Laboratory experiments with krill
The expansion of oil and gas exploitation in sub-arctic and arctic areas has increased the need for improved environmental risk tools that include data for key ecological species. Every new ecologically important species to be tested for its tolerance towards oil impact represents experimental challenges related to sampling, transport and exposure-/effect studies in the laboratory. In the first part of this project we have worked with these challenges and succeeded to master the use of adult krill (Meganyctiphanes norvegica) as test species and obtained results that can be used in environmental risk calculations regarding discharge of produced water and spills of crude oil. (Experiments with larvae were partly successful). After two weeks’ exposure of adult krill at three oil concentrations it was measured mortality, moulting rate, respiration activity, and biomarkers (GST, EROD and LMS).
It was found total acute mortality at the highest concentration, total mortality within a week in the medium, and 57% accumulated mortality after two weeks at the lowest concentration (respectively 56, 21 and 5 µg L-1 Total Polycyclic Aromatic Hydrocarbons (TPAH). Accumulated mortality in the control group was 21%. Moulting rate and respiration activity was lower in oil exposed animals, but without clear dose dependency. The biomarkers showed lower activity in krill than in other species. With optimization of the methods for krill it could possibly be generated biomarker data for modelling, but this was not achieved within the frames of the project. Higher order effect data at whole organism level were however generated in the experiments.
Environmental risk and monitoring
Data produced in in the laboratory experiments have been evaluated together with data from other projects and literature data in accordance with methods for risk calculation (SSDs and DREAM). Biosensor data have also been inspected with multivariate analyses to clarify if they can be implemented in existing risk methodologies.
In order to optimize risk assessment in arctic areas the results so far show that there is a need for more data from arctic species and further development of suitable monitoring methods. The project has focused on using the generated lab data to connect toxicity and risk assessment on (sub-) individual levels to population levels through DEB Tox.
Dynamic Energy Budget toxicity model (DEB Tox)
Krill was used as model organism in a toxicokinetic/-dynamic model, and data from the project’s exposure experiments were implemented in the model together with literature data. The model calculates toxic effects in organisms over time and can also be used to extrapolate effects under realistic time varying exposure scenarios. This modelling work is done within a bioenergetic framework ('General Unified Threshold model of Survival'; GUTSK; a DEB Tox tool). The model was calibrated with the project’s krill data and thereafter used to predict effects of three time varying exposures that had not been tested in expriments.
Ecosystem process modelling
In environmental management the concern for negative environmental effects is focused on ecological levels. While the above mentioned part studies are concentrated on effect measurements and modelling at individual organism levels (with a certain relevance to populations) it is in the project further examined how a combination of oil spill, climatic warming, and fishery can influence the sub-arctic Barents Sea at ecosystem level. In this context the effects of these influences and how the complexity of the modelled ecosystem would influence the output were examined. Available data for such modelling is temporarily partly incomplete and overlapping. A ‘Bayesian conditioning probability space’ framework was used to handle this, and to represent ecosystem processes and (simultaneously) errors in the known processes and observations. The results showed that there are potentially cascade effects up to predator fish at the highest trophic level (cod), but large uncertainties in the outputs makes it so far difficult to achieve clear predictions.
Integration of management tools
In the last phase of the project it will be prepared a report describing how different model- and monitoring tools can be used jointly to connect different assessment methods in the oil industry’s environmental management (predictions/risk assessments with monitoring at different biological/ecological levels). Further, it will be prepared a laboratory report and seven publications that constitute the scientific basis for some of the links described in the report.
The seven publications are about:
- Effect studies with oil exposure of krill
- Modelling of growth and oil impact on krill (2 articles, 1 submitted).
Modelled effect of mass mortality on nutrient web dynamics
- Biomarker responses to oil exposure in different fish species
- Update of biomarker based species sensitivity distributions w
Current trends in environmental management point towards a common aim: Ecosystem Based Management (EBM) to achieve a holistic approach to ocean management. Several assessment methods and tools exist for environmental management, but they are presently fra gmented with little communication capability.
The project will develop an Integrated Model System (IMS) to link effect/risk assessment of oil and gas industry discharges to EBM, focussing on the link from individual to population level and key ecological species in pelagic Arctic food webs. The model system includes plankton and fish, and will be relevant for operational and acute discharges of hydrocarbons in Arctic marine ecosystems.
The IMS will provide a link between prognostic and diagnostic assessme nt parameters, and between current tools for Environmental Risk Assessment and Indicators being developed in the Barents Sea and Lofoten Management Plan. This gives an opportunity to assess predicted effects/risk and monitoring data coherently using the s ame standards and requirements.
The project uses central ecological properties such as organism fitness (production, reproduction, growth and mortality), prognostic assessments (effect/risk predictions) and biological indicators at different organization levels and time scales for diagnostic assessments (field monitoring). To integrate discharges and risk assessment with population level effects, the focus from individual to population is balanced with a focus from discharge to individual effects.
Existin g methods will be used, but for Arctic application data on Arctic key species will be produced. Methods and models will be developed and adapted to these ecosystems. Approaches include data mining, laboratory studies and development of biological monitori ng tools and assessment procedures. Laboratory studies will focus on krill, which have key relevance in Arctic sea food webs. Analysis of existing material will produce new data for oil sensitivity in herring la