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.