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This study presents an Agent-Based Model (ABM) simulation to assess the impact of varying migration routes on sea lice (Caligus clemensi) infestation levels in juvenile wild sockeye salmon (Oncorhynchus nerka) in the Discovery Islands, British Columbia, Canada. This research highlights the importance of migratory routes in determining the extent of exposure to sea lice originating from nearby salmon farms. Three northward out-migration routes were modelled, each exposing the fish to different levels of infestation pressure based on proximity to salmon farms. The ABM incorporates spatially explicit migration patterns of juvenile sockeye salmon using a detailed raster map of the Discovery Islands. Key variables such as swimming speed, progression rate, and infestation levels were integrated into the model, offering a comprehensive analysis of migration and infestation dynamics. The study revealed that infestation rate is highly variable, depending on migration routes. Specifically, salmon traveling longer migration routes with lower infestation pressure may experience higher sea lice loads compared to those on shorter routes with higher infestation pressure. This underscores the role of low infestation pressures and the critical influence of swimming speed, which affects exposure time, and thus infestation rates. Additionally, the study conducted a sensitivity analysis to understand the influence of various parameters on infestation rates. This analysis highlighted the importance of swimming speed and progression rate, particularly in routes closer to the farms. The findings suggest that slower swimming speeds and meandering routes increase exposure to lice, thereby elevating infestation levels. The research contributes to understanding the dynamics of sea lice transmission and its relationship with salmon migration patterns. It underscores the necessity of considering migratory routes and farm proximity in managing and mitigating the impact of sea lice infestation on wild salmon populations. This study's insights are crucial for developing strategies to balance aquaculture practices with the conservation of wild salmon. [ABSTRACT FROM AUTHOR]

Objective: Efficiently managing sea lice on salmon farms through active surveillance, crucial for lice abundance estimation, is challenging due to the need for effective sampling schemes. To address this, we developed an application that considers infestation levels, farm structure, and management protocols, enhancing the precision of sampling strategies for sea louse abundance estimation. Methods: Simulation‐based methods are valuable for estimating suitable sample sizes in complex studies where standard formulae are inadequate. We introduce FishSampling, an open Web‐based application tailored to determine precise sample sizes for specific scenarios and objectives. Result: The model incorporates factors such as sea lice abundance, farm pen numbers, potential clustering effects among these pens, and the desired confidence level. Simulation outcomes within this application provide practical advice on how to decide on the number of fish and pens to sample, under varying levels of assumed clustering. Conclusion: This approach can be used across the salmon aquaculture sector to improve sampling strategies for sea lice abundance estimation and balance surveillance costs against health objectives. Impact statementThe open‐source application FishSampling enhances sea lice monitoring on salmon farms with a novel simulation‐based approach for sample size determination. It offers precise estimates of sea lice abundance, crucial for regulatory purposes, and aids in the efficient allocation of sampling resources. [ABSTRACT FROM AUTHOR]

The salmon louse Caligus rogercresseyi (Boxshall and Bravo 2000) is a common ectoparasite of farmed salmonids in Chile. Sea lice can negatively impact the growth of hosts, adversely affecting aquaculture productivity. Unlike Lepeophtheirus salmonis (Krøyer, 1838), whose life cycle parameters have been well studied due to its importance in the Northern Hemisphere, for C. rogercresseyi no single source exists that quantifies the parameters required to model this ectoparasite's life cycle. Given that different species of sea lice have substantially different biological characteristics, it is important to parameterize the life cycle of C. rogercresseyi using appropriate observational data, rather than simply trying to adapt parameters developed for L. salmonis. Using data from existing literature, we quantified the development and survival rates for each stage in the C. rogercresseyi life cycle. We illustrate how development rates are affected by water temperature and explore the important impacts of salinity on rates of survival. We present equations that can be used to model development periods and survival proportions given certain water temperature and salinity profiles. While key parameters to quantitatively model the life cycle of C. rogercresseyi are presented, further research is required to adequately model the complete population dynamics of this ectoparasite on Chilean salmon farms and consequently to support decision‐making to achieve effective control and mitigation. [ABSTRACT FROM AUTHOR]

A popular framing of Goodhart's Law states, "When a measure become a target, it ceases to be a good measure". The extent to which this may be the case in the reporting of sea louse infestation on salmon farms is explored here. Due to the importance of controlling sea louse infestation on salmon farms, monitoring programmes are active in most salmon producing regions and, in many, a maximum allowable sea louse level is specified. Using publicly accessible data from Norway and BC, Canada, this study investigated the extent to which the framing of these programmes, in particular the specification of low threshold levels, may be affecting the veracity of the reported sea louse infestation data. In BC, where the threshold level is set to 3 mobile Lepeophtheirus salmonis little evidence of anomalous patterns in the data and the overall proportion of females within the adult sea lice population is around 0.43. By contrast, in Norway where lower sea louse limits are in place (at either 0.5 or 0.2 adult female L. salmonis), there is evidence of unexpected and sharp reductions in the abundance of adult females reported around these threshold values. In addition, the average proportion of females is estimated to be only around 0.20 of the total adult L. salmonis population. The unexpected observations in the data were much more evident for farms in the southern areas of Norway and over the most recent years. These findings appear to support the case that the measurement of sea lice on salmon farms can be significantly influenced by targets (particularly those which are highly demanding), and that as such, researchers and fish health professionals should be aware of potential biases within these data. In addition, regulators should carefully consider the unintended consequences of setting certain sea louse thresholds and the ways in which the potential to effectively review data quality and accuracy may be impacted by the choice of sea louse stage(s) that are reported. • Reported proportions of adult female sea lice varied markedly by region. • In Norway, abrupt changes in proportion of adult female L. salmonis occurred. • Sharp fluctuations in abundance occurred around regulated sea lice target levels. • Anomalies were not observed where more attainable sea lice targets were in place. [ABSTRACT FROM AUTHOR]

The ectoparasite Lepeophtheirus salmonis has for decades been a serious challenge to the sustainability of salmonid aquaculture and has the potential to disrupt the ecosystem of wild salmonids. To tackle this challenge, efforts have been made to develop effective lice control strategies that deter ectoparasitic infestations on salmon farms. Infestation control can be enhanced by understanding L. salmonis population dynamics that may reveal critical points at which to intervene. Here, we investigate the impact of key parameters affecting L. salmonis population dynamics on salmon farms with a view to informing L. salmonis management. We built a system dynamics model to simulate L. salmonis populations in a hypothetical Atlantic salmon (Salmo salar L.) farm. Our model focused on three key parameters in the life cycle of L. salmonis : external pressure (rate of introduction of planktonic L. salmonis from outside the farm), attachment proportion (proportion of L. salmonis that successfully infest salmon), and treatment efficacy (proportion of L. salmonis that are killed as a result of treatment). By applying various combinations of plausible values for these three parameters, their modelled impacts on L. salmonis management were evaluated in terms of the total number of required treatments and the total infestation burden during a typical production cycle. These two modelled outcomes represent the cost of treatment and a proxy for other likely costs, such as lost growth or mortality caused by the parasite. Our results indicated that overall L. salmonis infestation levels were more sensitive to changes in the proportion of successful attachment, followed by changes in external pressure, and were least affected by changes in treatment efficacy. While attachment proportion and external pressure are involved at the pre-infestation stages in the life cycle of L. salmonis , treatment efficacy is involved in the post-infestation stages. Therefore, these findings suggest that preventing infestation before lice attach to fish is a more effective L. salmonis control strategy than treating already infested salmon. Overall, the presented results provide numerical evidence for the efficiency of prevention and support the development and application of prevention measures that have been on the rise in recent years. • L. salmonis infestation level was remarkably sensitive to the lice attachment. • L. salmonis infestation levels were less sensitive to treatment efficacy. • Attachment of L. salmonis to fish is involved in preventing the parasitic infestation. • L. salmonis are better managed by prevention than by treating post-infestation. [ABSTRACT FROM AUTHOR]

Effective sampling is essential to monitoring and controlling sea lice infestations on salmon farms. However, official sampling regimes are often inadequate, typically adopting a one-size-fits-all approach. Over the past decade, the thresholds at which mandatory treatment is required has also been reduced in many regions and, as such, the total infestation loads reported from salmon farms tend to be lower. Therefore, the use of prevalence, as opposed to the more conventionally used abundance, becomes a metric of interest and, from an analytical perspective, offers some beneficial characteristics. This paper explores a range of sampling scenarios and their impacts on the accuracy of sea lice estimation, particularly when prevalence is the adopted metric. Empirical sea lice count data demonstrated a good fit to the negative binomial distribution and provided a probable range of values that could be used to describe typical levels of over-dispersion. It was demonstrated that, when prevalence is low, it can be reliably used to predict abundance. Monte Carlo simulations of a hypothetical salmon farm were then used to test results for a variety of sample sizes and sea lice infestation scenarios. Different sea lice infestation levels between pens in a farm (i.e. a spatial clustering effect) and aggregations of sea lice on their hosts (i.e. the effect of over-dispersion) were simulated to explore a variety of conditions. The extent to which higher levels of clustering and/or over-dispersion necessitate the need for larger sample sizes to achieve similar levels of accuracy was explored. The level of accuracy that can be achieved in practice depends on many factors and what is considered to be an acceptable level of accuracy will, by definition, be subjective and vary according to the purpose for which the estimation is being carried out. This study includes a variety of possible situations to guide farm operators in choosing sample sizes according to their particular requirements. Furthermore, appropriate sample size determination can be expected to reduce overall sampling effort, achieve better overall control, help avoid unnecessary treatments, and reduce both associated costs and fish welfare impacts. • Sea lice counts on farmed salmon follow a negative binomial distribution. • Dispersion in sea lice count values is similar across farms and regions. • Prevalence can be used to reasonably accurately estimate abundance. • Sampling accuracy is affected by differences in prevalence among pens. [ABSTRACT FROM AUTHOR]