Download or read book Modeling Sea Lice Dispersal from Salmon Farms in the Broughton Archipelago, British Columbia, Canada written by Danielle Burnett and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Salmon farming has sustainability challenges, mainly due to issues associated with disease and parasite management. Arguably, the largest of these challenges is that of sea lice control. Here we present results from a sea lice dispersal study, which simulated larvae releases from 20 salmon farms in the Broughton Archipelago, British Columbia, Canada. This was done utilizing a coupled biological and physical model for a time period of wild salmon smolt out-migrations (March - July), simulating the physical conditions of 2009. The output from the physical model was then used as input for a particle-tracking model. Each of the 20 salmon farms simulated a release of 50 particles every hour, on the hour, for 129 days. The particles were also assigned a biological model, mimicking "pre-parasitic" nauplii maturing into parasitic copepods. Both time and the temperature each particle encountered impacted the maturation rate, and both time and the salinity each particle encountered impacted the survival of the particle. The output from the coupled simulation was used to: i. Quantify the infestation pressure each farm exerted on each other farm and on itself (self-infestation) (chapter 3) ii. Build a connectivity network among the farms (chapter 3) iii. Define the role each farm played in the network (chapter 3) iv. Explore temporal variability in connectivity (chapter 4) v. Quantify which environmental drivers were responsible for the emergent patterns seen in the connectivity (chapter 4) vi. Attempt ground-truthing using real sea lice abundance data collected by the veterinarians in the BA over 13 years (chapter 5) From this analysis, several key patterns emerged. The underlying pattern of connectivity showed certain clusters of farms strongly connected via biologically-modeled particles. There were also some farms that had, overall, low connectivity to all other farms, and thus their locations would be ideal candidates for siting future farm locations. Additionally, there was high temporal variability in the farm connectivity, with overall low connectivity for the study period, interrupted with pulses of high connectivity. These pulses coincided with warming events. Thus, it would be ideal to manage the farms with a plan for these spikes in connectivity. Furthermore, the sea lice larvae's survival was supressed throughout the simulation period due to an event typical of spring in the BA where the snowmelt from the surrounding mountains deluge the fjords of the BA (known as a freshet). The fresh, cold water of the freshet acts as a natural sea lice control aid, and highlights the risks associated with the possible lessening of the freshet due to climate change. Finally, we found support for the simulation's patterns in the observed sea lice abundance data set. The clustering in the observed data set was similar to the highly connected farms in the simulated data set. The similarities provided some confidence in the reliability of the simulation and thus the management recommendations that emerged from the analysis. The methodology employed here could be widely applicable to many epidemiological questions around marine disease dispersal pathways, and thus may be of interest to a wider audience than aquaculture and coastal managers.