Validation of a sea lice dispersal model: principles from ecological agent-based models applied to aquatic epidemiology

Abstract

Sea lice are one of the most economically costly and ecologically concerning problems facing the salmon farming industry. Here, we validated a coupled biological and physical model that simulated sea lice larvae dispersal from salmon farms in the Broughton Archipelago (BA), British Columbia, Canada. We employed a concept from ecological agent-based modeling known as ‘pattern matching’, which identifies similar emergent properties in both the simulated and observed data to confirm that the simulation contained sufficient complexity to recreate the emergent properties of the system. One emergent property from the biophysical simulations was the existence of sub-networks of farms. These were also identified in the observed sea lice count data in this study using a space-time scan statistic (SaTScan) to identify significant spatio-temporal clusters of farms. Despite finding support for our simulation in the observed data, which consisted of over a decade’s worth of monthly sea lice abundance counts from salmon farms in the BA, the validation was not entirely straightforward. The complexities associated with validating this biophysical dispersal simulation highlight the need to further develop validation techniques for agent-based models in general, and biophysical simulations in particular, which often result in patchiness in their dispersal fields. The methods utilised in this validation could be adopted as a template for other epidemiological dispersal models, particularly those related to aquaculture, which typically have robust disease monitoring data collection plans in place.

Next-generation matrices for marine metapopulations: the case of sea lice on salmon farms*

“…However, there is abundant evidence that in the Broughton Archipelago sea lice populations on many salmon farms will grow exponentially in the absence of treatment by farm managers [64,65,66,52,67]. This discrepancy may be due to the fact that while the hydrodynamic and particle tracking models give good estimates of sea louse dispersal between farms [52], they likely underestimate the number of sea louse larvae that remain inside a salmon farm.…”

“…However, there is abundant evidence that in the Broughton Archipelago sea lice populations on many salmon farms will grow exponentially in the absence of treatment by farm managers [64,65,66,52,67]. This discrepancy may be due to the fact that while the hydrodynamic and particle tracking models give good estimates of sea louse dispersal between farms [52], they likely underestimate the number of sea louse larvae that remain inside a salmon farm. In the hydrodynamic model the location of the farm does not alter the local hydrodynamics that occur within the farm, but in reality the the presence of salmon swimming within the net pens may alter the local hydrodynamics sufficiently that some proportion of larvae remain within the farm.…”

“…To determine the level of sea lice dispersal away from salmon farms, we use a hydrodynamic model, coupled with a particle tracking model, to track sea lice particles released from 20 historical farms in the Broughton Archipelago [51]. The coupled hydrodynamic and particle tracking models have been validated with sea louse counts on salmon farms in the Broughton [52]. We construct one next-generation matrix for the 20 historical farms in the region for which the hydrodynamic particle tracking model was run, as well as one for the 11 remaining farms in the area after 2023, subject to First Nations and governmental approval [47].…”

“…To determine the level of sea lice dispersal away from salmon farms, we use a hydrodynamic model, coupled with a particle tracking model, to track sea lice particles released from 20 historical farms in the Broughton Archipelago [51]. The coupled hydrodynamic and particle tracking models have been validated with sea louse counts on salmon farms in the Broughton [52].…”

Timing and probability of arrival for sea lice dispersing between salmon farms

“…In dense salmon farming regions such as Norway, Scotland and Canada there is evidence that sea lice populations on salmon farms are connected via larval dispersal, therefore acting as connected metapopulations, though the degree of larval dispersal between farms may not always be solely based on distance [10,11,[25][26][27]. Evidence for connectivity has been found by tracking sea lice particles in hydrodynamic models [9][10][11]26] and by fitting metapopulation models to data of sea lice counts on salmon farms [25,27]. In Norway, where most salmon farms are located in fjords along the coast, seaway distance has been used as a simple measure of farm connectivity over a large scale [25].…”