The relevance of larval biology on spatiotemporal patterns of pathogen connectivity among open-marine salmon farms

Abstract

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 metapopulations 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 versus survival and 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 accounting for 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.

Validating a biophysical parasite model with fish farm pen and plankton trawl data

“…Our simulations lack any influence of salinity on mortality (Bricknell et al 2006), or vertical movement of lice from the surface layers (Crosbie et al 2019) or the impacts of vertical diffusion (Visser 1997, Ross & Sharples 2004. These factors, and their interactions, have been found in some other studies to be important in determining the outcome of dispersal events (Johnsen et al 2016, Cantrell et al 2020, and are obvious candidates for future refinements to the dispersal model used here.…”

“…In order to characterise the spatial distribution of lice in the coastal environment, many studies have implemented biophysical models to describe the dispersal process. This research has been carried out over many years in all major salmon producing areas (Murray & Gillibrand 2006, Amundrud & Murray 2009, Stucchi et al 2010, Salama & Rabe 2013, Kragesteen et al 2018, Myksvoll et al 2018, Cantrell et al 2020, and has led to suggestions on approaches to managing aquaculture operations at local and regional scales (Adams et al 2015, Samsing et al 2019.…”

Next-generation matrices for marine metapopulations: the case of sea lice on salmon farms*

“…In order to use the next-generation matrix to calculate patch contribution or persistence we are assuming that our system is autonomous, and that the demographic rates do not change with time. In reality for most systems, including sea lice on salmon farms, environmental variables will fluctuate over time, potentially changing the demographic rates of the population [68]. In our case temperature and salinity change over the course of the spring, but we calculate the next-generation matrix using the mean temperature and salinity that sea lice experience during the particle tracking simulation window.…”

Estimating the dispersal of Lepeophtheirus salmonis sea lice within and among Atlantic salmon sites of the Bay of Fundy, New Brunswick

“…The application of different methods to the dataset may reveal variability in the impact of infestation pressures estimated (e.g. Cantrell et al., 2019 ; Kristoffersen et al., 2014 ).…”

“…Aldrin et al, 2013Aldrin et al, , 2019Kristoffersen et al, 2013Kristoffersen et al, , 2014Kristoffersen et al, , 2018. Cantrell et al, (2018Cantrell et al, ( , 2019 defined infestation pressure as the density of particles representing sea lice using a biophysical model in the Broughton Archipelago, British Columbia (BC). Sandvik et al, (2016) defined infestation pressure on a scale of low (0-1 louse) to high (>10 lice) resulting from a biophysical model informed from field data.…”