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Reduced within-population quantitative genetic variation is associated with climate harshness in maritime pine

Heredity volume 131, pages 68–78 (2023)Cite this article

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Abstract

How evolutionary forces interact to maintain genetic variation within populations has been a matter of extensive theoretical debates. While mutation and exogenous gene flow increase genetic variation, stabilizing selection and genetic drift are expected to deplete it. To date, levels of genetic variation observed in natural populations are hard to predict without accounting for other processes, such as balancing selection in heterogeneous environments. We aimed to empirically test three hypotheses: (i) admixed populations have higher quantitative genetic variation due to introgression from other gene pools, (ii) quantitative genetic variation is lower in populations from harsher environments (i.e., experiencing stronger selection), and (iii) quantitative genetic variation is higher in populations from heterogeneous environments. Using growth, phenological and functional trait data from three clonal common gardens and 33 populations (522 clones) of maritime pine (Pinus pinaster Aiton), we estimated the association between the population-specific total genetic variances (i.e., among-clone variances) for these traits and ten population-specific indices related to admixture levels (estimated based on 5165 SNPs), environmental temporal and spatial heterogeneity and climate harshness. Populations experiencing colder winters showed consistently lower genetic variation for early height growth (a fitness-related trait in forest trees) in the three common gardens. Within-population quantitative genetic variation was not associated with environmental heterogeneity or population admixture for any trait. Our results provide empirical support for the potential role of natural selection in reducing genetic variation for early height growth within populations, which indirectly gives insight into the adaptive potential of populations to changing environments.

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Fig. 1: Location of the three common gardens and the 33 populations used in the study.
Fig. 2: Median and 95% credible intervals of the βX coefficients, which stand for the association among within-population quantitative genetic variation and its potential underlying drivers on the x-axis (see equation (4)).
Fig. 3: Validation step using height and SLA measurements from a family-based progeny test near Asturias (data kindly provided by Dr. Ricardo Alı́a, CSIC, Madrid).

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Data availability

Data are publicly available. SNP data were deposited in the Dryad repository at https://doi.org/10.5061/dryad.8d6k1. Height data have been deposited in GENFORED, the Spanish Network of Genetic Trials (http://www.genfored.es). The scripts are available in the following Github repository: https://github.com/JulietteArchambeau/H2Pinpin.

References

  • Aase K, Jensen H, Muff S (2022) Genomic estimation of quantitative genetic parameters in wild admixed populations. Meth Ecol Evol 13:1014–1026

    Article  Google Scholar 

  • Aitken SN, Bemmels JB (2015) Time to get moving: Assisted gene flow of forest trees. Evol Appl 9:271–290

    Article  PubMed  PubMed Central  Google Scholar 

  • Alberto F et al. (2013) Potential for evolutionary responses to climate change - evidence from tree populations. Global Change Biol 19:1645–1661

    Article  Google Scholar 

  • Alberto F et al. (2011) Adaptive responses for seed and leaf phenology in natural populations of sessile oak along an altitudinal gradient. J Evolut Biol 24:1442–1454

    Article  CAS  Google Scholar 

  • Alía R, Chambel R, Notivol E, Climent J, González-Martínez SC (2014) Environment-dependent microevolution in a Mediterranean pine (Pinus pinaster Aiton). BMC Evol Biol 14:1–12

    Article  Google Scholar 

  • Anderegg LDL et al. (2021) Aridity drives coordinated trait shifts but not decreased trait variance across the geographic range of eight Australian trees. New Phytologist 229:1375–1387

    Article  PubMed  Google Scholar 

  • Archambeau J et al. (2022) Combining climatic and genomic data improves range-wide tree height growth prediction in a forest tree. Am Naturalist 200:E141–E159

    Article  Google Scholar 

  • Barton NH (1990) Pleiotropic models of quantitative variation. Genetics 124:773–782

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Benavides R et al. (2021) Phenotypes of Pinus sylvestris are more coordinated under local harsher conditions across Europe. J Ecol 109:2580–2596

    Article  Google Scholar 

  • Benito Garzón M, Robson TM, Hampe A (2019) ΔTraitSDMs: Species distribution models that account for local adaptation and phenotypic plasticity. New Phytol 222:1757–1765

    Article  PubMed  Google Scholar 

  • Bigler C, Veblen TT (2009) Increased early growth rates decrease longevities of conifers in subalpine forests. Oikos 118:1130–1138

    Article  Google Scholar 

  • Bucci G et al. (2007) Range-wide phylogeography and gene zones in Pinus pinaster Ait. revealed by chloroplast microsatellite markers. Mol Ecol 16:2137–2153

    Article  CAS  PubMed  Google Scholar 

  • Buffalo V (2021) Quantifying the relationship between genetic diversity and population size suggests natural selection cannot explain Lewontin’s Paradox. eLife 10:e67509

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Byers DL (2005) Evolution in heterogeneous environments and the potential of maintenance of genetic variation in traits of adaptive significance. Genetics of Adapt107–124. https://doi.org/10.1007/s10709-003-2721-5.

  • Carley LN, Morris WF, Walsh R, Riebe D, Mitchell-Olds T (2022) Are genetic variation and demographic performance linked? Evol Appl 15:1888–1906

    Article  PubMed  PubMed Central  Google Scholar 

  • Carpenter B et al. (2017) Stan: A probabilistic programming language. J Stat Softw 76 https://www.osti.gov/pages/biblio/1430202-stan-probabilistic-programming-language.

  • Charmantier A, Kruuk LEB, Blondel J, Lambrechts MM (2004) Testing for microevolution in body size in three blue tit populations. J Evol Biol 17:732–743

    Article  CAS  PubMed  Google Scholar 

  • Colautti RI, Eckert CG, Barrett SCH (2010) Evolutionary constraints on adaptive evolution during range expansion in an invasive plant. Proc R Soc B: Biolog Sci 277:1799–1806

    Article  Google Scholar 

  • Conrad O et al. (2015) System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosci Model Dev 8:1991–2007

    Article  Google Scholar 

  • Corcuera L, Gil-Pelegrin E, Notivol E (2010) Phenotypic plasticity in Pinus pinaster δ13C: Environment modulates genetic variation. Annals Forest Sci 67:812–812

    Article  Google Scholar 

  • Costa P, Durel CE (1996) Time trends in genetic control over height and diameter in maritime pine. Can J Forest Res 26:1209–1217

    Article  Google Scholar 

  • de- Lucas AI, Robledo-Arnuncio JJ, Hidalgo E, González-Martínez SC (2008) Mating system and pollen gene flow in mediterranean maritime pine. Heredity 100:390–399

    Article  CAS  PubMed  Google Scholar 

  • de Miguel M et al. (2022) Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (Pinus pinaster Ait., Pinaceae). Mol Ecol. https://doi.org/10.1111/mec.16367.

  • Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164:1567–1587

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Felsenstein J (1976) The theoretical population genetics of variable selection and migration. Ann Rev Gen 10:253–280

    Article  CAS  Google Scholar 

  • Fisher RA (1930) The genetical theory of natural selection. Clarendon Press, Oxford

  • Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conserv Biol 10:1500–1508

    Article  Google Scholar 

  • Gaspar MJ, Velasco T, Feito I, Alía R, Majada J (2013) Genetic variation of drought tolerance in Pinus pinaster at three hierarchical levels: A comparison of induced osmotic stress and field testing. PLOS ONE 8:e79094

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gauzere J et al. (2020) Where is the optimum? Predicting the variation of selection along climatic gradients and the adaptive value of plasticity. A case study on tree phenology. Evol Lett 4:109–123

    Article  PubMed  PubMed Central  Google Scholar 

  • Gibson G, Dworkin I (2004) Uncovering cryptic genetic variation. Nat Rev Genetics 5:681–690

    Article  CAS  PubMed  Google Scholar 

  • González-Martínez SC, Alía R, Gil L (2002) Population genetic structure in a Mediterranean pine (Pinus pinaster Ait.): A comparison of allozyme markers and quantitative traits. Heredity 89:199–206

    Article  PubMed  Google Scholar 

  • González-Martínez SC et al. (2007) Spatial genetic structure of an explicit glacial refugium of maritime pine (Pinus pinaster Aiton) insoutheastern Spain. In Weiss, S. & Ferrand, N. (eds.) Phylogeography of southern european refugia: Evolutionary perspectives on the origins and conservation of European biodiversity. Springer, Netherlands, 2007, p 257–269 https://doi.org/10.1007/1-4020-4904-8_9.

  • Grattapaglia D, Plomion C, Kirst M, Sederoff RR (2009) Genomics of growth traits in forest trees. Curr Opinion Plant Biol 12:148–156

    Article  CAS  Google Scholar 

  • Grivet D et al. (2011) Molecular footprints of local adaptation in two mediterranean conifers. Mol Biol Evol 28:101–116

    Article  CAS  PubMed  Google Scholar 

  • Hamrick JL (2004) Response of forest trees to global environmental changes. Forest Ecol Manag 197:323–335

    Article  Google Scholar 

  • Hedrick PW (1986) Genetic polymorphism in heterogeneous environments: A decade later. Annual Rev Ecol Syst 17:535–566

    Article  Google Scholar 

  • Hedrick PW (2006) Genetic polymorphism in heterogeneous environments: The age of genomics. Annual Rev Ecol, Evol Syst 37:67–93

    Article  Google Scholar 

  • Hereford J, Hansen TF, Houle D (2004) Comparing strengths of directional selection: How strong is strong? Evolution 58:2133–2143

    PubMed  Google Scholar 

  • Hiederer, R., European Commission, Joint Research Centre & Institute for Environment and Sustainability. Mapping Soil Properties for Europe Spatial Representation of Soil Database Attributes. (Publications Office of the European Union, 2013).

  • Hoffmann AA, Parsons P (1991) Evolutionary genetics and environmental stress. Oxford University Press https://agris.fao.org/agris-search/search.do?recordID=US201300695279.

  • Huang Y, Stinchcombe JR, Agrawal AF (2015) Quantitative genetic variance in experimental fly populations evolving with or without environmental heterogeneity. Evolution 69:2735–2746

    Article  CAS  PubMed  Google Scholar 

  • Hurel A et al. (2021) Genetic basis of growth, spring phenology, and susceptibility to biotic stressors in maritime pine. Evol Appl 14:2750–2772

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jaramillo-Correa J-P et al. (2015) Molecular proxies for climate maladaptation in a long-lived tree (Pinus pinaster Aiton, Pinaceae). Genetics 199:793–807

    Article  PubMed  Google Scholar 

  • Johnson GR, Sniezko RA, Mandel NL (1997) Age trends in Douglas-fir genetic parameters and implications for optimum selection age. Silvae Genetica 46:349–358

    Google Scholar 

  • Johnson T, Barton NH (2005) Theoretical models of selection and mutation on quantitative traits. Phil Trans R Soc B: Biol Sci 360:1411–1425

    Article  CAS  Google Scholar 

  • Jump AS, Peñuelas J (2005) Running to stand still: Adaptation and the response of plants to rapid climate change. Ecol Lett 8:1010–1020

    Article  PubMed  Google Scholar 

  • Körner, C. Alpine plant diversity: A global survey and functional interpretations. In Chapin, F. S. & Körner, C. (eds.) Arctic and Alpine Biodiversity: Patterns, Causes and Ecosystem Consequences, Ecological Studies, 45-62 (Springer, 1995). https://doi.org/10.1007/978-3-642-78966-3_4.

  • Kremer A et al. (2012) Long-distance gene flow and adaptation of forest trees to rapid climate change. Ecol Lett 15:378–392

    Article  PubMed  PubMed Central  Google Scholar 

  • Kroon J, Ericsson T, Jansson G, Andersson B (2011) Patterns of genetic parameters for height in field genetic tests of Picea abies and Pinus sylvestris in Sweden. Tree Genet Genomes 7:1099–1111

    Article  Google Scholar 

  • Kurt Y, González-Martínez SC, Alía R, Isik K (2012) Genetic differentiation in Pinus brutia Ten. using molecular markers and quantitative traits: The role of altitude. Annals Forest Sci 69:345–351

    Article  Google Scholar 

  • Kusnandar D, Galwey NW, Hertzler GL, Butcher TB (1998) Age trends in variances and heritabilities for diameter and height in maritime pine (Pinus pinaster Ait.) in western Australia. Silvae Genetica 47:136–141

    Google Scholar 

  • Lamy J-B et al. (2014) Limited genetic variability and phenotypic plasticity detected for cavitation resistance in a Mediterranean pine. New Phytol 201:874–886

    Article  PubMed  Google Scholar 

  • Lande R, Shannon S (1996) The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 50:434–437

    Article  PubMed  Google Scholar 

  • Lawton-Rauh A (2008) Demographic processes shaping genetic variation. Curr Opinion Plant Biol 11:103–109

    Article  Google Scholar 

  • Ledón-Rettig CC, Pfennig DW, Chunco AJ, Dworkin I (2014) Cryptic genetic variation in natural populations: A predictive framework. Integr Comp Biol 54:783–793

    Article  PubMed  Google Scholar 

  • Leimu R, Mutikainen P, Koricheva J, Fischer M (2006) How general are positive relationships between plant population size, fitness and genetic variation? J Ecol 94:942–952

    Article  Google Scholar 

  • Leites LP, Robinson AP, Rehfeldt GE, Marshall JD, Crookston NL (2012) Height-growth response to climatic changes differs among populations of Douglas-fir: A novel analysis of historic data. Ecol Appl 22:154–165

    Article  PubMed  Google Scholar 

  • Lemoine NP (2019) Moving beyond noninformative priors: Why and how to choose weakly informative priors in Bayesian analyses. Oikos 128:912–928

    Article  Google Scholar 

  • Lepoittevin C et al. (2011) Genetic parameters of growth, straightness and wood chemistry traits in Pinus pinaster. Annals Forest Sci 68:873–884

    Article  Google Scholar 

  • Lerner, I. M. The genetic basis of selection. In The genetic basis of selection. (Chapman & Hall, Ltd., London & John Wiley & Sons, Inc., New York, 1958). https://www.cabdirect.org/cabdirect/abstract/19591600905.

  • Levene H (1953) Genetic equilibrium when more than one ecological niche is available. Am Nat 87:331–333

    Article  Google Scholar 

  • Lin Y et al. (2013) Effect of genotype by spacing interaction on radiata pine genetic parameters for height and diameter growth. Forest Ecol Manag 304:204–211

    Article  Google Scholar 

  • Linhart YB, Grant MC (1996) Evolutionary significance of local genetic differentiation in plants. Annual Rev Ecol Syst 27:237–277

    Article  Google Scholar 

  • Mackay TFC (1981) Genetic variation in varying environments. Gen Res 37:79–93

    Article  Google Scholar 

  • Marchi M et al. (2020) ClimateEU, scale-free climate normals, historical time series, and future projections for Europe. Sci Data 7:428

    Article  PubMed  PubMed Central  Google Scholar 

  • McDonald TK, Yeaman S (2018) Effect of migration and environmental heterogeneity on the maintenance of quantitative genetic variation: A simulation study. J Evol Biol 31:1386–1399

    Article  PubMed  Google Scholar 

  • Merilä J, Sheldon B, Kruuk L (2001) Explaining stasis: Microevolutionary studies in natural populations. Genetica 112:199–222

    Article  PubMed  Google Scholar 

  • Merilä J, Söderman F, O’Hara R, Räsänen K, Laurila A (2004) Local adaptation and genetics of acid-stress tolerance in the moor frog, Rana arvalis. Conserv Gen 5:513–527

    Article  Google Scholar 

  • Mitchell PJ, Veneklaas EJ, Lambers H, Burgess SSO (2008) Leaf water relations during summer water deficit: Differential responses in turgor maintenance and variation in leaf structure among different plant communities in south-western Australia. Plant, Cell Environ 31:1791–1802

    Article  PubMed  Google Scholar 

  • Mitchell-Olds T, Willis JH, Goldstein DB (2007) Which evolutionary processes influence natural genetic variation for phenotypic traits? Nat Rev Gen 8:845–856

    Article  CAS  Google Scholar 

  • Morgenstern, M. Geographic Variation in Forest Trees: Genetic Basis and Application of Knowledge in Silviculture (Vancouver: Univ. B.C. Press, 1996).

  • Neale DB, Savolainen O (2004) Association genetics of complex traits in conifers. Trends Plant Sci 9:325–330

    Article  CAS  PubMed  Google Scholar 

  • Petit RJ, Hampe A (2006) Some evolutionary consequences of being a tree. Annual Rev Ecol Evol Syst 37:187–214

    Article  Google Scholar 

  • Plomion C et al. (2016) High-density SNP assay development for genetic analysis in maritime pine (Pinus pinaster). Mol Ecol Resources 16:574–587

    Article  CAS  Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • R Core Team. R: A Language and Environment for Statistical Computing (2020). https://www.R-project.org/.

  • Ramírez-Valiente JA, Etterson JR, Deacon NJ, Cavender-Bares J (2019) Evolutionary potential varies across populations and traits in the neotropical oak Quercus oleoides. Tree Physiol 39:427–439

    Article  PubMed  Google Scholar 

  • Rehfeldt GE, Ying CC, Spittlehouse DL, Hamilton DA (1999) Genetic responses to climate in Pinus contorta: Niche breadth, climate change, and reforestation. Ecol Monogr 69:375–407

    Article  Google Scholar 

  • Reid JM, Arcese P (2020) Recent immigrants alter the quantitative genetic architecture of paternity in song sparrows. Evol Lett 4:124–136

    Article  PubMed  PubMed Central  Google Scholar 

  • Reynolds VA, Anderegg LDL, Loy X, HilleRisLambers J, Mayfield MM (2018) Unexpected drought resistance strategies in seedlings of four Brachychiton species. Tree Physiol 38:664–677

    Article  CAS  PubMed  Google Scholar 

  • Richardson JL, Urban MC, Bolnick DI, Skelly DK (2014) Microgeographic adaptation and the spatial scale of evolution. Trends in Ecol Evol 29:165–176

    Article  Google Scholar 

  • Riley SJ, DeGloria SD, Elliot R (1999) Index that quantifies topographic heterogeneity. Intermountain J Sci 5:23–27

    Google Scholar 

  • Robledo-Arnuncio JJ, Gil L (2005) Patterns of pollen dispersal in a small population of Pinus sylvestris L. revealed by total-exclusion paternity analysis. Heredity 94:13–22

    Article  CAS  PubMed  Google Scholar 

  • Rodríguez-Quilón I et al. (2015) Local effects drive heterozygosity-fitness correlations in an outcrossing long-lived tree. Proc R Soc B: Biolog Sci 282:20152230

    Article  Google Scholar 

  • Rodríguez-Quilón I et al. (2016) Capturing neutral and adaptive genetic diversity for conservation in a highly structured tree species. Ecolog Appl 26:2254–2266

    Article  Google Scholar 

  • Santos-del Blanco L et al. (2022) On the feasibility of estimating contemporary effective population size (Ne) for genetic conservation and monitoring of forest trees. Biolog Conserv 273:109704

    Article  Google Scholar 

  • Savolainen O, Pyhäjärvi T, Knürr T (2007) Gene flow and local adaptation in trees. Annual Rev Ecol Evol Syst 38:595–619

    Article  Google Scholar 

  • Schlichting CD (2008) Hidden reaction norms, cryptic genetic variation, and evolvability. Annals NY Acad Sci 1133:187–203

    Article  Google Scholar 

  • Schuster WSF, Mitton JB (2000) Paternity and gene dispersal in limber pine (Pinus flexilis James). Heredity 84:348–361

    Article  CAS  PubMed  Google Scholar 

  • Scotti I, González-Martínez SC, Budde KB, Lalagüe H (2016) Fifty years of genetic studies: What to make of the large amounts of variation found within populations? Annals Forest Sci 73:69–75

    Article  Google Scholar 

  • Scotti-Saintagne C et al. (2004) Detection of quantitative trait loci controlling bud burst and height growth in Quercus robur L. Theor Appl Genetics 109:1648–1659

    Article  CAS  Google Scholar 

  • Spichtig M, Kawecki TJ (2004) The maintenance (or not) of polygenic variation by soft selection in heterogeneous environments. Am Nat 164:70–84

    Article  PubMed  Google Scholar 

  • Stein A, Gerstner K, Kreft H (2014) Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol Lett 17:866–880

    Article  PubMed  Google Scholar 

  • Stock AJ, Campitelli BE, Stinchcombe JR (2014) Quantitative genetic variance and multivariate clines in the Ivyleaf morning glory, Ipomoea hederacea. Philosoph Trans R Soc B: Biolog Sci 369:20130259

    Article  Google Scholar 

  • Storfer A (1996) Quantitative genetics: A promising approach for the assessment of genetic variation in endangered species. Trends Ecol Evol 11:343–348

    Article  CAS  PubMed  Google Scholar 

  • Thibault E, Soolanayakanahally R, Keller SR (2020) Latitudinal clines in bud flush phenology reflect genetic variation in chilling requirements in balsam poplar, Populus balsamifera. Am J Botany 107:1597–1605

    Article  Google Scholar 

  • Triantis KA, Mylonas M, Lika K, Vardinoyannis K (2003) A model for the species-area-habitat relationship. J Biogeogr 30:19–27

    Article  Google Scholar 

  • van Heerwaarden B et al. (2009) Testing evolutionary hypotheses about species borders: Patterns of genetic variation towards the southern borders of two rainforest Drosophila and a related habitat generalist. Proc R Soc B: Biol Sci 276:1517–1526

    Article  Google Scholar 

  • Viñas, R. A., Caudullo, G., Oliveira, S. & de Rigo, D. Pinus pinaster in Europe: Distribution, habitat, usage and threats (2016).

  • Vitasse, Y. V., Delzon, S. D., Bresson, C. C. B. C., Michalet, R. M. & Kremer, A. K. Altitudinal differentiation in growth and phenology among populations of temperate-zone tree species growing in a common garden. Canadian Journal of Forest Research (2009). https://doi.org/10.1139/X09-054.

  • Waddington CH (1953) Genetic assimilation of an acquired character. Evolution 7:118–126

    Article  Google Scholar 

  • Walsh B, Blows MW (2009) Abundant genetic variation + strong selection = multivariate genetic constraints: A geometric view of adaptation. Annual Rev Ecol Evol Syst 40:41–59

    Article  Google Scholar 

  • Walsh, B. & Lynch, M. Evolution and Selection of Quantitative Traits (Oxford University Press, 2018).

  • Warren CR, McGrath JF, Adams MA (2001) Water availability and carbon isotope discrimination in conifers. Oecologia 127:476–486

    Article  PubMed  Google Scholar 

  • Willi Y, van Buskirk J, Hoffmann AA (2006) Limits to the adaptive potential of small populations. Annual Rev Ecol Evol Syst 37:433–458

    Article  Google Scholar 

  • Wood JLA, Tezel D, Joyal D, Fraser DJ (2015) Population size is weakly related to quantitative genetic variation and trait differentiation in a stream fish. Evolution 69:2303–2318

    Article  PubMed  Google Scholar 

  • Wu HX, Ying CC (2004) Geographic pattern of local optimality in natural populations of lodgepole pine. Forest Ecol Manag 194:177–198

    Article  Google Scholar 

  • Yeaman S, Chen Y, Whitlock MC (2010) No effect of environmental heterogeneity on the maintenance of genetic variation in wing shape in Drosophila melanogaster. Evolution 64:3398–3408

    Article  PubMed  Google Scholar 

  • Yeaman S et al. (2016) Convergent local adaptation to climate in distantly related conifers. Science 353:1431–1433

    Article  CAS  PubMed  Google Scholar 

  • Yeaman S, Jarvis A (2006) Regional heterogeneity and gene flow maintain variance in a quantitative trait within populations of lodgepole pine. Proc R Soc B: Biolog Sci 273:1587–1593

    Article  CAS  Google Scholar 

  • Younginger BS, Sirová D, Cruzan MB, Ballhorn DJ (2017) Is biomass a reliable estimate of plant fitness? Appl Plant Sci 5:1600094

    Article  Google Scholar 

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Acknowledgements

We thank A. Saldaña, F. del Caño, E. Ballesteros and D. Barba (INIA) and the ‘Unité Expérimentale Forêt Pierroton’ (UEFP, INRAE; https://doi.org/10.15454/1.5483264699193726E12) for field assistance (plantation and measurements). Data used in this research are part of the Spanish Network of Genetic Trials (GENFORED, http://www.genfored.es). We thank all persons and institutions linked to the establishment and maintenance of field trials used in this study. We are very grateful to Ricardo Alía and Juan Majada who initiated and supervised the establishment of the CLONAPIN network and provided the progeny test height data that we used in the validation analysis. We thank Maurizio Marchi for kindly providing the raster files of the climate variables corresponding to the desired time period and spatial extent and for providing detailed explanations in response to our questions regarding the extraction of climatic data with the Climatic Downscaling Tool. JA was funded by the University of Bordeaux (ministerial grant). This study was funded by the ‘Initiative d’Excellence (IdEx) de l’Université de Bordeaux - Chaires d’installation 2015’ (EcoGenPin) and the European Union’s Horizon 2020 research and innovation program under grant agreements No 773383 (B4EST) and No 862221 (FORGENIUS).

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Authors and Affiliations

  1. INRAE, Univ. Bordeaux, BIOGECO, F-33610, Cestas, France

    Juliette Archambeau, Marta Benito Garzón, Marina de Miguel, Benjamin Brachi & Santiago C. González-Martínez

  2. UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, UK

    Juliette Archambeau

  3. EGFV, Univ. Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, F-33882, Villenave d’Ornon, France

    Marina de Miguel

  4. CNRS, Institute of Mathematics of Bordeaux, F-33400, Talence, France

    Frédéric Barraquand

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SCG-M designed the experiment and supervised the curation of field data. MdM cleaned and formatted the phenotypic data. SCG-M, JA, FB, MBG and BB conceived the paper methodology. JA and FB built the model equations and codes. JA conducted the data and simulation analyses. All authors interpreted the results. JA led the writing of the manuscript. All authors contributed to the manuscript and gave final approval for publication.

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Correspondence to Juliette Archambeau.

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Archambeau, J., Benito Garzón, M., de Miguel, M. et al. Reduced within-population quantitative genetic variation is associated with climate harshness in maritime pine. Heredity 131, 68–78 (2023). https://doi.org/10.1038/s41437-023-00622-9

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