (2017) Quercus canariensis. IUCN red List Threat species e.T7880925Goudet J (1995) FSTAT (Version 1.2): A computer program to calculate F-statistics. J Hered 86:485–486Article 

Google Scholar 
Hampe A, Petit RJ (2005) Conserving biodiversity under climate change: the rear edge matters. Ecol Lett 8:461–7PubMed 
Article 

Google Scholar 
Hardy OJ, Charbonnel N, Fréville H, Heuertz M (2003) Microsatellite allele sizes: a simple test to assess their significance on genetic differentiation. Genetics 163:1467–82CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620Article 
CAS 

Google Scholar 
Harvey-Brown Y, García Murillo PG, Buira A (2017) Quercus faginea. IUCN Red List Threat Species: e.T78916251A80570540.Henriques R, von der Heyden S, Matthee CA (2016) When homoplasy mimics hybridization: a case study of Cape hakes (Merluccius capensis and M. paradoxus). PeerJ 4:e1827PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hewitt GM (1999) Post-glacial re-colonization of European biota. Biol J Linn Soc 68:87–112Article 

Google Scholar 
Hey J, Won YJ, Sivasundar A, Nielsen R, Markert JA (2004) Using nuclear haplotypes with microsatellites to study gene flow between recently separated Cichlid species. Mol Ecol 13:909–919CAS 
PubMed 
Article 

Google Scholar 
Hoban S, Bruford M, D’Urban Jackson J, Lopes-Fernandes M, Heuertz M, Hohenlohe PA et al. (2020) Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol Conserv 248:108654Article 

Google Scholar 
Hoogenboom J, de Knijff P, Laros JFJ, de Leeuw RH, van der Gaag KJ, Sijen T (2016) FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise. Forensic Sci Int Genet 27:27–40PubMed 
Article 
CAS 

Google Scholar 
Hothorn T, Hornik K, van de Wiel MA, Zeileis A (2008) Implementing a class of permutation tests: the coin package. J Stat Softw 28:1–23Article 

Google Scholar 
Jamieson IG, Allendorf FW (2012) How does the 50/500 rule apply to MVPs? Trends Ecol Evol 27:578–584PubMed 
Article 

Google Scholar 
Jerome D, Vasquez F (2018) Quercus faginea. IUCN Red List Threat Species e.T7891625Kalinowski ST (2005) HP-RARE 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5:187–189CAS 
Article 

Google Scholar 
Kampfer S, Lexer C, Glössl J, Steinkellner H (1998) Characterization of (GA)n microsatellite loci from Quercus robur. Hereditas 129:183–186CAS 
Article 

Google Scholar 
Kivelä M, Arnaud-Haond S, Saramäki J (2015) EDENetworks: A user-friendly software to build and analyse networks in biogeography, ecology and population genetics. Mol Ecol Resour 15:117–122PubMed 
Article 

Google Scholar 
Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour 15:1179–1191CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Layton KKS, Dempson B, Snelgrove PVR, Duffy SJ, Messmer AM, Paterson IG et al. (2020) Resolving fine‐scale population structure and fishery exploitation using sequenced microsatellites in a northern fish. Evol Appl: eva.12922.Lepais O, Chancerel E, Boury C, Salin F, Manicki A, Taillebois L et al. (2020) Fast sequence-based microsatellite genotyping development workflow. PeerJ 8:e9085PubMed 
PubMed Central 
Article 

Google Scholar 
Lepais O, Leger V, Gerber S (2006) Short note: high throughput microsatellite genotyping in oak species. Silvae Genet 55:238Article 

Google Scholar 
Lepais O, Muller SD, Ben Saad-Limam S, Benslama M, Rhazi L, Belouahem-Abed D et al. (2013) High genetic diversity and distinctiveness of rear-edge climate relicts maintained by ancient tetraploidisation for Alnus glutinosa. PLoS One 8:e75029CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leroy T, Roux C, Villate L, Bodénès C, Romiguier J, Paiva JAP et al. (2017) Extensive recent secondary contacts between four European white oak species. N. Phytol 214:865–878CAS 
Article 

Google Scholar 
Lye GC, Lepais O, Goulson D (2011) Reconstructing demographic events from population genetic data: the introduction of bumblebees to New Zealand. Mol Ecol 20:2888–900CAS 
PubMed 
Article 

Google Scholar 
Magri D, Fineschi S, Bellarosa R, Buonamici A, Sebastiani F, Schirone B et al. (2007) The distribution of Quercus suber chloroplast haplotypes matches the palaeogeographical history of the western Mediterranean. Mol Ecol 16:5259–66CAS 
PubMed 
Article 

Google Scholar 
Marin J, Pudlo P, Estoup A, Robert C (2018) Likelihood-free model choice. In: Sisson S A, Fan Y, Beaumont M (eds) Handbook of Approximate Bayesian Computation, CRC Press, pp. 153.Médail F, Diadema K (2009) Glacial refugia influence plant diversity patterns in the Mediterranean Basin. J Biogeogr 36:1333–1345Article 

Google Scholar 
Moracho E, Moreno G, Jordano P, Hampe A (2016) Unusually limited pollen dispersal and connectivity of Pedunculate oak (Quercus robur) refugial populations at the species’ southern range margin. Mol Ecol 25:3319–3331CAS 
PubMed 
Article 

Google Scholar 
Mountain JL, Knight A, Jobin M, Gignoux C, Miller A, Lin AA et al. (2002) SNPSTRs: Empirically derived, rapidly typed, autosomal haplotypes for inference of population history and mutational processes. Genome Res 12:1766–1772CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Muir G, Lowe AJ, Fleming CC, Vogl C (2004) High nuclear genetic diversity, high levels of outcrossing and low differentiation among remnant populations of Quercus petraea at the margin of its range in Ireland. Ann Bot 93:691–697CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Neophytou C, Gärtner SM, Vargas-Gaete R, Michiels H-G (2015) Genetic variation of Central European oaks: shaped by evolutionary factors and human intervention? Tree Genet Genomes 11:1–15Article 

Google Scholar 
Payseur BA, Cutter AD (2006) Integrating patterns of polymorphism at SNPs and STRs. Trends Genet 22:424–429CAS 
PubMed 
Article 

Google Scholar 
Petit RJ, Brewer S, Bordács S, Burg K, Cheddadi R, Coart E et al. (2002) Identification of refugia and post-glacial colonisation routes of European white oaks based on chloroplast DNA and fossil pollen evidence. Ecol Manag 156:49–74Article 

Google Scholar 
Petit RJ, Hampe A, Cheddadi R (2005) Climate changes and tree phylogeography in the Mediterranean. Taxon 54:877–885Article 

Google Scholar 
Press MO, Hall AN, Morton EA, Queitsch C (2019) Substitutions are boring: Some arguments about parallel mutations and high mutation rates. Trends Genet 35:253–264CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Press MO, Mccoy RC, Hall AN, Akey JM, Queitsch C (2018) Massive variation of short tandem repeats with functional consequences across strains of Arabidopsis thaliana. Genome Res 28:1169–1178CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–59CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pudlo P, Marin J-M, Estoup A, Cornuet J-M, Gautier M, Robert CP (2016) Reliable ABC model choice via random forests. Bioinformatics 32:859–866CAS 
PubMed 
Article 

Google Scholar 
Ramakrishnan U, Mountain JL (2004) Precision and accuracy of divergence time estimates from STR and SNPSTR variation. Mol Biol Evol 21:1960–1971CAS 
PubMed 
Article 

Google Scholar 
Raynal L, Marin J-M, Pudlo P, Ribatet M, Robert CP, Estoup A (2019) ABC random forests for Bayesian parameter inference. Bioinformatics 35:1720–1728CAS 
PubMed 
Article 

Google Scholar 
Rodríguez-Sánchez F, Hampe A, Jordano P, Arroyo J (2010) Past tree range dynamics in the Iberian Peninsula inferred through phylogeography and palaeodistribution modelling: A review. Rev Palaeobot Palynol 162:507–521Article 

Google Scholar 
Šarhanová P, Pfanzelt S, Brandt R, Himmelbach A, Blattner FR (2018) SSR-seq: Genotyping of microsatellites using next-generation sequencing reveals higher level of polymorphism as compared to traditional fragment size scoring. Ecol Evol 8:10817–10833PubMed 
PubMed Central 
Article 

Google Scholar 
Scotti-Saintagne C, Mariette S, Porth I, Goicoechea PG, Barreneche T, Bodénès C et al. (2004) Genome scanning for interspecific differentiation between two closely related oak species [Quercus robur L. and Q. petraea (Matt.) Liebl.]. Genetics 168:1615–26CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vartia S, Villanueva-Cañas JL, Finarelli J, Farrell ED, Collins PC, Hughes GM et al. (2016) A novel method of microsatellite genotyping-by-sequencing using individual combinatorial barcoding. R Soc Open Sci 3:150565PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Viruel J, Haguenauer A, Juin M, Mirleau F, Bouteiller D, Boudagher-Kharrat M et al. (2018) Advances in genotyping microsatellite markers through sequencing and consequences of scoring methods for Ceratonia siliqua (Leguminosae). Appl Plant Sci 6:e01201PubMed 
PubMed Central 
Article 

Google Scholar 
Wang J (2016) Individual identification from genetic marker data: developments and accuracy comparisons of methods. Mol Ecol Resour 16:163–175CAS 
PubMed 
Article 

Google Scholar 
Waples RS, Do C (2010) Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol Appl 3:244–262PubMed 
Article 

Google Scholar 
Xie KT, Wang G, Thompson AC, Wucherpfennig JI, Reimchen TE, MacColl ADC et al. (2019) DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science (80-) 363:81–84CAS 
Article 

Google Scholar  More

Infection properties of clade A and clade B T7-like cyanophagesWe set out to test the hypothesis that the phylogenetic separation of T7-like cyanophages into two major clades reflects differences in their infection physiology. To do this we investigated a suite of infection properties of three pairs of clade A and B phages, each pair infecting the same Synechococcus host (Table 1) to allow us to control for variability in host genetics and physiology. These six cyanophages are representatives of 3 clade A and 2 clade B cyanophage subclades (SI Appendix, Table S1).Table 1 Summary of infection physiology of three pairs of clade A and clade B cyanophages infecting the same Synechococcus hosts.Full size tableWe began by investigating adsorption kinetics and the length of time taken to produce new phages in the infection cycle, the latent period, from phage growth curve experiments. In all three pairs of phages, adsorption was 7–15-fold more rapid in the clade A phage versus the clade B phage (Fig. 1, Table 1). Furthermore, the clade A phage had a faster infection cycle with a latent period that was 3-5-fold shorter than the clade B phage on the same host (Fig. 1a–c) (Table 1). To determine how representative these findings are for a greater diversity of T7-like cyanophages we report the latent period of nine additional non-paired phages that infect a variety of hosts and span the diversity of this cyanophage genus, measured here and taken from the literature (SI Appendix, Table S1). These phages showed the same pattern as observed between phage pairs, although one clade A phage had a relatively long latent period (see SI Appendix, Table S1). Overall, the 5 clade A phages representative of 5 subclades had a significantly shorter latent period (3.3 ± 3.6 h, n = 5 phages (mean ± SD) than the 10 clade B phages from 7 subclades (7.7 ± 2.0 h, n = 10 phages) (Kruskal-Wallis: χ2 = 4.72, df = 1; p = 0.029, n = 15). No significant differences in the length of the latent period were found for clade B phages that infected Synechococcus and Prochlorococcus (Kruskal-Wallis: χ2 = 1.13, df = 1; p = 0.29, n = 10).Fig. 1: Comparison of the infection physiology between pairs of clade A and clade B T7-like cyanophage infecting the same Synechococcus host.a–c Cyanophage growth curves, d–f burst sizes, g–i virulence as the percentage of lysed host cells, j–l decay as loss of infectivity, m–o plaque sizes. a, d, g, j, m Clade A Syn5 phage and clade B S-TIP37 phage infecting WH8109. b, e, h, k, n Clade A S-CBP42 phage and clade B S-RIP2 phage infecting WH7803. c, f, i, l, o Clade A S-TIP28 phage and clade B S-TIP67 phage infecting CC9605. The host strain is shown at the right of the panels. Red and blue lines or bars show results for clade A and clade B phages, respectively. a–c, g–I Error bars indicate standard deviations. d–f Burst size results are for single cells. j–l The solid line shows the fitted multi-level linear model. m–o The time after infection at which plaques were photographed appears above the images. *p value  More

Experimental design and crop managementField experiments were conducted at Chengyang Agricultural Experimental Station, Qingdao, China (36°18′ 11″/N, 120°21′ 13″/E) in 2019 and 2020. The soil type in the field was brown loam that contained 22.76 g kg−1 organic matter, 82.39 mg kg−1 alkali-hydrolysable N, 25.10 mg kg−1 Olsen-P and 94.89 mg kg−1 exchangeable K. The test cultivars of maize were Jinhai5 with strong lodging resistance and Xundan20 with weak lodging resistance, which were repeated four times in plots laying out in randomized block designs. Plant density was 7.5 plants / m2 with the row spacing of 60 cm. the plot consisted of 8 rows length of 15 m. Two–three seeds per hole were manually sowed at 5 cm on 20 April 2019 and 24 April 2020, and the seedlings were thinned to the target planting density at V2, and harvested on 10 September and 14 September, respectively. Fertilization and irrigation management followed local production practices in maize.Sampling and measurementPlant samples were taken at V8, V12, R1, R2 and R6. Ten typical plants of each tested cultivars were selected to be subjected to mechanical and above-ground morphological measurements at each sampling. The other three maize plants were used to measure morphological traits of roots. Xundan20 was seriously damaged due to the storm in the late stage of maize growth in 2020, resulting in the missing data for physiological maturity.Determination of leaf area vertical distributionLeaf area of expanded leaves each was computed by the coefficient method: Single leaf area = length * width * 0.75. Leaf area for unexpanded leaves was estimated by the leaf weight method. Leaf area per plant was the sum of all individual green leaf areas. Leaf height is the height from the ground to the leaf collar position of maize.Determination of max root side-pulling resistanceSample plants were surrounded with water-proof steel devices inserted into underground, and watered to soil moisture over saturation at one day before mechanical testing. When measured, due to the limited space, all leaves of sample plants are removed in order to improve the measurement accuracy. The defoliated stalks were immobilized by a pair of lengthwise steel clamps to prevent stalks from bending (Fig. 7). After the digital pole dynamometer18 with a 1.5 m long slider and a main unit was linked to the stalks at a height of 80 cm away from the ground, the operator by hand pulled at a slow and uniform speed until the roots were pulled out. Records of load force, declination angle and sensor position were automatically stored in main unit during this operation. The peak value of forces, extracted from records, was taken as the max root side-pulling resistance.Figure 7Schematic diagram for measuring max root side-pulling resistance.Full size imageRoot anti-lodging indexBased on the method of Cui et al.6, the force value comparison is changed to the moment value comparison to calculate root anti-lodging index:$${text{AL}}_{root} = M_{root} / , M_{wind} = F_{root} / , F_{wind}$$
(1)
where M root is the root failure moment, M wind is the wind resultant moment. Root anti-lodging index indicates the ability of plants to resist root lodging. The larger its value is, the stronger the resistance is, and vice versa.$${text{M}}_{root} = F , *d$$
(2)
where F is the max root side-pulling resistance, d is moment arm, i.e., the length of force arm. As a component of root anti-lodging index, the root failure moment represents the ability of the root system to resist lateral pulling. The greater its value is, the better the resistance is, and vice versa.With the base of the stem as the fulcrum,$${text{M}}_{wind} = sum 0.{5}CA_{i} rho V^{2} h_{i}$$
(3)
where C is coefficient of air resistance, ρ is air mass density ,V is the wind speed , Ai is the area of a single leaf , hi is the height of leaf, ∑ represents to sum up over all leaves. C value is set to be 0.219. When encountering wind speed at grade 6 or higher, maize is more prone to lodging. Unless stated explicitly, the following analysis was limited to the upper wind speed for grade 6 wind20.Root morphological traitsThe number and length of all primary nodal roots were measured. Root-soil balls each of two or three tested plants were obtained after lateral root-pulling testing. The images of the three frontal sides, 120 degrees apart from each other, of the root-soil balls were taken using a digital camera. Ball volumes were then evaluated by considering them to be rotationally symmetric. Average volumes were used for further analysis.Single root tensile resistanceRoots after counting the number of nodal roots were used to measure the single root tensile resistance. First, clean the dust off roots. Then, diameters of roots were determined with a vernier caliper. Single root tensile resistance was measured by HF-500 digital push–pull apparatus. Fixed the upper and lower ends of the root, then one end moved slowly and uniformly, the other end was still until the root breaks. The peak tension force displayed by the instrument was taken as the single root tensile resistance.Statistical analysisBased on variance analysis, the Tukey method was used to compare the differences among means. The logarithmic transformation of variables was carried out to improve the homogeneity of error variance if appropriate.The substantive effect or influence of various factors on the response variable can be expressed by effect size of factors, which can be calculated under the framework of variance analysis. Effect size is the proportion of the effect of a certain factor in the total effect, which is a dimensionless number21,22,23.The formula for calculating effect size of factors is:$$omega^{2} = frac{{df_{effect} times left( {MS_{effect} – MS_{error} } right)}}{{SS_{total} + MS_{error} }}$$
(4)
where df is the degree of freedom, MS represents mean square.Two conceptual models were used when dealing with effect size. One model was of components, i.e., taking the logarithm of both sides of Eq. (1):$${text{LOG}}left( {{text{AL}}_{{{text{root}}}} } right) , = {text{ LOG}}left( {{text{M}}_{{{text{root}}}} } right) , + {text{ LOG}}left( {{text{M}}_{{{text{wind}}}} } right)$$
(5)
where LOG denotes logarithmic transformation.The other was the factorial model, i.e.,$${text{factors affecting AL}}_{{{text{root}}}} = {text{ wind grade }} + {text{ cultivar }} + {text{ growth stage}}$$
(6)
Experimental research and field studies on plants including the collection of plant materialThe authors declare that the cultivation of plants and carrying out study in Chengyang Agricultural Experimental Station complies with all relevant institutional, national and international guidelines and treaties.Statement of permissions and/or licenses for collection of plant or seed specimensThe authors declare that the seed specimens used in this study are publicly accessible seed materials and we were given explicit written permission to use them for this research. More

Shah, S. et al. Ethno botanical study of medicinal plants of district charsadda, Khyber Pakhtoonkhwa, Pakistan. Int. J. Herb. Med. 8, 67–75 (2020).
Google Scholar 
Hu, R., Lin, C., Xu, W., Liu, Y. & Long, C. Ethnobotanical study on medicinal plants used by Mulam people in Guangxi, China. J. Ethnobiol. Ethnomed. 16, 1–50 (2020).Article 

Google Scholar 
Kooti, W. et al. Effective medicinal plant in cancer treatment, part 2: Review study. J. Evid. Based Complem. Altern. Med. 22, 982–995 (2017).CAS 
Article 

Google Scholar 
Mazalovska, M. & Kouokam, J. C. Transiently expressed mistletoe lectin ii in nicotiana benthamiana demonstrates anticancer activity in vitro. Molecules 25, 2562 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Shukla, S. & Mehta, A. Anticancer potential of medicinal plants and their phytochemicals: A review. Rev. Bras. Bot. 38, 199–210 (2015).Article 

Google Scholar 
Shaikh, A. M., Shrivastava, B., Apte, K. G. & Navale, S. D. Medicinal plants as potential source of anticancer agents: A review. J. Pharmacogn. Phytochem. APT Res. Found. 5, 291–295 (2016).CAS 

Google Scholar 
Iqbal, J. et al. Plant-derived anticancer agents: A green anticancer approach. Asian Pac. J. Trop. Biomed. 7, 1129–1150 (2017).Article 

Google Scholar 
Zuber, D. Biological flora of Central Europe: Viscum album L. Flora 199, 181–203 (2004).Article 

Google Scholar 
Bar-Sela, G. White-Berry Mistletoe (Viscum album L.) as complementary treatment in cancer: Does it help?. Eur. J. Integr. Med. 3, e55–e62 (2011).Article 

Google Scholar 
Vicaş, S. I., Ruginǎ, D. & Socaciu, C. Comparative study about antioxidant activities of viscum album from different host trees, harvested in different seasons. J. Med. Plants Res. 5, 2237–2244 (2011).
Google Scholar 
Gastauer, M. & Meira-Neto, J. A. A. Updated angiosperm family tree for analyzing phylogenetic diversity and community structure. Acta Bot. Brasilica 31, 191–198 (2017).Article 

Google Scholar 
Varga, I. et al. Changes in the distribution of European mistletoe (Viscum album) in hungary during the last hundred years. Folia Geobot. 49, 559–577 (2014).Article 

Google Scholar 
Lech, P., Żółciak, A. & Hildebrand, R. Occurrence of european mistletoe (Viscum album l.) On forest trees in poland and its dynamics of spread in the period 2008–2018. Forests 11, 83 (2020).Article 

Google Scholar 
Büssing, A., Suzart, K. & Schweizer, K. Differences in the apoptosis-inducing properties of Viscum album L. extracts. Anticancer. Drugs 8, S9–S14 (1997).PubMed 
Article 

Google Scholar 
Maier, G. & Fiebig, H. H. Absence of tumor growth stimulation in a panel of 16 human tumor cell lines by mistletoe extracts in vitro. Anticancer. Drugs 13, 373–379 (2002).CAS 
PubMed 
Article 

Google Scholar 
Orhan, D. D., Aslan, M., Sendogdu, N., Ergun, F. & Yesilada, E. Evaluation of the hypoglycemic effect and antioxidant activity of three Viscum album subspecies (European mistletoe) in streptozotocin-diabetic rats. J. Ethnopharmacol. 98, 95–102 (2005).PubMed 
Article 

Google Scholar 
Ofem, O. E. et al. Effect of crude aqueous leaf extract of Viscum album (mistletoe) in hypertensive rats. Indian J. Pharmacol. 39, 15–19 (2007).Article 

Google Scholar 
Gupta, G. et al. Sedative, antiepileptic and antipsychotic effects of Viscum album L. (Loranthaceae) in mice and rats. J. Ethnopharmacol. 141, 810–816 (2012).CAS 
PubMed 
Article 

Google Scholar 
Orhue, P. O., Edomwande, E. C., Igbinosa, E. & Al, E. Antibacterial activity of extracts of mistletoe (Tapinanthus dodoneifollus (dc) dancer) from Cocoa tree (Theobrama cacao). Int. J. Herbs Pharmacol. Res. 3, 24–29 (2014).
Google Scholar 
Karagöz, A., Önay, E., Arda, N. & Kuru, A. Antiviral potency of mistletoe (Viscum album ssp. album) extracts against human parainfluenza virus type 2 in Vero cells. Phyther. Res. 17, 560–562 (2003).Article 

Google Scholar 
Thronicke, A., Schad, F., Debus, M., Grabowski, J. & Soldner, G. Viscum album L. therapy in oncology—An update on current evidence. Complement. Med. Res. https://doi.org/10.1159/000524184 (2022).Article 
PubMed 

Google Scholar 
Lavastre, V., Cavalli, H., Ratthe, C. & Girard, D. Anti-inflammatory effect of Viscum album agglutinin-I (VAA-I): Induction of apoptosis in activated neutrophils and inhibition of lipopolysaccharide-induced neutrophilic inflammation in vivo. Clin. Exp. Immunol. 137, 272–278 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ćebović, T., Spasić, S. & Popović, M. Cytotoxic effects of the Viscum album L. extract on ehrlich tumour cells in vivo. Phyther. Res. 22, 1097–1103 (2008).Article 
CAS 

Google Scholar 
Tröger, W. et al. Viscum album [L.] extract therapy in patients with locally advanced or metastatic pancreatic cancer: A randomised clinical trial on overall survival. Eur. J. Cancer 49, 3788–3797 (2013).PubMed 
Article 

Google Scholar 
Ostermann, T. et al. A Systematic review and meta-analysis on the survival of cancer patients treated with a fermented Viscum album L. extract (Iscador): An update of findings. Complement. Med. Res. 27(260), 271 (2020).
Google Scholar 
Loef, M. & Walach, H. Quality of life in cancer patients treated with mistletoe: A systematic review and meta-analysis. BMC Complement. Med. Ther. 20, 227 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Szurpnicka, A., Kowalczuk, A. & Szterk, A. Biological activity of mistletoe: In vitro and in vivo studies and mechanisms of action. Arch. Pharm. Res. 43, 593–629 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, S., Kim, K.-C. & Lee, C. Mistletoe (Viscum album) extract targets Axl to suppress cell proliferation and overcome cisplatin- and erlotinib-resistance in non-small cell lung cancer cells. Phytomedicine 36, 183–193 (2017).CAS 
PubMed 
Article 

Google Scholar 
Urech, K. & Baumgartner, S. Chemical constituents of Viscum album L.: Implications for the pharmaceutical preparation of mistletoe. Transl. Res. Biomed. 4(11), 23 (2015).
Google Scholar 
Franz, H., Ziska, P. & Kindt, A. Isolation and properties of three lectins from mistletoe (Viscum album L.). Biochem. J. 195, 481–484 (1981).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ochocka, J. R. & Piotrowski, A. Biologically active compounds from European mistletoe (Viscum album L.). Can. J. Plant Pathol. 24, 21–28 (2002).CAS 
Article 

Google Scholar 
Hajtó, T. et al. Oncopharmacological perspectives of a plant lectin (Viscum album agglutinin-I): Overview of recent results from in vitro experiments and in vivo animal models, and their possible relevance for clinical applications. Evid. Based Complement. Altern. Med. 2, 59–67 (2005).Article 

Google Scholar 
Nazaruk, J. & Orlikowski, P. Phytochemical profile and therapeutic potential of Viscum album L. Nat. Prod. Res. 30, 373–385 (2016).CAS 
PubMed 
Article 

Google Scholar 
Samuelsson, G. Mistletoe toxins. Syst. Zool. 22, 566–569 (1973).CAS 
Article 

Google Scholar 
Debreczeni, J. É., Girmann, B., Zeeck, A., Krätzner, R. & Sheldrick, G. M. Structure of viscotoxin A3: Disulfide location from weak SAD data. Acta Crystallogr. Sect. D Biol. Crystallogr. 59, 2125–2132 (2003).Article 
CAS 

Google Scholar 
Parsakhoo, A. & Jalilvand, H. Effects of ironwood (Parrotia persica c A Meyer ) leaf litter on forest soil nutrients content. Am. J. Agric. Environ. Sci. 5, 244–249 (2009).CAS 

Google Scholar 
Hosseini, S. M. Inscription of the Hyrcanian forests on the UNESCO world heritage list nomination file, UNESCO, 492. Available at: https://whc.unesco.org/en/list/1584/documents/ (2019).Hosseini, S. M. et al. The effects of Viscum album L. on foliar weight and nutrients content of host trees in Caspian forests (Iran). Polish J. Ecol. 55, 579–583 (2007).MathSciNet 
CAS 

Google Scholar 
Sefidi, K., Marvie Mohadjer, M. R., Etemad, V. & Copenheaver, C. A. Stand characteristics and distribution of a relict population of persian ironwood (Parrotia persica CA Meyer) in northern Iran. Flora Morphol. Distrib. Funct. Ecol. Plants 206, 418–422 (2011).Article 

Google Scholar 
Cărăbuş, M. C., Leinemann, L., Curtu, A. L. & Şofletea, N. Preliminary results on the genetic diversity of Carpinus betulus in Carpathian populations. Bull. Transilv. Univ. Brasov, Ser. II For Wood Ind. Agric. Food Eng. 8, 1–6 (2015).
Google Scholar 
Barbasz, A., Kreczmer, B., Rudolphi-Skorska, E. & Sieprawska, A. Biologically active substances in plant extracts from mistletoe Viscum album and trees: fir (Abies alba Mill.), pine (Pinus sylvestris L.) and yew (Taxus baccata L.). Herba Pol. 58, 16–26 (2012).
Google Scholar 
Wójciak-Kosior, M. et al. Evaluation of seasonal changes of triterpenic acid contents in viscum album from different host trees. Pharm. Biol. 55, 1–4 (2017).PubMed 
Article 
CAS 

Google Scholar 
Stefanucci, A. et al. Viscum album L. homogenizer-assisted and ultrasound-assisted extracts as potential sources of bioactive compounds. J. Food Biochem. 44, 1–12 (2020).Article 
CAS 

Google Scholar 
Urech, K., Schaller, G. & Jäggy, C. Viscotoxins, mistletoe lectins and their isoforms in mistletoe (Viscum album L.) extracts Iscador: Analytical results on pharmaceutical processing of mistletoe. Drug Res. 56, 428–434 (2006).CAS 

Google Scholar 
Soursouri, A., Hosseini, S. M. & Fattahi, F. Biochemical analysis of European mistletoe (Viscum album L.) foliage and fruit settled on Persian ironwood (Parrotia persica C. A. Mey) and hornbeam (Carpinus betulus L.). Biocatal. Agric. Biotechnol. 22, 101360 (2019).Article 

Google Scholar 
Önay-Uçar, E., Karagöz, A. & Arda, N. Antioxidant activity of Viscum album ssp. album. Fitoterapia 77, 556–560 (2006).PubMed 
Article 

Google Scholar 
Simona, V., Rugina, D. & Socaciu, C. Antioxidant activities of Viscum album’s leaves from various host trees. Bull. Univ Agric. Sci. Vet. Med. Cluj Napoca Agric. 65, 327–332 (2008).
Google Scholar 
Schaller, G., Urech, K., Grazi, G. & Giannattasio, M. Viscotoxin composition of the three European subspecies of Viscum album. Planta Med. 64, 677–678 (1998).CAS 
PubMed 
Article 

Google Scholar 
Holandino, C. et al. Phytochemical analysis and in vitro anti-proliferative activity of Viscum album ethanolic extracts. BMC Complement. Med. Ther. 20, 215 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zuber, D. & Widmer, A. Phylogeography and host race differentiation in the European mistletoe (Viscum album L.). Mol. Ecol. 18, 1946–1962 (2009).CAS 
PubMed 
Article 

Google Scholar 
Schaller, G., Urech, K. & Giannattasio, M. Cytotoxicity of different viscotoxins and extracts from the European subspecies of Viscum album L. Phyther. Res. 10, 473–477 (1996).CAS 
Article 

Google Scholar 
Eggenschwiler, J. et al. Mistletoe lectin is not the only cytotoxic component in fermented preparations of Viscum album from white fir (Abies pectinata). BMC Complement. Altern. Med. 7, 14 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jaggy, C., Musielski, H., Urech, K. & Schaller, G. Quantitative determination of lectins in mistletoe preparations. Arzneimittel-Forschung/Drug Res. 45, 905–909 (1995).CAS 

Google Scholar  More