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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

Supplementary Material 11: Subset of populations of wild relatives of lentils in Europe selected through the Calibration Method of the Predictive Characterization technique and potentially resistant to lentil rust (Uromyces vicia-fabae (Pers.) Schröt)., 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 information. Peer Review File. Source Data, Peer reviewed

Peer reviewed

Genomic architectures of the plant viruses, detection by RT-qPCR, additional results of Cas12a-based detection, nuclease expression and purification scheme, additional results of Cas13a/d-based detection, nucleic acid sequences, and numerical data., Peer reviewed

S1 Data. Raw data. It collects the data from the surveys. No processing is included in this set. Data on gender, age, location and authors of the interview were considered potentially identifying information by the publisher, so they have been removed from the dataset provided with the article. All the answers of 284 respondents are included. S2 Data. Cleaned data. First day surveys are excluded. Data on gender, age, location and authors of the interview were considered potentially identifying information by the publisher, so they have been removed from the dataset provided with the article. Data were cleaned without removing any relevant information. S3 Data. Data with variables included in the analysis. The inputs (SDE variables) and output (Risk Indices) used for the analysis. S4 Data. Data with the variables appearing in the final model. This dataset contains only the variables appearing in the model with the binarized risk indices., S1 File. IRB results. Result of Lehigh University’s Institutional Review Board evaluation. S2 File. Consent statement of participants: Informed consent statement that was distributed to all the survey participants, in English and Krio. S3 File. Survey instrument: Survey questions and all possible answers, in English and Krio. S4 File. PLOS’ questionnaire on inclusivity in global research: A complete copy of PLOS’ questionnaire on inclusivity in global research in our manuscript. S5 File. Inclusivity in global research., S1 Fig. Results of the Xgboost algorithm. S2 Fig. Results of the UMAP analysis. S3 Fig. Results of the principal component analysis (PCA)., Peer reviewed

Table S1: Host range: response of legume species to isolates of powdery mildew., Peer reviewed

This Supplementary Material contains some details related to the Experimental Section, including Reagents, Experimental Methods and Instrumentation. Concerning the Results and Discussion section, different Figures and Tables are included showing experimental results related to optimizations of the immunocytochemistry assay, the analysis of HRPEsv cells by LA-ICP-MS and the characterisation of HRPEsv cells@AuNCs standards., Peer reviewed

Appendix S1. Details of data sets included in the EVA-W arable weed vegetation subset of the European Vegetation Archive. Appendix S2. Details of data sets included in Arable Weeds and Management in Europe data collection (as of 2021). Appendix S3. Record numbers presented in Figure 5 of the main paper. Appendix S4. Distribution of records over time in two data collections., Peer reviewed