Sea Lice Larvae Research Articles

The Use of Kernel Density Estimation With a Bio-Physical Model Provides a Method to Quantify Connectivity Among Salmon Farms: Spatial Planning and Management With Epidemiological Relevance.

Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.

Factors influencing the long-term dynamics of larval sea lice density at east and west coast locations in Scotland

Sea lice (Copepoda: Caligidae) are marine copepods that parasitize finfish, and in cases of high infestation can result in severe epithelial damage and mortality. In Scotland, 2 species of sea louse, Lepeophtheirus salmonis and Caligus elongatus, pose a significant economic burden to the marine Atlantic salmon aquaculture industry and potentially impact wild salmonids. The purpose of this study was to determine how the density of pelagic sea lice is affected by external variables, in order to improve our understanding of sea lice dynamics. Long-term data from 2 sampling sites on the east and west coasts of Scotland were modelled independently in conjunction with environmental and anthropogenic variables. Statistical analysis identified that at the east coast site, the most influential factor affecting lice density was salinity. On the west coast, salinity, rainfall and farmed salmon production year were most influential. Molecular and morphological techniques also showed that the individuals recorded on the east coast were C. elongatus, a generalist copepod parasite, whereas only the salmonid-specific L. salmonis were found on the west. These results reiterate the role of environmental factors in influencing sea lice dynamics, and that salmonids are the primary hosts of sea lice on the west coast, but there could be non-salmonid host species as well as salmonid species influencing east coast sea lice densities.

Ocean modelling for aquaculture and fisheries in Irish waters

Abstract. The Marine Institute, Ireland, runs a suite of operational regional and coastal ocean models. Recent developments include several tailored products that focus on the key needs of the Irish aquaculture sector. In this article, an overview of the products and services derived from the models are presented. The authors give an overview of a shellfish model developed in-house and that was designed to predict the growth, the physiological interactions with the ecosystem, and the level of coliform contamination of the blue mussel. As such, this model is applicable in studies on the carrying capacity of embayments, assessment of the impacts of pollution on aquaculture grounds, and the determination of shellfish water classes. Further services include the assimilation of the model-predicted shelf water movement into a new harmful algal bloom alert system used to inform end users of potential toxic shellfish events and high biomass blooms that include fish-killing species. Models are also used to identify potential sites for offshore aquaculture, to inform studies of potential cross-contamination in farms from the dispersal of planktonic sea lice larvae and other pathogens that can infect finfish, and to provide modelled products that underpin the assessment and advisory services on the sustainable exploitation of the resources of marine fisheries. This paper demonstrates that ocean models can provide an invaluable contribution to the sustainable blue growth of aquaculture and fisheries.

Sea-lice infection models for fishes

As free-living sea-lice larvae are difficult to sample directly, lice abundances on fish have recently been used to study larvae in the water. In the KLV problem, juvenile wild salmon migrate past a salmon farm, and the change of infection with distance along the migration route is used to estimate larvae production from the farm. In the farm problem, time-varying infection of sea-cage fish is used to estimate the time-variation of free-living larvae in waters near the farm. Both inverse problems require good forward models for infection. In the farm problem, hosts are relatively large and lice pathogenesis is seldom mortal, whereas in the KLV problem hosts are small and lice-induced host mortality can affect lice abundance; thus, infection models for the farm problem are special cases of models for the KLV problem. Here I give an infection model for the KLV problem that explicitly includes lice clumping and host mortality, showing that Krkosek et al. (Proc R Soc B 272:689-696, 2005) (KLV) probably underestimated larvae production by the salmon farm, and further, that if lice development rates were known from laboratory data, lice abundance field data could be directly inverted for lice-induced host mortality during migration. If lice-induced host mortality is negligible, or if lice are Poisson distributed, infection models of arbitrary complexity reduce to Erlang models. I give two useful Erlang models with their solutions for non-zero initial conditions.

The Effects of Water Temperature, Salinity, and Currents on the Survival and Distribution of the Infective Copepodid Stage of Sea Lice (Lepeophtheirus Salmonis) Originating on Atlantic Salmon Farms in the Broughton Archipelago of British Columbia, Canada

Recent reports claim that pink salmon fry are heavily infected by Lepeoptheirus salmonis as they pass salmon farms in the Broughton Archipelago. Hydrodynamic studies reveal that the top 25 to 40 m of water generally flows seaward through the archipelago under the influence of freshwater, reducing surface salinity from 15–25% from June through November of most years. Sea lice larvae do not consistently develop to an infectious stage at salinities < 30‰—providing a natural control in the archipelago. The larvae require 4.2–5.8 days to develop to the infective stage at the temperatures typical during the time that pink salmon fry are migrating. Net current speeds measured at 15 salmon farms are predicted to carry most sea lice nauplii 7.3–10.0 km downcurrent and out of the archipelago before they become infective. A particle-tracking model predicts that nauplii released in the vicinity of two farms located deep in the archipelago are carried 10–40 km from the farms before they become infective. These predictions are consistent with the European experience and suggest little potential for salmon farms to be self-infecting or to infect migrating wild fish in their immediate vicinity, except where net current vectors are zero. A clearer picture of sea lice epizootics requires detailed knowledge of site-specific oceanographic characteristics and the affects these have on sea lice survival and dispersal during development to the infectious copepodid stage.