BUSCADOR RECOLECTA

Supplementary Material 3: Lentil accessions evaluated in their resistance to lentil rust and used to calibrate the models of the Predictive Characterization., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary Material 4: Total number of evaluated accessions and number of accessions evaluated for lentil rust and broomrape. No. acc = number of accessions assessed; No. cty = number of countries were material was collected in origin. * Some countries host sampled populations of more than one taxon., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary Material 5: Complete dataset of wild lentil populations used and results, including Passport Information, Variables extraction per population and Predictive Characterization results. Explanations of each field can be found in the Lengend sheet., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary Material 6: List of top variables chosen by the Random Forest algorithm (in blue font), shown per group of variables and ordered by Mean Decrease Accuracy Value. Variables in red are correlated according to the bivariate correlations (See Supplementary Material 7)., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary Material 7: Table of bivariate correlations of the first 15 variables chosen by the Random Forest algorithm according to their Mean Decrease Accuracy values (See Supplementary Material 6). Number in brackets indicate selection order. Red values have Person correlation coefficient >0.50| and p-value <0.05, and considered to be correlated variables. Number in brackets relate to the variable selection order for subsequent analyses., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

1. Supplementary Data: -Genes involved in pollen development -Pectin Methyl-Esterases and biotic stress -MYB14 transcription factor modulated genes Supplementary table 1: A selection of few example queries using GraphQL, pyCOMPASS and rCOMPASS Supplementary Table 2: Comparison table between VESPUCCI v1 and v2 main features Supplementary Table 3: Comparison table between several on-line resources for grapevine and other plant species. Supplementary Figure 1. Bar Plot of VESPUCCI v1 sample distributions based on their condition annotation in Vitis vinifera (left) and non-vinifera (right) experiments. Samples are divided by tissues (X-axis) and their abundances in square root scale (Y-axis). In the left plot, different colors are used to denote untreated samples, biotic-treated samples and abiotic-treated samples. In the right plot, colors are used to differentiate between different Vitis species, subspecies and cross species. The great majority of samples (50%) come from Vitis vinifera untreated fruit samples taken at different developmental stages. Non-vinifera species and hybrids samples represent 7.5% of the dataset while 8.5% of the total are stress-related (1.4% being fruit) and 34% are Vitis vinifera untreated non-fruit samples., Peer reviewed

Supplementary Material 8: Variable values describing each of the ecogeographic categories of the Ecogeographic Land Characterization map for lentil wild species (Bio 1 values expressed in ºC). Mean, median, maximum, minimum and standard deviation values shown., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary Material 9: Populations of wild relatives of lentils in Europe selected through the Environmental Filtering Method of the Predictive Characterization technique and potentially tolerant to following abiotic stresses: Drought, soil salinity and waterlogging. Description of each field can be found in the Legend sheet., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary Material 10: True Statistic Skill values obtained for the nine tested algorithms (75% training data, 100 runs per algorithm) for lentil rust resistance., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

First of all, we must run the script called “Packages” in order to install all the packages that are needed. The rest of the code is structured in six folders, one folder for each model compiled. The structure of these folders is presented below. - Folder (1). It is called “1)Data” and it contains the datasets used as input at the folders from (2) to (6). - Folders from (2) to (6). They have one folder for each dataset and a txt file where the model is written using JAGS language. At each folder we find two R scripts, one where the model is estimated and the other for the prediction. The outputs of the estimating script are used as input for the predicting script. - Folder (2). It also contains an additional folder called “FemaleFamilies_DifferentSPBal”. Here we have estimate the proposed model using different selected principal balances to compare the results when extracting different percentage of variance to the data. This folder contains one folder for each combination of selected principal balances analysed., Peer reviewed