Modelling the Impact of Temperature-Induced Life History Plasticity and Mate Limitation on the Epidemic Potential of a Marine Ectoparasite

Abstract

Temperature is hypothesized to contribute to increased pathogenicity and virulence of many marine diseases. The sea louse (Lepeophtheirus salmonis) is an ectoparasite of salmonids that exhibits strong life-history plasticity in response to temperature; however, the effect of temperature on the epidemiology of this parasite has not been rigorously examined. We used matrix population modelling to examine the influence of temperature on demographic parameters of sea lice parasitizing farmed salmon. Demographically-stochastic population projection matrices were created using parameters from the existing literature on vital rates of sea lice at different fixed temperatures and yearly temperature profiles. In addition, we quantified the effectiveness of a single stage-specific control applied at different times during a year with seasonal temperature changes. We found that the epidemic potential of sea lice increased with temperature due to a decrease in generation time and an increase in the net reproductive rate. In addition, mate limitation constrained population growth more at low temperatures than at high temperatures. Our model predicts that control measures targeting preadults and chalimus are most effective regardless of the temperature. The predictions from this model suggest that temperature can dramatically change vital rates of sea lice and can increase population growth. The results of this study suggest that sea surface temperatures should be considered when choosing salmon farm sites and designing management plans to control sea louse infestations. More broadly, this study demonstrates the utility of matrix population modelling for epidemiological studies.

Lessons from sea louse and salmon epidemiology

“…The 1.5-to 2-year seawater production cycles for farmed salmon add additional variability in host density in coastal regions. [3,59] explain underlying mechanisms [37] project behaviours of systems [20,28,43] predict outcomes of interventions [15,27] raise questions and generate hypotheses [66] propose models [49] present definition functions and goals timescale find possible explanations for patterns and phenomena [18] generate hypotheses [4] specify new data needs [27] [20] regression models -statistical -descriptive/correlational -can include spatial effects GLM, GLMM, random effects, logistic regression -identifying epidemiological factors effecting sea lice abundance on salmon farms [21] -associations between aquaculture and sea louse infections on sea trout [22] survival functions and hazard functions -statistical -descriptive/correlational survival analysis -impacts of sea lice on salmon survival in the NE Atlantic [3] -effects of salinity on sea louse survival on juvenile salmon [23] stochastic processes -discrete-time or continuous-time dynamics [29] rstb.royalsocietypublishing.org Phil. Trans.…”

“…For example, a modified susceptible-exposed-infective-resistant ordinary differential equation model showed that stronger currents and higher stocking levels increased transmission of sea lice between two hypothetical farms [52]. A matrix model that explicitly accounted for demographic stochasticity among sea lice raised concerns about the impact of rising sea surface temperatures on sea louse dynamics by showing that both high temperatures and increased temperature variation can increase the sea louse basic reproduction number, R 0 , and decrease generation time of sea lice, resulting in faster population growth [20]. The development of these mathematical models that incorporated environmental impacts has been facilitated by numerous laboratory studies that allowed for the parametrization of stage-specific effects of temperature on sea louse development [53].…”

“…At both host and farm scales, this spatial heterogeneity has implications for sea louse demography and monitoring [20]. Monte Carlo simulations and matrix population modelling suggest that when sea louse abundance is maintained below a mean of three gravid females per host on farms, mate limitation decreases the rate of sea louse reproduction [20,55]. However, over-dispersion of sea lice on hosts can increase mating success by bringing males and females into contact more frequently [20].…”

“…Monte Carlo simulations and matrix population modelling suggest that when sea louse abundance is maintained below a mean of three gravid females per host on farms, mate limitation decreases the rate of sea louse reproduction [20,55]. However, over-dispersion of sea lice on hosts can increase mating success by bringing males and females into contact more frequently [20]. Over-dispersion may also have disadvantages for parasite fitness, because parasite-induced host mortality can have Figure 4.…”

Managing aquatic parasites for reduced drug resistance: lessons from the land

“…We adapted a temperature profile from [27] for our simulations. This profile was derived by fitting a sine curve to daily temperature data from 33 salmon farms across 5 years in Scotland [31].…”