Toward a new data standard for combined marine biological and environmental datasets - expanding OBIS beyond species occurrences

The Ocean Biogeographic Information System (OBIS) is the world's most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling events and species occurrences, and includes additional fields for property standardization. We also embrace the use of the new parentEventID DwC term, which enables the creation of a sampling event hierarchy. We believe that the adoption of this recommended practice as a new data standard for managing and sharing biological and associated environmental datasets by IODE and the wider international scientific community would be key to improving the effectiveness of the knowledge base, and will enhance integration and management of critical data needed to understand ecological and biological processes in the ocean, and on land.Fil: De Pooter, Daphnis. Flanders Marine Institute; BélgicaFil: Appeltans, Ward. UNESCO-IOC; BélgicaFil: Bailly, Nicolas. Hellenic Centre for Marine Research, MedOBIS; GreciaFil: Bristol, Sky. United States Geological Survey; Estados UnidosFil: Deneudt, Klaas. Flanders Marine Institute; BélgicaFil: Eliezer, Menashè. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; ItaliaFil: Fujioka, Ei. University Of Duke. Nicholas School Of Environment. Duke Marine Lab; Estados UnidosFil: Giorgetti, Alessandra. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; ItaliaFil: Goldstein, Philip. University of Colorado Museum of Natural History, OBIS; Estados UnidosFil: Lewis, Mirtha Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Lipizer, Marina. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; ItaliaFil: Mackay, Kevin. National Institute of Water and Atmospheric Research; Nueva ZelandaFil: Marin, Maria Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Moncoiffé, Gwenaëlle. British Oceanographic Data Center; Reino UnidoFil: Nikolopoulou, Stamatina. Hellenic Centre for Marine Research, MedOBIS; GreciaFil: Provoost, Pieter. UNESCO-IOC; BélgicaFil: Rauch, Shannon. Woods Hole Oceanographic Institution; Estados UnidosFil: Roubicek, Andres. CSIRO Oceans and Atmosphere; AustraliaFil: Torres, Carlos. Universidad Autonoma de Baja California Sur; MéxicoFil: van de Putte, Anton. Royal Belgian Institute for Natural Sciences; BélgicaFil: Vandepitte, Leen. Flanders Marine Institute; BélgicaFil: Vanhoorne, Bart. Flanders Marine Institute; BélgicaFil: Vinci, Mateo. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; ItaliaFil: Wambiji, Nina. Kenya Marine and Fisheries Research Institute; KeniaFil: Watts, David. CSIRO Oceans and Atmosphere; AustraliaFil: Klein Salas, Eduardo. Universidad Simon Bolivar; VenezuelaFil: Hernandez, Francisco. Flanders Marine Institute; Bélgic

Environmentally relevant concentration of caffeine-effect on activity and circadian rhythm in wild perch

We studied the ecological consequences of widespread caffeine contamination by conducting an experiment focused on changes in the behavioral traits of wild perch (Perca fluviatilis) after waterborne exposure to 10 mu g L-1 of caffeine. We monitored fish swimming performance during both light and dark conditions to study the effect of caffeine on fish activity and circadian rhythm, using a novel three-dimensional tracking system that enabled positioning even in complete darkness. All individuals underwent three behavioral trials-before exposure, after 24 h of exposure, and after 5 days of exposure. We did not observe any effect of the given caffeine concentration on fish activity under light or dark conditions. Regardless of caffeine exposure, fish swimming performance was significantly affected by both the light-dark conditions and repeating of behavioral trials. Individuals in both treatments swam significantly more during the light condition and their activity increased with time as follows: before exposure < after 24 h of exposure < after 5 days of exposure. We confirmed that the three-dimensional automated tracking system based on infrared sensors was highly effective for conducting behavioral experiments under completely dark conditions

Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review

Fish biomass estimation is one of the most common and important practices in aquaculture. The regular acquisition of fish biomass information has been identified as an urgent need for managers to optimize daily feeding, control stocking densities and ultimately determine the optimal time for harvesting. However, it is difficult to estimate fish biomass without human intervention because fishes are sensitive and move freely in an environment where visibility, lighting and stability are uncontrollable. Until now, fish biomass estimation has been mostly based on manual sampling, which is usually invasive, time‐consuming and laborious. Therefore, it is imperative and highly desirable to develop a noninvasive, rapid and cost‐effective means. Machine vision, acoustics, environmental DNA and resistivity counter provide the possibility of developing nonintrusive, faster and cheaper methods for in situ estimation of fish biomass. This article summarizes the development of these nonintrusive methods for fish biomass estimation over the past three decades and presents their basic concepts and principles. The strengths and weaknesses of each method are analysed and future research directions are also presented. Studies show that the applications of information technology such as advanced sensors and communication technologies have great significance to accelerate the development of new means and techniques for more effective biomass estimation. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Through close cooperation between fisheries experts and engineers, the precision and the level of intelligence for fish biomass estimation will be further improved based on the above methods

Real-time spatial modeling to detect and track resources on construction sites

For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin

Automatic Bluefin Tuna Sizing with a Combined Acoustic and Optical Sensor

[EN] A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor's field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes' ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna's Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing.This work was supported by funding from ACUSTUNA project ref. CTM2015-70446-R (MINECO/ERDF, EU) and PAID-10-19 (UPV).Muñoz-Benavent, P.; Puig Pons, V.; Andreu García, G.; Espinosa Roselló, V.; Atienza-Vanacloig, V.; Pérez Arjona, I. (2020). Automatic Bluefin Tuna Sizing with a Combined Acoustic and Optical Sensor. Sensors. 20(18):1-17. https://doi.org/10.3390/s20185294S117201

Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet

Automated processing of zebrafish imaging data: a survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines