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A tool to predict sea lice levels on salmon farms in Chile

Sophie St-Hilaire1*, Thitiwan Patanasatienkul1, Jenny Yu1, Anja B. Kristoffersen2, Henrik Stryhn1, Crawford Revie1, Rolando A. Ibarra3, Alfredo Tello3 and Gregor McEwan1
  • 1 University of Prince Edward Island, Department of Health Management, Canada
  • 2 National Veterinary Institute, Norway
  • 3 SalmonChile, Intesal, Chile

Caligus rogercresseyi is a host-dependent parasite. Farmed fish become infected with the free-swimming stage of the ectoparasite from three sources: 1) infected fish within the farm; 2) infected fish on nearby farms; and 3) infected wild fish (Kristoffersen et al 2013). Levels of lice on farms increase over time at relatively predictable levels in a closed system where the source of lice is known, the environment is conducive to the parasite’s survival, and the fish are not undergoing a treatment. In other words, adult sea lice on a given week can be estimated from the previous week’s surviving adult lice and any juvenile lice that matured on the fish. For farms that are predominantly infected from adult lice within their own farm, it should be possible to estimate lice levels from one week to the next. This principle formed the basis for the development of our sea lice predictive tool. Predicting sea lice levels from one week to the next is useful to producers for a number of reasons. First, it permits them to plan sea lice treatments more effectively. It also enables producers to estimate the overall number of sea lice treatments that a farm may need during a production cycle. Finally, if a producer’s fish are consistently above the expected sea lice threshold from its own infection pressure it is likely that the farm is exposed to sea lice from other sources, such as infected neighboring farms and or infected wild fish. These farms may benefit from coordinated sea lice management strategies (Arriagada et al. submitted). We developed a tool for the salmon industry in Chile to predict the abundance of sea lice on farms that were relatively isolated and likely not receiving high levels of sea lice exposure from neighboring farms. We used data on sea lice abundance collected through the weekly SalmonChile INTESAL sea lice monitoring program to estimate the relationship between the adult sea lice abundance from one week to the next on farms that had no known neighbors within 10km and no more than two neighbors within 20 seaway km. We identified farms that met our isolation criterion based on active sites in a GIS of salt water aquaculture farms provided by SalmonChile. We defined time intervals between sea lice treatments as from the week immediately post treatment until the week before the subsequent treatment. We restricted our dataset to intervals associated with bath treatments, when the water salinity was above 26ppt. Our final dataset of isolated farms consisted of 32 farms and 89 treatment intervals over a period spanning from 2009 to 2015. The temperature during these time intervals ranged between 7 and 160C and the starting level of adult sea lice abundance was always below 10 adult lice per fish. Using the observed abundance of adult sea lice on a farm the week post treatment as a starting point, our tool predicted the abundance of sea lice over the subsequent 8-week period. We optimized our predictive model by minimizing the difference between predicted and observed sea lice abundance on our isolated farms. We also determined the proportion of farms that were consistently above the predicted estimates and various other thresholds. For comparison, we explored our model’s predicted sea lice levels in relation to the observed sea lice on farms that were known to have greater than six neighbors within 10km and at least six additional neighbors within 20 seaway km of their sites. 77% of farms with no neighbors within 20 seaway km, observed sea lice levels below 1.5 times the model’s predicted sea lice abundance in the first three weeks post treatment. In contrast, 70% of the farms identified as having many neighbors, observed sea lice levels greater than this threshold on at least one of these three weeks post treatment. We have introduced our sea lice predictive tool to the Chilean industry for their feedback. The intention is to improve the tool over time to better predict the levels of sea lice on isolated farms with no neighbors within 20 seaway km. Once we are satisfied that our model is accurate at predicting sea lice within farms, we will use it to build a larger, multi-farm, system dynamics model to help identify the optimal biocapacity and site configuration of fish farming areas, while maintaining the frequency of treatments below a specified level.

Acknowledgements


This research was undertaken, in part, thanks to funding from the Canada Excellence Research Chair Program. We would like to thank Intesal-SalmonChile for the insightful discussions and providing access to Industry data. The authors wish to thank William Chalmers for editorial assistance in preparation of the manuscript.

References


Kristoffersen, A.B., Rees, E.E., Stryhn, H., Ibarra, R., Campisto, J.L., Revie, C.W., St-Hilaire, S.
2013. Understanding sources of sea lice for salmon farms in Chile. Prev. Vet. Med.
111, 165–175.

Arriagada GA, Stryhn H, Vanderstichel R., Campistó J.L., Rees E.E., Sanchez J., Ibarra R, St-Hilaire S. Evaluating the effect of synchronized sea lice treatments in Chile. Preventative Veterinary Medicine (Submitted April 2016).

Keywords: sea lice, predictive model, Caligus rogercresseyi, Chile, sources of sea lice

Conference: AquaEpi I - 2016, Oslo, Norway, 20 Sep - 22 Sep, 2016.

Presentation Type: Oral

Topic: Aquatic Animal Epidemiology

Citation: St-Hilaire S, Patanasatienkul T, Yu J, Kristoffersen AB, Stryhn H, Revie C, Ibarra RA, Tello A and McEwan G (2016). A tool to predict sea lice levels on salmon farms in Chile. Front. Vet. Sci. Conference Abstract: AquaEpi I - 2016. doi: 10.3389/conf.FVETS.2016.02.00032

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Received: 29 May 2016; Published Online: 14 Sep 2016.

* Correspondence: DVM, PhD. Sophie St-Hilaire, University of Prince Edward Island, Department of Health Management, Charlottetown, PEI, Canada, ssthilaire@upei.ca