Expert opinion elicitation and pathogen scoring
Snowball sampling resulted in a list of 54 potential stakeholder-expert participants, of which 25 agreed to participate (Supporting Information 2). Stakeholder-experts came from a variety of backgrounds but were generally categorized as academics, government officials (both state and federal), or members of the bait and fishing industries. The industry stakeholder group (n=4) reported the highest number of years of experience (mean=30 years, sd=14.2), followed by government officials (n=13, m=18, sd=11.7), and academics (n=8, m=17, sd=10.4). Confidence scores generally decreased as years of experience increased. Academics had the highest average confidence score (m=6.25, sd=2.12) followed by government officials (m=6.08, sd=1.61) and the industry stakeholders reported the lowest overall confidence scores (m=4.5, sd=1.73). No experts reported a conflict of interest.
Twenty-three stakeholder-experts (92%) assigned criteria weights ranging from 0.0 to 0.5 (up to 50% weight). Two stakeholder-experts (8%) indicated an equal weight (1/7 or 0.14 for each criterion). Beta-PERT distributions of the weightings varied in shape, indicating differences in the relative criterion’s importance (weight mean value) and levels of agreement (weight standard deviation) between the experts. The criterion with the highest mean weight determined by the experts across all pathogens was “Ecological impact if established” (mean weight=0.179) followed by “Colonization potential” (m=0.168) and “Host species” (m=0.149). Regarding agreement among experts (lowest standard deviation), “Prevalence” (sd=0.065) was the most agreed criterion followed by “Economic impact if established” (sd=0.071) and “Ecological impact if established” (sd=0.078) (Supporting Information 2).
Unweighted risk scoring (assuming equal weight by using Eq. 1) resulted in the microsporidian parasite Ovipleistophora ovariae as the highest-risk pathogen, followed by Asian fish tapewormSchizocotyle acheilognathi and viral hemorrhagic septicemia virus (VHSV) (tied at #2). However, multiple pathogens received the same risk score (4 pathogens with a score of 3, 3 pathogens with a score of 5 and 6 each) making it difficult to distinguish among them (Supporting Information 2). Only 7 risk ranking levels were obtained with the unweighted risk scoring system.
Weighted risk score simulations resulted in distinct distributions for the 15 pathogens evaluated (Figure 2). The pathogen with the highest mean risk score was Asian fish tapeworm (mean=2.01, sd = 0.36), followed by Ovipleistophora ovariae (mean=1.99, sd=0.30), and VHSV (mean=1.97, sd=0.40) (Table 4). The ‘highest-concern’ tier (risk scores 1.74-2.10) also included fathead minnow nidovirus (FHMNV), infectious pancreatic necrosis virus (IPNV), and the bacteria Yersinia ruckeri and Aeromonas salmonicida . The ‘moderate-concern’ (risk scores 1.38-1.74) included the microsporidian parasiteHeterosporis sutherlandae , golden shiner virus (GSV), and spring viremia of carp virus (SVCV). The ‘lowest-concern’ tier (risk scores 1.02-1.38) included white sucker bunyavirus (WSBV), fathead minnow picornavirus (FHMPV), epizootic hematopoietic necrosis virus (EHNV), the bacteria Edwardsiella ictaluri , and the macroparasiteNeascus spp. Mean risk values and overall distributions of weighted risk scores were significantly different among all pathogens by both pairwise t-tests and Kolmogorov-Smirnov tests (p<0.05), except for the mean risk values of IPNV and A. salmonicida(p=0.09). Two pairs of pathogens, including GSV and SVCV, and FHMPV and WSBV, were not significantly different from one another by either metric (Supporting Information 3). Total evidence uncertainty scores, indicating the amount of published support for assigned risk scores, ranged from 1-12 (mean=7.67) (Table 4). Uncertainty scores were generally negatively correlated with total risk scores (Figure 3a), i.e. higher-risk pathogens tended to have lower uncertainty scores; however, the relationship was not significant (p=0.14).