Quantitative analysis of mass mortality events in salmon aquaculture shows increasing scale of fish loss events around the world.

Globally, salmon aquaculture promises to contribute to sustainable sources of animal protein for a growing human population. However, the growth of the industry also includes increased reports of mass mortality events-disaster events where large numbers of fish die in short periods of time. As salmon production increases in scale and more technology is used to grow salmon in contexts otherwise not suited for them, there is a possibility for more frequent and more severe mortality events. Despite investigations into specific cases of mass mortality events-no global study has been conducted to see if large scale mortality is increasing in frequency and scale. Using a global dataset of publicly available and government-collated data on salmon mortality events including nations responsible for the majority of salmon aquaculture, we document trends in mortality events, showing that in some of the major salmon producing nations of the world (in particular Norway, Canada, and the UK), mass mortality events have increased in frequency from 2012 to 2022. We also show that the scope of mass mortality events has increased over time-that is, the upper bound of how many fish were killed in a specific mortality event has increased over time. Finally, the expected maximum size of a mass mortality event differs from country to country, but is likely much larger than site and jurisdictional thresholds of concern for animal welfare, early warning thresholds, and capacity to respond to mortality events. The consequences of the increased scale and scope of mass mortality events extend past aquaculture production to include severe consequences to aquaculture companies and to coastal communities who depend on aquaculture. Our results agree with predictions of the concept of "manufactured risk", which suggests that risk emerges from the aggressive use of technology to optimize production in variable environments, and we argue that there is a need for more fine-scale and standard data collection on salmon mortality events, and that future investigations into salmon aquaculture should increase focus on disaster potential and realization.

A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry.

The novel coronavirus (COVID-19) is highly detrimental, and its death distribution peculiarity has severely affected people's health and the operations of businesses. COVID-19 has wholly undermined the global economy, including inflicting significant damage to the ever-emerging biomass supply chain; its sustainability is disintegrating due to the coronavirus. The biomass supply chain must be sustainable and robust enough to adapt to the evolving and fluctuating risks of the market due to the coronavirus or any potential future pandemics. However, no such study has been performed so far. To address this issue, investigating how COVID-19 influences a biomass supply chain is vital. This paper presents a dynamic risk assessment methodological framework to model biomass supply chain risks due to COVID-19. Using a dynamic Bayesian network (DBN) formalism, the impacts of COVID-19 on the performance of biomass supply chain risks have been studied. The proposed model has been applied to the biomass supply chain of a U.S.-based Mahoney Environmental® company in Washington, USA. The case study results show that it would take one year to recover from the maximum damage to the biomass supply chain due to COVID-19, while full recovery would require five years. Results indicate that biomass feedstock gate availability (FGA) is 2%, due to pandemic and lockdown conditions. Due to the availability of vaccination and gradual business reopenings, this availability increases to 92% in the second year. Results also indicate that the price of fossil-based fuel will gradually increase after one year of the pandemic; however, the market prices of fossil-based fuel will not revert to pre-coronavirus conditions even after nine years. K-fold cross-validation is used to validate the DBN. Results of validation indicate a model accuracy of 95%. It is concluded that the pandemic has caused risks to the sustainability of biomass feedstock, and the current study can help develop risk mitigation strategies.

Dynamic ecological risk modelling of hydrocarbon release scenarios in Arctic waters.

The Arctic is an ecologically diverse area that is increasingly vulnerable to damages from oil spills associated with commercial vessels traversing newly open shipping lanes. The significance of such accidents on Arctic marine habitats and the potential for recovery can be examined using ecological risk assessment (ERA) coupled with a dynamic object-oriented Bayesian network (DOOBN). A DOOBN approach is useful to represent the probabilistic relationships inherent in the interactions between key events associated with an oil spill, including oil dispersion from the source, ice-oil slick interactions, seawater-oil slick formation, sedimentation, and exposures to different aquatic life. From such analysis, a probabilistic cost analysis can be performed to examine the theoretical cost of habitat services lost and restored. The application of an ERA-DOOBN model to assess oil spills in the Arctic is demonstrated using a case study. The utility of the model output for determining habitat restoration costs and developing policy guidelines for ecological response measures in the Arctic is also discussed.