Philippa Kaur

Listen to the latest from the world of science, with Benjamin Thompson.
Your browser does not support the audio element.

Download MP3

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

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

RESEARCH BRIEFINGS
07 December 2022

Ancient environmental DNA from northern Greenland opens a new chapter in genetic research, demonstrating that it is possible to track the ecology and evolution of biological communities two million years ago. The record shows an open boreal-forest ecosystem inhabited by large animals such as mastodons and reindeer. More

Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Chang. 4, 471–476 (2014).Article 
ADS 

Google Scholar 
Wright, S. J. Plant responses to nutrient addition experiments conducted in tropical forests. Ecol. Monogr. 89, e01382 (2019).Article 

Google Scholar 
Levy-Varon, J. H. et al. Tropical carbon sink accelerated by symbiotic dinitrogen fixation. Nat. Commun. 10, 5637 (2019).Article 
ADS 
CAS 

Google Scholar 
Batterman, S. A. et al. Key role of symbiotic dinitrogen fixation in tropical forest secondary succession. Nature 502, 224–227 (2013).Article 
ADS 
CAS 

Google Scholar 
Ter Steege, H. et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 443, 444–447 (2006).Article 
ADS 

Google Scholar 
Hedin, L. O., Brookshire, E. N. J., Menge, D. N. L. & Barron, A. R. The nitrogen paradox in tropical forest ecosystems. Annu. Rev. Ecol. Evol. Syst. 40, 613–635 (2009).Article 

Google Scholar 
Menge, D. N. L. et al. Patterns of nitrogen-fixing tree abundance in forests across Asia and America. J. Ecol. 107, 2598–2610 (2019).Article 
CAS 

Google Scholar 
Matson, W. J.Jr Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Syst. 11, 119–161 (1980).Article 

Google Scholar 
Coley, P. D., Bateman, M. L. & Kusar, T. A. The effects of plant quality on caterpillar growth and defense against natural enemies. Oikos 115, 219–228 (2006).Article 

Google Scholar 
Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).Article 
ADS 
CAS 

Google Scholar 
Barron, A. R., Purves, D. W. & Hedin, L. O. Facultative nitrogen fixation by canopy legumes in a lowland tropical forest. Oecologia 165, 511–520 (2011).Article 
ADS 

Google Scholar 
McCulloch, L. A. & Porder, S. Light fuels while nitrogen suppresses symbiotic nitrogen fixation hotspots in neotropical canopy gap seedlings. New Phytol. 231, 1734–1745 (2021).Article 
CAS 

Google Scholar 
Brookshire, E. N. J. et al. Symbiotic N fixation is sufficient to support net aboveground biomass accumulation in a humid tropical forest. Sci Rep. 9, 7571 (2019).Article 
ADS 
CAS 

Google Scholar 
Gei, M. et al. Legume abundance along successional and rainfall gradients in Neotropical forests. Nat. Ecol. Evol. 2, 1104–1111 (2018).Article 

Google Scholar 
Vance, C. P. in Nitrogen-fixing Leguminous Symbioses. Nitrogen Fixation: Origins, Applications, and Research Progress, Vol. 7 (eds Dilworth, M. J. et al.) (Springer, 2008).Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: how can it occur? Biogeochemistry 13, 87–115 (1991).Article 

Google Scholar 
Menge, D. N. L., Levin, S. A. & Hedin, L. O. Evolutionary tradeoffs can select against nitrogen fixation and thereby maintain nitrogen limitation. Proc. Natl Acad. Sci. USA 105, 1573–1578 (2008).Article 
ADS 
CAS 

Google Scholar 
Sheffer, E., Batterman, S. A., Levin, S. A. & Hedin, L. O. Biome-scale nitrogen fixation strategies selected by climatic constraints on nitrogen cycle. Nat. Plants 1, 15182 (2015).Article 
CAS 

Google Scholar 
Vitousek, P. M. & Field, C. B. Ecosystem constraints to symbiotic nitrogen fixers: a simple model and its implications. Biogeochemistry 46, 179–202 (1999).Article 
CAS 

Google Scholar 
Coley, P. D. & Barone, J. A. Herbivory and plant defenses in tropical forests. Annu. Rev. Ecol. Syst. 27, 305–335 (1996).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 (2009).Article 
ADS 

Google Scholar 
Batterman, S. A. et al. Phosphatase activity and nitrogen fixation reflect species differences, not nutrient trading or nutrient balance, across tropical rainforest trees. Ecol. Lett. 21, 1486–1495 (2018).Article 

Google Scholar 
Menge, D. N. L., Wolf, A. A. & Funk, J. L. Diversity of nitrogen fixation strategies in Mediterranean legumes. Nat. Plants 1, 15064 (2015).Article 
CAS 

Google Scholar 
Ritchie, M. E. & Tilman, D. Responses of legumes to herbivores and nutrients during succession on a nitrogen-poor soil. Ecol. Soc. Am. 76, 2648–2655 (1995).
Google Scholar 
Taylor, B. N. & Ostrowsky, L. R. Nitrogen-fixing and non-fixing trees differ in leaf chemistry and defence but not herbivory in a lowland Costa Rican rain forest. J. Trop. Ecol. 35, 270–279 (2019).Article 

Google Scholar 
Endara, M.-J. et al. Coevolutionary arms race versus host defense chase in a tropical herbivore–plant system. Proc. Natl Acad. Sci. USA 114, E7499–E7505 (2017).Article 
CAS 

Google Scholar 
Kursar, T. A. & Coley, P. D. Convergence in defense syndromes of young leaves in tropical rainforests. Biochem. Syst. Ecol. 31, 929–949 (2003).Article 
CAS 

Google Scholar 
Kursar, T. A. et al. The evolution of antiherbivore defenses and their contribution to species coexistence in the tropical tree genus Inga. Proc. Natl Acad. Sci. USA 106, 18073–18078 (2009).Article 
ADS 
CAS 

Google Scholar 
Taylor, B. N. & Menge, D. N. L. Light regulates tropical symbiotic nitrogen fixation more strongly than soil nitrogen. Nat. Plants 4, 655–661 (2018).Article 
CAS 

Google Scholar 
Adams, M., Turnbull, T., Sprent, J. & Buchmann, N. Legumes are different: leaf nitrogen, photosynthesis, and water use efficiency. Proc. Natl Acad. Sci. USA 113, 4098–4103 (2016).Article 
ADS 
CAS 

Google Scholar 
Coley, P. D. Effects of plant growth rate and leaf lifetime on the amount and type of anti-herbivore defense. Oecologia 74, 531–536 (1988).Article 
ADS 
CAS 

Google Scholar 
Batterman, S. A., Wurzburger, N. & Hedin, L. O. Nitrogen and phosphorus interact to control tropical symbiotic N2 fixation: a test in Inga punctata. J. Ecol. 101, 1400–1408 (2013).Article 
CAS 

Google Scholar 
Eichhorn, M. P., Nilus, R., Compton, S. G., Hartley, S. E. & Burslem, D. F. R. P. Herbivory of tropical rain forest tree seedlings correlates with future mortality. Ecology 91, 1092–1101 (2010).Article 

Google Scholar 
Wink, M. Evolution of secondary metabolites in legumes (Fabaceae). South African J. Bot. 89, 164–175 (2013).Article 
CAS 

Google Scholar 
Currano, E. D. & Jacobs, B. F. Bug-bitten leaves from the early Miocene of Ethiopia elucidate the impacts of plant nutrient concentrations and climate on insect herbivore communities. Glob. Planet. Change 207, 103655 (2021).Article 

Google Scholar 
Wieder, W. R., Cleveland, C. C., Lawrence, D. M. & Bonan, G. B. Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study. Environ. Res. Lett. 10, 044016 (2015).Article 
ADS 

Google Scholar 
Sprent, J. I. Legume Nodulation: A Global Perspective (John Wiley, 2009).Leigh, E. G. Jr Tropical Forest Ecology: A View from Barro Colorado Island (Oxford Univ. Press, 1999).Comita, L. S., Muller-Landau, H. C., Aguilar, S. & Hubbell, S. P. Asymmetric density dependence shapes species abundances in a tropical tree community. Science 329, 330–332 (2010).Article 
ADS 
CAS 

Google Scholar 
Queenborough, S. A., Metz, M. R., Valencia, R. & Wright, S. J. Demographic consequences of chromatic leaf defence in tropical tree communities: do red young leaves increase growth and survival? Ann. Bot. 112, 677–684 (2013).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 (2012).Article 
CAS 

Google Scholar 
Pasquini, S. C. & Santiago, L. S. Nutrients limit photosynthesis in seedlings of a lowland tropical forest tree species. Oecologia 168, 311–319 (2012).Article 
ADS 
CAS 

Google Scholar 
Collalti, A. & Prentice, I. C. Is NPP proportional to GPP? Waring’s hypothesis 20 years on. Tree Physiol. 39, 1473–1483 (2019).Article 
CAS 

Google Scholar 
Westbrook, J. W. et al. What makes a leaf tough? Patterns of correlated evolution between leaf toughness traits and demographic rates among 197 shade-tolerant woody species in a Neotropical forest. Am. Nat. 177, 800–811 (2011).Article 

Google Scholar 
Wright, S. J. et al. Functional traits and the growth–mortality trade‐off in tropical trees. Ecology 91, 3664–3674 (2010).Article 

Google Scholar 
Kitajima, K. et al. How cellulose-based leaf toughness and lamina density contribute to long leaf lifespans of shade-tolerant species. New Phytol. 195, 640–652 (2012).Article 

Google Scholar 
Kitajima, K., Wright, S. J. & Westbrook, J. W. Leaf cellulose density as the key determinant of inter- and intra-specific variation in leaf fracture toughness in a species-rich tropical forest. Interface Focus https://doi.org/10.1098/rsfs.2015.0100 (2016).Sedio, B. E., Echeverri, J. C. R., Boya, C. A. & Wright, S. J. Sources of variation in foliar secondary chemistry in a tropical forest tree community. Ecology 98, 616–623 (2017).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).Article 

Google Scholar 
Murphy, S. J., Xu, K. & Comita, L. S. Tree seedling richness, but not neighborhood composition, influences insect herbivory in a temperate deciduous forest community. Ecol. Evol. 6, 6310–6319 (2016).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. Preprint at https://arxiv.org/abs/1406.5823 (2014).Moles, A. T. & Westoby, M. Do small leaves expand faster than large leaves, and do shorter expansion times reduce herbivore damage? Oikos 90, 517–524 (2000).Article 

Google Scholar 
Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. https://doi.org/10.18637/jss.v080.i01 (2017). 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

The isotopic spread for each study siteThe overall linear relationship between δ2H and δ18O values for hair (n = 81) and toenails (n = 39), respectively, were (Fig. 2):$$delta^{2} {text{H}}_{{text{hair(VSMOW)}}} = , 0.89 times delta^{18} {text{O}}_{{text{hair(VSMOW)}}} {-} , 86.16,;{text{R}}^{2} = , 0.19,;p , < , 0.01$$ (1) $$delta^{2} {text{H}}_{{text{toenail(VSMOW)}}} = , 0.15 times delta^{18} {text{O}}_{{text{toenail(VSMOW)}}} {-} , 91.69,;{text{R}}^{2} = , 0.00,;p , = , 0.69$$ (2) Figure 2δ2H and δ18O values (‰) of all samples for both hair (δ2H: n = 81, δ18O: n = 82) and toenails (δ2H and δ18O: n = 39). The solid black line represents the Global Meteoric Water Line (GMWL) [δ2H = 8 (times) δ18O + 10] and is included in the graph for comparison purposes. The regression lines between oxygen and hydrogen values for hair [δ2Hhair(VSMOW) = 0.89 × δ18Ohair(VSMOW) − 86.16, R2 = 0.19, p  − 82‰ were then split further where any samples with δ2Hhair values less than − 73‰ were initially classified as Site 2. These samples were then split again to either Site 2 (δ2Hhair ≥ 76‰) or Site 4 (δ2Hhair  − 73‰ were classified as Site 4. No samples could be classified as originating from Site 3. The second CART model was built for stable hydrogen and oxygen isotopes of toenails (Model 2) (Fig. 5b). The model included only two decision nodes in which the first predictor variable was δ2Htoenail value, where samples with values less than − 93‰ were predicted to be from Site 1. For toenail samples with hydrogen values greater than − 93‰, oxygen values were used to determine whether they could be classified as Site 2 or Site 4. Those samples with δ18Otoenail values less than 9.6‰ were classified as Site 2 and those with values greater than 9.6‰ were predicted as Site 4. No samples were predicted to be from Site 3 purely from stable hydrogen and oxygen isotopes in toenails. Finally, the third model consisted of stable hydrogen and oxygen isotope values in both hair and toenail samples (Model 3) (Fig. 5c). Model 3 selected toenails as the best attribute for classification, which indicates that toenail isotope values are the better predictor when both hair and toenail samples are present for analysis from Sites 1–4. The model was similar to that of Model 2.Figure 5Decision trees developed from both δ2H and δ18O values of (a) hair [Model 1, trained with n = 65], (b) toenails [Model 2, trained with n = 32] and (c) of both hair and toenails [Model 3, trained with n = 28]. The predicted study site numbers are shown on the first row within each bubble. The proportions of samples in each node are shown as decimals for Sites 1, 2, 3, 4, respectively. The percentages indicate the proportion of samples within each sub-partition.Full size imageConfusion matrices (Table 1) were constructed for all three models to evaluate the performance of the classification models. Of the three models, Model 3 proved to be the most accurate model with an overall accuracy of 71.4% (see Supplementary Fig S2. online). The performance evaluation summary, including measures for sensitivity, specificity, positive predictive value, and negative predictive value for all three models, is provided in (see Supplementary Table S3. online).Intra-individual differencesBoth hair and toenail samples were retrieved from 35 of the 86 individuals. The paired difference between δ2H values in hair and toenails of the same individual was tested using the Wilcoxon Signed Rank's test for non-normal data as the dataset failed the Shapiro–Wilk's normality test at the α = 0.05 significance level. Significant differences were found between δ2H values of hair (n = 35, mean = − 78.0‰, s.d. = 3.06) and toenails (n = 35, mean = − 90.9‰, s.d. = 3.27) from the same individual (p  0.05. Overall, the isotopic values of δ2H in hair were higher than those of toenail from the same individual by 13.0‰, on average, with a standard deviation of 8.4‰. For δ18O, the average was 1.5‰ with a standard deviation of 4.6‰ (Fig. 6).Figure 6(a) δ2H and (b) δ18O values in hair and toenails for all individuals that provided both tissue types (n = 35). Study site information are also shown by shapes. The standard deviations of each sample, ran in either duplicates or triplicates, are shown by error bars. Note that error bars cannot be seen for some samples due to small standard deviations. The average difference between the isotopic values of hair and toenail from the same individual were 13.0‰ with a standard deviation of 8.4‰ for δ2H and 1.5‰ with a standard deviation of 4.6‰ for δ18O.Full size imageThe linear relationships between δ2H in hair and toenails for all individuals were (see Supplementary Fig S3. online):$$delta^{2} {text{H}}_{{{text{hair}}}} = , 0.48 times delta^{2} {text{H}}_{{text{toenail }}} {-} , 34.72,;{text{R}}^{2} = , 0.16,;p , < , 0.05$$ (19) and for δ18O:$$delta^{18} {text{O}}_{{{text{hair}}}} = , 0.55 times delta^{18} {text{O}}_{{{text{toenail}}}} + , 5.16,;{text{R}}^{2} = , 0.13,;p , < 0.05$$ (20) Overall, both equations showed a weak relationship, as seen by the small R2 values. 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