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\begin{document}

\title{An expert-based risk ranking framework for assessing pathogens in the
live baitfish trade}



\author[1]{Margaret McEachran}%
\author[1]{Fernando Sampedro Parra}%
\author[2]{Dominic Travis}%
\author[1]{Nicholas Phelps}%
\affil[1]{University of Minnesota Twin Cities}%
\affil[2]{University of Minnesota}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
As global trade of live animals expands, there is increasing need to
assess the risks of invasive organisms, including pathogens, that can
accompany these translocations. The movement and release of live
baitfish by recreational anglers has been identified as a particularly
high-risk pathway for the spread of aquatic diseases in the United
States. To provide risk-based decision support for preventing and
managing disease invasions from baitfish release, we developed a hazard
identification and ranking tool to identify the pathogens that pose the
highest risk to wild fish via this pathway. We created a screening
protocol and semi-quantitative stochastic risk ranking framework,
combining published data with expert elicitation (n=25) and applied the
framework to identify high-priority pathogens for the bait supply in
Minnesota, USA. Normalized scores were developed for seven risk criteria
(likelihood of transfer, prevalence in bait supply, likelihood of
colonization, current distribution, economic impact if established,
ecological impact if established, and host species) to characterize a
pathogen's ability to persist in the bait supply and cause impacts to
wild fish species of concern. The generalist macroparasite
\emph{Schizocotyle acheilognathi} was identified as presenting highest
overall threat, followed by the microsporidian \emph{Ovipleistophora
ovariae}, and viral hemorrhagic septicemia virus. Our findings provide
risk-based decision support for managers charged with maintaining both
the recreational fishing industry and sustainable, healthy natural
resources. The ranking process, implemented here for a single state case
study, provides a standardized conceptual framework that could be
applied across jurisdictions to inform risk-based management of the live
baitfish pathway.%
\end{abstract}%



\sloppy


An expert-based risk ranking framework for assessing potential pathogens
in the live baitfish trade

Running title: Expert-based ranking of pathogens in live baitfish

Authors: MC McEachran\textsuperscript{1,2} , F
Sampedro\textsuperscript{3}, DA Travis\textsuperscript{2,4} , NBD
Phelps\textsuperscript{1,2}*

* Corresponding author

\textsuperscript{1}Minnesota Aquatic Invasive Species Research Center,
University of Minnesota, St. Paul, MN, USA

\textsuperscript{2} Department of Fisheries, Wildlife, and Conservation
Biology, University of Minnesota

\textsuperscript{3} Environmental Health Sciences Division, School of
Public Health, University of Minnesota

\textsuperscript{4} One Health Division, Department of Veterinary
Population Medicine, College of Veterinary Medicine, University of
Minnesota

\textbf{Summary} :

As global trade of live animals expands, there is increasing need to
assess the risks of invasive organisms, including pathogens, that can
accompany these translocations. The movement and release of live
baitfish by recreational anglers has been identified as a particularly
high-risk pathway for the spread of aquatic diseases in the United
States. To provide risk-based decision support for preventing and
managing disease invasions from baitfish release, we developed a hazard
identification and ranking tool to identify the pathogens that pose the
highest risk to wild fish via this pathway. We created a screening
protocol and semi-quantitative stochastic risk ranking framework,
combining published data with expert elicitation (n=25) and applied the
framework to identify high-priority pathogens for the bait supply in
Minnesota, USA. Normalized scores were developed for seven risk criteria
(likelihood of transfer, prevalence in bait supply, likelihood of
colonization, current distribution, economic impact if established,
ecological impact if established, and host species) to characterize a
pathogen's ability to persist in the bait supply and cause impacts to
wild fish species of concern. The generalist
macroparasite\emph{Schizocotyle acheilognathi} was identified as
presenting highest overall threat, followed by the microsporidian
\emph{Ovipleistophora ovariae,} and viral hemorrhagic septicemia virus.
Our findings provide risk-based decision support for managers charged
with maintaining both the recreational fishing industry and sustainable,
healthy natural resources. The ranking process, implemented here for a
single state case study, provides a standardized conceptual framework
that could be applied across jurisdictions to inform risk-based
management of the live baitfish pathway.

\textbf{Acknowledgements:} Funding for this project was provided by the
Minnesota Environment and Natural Resources Trust Fund as recommended by
the Minnesota Aquatic Invasive Species Research Center and the
Legislative-Citizen Commission on Minnesota Resources. We thank the 25
expert stakeholders whose participation helped to inform this study.

\textbf{Conflict of interest} : None of the authors or experts whose
opinion was elicited in this study have a conflict of interest to
declare.

\textbf{Ethics approval} : This research was reviewed by the University
of Minnesota's Institutional Review Board and determined to be ``not
human research'' (STUDY00007170).

\textbf{Keywords} : risk assessment, hazard prioritization, hazard
identification, decision analysis, baitfish

\textbf{Introduction}

In an increasingly globalized world, there is growing evidence that
trade (both formal and illegal or unregulated) of live animals and
animal products is a significant driver of disease spread among wildlife
populations worldwide (Daszak et al. 2000; Meyerson \& Mooney 2007;
Smith et al. 2009; Hulme 2009; Daszak et al. 2000; Peeler et al. 2011;
Tompkins et al. 2015). Preventing the introduction or range expansion of
harmful pathogens in wildlife populations is critical, as introduced
pathogens can have devastating consequences to na\selectlanguage{ngerman}ïve populations with
potential implications for biodiversity and human health (Daszak et al.,
2000; Gozlan, Peeler, Longshaw, St-Hilaire, \& Feist, 2006; Smith, Sax,
\& Lafferty, 2006). The full extent to which animal trade and movement
drives disease spread is unknown, but likely underestimated (Cunningham,
1996).

Recently, collaborative efforts between veterinarians, public health
professionals, and conservation biologists have enhanced our toolkit for
proactive characterization and management of wildlife disease risks
(Cunningham, 1996; Jakob-Hoff et al., 2014). Wildlife disease risk
analysis (WDRA) comprises a suite of tools and methods to characterize,
communicate and mitigate the risk of disease spread via the intentional
(Hartley \& Sainsbury, 2017; Pavlin, Schloegel, \& Daszak, 2009) or
unintentional (G. Copp, Garthwaite, \& Gozlan, 2005) movement of live
animals (OIE \& IUCN 2014; Jakob-Hoff et al. 2014). Many introduction
risk analysis frameworks are largely designed for known - or at least
well described - hazards (Williams, Britton, \& Turnbull, 2013) and are
vulnerable to uncertainties associated with lesser-known disease agents
(Gaughan, 2001). This is particularly true for invasive species and
wildlife disease management, where management decisions must be made
without perfect knowledge of the biological system in question
(Beauvais, Zuther, Villeneuve, Kock, \& Guitian, 2019; Larson, Kueffer,
\& ZiF Working Group on Ecological Novelty, 2013; Regan et al., 2005;
Sainsbury \& Vaughan-Higgins, 2012). For example, disease introduction
is considered one of the greatest threats posed by introduced fishes to
native species (G. H. Copp, Garthwaite, \& Gozlan, 2005; Ganzhorn,
Rohovec, \& Fryer, 1992). Despite this concern, and the fact that live
fish have historically comprised over 90\% of live animal specimens
imported into the US (Smith et al. 2009; Smith et al., 2016), fish
movement remains a particularly poorly understood pathway for disease
spread (G. H. Copp et al., 2005; Gaughan, 2001; Jones, 2000; Travis \&
Hueston, 2000; Williams et al., 2013). Risk analyses for aquatic animals
therefore involve inherent uncertainty with respect to basic disease
information, disease status of wild fish populations, and the stochastic
nature of biological systems (Beauvais et al., 2019; Jones, 2000; Travis
\& Hueston, 2000).

The movement of live bait for use in recreational angling has been
identified as a particularly high-risk and poorly understood pathway for
the spread of several concerning aquatic invasive species and pathogens
(e.g. viral hemorrhagic septicemia virus) (McEachran et al, in review;
Boonthai et al. 2017, 2018; Mahon et al. 2018) in the Great Lakes region
of the United States (Litvak \& Mandrak 1996; Ludwig \& Leitch 1996;
Goodchild 2000; Drake \& Mandrak 2014). Baitfish are small fish, most
commonly minnows of the family Leuciscidae (formerly Cyprinidae)
(Schönhuth, Vuki\selectlanguage{polish}ć, Š\selectlanguage{english}anda, Yang, \& Mayden, 2018; Tan \& Armbruster,
2018), that are fed as forage in aquaculture settings and are used as
bait by recreational anglers. Live fish are the most popular bait in
many Great Lakes states, where millions are raised on farms or harvested
from the wild, moved long distances overland, and sporadically released
by anglers into the water (Litvak \& Mandrak, 1993; Ludwig \& Leitch,
1996). Mandatory disease testing is limited to certain baitfish species
and diseases (e.g. MN Statute 17.4991), and the health status of
baitfish populations is generally poorly understood (Goodwin, Peterson,
Meyers, \& Money, 2004; Jones, 2000). Pathogens typically rank among the
lowest invasive species in terms of angler awareness (Cole, Keller, \&
Garbach, 2016) yet are easily transferred with legal bait and can have
devastating consequences if introduced (Gozlan et al., 2006; Morant et
al., 2013). Consequently, the use of live baitfish presents a
significant opportunity for pathogen spread. At the same time, the live
baitfish industry is economically and culturally important in US states
like Minnesota where demand for minnows drives a \textgreater{}\$2.4
million live baitfish industry and supports an even larger recreational
fishing industry (United States Department of Agriculture, 2013). The
sheer volume of this pathway combined with recent baitfish shortages
have increased the scrutiny and demand for a safe, reliable bait supply,
igniting a debate about how to balance the risk for disease spread with
the value it provides to the state and the region.

Fish health researchers and aquatic resource managers are increasingly
in need of a system to triage (or identify, rank, and prioritize) the
large number of potential fish pathogens that could be introduced or
spread via the live baitfish pathway. Although some qualitative
assessments have been completed (Gunderson 2018; Boersen et al. 2017),
there is no formal framework to rank pathogens in the live baitfish
pathway. The purpose of this study was to develop a semi-quantitative
risk ranking framework to rank pathogens in the live baitfish supply
according to their potential impact on wild fish populations in
Minnesota. Given the importance of the bait and fishing industries,
significant uncertainty, and need for evidence-based risk management
strategies (Minns \& Cooley, 2000; Stohlgren \& Schnase, 2006),
multi-criteria decision analysis (MCDA) methodology was used as the
basis for the risk ranking framework. MCDA enabled the integration of
empirical data and value-based judgements for prioritizing hazards in
the live baitfish pathway.

Methods

\emph{Problem formulation and scoping}

A multi-step process centered around expert stakeholder input was
designed for the risk ranking exercise (Table 1). As first step, an
initial cluster of stakeholder-experts with expertise in fish health and
aquatic invasive species prevention were identified from the Minnesota
Department of Natural Resources (MNDNR) to provide input throughout the
process and to ensure study outcomes aligned with the state management
objectives.

Best practices indicate that clarifying the objective, question, or
endpoint of interest is critical for the accuracy and applicability of a
risk assessment (Jakob-Hoff et al., 2014). Therefore, the second step of
the risk ranking exercise was to define the primary question of the
analysis, which was formulated as: ``What pathogens are most likely to
present a risk to the health of wild fish via release of infected
baitfish?'' Although there is some evidence that potential human and
wildlife pathogens (Mahon et al. 2018; Picco et al. 2010) may be present
in live baitfish, the scope of this study was limited to pathogens of
fish. After the definition of the project question, an initial list of
pathogens to be assessed was obtained from existing qualitative
evaluations (Boersen et al. 2017; Gunderson 2018) and lists of important
(regulatory) fish pathogens curated by the OIE (Aquatic Animal Health
Code, OIE) and Minnesota law (MN Statute 17.4982). Inclusion/exclusion
criteria were developed based on host susceptibility for the initial
hosts, live baitfish that could be legally used in Minnesota as listed
in the 2018 fishing regulations handbook (accessible at
https://files.dnr.state.mn.us/rlp/regulations/fishing/fishing\_regs.pdf),
and the recipient population (described as ``fish of concern''), which
included game fish, fish listed as threatened or endangered by the
Minnesota Endangered Species Statute (MN Statute 84.0895), or fish
receiving management attention from MNDNR (Figure 1).

\emph{Development of the risk ranking framework}

The third step of the risk ranking process was to build a framework to
score the included pathogens according to defined risk criteria. A
semi-quantitative matrix based on the multicriteria decision analysis
(MCDA) methodology was developed to identify the high-risk fish
pathogens of concern (WHO and FAO 2007; Van der Fels-Klerx et al. 2018).
MCDA methodology allows for the inclusion of different types of risk
information, including empirical data and expert judgement. Risk ranking
criteria were developed based on previous evaluations of the bait
pathway (Boersen et al., 2017; J. L. Gunderson, 2018) and adapted to
reflect the likelihood of pathogen occurrence and the severity of its
impact due to spreading in the baitfish pathway. The MCDA risk ranking
framework was comprised of seven risk criteria: likelihood of transfer,
prevalence in the bait supply, colonization potential, ecological impact
if established, economic impact if established, current distribution in
Minnesota, and host species. Each criterion was assigned a normalized
risk score based on available literature (0-3, Table 2). An unweighted
risk score (assuming equal weight among all criteria) was calculated for
each pathogen by adding each individual criterion score using the
following equation:

\(Unweighted\ risk\ score=\sum_{i=1}^{7}S_{\text{ij}}\ \) (1)

where \emph{S\textsubscript{ij}} is the score for pathogen \emph{j} on
criterion \emph{i} . All data and calculations available in the
Supporting Information.

\emph{Expert opinion elicitation and pathogen scoring}

To incorporate value-based judgements into the weighting of the criteria
in this assessment (Havelaar et al., 2010; Krause, 2008; Walshe \&
Burgman, 2010), potential stakeholder-experts were identified based on
their interest in, influence on, and valuable knowledge of the live
baitfish pathway (Jakob-Hoff et al., 2014). Identified experts were then
validated by eligibility criteria including: current professional
position and years of experience related to fish health, aquatic
invasive species, or the production of live baitfish for recreational
angling. Stakeholder-experts were also asked to identify other potential
participants for our study, a process called ``snowball sampling'', by
which members of a narrowly-defined group identify other members of that
group (Hald et al., 2016). Willing and informed stakeholder-experts were
asked to assign a weight to each criterion such that all weights added
to one (Cox, Revie, \& Sanchez, 2012; Krause, 2008). The expert
weighting exercise was administered in the online survey platform
Qualtrics (Qualtrics, Provo, UT 2019).

\emph{Uncertainty estimation}

Three types of uncertainty were identified during the development of the
risk ranking framework. First, the uncertainty associated with the
criteria weights assigned by the stakeholder-experts was characterized
by a Beta-PERT distribution (Vose, 2008). For each pathogen, a total
weighted risk score was obtained by adding each individual risk
criterion score multiplied by values from the expert's weight
distribution for each criterion using the following equation (adapted
from ECDC 2017):

\(Weighted\ risk\ score=\ \sum_{i=1}^{7}{Wi*S_{\text{ij}}}\) (2)

\begin{equation}
W_{i}\sim\text{BetaPERT}(a,b,c)\nonumber \\
 \end{equation}

where \emph{S\textsubscript{ij}} is the score for pathogen \emph{j} on
criterion \emph{i} as in Equation 1, and \emph{W\textsubscript{i}} is
the probability distribution of the expert-designated weights for each
criterion \emph{i} . The Beta-PERT distribution was characterized by a
minimum (a), most likely (b) and maximum value (c). Latin hypercube
sampling (LHS) was performed in @Risk (Palisade, Inc.) to iterate over
Equation 2 and sample stratified random numbers from each probability
distribution of the expert-designated weights defined in the model
(Vose, 2008). Significant correlations between input values were
included in the model (Supporting Information). The LHS was repeated for
10,000 iterations to generate the final distribution of total weighted
risk scores with mean and standard deviation values that accurately
accounted for all possible weighted risk scores for a given set of
parameters defined. Pairwise t-tests with a Bonferroni correction and
nonparametric Kolmogorov-Smirnov tests (Arnold \& Emerson, 2011) were
applied to test for significant differences in mean total risk scores
and overall total weighted risk distributions between pathogens,
respectively.

The second type of uncertainty was related to the amount of published
evidence supporting the risk score assigned to each criterion. A
normalized scale (0-2, Table 3) was developed to estimate the evidence
uncertainty associated with the total weighted and unweighted risk
scores for each pathogen. If we were unable to find published
information about a particular criterion for a particular pathogen, the
risk score was extrapolated from similar pathogens and was assigned a
high uncertainty score (2) for that criterion. Total evidence
uncertainty score for each pathogen was estimated using Equation
3:\(Total\ evidence\ uncertainty=\sum_{i=1}^{7}U_{\text{ij}}\) (3)

where \emph{U\textsubscript{ij}} is the normalized uncertainty score for
pathogen \emph{j} on criterion \emph{i.} Total evidence uncertainty
scores for each pathogen are reported in Table 4.

A third type of uncertainty was related to the `confidence level' of the
stakeholder-experts in assigning the weight values. Experts indicated
their confidence in the assigned weights by a score between 1 (low) and
10 (high, integer number). The confidence scores were intended to
illustrate the range and variety of confidence from various experts and
not used in the final calculations of the risk ranking.

\emph{Sensitivity analysis}

A sensitivity analysis was carried out to measure the impact of expert
opinion value judgements on risk ranking using the tornado graph feature
in @Risk to determine which expert weights had the greatest impact on
overall weighted risk score for each pathogen. A positive Spearman
correlation value indicated a positive relationship between the weight
for that criterion and the total risk score. The criterion with the
highest absolute value was identified as the most impactful risk factor
for future risk management strategies.

Results

\emph{Problem formulation and scoping}

A total of 33 fish pathogens were identified as potential hazards
(Supporting Information). Using the inclusion/exclusion criteria
established (Figure 1), pathogens were excluded due to lack of
sufficient evidence for transmission in live baitfish and/or
susceptibility of fish of concern. A final list of 15 pathogens were
identified for the risk ranking exercise.

\emph{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 \emph{Ovipleistophora ovariae} as the
highest-risk pathogen, followed by Asian fish tapeworm\emph{Schizocotyle
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 \emph{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 \emph{Yersinia
ruckeri} and \emph{Aeromonas salmonicida} . The `moderate-concern' (risk
scores 1.38-1.74) included the microsporidian parasite\emph{Heterosporis
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
\emph{Edwardsiella ictaluri} , and the macroparasite\emph{Neascus} 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\textless{}0.05), except for the mean risk
values of IPNV and \emph{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).

\emph{Sensitivity analysis}

The impact of the expert-designated criteria weights on overall risk
scores for each pathogen was examined by calculating Spearman
correlation coefficients (Table 4). All of the most impactful criteria
weights had a positive correlation with overall risk scores, meaning
that an increase in the criteria weighting produced an increase in the
overall risk score. The ``host species'' criteria was identified as the
most impactful in the highest number of pathogens (nine pathogens). The
``likelihood of transfer'' and ``current distribution in Minnesota''
were impactful for two pathogens each, whereas the ``economic impact if
established'' and ``likelihood of colonization'' were most impactful for
one pathogen each.

Discussion

In this study, a MCDA risk ranking framework integrating empirical data
and expert opinion was used to rank pathogens in the live baitfish
pathway. Applying the framework as a case study to the problem of
pathogen introduction via the Minnesota bait pathway resulted in
distinct risk scores for each of the 15 pathogens assessed. The
highest-risk pathogen group included the Asian fish tapeworm, \emph{O.
ovariae,} VHSV, FHMNV, IPNV, \emph{A. salmonicida} and \emph{Y. ruckeri}
. To our knowledge, this is the first study that has employed both
semi-quantitative scores and expert opinions to evaluate and rank
pathogens in the live baitfish pathway. The inclusion of expert
judgement in the risk ranking exercise allowed a more detailed ranking
analysis with distinct risk scores, avoiding the risk score clustering
observed in the unweighted system. The weighted framework also made
explicit the impact of subjective beliefs about which criteria were most
important, emphasizing the importance of considering value judgements
when making decisions about which pathogens to manage.

The Asian fish tapeworm, \emph{O. ovariae,} and VHSV were the top-ranked
pathogens in both the unweighted and weighted risk scoring systems,
confirming the relevance of these three fish pathogens to the bait
supply pathway. The highest ranked pathogen was the non-native Asian
fish tapeworm, a generalist fish parasite that can infect hundreds of
fish species and known to be present in the live baitfish supply in the
region (Boonthai et al. 2017; Kuchta et al. 2018). \emph{Ovipleistophora
ovariae} is an obligate intracellular and vertically transmitted
parasite, infecting the ovarian tissue of golden shiners, leading to
significant declines in fecundity by age-2 (Phelps \& Goodwin, 2008).
Although \emph{O. ovariae} is believed to be widely distributed and
highly prevalent in the golden shiner supply chain, surveys of wild
populations to confirm establishment have not been completed (McEachran
et al. in review), and the parasite remains of concern. Indeed, a
previous qualitative risk assessment for golden shiners imported from
Arkansas bait producers identified both Asian fish tapeworm and \emph{O.
ovariae} as high-risk (J. L. Gunderson, 2018)(J. Gunderson, 2004).VHSV
is a broadly recognized risk to fish health globally (Escobar,
Escobar-Dodero, \& Phelps, 2018), and following its invasion in the
Great Lakes in 2003 (Elsayed et al., 2006), has been identified as a
concern in previous evaluations of the Minnesota bait industry (Boersen
et al., 2017; Phelps, Craft, Travis, Pelican, \& Goyal, 2014).

The results of the risk ranking framework highlight the paradoxes of
risk management efforts that focus on the host species, rather than the
pathogen of interest. For example, the ranking framework identified the
Minnesota certifiable diseases IPNV, \emph{A. salmonicida} , and
\emph{Y. ruckeri} as high-risk hazards for the bait pathway. These
pathogens can have serious fish health implications for salmonid species
(Furones, Rodgers, \& Munn, 1993; Roberts \& Pearson, 2005; Wiklund \&
Dalsgaard, 1998) and are consequently regulated in Minnesota to limit
introduction and spread (MN Statute 17.4982). However, these regulations
only apply to salmonid species, despite known susceptibility and
evidence of at least \emph{A. salmonicida} and \emph{Y. ruckeri} in the
local retail baitfish supply (McEachran et al, in review). In contrast,
VHSV is another state-certifiable pathogen identified as high-risk in
this study, but it is managed at the pathogen level, with all
susceptible species (including legal bait species) subject to regulatory
conditions (MN Statute 17.4991). These paradoxes highlight the
importance of managing specific invasive pathogens of known risk, rather
than host species, when attempting to reduce the risk of pathogen spread
via any live animal movement pathway.

Estimates of evidence uncertainty varied across pathogens, with some
pathogens having higher or lower uncertainty than average (Figure 3a).
Some pathogens in the high-risk group (e.g. FHMNV and IPNV) and low-risk
pathogens (e.g. WSBV, FHMPV) obtained high uncertainty scores,
suggesting that as more information becomes available in the future, the
risk ranking may change for these less well-described pathogens. Because
of the high number of fish species and increasing rates of pathogen
reporting and surveillance, pathogens of fish account for a large number
of emerging diseases of wildlife (Tompkins et al., 2015), and so
invasion management tools must be equipped to dealing with both emergent
and well-documented pathogens. Fish health managers could apply the risk
ranking to evaluate potential risk and determine what type of action, if
any, is warranted, based on their own tolerance for uncertainty and risk
(Figure 3b). If new evidence emerges in the future, the risk ranking
framework can be updated and risk ranking scores recalculated, providing
support for risk-based disease management.

It is important to note that while the risk ranking framework identifies
pathogens of importance (`high risk') in the live baitfish supply, this
does not directly translate to an inevitable impact on wild fish
populations. Like all invasion scenarios, many factors must align to
result in the successful establishment and negative outcome of a hazard
(e.g. baitfish pathogen) to a new environment (e.g. na\selectlanguage{ngerman}ïve wild
population of concern) (Simberloff, 2009; Stohlgren \& Schnase, 2006;
Wang \& Jackson, 2011). Examples of failed introductions are impossible
to quantify given the limited information for the disease status of
baitfish and their movement patterns, and the disease status of wild
populations. For VHSV, a pathogen where significant surveillance has
occurred (i.e. Phelps et al. 2014), no detections have occurred in the
Minnesota baitfish supply and therefore transmission via this pathway is
presumed to be nonexistent. Evaluating the current distribution and
potential for establishment of high-risk pathogens known to be in the
baitfish supply (e.g. \emph{O. ovariae} , \emph{A. salmonicida} ,
\emph{Y. ruckeri} ) is warranted to better inform future risk
assessments. Regardless, the risk ranking framework is a useful tool to
identify and prioritize pathogens for further management consideration
and provide justification for proactive prevention efforts.

Incorporating variability and uncertainty from multiple different
stakeholder groups (managers, academia, and industry), and not just a
single sector, is increasingly recognized as a critical part of managing
invasive species (Shackleton et al., 2019). The expert opinion-based
risk ranking framework developed in this study incorporates expert
opinion with empirical evidence, and improves on previous qualitative
evaluations and unweighted rankings to distinguish between high-,
medium-, and low-risk pathogens in the live baitfish supply. Where
uncertainty exists, the precautionary principle is often employed,
whereby novel and highly uncertain pathogens are automatically assigned
a high-priority ranking and allocated resources and risk management
efforts (Larson et al., 2013; Sainsbury \& Vaughan-Higgins, 2012) This
approach risks obfuscating management plans and creating burdensome
regulations for producers (van Senten \& Engle, 2017). Conversely,
failure to systematically assess all possible hazards may indeed
overlook important pathogens, leaving fish populations at risk (Gaughan,
2001). Although the framework discussed in this study provides a
relative, rather than an absolute determination of risk, disease
management in the absence of a structured and inclusive process for
identifying hazards could result in the over or under-inclusion of
important pathogens. This framework has broad applicability for
understanding risks and will provide support for difficult conservation
decisions to balance invasion risks with the economic, cultural, and
societal benefits associated with live baitfish use. In addition, its
flexibility in application means it could be modified to assess risks in
other pathways, jurisdictions, or other taxa of invasive species as
invasion prevention needs emerge.

\textbf{Acknowledgements:}

Funding for this project was provided by the Minnesota Environment and
Natural Resources Trust Fund as recommended by the Minnesota Aquatic
Invasive Species Research Center and the Legislative-Citizen Commission
on Minnesota Resources.

We thank the 25 expert stakeholders whose participation helped to inform
this study.

\textbf{Conflict of interest} :

None of the authors of the manuscript or experts whose opinion was
elicited in this study have a conflict of interest to declare.

\textbf{Ethics approval} :

This research was reviewed by the University of Minnesota's
Institutional Review Board and determined to be ``not human research''
(STUDY00007170).

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\textbf{Tables}

Table 1: Outline of the risk ranking process\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Project Stage & Description\tabularnewline
\midrule
\endhead
Stakeholder identification & Identification of initial cluster of
stakeholder-experts within Minnesota DNR\tabularnewline
Problem formulation and scoping & Determination of inclusion/exclusion
criteria for pathogen selection\tabularnewline
Risk ranking framework development & Development of criteria and
normalized risk scores\tabularnewline
Expert opinion elicitation and pathogen scoring & Stochastic simulation
of weighted pathogen scores based on expert-designated
weights\tabularnewline
Sensitivity analysis & Spearman rank correlations to quantify the effect
of subjective weight judgements on overall risk scores\tabularnewline
\bottomrule
\end{longtable}

Table 2: Description of normalized scoring scheme for risk ranking
criteria.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& 0 & 1 & 2 & 3\tabularnewline
\midrule
\endhead
Criteria & & & &\tabularnewline
Likelihood of transfer & Not likely, due to extensive testing and
surveillance and strict protocols to prevent transfer & Low, several
management practices and disease testing and surveillance is done &
Moderate, some risk reduction measures, but testing is incomplete (i.e.
not on all bait species) & High, no routine testing of bait species, not
able to be detected visually\tabularnewline
Prevalence in bait supply & Has not been found in MN bait supply & Low
prevalence in bait supply, 1-33\% & Moderate prevalence in bait supply,
34-66\% & High prevalence in bait supply, 67-100\%\tabularnewline
Colonization potential & Not likely, organism will not be established
due to climate mismatch, life cycle limitation or lack of suitable hosts
& organism has a low probability of becoming established on the basis of
climatic, life cycle, or host requirements & Organism has a medium
probability of becoming established on the basis of climatic, life
cycle, or host requirements & Organism has a high probability of
becoming established on the basis of climatic, life cycle, or host
requirements, or has been introduced in some areas of MN\tabularnewline
Current distribution in MN & Common, frequently encountered, widespread
& Fairly common, either widespread but not abundant in any location or
abundant in some areas & Uncommon, not widespread or abundant in any
location & Not Detected, surveys have been conducted but the organism
has never been found\tabularnewline
Economic impact & No known impact on any game species, fishery, tourism
or species of interest & Mild impact on economic contribution of game
species, fishery, tourism or species of interest & Moderate impact on
economic contribution of game species, fishery, tourism or species of
interest & Severe impact on economic contribution of game species,
fishery, tourism, or species of interest\tabularnewline
Ecological impact if established & No or negligible impact on
population, community, or ecosystem ecology & Mild ecological impact
(e.g. minor shift in food web) & Moderate ecological impact (e.g. some
habitat degradation, some food web impact, etc.) & Severe ecological
impact (e.g. fishery collapse, cascading effects, habitat degradation,
etc.)\tabularnewline
Host species & No known hosts in MN or single non-game, non-threatened
\& endangered (T\&E) or management-relevant species affected & Single
game, T\&E, or management-relevant species affected & More than one
game, T\&E or management-relevant species affected & Several game, T\&E
or management-relevant species greatly affected (i.e. high
mortality)\tabularnewline
\bottomrule
\end{longtable}

Table 3: Description of normalized scoring criteria for evidence
uncertainty metric.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Score & Description\tabularnewline
\midrule
\endhead
0 & definitive published evidence or internationally accepted
conclusion\tabularnewline
1 & some uncertainty or lack of definitive information in published
literature+\tabularnewline
2 & little or no data or information++\tabularnewline
\bottomrule
\end{longtable}

+: Uncertainty score automatically set at 1 for pathogens not detected
in Minnesota waters or bait supply due to inherent uncertainty in
disease testing unless there was significant evidence (i.e. nearly
complete sampling coverage) for absence of pathogen.

++: Uncertainty score automatically set at 2 for pathogens where no
information was found.

Table 4: Results of weighted risk ranking and evidence uncertainty
scoring.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Pathogen\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Total weighted risk score (mean \selectlanguage{ngerman}± sd)\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Weighted rank\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Most influential criterion weight (Spearman rank coefficient)\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Evidence uncertainty score\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Asian fish tapeworm\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.01 \selectlanguage{ngerman}± 0.36\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.69)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Ovipleistophora ovariae}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.99 \selectlanguage{ngerman}± 0.30\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
economic impact (0.67)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
viral hemorrhagic septicemia virus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.97 \selectlanguage{ngerman}± 0.40\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.65)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
fathead minnow nidovirus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.80 \selectlanguage{ngerman}± 0.34\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.75)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
10\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
infectious pancreatic necrosis virus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.79 \selectlanguage{ngerman}± 0.37\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.68)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Aeromonas salmonicida}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.78 \selectlanguage{ngerman}± 0.33\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.76)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Yersinia ruckeri}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.75 \selectlanguage{ngerman}± 0.34\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.75)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Heterosporis sutherlandae}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.67 \selectlanguage{ngerman}± 0.37\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.77)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
golden shiner virus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.42 \selectlanguage{ngerman}± 0.26\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.64)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
spring viremia of carp virus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.41 \selectlanguage{ngerman}± 0.27\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
host species (0.62)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Neascus} spp.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.33 \selectlanguage{ngerman}± 0.30\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
colonization potential (0.57)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
epizootic hematopoietic necrosis virus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.15 \selectlanguage{ngerman}± 0.38\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
current distribution (0.67)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
fathead minnow picornavirus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.12 \selectlanguage{ngerman}± 0.20\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
likelihood of transfer (0.69)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
white sucker bunyavirus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.12 \selectlanguage{ngerman}± 0.20\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
likelihood of transfer (0.69)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Edwardsiella ictaluri}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.02 \selectlanguage{ngerman}± 0.27\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
current distribution (0.81)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure Legends}

Figure 1: Inclusion criteria decision tree for pathogen selection.

Figure 2: Simulated risk score distributions for selected pathogens
(n=15).

Figure 3: Relationship between uncertainty and risk. (\textbf{A} )
Summed ``evidence'' uncertainty scores vs. mean weighted risk scores for
the 15 pathogens assessed. Total uncertainty scores were calculated
according to Table 3 and Eq. 3 for each pathogen. Solid line indicates
the average uncertainty score (7.6) (\textbf{B} ) Conceptual diagram for
the theoretical risk-uncertainty-response nexus. Hypothetical thresholds
for decision-making are represented by the solid line (higher risk
tolerance scenario) and the dashed line (lower risk tolerance scenario).

Figure 1: Inclusion criteria decision tree for pathogen selection\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

* OIE: Diseases listed by the World Organisation for Animal Health
(Aquatic Animal Health Code, 2017); MN Certifiable Fish Diseases (MN
Statute 17.4982); 2018 Minnow Import Risk Report (Gunderson, 2018);
Hazard Analysis for Bait and Aquaculture Industry (Boersen, 2017); MNDNR
Fish disease webpage (accessible at
https://www.dnr.state.mn.us/fish\_diseases/index.html)

+ Live legal bait species according to the 2018 Minnesota Fishing
Regulations Handbook, accessible at
https://files.dnr.state.mn.us/rlp/regulations/fishing/fishing\_regs.pdf).
Members of the minnow family, except carp and goldfish; bullheads, cisco
(tullibee), lake whitefish, goldeyes, and mooneyes (not over 7 inches
long); suckers (not over 12 inches long); mudminnows, tadpole madtoms,
and stonecats. ``Leeches'' are designated ``minnows'' by the MN Fishing
Regulations Handbook, but are not considered in this hazard assessment.

++ Fish of concern were defined as any fish species receiving management
attention from the MNDNR, including but not limited to game species,
threatened and endangered species, or species that support commercial
fisheries.

Figure 2: Simulated risk score distributions for selected pathogens
(n=15).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3: Relationship between uncertainty and risk. (\textbf{A} )
Summed ``evidence'' uncertainty scores vs. mean weighted risk scores for
the 15 pathogens assessed. Total uncertainty scores were calculated
according to Table 3 and Eq. 3 for each pathogen. Solid line indicates
the average uncertainty score (7.6) (\textbf{B} ) Conceptual diagram for
the theoretical risk-uncertainty-response nexus. Hypothetical thresholds
for decision-making are represented by the solid line (higher risk
tolerance scenario) and the dashed line (lower risk tolerance scenario).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

