Using fatty acid markers to distinguish between effects of salmon (Salmo salar) and halibut (Hippoglossus hippoglossus) farming on mackerel (Scomber scombrus) and whiting (Merlangius merlangus)

Presence of coastal aquaculture activities in marine landscapes is growing with impacts on the wild fish that share these habitats. However, it is difficult to disentangle subsequent ecological interactions between these activities and marine fish communities. We evaluated the impact of both salmon and halibut farms on mackerel (Scomber scombrus) and whiting (Merlangius merlangus) sampled near sea cages using condition indices and fatty acid (FA) biomarkers. Results of the stomach content analysis indicated that mackerel and whiting consumed waste feed which was also reflected in their modified FA profiles. Both mackerel and whiting had elevated levels of FAs that are of vegetable oils origin. The use of vegetable oils as replacement for marine oils is a lot more common in salmon farming than halibut farming. Additionally, the overall effects of the two fish farms were more pronounced in whiting than in mackerel sampled near the sea cages. By allowing discrimination between sources of trophic interactions, this method could lead to more informed decisions in managing different farming activities

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence