Connected networks of sea lice populations: dynamics and implications for control

Abstract

In studies of the population dynamics of parasitic sea lice and the implications of outbreaks for salmon farms, several types of mathematical models have been implemented. Delay differential equation models describe the temporal dynamics of average adult lice densities over many farm sites. In contrast, larval transport models consider the relative densities of lice at farm sites by modelling larval movements between them but do not account for temporal dynamics or feedbacks created by reproduction. Finally, several recent studies have investigated spatiotemporal variation in site lice abundances using statistical models and distance-based proxies for connectivity. We developed a model which integrates connectivity estimates from larval transport models into the delay differential equation framework. This allows representation of sea lice developmental stages, dispersal between sites, and the impact of management actions. Even with identical external infection rates, lice abundances differ dramatically between farms over a production cycle (dependent on oceanographic conditions and resulting between-farm connectivity). Once infected, lice dynamics are dominated by site reproduction and subsequent dispersal. Lice control decreases actual lice abundances and also reduces variation in abundance between sites (within each simulation) and between simulation runs. Control at sites with the highest magnitude of incoming connections, computed directly from connectivity modelling, had the greatest impact on lice abundances across all sites. Connectivity metrics may therefore be a reasonable approximation of the effectiveness of management practices at particular sites. However, the model also provides new opportunities for investigation and prediction of lice abundances in interconnected systems with spatially varying infection and management.

Identifying salmon lice transmission characteristics between Faroese salmon farms

“…Farms 16, 18, and 21 (if all farms contribute equally). On the contrary, Adams et al (2015) combined connectivity estimates with a salmon lice population dynamic model developed by Revie et al (2005) and found that management was most effective when targeting the highest receiving or influx farms, which is somewhat counter-intuitive. This finding suggests the need to include population dynamics, specifically to examine the network response to different treatment strategies.…”

“…The estimated treatment cost per kg, although widely used, may be outdated (Shinn et al 2015). In addition, the parasites impact the global aquaculture industry with negative publicity (Adams et al 2015) due to the possible damage they have on the environment as a result of chemical treatments and influence on local wildlife (Ford & Myers 2008). Consequently, salmon lice are a major obstacle in any further expansion of the salmon aquaculture industry.…”

“…Coordinated treatment management plans are thus essential to achieve long-term sustainable control of salmon lice. Achieving control requires a thorough understanding of the sea lice dispersion patterns, a factor highly dependent on regional and local hydrography (Adams et al 2015).…”

A simple model of the role of area management in the control of sea lice

“…These models are being combined to include transport processes that reflect local hydrodynamics and population models that detail the dynamics of sea lice populations (Gettinby et al, 2011, Adams et al, 2015Peacock et al, in press). These detailed models are being used to inform effective controls for sea lice, and will increasingly be applied to inform specific management practices for specific farms as more widespread hydrodynamic model output of sufficient resolution becomes available and quality data on lice counts is rapidly made available to managers.…”

“…Amundrud and Murray, 2009;Adams et al, 2012;Adams et al, 2015), aspects of population modelling (e.g. Revie et al, 2005;Gettinby et al, 2011, McEwan et al, 2015, and statistical modelling such as of factors influencing lice treatment rates (Revie et al, 2003;Murray and Hall, 2014).…”