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

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

Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon

Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses.In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out. Keywords: State-space models, State process, Prediction horizon, Sea lice abundance, Atlantic salmo

Association between sea lice (Lepeophtheirus salmonis) infestation on Atlantic salmon farms and wild Pacific salmon in Muchalat Inlet, Canada

Abstract Growth in salmon aquaculture over the past two decades has raised concerns regarding the potential impacts of the industry on neighboring ecosystems and wild fish productivity. Despite limited evidence, sea lice have been identified as a major cause for the decline in some wild Pacific salmon populations on the west coast of Canada. We used sea lice count and management data from farmed and wild salmon, collected over 10 years (2007–2016) in the Muchalat Inlet region of Canada, to evaluate the association between sea lice recorded on salmon farms with the infestation levels on wild out-migrating Chum salmon. Our analyses indicated a significant positive association between the sea lice abundance on farms and the likelihood that wild fish would be infested. However, increased abundance of lice on farms was not significantly associated with the levels of infestation observed on the wild salmon. Our results suggest that Atlantic salmon farms may be an important source for the introduction of sea lice to wild Pacific salmon populations, but that the absence of a dose response relationship indicates that any estimate of farm impact requires more careful evaluation of causal inference than is typically seen in the extant scientific literature

Video_1_The Use of Kernel Density Estimation With a Bio-Physical Model Provides a Method to Quantify Connectivity Among Salmon Farms: Spatial Planning and Management With Epidemiological Relevance.AVI

<p>Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.</p

Comparison of infectious agents detected from hatchery and wild juvenile Coho salmon in British Columbia, 2008-2018.

Infectious diseases are potential contributors to decline in Coho salmon (Oncorhynchus kisutch) populations. Although pathogens are theoretically considered to pose higher risk in high-density rearing environments like hatcheries, there is no direct evidence that hatchery-origin Coho salmon increase the transmission of infectious agents to sympatric wild populations. This study was undertaken to compare prevalence, burden, and diversity of infectious agents between hatchery-reared and wild juvenile Coho salmon in British Columbia (BC), Canada. In total, 2,655 juvenile Coho salmon were collected between 2008 and 2018 from four regions of freshwater and saltwater in BC. High-throughput microfluidics qPCR was employed for simultaneous detection of 36 infectious agents from mixed-tissue samples (gill, brain, heart, liver, and kidney). Thirty-one agents were detected at least once, including ten with prevalence >5%. Candidatus Brachiomonas cysticola, Paraneuclospora theridion, and Parvicapsula pseudobranchiocola were the most prevalent agents. Diversity and burden of infectious agents were substantially higher in marine environment than in freshwater. In Mainland BC, infectious burden and diversity were significantly lower in hatchery smolts than in wild counterparts, whereas in other regions, there were no significant differences. Observed differences in freshwater were predominantly driven by three parasites, Loma salmonae, Myxobolus arcticus, and Parvicapsula kabatai. In saltwater, there were no consistent differences in agent prevalence between hatchery and wild fish shared among the west and east coasts of Vancouver Island. Although some agents showed differential infectious patterns between regions, annual variations likely contributed to this signal. Our findings do not support the hypothesis that hatchery smolts carry higher burdens of infectious agents than conspecific wild fish, reducing the potential risk of transfer to wild smolts at this life stage. Moreover, we provide a baseline of infectious agents in juvenile Coho salmon that will be used in future research and modeling potential correlations between infectious profiles and marine survival

Transmission of Piscirickettsia salmonis among salt water salmonid farms in Chile

Salmon Rickettsia Syndrome (SRS), also known as Salmon Rickettsia Septicemia, caused by Piscirickettsia salmonis is the most common bacterial disease in the Chilean salmon aquaculture industry. The epidemiology of this pathogen is relatively unknown. Using the Chilean salmon aquaculture industry monthly diagnostic data collected between January 2009 and December 2012, we assessed the spatial and environmental factors associated with the reporting of SRS on Atlantic salmon, coho salmon, and rainbow trout farms. Crops of fish were on average 60% likely to report SRS during our study time period. The probability of a farm reporting SRS was positively associated with temperature, time in salt water, and the number of SRS infected neighbors. The distance effect from infected neighboring farms that best fit the model for predicting SRS on a farm varied by species but ranged between 7.5 and 10 km. Our study suggests reducing disease on farms will not only benefit the producer it will also reduce the probability that P. salmonis is transferred to neighboring farms

Experimental infection of highly pathogenic avian influenza virus H5N1 in black-headed gulls (Chroicocephalus ridibundus)

International audienceHistorically, highly pathogenic avian influenza viruses (HPAIV) rarely resulted in infection or clinical disease in wild birds. However, since 2002, disease and mortality from natural HPAIV H5N1 infection have been observed in wild birds including gulls. We performed an experimental HPAIV H5N1 infection of black-headed gulls (Chroicocephalus ridibundus) to determine their susceptibility to infection and disease from this virus, pattern of viral shedding, clinical signs, pathological changes and viral tissue distribution. We inoculated sixteen black-headed gulls with 1 × 104 median tissue culture infectious dose HPAIV H5N1 (A/turkey/Turkey/1/2005) intratracheally and intraesophageally. Birds were monitored daily until 12 days post inoculation (dpi). Oropharyngeal and cloacal swabs were collected daily to detect viral shedding. Necropsies from birds were performed at 2, 4, 5, 6, 7, and 12 dpi. Sampling from selected tissues was done for histopathology, immunohistochemical detection of viral antigen, PCR, and viral isolation. Our study shows that all inoculated birds were productively infected, developed systemic disease, and had a high morbidity and mortality rate. Virus was detected mainly in the respiratory tract on the first days after inoculation, and then concentrated more in pancreas and central nervous system from 4 dpi onwards. Birds shed infectious virus until 7 dpi from the pharynx and 6 dpi from the cloaca. We conclude that black-headed gulls are highly susceptible to disease with a high mortality rate and are thus more likely to act as sentinel species for the presence of the virus than as long-distance carriers of the virus to new geographical areas