Ecology

Abdel-Ghani AH, Parzies HK, Omary A, Geiger HH (2004) Estimating the outcrossing rate of barley landraces and wild barley populations collected from ecologically different regions of Jordan Theor Appl Genet 109(3):588–595PubMed 

Google Scholar 
Akerman A, Bürger R (2014) The consequences of gene flow for local adaptation and differentiation: a two-locus two-deme model J Math Biol 68(5):1135–1198PubMed 

Google Scholar 
Al-Asadi H, Petkova D, Stephens M, Novembre J (2019) Estimating recent migration and population-size surfaces PLoS Genet 15(1):e1007908PubMed 
PubMed Central 

Google Scholar 
Baker HG (1967) Support for Baker’s law-as a rule Evolution 21(4):853–856PubMed 

Google Scholar 
Baker K, Baker K, Bayer M, Cook N, Dreißig S, Dhillon T, Russell J, Hedley PE, Morris J, Ramsay L, Colas I et al. (2014) The low-recombining pericentromeric region of barley restricts gene diversity and evolution but not gene expression Plant J 79(6):981–992CAS 
PubMed 
PubMed Central 

Google Scholar 
Battey C, Ralph PL, Kern AD (2020) Space is the place: effects of continuous spatial structure on analysis of population genetic data Genetics 215(1):193–214CAS 
PubMed 
PubMed Central 

Google Scholar 
Baum M, Grando S, Backes G, Jahoor A, Sabbagh A, Ceccarelli S (2003) QTLs for agronomic traits in the mediterranean environment identified in recombinant inbred lines of the cross’ Arta’ × H. spontaneum 41-1 Theor Appl Genet 107(7):1215–1225CAS 
PubMed 

Google Scholar 
Bedada G, Westerbergh A, Nevo E, Korol A, Schmid KJ (2014) DNA sequence variation of wild barley Hordeum spontaneum (L.) across environmental gradients in Israel Heredity 112(6):646–655CAS 
PubMed 
PubMed Central 

Google Scholar 
Berner D, Roesti M (2017) Genomics of adaptive divergence with chromosome-scale heterogeneity in crossover rate Mol Ecol 26(22):6351–6369CAS 
PubMed 

Google Scholar 
Bhatia G, Patterson N, Sankararaman S, Price AL (2013) Estimating and interpreting FST: the impact of rare variants Genome Res 23(9):1514–1521CAS 
PubMed 
PubMed Central 

Google Scholar 
Bohra A, Kilian B, Kilian B, Sivasankar S, Caccamo M, Mba, C, McCouch SR, Varshney RK (2021) Reap the crop wild relatives for breeding future crops. Trends Biotechnol. https://doi.org/10.1016/j.tibtech.2021.08.009Bradburd GS, Coop GM, Ralph PL (2018) Inferring continuous and discrete population genetic structure across space. Genetics 210(1):33–52PubMed 
PubMed Central 

Google Scholar 
Bürger R, Akerman A (2011) The effects of linkage and gene flow on local adaptation: a two-locus continent–island model. Theor Popul Biol 80(4):272–288PubMed 
PubMed Central 

Google Scholar 
Cabreros I, Storey JD (2019) A likelihood-free estimator of population structure bridging admixture models and principal components analysis. Genetics 212(4):1009–1029PubMed 
PubMed Central 

Google Scholar 
Caldwell KS, Russell J, Langridge P, Powell W (2006) Extreme population-dependent linkage disequilibrium detected in an inbreeding plant species, Hordeum vulgare. Genetics 172(1):557–567CAS 
PubMed 
PubMed Central 

Google Scholar 
Capblancq T, Luu K, Blum MG, Bazin E (2018) Evaluation of redundancy analysis to identify signatures of local adaptation. Mol Ecol Resour 18(6):1223–1233CAS 
PubMed 

Google Scholar 
Caye K, Jumentier B, Lepeule J, François O (2019) LFMM 2: fast and accurate inference of gene-environment associations in genome-wide studies. Mol Biol Evol 36(4):852–860CAS 
PubMed 
PubMed Central 

Google Scholar 
Contreras-Moreira B, Serrano-Notivoli R, Mohammed NE, Cantalapiedra CP, Beguería S, Casas AM, Igartua E (2019) Genetic association with high-resolution climate data reveals selection footprints in the genomes of barley landraces across the Iberian Peninsula. Mol Ecol 28(8):1994–2012PubMed 
PubMed Central 

Google Scholar 
Dawson IK, Russell J, Powell W, Steffenson B, Thomas WTB, Waugh R (2015) Barley: a translational model for adaptation to climate change. New Phytol 206(3):913–931PubMed 

Google Scholar 
Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (pcnm). Ecol Model 196(3-4):483–493
Google Scholar 
Dray S, Bauman D, Blanchet G, Borcard D, Clappe S, Guenard G, Jombart T, Larocque G, Legendre P, Madi N, Wagner HH (2019) adespatial: multivariate multiscale spatial analysis. R package version 0.3-7. https://CRAN.R-project.org/package=adespatialElshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6(5):e19379CAS 
PubMed 
PubMed Central 

Google Scholar 
Excoffier L, Hofer T, Foll M (2009) Detecting loci under selection in a hierarchically structured population. Heredity 103(4):285–298CAS 
PubMed 

Google Scholar 
Fang Z, Gonzales AM, Clegg MT, Smith KP, Muehlbauer GJ, Steffenson BJ, Morrell PL (2014) Two genomic regions contribute disproportionately to geographic differentiation in wild barley. G34(7):1193–1203PubMed 
PubMed Central 

Google Scholar 
Fick SE, Hijmans RJ (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315
Google Scholar 
Forester BR, Lasky JR, Wagner HH, Urban DL (2018) Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations. Mol Ecol 27(9):2215–2233CAS 
PubMed 

Google Scholar 
Forester BR, Jones MR, Joost S, Landguth EL, Lasky JR (2016) Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol Ecol 25(1):104–120CAS 
PubMed 

Google Scholar 
Galkin E, Dalal A, Evenko A, Fridman E, Kan I, Wallach R, Moshelion M (2018) Risk-management strategies and transpiration rates of wild barley in uncertain environments. Physiol Plant 164(4):412–428CAS 
PubMed 

Google Scholar 
Gautier M (2015) Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics 201(4):1555–1579CAS 
PubMed 
PubMed Central 

Google Scholar 
Gibson MJ, Moyle LC (2020) Regional differences in the abiotic environment contribute to genomic divergence within a wild tomato species. Mol Ecol 29(12):2204–2217CAS 
PubMed 

Google Scholar 
Günther T, Coop G (2013) Robust identification of local adaptation from allele frequencies. Genetics 195(1):205–220PubMed 
PubMed Central 

Google Scholar 
Gutaker RM, Groen SC, Bellis ES, Choi JY, Pires IS, Bocinsky RK, Slayton ER, Wilkins O, Castillo CC, Negrão S et al. (2020) Genomic history and ecology of the geographic spread of rice. Nat Plants 6(5):492–502PubMed 

Google Scholar 
Hämälä T, Savolainen O (2019) Genomic patterns of local adaptation under gene flow in arabidopsis lyrata. Mol Biol Evol 36(11):2557–2571
Google Scholar 
Harlan JR, Zohary D (1966) Distribution of wild wheats and barley. Science 153(3740):1074–1080CAS 
PubMed 

Google Scholar 
Hartfield M, Bataillon T, Glémin S (2017) The evolutionary interplay between adaptation and self-fertilization. Trends Genet 33(6):420–431CAS 
PubMed 
PubMed Central 

Google Scholar 
Hendrick MF, Finseth FR, Mathiasson ME, Palmer KA, Broder EM, Breigenzer P, Fishman L (2016) The genetics of extreme microgeographic adaptation: an integrated approach identifies a major gene underlying leaf trichome divergence in yellowstone mimulus guttatus. Mol Ecol 25(22):5647–5662CAS 
PubMed 

Google Scholar 
Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B et al. (2017) Soilgrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2):e0169748PubMed 
PubMed Central 

Google Scholar 
Herzig P, Herzig P, Maurer A, Draba V, Sharma R, Draicchio F, Bull H, Milne L, Thomas WTB, Flavell AJ, Pillen K (2018) Contrasting genetic regulation of plant development in wild barley grown in two European environments revealed by nested association mapping. J Exp Bot 69(7):1517–1531CAS 
PubMed 
PubMed Central 

Google Scholar 
Hill W, Weir B (1988) Variances and covariances of squared linkage disequilibria in finite populations. Theor Popul Biol 33(1):54–78CAS 
PubMed 

Google Scholar 
Hodgins KA, Yeaman S (2019) Mating system impacts the genetic architecture of adaptation to heterogeneous environments. New Phytol 224(3):1201–1214PubMed 

Google Scholar 
House GL, Hahn MW (2018) Evaluating methods to visualize patterns of genetic differentiation on a landscape. Mol Ecol Resour 18(3):448–460PubMed 

Google Scholar 
Hübner S, Korol AB, Schmid KJ (2015) Rna-seq analysis identifies genes associated with differential reproductive success under drought-stress in accessions of wild barley hordeum spontaneum. BMC Plant Biol15(1):1–14
Google Scholar 
Hübner S, Bdolach E, Ein-Gedy S, Schmid KJ, Korol A, Fridman E (2013) Phenotypic landscapes: phenological patterns in wild and cultivated barley. J Evol Biol 26(1):163–174PubMed 

Google Scholar 
Hübner S, Günther T, Flavell A, Fridman E, Graner A, Korol A, Schmid KJ (2012) Islands and streams: clusters and gene flow in wild barley populations from the Levant. Mol Ecol 21(5):1115–1129PubMed 

Google Scholar 
Hübner S, Höffken M, Oren E, Haseneyer G, Stein N, Graner A, Schmid K, Fridman E (2009) Strong correlation of wild barley (Hordeum spontaneum) population structure with temperature and precipitation variation. Mol Ecol 18(7):1523–1536PubMed 

Google Scholar 
Jakob SS, Rödder D, Engler JO, Shaaf S, Özkan H, Blattner FR, Kilian B (2014) Evolutionary history of wild barley (Hordeum vulgare subsp. spontaneum) analyzed using multilocus sequence data and paleodistribution modeling. Genome Biol Evol 6(3):685–702CAS 
PubMed 
PubMed Central 

Google Scholar 
Jayakodi M, Padmarasu S, Haberer G, Bonthala VS, Gundlach H, Monat C, Lux T, Kamal N, Lang D, Himmelbach A, Ens J, Zhang X-Q, Angessa TT, Zhou G, Tan C, Hill C, Wang P, Schreiber M, Boston LB, Plott C, Jenkins J, Guo Y, Fiebig A, Budak H, Xu D, Zhang J, Wang C, Grimwood J, Schmutz J, Guo G, Zhang G, Mochida K, Hirayama T, Sato K, Chalmers KJ, Langridge P, Waugh R, Pozniak CJ, Scholz U, Mayer KFX, Spannagl M, Li C, Mascher M, Stein N (2020) The barley pan-genome reveals the hidden legacy of mutation breeding Nature 588:284–289. https://doi.org/10.1038/s41586-020-2947-8CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation. Ecol Lett 7(12):1225–1241
Google Scholar 
Kilian B, Özkan H, Kohl J, von Haeseler A, Barale F, Deusch O, Brandolini A, Yucel C, Martin W, Salamini F (2006) Haplotype structure at seven barley genes: relevance to gene pool bottlenecks, phylogeny of ear type and site of barley domestication. Mol Genet Genom 276(3):230–241CAS 

Google Scholar 
Lasky JR, Des Marais DL, McKAY JK, Richards JH, Juenger TE, Keitt TH (2012) Characterizing genomic variation of arabidopsis thaliana: the roles of geography and climate. Mol Ecol 21(22):5512–5529PubMed 

Google Scholar 
Lasky JR, Upadhyaya HD, Ramu P, Deshpande S, Hash CT, Bonnette J, Juenger TE, Hyma K, Acharya C, Mitchell SE et al. (2015) Genome-environment associations in sorghum landraces predict adaptive traits. Sci Adv 1(6):e1400218PubMed 
PubMed Central 

Google Scholar 
Lawson DJ, Van Dorp L, Falush D (2018) A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nat Commun 9(1):1–11CAS 

Google Scholar 
Lee C-R, Mitchell-Olds T (2011) Quantifying effects of environmental and geographical factors on patterns of genetic differentiation. Mol Ecol 20(22):4631–4642PubMed 
PubMed Central 

Google Scholar 
Leek JT (2011) Asymptotic conditional singular value decomposition for high-dimensional genomic data. Biometrics 67(2):344–352PubMed 

Google Scholar 
Legendre P, Legendre L (2012) Canonical analysis. In: Numerical ecology, 3rd English edn, chap. 11. Elsevier Science BV, The Netherlands, pp 625–710López-Goldar X, Agrawal AA (2021) Ecological interactions, environmental gradients, and gene flow in local adaptation Trends Plant Sci 26(8):796–809PubMed 

Google Scholar 
Lotterhos KE, Whitlock MC (2015) The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol Ecol 24(5):1031–1046PubMed 

Google Scholar 
Lundgren E, Ralph PL (2019) Are populations like a circuit? Comparing isolation by resistance to a new coalescent-based method. Mol Ecol Resour 19(6):1388–1406PubMed 

Google Scholar 
Makowski D, Ben-Shachar M, Lüdecke D (2019) bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J Open Source Softw 4(40):1541
Google Scholar 
Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, Radchuk V, Dockter C, Hedley PE, Russell J et al. (2017) A chromosome conformation capture ordered sequence of the barley genome. Nature 544(7651):427–433CAS 
PubMed 

Google Scholar 
Mascher M (2019) Pseudomolecules and annotation of the second version of the reference genome sequence assembly of barley cv. morex [morex v2]. https://doi.ipk-gatersleben.de:443/DOI/83e8e186-dc4b-47f7-a820-28ad37cb176b/d1067eba-1d08-42e2-85ec-66bfd5112cd8/2McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5(10):e1000686Mee JA, Yeaman S (2019) Unpacking conditional neutrality: genomic signatures of selection on conditionally beneficial and conditionally deleterious mutations. Am Nat 194(4):529–540PubMed 

Google Scholar 
Milner SG, Jost M, Taketa S, Mazón ER, Himmelbach A, Oppermann M, Weise S, Knüpffer H, Basterrechea M, König P et al. (2019) Genebank genomics highlights the diversity of a global barley collection. Nat Genet 51(2):319–326CAS 
PubMed 

Google Scholar 
Morrell PL, Toleno DM, Lundy KE, Clegg MT (2005) Low levels of linkage disequilibrium in wild barley (Hordeum vulgare ssp. spontaneum) despite high rates of self-fertilization. Proc Natl Acad Sci USA 102(7):2442–2447CAS 
PubMed 
PubMed Central 

Google Scholar 
Navarro JAR, Willcox M, Burgueño J, Romay C, Swarts K, Trachsel S, Preciado E, Terron A, Delgado HV, Vidal V et al. (2017) A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat Genet 49(3):476
Google Scholar 
Nevo E, Zohary D, Brown A, Haber M (1979) Genetic diversity and environmental associations of wild barley, Hordeum spontaneum, in Israel. Evolution 33(3):815–833CAS 
PubMed 

Google Scholar 
Nevo E, Beharav A, Meyer RC, Hackett CA, Forster BP, Russell JR, Powell W (2005) Genomic microsatellite adaptive divergence of wild barley by microclimatic stress in ‘Evolution Canyon’, Israel. Biol J Linn Soc84(2):205–224
Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2019) vegan: community ecology package. R package version 2.5-6. https://CRAN.R-project.org/package=veganPankin A, Altmüller J, Becker C, von Korff M (2018) Targeted resequencing reveals genomic signatures of barley domestication. New Phytol 218(3):1247–1259CAS 
PubMed 
PubMed Central 

Google Scholar 
Pekel J-F, Cottam A, Gorelick N, Belward AS (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540(7633):418–422CAS 

Google Scholar 
Pembleton L, Cogan N, Forster J (2013) StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations Mol Ecol Res 13:946–952. https://doi.org/10.1111/1755-0998.12129CAS 
Article 

Google Scholar 
Peterman WE (2018) Resistancega: an r package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol Evol 9(6):1638–1647
Google Scholar 
Petkova D, Novembre J, Stephens M (2016) Visualizing spatial population structure with estimated effective migration surfaces. Nat Genet 48(1):94CAS 
PubMed 

Google Scholar 
Pham A-T, Maurer A, Pillen K, Brien C, Dowling K, Berger B, Eglinton JK, March TJ (2019) Genome-wide association of barley plant growth under drought stress using a nested association mapping population. BMC Plant Biol 19(1):134PubMed 
PubMed Central 

Google Scholar 
Poland JA, Brown PJ, Sorrells ME, Jannink J-L (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7(2):e32253CAS 
PubMed 
PubMed Central 

Google Scholar 
Pyhäjärvi T, Hufford MB, Mezmouk S, Ross-Ibarra J (2013) Complex patterns of local adaptation in teosinte. Genome Biol Evol 5(9):1594–1609PubMed 
PubMed Central 

Google Scholar 
Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24(17):4348–4370PubMed 

Google Scholar 
Renaut S, Grassa CJ, Yeaman S, Moyers BT, Lai Z, Kane NC, Bowers JE, Burke JM, Rieseberg LH (2013) Genomic islands of divergence are not affected by geography of speciation in sunflowers. Nat Commun 4(1):1–8
Google Scholar 
Russell J, Mascher M, Dawson IK, Kyriakidis S, Calixto C, Freund F, Bayer M, Milne I, Marshall-Griffiths T, Heinen S et al. (2016) Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation. Nat Genet 48(9):1024CAS 
PubMed 

Google Scholar 
Samuk K, Samuk K, Owens GL, Delmore KE, Miller SE, Rennison DJ, Schluter D (2017) Gene flow and selection interact to promote adaptive divergence in regions of low recombination. Mol Ecol 26(17):4378–4390PubMed 

Google Scholar 
Sato K, Mascher M, Himmelbach A, Haberer G, Spannagl M, Stein N (2021) Chromosome-scale assembly of wild barley accession ‘OUH602’. G3 11(10):jkab244PubMed 
PubMed Central 

Google Scholar 
Schmid K, Kilian B. Russell J (2018) Barley domestication, adaptation and population genomics. In: The Barley Genome, Springer International Publishing: Cham, pp 317–336Szkiba D, Kapun M, von Haeseler A, Gallach M (2014) SNP2GO: functional analysis of genome-wide association studies. Genetics 197(1):285–289PubMed 
PubMed Central 

Google Scholar 
Terrazas RA, Balbirnie-Cumming K, Morris J, Hedley PE, Russell J, Paterson E, Baggs EM, Fridman E, Bulgarelli D (2020) A footprint of plant eco-geographic adaptation on the composition of the barley rhizosphere bacterial microbiota. Sci Rep 10(1):1–13
Google Scholar 
Tiffin P, Ross-Ibarra J (2014) Advances and limits of using population genetics to understand local adaptation. Trends Ecol Evol 29(12):673–680PubMed 

Google Scholar 
Tsuda Y, Chen J, Stocks M, Källman T, Sønstebø JH, Parducci L, Semerikov V, Sperisen C, Politov D, Ronkainen T et al. (2016) The extent and meaning of hybridization and introgression between siberian spruce (picea obovata) and norway spruce (picea abies): cryptic refugia as stepping stones to the west? Mol Ecol 25(12):2773–2789CAS 
PubMed 

Google Scholar 
Turner-Hissong SD, Mabry ME, Beissinger TM, Ross-Ibarra J, Pires JC (2020) Evolutionary insights into plant breeding Curr Opin Plant Biol 54:93–100. https://doi.org/10.1016/j.pbi.2020.03.003CAS 
Article 
PubMed 

Google Scholar 
de Villemereuil P, Frichot É, Bazin É, François O, Gaggiotti OE (2014) Genome scan methods against more complex models: when and how much should we trust them? Mol Ecol 23(8):2006–2019PubMed 

Google Scholar 
Volis S (2011) Adaptive genetic differentiation in a predominantly self-pollinating species analyzed by transplanting into natural environment, crossbreeding and QST-FST test. New Phytol 192(1):237–248CAS 
PubMed 

Google Scholar 
Volis S, Mendlinger S, Ward D (2002a) Differentiation in populations of Hordeum spontaneum along a gradient of environmental productivity and predictability: life history and local adaptation. Biol J Linn Soc 77(4):479–490
Google Scholar 
Volis S, Mendlinger S, Ward D (2002b) Adaptive traits of wild barley plants of Mediterranean and desert origin. Oecologia 133(2):131–138PubMed 

Google Scholar 
Volis S, Zaretsky M, Shulgina I (2010) Fine-scale spatial genetic structure in a predominantly selfing plant: role of seed and pollen dispersal. Heredity 105(4):384–393CAS 
PubMed 

Google Scholar 
Volis S, Shulgina I, Ward D, Mendlinger S (2003) Regional subdivision in wild barley allozyme variation: adaptive or neutral? J Hered 94(4):341–351CAS 
PubMed 

Google Scholar 
Volis S, Verhoeven K, Mendlinger S, Ward D (2004) Phenotypic selection and regulation of reproduction in different environments in wild barley. J Evol Biol 17(5):1121–1131CAS 
PubMed 

Google Scholar 
Volis S, Yakubov B, Shulgina I, Ward D, Mendlinger S (2005) Distinguishing adaptive from nonadaptive genetic differentiation: comparison of q st and f st at two spatial scales. Heredity 95(6):466–475CAS 
PubMed 

Google Scholar 
Volis S, Yakubov B, Shulgina I, Ward D, Zur V, Mendlinger S (2001) Tests for adaptive RAPD variation in population genetic structure of wild barley, Hordeum spontaneum Koch. Biol J Linn Soc 74(3):289–303
Google Scholar 
Wang X, Chen Z-H, Yang C, Zhang X, Jin G, Chen G, Wang Y, Holford P, Nevo E, Zhang G et al. (2018) Genomic adaptation to drought in wild barley is driven by edaphic natural selection at the Tabigha Evolution Slope. Proc Natl Acad Sci USA 115(20):5223–5228CAS 
PubMed 
PubMed Central 

Google Scholar 
Wiegmann M, Wiegmann M, Maurer A, Pham A, March TJ, Al-Abdallat A, Thomas WTB, Bull HJ, Shahid M, Eglinton J, Baum M, Flavell AJ, Tester M, Pillen K (2019) Barley yield formation under abiotic stress depends on the interplay between flowering time genes and environmental cues Sci Rep 9(1):6397. https://doi.org/10.1038/s41598-019-42673-1CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yeaman S, Whitlock MC (2011) The genetic architecture of adaptation under migration–selection balance. Evolution 65(7):1897–1911PubMed 

Google Scholar 
Zheng X, Levine D, Shen J, Gogarten S, Laurie C, Weir B (2012) A high-performance computing toolset for relatedness and principal component analysis of snp data Bioinformatics 28(24):3326–3328. https://doi.org/10.1093/bioinformatics/bts606CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Plant defence traitsWe compiled species level data for five plant traits: wood density (WD), leaf and stem spinescence, latex production, and leaf size, for tropical and extra-tropical South and Central American woody species (i.e., the Neotropical biogeographic realm). WD was obtained for 2577 species from ref. 44. We only used wood density data from Zanne et al.44, because this study used WD measured in stems, whereas most other studies with available data used WD measured in branches. Leaf size data were obtained for 2660 woody species from Wright et al.37. We did not include leaf size from herbaceous species because herbaceous and woody species are influence by different megafauna guilds, suggesting distinct mechanisms, and because this dataset37 only included data for 253 Neotropical herbaceous species. The presence or absence of stem (and/or branch) spines (mostly thorns, but also prickles) were obtained from Dantas and Pausas45 for Neotropical savanna and forest species (1004 species) and complemented with other literature sources for other ecoregions (listed in the supplementary materials) using the names of the species for which we had WD and Leaf Size data. Our final stem spines dataset included 2843 woody species. We also compiled data on the presence of latex in plant stems and leaves for all the species for which we had data on other traits (3160 species; references in the supplementary materials). Finally, we also compiled data on leaf spines. While we managed to find leaf spine data for a total of 2173 woody species, we found spinescence in leaves to be especially concentrated in the palm Family (Arecaceae; 198 out of 221 species with leaf spines). Moreover, out of the non-palm species, all but three species also presented stem spines, indicating that, for other taxa, leaf spines might be dependent on the presence of stem spines at the region (in palms, 51% have stem spines). Thus, we only used leaf spinescence data of palm species (694 species) from the global Palm Traits Database 1.046.For wood density and leaf size, we often had more than one trait value per species (1005 and 831 species with more than one trait value, respectively). Thus, we computed the species mean trait value. This rarely occurred for binary traits (spinescence and latex) and, when occurred, the maximum value was used (0 for absence and 1 for presence). This later decision was based in the assumption that omitting the presence of spines or latex is more likely than incorrectly reporting the presence when it is absent. Moreover, some of these traits can be plastic18.From species to ecoregionsWe searched for geographical distribution data (coordinates) from the Global Biodiversity Information Facility (GBIF) for all of the species in each species-trait dataset (Data available from GBIF using the following doi: WD: https://doi.org/10.15468/dl.3vua3x; Stem spines: https://doi.org/10.15468/dl.ar5ddj; Latex: https://doi.org/10.15468/dl.m8dzjd; Leaf spines: https://doi.org/10.15468/dl.vv8gw4; Leaf size: https://doi.org/10.15468/dl.k98nxc). For this search, we used tools provided by the “rgbif” package for R in which species names are updated to the most recent classification and the returned occurrences also include those associated with synonyms (i.e., the “backbone” method). We labelled the obtained geographical coordinates according to their ecoregion and biogeographical realm (following Dinerstein et al.47) and cropped out occurrences falling outside of the Neotropical realm. Since occurrence data was not available to all the species in our initial trait dataset, the number of species used to calculate ecoregion level means was reduced to 2110 species, for wood density, 2133, for leaf size, 2629, for stem spines, 2714, for latex, and 657, for leaf spines. A detailed evaluation of the representativity of this data in relation to ecoregion- and Neotropical- level patterns can be found in the Supplementary Methods. Based on the occurrence data and their ecoregion label, we built a species abundance (columns) by ecoregion (rows) matrix for each trait.We obtained ecoregion scale abundance-weighted means for continuous traits (WD and Leaf Size) by: (1) Multiplying species abundance in each grid cell of the ecoregion by the mean species value; (2) Summing up the row values; (3) dividing the resulting row sum by the total species abundance (row sum prior to trait multiplication), and (4) calculating the ecoregions’ means (across all of the grid cells). For Stem Spines and Latex (binary traits), we used a similar procedure, but the maximum (0 for absence and 1 for presence) value was used instead of the mean in step (1), and step (2) was directly used to calculate the number of presences (i.e., 1 s). Moreover, instead of the steps (3) and (4), we calculated the number of absences as the difference between the total abundance (row sums before trait multiplication) and the values obtained in step (2). This process resulted in weighted means for WD and stem spinescence for 173 ecoregions, and Leaf Size and Latex for 174, out of the 179 Neotropical ecoregions. For leaf spinescence, we used a similar approach, although, because of the fewer species, the abundance estimate from GBIF was less reliable. Thus, we transformed the ecoregion species abundance to presence/absence before multiplying the trait values (0/1 for absence/presence). We obtained leaf spinescence data for 159 out of the 179 Neotropical ecoregions. The species- and ecoregion- level data is provided in the Supplementary Data and in ref. 47.Historical megafauna distributionWe obtained data on historical distribution of megafauna species from the MegaPast2Future/PHYLACINE_1.2 dataset24, a dataset containing distribution maps (96.5 km of spatial resolution) and functional traits for mammal species of the last 130,000 years. From this dataset, we obtained the probable past distribution of extinct large mammal herbivore (hereafter, “megafauna”) species, if these species were still alive today (“Present Natural” scenario; see details below). The “Present Natural” distribution of extinct species in this database is based on the estimated historical distribution (i.e., preceding anthropogenic range modifications) of extant species that are known (from the fossil record) to have coexisted with the extinct species. In this approach, an extinct species is considered to have been present in a given grid cell if at least 50% of the extant species that were found coexisting with the extinct species in the fossil (and subfossil) record was predicted to have occurred in the same cell prior to anthropogenic range modifications24,48. This approach assumes that, since extant and extinct species coexisted in the same locations, they must have had similar ecological requirements. It also assumes that megafauna extinction had anthropogenic causes, instead of causes related to climate change49, which is largely accepted in the literature50.We extracted the “Present Natural” distribution of extinct mammal (coded “EP” for IUCN status; i.e., “extinct in prehistory”, meaning before 1500 CE) whose body mass was higher than 50 kg (megafauna), and for which at least 90% of their diet consisted of plants (i.e., strict herbivores). For each Ecoregion, we began by calculating two megafauna-related metrics: extinct megafauna species richness (Mrich) and their mean body mass (Mbm). For this, we cropped the distribution maps of the megafauna species (containing 1 for presence and 0 for absence of each species) to the Neotropical realm. To calculate Mrich, we (1) counted species presences within each of the grid cells in the global grid (i.e., calculated the cell’s megafauna richness); (2) assigned the corresponding ecoregion label to the resulting richness grid cells, subset the richness cell values corresponding to the Neotropical region; and (3) calculated the mean for each Neotropical ecoregion. For Mbm, we replaced the presences of the megafauna species in the initial raster object (grid cell map of each megafauna species) by their body masses and calculated the grid cell-level mean body mass, before calculating the ecoregion-level means. We also calculated megafauna density and secondary productivity based on allometric equations that relate these metrics to megafauna body mass. However, we did not used megafauna density and secondary productivity because they were strongly correlated to megafauna richness (Supplementary Fig. 3). More details on how these metrics were calculated can be found in the Supplementary Methods.We also obtained diet preference information from the literature for most megafauna species that occurred in the Neotropical region (details and references in the Supplementary Material). Based on these information, we calculated the richness of large browser (MBrich for megabrowser richness), grazer (MGrich for megagrazer richness), and mixed-feeder (MMfrich for mega mixed-feeder richness) species by sub setting the megafauna species by grid cell array before the richness calculation in order to select only species that were classified within the correspondent subgroup.Extant herbivore mammal distributionWe also compiled data on the distribution, body mass and diet of extant and recently extinct (i.e., extinct after 1500 CE) herbivore mammal species (for simplicity, called ‘extant’ species in this study). As with megafauna maps, the distributions used represented reconstructions for periods preceding anthropogenic reduction of extant herbivores ranges (“Present Natural” scenario), based on abiotic, biotic and geographic variables48, rather than the currently observed distribution. This scenario was used because modern anthropogenic range reductions are too recent to produce substantial geographic effects at this spatial scale. These data were obtained by sub setting the MegaPast2Future/PHYLACINE_1.2 dataset to exclude species that were coded “EP” for IUCN status and that were not strict herbivores (at least 90% of the diet constituting of plants). We subsequently associated diet information to these species using data from ref. 51 and excluded all species that did not feed mainly on aboveground vegetative plant tissues (i.e., species that fed mostly on fruits, seed, roots were excluded). This later filtering was because the number of herbivores that feed mostly on seed and fruit increase with decreasing size (and this dataset included small mammals). We subsequently calculated the same metrics as for the extinct megafauna species (except for the richness of mixed-feeders as our source for diets50 labelled species according to dominant feeding pattern). For this, we used the same approach described for extinct megafauna species. We did not use a size threshold for extant species because there were only 13 extant mammal herbivore species with over 50 kg in the Neotropical region, most of which were grazers (9 species; 4 species were mixed-feeders and none were browsers). Therefore, we relied on the mean body mass metric calculated for extant mammals to detect potential size-related effects.Climate, soil, fire, insularity, and hurricanesFor each Ecoregion, we obtained data on climate (mean annual precipitation and temperature, and rainfall seasonality) and soil (sand content, pH, and cation exchange capacity) variables. Climate data was obtained from WorldClim 2.1 (10 min spatial resolution) and was based on climate data from 1970 to 200052. Soil data were obtained from SoilGrids (5 km of spatial resolution)53, and consisted of mean values for two depths, 0.05 and 2 m. We calculated Ecoregion level means for all of the soil and climate variables after intersecting the climate and soil grid maps with the ecoregion map.We obtained the number (a proxy for frequency) and intensity of wildfires per ecoregion area using the MODIS active fire location product (MCD14ML)54. We only considered fires (i.e., hotspots) with detection confidence of 95% or higher occurring from November 2000 to December 2019 (both included). To ensure that only wildfires were considered, we associated each fire pixel with a land cover type (300 m of spatial resolution) from ref. 55 for a buffer area of 1000 m surrounding the fire pixel centroid. We excluded all of the fires occurring in areas in which more than 10% of the surrounding land cover pixels corresponded to agricultural, urban and water classes. We calculated the number of wildfires per ecoregion area by dividing the fire count of each Ecoregion by the ecoregion area, and multiplying the resulting value by the proportion of vegetated land cover pixels (same classes used to exclude fires in anthropogenic areas and water bodies above). Fire intensity was estimated as the average fire radiative power across all detected MODIS hotspots in the ecoregion. Ecoregions lacking large preserved vegetated areas (criteria above) were excluded from subsequent analyses.Using the ecoregion map, we also classified ecoregions into insular (1), when most of the ecoregion area was located in islands, vs. continental (0), otherwise. This was performed because island biogeography theory predicts that, in island, species richness should be low due to low colonization and high extinction rates. Insularity has also been shown to reduce megafauna body size (i.e., the island rule), even though the mechanisms are not fully understood56. We also compiled data on hurricane activity, as woody density was suggested to confer resistance against this disturbance57. We used data from 1990 to 2019 from the HURDAT2 dataset58, containing six-hourly information about the location of all of the known tropical and subtropical cyclones (0.1° latitude/longitude). We used the sum of hurricane occurrences per ecoregions divided by ecoregion area as an indicator of hurricane activity.Statistical analysesTo understand megafauna patterns, we began by fitting (multiple) regression models with habitat-related (fire, climate, soil) and geographical (insularity) variables as predictors. We expected that megafauna richness in general was higher under savanna conditions (arid nutrient-rich or mesic nutrient-poor environments with frequent fires)1,22. We also expected that megafauna richness and body mass were affected negatively by insularity (i.e., following the island biogeography theory and island rule). Before the analyses, we tested the correlations among all of the variables that would eventually be entered as predictors in the same model for both the megafauna and trait models (Supplementary Table 1), in order to avoid multicollinearity associated with highly correlated variables (here, r ≥ 0.60). Since mean annual precipitation and soil pH were strongly positively correlated (r = −0.78), for all of the analyses (including the analyses with functional traits, described below), model selection was performed separately for these two variables (i.e., two different model selection procedures, one containing each of the two variables among the initial set of predictors). We selected the best among the two resulting models as that with the lowest AIC (differences higher than two points in all of the cases). To make sure that no multicollinearity remained we also calculated the Variation Inflation Factor (VIF) for all of the predictor variables as 1/tolerance, where tolerance is calculated as 1 minus the R2 of all of the model regressing a predictive variable against all of the other predictors. In all of the models, VIF was 3.33 or smaller (i.e., a tolerance of 0.30 or higher), indicating absence of multicollinearity.Model simplification was carried interactively using stepwise (both forward and backward) searching for the model with the lowest AIC (using R’s “step” function) and subsequently retaining only the significant variables (p ≤ 0.05). We calculated the Pearson r statistics as a measure of effect size for the selected variables as well as the associated confidence intervals, using the packages “parameters” and “effectsize” for R. The average contribution of each predictor variable was also calculated, using the package “dominanceanalysis”, as the mean difference in R2 before and after removing the target variable from models containing all of the possible subset combinations of the selected predictor variables, including the full selected model.For testing whether the studied plant functional traits were related to our megafauna indicators, we fit linear models to WD and leaf size, and generalized linear models (GLM; binomial family) for spinescence and latescence, using ecoregion as the unit. For spinescence and latescence, we used the matrix containing the count of spiny/latex and non-spiny/non-latex plants (species abundance; for stem spines and latex) or number of species with or without spines (for leaf spines; see above) as response variables. The predictor variables included the animal indicators for extinct megafauna and extant herbivores, as well as climate, soil, and fire predictors (and, for WD, hurricane counts). Because total, as well as megagrazer, megabrowser, and mega mixed-feeder species richness were strongly positively correlated (Supplementary Table 1), we used the richness difference between grazers and browsers to evaluate the effect of diet (Supplementary Fig. 1). For consistency, we used the same diet variable for extant and extinct species. Since we did not identify strong correlations among extinct megafauna and extant herbivore indicators (Supplementary Table 1), these variables were all entered simultaneously in the same initial models. As with the analyses of the megafauna indices, we also used r as effect size and calculated the average predictor contribution in terms of R2 for these models. For the later, we used the MacFadden Pseudo-R2 in the GLM models as implemented in the “pscl” and “dominanceanalysis” packages for R, as this statistic is the most comparable with R2 from linear multiple regression (Maximum Likelihood and Cragg and Uhler’s Pseudo-R2 were also calculated for the logistic models), and adjusted R2 for continuous traits. Islands were not included in these models, as island plants were expected to respond differently due to the effects of insularity on animal species richness, precluding megafauna and extant mammal richness from being accurate proxies for consumer abundance. For stem spines, we always included a quadratic term to both megafauna and extant mammal herbivore body mass, as evidence suggest that medium-size herbivores (i.e., approximately 250 kg) are important selective drivers of this trait12. If a significant relationship with our herbivory indicators (both extant and extinct) were significant but not indicative of a selective effect by herbivores (for more defended plants), this relationship was discarded (along with related variables, such as diet); this happened only once, for leaf size, which increased with extant herbivore richness (Supplementary Table 8).For all of the general linear regression models, assumptions of normality, homoscesticity and lack of spatial autocorrelation in the residuals were checked using the Kolmogorov–Smirnov, Breusch–Pagan and Moran’s I tests, respectively. For the later, ecoregions were considered neighbours when they were adjacent and non-neighbour otherwise. In some cases, heteroscesticity was detected and, thus, the significance of the coefficients was tested using heteroskedasticity-consistent covariance matrix estimation. If one or more variable lost their significance they were stepwise removed from the final model, beginning by the least significant, until all remaining variables had a significant effect. Overdispersion in the generalized linear model was also detected and dealt with using overdispersed binomial logit models, as implemented in the “dispmod” package for R, in which weights are interactively calculated and used to maintain the residual deviance lower than the degrees of freedom. To confirm that the detected associations between megafauna indices and plant traits were robust, we also tested the coefficient significance using randomization of the plant species by ecoregion matrices (see Supplementary Methods for details).To test the prediction that Neotropical ecoregions could be broadly classified into the three hypothesised antiherbiomes, we used hierarchical clustering on principal component axes of the ecoregion by trait matrix (five plant traits, standardized to zero mean and unit variance). We selected the number of clusters associated with the highest loss of inertia (within group variability) when progressively increasing the number of clusters, using the R package “FactoMineR”. This procedure allowed the recognition of large regions characterised by specific patterns of defence strategies (‘antiherbiomes’). We subsequently tested for axes score, megafauna and environmental differences among the resulting antiherbiomes to verify whether and how trait, climate and soil patterns matched those described for African ecosystems, and to understand the megafaunal differences among the antiherbiomes. For these comparisons, we used Kruskal-Wallis and post-hoc pairwise Dunn tests, using the Benjamini & Hochberg59 (1995) correction of P-values for multiple comparisons in both cases, and exclusively included continental ecoregions. For spines, we used the proportion of spinescent plants/species (rather than the number of “yes” and “no” used on previous analyses) in the principal component analysis. Because palms were missing from 20 ecoregions, we completed the values for these ecoregions using predicted model probabilities. To better understand these associations between traits and the environmental and megafauna variables, we also regressed the PCA axes against the same predictors used for traits.We also developed a framework to identify forest ecoregions most likely to have experienced a biome shift after megafauna extinction using antiherbiome, biome and megafauna distribution data. Ecoregions likely to have experienced a savanna-to-forest shift since the Pleistocene are those that: (1) are currently forest-dominated; (2) are classified in antiherbiomes analogous to African arid nutrient-rich or mesic nutrient-poor savannas; and (3) were megafauna- and, especially, megagrazer- rich during the Pleistocene (richness equal or greater than the 0.75 quantile: 14 species for Mrich, and 3 for exclusively grazing species; MGrich). We validated the distribution of these areas with fossil evidence (22 sites) from the Last Glacial Maximum and mid-Holocene (see Supplementary Methods and Supplementary Table 9). For this, we also used information about the present dominant vegetation type in the fossil sites, extracted from the reference sources (see Supplementary Table 9), to segregate savanna-forest shifts from data coming from stable savanna patches within forest or long-term savanna regions. We also contrasted the predicted patterns with the present location of savanna patches within the Amazon Forest region from ref. 60.All statistical analyses and data handling were carried out in the R (v.4.0.2) environment, using the previously mentioned packages, in addition to FSA, gridExtra, grid, lattice, lmtest, latticeExtra, olsrr, raster, rgdal, rgeos, sandwich, spatialreg, spdep and vegan, using codes provided in ref. 47.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Gaston, K. J., Jackson, S. F., Cantú-Salazar, L. & Cruz-Piñón, G. The ecological performance of protected areas. Annu. Rev. Ecol. Evol. Syst. 39, 93–113 (2008).
Google Scholar 
2.Staver, A. C., Abraham, J. O., Hempson, G. P., Karp, A. T. & Faith, J. T. The past, present, and future of herbivore impacts on savanna vegetation. J. Ecol. 109, 2804–2822 (2021).
Google Scholar 
3.Bond, W. J. Keystone species. In Biodiversity and Ecosystem Function (eds Schulze, E. D. & Mooney, H. A.) 237–253 (Springer, 1994).
Google Scholar 
4.Cole, M. M. The influence of soils, geomorphology and geology on the distribution of plant communities in savanna ecosystems. In Ecology of Tropical Savannas (eds Huntley, B. J. & Walker, B. H.) 145–174 (Springer, 1982).
Google Scholar 
5.Huntley, B. J. & Walker, B. H. (eds) Ecology of Tropical Savannas Vol. 42 (Springer, 1982).
Google Scholar 
6.Frost, P. et al. Responses of Savannas to Stress and Disturbance: A Proposal for a Collaborative Programme of Research (Biology International 10, 1985).
Google Scholar 
7.Medina, E. & Silva, J. F. Savannas of northern South America: A steady state regulated by water–ire interactions on a background of low nutrient availability. J. Biogeogr. 17, 403–413 (1990).
Google Scholar 
8.Fensham, R. J., Fairfax, R. J. & Archer, S. R. Rainfall, land use and woody vegetation cover change in semi-arid Australian savanna. J. Ecol. 93, 596–606 (2005).
Google Scholar 
9.Mucina, L. & Rutherford, M. C. The Vegetation of South Africa, Lesotho and Swaziland (South African National Biodiversity Institute, 2006).
Google Scholar 
10.Staver, A. C., Botha, J. & Hedin, L. Soils and fire jointly determine vegetation structure in an African savanna. New Phytol. 216, 1151–1160 (2017).CAS 
PubMed 

Google Scholar 
11.Jakubka, D. et al. Effects of climate, habitat and land use on the cover and diversity of the savanna herbaceous layer in Burkina-Faso, West Africa. Folia Geobot. 52, 129–142 (2017).
Google Scholar 
12.Bond, W. J. Open Ecosystems: Ecology and Evolution Beyond the Forest Edge (Oxford University Press, 2019).
Google Scholar 
13.O’Connor, T. G. Composition and population responses of an African savanna grassland to rainfall and grazing. J. Appl. Ecol. 31, 155–171 (1994).
Google Scholar 
14.Walker, B. & Langridge, J. Predicting savanna vegetation structure on the basis of plant available moisture (PAM) and plant available nutrients (PAN): A case study from Australia. J. Biogeogr. 24, 813–825 (1997).
Google Scholar 
15.Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).CAS 
ADS 
PubMed 

Google Scholar 
16.Bucini, G. & Hanan, N. P. A continental-scale analysis of tree cover in African savannas. Glob. Ecol. Biogeogr. 16, 593–605 (2007).
Google Scholar 
17.Venter, F. J., Scholes, R. J. & Eckhardt, H. C. The abiotic template and its associated vegetation pattern. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 83–129 (Island Press, Washington, D.C., 2003).18.Valeix, M. et al. Vegetation structure and ungulate abundance over a period of increasing elephant abundance in Hwange National Park, Zimbabwe. J. Trop. Ecol. 23, 87–93 (2007).
Google Scholar 
19.Asner, G. P. et al. Large-scale impacts of herbivores on the structural diversity of African savannas. Proc. Natl Acad. Sci. USA 106, 4947–4952 (2009).CAS 
ADS 
PubMed 
PubMed Central 

Google Scholar 
20.Archibald, S. & Hempson, G. P. Competing consumers: Contrasting the patterns and impacts of fire and mammalian herbivory in Africa. Philos. Trans. R. Soc. B 371, 20150309 (2016).
Google Scholar 
21.Archibald, S., Bond, W. J., Stock, W. D. & Fairbanks, D. H. K. Shaping the landscape: Fire–grazer interactions in an African savanna. Ecol. Appl. 15, 96–109 (2005).
Google Scholar 
22.Staver, A. C. & Bond, W. J. Is there a ‘browse trap’? Dynamics of herbivore impacts on trees and grasses in an African savanna. J. Ecol. 102, 595–602 (2014).
Google Scholar 
23.Jeltsch, F., Weber, G. E. & Grimm, V. Ecological buffering mechanisms in savannas: A unifying theory of long-term tree-grass coexistence. Plant Ecol. 105, 161–171 (2000).
Google Scholar 
24.February, E. C., Higgins, S. I., Bond, W. J. & Swemmer, L. Influence of competition and rainfall manipulation on the growth responses of savanna trees and grasses. Ecology 94, 1155–1164 (2013).PubMed 

Google Scholar 
25.Savadogo, P., Tiveau, D., Sawadogo, L. & Tigabu, M. Herbaceous species responses to long-term effects of prescribed fire, grazing and selective tree cutting in the savanna-woodlands of West Africa. Perspect. Plant Ecol. Evol. Syst. 10, 179–195 (2008).
Google Scholar 
26.Smith, M. D. et al. Long-term effects of fire frequency and season on herbaceous vegetation in savannas of the Kruger National Park, South Africa. J. Plant Ecol. 6, 71–83 (2013).
Google Scholar 
27.Thrash, I. Impact of water provision on herbaceous vegetation in Kruger National Park, South Africa. J. Arid Environ. 38, 437–450 (1998).ADS 

Google Scholar 
28.Staver, A. C., Bond, W. J., Stock, W. D., van Rensburg, S. J. & Waldram, M. S. Browsing and fire interact to suppress tree density in an African savanna. Ecol. Appl. 19, 1909–1919 (2009).PubMed 

Google Scholar 
29.Smit, I. P. J. & Ferreira, S. M. Management intervention affects river-bound spatial dynamics of elephants. Biol. Conserv. 143, 2172–2181 (2010).
Google Scholar 
30.Loarie, S. R., van Aarde, R. J. & Pimm, S. L. Elephant seasonal vegetation preferences across dry and wet savannas. Biol. Conserv. 142, 3099–3107 (2009).
Google Scholar 
31.Young, K. D., Ferreira, S. M. & Van Aarde, R. J. Elephant spatial use in wet and dry savannas of southern Africa. J. Zool. 278, 189–205 (2009).
Google Scholar 
32.Codron, J. et al. Elephant (Loxodonta africana) diets in Kruger National Park, South Africa: spatial and landscape differences. J. Mammal. 87, 27–34 (2006).
Google Scholar 
33.Timberlake, J. Colophospermum mopane: Annotated Bibliography and Review (Zimbabwe Bulletin of Forestry Research No. 11, 1995).
Google Scholar 
34.Pyšek, P. et al. Into the great wide open: Do alien plants spread from rivers to dry savanna in the Kruger National Park?. NeoBiota 60, 61–77 (2020).
Google Scholar 
35.Eckhardt, H. C., van Wilgen, B. W. & Biggs, H. C. Trends in woody vegetation cover in the Kruger National Park, South Africa, between 1940 and 1998. Afr. J. Ecol. 38, 108–115 (2000).
Google Scholar 
36.Groen, T. A. Spatial Matters: How Spatial Patterns and Processes Affect Savanna Dynamics. PhD Thesis, Wageningen University, The Netherlands (2007).37.MacFadyen, S., Hui, C., Verburg, P. H. & Van Teeffelen, A. J. A. Quantifying spatiotemporal drivers of environmental heterogeneity in Kruger National Park, South Africa. Landsc. Ecol. 31, 2013–2029 (2016).
Google Scholar 
38.Munyati, C. & Ratshibvumo, T. Differentiating geological fertility derived vegetation zones in Kruger National Park, SouthAfrica, using Landsat and MODIS imagery. J. Nat. Conserv. 18, 169–179 (2010).
Google Scholar 
39.Colgan, M. S., Asner, G. P., Levick, S. R., Martin, R. E. & Chadwick, O. A. Topo-edaphic controls over woody plant biomass in South African savannas. Biogeosciences 9, 1809–1821 (2012).ADS 

Google Scholar 
40.Walter, H. & Burnett, J. H. Ecology of Tropical and Subtropical Vegetation Vol. 539 (Oliver and Boyd, 1971).
Google Scholar 
41.Scholes, R. J., Bond, W. J. & Eckhardt, H. C. Vegetation dynamics in the Kruger ecosystem. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 243–262 (Island Press, 2003).
Google Scholar 
42.Knoop, W. T. & Walker, B. H. Interactions of woody and herbaceous vegetation in southern African savanna. J. Ecol. 73, 235–253 (1985).
Google Scholar 
43.Tilman, D. The resource-ratio hypothesis of plant succession. Am. Nat. 125, 827–852 (1985).
Google Scholar 
44.Scholes, R. J. & Archer, S. R. Tree–grass interactions in savannas. Annu. Rev. Ecol. Syst. 28, 517–544 (1997).
Google Scholar 
45.Muvengwi, J., Davies, A. B., Parrini, F. & Witkowski, E. T. F. Contrasting termite diversity and assemblages on granitic and basaltic African savanna landscapes. Insect. Soc. 65, 25–35 (2018).
Google Scholar 
46.Trollope, W. S. W., Potgieter, A. L. F. & Zambatis, N. Assessing veld condition in the Kruger National Park using key grass species. Koedoe 32, 67–93 (1989).
Google Scholar 
47.Ehleringer, J. R. & Monson, R. K. Evolutionary and ecological aspects of photosynthetic pathway variation. Annu. Rev. Ecol. Syst. 24, 411–439 (1993).
Google Scholar 
48.Chytrý, M., Tichý, L. & Roleček, J. Local and regional patterns of species richness in Central European vegetation types along the pH/calcium gradient. Folia Geobot. 38, 429–442 (2003).
Google Scholar 
49.Owen-Smith, N., Page, B., Teren, G. & Druce, D. J. Megabrowser impacts on woody vegetation in savannas. In Savanna Woody Plants and Large Herbivores (eds Scogings, P. F. & Sankaran, M.) 585–611 (Wiley, 2019).
Google Scholar 
50.Nasseri, N. A., McBrayer, L. D. & Schulte, B. A. The impact of tree modification by African elephant (Loxodonta africana) on herpetofaunal species richness in northern Tanzania. Afr. J. Ecol. 49, 133–140 (2010).
Google Scholar 
51.Asner, G. P. & Levick, S. R. Landscape-scale effects of herbivores on treefall in African savannas. Ecol. Lett. 15, 1211–1217 (2012).PubMed 

Google Scholar 
52.Haynes, G. Elephants (and extinct relatives) as earth-movers and ecosystem engineers. Geomorphology 157, 99–107 (2012).ADS 

Google Scholar 
53.Kruger, L. M., Coetzee, J. A. & Vickers, K. The Impacts of Elephants on Woodlands and Associated Biodiversity (Summary report to South African National Parks, Organization for Tropical Studies, 2007).54.Guy, P. R. The influence of elephants and fire on a Brachystegia julbernardia woodland in Zimbabwe. J. Trop. Ecol. 5, 215–226 (1989).
Google Scholar 
55.Laws, R. M., Parker, I. S. C. & Johnstone, R. C. B. Elephants and Their Habitats: The Ecology of Elephants in North Bunyoro, Uganda (Clarendon Press, 1975).
Google Scholar 
56.Thompson, P. J. The role of elephants, fire and other agents in the decline of Brachystegia woodlands. J. S. Afr. Wildl. Manag. Assoc. 5, 11–18 (1975).
Google Scholar 
57.Barnes, M. E. Effects of large herbivores and fire on the regeneration of Acacia erioloba woodlands in Chobe National Park, Botswana. Afr. J. Ecol. 39, 340–350 (2001).
Google Scholar 
58.Scogings, P. F., Johansson, T., Hjältén, J. & Kruger, J. Responses of woody vegetation to exclusion of large herbivores in semi-arid savannas. Austral Ecol. 37, 56–66 (2012).
Google Scholar 
59.Verweij, R. J. T., Higgins, S. I., Bond, W. J. & February, E. C. Water sourcing by trees in a mesic savanna: Responses to severing deep and shallow roots. Environ. Exp. Bot. 74, 229–236 (2011).
Google Scholar 
60.Smit, I. et al. Effects of fire on woody vegetation structure in African savanna. Ecol. Appl. 20, 1865–1875 (2010).PubMed 

Google Scholar 
61.MacFadyen, S., Hui, C., Verburg, P. H. & Van Teeffelen, A. J. A. Spatiotemporal distribution dynamics of elephants in response to density, rainfall, rivers and fire in Kruger National Park, South Africa. Divers. Distrib. 25, 880–894 (2019).
Google Scholar 
62.O’Connor, T. G., Goodman, P. S. & Clegg, B. A functional hypothesis of the threat of local extirpation of woody plant species by elephant in Africa. Biol. Conserv. 136, 329–345 (2007).
Google Scholar 
63.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. Accessed 2021-06-08 from https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).64.Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).CAS 
ADS 
PubMed 
PubMed Central 

Google Scholar 
65.Bohdalková, E., Toszogyova, A., Šímová, I. & Storch, D. Universality in biodiversity patterns: variation in species-temperature and species-productivity relationships reveals a prominent role of productivity in diversity gradients. Ecography 44, 1366–1378 (2021).
Google Scholar 
66.Knight, R. S., Crowe, T. M. & Siegfried, W. R. Distribution and species richness of trees in southern Africa. J. S. Afr. Bot. 48, 455–480 (1982).
Google Scholar 
67.Gaylard, A., Owen-Smith, N. & Redfern, J. Surface water availability: implications for heterogeneity and eco-system processes. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 171–188 (Island Press, 2003).
Google Scholar 
68.Venter, F. J. A Classification of Land for Management Planning in the Kruger National Park. PhD Thesis, University of South Africa (1990).69.du Toit, J. T. et al. (eds) The Kruger Experience: Ecology and Management of Savanna Heterogeneity (Island Press, 2003).
Google Scholar 
70.Smit, I. P. J., Smit, C. F., Govender, N., van der Linde, M. & MacFadyen, S. Rainfall, geology and landscape position generate large-scale spatiotemporal fire pattern heterogeneity in an African savanna. Ecography 36, 447–459 (2013).
Google Scholar 
71.Obermeijer, A. A. A preliminary list of plants found in the Kruger National Park. Ann. Transvaal Mus. 17, 185–227 (1937).
Google Scholar 
72.van der Schijff, H. P. ‘n Ekologiese Studie van die Flora van die Nasionale Krugerwildtuin. D.Sc. Thesis, Potchefstroom University (1957).73.van der Schijff, H. P. The affinities of the flora of the Kruger National Park. Kirkia 7, 109–120 (1968).
Google Scholar 
74.Coetzee, B. J. Phytosociology, Vegetation Structure and Landscapes of the Central District. Kruger National Park, South Africa. Dissertationes Botanicae, J. Cramer, Vaduz (1983).75.Gertenbach, W. P. D. Landscapes of the Kruger National Park. Koedoe 26, 9–121 (1983).
Google Scholar 
76.Siebert, F. & Eckhardt, H. C. The vegetation and floristics of the Nkhuhlu exclosures, Kruger National Park. Koedoe 50, 126–144 (2008).
Google Scholar 
77.Siebert, F., Eckhardt, H. C. & Siebert, S. J. The vegetation and floristics of the Letaba exclosures, Kruger National Park, South Africa. Koedoe 52, 1–12 (2010).
Google Scholar 
78.Wigley, B. J., Fritz, H., Coetsee, C. & Bond, W. J. Herbivores shape woody plant communities in the Kruger National Park: Lessons from three long-term exclosures. Koedoe 56, 1–12 (2014).
Google Scholar 
79.Enslin, B. W., Potgieter, A. L. F., Biggs, H. C. & Biggs, R. Long term effects of fire frequency and season on the woody vegetation dynamics of the Sclerocarya birrea/Acacia nigrescens savanna of the Kruger National Park. Koedoe 43, 27–37 (2000).
Google Scholar 
80.Brits, J., Van Rooyen, M. W. & Van Rooyen, N. Ecological impact of large herbivores on the woody vegetation at selected watering points on the eastern basaltic soils in the Kruger National Park. Afr. J. Ecol. 40, 53–60 (2002).
Google Scholar 
81.Todd, S. W. Gradients in vegetation cover, structure and species richness of Nama-Karoo shrublands in relation to distance from livestock watering points. J. Appl. Ecol. 43, 293–304 (2006).
Google Scholar 
82.Foxcroft, L. C., Henderson, L., Nichols, G. R. & Martin, B. W. A revised list of alien plants for the Kruger National Park. Koedoe 46, 21–44 (2003).
Google Scholar 
83.Foxcroft, L. C., Richardson, D. M. & Wilson, J. R. Ornamental plants as invasive aliens: Problems and solutions in Kruger National Park, South Africa. Environ. Manag. 41, 32–51 (2008).ADS 

Google Scholar 
84.Mueller-Dombois, D. & Ellenberg, H. Aims and Methods of Vegetation Ecology (Wiley, 1974).
Google Scholar 
85.van der Maarel, E. Transformation of cover-abundance values in phytosociology and its effects on community similarity. Vegetatio 38, 97–114 (1979).
Google Scholar 
86.Magurran, A. E. Ecological Diversity and Its Measurement (Croom Helm, 1988).
Google Scholar 
87.Pooley, E. Wildflowers of Kwazulu-Natal and the Eastern Region (Natal Flora Publications Trust, 1998).
Google Scholar 
88.Schmidt, E., Lötter, M. & McCleland, W. Trees and Shrubs of Mpumalanga and Kruger National Park (Jacana Media, 2002).
Google Scholar 
89.van der Walt, R. Wild Flowers of the Limpopo Valley (Retha van der Walt, 2009).
Google Scholar 
90.Oudtshoorn, F. V. Guide to Grasses of Southern Africa 3rd edn. (Briza Publications, 2018).
Google Scholar 
91.R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2013).
Google Scholar 
92.Lenth, R. Emmeans: Estimated Marginal Means, aka Least-Square Means. R package version 1.2.1. https://CRAN.R-project.org/package=emmeans (2018).93.Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data Using CANOCO 5 (Cambridge University Press, 2014).MATH 

Google Scholar 
94.Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: A comprehensive framework 562 for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Modell. 196, 483–563 (2006).
Google Scholar 
95.Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using Canoco 5 2nd edn. (Cambridge University Press, 2014).MATH 

Google Scholar  More

1.Bastviken, D. Methane. in Encyclopedia of Inland Waters (ed. Likens, G. E.) 783–805 (Elsevier, 2009). https://doi.org/10.1016/B978-012370626-3.00117-42.Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Thottathil, S. D., Reis, P. C. J., del Giorgio, P. A. & Prairie, Y. T. The extent and regulation of summer methane oxidation in Northern Lakes. J. Geophys. Res. Biogeosciences 123, 3216–3230 (2018).CAS 
ADS 

Google Scholar 
4.Kankaala, P., Taipale, S., Nykänen, H. & Jones, R. I. Oxidation, efflux, and isotopic fractionation of methane during autumnal turnover in a polyhumic, boreal lake. J. Geophys. Res. Biogeosciences 112, 1–7 (2007).
Google Scholar 
5.Kankaala, P., Huotari, J., Peltomaa, E., Saloranta, T. & Ojala, A. Methanotrophic activity in relation to methane efflux and total heterotrophic bacterial production in a stratified, humic, boreal lake. Limnol. Oceanogr. 51, 1195–1204 (2006).CAS 
ADS 

Google Scholar 
6.Bastviken, D., Ejlertsson, J., Sundh, I. & Tranvik, L. Methane as a source of carbon and energy for lake pelagic food webs. Ecology 84, 969–981 (2003).
Google Scholar 
7.Kankaala, P., Lopez Bellido, J., Ojala, A., Tulonen, T. & Jones, R. I. Variable production by different pelagic energy mobilizers in Boreal Lakes. Ecosystems 16, 1152–1164 (2013).CAS 

Google Scholar 
8.Morana, C. et al. Methanotrophy within the water column of a large meromictic tropical lake (Lake Kivu, East Africa). Biogeosciences 12, 2077–2088 (2015).ADS 

Google Scholar 
9.Grey, J. The incredible lightness of being methane-fuelled: stable isotopes reveal alternative energy pathways in aquatic ecosystems and beyond. Front. Ecol. Evol. 4, 1–14 (2016).
Google Scholar 
10.Jones, R. I. & Grey, J. Biogenic methane in freshwater food webs. Freshw. Biol. 56, 213–229 (2011).CAS 

Google Scholar 
11.Kankaala, P., Taipale, S. & Grey, J. Experimental d13C evidence for a contribution of methane to pelagic food webs in lakes. Limnol. Oceanogr. 51, 2821–2827 (2006).CAS 
ADS 

Google Scholar 
12.Guérin, F. & Abril, G. Significance of pelagic aerobic methane oxidation in the methane and carbon budget of a tropical reservoir. J. Geophys. Res. 112, 1–14 (2007).
Google Scholar 
13.Rasilo, T., Hutchins, R. H. S., Ruiz-González, C. & del Giorgio, P. A. Transport and transformation of soil-derived CO2, CH4 and DOC sustain CO2 supersaturation in small boreal streams. Sci. Total Environ. 579, 902–912 (2017).CAS 
PubMed 
ADS 

Google Scholar 
14.Soued, C. & Prairie, Y. T. The carbon footprint of a Malaysian tropical reservoir: measured versus modeled estimates highlight the underestimated key role of downstream processes. Biogeosciences 17, 515–227 (2020).CAS 
ADS 

Google Scholar 
15.Del Giorgio, P. A. & Gasol, J. M. Physiological structure and single-cell activity in marine bacterioplankton. in Microbial Ecology of the Oceans: Second Edition (ed. Kirchman, D. L.) 243–298 (John Wiley & Sons, Inc, 2008). https://doi.org/10.1002/9780470281840.ch816.Reis, P. C. J., Ruiz-González, C., Soued, C., Crevecoeur, S. & Prairie, Y. T. Rapid shifts in methanotrophic bacterial communities mitigate methane emissions from a tropical hydropower reservoir and its downstream river. Sci. Total Environ. 748, 141374 (2020).CAS 
PubMed 
ADS 

Google Scholar 
17.Thottathil, S. D., Reis, P. C. J. & Prairie, Y. T. Methane oxidation kinetics in northern freshwater lakes. Biogeochemistry 143, 105–116 (2019).CAS 

Google Scholar 
18.Milucka, J. et al. Methane oxidation coupled to oxygenic photosynthesis in anoxic waters. ISME J. 9, 1991–2002 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Zigah, P. K. et al. Methane oxidation pathways and associated methanotrophic communities in the water column of a tropical lake. Limnol. Oceanogr. 60, 553–572 (2015).ADS 

Google Scholar 
20.Mayr, M. J. et al. Growth and rapid succession of methanotrophs effectively limit methane release during lake overturn. Commun. Biol. 3, 1–9 (2020).
Google Scholar 
21.Bussmann, I., Rahalkar, M. & Schink, B. Cultivation of methanotrophic bacteria in opposing gradients of methane and oxygen. FEMS Microbiol. Ecol. 56, 331–344 (2006).CAS 
PubMed 

Google Scholar 
22.Kankaala, P., Eller, G. & Jones, R. I. Could bacterivorous zooplankton affect lake pelagic methanotrophic activity? Fundam. Appl. Limnol. / Arch. f.ür. Hydrobiol. 169, 203–209 (2007).
Google Scholar 
23.Khmelenina, V. N. et al. Structural and functional features of methanotrophs from hypersaline and alkaline lakes. Microbiology 79, 472–482 (2010).CAS 

Google Scholar 
24.Westfall, C. S. & Levin, P. A. Bacterial cell size: multifactorial and multifaceted. Annu. Rev. Microbiol. 71, 499–517 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Chien, A. C., Hill, N. S. & Levin, P. A. Cell size control in bacteria. Curr. Biol. 22, R340–R349 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Velimirov, B. Nanobacteria, ultramicrobacteria and starvation forms: a search for the smallest metabolizing bacterium. Microbes Environ. 16, 67–77 (2001).
Google Scholar 
27.Reis, P. C. J., Thottathil, S. D., Ruiz-González, C. & Prairie, Y. T. Niche separation within aerobic methanotrophic bacteria across lakes and its link to methane oxidation rates. Environ. Microbiol. 22, 738–751 (2020).CAS 
PubMed 

Google Scholar 
28.Garcia-Chaves, M. C., Cottrell, M. T., Kirchman, D. L., Ruiz-González, C. & del Giorgio, P. A. Single-cell activity of freshwater aerobic anoxygenic phototrophic bacteria and their contribution to biomass production. Isme J. 10, 1579–1588 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Jürgens, K. & Matz, C. Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 81, 413–434 (2002).
Google Scholar 
30.Rautio, M. & Vincent, W. F. Benthic and pelagic food resources for–zooplankton in shallow high-latitude lakes and ponds. Freshw. Biol. 51, 1038–1052 (2006).CAS 

Google Scholar 
31.Rissanen, A. J. et al. Gammaproteobacterial methanotrophs dominate methanotrophy in aerobic and anaerobic layers of boreal lake waters. Aquat. Microb. Ecol. 81, 257–276 (2018).
Google Scholar 
32.Zimmermann, M. et al. Microbial methane oxidation efficiency and robustness during lake overturn. Limnol. Oceanogr. Lett. 6, 320–328 (2021).CAS 

Google Scholar 
33.Puri, A. W. et al. Genetic tools for the industrially promising methanotroph Methylomicrobium buryatense. Appl. Environ. Microbiol. 81, 1775–1781 (2015).PubMed 
PubMed Central 
ADS 

Google Scholar 
34.Strong, P. J., Kalyuzhnaya, M., Silverman, J. & Clarke, W. P. A methanotroph-based biorefinery: potential scenarios for generating multiple products from a single fermentation. Bioresour. Technol. 215, 314–323 (2016).CAS 
PubMed 

Google Scholar 
35.Oswald, K. et al. Aerobic gammaproteobacterial methanotrophs mitigate methane emissions from oxic and anoxic lake waters. Limnol. Oceanogr. 61, S101–S118 (2016).
Google Scholar 
36.Smith, E. M. & Prairie, Y. T. Bacterial metabolism and growth efficiency in lakes: the importance of phosphorus availability. Limnol. Oceanogr. 49, 137–147 (2004).CAS 
ADS 

Google Scholar 
37.Del Giorgio, P. A., Cole, J. J., Caraco, N. F. & Peters, R. H. Linking planktonic biomass and metabolism to net gas fluxes in northern temperate lakes. Ecology 80, 1422–1431 (1999).
Google Scholar 
38.Kellerman, A. M., Dittmar, T., Kothawala, D. N. & Tranvik, L. J. Chemodiversity of dissolved organic matter in lakes driven by climate and hydrology. Nat. Commun. 5, 3804 (2014).CAS 
PubMed 
ADS 

Google Scholar 
39.Sun, L., Perdue, E. M., Meyer, J. L. & Weis, J. Use of elemental composition to predict bioavailability of dissolved organic matter in a Georgia river. Limnol. Oceanogr. 42, 714–721 (1997).CAS 
ADS 

Google Scholar 
40.Kellerman, A. M., Kothawala, D. N., Dittmar, T. & Tranvik, L. J. Persistence of dissolved organic matter in lakes related to its molecular characteristics. Nat. Geosci. 8, 454–457 (2015).CAS 
ADS 

Google Scholar 
41.Guillemette, F. & del Giorgio, P. A. Reconstructing the various facets of dissolved organic carbon bioavailability in freshwater ecosystems. Limnol. Oceanogr. 56, 734–748 (2011).CAS 
ADS 

Google Scholar 
42.Logue, J. B. et al. Experimental insights into the importance of aquatic bacterial community composition to the degradation of dissolved organic matter. ISME J. 10, 533–545 (2016).CAS 
PubMed 

Google Scholar 
43.Salcher, M. M., Posch, T. & Pernthaler, J. In situ substrate preferences of abundant bacterioplankton populations in a prealpine freshwater lake. ISME J. 7, 896–907 (2013).CAS 
PubMed 

Google Scholar 
44.Sobek, S., Tranvik, L. J., Prairie, Y., Kortelainen, P. & Cole, J. J. Patterns and regulation of dissolved organic carbon: an analysis of 7,500 widely distributed lakes. Limnol. Oceanogr. 52, 1208–1219 (2007).CAS 
ADS 

Google Scholar 
45.Kalyuzhnaya, M. G. et al. Highly efficient methane biocatalysis revealed in a methanotrophic bacterium. Nat. Commun. 4, 2785 (2013).CAS 
PubMed 
ADS 

Google Scholar 
46.Oshkin, I. Y. et al. Methane-fed microbial microcosms show differential community dynamics and pinpoint taxa involved in communal response. ISME J. 9, 1119–1129 (2014).PubMed 
PubMed Central 

Google Scholar 
47.Chistoserdova, L. & Kalyuzhnaya, M. G. Current trends in methylotrophy. Trends Microbiol 26, 703–714 (2018).CAS 
PubMed 

Google Scholar 
48.Martinez-Cruz, K. et al. Anaerobic oxidation of methane by aerobic methanotrophs in sub-Arctic lake sediments. Sci. Total Environ. 607–608, 23–31 (2017).PubMed 
ADS 

Google Scholar 
49.Samad, M. S. & Bertilsson, S. Seasonal variation in abundance and diversity of bacterial methanotrophs in five temperate lakes. Front. Microbiol. 8, 1–12 (2017).
Google Scholar 
50.Ricão Canelhas, M., Denfeld, B. A., Weyhenmeyer, G. A., Bastviken, D. & Bertilsson, S. Methane oxidation at the water-ice interface of an ice-covered lake. Limnol. Oceanogr. 61, S78–S90 (2016).ADS 

Google Scholar 
51.Houser, J. N. Water color affects the stratification, surface temperature, heat content, and mean epilimnetic irradiance of small lakes. Can. J. Fish. Aquat. Sci. 63, 2447–2455 (2006).
Google Scholar 
52.Caplanne, S. & Laurion, I. Effect of chromophoric dissolved organic matter on epilimnetic stratification in lakes. Aquat. Sci. 70, 123–133 (2008).CAS 

Google Scholar 
53.Oswald, K. et al. Light-dependent aerobic methane oxidation reduces methane emissions from seasonally stratified lakes. PLoS ONE 10, e0132574 (2015).PubMed 
PubMed Central 

Google Scholar 
54.Savvichev, A. S. et al. Light-dependent methane oxidation is the major process of the methane cycle in the water column of the Bol’shie Khruslomeny Polar Lake. Microbiology 88, 370–374 (2019).CAS 

Google Scholar 
55.Baines, S. B. & Pace, M. L. The production of dissolved organic matter by phytoplankton and its importance to bacteria: patterns across marine and freshwater systems. Limnol. Oceanogr. 36, 1078–1090 (1991).ADS 

Google Scholar 
56.Cole, J. J., Likens, G. E. & Strayer, D. L. Photosynthetically produced dissolved organic carbon: an important carbon source for planktonic bacteria. Limnol. Oceanogr. 27, 1080–1090 (1982).CAS 
ADS 

Google Scholar 
57.Dumestre, J. et al. Influence of light intensity on methanotrophic bacterial activity in Petit Saut reservoir, French Guiana. Appl. Environ. Microbiol. 65, 534–539 (1999).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
58.Murase, J. & Sugimoto, A. Inhibitory effect of light on methane oxidation in the pelagic water column of a mesotrophic lake (Lake Biwa, Japan). Limnol. Oceanogr. 50, 1339–1343 (2005).CAS 
ADS 

Google Scholar 
59.Moran, M. A. & Hodson, R. E. Bacterial production on humic and nonhumic components of dissolved organic carbon. Limnol. Oceanogr. 35, 1744–1756 (1990).CAS 
ADS 

Google Scholar 
60.Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 

Google Scholar 
61.Roulet, N. & Moore, T. R. Browning the waters. Nature 444, 283–284 (2006).CAS 
PubMed 
ADS 

Google Scholar 
62.Weyhenmeyer, G. A., Prairie, Y. T. & Tranvik, L. J. Browning of boreal freshwaters coupled to carbon-iron interactions along the aquatic continuum. PLoS ONE 9, e88104 (2014).PubMed 
PubMed Central 
ADS 

Google Scholar 
63.O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 10773–10781 (2015).ADS 

Google Scholar 
64.Yamamoto, S., Alcauskas, J. B. & Crozier, T. E. Solubility of methane in distilled water and seawater. J. Chem. Eng. Data 21, 78–80 (1976).CAS 

Google Scholar 
65.Cantin, A., Beisner, B. E., Gunn, J. M., Prairie, Y. T. & Winter, J. G. Effects of thermocline deepening on lake plankton communities. Can. J. Fish. Aquat. Sci. 68, 260–276 (2011).
Google Scholar 
66.Smith, D. C. & Azam, F. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Mar. Microb. Food Webs 6, 107–114 (1992).
Google Scholar 
67.Del Giorgio, P. A., Pace, M. L. & Fischer, D. Relationship of bacterial growth efficiency to spatial variation in bacterial activity in the Hudson River. Aquat. Microb. Ecol. 45, 55–67 (2006).
Google Scholar 
68.Eller, G., Stubner, S. & Frenzel, P. Group-specific 16S rRNA targeted probes for the detection of type I and type II methanotrophs by fluorescence in situ hybridisation. FEMS Microbiol. Lett. 198, 91–97 (2001).CAS 
PubMed 

Google Scholar 
69.Zeder, M. ACME tool3. (2014).70.Callieri, C. et al. Bacteria, Archaea, and Crenarchaeota in the epilimnion and hypolimnion of a deep holo-oligomictic lake. Appl. Environ. Microbiol. 75, 7298–7300 (2009).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
71.Lew, S. & Glińska-Lewczuk, K. Environmental controls on the abundance of methanotrophs and methanogens in peat bog lakes. Sci. Total Environ. 645, 1201–1211 (2018).CAS 
PubMed 
ADS 

Google Scholar 
72.Fagerbakke, K. M., Heldal, M. & Norland, S. Content of carbon, nitrogen, oxygen, sulfur and phosphorus in native aquatic and cultured bacteria. Aquat. Microb. Ecol. 10, 15–27 (1996).
Google Scholar 
73.Read J. S. et al. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environmental Modelling and Software. 26, 1325–1336 (2011).
Google Scholar 
74.R Core Team. R: a language and environment for statistical computing. (2019). https://www.R-project.org/.75.RStudio Team. RStudio: Integrated Development for R. RStudio, Inc., Boston, MAURL. (2018). http://www.rstudio.com/.76.Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. (2019). https://cran.r-project.org/package=dplyr.77.Sievert, C. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida. (2020).78.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, 2016). https://ggplot2.tidyverse.org.79.Wilke, C.O. cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 1.0.0. (2019). https://CRAN.R-project.org/package=cowplot.80.Auguie, B. gridExtra: Miscellaneous Functions for ‘Grid’ Graphics. R package version 2.3. (2017). https://CRAN.R-project.org/package=gridExtra.81.Reis, P. C. J., Thottathil, S. D. & Prairie, Y. T. Dataset: the role of methanotrophy in the microbial carbon metabolism of temperate lakes. (1.0.0) [Data set]. Zenodo. (2021). https://doi.org/10.5281/zenodo.5737277. More

1.Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. P Natl Acad. Sci. USA 103, 12115–12120 (2006).CAS 
ADS 

Google Scholar 
2.Pedros-Alio, C. The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 449–466 (2012).PubMed 

Google Scholar 
3.Lynch, M. D. J. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).CAS 
PubMed 

Google Scholar 
4.Campbell, B. J., Yu, L. Y., Heidelberg, J. F. & Kirchman, D. L. Activity of abundant and rare bacteria in a coastal ocean. P Natl Acad. Sci. USA 108, 12776–12781 (2011).CAS 
ADS 

Google Scholar 
5.Gobet, A. et al. Diversity and dynamics of rare and of resident bacterial populations in coastal sands. Isme J. 6, 542–553 (2012).PubMed 

Google Scholar 
6.Wilhelm, L. et al. Rare but active taxa contribute to community dynamics of benthic biofilms in glacier-fed streams. Environ. Microbiol. 16, 2514–2524 (2014).CAS 
PubMed 

Google Scholar 
7.Lawson, C. E. et al. Rare taxa have potential to make metabolic contributions in enhanced biological phosphorus removal ecosystems. Environ. Microbiol. 17, 4979–4993 (2015).CAS 
PubMed 

Google Scholar 
8.Newton, R. J. & Shade, A. Lifestyles of rarity: understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat. Micro. Ecol. 78, 51–63 (2016).
Google Scholar 
9.Lauro, F. M. et al. The genomic basis of trophic strategy in marine bacteria. P Natl Acad. Sci. USA 106, 15527–15533 (2009).CAS 
ADS 

Google Scholar 
10.Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. RRNA operon copy number reflects ecological strategies of bacteria. Appl Environ. Micro. 66, 1328–1333 (2000).CAS 
ADS 

Google Scholar 
11.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Polz, M. F. & Cordero, O. X. Bacterial evolution: genomics of metabolic trade-offs. Nat. Microbiol. 1, 16181 (2016).CAS 
PubMed 

Google Scholar 
13.Giovannoni, S. J. SAR11 bacteria: the most abundant plankton in the oceans. Ann. Rev. Mar. Sci. 9, 231–255 (2017).PubMed 

Google Scholar 
14.Elser, J. J. et al. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 3, 540–550 (2000).
Google Scholar 
15.Hessen, D. O., Elser, J. J., Sterner, R. W. & Urabe, J. Ecological stoichiometry: an elementary approach using basic principles. Limnol. Oceanogr. 58, 2219–2236 (2013).CAS 
ADS 

Google Scholar 
16.Acharya, K., Kyle, M. & Elser, J. J. Biological stoichiometry of Daphnia growth: an ecophysiological test of the growth rate hypothesis. Limnol. Oceanogr. 49, 656–665 (2004).CAS 
ADS 

Google Scholar 
17.Hendrixson, H. A., Sterner, R. W. & Kay, A. D. Elemental stoichiometry of freshwater fishes in relation to phylogeny, allometry and ecology. J. Fish. Biol. 70, 121–140 (2007).
Google Scholar 
18.Matzek, V. & Vitousek, P. M. N: P stoichiometry and protein: RNA ratios in vascular plants: an evaluation of the growth-rate hypothesis. Ecol. Lett. 12, 765–771 (2009).PubMed 

Google Scholar 
19.Ghoul, M. & Mitri, S. The ecology and evolution of microbial competition. Trends Microbiol 24, 833–845 (2016).CAS 
PubMed 

Google Scholar 
20.Laland, K., Matthews, B. & Feldman, M. W. An introduction to niche construction theory. Evol. Ecol. 30, 191–202 (2016).PubMed 
PubMed Central 

Google Scholar 
21.Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 

Google Scholar 
22.Větrovský, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. Plos ONE 8, e57923 (2013).PubMed 
PubMed Central 
ADS 

Google Scholar 
23.Molenaar, D., van Berlo, R., de Ridder, D. & Teusink, B. Shifts in growth strategies reflect tradeoffs in cellular economics. Mol. Syst. Biol. 5, 323–323 (2009).PubMed 
PubMed Central 

Google Scholar 
24.Nemergut, D. R. et al. Decreases in average bacterial community rRNA operon copy number during succession. Isme J. 10, 1147–1156 (2016).CAS 
PubMed 

Google Scholar 
25.Wu, L. W. et al. Microbial functional trait of rRNA operon copy numbers increases with organic levels in anaerobic digesters. Isme J. 11, 2874–2878 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Wu, L. W. et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat. Microbiol. 4, 2579–2579 (2019).PubMed 

Google Scholar 
27.Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. K. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 43, D593–D598 (2015).CAS 
PubMed 

Google Scholar 
28.Dai, T. et al. Dynamics of coastal bacterial community average ribosomal RNA operon copy number reflect its response and sensitivity to ammonium and phosphate. Environ. Pollut. 260, 113971 (2020).CAS 
PubMed 

Google Scholar 
29.Zhou, J., Deng, Y., Luo, F., He, Z. & Yang, Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. Mbio 2, e00122–00111 (2011).PubMed 
PubMed Central 

Google Scholar 
30.Vellend, M. Conceptual Synthesis in Community Ecology. Q Rev. Biol. 85, 183–206 (2010).PubMed 

Google Scholar 
31.Frey, E. Evolutionary game theory: Theoretical concepts and applications to microbial communities. Phys. A 389, 4265–4298 (2010).MathSciNet 
CAS 
MATH 

Google Scholar 
32.Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. P Natl Acad. Sci. USA 116, 11824 (2019).CAS 

Google Scholar 
33.Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
34.Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic Interactions and the Drivers of Microbial Community Assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 

Google Scholar 
35.Gorter, F. A., Manhart, M. & Ackermann, M. Understanding the evolution of interspecies interactions in microbial communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190256 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Gandhi, S. R., Korolev, K. S. & Gore, J. Cooperation mitigates diversity loss in a spatially expanding microbial population. P Natl Acad. Sci. USA 116, 23582–23587 (2019).CAS 

Google Scholar 
37.Calatayud, J. et al. Positive associations among rare species and their persistence in ecological assemblages. Nat. Ecol. Evol. 4, 40–45 (2020).PubMed 

Google Scholar 
38.Furman, O. et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat. Commun. 11, 1904 (2020).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
39.Tardy, V. et al. Stability of soil microbial structure and activity depends on microbial diversity. Env. Microbiol. Rep. 6, 173–183 (2014).CAS 

Google Scholar 
40.Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).CAS 
PubMed 
ADS 

Google Scholar 
41.Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).PubMed 

Google Scholar 
42.Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol. 6, 196–208 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Chatzinikolaou, E. et al. Spatio-temporal benthic biodiversity patterns and pollution pressure in three Mediterranean touristic ports. Sci. Total Environ. 624, 648–660 (2018).CAS 
PubMed 
ADS 

Google Scholar 
44.Filippini, G. et al. Sediment bacterial communities associated with environmental factors in Intermittently Closed and Open Lakes and Lagoons (ICOLLs). Sci. Total Environ. 693, 133462 (2019).CAS 
PubMed 
ADS 

Google Scholar 
45.Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Huse, S. M. et al. Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing. Plos Genet. 4, e1000255 (2008).PubMed 
PubMed Central 

Google Scholar 
47.Salas-González, I. et al. Coordination between microbiota and root endodermis supports plant mineral nutrient homeostasis. Science 371, eabd0695 (2021).PubMed 

Google Scholar 
48.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. P Natl Acad. Sci. USA 108, 4516 (2011).CAS 
ADS 

Google Scholar 
49.Wear, E. K., Wilbanks, E. G., Nelson, C. E. & Carlson, C. A. Primer selection impacts specific population abundances but not community dynamics in a monthly time-series 16S rRNA gene amplicon analysis of coastal marine bacterioplankton. Environ. Microbiol. 20, 2709–2726 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
51.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
52.Wu, L. W. et al. Long-term successional dynamics of microbial association networks in anaerobic digestion processes. Water Res. 104, 1–10 (2016).PubMed 

Google Scholar 
53.Galand, P. E., Casamayor, E. O., Kirchman, D. L. & Lovejoy, C. Ecology of the rare microbial biosphere of the Arctic Ocean. P Natl Acad. Sci. USA 106, 22427–22432 (2009).CAS 
ADS 

Google Scholar 
54.Ju, F. & Zhang, T. Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant. Isme J. 9, 683–695 (2015).CAS 
PubMed 
ADS 

Google Scholar  More

1.Lyson, T. R. et al. Origin of the unique ventilatory apparatus of turtles. Nat. Commun. 5(5211), 1–11. https://doi.org/10.1038/ncomms6211 (2014).CAS 
Article 

Google Scholar 
2.Gans, C. & Hughes, G. The mechanism of lung ventilation in the tortoise Testudo graeca Linné. J. Exp. Biol. 47(1), 1–20 (1967).CAS 
Article 
PubMed 

Google Scholar 
3.Jackson, D. C., Singer, J. H. & Downey, P. T. Oxidative cost of breathing in the turtle Chrysemys picta bellii. Am. J. Physiol. 261, R1325–R1328 (1991).CAS 
PubMed 

Google Scholar 
4.Landberg, T., Mailhot, J. D. & Brainerd, E. L. Lung ventilation during treadmill locomotion in a semi-aquatic turtle, Trachemys scripta. J. Exp. Zool. 311A, 551–562. https://doi.org/10.1002/jez.478 (2009).Article 

Google Scholar 
5.Ruhr, I., Rose, K., Sellers, W., Crossley, D. II. & Codd, J. Turning turtle: Scaling relationships and self-righting ability in Chelydra serpentina. Proc. R. Soc. B. 288, 20210213. https://doi.org/10.1098/rspb.2021.0213 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Pritchard, P. C. H. Encyclopaedia of Turtles (TFH, 1979).
Google Scholar 
7.Carr, A. Handbook of Turtles: The Turtles of the United States, Canada, and Baja California (Cornell University Press, 1952).
Google Scholar 
8.Rivera, G. Ecomorphological variation in shell shape of the freshwater turtle Pseudemys concinna inhabiting different aquatic flow regimes. Int. Comp. Biol. 48(6), 769–787. https://doi.org/10.1093/icb/icn088 (2008).Article 

Google Scholar 
9.McNeill Alexander, R. Gaits of mammals and turtles. J. R. Soc. Jpn. 11(3), 314–319 (1993).Article 

Google Scholar 
10.Zani, P. A. & Kram, R. Low metabolic cost of locomotion in ornate box turtles, Terrapene ornate. J. Exp. Biol. 211, 3671–3676. https://doi.org/10.1242/jeb.019869 (2008).Article 
PubMed 

Google Scholar 
11.Sellers, W. I., Rose, K. A. R., Crossley, D. A. II. & Codd, J. R. Inferring cost of transport from whole-body kinematics in three sympatric turtle species with different locomotor habits. Comp. Biochem. Physiol. A. 247, 110739. https://doi.org/10.1016/j.cbpa.2020.110739 (2020).CAS 
Article 

Google Scholar 
12.Chiari, Y., van der Meijden, A., Caccone, A., Claude, J. & Gilles, B. Self-righting potential and the evolution of shell shape in Galápagos tortoises. Sci. Rep. 7(1), 1–8. https://doi.org/10.1038/s41598-017-15787-7 (2017).CAS 
Article 

Google Scholar 
13.Woledge, R. C. The energetics of tortoise muscle. J. Physiol. 197(3), 685–707 (1968).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Steyermark, A. C. & Spotila, J. R. Body temperature and maternal identity affect snapping turtle (Chelydra serpentina) righting response. Copeia 4, 1050–1057. https://doi.org/10.1643/0045-8511(2001)001[1050:BTAMIA]2.0.CO;2 (2001).Article 

Google Scholar 
15.Rubin, A. M., Blob, R. W. & Mayerl, C. J. Biomechanical factors influencing successful self-righting in the Pleurodire turtle, Emydura subglobosa. J. Exp. Biol. 221, jeb182642. https://doi.org/10.1242/jeb.182642 (2018).Article 
PubMed 

Google Scholar 
16.Penn, D. & Brockmann, H. J. Age-biased stranding and righting in male horseshoe crabs, Limulus polyphemus. Anim. Behav. 49, 1531–1539. https://doi.org/10.1016/003-3472(95)90074-8 (1995).Article 

Google Scholar 
17.Bonnet, X. et al. Sexual dimorphism in steppe tortoises (Testudo horsfieldii): Influence of the environment and sexual selection on body shape and mobility. Biol. J. Linn. Soc. 72, 357–372. https://doi.org/10.1006/bjls.2000.0504 (2001).Article 

Google Scholar 
18.Zuffi, M. A. L. & Corti, C. Aspects of population ecology of Testudo hermanni hermanni from Asinara Island, NW Sardinia (Italy, Western Mediterranean Sea): Preliminary data. Amphib-Reptil. 24, 441–447 (2003).Article 

Google Scholar 
19.Domokos, G. & Várkonyi, P. L. Geometry and self-righting of turtles. Proc. R. Soc. B. 275(1630), 11–17. https://doi.org/10.1098/rspb.2007.1188 (2008).Article 
PubMed 

Google Scholar 
20.Mann, G. K. H., O’Riain, M. J. & Hofmeyr, M. D. Shaping up to fight: Sexual selection influences body shape and size in the fighting tortoise (Chersina angulata). J. Zool. 269, 373–379. https://doi.org/10.1111/j.1469-7998.2006.00079x (2006).Article 

Google Scholar 
21.Golubović, A., Bonnet, X., Djordjević, S., Djurakic, M. & Tomović, L. Variations in righting behavior across Hermann’s tortoise populations. J. Zool. 291, 69–75. https://doi.org/10.1111/jzo.12047 (2013).Article 

Google Scholar 
22.Golubović, A., Andelkovic, M., Arsovski, D., Bonnet, X. & Tomović, L. Locomotor performances reflect habitat constraints in an armoured species. Behav. Ecol. Sociobiol. 71, 93. https://doi.org/10.1007/s00265-017-2318-0 (2017).Article 

Google Scholar 
23.Ashe, V. M. The righting reflex in turtles: A description and comparison. Psychol. Sci. 20, 150–152. https://doi.org/10.3758/BF03335647 (1970).Article 

Google Scholar 
24.Golubović, A., Tomović, L. & Ivanović, A. Geometry of self-righting: The case of Hermann’s tortoises. Zool. Anz. 254, 99–105. https://doi.org/10.1016/j.jcz.2014.12.003 (2015).Article 

Google Scholar 
25.Finkler, M. S. Influence of water availability during hatching on hatchling size, body composition, desiccation tolerance, and terrestrial locomotor performance in the snapping turtle, Chelydra serpentina. Physiol. Biochem. Zool. 72, 714–722. https://doi.org/10.1086/316711 (1999).CAS 
Article 
PubMed 

Google Scholar 
26.Stojadinović, D., Milošević, D. & Crnobrnja-Isailović, J. Righting time versus shell size and shape dimorphism in adult Hermann’s tortoises: Field observations meet theoretical predictions. Anim. Biol. 63(4), 381–396. https://doi.org/10.1163/15707563-00002420 (2013).Article 

Google Scholar 
27.Delmas, V., Baudry, E., Girondot, M. & Prevot-Julliard, A.-C. The righting reflex as a fitness indicator in freshwater turtles. Biol. J. Linn. Soc. 91, 99–109. https://doi.org/10.1111/j.1095-8312/2007.00780.x (2007).Article 

Google Scholar 
28.Burger, J. Behavior of hatchling diamondback terrapins (Malaclemys terrapin) in the field. Copeia 1976, 742. https://doi.org/10.2307/1443457 (1976).Article 

Google Scholar 
29.Landberg, T., Mailhot, J. D. & Brainerd, E. L. Lung ventilation during treadmill locomotion in a terrestrial turtle, Terrapene carolina. J. Exp. Biol. 206, 3391–3404. https://doi.org/10.1242/jeb.00553 (2003).Article 
PubMed 

Google Scholar 
30.Gaunt, A. S. & Gans, C. Mechanics of respiration in the snapping turtle, Chelydra serpentina (Linné). J. Morph. 128, 195–227. https://doi.org/10.1002/jmor.1051280205 (1969).Article 

Google Scholar 
31.Lambertz, M., Böhme, W. & Perry, S. F. The anatomy of the respiratory system in Platysternon megacephalum Gray, 1831 (Testudines: Crytodira) and related species, and its phylogenetic implications. Comp. Biochem. Physiol. 156, 330–336. https://doi.org/10.1016/j.cbpa.2009.12.016 (2010).CAS 
Article 

Google Scholar 
32.de Souza, R. B. B. & Klein, W. The influence of the post-pulmonary septum and submersion on the pulmonary mechanics of Trachemys scripta (Cryptodira: Emydidae). J. Exp. Biol. 224(12), 242386. https://doi.org/10.1242/jeb.242386 (2021).Article 

Google Scholar 
33.Jodice, P. G. R., Epperson, D. M. & Visser, G. H. Daily energy expenditure in free-ranging gopher tortoises (Gopherus polyphemus). Copeia 2006(1), 129–136. https://doi.org/10.1643/0045-8511(2006)006[0129:DEEIFG]2.0.CO;2 (2006).Article 

Google Scholar 
34.Zera, A. J. & Harshman, L. G. The physiology of life history trade-offs in animals. Ann. Rev. Ecol. Syst. 32, 95–126. https://doi.org/10.1146/annurev.ecolsys.32.081501.114006 (2001).Article 

Google Scholar 
35.Shadmehr, R., Huang, H. J. & Ahmed, A. A. A representation of effort in decision-making and motor control. Curr. Biol. 26, 1929–1934. https://doi.org/10.1016/j.cub.2016.05.065 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Shepard, E. L. C. et al. Energy landscapes shapes animal movement ecology. Am. Nat. 182(3), 298–312. https://doi.org/10.1086/671257 (2013).Article 
PubMed 

Google Scholar 
37.Baudinette, R. V., Miller, A. M. & Sarre, M. P. Aquatic and terrestrial locomotory energetics in a toad and a turtle: A search for generalisations among ectotherms. Physiol. Biochem. Zool. 73(6), 672–682. https://doi.org/10.1086/318101 (2000).CAS 
Article 
PubMed 

Google Scholar 
38.Hailey, A. & Coulson, I. M. Measurement of time budgets from continuous observation of thread-trailed tortoises (Kinixys spekii). Herp. J. 9, 15–20 (1999).
Google Scholar 
39.Kram, R. & Taylor, C. R. Energetics of running: A new perspective. Nature 346, 265–267. https://doi.org/10.1038/346265a0 (1990).CAS 
Article 
ADS 
PubMed 

Google Scholar 
40.Taylor, C. R. Relating mechanics and energetics during exercise. Adv. Vet. Sci. Comp. Med. 38A, 181–215 (1994).CAS 
PubMed 

Google Scholar 
41.Cavagna, G. A. & Kaneko, M. Mechanical work and efficiency in level walking and running. J. Physiol. 268(2), 467–481. https://doi.org/10.1113/jphysiol.1977.sp011866 (1977).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Carrier, D. R., Deban, S. M. & Fischbein, T. Locomotor function of the pectoral girdle “muscular sling” in trotting dogs. J. Exp. Biol. 209, 2224–2237. https://doi.org/10.1242/jeb.02236 (2006).Article 
PubMed 

Google Scholar 
43.Heglund, N. C. & Cavagna, G. A. Efficiency of vertebrate locomotory muscles. J. Exp. Biol. 115, 283–292. https://doi.org/10.1242/jeb.115.1.283 (1985).CAS 
Article 
PubMed 

Google Scholar 
44.Barclay, C. J. The basis of difference in thermodynamic efficiency among skeletal muscles. Clin. Exp. Pharm. Physiol. 44(12), 1279–1286. https://doi.org/10.1111/1440-1681.12850 (2017).MathSciNet 
CAS 
Article 

Google Scholar 
45.Nwoye, L. O. & Goldspink, G. Biochemical efficiency and intrinsic shortening speed in selected fast and slow muscles. Experientia 37, 856–857. https://doi.org/10.1007/BF1985678 (1981).CAS 
Article 
PubMed 

Google Scholar 
46.Lambert, M. Temperature, activity and field sighting in the Mediterranean spur-thighed or common garden tortoise Testudo graeca. Biol. Conserv. 21, 39–54. https://doi.org/10.1016/0006-3207(81)90067-7 (1981).Article 

Google Scholar 
47.Tracy, R., Zimmerman, L., Tracy, C., Bradley, K. & Castle, K. Rates of food passage in the digestive tract of young desert tortoises: Effects of body size and diet quality. Chelonian Conserv. Biol. 5(2), 269–273. https://doi.org/10.2744/1071-8443(2006)5[269:ROFPIT]2.0.co;2 (2006).Article 

Google Scholar 
48.Huey, R. & Kingsolver, J. Evolution of thermal sensitivity of ectotherm performance. Trends Ecol. Evol. 4(5), 131–135. https://doi.org/10.1016/0169-5347(89)90211-5 (1989).CAS 
Article 
PubMed 

Google Scholar 
49.Lailvaux, S. & Irschick, D. Effects of temperature and sex on jump performance and biomechanics in the lizard Anolis carolinensis. Funct. Ecol. 21(3), 534–543. https://doi.org/10.1111/j.1365-2435.2007.01263.x (2007).Article 

Google Scholar 
50.Lighton, J. Measuring Metabolic Rates: A Manual for Scientists (Oxford University Press, 2008).Book 

Google Scholar 
51.Brody, S. Bioenergetics and Growth (Reinhold, 1945).
Google Scholar  More

1.Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl Acad. Sci. 108, 17905–17909 (2011).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
2.Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & Garcia-Herrera, R. The Hot Summer of 2010: Redrawing the temperature record map of Europe. Science 332, 220–224 (2011).CAS 
PubMed 
ADS 

Google Scholar 
3.Fischer, E. M. & Knutti, R. Detection of spatially aggregated changes in temperature and precipitation extremes. Geophys. Res. Lett. 41, 547–554 (2014).ADS 

Google Scholar 
4.Della-Marta, P. M., Haylock, M. R., Luterbacher, J. & Wanner, H. Doubled length of western European summer heat waves since 1880. J. Geophys. Res. 112, D15103 (2007).ADS 

Google Scholar 
5.Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Chang. 8, 469–477 (2018).ADS 

Google Scholar 
6.Teskey, R. et al. Responses of tree species to heat waves and extreme heat events. Plant, Cell Environ. 38, 1699–1712 (2015).
Google Scholar 
7.Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).CAS 
PubMed 
ADS 

Google Scholar 
8.Steppe, K., Sterck, F. & Deslauriers, A. Diel growth dynamics in tree stems: linking anatomy and ecophysiology. Trends Plant Sci. 20, 335–343 (2015).CAS 
PubMed 

Google Scholar 
9.Peters, R. L. et al. Turgor—a limiting factor for radial growth in mature conifers along an elevational gradient. N. Phytol. 229, 213–229 (2021).CAS 

Google Scholar 
10.Meinzer, F. C., Johnson, D. M., Lachenbruch, B., McCulloh, K. A. & Woodruff, D. R. Xylem hydraulic safety margins in woody plants: Coordination of stomatal control of xylem tension with hydraulic capacitance. Funct. Ecol. 23, 922–930 (2009).
Google Scholar 
11.Anderegg, W. R. L., Berry, J. A. & Field, C. B. Linking definitions, mechanisms, and modeling of drought-induced tree death. Trends Plant Sci. 17, 693–700 (2012).CAS 
PubMed 

Google Scholar 
12.Martínez‐Vilalta, J., Anderegg, W. R. L., Sapes, G. & Sala, A. Greater focus on water pools may improve our ability to understand and anticipate drought‐induced mortality in plants. N. Phytol. 223, 22–32 (2019).
Google Scholar 
13.Zweifel, R., Haeni, M., Buchmann, N. & Eugster, W. Are trees able to grow in periods of stem shrinkage? N. Phytol. 211, 839–849 (2016).
Google Scholar 
14.Dietrich, L., Zweifel, R. & Kahmen, A. Daily stem diameter variations can predict the canopy water status of mature temperate trees. Tree Physiol. 38, 941–952 (2018).PubMed 

Google Scholar 
15.Zweifel, R. et al. Why trees grow at night. N. Phytol. 231, 2174–2185 (2021).
Google Scholar 
16.Buras, A., Rammig, A. & Zang, C. S. Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003. Biogeosciences 17, 1655–1672 (2020).ADS 

Google Scholar 
17.Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
18.Peters, W., Bastos, A., Ciais, P. & Vermeulen, A. A historical, geographical, and ecological perspective on the 2018 European summer drought. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190505 (2020).
Google Scholar 
19.Albergel, C. et al. Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation. Remote Sens. 11, 520 (2019).ADS 

Google Scholar 
20.Smith, N. E. et al. Spring enhancement and summer reduction in carbon uptake during the 2018 drought in northwestern. Eur. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190509 (2020).CAS 

Google Scholar 
21.Brun, P. et al. Large‐scale early‐wilting response of Central European forests to the 2018 extreme drought. Glob. Chang. Biol. 26, 7021–7035 (2020).PubMed 
PubMed Central 
ADS 

Google Scholar 
22.Ramonet, M. et al. The fingerprint of the summer 2018 drought in Europe on ground-based atmospheric CO2 measurements. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190513 (2020).CAS 

Google Scholar 
23.Bastos, A. et al. Impacts of extreme summers on European ecosystems: A comparative analysis of 2003, 2010 and 2018. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190507 (2020).CAS 

Google Scholar 
24.Lin, Y.-S. et al. Optimal stomatal behaviour around the world. Nat. Clim. Chang. 5, 459–464 (2015).CAS 
ADS 

Google Scholar 
25.Schuldt, B. et al. A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol. 45, 86–103 (2020).
Google Scholar 
26.Rita, A. et al. The impact of drought spells on forests depends on site conditions: The case of 2017 summer heat wave in southern Europe. Glob. Chang. Biol. 26, 851–863 (2020).PubMed 
ADS 

Google Scholar 
27.Hanewinkel, M., Cullmann, D. A., Schelhaas, M.-J., Nabuurs, G.-J. & Zimmermann, N. E. Climate change may cause severe loss in the economic value of European forest land. Nat. Clim. Chang. 3, 203–207 (2013).ADS 

Google Scholar 
28.Larysch, E., Stangler, D. F., Nazari, M., Seifert, T. & Kahle, H.-P. Xylem phenology and growth response of European beech, silver fir and scots pine along an elevational gradient during the extreme drought year 2018. Forests 12, 75 (2021).
Google Scholar 
29.Rohner, B., Kumar, S., Liechti, K., Gessler, A. & Ferretti, M. Tree vitality indicators revealed a rapid response of beech forests to the 2018 drought. Ecol. Indic. 120, 106903 (2021).
Google Scholar 
30.Scharnweber, T., Smiljanic, M., Cruz-García, R., Manthey, M. & Wilmking, M. Tree growth at the end of the 21st century – the extreme years 2018/19 as template for future growth conditions. Environ. Res. Lett. 15, 074022 (2020).ADS 

Google Scholar 
31.Kowalska, N. et al. Analysis of floodplain forest sensitivity to drought. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190518 (2020).CAS 

Google Scholar 
32.Zweifel, R. et al. Baumwasserdefizite erreichten im Sommer 2018 Höchstwerte–war das aus dem All erkennbar. Schweiz Z. Forstwes. 171, 302–305 (2020).
Google Scholar 
33.Cuny, H. E. et al. Woody biomass production lags stem-girth increase by over one month in coniferous forests. Nat. Plants 1, 15160 (2015).CAS 
PubMed 

Google Scholar 
34.D’Orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Chang. Biol. 24, 2339–2351 (2018).PubMed 
ADS 

Google Scholar 
35.Delpierre, N., Berveiller, D., Granda, E. & Dufrêne, E. Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest. N. Phytol. 210, 459–470 (2016).CAS 

Google Scholar 
36.Babst, F. et al. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. N. Phytol. 201, 1289–1303 (2014).CAS 

Google Scholar 
37.Zweifel, R. et al. Determinants of legacy effects in pine trees – implications from an irrigation‐stop experiment. N. Phytol. 227, 1081–1096 (2020).CAS 

Google Scholar 
38.Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).CAS 
PubMed 
ADS 

Google Scholar 
39.Zweifel, R., Zimmermann, L. & Newbery, D. M. Modeling tree water deficit from microclimate: An approach to quantifying drought stress. Tree Physiol. 25, 147–156 (2005).CAS 
PubMed 

Google Scholar 
40.Gleason, S. M. et al. Weak tradeoff between xylem safety and xylem-specific hydraulic efficiency across the world’s woody plant species. N. Phytol. 209, 123–136 (2016).CAS 

Google Scholar 
41.Duursma, R. A. et al. On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls. N. Phytol. 221, 693–705 (2019).
Google Scholar 
42.Poyatos, R., Aguadé, D. & Martínez-Vilalta, J. Below-ground hydraulic constraints during drought-induced decline in Scots pine. Ann. Sci. 75, 100 (2018).
Google Scholar 
43.Johnson, D. M., McCulloh, K. A., Woodruff, D. R. & Meinzer, F. C. Hydraulic safety margins and embolism reversal in stems and leaves: Why are conifers and angiosperms so different? Plant Sci. 195, 48–53 (2012).CAS 
PubMed 

Google Scholar 
44.Brodribb, T. J., McAdam, S. A. M., Jordan, G. J. & Martins, S. C. V. Conifer species adapt to low-rainfall climates by following one of two divergent pathways. Proc. Natl Acad. Sci. U.S.A 111, 14489–14493 (2014).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
45.Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).CAS 
PubMed 
ADS 

Google Scholar 
46.Drake, J. E. et al. Trees tolerate an extreme heatwave via sustained transpirational cooling and increased leaf thermal tolerance. Glob. Chang. Biol. 24, 2390–2402 (2018).PubMed 
ADS 

Google Scholar 
47.Anderegg, W. R. L., Trugman, A. T., Badgley, G., Konings, A. G. & Shaw, J. Divergent forest sensitivity to repeated extreme droughts. Nat. Clim. Chang. 10, 1091–1095 (2020).ADS 

Google Scholar 
48.Leuzinger, S., Zotz, G., Asshoff, R. & Korner, C. Responses of deciduous forest trees to severe drought in Central Europe. Tree Physiol. 25, 641–650 (2005).PubMed 

Google Scholar 
49.Brinkmann, N., Eugster, W., Zweifel, R., Buchmann, N. & Kahmen, A. Temperate tree species show identical response in tree water deficit but different sensitivities in sap flow to summer soil drying. Tree Physiol. 36, 1508–1519 (2016).PubMed 

Google Scholar 
50.Rosengren, U. et al. Functional biodiversity aspects on the nutrient sustainability in forests-Importance of root distribution. J. Sustain. 21, 77–100 (2006).
Google Scholar 
51.Salomón, R. L., Limousin, J.-M., Ourcival, J.-M., Rodríguez-Calcerrada, J. & Steppe, K. Stem hydraulic capacitance decreases with drought stress: implications for modelling tree hydraulics in the Mediterranean oak Quercus ilex. Plant. Cell Environ. 40, 1379–1391 (2017).PubMed 

Google Scholar 
52.Mencuccini, M. et al. Leaf economics and plant hydraulics drive leaf: wood area ratios. N. Phytol. 224, 1544–1556 (2019).
Google Scholar 
53.Guerrero-Ramírez, N. R. et al. Global root traits (GRooT). Database Glob. Ecol. Biogeogr. 30, 25–37 (2021).
Google Scholar 
54.Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).PubMed 
ADS 

Google Scholar 
55.van der Maaten, E. et al. Species distribution models predict temporal but not spatial variation in forest growth. Ecol. Evol. 7, 2585–2594 (2017).PubMed 
PubMed Central 

Google Scholar 
56.Körner, C. No need for pipes when the well is dry—a comment on hydraulic failure in trees. Tree Physiol. 39, 695–700 (2019).PubMed 

Google Scholar 
57.Walthert, L. et al. From the comfort zone to crown dieback: Sequence of physiological stress thresholds in mature European beech trees across progressive drought. Sci. Total Environ. 753, 141792 (2021).CAS 
PubMed 
ADS 

Google Scholar 
58.Preisler, Y., Tatarinov, F., Grünzweig, J. M. & Yakir, D. Seeking the “point of no return” in the sequence of events leading to mortality of mature trees. Plant. Cell Environ. 44, 1315–1328 (2021).CAS 
PubMed 

Google Scholar 
59.Poyatos, R. et al. Global transpiration data from sap flow measurements: The SAPFLUXNET database. Earth Syst. Sci. Data 13, 2607–2649 (2021).ADS 

Google Scholar 
60.Steppe, K., von der Crone, J. S. & De Pauw, D. J. W. TreeWatch.net: A water and carbon monitoring and modeling network to assess instant tree hydraulics and carbon status. Front. Plant Sci. 7, 993 (2016).PubMed 
PubMed Central 

Google Scholar 
61.Sass-Klaassen, U. et al. A tree-centered approach to assess impacts of extreme climatic events on forests. Front. Plant Sci. 7, 1–6 (2016).
Google Scholar 
62.Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Chang. Biol. 23, 1675–1690 (2017).PubMed 
ADS 

Google Scholar 
63.Sparks, A. H., Hengl, T. & Nelson, A. GSODR: Global summary daily weather data in R. J. Open Source Softw. 2, 177 (2017).ADS 

Google Scholar 
64.Muñoz-Sabater, J. et al. ERA5-Land: An improved version of the ERA5 reanalysis land component. in Joint ISWG and LSA-SAF Workshop IPMA. 26–28 (2018).65.Granier, A. et al. Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003. Agric. Meteorol. 143, 123–145 (2007).
Google Scholar 
66.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 

Google Scholar 
67.Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M. & Jones, P. D. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
Google Scholar 
68.Frich, P. et al. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19, 193–212 (2002).ADS 

Google Scholar 
69.Alexander, L. V. et al. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. Atmos. 111, 1–22 (2006).
Google Scholar 
70.Knüsel, S., Peters, R. L., Haeni, M., Wilhelm, M. & Zweifel, R. Processing and extraction of seasonal tree physiological parameters from stem radius time series. Forests 12, 765 (2021).
Google Scholar 
71.Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
Google Scholar 
72.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
73.R. Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2019). More

The broad categories of data included in the repository are summarized in Online-only Table 2 and visualized in Fig. 2. Each category is explained in greater detail below, while full details together with accompanying notes are given in the repository (Database_structure.csv) and in Supplementary File 1. Online-only Table 2 gives an overview of data coverage per category, both across all species and for native species separately. A complete list of data sources is available in Supplementary File 2.Fig. 2Visualization of the attributes presented in the database.Full size imageGeneration of the species listTaxon names listed in the most recent and widely accepted New Flora of the British Isles’ index12 were digitized via the Optical Character Recognition Software ReadirisTM 17 (IRIS). Results from the digitization were transferred into a spreadsheet and obvious recognition errors were fixed. The resulting table contained 5,687 taxa and associated taxonomic authorities. A total of 360 unnamed hybrids were excluded, as well as species noted to have only questionable or unconfirmed records, leaving 5,038 species. Forty-one intergeneric hybrid species, 827 entries relating to (notho)subspecies, (notho)varieties, cultivars and forma were also removed along with 720 named hybrids. Species that were included by Stace12 but which he considered not to be part of the flora (i.e. listed as ‘other species’ and ‘other genera’, e.g. genus Tragus or Coreopsis verticillata) were also excluded. Seven species that were labelled ‘extinct’ in the flora were included as there were indications that the species might be in the process of reintroduction (e.g. Bromus interruptus, Bupleurum falcatum and Schoenoplectus pungens). Extinct native and archaeophyte species without any signs of reintroduction (e.g. Dryopteris remota) are also listed but no additional data are provided and they are not included in calculations of completeness of data (Online-only Table 2). The final number of extant species listed here is therefore 3,209 (comprising 1,468 natives, 1,690 aliens and 51 species with unknown status), plus 18 formally extinct species (natives and archaeophytes not seen in the study region since 1999). Species names and taxonomic authorities were revised according to the 2021 reprint of the New Flora of the British Isles, communicated to us by C.A.S. ahead of publication. Genera with less well-defined species – for example due to apomixis – contain additional information on subgenera, sections, and aggregates, as per Stace12. Since misidentifications are common in these groups, we include a column termed ‘unclear_species_marker’ that allows for these species to be quickly identified and excluded from analyses if appropriate. Such genera are often incompletely listed in our database since most microspecies are not sufficiently well defined.TaxonomyNomenclature of the list was checked by Global Names Resolver in the R package ‘taxize’20,21, using the International Plant Names Index (IPNI)22 as the data source, to remove any digitisation errors. Resolved names were used to determine accepted higher taxonomic hierarchy (family, order) again using taxize, with the National Center for Biotechnology Information (NCBI) database. Species that could not be resolved by the Global Names Resolver or did not yield matches in the NCBI database for their higher taxonomic ranks were manually checked for name matches in the World Checklist of Vascular Plants (WCVP)17. Species within the original species list that were found to be identical to a different spelling in WCVP were retained in the database. In such instances, and when slight spelling differences occurred, the columns ‘taxon_name‘ and ‘taxon_name_WCVP‘ differ. To improve clarity, each species is presented here with its unique identification number according to the WCVP (listed as ‘kew_id’) together with three additional columns (i.e. WCVP.URL, POWO.URL and IPNI.URL) which contain hyperlinks to the freely accessible taxon description websites of the (WCVP)17, Plants of the World Online (POWO)23 and (IPNI)22, respectively. Thus, while the taxon names used in the database correspond to those used by Stace12, changes in the accepted species name since publication can be traced in columns ‘taxonomic_status’ and ‘accepted_kew_id’. The family classification of WCVP follows APG IV24 for angiosperms, Christenhusz et al. (2011)25 for gymnosperms and Christenhusz & Chase (2014)26 for ferns and lycopods.Native statusWe offer three different datasets which describe the status of a species as native or non-native, and its level of establishment in BI. The first is extracted from Stace (2019)12, the second contains the status codes used in PLANTATT10 and the unpublished ALIENATT (pers. comm. author K.J.W.) dataset, and the third is extracted from Alien Plants13. The status from Stace12 and Stace & Crawley13 assigns a species to either native or alien status, with aliens subdivided into archaeophytes and neophytes at different levels of establishment (e.g. denizen, colonist etc., see Online-only Table 1). Status codes from the BSBI can be either AC (alien casual), AN (neophyte), AR (archaeophyte), N (native), NE (native endemic) or NA (native status doubtful).Functional traitsData for five ecologically relevant functional traits (i.e. seed mass, specific leaf area [SLA], leaf area, leaf dry matter content [LDMC] and vegetative height) were downloaded from public data available in the TRY database27 (for specific authors see Supplementary File 1 and Supplementary File 2). Averages were calculated using the available measurements downloaded for each species, excluding rows where the measurement was 0. In addition, the maximum vegetative height for each species is given, where available.Realized niche descriptionRealized niche descriptions based on assessments made on plants living in BI are given in the form of Ellenberg indicator values18, as published in PLANTATT10. Ellenberg indicator values place each species along an environmental gradient (e.g. light or salinity) by assigning a number on an ordinal scale, depending on the species preference for the specific gradient (Online-only Table 2). This information is often used to gain insights into environmental changes based on species occurrences28. For species listed under a previously accepted name in PLANTATT, the information was associated with the accepted synonym in Stace (2019)12. Due to the low coverage of PLANTATT for non-native species included in our list, we additionally include Ellenberg indicator values based on Central European assessments, as made available by Döring29. Each Ellenberg category is listed in a separate column, keeping the information from both data sources separate to avoid confounding of assessments based on two different regions (i.e. Britain and Ireland versus Central Europe).Life strategyTo characterize the life strategy of a species, we used the CSR scheme developed by Grime19, which classifies each species as either a competitor (C), stress tolerator (S), ruderal (R) or a combination of these (e.g. CS, SR). CSR classifications were obtained from the Electronic Comparative Plant Ecology database30. Due to the low coverage of available CSR assessments for species in our database (i.e. data available for just 460 out of 3,209 species) we imputed CSR strategies for a further 981 species using available functional trait data, following the method proposed by Pierce et al.31. The functional leaf traits required for this method – i.e. specific leaf area, leaf area, leaf dry matter content – were obtained from the TRY database27. Pre-existing30 and newly imputed CSR strategies are listed in separate columns.Growth form, succulence and life-formPlant growth form descriptions were obtained from the TRY database27 and filtered for those entries given by specific contributors (Online-only Table 2) to maintain consistent use of growth form categories. Information on whether a species was considered to be a succulent was obtained by screening the entire growth form information obtained from the TRY database for the phrase ‘succulence’ or ‘succulent’.Species life-form categories according to Raunkiaer32 were determined for each species in our dataset with regard to the typical life-form of the species as it grows in BI (pers. comm. M.J.M.C.).Associated biome and originInformation given in the Ecoflora database3 for the biome that each species is associated with was matched to the species names according to Stace12. The recognized biome categories follow Preston & Hill33 and are ‘Arctic montane’, ‘Boreal Montane’, ‘Boreo-Arctic Montane’, ‘Boreo-Temperate’, ‘Mediterranean’, ‘Mediterranean-Atlantic’, ‘Southern Temperate’, ‘Temperate’, ‘Wide Boreal’ and ‘Wide Temperate’.For non-native species, the assumed origin (i.e. the region that plants were most likely to have been introduced to BI from, rather than the full non-BI distribution of a species) was adapted from Stace12 into a brief description of their country or region of origin. In addition, these descriptions were manually allocated to the TDWG level 1 regions listed in the World Geographical Scheme for Recording Plant Distributions (WGSRPD, TDWG)34.Species distributionsDistribution metrics for each species are given as the number of 10-km square hectads in BI with records for the species in question within a specified time window. The data were derived from the BSBI Distribution Database35 and were extracted for each species, dividing the study region into Great Britain (incl. Isle of Man), Ireland and the Channel Islands, as previously partitioned for data available in PLANTATT10. The database was queried using species and hectads for grouping, showing only records ‘matching or within 2 km of county boundary’ and excluding ‘do-not-map-flagged occurrences’. The data were not corrected for sampling bias and should therefore only be used as an indication of trends.Hybrid propensityData on hybridization is provided for 641 species, obtained from the Hybrid flora of the British Isles36 which enumerates every hybrid reported in BI up until 2015 (pers. comm. M.R.B.). Each entry was transcribed manually, and then filtered to exclude (a) hybrids that have been recorded, but not formed in the British Isles, (b) triple hybrids (mainly reported for the genus Salix), (c) doubtful records, (d) hybrids between subspecific ranks, and (e) hybrids where at least one parent is not native (only archaeophytes included). This left 821 hybrid combinations for data aggregation. The metric chosen here is hybrid propensity, which is a per-species metric of how many other species a focal species hybridizes with (sensu Whitney et al., 201037). A scaled hybrid propensity metric is also given which was calculated by weighting the hybrid propensity score by the number of intrageneric combinations for a given genus, to account for the greater opportunities of hybridization in larger genera.DNA barcodesDNA barcode sequences for plant species present in BI are currently available for 1,413 species in our database. The information was derived from a dataset of rbcL, matK and ITS2 sequences compiled for the UK flora generated by the National Botanic Garden of Wales and the Royal Botanic Garden Edinburgh38,39 (pers. comm. L.J. and N.D.V.). The data are given as a hyperlink to the record’s page on the Barcode of Life Data Systems (BOLD40) which includes the DNA barcode sequences as well as scans of the herbarium specimen and information on the sample’s collection. Most species have multiple record pages associated with them, due to the sampling of more than one individual. We include a maximum of three BOLD accessions per species; the full range of individuals sampled can be accessed via the original publications38,39. DNA barcodes are almost exclusively available for native species. Future releases of our database will increase the coverage of the non-native flora significantly. Where species in the BOLD database are attributed to a species name that is considered synonymous with another name in our list, the hyperlink is matched to the latest nomenclature12. 1,421 species have at least one sequence associated with them and 935 species have sequence data for all three sequences (rbcL, matK and ITS2).Genome size and chromosome numbersGenome size data for 2,117 specimens (at least one measurement per species) were obtained from various sources. Measurements for a total of 467 species were newly estimated using plant material of known BI origin, often sourced  from the Millennium Seedbank of the Royal Botanic Gardens, Kew (RBG Kew)41. The measurements were made by flow cytometry using seeds or seedlings and following an established protocol42. Information on the extraction buffers and calibration standard species used are available in the file GS_Kew_BI.csv, along with peak CV values of the measurements as a quality control. Where more than one measurement is reported per species, the measurements were made on plant material from different populations or using different buffers. Previously published data for additional species were obtained from reports on the Czech flora43, the Dutch flora44, and prime values listed in the Plant DNA C-values database45,46. Since significant intraspecific differences in genome size between plant material from different geographical origins have previously been described, predominantly due to cytotype diversity in ploidy level47, genome size measurements from previously published sources were assessed with regard to the origin of the material. The column ‘from_BI_material’ (GS_BI.csv, BI_main.csv) allows users to filter for measurements made on material from BI to exclude a potential bias. The information was obtained from the original publication source of each measurement.Chromosome numbers for 1,410 species (at least one chromosome number per species) determined exclusively from material collected in BI were obtained from an extensive dataset compiled by R.J.G. from various published studies, unpublished theses and personal communications from trusted sources. The counts were made between 1898 and 2017, with a large proportion stemming from efforts to achieve greater coverage of the flora by a team of cytologists based at the University of Leicester and headed by R.J.G. Part of the dataset was previously incorporated into the BSBI’s data catalogue5 but has since undergone revisions to incorporate new information and changes in taxonomy. The dataset contained many measurements at subspecies level which were allocated to the species level taxon in our list. This served to include as much of the often considerable infraspecific variation as possible. Since some species for which chromosome counts have been reported elsewhere are lacking chromosome counts from British or Irish material, they are absent from this dataset. To fill such gaps, we also present chromosome numbers from reports on the Czech flora43, the Dutch flora44, and the Plant DNA C-values database45,46. More