Warming waters are changing marine pathogen dispersal patterns and infectivity worldwide. Coupled biological-physical modelling has been used in many systems to determine the connectivity of meta-populations via infectious disease particles. Here we model the connectivity of sea lice larvae (Lepeophtheirus salmonis) among salmon farms in the Broughton Archipelago, British Columbia, Canada, using a coupled biological-physical model. The physical model simulated pathogen dispersal, while the biological component influenced the survival and developmental rates of the sea lice. Model results predicted high temporal variability in connectivity strength among farms; an emergent effect from the interacting parts of the simulation (dispersion vs. survival/development). Drivers of temporal variability were disentangled using generalized additive modeling, which revealed the variability was most strongly impacted by the spring freshet, which can act as a natural aid for sea lice control in the Broughton Archipelago. Our results suggest that farm management strategies can benefit by taking into account short-term spikes in regional pathogen connectivity among farms. Additionally, future scenarios of a warming climate with reduced snowpack can make sea lice control more challenging.
|Number of pages||15|
|Journal||Canadian Journal of Fisheries and Aquatic Sciences|
|Early online date||16 Aug 2019|
|Publication status||Published - 31 Mar 2020|
- salmon farms
- marine pathogens