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

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

Evaluation of water salinity effects on the sea lice Lepeophtheirus salmonis found on farmed Atlantic salmon in Muchalat Inlet, British Columbia, Canada

The sea louse Lepeophtheirus salmonis is a major ectoparasite of both farmed and wild salmonids that causes substantial economic losses to the salmon industry worldwide. However, in British Columbia (BC) sea lice do not typically represent a significant health threat to farmed salmon. Sea lice patterns on Atlantic salmon farms in BC are not fully understood, but it is believed they are highly influenced by sea water salinity levels, which vary dramatically over the year. The objective of this investigation was to evaluate the effects of changes in water salinity on mobile L. salmonis found in farmed salmonids in the Muchalat Inlet, BC, while controlling for potential confounding factors. Using daily farm-based salinity measurements over a 13-year period, we built different salinity metrics to summarize salinity drops within specific periods of time prior to sea lice sampling events. Our results suggest that reduced salinity negatively impacted mobile sea lice in three different ways: first, a direct effect on mobile lice, lasting no more than one day; second, an effect mediated by detrimental impacts on pre-mobile lice stages; and third, an effect possibly associated with reduced fecundity of parents of that lice cohort. These findings confirm the important role of salinity on sea lice population dynamics in BC, and contribute new knowledge which is useful in understanding sea lice patterns and determinants in this region. Relevance statement We provided evidence that salinity can naturally control sea lice in British Columbia

Effect of timing of count events on estimates of sea lice abundance and interpretation of effectiveness following bath treatments

Effectiveness of sea lice bath treatment is often assessed by comparing pre- and post-treatment counts. However, in practice, the post-treatment counting window varies from the day of treatment to several days after treatment. In this study, we assess the effect of post-treatment lag time on sea lice abundance estimates after chemical bath treatment using data from the sea lice data management program (Fish-iTrends) between 2010 and 2014. Data on two life stages, (i) adult female (AF) and (ii) pre-adult and adult male (PAAM), were aggregated at the cage level and log-transformed. Average sea lice counts by post-treatment lag time were computed for AF and PAAM and compared relative to treatment day, using linear mixed models. There were 720 observations (treatment events) that uniquely matched pre- and post-treatment counts from 53 farms. Lag time had a significant effect on the estimated sea lice abundance, which was influenced by season and pre-treatment sea lice levels. During summer, sea lice were at a minimum when counted 1 day post-treatment irrespective of pre-treatment sea lice levels, whereas in the spring and autumn, low levels were observed for PAAM over a longer interval of time, provided the pre-treatment sea lice levels were >5-10

Evaluating bath treatment effectiveness in the control of sea lice burdens on Atlantic salmon in New Brunswick, Canada

The use of medicinal bath treatment for sea lice is becoming more common, due to increasing resistance to in-feed treatments with emamectin benzoate. Common treatment modalities in New Brunswick, Canada, include Salmosan administered by tarpaulin or wellboat, and Paramove administered by wellboat. In this study, we assessed the effectiveness of these treatment modalities in the field between 2010 and 2015 using a web-based sea lice data management system (Fish-iTrends© ). Effectiveness was evaluated for adult female (AF) and for pre-adult and adult male (PAAM) life stages separately. We also investigated the impact of variability in pretreatment lead and post-treatment lag time on effectiveness measures. There were 1185 treatment events at 57 farms that uniquely matched our pre- and post-treatment count criteria. The effectiveness of treatment modality was significantly influenced by season, pretreatment level of sea lice and by lead and lag times. In summer, Salmosan administered by tarpaulin had the greatest effectiveness on both AF and PAAM, when pretreatment levels were above 10 sea lice; whereas in autumn, the performance of treatment modalities varied significantly, depending on the pretreatment levels for the life stages. Ignoring the lead or lag time effect generally resulted in an underestimation of treatment effectiveness

Variation in pre-treatment count lead time and its effect on baseline estimates of cage-level sea lice abundance

Treatment efficacy studies typically use pre-treatment sea lice abundance as the baseline. However, the pre-treatment counting window often varies from the day of treatment to several days before treatment. We assessed the effect of lead time on baseline estimates, using historical data (2010-14) from a sea lice data management programme (Fish-iTrends). Data were aggregated at the cage level for three life stages: (i) chalimus, (ii) pre-adult and adult male and (iii) adult female. Sea lice counts were log-transformed, and mean counts by lead time relative to treatment day were computed and compared separately for each life stage, using linear mixed models. There were 1,658 observations (treatment events) from 56 sites in 5 Bay Management Areas. Our study showed that lead time had a significant effect on the estimated sea lice abundance, which was moderated by season. During the late summer and autumn periods, counting on the day of treatment gave significantly higher values than other days and would be a more appropriate baseline estimate, while during spring and early summer abundance estimates were comparable among counts within 5 days of treatment. A season-based lead time window may be most appropriate when estimating baseline sea lice levels

Connectivity-based risk ranking of infectious salmon anaemia virus (ISAv) outbreaks for targeted surveillance planning in Canada and the USA

Infectious salmon anaemia (ISA) can be a serious viral disease of farmed Atlantic salmon (Salmo salar). A tool to rank susceptible farms based on the risk of ISA virus (ISAv) infection spread from infectious farms after initial incursion or re-occurrence in an endemic area, can help guide monitoring and surveillance activities. Such a tool could also support the response strategy to contain virus spread, given available resources. We developed a tool to rank ISAv infection risks using seaway distance and hydrodynamic information separately and combined. The models were validated using 2002-2004 ISAv outbreak data for 30 farms (24 in New Brunswick, Canada and 6 in Maine, United States). Time sequence of infection spread was determined from the outbreak data that included monthly infection status of the cages on these farms. The first infected farm was considered as the index site for potential spread of ISAv to all other farms. To assess the risk of ISAv spreading to susceptible farms, the second and subsequent infected farms were identified using the farm status in the given time period and all infected farms from the previous time periods. Using the three models (hydrodynamic only, seaway-distance, and combined hydrodynamic-seaway-distance based models), we ranked susceptible farms within each time interval by adding the transmission risks from surrounding infected farms and sorting them from highest to lowest. To explore the potential efficiency of targeted sampling, we converted rankings to percentiles and assessed the model's predictive performance by comparing farms identified as high risk based on the rank with those that were infected during the next time interval as observed in the outbreak data. The overall predictive ability of the models was compared using area under the ROC curve (AUC). Farms that become infected in the next period were always within the top 65% of the rank predicted by our models. The overall predictive ability of the combined (hydrodynamic-seaway-distance based model) model (AUC = 0.833) was similar to the model that only used seaway distance (AUC = 0.827). Such models can aid in effective surveillance planning by balancing coverage (number of farms included in surveillance) against the desired level of confidence of including all farms that become infected in the next time period. Our results suggest that 100% of the farms that become infected in the next time period could be targeted in a surveillance program, although at a significant cost of including many false positives

Investigating mortality caused by environmental doses of urban pollutants, the phthalates DnBP and DEHP, on the urban pollinator Bombus terrestris

Cities are environments with severe environmental constraints (e.g. fragmented habitats, urban heat islands, air and soil pollutants) which can be detrimental to wildlife development. We focus our research on the impact of urban pollutant chronic exposure, such as phthalates, on pollinators. These ubiquitous emerging pollutants are endocrine disruptors known to affect invertebrates through immune stress, reproductive impairments, and developmental issues. In this study, we first evaluated phthalates exposure at atmospheric and cuticular levels on city-caught Bombus terrestris. Then, we tested under laboratory conditions the effects of two commonly found phthalates, DEHP (Di(2-ethylhexyl) phthalate) and DnBP (Di-n-butyl phthalate), on the individual and colonial health of Bombus terrestris. Our first results suggest that environmental level exposure to those chemicals can lead to mortality in Bombus terrestris. Faced with these results, it appears that there is a need for deeper investigations of the role of endocrine disruptors, such as phthalates, in the current global decline of insects, and particularly, pollinator populations.5438 - ASPI - Michez - Abeilles sauvages en ville : effets des polluants urbains sur la santé des insectes et sur les interactions plantes-pollinisateurs - Sources privée

The relevance of larval biology on spatiotemporal patterns of pathogen connectivity among open-marine salmon farms

Warming waters are changing marine pathogen dispersal patterns and infectivity worldwide. Coupled biological-physical modelling has been used in many systems to determine the connectivity of meta-populations via infectious disease particles. Here we model the connectivity of sea lice larvae (Lepeophtheirus salmonis) among salmon farms in the Broughton Archipelago, British Columbia, Canada, using a coupled biological-physical model. The physical model simulated pathogen dispersal, while the biological component influenced the survival and developmental rates of the sea lice. Model results predicted high temporal variability in connectivity strength among farms; an emergent effect from the interacting parts of the simulation (dispersion vs. survival/development). Drivers of temporal variability were disentangled using generalized additive modeling, which revealed the variability was most strongly impacted by the spring freshet, which can act as a natural aid for sea lice control in the Broughton Archipelago. Our results suggest that farm management strategies can benefit by taking into account short-term spikes in regional pathogen connectivity among farms. Additionally, future scenarios of a warming climate with reduced snowpack can make sea lice control more challenging