Forêts dégradées, bois surexploité... C’est un constat préoccupant sur l’exploitation de la ressource (...)
FLAG
Full title and Summary
FOREST TREE ECOLOGICAL GENETICS: INTERPLAY OF GENE FLOW AND ENVIRONMENTAL VARIABILITY IN SHAPING LOCAL ADAPTATION AND GENETIC ADAPTIVE POTENTIAL
Environmental gradients and patchiness shape the level and the distribution of genetic diversity of adaptive significance. A rich literature describes how gene frequencies vary along gradients and deals with the interaction of local selection and global gene flow. The implicit assumption is that populations evolving according to divergent selective pressures have to be sufficiently isolated for long term selective divergence to take place. Genetic pools differentiating along gradients and between patches store large amounts of genetic variability and thus favour the maintenance of a species’ adaptive potential. Measuring and modelling the amount of divergence among populations under divergent ecological conditions is therefore central to the prediction of how populations may respond to future local and global environmental changes.
The turnover of genetic variants over gradients and habitat patches has traditionally been studied on geographical scales between regional and global. However, it can be argued that, for long-lived organisms, the large amounts of observed within-population genetic diversity may be maintained at least partly by local selection. In some instances, environmental conditions vary on a spatial scale that is comparable to, or even shorter than, average gene flow distances, thus setting the conditions for the interplay between selection and dispersal in the generation of observed patterns of diversity. Under these conditions, local differentiation among sub-populations for adaptive traits and genes may be maintained in spite of, and in some cases thanks to, ongoing gene flow. A combination of landscape and ecological genetic approaches makes it possible to investigate these phenomena, which have been shown to occur only for a limited set of plant species.
Strategies to infer and model the strength of selection at target genes require prior knowledge of the loci under selection; however, current sequencing technology, combined with intensive field sampling of natural populations, can lead to the identification of selectively significant loci by population genomic approaches, even for non-model species. Providing genotypes for thousands of Single Nucleotide Polymorphisms (SNP) for hundreds of individuals has currently become feasible and relatively cheap. Quantitative genetics tools can now be successfully used to validate the association between SNP frequencies, phenotypic values, and environmental gradients. The time is therefore ripe for starting to address these major ecological-genetic questions directly in ecologically relevant species.
The present project aims at investigating the complex genome-wide effects of local adaptation in nine keystone tree species from four major terrestrial ecosystems. Here we address the question of whether and how genetic diversity in forest tree stands, occurring across environmental gradients, is spatially structured by selective forces, and we propose to estimate the proportion of the genome undergoing such processes as well as to identify genes under selection. We propose an original use of classical quantitative genetic tools applied to in-situ progeny tests (reciprocal transplant experiment and replicated provenance tests) to validate SNP-trait-environment associations. The intensity of migratory and adaptive processes will be modelled thanks to advanced modelling strategies and will allow us to provide predictions of the response of forests to climate changes. We focus here on water availability gradients in particularly sensitive forest areas, such as the Guiana shield, the Mediterranean basin, Sub-Saharan Africa and the Brazilian Cerrado; all these regions suffer broad seasonal changes in soil water availability and are expected to undergo abrupt rainfall changes in the near future. Therefore, studying how forest tree populations cope with ecological gradients is at the fore among tools to predict the impact of expected future environmental changes.
Partnership
Seven laboratories are involved in the FLAG program: one in French Guiana, three in continental France, one in Brazil, one in Spain and One in Italy. All have a strong background in forest tree population genetics and genomics, in (wet and dry) tropical, Mediterranean or temperate species.
Project description
We propose here to address the genetic response of forest tree populations to gradients and habitat contrasts related to soil water content (SWC) and temperature. These gradients are major determinants of tree growth and survival, and the physiological and molecular properties of plant responses to heat, cold, drought and flooding stress are among the most finely characterised processes in plant biology (e.g. see Jung et al. 2009; Parelle et al. 2007; Tournaire-Roux et al. 2003 for recent examples). Therefore, studying evolutionary-genetic patterns related to these stresses in natural populations of non-model organisms will take advantage of the field’s large body of knowledge. The fact that such environmental gradients are relevant in tree populations is proven by several case studies reporting survival and growth response to changes in temperature and SWC (Baraloto et al. 2006; Baraloto et al. 2005; Baraloto et al. 2007; Cohen et al. 2007; Mayle& Power 2008; Meir& Woodward 2010; Phillips et al. 2009).
Our strategy is three-pronged, and is based on two experimental phases, addressing questions in population genomics and quantitative genetics, and a modelling phase, building on the data gathered in the experimental phases to model the processes underlying the maintenance of genetic diversity and to predict future changes.
SNP and indel markers will be identified from ongoing or new high-throughput genome sequence activities, and used to genotype natural populations of each species, growing in sites displaying a gradient or contrast of environmental (drought / flooding / temperature) conditions, for which relevant environmental information is available or will be obtained during the project. Each adult tree will be genotyped and the frequency of alleles at each locus will be tested for correlation with the gradients (or association with the contrasts).
Between1000 and 2000 loci per species will be screened by next-generation sequencing of short DNA fragments containing the loci, based on the Restriction Associated DNA (RAD) technique (Miller et al. 2007), a novel method to genotype whole genomes directly without the need for prior sequence information, that has recently been applied to the study of population divergence by genome scan (Hohenlohe et al. 2010). FLAG will be one of the earliest applications of RAD to population genetics and will provide an unprecedented wealth of data. This activity will lead to the estimate of the amount of loci under selection along the gradient as well as to the identification of loci and polymorphisms related to stress response / adaptation to the environment. Moreover, cross-species, cross-biome comparisons will allow us to look for conserved patterns of response to stresses in multiple environments. SNP markers found to be correlated to environmental gradients will be used to genotype seedling being grown in reciprocal transplant experiments and replicated common gardens involving the same populations and sites as the adult stands screened as described above. As the reciprocal transplant and common garden populations are recurrently measured for growth, physiological and survival traits, SNP markers will be checked for association with those traits to validate the link between selection at the molecular level and phenotypes.
Three major points make this project entirely novel. (1) Identifying general patterns and shared processes in ecologically relevant species. The program aims at searching and identifying loci underlying adaptation to similar climatic gradients in forests from very different ecosystems, by a genome scan approach. This makes it possible to search for common trends across species, thus eventually providing “universal” markers of environmental adaptation in a large array of forest ecosystems and botanical families. (2) Linking ecology, genetics and physiology. The two-step strategy of first searching for loci by correlation with environmental gradients, and then validating them by association with growth, physiological, and survival traits in juveniles is entirely novel, and will possibly lead to the direct identification of characters that contribute to adaptation. This strategy rests on the fact that potentially adaptive traits (“functional” traits sensu Violle et al. (2007)) can be identified based on knowledge gathered on model species. In particular, characters involved in the response to particular environmental stresses (temperature, drought) can be singled out and measured precisely to test the adaptation of populations to these constraints. Therefore, it makes sense to first identify loci under divergent selection in natural populations, and then test whether they contribute to the genetic control of traits putatively involved, or proven to be involved, in the response to environmental divergence. The simultaneous detection of selection at a locus and of its involvement in the control of an adaptive trait, if found, is compelling evidence that the locus acts on adaptive processes through the expression of the trait (but absence of detection of selected loci linked to a given trait cannot be taken as evidence of non adaptive value of the trait; such a test is beyond the scope of this program). This project will contribute novel and original data to the study of the ecological genetics of forests worldwide and to the knowledge of their standing capacity to adapt to environmental variation and to respond to future changes. It specifically includes tropical forests, which have much higher biodiversity than the mostly temperate models in which molecular genetic studies of adaptation to gradients have so far been carried out, and Mediterranean forests, also genetically diverse, which will suffer increased drought stress due to climate change. The newly developed markers and genotyping strategies will be available for the screening of other forests in the context of conservation strategies and assessment of biological diversity. The high dimensionality of the data collected here (several hundreds of individuals x several hundreds of markers per species) will provide a very powerful basis for modelling evolutionary forces , the interactions between selection and gene flow and correlation between loci in their response to environmental pressure. (3) modelling non-equilibrium population-genetic processes and predicting the impact of global change. The data gathered within the FLAG project will be used to calibrate eco-genetic models currently in use or under development by the partnership. Partner 2 in particular is currently developing physio-demo-genetic models that couple (1) a physical and physiological module simulating the tree response to environmental variations, (2) a demographic module converting tree reserves into seed productions and tree mortality and modelling migration and (3) a quantitative genetics model allowing to account for genetic variation among individuals for various functional traits (Davi & Oddou-Muratorio 2010). FLAG will explicitly model the processes leading to the observed patterns of genetic divergence, then use this model to predict the effect of changes in selective regimes. This will allow us to simulate adaptive dynamic trajectories of trees populations in response to realistic climatic scenarios. The results obtained with this activity will make it possible to better understand the building of local adaptation in tree population along climatic gradient, and to gauge the quantitative impact of selection, plasticity, migration and species life history traits on the evolutionary dynamics of tree populations under environmental change.
References
Baraloto C, Bonal D, Goldberg D (2006) Differential seedling growth response to soil resource availability among nine neotropical tree species. Journal of tropical ecology 22, 487-497.
Baraloto C, Goldberg D, Bonal D (2005) Performance trade-offs among tropical tree seedlings in contrasting microhabitats. Ecology 86, 2461-2472.
Baraloto C, Morneau F, Bonal D, Blanc L, Ferry B (2007) Seasonal water stress tolerance and habitat associations within four neotropical tree genera. Ecology 88, 478-489.
Cohen AS, Stone JR, Beuning KRM, et al. (2007) Ecological consequences of early Late Pleistocene megadroughts in tropical Africa. Proceedings of the National Academy of Sciences 104, 16422-16427.
Jung V, Hoffmann L, Muller S (2009) Ecophysiological responses of nine floodplain meadow species to changing hydrological conditions. Plant Ecology 201, 589-598.
Mayle FE, Power MJ (2008) Impact of a drier Early-Mid-Holocene climate upon Amazonian forests. Philosophical Transactions of the Royal Society B: Biological Sciences 363, 1829-1838.
Meir P, Woodward IF (2010) Amazonian rain forests and drought: response and vulnerability. New Phytologist 187, 553-557.
Parelle J, Zapater M, Scotti-Saintagne C, et al. (2007) Quantitative trait loci of tolerance to waterlogging in a European oak (Quercus robur L.): physiological relevance and temporal effect patterns. Plant, Cell and Environment 30, 422-434.
Phillips OL, Aragao LEOC, Lewis SL, et al. (2009) Drought Sensitivity of the Amazon Rainforest. Science 323, 1344-1347.
Tournaire-Roux C, Sutka M, Javot H, et al. (2003) Cytosolic pH regulates root water transport during anoxic stress through gating of aquaporins. Nature 425, 393-396.
Workpackages
The project is composed of six workpackages.
Workpackage 1 (Coordination)
will have the goal of supervising the coherence of the project (harmonisation of data collection and treatment among species), insuring that tasks are completed on time, and organising meetings among partners. It will involve continuous checking of financial and administrative coherence of the project, streamlining regular publication of technical reports and scientific publications, and the organisation of a workshop within an international conference (e.g. IUFRO, ESEB, Plant and Animal Genome) on the genomics of adaptation to local gradients. It will as well organise technology transfer to stakeholder and communication of results to the general public.
Workpackage 2 (Genome scan)
will provide the population genomic data on which rests the analysis of levels of selection at the molecular level. Data will be acquired by RAD on replicated cases of populations distributed across environmental gradients and contrasts. Allele frequencies will be characterised for 1000-2000 polymorphisms per species.
Workpackage 3 (seedling trait measurement)
will rest on reciprocal transplants and replicated common gardens to obtain quantitative trait data from seedlings belonging to the same populations as the adult stand, and planted in plots close to, or within the perimeter of, the adults stands. The seedling will be measured repeatedly for the duration of the project.
Workpackage 4 (Genomics of adaptation)
is the first data analysis step. It will lead to the identification of SNP/indel markers that are statistically associated to environments and are submitted to divergent selection. This will be achieved by applying an array of statistical techniques based on correlation, on population divergence and on estimation of genetic diversity. The loci under selection identified in Task 4 will be used in Task 5.
Workpackage 5 (Association genetics)
will test the association of loci under selection with characters putatively under selection in contrasting environments and gradients, building on results from Task 3 and from Task 4. The association between traits and SNPs will be investigated by standard association genetics approaches, by taking into account population structure both within and between sites.
Workpackage 6 (modelling and simulation)
will allow us to estimate selection coefficients at molecular loci and on quantitative characters and to model the interplay between gene flow and selection in shaping patterns of genetic diversity at adaptive and neutral genes. For this task, we will use models or modelling platform already developed in the frame of the EU Evoltree NoE (http://www.evoltree.eu/index.php/modelling-platform) and/or under development in the frame of the EU Biodiversa Linktree project (http://www.igv.fi.cnr.it/linktree/?project/8). Finally, adaptive potential of populations to future environmental changes will be modelled.
Project updates
11 December 2013 Alternative GBS method proposed to the consortium: exome capture. We plan to obtain 10k SNPs per species, 100 individually tagged trees per species.
15 November 2013 Isabelle Lesur hired as bioinformatics specialist for one year to deal with NGS and GBS data.
October 2013. Data collection of full genome sequencing in Eperua falcata.
September 2013 Larch reciprocal transplant established; first trait measurement campaign on Eperua reciprocal transplant.
15 July 2013 Decision of the performance of in-house RADseq protocol. RADseq are deemed unsatisfactory and other options for GBS are considered. See the paper by Arnold et al. for methodological considerations.
July 2013. Data analysis of RADseq data in Eperua falcata from outsourced genotyping.
30 June 2013 (scheduled) Delivery of 6-month project report to the funding agency
3 June 2013: all samples received and accepted by Partner 6 for RADseq tests.
20 May 2013: RADseq tests started on a limited set of samples by Partner 6.
25-26 March 2013: meeting between the Coordinator and Partner 6 to discuss technical details of RADseq genotyping.
20 March 2013 Postdoc at EcoFoG recruited, scheduled start of the contract: 1st July 2013.
5 March 2013 Draft of the Kick-off meeting minutes sent to the partners.
4 March 2013 Shortlist of candidates for the FLAG postdoc at EcoFoG (see “Open Positions” folder.
End of February 2013 - Christophe BOURY joins the project (RADseq for all species, Partner 6).
End of January 2013 (scheduled) delivery of DNA samples to Partner 6 for the preparation of RADseq libraries
15-18 January 2013 Kick-off meeting In Avignon (France), joint with the ERANET-TIPTREE Kick-off meeting.
10 January 2013 Project presentation at the Kick-off meeting of the ANR-BIOADAPT funding program (ANR, Paris)
30 December 2012 : Post-doc position in population genomics open (see “Open positions” folder)
1st November 2012: Project started
Useful References
In this section you will find a mixed bag of references on “local adaptation” (whatever it may mean for you, the reader). The list of references will be recurrently updated with the contribution of project partners.
V. L. Sork, S. N. Aitken, R. J. Dyer, A. J. Eckert, P. Legendre and D. B. Neale (2013) Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate. Tree Genetics & Genomes 9: 901-911. http://dx.doi.org/10.1007/s11295-013-0596-x
A. Shafer and J. Wolf (2013) Widespread evidence for incipient ecological speciation: a meta-analysis of isolation-by-ecology. Ecology letters
H. Lalagüe (2013) Genetic response of tree population to spatial climatic variation : an experimental genomic and simulation approach in Fagus sylvatica populations along altitudinal gradients. PhD:
T. Günther and G. Coop (2013) Robust identification of local adaptation from allele frequencies. Genetics 195: 205-220.
S. De Mita, A.-C. Thuillet, L. Gay, N. Ahmadi, S. Manel, J. Ronfort and Y. Vigouroux (2013) Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations. Molecular Ecology 22: 1383-1399. http://dx.doi.org/10.1111/mec.12182
D. Audigeos, L. Brousseau, S. Traissac, C. Scotti-Saintagne and I. Scotti (2013) Molecular divergence in tropical tree populations occupying environmental mosaics. Journal of Evolutionary biology 26: 529-544.
F. J. Alberto, S. N. Aitken, R. Alía, S. C. González-Martínez, H. Hänninen, A. Kremer, F. Lefèvre, T. Lenormand, S. Yeaman, R. Whetten and O. Savolainen (2013) Potential for evolutionary responses to climate change – evidence from tree populations. Global Change Biology 19: 1645-1661. http://dx.doi.org/10.1111/gcb.12181
J.-P. Soularue and A. Kremer (2012) Assortative mating and gene flow generate clinal phenological variation in trees. BMC Evolutionary Biology 12: 79. http://www.biomedcentral.com/1471-2148/12/79
S. D. Schoville, A. l. Bonin, O. François, S. p. Lobreaux, C. Melodelima and S. p. Manel (2012) Adaptive genetic variation on the landscape: methods and cases. Annual Review of Ecology, Evolution, and Systematics 43: 23-43. http://www.annualreviews.org/doi/abs/10.1146/annurev-ecolsys-110411-160248
A. Duputié, F. Massol, I. Chuine, M. Kirkpatrick and O. Ronce (2012) How do genetic correlations affect species range shifts in a changing environment? Ecology letters 15: 251-259. http://dx.doi.org/10.1111/j.1461-0248.2011.01734.x
F. Blanquart, S. Gandon and S. L. Nuismer (2012) The effects of migration and drift on local adaptation to a heterogeneous environment. Journal of Evolutionary biology 25: 1351-1363. http://dx.doi.org/10.1111/j.1420-9101.2012.02524.x
B. Rhoné, R. Vitalis, I. Goldringer and I. Bonnin (2010) Evolution of flowering time in experimental wheat populations: a comprehensive approach to detect genetic signatures of natural selection. Evolution 64: 2110-2125. http://dx.doi.org/10.1111/j.1558-5646.2010.00970.x
B. Pujol, A. J. Wilson, R. I. C. Ross and J. R. Pannell (2008) Are QST–FST comparisons for natural populations meaningful? Molecular Ecology 17: 4782-4785. http://dx.doi.org/10.1111/j.1365-294X.2008.03958.x
P. Nosil, S. P. Egan and D. J. Funk (2008) Heterogeneous genomic differentiation between walking-stick ecotypes: “isolation by adaptation” and multiple roles for divergent selection
Evolution 62: 316-336. http://dx.doi.org/10.1111/j.1558-5646.2007.00299.x
D. Garant, S. E. Forde and A. P. Hendry (2007) The multifarious effects of dispersal and gene flow on contemporary adaptation. Functional ecology 21: 434-443.
A. Bonin, P. Taberlet, C. Miaud and F. Pompanon (2006) Explorative genome scan to detect candidate loci for adaptation along a gradient of altitude in the common frog (Rana temporaria). Molecular Biology and Evolution 23: 773-783.
M. Alleaume-Benharira, I. R. Pen and O. Ronce (2006) Geographical patterns of adaptation within a species’ range: interactions between drift and gene flow. Journal of Evolutionary biology 19: 203-215. http://dx.doi.org/10.1111/j.1420-9101.2005.00976.x
E. Porcher, T. Giraud, I. Goldringer and C. Lavigne (2004) Experimental demonstration of a causal relationship between heterogeneity of selection and genetic differentiation in quantitative traits. Evolution 58: 1434-1445. http://dx.doi.org/10.1111/j.0014-3820.2004.tb01725.x
M. J. Hovenden and J. K. Vander Schoor (2004) Nature vs nurture in the leaf morphology of Southern beech, Nothofagus cunninghamii (Nothofagaceae). New Phytologist 161: 585-594. http://dx.doi.org/10.1046/j.1469-8137.2003.00931.x
C. Schlötterer (2002) Towards a molecular characterization of adaptation in local populations. Current Opinion in Genetics & Development 12: 683-687. http://www.sciencedirect.com/science/article/pii/S0959437X02003490
T. Lenormand (2002) Gene flow and the limits to natural selection. Trends in Ecology & Evolution 17: 183-189. http://www.sciencedirect.com/science/article/pii/S0169534702024977
O. Ronce and M. Kirkpatrick (2001) When sources become sinks: migrational meltdown in heterogeneous habitats. Evolution 55: 1520-1531.
F. W. Allendorf and R. F. Leary (1986) Heterozygosity and fitness in natural populations of animals. Conservation Biology: the science of scarcity and diversity.
T. Nagylaki (1978) Clines with asymmetric migration. Genetics 88: 813-827. http://www.genetics.org/content/88/4/813.abstract
M. Caisse and J. Antonovics (1978) Evolution in closely adjacent plant populations. Heredity 40: 371-384. http://dx.doi.org/10.1038/hdy.1978.44
J. Antonovics (1971) The effect of a heterogeneous environment on the genetics of natural populations. The American scientist 59: 593-599.
S. K. Jain and A. D. Bradshaw (1966) Evolutionary divergence among adjacent plant populations I. The evidence and its theoretical analysis. Heredity 21: 407-441. http://dx.doi.org/10.1038/hdy.1966.42
Authorship
The FLAG webpages are maintained by Ivan Scotti (ivan.scotti[at]EcoFoG.gf)