Dahlia common mosaic virus (DCMV) was previously reported to infect only dahlias (Dahlia variabilis). However, DCMV was identified for the first time in association with Silphium spp. The complete genome of DCMV was cloned and sequenced from naturally occurring symptomatic silphium plants grown in Kansas. The two DCMV silphium isolates sequenced showed over 98% sequence identity with those reported from dahlia (D. variabilis). The D. variabilis endogenous pararetroviral sequence (DvEPRS) was also detected in all 19 silphium samples tested in this study, suggesting that DvEPRS may exist as an integrated sequence in the silphium genome as well. Our findings indicate that the host range of DCMV appears to include other plant species besides dahlia. Copyright (c) 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

All figures and code were generated in RStudio 2022.02.3+492 "Prairie Trillium" Release. All packages needed to run the R code are shown in the RMD’s. Code to generate figures and statistical analysis: DVTindex_help.Rmd Code to generate figure 3 Figure5_PrairieVSCommonGarden Code to generate figure 5 PATHOFigs_for_Manu_FEB22.Rmd Code to generate figures 1, 2, 4 SUPP_Figure3_DimPathoEDIT.Rmd Code to generate supplementary figure 3 (fig S3) STATSAnalysis_PathoDim2B.Rmd Code to generate all tables and statistical analysis in the manuscript Description of data files: 2019and2020Prairie_data.txt This file contains data collected from the prairie sites in 2019 and 2020 Dim2b_latlon.csv The latitudinal and longitudinal coordinates for the common garden sites and prairie sites as well as precipitation data DVTINDEX_from_DVT.csv A separate file that contains the data that produced fig 3. This data file is sourced from SEPT2019_2020_COMPILED_2b_DATACOLL.txt SEPT2019_2020_COMPILED_2b_DATACOLL.txt SEPT2019_2020_COMPILED_2b_DATACOLL_plots.txt SEPT2019_2020_COMPILED_2b_DATACOLL_plots_longversion.txt These 3 data files are different versions of the same raw data that was collected from the common garden sites in 2019 and 2020. The code calls for all 3 at different points to generate plots and run statistical analyses Summary [ insert unique name here] Various data frames generated from r mark downs that contain summary statistics of the data. These are used to produce the figures in PATHOFigs_for_Manu_FEB22.Rmd graphs.

Sainfoin (Onobrychis spp.) is a perennial forage legume that is also attracting attention as a perennial pulse with potential for human consumption. The dual use of sainfoin underpins diverse research and breeding programs focused on improving sainfoin lines for forage and pulses, which is driving the generation of complex datasets describing high dimensional phenotypes in the post-omics era. To ensure that multiple user groups, for example, breeders selecting for forage and those selecting for edible seed, can utilize these rich datasets, it is necessary to develop common ontologies and accessible ontology platforms. One such platform, Crop Ontology, was created in 2008 by the Consortium of International Agricultural Research Centers (CGIAR) to host crop-specific trait ontologies that support standardized plant breeding databases. In the present study, we describe the sainfoin crop ontology (CO). An in-depth literature review was performed to develop a comprehensive list of traits measured and reported in sainfoin. Because the same traits can be measured in different ways, ultimately, a set of 98 variables (variable = plant trait + method of measurement + scale of measurement) used to describe variation in sainfoin were identified. Variables were formatted and standardized based on guidelines provided here for inclusion in the sainfoin CO. The 98 variables contained a total of 82 traits from four trait classes of which 24 were agronomic, 31 were morphological, 19 were seed and forage quality related, and 8 were phenological. In addition to the developed variables, we have provided a roadmap for developing and submission of new traits to the sainfoin CO.