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 naïve 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, Vukić, Šanda, 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 >$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.