Module L: New Models of

Salmon Health Management

Overview of Planned Research

1

November 7, 2017

Dr. Ian Gardner

on behalf of the team

2

Ocean Data & Technology

Sustainable Fisheries

Sustainable Aquaculture

Marine Safety

Ocean Change

Ocean Solutions

J

Atmosphere-Ocean

Interactions

J

Shifting

Ecosystems

A: Marine Atmospheric Composition & Visibility

B: Auditing the Northwest Atlantic Carbon Sink

C: Dynamic microbial communities

D: Impact of Warming on Groundfish

E: Ecosystem Indicators

F. Cooperative Physical Modelling

G: Future-proofing Marine Protected Area

Networks

H: Novel Stock Assessment Models

I. Informing Governance Responses

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

N: Safe Navigation & Environmental Protection

O: Transforming Ocean Observations

P: Research Data Management

MUN

Dal

UPEI

Dal

Linkages of aquaculture modules

3

Novel sensors

(Dal - Module K)

Improving sustainability

(MUN - Module J)

Module L

Projects

Social license

(Dal- Module M)

Data

Biocapacity estimates

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

Module L: New Models of

Salmon Health Management

4

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

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

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

2002 -2004 ISAv outbreak in Bay of Fundy (24

farms in NB and 6 in Maine)

7

Example

Model

Output

Infected sites

Highest risk

(non-infected) sites

Medium risk

(non-infected) sites

Low risk

(non-infected) sites

8

Project 2 – Improving antibacterial

treatment efficacy

• PI:

– Sophie St-Hilaire

• Collaborators:

– Eastern Aquatic Veterinary Association

– Industry and provincial veterinarians

– Salmon companies

9

Treatment failure may occur if fish

have antibiotic concentrations

lower than the MIC

~11% below the MIC90

~46% below the MIC90

1000 2000

10

MIC90: Minimum Inhibitory Concentration for 90% of isolates

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

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

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

Project 3 – Modelling tools to

investigate disease occurrences,

transmission patterns, and mitigation

strategies

• PIs: – Raphael Vanderstichel

– Henrik Stryhn

– Crawford Revie

• Collaborators:

– Jon Grant – Dalhousie University;

– Tor Horsberg – Norwegian School Vet Sciences;

– Anja Kristoffersen, Peder Jansen - Norwegian

Veterinary Institute 14

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

Modelling Tools

16

Ocean circulation models

Observed Disease

Occurrence = Internal Expected External Factors + + error

Pathogen biological parameters

e.g. historical pathogen levels, farm size,

temperature, etc.

Hypothesized hydrological parameters

e.g. particle intensities weighted by

neighbouring farm pathogen levels

Mapping errors

Values from model residuals

describe transmission patterns

Epi-Statistical models

Informing

policy and

management

decisions

Epi-Simulation models

Farm A

Farm B

Farm C Self-infection

Unidirectional or bidirectional

farm-to-farm infection

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

Extending the Models

• State-space models

– Apply a multivariate state-

space 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

18

BMA: Bay Management Area

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

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

Novel data streams from pen-level

sensors

• Environmental data from each cage

• Water movement/flow patterns

within and

around cages

21

SINTEF

LiceRisk

Novel data streams from pen-level

sensors

• Video and other signals

22

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

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

Questions:

24