RAPID : research on automated plankton identification

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 2 (2007): 172-187.When Victor Hensen deployed the first true plankton1 net in 1887, he and his colleagues were attempting to answer three fundamental questions: What planktonic organisms are present in the ocean? How many of each type are present? How does the plankton’s composition change over time? Although answering these questions has remained a central goal of oceanographers, the sophisticated tools available to enumerate planktonic organisms today offer capabilities that Hensen probably could never have imagined.This material is based upon work supported by the National Science Foundation under Grants OCE-0325018, OCE-0324937, OCE-0325167 and OCE-9423471, and the European Union under grants Q5CR-2002-71699, MAS3-ct98-0188, and MAS2-ct92-0015

In situ real-time Zooplankton Detection and Classification

Zooplankton plays a key-role on Earth’s ecosystem, emerging in the oceans and rivers in great quantities and diversity, making it an important and rather common topic on scientific studies. It serves as prey for many large living beings, such as fish and whales, and helps to keep the food chain stabilized by acting not only as prey to other animals but also as a consumer of phytoplankton, the main producers of oxygen on the planet. Zooplankton are also good indicators of environmental changes, such as global warming or rapid fluctuations in carbon dioxide in the atmosphere, since their abundance and existence is dependent on many environmental factors that indicate such changes. Not only is it important to study the numbers of zooplankton in the water masses, but also to know of what different species these numbers are composed of, as different species can provide information of different environmental attributes. In this thesis a possible solution for the zooplankton in situ detection and classification problem in real-time is proposed using a portable deep learning approach based on CNNs (Convolutional Neural Networks) deployed on INESC TEC’s MarinEye system. The proposed solution makes use of two different CNNs, one for the detection problem and another for the classification problem, running in MarinEye’s plankton imaging system, and portability is guaranteed by the use of the Movidius™ Neural Compute Stick as the deep learning motor in the hardware side. The software was implemented as a ROS node, which guarantees not only portability but facilitates communication between the imaging system and other MarinEye’s modules.O zooplâncton representa um papel fundamental no ecossistema do planeta, surgindo nos oceanos e rios em grandes quantidades numa elevada diversidade de espécies, sendo um objecto de estudo comum em publicações e artigos produzidos pela comunidade científica. A sua importância vem de entre outros factores do facto de ser a principal fonte de alimento de uma grande parte da vida marinha, desde pequenos peixes a baleias, e de ser um grande consumidor de fitoplâncton, a principal fonte de oxigénio do planeta. O zooplâncton é também um bom indicador de alterações ambientais, como o aquecimento global ou variações rápidas na quantidade de dióxido de carbono na atmosfera, uma vez que a sua abundância depende de diversos factores ambientais relacionados com tais mudanças, sendo não só importante perceber em que quantidades existe nas massas de água do planeta, mas também por que diferentes espécies está distribuído. Nesta tese é apresentada uma possível solução para a deteção e classificação de zooplâncton in situ e em tempo real, recorrendo a uma abordagem facilmente portável de Deep Learning, baseada em Redes Neuronais Convolucionais implementado no sistema MarinEye do INESC TEC. A solução proposta faz uso de duas arquitecturas de redes diferentes, uma dedicada à tarefa de deteção do zooplâncton, e outra dedicada `a sua classificação, implementadas no módulo de aquisição de imagens de plâncton do sistema MarinEye. A portabilidade e flexibilidade do sistema foi garantida através do uso da Movidius™ Neural Compute Stick como motor de deep learning, assim como da implementação do software como um nó de ROS, que garante não só a portabilidade do sistema, como também permite uma facilidade de comunicação entre os diferentes módulos do MarinEye

Tracking fish abundance by underwater image recognition

Marine cabled video-observatories allow the non-destructive sampling of species at frequencies and durations that have never been attained before. Nevertheless, the lack of appropriate methods to automatically process video imagery limits this technology for the purposes of ecosystem monitoring. Automation is a prerequisite to deal with the huge quantities of video footage captured by cameras, which can then transform these devices into true autonomous sensors. In this study, we have developed a novel methodology that is based on genetic programming for content-based image analysis. Our aim was to capture the temporal dynamics of fish abundance. We processed more than 20,000 images that were acquired in a challenging real-world coastal scenario at the OBSEA-EMSO testing-site. The images were collected at 30-min. frequency, continuously for two years, over day and night. The highly variable environmental conditions allowed us to test the effectiveness of our approach under changing light radiation, water turbidity, background confusion, and bio-fouling growth on the camera housing. The automated recognition results were highly correlated with the manual counts and they were highly reliable when used to track fish variations at different hourly, daily, and monthly time scales. In addition, our methodology could be easily transferred to other cabled video-observatories.Peer ReviewedPostprint (published version

THE DEVELOPMENT OF NOVEL TECHNIQUES FOR CHARACTERISATION OF MARINE ZOOPLANKTON OVER VERY LARGE SPATIAL SCALES

Marine zooplankton play an important role in the transfer of CO2 from the atmosphere/ocean system to deeper waters and the sediments. They also provide food for much of the world's fish stocks and in some areas of the ocean depleted of nutrients they sustain phytoplankton growth by recycling nutrients. They therefore have a profound effect on the carbon cycle and upon life in the oceans. There is a perceived lack of information about global distributions of zooplankton needed to validate ecosystems dynamics models, and the traditional methods of survey are inadequate to provide this information. There is a need to develop new technologies for the large scale survey of zooplankton, which should provide data either suitable for quick and easy subsequent processing, or better still, processed in real time. New technologies for large scale zooplankton survey fall into three main categories: acoustic, optical and video. No single method is capable of providing continuous real time data at the level of detail required. A combination of two of the new technologies (optical and video) has the potential to provide broad scale data on abundance, size and species distributions of zooplankton routinely, reliably, rapidly and economically. Such a combined method has been developed in this study. The optical plankton counter (OPC) is a fairly well established instrument in marine and freshwater zooplankton survey. A novel application of the benchtop version of this instrument (OPC-IL) for real time data gathering at sea over ocean basin scales has been developed in this study. A new automated video zooplankton analyser (ViZA) has been designed and developed to operate together with the OPC-IL. The two devices are eventually to be deployed in tandem on the Undulating Oceanographic Recorder (UOR) for large scale ocean survey of zooplankton. During the initial development of the system, the two devices are used in benchtop flow through mode using the ship's uncontaminated sea water supply. The devices have been deployed on four major oceanographic cruises in the North and South Atlantic, covering almost 40,000 km. of transect. Used in benchtop mode, it has been shown that the OPC can simply and reliably survey thousands of kilometres of ocean surface waters for zooplankton abundance and size distribution in the size range 250|im. to 11.314 mm. in real time. The ViZA system can add the dimension of shape to the OPC size data, and provide supporting data on size distributions and abundance. Sampling rate in oligotrophic waters, and image quality problems are two main limitations to current ViZA performance which must be addressed, but where sufficient abundance exists and good quality images are obtained, the initial version of the ViZA system is shown to be able reliably to classify zooplankton to six major groups. The four deployments have shown that data on zooplankton distributions on oceanic scales can be obtained without the delays and prohibitive costs associated with sample analysis for traditional sampling methods. The results of these deployments are presented, together with an assessment of the performance of the system and proposals for improvements to meet the requirements specified before a fiill in-situ system is deployed.Plymouth Marine Laborator

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Diet and stable isotope analyses reveal the feeding ecology of the orangeback squid Sthenoteuthis pteropus (Steenstrup 1855) (Mollusca, Ommastrephidae) in the eastern tropical Atlantic

In the eastern tropical Atlantic, the orangeback flying squid Sthenoteuthis pteropus (Steenstrup 1855) (Cephalopoda, Ommastrephidae) is a dominant species of the epipelagic nekton community. This carnivore squid has a short lifespan and is one of the fastest-growing squids. In this study, we characterise the role of S. pteropus in the pelagic food web of the eastern tropical Atlantic by investigating its diet and the dynamics of its feeding habits throughout its ontogeny and migration. During three expeditions in the eastern tropical Atlantic in 2015, 129 specimens were caught by hand jigging. Stomach content analyses (via visual identification and DNA barcoding) were combined with stable isotope data (∂15N and ∂13C) of muscle tissue to describe diet, feeding habits and trophic ecology of S. pteropus. Additionally, stable isotope analyses of incremental samples along the squid’s gladius—the chitinous spiniform structure supporting the muscles and organs—were carried out to explore possible diet shifts through ontogeny and migration. Our results show that S. pteropus preys mainly on myctophid fishes (e.g. Myctophum asperum, Myctophum nitidulum, Vinciguerria spp.), but also on other teleost species, cephalopods (e.g. Enoploteuthidae, Bolitinidae, Ommastrephidae), crustaceans and possibly on gelatinous zooplankton as well. The squid shows a highly opportunistic feeding behaviour that includes cannibalism. Our study indicates that the trophic position of S. pteropus may increase by approximately one trophic level from a mantle length of 15 cm to 47 cm. The reconstructed isotope-based feeding chronologies of the gladii revealed high intra- and inter-individual variability in the squid’s trophic position and foraging area. These findings are not revealed by diet or muscle tissue stable isotope analysis. This suggests a variable and complex life history involving individual variation and migration. The role of S. pteropus in transferring energy and nutrients from lower to higher trophic levels may be underestimated and important for understanding how a changing ocean impacts food webs in the eastern Atlantic

A Model-based and data-driven Operational Ecological Biomass Estimator

Editors: Einar Svendsen (chairman); Jens Glad Balchen; Johnny Johannessen; Bjarte Bogstad; Jo Arve Alfredsen; Dag Slagstad; Morten Skogen; Kurt TandeA 10-year multidisciplinary research and development project to improve the understanding of the dynamics of the marine ecosystems, and to produce a tool to meet the future increasing demands for an ecological approach to marine management based on precautionary principles

Climate Change and Copepod Size Distribution: Comparison of Two Coastal Long-Term Series in the Western Mediterranean Sea

This thesis was aimed at investigating long-term dynamics of zooplankton communities in relation to climate change. It represents a pioneer study in comparing Mediterranean time series with a standardised methodology, i.e., the ZooScan, a digital imaging system for counting and sizing mesozooplankton from preserved samples. This study has proven that copepod size spectra (i.e., histogram of organisms arranged by size classes) obtained with the ZooScan is a powerful synthetic index to monitor changes in the pelagic system. Copepod abundance and size spectra were analyzed in the zooplankton time series conducted at stn MC (Gulf of Naples, Tyrrhenian Sea) and Point B (Villefranche Bay, Ligurian Sea) for the years 1986-2005 and 1974-2003, respectively. In both time series, the proportion of large individuals in the copepod community increased over the years, with a shift in the early 1990s at stn MC and in 1987 at Point B. In both cases, the 1990s copepod reproduction might have decreased due to earlier and stronger stratification driven by rising temperature. At stn MC, the shift to dominance of small phytoplankton cells in the 1990s seemed to be the direct cause. At Point B, in addition to the detrimental effect of earlier stratification in the 1990s, the 1980s seemed to be very productive due to strong winter convection driven by cool salty winters. High salinity was related to low winter atmospheric pressure linked to the North Atlantic Oscillation (+ winter NAO). In both locations, the frequency of occurrence of typical offshore species increased over the years, suggesting changes in coastal-offshore interactions. Mechanisms controlling the copepod communities in both sites seem to be different. Stn MC has a more coastal character than Point B and thus the former is more affected by local conditions as terrestrial nutrient inputs. At Point B, stratification-destratification dynamics seem to control production

SPATIAL AND TEMPORAL DYNAMICS OF THE CHESAPEAKE BAY SEA NETTLE

The jellyfish Chrysaora chesapeakei forms large summer blooms in Chesapeake Bay, and has substantial ecological and economic impacts on local ecosystems. Limited information on this species is mostly due to difficulties collecting spatial information on jellyfish in dynamic coastal ecosystems. Spatial gaps of C. chesapeakei were addressed by applying a multi-scale approach across life stages and within a source-sink context, reflected by the ecology and habitat utilization of C. chesapeakei. An Adaptive Resolution Imaging System (ARIS, SoundMetrics, Inc.) was used to collect high-resolution data on medusae in 2016 and 2017, within a Patuxent River waterscape. Polyp settlement plates were deployed at eight sites to understand the distributional range of the sessile benthic stage in Chesapeake Bay, but polyps successfully overwintered at only one of the sites, indicating that settlement alone was insufficient to explain C. chesapeakei dispersal to new habitat. Using high-resolution sonar data, a multi-scale spatial analysis was conducted to understand medusae dispersion and abundance. Medusae were three times more abundant in 2017 than in 2016. However, differences in water-column concentration were not apparent at the fine-scale (<5m) where medusae were randomly dispersed in both years. At the mesoscale (10km), spatial dependency was observed in both years, with more transport of jellyfish to dispersal habitat in the high-abundance year (2017). Overall, polyp settlement and overwintering survival in potential habitat seem to control the spatial distribution of C. chesapeakei at the Bay-wide scale while medusae appear responsible for mesoscale dispersal to new habitat, demonstrating high dispersal to sink habitat in a high-density year and low dispersal in a low-density year