ORCID Profile
0000-0001-5959-0576
Current Organisation
Modail Mara Inc.
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Publisher: Springer Science and Business Media LLC
Date: 30-05-2017
Publisher: ACM
Date: 30-09-2009
Publisher: Public Library of Science (PLoS)
Date: 20-02-2018
Publisher: Springer London
Date: 2009
Publisher: Canadian Science Publishing
Date: 08-2016
Abstract: Sea lice are common ectoparasites of farmed and wild salmonids and can cause substantial morbidity and mortality in their hosts. While sea lice infections are common in estuarine areas with variable salinity, the effects of salinity on population dynamics are poorly understood. We used existing literature to parameterize salinity-dependent logistic mortality curves for different life stages of sea lice. We then used population matrix models to characterize the effects of temperature and salinity on sea louse population growth. Our models showed that low salinity decreases survival, while low temperature retards sea louse development. In contrast with the linear effects of temperature on sea louse development, salinity has a nonlinear effect on sea louse survival values below 20 psu cause mortality, while values above 20 psu have little effect on survival. Simulations showed that sea louse population growth can be greatest in zones that are intermediate between estuarine and oceanic. In these cases population growth is not limited by the low salinities found in more estuarine sites or the low temperatures found in more oceanic sites.
Publisher: The Royal Society
Date: 12-2016
Abstract: Atlantic salmon farming is one of the largest aquaculture industries in the world. A major problem in salmon farms is the sea louse ectoparasite Lepeophtheirus salmonis , which can cause stress, secondary infection and sometimes mortality in the salmon host. Sea lice have substantial impacts on farm economics and potentially nearby wild salmonid populations. The most common method of controlling sea louse infestations is application of chemicals. However, most farming regions worldwide have observed resistance to the small set of treatment chemicals that are available. Despite this, there has been little investigation of treatment strategies for managing resistance in aquaculture. In this article, we compare four archetypical treatment strategies inspired by agriculture, where the topic has a rich history of study, and add a fifth strategy common in aquaculture. We use an agent-based model (ABM) to simulate these strategies and their varying applications of chemicals over time and space. We analyse the ABM output to compare how the strategies perform in controlling louse abundance, number of treatments required and levels of resistance in the sea louse population. Our results indicated that among the approaches considered applying chemicals in combination was the most effective over the long term.
Publisher: Springer London
Date: 2009
Publisher: ACM Press
Date: 2005
Publisher: ACM
Date: 28-11-2007
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: ACM Press
Date: 2007
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.PREVETMED.2018.08.001
Abstract: Caligus rogercresseyi is a host-dependent parasite that affects rainbow trout and Atlantic salmon in Chile. Numbers of sea lice on fish increase over time at relatively predictable rates when the environment is conducive to the parasite's survival and fish are not undergoing treatment. We developed a tool for the salmon industry in Chile that predicts the abundance of adult sea lice over time on farms that are relatively isolated. We used data on sea louse abundance collected through the SalmonChile INTESAL sea lice monitoring program to create series of weekly lice counts between lice treatment events on isolated farms. We defined isolated farms as those with no known neighbors within a 10 km seaway distance and no more than two neighbors within a 20 km seaway distance. We defined the time between sea lice treatments as starting the week immediately post treatment and ending the week before a subsequent treatment. Our final dataset of isolated farms consisted of 65 series from 32 farms, between 2009 and 2015. Given an observed abundance at time t = 0, we built a model that predicted 8 consecutive weekly sea louse abundance levels, based on the preceding week's lice prediction. We calibrated the parameters in our model on a randomly selected subset of training data, choosing the parameter combinations that minimized the absolute difference between the predicted and observed sea louse abundance values. We validated the parameters on the remaining, unseen, subset of data. We encoded our model and made it available as a Web-accessible applet for producers. We determined a threshold, based on the upper 97.5% predictive interval, as a guideline for producers using the tool. We hypothesize that if farms exceed this threshold, especially if the sea lice levels are above this threshold 2 and 4 weeks into the model predictions, the sea louse population on the farm is likely influenced by sources other than lice within the farm.
Publisher: ACM
Date: 05-04-2008
Publisher: ACM
Date: 11-02-2012
No related grants have been discovered for Gregor McEwan.