Using data-driven models to explore sea louse infestations on wild and farmed salmon

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1 Using data-driven models to explore sea louse infestations on wild and farmed salmon Maya Groner Atlantic Veterinary College University of Prince Edward Island Canada

2 Sea louse think tank Erin Rees Marty Krkosek Mark Lewis Stephanie Peacock Andrew Bateman Neil Frazer Crawford Revie Gregor McEwan Mark Fast Luke Rogers Uli Schlagel Sophie St. Hilaire

3 Sea louse parasites of salmon

4 Groner et al. In review Groner et al. in review

5 Sea Lice serious farm concern St Andrews, March 2015

6 Sea Lice - the wild angle St Andrews, March 2015

7 Sea Lice - the wild angle St Andrews, March 2015

8 Potential factors affecting sea louse infestations

9 Simulations and mathematical models are useful for exploring hypothetical scenarios, intractable systems, long term projections, and testing general theory

10 PROVIDED YOU HAVE THE RIGHT DATA.

11 BAMP DATA Broughton Archipelago Monitoring Plan (BAMP) - tripartite: engos / Producers / DFO Data from wild field sampling ( ) - specific fish-level data (177,152 records) - individual lice details (198,392 samples) Complementary data from farms St Andrews, March 2015 BAMP.ca

12 Questions we may want to address with simulations How does current and projected environmental variation influence sea louse populations? (Temperature, salinity, hydrodynamics) How can we manage salmon farms to reduce sea lice infections? How does sea louse population structure impact evolutionary processes such as resistance to chemicals?

13 Modeling approaches Factors to incorporate Differential Equation Population matrix model Delaydifferential Equation Individual- Based Model Sea Lice Growth and Survival Dynamic processes (e.g. density dependence) Instantaneous events (e.g., treatments) Behaviour and individual variation Stochastic processes Solve for equilibriums

14 Modeling approaches Factors to incorporate Differential Equation Population matrix model Delaydifferential Equation Individual- Based Model Sea Lice Growth and Survival Dynamic processes (e.g. density dependence) Instantaneous events (e.g., treatments) Behaviour and individual variation Stochastic processes Solve for equilibriums

15 Questions we may want to address with simulations How does current and projected environmental variation influence sea louse populations? How can we manage salmon farms to reduce sea lice infections? How does sea louse population structure impact evolutionary processes such as resistance to chemicals?

16 Salinity influences sea louse survival, while temperature alters development Copepodid mortality Salinity Development time Temperature Stien et al. 2005

17 Quantifying the role of temperature and salinity on sea lice Population matrix model Adult P 1 P 2 P 3 P 4 P 5 P 6 P 7 Egg G 1 Larvae (freeswimming) Chalimu s G 2 G 3 Preadult G 4 Gravid I G 5 Between - Clutch G 6 G 7 Gravid II F 5 F 7 Groner et al PLoS one, 2015 MEPS

18 Influence of ocean conditions on sea lice life history in the absence of migration Number of individuals 750 Copepodids Chalimus Preadults Adult Oceanic Mid Estuarine Days

19 Solving for equilibriums using population matrix models Daily population growth rate (Lambda) C 12 C 8 C 4 C Salinity (psu) Groner et al PLoS one, 2015 MEPS

20 Solving for equilibriums using population matrix models Temperature increases population growth by increasing in reproductive success and decreasing generation time. Salinity increase population growth by increasing reproductive success Daily population growth rate (Lambda) Generation Time (days) Basic reproductive Ratio(R0) Salinity (psu) 16 C 12 C 8 C 4 C

21 How does yearly variation impact population growth?

22 How can we evaluate transmission processes? Finite-Volume, primitive equation Community Ocean Model (FVCOM)

23 Simulated environmental transport distances of Lepeophtheirus salmonis in Loch Linnhe, Scotland, for informing aquaculture area management structures Salama et al. 2015, JOFD

24 How can we evaluate transmission processes? Foreman et al. 2015

25 Various levels of complexity ODE models w/ demographic stochasticity Agent Based Models w/ demographic stochasticity Hydrodynamic transmission model- coupled with ODE model of farm transmission processes

26 Questions we may want to address with simulations How does current and projected environmental variation influence sea louse populations? How can we manage salmon farms to reduce sea lice infections? How does sea louse population structure impact evolutionary processes such as resistance to chemicals?

27 Numerous methods are used to control these parasites Bay Management Areas Monitoring, reporting Decrease sea lice Chemical Treatments Still very dependent on chemicals for control Coordinating salmon cohorts Fallowing Wrasse

28 What are the alternatives to chemicals? Can we use modeling techniques to quantify optimal stocking levels for wrasse? To what extent does using wrasse reduce the need for chemical treatments?

29 Individual-based model of sea lice

30 Individual-based model of sea lice

31 Additional causes of mortality Treatments Wrasse Predation Feed at a constant rate Wrasse: salmon ratios 0, 1:200, 1:100, 1:50, 1:25, 1:10

32 So.are these really different?

33 Some results from simulations No control of sea lice Wrasse only Chemical treatments only 17 Treatments Chemical treatments and wrasse 10 Treatments Groner et al. 2013, JOFD Time (days)

34 Using wrasse can reduce the number of chemical treatments for all infection scenarios Number of Chemical Treatments required in a year Wrasse: Salmon Stocking Ratio

35 Questions we may want to address with simulations How does current and projected environmental variation influence sea louse populations? How can we manage salmon farms to reduce sea lice infections? How does sea louse population structure impact evolutionary processes such as resistance to chemicals?

36 How does a refugia from chemical treatments impact the evolution of resistance? Treatment Salmon Farm Attached Lice Wild Salmon Attached Lice Planktonic Lice Wild Trout Attached Lice

37 Modified individual-based model to understand evolutionary processes 1) Model both the host and the parasite salmon from farmed and wild populations 2) Resistance is conferred through a single gene with resistant and susceptible alleles 3) Genes are transferred through Mendelian genetics 4) Emergent properties of interest populations size population-level resistance

38 Model Structure Upper level variables

39 Proportion of resistant alleles Wild salmon refugia reduces the evolution of resistance (in review) McEwan et al. in review Time (Days)

40 What have we learned about sea lice from simulations? Predictions of how salinity and temperature on influence sea louse population growth Recommendations for more sustainable management of sea lice on fish farms Predictions for how wild refugia influence evolution of chemical resistance in sea lice - validation required

41

42 Questions

43 Data and modeling All models are wrong but some are useful It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with the experiment [data], it's wrong. (Richard Feyman) St Andrews, March 2015

44 Modeling take home message? St Andrews, March 2015

45 Modeling take home message? St Andrews, March 2015

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