A data science approach for multi-sensor marine observatory data monitoring cold water corals (Paragorgia arborea) in two campaigns.

Fixed underwater observatories (FUO), equipped with digital cameras and other sensors, become more commonly used to record different kinds of time series data for marine habitat monitoring. With increasing numbers of campaigns, numbers of sensors and campaign time, the volume and heterogeneity of the data, ranging from simple temperature time series to series of HD images or video call for new data science approaches to analyze the data. While some works have been published on the analysis of data from one campaign, we address the problem of analyzing time series data from two consecutive monitoring campaigns (starting late 2017 and late 2018) in the same habitat. While the data from campaigns in two separate years provide an interesting basis for marine biology research, it also presents new data science challenges, like the the marine image analysis in data form more than one campaign. In this paper, we analyze the polyp activity of two Paragorgia arborea cold water coral (CWC) colonies using FUO data collected from November 2017 to June 2018 and from December 2018 to April 2019. We successfully apply convolutional neural networks (CNN) for the segmentation and classification of the coral and the polyp activities. The result polyp activity data alone showed interesting temporal patterns with differences and similarities between the two time periods. A one month "sleeping" period in spring with almost no activity was observed in both coral colonies, but with a shift of approximately one month. A time series prediction experiment allowed us to predict the polyp activity from the non-image sensor data using recurrent neural networks (RNN). The results pave a way to a new multi-sensor monitoring strategy for Paragorgia arborea behaviour.

Exploring time series of hyperspectral images for cold water coral stress response analysis.

Hyperspectral imaging (HSI) is a promising technology for environmental monitoring with a lot of undeveloped potential due to the high dimensionality and complexity of the data. If temporal effects are studied, such as in a monitoring context, the analysis becomes more challenging as time is added to the dimensions of space (image coordinates) and wavelengths. We conducted a series of laboratory experiments to investigate the impact of different stressor exposure patterns on the spectrum of the cold water coral Desmophyllum pertusum. 65 coral samples were divided into 12 groups, each group being exposed to different types and levels of particles. Hyperspectral images of the coral samples were collected at four time points from prior to exposure to 6 weeks after exposure. To investigate the relationships between the corals' spectral signatures and controlled experimental parameters, a new software tool for interactive visual exploration was developed and applied, the HypIX (Hyperspectral Image eXplorer) web tool. HypIX combines principles from exploratory data analysis, information visualization and machine learning-based dimension reduction. This combination enables users to select regions of interest (ROI) in all dimensions (2D space, time point and spectrum) for a flexible integrated inspection. We propose two HypIX workflows to find relationships in time series of hyperspectral datasets, namely morphology-based filtering workflow and embedded driven response analysis workflow. With these HypIX workflows three users identified different temporal and spatial patterns in the spectrum of corals exposed to different particle stressor conditions. Corals exposed to particles tended to have a larger change rate than control corals, which was evident as a shifted spectrum. The responses, however, were not uniform for coral samples undergoing the same exposure treatments, indicating individual tolerance levels. We also observed a good inter-observer agreement between the three HyPIX users, indicating that the proposed workflow can be applied to obtain reproducible HSI analysis results.

Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory.

An array of sensors, including an HD camera mounted on a Fixed Underwater Observatory (FUO) were used to monitor a cold-water coral (Lophelia pertusa) reef in the Lofoten-Vesterålen area from April to November 2015. Image processing and deep learning enabled extraction of time series describing changes in coral colour and polyp activity (feeding). The image data was analysed together with data from the other sensors from the same period, to provide new insights into the short- and long-term dynamics in polyp features. The results indicate that diurnal variations and tidal current influenced polyp activity, by controlling the food supply. On a longer time-scale, the coral's tissue colour changed from white in the spring to slightly red during the summer months, which can be explained by a seasonal change in food supply. Our work shows, that using an effective integrative computational approach, the image time series is a new and rich source of information to understand and monitor the dynamics in underwater environments due to the high temporal resolution and coverage enabled with FUOs.

Water column monitoring near oil installations in the North Sea 2001-2004.

Fisheries have been vital to coastal communities around the North Sea for centuries, but this semi-enclosed sea also receives large amounts of waste. It is therefore important to monitor and control inputs of contaminants into the North Sea. Inputs of effluents from offshore oil and gas production platforms (produced water) in the Norwegian sector have been monitored through an integrated chemical and biological effects programme since 2001. The programme has used caged Atlantic cod and blue mussels. PAH tissue residues in blue mussels and PAH bile metabolites in cod have confirmed exposure to effluents, but there was variation between years. Results for a range of biological effects methods reflected exposure gradients and indicated that exposure levels were low and caused minor environmental impact at the deployment locations. There is a need to develop methods that are sufficiently sensitive to components in produced water at levels found in marine ecosystems.

Integrated environmental monitoring and multivariate data analysis-A case study.

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC.

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation.

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, [Formula: see text]) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors.

Integrated environmental mapping and monitoring, a methodological approach to optimise knowledge gathering and sampling strategy.

New technology has led to new opportunities for a holistic environmental monitoring approach adjusted to purpose and object of interest. The proposed integrated environmental mapping and monitoring (IEMM) concept, presented in this paper, describes the different steps in such a system from mission of survey to selection of parameters, sensors, sensor platforms, data collection, data storage, analysis and to data interpretation for reliable decision making. The system is generic; it can be used by authorities, industry and academia and is useful for planning- and operational phases. In the planning process the systematic approach is also ideal to identify areas with gap of knowledge. The critical stages of the concept is discussed and exemplified by two case studies, one environmental mapping and one monitoring case. As an operational system, the IEMM concept can contribute to an optimised integrated environmental mapping and monitoring for knowledge generation as basis for decision making.

Assessing the potential impact of water-based drill cuttings on deep-water calcareous red algae using species specific impact categories and measured oceanographic and discharge data.

The potential impact of drill cuttings on the two deep water calcareous red algae Mesophyllum engelhartii and Lithothamnion sp. from the Peregrino oil field was assessed. Dispersion modelling of drill cuttings was performed for a two year period using measured oceanographic and discharge data with 24 h resolution. The model was also used to assess the impact on the two algae species using four species specific impact categories: No, minor, medium and severe impact. The corresponding intervals for photosynthetic efficiency (ΦPSIImax) and sediment coverage were obtained from exposure-response relationship for photosynthetic efficiency as function of sediment coverage for the two algae species. The temporal resolution enabled more accurate model predictions as short-term changes in discharges and environmental conditions could be detected. The assessment shows that there is a patchy risk for severe impact on the calcareous algae stretching across the transitional zone and into the calcareous algae bed at Peregrino.

The effect of sediment mimicking drill cuttings on deep water rhodoliths in a flow-through system: Experimental work and modeling.

The impact of sediment coverage on two rhodolith-forming calcareous algae species collected at 100m water depth off the coast of Brazil was studied in an experimental flow-through system. Natural sediment mimicking drill cuttings with respect to size distribution was used. Sediment coverage and photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ϕPSIImax) were measured as functions of light intensity, flow rate and added amount of sediment once a week for nine weeks. Statistical experimental design and multivariate data analysis provided statistically significant regression models which subsequently were used to establish exposure-response relationship for photosynthetic efficiency as function of sediment coverage. For example, at 70% sediment coverage the photosynthetic efficiency was reduced 50% after 1-2weeks of exposure, most likely due to reduced gas exchange. The exposure-response relationship can be used to establish threshold levels and impact categories for environmental monitoring.

Approaches for derivation of environmental quality criteria for substances applied in risk assessment of discharges from offshore drilling operations.

In order to achieve the offshore petroleum industries "zero harm" goal to the environment, the environmental impact factor for drilling discharges was developed as a tool to identify and quantify the environmental risks associated with disposal of drilling discharges to the marine environment. As an initial step in this work the main categories of substances associated with drilling discharges and assumed to contribute to toxic or nontoxic stress were identified and evaluated for inclusion in the risk assessment. The selection were based on the known toxicological properties of the substances, or the total amount discharged together with their potential for accumulation in the water column or sediments to levels that could be expected to cause toxic or nontoxic stress to the biota. Based on these criteria 3 categories of chemicals were identified for risk assessment the water column and sediments: Natural organic substances, metals, and drilling fluid chemicals. Several approaches for deriving the environmentally safe threshold concentrations as predicted no effect concentrations were evaluated in the process. For the water column consensus were reached for using the species sensitivity distribution approach for metals and the assessment factor approach for natural organic substances and added drilling chemicals. For the sediments the equilibrium partitioning approach was selected for all three categories of chemicals. The theoretically derived sediment quality criteria were compared to field-derived threshold effect values based on statistical approaches applied on sediment monitoring data from the Norwegian Continental Shelf. The basis for derivation of predicted no effect concentration values for drilling discharges should be consistent with the principles of environmental risk assessment as described in the Technical Guidance Document on Risk Assessment issued by the European Union.