Longitudinal Analysis of the Gill microbiomes of Atlantic Salmon from four Scottish farms reveals dynamics in bacterial richness and seasonal trends in diversity.

Atlantic Salmon aquaculture in Scotland is a major industry being both Scotland, and the UK’s largest food export. Gill disease, in particular Complex Gill Disease, is a significant challenge of salmon production. It is increasingly understood that the microbiome can influence host health and immunity. Therefore, the objective of the study is to identify and characterise the gill microbiome from stocking to harvest from four sites in Scotland 2018-2020. At each site, mucosal gill swabs were collected fortnightly (sites A &amp; C) or monthly (sites B &amp; G) from eight fish in two pens (n=623 fish). Gill samples underwent 16S rRNA Illumina MiSeq amplicon library preparation and analysis to characterise changes in the gill mucosal communities. Complex Gill disease was identified in sampled fish from each site (A: 20%, B: 11%, C: 24%, G: 13%).At the four sites we showed species richness (alpha diversity) varied over time ranging from 68 ±SD31 to 777 ±SD152 (average 353 ±SD 158). Interestingly, 1100–1500 degree-days after seawater transfer, a distinct decline in species richness and evenness was observed at three of the four sties (A:410 SD± 134 to 276 SD±86 , B:264 SD±67 to 156 SD±71 , C:356 SD±130 to 228 SD±89). In terms of community composition, 1) while there were similarities between all four sites, the communities were statistically different (R = 0.067, P&lt;0.001) from each farm, indicating that sites contributes to differences seen in the microbiome. Within each farm, a seasonal pattern in the microbiome was seen, with community shifts through winter-spring-summer-autumn (A: R2 = 0.11, P&lt;0.001, B: R2 = 0.30, P&lt;0.001, C: R2 = 0.22, P&lt;0.001, G: R2 = 0.11, P&lt;0.001). Proteobacteria dominated the gills (average: 73.6%), with Bacteriodota (average: 18.2%) also highly abundant at all sites. Overall, we have shown changes in the bacterial communities over time and between sites indicating both seasonal and temporal changes in the gill microbiome. Understanding this will help us to better understand the role of the gill microbiome and its role in fish health. <br/

Longitudinal Analysis of the Gill microbiomes of Atlantic Salmon from four Scottish farms reveals dynamics in bacterial richness and seasonal trends in diversity.

Atlantic Salmon aquaculture in Scotland is a major industry being both Scotland, and the UK’s largest food export. Gill disease, in particular Complex Gill Disease, is a significant challenge of salmon production. It is increasingly understood that the microbiome can influence host health and immunity. Therefore, the objective of the study is to identify and characterise the gill microbiome from stocking to harvest from four sites in Scotland 2018-2020. At each site, mucosal gill swabs were collected fortnightly (sites A &amp; C) or monthly (sites B &amp; G) from eight fish in two pens (n=623 fish). Gill samples underwent 16S rRNA Illumina MiSeq amplicon library preparation and analysis to characterise changes in the gill mucosal communities. Complex Gill disease was identified in sampled fish from each site (A: 20%, B: 11%, C: 24%, G: 13%).At the four sites we showed species richness (alpha diversity) varied over time ranging from 68 ±SD31 to 777 ±SD152 (average 353 ±SD 158). Interestingly, 1100–1500 degree-days after seawater transfer, a distinct decline in species richness and evenness was observed at three of the four sties (A:410 SD± 134 to 276 SD±86 , B:264 SD±67 to 156 SD±71 , C:356 SD±130 to 228 SD±89). In terms of community composition, 1) while there were similarities between all four sites, the communities were statistically different (R = 0.067, P&lt;0.001) from each farm, indicating that sites contributes to differences seen in the microbiome. Within each farm, a seasonal pattern in the microbiome was seen, with community shifts through winter-spring-summer-autumn (A: R2 = 0.11, P&lt;0.001, B: R2 = 0.30, P&lt;0.001, C: R2 = 0.22, P&lt;0.001, G: R2 = 0.11, P&lt;0.001). Proteobacteria dominated the gills (average: 73.6%), with Bacteriodota (average: 18.2%) also highly abundant at all sites. Overall, we have shown changes in the bacterial communities over time and between sites indicating both seasonal and temporal changes in the gill microbiome. Understanding this will help us to better understand the role of the gill microbiome and its role in fish health. <br/

Agile training to help enable standardisation of phytoplankton sampling and gross gill terminology across the Scottish sector

Current aquaculture operations in the UK are dominated by finfish farming in Scotland, contributing over £1.8 billion to the Scottish economy with the ambition to double this value by 2030. Finfish health is the top priority across the sector as healthy fish enjoy higher survival rates. One of the most important threats is the occurrence of gill disease, e.g. due to HABs, with potentially devastating impacts on fish health resulting in mortality, reduced welfare, and associated losses in profit on the rise. To understand this threat better, high-quality data generation for reporting is essential. For example, a significant body of work – catalysed by the Scottish Government’s Farmed Fish Health Framework and involving SAIC, agencies, regulators, and a large representation from producers within the sector – acknowledged the need for procedures for sustained and standardised surveillance and reporting of algal blooms, and a standardised operating procedure was developed. The sector representatives are unanimous in the need for developing specific skills to operate under the HABs SOP and in fish health generally. Two courses have been funded by Defra, UK, in the area of aquaculture operators’ skills development. The first course aims for standardisation of HABs sampling and classification, and understanding of the data and modelling associated with mitigation and management of HABs events, and will be delivered through a partnership between SAIC, the Scottish Association for Marine Science (SAMS) and Lantra. The second course is under the health framework and aims to improve the skills and knowledge of technicians and veterinary professionals currently working in, or interested in diversifying into, the seafood sector. An initial aim of the latter course, a partnership between SRUC and SSF, is to standardise salmon gross gill health monitoring terminology

Agile training to help enable standardisation of phytoplankton sampling and gross gill terminology across the Scottish sector

Current aquaculture operations in the UK are dominated by finfish farming in Scotland, contributing over £1.8 billion to the Scottish economy with the ambition to double this value by 2030. Finfish health is the top priority across the sector as healthy fish enjoy higher survival rates. One of the most important threats is the occurrence of gill disease, e.g. due to HABs, with potentially devastating impacts on fish health resulting in mortality, reduced welfare, and associated losses in profit on the rise. To understand this threat better, high-quality data generation for reporting is essential. For example, a significant body of work – catalysed by the Scottish Government’s Farmed Fish Health Framework and involving SAIC, agencies, regulators, and a large representation from producers within the sector – acknowledged the need for procedures for sustained and standardised surveillance and reporting of algal blooms, and a standardised operating procedure was developed. The sector representatives are unanimous in the need for developing specific skills to operate under the HABs SOP and in fish health generally. Two courses have been funded by Defra, UK, in the area of aquaculture operators’ skills development. The first course aims for standardisation of HABs sampling and classification, and understanding of the data and modelling associated with mitigation and management of HABs events, and will be delivered through a partnership between SAIC, the Scottish Association for Marine Science (SAMS) and Lantra. The second course is under the health framework and aims to improve the skills and knowledge of technicians and veterinary professionals currently working in, or interested in diversifying into, the seafood sector. An initial aim of the latter course, a partnership between SRUC and SSF, is to standardise salmon gross gill health monitoring terminology

A 2-stage hierarchical interrupted time-series analysis to quantify the long-term effect of subclinical bacterial kidney disease on performance of farmed Atlantic salmon (Salmo salar L.).

Bacterial Kidney Disease (BKD) is an economically significant disease in salmonid aquaculture and commonly requires antibiotic treatments to reduce its impact. Once a pen of fish is diagnosed with BKD, fish are considered chronically infected, potentially until harvest. Although there appears to be little or no evidence to support it, it is often assumed that subclinical infections affect productivity over the long term. We used a 2-stage hierarchical interrupted time series (ITS) analysis in an attempt to quantify the effect of subclinical BKD on mortality, growth, and food conversion ratio (FCR) of Atlantic salmon cultured in marine farms in Atlantic Canada. For all three outcomes, BKD had for some site cycles a positive effect, and for others a negative effect. Overall, the effect of BKD on mortality and growth could not be detected (effect -0.08 ((95% ci: -0.51, 0.35) and 0.00 (-0.02, 0.02)), while a very small effect showing an increase in FCR was detected (0.07 (-0.01, 0.15)). We hypothesized that minimal interference with fish performance may be compatible with the ecology of Renibacterium salmoninarum, the causative agent of BKD. For this organism, vertical transmission is a primary mode of propagation in low-density host populations as found in the wild. Since farms are always adapting and optimizing their farm management of BKD, these constant adjustments may also have negated our ability to detect the effect of many factors contributing to BKD productivity impacts. Hierarchical ITS analysis is considered an appropriate methodology to investigate the complex relationships with productivity measures over time under farming conditions. In the highly innovative salmon aquaculture industry, health records generating data available for time-series analysis is expected to become more accurate and abundant in the future, providing more opportunities for time-series regression studies

Sea lice management measures for farmed Atlantic salmon (Salmo salar) in Scotland: Costs and effectiveness

Cultured and wild Atlantic salmon around the world are affected by sea lice. Salmon culturing countries have policies in place to minimize sea lice abundance on cultured salmon in open net pens in the marine environment. To adhere to these policies, salmon producers deploy a range of management measures against sea lice throughout the production cycle. The cost effectiveness of these sea lice management measures is not well quantified. This study provides estimates for cost effectiveness in Scotland of (1) individual sea lice management measures and (2) integrated management strategies that span an entire production cycle. Estimates were based on the cost-effectiveness ratio, in which costs consist of those associated with equipment, implementation, environment and side effects (mortality). Effectiveness was based on interviews and expert opinions. For single measures, skirts and the use of in-feed medicines had the best cost-effectiveness. Cleaner fish, fresh or brackish water baths, the physical removal measures (thermolicer and hydrolicer) and medicinal baths were among the next most cost-effective measures, followed by hydrogen peroxide baths. Tarpaulins were more cost-effective than well boats due to lower costs under the assumption of equal effectiveness. Direct comparison of cost effectiveness among measures may not always be constructive as they are deployed at different times in the production cycle and their functionality is different. A holistic approach to sea lice management, a common practice in industry as shown by the integrated management strategies, may reduce risk of developing resistance. For the single measures, carbon costs were insignificant compared to other costs. If measures would have a lasting effect on production through to harvest, such as ongoing increased mortality as a result of a management measure, carbon costs may become significant. Better quantification of effectiveness is important because the scarcity of data led to uncertainty that had a large impact on cost-effectiveness estimates. Generally, this study demonstrated a lack of reliable publicly available data and lack of standardization of data, which constrains research. Highlighted gaps in knowledge can serve as a guide to improve further understanding.<br/

Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images

Measuring hyperplasia in Atlantic salmon gills can provide valuable insights into fish health. In this study, we propose an innovative technique for classifying histology images to identify regions of hyperplasia. Our pipeline utilises novel signal processing techniques in conjunction with prototypical deep learning methods to analyse image texture. We hypothesise and demonstrate that our method effectively captures distinct features of gill histopathology whole-slide images, thereby enhancing the classification task. Compared to conventional deep learning methods, our hybrid approach exhibits exceptional performance in speed and accuracy. When further developed, the concept can support conventional histopathological assessment by providing a computer-assisted hyperplasia score as an objective quantitative histopathological endpoint. The workflow is translatable to other gill conditions and histopathology images beyond gills

Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images

Measuring hyperplasia in Atlantic salmon gills can provide valuable insights into fish health. In this study, we propose an innovative technique for classifying histology images to identify regions of hyperplasia. Our pipeline utilises novel signal processing techniques in conjunction with prototypical deep learning methods to analyse image texture. We hypothesise and demonstrate that our method effectively captures distinct features of gill histopathology whole-slide images, thereby enhancing the classification task. Compared to conventional deep learning methods, our hybrid approach exhibits exceptional performance in speed and accuracy. When further developed, the concept can support conventional histopathological assessment by providing a computer-assisted hyperplasia score as an objective quantitative histopathological endpoint. The workflow is translatable to other gill conditions and histopathology images beyond gills