A stepwise integrated risk-assessment framework in aquaculture: the case of sea lice tolerance to freshwater treatments on salmon farms

E Laurin,J Sanchez,C Revie,M Stormoen

doi:10.3354/aei00373

Abstract

Aquaculture studies are often faced with data limitations when carrying out a quantitative risk assessment. Consolidating results from a literature search of potentially applicable methods, we propose a stepwise integrated methods approach that incorporates foundations from an antimicrobial resistance framework, the Office International Epizooties risk model, quantitative microbial risk assessment and infectious disease transmission models. We suggest that an initial ranking profile can be used to prioritize more in-depth qualitative and quantitative risk assessments, when data are available. The ranking method was done using a software that provides practical and interactive graphics for visualizing the impact of different factors and their respective weights on the likelihood of undesirable events (hazards) occurring. For this step, we illustrate how to include available data to obtain ranking results for decision makers using information from a recent sea lice freshwater tolerance literature review (Groner et al. 2019) that identified a gap in quantitative data. In our case example, for copepodid sea lice life stages, hypothetically changing how much experts believe that location and time are important factors revealed the most impact on the ranking for different degrees of freshwater tolerance evolution (no evolution, various partial options, known evolution). The factors ‘location’ and ‘time’, as well as ‘freshwater treatment’, have the greatest impact on the ranking for the preadult sea lice life-stages model. Results from our proposed ranking method can help to drive decisions around interpreting the various factors as they apply to mitigation planning and prioritizing those that should be included in further research. Additionally, we identify where quantitative data could be incorporated, as they become available, into a full risk assessment model with suggested models for a freshwater tolerance risk analysis framework.

Highlights

Risk analyses (RAs) look at the likelihood that undesirable events will occur, together with the consequences of their occurrence (Greiner et al 2004, Peeler et al 2007)
These models are based on susceptible−infectious− recovered (SIR) models to define the interacting population groups. Outputs from these models, in particular system dynamic infectious disease transmission (IDT) models, can provide unit frequency that are dependent on different conditions and can be used for determining the probability and likelihood of the hazard occurring. Because it is set in a Bayesian framework to handle uncertainty, we suggest that the Aldrin et al (2017) salmon lice (L. salmonis) model may be adapted for a quantitative RA, with factors such as temperature dependence, population and individual fish demographics, transmission rates and control measures
The 4 steps required for the OIE RA are: (1) hazard identification; (2) risk assessment; (3) risk management; and (4) risk communication

Summary

Introduction

Risk analyses (RAs) look at the likelihood (see Table 1 for a definition of RA terminology used) that undesirable events (hazards) will occur, together with the consequences of their occurrence (Greiner et al 2004, Peeler et al 2007). Semi-quantitative methods have been proposed to provide score results when too few quantitative data are available, as is often the case for aquatic-focused RAs (Vose 2001, Peeler et al 2007, Beaudequin et al 2015). How a factor or subfactor will be measured in order to estimate the score or directional result of each one. Preference, preference level, The quantitative or score value that identifies which outcome is preferred over another, preference value i.e. it is more influential on the risk estimate or score Positive flow (phi+) indicates the level to which a particular outcome is dominating all others, negative flow (phi−) indicates the level to which a given outcome is being dominated, and net flow (net phi) is used to provide an approximate measure of the overall preference of an outcome

Results

Discussion

Conclusion