Differences in diversity and community composition of the shell microbiome of apparently healthy lobsters Homarus americanus across Atlantic Canada.

Host-microbe dynamics are of increasing interest in marine research due to their role in host health and productivity. Changes in the shell microbiome of American lobsters have been associated with epizootic shell disease, a syndrome that is spreading northwards across the eastern U.S. and Canadian Atlantic coast. This study analyzed differences in alpha and beta diversity, as well as differentially abundant taxa, in the shell-associated bacterial community of apparently healthy lobsters from four lobster fishing areas (LFAs) in Atlantic Canada. Over 180 lobsters from New Brunswick, Nova Scotia and Prince Edward Island (PEI) were sampled during seven sampling events over four sampling months. The bacterial community was identified using novel PacBio long-read sequencing, while alpha and beta diversity parameters were analyzed using linear regression models and weighted UniFrac distances. The bacterial richness, diversity and evenness differed by sampling location, sampling month, and molt stage, but not by lobster sex or size, nor sampling depth. Similarly, based on LFA, sampling month, year and lobster molt stage, the shell microbiome differed in microbial community composition with up to 34 out of 162 taxa differing significantly in abundance between sampling groups. This large-scale microbial survey suggests that the shell microbial diversity of apparently healthy lobsters is influenced by spatial and temporal factors such as geographic location, as well as the length of time the carapace is exposed to the surrounding seawater.

Observed size distribution changes in American lobsters over a 12-year period in southwestern Nova Scotia, Canada.

Size distribution and size frequency information of American lobsters (Homarus americanus) are often used to help estimate the age distributions, and reproductive output for the species and to guide the determination of appropriate minimum legal sizes for the fishery. This study used truncated linear regression models to estimate the effects of sampling year, sampling month, lobster sex and water depth on the lobster size. A dataset of almost 130,000 trap-caught lobsters from the two most important lobster fishing areas of Atlantic Canada collected over a 12-year period (2004-2015) was analyzed. It was shown that truncated models can help to account for biases due to the trap sampling method from vessels and from wharf samplings. There were significant annual and seasonal changes in size distribution, and data collected outside the fishing season showed a significant increase in carapace length in 2014 and 2015, potentially reflecting a northward shift of the range of lobster populations due to more favourable settlement and recruitment habitats. Size also increased in late summer, likely due to moult. Our results demonstrated that landed lobsters, especially females, were smaller than the predicted size-at-maturity in the region (96.5 mm carapace length), which could have long-term repercussions for the stock's reproductive potential.

Long-read sequencing reveals the shell microbiome of apparently healthy American lobsters Homarus americanus from Atlantic Canada.

The shell microbial community of lobsters-a key factor in the development of epizootic shell disease (ESD)-is still insufficiently researched in Atlantic Canada and many knowledge gaps remain. This study aimed to establish a baseline description and analysis of the shell microbiome of apparently healthy lobsters from four locations in the region. More than 180 lobster shell swab samples were collected from New Brunswick, Nova Scotia and Prince Edward Island (PEI). PacBio long-read 16S rDNA sequencing and bioinformatic analyses in QIIME2 identified the shell-associated bacteria. The shell microbiome of healthy lobsters consisted mainly of the bacterial classes Gammaproteobacteria, Saprospiria, Verrucomicrobiae, Alphaproteobacteria, Flavobacteriia, Acidimicrobiia and Planctomycetia. The microbial composition differed regionally and seasonally, with some classes showing decreased or increased relative abundances in the PEI samples as well as in the winter and spring samples in Nova Scotia. The core shell microbiome included potentially pathogenic as well as beneficial bacterial taxa, of which some were present only in certain regions. Bacterial taxa that have previously been associated with ESD were present on healthy lobsters in Atlantic Canada, but their frequency differed by location, sampling time, and moult stage. This study indicated that geographical and seasonal factors influenced the shell microbiome of apparently healthy lobsters more than host factors such as sex, size, and moult stage. Our results provide valuable reference microbial data from lobsters in a disease-free state.

Data-fed, needs-driven: Designing analytical workflows fit for disease surveillance.

Syndromic surveillance has been an important driver for the incorporation of "big data analytics" into animal disease surveillance systems over the past decade. As the range of data sources to which automated data digitalization can be applied continues to grow, we discuss how to move beyond questions around the means to handle volume, variety and velocity, so as to ensure that the information generated is fit for disease surveillance purposes. We make the case that the value of data-driven surveillance depends on a "needs-driven" design approach to data digitalization and information delivery and highlight some of the current challenges and research frontiers in syndromic surveillance.

Fisheries dataset on moulting patterns and shell quality of American lobsters H. americanus in Atlantic Canada.

Monitoring the moulting phenology of American lobsters (Homarus americanus) is important for maintaining sustainable lobster stocks. Changes in lobster landings can affect reproduction and disease susceptibility. Data on lobster moult indicators and on life-history traits (sex, size) were collated from a twelve-year monitoring program (2004-2015) in six lobster fishing areas in Atlantic Canada. A total of 141,659 lobsters were sampled over 1,195 sampling events using a standardized protocol and commercial lobster fishing traps. The dataset contains pleopod stages, estimated hemolymph protein levels (°Brix values) and shell hardness as well as lobster sex and size. Evaluation of sex ratio dynamics is also possible but existing biases in sampling males and females needs to be noted. This dataset is valuable in terms of inferring spatio-temporal trends in the life history of lobsters, as well as in the analysis of their moult cycle, and hence more generally for fisheries science and marine ecology.

Quantifying key parameters related to the life cycle of Caligus rogercresseyi.

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.

Corrigendum: Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making.

[This corrects the article DOI: 10.3389/fvets.2021.633977.].

Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making.

The biggest change brought about by the "era of big data" to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex "variety" dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.

Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean.

Variation in circulus spacing on the scales of wild Atlantic salmon is indicative of changes in body length growth rate. We analyzed scale circulus spacing during the post-smolt growth period for adult one sea-winter salmon (n = 1947) returning to Scotland over the period 1993-2011. The growth pattern of the scales was subjectively and visually categorized according to the occurrence and zonal sequence of three intercirculus spacing criteria ("Slow", "Fast" and "Check" zones). We applied hierarchical time-series cluster analysis to the empirical circulus spacing data, followed by post hoc analysis of significant changes in growth patterns within the 20 identified clusters. Temporal changes in growth pattern frequencies showed significant correlation with sea surface temperature anomalies during the early months of the post-smolt growth season and throughout the Norwegian Sea. Since the turn of the millennium, we observed (a) a marked decrease in the occurrence of continuous Fast growth; (b) increased frequencies of fish showing an extended period of initially Slow growth; and (c) the occurrence of obvious growth Checks or hiatuses. These changes in post-smolt growth pattern were manifest also in decreases in the mean body length attained by the ocean midwinter, as sea surface temperatures have risen.

Modeling Pathogen Dispersal in Marine Fish and Shellfish.

In marine ecosystems, oceanographic processes often govern host contacts with infectious agents. Consequently, many approaches developed to quantify pathogen dispersal in terrestrial ecosystems have limited use in the marine context. Recent applications in marine disease modeling demonstrate that physical oceanographic models coupled with biological models of infectious agents can characterize dispersal networks of pathogens in marine ecosystems. Biophysical modeling has been used over the past two decades to model larval dispersion but has only recently been utilized in marine epidemiology. In this review, we describe how biophysical models function and how they can be used to measure connectivity of infectious agents between sites, test hypotheses regarding pathogen dispersal, and quantify patterns of pathogen spread, focusing on fish and shellfish pathogens.

Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology.

BACKGROUND: A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations. METHODS: The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups. RESULTS: From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were 'long-term care', 'palliative', 'major acute', 'major chronic', 'major cancer', 'major newborn', 'major mental health' and 'moderate chronic'. Both 'major acute' and 'major cancer' health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, 'major chronic', 'moderate chronic', 'major newborn' and 'other mental health', a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use. INTERPRETATION: Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.

Assessment of farmers' compliance in implementing recommended cow comfort changes and their effects on lying time, stall and cow cleanliness within smallholder dairy farms in Kenya.

Our study aimed to evaluate farmers' compliance in implementing recommendations of farm-specific cow comfort changes, and the effects of these changes on lying time, stall cleanliness and cow cleanliness using a randomized controlled trial carried out on 100 smallholder dairy farms in Kenya, with 62 and 11 farms remaining in the intervention and control groups, respectively. On the first farm visit, data loggers were attached on lactating cows to determine lying time and questionnaires utilized to collect baseline data. Three days later, stall design and management recommendations were given to the intervention group of farmers orally and in written form. After an average of 39 ±â€¯7 days, data loggers were re-attached, compliance was assessed, and a post-intervention questionnaire was administered to the intervention group on the third visit. Three days subsequent to the first and third visits, data loggers were removed from all cows. Data were analysed in Stata 14.2® using proportion tests and Kruskal-Wallis rank tests to compare cleanliness scores and lying time, respectively. Interaction effects between treatment groups and visits were assessed using multivariable mixed linear and logistic regression models. While 46 of the 62 intervention farmers (74%) made at least one recommended change to cow comfort, 63% of the 324 overall recommendations were implemented. The odds of a recommendation being implemented were significantly higher when:1) major recommendations were given relative to minor recommendations (OR = 6.28); 2) recommendations were related to floor characteristics (floor softness and flatness) in comparison to recommendations related to stall design (OR = 3.14). The odds of compliance were lower on: 1) farms where the farm-hands received the recommendations compared to farms that had the female principal farmer receive the recommendations (OR = 0.01); 2) farms that had recommended changes related to roof, alley and sharps fixes relative to stall design fixes (OR = 0.13). Post-intervention, stall, udder and upper hind-leg cleanliness scores improved significantly (p < 0.0001, p = 0.021 and p = 0.017, respectively) in the intervention farms but not in the control farms. There was no significant difference in lying times between intervention and control farms, with 0.6 and 0.2 h/day increases being recorded in the intervention and control groups, from the 10.9 and 10.4 h/day at baseline, respectively. Giving farm-specific cow comfort recommendations to smallholder dairy farmers in Kenya, and providing them with a participatory role in the formulation and implementation of improvement recommendations ensured good acceptance and a high degree of implementation, and led to a subsequent improvement in cow comfort and cleanliness.

Updating the global occurrence of Culicoides imicola, a vector for emerging viral diseases.

Culicoides imicola is the main vector transmitting viruses causing animal diseases such as Bluetongue, African Horse Sickness, and Schmallenberg. It has become widely distributed, with reports from South Africa to southern Europe, and from western Africa to southern China. This study presents a global compendium of Culicoides imicola occurrence between 1943 and 2018, reflecting the most recently compiled and harmonized global dataset derived from peer-reviewed literature. The procedures used in producing the data, as well as the geo-coding methods, database management and technical validation procedures are described. The study provides an updated and comprehensive global database of C. imicola occurrence, consisting of 1 039 geo-coded records from 50 countries. The datasets can be used for risk mapping of the diseases transmitted by C. imicola as well as to develop the global habitat suitability for the vector.

Modeling the global distribution of Culicoides imicola: an Ensemble approach.

Culicoides imicola is a midge species serving as vector for a number of viral diseases of livestock, including Bluetongue, and African Horse Sickness. C. imicola is also known to transmit Schmallenberg virus experimentally. Environmental and demographic factors may impose rapid changes on the global distribution of C. imicola and aid introduction into new areas. The aim of this study is to predict the global distribution of C. imicola using an ensemble modeling approach by combining climatic, livestock distribution and land cover covariates, together with a comprehensive global dataset of geo-positioned occurrence points for C. imicola. Thirty individual models were generated by 'biomod2', with 21 models scoring a true skill statistic (TSS) >0.8. These 21 models incorporated weighted runs from eight of ten algorithms and were used to create a final ensemble model. The ensemble model performed very well (TSS = 0.898 and ROC = 0.991) and indicated high environmental suitability for C. imicola in the tropics and subtropics. The habitat suitability for C. imicola spans from South Africa to southern Europe and from southern USA to southern China. The distribution of C. imicola is mainly constrained by climatic factors. In the ensemble model, mean annual minimum temperature had the highest overall contribution (42.9%), followed by mean annual maximum temperature (21.1%), solar radiation (13.6%), annual precipitation (11%), livestock distribution (6.2%), vapor pressure (3.4%), wind speed (0.8%), and land cover (0.1%). The present study provides the most up-to-date predictive maps of the potential distributions of C. imicola and should be of great value for decision making at global and regional scales.

Impact of One-Health framework on vaccination cost-effectiveness: A case study of rabies in Ethiopia.

Livestock losses due to rabies and health and the corresponding benefits of controlling the disease are not often considered when the cost-effectiveness of rabies control is evaluated. In this research, assessed the benefits of applying a One Health perspective that includes these losses to the case of canine rabies vaccination in Ethiopia. We constructed a dynamic epidemiological model of rabies transmission. The model was fit to district-specific data on human rabies exposures and canine demography for two districts with distinct agro-ecologies. The epidemiological model was coupled with human and livestock economic outcomes to predict the health and economic impacts under a range of vaccination scenarios. The model indicates that human exposures, human deaths, and rabies-related livestock losses would decrease monotonically with increasing vaccination coverage. In the rural district, all vaccination scenarios were found to be cost-saving compared to the status quo of no vaccination, as more money could be saved by preventing livestock losses than would be required to fund the vaccination campaigns. Vaccination coverages of 70% and 80% were identified as most likely to provide the greatest net health benefits at the WHO cost-effectiveness threshold over a period of 5 years, in urban and rural districts respectively. Shorter time frames led to recommendations for higher coverage in both districts, as did even a minor threat of rabies re-introduction. Exclusion of rabies-related livestock losses reduced the optimal vaccination coverage for the rural district to 50%. This study demonstrated the importance of including all economic consequences of zoonotic disease into control decisions. Analyses that include cattle and other rabies-susceptible livestock are likely better suited to many rural communities in Africa wishing to maximize the benefits of canine vaccination.

The Application of a Mathematical Model to Evaluate the Effectiveness of Control Strategies Against Ciona intestinalis in Mussel Production.

The Prince Edward Island (PEI) mussel industry has faced challenges associated with invasive tunicate species over the past two decades. Field experiments to find suitable mitigation strategies require considerable time and are resource intensive. This study demonstrates the application of a mathematical model to assess several control strategies against Ciona intestinalis populations under different temperature conditions in a mussel production area in PEI. A temperature dependent compartmental model was used to model the total abundance of C. intestinalis. Each mitigation strategy was defined in terms of a combination of timing and frequency of treatments. Various strategies were explored to obtain the combination that maximized the difference in predicted abundances between the control (untreated) and the different mitigation strategies. Treatment frequency was allowed to vary between one and four times over a given production year. The model was assessed under baseline conditions, which mimicked water temperatures from Georgetown Harbor, PEI, in 2008; as well as under scenarios that reflected prolonged summer or warm spring temperatures. Furthermore, the sensitivity of the model to variations in presumed treatment efficacy was evaluated. The use of all four available treatments, starting around the first week of July and correctly timed thereafter, provided the most effective strategy, assuming the baseline temperature scenario. However, the effectiveness of this mitigation strategy depended on temperature conditions. The mathematical model developed in this study allows decision makers to explore different strategies to control the abundance of C. intestinalis in mussel production areas under different environmental conditions. In addition, the modeling framework developed could be adapted to simulate comparable ectoparasitic infestation in aquatic environments.

Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature and treatment rules.

Atlantic salmon farming is one of the largest aquaculture sectors in the world. A major impact on farm economics, fish welfare and, potentially, nearby wild salmonid populations, is the sea louse ectoparasite Lepeophtheirus salmonis. Sea louse infestations are most often controlled through application of chemicals, but in most farming regions, sea lice have evolved resistance to the small set of available chemicals. Therefore, alternative treatment methodologies are becoming more widely used. One increasingly common alternative treatment involves the co-culture of farmed salmon with cleaner fish, which prey on sea lice. However, despite their wide use, little is understood about the situations in which cleaner fish are most effective. For example, previous work suggests that a low parasite density results in sea lice finding it difficult to acquire mates, reducing fecundity and population growth. Other work suggests that environmental conditions such as temperature and external sea louse pressure have substantial impact on this mate limitation threshold and may even remove the effect entirely. We used an Agent-Based Model (ABM) to simulate cleaner fish on a salmon farm to explore interactions between sea louse mating behaviour, cleaner fish feeding rate, temperature and external sea louse pressure. We found that sea louse mating has a substantial effect on sea louse infestations under a variety of environmental conditions. Our results suggest that cleaner fish can control sea louse infestations most effectively by maintaining the population below critical density thresholds.

Drivers for the development of an Animal Health Surveillance Ontology (AHSO).

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

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.

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.