Philippa Kaur

Coyne, J. A. & Orr, H. A. Speciation 83–178 (Sinauer, 2004).Dieckmann, U., Doebeli, M., Metz, J. A. & Tautz, D. Adaptive Speciation (Cambridge University Press, 2004).Butlin, R. K., Galindo, J. & Grahame, J. W. Sympatric, parapatric or allopatric: the most important way to classify speciation? Philos. T. Roy. Soc. B 363, 2997–3007 (2008).Article 

Google Scholar 
Smadja, C. M. & Butlin, R. K. A framework for comparing processes of speciation in the presence of gene flow. Mol. Ecol. 20, 5123–5140 (2011).Article 

Google Scholar 
Seehausen, O. et al. Genomics and the origin of species. Nat. Rev. Genet. 15, 176–192 (2014).Article 
CAS 

Google Scholar 
Kulmuni, J., Butlin, R. K., Lucek, K., Savolainen, V. & Westram, A. M. Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers. Philos. T. Roy. Soc. B 375, 20190528 (2020).Article 

Google Scholar 
Hofreiter, M. & Stewart, J. Ecological change, range fluctuations and population dynamics during the Pleistocene. Curr. Biol. 19, R584–R594 (2009).Article 
CAS 

Google Scholar 
Longman, J., Mills, B. J. W., Manners, H. R., Gernon, T. M. & Palmer, M. R. Late Ordovician climate change and extinctions driven by elevated volcanic nutrient supply. Nat. Geosci. 14, 924–929 (2021).Article 
ADS 
CAS 

Google Scholar 
Thomson, R. C., Spink, P. Q. & Shaffer, H. B. A global phylogeny of turtles reveals a burst of climate-associated diversification on continental margins. Proc. Natl Acad. Sci. USA 118, e2012215118 (2021).Article 
CAS 

Google Scholar 
Chaboureau, A. C., Sepulchre, P., Donnadieu, Y. & Franc, A. Tectonic-driven climate change and the diversification of angiosperms. Proc. Natl Acad. Sci. USA 111, 14066–14070 (2014).Article 
ADS 
CAS 

Google Scholar 
Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).Article 
ADS 
CAS 

Google Scholar 
Schmitt, T. Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Front. Zool. 4, 11 (2007).Article 

Google Scholar 
Haffer, J. Speciation in Amazonian forest birds. Science 165, 131–137 (1969).Article 
ADS 
CAS 

Google Scholar 
Ebdon, S. et al. The Pleistocene species pump past its prime: evidence from European butterfly sister species. Mol. Ecol. 30, 3575–3589 (2021).Article 

Google Scholar 
Excoffier, L., Foll, M. & Petit, R. J. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).Article 

Google Scholar 
Baker, H. G. Self-compatibility and establishment after ‘long-distance’ dispersal. Evolution 9, 347–349 (1955).
Google Scholar 
Fisher, R. The Genetical Theory of Natural Selection 125–129 (Oxford University Press, 1930).Endler, J. A. Geographic Variation, Speciation, and Clines. Monographs in Population Biology Vol. 10, 53–65, 142–150 (Princeton University Press, 1977).Doebeli, M. & Dieckmann, U. Speciation along environmental gradients. Nature 421, 259–264 (2003).Article 
ADS 
CAS 

Google Scholar 
Ispolatov, J. & Doebeli, M. Diversification along environmental gradients in spatially structured populations. Evol. Ecol. Res. 11, 295–304 (2009).
Google Scholar 
Rettelbach, A., Servedio, M. R. & Hermisson, J. Speciation in peripheral populations: effects of drift load and mating systems. J. Evol. Biol. 29, 1073–1090 (2016).Article 
CAS 

Google Scholar 
Wright, S. I., Kalisz, S. & Slotte, T. Evolutionary consequences of self-fertilization in plants. Proc. R. Soc. Lond. Ser. B 280, 20130133 (2013).
Google Scholar 
Hu, X.-S. Mating system as a barrier to gene flow. Evolution 69, 1158–1177 (2015).Article 
CAS 

Google Scholar 
Glémin, S. How are deleterious mutations purged? Drift versus nonrandom mating. Evolution 57, 2678–2687 (2003).
Google Scholar 
Warwick, S. I., Francis, A. & Al-Shehbaz, I. A. Brassicaceae: species checklist and database on CD-Rom. Plant Syst. Evol. 259, 249–258 (2006).Article 

Google Scholar 
Warwick, S. I., Al-Shehbaz, I. A. & Sauder, C. A. Phylogenetic position of Arabis arenicola and generic limits of Aphragmus and Eutrema (Brassicaceae) based on sequences of nuclear ribosomal DNA. Can. J. Bot. 84, 269–281 (2006).Article 
CAS 

Google Scholar 
Hohmann, N. et al. Taming the wild: resolving the gene pools of non-model Arabidopsis lineages. BMC Evol. Biol. 14, e224 (2014).Article 

Google Scholar 
Novikova, P. Y. et al. Sequencing of the genus Arabidopsis identifies a complex history of nonbifurcating speciation and abundant trans-specific polymorphism. Nat. Genet. 48, 1077–1082 (2016).Article 
CAS 

Google Scholar 
Perrier, A. & Willi, Y. Intraspecific variation in reproductive barriers between two recently-diverged, allopatric Arabidopsis species. J. Evol. Biol. https://doi.org/10.1111/jeb.14122 (2022). (in press).Griffin, P. C. & Willi, Y. Evolutionary shifts to self-fertilisation restricted to geographic range margins in North American Arabidopsis lyrata. Ecol. Lett. 17, 484–490 (2014).Article 
CAS 

Google Scholar 
Willi, Y., Fracassetti, M., Zoller, S. & Van Buskirk, J. Accumulation of mutational load at the edges of a species range. Mol. Biol. Evol. 35, 781–791 (2018).Article 
CAS 

Google Scholar 
Schmickl, R., Jørgensen, M. H., Brysting, A. K. & Koch, M. A. The evolutionary history of the Arabidopsis lyrata complex: a hybrid in the Amphi-Beringian area closes a large distribution gap and builds up a genetic barrier. BMC Evol. Biol. 10, e98 (2010).Article 

Google Scholar 
Pyhäjärvi, T., Aalto, E. & Savolainen, O. Time scales of divergence and speciation among natural populations and subspecies of Arabidopsis lyrata (Brassicaceae). Am. J. Bot. 99, 1314–1322 (2012).Article 

Google Scholar 
Dyke, A. S. in Quaternary Glaciations – Extent and Chronology, Part II: North America (Elsevier, Amsterdam, 2004).Kirkpatrick, M. & Ravigné, V. Speciation by natural and sexual selection: models and experiments. Am. Nat. 159, S22–S35 (2002).Article 

Google Scholar 
Igic, B., Lande, R. & Kohn, J. R. Loss of self‐incompatibility and its evolutionary consequences. Int. J. Plant Sci. 169, 93–104 (2008).Article 

Google Scholar 
Willi, Y. & Määttänen, K. Evolutionary dynamics of mating system shifts in Arabidopsis lyrata. J. Evol. Biol. 23, 2123–2131 (2010).Article 
CAS 

Google Scholar 
Lucek, K. & Willi, Y. Drivers of linkage disequilibrium across a species’ geographic range. PLoS Genet. 17, e1009477 (2021).Article 
CAS 

Google Scholar 
Pironon, S. et al. Geographic variation in genetic and demographic performance: new insights from an old biogeographical paradigm: the centre-periphery hypothesis. Biol. Rev. 92, 1877–1909 (2017).Article 

Google Scholar 
Encinas-Viso, F., Young, A. G. & Pannell, J. R. The loss of self-incompatibility in a range expansion. J. Evol. Biol. 33, 1235–1244 (2020).Article 

Google Scholar 
Jarne, P. & Auld, J. R. Animals mix it up too: the distribution of self-fertilization among hermaphroditic animals. Evolution 60, 1816–1824 (2006).
Google Scholar 
Foxe, J. P. et al. Reconstructing origins of loss of self-incompatibility and selfing in North American Arabidopsis lyrata: a population genetic context. Evolution 64, 3495–3510 (2010).Article 

Google Scholar 
Koski, M. H., Layman, N. C., Prior, C. J., Busch, J. W. & Galloway, L. F. Selfing ability and drift load evolve with range expansion. Evol. Lett. 3, 500–512 (2019).Article 

Google Scholar 
Prior, C. J. & Busch, J. W. Selfing rate variation within species is unrelated to life‐history traits or geographic range position. Am. J. Bot. 108, 2294–2308 (2021).Article 

Google Scholar 
Skeels, A. & Cardillo, M. Reconstructing the geography of speciation from contemporary biodiversity data. Am. Nat. 193, 240–254 (2019).Article 

Google Scholar 
Sánchez-Castro, D., Perrier, A. & Willi, Y. Reduced climate adaptation at range edges in North American Arabidopsis lyrata. Glob. Ecol. Biogeogr. 31, 1066–1077 (2022).Article 

Google Scholar 
Roessler, K. et al. The genome-wide dynamics of purging during selfing in maize. Nat. Plants 5, 980–990 (2019).Article 
CAS 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).Hu, T. T. et al. The Arabidopsis lyrata genome sequence and the basis of rapid genome size change. Nat. Genet. 43, 476–481 (2011).Article 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).Article 

Google Scholar 
McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).Article 
CAS 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).Article 
CAS 

Google Scholar 
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).Article 
CAS 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).Article 
CAS 

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

Google Scholar 
Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).Article 
CAS 

Google Scholar 
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).Article 

Google Scholar 
Marchi, N. et al. The genomic origins of the world’s first farmers. Cell 185, 1842–1859 (2022).Article 
CAS 

Google Scholar 
Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).Article 
CAS 

Google Scholar 
Genete, M., Castric, V. & Vekemans, X. Genotyping and de novo discovery of allelic variants at the Brassicaceae self-incompatibility locus from short-read sequencing data. Mol. Biol. Evol. 7, 1193–1201 (2020).Article 

Google Scholar 
Lynch, M. et al. Genome-wide linkage-disequilibrium profiles from single individuals. Genetics 198, 269–281 (2014).Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).Article 
CAS 

Google Scholar 
Nychka, D., Furrer, R., Paige, J. & Sain, S. fields: tools for spatial data. R package version 14.1 https://github.com/dnychka/fieldsRPackage (2021).Asquith, W. lmomco—L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions. R package version 2.4.7 (2022).Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
Lemon, J. Plotrix: a package in the red light district of R. R. N. 6, 8–12 (2006).
Google Scholar 
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R. N. 5, 9–13 (2005).
Google Scholar 
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R Second edition (Springer, 2013). More

Department of Anthropology, East Carolina University, Greenville, NC, USARyan SchachtDepartment of Environmental Science, Policy and Management and Museum of Vertebrate Zoology, University of California, Berkeley, CA, 94720, USASteven R. BeissingerDepartment of Ecology and Evolution, University of Lausanne, 1015, Lausanne, SwitzerlandClaus WedekindEcology & Evolution, Research School of Biology, The Australian National University, Acton, Canberra, 2601, AustraliaMichael D. JennionsMARBEC Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, FranceBenjamin GeffroyELKH-PE Evolutionary Ecology Research Group, University of Pannonia, 8210, Veszprém, HungaryAndrás LikerBehavioural Ecology Research Group, Center for Natural Sciences, University of Pannonia, 8210, Veszprém, HungaryAndrás LikerBehavioral Ecology and Sociobiology Unit, German Primate Center, Leibniz Institute of Primate Biology, 37077, Göttingen, GermanyPeter M. KappelerDepartment of Sociobiology/Anthropology, University of Göttingen, 37077, Göttingen, GermanyPeter M. KappelerGroningen Institute for Evolutionary Life Sciences, University of Groningen, 9747 AG, Groningen, The NetherlandsFranz J. WeissingDepartment of Anthropology, University of Utah, Salt Lake City, UT, USAKaren L. KramerInstitute of Global Health, University College London, London, UKTherese HeskethCentre for Global Health, Zhejiang University School of Medicine, Hangzhou, P.R. ChinaTherese HeskethIHPE Univ Perpignan Via Domitia, CNRS, Ifremer, Univ Montpellier, Perpignan, FranceJérôme BoissierStockholm University Demography Unit, Sociology Department, Stockholm University, 106 91, Stockholm, SwedenCaroline UgglaKem C. Gardner Policy Institute, David Eccles School of Business, University of Utah, Salt Lake City, UT, USAMike HollingshausMilner Centre for Evolution, University of Bath, Bath, BA2 7AY, UKTamás SzékelyELKH-DE Reproductive Strategies Research Group, Department of Zoology and Human Biology, University of Debrecen, H-4032, Debrecen, HungaryTamás Székely More

Bruijnzeel, L. A., Mulligan, M. & Scatena, F. N. Hydrometeorology of tropical montane cloud forests: emerging patterns. Hydrol. Processes 25, 25 (2011).
Google Scholar 
Myster, R. W. The Andean Cloud Forest. Andean Cloud Forest https://doi.org/10.1007/978-3-030-57344-7 (2021).Article 

Google Scholar 
Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Change 5, 424–430 (2015).Article 
ADS 

Google Scholar 
Hu, J. & Riveros-Iregui, D. A. Life in the clouds: are tropical montane cloud forests responding to changes in climate?. Oecologia 180, 1061–1073 (2016).Article 
ADS 

Google Scholar 
Peterson, A. T. Ecological niche conservatism: A time-structured review of evidence. J. Biogeogr. 38, 817–827 (2011).Article 

Google Scholar 
Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical forests in the anthropocene. Annu. Rev. Environ. Resour. 39, 125–159 (2014).Article 

Google Scholar 
Johnstone, J. F. et al. Changing disturbance regimes, ecological memory, and forest resilience. Front. Ecol. Environ. 14, 369–378 (2016).Article 

Google Scholar 
Trugman, A. T. et al. Tree carbon allocation explains forest drought-kill and recovery patterns. Ecol. Lett. 21, 1552–1560 (2018).Article 
CAS 

Google Scholar 
Hacket-Pain, A. J., Friend, A. D., Lageard, J. G. A. & Thomas, P. A. The influence of masting phenomenon on growth-climate relationships in trees: explaining the influence of previous summers’ climate on ring width. Tree Physiol. 00, 1–12 (2015).
Google Scholar 
Lourenço, J. et al. Hydraulic tradeoffs underlie local variation in tropical forest functional diversity and sensitivity to drought. New Phytol. https://doi.org/10.1111/nph.17944 (2022).Article 

Google Scholar 
Fonti, P., von Arx, G., García-González, I. & Sass-Klaassen, U. Studying global change through investigation of the plastic responses of xylem anatomy in tree rings. New Phytol. 185, 42–53 (2010).Article 

Google Scholar 
Jupa, R., Krabičková, D., Plichta, R., Mayr, S. & Gloser, V. Do angiosperm tree species adjust intervessel lateral contact in response to soil drought?. Physiol. Plant. 20, 1–11. https://doi.org/10.1111/ppl.13435 (2021).Article 
CAS 

Google Scholar 
Peters, J. M. R. et al. Living on the edge: a continental-scale assessment of forest vulnerability to drought. Glob. Change Biol. 20, 1–22. https://doi.org/10.1111/gcb.15641 (2021).Article 
CAS 

Google Scholar 
Rita, A., Borghetti, M., Todaro, L. & Saracino, A. Interpreting the climatic effects on xylem functional traits in two Mediterranean oak species: the role of extreme climatic events. Front. Plant Sci. 7, 1–11 (2016).Article 

Google Scholar 
Rodríguez-Ramírez, E. C., Vázquez-García, J. A., García-González, I., Alcántara-Ayala, O. & Luna-Vega, I. Drought effects on the plasticity in vessel traits of two endemic Magnolia species in the tropical montane cloud forests of eastern Mexico. J. Plant Ecol. 13, 331–340. https://doi.org/10.1093/jpe/rtaa019 (2020).Article 

Google Scholar 
Aide, T. M. & Grau, H. R. Globalization, migration and Latin American ecosystems. Science 305, 1915–1917 (2004).Article 

Google Scholar 
Oliveira, R. S., Eller, C. B., Bittencourt, P. R. L. & Mulligan, M. The hydroclimatic and ecophysiological basis of cloud forest distributions under current and projected climates. Ann. Bot. 113, 909–920 (2014).Article 

Google Scholar 
Pereyra-Espinoza, M. J., Inga-Guillen, G. J., Santos-Morales, M. & Rodríguez-Arisméndiz, R. Potencialidad de Cedrela odorata (Meliaceae) para estudios dendrocronológicos en la selva central del Perú. Rev. Biol. Trop. 62, 783–793 (2014).Article 

Google Scholar 
Layme-Huaman, E. T., Ferrero, M. E., Palacios-Lazaro, K. S. & Requena-Rojas, E. J. Cedrela nebulosa: A novel species for dendroclimatological studies in the montane tropics of South America. Dendrochronologia 50, 105–112 (2018).Article 

Google Scholar 
Rodríguez-Ramírez, E. C., Valdez-Nieto, J. A., Vázquez-García, J. A., Dieringer, G. & Luna-Vega, I. Plastic responses of Magnolia schiedeana Schltdl., a relict-endangered Mexican cloud forest tree, to climatic events: Evidences from leaf venation and wood vessel anatomy. Forests 11, 25 (2020).Article 

Google Scholar 
Carlquist, S. Ecological factors in wood evolution: a floristic approach. Am. J. Bot. 64, 887–896 (2020).Article 

Google Scholar 
Speer, B. J. H. Fundamentals of tree-ring research. 509 (2010). https://doi.org/10.1002/gea.20357.Rita, A., Cherubini, P., Leonardi, S., Todaro, L. & Borghetti, M. Functional adjustments of xylem anatomy to climatic variability: Insights from long-Term Ilex aquifolium tree-ring series. Tree Physiol. 35, 817–828 (2015).Article 

Google Scholar 
Paredes-Villanueva, K., López, L. & Navarro Cerrillo, R. M. Regional chronologies of Cedrela fissilis and Cedrela angustifolia in three forest types and their relation to climate. Trees Struct. Funct. 30, 1581–1593 (2016).Article 

Google Scholar 
Köhl, M., Lotfiomran, N. & Gauli, A. Influence of local climate and ENSO on the growth of Cedrela odorata L. in Suriname. Atmosphere 13, 1119 (2022).Article 
ADS 

Google Scholar 
Menezes, I. R. N., Aragão, J. R. V., Pagotto, M. A. & Lisi, C. S. Teleconnections and edaphoclimatic effects on tree growth of Cedrela odorata L in a seasonally dry tropical forest in Brazil. Dendrochronologia 72, 125923 (2022).Article 

Google Scholar 
Jiménez-Rodríguez, C. D., Coenders-Gerrits, M., Schilperoort, B., González-Angarita, A. D. P. & Savenije, H. Vapor plumes in a tropical wet forest: Spotting the invisible evaporation. Hydrol. Earth Syst. Sci. 25, 619–635 (2021).Article 
ADS 

Google Scholar 
Bräuning, A. et al. Climatic control of radial growth of Cedrela montana in a humid mountain rainforest in southern Ecuador. Erdkunde 63, 337–345 (2009).Article 

Google Scholar 
Goldsmith, G. R., Matzke, N. J. & Dawson, T. E. The incidence and implications of clouds for cloud forest plant water relations. Ecol. Lett. 16, 307–314 (2013).Article 

Google Scholar 
Toledo, M. et al. Climate is a stronger driver of tree and forest growth rates than soil and disturbance. J. Ecol. 99, 254–264 (2011).Article 

Google Scholar 
Pandey, S., Carrer, M., Castagneri, D. & Petit, G. Xylem anatomical responses to climate variability in Himalayan birch trees at one of the world’s highest forest limit. Perspect. Plant Ecol. Evol. Syst. 33, 34–41 (2018).Article 

Google Scholar 
Bose, A. K. et al. Growth and resilience responses of Scots pine to extreme droughts across Europe depend on predrought growth conditions. Glob. Change Biol. 26, 4521–4537 (2020).Article 
ADS 

Google Scholar 
Aloni, R. Ecophysiological implications of vascular differentiation and plant evolution. Trees Struct. Funct. 29, 25 (2015).Article 

Google Scholar 
Venegas-González, A., von Arx, G., Chagas, M. P. & Filho, M. T. Plasticity in xylem anatomical traits of two tropical species in response to intra-seasonal climate variability. Trees Struct. Funct. 29, 423–435 (2015).Article 

Google Scholar 
Fonti, P. et al. Studying global change through investigation of the plastic responses of xylem anatomy in tree rings. New Phytol. 185, 42–53 (2010).Article 

Google Scholar 
Scholz, A., Klepsch, M., Karimi, Z. & Jansen, S. How to quantify conduits in wood?. Front. Plant Sci. 4, 1–11 (2013).Article 

Google Scholar 
García-González, I., Souto-Herrero, M. & Campelo, F. Ring-porosity and earlywood vessels: a review on extracting environmental information through time. IAWA J. 37, 295–314 (2016).Article 

Google Scholar 
Scholz, A., Stein, A., Choat, B. & Jansen, S. How drought and deciduousness shape xylem plasticity in three Costa Rican woody plant species. IAWA J. 35, 337–355 (2014).Article 

Google Scholar 
von Arx, G., Kueffer, C. & Fonti, P. Quantifying plasticity in vessel grouping—added value from the image analysis tool ROXAS. IAWA J. 34, 433–445 (2013).Article 

Google Scholar 
Koecke, A. V., Muellner-Riehl, A. N., Pennington, T. D., Schorr, G. & Schnitzler, J. Niche evolution through time and across continents: The story of Neotropical Cedrela (Meliaceae). Am. J. Bot. 100, 1800–1810 (2013).Article 

Google Scholar 
Sperry, J. S. & Saiendra, N. Z. Intra- and inter-plant variation in xylem cavitation in Betula occidentalis. Plant. Cell Environ. 17, 1233–1241 (1994).Article 

Google Scholar 
Rodríguez-Ramírez, E. C., Crispín-DelaCruz, D. B., Ticse-Otarola, G. & Requena-Rojas, E. J. Assessing the hydric deficit on two Polylepis species from the Peruvian Andean mountains: Xylem vessel anatomic adjusting. Forest 13, 633 (2022).
Google Scholar 
Islam, M., Rahman, M. & Bräuning, A. Xylem anatomical responses of diffuse porous Chukrasia tabularis to climate in a South Asian moist tropical forest. For. Ecol. Manage. 412, 9–20 (2018).Article 

Google Scholar 
Abrantes, J., Campelo, F., García-González, I. & Nabais, C. Environmental control of vessel traits in Quercus ilex under Mediterranean climate: Relating xylem anatomy to function. Trees Struct. Funct. 27, 655–662 (2013).Article 

Google Scholar 
Fahey, T. J., Sherman, R. E. & Tanner, E. V. J. Tropical montane cloud forest: environmental drivers of vegetation structure and ecosystem function. J. Trop. Ecol. 20, 1–13 (2015).
Google Scholar 
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen–Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).Article 
ADS 

Google Scholar 
FAO-UNESCO. Soil Map of the World: Revised Legend (World Soil Resources Report 60. FAO-UNESCO, 1998).Stokes, M. & Smiley, T. L. An Introduction to Tree-Ring Dating (University of Arizona Press, 1996).
Google Scholar 
Speer, J. H. Oak mast history from dendrochronology: A new technique demonstrated in the Southern Appalachian region. Science 20, 257 (2001).
Google Scholar 
Schulman, E. Dendroclimatic Changes in Semiarid America (University of Arizona Press, 1956).
Google Scholar 
Holmes, R. L. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 43, 69–78 (1983).
Google Scholar 
Grissino-Mayer, H. D. Evaluating crossdating accuracy: A manual and tutorial for the computer program COFECHA. Tree Ring Res. 57, 205–221 (2001).
Google Scholar 
Wigley, T. M. L., Briffa, K. R. & Jones, P. D. On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Clim. Appl. Meteorol. 23, 201–213 (1984).Article 
ADS 

Google Scholar 
Cook, E. RCSigFree, Software Specialized in Dendrochronology (2017).Barichivich, J., Sauchyn, D. J. & Lara, A. Climate signals in high elevation tree-rings from the semiarid Andes of north-central Chile: responses to regional and large-scale variability. Palaeogeogr. Palaeoclimatol. Palaeoecol. 281, 320–333 (2009).Article 

Google Scholar 
Briffa, K. R. Interpreting high-resolution proxy climate data-The example of dendroclimatology. In Analysis of Climate Variability vol 0500 (eds von Storch, H. et al.) 77–94 (Springer, 1999).Chapter 

Google Scholar 
Marengo, J. A., Nobre, C. A., Tomasella, J., Cardoso, M. F. & Oyama, M. D. Hydro-climatic and ecological behaviour of the drought of Amazonia in 2005. Philos. Trans. R. Soc. B Biol. Sci. 363, 1773–1778 (2008).Article 
CAS 

Google Scholar 
Jimenez, J. C. et al. Spatio-temporal patterns of thermal anomalies and drought over tropical forests driven by recent extreme climatic anomalies. Philos. Trans. R. Soc. B Biol. Sci. 373, 25 (2018).Article 

Google Scholar 
Gloor, M. et al. Recent Amazon climate as background for possible ongoing Special Section. Glob. Biogeochem. Cycles 29, 1384–1399 (2015).Article 
ADS 
CAS 

Google Scholar 
Mooney, C. Z., Mooney, C. F., Duval, R. D. & Duvall, R. Bootstrapping: A Nonparametric Approach to Statistical Inference (Sage Publications, 1993).Book 

Google Scholar 
Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

Google Scholar 
Lloret, F., Keeling, E. G. & Sala, A. Components of tree resilience: Effects of successive low-growth episodes in old ponderosa pine forests Published by Wiley on behalf of Nordic Society Oikos Stable. https://www.jstor.org/stable/41316009 Linked references are available on JSTOR for. Oikos 120, 1909–1920 (2011).Baker, J. C. A., Santos, G. M., Gloor, M. & Brienen, R. J. W. Does Cedrela always form annual rings? Testing ring periodicity across South America using radiocarbon dating. Trees Struct. Funct. 31, 1999–2009 (2017).Article 

Google Scholar 
Palacios, W. A., Santiana, J. & Iglesias, J. A new species of Cedrela (Meliaceae) from the eastern flanks of Ecuador. Phytotaxa 393, 84–88 (2019).Article 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
CAS 

Google Scholar 
Souto-Herrero, M., Rozas, V. & García-González, I. Earlywood vessels and latewood width explain the role of climate on wood formation of Quercus pyrenaica Willd. across the Atlantic-Mediterranean boundary in NW Iberia. For. Ecol. Manage. 425, 126–137 (2018).Article 

Google Scholar 
Oksanen, J. et al. Vegan: Community ecology package. R Package version 2.4-1. https://cran.r-project.org/web/packages/vegan/index.html. (2016).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Media Vol 35 (Springer, 2016).Book 
MATH 

Google Scholar 
Ver Hoef, J. M. & Boveng, P. L. Binomial Regression: How should we model overdispersed count data?. Ecology 88, 2766–2772 (2007).Article 

Google Scholar 
Karger, D., Nobis, M., Normand, S., Graham, C. & Zimmermann, N. CHELSA-TraCE21k v1.0. Downscaled transient temperature and precipitation data since the last glacial maximum. Clim. Past Discuss. https://doi.org/10.5194/cp-2021-30 (2021).Hurvich, C. M. & Tsai, C. L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).Article 
MathSciNet 
MATH 

Google Scholar 
Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R (Springer, 2011). https://doi.org/10.1007/978-1-4419-7976-6.Book 
MATH 

Google Scholar 
‘glm2’, P. http://mirror.psu.ac.th/pub/cran/web/packages/glm2/glm2.pdf. Accessed 20 Mar 2020. 4–11 http://mirror.psu.ac.th/pub/cran/web/packages/glm2/glm2.pdf (2020).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 25 (2015).Article 

Google Scholar 
Barton, K. Package ‘ MuMIn ’ Version 1.46.0. R Package (2022). More

Predictors of reservoir statusOur analyses include all known rodent reservoirs for zoonotic pathogens (282 species). These reservoirs harbour a total of 95 known zoonotic pathogens (34 viruses, 26 bacteria, 17 helminths, 12 protozoa and six fungi) employing all known modes of transmission (43 vector-borne, 32 close-contact, 28 non-close contact, and 13 using multiple transmission modes) (Supplementary Data 2). Compared to presumed non-reservoirs (species currently not known to harbour any zoonotic pathogens), we observed that reservoir rodents are strikingly synanthropic (Figs. 2, 3a, Table 1). Despite potential geographic biases, and the general possibility that synanthropic species are better studied compared to non-synanthropic species (see Sampling bias and Supplementary Figs. 1, 2), synanthropy emerged as a defining characteristic of nearly all (95%) currently known rodent reservoirs. Of the 155 synanthropic species, only six are considered as truly synanthropic, i.e., predominately, if not exclusively, occurring in or near human dwellings, while the remaining species only occasionally show synanthropic behaviour (Supplementary Data 1).Fig. 2: Predictors of reservoir status.Final structural equation model linking reservoir status of rodent species (n = 269) with their synanthropy and hunting status, population fluctuations (s-index, log-transformed), and adult body mass, controlling for their occurrence in a range of habitats and the number of studies available per species. One-sided (directional) arrows represent a causal influence originating from the variable at the base of the arrow, with the width of the arrow and associated value representing the standardised strength of the relationship. The small double-sided arrows and numbers next to each response (endogenous) variable represent the error variance.Full size imageFig. 3: Characteristics of reservoir and synanthropic rodents.a Reservoir rodents are predominately synanthropic (n = 436 with n (non-reservoir) = 154, n (reservoir) = 282). b Synanthropic rodents display high population fluctuations (high s-index) (n = 269) and c, occur in multiple artificial habitats (n = 269) (Tables 1–3). In a, estimated probability and 95% confidence intervals are shown and in b–c, estimated probability is shown and shaded areas show 95 % confidence intervals.Full size imageTable 1 Summary of best-fit generalized linear mixed effects model for reservoir status (n = 436)Full size tableCompared to non-reservoirs, we also found that rodent reservoirs are disproportionately exploited by humans (hunted for meat and fur). Seventy-two of the regularly hunted rodent species (n = 83) are reservoirs (87%), and hunted rodent species harbour on average five times the number of zoonotic pathogens than non-hunted species (Table 2).Table 2 Summary of rodent characteristics divided by rodent group with respect to hunting, reservoir status, and synanthropic behaviourFull size tableWe explored causal pathways using a structural equation model (SEM) linking synanthropy, reservoir status, and their hypothesized predictors. The final model, which we established a priori, had 17 free parameters and 21 degrees of freedom (n = 269). The model fit, based on the SRMR (standardized root mean squared residual) and the RMSEA (root mean squared error of approximation) indicated a good fit (see Methods). From the initially formulated full model, the pathways linking reservoir status to population fluctuations (s-index, Methods), occurrence in grasslands, number of artificial habitats a species occurs in, and number of studies found per species were not significant and thus removed from the final model (Supplementary Fig. 3). Similarly, pathways linking synanthropy and occurrence in grasslands were not significant and also removed. All reported coefficients for pathways are standardized to facilitate comparisons among the different relationships. The relationships and coefficients below all refer to those in the final model.The focal variable in the model was reservoir status, which was strongly and positively associated with synanthropy and had the highest estimated pathway coefficient (standardised estimate = 0.58, 95% CI 0.49–0.66, Fig. 2). Controlling for synanthropy, species were more likely to be a reservoir with increasing adult weight (0.13, 0.04–0.22). Species that occur in savanna were less likely to be reservoirs (−0.13, −0.22 to −0.04), while hunted species were more likely to be reservoirs (Fig. 2, 0.20, 0.11–0.30).Synanthropy was influenced by four habitat variables: a species was more likely to be synanthropic if it occurs in a higher number of artificial habitats (0.17, 0.04–0.31), and occurs in urban areas (0.14, 0.01–0.27), deserts (0.12, 0.01–0.23), or forests (0.13, 0.02–0.24). Notably, species with higher s-index, and thus larger population fluctuations, were more likely to be synanthropic (0.12, 0.01–0.22), and the s-index itself decreased as adult weight increased (−0.16, −0.27 to −0.04). Finally, hunted species were characterized by higher adult bodyweight (0.35, 0.25–0.44) (Fig. 2).The number of studies per species was positively associated with both a species’ synanthropic behaviour (0.29, 0.19–0.39) and its reservoir status (0.09, 0.00– 0.19), albeit with weaker evidence for the latter effect (p = 0.054) (Fig. 2),The confirmatory generalized linear mixed effects models (GLMMs) (Tables 1, 3), which control for correlation among species within the same family, showed that our SEM results were robust. Indeed, synanthropy was a significant predictor of reservoir status. These models underscore synanthropy as the most important predictor of reservoir status in our analysis (Table 1, Figs. 2–3).Table 3 Summary of best-fit generalized linear mixed effects model for synanthropic status (n = 269)Full size tablePopulation fluctuations affect transmission riskOur newly compiled data on the magnitude of population fluctuations enabled comparative investigations beyond theoretically straightforward predictions that transmission risk increases with reservoir abundance for density-dependent systems. We show that while strong population fluctuations (measured as the s-index) are found frequently in both reservoir and non-reservoir rodents (Table 2), synanthropic rodents exhibit much larger population fluctuations compared to non-synanthropic rodents (Table 2, Figs. 2–3). This pattern was apparent despite broad confidence intervals in the relationship between the s-index and the probability of being synanthropic (Fig. 3b, Tables 2, 3). Taken together, our results suggest that larger population fluctuations in reservoir species increase zoonotic transmission risk via synanthropic behaviours of rodents, thereby increasing the likelihood of zoonotic spillover infection to humans.Habitat generalism and habitat transformation increase transmission riskWe also find that reservoir species thrive in human-created (artificial) habitats (Fig. 3a, c, Tables 2–3), which reflects a general flexibility in their use of diverse habitat types compared to non-reservoir species (Fig. 4a, Table 2). In addition, the number of zoonotic pathogens harboured by a rodent species increased with habitat breadth (r436 = 0.34, p  More

Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892, https://doi.org/10.1111/j.2007.0030-1299.15559.x (2007).Article 

Google Scholar 
Aerts, R. & Chapin, F. S. The mineral nutrition of wild plants revisited: A re-evaluation of processes and patterns. Advances in Ecological Research, Vol 30 30, 1–67 (2000).CAS 

Google Scholar 
Grime, J. P. Plant Strategies, Vegetation Processes, and Ecosystem Properties., (John Wiley & Sons, 2001).Diaz, S. et al. The plant traits that drive ecosystems: Evidence from three continents. Journal of Vegetation Science 15, 295–304, https://doi.org/10.1111/j.1654-1103.2004.tb02266.x (2004).Article 

Google Scholar 
Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Functional Ecology 16, 545–556 (2002).Article 

Google Scholar 
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany 61, 167, https://doi.org/10.1071/bt12225 (2013).Article 

Google Scholar 
Garnier, E., Navas, M.-L. & Grigulis, K. Plant Functional Diversity. (Oxford University Press, 2016).Pausas, J. G., Bradstock, R. A., Keith, D. A. & Keeley, J. E. Plant functional traits in relation to fire in crown-fire ecosystems. Ecology 85, 1085–1100 (2004).Article 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171, https://doi.org/10.1038/nature16489 (2016).Article 
ADS 
CAS 

Google Scholar 
Kattge, J. et al. TRY – a global database of plant traits. Global Change Biology 17, 2905–2935, https://doi.org/10.1111/j.1365-2486.2011.02451.x (2011).Article 
ADS 

Google Scholar 
Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Global Change Biology 26, 119–188, https://doi.org/10.1111/gcb.14904 (2020).Article 
ADS 

Google Scholar 
Royal Botanic Gardens, Kew. The State of the World’s Plants Report – 2016. (Royal Botanic Gardens, Kew, 2016).Kattge, J. et al. TRY – Categorical Traits Dataset. Data from: TRY – a global database of plant traits. TRY File Archive https://www.try-db.org/TryWeb/Data.php – 3 (2012).Garnier, E. et al. Towards a thesaurus of plant characteristics: an ecological contribution. Journal of Ecology 105, 298–309, https://doi.org/10.1111/1365-2745.12698 (2016).Article 

Google Scholar 
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Australian Journal of Botany 51, 335, https://doi.org/10.1071/bt02124 (2003).Article 

Google Scholar 
Kleyer, M. et al. The LEDA Traitbase: a database of life-history traits of the Northwest European flora. Journal of Ecology 96, 1266–1274, https://doi.org/10.1111/j.1365-2745.2008.01430.x (2008).Article 

Google Scholar 
Adler, P. B., Milchunas, D. G., Lauenroth, W. K., Sala, O. E. & Burke, I. C. Functional traits of graminoids in semi-arid steppes: a test of grazing histories. Journal of Applied Ecology 41, 653–663, https://doi.org/10.1111/j.0021-8901.2004.00934.x (2004).Article 

Google Scholar 
Adler, P. B. A comparison of livestock grazing effects on sagebrush steppe, USA, and Patagonian steppe, Argentina. PhD thesis, Colorado State University, (2003).Atkin, O. K., Westbeek, M. H. M., Cambridge, M. L., Lambers, H. & Pons, T. L. Leaf Respiration in Light and Darkness (A Comparison of Slow- and Fast-Growing Poa Species. Plant Physiology 113, 961–965, https://doi.org/10.1104/pp.113.3.961 (1997).Article 
CAS 

Google Scholar 
Campbell, C. et al. Acclimation of photosynthesis and respiration is asynchronous in response to changes in temperature regardless of plant functional group. New Phytologist 176, 375–389, https://doi.org/10.1111/j.1469-8137.2007.02183.x (2007).Article 
CAS 

Google Scholar 
Atkin, O. K., Schortemeyer, M., McFarlane, N. & Evans, J. R. The response of fast- and slow-growing Acacia species to elevated atmospheric CO2: an analysis of the underlying components of relative growth rate. Oecologia 120, 544–554, https://doi.org/10.1007/s004420050889 (1999).Article 
ADS 

Google Scholar 
Loveys, B. R. et al. Thermal acclimation of leaf and root respiration: an investigation comparing inherently fast- and slow-growing plant species. Global Change Biology 9, 895–910, https://doi.org/10.1046/j.1365-2486.2003.00611.x (2003).Article 
ADS 

Google Scholar 
Bahn, M. et al. in Land-use changes in European mountain ecosystems. ECOMONT- Concept and Results (eds A. Cernusca, U. Tappeiner, & N. Bayfield) 247-255 (Blackwell Wissenschaft, Berlin, 1999).Wohlfahrt, G. et al. Inter-specific variation of the biochemical limitation to photosynthesis and related leaf traits of 30 species from mountain grassland ecosystems under different land use. Plant, Cell and Environment 22, 1281–1296, https://doi.org/10.1046/j.1365-3040.1999.00479.x (1999).Article 

Google Scholar 
Wilson, K. B., Baldocchi, D. D. & Hanson, P. J. Spatial and seasonal variability of photosynthetic parameters and their relationship to leaf nitrogen in a deciduous forest. Tree Physiology 20, 565–578, https://doi.org/10.1093/treephys/20.9.565 (2000).Article 

Google Scholar 
Xu, L. & Baldocchi, D. D. Seasonal trends in photosynthetic parameters and stomatal conductance of blue oak (Quercus douglasii) under prolonged summer drought and high temperature. Tree Physiology 23, 865–877, https://doi.org/10.1093/treephys/23.13.865 (2003).Article 

Google Scholar 
Baraloto, C. et al. Decoupled leaf and stem economics in rain forest trees. Ecology Letters 13, 1338–1347, https://doi.org/10.1111/j.1461-0248.2010.01517.x (2010).Article 

Google Scholar 
Baraloto, C. et al. Functional trait variation and sampling strategies in species-rich plant communities. Functional Ecology 24, 208–216, https://doi.org/10.1111/j.1365-2435.2009.01600.x (2010).Article 

Google Scholar 
Blonder, B. et al. The leaf-area shrinkage effect can bias paleoclimate and ecology research. American Journal of Botany 99, 1756–1763, https://doi.org/10.3732/ajb.1200062 (2012).Article 

Google Scholar 
Blonder, B. et al. Testing models for the leaf economics spectrum with leaf and whole-plant traits in Arabidopsis thaliana. AoB Plants 7, plv049, https://doi.org/10.1093/aobpla/plv049 (2015).Article 

Google Scholar 
Blonder, B., Violle, C. & Enquist, B. J. Assessing the causes and scales of the leaf economics spectrum using venation networks in Populus tremuloides. Journal of Ecology 101, 981–989, https://doi.org/10.1111/1365-2745.12102 (2013).Article 

Google Scholar 
Blonder, B., Violle, C., Bentley, L. P. & Enquist, B. J. Venation networks and the origin of the leaf economics spectrum. Ecology Letters 14, 91–100, https://doi.org/10.1111/j.1461-0248.2010.01554.x (2010).Article 

Google Scholar 
Bond-Lamberty, B., Wang, C. & Gower, S. T. Aboveground and belowground biomass and sapwood area allometric equations for six boreal tree species of northern Manitoba. Canadian Journal of Forest Research 32, 1441–1450, https://doi.org/10.1139/x02-063 (2002).Article 

Google Scholar 
Bond-Lamberty, B., Wang, C., Gower, S. T. & Norman, J. Leaf area dynamics of a boreal black spruce fire chronosequence. Tree Physiology 22, 993–1001, https://doi.org/10.1093/treephys/22.14.993 (2002).Article 
CAS 

Google Scholar 
Bond-Lamberty, B., Wang, C. & Gower, S. T. The use of multiple measurement techniques to refine estimates of conifer needle geometry. Canadian Journal of Forest Research 33, 101–105, https://doi.org/10.1139/x02-166 (2003).Article 

Google Scholar 
Brown, K. A. et al. Assessing Natural Resource Use by Forest-Reliant Communities in Madagascar Using Functional Diversity and Functional Redundancy Metrics. PLoS ONE 6, e24107, https://doi.org/10.1371/journal.pone.0024107 (2011).Article 
ADS 
CAS 

Google Scholar 
Burrascano, S. et al. Wild boar rooting intensity determines shifts in understorey composition and functional traits. Community Ecology 16, 244–253, https://doi.org/10.1556/168.2015.16.2.12 (2015).Article 

Google Scholar 
Butterfield, B. J. & Briggs, J. M. Regeneration niche differentiates functional strategies of desert woody plant species. Oecologia 165, 477–487, https://doi.org/10.1007/s00442-010-1741-y (2010).Article 
ADS 

Google Scholar 
Byun, C., de Blois, S. & Brisson, J. Plant functional group identity and diversity determine biotic resistance to invasion by an exotic grass. Journal of Ecology 101, 128–139, https://doi.org/10.1111/1365-2745.12016 (2012).Article 

Google Scholar 
Campetella, G. et al. Patterns of plant trait–environment relationships along a forest succession chronosequence. Agriculture, Ecosystems & Environment 145, 38–48, https://doi.org/10.1016/j.agee.2011.06.025 (2011).Article 

Google Scholar 
Cavender-Bares, J., Keen, A. & Miles, B. Phylogenetic structure of floridian plant communities depends on taxonomic and spatial scale. Ecology 87, S109–S122, https://doi.org/10.1890/0012-9658(2006)87[109:psofpc]2.0.co;2 (2006).Article 

Google Scholar 
Cerabolini, B. E. L. et al. Can CSR classification be generally applied outside Britain? Plant Ecology 210, 253–261, https://doi.org/10.1007/s11258-010-9753-6 (2010).Article 

Google Scholar 
Pierce, S., Brusa, G., Sartori, M. & Cerabolini, B. E. L. Combined use of leaf size and economics traits allows direct comparison of hydrophyte and terrestrial herbaceous adaptive strategies. Annals of Botany 109, 1047–1053, https://doi.org/10.1093/aob/mcs021 (2012).Article 

Google Scholar 
Cornelissen, J. H. C. et al. Leaf digestibility and litter decomposability are related in a wide range of subarctic plant species and types. Functional Ecology 18, 779–786, https://doi.org/10.1111/j.0269-8463.2004.00900.x (2004).Article 

Google Scholar 
Quested, H. M. et al. Decomposition of sub-arctic plants with differenting nitogen economies: a functional role for hemiparasites. Ecology 84, 3209–3221, https://doi.org/10.1890/02-0426 (2003).Article 

Google Scholar 
Cornelissen, J. H. C., Diez, P. C. & Hunt, R. Seedling Growth, Allocation and Leaf Attributes in a Wide Range of Woody Plant Species and Types. The Journal of Ecology 84, 755, https://doi.org/10.2307/2261337 (1996).Article 

Google Scholar 
Cornelissen, J. H. C., Werger, M. J. A. & CastroDiez, P. vanRheenen, J. W. A. & Rowland, A. P. Foliar nutrients in relation to growth, allocation and leaf traits in seedlings of a wide range of woody plant species and types. Oecologia 111, 460–469 (1997).Article 
ADS 
CAS 

Google Scholar 
Cornelissen, J. H. C. et al. Functional traits of woody plants: correspondence of species rankings between field adults and laboratory-grown seedlings? Journal of Vegetation Science 14, 311, https://doi.org/10.1658/1100-9233(2003)014[0311:ftowpc]2.0.co;2 (2003).Article 

Google Scholar 
Castro-Díez, P., Puyravaud, J. P., Cornelissen, J. H. C. & Villar-Salvador, P. Stem anatomy and relative growth rate in seedlings of a wide range of woody plant species and types. Oecologia 116, 57–66, https://doi.org/10.1007/s004420050563 (1998).Article 
ADS 

Google Scholar 
Cornelissen, J. H. C. A triangular relationship between leaf size and seed size among woody species: allometry, ontogeny, ecology and taxonomy. Oecologia 118, 248–255, https://doi.org/10.1007/s004420050725 (1999).Article 
ADS 
CAS 

Google Scholar 
Cornelissen, J. H. C. An Experimental Comparison of Leaf Decomposition Rates in a Wide Range of Temperate Plant Species and Types. The Journal of Ecology 84, 573, https://doi.org/10.2307/2261479 (1996).Article 

Google Scholar 
Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecology Letters 11, 1065–1071, https://doi.org/10.1111/j.1461-0248.2008.01219.x (2008).Article 

Google Scholar 
Preston, K. A., Cornwell, W. K. & DeNoyer, J. L. Wood density and vessel traits as distinct correlates of ecological strategy in 51 California coast range angiosperms. New Phytologist 170, 807–818, https://doi.org/10.1111/j.1469-8137.2006.01712.x (2006).Article 

Google Scholar 
Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: Convex hull volume. Ecology 87, 1465–1471, https://doi.org/10.1890/0012-9658(2006)87[1465:attfhf]2.0.co;2 (2006).Article 

Google Scholar 
Ackerly, D. D. & Cornwell, W. K. A trait-based approach to community assembly: partitioning of species trait values into within- and among-community components. Ecology Letters 10, 135–145, https://doi.org/10.1111/j.1461-0248.2006.01006.x (2007).Article 
CAS 

Google Scholar 
Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecological Monographs 79, 109–126, https://doi.org/10.1890/07-1134.1 (2009).Article 

Google Scholar 
Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytologist 183, 980–992, https://doi.org/10.1111/j.1469-8137.2009.02917.x (2009).Article 
CAS 

Google Scholar 
Craine, J. M. et al. Functional consequences of climate change-induced plant species loss in a tallgrass prairie. Oecologia 165, 1109–1117, https://doi.org/10.1007/s00442-011-1938-8 (2011).Article 
ADS 

Google Scholar 
Craine, J. M., Towne, E. G., Ocheltree, T. W. & Nippert, J. B. Community traitscape of foliar nitrogen isotopes reveals N availability patterns in a tallgrass prairie. Plant and Soil 356, 395–403, https://doi.org/10.1007/s11104-012-1141-7 (2012).Article 
CAS 

Google Scholar 
Tucker, S. S., Craine, J. M. & Nippert, J. B. Physiological drought tolerance and the structuring of tallgrass prairie assemblages. Ecosphere 2, art48, https://doi.org/10.1890/es11-00023.1 (2011).Article 

Google Scholar 
Craine, J. M., Lee, W. G., Bond, W. J., Williams, R. J. & Johnson, L. C. Environmental constraints on a global relationship among leaf and root traits of grasses. Ecology 86, 12–19, https://doi.org/10.1890/04-1075 (2005).Article 

Google Scholar 
Craven, D. et al. Between and within-site comparisons of structural and physiological characteristics and foliar nutrient content of 14 tree species at a wet, fertile site and a dry, infertile site in Panama. Forest Ecology and Management 238, 335–346, https://doi.org/10.1016/j.foreco.2006.10.030 (2007).Article 

Google Scholar 
Craven, D. et al. Seasonal variability of photosynthetic characteristics influences growth of eight tropical tree species at two sites with contrasting precipitation in Panama. Forest Ecology and Management 261, 1643–1653, https://doi.org/10.1016/j.foreco.2010.09.017 (2011).Article 

Google Scholar 
Bragazza, L. Conservation priority of Italian Alpine habitats: a floristic approach based on potential distribution of vascular plant species. Biodiversity and Conservation 18, 2823–2835, https://doi.org/10.1007/s10531-009-9609-3 (2009).Article 

Google Scholar 
Dainese, M. & Bragazza, L. Plant traits across different habitats of the Italian Alps: a comparative analysis between native and alien species. Alpine Botany 122, 11–21, https://doi.org/10.1007/s00035-012-0101-4 (2012).Article 

Google Scholar 
de Araujo, A. C. et al. LBA-ECO CD-02 C and N Isotopes in Leaves and Atmospheric CO2, Amazonas, Brazil. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. (2011).Royal Botanical Gardens KEW. Seed Information Database (SID). Version 7.1. Available from: http://data.kew.org/sid/ (accessed May 2011). (2008).Domingues, T. F., Berry, J. A., Martinelli, L. A., Ometto, J. P. H. B. & Ehleringer, J. R. Parameterization of Canopy Structure and Leaf-Level Gas Exchange for an Eastern Amazonian Tropical Rain Forest (Tapajós National Forest, Pará, Brazil). Earth Interactions 9, 1–23, https://doi.org/10.1175/ei149.1 (2005).Article 
ADS 

Google Scholar 
Domingues, T. F., Martinelli, L. A. & Ehleringer, J. R. Ecophysiological traits of plant functional groups in forest and pasture ecosystems from eastern Amazônia, Brazil. Plant Ecology 193, 101–112, https://doi.org/10.1007/s11258-006-9251-z (2007).Article 

Google Scholar 
Domingues, T. F. et al. Co-limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa woodlands. Plant, Cell & Environment 33, 959–980, https://doi.org/10.1111/j.1365-3040.2010.02119.x (2010).Article 
CAS 

Google Scholar 
Kerkhoff, A. J., Fagan, W. F., Elser, J. J. & Enquist, B. J. Phylogenetic and Growth Form Variation in the Scaling of Nitrogen and Phosphorus in the Seed Plants. The American Naturalist 168, E103–E122, https://doi.org/10.1086/507879 (2006).Article 

Google Scholar 
Fagúndez, J. & Izco, J. Seed morphology of the European species of Erica L. sect. Arsace Salisb. ex Benth. (Ericaceae). Acta Botanica Gallica 157, 45–54, https://doi.org/10.1080/12538078.2010.10516188 (2010).Article 

Google Scholar 
Han, W., Fang, J., Guo, D. & Zhang, Y. Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytologist 168, 377–385, https://doi.org/10.1111/j.1469-8137.2005.01530.x (2005).Article 
CAS 

Google Scholar 
He, J.-S. et al. A test of the generality of leaf trait relationships on the Tibetan Plateau. New Phytologist 170, 835–848, https://doi.org/10.1111/j.1469-8137.2006.01704.x (2006).Article 

Google Scholar 
He, J.-S. et al. Leaf nitrogen:phosphorus stoichiometry across Chinese grassland biomes. Oecologia 155, 301–310, https://doi.org/10.1007/s00442-007-0912-y (2007).Article 
ADS 

Google Scholar 
Bocanegra, K., Fernández, F. & Galvis, J. Grupos funcionales de arboles en bosques secundarios de la region Bajo Calima (Buenaventura, Colombia). Boletín Científico. Centro de Museos. Museo de Historia Natural 19, 17–40, https://doi.org/10.17151/bccm.2015.19.1.2 (2015).Article 

Google Scholar 
Fitter, A. H. & Peat, H. J. The Ecological Flora Database. The Journal of Ecology 82, 415, https://doi.org/10.2307/2261309 (1994).Article 

Google Scholar 
Frenette-Dussault, C., Shipley, B., Léger, J.-F., Meziane, D. & Hingrat, Y. Functional structure of an arid steppe plant community reveals similarities with Grime’s C-S-R theory. Journal of Vegetation Science 23, 208–222, https://doi.org/10.1111/j.1654-1103.2011.01350.x (2011).Article 

Google Scholar 
Kichenin, E., Wardle, D. A., Peltzer, D. A., Morse, C. W. & Freschet, G. T. Contrasting effects of plant inter- and intraspecific variation on community-level trait measures along an environmental gradient. Functional Ecology 27, 1254–1261, https://doi.org/10.1111/1365-2435.12116 (2013).Article 

Google Scholar 
Freschet, G. T., Cornelissen, J. H. C., van Logtestijn, R. S. P. & Aerts, R. Evidence of the ‘plant economics spectrum’ in a subarctic flora. Journal of Ecology 98, 362–373, https://doi.org/10.1111/j.1365-2745.2009.01615.x (2010).Article 

Google Scholar 
Freschet, G. T., Cornelissen, J. H. C., van Logtestijn, R. S. P. & Aerts, R. Substantial nutrient resorption from leaves, stems and roots in a subarctic flora: what is the link with other resource economics traits. New Phytologist 186, 879–889, https://doi.org/10.1111/j.1469-8137.2010.03228.x (2010).Article 
CAS 

Google Scholar 
Gallagher, R. V. & Leishman, M. R. A global analysis of trait variation and evolution in climbing plants. Journal of Biogeography 39, 1757–1771, https://doi.org/10.1111/j.1365-2699.2012.02773.x (2012).Article 

Google Scholar 
Garnier, E. et al. Assessing the Effects of Land-use Change on Plant Traits, Communities and Ecosystem Functioning in Grasslands: A Standardized Methodology and Lessons from an Application to 11 European Sites. Annals of Botany 99, 967–985, https://doi.org/10.1093/aob/mcl215 (2007).Article 

Google Scholar 
Pakeman, R. J., Lepš, J., Kleyer, M., Lavorel, S. & Garnier, E. Relative climatic, edaphic and management controls of plant functional trait signatures. Journal of Vegetation Science 20, 148–159, https://doi.org/10.1111/j.1654-1103.2009.05548.x (2009).Article 

Google Scholar 
Pakeman, R. J. et al. Impact of abundance weighting on the response of seed traits to climate and land use. Journal of Ecology 96, 355–366 (2008).Article 

Google Scholar 
Fortunel, C. et al. Leaf traits capture the effects of land use changes and climate on litter decomposability of grasslands across Europe. Ecology 90, 598–611 (2009).Article 

Google Scholar 
Gillison, A. N. & Carpenter, G. A generic plant functional attribute set and grammar for dynamic vegetation description and analysis. Functional Ecology 11, 775–783, https://doi.org/10.1046/j.1365-2435.1997.00157.x (1997).Article 

Google Scholar 
Hill, M. O., Preston, C. D. & Roy, D. B. PLANTATT – attributes of British and Irish Plants: status, size, life history, geography and habitats. (Huntingdon: Centre for Ecology and Hydrology, 2004).Green, W. USDA PLANTS Compilation, version 1, 09-02-02. (http://bricol.net/downloads/data/PLANTSdatabase/) NRCS: The PLANTS Database (http://plants.usda.gov, 1 Feb 2009). National Plant Data Center: Baton Rouge, LA 70874-74490 USA (2009).Guerin, G. R., Wen, H. & Lowe, A. J. Leaf morphology shift linked to climate change. Biology Letters 8, 882–886, https://doi.org/10.1098/rsbl.2012.0458 (2012).Article 

Google Scholar 
Gutiérrez, A. G. & Huth, A. Successional stages of primary temperate rainforests of Chiloé Island, Chile. Perspectives in Plant Ecology, Evolution and Systematics 14, 243–256, https://doi.org/10.1016/j.ppees.2012.01.004 (2012).Article 

Google Scholar 
Han, W. et al. Floral, climatic and soil pH controls on leaf ash content in China’s terrestrial plants. Global Ecology and Biogeography 21, 376–382, https://doi.org/10.1111/j.1466-8238.2011.00677.x (2011).Article 

Google Scholar 
Chen, Y., Han, W., Tang, L., Tang, Z. & Fang, J. Leaf nitrogen and phosphorus concentrations of woody plants differ in responses to climate, soil and plant growth form. Ecography 36, 178–184, https://doi.org/10.1111/j.1600-0587.2011.06833.x (2011).Article 

Google Scholar 
Meng, T.-T. et al. Responses of leaf traits to climatic gradients: adaptive variation versus compositional shifts. Biogeosciences 12, 5339–5352, https://doi.org/10.5194/bg-12-5339-2015 (2015).Article 
ADS 

Google Scholar 
Prentice, I. C. et al. Evidence of a universal scaling relationship for leaf CO2 drawdown along an aridity gradient. New Phytologist 190, 169–180, https://doi.org/10.1111/j.1469-8137.2010.03579.x (2010).Article 
CAS 

Google Scholar 
He, T., Pausas, J. P., Belcher, C. M., Schwilk, D. W. & Lamont, B. B. Fire-adapted traits of Pinus arose in the fiery Cretaceous. New Phytologist 194, 751–759, https://doi.org/10.1111/j.1469-8137.2012.04079.x (2012).Article 

Google Scholar 
He, T., Lamont, B. B. & Downs, K. S. Banksias born to burn. New Phytologist 191, 184–196, https://doi.org/10.1111/j.1469-8137.2011.03663.x. (2011).Article 

Google Scholar 
Hickler, T. Plant functional types and community characteristics along environmental gradients on Öland’s Great Alvar (Sweden) Master thesis, University of Lund, Sweden, (1999).Vergutz, L., Manzoni, S., Porporato, A., Novais, R. F. & Jackson, R. B. Global resorption efficiencies and concentrations of carbon and nutrients in leaves of terrestrial plants. Ecological Monographs 82, 205–220, https://doi.org/10.1890/11-0416.1 (2012).Article 

Google Scholar 
Vergutz, L., Manzoni, S., Porporato, A., Novais, R. F. & Jackson, R. B. A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1106 (2012).Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755, https://doi.org/10.1038/nature11688 (2012).Article 
ADS 
CAS 

Google Scholar 
Kattge, J., Knorr, W., Raddatz, T. & Wirth, C. Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. Global Change Biology 15, 976–991, https://doi.org/10.1111/j.1365-2486.2008.01744.x (2009).Article 
ADS 

Google Scholar 
Kirkup, D., Malcolm, P., Christian, G. & Paton, A. Towards a Digital African Flora. Taxon 54, 457, https://doi.org/10.2307/25065373 (2005).Article 

Google Scholar 
Koike, F. Plant traits as predictors of woody species dominance in climax forest communities. Journal of Vegetation Science 12, 327–336, https://doi.org/10.2307/3236846 (2001).Article 

Google Scholar 
Koike, F., Clout, M., Kawamichi, M., De Poorter, M. & Iwatsuki, K. Assessment and Control of Biological Invasion Risks. (Cambridge, UK and Shoukadoh Book Sellers, Kyoto, Japan, and IUCN, Gland, Switzerland, 2006).Kraft, N. J. B. & Ackerly, D. D. Functional trait and phylogenetic tests of community assembly across spatial scales in an Amazonian forest. Ecological Monographs 80, 401–422, https://doi.org/10.1890/09-1672.1 (2010).Article 

Google Scholar 
Kraft, N. J. B., Valencia, R. & Ackerly, D. D. Functional Traits and Niche-Based Tree Community Assembly in an Amazonian Forest. Science 322, 580–582, https://doi.org/10.1126/science.1160662 (2008).Article 
ADS 
CAS 

Google Scholar 
Kühn, I., Durka, W. & Klotz, S. BiolFlor – a new plant-trait database as a tool for plant invasion ecology. Diversity and Distribution 10, 363–365 (2004).Article 

Google Scholar 
Otto, B. Merkmale von Samen, Früchten, generativen Germinulen und generativen Diasporen. In: Klotz, S., Kühn, I. & Durka, W. [eds.]: BIOLFLOR – Eine Datenbank zu biologisch-ökologischen Merkmalen der Gefäßpflanzen in Deutschland. Schriftenreihe für Vegetationskunde 38. Bundesamt für Naturschutz, Bonn (2002).Kurokawa, H. & Nakashizuka, T. Leaf herbivory and decomposability in a Malaysian tropical rain forest. Ecology 89, 2645–2656, https://doi.org/10.1890/07-1352.1 (2008).Article 

Google Scholar 
Guy, A. L., Mischkolz, J. M. & Lamb, E. G. Limited effects of simulated acidic deposition on seedling survivorship and root morphology of endemic plant taxa of the Athabasca Sand Dunes in well-watered greenhouse trials. Botany 91, 176–181, https://doi.org/10.1139/cjb-2012-0162 (2013).Article 

Google Scholar 
Mishkolz, J. M. Selecting and evaluating native forage mixtures for the mixed grass prairie. (University of Saskatchewan, Saskatoon, SK., 2013).Laughlin, D. C., Leppert, J. J., Moore, M. M. & Sieg, C. H. A multi-trait test of the leaf-height-seed plant strategy scheme with 133 species from a pine forest flora. Functional Ecology 24, 493–501, https://doi.org/10.1111/j.1365-2435.2009.01672.x (2009).Article 

Google Scholar 
Laughlin, D. C., Fulé, P. Z., Huffman, D. W., Crouse, J. & Laliberté, E. Climatic constraints on trait-based forest assembly. Journal of Ecology 99, 1489–1499, https://doi.org/10.1111/j.1365-2745.2011.01885.x (2011).Article 

Google Scholar 
Fyllas, N. M. et al. Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. Biogeosciences 6, 2677–2708, https://doi.org/10.5194/bg-6-2677-2009 (2009).Article 
ADS 

Google Scholar 
Baker, T. R. et al. Do species traits determine patterns of wood production in Amazonian forests. Biogeosciences 6, 297–307, https://doi.org/10.5194/bg-6-297-2009 (2009).Article 
ADS 
CAS 

Google Scholar 
Patiño, S. et al. Branch xylem density variations across the Amazon Basin. Biogeosciences 6, 545–568, https://doi.org/10.5194/bg-6-545-2009 (2009).Article 
ADS 

Google Scholar 
Louault, F., Pillar, V. D., Aufrère, J., Garnier, E. & Soussana, J. F. Plant traits and functional types in response to reduced disturbance in a semi-natural grassland. Journal of Vegetation Science 16, 151–160, https://doi.org/10.1111/j.1654-1103.2005.tb02350.x (2005).Article 

Google Scholar 
Malhado, A. C. M. et al. Spatial distribution and functional significance of leaf lamina shape in Amazonian forest trees. Biogeosciences 6, 1577–1590, https://doi.org/10.5194/bg-6-1577-2009 (2009).Article 
ADS 

Google Scholar 
Manning, P., Houston, K. & Evans, T. Shifts in seed size across experimental nitrogen enrichment and plant density gradients. Basic and Applied Ecology 10, 300–308, https://doi.org/10.1016/j.baae.2008.08.004 (2009).Article 
CAS 

Google Scholar 
Fry, E. L., Power, S. A. & Manning, P. Trait-based classification and manipulation of plant functional groups for biodiversity-ecosystem function experiments. Journal of Vegetation Science 25, 248–261, https://doi.org/10.1111/jvs.12068 (2013).Article 

Google Scholar 
Everwand, G., Fry, E. L., Eggers, T. & Manning, P. Seasonal Variation in the Capacity for Plant Trait Measures to Predict Grassland Carbon and Water Fluxes. Ecosystems 17, 1095–1108, https://doi.org/10.1007/s10021-014-9779-z (2014).Article 
CAS 

Google Scholar 
Medlyn, B. E. & Jarvis, P. G. Design and use of a database of model parameters from elevated [CO2] experiments. Ecological Modelling 124, 69–83, https://doi.org/10.1016/s0304-3800(99)00148-9 (1999).Article 
CAS 

Google Scholar 
Medlyn, B. E. et al. Effects of elevated [CO2] on photosynthesis in European forest species: a meta-analysis of model parameters. Plant, Cell & Environment 22, 1475–1495, https://doi.org/10.1046/j.1365-3040.1999.00523.x (1999).Article 
CAS 

Google Scholar 
Medlyn, B. E. et al. Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis. New Phytologist 149, 247–264, https://doi.org/10.1046/j.1469-8137.2001.00028.x (2001).Article 
CAS 

Google Scholar 
Meir, P. et al. Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area. Plant, Cell and Environment 25, 343–357, https://doi.org/10.1046/j.0016-8025.2001.00811.x (2002).Article 

Google Scholar 
Carswell, F. E. et al. Photosynthetic capacity in a central Amazonian rain forest. Tree Physiology 20, 179–186, https://doi.org/10.1093/treephys/20.3.179 (2000).Article 

Google Scholar 
Meir, P., Levy, P. E., Grace, J. & Jarvis, P. G. Photosynthetic parameters from two contrasting woody vegetation types in West Africa. Plant Ecology 192, 277–287, https://doi.org/10.1007/s11258-007-9320-y (2007).Article 

Google Scholar 
Mencuccini, M. The ecological significance of long-distance water transport: short-term regulation, long-term acclimation and the hydraulic costs of stature across plant life forms. Plant, Cell and Environment 26, 163–182, https://doi.org/10.1046/j.1365-3040.2003.00991.x (2003).Article 

Google Scholar 
Messier, J., McGill, B. J. & Lechowicz, M. J. How do traits vary across ecological scales? A case for trait-based ecology. Ecology Letters 13, 838–848, https://doi.org/10.1111/j.1461-0248.2010.01476.x (2010).Article 

Google Scholar 
Milla, R. & Reich, P. B. Multi-trait interactions, not phylogeny, fine-tune leaf size reduction with increasing altitude. Annals of Botany 107, 455–465, https://doi.org/10.1093/aob/mcq261 (2011).Article 

Google Scholar 
Minden, V. & Kleyer, M. Testing the effect-response framework: key response and effect traits determining above-ground biomass of salt marshes. Journal of Vegetation Science 22, 387–401, https://doi.org/10.1111/j.1654-1103.2011.01272.x (2011).Article 

Google Scholar 
Minden, V., Andratschke, S., Spalke, J., Timmermann, H. & Kleyer, M. Plant trait–environment relationships in salt marshes: Deviations from predictions by ecological concepts. Perspectives in Plant Ecology, Evolution and Systematics 14, 183–192, https://doi.org/10.1016/j.ppees.2012.01.002 (2012).Article 

Google Scholar 
Moles, A. T., Falster, D. S., Leishman, M. R. & Westoby, M. Small-seeded species produce more seeds per square metre of canopy per year, but not per individual per lifetime. Journal of Ecology 92, 384–396, https://doi.org/10.1111/j.0022-0477.2004.00880.x (2004).Article 

Google Scholar 
Moles, A. T. et al. Factors that shape seed mass evolution. Proceedings of the National Academy of Sciences 102, 10540–10544, https://doi.org/10.1073/pnas.0501473102 (2005).Article 
ADS 
CAS 

Google Scholar 
Lavergne, S., Muenke, N. J. & Molofsky, J. Genome size reduction can trigger rapid phenotypic evolution in invasive plants. Annals of Botany 105, 109–116, https://doi.org/10.1093/aob/mcp271 (2009).Article 
CAS 

Google Scholar 
Lavergne, S. & Molofsky, J. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proceedings of the National Academy of Sciences 104, 3883–3888, https://doi.org/10.1073/pnas.0607324104 (2007).Article 
ADS 
CAS 

Google Scholar 
Givnish, T. J., Montgomery, R. A. & Goldstein, G. Adaptive radiation of photosynthetic physiology in the Hawaiian lobeliads: light regimes, static light responses, and whole-plant compensation points. American Journal of Botany 91, 228–246, https://doi.org/10.3732/ajb.91.2.228 (2004).Article 
CAS 

Google Scholar 
Moretti, M. & Legg, C. Combining plant and animal traits to assess community functional responses to disturbance. Ecography 32, 299–309, https://doi.org/10.1111/j.1600-0587.2008.05524.x (2009).Article 

Google Scholar 
Niinemets, U. Global-Scale Climatic Controls of Leaf Dry Mass per Area, Density, and Thickness in Trees and Shrubs. Ecology 82, 453, https://doi.org/10.2307/2679872 (2001).Article 

Google Scholar 
Niinemets, Ü. Research review. Components of leaf dry mass per area – thickness and density – alter leaf photosynthetic capacity in reverse directions in woody plants. New Phytologist 144, 35–47, https://doi.org/10.1046/j.1469-8137.1999.00466.x (1999).Article 

Google Scholar 
Ciocarlan, V. The illustrated Flora of Romania. Pteridophyta et Spermatopyta. 1141 (Editura Ceres, 2009).Sanda, V., Bita-Nicolae, C. D. & Barabas, N. The flora of spontane and cultivated cormophytes from Romania. (Editura “Ion Borcea”, Bacau, 2003).Onoda, Y. et al. Global patterns of leaf mechanical properties. Ecology Letters 14, 301–312, https://doi.org/10.1111/j.1461-0248.2010.01582.x (2011).Article 

Google Scholar 
Ordoñez, J. C. et al. Plant Strategies in Relation to Resource Supply in Mesic to Wet Environments: Does Theory Mirror Nature? The American Naturalist 175, 225–239, https://doi.org/10.1086/649582 (2010).Article 

Google Scholar 
Ordoñez, J. C. et al. Leaf habit and woodiness regulate different leaf economy traits at a given nutrient supply. Ecology, 100413130925016, https://doi.org/10.1890/09-1509 (2010).Pahl, A. T., Kollmann, J., Mayer, A. & Haider, S. No evidence for local adaptation in an invasive alien plant: field and greenhouse experiments tracing a colonization sequence. Annals of Botany 112, 1921–1930, https://doi.org/10.1093/aob/mct246 (2013).Article 

Google Scholar 
Paula, S. et al. Fire-related traits for plant species of the Mediterranean Basin. Ecology 90, 1420–1420, https://doi.org/10.1890/08-1309.1 (2009).Article 

Google Scholar 
Paula, S. & Pausas, J. G. Burning seeds: germinative response to heat treatments in relation to resprouting ability. Journal of Ecology 96, 543–552, https://doi.org/10.1111/j.1365-2745.2008.01359.x (2008).Article 

Google Scholar 
Peco, B., de Pablos, I., Traba, J. & Levassor, C. The effect of grazing abandonment on species composition and functional traits: the case of dehesa grasslands. Basic and Applied Ecology 6, 175–183, https://doi.org/10.1016/j.baae.2005.01.002 (2005).Article 

Google Scholar 
Ogaya, R. & Peñuelas, J. Comparative field study of Quercus ilex and Phillyrea latifolia: photosynthetic response to experimental drought conditions. Environmental and Experimental Botany 50, 137–148, https://doi.org/10.1016/s0098-8472(03)00019-4 (2003).Article 

Google Scholar 
Ogaya, R. & Penuelas, J. Contrasting foliar responses to drought in Quercus ilex and Phillyrea latifolia. Biologia Plantarum 50, 373–382, https://doi.org/10.1007/s10535-006-0052-y (2006).Article 

Google Scholar 
Ogaya, R. & Peñuelas, J. Tree growth, mortality, and above-ground biomass accumulation in a holm oak forest under a five-year experimental field drought. Plant Ecology 189, 291–299, https://doi.org/10.1007/s11258-006-9184-6 (2006).Article 

Google Scholar 
Ogaya, R. & Peñuelas, J. Changes in leaf δ13C and δ15N for three Mediterranean tree species in relation to soil water availability. Acta Oecologica 34, 331–338, https://doi.org/10.1016/j.actao.2008.06.005 (2008).Article 
ADS 

Google Scholar 
Sardans, J., Peñuelas, J. & Ogaya, R. Drought-induced changes in C and N stoichiometry in a Quercus ilex Mediterranean forest. Forest Science 54, 513–522 (2008).
Google Scholar 
Sardans, J., Peñuelas, J., Prieto, P. & Estiarte, M. Changes in Ca, Fe, Mg, Mo, Na, and S content in a Mediterranean shrubland under warming and drought. Journal of Geophysical Research 113, https://doi.org/10.1029/2008jg000795 (2008).Peñuelas, J. et al. Faster returns on ‘leaf economics’ and different biogeochemical niche in invasive compared with native plant species. Global Change Biology 16, 2171–2185, https://doi.org/10.1111/j.1365-2486.2009.02054.x (2009).Article 
ADS 

Google Scholar 
Peñuelas, J. et al. Higher Allocation to Low Cost Chemical Defenses in Invasive Species of Hawaii. Journal of Chemical Ecology 36, 1255–1270, https://doi.org/10.1007/s10886-010-9862-7 (2010).Article 
CAS 

Google Scholar 
Pierce, S., Brusa, G., Vagge, I. & Cerabolini, B. E. L. Allocating CSR plant functional types: the use of leaf economics and size traits to classify woody and herbaceous vascular plants. Functional Ecology 27, 1002–1010, https://doi.org/10.1111/1365-2435.12095 (2013).Article 

Google Scholar 
Pierce, S., Ceriani, R. M., De Andreis, R., Luzzaro, A. & Cerabolini, B. The leaf economics spectrum of Poaceae reflects variation in survival strategies. Plant Biosystems – An International Journal Dealing with all Aspects of Plant Biology 141, 337–343, https://doi.org/10.1080/11263500701627695 (2007).Article 

Google Scholar 
Pierce, S., Luzzaro, A., Caccianiga, M., Ceriani, R. M. & Cerabolini, B. Disturbance is the principal α-scale filter determining niche differentiation, coexistence and biodiversity in an alpine community. Journal of Ecology 95, 698–706, https://doi.org/10.1111/j.1365-2745.2007.01242.x (2007).Article 

Google Scholar 
Müller, S. C., Overbeck, G. E., Pfadenhauer, J. & Pillar, V. D. Plant Functional Types of Woody Species Related to Fire Disturbance in Forest–Grassland Ecotones. Plant Ecology 189, 1–14, https://doi.org/10.1007/s11258-006-9162-z (2006).Article 

Google Scholar 
Pillar, V. D. & Sosinski, E. E. An improved method for searching plant functional types by numerical analysis. Journal of Vegetation Science 14, 323–332, https://doi.org/10.1111/j.1654-1103.2003.tb02158.x (2003).Article 

Google Scholar 
Duarte, Ld. S., Carlucci, M. B., Hartz, S. M. & Pillar, V. D. Plant dispersal strategies and the colonization of Araucaria forest patches in a grassland-forest mosaic. Journal of Vegetation Science 18, 847–858, https://doi.org/10.1111/j.1654-1103.2007.tb02601.x (2007).Article 

Google Scholar 
Blanco, C., Sosinski, E., Santos, B., Silva, M. & Pillar, V. On the overlap between effect and response plant functional types linked to grazing. Community Ecology 8, 57–65, https://doi.org/10.1556/comec.8.2007.1.8 (2007).Article 

Google Scholar 
Overbeck, G. E., Müller, S. C., Pillar, V. D. & Pfadenhauer, J. Fine-scale post-fire dynamics in southern Brazilian subtropical grassland. Journal of Vegetation Science 16, 655, https://doi.org/10.1658/1100-9233(2005)016[0655:fpdisb]2.0.co;2 (2005).Article 

Google Scholar 
Overbeck, G. E. & Pfadenhauer, J. Adaptive strategies in burned subtropical grassland in southern Brazil. Flora – Morphology, Distribution, Functional Ecology of Plants 202, 27–49, https://doi.org/10.1016/j.flora.2005.11.004 (2007).Article 

Google Scholar 
Poorter, H., Niinemets, Ü., Poorter, L., Wright, I. J. & Villar, R. Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytologist 182, 565–588, https://doi.org/10.1111/j.1469-8137.2009.02830.x (2009).Article 

Google Scholar 
Powers, J. S. & Tiffin, P. Plant functional type classifications in tropical dry forests in Costa Rica: leaf habit versus taxonomic approaches. Functional Ecology 24, 927–936, https://doi.org/10.1111/j.1365-2435.2010.01701.x (2010).Article 

Google Scholar 
Price, C. A. & Enquist, B. J. Scaling of mass and morphology in Dicotyledonous leaves: an extension of the WBE model. Ecology 88, 1132–1141, https://doi.org/10.1890/06-1158 (2007).Article 

Google Scholar 
Price, C. A., Enquist, B. J. & Savage, V. M. A general model for allometric covariation in botanical form and function. Proceedings of the National Academy of Sciences 104, 13204–13209, https://doi.org/10.1073/pnas.0702242104 (2007).Article 
ADS 
CAS 

Google Scholar 
Willis, C. G. et al. Phylogenetic community structure in Minnesota oak savanna is influenced by spatial extent and environmental variation. Ecography, no-no, https://doi.org/10.1111/j.1600-0587.2009.05975.x (2009).Reich, P. B., Oleksyn, J. & Wright, I. J. Leaf phosphorus influences the photosynthesis–nitrogen relation: a cross-biome analysis of 314 species. Oecologia 160, 207–212, https://doi.org/10.1007/s00442-009-1291-3 (2009).Article 
ADS 

Google Scholar 
Reich, P. B. et al. Scaling of respiration to nitrogen in leaves, stems and roots of higher land plants. Ecology Letters 11, 793–801, https://doi.org/10.1111/j.1461-0248.2008.01185.x (2008).Article 

Google Scholar 
Cavender-Bares, J., Sack, L. & Savage, J. Atmospheric and soil drought reduce nocturnal conductance in live oaks. Tree Physiology 27, 611–620, https://doi.org/10.1093/treephys/27.4.611 (2007).Article 

Google Scholar 
Coomes, D. A., Heathcote, S., Godfrey, E. R., Shepherd, J. J. & Sack, L. Scaling of xylem vessels and veins within the leaves of oak species. Biology Letters 4, 302–306, https://doi.org/10.1098/rsbl.2008.0094 (2008).Article 

Google Scholar 
Cornwell, W. K., Bhaskar, R., Sack, L., Cordell, S. & Lunch, C. K. Adjustment of structure and function of Hawaiian Metrosideros polymorpha at high vs. low precipitation. Functional Ecology 21, 1063–1071, https://doi.org/10.1111/j.1365-2435.2007.01323.x (2007).Article 

Google Scholar 
Dunbar‐Co, S., Sporck, Margaret, J. & Sack, L. Leaf Trait Diversification and Design in Seven Rare Taxa of the Hawaiian Plantago Radiation. International Journal of Plant Sciences 170, 61–75, https://doi.org/10.1086/593111 (2009).Article 

Google Scholar 
Hao, G.-Y., Sack, L., Wang, A.-Y., Cao, K.-F. & Goldstein, G. Differentiation of leaf water flux and drought tolerance traits in hemiepiphytic and non-hemiepiphytic Ficus tree species. Functional Ecology 24, 731–740, https://doi.org/10.1111/j.1365-2435.2010.01724.x (2010).Article 

Google Scholar 
Hoof, J., Sack, L., Webb, D. T. & Nilsen, E. T. Contrasting Structure and Function of Pubescent and Glabrous Varieties of Hawaiian Metrosideros polymorpha (Myrtaceae) at High Elevation. Biotropica 0, 070606001740001-???, https://doi.org/10.1111/j.1744-7429.2007.00325.x (2007).Article 

Google Scholar 
Martin, R. E., Asner, G. P. & Sack, L. Genetic variation in leaf pigment, optical and photosynthetic function among diverse phenotypes of Metrosideros polymorpha grown in a common garden. Oecologia 151, 387–400, https://doi.org/10.1007/s00442-006-0604-z (2006).Article 
ADS 

Google Scholar 
Nakahashi, C. D., Frole, K. & Sack, L. Bacterial Leaf Nodule Symbiosis in Ardisia (Myrsinaceae): Does it Contribute to Seedling Growth Capacity. Plant Biology 7, 495–500, https://doi.org/10.1055/s-2005-865853 (2005).Article 
CAS 

Google Scholar 
Quero, J. L. et al. Relating leaf photosynthetic rate to whole-plant growth: drought and shade effects on seedlings of four Quercus species. Functional Plant Biology 35, 725, https://doi.org/10.1071/fp08149 (2008).Article 

Google Scholar 
Sack, L. Responses of temperate woody seedlings to shade and drought: do trade-offs limit potential niche differentiation. Oikos 107, 110–127, https://doi.org/10.1111/j.0030-1299.2004.13184.x (2004).Article 

Google Scholar 
Sack, L. & Frole, K. Leaf structural diversity is related to hydraulic capacity in tropical rain forest trees. Ecology 87, 483–491, https://doi.org/10.1890/05-0710 (2006).Article 

Google Scholar 
Sack, L., Tyree, M. T. & Holbrook, N. M. Leaf hydraulic architecture correlates with regeneration irradiance in tropical rainforest trees. New Phytologist 167, 403–413, https://doi.org/10.1111/j.1469-8137.2005.01432.x (2005).Article 

Google Scholar 
Sack, L., Cowan, P. D., Jaikumar, N. & Holbrook, N. M. The ‘hydrology’ of leaves: co-ordination of structure and function in temperate woody species. Plant, Cell and Environment 26, 1343–1356, https://doi.org/10.1046/j.0016-8025.2003.01058.x (2003).Article 

Google Scholar 
Sack, L., Melcher, P. J., Liu, W. H., Middleton, E. & Pardee, T. How strong is intracanopy leaf plasticity in temperate deciduous trees. American Journal of Botany 93, 829–839, https://doi.org/10.3732/ajb.93.6.829 (2006).Article 

Google Scholar 
Scoffoni, C., Pou, A., Aasamaa, K. & Sack, L. The rapid light response of leaf hydraulic conductance: new evidence from two experimental methods. Plant, Cell & Environment 31, 1803–1812, https://doi.org/10.1111/j.1365-3040.2008.01884.x (2008).Article 

Google Scholar 
Sandel, B., Corbin, J. D. & Krupa, M. Using plant functional traits to guide restoration: a case study in California coastal grassland. Ecosphere 2, https://doi.org/10.1890/ES10-00175.1 (2011).Scherer-Lorenzen, M., Schulze, E., Don, A., Schumacher, J. & Weller, E. Exploring the functional significance of forest diversity: A new long-term experiment with temperate tree species (BIOTREE. Perspectives in Plant Ecology, Evolution and Systematics 9, 53–70, https://doi.org/10.1016/j.ppees.2007.08.002 (2007).Article 

Google Scholar 
Schweingruber, F. H. & Landolt, W. The Xylem Database. (Swiss Federal Research Institute WSL, 2005).Schweingruber, F. H. & Poschlod, P. Growth rings in herbs and shrubs: Life span, age determination and stem anatomy. Forest, Snow and Landscape Research 79, 195–415 (2005).
Google Scholar 
Sheremetev, S. N. Herbs on the soil moisture gradient (water relations and the structural-functional organization). (KMK Scientific Press Ltd, Moscow, 2005).Shiodera, S., Rahajoe, J. S. & Kohyama, T. Variation in longevity and traits of leaves among co-occurring understorey plants in a tropical montane forest. Journal of Tropical Ecology 24, 121–133, https://doi.org/10.1017/s0266467407004725 (2008).Article 

Google Scholar 
Shipley, B. Trade-offs between net assimilation rate and specific leaf area in determining relative growth rate: relationship with daily irradiance. Functional Ecology 16, 682–689, https://doi.org/10.1046/j.1365-2435.2002.00672.x (2002).Article 

Google Scholar 
Meziane, D. & Shipley, B. Interacting components of interspecific relative growth rate: constancy and change under differing conditions of light and nutrient supply. Functional Ecology 13, 611–622, https://doi.org/10.1046/j.1365-2435.1999.00359.x (1999).Article 

Google Scholar 
McKenna, M. F. & Shipley, B. Interacting determinants of interspecific relative growth: Empirical patterns and a theoretical explanation. Écoscience 6, 286–296, https://doi.org/10.1080/11956860.1999.11682529 (1999).Article 

Google Scholar 
Shipley, B. & Vu, T.-T. Dry matter content as a measure of dry matter concentration in plants and their parts. New Phytologist 153, 359–364, https://doi.org/10.1046/j.0028-646x.2001.00320.x (2002).Article 

Google Scholar 
Shipley, B. & Parent, M. Germination Responses of 64 Wetland Species in Relation to Seed Size, Minimum Time to Reproduction and Seedling Relative Growth Rate. Functional Ecology 5, 111, https://doi.org/10.2307/2389561 (1991).Article 

Google Scholar 
Shipley, B. & Lechowicz, M. J. The functional co-ordination of leaf morphology, nitrogen concentration, and gas exchange in40 wetland species. Écoscience 7, 183–194, https://doi.org/10.1080/11956860.2000.11682587 (2000).Article 

Google Scholar 
Pyankov, V. I., Kondratchuk, A. V. & Shipley, B. Leaf structure and specific leaf mass: the alpine desert plants of the Eastern Pamirs, Tadjikistan. New Phytologist 143, 131–142, https://doi.org/10.1046/j.1469-8137.1999.00435.x (1999).Article 

Google Scholar 
Meziane, D. & Shipley, B. Interacting determinants of specific leaf area in 22 herbaceous species: effects of irradiance and nutrient availability. Plant, Cell & Environment 22, 447–459, https://doi.org/10.1046/j.1365-3040.1999.00423.x (1999).Article 

Google Scholar 
Shipley, B. Structured Interspecific Determinants of Specific Leaf Area in 34 Species of Herbaceous Angiosperms. Functional Ecology 9, 312, https://doi.org/10.2307/2390579 (1995).Article 

Google Scholar 
Kazakou, E., Vile, D., Shipley, B., Gallet, C. & Garnier, E. Co-variations in litter decomposition, leaf traits and plant growth in species from a Mediterranean old-field succession. Functional Ecology 20, 21–30, https://doi.org/10.1111/j.1365-2435.2006.01080.x (2006).Article 

Google Scholar 
Vile, D. Significations fonctionnelle et ecologique des traits des especes vegetales: exemple dans une succession post-cultural mediterraneenne et generalisations PhD thesis, Université de Sherbrooke, Sherbrooke (Quebec), (2005).Auger, S. L’importance de la variabilité interspécifique des traits fonctionnels par rapport à la variabilité intraspécifique chez les jeunes arbres en forêt mature Msc thesis, Université de Sherbrooke, Sherbrooke (Quebec) (2012).Auger, S. & Shipley, B. Inter-specific and intra-specific trait variation along short environmental gradients in an old-growth temperate forest. Journal of Vegetation Science 24, 419–428, https://doi.org/10.1111/j.1654-1103.2012.01473.x (2012).Article 

Google Scholar 
Soudzilovskaia, N. A. et al. Functional traits predict relationship between plant abundance dynamic and long-term climate warming. Proceedings of the National Academy of Sciences 110, 18180–18184, https://doi.org/10.1073/pnas.1310700110 (2013).Article 
ADS 

Google Scholar 
Elumeeva, T. G. et al. Long-term vegetation dynamic in the Northwestern Caucasus: which communities are more affected by upward shifts of plant species? Alpine Botany 123, 77–85, https://doi.org/10.1007/s00035-013-0122-7 (2013).Article 

Google Scholar 
Spasojevic, M. J. & Suding, K. N. Inferring community assembly mechanisms from functional diversity patterns: the importance of multiple assembly processes. Journal of Ecology 100, 652–661, https://doi.org/10.1111/j.1365-2745.2011.01945.x (2012).Article 

Google Scholar 
Swaine, E. K. Ecological and evolutionary drivers of plant community assembly in a Bornean rain forest PhD thesis, University of Aberdeen, (2007).Zheng, W. Silva Sinica: Volume 1-4. (China Forestry Publishing House, Beijing 1983).Pan, Y., Cieraad, E. & van Bodegom, P. M. Are ecophysiological adaptive traits decoupled from leaf economics traits in wetlands. Functional Ecology 33, 1202–1210, https://doi.org/10.1111/1365-2435.13329 (2019).Article 

Google Scholar 
Douma, J. C., Bardin, V., Bartholomeus, R. P. & van Bodegom, P. M. Quantifying the functional responses of vegetation to drought and oxygen stress in temperate ecosystems. Functional Ecology 26, 1355–1365, https://doi.org/10.1111/j.1365-2435.2012.02054.x (2012).Article 

Google Scholar 
van Bodegom, P. M., Sorrell, B. K., Oosthoek, A., Bakker, C. & Aerts, R. Separating the effects of partial submergence and soil oxygen demand on plant physiology. Ecology 89, 193–204, https://doi.org/10.1890/07-0390.1 (2008).Article 

Google Scholar 
Bakker, C., Van Bodegom, P. M., Nelissen, H. J. M., Ernst, W. H. O. & Aerts, R. Plant responses to rising water tables and nutrient management in calcareous dune slacks. Plant Ecology 185, 19–28, https://doi.org/10.1007/s11258-005-9080-5 (2006).Article 

Google Scholar 
Bakker, C., Rodenburg, J. & Van Bodegom, P. M. Effects of Ca- and Fe-rich seepage on P availability and plant performance in calcareous dune soils. Plant and Soil 275, 111–122 (2005).Article 
CAS 

Google Scholar 
Adriaenssens, S. Dry deposition and canopy exchange for temperate tree species under high nitrogen deposition PhD thesis, Ghent University, Ghent, Belgium, (2012).Von Holle, B. & Simberloff, D. Testing Fox’s assembly rule: does plant invasion depend on recipient community structure? Oikos 105, 551–563, https://doi.org/10.1111/j.0030-1299.2004.12597.x (2004).Article 

Google Scholar 
Williams, M., Shimabokuro, Y. E. & Rastetter, E. B. LBA-ECO CD-09 Soil and Vegetation Characteristics, Tapajos National Forest, Brazil, Dataset. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA https://doi.org/10.3334/ORNLDAAC/1104 (2012).Article 

Google Scholar 
Wirth, C. & Lichstein, J. W. in Old-Growth Forests 81–113 (Springer Berlin Heidelberg, 2009).Fonseca, C. R., Overton, J. M., Collins, B. & Westoby, M. Shifts in trait-combinations along rainfall and phosphorus gradients. Journal of Ecology 88, 964–977, https://doi.org/10.1046/j.1365-2745.2000.00506.x (2000).Article 

Google Scholar 
McDonald, P. G., Fonseca, C. R., Overton, J. M. & Westoby, M. Leaf-size divergence along rainfall and soil-nutrient gradients: is the method of size reduction common among clades? Functional Ecology 17, 50–57, https://doi.org/10.1046/j.1365-2435.2003.00698.x (2003).Article 

Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827, https://doi.org/10.1038/nature02403 (2004).Article 
ADS 
CAS 

Google Scholar 
Wright, I. J. et al. Irradiance, temperature and rainfall influence leaf dark respiration in woody plants: evidence from comparisons across 20 sites. New Phytologist 169, 309–319, https://doi.org/10.1111/j.1469-8137.2005.01590.x (2005).Article 
CAS 

Google Scholar 
Wright, I. J. et al. Relationships Among Ecologically Important Dimensions of Plant Trait Variation in Seven Neotropical Forests. Annals of Botany 99, 1003–1015, https://doi.org/10.1093/aob/mcl066 (2006).Article 

Google Scholar 
Wright, J. P. & Sutton-Grier, A. Does the leaf economic spectrum hold within local species pools across varying environmental conditions. Functional Ecology 26, 1390–1398, https://doi.org/10.1111/1365-2435.12001 (2012).Article 

Google Scholar 
Wright, S. J. et al. Functional traits and the growth-mortality tradeoff in tropical trees. Ecology, 100514035422098, https://doi.org/10.1890/09-2335 (2010).Yguel, B. et al. Phytophagy on phylogenetically isolated trees: why hosts should escape their relatives. Ecology Letters 14, 1117–1124, https://doi.org/10.1111/j.1461-0248.2011.01680.x (2011).Article 

Google Scholar 
Zanne, A. E. et al. in Data from: Towards a worldwide wood economics spectrum. Dataset, https://doi.org/10.5061/dryad.234 (Dryad, 2009).Chave, J. et al. Towards a worldwide wood economics spectrum. Ecology Letters 12, 351–366, https://doi.org/10.1111/j.1461-0248.2009.01285.x (2009).Article 

Google Scholar 
Wang, H. et al. The China Plant Trait Database: toward a comprehensive regional compilation of functional traits for land plants. Ecology 99(2) 500–500 https://doi.org/10.1002/ecy.2091 (2018).Article 

Google Scholar 
Boyle, B. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. BMC Bioinformatics 14, https://doi.org/10.1186/1471-2105-14-16 (2013).The Taxonomic Name Resolution Service [Internet]. iPlant Collaborative. Version 4.0 [Accessed: Sep 2015]. Available from: http://tnrs.iplantcollaborative.org.Büntgen, U., Psomas, A. & Schweingruber, F. H. Introducing wood anatomical and dendrochronological aspects of herbaceous plants: applications of the Xylem Database to vegetation science. Journal of Vegetation Science 25, 967–977, https://doi.org/10.1111/jvs.12165 (2014).Article 

Google Scholar 
Page, C. N. The ferns of Britain and Ireland. (Cambridge Univ. Press, 1997).Lloyd, R. M. Spore morphology of the Hawaiian genus Sadleria (Blechnaceae). Am. Fern J. 66, 1–7 (1976).Article 

Google Scholar 
Conway, E. Spore production in bracken (Pteridium aquilinum (L.) Kuhn). J. Ecol. 45, 273–284 (1957).Article 

Google Scholar 
Stoor, A. M., Boudrie, M., Jéröme, C., Horn, K. & Bennert, H. W. Diphasiastrum oellgaardii (Lycopodiaceae, Pteridophyta), a new lycopod species from Central Europe and France. Feddes Repert. 107, 149–157 (1996).Article 

Google Scholar 
Shan, H. et al. Trait prediction using hierarchical probabilistic matrix factorization. In J. Langford (Ed.) Proceedings of the International Conference for Machine Learning (ICML). Edinburgh: International Conference on Machine Learning, 1303–1310 (2012).Fazayeli F, Banerjee, A., Kattge, J., Schrodt, F. & Reich, P. B. Uncertainty Quantified Matrix Completion using Bayesian Hierarchical Matrix Factorization. 13th International Conference on Machine Learning and Applications (ICMLA), Detroit, USA December 3–6, https://doi.org/10.1109/ICMLA.2014.56 (2014).Schrodt, F. et al. BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. Global Ecology and Biogeography, https://doi.org/10.1111/geb.12335 (2015).Díaz, S. et al. The global spectrum of plant form and function: enhanced species-level trait dataset. TRY File Archive https://doi.org/10.17871/TRY.81 (2022).New, M., Hulme, M. & Jones, P. Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology. Journal of Climate 12, 829–856, https://doi.org/10.1175/1520-0442(1999)0122.0.CO;2 (1999).Whittaker, R. J. Communities and Ecosystems. (Macmillan, 1975).Weigelt, P., König, C. & Kreft, H. The Global Inventory of Floras and Traits (GIFT) database. Available: http://gift.uni-goettingen.de (2018).Weigelt, P., König, C. & Kreft, H. GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography. Journal of Biogeography 47(1) 16–43 https://doi.org/10.1111/jbi.13623 (2020)Article 

Google Scholar  More

Bacterial strains and cultivationAll marine bacteria used in this study were cultivated using the ½ YTSS (yeast-tryptone-sea salt) medium (DSMZ 974), containing yeast extract 2 g/L, tryptone 1.25 g/L and Sigma sea salts 20 g/L or the defined marine ammonium mineral salt (MAMS) medium (DSMZ 1313) where HEPES (10 mM, pH 8.0) replaced the phosphate buffer [16]. All cultures were grown at 30 °C aerobically in a shaker (150 rpm).For growth competition assays between the WT and the olsA mutant, cultures of bacteria were grown in 10 mL ½ YTSS medium for the WT strain, or with the addition of 10 µg/mL gentamicin for the olsA mutant since a gentamicin cassette was inserted to construct the mutant [4]. Cells were harvested at mid-late exponential phase and diluted to an optical density measured at 540 nm (OD540) of 1.0. These cells were then both inoculated at 1% (v/v) into 250 mL flasks containing 50 mL growth media (either ½ YTSS or MAMS + 0.5 mM Pi) in triplicate and grown at 30 °C with shaking at 140 rpm. At time point 0 h, 100 µL samples were removed in triplicate from each flask. These samples were then ten-fold serially diluted in the same growth media to a dilution of 10−9. From each serial dilution tube, 10 µL droplets were pipetted in triplicate onto agar plates containing either ½ YTSS agar (to count both the WT and the olsA mutant) or ½ YTSS agar + 10 µg/mL gentamicin (to count just the olsA mutant). Once the droplets were dry, plates were incubated at 30 °C for 3-4 days. Colony forming units (CFU) were determined by counting the number of colonies in the dilution number where single colonies were clearly visible. For the cultures grown in ½ YTSS medium, samples were removed and enumerated using the same method at time points 24 h and 96 h. For the cultures grown in MAMS media + 0.5 mM Pi, samples were removed and enumerated at time points 0 h, 48 h and 96 h.Membrane separation by sucrose density gradient ultracentrifugationThe WT strain and the olsA mutant were grown in ½ YTSS medium to OD540 ~0.8. One litre of culture was then collected by centrifugation at 12,300 × g at 4 °C for 10 minutes, using a JLA 10.5 rotor. Cells were washed and resuspended in 50 mL HEPES buffer (pH 8.0, 10 mM). Cells were then pelleted by centrifugation at 4,500 × g at 4 °C for 10 min, before resuspending the pellet in 3 mL HEPES buffer (pH 8.0, 10 mM), containing 1.6X cOmplete Protease Inhibitor cocktail (Roche), 3X DNAse I buffer (NEB) and 6 units/mL DNase I (NEB). Cells were then lysed using a French Press at 1000 PSI. Cell debris was removed by centrifugation at 4,500 × g at 4 °C for 10 min and the supernatant was transferred to a new Oakridge centrifuge tube for pelleting total membranes by centrifugation at 75,600 × g at 10 °C for 45 min in a JA25.5 rotor. Pelleted membranes were then washed and resuspended in 20% (w/v) sucrose in HEPES buffer (10 mM, pH 8.0). Resuspended membrane samples were then layered on top of a stepwise gradient containing 3.3 mL 73% (w/v) sucrose at the bottom and 6.7 mL 53% (w/v) sucrose in between. Inner (IM) and outer (OM) membranes were separated by centrifugation at 140,000 × g at 4 °C, for 16 hours in a SW40-Ti rotor. The IM resided in the interface between the 53% (w/v) and 20% (w/v) sucrose layers and the OM in the interface between the 53% (w/v) and 73% (w/v) sucrose layers. Both IM and OM samples were removed from the sucrose density interface, diluted with 30 mL HEPES buffer (10 mM, pH 8.0), and pelleted by centrifugation at 75,600 × g for 45 min. IM and OM were then resuspended in 1 mL of the same HEPES buffer before lipid and protein extractions.Proteomics sample preparation, in-gel digestion and nanoLC-MS analysisIM and OM samples were carefully dissolved in 100 μL 1X LDS loading buffer (Invitrogen) before loading on a precast Tris-Bis NuPAGE gel (Invitrogen) using 1X MOPS running solution (Invitrogen). SDS-polyacrylamide gel electrophoresis was run for approximately 5 min to purify polypeptides in the polyacrylamide gel by removing contaminants. Polyacrylamide gel bands containing the membrane proteome were excised and digested by trypsin (Roche) proteolysis. The resulting tryptic peptides were extracted using formic acid-acetonitrile (5%:25%, v/v) before resuspension in acetonitrile-trifluoroacetate (2.5%:0.05%, v/v). Tryptic peptides were separated by nano-liquid chromatography (nanoLC) using an Ultimate 3000 LC system with an Acclaim PepMap RSLC C18 reverse phase column (ThermoFisher) at the Proteomics Research Technology Platform (PRTP) at the University of Warwick. MS/MS spectra were collected using an Orbitrap Fusion mass spectrometer (ThermoFisher) in electrospray ionization (ESI) mode. Survey scans of peptides from m/z 350 to 1500 were collected for each sample in a 1.5-hr LC-MS run. This resulted in 12 mass spectra (3 biological replicates of IM and OM of WT and the olsA mutant) with a total of ~ 7.5 G of MS/MS data.MS/MS data search and statistical analysesCompiled MS/MS raw files were searched against the genome of Ruegeria pomeroyi DSS-3 using the MaxQuant software package [17, 18]. Default settings were used and samples were matched between runs. The software package Perseus (v1.6.5.0) was used to determine differentially expressed proteins with a false discovery rate (FDR) of 0.01 [19]. The LFQ (label-free quantitation) intensity of each protein was normalized by dividing the total peptide intensity of each sample by the length of each protein. Peptides were retained for further analyses only if they were consistently found in all three biological replicates in at least one set of the four samples (IM_WT, IM_olsA, OM_WT, OM_olsA). Missing values were imputed using the default parameters (width, 0.3; down-shift 1.8) and statistical analyses were performed using a two-sample Student’s t-test. Principle component analysis (PCA) plots and volcano plots were generated using default settings in the Perseus package.To analyse the pathways of differentially expressed proteins between the wild-type and the mutant, the sequences of those proteins that were significantly overrepresented (FDR  More

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In this episode:00:45 World’s oldest DNA shows that mastodons roamed ancient GreenlandDNA recovered from ancient permafrost has been used to reconstruct what an ecosystem might have looked like two million years ago. Their work suggests that Northern Greenland was much warmer than the frozen desert it is today, with a rich ecosystem of plants and animals.Research Article: Kjær et al.Nature Video: The world’s oldest DNA: Extinct beasts of ancient Greenland08:21 Research HighlightsWhy low levels of ‘good’ cholesterol don’t predict heart disease risk in Black people, and how firework displays affect the flights of geese.Research Highlight: ‘Good’ cholesterol readings can lead to bad results for Black peopleResearch Highlight: New Year’s fireworks chase wild geese high into the sky10:31 Modelling the potential emissions of plasticsWhile the global demand for plastics is growing, the manufacturing and disposal of these ubiquitous materials is responsible for significant CO2 emissions each year. This week, a team have modelled how CO2 emissions could vary in the context of different strategies for mitigating climate change. They reveal how under specific conditions the industry could potentially become a carbon sink.Research Article: Stegmann et al.News and Views: Plastics can be a carbon sink but only under stringent conditionsSubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode. Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. An RSS feed for Nature Podcast is available too. More

SamplingSediment samples were obtained from the Kap København Formation in North Greenland (82° 24′ 00″ N 22° 12′ 00″ W) in the summers of 2006, 2012 and 2016 (see Supplementary Table 3.1.1). Sampled material consisted of organic-rich permafrost and dry permafrost. Prior to sampling, profiles were cleaned to expose fresh material. Samples were hereafter collected vertically from the slope of the hills either using a 10 cm diameter diamond headed drill bit or cutting out ~40 × 40 × 40 cm blocks. Sediments were kept frozen in the field and during transportation to the lab facility in Copenhagen. Disposable gloves and scalpels were used and changed between each sample to avoid cross-contamination. In a controlled laboratory environment, the cores and blocks were further sub-sampled for material taking only the inner part of sediment cores, leaving 1.5–2 cm between the inner core and the surface that provided a subsample of approximately 6–10 g. Subsequently, all samples were stored at temperatures below −22 °C.We sampled organic-rich sediment by taking samples and biological replicates across the three stratigraphic units B1, B2 and B3, spanning 5 different sites, site: 50 (B3), 69 (B2), 74a (B1), 74b (B1) and 119 (B3). Each biological replicate from each unit at each site was further sampled in different sublayers (numbered L0–L4, Source Data 1, sheet 1).Absolute age datingIn 2014, Be and Al oxide targets from 8× 1 kg quartz-rich sand samples collected at modern depths ranging from 3 to 21 m below stream cut terraces were analysed by accelerator mass spectrometry and the cosmogenic isotope concentrations interpreted as maximum ages using a simple burial dating approach1 (26Al:10Be versus normalized 10Be). The 26Al and 10Be isotopes were produced by cosmic ray interactions with exposed quartz in regolith and bedrock surfaces in the mountains above Kap København prior to deposition. We assume that the 26Al:10Be was uniform and steady for long time periods in the upper few metres of these gradually eroding palaeo-surfaces. Once eroded by streams and hillslope processes, the quartz sand was deposited in sandy braided stream sediment, deltaic distributary systems, or the near-shore environment and remained effectively shielded from cosmic ray nucleons buried (many tens of metres) under sediment, intermittent ice shelf or ice sheet cover, and—at least during interglacials—the marine water column until final emergence. The simple burial dating approach assumes that the sand grains experienced only one burial event. If multiple burial events separated by periods of re-exposure occurred, then the starting 26Al:10Be before the last burial event would be less than the initial production ratio (6.75 to 7.42, see discussion below) owing to the relatively faster decay of 26Al during burial, and therefore the calculated burial age would be a maximum limiting age. Multiple burial events can be caused by shielding by thick glacier ice in the source area, or by sediment storage in the catchment prior to final deposition. These shielding events mean that the 26Al:10Be is lower, and therefore a calculated burial age assuming the initial production ratio would overestimate the final burial duration. We also consider that once buried, the sand grains may have been exposed to secondary cosmogenic muons (their depth would be too great for submarine nucleonic production). As sedimentation rates in these glaciated near-shore environments are relatively rapid, we show that even the muonic production would be negligible (see Supplemental Information). However, once the marine sediments emerged above sea level, in-situ production by both nucleogenic and muogenic production could alter the 26Al:10Be. The 26Al versus 10Be isochron plot reveals this complex burial history (Supplementary Information, section 3) and the concentration versus depth composite profiles for both 26Al and 10Be reveal that the shallowest samples may have been exposed during a period of time (~15,000 years ago) that is consistent with deglaciation in the area (Supplemental Information). While we interpret the individual simple burial age of all samples as a maximum limiting age of deposition of the Kap København Formation Member B, we recommend using the three most deeply shielded samples in a single depth profile to minimize the effect of post-depositional production. We then calculate a convolved probability distribution age for these three samples (KK06A, B and C). However, this calculation depends on the 26Al:10Be production ratio we use (that is, between 6.75 and 7.42) and on whether we adjust for erosion in the catchment. So, we repeat the convolved probability distribution function age for the lowest and highest production ratio and zero to maximum possible erosion rate, to obtain the minimum and maximum limiting age range at 1σ confidence (Supplementary Information, section 3). Taking the midpoint between the negative and positive 3σ confidence limits, we obtain a maximum burial age of 2.70 ± 0.46 Myr. This age is also supported by the position of those three samples on the isochron plot, which suggests the true age may not be significantly different that this maximum limiting age.Thermal ageThe extent of thermal degradation of the Kap København DNA was compared to the DNA from the Krestovka Mammoth molar. Published kinetic parameters for DNA degradation64 were used to calculate the relative rate difference over a given interval of the long-term temperature record and to quantify the offset from the reference temperature of 10 °C, thus estimating the thermal age in years at 10 °C for each sample (Supplementary Information, section 4). The mean annual air temperature (MAT) for the the Kap København sediment was taken from Funder et al. (2001)6 and for the Krestovka Mammoth the MAT was calculated using temperature data from the Cerskij Weather Station (WMO no. 251230) 68.80° N 161.28° E, 32 m from the International Research Institute Data Library (https://iri.columbia.edu/) (Supplementary Table 4.4.1).We did not correct for seasonal fluctuation for the thermal age calculation of the Kap København sediments or from the Krestovka Mammoth. We do provide theoretical average fragment length for four different thermal scenarios for the DNA in the Kap København sediments (Supplementary Table 4.4.2). A correction in the thermal age calculation was applied for altitude using the environmental lapse rate (6.49 °C km−1). We scaled the long-term temperature model of Hansen et al. (2013)65 to local estimates of current MATs by a scaling factor sufficient to account for the estimates of the local temperature decline at the last glacial maximum and then estimated the integrated rate using an activation energy (Ea) of 127 kJ mol−1 (ref. 64).Mineralogic compositionThe minerals in each of the Kap København sediment samples were identified using X-ray diffraction and their proportions were quantified using Rietveld refinement. The samples were homogenized by grinding ~1 g of sediment with ethanol for 10 min in a McCrone Mill. The samples were dried at 60 °C and added corundum (CR-1, Baikowski) as the internal standard to a final concentration of 20.0 wt%. Diffractograms were collected using a Bruker D8 Advance (Θ–Θ geometry) and the LynxEye detector (opening 2.71°), with Cu Kα1,2 radiation (1.54 Å; 40 kV, 40 mA) using a Ni-filter with thickness of 0.2 mm on the diffracted beam and a beam knife set at 3 mm. We scanned from 5–90° 2θ with a step size of 0.1° and a step time of 4 s while the sample was spun at 20 rpm. The opening of the divergence slit was 0.3° and of the antiscatter slit 3°. Primary and secondary Soller slits had an opening of 2.5° and the opening of the detector window was 2.71°. For the Rietveld analysis, we used the Profex interface for the BGMN software66,67. The instrumental parameters and peak broadening were determined by the fundamental parameters ray-tracing procedure68. A detailed description of identification of clay minerals can be found in the supporting information.AdsorptionWe used pure or purified minerals for adsorption studies. The minerals used and treatments for purifying them are listed in Supplementary Table 4.2.6. The purity of minerals was checked using X-ray diffraction with the same instrumental parameters and procedures as listed in the above section i.e., mineralogical composition. Notes on the origin, purification and impurities can be found in the Supplementary Information section 4. We used artificial seawater69 and salmon sperm DNA (low molecular weight, lyophilized powder, Sigma Aldrich) as a model for eDNA adsorption. A known amount of mineral powder was mixed with seawater and sonicated in an ultrasonic bath for 15 min. The DNA stock was then added to the suspension to reach a final concentration between 20–800 μg ml−1. The suspensions were equilibrated on a rotary shaker for 4 h. The samples were then centrifuged and the DNA concentration in the supernatant determined with UV spectrometry (Biophotometer, Eppendorf), with both positive and negative controls. All measurements were done in triplicates, and we made five to eight DNA concentrations per mineral. We used Langmuir and Freundlich equations to fit the model to the experimental isotherm and to obtain adsorption capacity of a mineral at a given equilibrium concentration.PollenThe pollen samples were extracted using the modified Grischuk protocol adopted in the Geological Institute of the Russian Academy of Science which utilizes sodium pyrophosphate and hydrofluoric acid70. Slides prepared from 6 samples were scanned at 400× magnification with a Motic BA 400 compound microscope and photographed using a Moticam 2300 camera. Pollen percentages were calculated as a proportion of the total palynomorphs including the unidentified grains. Only 4 of the 6 samples yielded terrestrial pollen counts ≥50. In these, the total palynomorphs identified ranged from 225 to 71 (mean = 170.25; median = 192.5). Identifications were made using several published keys71,72. The pollen diagram was initially compiled using Tilia version 1.5.1273 but replotted for this study using Psimpoll 4.1074.DNA recoveryFor recovery calculation, we saturated mineral surfaces with DNA. For this, we used the same protocol as for the determination of adsorption isotherms with an added step to remove DNA not adsorbed but only trapped in the interstitial pores of wet paste. This step was important because interstitial DNA would increase the amount of apparently adsorbed DNA and overestimate the recovery. To remove trapped DNA after adsorption, we redispersed the minerals in seawater. The process of redispersing the wet paste in seawater, ultracentrifugation and removal of supernatant lasted less than 2.5 min. After the second centrifugation, the wet pastes were kept frozen until extraction. We used the same extraction protocol as for the Kap København sediments. After the extraction, the DNA concentration was again determined using UV spectrometry.MetagenomesA total of 41 samples were extracted for DNA75 and converted to 65 dual-indexed Illumina sequencing libraries (including 13 negative extraction- and library controls)30. 34 libraries were thereafter subjected to ddPCR using a QX200 AutoDG Droplet Digital PCR System (Bio-Rad) following manufacturer’s protocol. Assays for ddPCR include a P7 index primer (5′-AGCAGAAGACGGCATAC-3′) (900nM), gene-targeting primer (900 nM), and a gene-targeting probe (250nM). We screened for Viridiplantae psbD (primer: 5′-TCATAATTGGACGTTGAACC-3′, probe: 5′-(FAM)ACTCCCATCATATGAAA(BHQ1)-3′) and Poaceae psbA (primer: 5′-CTCACAACTTCCCTCTAGAC-3′, probe 5′-(HEX)AGCTGCTGTTGAAGTTC(BHQ1)-3′). Additionally, 34 of the 65 libraries were enriched using targeted capture enrichment, for mammalian mitochondrial DNA using the PaleoChip Arctic1.0 bait-set31 and all libraries were hereafter sequenced on an Illumina HiSeq 4000 80 bp PE or a NovaSeq 6000 100 bp PE. We sequenced a total of 16,882,114,068 reads which, after low complexity filtering (Dust = 1), quality trimming (q ≥ 25), duplicate removal and filtering for reads longer than 29 bp (only paired read mates for NovaSeq data) resulted in 2,873,998,429 reads that were parsed for further downstream analysis. We next estimated kmer similarity between all samples using simka32 (setting heuristic count for max number of reads (-max-reads 0) and a kmer size of 31 (-kmer-size 31)), and performed a principal component analysis (PCA) on the obtained distance matrix (see Supplementary Information, ‘DNA’). We hereafter parsed all QC reads through HOLI33 for taxonomic assignment. To increase resolution and sensitivity of our taxonomic assignment, we supplemented the RefSeq (92 excluding bacteria) and the nucleotide database (NCBI) with a recently published Arctic-boreal plant database (PhyloNorway) and Arctic animal database34 as well as searched the NCBI SRA for 139 genomes of boreal animal taxa (March 2020) of which 16 partial-full genomes were found and added (Source Data 1, sheet 4) and used the GTDB microbial database version 95 as decoy. All alignments were hereafter merged using samtools and sorted using gz-sort (v. 1). Cytosine deamination frequencies were then estimated using the newly developed metaDMG, by first finding the lowest common ancestor across all possible alignments for each read and then calculating damage patterns for each taxonomic level36 (Supplementary Information, section 6). In parallel, we computed the mean read length as well as number of reads per taxonomic node (Supplementary Information, section 6). Our analysis of the DNA damage across all taxonomic levels pointed to a minimum filter for all samples at all taxonomic levels with a D-max ≥ 25% and a likelihood ratio (λ-LR) ≥ 1.5. This ensured that only taxa showing ancient DNA characteristics were parsed for downstream profiling and analysis and resulted in no taxa within any controls being found (Supplementary Information, section 6).Marine eukaryotic metagenomeWe sought to identify marine eukaryotes by first taxonomically labelling all quality-controlled reads as Eukaryota, Archaea, Bacteria or Virus using Kraken 276 with the parameters ‘–confidence 0.5 –minimum-hit-groups 3’ combined with an extra filtering step that only kept those reads with root-to-leaf score >0.25. For the initial Kraken 2 search, we used a coarse database created by the taxdb-integration workflow (https://github.com/aMG-tk/taxdb-integration) covering all domains of life and including a genomic database of marine planktonic eukaryotes63 that contain 683 metagenome-assembled genomes (MAGs) and 30 single-cell genomes (SAGs) from Tara Oceans77, following the naming convention in Delmont et al.63, we will refer to them as SMAGs. Reads labelled as root, unclassified, archaea, bacteria and virus were refined through a second Kraken 2 labelling step using a high-resolution database containing archaea, bacteria and virus created by the taxdb-integration workflow. We used the same Kraken 2 parameters and filtering thresholds as the initial search. Both Kraken 2 databases were built with parameters optimized for the study read length (–kmer-len 25 –minimizer-len 23 –minimizer-spaces 4).Reads labelled as eukaryota, root and unclassified were hereafter mapped with Bowtie278 against the SMAGs. We used MarkDuplicates from Picard (https://github.com/broadinstitute/picard) to remove duplicates and then we calculated the mapping statistics for each SMAG in the BAM files with the filterBAM program (https://github.com/aMG-tk/bam-filter). We furthermore estimated the postmortem damage of the filtered BAM files with the Bayesian methods in metaDMG and selected those SMAGs with a D-max ≥ 0.25 and a fit quality (λ-LR) higher than 1.5. The SMAGs with fewer than 500 reads mapped, a mean read average nucleotide identity (ANI) of less than than 93% and a breadth of coverage ratio and coverage evenness of less than 0.75 were removed. We followed a data-driven approach to select the mean read ANI threshold, where we explored the variation of mapped reads as a function of the mean read ANI values from 90% to 100% and identified the elbow point in the curve (Supplementary Fig. 6.11.1). We used anvi’o79 in manual mode to plot the mapping and damage results using the SMAGs phylogenomic tree inferred by Delmont et al. as reference. We used the oceanic signal of Delmont et al. as a proxy to the contemporary distribution of the SMAGs in each ocean and sea (Fig. 5 and Supplementary Information, section 6).Comparison of DNA, macrofossil and pollenTo allow comparison between records in DNA, macrofossil and pollen, the taxonomy was harmonized following the Pan Arctic Flora checklist43 and NCBI. For example, since Bennike (1990)18, Potamogeton has been split into Potamogeton and Stuckenia, Polygonym has been split to Polygonum and Bistorta, and Saxifraga was split to Saxifraga and Micranthes, whereas others have been merged, such as Melandrium with Silene40. Plant families have changed names—for instance, Gramineae is now called Poaceae and Scrophulariaceae has been re-circumscribed to exclude Plantaginaceae and Orobancheae80. We then classified the taxa into the following: category 1 all identical genus recorded by DNA and macrofossils or pollen, category 2 genera recorded by DNA also found by macrofossils or pollen including genus contained within family level classifications, category 3 taxa only recorded by DNA, category 4 taxa only recorded by macrofossils or pollen (Source Data 1).Phylogenetic placementWe sought to phylogenetically place the set of ancient taxa with the most abundant number of reads assigned, and with a sufficient number of reference sequences to build a phylogeny. These taxa include reads mapped to the chloroplast genomes of the flora genera Salix, Populus and Betula, and to the mitochondrial genomes of the fauna families Elephantidae, Cricetidae, Leporidae, as well as the subfamilies Capreolinae and Anserinae. Although the evolution of the chloroplast genome is somewhat less stable than that of the plant mitochondrial genome, it has a faster rate of evolution, and is non-recombining, and hence is more likely to contain more informative sites for our analysis than the plant mitochondria81. Like the mitochondrial genome, the chloroplast genome also has a high copy number, so that we would expect a high number of sedimentary reads mapping to it.For each of these taxa, we downloaded a representative set of either whole chloroplast or whole mitochondrial genome fasta sequences from NCBI Genbank82, including a single representative sequence from a recently diverged outgroup. For the Betula genus, we also included three chloroplast genomes from the PhyloNorway database34,83. We changed all ambiguous bases in the fasta files to N. We used MAFFT84 to align each of these sets of reference sequences, and inspected multiple sequence alignments in NCBI MSAViewer to confirm quality85. We trimmed mitochondrial alignments with insufficient quality due to highly variable control regions for Leporidae, Cricetidae and Anserinae by removing the d-loop in MegaX86.The BEAST suite49 was used with default parameters to create ultrametric phylogenetic trees for each of the five sets of taxa from the multiple sequence alignments (MSAs) of reference sequences, which were converted from Nexus to Newick format in Figtree (https://github.com/rambaut/figtree). We then passed the multiple sequence alignments to the Python module AlignIO from BioPython87 to create a reference consensus fasta sequence for each set of taxa. Furthermore, we used SNPSites88 to create a vcf file from each of the MSAs. Since SNPSites outputs a slightly different format for missing data than needed for downstream analysis, we used a custom R script to modify the vcf format appropriately. We also filtered out non-biallelic SNPs.From the damage filtered ngsLCA output, we extracted all readIDs uniquely classified to reference sequences within these respective taxa or assigned to any common ancestor inside the taxonomic group and converted these back to fastq files using seqtk (https://github.com/lh3/seqtk). We merged reads from all sites and layers to create a single read set for each respective taxon. Next, since these extracted reads were mapped against a reference database including multiple sequences from each taxon, the output files were not on the same coordinate system. To circumvent this issue and avoid mapping bias, we re-mapped each read set to the consensus sequence generated above for that taxon using bwa89 with ancient DNA parameters (bwa aln -n 0.001). We converted these reads to bam files, removed unmapped reads, and filtered for mapping quality  > 25 using samtools90. This produced 103,042, 39,306, 91,272, 182 and 129 reads for Salix, Populus, Betula, Elephantidae and Capreolinae, respectively.We next used pathPhynder62, a phylogenetic placement algorithm that identifies informative markers on a phylogeny from a reference panel, evaluates SNPs in the ancient sample overlapping these markers, and traverses the tree to place the ancient sample according to its derived and ancestral SNPs on each branch. We used the transversions-only filter to avoid errors due to deamination, except for Betula, Salix and Populus in which we used no filter due to sufficiently high coverage. Last, we investigated the pathPhynder output in each taxon set to determine the phylogenetic placement of our ancient samples (see Supplementary Information for discussion on phylogenetic placement).Based on the analysis described above we further investigated the phylogenetic placement within the genus Mammut, or mastodons. To avoid mapping reference biases in the downstream results, we first built a consensus sequence from all comparative mitochondrial genomes used in said analysis and mapped the reads identified in ngsLCA as Elephantidae to the consensus sequence. Consensus sequences were constructed by first aligning all sequences of interest using MAFFT84 and taking a majority rule consensus base in Geneious v2020.0.5 (https://www.geneious.com). We performed three analyses for phylogenetic placement of our sequence: (1) Comparison against a single representative from each Elephantidae species including the sea cow (Dugong dugon) as outgroup, (2) Comparison against a single representative from each Elephantidae species, and (3) Comparison against all published mastodon mitochondrial genomes including the Asian elephant as outgroup.For each of these analyses we first built a new reference tree using BEAST v1.10.4 (ref. 47) and repeated the previously described pathPhynder steps, with the exception that the pathPhynder tree path analysis for the Mammut SNPs was based on transitions and transversions, not restricting to only transversions due to low coverage.
Mammut americanum
We confirmed the phylogenetic placement of our sequence using a selection of Elephantidae mitochondrial reference sequences, GTR+G, strict clock, a birth-death substitution model, and ran the MCMC chain for 20,000,000 runs, sampling every 20,000 steps. Convergence was assessed using Tracer91 v1.7.2 and an effective sample size (ESS)  > 200. To determine the approximate age of our recovered mastodon mitogenome we performed a molecular dating analysis with BEAST47 v1.10.4. We used two separate approaches when dating our mastodon mitogenome, as demonstrated in a recent publication92. First, we determined the age of our sequence by comparing against a dataset of radiocarbon-dated specimens (n = 13) only. Secondly, we estimated the age of our sequence including both molecularly (n = 22) and radiocarbon-dated (n = 13) specimens using the molecular dates previously determined92. We utilized the same BEAST parameters as Karpinski et al.92 and set the age of our sample with a gamma distribution (5% quantile: 8.72 × 104, Median: 1.178 × 106; 95% quantile: 5.093 × 106; initial value: 74,900; shape: 1; scale: 1,700,000). In short, we specified a substitution model of GTR+G4, a strict clock, constant population size, and ran the Markov Chain Monte Carlo chain for 50,000,000 runs, sampling every 50,000 steps. Convergence of the run was again determined using Tracer.Molecular dating methodsIn this section, we describe molecular dating of the ancient birch (Betula) chloroplast genome using BEAST v1.10.4 (ref. 47). In principle, the genera Betula, Populus and Salix had both sufficiently high chloroplast genome coverage (with mean depth 24.16×, 57.06× and 27.04×, respectively, although this coverage is highly uneven across the chloroplast genome) and enough reference sequences to attempt molecular dating on these samples. Notably, this is one of the reasons we included a recently diverged outgroup with a divergence time estimate in each of these phylogenetic trees. However, our Populus sample clearly contained a mixture of different species, as seen from its inconsistent placement in the pathPhynder output. In particular, there were multiple supporting SNPs to both Populus balsamifera and Populus trichocarpa, and both supporting and conflicting SNPs on branches above. Furthermore, upon inspection, our Salix sample contained a surprisingly high number of private SNPs which is inconsistent with any ancient or even modern age, especially considering the number of SNPs assigned to the edges of the phylogenetic tree leading to other Salix sequences. We are unsure what causes this inconsistency but hypothesize that our Salix sample is also a mixed sample, containing multiple Salix species that diverged from the same placement branch on the phylogenetic tree at different time periods. This is supported by looking at all the reads that cover these private SNP sites, which generally appear to be from a mixed sample, with reads containing both alternate and reference alleles present at a high proportion in many cases. Alternatively, or potentially jointly in parallel, this could be a consequence of the high number of nuclear plastid DNA sequences (NUPTs) in Salix93. Because of this, we continued with only Betula.First, we downloaded 27 complete reference Betula chloroplast genome sequences and a single Alnus chloroplast genome sequence to use as an outgroup from the NCBI Genbank repository, and supplemented this with three Betula chloroplast sequences from the PhyloNorway database generated in a recent study29, for a total of 31 reference sequences. Since chloroplast sequences are circular, downloaded sequences may not always be in the same orientation or at the same starting point as is necessary for alignment, so we used custom code (https://github.com/miwipe/KapCopenhagen) that uses an anchor string to rotate the reference sequences to the same orientation and start them all from the same point. We created a MSA of these transformed reference sequences with Mafft84 and checked the quality of our alignment by eye in Seqotron94 and NCBI MsaViewer. Next, we called a consensus sequence from this MSA using the BioAlign consensus function87 in Python, which is a majority rule consensus caller. We will use this consensus sequence to map the ancient Betula reads to, both to avoid reference bias and to get the ancient Betula sample on the same coordinates as the reference MSA.From the last common ancestor output in metaDMG36, we extracted read sets for all units, sites and levels that were uniquely classified to the taxonomic level of Betula or lower, with at a minimum sequence similarity of 90% or higher to any Betula sequence, using Seqtk95. We mapped these read sets against the consensus Betula chloroplast genome using BWA89 with ancient DNA parameters (-o 2 -n 0.001 -t 20), then removed unmapped reads, quality filtered for read quality ≥25, and sorted the resulting bam files using samtools89. For the purpose of molecular dating, it is appropriate to consider these read sets as a single sample, and so we merged the resulting bam files into one sample using samtools. We used bcftools89 to make an mpileup and call a vcf file, using options for haploidy and disabling the default calling algorithm, which can slightly biases the calls towards the reference sequence, in favour of a majority call on bases that passed the default base quality cut-off of 13. We included the default option using base alignment qualities96, which we found greatly reduced the read depths of some bases and removed spurious SNPs around indel regions. Lastly, we filtered the vcf file to include only single nucleotide variants, because we do not believe other variants such as insertions or deletions in an ancient environmental sample of this type to be of sufficiently high confidence to include in molecular dating.We downloaded the gff3 annotation file for the longest Betula reference sequence, MG386368.1, from NCBI. Using custom R code97, we parsed this file and the associated fasta to label individual sites as protein-coding regions (in which we labelled the base with its position in the codon according to the phase and strand noted in the gff3 file), RNA, or neither coding nor RNA. We extracted the coding regions and checked in Seqotron94 and R that they translated to a protein alignment well (for example, no premature stop codons), both in the reference sequence and the associated positions in the ancient sequence. Though the modern reference sequence’s coding regions translated to a high-quality protein alignment, translating the associated positions in the ancient sequence with no depth cut-off leads to premature stop codons and an overall poor quality protein alignment. On the other hand, when using a depth cut-off of 20 and replacing sites in the ancient sequence which did not meet this filter with N, we see a high-quality protein alignment (except for the N sites). We also interrogated any positions in the ancient sequence which differed from the consensus, and found that any suspicious regions (for example, with multiple SNPs clustered closely together spatially in the genome) were removed with a depth cut-off of 20. Because of this, we moved forward only with sites in both the ancient and modern samples which met a depth cut-off of at least 20 in the ancient sample, which consisted of about 30% of the total sites.Next, we parsed this annotation through the multiple sequence alignment to create partitions for BEAST47. After checking how many polymorphic and total sites were in each, we decided to use four partitions: (1) sites belonging to protein-coding positions 1 and 2, (2) coding position 3, (3) RNA, or (4) non-coding and non-RNA. To ensure that these were high confidence sites, each partition also only included those positions which had at least depth 20 in the ancient sequence and had less than 3 total gaps in the multiple sequence alignment. This gave partitions which had 11,668, 5,828, 2,690 and 29,538 sites, respectively. We used these four partitions to run BEAST47 v1.10.4, with unlinked substitution models for each partition and a strict clock, with a different relative rate for each partition. (There was insufficient information in these data to infer between-lineage rate variation from a single calibration). We assigned an age of 0 to all of the reference sequences, and used a normal distribution prior with mean 61.1 Myr and standard deviation 1.633 Myr for the root height48; standard deviation was obtained by conservatively converting the 95% HPD to z-scores. For the overall tree prior, we selected the coalescent model. The age of the ancient sequence was estimated following the overall procedures of Shapiro et al. (2011)98. To assess sensitivity to prior choice for this unknown date, we used two different priors, namely a gamma distribution metric towards a younger age (shape = 1, scale = 1.7); and a uniform prior on the range (0, 10 Myr). We also compared two different models of rate variation among sites and substitution types within each partition, namely a GTR+G with four rate categories, and base frequencies estimated from the data, and the much simpler Jukes Cantor model, which assumed no variation between substitution types nor sites within each partition. All other priors were set at their defaults. Neither rate model nor prior choice had a qualitative effect on results (Extended Data Fig. 10). We also ran the coding regions alone, since they translated correctly and are therefore highly reliable sites and found that they gave the same median and a much larger confidence interval, as expected when using fewer sites (Extended Data Fig. 10). We ran each Markov chain Monte Carlo for a total of 100 million iterations. After removing a burn-in of the first 10%, we verified convergence in Tracer91 v1.7.2 (apparent stationarity of traces, and all parameters having an Effective Sample Size  > 100). We also verified that the resulting MCC tree from TreeAnnotator47 had placed the ancient sequence phylogenetically identically to pathPhynder62 placement, which is shown in Extended Data Fig. 9. For our major results, we report the uniform ancient age prior, and the GTR+G4 model applied to each of the four partitions. The associated XML is given in Source Data 3. The 95% HPD was (2.0172,0.6786) for the age of the ancient Betula chloroplast sequence, with a median estimate of 1.323 Myr, as shown in Fig. 2.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More