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

Abstract

The objective of this study was to estimate the impact of infestation pressures on the abundance of the parasitic sea louse, Lepeophtheirus salmonis , in the Bay of Fundy, New Brunswick (NB), Canada, using the Fish‐iTrends database for the years 2009–2018. Infestation pressures were calculated as time‐lagged weighted averages of the abundance of adult female (AF) sea lice within a site (internal infestation pressure: IIP) and among sites (external infestation pressure: EIP). The EIP weights were calculated from seaway distances among sites and a Gaussian kernel density for bandwidths of 5 to 60 km. The EIP with a bandwidth of 10 km had the best fit, as determined with Akaike's information criterion, and historical AF sea lice abundance. This estimated dispersal distance of 10 km was similar to previous studies in Norway, Scotland and in New Brunswick. The infestation pressures estimated from empirical AF sea lice abundance within and among sites significantly increased the abundance of AF sea lice ( p < .001). This study concludes that sea lice burdens within Atlantic salmon farms in the Bay of Fundy, NB, are affected by within site management and could be improved by synchronizing treatments between sites.

Modelling the dispersion of infectious salmon anemia virus from Atlantic salmon farms in the Quoddy Region of New Brunswick, Canada and Maine, USA

“…Seaway distance has frequently been used in previous studies to estimate the connectivity among farms (Aldrin et al 2011;Gautam et al 2018;Parent et al 2021). Our results indicate that accounting for seaway distance is essential when predicting connectivity between farms, given that most farms separated by short seaway distances showed high potential infectious connectivity.…”

Imperfect Estimation of Lepeophtheirus salmonis Abundance and Its Impact on Salmon Lice Treatment on Atlantic Salmon Farms

“…Consequently, sea lice dynamics on a single farm were simulated using deterministic models in order to preclude stochasticity and other sources of variability in the modeled outcomes. Nevertheless, future studies should account for other factors that could potentially influence the accuracy of sea lice abundance estimates and timing of treatment, such as within and between site variability in abundance (Parent et al, 2021), resistance to applied chemotherapeutics (Aaen et al, 2015), and variability in environmental factors (Rittenhouse et al, 2016) or external sources of infestation (Adams et al, 2012).…”

A Scoping Review on GIS Technologies Applied to Farmed Fish Health Management

“…Seaway distance; buffer Spatiotemporal model; stochastic model; risk map; disease spread [10] Spatiotemporal analysis; risk factor assessment; surveillance support [30] Simulation scenario model; stochastic model; disease management practice [31] Spatiotemporal model; risk factor assessment and map [32] Seaway distance; kernel density Spatial and spatiotemporal model; spatial distribution; disease spread and management [33,34] Euclidean distance; grid calculation Spatiotemporal model; risk assessment; disease management [35] Seaway distance; centroid; kernel density Spatial clustering and dispersal pathway; estimation of infestation pressure [36] Euclidean distance; nearest neighbour; grid calculation Spatial planning; risk assessment [37] Seaway distance; nearest neighbour; kernel density Stochastic model; risk factor assessment and map; disease spread [18] Seaway distance; Thiessen polygon; classification Spatiotemporal analysis; spatial distribution; risk assessment and map [38] Nearest neighbour; centroid; Haversine distance Risk factor assessment; disease study [39] Seaway…”