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).