Effects of suspended drill cuttings on the coral Lophelia pertusa using pulsed and continuous exposure scenarios.

This study aimed at providing confidence in the predictability of the impacts of drill cuttings (DC) discharge on the cold-water coral Lophelia pertusa. L. pertusa was exposed to DC from offshore exploration in the lab with the goal to assess precautionary thresholds of effects.

Effects of chronic crude oil exposure on early developmental stages of the Northern krill (Meganyctiphanes norvegica).

Rising oil and gas activities in northern high latitudes have led to an increased risk of petroleum pollution in these ecosystems. Further, seasonal high UV radiation at high latitudes may elevate photo-enhanced toxicity of petroleum pollution to marine organisms. Zooplanktons are a key ecological component of northern ecosystems; therefore, it is important to assess their sensitivity to potential pollutants of oil and gas activity.

Physiological responses and lipid storage of the coral Lophelia pertusa at varying food density.

Despite the importance of the cold-water coral Lophelia pertusa to deep-sea reef ecosystem functioning, current knowledge of key physiological responses to available food resources is scarce. Scenarios with varying food density may help to understand how corals deal with seasonal variations in the dark ocean and might be used to study consequences of anthropogenic activities potentially affecting food availability. Thus, the physiological responses of L.

Cascading effects of mass mortality events in Arctic marine communities.

Mass mortality events caused by pulse anthropogenic or environmental perturbations (e.g., extreme weather, toxic spills or epizootics) severely reduce the abundance of a population in a short time.

Robust predictive modelling of water pollution using biomarker data.

This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels. The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required 'under the hood' knowledge.