Module L: New Models of Salmon Health Management

Transcription

1 Module L: New Models of Salmon Health Management Overview of Planned Research November 7, 2017 Dr. Ian Gardner on behalf of the team 1

2 Atmosphere-Ocean Interactions J Ocean Change Shifting Ecosystems J A: Marine Atmospheric Composition & Visibility B: Auditing the Northwest Atlantic Carbon Sink Marine Safety N: Safe Navigation & Environmental Protection Ocean Data & Technology O: Transforming Ocean Observations P: Research Data Management Ocean Solutions MUN Dal UPEI Dal C: Dynamic microbial communities D: Impact of Warming on Groundfish E: Ecosystem Indicators F. Cooperative Physical Modelling Sustainable Fisheries G: Future-proofing Marine Protected Area Networks H: Novel Stock Assessment Models I. Informing Governance Responses Sustainable Aquaculture J: Improving Aquaculture Sustainability K: Novel sensors for fish health and welfare L: New Models of Salmon Health Management M: Social License & Planning in Coastal Communities 2

3 Linkages of aquaculture modules Novel sensors (Dal - Module K) Improving sustainability (MUN - Module J) Data Module L Projects Biocapacity estimates Social license (Dal- Module M) 3

4 Module L: New Models of Salmon Health Management Project 1: Risk-based models to reduce spread of Infectious Salmon Anaemia virus (ISAv) Project 2: Improving antimicrobial treatment efficacy in Atlantic salmon Project 3: Modeling tools to investigate disease occurrences, transmission patterns, and mitigation strategies, in the context of biocapacity Project 4: Interpretation of novel data streams from pen-level sensors and microscale current patterns for fish health monitoring and parasite control 4

5 Project 1: Risk ranking of Atlantic salmon farms to minimize spread of ISAv PI: Ian Gardner Collaborators: Kim Klotins, Raju Gautum CFIA Lori Gustafson USDA:APHIS:VS Mike Beattie Gas infusion systems 5

6 Project 1 Why? CFIA is responsible for management of outbreaks caused by World Organisation of Animal Health (OIE) listed diseases Needs science-based guidance on prioritization of salmon farm surveillance during outbreaks Outcomes and impacts Risk ranking tool (R software) in real time More efficient disease response 6

7 Objectives and approach Compare seaway distance and a combination of seaway distance and hydrodynamic connectivity data for predicting risk after ISAv spread from an index site Susceptible-Infected (SI) model coded in R software Validation of model with monthly case data from ISAv outbreak in Bay of Fundy (24 farms in NB and 6 in Maine) 7

8 Example Model Output Infected sites Highest risk (non-infected) sites Medium risk (non-infected) sites Low risk (non-infected) sites 8

9 Project 2 Improving antibacterial treatment efficacy PI: Sophie St-Hilaire Collaborators: Eastern Aquatic Veterinary Association Industry and provincial veterinarians Salmon companies 9

10 Treatment failure may occur if fish have antibiotic concentrations lower than the MIC ~46% below the MIC90 ~11% below the MIC MIC90: Minimum Inhibitory Concentration for 90% of isolates 10

11 Project 2 Why? Early detection of bacterial disease is desirable More effective in-feed delivery systems are needed Outcomes and impacts Reduced use of antibiotics while salmon are in sea-water Best Practices guide developed 11

12 Improving antibacterial treatment efficacy Objective Determine ways to improve the distribution of antibiotics in salmon aquaculture to achieve better coverage within the population At different temperatures For different size fish For different antibiotic classes 12

13 Approach: starting in 2019 Industry focus group meeting Pilot study to assess post-treatment antibiotic tissue concentrations Update focus group meeting on pilot & make adjustment based on feedback Initiate larger-scale longitudinal study 13

14 Project 3 Modelling tools to investigate disease occurrences, transmission patterns, and mitigation PIs: Raphael Vanderstichel Henrik Stryhn Crawford Revie Collaborators: strategies Jon Grant Dalhousie University; Tor Horsberg Norwegian School Vet Sciences; Anja Kristoffersen, Peder Jansen - Norwegian Veterinary Institute 14

15 Background Why? Responding to external requests to improve models that reduce and better manage diseases (e.g. sea lice) in farm/zones Outcomes and impacts Utilize the more rich and complicated data now available Expand current farm-level models to zone level 15

16 Ocean circulation models Modelling Tools Epi-Statistical models Hypothesized hydrological parameters e.g. particle intensities weighted by neighbouring farm pathogen levels Observed Disease Occurrence = Internal Expected + External Factors + error Pathogen biological parameters e.g. historical pathogen levels, farm size, temperature, etc. Mapping errors Values from model residuals describe transmission patterns Epi-Simulation models Farm A Farm B Unidirectional or bidirectional farm-to-farm infection Farm C Self-infection Informing policy and management decisions 16

17 Extending the Models Unobserved disease status (Simulation models) Current agent-based models exist for individual farm simulations Evolution of chemotherapeutant resistance Future goal to expand this to represent a larger area with multiple farms Build connectivity among farms Solve scalability issues Computational demands 17

18 Extending the Models State-space models Apply a multivariate statespace model for sea lice for active sites in Grand Manan area Quantify internal and external infection pressure of sea lice on salmon sites Evaluate the predictive accuracy of the model BMA: Bay Management Area 18

19 Project 4 Interpretation of novel data streams (pen sensors and microscale current patterns) for fish health monitoring and parasite control PI: Crawford Revie Collaborators: SINTEF Norway 19

20 Project 4 Why? Increased numbers/types of pen-level data Uncertainty as to how these can best be used in the context of fish health Outcomes and impacts New methods to interpret data and better target cage-level health interventions 20

21 Novel data streams from pen-level sensors Environmental data from each cage Water movement/flow patterns within and around cages SINTEF LiceRisk 21

22 Novel data streams from pen-level sensors Video and other signals 22

23 New methods for interpretation of data Integration of data sources/types Multivariate statistics to filter/summarise signals Machine learning algorithms to detect trends and associations between signals and fish health 23

24 Questions: Project 1: Risk-based models to reduce spread of Infectious Salmon Anaemia virus (ISAv) Project 2: Improving antimicrobial treatment efficacy in Atlantic salmon Project 3: Modeling tools to investigate disease occurrences, transmission patterns, and mitigation strategies, in the context of biocapacity Project 4: Interpretation of novel data streams from pen-level sensors and microscale current patterns for fish health monitoring and parasite control 24