Philippa Kaur

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

1.Meredith, M. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate Ch. 3 (eds Pörtner, H.-O. et al.) (Intergovernmental Panel on Climate Change, 2019).2.Blok, D. et al. Shrub expansion may reduce summer permafrost thaw in Siberian tundra. Glob. Change Biol. 16, 1296–1305 (2010). A field study in which dwarf-shrub canopies were removed experimentally, resulting in increased thaw depths, thereby, underscoring the protective role of vegetation cover on permafrost.
Google Scholar 
3.van Huissteden, J. Thawing Permafrost: Permafrost Carbon in a Warming Arctic (Springer, 2020).4.Jorgenson, M. et al. Resilience and vulnerability of permafrost to climate change. Can. J. For. Res. 40, 1219–1236 (2010).
Google Scholar 
5.Kropp, H. et al. Shallow soils are warmer under trees and tall shrubs across Arctic and Boreal ecosystems. Environ. Res. Lett. 16, 015001 (2020).
Google Scholar 
6.Myers-Smith, I. H. & Hik, D. S. Shrub canopies influence soil temperatures but not nutrient dynamics: an experimental test of tundra snow–shrub interactions. Ecol. Evol. 3, 3683–3700 (2013).
Google Scholar 
7.Sturm, M. et al. Snow–shrub interactions in Arctic tundra: a hypothesis with climatic implications. J. Clim. 14, 336–344 (2001).
Google Scholar 
8.Sturm, M. et al. Winter biological processes could help convert arctic tundra to shrubland. BioScience 55, 17–26 (2005).
Google Scholar 
9.Chapin, F. S. et al. Role of land-surface changes in Arctic summer warming. Science 310, 657–660 (2005).
Google Scholar 
10.Loranty, M. M. et al. Reviews and syntheses: Changing ecosystem influences on soil thermal regimes in northern high-latitude permafrost regions. Biogeosciences 15, 5287–5313 (2018). Review article showing how Arctic ecosystem processes can influence soil thermal dynamics in permafrost soil.
Google Scholar 
11.Shur, Y. L. & Jorgenson, M. T. Patterns of permafrost formation and degradation in relation to climate and ecosystems. Permafr. Periglac. Process. 18, 7–19 (2007).
Google Scholar 
12.Chadburn, S. E. et al. An observation-based constraint on permafrost loss as a function of global warming. Nat. Clim. Change 7, 340–344 (2017).
Google Scholar 
13.Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth. Environ. 3 https://doi.org/10.1038/s43017-021-00240-1 (2022).14.Ksenofontov, S., Backhaus, N. & Schaepman-Strub, G. ‘There are new species’: indigenous knowledge of biodiversity change in Arctic Yakutia. Polar Geogr. 42, 34–57 (2019).
Google Scholar 
15.Schuur, E. A. et al. Vulnerability of permafrost carbon to climate change: implications for the global carbon cycle. BioScience 58, 701–714 (2008).
Google Scholar 
16.Kokelj, S. V. & Jorgenson, M. Advances in thermokarst research. Permafr. Periglac. Process. 24, 108–119 (2013).
Google Scholar 
17.Keuper, F. et al. A frozen feast: thawing permafrost increases plant-available nitrogen in subarctic peatlands. Glob. Change Biol. 18, 1998–2007 (2012).
Google Scholar 
18.Salmon, V. G. et al. Nitrogen availability increases in a tundra ecosystem during five years of experimental permafrost thaw. Glob. Change Biol. 22, 1927–1941 (2016).
Google Scholar 
19.Blume-Werry, G., Milbau, A., Teuber, L. M., Johansson, M. & Dorrepaal, E. Dwelling in the deep–strongly increased root growth and rooting depth enhance plant interactions with thawing permafrost soil. New Phytol. 223, 1328–1339 (2019).
Google Scholar 
20.Wang, P. et al. Above- and below-ground responses of four tundra plant functional types to deep soil heating and surface soil fertilization. J. Ecol. 105, 947–957 (2017).
Google Scholar 
21.Nauta, A. L. et al. Permafrost collapse after shrub removal shifts tundra ecosystem to a methane source. Nat. Clim. Change 5, 67–70 (2015).
Google Scholar 
22.Osterkamp, T. et al. Physical and ecological changes associated with warming permafrost and thermokarst in interior Alaska. Permafr. Periglac. Process. 20, 235–256 (2009).
Google Scholar 
23.Schuur, E. A. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).
Google Scholar 
24.Koven, C. D. et al. Permafrost carbon-climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).
Google Scholar 
25.Abbott, B. W. & Jones, J. B. Permafrost collapse alters soil carbon stocks, respiration, CH4, and N2O in upland tundra. Glob. Change Biol. 21, 4570–4587 (2015).
Google Scholar 
26.Voigt, C. et al. Warming of subarctic tundra increases emissions of all three important greenhouse gases – carbon dioxide, methane, and nitrous oxide. Glob. Change Biol. 23, 3121–3138 (2017).
Google Scholar 
27.Lenton, T. M. et al. Climate tipping points – too risky to bet against. Nature 575, 592–595 (2019).
Google Scholar 
28.Miner, K. R. Permafrost carbon emissions in a changing Arctic. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00230-3 (2022).Article 

Google Scholar 
29.Peterson, K. & Billings, W. Tundra vegetational patterns and succession in relation to microtopography near Atkasook, Alaska. Arct. Alp. Res. 12, 473–482 (1980).
Google Scholar 
30.Bliss, L. in North American Terrestrial Vegetation (eds Barbour, M. G. & Billings W. D.) (Cambridge Univ. Press, 1988).31.Walker, D. A. et al. The circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282 (2005).
Google Scholar 
32.Frost, G. V., Epstein, H. E. & Walker, D. A. Regional and landscape-scale variability of Landsat-observed vegetation dynamics in northwest Siberian tundra. Environ. Res. Lett. 9, 025004 (2014).
Google Scholar 
33.Walker, D. A. et al. Environment, vegetation and greenness (NDVI) along the North America and Eurasia Arctic transects. Environ. Res. Lett. 7, 015504 (2012).
Google Scholar 
34.Raynolds, M. K. et al. A raster version of the Circumpolar Arctic Vegetation Map (CAVM). Remote Sens. Environ. 232, 111297 (2019).
Google Scholar 
35.Chernov, Y. I. & Matveyeva, N. in Polar Alpine Tundra (ed. Wielgolaski, F. E.) 361–507 (Elsevier, 1997).36.Elvebakk, A. in The Species Concept in the High North: A Panarctic Flora Initiative (eds Nordal, I. & Razzhivin, V. Y.) 81–112 (The Norwegian Academy of Science and Letters, 1999).37.Yurtsev, B. A. Floristic division of the Arctic. J. Veg. Sci. 5, 765–776 (1994).
Google Scholar 
38.Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Change 2, 453–457 (2012). A meta-analysis of field-observed vegetation changes from 46 polar sites indicating widespread increases of shrub vegetation and increased plant size.
Google Scholar 
39.Iversen, C. M. et al. The unseen iceberg: plant roots in arctic tundra. New Phytol. 205, 34–58 (2015).
Google Scholar 
40.Hobbie, J. E. & Hobbie, E. A. 15N in symbiotic fungi and plants estimates nitrogen and carbon flux rates in Arctic tundra. Ecology 87, 816–822 (2006).
Google Scholar 
41.Nielsen, U. N. & Wall, D. H. The future of soil invertebrate communities in polar regions: different climate change responses in the Arctic and Antarctic? Ecol. Lett. 16, 409–419 (2013).
Google Scholar 
42.Clemmensen, K. E. et al. A tipping point in carbon storage when forest expands into tundra is related to mycorrhizal recycling of nitrogen. Ecol. Lett. 24, 1193–1204 (2021).
Google Scholar 
43.Minke, M., Donner, N., Karpov, N., de Klerk, P. & Joosten, H. Patterns in vegetation composition, surface height and thaw depth in polygon mires in the Yakutian Arctic (NE Siberia): a microtopographical characterisation of the active layer. Permafr. Periglac. Process. 20, 357–368 (2009).
Google Scholar 
44.Liljedahl, A. K. et al. Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nat. Geosci. 9, 312–318 (2016).
Google Scholar 
45.Grunberg, I., Wilcox, E. J., Zwieback, S., Marsh, P. & Boike, J. Linking tundra vegetation, snow, soil temperature, and permafrost. Biogeosciences 17, 4261–4279 (2020). A field study reporting that large variations in soil temperatures and thaw depths can be explained by vegetation-mediated differences in snow height.
Google Scholar 
46.Magnússon, R. I. et al. Rapid vegetation succession and coupled permafrost dynamics in Arctic thaw ponds in the Siberian lowland tundra. J. Geophys. Res. Biogeosci. 125, e2019JG005618 (2020).
Google Scholar 
47.Jorgenson, M. et al. Role of ground ice dynamics and ecological feedbacks in recent ice wedge degradation and stabilization. J. Geophys. Res. Earth Surf. 120, 2280–2297 (2015). Outlines the role of ground ice and vegetation succession in thermokarst terrain, including first estimates of recovery times.
Google Scholar 
48.Bjorkman, A. D. et al. Status and trends in Arctic vegetation: evidence from experimental warming and long-term monitoring. Ambio 49, 678–692 (2020). A meta-analysis of plant species responses to experimental climate warming across Arctic sites, finding that shrubs and graminoids generally responded positively to warming, whereas lichens and bryophytes responded more negatively.
Google Scholar 
49.Frost, G. V. et al. Arctic Report Card 2020: Tundra Greenness. https://doi.org/10.25923/46rm-0w23 (NOAA, 2020). Provides an annual update of Arctic NDVI, offering a long-standing record of Arctic greening and browning.50.Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020). Review article outlining complexity in Arctic greening and browning dynamics. The temporal and spatial scale of spectral data and the role of non-vegetation-related processes and ground-truthing remains essential.
Google Scholar 
51.Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).
Google Scholar 
52.Sistla, S. A. et al. Long-term warming restructures Arctic tundra without changing net soil carbon storage. Nature 497, 615–618 (2013).
Google Scholar 
53.Bhatt, U. S. et al. Circumpolar Arctic Tundra vegetation change is linked to sea ice decline. Earth Interact. 14, 1–20 (2010).
Google Scholar 
54.Oechel, W. C. & Billings, W. in Arctic Ecosystems in a Changing Climate: an Ecophysiological Perspective (eds Chapin, F. S. III et al.) 139–168 (Academic Press, 1992).55.Shaver, G. R. et al. Species composition interacts with fertilizer to control long-term change in tundra productivity. Ecology 82, 3163–3181 (2001).
Google Scholar 
56.Bret-Harte, M. S., Shaver, G. R. & Chapin, F. S. III Primary and secondary stem growth in arctic shrubs: implications for community response to environmental change. J. Ecol. 90, 251–267 (2002).
Google Scholar 
57.Mack, M. C., Schuur, E. A. G., Bret-Harte, M. S., Shaver, G. R. & Chapin, F. S. Ecosystem carbon storage in arctic tundra reduced by long-term nutrient fertilization. Nature 431, 440–443 (2004).
Google Scholar 
58.Myers-Smith, I. H. et al. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Change 5, 887–891 (2015).
Google Scholar 
59.McGuire, A. D. et al. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 79, 523–555 (2009).
Google Scholar 
60.van der Kolk, H.-J., Heijmans, M. M., van Huissteden, J., Pullens, J. W. & Berendse, F. Potential Arctic tundra vegetation shifts in response to changing temperature, precipitation and permafrost thaw. Biogeosciences 13, 6229–6245 (2016).
Google Scholar 
61.Myers-Smith, I. H. et al. Eighteen years of ecological monitoring reveals multiple lines of evidence for tundra vegetation change. Ecol. Monogr. 89, e01351 (2019).
Google Scholar 
62.Leffler, A. J., Klein, E. S., Oberbauer, S. F. & Welker, J. M. Coupled long-term summer warming and deeper snow alters species composition and stimulates gross primary productivity in tussock tundra. Oecologia 181, 287–297 (2016).
Google Scholar 
63.Euskirchen, E. et al. Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high-latitude ecosystems. Glob. Change Biol. 12, 731–750 (2006).
Google Scholar 
64.McGuire, A. D. et al. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl Acad. Sci. USA 115, 3882–3887 (2018).
Google Scholar 
65.National Academies of Sciences, Engineering, and Medicine. Understanding Northern Latitude Vegetation Greening and Browning: Proceedings of a Workshop (The National Academies Press, 2019).66.Phoenix, G. K. & Bjerke, J. W. Arctic browning: extreme events and trends reversing arctic greening. Glob. Change Biol. 22, 2960–2962 (2016).
Google Scholar 
67.Bokhorst, S. et al. Impacts of extreme winter warming in the sub-Arctic: growing season responses of dwarf shrub heathland. Glob. Change Biol. 14, 2603–2612 (2008).
Google Scholar 
68.Bret-Harte, M. S. et al. The response of Arctic vegetation and soils following an unusually severe tundra fire. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120490 (2013).
Google Scholar 
69.Farquharson, L. M. et al. Climate change drives widespread and rapid thermokarst development in very cold permafrost in the Canadian High Arctic. Geophys. Res. Lett. 46, 6681–6689 (2019).
Google Scholar 
70.Turetsky et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–34 (2019). Reveals that abrupt thaw of permafrost could double the estimated future release of greenhouse gases from permafrost soils compared with scenarios of gradual thaw.
Google Scholar 
71.Bokhorst, S. F., Bjerke, J. W., Tømmervik, H., Callaghan, T. V. & Phoenix, G. K. Winter warming events damage sub-Arctic vegetation: consistent evidence from an experimental manipulation and a natural event. J. Ecol. 97, 1408–1415 (2009).
Google Scholar 
72.Bjerke, J. W. et al. Record-low primary productivity and high plant damage in the Nordic Arctic Region in 2012 caused by multiple weather events and pest outbreaks. Environ. Res. Lett. 9, 084006 (2014).
Google Scholar 
73.Treharne, R., Bjerke, J. W., Tømmervik, H., Stendardi, L. & Phoenix, G. K. Arctic browning: impacts of extreme climatic events on heathland ecosystem CO2 fluxes. Glob. Change Biol. 25, 489–503 (2019).
Google Scholar 
74.Olofsson, J., Tommervik, H. & Callaghan, T. V. Vole and lemming activity observed from space. Nat. Clim. Change 2, 880–883 (2012).
Google Scholar 
75.Lara, M. J., Nitze, I., Grosse, G., Martin, P. & McGuire, A. D. Reduced arctic tundra productivity linked with landform and climate change interactions. Sci. Rep. 8, 2345 (2018).
Google Scholar 
76.Verdonen, M., Berner, L. T., Forbes, B. C. & Kumpula, T. Periglacial vegetation dynamics in Arctic Russia: decadal analysis of tundra regeneration on landslides with time series satellite imagery. Environ. Res. Lett. 15, 105020 (2020).
Google Scholar 
77.Assmann, J. J., Myers-Smith, I. H., Kerby, J. T., Cunliffe, A. M. & Daskalova, G. N. Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites. Environ. Res. Lett. 15, 125002 (2020).
Google Scholar 
78.Raynolds, M. K. & Walker, D. A. Increased wetness confounds Landsat-derived NDVI trends in the central Alaska North Slope region, 1985–2011. Environ. Res. Lett. 11, 085004 (2016).
Google Scholar 
79.Magnússon, R. Í. et al. Shrub decline and expansion of wetland vegetation revealed by very high resolution land cover change detection in the Siberian lowland tundra. Sci. Total Environ. 782, 146877 (2021).
Google Scholar 
80.Nitze, I. & Grosse, G. Detection of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks. Remote Sens. Environ. 181, 27–41 (2016).
Google Scholar 
81.Chen, Y., Hu, F. S. & Lara, M. J. Divergent shrub-cover responses driven by climate, wildfire, and permafrost interactions in Arctic tundra ecosystems. Glob. Change Biol. 27, 652–663 (2021).
Google Scholar 
82.Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).
Google Scholar 
83.Siewert, M. B. & Olofsson, J. Scale-dependency of Arctic ecosystem properties revealed by UAV. Environ. Res. Lett. 15, 094030 (2020).
Google Scholar 
84.Beamish, A. et al. Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: a review and outlook. Remote Sens. Environ. 246, 111872 (2020).
Google Scholar 
85.Blok, D. et al. The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo and temperature. Environ. Res. Lett. 6, 035502 (2011).
Google Scholar 
86.Boelman, N. T., Gough, L., McLaren, J. R. & Greaves, H. Does NDVI reflect variation in the structural attributes associated with increasing shrub dominance in arctic tundra? Environ. Res. Lett. 6, 035501 (2011).
Google Scholar 
87.Sturm, M., Racine, C. & Tape, K. Climate change – increasing shrub abundance in the Arctic. Nature 411, 546–547 (2001).
Google Scholar 
88.Tape, K., Sturm, M. & Racine, C. The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Glob. Change Biol. 12, 686–702 (2006).
Google Scholar 
89.Jorgenson, J. C., Raynolds, M. K., Reynolds, J. H. & Benson, A. M. Twenty-five year record of changes in plant cover on tundra of northeastern Alaska. Arct. Antarctic Alp. Res. 47, 785–806 (2015).
Google Scholar 
90.Jorgenson, J. C., Jorgenson, M. T., Boldenow, M. L. & Orndahl, K. M. Landscape change detected over a half century in the Arctic National Wildlife Refuge using high-resolution aerial imagery. Remote Sens. 10, 1305 (2018).
Google Scholar 
91.Hobbie, J. E. et al. Ecosystem responses to climate change at a Low Arctic and a High Arctic long-term research site. Ambio 46, 160–173 (2017).
Google Scholar 
92.Virkkala, A.-M., Abdi, A. M., Luoto, M. & Metcalfe, D. B. Identifying multidisciplinary research gaps across Arctic terrestrial gradients. Environ. Res. Lett. 14, 124061 (2019).
Google Scholar 
93.Ropars, P. & Boudreau, S. Shrub expansion at the forest-tundra ecotone: spatial heterogeneity linked to local topography. Environ. Res. Lett. 7, 015501 (2012).
Google Scholar 
94.Ropars, P., Levesque, E. & Boudreau, S. How do climate and topography influence the greening of the forest-tundra ecotone in northern Québec? A dendrochronological analysis of Betula glandulosa. J. Ecol. 103, 679–690 (2015).
Google Scholar 
95.Tremblay, B., Levesque, E. & Boudreau, S. Recent expansion of erect shrubs in the Low Arctic: evidence from Eastern Nunavik. Environ. Res. Lett. 7, 035501 (2012).
Google Scholar 
96.Boulanger-Lapointe, N., Levesque, E., Boudreau, S., Henry, G. H. R. & Schmidt, N. M. Population structure and dynamics of Arctic willow (Salix arctica) in the High Arctic. J. Biogeogr. 41, 1967–1978 (2014).
Google Scholar 
97.Frost, G. V., Epstein, H. E., Walker, D. A., Matyshak, G. & Ermokhina, K. Patterned-ground facilitates shrub expansion in Low Arctic tundra. Environ. Res. Lett. 8, 015035 (2013).
Google Scholar 
98.Lantz, T. C., Kokelj, S. V., Gergel, S. E. & Henry, G. H. Relative impacts of disturbance and temperature: persistent changes in microenvironment and vegetation in retrogressive thaw slumps. Glob. Change Biol. 15, 1664–1675 (2009).
Google Scholar 
99.Huebner, D. C. & Bret-Harte, M. S. Microsite conditions in retrogressive thaw slumps may facilitate increased seedling recruitment in the Alaskan Low Arctic. Ecol. Evol. 9, 1880–1897 (2019).
Google Scholar 
100.Lantz, T. C., Marsh, P. & Kokelj, S. V. Recent shrub proliferation in the Mackenzie Delta uplands and microclimatic implications. Ecosystems 16, 47–59 (2013).
Google Scholar 
101.Hu, F. S. et al. Arctic tundra fires: natural variability and responses to climate change. Front. Ecol. Environ. 13, 369–377 (2015).
Google Scholar 
102.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
Google Scholar 
103.Didan, K. MYD13Q1 MODIS/Aqua vegetation indices 16-day L3 global 250 m SIN grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MYD13Q1.006 (2015).Article 

Google Scholar 
104.Didan, K. MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250 m SIN grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).Article 

Google Scholar 
105.Dorigo, W. et al. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).
Google Scholar 
106.Brown, J., Ferrians, O. Jr, Heginbottom, J. A. & Melnikov, E. Circum-Arctic Map of Permafrost and Ground-ice Conditions (US Geological Survey, 1997).107.Jones, G. A. & Henry, G. H. Primary plant succession on recently deglaciated terrain in the Canadian High Arctic. J. Biogeogr. 30, 277–296 (2003).
Google Scholar 
108.Cornelissen, J. H. C. et al. Global change and arctic ecosystems: is lichen decline a function of increases in vascular plant biomass? J. Ecol. 89, 984–994 (2001).
Google Scholar 
109.Aguirre, D., Benhumea, A. E. & McLaren, J. R. Shrub encroachment affects tundra ecosystem properties through their living canopy rather than increased litter inputs. Soil Biol. Biochem. 153, 108121 (2021).
Google Scholar 
110.Gornall, J. L., Jonsdottir, I. S., Woodin, S. J. & Van der Wal, R. Arctic mosses govern below-ground environment and ecosystem processes. Oecologia 153, 931–941 (2007).
Google Scholar 
111.Soudzilovskaia, N. A., Bodegom, P. M. & Cornelissen, J. H. Dominant bryophyte control over high-latitude soil temperature fluctuations predicted by heat transfer traits, field moisture regime and laws of thermal insulation. Funct. Ecol. 27, 1442–1454 (2013).
Google Scholar 
112.Blok, D. et al. The cooling capacity of mosses: controls on water and energy fluxes in a Siberian tundra site. Ecosystems 14, 1055–1065 (2011).
Google Scholar 
113.Belke-Brea, M., Domine, F., Barrere, M., Picard, G. & Arnaud, L. Impact of shrubs on winter surface albedo and snow specific surface area at a low Arctic site: In situ measurements and simulations. J. Clim. 33, 597–609 (2020).
Google Scholar 
114.Wilcox, E. J. et al. Tundra shrub expansion may amplify permafrost thaw by advancing snowmelt timing. Arct. Sci. 5, 202–217 (2019).
Google Scholar 
115.Frost, G. V., Epstein, H. E., Walker, D. A., Matyshak, G. & Ermokhina, K. Seasonal and long-term changes to active-layer temperatures after tall shrubland expansion and succession in Arctic tundra. Ecosystems 21, 507–520 (2018).
Google Scholar 
116.Wilson, M. A., Burn, C. & Humphreys, E. in Cold Regions Engineering 2019 (eds Bilodeau, J.-P., Nadeau, D. F., Fortier, D. & Conciatori, D.) 687–695 (American Society of Civil Engineers, 2019).117.Liljedahl, A. K., Timling, I., Frost, G. V. & Daanen, R. P. Arctic riparian shrub expansion indicates a shift from streams gaining water to those that lose flow. Commun. Earth Environ. 1, 50 (2020).
Google Scholar 
118.Paradis, M., Lévesque, E. & Boudreau, S. Greater effect of increasing shrub height on winter versus summer soil temperature. Environ. Res. Lett. 11, 085005 (2016).
Google Scholar 
119.Beringer, J., Chapin, F. S., Thompson, C. C. & McGuire, A. D. Surface energy exchanges along a tundra-forest transition and feedbacks to climate. Agric. For. Meteorol. 131, 143–161 (2005).
Google Scholar 
120.Kemppinen, J. et al. Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems 24, 1378–1392 (2021). Quantifies the effects of shrub abundance on the soil thermal regime using a distinction between a rough, tall-shrub canopy and an aerodynamic, dwarf-shrub canopy.
Google Scholar 
121.Jorgenson, M. T., Ely, C. & Terenzi, J. in Shared Science Needs: Report from the Western Alaska Landscape Conservation Cooperative Science Workshop (eds Reynolds, J. H. & Wiggins, H. V.) 130–137 (2012).122.Sturm, M., Douglas, T., Racine, C. & Liston, G. E. Changing snow and shrub conditions affect albedo with global implications. J. Geophys. Res. Biogeosci. 110, G01004 (2005).
Google Scholar 
123.Zhang, T. Influence of the seasonal snow cover on the ground thermal regime: an overview. Rev. Geophys. 43, RG4002 (2005).
Google Scholar 
124.Domine, F., Barrere, M. & Morin, S. The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime. Biogeosciences 13, 6471–6486 (2016).
Google Scholar 
125.Lawrence, D. M. & Swenson, S. C. Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environ. Res. Lett. 6, 045504 (2011).
Google Scholar 
126.Barrere, M., Domine, F., Belke-Brea, M. & Sarrazin, D. Snowmelt events in autumn can reduce or cancel the soil warming effect of snow–vegetation interactions in the Arctic. J. Clim. 31, 9507–9518 (2018).
Google Scholar 
127.Loranty, M. M., Goetz, S. J. & Beck, P. S. Tundra vegetation effects on pan-Arctic albedo. Environ. Res. Lett. 6, 024014 (2011).
Google Scholar 
128.Bonfils, C. et al. On the influence of shrub height and expansion on northern high latitude climate. Environ. Res. Lett. 7, 015503 (2012).
Google Scholar 
129.Williamson, S. N., Barrio, I. C., Hik, D. S. & Gamon, J. A. Phenology and species determine growing-season albedo increase at the altitudinal limit of shrub growth in the sub-Arctic. Glob. Change Biol. 22, 3621–3631 (2016).
Google Scholar 
130.Juszak, I., Eugster, W., Heijmans, M. & Schaepman-Strub, G. Contrasting radiation and soil heat fluxes in Arctic shrub and wet sedge tundra. Biogeosciences 13, 4049–4064 (2016).
Google Scholar 
131.Göckede, M. et al. Negative feedback processes following drainage slow down permafrost degradation. Glob. Change Biol. 25, 3254–3266 (2019).
Google Scholar 
132.Bonan, G. Ecological Climatology: Concepts and Applications (Cambridge Univ. Press, 2015).133.Eugster, W. et al. Land–atmosphere energy exchange in Arctic tundra and boreal forest: available data and feedbacks to climate. Glob. Change Biol. 6, 84–115 (2000).
Google Scholar 
134.Liljedahl, A. et al. Nonlinear controls on evapotranspiration in arctic coastal wetlands. Biogeosciences 8, 3375–3389 (2011).
Google Scholar 
135.Zwieback, S., Chang, Q., Marsh, P. & Berg, A. Shrub tundra ecohydrology: rainfall interception is a major component of the water balance. Environ. Res. Lett. 14, 055005 (2019).
Google Scholar 
136.Subin, Z. M. et al. Effects of soil moisture on the responses of soil temperatures to climate change in cold regions. J. Clim. 26, 3139–3158 (2013).
Google Scholar 
137.Aalto, J., Scherrer, D., Lenoir, J., Guisan, A. & Luoto, M. Biogeophysical controls on soil-atmosphere thermal differences: implications on warming Arctic ecosystems. Environ. Res. Lett. 13, 074003 (2018).
Google Scholar 
138.Asmus, A. L. et al. Shrub shading moderates the effects of weather on arthropod activity in arctic tundra. Ecol. Entomol. 43, 647–655 (2018).
Google Scholar 
139.Hinkel, K., Paetzold, F., Nelson, F. & Bockheim, J. Patterns of soil temperature and moisture in the active layer and upper permafrost at Barrow, Alaska: 1993–1999. Glob. Planet. Change 29, 293–309 (2001).
Google Scholar 
140.Douglas, T. A., Turetsky, M. R. & Koven, C. D. Increased rainfall stimulates permafrost thaw across a variety of Interior Alaskan boreal ecosystems. NPJ Clim. Atmos. Sci. 3, 28 (2020).
Google Scholar 
141.Neumann, R. B. et al. Warming effects of spring rainfall increase methane emissions from thawing permafrost. Geophys. Res. Lett. 46, 1393–1401 (2019).
Google Scholar 
142.Aartsma, P., Asplund, J., Odland, A., Reinhardt, S. & Renssen, H. Microclimatic comparison of lichen heaths and shrubs: shrubification generates atmospheric heating but subsurface cooling during the growing season. Biogeosciences 18, 1577–1599 (2021).
Google Scholar 
143.Fisher, J. P. et al. The influence of vegetation and soil characteristics on active-layer thickness of permafrost soils in boreal forest. Glob. Change Biol. 22, 3127–3140 (2016).
Google Scholar 
144.Van Cleve, K. et al. Taiga ecosystems in interior Alaska. BioScience 33, 39–44 (1983).
Google Scholar 
145.Kade, A., Romanovsky, V. & Walker, D. The n-factor of nonsorted circles along a climate gradient in Arctic Alaska. Permafr. Periglac. Process. 17, 279–289 (2006).
Google Scholar 
146.Atchley, A. L., Coon, E. T., Painter, S. L., Harp, D. R. & Wilson, C. J. Influences and interactions of inundation, peat, and snow on active layer thickness. Geophys. Res. Lett. 43, 5116–5123 (2016).
Google Scholar 
147.Klene, A. E., Nelson, F. E., Shiklomanov, N. I. & Hinkel, K. M. The n-factor in natural landscapes: variability of air and soil-surface temperatures, Kuparuk River Basin, Alaska, USA. Arct. Antarct. Alp. Res. 33, 140–148 (2001).
Google Scholar 
148.van Everdingen, R. O. Multi-Language Glossary of Permafrost and Related Ground-Ice Terms (National Snow and Ice Data Center/World Data Center for Glaciology, 2005).149.Iwahana, G. et al. Geocryological characteristics of the upper permafrost in a tundra-forest transition of the Indigirka River Valley, Russia. Polar Sci. 8, 96–113 (2014).
Google Scholar 
150.Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment. Nat. Commun. 10, 1329 (2019).
Google Scholar 
151.Kanevskiy, M. et al. Degradation and stabilization of ice wedges: implications for assessing risk of thermokarst in northern Alaska. Geomorphology 297, 20–42 (2017).
Google Scholar 
152.Olefeldt, D. et al. Circumpolar distribution and carbon storage of thermokarst landscapes. Nat. Commun. 7, 13043 (2016).
Google Scholar 
153.Jorgenson, M., Shur, Y. L. & Pullman, E. R. Abrupt increase in permafrost degradation in Arctic Alaska. Geophys. Res. Lett. 33, L02503 (2006).
Google Scholar 
154.Stieglitz, M., Déry, S., Romanovsky, V. & Osterkamp, T. The role of snow cover in the warming of arctic permafrost. Geophys. Res. Lett. 30, 1721 (2003).
Google Scholar 
155.Anisimov, O. & Zimov, S. Thawing permafrost and methane emission in Siberia: Synthesis of observations, reanalysis, and predictive modeling. Ambio 50, 2050–2059 (2021).
Google Scholar 
156.Tei, S. et al. An extreme flood caused by a heavy snowfall over the Indigirka River basin in Northeastern Siberia. Hydrol. Process. 34, 522–537 (2020).
Google Scholar 
157.Jones, B. M. et al. Recent Arctic tundra fire initiates widespread thermokarst development. Sci. Rep. 5, 15865 (2015).
Google Scholar 
158.Fraser, R. H. et al. Climate sensitivity of high Arctic permafrost terrain demonstrated by widespread ice-wedge thermokarst on Banks Island. Remote Sens. 10, 954 (2018).
Google Scholar 
159.Kokelj, S. V., Lantz, T. C., Tunnicliffe, J., Segal, R. & Lacelle, D. Climate-driven thaw of permafrost preserved glacial landscapes, northwestern Canada. Geology 45, 371–374 (2017).
Google Scholar 
160.Raynolds, M. K. et al. Cumulative geoecological effects of 62 years of infrastructure and climate change in ice-rich permafrost landscapes, Prudhoe Bay Oilfield, Alaska. Glob. Change Biol. 20, 1211–1224 (2014).
Google Scholar 
161.Yang, M., Nelson, F. E., Shiklomanov, N. I., Guo, D. & Wan, G. Permafrost degradation and its environmental effects on the Tibetan Plateau: a review of recent research. Earth Sci. Rev. 103, 31–44 (2010).
Google Scholar 
162.Payette, S., Delwaide, A., Caccianiga, M. & Beauchemin, M. Accelerated thawing of subarctic peatland permafrost over the last 50 years. Geophys. Res. Lett. 31, L18208 (2004).
Google Scholar 
163.French, H. & Shur, Y. The principles of cryostratigraphy. Earth Sci. Rev. 101, 190–206 (2010).
Google Scholar 
164.Burn, C. R. & Friele, P. Geomorphology, vegetation succession, soil characteristics and permafrost in retrogressive thaw slumps near Mayo, Yukon Territory. Arctic 42, 31–40 (1989).
Google Scholar 
165.Walvoord, M. A. & Kurylyk, B. L. Hydrologic impacts of thawing permafrost — a review. Vadose Zone J. 15, vzj2016-01 (2016).
Google Scholar 
166.Zona, D. et al. Characterization of the carbon fluxes of a vegetated drained lake basin chronosequence on the Alaskan Arctic Coastal Plain. Glob. Change Biol. 16, 1870–1882 (2010).
Google Scholar 
167.Jorgenson, M. T. & Shur, Y. Evolution of lakes and basins in northern Alaska and discussion of the thaw lake cycle. J. Geophys. Res. Earth Surf. 112, F02S17 (2007).
Google Scholar 
168.Cray, H. A. & Pollard, W. H. Vegetation recovery patterns following permafrost disturbance in a Low Arctic setting: case study of Herschel Island, Yukon, Canada. Arct. Antarct. Alp. Res. 47, 99–113 (2015).
Google Scholar 
169.Baltzer, J. L., Veness, T., Chasmer, L. E., Sniderhan, A. E. & Quinton, W. L. Forests on thawing permafrost: fragmentation, edge effects, and net forest loss. Glob. Change Biol. 20, 824–834 (2014).
Google Scholar 
170.Scheffer, M., Hirota, M., Holmgren, M., Van Nes, E. H. & Chapin, F. S. Thresholds for boreal biome transitions. Proc. Natl Acad. Sci. USA 109, 21384–21389 (2012).
Google Scholar 
171.Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E. & Boike, J. Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun. 9, 5423 (2018).
Google Scholar 
172.Elmendorf, S. C. et al. Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time. Ecol. Lett. 15, 164–175 (2012).
Google Scholar 
173.Strauss, J. et al. Deep Yedoma permafrost: a synthesis of depositional characteristics and carbon vulnerability. Earth Sci. Rev. 172, 75–86 (2017).
Google Scholar 
174.Hjort, J. E. A. Impacts of permafrost degradation on infrastructure. Nat. Rev. Earth. Environ. 3 https://doi.org/10.1038/s43017-021-00247-8 (2022).175.Kumpula, T., Pajunen, A., Kaarlejärvi, E., Forbes, B. C. & Stammler, F. Land use and land cover change in Arctic Russia: Ecological and social implications of industrial development. Glob. Environ. Change 21, 550–562 (2011).
Google Scholar 
176.Nitzbon, J. et al. Fast response of cold ice-rich permafrost in northeast Siberia to a warming climate. Nat. Commun. 11, 2201 (2020).
Google Scholar 
177.Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J. & Slater, A. Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions. Environ. Res. Lett. 10, 094011 (2015).
Google Scholar 
178.Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267 (2017).
Google Scholar 
179.Mekonnen, Z. A., Riley, W. J., Grant, R. F. & Romanovsky, V. E. Changes in precipitation and air temperature contribute comparably to permafrost degradation in a warmer climate. Environ. Res. Lett. 16, 024008 (2021).
Google Scholar 
180.Mikhailov, I. Changes in the soil-plant cover of the high Arctic of Eastern Siberia. Eurasian Soil. Sci. 53, 715–723 (2020).
Google Scholar 
181.Frost, G. V. et al. Multi-decadal patterns of vegetation succession after tundra fire on the Yukon-Kuskokwim Delta, Alaska. Environ. Res. Lett. 15, 025003 (2020).
Google Scholar 
182.Whitley, M. A. et al. Assessment of LiDAR and spectral techniques for high-resolution mapping of sporadic permafrost on the Yukon-Kuskokwim Delta, Alaska. Remote Sens. 10, 258 (2018).
Google Scholar  More

1.Caddy, J. F. & Gulland, J. A. Historical patterns of fish stocks. Mar. Policy 7, 267–278 (1983).
Google Scholar 
2.Steele, J. H. & Henderson, E. W. Coupling between physical and biological scales. Philos. Trans. R. Soc. Lond. Ser. B-Biol. Sci. 343, 5–9 (1994).
Google Scholar 
3.Bjornstad, O. N., Fromentin, J. M., Stenseth, N. C. & Gjosaeter, J. Cycles and trends in cod populations. Proc. Natl Acad. Sci. USA 96, 5066–5071 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Piatt, J. F. et al. Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014–2016. PLoS One 15, e0226087 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Oremus, K. L. Climate variability reduces employment in New England fisheries. Proc. Natl Acad. Sci. USA 16, 26444–26449 (2018).
Google Scholar 
6.Shelton, A. O. & Mangel, M. Fluctuations of fish populations and the magnifying effect of fishing. Proc. Natl Acad. Sci. USA 108, 7075–7080 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Essington, T. E. et al. Fishing amplifies forage fish population collapses. Proc. Natl Acad. Sci. USA 112, 6648–6652 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Memarzadeha, M., Britten, G. L., Wormd, B. & Boettigere, C. Rebuilding global fisheries under uncertainty. Proc. Natl Acad. Sci. USA 116, 15985–15990 (2019).
Google Scholar 
9.Pauly, D. & Zeller, D. Sea Around Us Concepts, Design and Data (seaaroundus.org) (2015).10.Bjornstad, O. N., Nisbet, R. M. & Fromentin, J. M. Trends and cohort resonant effects in age-structured populations. J. Anim. Ecol. 73, 1157–1167 (2004).
Google Scholar 
11.Botsford, L. W., Holland, M. D., Field, J. C. & Hastings, A. Cohort resonance: a significant component of fluctuations in recruitment, egg production, and catch of fished populations. ICES J. Mar. Sci. 71, 2158–2170 (2014).
Google Scholar 
12.Di Lorenzo, E. & Ohman, M. D. A double-integration hypothesis to explain ocean ecosystem response to climate forcing. Proc. Natl Acad. Sci. USA 110, 2496–2499 (2013).PubMed 
PubMed Central 

Google Scholar 
13.Bjorkvoll, E. et al. Stochastic population dynamics and life-history variation in marine fish species. Am. Naturalist 180, 372–387 (2012).
Google Scholar 
14.Hsieh, C. H. et al. Fishing elevates variability in the abundance of exploited species. Nature 443, 859–862 (2006).CAS 
PubMed 

Google Scholar 
15.Beamish, R. J., McFarlane, G. A. & Benson, A. Longevity overfishing. Prog. Oceanogr. 68, 289–302 (2006).
Google Scholar 
16.Anderson, C. N. K. et al. Why fishing magnifies fluctuations in fish abundance. Nature 452, 835–839 (2008).CAS 
PubMed 

Google Scholar 
17.Hutchings, J. A. & Myers, R. A. Effect of age on the seasonality of maturation and spawning of Atlantic cod, Gadus morhua, in the northwest Atlantic. Can. J. Fish. Aquat. Sci. 50, 2468–2474 (1993).
Google Scholar 
18.Bobko, S. J. & Berkeley, S. A. Maturity, ovarian cycle, fecundity, and age-specific parturition of black rockfish (Sebastes melanops). Fish. Bull. 102, 418–429 (2004).
Google Scholar 
19.Berkeley, S. A., Chapman, C. & Sogard, S. M. Maternal age as a determinant of larval growth and survival in a marine fish, Sebastes melanops. Ecology 85, 1258–1264 (2004).
Google Scholar 
20.Longhurst, A. Murphy’s law revisited: longevity as a factor in recruitment to fish populations. Fish. Res. 56, 125–131 (2002).
Google Scholar 
21.Stawitz, C. C. & Essington, T. E. Somatic growth contributes to population variation in marine fishes. J. Anim. Ecol. 88, 315–329 (2019).PubMed 

Google Scholar 
22.Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).CAS 
PubMed 

Google Scholar 
23.Hollowed, A. B., Hare, S. R. & Wooster, W. S. Pacific basin climate variability and patterns of Northeast Pacific marine fish production. Prog. Oceanogr. 49, 257–282 (2001).
Google Scholar 
24.Holsman, K. K., Aydin, K., Sullivan, J., Hurst, T. & Kruse, G. H. Climate effects and bottom-up controls on growth and size-at-age of Pacific halibut (Hippoglossus stenolepis) in Alaska (USA). Fish. Oceanogr. 28, 345–358 (2019).
Google Scholar 
25.Whitten, A. R., Klaer, N. L., Tuck, G. N. & Day, R. W. Accounting for cohort-specific variable growth in fisheries stock assessments: A case study from south-eastern Australia. Fish. Res. 142, 27–36 (2013).
Google Scholar 
26.Heessen, H. J. L., Daan, N. & Ellis, J. R. Fish atlas of the Cebtic Sea, North Sea, and Baltic Sea (KNNV Publishing and Wageningen Academic Publishers, 2015).27.Froese, R. & Pauly, D. FishBase, version (01/2021) https://www.fishbase.org (2021).28.Munch, S. B. & Salinas, S. Latitudinal variation in lifespan within species is explained by the metabolic theory of ecology. Proc. Natl Acad. Sci. USA 106, 13860–13864 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Beukhof, E. et al. Marine fish traits follow fast-slow continuum across oceans. Sci. Rep. 9, 17878 (2019).30.Pauly, D. On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks. J. Cons. int. Explor. Mer. 39, 175–192 (1980).
Google Scholar 
31.Audzijonyte, A. et al. Fish body sizes change with temperature but not all species shrink with warming. Nat. Ecol. Evol. 4, 1–6 (2020).
Google Scholar 
32.Audzijonyte, A. et al. Is oxygen limitation in warming waters a valid mechanism to explain decreased body sizes in aquatic ectotherms? Glob. Ecol. Biogeogr. 28, 64–77 (2019).
Google Scholar 
33.Forster, J., Hirst, A. G. & Atkinson, D. Warming-induced reductions in body size are greater in aquatic than terrestrial species. Proc. Natl Acad. Sci. USA 109, 19310–19314 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).CAS 
PubMed 

Google Scholar 
35.Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).CAS 

Google Scholar 
36.Ives, A. R. Measuring resilience in stochastic-systems. Ecol. Monogr. 65, 217–233 (1995).
Google Scholar 
37.Alheit, J. & Niquen, M. Regime shifts in the Humboldt Current ecosystem. Prog. Oceanogr. 60, 201–222 (2004).
Google Scholar 
38.Pinsky, M. L., Jensen, O. P., Ricard, D. & Palumbi, S. R. Unexpected patterns of fisheries collapse in the world’s oceans. Proc. Natl Acad. Sci. USA 108, 8317–8322 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Spencer, P. D. & Collie, J. S. Patterns of population variability in marine fish stocks. Fish. Oceanogr. 6, 188–204 (1997).
Google Scholar 
40.FAO. The State of World Fisheries and Aquaculture 2018 – Meeting the Sustainable Development Goals. (Food and Agriculture Organization of the United Nations, Rome, 2018).41.Barnett, L. A. K., Branch, T. A., Ranasinghe, R. A. & Essington, T. E. Old-growth fishes become scarce under fishing. Curr. Biol. 27, 2843–2848 (2017).CAS 
PubMed 

Google Scholar 
42.Rouyer, T. et al. Shifting dynamic forces in fish stock fluctuations triggered by age truncation? Glob. Change Biol. 17, 3046–3057 (2011).
Google Scholar 
43.Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).
Google Scholar 
44.Easterling, D. R. et al. Climate extremes: Observations, modeling, and impacts. Science 289, 2068–2074 (2000).CAS 
PubMed 

Google Scholar 
45.Portner, H. O. & Peck, M. A. Climate change effects on fishes and fisheries: towards a cause-and-effect understanding. J. Fish. Biol. 77, 1745–1779 (2010).CAS 
PubMed 

Google Scholar 
46.Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).CAS 
PubMed 

Google Scholar 
47.de Gee, A. & Kikkert, A. H. Analysis of the grey gurnard (Eutrigla gurnardus) samples collected during the 1991 international stomach sample project. ICES Document CM 1993/G:14, 25 (1993).48.Sparholt, H. In Fish Atlas of the Celtic Sea, North Sea, and Baltic Sea (eds Heessen, H., Daan, N., & Ellis, J. R.) 377–381 (KNNV Publishiing and Wageningen Academic Publishers, 2015).49.Arnott, S. A. & Ruxton, G. D. Sandeel recruitment in the North Sea: demographic, climate and trophic effects. Mar. Ecol. Prog. Ser. 238, 199–210 (2002).
Google Scholar 
50.van Deurs, M., van Hal, R., Tomczak, M. T., Jonasdottir, S. H. & Dolmer, P. Recruitment of lesser sandeel Ammodytes marinus in relation to density dependence and zooplakton composition. Mar. Ecol. Prog. Ser. 381, 249–258 (2009).
Google Scholar 
51.Capuzzo, E. et al. A decline in primary production in the North Sea over 25 years, associated with reductions in zooplankton abundance and fish stock recruitment. Glob. Change Biol. 24, E352–E364 (2018).
Google Scholar 
52.Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. D: Atmospheres 108, ACL 2-1–ACL 2–29 (2003).
Google Scholar 
53.Papworth, D. J., Marini, S. & Conversi, A. Novel, unbiased analysis approach for investigating population dynamics: A case study on Calanus finmarchicus and its decline in the North Sea. PLoS One 11, e0158230 (2016).PubMed 
PubMed Central 

Google Scholar 
54.Bergstad, O. A., Hoines, A. S. & Jorgensen, T. Growth of sandeel Ammodytes marinus, in the northern North Sea and Norwegian coastal waters. Fish. Res. 56, 9–23 (2002).
Google Scholar 
55.Wright, P. J. Otolith microstructure of the lesser sandeel, Ammodytes marinus. J. Mar. Biol. Assoc. U.K. 73, 245–248 (1993).
Google Scholar 
56.Sell, A. & Heessen, H. in Fish atlas of the Celtic Sea, North Sea, and Baltic Sea (eds Heessen, H., Daan, N., & Ellis, J. R.) 295−299 (KNNV Publishing and Wageningen Academic Publishers, 2015).57.Bergstad, O. A., Hoines, A. S. & Kruger-Johnsen, E. M. Spawning time, age and size at maturity, and fecundity of sandeel, Ammodytes marinus, in the north-eastern North Sea and in unfished coastal waters off Norway. Aquat. Living Resour. 14, 293–301 (2001).
Google Scholar 
58.Pyper, B. J. & Peterman, R. M. Comparison of methods to account for autocorrelation in correlation analyses of fish data. Can. J. Fish. Aquat. Sci. 55, 2127–2140 (1998).
Google Scholar 
59.van der Sleen, P. et al. Non-stationary responses in anchovy (Engraulis encrasicolus) recruitment to coastal upwelling in the Southern Benguela. Mar. Ecol. Prog. Ser. 596, 155–164 (2018).
Google Scholar 
60.Cushing, D. H. Upwelling and production on fish. Adv. Mar. Biol. 9, 255–334 (1971).
Google Scholar 
61.Pauly, D. & Lam, V. W. Y. In Large marine ecosystems: Status and Trends (eds IOC-UNESCO and UNEP) 113–137 (United Nations Environmental Programme, 2016). More

1.Galloway, J. N. et al. Trace metals in atmospheric deposition: A review and assessment. Atmos. Environ. 16, 1677–1700 (1982).CAS 
ADS 

Google Scholar 
2.Dodds, W. K., Perkin, J. S. & Gerken, J. E. Human impact on freshwater ecosystem services: A global perspective. Environ. Sci. Technol. 47, 9061–9068 (2013).CAS 
PubMed 
ADS 

Google Scholar 
3.Rigosi, A., Carey, C. C., Ibelings, B. W. & Brookes, J. D. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol. Oceanogr. 59, 99–114 (2014).ADS 

Google Scholar 
4.Dokulil, M. T. & Teubner, K. Eutrophication and climate change: Present situation and future scenarios. In Eutrophication: Causes, Consequences and Control (eds Ansari, A. A. et al.) 1–16 (Springer, 2011).
Google Scholar 
5.Codd, G. A., Lindsay, J., Young, F. M., Morrison, L. F. & Metcalf, J. S. Harmful Cyanobacteria (Springer, 2005).
Google Scholar 
6.Harland, F. M. J., Wood, S. A., Moltchanova, E., Williamson, W. M. & Gaw, S. Phormidium autumnale growth and anatoxin-a production under iron and copper stress. Toxins (Basel). 5, 2504–2521 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Zurawell, R. W., Chen, H., Burke, J. M. & Prepas, E. E. Hepatotoxic cyanobacteria: A review of the biological importance of microcystins in freshwater environments. J. Toxicol. Environ. Health B 8, 1–37 (2005).CAS 

Google Scholar 
8.Funari, E. & Testai, E. Human health risk assessment related to cyanotoxins exposure. Crit. Rev. Toxicol. 38, 97–125 (2008).CAS 
PubMed 

Google Scholar 
9.Brooks, B. W. et al. Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems?. Environ. Toxicol. Chem. 35, 6–13 (2016).CAS 
PubMed 

Google Scholar 
10.Pick, F. R. & Lean, D. R. S. The role of macronutrients (C, N, P) in controlling cyanobacterial dominance in temperate lakes. N. Z. J. Mar. Freshw. Res. 21, 425–434 (1987).CAS 

Google Scholar 
11.Schindler, A. D. W. Evolution of phosphorus limitation in lakes. Science 195, 260–262 (1977).CAS 
PubMed 
ADS 

Google Scholar 
12.Kumar, K., Mella-Herrera, R. A. & Golden, J. W. Cyanobacterial heterocysts. Cold Spring Harb. Perspect. Biol. 2, 1–20 (2010).
Google Scholar 
13.Paerl, H. W., Fulton, R. S., Moisander, P. H. & Dyble, J. Harmful freshwater algal blooms, with an emphasis on cyanobacteria. Sci. World J. 1, 76–113 (2001).CAS 

Google Scholar 
14.Paerl, H. W., Hall, N. S. & Calandrino, E. S. Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Sci. Total Environ. 409, 1739–1745 (2011).CAS 
PubMed 
ADS 

Google Scholar 
15.Higgins, S. N. et al. Biological nitrogen fixation prevents the response of a eutrophic lake to reduced loading of nitrogen: Evidence from a 46-year whole-lake experiment. Ecosystems 21, 1088–1100 (2018).CAS 

Google Scholar 
16.Dolman, A. M. et al. Cyanobacteria and cyanotoxins: The influence of nitrogen versus phosphorus. PLoS ONE 7, e38757 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
17.Schoffman, H., Lis, H., Shaked, Y. & Keren, N. Iron-nutrient interactions within phytoplankton. Front. Plant Sci. 7, 1223 (2016).PubMed 
PubMed Central 

Google Scholar 
18.Needoba, J. A., Foster, R. A., Sakamoto, C., Zehr, J. P. & Johnson, K. S. Nitrogen fixation by unicellular diazotrophic cyanobacteria in the temperate oligotrophic North Pacific Ocean. Limnol. Oceanogr. 52, 1317–1327 (2007).CAS 
ADS 

Google Scholar 
19.Romero, I. C., Klein, N. J., Sañudo-Wilhelmy, S. A. & Capone, D. G. Potential trace metal co-limitation controls on N2 fixation and NO3- uptake in lakes with varying trophic status. Front. Microbiol. 4, 1–12 (2013).CAS 

Google Scholar 
20.Newton, W. E. Physiology, biochemistry, and molecular biology of nitrogen fixation. In Biology of the Nitrogen Cycle 109–129 (Elsevier B. V, 2007).
Google Scholar 
21.Salama, Z. A., El-Fouly, M. M., Lazova, G. & Popova, L. P. Carboxylating enzymes and carbonic anhydrase functions were suppressed by zinc deficiency in maize and chickpea plants. Acta Physiol. Plant. 28, 445–451 (2006).CAS 

Google Scholar 
22.Sültemeyer, D. Carbonic anhydrase in eukaryotic algae: Characterization, regulation, and possible function during photosynthesis. Can. J. Bot. 76, 962–972 (1998).
Google Scholar 
23.Vallee, B. L. & Auld, D. S. Zinc coordination, function, and structure of zinc enzymes and other proteins. Biochemistry 29, 5647–5659 (1990).CAS 
PubMed 

Google Scholar 
24.Wu, F. Y. & Wu, C. W. Zinc in DNA replication and transcription. Annu. Rev. Nutr. 7, 251–272 (1987).CAS 
PubMed 

Google Scholar 
25.Beyer, W., Imlay, J. & Fridovich, I. Superoxide dismutases. Prog. Nucleic Acid Res. Mol. Biol. 40, 221–253 (1991).CAS 
PubMed 

Google Scholar 
26.Holm-Hansen, O., Gerloff, G. H. & Skogg, F. Cobalt as an essential element for blue-green algae. Physiol. Plant. 7, 665–675 (1954).CAS 

Google Scholar 
27.Sunda, W. G. & Huntsman, S. A. Cobalt and zinc interreplacement in marine phytoplankton: Biological and geochemical implications. Limnol. Oceanogr. 40, 1404–1417 (1995).CAS 
ADS 

Google Scholar 
28.Steffens, G. C. M., Biewald, R. & Buse, G. Cytochrome c oxidase is three-copper, two-heme-A protein. Eur. J. Biochem. 164, 295–300 (1987).CAS 
PubMed 

Google Scholar 
29.Price, R. C., Mortimer, N., Smith, I. E. M. & Maas, R. Whole-rock geochemical reference data for Torlesse and Waipapa terranes, North Island, New Zealand. N. Z. J. Geol. Geophys. 58, 213–228 (2015).CAS 

Google Scholar 
30.Downs, T. M., Schallenberg, M. & Burns, C. W. Responses of lake phytoplankton to micronutrient enrichment: A study in two New Zealand lakes and an analysis of published data. Aquat. Sci. 70, 347–360 (2008).CAS 

Google Scholar 
31.Bayer, T. K., Schallenberg, M. & Martin, C. E. Investigation of nutrient limitation status and nutrient pathways in Lake Hayes, Otago, New Zealand: A case study for integrated lake assessment. N. Z. J. Mar. Freshw. Res. 42, 285–295 (2008).CAS 

Google Scholar 
32.Glass, J. B., Axler, R. P., Chandra, S. & Goldman, C. R. Molybdenum limitation of microbial nitrogen assimilation in aquatic ecosystems and pure cultures. Front. Microbiol. 3, 1–11 (2012).
Google Scholar 
33.Sterner, R. W. et al. Phosphorus and trace metal limitation of algae and bacteria in Lake Superior. Limnol. Oceanogr. 49, 495–507 (2004).CAS 
ADS 

Google Scholar 
34.Vrede, T. & Tranvik, L. J. Iron constraints on planktonic primary production in oligotrophic lakes. Ecosystems 9, 1094–1105 (2006).CAS 

Google Scholar 
35.North, R. L., Guildford, S. J., Smith, R. E. H., Havens, S. M. & Twiss, M. R. Evidence for phosphorus, nitrogen, and iron colimitation of phytoplankton communities in Lake Erie. Limnol. Oceanogr. 52, 315–328 (2007).CAS 
ADS 

Google Scholar 
36.Kelly, L. T. et al. Trace metal and nitrogen concentrations differentially affect bloom forming cyanobacteria of the genus Dolichospermum. Aquat. Sci. 83, 1–11 (2021).
Google Scholar 
37.Sorichetti, R. J., Creed, I. F. & Trick, C. G. Iron and iron-binding ligands as cofactors that limit cyanobacterial biomass across a lake trophic gradient. Freshw. Biol. 61, 146–157 (2016).CAS 

Google Scholar 
38.Wood, S. A. et al. Contrasting cyanobacterial communities and microcystin concentrations in summers with extreme weather events: Insights into potential effects of climate change. Hydrobiologia 785, 71–89 (2017).CAS 

Google Scholar 
39.Li, X., Dreher, T. W. & Li, R. An overview of diversity, occurrence, genetics and toxin production of bloom-forming Dolichospermum (Anabaena) species. Harmful Algae 54, 54–68 (2016).CAS 
PubMed 

Google Scholar 
40.Hawes, I. & Smith, R. Seasonal dynamics of epilithic periphyton in oligotrophic lake Taupo, New Zealand. N. Z. J. Mar. Freshw. Res. 28, 1–12 (1994).
Google Scholar 
41.Verburg, P. & Albert, A. Taupo Long Term Monitoring (Springer, 2018).
Google Scholar 
42.Marañón, E. Cell Size as a key determinant of phytoplankton metabolism and community structure. Ann. Rev. Mar. Sci. 7, 241–264 (2015).PubMed 

Google Scholar 
43.Kagami, M. & Urabe, J. Phytoplankton growth rate as a function of cell size: An experimental test in Lake Biwa. Limnology 2, 111–117 (2001).
Google Scholar 
44.Kraemer, S. M., Duckworth, O. W., Harrington, J. M. & Schenkeveld, W. D. C. Metallophores and trace metal biogeochemistry. Aquat. Geochem. 21, 159–195 (2015).CAS 

Google Scholar 
45.Twiss, M. R., Auclair, J.-C. & Charlton, M. N. An investigation into iron-stimulated phytoplankton productivity in epipelagic Lake Erie during thermal stratification using trace metal clean techniques. Can. J. Fish. Aquat. Sci. 57, 86–95 (2000).CAS 

Google Scholar 
46.Feng, Y., Fu, F. & Hutchins, D. A. Trace metal clean culture techniques. Res. Methods Environ. Physiol. Aquat. Sci. https://doi.org/10.1007/978-981-15-5354-7_36 (2021).Article 

Google Scholar 
47.Rhodes, L. et al. The Cawthron institute culture collection of micro-algae: A significant national collection. N. Z. J. Mar. Freshw. Res. 50, 291–316 (2016).
Google Scholar 
48.Bolch, C. J. S. & Blackburn, S. I. Isolation and purification of Australian isolates of the toxic cyanobacterium Microcystis aeruginosa Kütz. J. Appl. Phycol. 8, 5–13 (1996).
Google Scholar 
49.Worms, I., Simon, D. F., Hassler, C. S. & Wilkinson, K. J. Bioavailability of trace metals to aquatic microorganisms: Importance of chemical, biological and physical processes on biouptake. Biochimie 88, 1721–1731 (2006).CAS 
PubMed 

Google Scholar 
50.Gobler, C. J., Hutchins, D. A., Fisher, N. S., Cosper, E. M. & Sañudo-Wilhelmy, S. A. Release and bioavailability of C, N, P, Se, and Fe following viral lysis of a marine chrysophyte. Limnol. Oceanogr. 42, 1492–1504 (1997).CAS 
ADS 

Google Scholar 
51.Bell, W. & Mitchell, R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol. Bull. 143, 265–277 (1972).
Google Scholar 
52.Seymour, J. R., Amin, S. A., Raina, J. B. & Stocker, R. Zooming in on the phycosphere: The ecological interface for phytoplankton-bacteria relationships. Nat. Microbiol. 2, 65 (2017).
Google Scholar 
53.Helliwell, K. E. et al. Cyanobacteria and eukaryotic algae use different chemical variants of Vitamin B12. Curr. Biol. 26, 999–1008 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Anderson, M. A. & Morel, F. M. M. The influence of aqueous iron chemistry on the uptake of iron by the coastal diatom Thallasiosira weissflogii. Limnol. Oceanogr. 27, 789–813 (1982).CAS 
ADS 

Google Scholar 
55.Lis, H., Kranzler, C., Keren, N. & Shaked, Y. A comparative study of Iron uptake rates and mechanisms amongst marine and fresh water Cyanobacteria: Prevalence of reductive Iron uptake. Life 5, 841–860 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Bruland, K. W., Knauer, G. A. & Martin, J. H. Zinc in north-east Pacific water. Nature 271, 741–743 (1978).CAS 
ADS 

Google Scholar 
57.Saeed, H. et al. Regulation of phosphorus bioavailability by iron nanoparticles in a monomictic lake. Sci. Rep. 8, 1–14 (2018).
Google Scholar 
58.Baken, S., Degryse, F., Verheyen, L., Merckx, R. & Smolders, E. Metal complexation properties of freshwater dissolved organic matter are explained by its aromaticity and by anthropogenic ligands. Environ. Sci. Technol. 45, 2584–2590 (2011).CAS 
PubMed 
ADS 

Google Scholar 
59.Campbell, P. G. C. Interactions between trace metals and aquatic organisms: A critique of the free-ion activity model. In Metal Speciation and Bioavailability in Aquatic Systems (eds Tessier, A. & Turner, D. R.) 45–102 (Wiley, 1995).
Google Scholar 
60.Scharek, R., Van Leeuwe, M. A. & De Baar, H. J. W. Responses of Southern Ocean phytoplankton to the addition of trace metals. Deep. Res. Part II 44, 209–227 (1997).CAS 

Google Scholar 
61.Facey, J. A., Apte, S. C. & Mitrovic, S. M. A review of the effect of trace metals on freshwater cyanobacterial growth and toxin production. Toxins (Basel). 11, 1–18 (2019).
Google Scholar 
62.Zhang, X. et al. Effect of micronutrients on algae in different regions of Taihu, a large, spatially diverse, hypereutrophic lake. Water Res. 151, 500–514 (2019).CAS 
PubMed 

Google Scholar 
63.Wever, A. D. et al. Differential response of phytoplankton to additions of nitrogen, phosphorus and iron in Lake Tanganyika. Freshw. Biol. 53, 264–277 (2008).
Google Scholar 
64.Nalewajko, C. & Murphy, T. P. Effects of temperature, and availability of nitrogen and phosphorus on the abundance of Anabaena and Microcystis in Lake Biwa, Japan: An experimental approach. Limnology 2, 45–48 (2001).
Google Scholar 
65.Kagami, M., Gurung, T. B., Yoshida, T. & Urabe, J. To sink or to be lysed? Contrasting fate of two large phytoplankton species in Lake Biwa. Limnol. Oceanogr. 51, 2775–2786 (2006).ADS 

Google Scholar 
66.Hartig, J. H. & Wallen, D. G. The influence of light and temperature on growth and photosynthesis of fragilaria crotonensis kitton. J. Freshw. Ecol. 3, 371–382 (1986).
Google Scholar 
67.Tilman, D. Tests of resource competition theory using four species of Lake Michigan algae. Ecology 62, 802–815 (1981).
Google Scholar 
68.Tompkins, T. & Blinn, D. W. The effect of mercury on the growth rate of Fragilaria crotonensis kitton and Asterionella formosa Hass. Hydrobiologia 49, 111–116 (1976).CAS 

Google Scholar 
69.Kazamia, E. et al. Endocytosis-mediated siderophore uptake as a strategy for Fe acquisition in diatoms. Sci. Adv. 4, aar4536 (2018).ADS 

Google Scholar 
70.Strzepek, R. F. & Harrison, P. J. Photosynthetic architecture differs in coastal and oceanic diatoms. Nature 431, 689–692 (2004).CAS 
PubMed 
ADS 

Google Scholar 
71.Strzepek, R. F., Boyd, P. W. & Sunda, W. G. Photosynthetic adaptation to low iron, light, and temperature in Southern Ocean phytoplankton. Proc. Natl. Acad. Sci. U. S. A. 116, 4388–4393 (2019).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
72.Raven, J. A. The iron and molybdenum use efficiencies of plant growth with different energy, carbon and nitrogen sources. New Phytol. 109, 279–287 (1988).CAS 

Google Scholar 
73.Kranzler, C., Rudolf, M., Keren, N. & Schleiff, E. Iron in cyanobacteria. Adv. Bot. Res. 65, 57–105 (2013).CAS 

Google Scholar  More

Characterization of farm typesThe principal component analysis (PCA) resulted in extraction of the first three principal components (PCs) based on eigen-value criterion (eigen-value  > 1) (Fig. 2A) explaining about 87% of the variability in surveyed farm households (Fig. 2B). The first principal component (PC 1) explained the greatest part of the variation, about 43.1% of the variability in surveyed farm households. PC 1 was more closely related to the variables describing the use of farm machinery, land area foodgrain, and income foodgrain. (Fig. 1A and Fig. 2C). The second principal component (PC 2) explained 27.1% of the variability in surveyed farm households and was strongly associated with land area fruit and vegetable, income fruit and vegetable, income on-farm, expense all farm enterprises (Fig. 1A and Fig. 2C). The third principal component (PC 3) explained 16.8% of the variability in surveyed farm households and described land area fodder, income fodder (Fig. 1B and Fig. 2C). Thus, the first three principal components explained the use of farm machinery, land use, income, and expense of farm households, giving insight into the production objective of households. The results from hierarchical clustering suggested a four-cluster cutoff point (Fig. 3A and Fig. 3B) and the non-hierarchical clustering assigned households to identified clusters (Fig. 1C and Fig. 1D). Thus households of the study area could be grouped into four farm types contrasted by their structural characteristics that describe resource endowment and functional characteristics that describe livelihood strategies. Traditionally, farm households were divided into four categories based on the size of their land holdings: marginal, small, medium, and large farmer19. The typologies created in this study are based on the possession of resources such as crops and animals, as well as decisions made by them regarding crop and livestock rearing. Based on structural factors, cropping system, livestock owned, source of income, and differences among different farm households, our study divided the farm households into four farm types. The similar type of categorization was done for smallholder’s farms in Indo‑Gangetic Plains of India20.Farm type-1. Resource constraint households with low farm income (n = 93, 46.5%): Farm type-1 was the largest cluster of sampled farm households, distinguishable from other farm types by smallest land owned by household (Table 1). The cropping system dominated by plantation crop, had fruits and vegetables. Nearly half of fruits and vegetables as sole crops and the rest are intercropped in coconut. The livestock system exhibited a low abundance of large ruminant and a high abundance of poultry, average ownership was limited to the isolated presence of cattle and 25 poultry. Egg production was highest among farm types. On-farm income were the lowest among farm types. Crop produce sales were the main source of on-farm income 76%, complemented by income from livestock 24%. Furthermore, the production cost of ₹69,000 was the lowest among farm types. Due to variables such as fluctuating commodity prices, labour shortages during peak agriculture season, farmers’ concentration shifted to adoption of few enterprises as a result of land fragmentation and economic liberalization in the 1990s21,22. These variables have had a significant impact on resource constraint farm types.Farm type-2. Resource endowed diversified households with high farm income (n = 25, 12.5%): Farm type-2 exhibited the smallest cluster of sampled farm households, mostly dominated by fruit and vegetable, plantation crop (Table 1). Nearly one-fourth of fruit and vegetable as the sole crop and the rest are intercropped in coconut in upland. Complementary and supplementary enterprises viz. apiculture, pisciculture, nutritional kitchen garden, agro-processing, and value addition generated income ₹5,010 which was substantially high in this cluster. Livestock production centered around a moderate abundance of large ruminant and moderate abundance of poultry, average ownership of 1 cattle and 17 poultry. This cluster had the highest on-farm income ₹1,25,600 among farm types. Crop produce sales provided 63% of on-farm income, complemented by income from livestock 33%. Moreover, the production cost of ₹2,02,000 was relatively high among farm types. These farm households adapted crop diversification. Diversification is a method for making better use of land, water, and other resources by growing more profitable crops. It allows farmers to choose which crops to grow on their farm in order to maximize returns, and most farmers grow multiple crops to reduce risk and uncertainty caused by climatic and biological fluctuations23. Diversification refers to switching from less profitable and non-sustainable crops to more profitable and long-term crops. It has emerged as a viable option for ensuring natural resource sustainability, ecological balance, job creation, and risk generation24.Farm type-3. Resource endowed mechanized households with low farm income (n = 43, 21.5%): Farm type-3 comprised of sampled farm households distinguishable from other farm types by the largest cropped area under foodgrain (Table 1). The foodgrain area dedicated to rice cultivation was located mostly in wetland, while the plantation crop area largely established with coconut was on paddy field bunds and in the garden land. Livestock production centered around a moderate abundance of large ruminant and low abundance of poultry, average ownership of 1 cattle and 5 poultry. This cluster had an on-farm income of ₹63,300, the main source being crop produce sales 58%, complemented by income from livestock 42%. Besides, the production cost of ₹1,79,000 was relatively high among farm types. In these farm households the farm mechanization has brought significant change in the livelihood. Especially, paddy field preparation through puddling, mechanical transplantation, and paddy combine harvester reduced the greater dependence of external labourers. The relative shortage of agricultural workers, and the comparatively high wage rate in agriculture has bought small and large scale mechanization in Kerala agricultural system21.Farm type-4. Resource endowed medium farm income households with livestock dominance (n = 39, 19.5%): A main distinguishing feature of sampled farm households in farm type-4 was the largest fodder area among farm types, established mostly in coconut garden (Table 1). A considerable number of households had a foodgrain area of in wetland, mainly dedicated to rice cultivation. The livestock system exhibited a high abundance of large ruminant and low abundance of poultry, comprised mostly of milch animal, average ownership of 2 cattle and 2 poultry. Milk production 3.84 × 103 L/year was the highest among farm types. On-farm income was ₹84,100. The main income source was livestock which constituted 65% of on-farm income, complemented by income from crop produces 35%. Production cost ₹1,54,000 was relatively high among farm types. These farmers adapted livestock has their source of livelihood and alternate means of employment especially farm women’s. The major benefit of livestock components like cattle and poultry is that they provide regular income to sustain farm family and also they provide nutritional security. Crossbred cattle adoption and crossbred milk output are important factors in increasing livestock revenue. To increase income from animal sources, a crossbreeding strategy should be implemented25.Farming system patternsDistinguishing characteristics of a farming system are highly location-specific, depend on adaptive strategies devised by farmers to cope with the adverse situations as well as take advantage of the potential opportunities for intensification and diversification of agriculture at the household level. Studies have shown that farmers come up with strategies to get along with adverse situations viz. volatile price, crop failure, flood, drought, declining soil fertility, land scarcity, climate change and also make use of potential opportunities viz. use of new technologies, value addition, which allowed for sustainable production and income10,26,27,28. These distinguishing characteristics of a farming system are discussed in relation to clustering variables grouped according to the theme, their interrelationships, and the identified farm types in the following sections.Farm household: The basic unit of social organization is the farm household where the head, typically a male lives with his nuclear family most often in a concrete roofed house. Farm households residing in traditional clay tile-roofed houses are also found occasionally. Farm households had an average size of four members (Table 1). Households were headed by the oldest male member aged 60 years. Both household size and age of household head remained unchanged across farm types. Land owned by households 0.42 ha is typically inherited (Table 1). Purchase is the less common access route to land ownership. Land owned by a household is commonly taken as a proxy for the wealth of a household as it correlates positively with livestock assets and crop production29. Results revealed variation in land owned by households across farm types with the smallest land 0.34 ha owned by the resource-constrained type-1 household. Interestingly, type-1 farmers accounted for a major proportion (46.5%) of farm households surveyed. The traditional practice of land owned by households typically fragmented into smaller parcels that are allocated to children at the time of their marriage, favors an increase in the number of small farm holdings. Eventually, the married children who had started in a household, leave the household with one’s spouse and consequently their children to build their own house and live separately in their inherited land, thus forming a new household. Small land holdings characterize Kerala agriculture. The core cause of poverty in Kerala is the tremendous fragmentation of agricultural land, and the fact that this fragmentation is only getting worse and is becoming a unique development issue. This current state of significant fragmentation, highlight the massive increase in the number of marginal farms as the area covered by large farms decreases30.Labour: A combination of family and hired wage labour was used for agricultural production in the study area. Family labour is comprised of individuals in a household who are related by blood and kinship. With all households having only one family member working on-farm on a full-time basis and the average household size being only four members, family labour availability is less (Table 1). Household size is commonly taken as a proxy for family labour availability thereby requiring the hiring of wage labour to deal with family labour shortage 11. Shortage of family labour is further exacerbated by one member in each household across farm types working non-farm on a full-time basis, either making a livelihood from overseas, running small businesses, or earning a salary from the service sector. The study area is located on the outskirts of the state capital, the educated youth in farm households have ample employment opportunities in the secondary sector namely construction, and in the tertiary sector namely health service, transportation, education, entertainment, tourism, finance, sales, and retail. Wage labourers were hence hired on a seasonal basis for labour-intensive activities such as land preparation, planting, and harvesting. The local wage rate for farm laborers in the study area were ₹650 and ₹600 per man-day for men and women respectively, which were the highest in the nation. For farmers and labourers, agriculture is not a reliable source of revenue and employment. Kerala’s labour distribution has shifted in favor of the non-agricultural sector, especially the service sector. Kerala has seen a significant increase in non-agricultural employment in both rural and urban areas, resulting in a shift in the workforce’s industrial distribution. The structure of rural employment in Kerala has transitioned from agricultural to non-agricultural enterprises as a result of these changes. The specialized agriculture practices and mono-cropping increased production cost, risk of crop failure, and lower market price31. Due to this, the small and marginal farmers migrated to neighboring cities in search of jobs and livelihood. In this scenario, IFS will be a solution to reduce the economic risk with improved employment generation. The continuous labour requirement for multiple crops and livestock systems provides an option for higher employment generation and keeps the farm families engaged in the farm activities. This holds good even during the COVID-19 pandemic for meeting the employment needs of reverse migrants (urban to rural). In IFS, farm activities are continued round the year, thus the farm family is effectively engaged in farm. The adoption of such systems avoids migration of farmers and rural youth to nearby cities and towns in search of contractual employment.Results showed increased use of farm machinery, 4.43 h/year in the type-3 household having a considerable land area under foodgrain (Table 1). Tractor-operated rotavator for puddling and combined harvester for reaping, threshing, and winnowing were extensively custom hired in the type-3 household. Mechanization in foodgrain cultivation was limited to custom hiring of tractor-operated rotavator for puddling in type-4 households resulting in the use of farm machinery1.40 h/year (Table 1). Brush cutter for trimming weeds, coconut tree climber for harvesting coconut, and plant protection sprayers were some of the machinery owned by a limited number of households across all farm types. The variables viz. use of farm machinery, land area under foodgrain, and net income from foodgrain sales were positively correlated, attributable to substitution of wage labourers with machines in agricultural enterprises having high work and maintenance requirements so that such enterprises remain economically viable (Fig. 1A, B; Table 1).Land use: Coconut plantation in upland and rice in lowland is the major land use. The two crop variables retained for principal component analysis (PCA) namely foodgrain area, fruit, and vegetable area, were negatively correlated to each other, suggesting that farms that dedicated large areas to field crops especially rice cultivation did so at the expense of fruits and vegetable crops especially banana, amaranth, cowpea and vice versa (Fig. 1A and Fig. 1B ; Table 1). Resource-constrained type-1 and resource endowed type-2 households exhibited the smallest cropped area under foodgrain (Table 1). The meager food grain area in type-1 and 2 households were under direct-seeded upland rice, cultivated as part of the latest efforts to diversify the existing cropping system in these households. Rice is the most widely consumed staple in the study area. The lower proportion of food grain in these households suggests that land resources had been preferentially allocated for production-oriented towards high-value crops especially fruit and vegetables (Table 1). This may be partially explained by copious non-farm income generated by type-1 and 2 households and apparent re-investment of that income preferentially for high-value crops especially fruit and vegetables.Results suggest that in resource-constrained type-1 and resource endowed type-2 households with ample off-farm and non-farm income having ensured access to market for foodgrain needs, land owned was preferentially allocated for production-oriented towards fruit and vegetables, to ensure nutritional security. It might have been otherwise utilized for land resource allocation in type-1 and 2 households had there been insufficient off-farm and non-farm income. A marginal shift from staple foodgrain to horticulture does not adversely affect food security at the household32.Resource endowed type-3 and 4 households, though had sufficient off-farm and non-farm income comparable with type-1 and 2 households, did not follow this pattern, with foodgrain area being more abundant among them. This suggested that farm households that dedicated large areas to field crops especially rice cultivation did so due to land topography favoring the prolonged presence of water creating wetlands. The rice crop residues were utilized to reduce the feeding cost of high-valued large ruminants especially cattle maintained in type-3 and 4 households (Table 1). In addition to the utilization of rice crop residues as feed for large ruminants, type-4 households had a higher proportion of land area dedicated to fodder, reducing even further their feeding cost.Livestock: The livestock species and their number owned represent the wealth of a farm household. Large ruminant cattle are the most valuable livestock. Small ruminant goats, though hardy and prolific, are less valued. Rearing of large and small ruminants is a crucial form of fortification against extreme shocks such as crop failure or medical emergency of household members, providing immediate cash. Results showed higher large ruminant ownership 1.08 LU in type-4 households (Table 1). Type-4 households recorded the highest milk production, followed by type-3 households, presumably due to higher fodder area in type-4 households leading to better feed quality and quantity, improved animal performance, and increased carrying capacity of cattle by maximizing stocking rate. The presence of state-owned milk marketing cooperative in the study area had played a role in the large ruminant ownership, due to the added advantage of assured steady market and stable milk price. Small ruminant ownership of 0.03 LU tended to be quite similar across farm types (Table 1).Households in all farm types had poultry flock kept in the traditional backyard poultry system, as a source of quick cash and protein-rich food (Table 1). The traditional backyard poultry system is characterized by an indigenous night shelter system, a scavenging system with scant supplementary feed, natural hatching of chicks, low productivity of birds, local marketing, and minimal health care practices24. Results indicated that the size of the poultry flock tended to increase as farm resource endowment decreased (Table 1). Resource constrained type-1 household exemplified this, as it had the highest poultry flock size of 0.25 LU and exhibited the highest income from poultry sales. Poultry flock size tended to be quite low and similar in resource endowed type-3 and 4 households. Backyard poultry system due to its least demanding nature in terms of infrastructure has been widely accepted by resource constrained households, enabling them to make a profit from the sale of poultry products11,33. Relatively high income from poultry sales in type-1 and 2 households represent a coping strategy to prop up household finances to access the local market for foodgrain needs. Farm households depending on traditional backyard poultry generally lacked access to adequate low-cost organic fertilizers especially farmyard manure, resulting in low productivity of crops, which may further exacerbate food insecurity28.Income: Shortfalls in agricultural production and thus agricultural income were common in the study area, compelling households to diversify their livelihoods. Sources of farm household income are on-farm, off-farm, and non-farm income34. On-farm income comprised of sales income from the crop, livestock, complementary, and supplementary enterprises (Table 1). Type-2 farm households recorded a high on-farm income of ₹1,25,600, as it befitted from a livelihood strategy of production of high valued fruit and vegetable in addition to plantation crops. Crop sales contributed 63% to on-farm income in type-2 farm households. Type-4 farm households recorded medium on-farm income ₹84,100, as it befitted from a livelihood strategy of production of fodder in addition to food grain and plantation crops. This resulted in increased carrying capacity and maximized stocking rate of large ruminant 1.08 LU. Livestock sales contributed 65% to on-farm income in type-4 farm households. Other farm enterprises viz. complementary and supplementary enterprises contributed 4% to on-farm income in type-2 farm households.The off-farm income included wages for working as hired casual labourers in farms of wealthier neighbors, wages for doing unskilled manual work under Kerala Rural Employment Guarantee Scheme (KREGS), and wages for manual work under women’s labour collectives. KREGS operating under the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) of the Government of India, provides 100 days of guaranteed employment in a year to every adult household member in need of wage employment and desire to do manual or unskilled work in and around the village. Works related to building and maintenance of canals, renovation of ponds, wells, and farmland, afforestation, etc. are undertaken under KREGS. Many women in the study area, who are homemakers had come together to form women’s labour collectives, locally known as ‘Thozil Koottam’, to take up agricultural activities related to the cultivation of paddy, banana, tubers, coconut palm, and land terracing. Once these women exhaust the 100 days of work under MGNREGS, they move out to the open market as a collective to seek work in private lands in neighboring areas. For the landowners, this meant labour availability in the local market at a reasonable rate, at a time when it had become difficult to find labourers to work. In converse, in some areas during peak agriculture season, the farmers are experiencing shortage of labour due to government’s schemes like KREGS and MGNREGS leading to increased labour wages and cost of production. In addition, reduced participation of youths in agricultural activity also led to increased shortage of labour in agricultural activity35.Non-farm income consisted of overseas remittances, running small businesses in the unorganized sector, and salary from the service sector. The proximity of the study area to the state capital provided educated youth in farm households with ample non-farm employment opportunities. Nevertheless, the dependence of farm households on off-farm and non-farm income was quite high since they contributed more than 65% to farm household income across all farm types (Table 1). Studies have shown that farm households are compelled to diversify their livelihood in times of shortfall in agricultural production36,37.Constraints to agricultural production identified for targeted farming systems interventionsThe typology results had identified four farm types based on resource endowment and livelihood strategy (Table 1). The target group is the households in a farm type who rely on research findings for ideas and strategies to improve the way they do agriculture. For solving agricultural production problems, identification of constraints that work as a bottleneck by hindering the problem-solving process is a vital step, so that targeted farming systems interventions based on research findings can be made, enabling the farm household to push against that constraint and overcome it. Research-for-development programs seeking to sustainably intensify agricultural production in the target communities should take into account the opportunities and constraints identified across the farm types and tailor their development strategies, interventions and policies accordingly 11. Cost-effective socially acceptable farming systems interventions were envisaged based on production constraints identified in farm households in each farm type, to optimize resource utilization in households within a farm type, and also to promote resource flow and interactions between farm types, to ensure the stability of existing farming systems (Table 2). Farm typologies are classifications based on a set of criteria, and farm types are generally uniform in terms of these criteria, with some intra-group variation. As a result, typologies are useful for bringing together farmers for discussion so that groups of farmers who manage their farms similarly, have similar basic goals, or have similar constraints and possibilities can be formed20,38. The following sections reflect on production constraints identified and targeted farming systems interventions envisaged in each farm type.Table 2 Constraints to agricultural production in farm types and farming systems interventions envisaged.Full size tableFarm household: Farm household is the centrepiece of the farming system. Improvements in the existing farming system involve intensification, diversification, and an increase in the operational area of the farm household. Crop-livestock farming systems are the backbone of small-holder agriculture in developing countries39. The largest share of surveyed farm households comprised of resource-constrained type-1 households 46.5% having limited access to land (Table 1). The rest of the households though had marginally higher land availability offers little scope for increasing agricultural production through land area expansion. Kerala with a high literacy rate of 94% has the highest overall life expectancy at birth, at 72 years for men and 78 years for women 40 (GoK, 2019). Household heads in all surveyed households were elderly males aged 60 years who are the decision-makers in the utilization of household land for agricultural activities (Table 1). Targeted farming systems interventions envisaged for intensification and diversification of existing farming system, therefore must be pragmatic and problem-solving to find acceptance among the increasingly aging household head, who tend to show reluctance towards drastic changes in the existing farming system.Dependence on off-farm and non-farm income was quite high among all surveyed households (Table 1). Only one out of four household members in each surveyed household were found working on-farm. Scarcity of household labour and the high cost of hired labour is likely to hamper efforts at diversification into supplementary enterprises having low-profit margins like a nutritional kitchen garden, except as part of increased awareness of health benefits to household members. Similarly, households are less likely to intensify existing rice-rice-fallow cropping system with legume cowpea in summer fallow and stop burning of crop residues in the field for clean cultivation, except as part of increased awareness about soil health and environmental pollution respectively (Table 2). Targeted farming systems interventions were therefore envisaged to be delivered through a capacity building and training program, to bring about a change in knowledge, attitude, and skill of the farm household for efficient farm operations.Foodgrain: Rice was the major foodgrain in the study area. Constraints of high severity in a type-3 household that had the largest area under food grain were low yield due to traditional variety, soil acidity, and imbalanced fertilization (Table 2). Crop loss due to pests was a constraint of high severity in type-4 households. The stale seedbed for weed management was the farming systems intervention envisaged to manage weeds in rice, which was a constraint of medium severity in the type-3 household. Farming systems intervention envisaged in summer rice fallow was raising cowpea utilizing the limited water available during the season. In general, the agricultural activity of Kerala is affected by limited water availability during winter rabi and summer season, poor soil fertility due to low nutrient holding capacity of the soil, inadequate crop protection, non-availability of quality seed material, and increased cost of cultivation. The farmers need to adapt soil test based fertilizer recommendation to meet the crop nutrient demand for reducing yield gap. Suitable pest and weed management are very much necessary to combat the crop loss. Adaption of climate resilient improved cultivars, bringing more area under irrigation, intercropping, crop rotation, and mulching are imperative to increase food grain production and to achieve food security of small and marginal farmers41.Horticulture: Banana, cowpea, cassava, and elephant foot yam were the widely cultivated fruit and vegetable in the study area (Table 2). Crop loss due to pests in banana and disease in cowpea were constraints of very high severity in type-1 households. The constraint in fruit and vegetable production due to traditional variety and imbalanced fertilization were of high to very high severity in type-2 households, which had a large area under fruit and vegetable. Raising cowpea is envisaged in farming systems interventions to utilize vacant interspaces of cassava and thus substantially lower the nitrogen fertilizer requirement of cassava. Cultivation of traditional poor-yielding turmeric varieties along with imbalanced fertilization were constraints of medium severity in the type-1 household (Table 2). Coconut was an important plantation crop in the study area, occupying the substantial cropped area in type-2 households (Table 2). Soil acidity and imbalanced fertilization were constraints of high severity in coconut in type-2 households. Crop loss in coconut due to pests was a constraint of high severity in type-3 and 4 households. Low green fodder availability due to poor yielding traditional fodder variety was a constraint of medium severity in type-2 and 3 households (Table 2). A multi-storeyed cropping system having cowpea, cassava, elephant foot yam, turmeric, banana, papaya, and fodder was the farming systems intervention envisaged to effectively utilize vacant interspaces of coconut. The Kerala state is major spice cultivating state and majority of the small, medium and large farmers are actively involved in the spice and plantation crops cultivation. The high value of spice and plantation crops is attracting rural youths also into horticulture sector, especially in processing of spices and their export to Gulf and European market. Kerala government is also promoting organic spice production to boost the local and international organic market for their products. In addition, Kerala’s home gardens are typical examples of low to medium-input sustainable agroecosystems. Home gardens are assemblages of plants, which may include trees, shrubs, and herbaceous plants that grow in or close to a homestead, are planted and managed by members of the household, and the products and services are primarily for household consumption. These home gardens are having great importance in meeting farm family food and nutritional security35.Livestock: Low milk yield in dairy cattle due to lack of awareness about mastitis infection was a constraint of high severity in type-2 and 3 households (Table 2). Raising awareness about hygiene to prevent mastitis and inclusion of mineral mixture in feeding schedule to increase milk fat content are the farming systems interventions envisaged for dairy cattle. Poor egg production in layer chicken due to rearing of non-descript desi chicken breed was a constraint of medium severity in the type-2 household (Table 2). Regular deworming was the farming systems intervention envisaged to improve livestock health in all households (Table 2). The dairy farmers of Kerala are experiencing several problems like high cost of veterinary service and medicine, high cost of cattle feed ,non-availability of green and dry fodder round the year, high labour cost, lack of need based training, non-availability of high yielding milch animals42. The government and Veterinary department of Kerala needs to address these issues to boost the livestock production and farmers income.Complementary enterprises: Complementary enterprises in a system support one another43. Vermicomposting and Azolla cultivation were the complementary enterprises envisaged in farming systems interventions. Crop residues interfering with field operations was a problem, with the farmer often resorting to burning crop residue in situ, causing loss of nutrients and organic matter to the soil. Lack of awareness about environmentally safe ways to manage crop residues was a constraint of low to medium severity in all households (Table 2). Promoting the use of crop residues for vermicomposting and as mulch in banana and coconut for soil moisture conservation were the farming systems interventions envisaged to discourage the burning of crop residues (Table 2). The establishment of the Azolla plot and inclusion of Azolla in the feeding schedule of livestock were envisaged in farming systems interventions to reduce feed cost (Table 2).Supplementary enterprises: Supplementary enterprises in a system utilize the otherwise unutilized resources43. Nutritional kitchen garden, agro-processing, and value addition were the supplementary enterprises envisaged in farming systems interventions. Fruits and vegetables for household consumption were found purchased from the local market due to production shortfall within the household, which was a constraint of low to high severity in all households (Table 2). The establishment of the nutritional kitchen garden and the growing of fruit trees in the backyard were the farming systems interventions envisaged ensuring nutritional security to the household. Encouraging farmers to take control of agro-processing and local marketing of primary production to capture the value that is added to it, thus fetching a better price for the produce, was the farming systems intervention envisaged for coconut, paddy, and milk, as per their recorded severity of constraints in respective farm types (Table 2).Importance of public distribution system (PDS) for food distributionThe Public Distribution System (PDS) was created as a way to manage scarcity and distribute food grains at low rates. PDS has evolved into a key component of the government’s food economy management strategy. PDS is a supplemental program that is not meant to meet a household’s or a part of society’s complete need for any of the commodities given under it. Historically, Kerala’s agricultural production has been directed toward cash crops, rather than food crops such as rice and wheat. As a result, the problem of food scarcity in Kerala has worsened. PDS is becoming more important in Kerala, where population density is high and farming patterns are mostly dependent on rains, with no consistent irrigation infrastructure, causing food supply availability to fluctuate over time, resulting in uncertainty. In order to avoid such situations and maintain the supply of required commodities, a PDS system is essential. Kerala’s below-poverty-line (BPL) households consume 40–55 percent of their rice through PDS. The PDS supplied a higher percentage of the rice requirements. It is also clear that rural areas have done marginally better than urban areas in terms of PDS system utilization. It is worth noting that in Kerala, about 80% of BPL households still have access to the PDS, even at various levels of utilization, thereby reducing the pressure on local farmland44. More

1.Granwehr, B. P. et al. West Nile virus: Where are we now? Lancet. Infect. Dis. 4, 547–556 (2004).PubMed 

Google Scholar 
2.Campbell, G. L., Marfin, A. A., Lanciotti, R. S. & Gubler, D. J. West Nile virus. Lancet. Infect. Dis. 2, 519–529 (2002).PubMed 

Google Scholar 
3.Petersen, L. R., Brault, A. C. & Nasci, R. S. West Nile virus: Review of the literature. JAMA. 310, 308–315 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Gamino, V. & Höfle, U. Pathology and tissue tropism of natural West Nile virus infection in birds: A review. Vet. Res. 44, 39 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Bunning, M. L. et al. Experimental infection of horses with West Nile virus. Emerg. Infect. Dis. 8, 380-386 (2002).PubMed 
PubMed Central 

Google Scholar 
6.Hayes, E. B. et al. Virology, pathology, and clinical manifestations of West Nile virus disease. Emerg. Infect. Dis. 11, 1174–1179 (2005).PubMed 
PubMed Central 

Google Scholar 
7.Saiz, J.-C. Animal and Human Vaccines against West Nile Virus. Pathogens. 9, 1073 (2020).PubMed 
PubMed Central 

Google Scholar 
8.Rizzoli, A. et al. Parasites and wildlife in a changing world: The vector-host- pathogen interaction as a learning case. Int. J. Parasitology: Parasites. Wildl. 9, 394–401 (2019).
Google Scholar 
9.Wang, Y., Yim, S. H. L., Yang, Y. & Morin, C. W. The effect of urbanization and climate change on the mosquito population in the Pearl River Delta region of China. Int. J. Biometeorol. 64, 501–512 (2020).PubMed 

Google Scholar 
10.Braack, L., Gouveia de Almeida, A. P., Cornel, A. J., Swanepoel, R. & de Jager, C. Mosquito-borne arboviruses of African origin: Review of key viruses and vectors. Parasites. Vectors. 11, 29 (2018).PubMed 
PubMed Central 

Google Scholar 
11.Johnson, N. et al. Emerging mosquito-borne threats and the response from european and eastern mediterranean countries. Int. J. Environ. Res. Public. Health. 15, 2775 (2018).PubMed Central 

Google Scholar 
12.Lourenço, J. et al. Epidemiological and ecological determinants of Zika virus transmission in an urban setting. Elife. 6, e29820 (2017).PubMed 
PubMed Central 

Google Scholar 
13.Giovanetti, M. et al. Genomic and Epidemiological Surveillance of Zika Virus in the Amazon Region. Cell Rep. 30, 2275–2283.e7 (2020).CAS 
PubMed 

Google Scholar 
14.Faria, N. R. et al. Genomic and epidemiological monitoring of yellow fever virus transmission potential. Science. 361, 894–899 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Wu, J. T., Peak, C. M., Leung, G. M. & Lipsitch, M. Fractional dosing of yellow fever vaccine to extend supply: a modelling study. Lancet. 388, 2904–2911 (2016).PubMed 
PubMed Central 

Google Scholar 
16.Murgue, B., Zeller, H. & Deubel, V. The ecology and epidemiology of West Nile virus in Africa, Europe, and Asia. Curr. Top. Microbiol. Immunol. 267, 195–221 (2002).CAS 
PubMed 

Google Scholar 
17.Pybus, O. G. et al. Unifying the spatial epidemiology and molecular evolution of emerging epidemics. Proc. Natl Acad. Sci. USA 109, 15066–15071 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Dellicour, S. et al. Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework. Nat. Commun. 11, 1–11 (2020).
Google Scholar 
19.Shocket, M. S. et al. Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23 °C and 26 °C. Elife. 9, e58511 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Haussig, J. M. et al. Early start of the West Nile fever transmission season 2018 in Europe. Euro. Surveill. 23, 1800428 (2018).PubMed Central 

Google Scholar 
21.Riccardo, F. et al. West Nile virus in Europe: after action reviews of preparedness and response to the 2018 transmission season in Italy, Slovenia, Serbia and Greece. Glob. Health. 16, 47 (2020).
Google Scholar 
22.Bakonyi, T. & Haussig, J. M. West Nile virus keeps on moving up in Europe. Eurosurveillance. 25, 2001938 (2020).PubMed Central 

Google Scholar 
23.Vlaskamp, D. R. M. et al. First autochthonous human West Nile virus infections in the Netherlands, July to August 2020. Eurosurveillance. 25, 2001904 (2020).24.West Nile virus in Europe in 2020 – human cases compared to previous seasons, updated 8 October 2020. https://www.ecdc.europa.eu/en/publications-data/west-nile-virus-europe-2020-human-cases-compared-previous-seasons-updated-8 (2020).25.Weekly updates: 2020 West Nile virus transmission season. https://www.ecdc.europa.eu/en/west-nile-fever/surveillance-and-disease-data/disease-data-ecdc.26.Council Directive 82/894/EEC of 21 December 1982 on the notification of animal diseases within the Community. EUR-Lex https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:31982L0894.27.European Food Safety Authority. https://www.efsa.europa.eu/en.28.European Centre for Disease Prevention and Control – West Nile virus. https://www.ecdc.europa.eu/en/west-nile-virus-infection.29.REVIVE – Rede de Vigilância de Vetores. http://www2.insa.pt/sites/INSA/Portugues/AreasCientificas/DoencasInfecciosas/AreasTrabalho/EstVectDoencasInfecciosas/Paginas/Revive.aspx.30.Osório, H. C., Zé-Zé, L., Amaro, F. & Alves, M. J. Mosquito surveillance for prevention and control of emerging mosquito-borne diseases in Portugal – 2008-2014. Int. J. Environ. Res. Public. Health. 11, 11583–11596 (2014).PubMed 
PubMed Central 

Google Scholar 
31.European network for sharing data on the geographic distribution of arthropod vectors, transmitting human and animal disease agents (VectorNet). https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net.32.Filipe, A. R. Anticorpos contra virus transmitidos por artropodos-arbovirus do grupo B em animais do Sul de Portugal: inquérito serológico preliminar com o vírus West Nile, estirpe Egypt 101. Ann. Esc. Nacional de. Saúde. Pública de. Med. Tropical 1, 197–204 (1967).CAS 

Google Scholar 
33.Filipe, A. R. & Pinto, M. R. Survey for antibodies to arboviruses in serum of animals from southern Portugal. Am. J. Trop. Med. Hyg. 18, 423–426 (1969).CAS 
PubMed 

Google Scholar 
34.Filipe, A. R. & Campaniço, M. Encefalomielite equina por arbovírus. A propósito de uma epizootia presuntiva causada pelo vírus West Nile. Revista Portuguesa de Ciências Veterinárias LXVIII, (1973).35.Filipe, A. R. Isolation in Portugal of West Nile virus from Anopheles maculipennis mosquitoes. Acta Virol. 16, 361 (1972).CAS 
PubMed 

Google Scholar 
36.Filipe, A. R. Anticorpos contra arbovírus na população de Portugal. Separata de O Médico. LXVII, 731–732 (1973).37.Formosinho, P. et al. O vírus West Nile em Portugal – estudos de vigilância epidemiológica. Rev. Portuguesa de. Ciências Veterinárias 101, 61–68 (2006).
Google Scholar 
38.Barros, S. C. et al. Serological evidence of West Nile virus circulation in Portugal. Vet. Microbiol. 152, 407–410 (2011).PubMed 

Google Scholar 
39.Almeida, A. P. G. et al. Potential mosquito vectors of arboviruses in Portugal: Species, distribution, abundance and West Nile infection. Trans. R. Soc. Trop. Med. Hyg. 102, 823–832 (2008).CAS 
PubMed 

Google Scholar 
40.Esteves, A. et al. West Nile virus in Southern Portugal, 2004. Vector Borne Zoonotic Dis. 5, 410–413 (2005).PubMed 

Google Scholar 
41.Barros, S. C. et al. West Nile virus in horses during the summer and autumn seasons of 2015 and 2016, Portugal. Vet. Microbiol. 212, 75–79 (2017).PubMed 

Google Scholar 
42.World Organization for Animal Health (OIE) – West Nile reports. Information received on 03/09/2015 from Prof. Dr Álvaro Mendonça, Director General, Direcção Geral de Alimentação e Veterinária, Ministério da Agricultura E do Mar, Lisboa, Portugal https://www.oie.int/wahis_2/public/wahid.php/Reviewreport/Review?page_refer=MapFullEventReport&reportid=18585 (2015).43.Connell, J. et al. Two linked cases of West Nile virus (WNV) acquired by Irish tourists in the Algarve, Portugal. Weekly releases (1997–2007) 8, 2517 (2004).44.Alves, M. J. et al. Infecção por vírus West Nile [Flavivírus] em Portugal. Considerações acerca de. um. caso cl.ínico de. s.índrome febril com. exantema 8, 46–51 (2012).
Google Scholar 
45.Zé-Zé, L. et al. Human case of West Nile neuroinvasive disease in Portugal, summer 2015. Eurosurveillance 20, 30024 (2015).
Google Scholar 
46.Direcção-Geral de Veterinária (Directorate-General of Veterinary). National statistics on official number of equines in subregions of Portugal. http://srvbamid.dgv.min-agricultura.pt/portal/page/portal/DGV/genericos?actualmenu=23555&generico=33698230&cboui=33698230.47.Osório, H. C., Zé-Zé, L., Amaro, F., Nunes, A. & Alves, M. J. Sympatric occurrence of Culex pipiens (Diptera, Culicidae) biotypes pipiens, molestus and their hybrids in Portugal, Western Europe: feeding patterns and habitat determinants. Med. Vet. Entomol. 28, 103–109 (2014).PubMed 

Google Scholar 
48.Gottdenker, N. L., Streicker, D. G., Faust, C. L. & Carroll, C. R. Anthropogenic land use change and infectious diseases: A review of the evidence. Ecohealth. 11, 619–632 (2014).PubMed 

Google Scholar 
49.Paz, S. & Semenza, J. C. Environmental drivers of West Nile fever epidemiology in Europe and Western Asia–a review. Int. J. Environ. Res. Public. Health. 10, 3543–3562 (2013).PubMed 
PubMed Central 

Google Scholar 
50.Eisen, L. et al. Irrigated agriculture is an important risk factor for West Nile virus disease in the hyperendemic Larimer-Boulder-Weld area of north central Colorado. J. Med. Entomol. 47, 939–951 (2010).PubMed 

Google Scholar 
51.Gates, M. C. & Boston, R. C. Irrigation linked to a greater incidence of human and veterinary West Nile virus cases in the United States from 2004 to 2006. Prev. Vet. Med 89, 134–137 (2009).PubMed 

Google Scholar 
52.Kovach, T. J. & Kilpatrick, A. M. Increased human incidence of West Nile virus disease near rice fields in California but Not in Southern United States. Am. J. Trop. Med. Hyg. 99, 222–228 (2018).PubMed 
PubMed Central 

Google Scholar 
53.Rocheleau, J. P. et al. Characterizing environmental risk factors for West Nile virus in Quebec, Canada, using clinical data in humans and serology in pet dogs. Epidemiol. Infect. 145, 2797–2807 (2017).CAS 
PubMed 

Google Scholar 
54.Lourenço, J., Thompson, R. N., Thézé, J. & Obolski, U. Characterising West Nile virus epidemiology in Israel using a transmission suitability index. Euro Surveill. 25, 1900629 (2020).PubMed Central 

Google Scholar 
55.Obolski, U. et al. MVSE: An R-package that estimates a climate-driven mosquito-borne viral suitability index. Methods Ecol. Evol. 10, 1357–1370 (2019).PubMed 
PubMed Central 

Google Scholar 
56.Petrone, M. E. et al. Asynchronicity of endemic and emerging mosquito-borne disease outbreaks in the Dominican Republic. Nat. Commun. 12, 151 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Hansen, B. B., Grøtan, V., Herfindal, I. & Lee, A. M. The Moran effect revisited: spatial population synchrony under global warming. Ecography 43, 1591–1602 (2020).
Google Scholar 
58.Arizaga, J. et al. Migratory Connectivity in European Bird Populations: Feather stable isotope values correlate with biometrics of breeding and wintering BluethroatsLuscinia svecica. Ardeola. 62, 255–267 (2015).
Google Scholar 
59.Pakanen, V.-M. et al. Migration strategies of the Baltic dunlin: Rapid jump migration in the autumn but slower skipping type spring migration. J. Avian Biol. 49, jav–01513 (2018).
Google Scholar 
60.Pardal, S. et al. Shorebird low spillover risk of mosquito-borne pathogens on Iberian wetlands. J. Ornithol. 155, 549–554 (2013).
Google Scholar 
61.Rizzoli, A. et al. Understanding West Nile virus ecology in Europe: Culex pipiens host feeding preference in a hotspot of virus emergence. Parasit. Vectors. 8, 1–13 (2015).
Google Scholar 
62.Kilpatrick, A. M., Kramer, L. D., Jones, M. J., Marra, P. P. & Daszak, P. West Nile virus epidemics in North America are driven by shifts in mosquito feeding behavior. PLoS Biol. 4, e82 (2006).PubMed 
PubMed Central 

Google Scholar 
63.Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568 (2017).PubMed 
PubMed Central 

Google Scholar 
64.Vogels, C. B. F., Fros, J. J., Göertz, G. P., Pijlman, G. P. & Koenraadt, C. J. M. Vector competence of northern European Culex pipiens biotypes and hybrids for West Nile virus is differentially affected by temperature. Parasit. Vectors 9, 393 (2016).PubMed 
PubMed Central 

Google Scholar 
65.Chuang, T.-W., Hockett, C. W., Kightlinger, L. & Wimberly, M. C. Landscape-level spatial patterns of West Nile virus risk in the northern Great Plains. Am. J. Trop. Med. Hyg. 86, 724–731 (2012).PubMed 
PubMed Central 

Google Scholar 
66.Crowder, D. W. et al. West nile virus prevalence across landscapes is mediated by local effects of agriculture on vector and host communities. PLoS One 8, e55006 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.García-Bocanegra, I. et al. Epidemiology and spatio-temporal analysis of West Nile virus in horses in Spain between 2010 and 2016. Transbound. Emerg. Dis. 65, 567–577 (2018).PubMed 

Google Scholar 
68.Lourenco, J. MVSE – WNV related files for Portugal. https://doi.org/10.6084/m9.figshare.c.5281664.v1 (2021).69.Jiguet, F. et al. Bird population trends are linearly affected by climate change along species thermal ranges. Proc. Biol. Sci. 277, 3601–3608 (2010).PubMed 
PubMed Central 

Google Scholar 
70.Cator, L. J. et al. The Role of Vector Trait Variation in Vector-Borne Disease Dynamics. Front Ecol. Evol. 8, 189 (2020).PubMed 
PubMed Central 

Google Scholar 
71.Kraemer, M. U. G. et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4, e08347 (2015).PubMed 
PubMed Central 

Google Scholar 
72.Hamlet, A. et al. The seasonal influence of climate and environment on yellow fever transmission across Africa. PLoS Negl. Trop. Dis. 12, e0006284 (2018).PubMed 
PubMed Central 

Google Scholar 
73.Thézé, J. et al. Genomic Epidemiology Reconstructs the Introduction and Spread of Zika Virus in Central America and Mexico. Cell Host. Microbe. 23, 855–864.e7 (2018).PubMed 
PubMed Central 

Google Scholar 
74.Perez-Guzman, P. N. et al. Measuring Mosquito-borne Viral Suitability in Myanmar and Implications for Local Zika Virus Transmission. PLoS Curr. 10, (2018).75.Pereira Gusmão Maia, Z. et al. Return of the founder Chikungunya virus to its place of introduction into Brazil is revealed by genomic characterization of exanthematic disease cases. Emerg. Microbes Infect. 9, 53–57 (2020).PubMed 

Google Scholar 
76.Copernicus Climate Data Store. https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecv-for-climate-change?tab=overview.77.Lourenço, J. & Obolski, U. MVSE R-package official page. https://sourceforge.net/projects/mvse/.78.R-Forge: Circular Statistics: Project Home. https://r-forge.r-project.org/projects/circular/.79.Geraci, M. Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression. J. Stat. Softw. 57, 1–29 (2014).
Google Scholar 
80.Damineli, D. S. C., Portes, M. T. & Feijó, J. A. Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity, and synchronization in growing cells. J. Exp. Bot. 68, 3267–3281 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.wavelets: Functions for Computing Wavelet Filters, Wavelet Transforms and Multiresolution Analyses. https://CRAN.R-project.org/package=wavelets.82.biwavelet GitHub repository. https://github.com/tgouhier/biwavelet.83.Barros, S. C. et al. Simultaneous detection of West Nile and Japanese encephalitis virus RNA by duplex TaqMan RT-PCR. J. Virol. Methods 193, 554–557 (2013).CAS 
PubMed 

Google Scholar 
84.Copernicus Climate Data Store. https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview.85.Filipe, A. R. & de Andrade, H. R. Arboviruses in the Iberian Peninsula. Acta Virol. 34, 582–591 (1990).CAS 
PubMed 

Google Scholar 
86.Almeida, A. P. G. et al. Mosquito surveys and West Nile virus screening in two different areas of southern Portugal, 2004-2007. Vector. Borne. Zoonotic Dis. 10, 673–680 (2010).PubMed 

Google Scholar 
87.Freitas, F. B., Novo, M. T., Esteves, A. & de Almeida, A. P. Species Composition and WNV Screening of Mosquitoes from Lagoons in a Wetland Area of the Algarve, Portugal. Front. Physiol. 2, 122 (2012).PubMed 
PubMed Central 

Google Scholar 
88.Parreira, R. et al. Two distinct introductions of the West Nile virus in Portugal disclosed by phylogenetic analysis of genomic sequences. Vector. Borne. Zoonotic. Dis. 7, 344–352 (2007).CAS 
PubMed 

Google Scholar 
89.Fotakis, E. A. et al. Identification and detection of a novel point mutation in the Chitin Synthase gene of Culex pipiens associated with diflubenzuron resistance. PLoS Negl. Trop. Dis. 14, e0008284 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Mixão, V. et al. Comparative morphological and molecular analysis confirms the presence of the West Nile virus mosquito vector, Culex univittatus, in the Iberian Peninsula. Parasit. Vectors. 9, 601 (2016).PubMed 
PubMed Central 

Google Scholar 
91.Osório, H. C., Zé-Zé, L. & Alves, M. J. Host-feeding patterns of Culex pipiens and other potential mosquito vectors (Diptera: Culicidae) of West Nile virus (Flaviviridae) collected in Portugal. J. Med. Entomol. 49, 717–721 (2012).PubMed 

Google Scholar 
92.Gomes, B. et al. The Culex pipiens complex in continental Portugal: distribution and genetic structure. J. Am. Mosq. Control. Assoc. 28, 75–80 (2012).PubMed 

Google Scholar 
93.Gomes, B. et al. Limited genomic divergence between intraspecific forms of Culex pipiens under different ecological pressures. BMC Evol. Biol. 15, 197 (2015).PubMed 
PubMed Central 

Google Scholar 
94.Calzolari, M. et al. Detection of mosquito-only flaviviruses in Europe. J. Gen. Virol. 93, 1215–1225 (2012).CAS 
PubMed 

Google Scholar 
95.Hernández-Triana, L. M. et al. Genetic diversity and population structure of Culex modestus across Europe: does recent appearance in the United Kingdom reveal a tendency for geographical spread? Med. Vet. Entomol. 34, 86–96 (2020).PubMed 

Google Scholar 
96.Alves, J. M. et al. Flavivírus transmitidos por mosquitos: um risco potencial para Portugal. Investigação em ambiente e saúde – desafios e estratégias (Universidade de Aveiro) (2009).97.Conte, A. et al. Spatio-temporal identification of areas suitable for West Nile Disease in the Mediterranean Basin and Central Europe. PLoS. One. 10, e0146024 (2015).PubMed 
PubMed Central 

Google Scholar 
98.García-Carrasco, J.-M., Muñoz, A.-R., Olivero, J., Segura, M. & Real, R. Predicting the spatio-temporal spread of West Nile virus in Europe. PLoS Negl. Trop. Dis. 15, e0009022 (2021).PubMed 
PubMed Central 

Google Scholar 
99.Marini, G., Manica, M., Delucchia, L., Pugliesed, A. & Rosa, R. Spring temperature shapes West Nile virus transmission in Europe. Acta. Trop. 215, 105796 (2021).PubMed 

Google Scholar  More

1.Zehr, J. P. & Capone, D. G. Changing perspectives in marine nitrogen fixation. Science 9514, 729 (2020).
Google Scholar 
2.Karl, D. et al. Dinitrogen fixation in the world’s oceans. Biogeochemistry 57–58, 47–98 (2002).
Google Scholar 
3.Dugdale, R. & Wilkerson, F. in Primary Productivity and Biogeochemical Cycles in the Sea (eds Falkowski, P. G. et al.) 107–122 (Springer, 1992).4.Carpenter, E. J. & Capone, D. G. in Nitrogen in the Marine Environment 2nd edn (eds Capone, D. G., Bronk, D. A., Mulholland, M. R. & Carpenter, E. J.) Ch. 4 (Elsevier, 2008).5.Gruber, N. & Sarmiento, J. L. Global patterns of marine nitrogen fixation and denitrification. Global Biogeochem. Cycles 11, 23–266 (1997).
Google Scholar 
6.Buchanan, P. J., Chase, Z., Matear, R. J., Phipps, S. J. & Bindoff, N. L. Marine nitrogen fixers mediate a low latitude pathway for atmospheric CO2 drawdown. Nat. Commun. https://doi.org/10.1038/s41467-019-12549-z (2019).7.Monteiro, F. M., Follows, M. J. & Dutkiewicz, S. Distribution of diverse nitrogen fixers in the global ocean. Global Biogeochem. Cycles 24, 1–16 (2010).
Google Scholar 
8.Church, M. J., Björkman, K. M., Karl, D. M., Saito, M. A. & Zehr, J. P. Regional distributions of nitrogen-fixing bacteria in the Pacific Ocean. Limnol. Oceanogr. 53, 63–77 (2008).CAS 

Google Scholar 
9.Monteiro, F. M., Dutkiewicz, S. & Follows, M. J. Biogeographical controls on the marine nitrogen fixers. Global Biogeochem. Cycles 25, 1–8 (2011).
Google Scholar 
10.Dutkiewicz, S., Ward, B. A., Monteiro, F. & Follows, M. J. Interconnection of nitrogen fixers and iron in the Pacific Ocean: theory and numerical simulations. Global Biogeochem. Cycles 26, 1–16 (2012).
Google Scholar 
11.Walworth, N. G. et al. Nutrient-colimited Trichodesmium as a nitrogen source or sink in a future ocean. Appl. Environ. Microbiol. 84, 1–14 (2018).CAS 

Google Scholar 
12.McGillicuddy, D. J. Jr. Do Trichodesmium spp. populations in the North Atlantic export most of the nitrogen they fix? Global Biogeochem. Cycles 28, 103–114 (2014).CAS 

Google Scholar 
13.Carpenter, E. J. & Romans, K. Major role of the cyanobacterium Trichodesmium in nutrient cycling in the North Atlantic Ocean. Science 254, 1989–1992 (1991).
Google Scholar 
14.Bergman, B., Sandh, G., Lin, S., Larsson, J. & Carpenter, E. J. Trichodesmium – a widespread marine cyanobacterium with unusual nitrogen fixation properties. FEMS Microbiol. Rev. 37, 286–302 (2013).CAS 
PubMed 

Google Scholar 
15.Capone, D. G. Trichodesmium, a globally significant marine cyanobacterium. Science 276, 1221–1229 (1997).CAS 

Google Scholar 
16.Gallon, J. R. The oxygen sensitivity of nitrogenase: a problem for biochemists and micro-organisms. Trends Biochem. Sci. 6, 19–23 (1981).CAS 

Google Scholar 
17.Saito, M. A. et al. Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc. Natl Acad. Sci. USA 108, 2184–2189 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Dron, A. et al. Light-dark (12:12) cycle of carbon and nitrogen metabolism in Crocosphaera watsonii WH8501: relation to the cell cycle. Environ. Microbiol. 14, 967–981 (2012).CAS 
PubMed 

Google Scholar 
19.Mohr, W., Intermaggio, M. P. & LaRoche, J. Diel rhythm of nitrogen and carbon metabolism in the unicellular, diazotrophic cyanobacterium Crocosphaera watsonii WH8501. Environ. Microbiol. 12, 412–421 (2010).CAS 
PubMed 

Google Scholar 
20.Flores, E. & Herrero, A. Compartmentalized function through cell differentiation in filamentous cyanobacteria. Nat. Rev. Microbiol. 8, 39–50 (2010).CAS 
PubMed 

Google Scholar 
21.Burnat, M., Herrero, A. & Flores, E. Compartmentalized cyanophycin metabolism in the diazotrophic filaments of a heterocyst-forming cyanobacterium. Proc. Natl Acad. Sci. USA 111, 3823–3828 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Sherman, D. M., Tucker, D. & Sherman, L. A. Heterocyst development and localization of cyanophycin in N2-fixing cultures of Anabaena sp. PCC 7120 (Cyanobacteria). J. Phycol. 941, 932–941 (2000).
Google Scholar 
23.Lamont, H. C., Silvester, W. B. & Torrey, J. G. Nile red fluorescence demonstrates lipid in the envelope of vesicles from N2-fixing cultures of Frankia. Can. J. Microbiol. 34, 656–660 (1988).CAS 

Google Scholar 
24.Saino, T. Diel variation in nitrogen fixation by a marine blue-green alga, Trichodesmium thiebautii. Deep Sea Res. 25, 1259–1263 (1978).
Google Scholar 
25.Saino, T. & Hattori, A. Aerobic nitrogen fixation by the marine non-heterocystous cyanobacterium Trichodesmium (Oscillatoria) spp.: its protective mechanism against oxygen. Mar. Biol. 70, 251–254 (1982).
Google Scholar 
26.Berman-Frank, I. et al. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium Trichodesmium. Science 294, 1534–1537 (2001).CAS 
PubMed 

Google Scholar 
27.Ohki, K. & Taniuchi, Y. Detection of nitrogenase in individual cells of a natural population of Trichodesmium using immunocytochemical methods for fluorescent cells. J. Oceanogr. 65, 427–432 (2009).CAS 

Google Scholar 
28.Eichner, M. et al. N2 fixation in free-floating filaments of Trichodesmium is higher than in transiently suboxic colony microenvironments. New Phytol. 222, 852–863 (2019).CAS 
PubMed 

Google Scholar 
29.Ohki, K. Intercellular localization of nitrogenase in a non-heterocystous cyanobacterium (cyanophyte), Trichodesmium sp. NIBB1067. J. Oceanogr. 64, 211–216 (2008).CAS 

Google Scholar 
30.Ohki, K., Zehr, F. & Fujita, Y. Regulation of nitrogenase activity in relation to the light-dark regime in the filamentous non-heterocystous cyanobacterium Trichodesmium sp. NIBB 1067. J. Gen. Microbiol. 138, 2679–2685 (1992).CAS 

Google Scholar 
31.Finzi-Hart, J. A. et al. Fixation and fate of C and N in the cyanobacterium Trichodesmium using nanometer-scale secondary ion mass spectrometry. Proc. Natl Acad. Sci. USA 106, 9931 (2009).CAS 

Google Scholar 
32.Sandh, G., El-Shehawy, R., Díez, B. & Bergman, B. Temporal separation of cell division and diazotrophy in the marine diazotrophic cyanobacterium Trichodesmium erythraeum IMS101. FEMS Microbiol. Lett. 295, 281–288 (2009).CAS 
PubMed 

Google Scholar 
33.Küpper, H. et al. Traffic lights in Trichodesmium. Regulation of photosynthesis for nitrogen fixation studied by chlorophyll fluorescence kinetic microscopy. Plant Physiol. 135, 2120–2133 (2019).
Google Scholar 
34.Ohki, K. & Fujita, Y. Aerobic nitrogenase activity measured as acetylene reduction in the marine non-heterocystous cyanobacterium Trichodesmium spp. grown under artificial conditions. Mar. Biol. 98, 111–114 (1988).CAS 

Google Scholar 
35.Waterbury, J. B. & Willey, J. M. Isolation and growth of marine planktonic Cyanobacteria. Methods Enzymol. 167, 100–105 (1988).CAS 

Google Scholar 
36.Chen, Y. B., Zehr, J. P. & Mellon, M. Growth and nitrogen fixation of the diazotrophic filamentous nonheterocystous cyanobacterium Trichodesmium sp. IMS 101 in defined media: evidence for a circadian rhythm. J. Phycol. 32, 916–923 (1996).
Google Scholar 
37.Berman-Frank, I., Bidle, K. D., Haramaty, L. & Falkowski, P. G. The demise of the marine cyanobacterium, Trichodesmium spp., via an autocatalyzed cell death pathway. Limnol. Oceanogr. 49, 997–1005 (2004).
Google Scholar 
38.Bell, P. R. F. et al. Laboratory culture studies of Trichodesmium isolated from the Great Barrier Reef lagoon, Australia. Hydrobiologia 532, 9–21 (2005).
Google Scholar 
39.Tzubari, Y., Magnezi, L., Be’Er, A. & Berman-Frank, I. Iron and phosphorus deprivation induce sociality in the marine bloom-forming cyanobacterium Trichodesmium. ISME J. 12, 1682–1693 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Held, N. A., McIlvin, M. R., Moran, D. M., Laub, M. T. & Saito, M. A. Unique patterns and biogeochemical relevance of two-component sensing in marine bacteria. mSystems 4, 1–16 (2019).
Google Scholar 
41.Aryal, U. K. & Sherman, L. A. in Cyanobacteria Omics Manipulation (ed. Los, D. A.) Ch. 6 (Caister Academic Press, 2017).42.Held, N. A. et al. Co-occurrence of Fe and P stress in natural populations of the marine diazotroph Trichodesmium. Biogeosciences 17, 2537–2551 (2020).
Google Scholar 
43.Klugkist, J., Haaker, H., Wassink, H. & Veeger, C. The catalytic activity of nitrogenase in intact Azotobacter vinelandii cells. Eur. J. Biochem. 146, 509–515 (1985).CAS 
PubMed 

Google Scholar 
44.Zehr, J. P., Wyman, M., Miller, V., Capone, D. G. & Duguay, L. Modification of the Fe protein of nitrogenase in natural populations of Trichodesmium thiebautii. Appl. Environ. Microbiol. 59, 669–676 (1993).CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Rodriguez, I. B. & Ho, T.-Y. Diel nitrogen fixation pattern of Trichodesmium: the interactive control of light and Ni. Sci. Rep. 4, 4445 (2014).PubMed 
PubMed Central 

Google Scholar 
46.Eichner, M., Kranz, S. A. & Rost, B. Combined effects of different CO2 levels and N sources on the diazotrophic cyanobacterium Trichodesmium. Physiol. Plant. 152, 316–330 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Hutchins, D. A. et al. Irreversibly increased nitrogen fixation in Trichodesmium experimentally adapted to elevated carbon dioxide. Nat. Commun. 6, 1–7 (2015).
Google Scholar 
48.Levitan, O. et al. Combined effects of CO2 and light on the N2-fixing cyanobacterium Trichodesmium IMS101: a mechanistic view. Plant Physiol. 154, 346–356 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Villareal, T. A. & Carpenter, E. J. Buoyancy regulation and the potential for vertical migration in the oceanic cyanobacterium Trichodesmium. Microb. Ecol. 45, 1–10 (2003).CAS 
PubMed 

Google Scholar 
50.Rabouille, S., Staal, M., Stal, L. J. & Soetaert, K. Modeling the dynamic regulation of nitrogen fixation in the cyanobacterium Trichodesmium sp. Appl. Environ. Microbiol. 72, 3217–3227 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Breitbarth, E., Wohlers, J., Kläs, J., LaRoche, J. & Peeken, I. Nitrogen fixation and growth rates of Trichodesmium IMS-101 as a function of light intensity. Mar. Ecol. Prog. Ser. 359, 25–36 (2008).CAS 

Google Scholar 
52.Chen, Y. B. et al. Circadian rhythm of nitrogenase gene expression in the diazotrophic filamentous nonheterocystous cyanobacterium Trichodesmium sp. strain IMS101. J. Bacteriol. 180, 3598–3605 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Rabouille, S., Staal, M., Stal, L. J. & Soetaert, K. Modeling the dynamic regulation of nitrogen fixation in the Cyanobacterium Trichodesmium sp. Appl. Environ. Microbiol. 72, 3217–3227 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Capone, D. G., O’Neill, J. M., Zehr, J. & Carpenter, E. J. Basis for diel variation in nitrogenase activity in the marine planktonic cyanobacterium Trichodesmium thiebautti. Appl. Environ. Microbiol. 56, 3532–3536 (1990).CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Gründel, M., Scheunemann, R., Lockau, W. & Zilliges, Y. Impaired glycogen synthesis causes metabolic overflow reactions and affects stress responses in the cyanobacterium Synechocystis sp. PCC 6803. Microbiology 158, 3032–3043 (2012).PubMed 

Google Scholar 
56.Jackson, S. A., Eaton-Rye, J. J., Bryant, D. A., Posewitz, M. C. & Davies, F. K. Dynamics of photosynthesis in a glycogen-deficient glgC mutant of Synechococcus sp. strain PCC 7002. Appl. Environ. Microbiol. 81, 6210–6222 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Boatman, T. G., Davey, P. A., Lawson, T. & Geider, R. J. The physiological cost of diazotrophy for Trichodesmium erythraeum IMS101. PLoS ONE 13, 1–24 (2018).
Google Scholar 
58.Chappell, P. D., Moffett, J. W., Hynes, A. M. & Webb, E. A. Molecular evidence of iron limitation and availability in the global diazotroph Trichodesmium. ISME J. 6, 1728–1739 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Chappell, P. D. & Webb, E. A. A molecular assessment of the iron stress response in the two phylogenetic clades of Trichodesmium. Environ. Microbiol. 12, 13–27 (2010).CAS 
PubMed 

Google Scholar 
60.Walsby, A. E. The properties and buoyancy-providing role of gas vacuoles in Trichodesmium Ehrenberg. Br. Phycol. J. 13, 103–116 (1978).
Google Scholar 
61.Villareal, T. A. & Carpenter, E. J. Diel buoyancy regulation in the marine diazotrophic cyanobacterium Trichodesmium thiebautii. Limnol. Oceanogr. 35, 1832–1837 (1990).
Google Scholar 
62.Romans, K. M., Carpenter, E. J. & Bergman, B. Buoyancy regulation in the colonial diazotrophic cyanobacterium Trichodesmium tenue: ultrastructure and storage of carbohydrate, polyphosphate, and nitrogen. J. Phycol. 30, 935–942 (1994).
Google Scholar 
63.Wang, L. et al. Molecular structure of glycogen in Escherichia coli. Biomacromolecules 20, 2821–2829 (2019).CAS 
PubMed 

Google Scholar 
64.Berman-Frank, I., Cullen, J. T., Shaked, Y., Sherrell, R. M. & Falkowski, P. G. Iron availability, cellular iron quotas, and nitrogen fixation in Trichodesmium. Limnol. Oceanogr. 46, 1249–1260 (2001).CAS 

Google Scholar 
65.Kustka, A. B. et al. Iron requirements for dinitrogen- and ammonium-supported growth in cultures of Trichodesmium (IMS 101): comparison with nitrogen fixation rates and iron:carbon ratios of field populations. Limnol. Oceanogr. 49, 1224 (2004).CAS 

Google Scholar 
66.Paerl, H. W., Prufert-Bebout, I. L. E., Guo, C. & Carolina, N. Iron-stimulated N2 fixation and growth in natural and cultured populations of the planktonic marine cyanobacteria Trichodesmium spp. Appl. Environ. Microbiol. 60, 1044–1047 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Rubin, M., Berman-Frank, I. & Shaked, Y. Dust- and mineral-iron utilization by the marine dinitrogen-fixer Trichodesmium. Nat. Geosci. 4, 529–534 (2011).CAS 

Google Scholar 
68.Polyviou, D. et al. Desert dust as a source of iron to the globally important diazotroph Trichodesmium. Front. Microbiol. 8, 1–12 (2018).
Google Scholar 
69.Basu, S. & Shaked, Y. Mineral iron utilization by natural and cultured Trichodesmium and associated bacteria. Limnol. Oceanogr. 63, 2307–2320 (2018).CAS 

Google Scholar 
70.Held, N. A. et al. Mechanisms and heterogeneity of in situ mineral processing by the marine nitrogen fixer Trichodesmium revealed by single-colony metaproteomics. ISME Commun. 1, 35 (2021).
Google Scholar 
71.Basu, S., Gledhill, M., de Beer, D., Prabhu Matondkar, S. G. & Shaked, Y. Colonies of marine cyanobacteria Trichodesmium interact with associated bacteria to acquire iron from dust. Commun. Biol. 2, 1–8 (2019).CAS 

Google Scholar 
72.Tyrrell, T. et al. Large-scale latitudinal distribution of Trichodesmium spp. in the Atlantic Ocean. J. Plankton Res. 25, 405–416 (2003).CAS 

Google Scholar 
73.Robson, R. L. & Postgate, J. R. Oxygen and hydrogen in biological nitrogen fixation. Annu. Rev. Microbiol. 34, 183–207 (1980).74.Zehr, J. P. Nitrogen fixation by marine cyanobacteria. Trends Microbiol. 19, 162–173 (2011).CAS 
PubMed 

Google Scholar 
75.Bergman, B. & Carpenter, E. J. Nitrogenase confined to randomly distributed trichomes in the marine cyanobacterium Trichodesmium thiebautii. J. Phycol. 27, 158–165 (1991).CAS 

Google Scholar 
76.Inomura, K., Wilson, S. T. & Deutsch, C. Mechanistic model for the coexistence of nitrogen fixation and photosynthesis in marine Trichodesmium. mSystems 4, 1–13 (2019).
Google Scholar 
77.Janson, S., Matveyev, A. & Bergman, B. The presence and expression of hetR in the non-heterocystous cyanobacterium Symploca PCC 8002. FEMS Microbiol. Lett. 168, 173–179 (1998).CAS 
PubMed 

Google Scholar 
78.Zhang, J. Y., Chen, W. L. & Zhang, C. C. hetR and patS, two genes necessary for heterocyst pattern formation, are widespread in filamentous nonheterocyst-forming cyanobacteria. Microbiology 155, 1418–1426 (2009).CAS 
PubMed 

Google Scholar 
79.Moore, J. K., Doney, S. C., Glover, D. M. & Fung, I. Y. Iron cycling and nutrient-limitation patterns in surface waters of the world ocean. Deep Sea Res. 2 Top. Stud. Oceanogr. 49, 463–507 (2001).
Google Scholar 
80.Chisholm, S. W. in Primary Productivity and Biogeochemical Cycles in the Sea (eds Falkowski, P. G. et al.) 213–237 (Springer, 1992).https://doi.org/10.1007/978-1-4899-0762-2_1281.Young, K. D. The selective value of bacterial shape. Microbiol. Mol. Biol. Rev. 70, 660–703 (2006).PubMed 
PubMed Central 

Google Scholar 
82.Lu, X. & Zhu, H. Tube-gel digestion: a novel proteomic approach for high-throughput analysis of membrane proteins. Mol. Cell Proteom. 4, 1948–1958 (2005).CAS 

Google Scholar 
83.Saito, M. A. et al. Multiple nutrient stresses at intersecting Pacific Ocean biomes detected by protein biomarkers. Science 345, 1173–1177 (2014).CAS 
PubMed 

Google Scholar 
84.McIlvin, M. R. & Saito, M. A. Online nanoflow two-dimension comprehensive active modulation reversed phase-reversed phase liquid chromatography high-resolution mass spectrometry for metaproteomics of environmental and microbiome samples. J. Proteome Res. 20, 4589–4597 (2021).CAS 
PubMed 

Google Scholar 
85.Lee, M. D. et al. Transcriptional activities of the microbial consortium living with the marine nitrogen-fixing cyanobacterium Trichodesmium reveal potential roles in community-level nitrogen cycling. Appl. Environ. Microbiol. 84, AEM.02026-17 (2017).
Google Scholar 
86.Zhang, Y., Wen, Z., Washburn, M. P. & Florens, L. Refinements to label-free proteome quantitation: how to deal with peptides shared by multiple proteins. Anal. Chem. 82, 2272–2281 (2010).CAS 
PubMed 

Google Scholar 
87.Gallien, S., Bourmaud, A., Kim, S. Y. & Domon, B. Technical considerations for large-scale parallel reaction monitoring analysis. J. Proteom. 100, 147–159 (2014).CAS 

Google Scholar 
88.Pino, L. K. et al. The skyline ecosystem: informatics for quantitative mass spectrometry proteomics. Mass Spectrom. Rev. 176, 139–148 (2019).
Google Scholar 
89.Held, N. A. et al. Mechanisms and heterogeneity of in situ mineral processing by the marine nitrogen fixer Trichodesmium revealed by single-colony metaproteomics. ISME Commun. https://doi.org/10.1038/s43705-021-00034-y (2021).90.White, A. E., Spitz, Y. H. & Letelier, R. M. Modeling carbohydrate ballasting by Trichodesmium spp. Mar. Ecol. Prog. Ser. 323, 35–45 (2006).
Google Scholar 
91.Morrison, F. A. An Introduction to Fluid Mechanics (Cambridge Univ. Press, 2013).92.Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Google Scholar 
93.Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
94.Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using NetworkX. In 7th Python Scientific Conference (SciPy 2008) 11–15 (2008). More

Study areaAll simulations were run at a 100 × 100 m resolution for the entire European Alps, which cover ~200,000 km². Elevations reach 4,810 m above sea level at the highest peak (Mont Blanc, elevational data were obtained from ref. 44). Mean annual temperature ranges from approximately −13 up to 16 °C and annual precipitation sums reach up to ~3,600 mm (climatic conditions were obtained from WorldClim45).Species dataTrue presences/absences were derived from complete species lists of 14,040 localized plots covering subalpine and alpine non-forest vegetation of the Alps, compiled from published46 and unpublished data sources. For more information see the supplementary information in ref. 21.Data on demographic rates as well as dispersal parameters were taken from ref. 21, Supplementary Table 1. Detailed values are provided in Supplementary Table 1.Environmental variablesCurrent climate dataMaps of current climatic conditions were generated on the basis of mean, minimum and maximum monthly temperature obtained from WorldClim45 and monthly precipitation sums derived from ref. 47 at a resolutions of 0.5 arcmin and 5 km, respectively. Temperature and precipitation data were downscaled to 100 m as described in ref. 21 and using ordinary kriging with elevation as covariable. As the reference periods of these two datasets do not match (temperature values represent averages for 1950–2000, while precipitation covers 1970–2005) temperature values were adapted using the E-OBS climate grids available online (www.ecad.eu/download/ensembles/download.php). We used these spatially refined temperature and precipitation grids to derive maps of mean annual temperature and mean annual precipitation sum. We chose only two climatic variables to keep models simple and, therefore, interpretation of results more straightforward. In fact, the climatic drivers of population performance and distribution can be more complex48 and vary among species, life history stages and vital rates49. However, as correlations between different descriptors of temperature (such as coldest month or warmest month temperature, Pearson correlation of 0.84) as well as between precipitation variables are high in the European Alps, we chose mean annual temperature and mean annual precipitation sum as they give the most basic description of how climatic conditions change over geographical and elevational gradients.Future climate dataMonthly time series of mean temperature as well as precipitation sums predicted for the twenty-first century were extracted from the Cordex data portal (http://cordex.org). We chose two IPCC5 scenarios from the RCP families representing mild (RCP 2.6) and severe (RCP 8.5) climate change to consider the uncertainty in the future climate predictions. Both scenarios were generated by Météo-France/Centre National de Recherches Météorologiques using the CNRM-ALADIN53 model, fed by output from the global circulation model CNRM-CM5 (ref. 50). The RCP 2.6 scenario assumes that radiative forcing reaches nearly 3 W m−2 (equal to 490 ppm CO2 equivalent) mid-century and will decrease to 2.6 W m−2 by 2100. In the RCP 8.5 scenario, radiative forcing continues to rise throughout the twenty-first century and reaches >8.5 W m−2 (equal to 1,370 ppm CO2 equivalent) in 210024.These time series were statistically downscaled (delta method) by (1) calculating differences (deltas) between monthly temperature and precipitation values hindcasted for the current climatic conditions (mean 1970–2005) and forecasted future values at the original spatial resolution of 11′; (2) spatially interpolating these differences to a resolution of 100 × 100 m using cubic splines and (3) adding them to the downscaled current climate data separately for each climatic variable21,36. Subsequently, we calculated running means (averaged over 35 years) for every tenth year (2030, 2040 through to 2080) for each climatic variable and finally derived, on the basis of the monthly data, mean annual temperature and mean annual precipitation sums for these decadal time steps. The application of 35-yr running means ensures that we use the same time interval for parameterization and prediction. Furthermore, for long-lived species such as alpine plants, running means over long time intervals appear most appropriate to characterize climatic niches33.Soil dataIn addition to the climatic data we used a map of the percentage of calcareous substrate within a cell (5′ longitude × 3′ latitude dissolved to 100 m resolution; further referred to as soil) as described in the supplementary information of ref. 21.Environmental suitability modellingWe parameterized logistic regression models (LRMs) with a logit link function using species presence/absence data as response and the three environmental (two bioclimatic and one soil) variables as predictors. All predictor variables entered the model as second-order polynomials in agreement with the standard unimodal niche concept.From the models, we also derived a threshold value to use for translating predicted probability of occurrence (as a measure of site suitability) into predicted presence or absence of each species at a site (called occurrence threshold, OT, henceforth). The threshold was defined such that it optimized the true skills statistic (TSS), a measure of predictive accuracy derived from comparing observed and predicted presence–absence maps51.Genetic model and niche partitioningSpecies-specific suitability curves for the annual mean temperature gradient derived from the LRMs were partitioned into suitability curves of ecologically different haplotypes of a species as defined by allelic variation in up to three diploid loci (Extended Data Fig. 6). The number of alleles was varied (n = 1, 2, 3, 5 and 10 alleles) as was the proportion of the entire species’ (temperature) niche covered by each haplotype. Models with more than one locus were run with diallelic loci, as otherwise computational efforts would have increased excessively (for each haplotype the number of seeds, juveniles and adults has to be stored and all seeds have to be distributed separately). Each combination of haplotype number and allelic niche size used in a particular simulation is further referred to as setting. Species-specific suitability curves along the other two dimensions (precipitation and soil) remained unpartitioned to ease interpretation of results. The implications of relaxing this assumption by modelling niche partitioning along different environmental gradients are hard to predict. Outcomes would probably depend on the direction and strength of individual specialization along these gradients, whether they are positively or negatively correlated, as well as on how both temperature and precipitation patterns will be affected by climate change. As a consequence, the patterns we found could be re-enforced, for example when the replacement of cold-adapted haplotypes within the species elevational range is further delayed, for example, because those haplotypes adapted to warmer conditions can cope less well with higher precipitation at higher elevations. Vice versa, maladaptation to the warming temperatures might be attenuated, for example, if temperature increase is paralleled by precipitation decrease and emerging drought stress. If, in this case, haplotypes from lower elevations can better cope with both higher temperatures and less water availability than those of median elevations, they may replace the latter faster at these median elevations and hence shorten the phase of maladaptation.Allelic effects were assumed to define the temperature optimum additively. Hence, the heterozygotes’ optimum is always exactly between the optima of the two corresponding homozygotes, corresponding to a codominant genetic model. Finally, all haplotypes corresponding to one setting were assumed to have constant (temperature) niche size, defined as a proportion (ω = 50%, 75% and 100%, for one haplotype only 100%) of the entire species’ (temperature) niche. The temperature niche was computed as the difference between the upper and lower temperature values at which the LRM-derived suitability curve predicted a suitability equal to OT (with precipitation and soil set to the respective optima of the species, also derived from the LRMs). To derive the same geographic distribution under current climatic conditions for each setting, the union of the niches of all haplotypes of one set has to approximate the niche of the single-species model (Extended Data Fig. 6). Note, however, that, the aspired equality of niches is impossible, as the niches resulting from a logistic regression with quadratic terms are always elliptic in shape. Therefore, the optima of the two extreme homozygotes (that is, those carrying haplotypes adapted to the coldest or warmest margins of the entire species’ niche) are fixed at: niche limits ± 1/2 × ω × niche size and all other optima are distributed between them at equal distances (Extended Data Fig. 6 gives a schematic illustration). As a consequence, the performance of the extreme haplotypes, both coldest and warmest, is modelled to be somewhat higher towards the cold and warm margins of the temperature niche, respectively, compared with the performance modelled for the species without intraspecific niche partitioning (compare the overlap of the black with the red and blue curves in Extended Data Fig. 6a). However, as haplotype number did not affect modelled range loss (compare Table 1), this marginal mismatch does not apparently impact our results. Homozygotes were ordered from the cold-adapted A1A1 up to the warm-adapted AnAn.Finally, the suitability curves of the genotypes were assumed to have the same value at their optimum as the species-specific suitability curve at this point (Extended Data Fig. 6).Artificial landscapesArtificial landscapes were defined as a raster of 50 × 112 cells (of 100 × 100 m). These rasters were homogeneous with respect to precipitation and soil carbon conditions which were set to the values optimal for each species according to the LRMs. With respect to temperature, by contrast, we implemented a gradient across the raster running from the minimum –9.1 °C to the maximum +3.8 °C temperature for which the LRM predicts values >OT across all six species. Buffering by 1 °C at both limits was done to avoid truncating simulation results. Further 4 °C at the lower limit were added to consider simulated temperature increase (below). The final temperature range represented by the raster ran from −14.1 to +4.8 °C. Temperature increased linearly along this axis by an increment of 0.171 °C per cell, derived from a 20° slope and a temperature decrease of 0.5 °C per 100 m of elevational change. Along the 50-cell breadth of the landscape, temperature values were kept constant. On the basis of these grids, we implemented a moderate and a severe climate change scenario, characterized by temperature increases of 2 and 4 °C, respectively, over 80 yr. Therefore, temperature of each raster cell increased annually by 0.025 and 0.05 °C, respectively.Simulations of spatiotemporal range dynamicsCATS21 is a spatially and temporarily explicit model operating on a two-dimensional grid (of 100 m mesh size in this case). CATS combines simulations of local species’ demography with species’ distribution models by scaling demographic rates relative to occurrence probabilities (suitabilities) predicted for the respective site or grid cell by the latter. Dispersal among grid cells is modelled as a combination of wind, exozoochoric and endozoochoric (that is, animal dispersal via attachment to the fur or ingestion and defecation, respectively) dispersal of plant seeds. Time proceeds in annual steps. The annually changing occurrence probabilities at each site affect the demography of local populations and hence, eventually, the number of seeds that are produced in each grid cell in the respective year. As a consequence, local populations grow or decrease, become extinct or establish anew and hence the species’ distribution across the whole study area changes as a function of the changing climate.Demographic modellingClimate-dependence of local demography was modelled by linking demographic rates (seed persistence, germination, survival, flowering frequency, seed yield and clonal reproduction) and carrying capacity to occurrence probabilities predicted by LRMs by means of sigmoidal functions. Furthermore, all rates were fixed at OT at a value ensuring stable population sizes; for more information see refs. 21,33. Demographic rates were confined by zero and a species-specific maximum value (Supplementary Table 1), which was assumed to be the same for all genotypes of a species. As a corollary, the demographic rates of all genotypes of a species are the same under optimal environmental conditions but their actual values for a particular site in a particular year differ due to different temperature optima of genotypes. In addition, germination, survival and clonal reproduction were modelled as density-dependent processes to account for intraspecific competition between genotypes. In our application, for all density-dependent functions, the species abundance is defined as the sum of all adult individuals in a given cell, regardless of their genotypes. Density dependence is commonly achieved by multiplying rates with (C – N)/C, where N is the species abundance and C is the (site- and genotype-specific) carrying capacity. This correction for density dependence causes the functions to drop to zero when N approximates C. To avoid the subsequent collapse of population sizes, we defined density-dependent rates as (C – N)/C × rate() + N/C × rate(OT), which ensures stable population sizes at densely populated sites occupied by only one genotype. To account for uncertainty in parameters of demographic rates, we assigned each species two value sets representing the upper and lower end of a plausible range of values on the basis of information derived from databases and own measurements (Supplementary Table 1).The simulations allowed for cross-pollination between genotypes. We used the relative amount of flowers (genotype-specific flowering frequency as defined by the sigmoid curve for the given suitability in the given raster cell for the given year × number of adults of that genotype in the population of that cell) to derive an estimate of the haplotype frequencies in the total pollen produced by the population within a grid cell. For the multiallelic case we allowed for recombination between loci with a recombination rate of 0.1%. These frequencies were set equal to the probability that particular haplotypes are transmitted to each year’s seed yield by pollination. Spatial pollen dispersal was accounted for in the following way: in each cell, 5% of the pollen involved in producing the annual seed yield, was assumed to stem from outside the respective raster cell. The proportions of different haplotypes in this 5% were derived from the overall pollen frequencies in all cells within a 700 m radius around the target cell (following estimates in ref. 52). Subsequently, produced seeds of each genotype were divided into resulting genotypes regarding the adult’s haplotype composition and the haplotype frequencies in the cells’ entire pollen load.Dispersal modellingFor wind dispersal of plant species we parameterized the analytical WALD kernel53 on the basis of measured seed traits and wind speed data from a meteorological station in the Central Alps of Austria. Exozoochorous and endozoochorous plant kernels were parameterized on the basis of correlated random walk simulations for the most frequent mammal dispersal vector in the study area, the chamois (Rupicapra rupicapra L.). For more details, see ref. 33. To account for uncertainties in species-specific dispersal rates, the proportion of seeds dispersed by the more far-reaching zoochorous kernels was assumed either as high (1–5%) or low (0.1–0.5%), setting upper and lower boundaries of a credible range of the dispersal ability of species.Simulation set up and simulation initializationTo test for the effects of climate change on genetic diversity in 2080, we ran CATS over the period 2000 to 2080 for each of the six study species across the entire Alps under a full factorial combination of (1) three niche sizes (50%, 75% and 100%); (2) six numbers of haplotypes (equal to two, three, five and ten alleles for one locus and four and eight for the diallelic two- and three-locus models, respectively); (3) three climate scenarios (current climate, RCP 2.6 and RCP 8.5); and (4) two sets of demographic and dispersal parameters. As a ‘control’ we also ran simulations for all climate scenarios and the two demographic and dispersal parameter sets for a setting with one genotype filling the whole niche of the species. To account for stochastic elements in CATS four replications were run for each combination of ‘treatments’.For simulations in artificial landscapes we used, instead of RCP 2.6 and RCP 8.5, ‘artificial’ climatic scenarios with an assumed warming of 2 and 4 °C, respectively, and no change in precipitation.All simulation runs were started with homozygotic individuals only. As initial distribution, for each simulation run each cell predicted to be environmentally suitable to the species (that is, occurrence probability of species >OT)—and within the real distribution range of the species28 (not relevant for simulations in artificial landscapes, of course)—was assumed to be occupied by an equal number of adults of each (homozygotic) genotype, with the total sum equal to the carrying capacity of the site. To accommodate this arbitrary within-cell genetic mixture of homozygotes (and missing heterozygotes) to actual local conditions we started simulations of range dynamics with a burn-in phase of 200 yr, run under constant current climatic conditions. To have a smooth transition from the burn-in phase under current climate (corresponding to the climate of the years 1970–2005; see current climate data) to future climate projections (starting with 2030) and to derive annual climate series, climate data were linearly interpolated between these two time intervals.Statistical analysisWe evaluated the contribution of climate scenario, haplotype number and haplotype niche size to overall species’ range change as well as the change in the frequency of the warm-adapted haplotype by means of linear models. In these models, log(range size in 2080/range size in 2000) and log(percentage of warm-adapted haplotype in 2080/percentage of warm-adapted haplotype in 2000), averaged over the four replicates and the two demographic and dispersal parameter sets, were the response variables. For the analysis of the change of the warm-adapted haplotype simulation settings with 100% niche size were ignored, as in this setting all haplotypes have the same temperature optimum (that is, neutral genetic variation). Approximate normality of residuals was confirmed by visual inspection.As indicator of the ‘topographic opportunity’ remaining to the species in the real world we calculated the area colonizable at elevations higher than those already occupied at the end of the simulation period. We therefore drew a buffer of 1 km around each cell predicted to be occupied in 2080 and then summed the area within these buffers at a higher elevation than the focal, occupied cell. Overlapping buffer areas were only counted once. This calculation was done for simulations conducted with one full-niche genotype per species only.Sensitivity analysisWe interpret the simulated relative decrease of warm-adapted haplotypes mainly as an effect of (1) the unrestricted expansion of cold-adapted haplotypes at the leading edge and (2) the resistance of the locally predominating haplotype that becomes increasingly maladapted with progressive climate warming, to recruitment by better-adapted haplotypes from below that are either rare or not present at all initially. However, the results achieved, and our conclusions, may be sensitive to assumptions about particular parameter values. Parameters that control the longevity of adult plants, and indirectly the rate of recruitment of new individuals, as well as those controlling gene flow via pollen (instead of seeds) may be particularly influential in this respect. We additionally ran simulations on artificial landscapes under alternative values of these parameters. In particular, we set the maximum age of plants to 10 yr instead of 100 yr and raised the proportion of locally produced pollen assumed to be transported up to 700 m to 10%. Both of these values are thus probably set to rather extreme levels: a maximum age of 10 yr is much shorter than the 30–50 yr assumed to be the standard age of (non-clonal) alpine plants31; and a cross-pollination rate between cells of 10% is high given that among the most important alpine pollinators only bumblebees are assumed to transport pollen >100 m regularly54,55. We add that we ran these additional simulations only in combination with the demographic species parameters set to high values because a short life span combined with low-level demographic parameters did not allow for stable populations of some species, even under current climatic conditions.For individual species, adapting plant age and cross-pollination rate between cells (Extended Data Fig. 7), did change the magnitude of loss of the warm-adapted haplotype. Nevertheless, for all of them the warm-adapted haplotype still became rarer when climate warmed and this effect increased with the level of warming. We are confident that our conclusions are qualitatively insensitive to variation of these parameters within a realistic range.Finally, in the simulations where we assumed a multilocus structure of the temperature niche, the recombination rate may also affect simulation results because it determines the rate by which new haplotypes can emerge. We also tested sensitivity of our simulations to doubling the recombination rate to 0.2%. Again, we found that a higher recombination rate had little qualitative effect on the results. Quantitatively, it resulted in a slightly pronounced relative decrease of the warmth-adapted haplotype in most species (Extended Data Fig. 8).Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More