Supplementary Materials Detailed methods for literature selection and analysis Using Google Scholar, we employed combinations of broad search terms (e.g., virus-host-vector interactions, plant virus, insect vector, non-persistently transmitted virus, persistent-circulative virus, persistent-propagative virus, plant virus chemical ecology, vector behavior, vector performance) and specific search terms (family and species names of viruses and their vectors) to identify a primary set of literature that examined any aspect of insect vector attraction, settling and feeding, and performance in relation to infected and healthy plants. References cited within each identified paper were then assessed to identify additional papers addressing these three interactions. Finally, this expanded set of papers was re-entered into Google Scholar to identify still other papers that cited this literature. We believe that this sampling procedure, combining both search terms and human assessment of additional literature, was adequate to capture a large proportion of the relevant peer-reviewed literature. Our final list comprised 55 individual papers (studies of PT viruses were considerably better represented in this study, as only 14 of the 55 studies examined NPT viruses). Working with this literature, we parsed each study into individual experiments, each addressing a single virus strain or isolate by host species (or cultivar) by vector interaction (224 experiments total). All experiments in the 55 papers were included in this total except (i) those employing transgenic plants expressing viral genes (a subset of experiments from Jiménez-Martínez et al. 2004 a&b and Medina-Ortega et al. 2009)—because the expression of viral genes by transgenic plants may influence virus-vector-plant interactions and thereby confound results (Jiménez-Martínez et al. 2004 a&b); and (ii) those employing vector responses to macerated plant tissue extracts as sources of odor cues (some experiments in Fereres, Kampmeier & Irwin 1999)—because such extracts are not necessarily good approximations of the cues available to vectors in nature. Additionally, we did not include one paper where the authors did not identify the virus species and family (Kennedy 1951, Nature 168: 825-826), one paper where the host plant was bypassed and vectors were injected with virions to make them viruliferous (as this may have unintended effects on vector physiology) (Sylvester and Richardson 1969, Virology 37: 26-31), and one paper (Stumpf and Kennedy 2005) where the inclusion of a large number of biotic and abiotic factors in the analysis (and inconsistent pooling of data across these factors) prevented us from drawing conclusions about the overall effects of each virus isolate on a thrips vector (the isolates are instead represented in our analysis by the later Stumpf and Kennedy 2007 paper). Virus strains or serotypes in our synthesis were designated according to the names provided by the authors, including standard (e.g. BYDV strain MAV [BYDV-MAV]) or alternative (e.g. “CMV-common” from common strain of CMV) naming conventions. Isolates for which strain names were not provided were designated by the location where the study was performed (e.g. PLRV used in studies performed at Wageningen University was called PLRV-Wageningen to distinguish it from the PLRV isolate used in studies at the University of Idaho). Otherwise, strains were designated “unknown” (e.g. “PVY-unknown” for unidentified Potato virus Y) or named for the host from which they were isolated (if stated). Each of the 224 experiments identified was categorized as pertaining to one of the three types of virus-host-vector effects (attraction, settling and feeding, and performance) that related to the three hypotheses presented above. Results of each were then further categorized based on outcomes: For experiments measuring attraction or settling and feeding, results were categorized as demonstrating a vector preference for virus-infected plants, a preference for healthy plants, or no preference. For assays of vector performance, results were categorized as indicating a positive, neutral, or negative effect on vector performance (i.e., development, survival, or fecundity). Where equal numbers of positive and negative effects were observed in a single experiment, the experiment was categorized as neutral (for example, an aphid vector has slightly higher fecundity on virus infected plants, but significantly lower longevity). In the absence of further knowledge of the ecology of the organisms involved, it is difficult to determine the relative contributions of different performance parameters to overall virus transmission and our approach to dealing with conflicting measures in a single experiment is the most conservative. Results for each transmission mechanism are presented as the number of experiments falling into each category within the three virus-host-vector interactions (Figs 2 through 4; Supplementary Tables 1 through 3). We have also color-coded individual virus species in our figures to reveal the extent to which overrepresentation of particular interactions may contribute to the overall pattern observed. Tabulated results were evaluated for departure from an expected even distribution of effects (positive: neutral: negative, or virus-infected: no preference: healthy) across the three interaction types using chi-square tests (see figure captions). To examine the distribution of experiments among different plant virus lineages in the categories of vector performance and vector settling and feeding preference, results are presented based on virus taxonomy in Table 1. Supplementary Table 1: References used to construct Figure 2, virus identifiers, and pathosystems described in each reference. (Full citations for Supplementary Tables 1-3 can be found at the end of this document) Supplementary Table 2: References used to construct Figure 3, virus identifiers, and pathosystems described in each reference. 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