Juliette Archambeau^1,2, Marta Benito-Garzón¹, Marina de-Miguel^1,3, Alexandre Changenet¹, Francesca Bagnoli⁴, Frédéric Barraquand⁵, Maurizio Marchi⁴, Giovanni G. Vendramin⁴, Stephen Cavers², Annika Perry² and Santiago C. González-Martínez¹

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

2 UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, United Kingdom

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

4 Institute of Biosciences and BioResources, National Research Council, 50019 Sesto Fiorentino, Italy

5 CNRS, Institute of Mathematics of Bordeaux, F-33400 Talence, France

Corresponding author: Juliette Archambeau, juli.archambeau@gmail.com

Author contributions

SCG-M designed the experiment and supervised the curation of field data. MdM cleaned and formatted the phenotypic data in the common gardens. MM extracted the climatic values at the location of the populations and the National Forest Inventory plots, and provided the raster files to project the genomic offset predictions across the species range. F Bagnoli, SCG-M and GGV did the DNA extractions, production, cleaning and checking of the genomic data. AC cleaned and formatted mortality data from the National Forest Inventory plots. SCG-M, JA and MBG conceived the paper methodology. JA and F Barraquand built the Bayesian models and codes in the validation steps. JA conducted the data analyses and wrote the code available in the present repository.

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Data

Genomic data

Fo genomic data filtering and formatting, see 2_FormattingGenomicData.qmd.

Population genetic structure

PopulationStructureCorrea2015.csv

Proportion of assignment to the six gene pools identified in Jaramillo-Correa et al. (2015). This dataset contains 523 genotypes but only 454 genotypes were used in the present study because the other genotypes had no genomic data or too much missing data. Meaning of the columns:

1. clon: clone (i.e., genotype)
2. pop: population (i.e., provenance)
3. Q1: proportion of assignment to the northern African (NA) gene pool for each clone.
4. Q2: proportion of assignment to the Corsican (C) gene pool for each clone.
5. Q3: proportion of assignment to the central Spain (CS) gene pool for each clone.
6. Q4: proportion of assignment to the French Atlantic (FA) gene pool for each clone.
7. Q5: proportion of assignment to the Iberian Atlantic (IA) gene pool for each clone.
8. Q6: proportion of assignment to the south-eastern Spain (SES) gene pool for each clone.
9. max.Q: ID of the main gene pool of each clone.
10. main_gp: full name of the main gene pool of each clone.
11. color_main_gp: color attributed to the main gene pool of each clone in the figures.

Raw genomic data

RawGenomicData.csv

Genomic data before data filtering and formatting. NAs are indicated with ---. Meaning of the columns:

1. clone: clone ID.
2. assay: assay in which the clone was genotyped, either the Infinium assay (only_Inf), the Axiom assay (only_Affx) or both assays (both_Inf_Affx).
3. snp_1 to snp_14016: genotype for each of the 14,016 SNPs.

Filtered and formatted allele counts

FormattedFilteredGenomicData_AlleleCounts_withmaf.csv and FormattedFilteredGenomicData_AlleleCounts_withoutmaf.csv

FormattedFilteredGenomicData_AlleleCounts_withmaf.csv contain alleles not filtered for minor allele frequencies (MAF); and FormattedFilteredGenomicData_AlleleCounts_withoutmaf.csv containes alleles filtered for MAF < 1%..
These datasets are allele counts of each genotype (coded as 0, 1 or 2). SNPs in rows and genotypes in columns. Meaning of the columns:

1. snp_ID: SNP ID.
2. to 455. ALT10 to VER9: Clone (i.e., genotype) ID.

Filtered and formatted allele frequencies

FormattedFilteredGenomicData_AlleleFrequencies_withoutmaf.csv

Allele frequencies of the populations filtered for MAF < 1%. SNPs in columns and populations in rows. Meaning of the columns:

1. pop: population.
2. to 9818. SNPs ID.

Imputed allele counts

Missing values were imputed based on the most common allele within the main gene pool of the genotype of concern (although we acknowledge that some genotypes had high admixture rates).

ImputedGenomicData_AlleleCounts_withmaf.csv and ImputedGenomicData_AlleleCounts_withmaf.csv

ImputedGenomicData_AlleleCounts_withmaf.csv is not filtered for MAF. ImputedGenomicData_AlleleCounts_withoutmaf.csv is filtered for MAF < 1%.

These datasets contain imputed allele counts for each genotype (coded as 0, 1 or 2) . SNPs in rows and genotypes in columns.Meaning of the columns:

1. snp_ID: SNP ID.
2. to 455. ALT10 to VER9: Clone (i.e., genotype) ID.

Imputed allele frequencies

ImputedGenomicData_AlleleFrequencies_withmaf.csv and ImputedGenomicData_AlleleFrequencies_withoutmaf.csv

ImputedGenomicData_AlleleFrequencies_withmaf.csv is not filtered for MAF. ImputedGenomicData_AlleleFrequencies_withoutmaf.csv is filtered for MAF < 1%.

Theses datasets contain imputed allele frequencies of the populations. SNPs in columns and populations in rows. Meaning of the columns:

1. pop: population.
2. to 9818. SNPs ID.

SNP information

SNPsInformation.csv.

Information of the different SNP IDs used across studies/assays, SNP position on the genome, SNP annotation and whether the SNPs have a minor allele frequency. SNPs are in rows. Meaning of the columns:

1. snp_ID: SNP ID used in the present study.
2. original_ID: Original ID of the SNP.
3. affx_ID: SNP ID in the Axiom assay.
4. infinium_ID: SNP ID in the Infinium assay.
5. scaffold/contig: contig on which the SNP is located. The term scaffold is also used as some SNPs were obtained from the alignment of NGS short-reads from a pseudoreference genome in Pinus pinaster that were called scaffolds (even though there are not really scaffolds).
6. genome_position: position of the SNP on the scaffold/contig (in bp).
7. annotation: SNP name (including possible alternative names).
8. MAF_filtering: REMOVED for SNPs with minor allele frequencies, which are removed for GEAs analyses but not for estimating the neutral population genetic structure; KEPT otherwise.

SNPsInformation_WithOutliers.csv.

This dataset is similar to SNPsInformation.csv (the first eight columns are identical) but includes additional information about the identification of SNPs by the different gene-environment association (GEA) methods (GF, RDA, pRDA, LFMM and BayPass), and about the inclusion of the SNPs in the final sets of control and candidate SNPs (see 7_GeneratingSNPsets.qmd report). Meaning of the columns:

1. RDA_outliers: TRUE if the SNP was identified as an outlier in the RDA; FALSE otherwise.
2. pRDA_outliers: TRUE if the SNP was identified as an outlier in the pRDA; FALSE otherwise.
3. GF_outliers: TRUE if the SNP was identified as an outlier with the GF algorithm; FALSE otherwise.
4. LFMM_outliers: TRUE if the SNP was identified as an outlier woth LFMM; FALSE otherwise.
5. BayPass_outliers: TRUE if the SNP was identified as an outlier with BayPass; FALSE otherwise.
6. all_candidate_SNPs: TRUE if the SNP was an outlier identified by at least one GEA method among RDA, pRDA, LFMM, BayPass and GF; FALSE otherwise.
7. common_candidate_SNPs: TRUE if the SNP was an outlier identified by at least two GEA methods among RDA, pRDA, LFMM, BayPass and GF; FALSE otherwise.
8. candidate_SNPs_corrected_pop_structure: TRUE if the SNP was an outlier identified by at least one GEA method correcting for population genetic structure (i.e., pRDA, LFMM or BayPass); FALSE otherwise.
9. random_control_SNPs: TRUE if the SNP was a control SNP randomly sampled among the SNPs not identified by any GEA method; FALSE otherwise.
10. control_SNPs_matching_allele_freq: TRUE if the SNP was a control SNP sampled among the SNPs not identified by any GEA method, and has a similar allele frequency to a candidate SNP; FALSE otherwise.
11. control_SNPs_without_NAs: TRUE if the SNP had no mising data; FALSE otherwise.

maritime_pine_coord.txt

SNP positions on the chromosome-scale reference genome of a related species (i.e., Pinus tabuliformis; Niu et al. 2022), as such a reference is not yet available for maritime pine. Meaning of the columns:

1. AxiomID: SNP ID in the Axiom assay.

2. CHROM: assigned chromosome of the SNP on the reference genome. Contig was indicated when no chromosome was assigned.

3. Absolute_POS: position of the SNP on the reference genome.

Climatic data

Climatic data was extracted with the Climate Downscaling Tool (ClimateDT). The full name, units and calculation method of the climatic variables can be found on the ClimateDT website: https://www.ibbr.cnr.it/climate-dt/?action=fldlist. Note that the variable called mean summer precipitation (MSP) in ClimateDT was renamed summer precipitation (SP) in the present study.

• tmn01 to tmn12: monthly minimum temperature (°C).
• tmx01 to tmx12: monthly maximum temperature (°C).
• prc01 to prc12: monthly total precipitation (mm).
• bio1: mean annual temperature (°C).
• bio2: mean diurnal range (mean of monthly (maximum temperature - minimum temperature)) (°C).
• bio3: isothermality (bio2/bio7) (×100) (index).
• bio4: temperature seasonality (standard deviation ×100) (°C).
• bio5: maximum temperature of the warmest month (°C).
• bio6: minimum temperature of the coldest month (°C).
• bio7: temperature annual range (bio5-bio6) (°C).
• bio8: mean temperature of the wettest quarter (°C).
• bio9: mean temperature of the driest quarter (°C).
• bio10: mean temperature of the warmest quarter (°C).
• bio11: mean temperature of the coldest quarter (°C).
• bio12: annual precipitation (mm).
• bio13: precipitation of the wettest month (mm).
• bio14: precipitation of the driest month (mm).
• bio15: precipitation seasonnality (coefficient of variation) (index).
• bio16: precipitation of the wettest quarter (mm).
• bio17: precipitation of the driest quarter (mm).
• bio18: precipitation of the warmest quarter (mm).
• bio19: precipitation of the coldest quarter (mm).
• Eref: Hargreaves reference evaporation (mm).
• CMD: Hargreaves climatic moisture deficit (mm).
• MCMT:mean coldest month temperature (°C).
• SP: summer (May to September) precipitation (mm).
• MWMT: summer (May to September) precipitation (mm).
• GDD0: degree-days above 0°C, chilling degree-days (°C * days).
• GDD5: degree-days above 5°C, chilling degree-days (°C * days).
• GDD18: degree-days above 18°C, chilling degree-days (°C * days).
• NFFD: number of frost-free days (day).
• DMA: De Martonne aridity index (index).
• EPQ: Emberger pluviothermic quotient (index).
• RMT: Rivas-Martinex thermic index (index).
• AHM: annual heat moisture index (bio1+10)/(bio12/1000)) (°C/mm).
• SHM: summer heat moisture index MWMT/(MSP/1000) (°C/mm).
• TD: temperature difference MWMT-MCMT (°C).
• EMT: extreme minimum temperature over 30 years (°C).
• bFFP: Julian date on which FFP begins (day).
• eFFP: Julian date on which FFP ends (day).
• FFP: frost-free period (day).
• PAS: precipitation as snow (mm).
• spi01 to spi12: monthly standardized precipitation index (index).
• spei01 to spei12: monthly standardized precipitation index (index).

This information on the climatic variables used in the present study is also available in the ClimateDT_Metadata.csv dataset. In this dataset, the first column contains the full names of the climatic variables, the second column lists their units, and the third column provides their codes.

Climate in the common gardens

ClimateDT_CommonGardens.csv

Annual climatic data (from 2010 to 2018) at the location of the five common gardens: Asturias (Spain), Bordeaux (France), Cáceres (Spain), Madrid (Spain) and Fundão (Portugal).

1. cg: common garden.
2. longitude of the common garden.
3. latitude of the common garden.
4. elevation of the common garden.
5. year between 2010 and 2018.
6. to 104. climatic variables (see names above).

Climate at the location of the populations

AnnualClimaticData_FromClimateDT_PopulationLocations.csv

Annual climatic data (from 1901 to 2021) at the location of the 34 CLONAPIN populations.

1. pop: population.
2. longitude of the population.
3. latitude of the population.
4. elevation of the population.
5. year between 1901 and 2021.
6. to 103. climatic variables (see names above)
    

AveragedClimaticVariables_ReferencePeriod_PopulationLocations.csv

Averages of the climatic variables over the reference period 1901-1950 at the location of the 34 CLONAPIN populations.

1. pop: population.
2. longitude of the population.
3. latitude of the population.
4. elevation of the population.
5. to 64. climatic variables (see names above).

AveragedClimaticVariables_FuturePeriod_PopulationLocations.csv

Predicted averages of the climatic variables at the location of the 34 CLONAPIN populations over the period 2041-2070, under the scenario SSP3.7-0 and for five climatic general circulation models (GCMs): GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0 and UKESM1-0-LL (column gcm).

1. pop: population.
2. gcm: general circulation model.
3. to 26. climatic variables (see names above).

Climate in the inventory plots

ClimateDT_NFIPlots_PastClimates.csv

Averaged climatic data at the location of the NFI plots for the reference period 1901-1950.

1. plotcode: code of the inventory plot.
2. longitude of the plot.
3. latitude of the plot.
4. elevation of the plot.
5. to 64. climatic variables (see names above).

 

ClimateDT_NFIPlots_SurveyClimates.csv

Averaged climatic data at the location of the NFI plots for the survey periods specific to each plot.

1. plotcode: code of the inventory plot.
2. longitude of the plot.
3. latitude of the plot.
4. elevation of the plot.
5. to 64. climatic variables (see names above).

National Forest Inventory (NFI) data

NFIdata_cleaned.csv

This dataset contains mortality data from the natural populations of the Spanish and French NFI plots in which maritime pines were recorded. Meaning of the columns:

1. plotcode: code of the NFI plot.
2. longitude: longitude of the NFI plot.
3. latitude: latitude of the NFI plot.
4. country: country of the NFI plot (ES = Spain, FR = France).
5. nb_years: number of years between surveys. It corresponds to the number of years between the first and second inventory in Spain and is equal to 5 in the French inventory as mortality was estimated in the five years before the survey date.
6. first_survey: year of the first survey in the Spanish NFI (NA for the French NFI)
7. second_survey: year of the second survey in the Spanish NFI and the unique survey in the French NFI
8. nb_dead: number of dead maritime pines in the NFI plot.
9. nb_tot: total number of maritime pines in the NFI plot (including the dead ones).
10. basal_area: basal area of all tree species in the NFI plot (proxy of the competition among trees).
11. mean_DBH: mean DBH of the maritime pines in the NFI plot including dead trees, adults and saplings/seedlings (recruitment).
12. prop_dead: proportion of dead maritime pines in the NFI plot.
13. annual_prop_dead: annual proportion of maritime pine mortality in the NFI plot.

 

Common garden data

CommonGardendata_cleaned.csv

This dataset contains mortality and height data from five clonal common gardens. Meaning of the columns:

1. cg: name of the common garden.
2. block: block in which the tree was planted.
3. pop: population of the tree.
4. clon: genotype of the tree.
5. tree: tree ID.
6. AST_htmar14: height measurements in March 2014 in Asturias (Spain) when the trees were 37 month-old (trees planted in February 2011).
7. BDX_htnov2018: height measurements in November 2018 in Bordeaux (France) when the trees were 85 month-old (trees planted in October 2011).
8. CAC_htdec11: height measurements in December 2011 in Cáceres (Spain) when the trees were 8 month-old (trees planted in April 2011).
9. MAD_htdec11: height measurements in December 2011 in Madrid (Spain) when the trees were 13 month-old (trees planted in November 2010).
10. POR_htmay13: height measurements in May 2013 in Fundão (Portugal) when the trees were 27 month-old (trees planted in February 2011).
11. AST_survmar14: survival data (0 for dead trees and 1 for survivors) in March 2014 in Asturias.
12. BDX_surv18: survival data in November 2018 in Bordeaux.
13. CAC_survdec11: survival data in December 2011 in Cáceres.
14. MAD_survdec11: survival data in December 2011 in Madrid.
15. POR_survmay13: survival data in May 2013 in Fundão.

 

HeightIntercepts_Archambeauetal2023.csv

This dataset contains the population height intercepts calculated across the five common gardens in the model 1 of Archambeau et al. (2022). Meaning of the columns:

1. pop: population ID.
2. height: mean of the posterior distributions of the population varying intercepts.
3. std.error: standard error of the mean of the posterior distributions of the population varying intercepts.
4. and 5. conf.low and conf.high: credible intervals of the posterior distributions of the population varying intercepts.

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Results

Genomic offset predictions and climatic distances at the location of the populations

go_predictions_climdist.csv

Dataset generated in 11_ComparingGenomicOffsetPredictions.qmd. Meaning of the columns:

1. var_name: name of the set of SNPs or climatic variable.
2. method_name: name of the genomic offset method or CD for climatic distance.
3. var_code: code of the set of SNPs or climatic variable.
4. pop: population.
5. val: value of the genomic offset or the climatic distance.
6. gcm: general circulation model used for future climate (2041-2060; SSP 3.7-0).
7. main_gp_pop: main gene pool of the population.
8. color_main_gene_pool: color used in the figures for the main gene pool of each population.

Genomic offset predictions and climatic distances at the location of the inventory plots

go_nfi_predictions.csv

Dataset generated in 12_ValidationNFI.qmd. Meaning of the columns:

1. snp_set_code: code of the set of SNPs (or code of the climatic variables for the climatic distances).
2. snp_set_name: name of the set of SNPs (or name of the climatic variables for the climatic distances).
3. plotcode: code of the inventory plot.
4. method: name of the genomic offset method or CD for climatic distances.
5. GO: value of the genomic offset or the climatic distance.
6. method_snp_set_code: code of the combination of the genomic offset method (or climatic distance) and the set of SNPs (or climatic variable).
7. method_snp_set_name: full name of the combination of the genomic offset method (or climatic distance) and the set of SNPs (or climatic variable).

go_cg_predictions.csv

Dataset generated in 13_ValidationCommonGardens.qmd. Meaning of the columns:

1. cg: name of the common garden.
2. pop: population.
3. input_code: code of the set of SNPs (or code of the climatic variables for the climatic transfer distances).
4. varX: value of the genomic offset or the climatic transfer distance.
5. method: name of the genomic offset method or CTD for climatic transfer distances.
6. method_input_code: code of the combination of the genomic offset method (or climatic transfer distance) and the set of SNPs (or climatic variable).
7. input_name: name of the set of SNPs (or code of the climatic variables for the climatic transfer distances).
8. method_input_name: full name of the combination of the genomic offset method (or climatic transfer distance) and the set of SNPs (or climatic variable).

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Scripts

Main reports

Quarto (or rmarkdown) and html versions of these scripts are available in the DRYAD repository.

All data used in these scripts are available in the DRYAD repository, except for some large rasters used to project genomic offset predictions across the range of maritime pine. These climatic data are available on the ClimateDT website: https://www.ibbr.cnr.it/climate-dt/.

The objects computed in these scripts are stored in an /outputs folder that users will need to create on their own computers to run the scripts successfully.

1_MainPopulationGenePool_CommonGardenClimates.qmd

• Attributing to each clone and population its main gene pool based on the ancestry coefficients estimated with the STRUCTURE software in Jaramillo-Correa et al. (2015).
• Calculating the average of the climatic variables between the planting and the measurement dates in the common gardens.

2_FormattingGenomicData.Rmd

• Formatting genomic data: converting letters (e.g. A/A, A/G) to numbers (0,1 or 2), and --- to NA.
• Filtering genomic data for monomorphic SNPs, minor allele counts (MAC), proportion of missing data per clone and per SNP, minor allele frequencies (MAF).
• Determining SNPs position on the genome.
• Exploring genomic data, e.g., number of SNPs/clones genotyped in each assay, Average and maximum number of missing values per clone.
• Imputation of missing data.

3_ReduncancyAnalysis_VariancePartionning_IdentificationCandidateSNPs.Rmd

• Selection of the climatic variables based on their biological relevance for maritime pine, their contribution to the genetic variance using a RDA-based stepwise selection (Capblancq and Forester 2021) and the magnitude of their exposure to climate change.
• Partitioning genomic variation among climate, neutral population genetic structure (accounted for with the main axes of a PCA) and geography (accounted for with population coordinates or distance-based Moran’s eigenvector maps).
• Identification of the outlier SNPs using Redundancy analysis (RDA) and partial RDA; approach developed in Capblancq et al. (2018) and Capblancq and Forester (2021).

4_GradientForest_IdentificationCandidateSNPs.qmd

• Identification of outlier SNPs with the Gradient Forest (GF) algorithm as described in Fitzpatrick et al. (2021) and Capblancq et al. (2023).

5_BaypassAnalysis_IdentificationCandidateSNPs.qmd

• Identification of outlier SNPs with BayPass; an approach described in Gautier (2015).

6_LFMM_IdentificationCandidateSNPs_GenomicOffsetPredictions.qmd

• Identification of outlier SNPs and estimation of the genetic gap (i.e., genomic offset) with the LEA R package (Gain & Francois 2021), which uses the latent factor mixed model (LFMM) approach (Frichot et al. 2013, Caye et al. 2019). Mathematical and theoretical details of the approach in Gain et al. (2023).

7_GeneratingSNPsets.qmd

• Identifying common outlier SNPs across the different gene-environment association (GEA) methods.
• Checking the genome position of the outlier SNPs; when some of them were located on the same scaffold/contig, only the SNP with the lower $p$-value in the RDA was kept in the final sets of candidate SNPs.
• Mapping SNP position on the reference genome of Pinus tabuliformis (Niu et al. 2022).
• Generating three sets of candidate SNPs and two sets of control SNPs (with the same number of SNPs as in the set with all candidate SNPs).

8_RedundancyAnalysis_GenomicOffsetPredictions.qmd

• Genomic offset predictions with Redundancy Analysis (RDA) and partial RDA; approach described in Capblancq and Forester (2021).
• Projections of the genomic offset predictions across the maritime pine range. Note that the rasters used for these projections (i.e., one for each climatic variable, and each general circulation model, so 6 rasters for the 1901-1950 reference climate; and 6*5=30 rasters for the 2041-2060 future climates) are not available in the DRYAD repository but are available on the ClimateDT website.

9_GeneralizedDissimilarityModelling_GenomicOffsetPredictions.qmd

• Genomic offset predictions with the Generalized Dissimilarity Modelling (GDM) approach, as described in Fitzpatrick & Keller (2015), Mokany et al. (2022) and the GDM website.

10_GradientForest_GenomicOffsetPredictions.qmd

• Genomic offset predictions with the Gradient Forest (GF) algorithm, approach described in Fitzpatrick & Keller (2015) and Gougherty et al. (2021).

11_ComparingGenomicOffsetPredictions.qmd

• Comparing genomic offset predictions across the different methods (GF, GDM, LFMM, RDA and pRDA), SNPs sets (control and candidate SNPs) and GCMs.

12_ValidationNFI.qmd

• Exploring mortality data from the National Forest Inventory (NFI) plots of France and Spain; see Changenet et al. (2021).
• Calculating the climatic distances between the reference climate (1901-1950) and the climate during inventories at the location of the inventory plots.
• Estimating the association between the genomic offset predictions and mortality rates in the NFI plots (with regression coefficients or correlations).

13_ValidationCommonGardens.qmd

• Calculating the climate transfer distances (CTDs), i.e., absolute climatic difference between the climates at the location of the populations and the common gardens.
• Estimating the association between genomic offset predictions / CTDs and mortality and height data from the five clonal common gardens (CLONAPIN network).

RPackageCitations.qmd

• Citations of the R packages used in the present study.

Additional functions

Additionnal functions used in the main scripts:

• calc_avg_clim_var.R - function to calculate average climatic variables over a specific period, based on the monthly climatic data from ClimateDT. In this function, the window function comes from the dismo R package. Under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007.
• corpmat.R - to compute a matrix of p-values; from Statistical Tools for High-throughput Data Analysis & Visualization (STHDA) website: http://www.sthda.com/english/wiki/visualize-correlation-matrix-using-correlogram.
• detectoutliers.R - to identify outliers based on their RDA loadings (from Forester et al. 2018); from Forester et al. (2018): https://popgen.nescent.org/2018-03-27_RDA_GEA.html.
• kable_mydf.R - my own function to print table using the kableExtra R package.
• make_eucli_plot.R - my own function to plot genomic offset predicions against Euclidean climatic distances, using R base plots.
• make_go_map.R - my own function to plot genomic offset predictions at specific locations.
• rdadapt.R - to conduct a RDA based genome scan; from Capblancq & Forester (2021): https://github.com/Capblancq/RDA-landscape-genomics/blob/main/src/rdadapt.R.

Stan code of the models

Stan code of the models used in the validation steps:

• ValidationNFI_stancode.stan - model used to estimate the association between mortality rates and genomic offset predictions/climatic distances in the inventory plots.
• ValidationNFI_stancode_with_predictions.stan - same model as ValidationNFI_stancode.stan but including predictions of mortality rates in the model outputs.
• ValidationNFI_stancode_with_predictions_withoutGO.stan - same model as ValidationNFI_stancode_with_predictions.stan but without genomic offset predictions or climatic distances as predictors (model used to estimate correlations between the model residuals and genomic offset predictions/climatic distances).
• ValidationCommonGarden_BinomialMortalityModel.stan - model used to estimate the association between mortality rates and genomic offset predictions/CTDs in the common gardens.
• ValidationCommonGarden_BinomialMortalityModel_WithoutGO.stan - same model as ValidationCommonGarden_BinomialMortalityModel.stan but without genomic offset predictions or climatic distances as predictors (model used to estimate correlations between the model residuals and genomic offset predictions/climatic distances).
• ValidationCommonGarden_HeightModel.stan - model used to estimate the association between height and genomic offset predictions/CTDs in the common gardens.
• ValidationCommonGarden_HeightModel_WithoutPredictor.stan - same model as ValidationCommonGarden_HeightModel.stan but without genomic offset predictions or climatic distances as predictors, model used to estimate the proportion of variance explained in a model that does not include the predictor of interest.
• ValidationCommonGarden_HeightModel_WithoutPredictor_WithPredictions.stan - same model as ValidationCommonGarden_HeightModel_WithoutPredictor.stan but including height predictions in the model outputs.

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License

The code of this repository is under the MIT license.

MIT license

Copyright (c) 2024 Authors

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Software versions

Analyses were undertaken with R version 4.3.3. R package versions are shown at the end of each script and in RPackageCitations.html.

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References

Archambeau J, Benito Garzón M, Barraquand F, de Miguel M, Plomion C and González-Martı́nez SC (2022). Combining climatic and genomic data improves range-wide tree height growth prediction in a forest tree. The American Naturalist 200(4):E141–E159.

Capblancq T, Luu K, Blum MGB and Bazin E (2018). Evaluation of redundancy analysis to identify signatures of local adaptation. Molecular Ecology Resources 18(6):1223–1233.

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Version changes

29-jan-2025.

These updates were made in response to revisions following the first round of review in The American Naturalist.

The abstract was modified.

The dataset SNPsInformation_WithOutliers.csv was also updated, here are the columns that were modified or added:

1. all_candidate_SNPs: TRUE if the SNP was an outlier identified by at least one GEA method among RDA, pRDA, LFMM, BayPass and GF; FALSE otherwise.
2. common_candidate_SNPs: TRUE if the SNP was an outlier identified by at least two GEA methods among RDA, pRDA, LFMM, BayPass and GF; FALSE otherwise.
3. candidate_SNPs_corrected_pop_structure: TRUE if the SNP was an outlier identified by at least one GEA method correcting for population genetic structure (i.e., pRDA, LFMM or BayPass); FALSE otherwise.
4. random_control_SNPs: TRUE if the SNP was a control SNP randomly sampled among the SNPs not identified by any GEA method; FALSE otherwise.
5. control_SNPs_matching_allele_freq: TRUE if the SNP was a control SNP sampled among the SNPs not identified by any GEA method, and has a similar allele frequency to a candidate SNP; FALSE otherwise.

Reports and scripts were updated to estimate the genomic offset for six sets of SNPs: three sets of candidate SNPs and three sets of control SNPs. Additionally, the estimation of genomic offset using pRDA was incorporated. Following these new analyses, the scripts for generating tables and figures were also updated.

10-apr-2025.

These updates were made to format the data and code according to the standards of The American Naturalist. They are specified in the Version changes of the README.

Modifications of the datasets:

• SNPsInformation_WithOutliers.csv was divided into two datasets: SNPsInformation.csv, which contains information only about SNP IDs, genomic positions, annotations, and MAF; and SNPsInformation_WithOutliers.csv, which is the same dataset but includes additional information about outlier identification and the inclusion of SNPs in the sets of candidate and control SNPs.
• Three datasets containing the genomic offset predictions and climatic distances estimated in the present study were uploaded in the following datasets: go_predictions_climdist.csv, go_nfi_predictions.csv and go_cg_predictions.csv.
• In the dataset ClimateDT_NFIPlots_SurveyClimates.csv, the plots have been reordered to match the order in the dataset ClimateDT_NFIPlots_PastClimates.csv.
• The ClimateDT_Metadata.csv dataset was added to provide information on the climatic variables from ClimateDT.
• The maritime_pine_coord.txt dataset was added to provide the SNP positions on a reference genome (i.e., the Pinus tabuliformis genome).

Modifications of the scripts:

• All reports have been revised between versions V2 and V3 to ensure the code is reproducible and includes only the sections of code relevant to the paper.
• 0_FormattingPopulationCoordinatesElevationClimateData.qmd and 2_CommonGardenData.qmd were merged into 1_MainPopulationGenePool_CommonGardenClimates.qmd.
• 1_FormattingGenomicData.Rmd was renamed to 2_FormattingGenomicData.Rmd.
• 3_CheckingPastFutureClimatesPopulationLocations.qmd, 14_ValidationNFI_ModelComparison.qmd and 16_PCAplotIDpops.qmd were removed because they did not contain code that was directly used in the paper.
• 4_ReduncancyAnalysis_VariancePartionning_IdentificationCandidateSNPs.Rmd was renamed to 3_ReduncancyAnalysis_VariancePartionning_IdentificationCandidateSNPs.Rmd.
• 5_GradientForest_IdentificationCandidateSNPs.qmd was renamed to 4_GradientForest_IdentificationCandidateSNPs.qmd.
• 6_BaypassAnalysis_IdentificationCandidateSNPs.qmd was renamed to 5_BaypassAnalysis_IdentificationCandidateSNPs.qmd.
• 7_LFMM_IdentificationCandidateSNPs_GenomicOffsetPredictions.qmd was renamed to 6_LFMM_IdentificationCandidateSNPs_GenomicOffsetPredictions.qmd.
• 8_GeneratingSNPsets.qmd was renamed to 7_GeneratingSNPsets.qmd.
• 11_RedundancyAnalysis_GenomicOffsetPredictions.qmd was renamed to 8_RedundancyAnalysis_GenomicOffsetPredictions.qmd.
• 12_ComparingGenomicOffsetPredictions.qmd was renamed to 11_ComparingGenomicOffsetPredictions.qmd.
• 13_ValidationNFI.qmd was renamed to 12_ValidationNFI.qmd.
• 15_ValidationCommonGardens.qmd was renamed to 13_ValidationCommonGardens.qmd.
• The names of the files containing the Stan code for the models have been updated for clarity, and the content of each file is now explicitly described in the README.
• Some additional scripts were removed as they were not relevant for the paper: make_high_go_pop_maps.R, project_adaptive_index.R, generate_scaled_nfi_clim_datasets.R, generate_scaled_clim_datasets.R, extract_clim_from_rasters.R, extract_climatedt_metadata.R and baypass_utils.R