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.

Towards a scientific community consensus on designating Vulnerable Marine Ecosystems from imagery.

Management of deep-sea fisheries in areas beyond national jurisdiction by Regional Fisheries Management Organizations/Arrangements (RFMO/As) requires identification of areas with Vulnerable Marine Ecosystems (VMEs). Currently, fisheries data, including trawl and longline bycatch data, are used by many RFMO/As to inform the identification of VMEs. However, the collection of such data creates impacts and there is a need to collect non-invasive data for VME identification and monitoring purposes. Imagery data from scientific surveys satisfies this requirement, but there currently is no established framework for identifying VMEs from images. Thus, the goal of this study was to bring together a large international team to determine current VME assessment protocols and establish preliminary global consensus guidelines for identifying VMEs from images. An initial assessment showed a lack of consistency among RFMO/A regions regarding what is considered a VME indicator taxon, and hence variability in how VMEs might be defined. In certain cases, experts agreed that a VME could be identified from a single image, most often in areas of scleractinian reefs, dense octocoral gardens, multiple VME species' co-occurrence, and chemosynthetic ecosystems. A decision flow chart is presented that gives practical interpretation of the FAO criteria for single images. To further evaluate steps of the flow chart related to density, data were compiled to assess whether scientists perceived similar density thresholds across regions. The range of observed densities and the density values considered to be VMEs varied considerably by taxon, but in many cases, there was a statistical difference in what experts considered to be a VME compared to images not considered a VME. Further work is required to develop an areal extent index, to include a measure of confidence, and to increase our understanding of what levels of density and diversity correspond to key ecosystem functions for VME indicator taxa. Based on our results, the following recommendations are made: 1. There is a need to establish a global consensus on which taxa are VME indicators. 2. RFMO/As should consider adopting guidelines that use imagery surveys as an alternative (or complement) to using bycatch and trawl surveys for designating VMEs. 3. Imagery surveys should also be included in Impact Assessments. And 4. All industries that impact the seafloor, not just fisheries, should use imagery surveys to detect and identify VMEs.

Benthic invertebrates in Svalbard fjords-when metabarcoding does not outperform traditional biodiversity assessment.

To protect and restore ecosystems and biodiversity is one of the 10 challenges identified by the United Nations's Decade of the Ocean Science. In this study we used eDNA from sediments collected in two fjords of the Svalbard archipelago and compared the taxonomic composition with traditional methods through metabarcoding, targeting mitochondrial CO1, to survey benthos. Clustering of 21.6 mill sequence reads with a d value of 13 in swarm, returned about 25 K OTU reads. An identification search with the BOLD database returned 12,000 taxonomy annotated sequences spanning a similarity range of 50% to 100%. Using an acceptance filter of minimum 90% similarity to the CO1 reference sequence, we found that 74% of the ca 100 taxon identified sequence reads were Polychaeta and 22% Nematoda. Relatively few other benthic invertebrate species were detected. Many of the identified sequence reads were extra-organismal DNA from terrestrial, planktonic, and photic zone sources. For the species rich Polychaeta, we found that, on average, only 20.6% of the species identified from morphology were also detected with DNA. This discrepancy was not due to missing reference sequences in the search database, because 90-100% (mean 96.7%) of the visually identified species at each station were represented with barcodes in Boldsystems. The volume of DNA samples is small compared with the volume searched in visual sorting, and the replicate DNA-samples in sum covered only about 2% of the surface area of a grab. This may considerably reduce the detection rate of species that are not uniformly distributed in the sediments. Along with PCR amplification bias and primer mismatch, this may be an important reason for the limited congruence of species identified with the two approaches. However, metabarcoding also identified 69 additional species that are usually overlooked in visual sample sorting, demonstrating how metabarcoding can complement traditional methodology by detecting additional, less conspicuous groups of organisms.

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.

A framework for the development of a global standardised marine taxon reference image database (SMarTaR-ID) to support image-based analyses.

Video and image data are regularly used in the field of benthic ecology to document biodiversity. However, their use is subject to a number of challenges, principally the identification of taxa within the images without associated physical specimens. The challenge of applying traditional taxonomic keys to the identification of fauna from images has led to the development of personal, group, or institution level reference image catalogues of operational taxonomic units (OTUs) or morphospecies. Lack of standardisation among these reference catalogues has led to problems with observer bias and the inability to combine datasets across studies. In addition, lack of a common reference standard is stifling efforts in the application of artificial intelligence to taxon identification. Using the North Atlantic deep sea as a case study, we propose a database structure to facilitate standardisation of morphospecies image catalogues between research groups and support future use in multiple front-end applications. We also propose a framework for coordination of international efforts to develop reference guides for the identification of marine species from images. The proposed structure maps to the Darwin Core standard to allow integration with existing databases. We suggest a management framework where high-level taxonomic groups are curated by a regional team, consisting of both end users and taxonomic experts. We identify a mechanism by which overall quality of data within a common reference guide could be raised over the next decade. Finally, we discuss the role of a common reference standard in advancing marine ecology and supporting sustainable use of this ecosystem.

Marine litter in the Nordic Seas: Distribution composition and abundance.

Litter has been found in all marine environments and is accumulating in seabirds and mammals in the Nordic Seas. These ecosystems are under pressure from climatic change and fisheries while the human population is small. The marine landscapes in the area range from shallow fishing banks to deep-sea canyons. We present density, distribution and composition of litter from the first large-scale mapping of sea bed litter in arctic and subarctic waters. Litter was registered from 1778 video transects, of which 27% contained litter. The background density of litter in the Barents Sea and Norwegian Sea is 202 and 279 items/km2 respectively, and highest densities were found close to coast and in canyons. Most of the litter originated from the fishing industry and plastic was the second most common litter. Background levels were comparable to European records and areas with most littering had higher densities than in Europe.

Using EUNIS habitat classification for benthic mapping in European seas: present concerns and future needs.

The EUNIS (European Union Nature Information System) habitat classification system aims to provide a common European reference set of habitat types within a hierarchical classification, and to cover all terrestrial, freshwater and marine habitats of Europe. The classification facilitates reporting of habitat data in a comparable manner, for use in nature conservation (e.g. inventories, monitoring and assessments), habitat mapping and environmental management. For the marine environment the importance of a univocal habitat classification system is confirmed by the fact that many European initiatives, aimed at marine mapping, assessment and reporting, are increasingly using EUNIS habitat categories and respective codes. For this reason substantial efforts have been made to include information on marine benthic habitats from different regions, aiming to provide a comprehensive geographical coverage of European seas. However, there still remain many concerns on its applicability as only a small fraction of Europe's seas are fully mapped and increasing knowledge and application raise further issues to be resolved. This paper presents an overview of the main discussion and conclusions of a workshop, organised by the MeshAtlantic project, focusing upon the experience in using the EUNIS habitats classification across different countries and seas, together with case studies. The aims of the meeting were to: (i) bring together scientists with experience in the use of the EUNIS marine classification and representatives from the European Environment Agency (EEA); (ii) agree on enhancements to EUNIS that ensure an improved representation of the European marine habitats; and (iii) establish practices that make marine habitat maps produced by scientists more consistent with the needs of managers and decision-makers. During the workshop challenges for the future development of EUNIS were identified, which have been classified into five categories: (1) structure and hierarchy; (2) biology; (3) terminology; (4) mapping; and (5) future development. The workshop ended with a declaration from the attendees, with recommendations to the EEA and European Topic Centre on Biological Diversity, to take into account the outputs of the workshop, which identify weaknesses in the current classification and include proposals for its modification, and to devise a process to further develop the marine component of the EUNIS habitat classification.