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

D Cantrell,R Filgueira,J Grant,Cw Revie,R Vanderstichel

doi:10.3354/aei00390

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.

Highlights

Agent-based models (ABMs) refer to simulations where individual agents are given the ability to interact with each other as well as with their environment (Grimm et al 2005, Railsback & Grimm 2010)
One area in which ABMs have only recently been utilised effectively is in marine epidemiology, where disease particles act as individual agents, interacting with each other and hosts as well as with ocean currents and other physical parameters of the waters they inhabit, such as temperature and salinity (Asplin et al 2004, Salama et al 2013, Skarðhamar et al 2018)
In Cantrell et al (2020a), we identified an emergent behaviour of 3 sub-networks of farms in the Broughton Archipelago (BA) that are more highly connected to each other than to the rest of the farms in the area

Summary

Introduction

Agent-based models (ABMs) refer to simulations where individual agents are given the ability to interact with each other as well as with their environment (Grimm et al 2005, Railsback & Grimm 2010). One area in which ABMs have only recently been utilised effectively is in marine epidemiology, where disease particles (bacteria, viruses, protists, parasites, etc.) act as individual agents, interacting with each other and hosts as well as with ocean currents and other physical parameters of the waters they inhabit, such as temperature and salinity (Asplin et al 2004, Salama et al 2013, Skarðhamar et al 2018) These types of simulations are able to create an ABM in the ocean by combining a particle-tracking model with biological characteristics assigned to the particles, which allows them to interact with the physical environment (including underlying circulation) and sometimes with each other. This type of biophysical model has been used to simulate dispersal of a wide variety of waterborne particles, from neonate sea turtles (Robson et al 2017), to the causative agents of cholera (Augustijn et al 2016), and to salmonid pathogens such as infectious hematopoietic necrosis virus or infectious salmon anaemia virus (Foreman et al 2015, Gautam et al 2018)

Methods

Results

Discussion

Conclusion