Distally deposited tephra from explosive volcanic eruptions can be a powerful tool for precise dating and correlation of sedimentary archives and landforms. However, the morphostratigraphic and chronological potential of ocean-rafted pumice has been under-utilized considering its long observational history and widespread distribution on modern and palaeo-shorelines around the world. Here we analyze the geochemical composition and elevation data of 60 samples of ocean-rafted pumice collected since 1958 from raised beaches on Svalbard. Comparison of pumice data with postglacial relative sea-level history suggests eight distinct pumice rafting events throughout the North Atlantic during the Middle and Late Holocene. Analyzed ocean-rafted pumice exhibit consistent silicic composition characteristic of deposits from Iceland’s volcanic system, Katla. Eruption-triggered jökulhlaups are key drivers of the transport of pumice from the Katla caldera to beyond the coast of Iceland and into the surface currents of the North Atlantic Ocean. Thus, the correlation of distinct, high-concentration pumice horizons from Katla deposited along raised Middle Holocene beach ridges in Svalbard further advocates for the persistence of the Mýrdalsjökull ice cap through the Holocene thermal maximum.
Tools and Data
Photo-identification (photo-id) is a method used in field studies by biologists to monitor animals according to their density, movement patterns and behavior, with the aim of predicting and preventing ecological risks. However, these methods can introduce subjectivity when manually classifying an individual animal, creating uncertainty or inaccuracy in the data as a result of the human criteria involved. One of the main objectives in photo-id is to implement an automated mechanism that is free of biases, portable, and easy to use. The main aim of this work is to develop an autonomous and portable photo-id system through the optimization of image classification algorithms that have high statistical dependence, with the goal of classifying dorsal fin images of the blue whale through offline information processing on a mobile platform. The new proposed methodology is based on the Scale Invariant Feature Transform (SIFT) that, in conjunction with statistical discriminators such as the variance and the standard deviation, fits the extracted data and selects the closest pixels that comprise the edges of the dorsal fin of the blue whale. In this way, we ensure the elimination of the most common external factors that could affect the quality of the image, thus avoiding the elimination of relevant sections of the dorsal fin. The photo-id method presented in this work has been developed using blue whale images collected off the coast of Baja California Sur. The results shown have qualitatively and quantitatively validated the method in terms of its sensitivity, specificity and accuracy on the Jetson Tegra TK1 mobile platform. The solution optimizes classic SIFT, balancing the results obtained with the computational cost, provides a more economical form of processing and obtains a portable system that could be beneficial for field studies through mobile platforms, making it available to scientists, government and the general public.
Knowledge mobilisation is required to “bridge the gap” between research, policy and practice. This activity is dependent on the amount, richness and quality of the data published. To understand the impact of a changing climate on commercial species, stakeholder communities require better knowledge of their past and current situations. The common cockle (Cerastoderma edule) is an excellent model species for this type of analysis, as it is well-studied due to its cultural, commercial and ecological significance in west Europe. Recently, C. edule harvests have decreased, coinciding with frequent mass mortalities, due to factors such as a changing climate and diseases. In this study, macro and micro level marine historical ecology techniques were used to create datasets on topics including: cockle abundance, spawning duration and harvest levels, as well as the ecological factors impacting those cockle populations. These data were correlated with changing climate and the Atlantic Multidecadal Oscillation (AMO) index to assess if they are drivers of cockle abundance and harvesting. The analyses identified the key stakeholder communities involved in cockle research and data acquisition. It highlighted that data collection was sporadic and lacking in cross-national/stakeholder community coordination. A major finding was that local variability in cockle populations is influenced by biotic (parasites) and abiotic (temperature, legislation and harvesting) factors, and at a global scale by climate (AMO Index). This comprehensive study provided an insight into the European cockle fishery but also highlights the need to identify the type of data required, the importance of standardised monitoring, and dissemination efforts, taking into account the knowledge, source, and audience. These factors are key elements that will be highly beneficial not only to the cockle stakeholder communities but to other commercial species.
Autonomous platforms already make observations over a wide range of temporal and spatial scales, measuring salinity, temperature, nitrate, pressure, oxygen, biomass, and many other parameters. However, the observations are not comprehensive. Future autonomous systems need to be more affordable, more modular, more capable and easier to operate. Creative new types of platforms and new compact, low power, calibrated and stable sensors are under development to expand autonomous observations. Communications and recharging need bandwidth and power which can be supplied by standardized docking stations. In situ power generation will also extend endurance for many types of autonomous platforms, particularly autonomous surface vehicles. Standardized communications will improve ease of use, interoperability, and enable coordinated behaviors. Improved autonomy and communications will enable adaptive networks of autonomous platforms. Improvements in autonomy will have three aspects: hardware, control, and operations. As sensors and platforms have more onboard processing capability and energy capacity, more measurements become possible. Control systems and software will have the capability to address more complex states and sophisticated reactions to sensor inputs, which allows the platform to handle a wider variety of circumstances without direct operator control. Operational autonomy is increased by reducing operating costs. To maximize the potential of autonomous observations, new standards and best practices are needed. In some applications, focus on common platforms and volume purchases could lead to significant cost reductions. Cost reductions could enable order-of-magnitude increases in platform operations and increase sampling resolution for a given level of investment. Energy harvesting technologies should be integral to the system design, for sensors, platforms, vehicles, and docking stations. Connections are needed between the marine energy and ocean observing communities to coordinate among funding sources, researchers, and end users. Regional teams should work with global organizations such as IOC/GOOS in governance development. International networks such as emerging glider operations (EGO) should also provide a forum for addressing governance. Networks of multiple vehicles can improve operational efficiencies and transform operational patterns. There is a need to develop operational architectures at regional and global scales to provide a backbone for active networking of autonomous platforms.
The use of social media (SM) data has emerged as a promising tool for the assessment of cultural ecosystem services (CES). Most studies have focused on the use of single SM platforms and on the analysis of photo content to assess the demand for CES. Here, we introduce a novel methodology for the assessment of CES using SM data through the application of graph theory network analyses (GTNA) on hashtags associated to SM posts and compare it to photo content analysis. We applied the proposed methodology on two SM platforms, Instagram and Twitter, on three worldwide known case study areas, namely Great Barrier Reef, Galapagos Islands and Easter Island. Our results indicate that the analysis of hashtags through graph theory offers similar capabilities to photo content analysis in the assessment of CES provision and the identification of CES providers. More importantly, GTNA provides greater capabilities at identifying relational values and eudaimonic aspects associated to nature, elusive aspects for photo content analysis. In addition, GTNA contributes to the reduction of the interpreter’s bias associated to photo content analyses, since GTNA is based on the tags provided by the users themselves. The study also highlights the importance of considering data from different SM platforms, as the type of users and the information offered by these platforms can show different CES attributes. The ease of application and relative short computing processing times involved in the application of GTNA makes it a cost-effective method with the potential of being applied to large geographical scales.
Physical, chemical, geological, and biological factors interact in marine environments to shape complex but recurrent patterns of organization of life on multiple spatial and temporal scales. These factors define biogeographic regions in surface waters that we refer to as seascapes. We characterize seascapes for the Florida Keys National Marine Sanctuary (FKNMS) and southwest Florida shelf nearshore environment using multivariate satellite and in situ measurements of Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs). The study focuses on three periods that cover separate oceanographic expeditions (March 11–18, May 9–13, and September 12–19, 2016). We collected observations on bio-optical parameters (particulate and dissolved spectral absorption coefficients), phytoplankton community composition, and hydrography from a ship. Phytoplankton community composition was evaluated using (1) chemotaxonomic analysis (CHEMTAX) based on high-performance liquid chromatography (HPLC) pigment measurements, and (2) analysis of spectral phytoplankton absorption coefficients (aphy). Dynamic seascapes were derived by combining satellite time series of sea surface temperature, chlorophyll-a concentration, and normalized fluorescent line height (nFLH) using a supervised thematic classification. The seascapes identified areas of different salinity and nutrient concentrations where different phytoplankton communities were present as determined by hierarchical cluster analyses of HPLC pigments and aphy spectra. Oligotrophic, Mesotrophic, and Transition seascape classes of deeper offshore waters were dominated by small phytoplankton (<2 μm; ∼ 40–60% of total cell abundance). In eutrophic, optically shallow coastal seascapes influenced by fresh water discharge, the phytoplankton was dominated by larger taxa (>60%). Spectral analysis of aphy indicated higher absorption levels at 492 and 550 nm wavelengths in seascapes carrying predominantly small phytoplankton than in classes dominated by larger taxa. Seascapes carrying large phytoplankton showed absorption peaks at the 673 nm wavelength. The seascape framework promises to be a tool to detect different biogeographic domains quickly, providing information about the changing environmental conditions experienced by coral reef organisms including coral, sponges, fish, and higher trophic levels. The effort illustrates best practices developed under the Marine Biodiversity Observation Network (MBON) demonstration project, in collaboration with the South Florida Ecosystem Restoration Research (SFER) project managed by the Atlantic Oceanographic and Meteorological Laboratory of NOAA (AOML-NOAA).
Acoustic telemetry techniques are very useful tools to monitor in detail the swimming behavior and spatial use of fish in artificial rearing environments at individual and group levels. We evaluated the feasibility of using passive acoustic telemetry to monitor fish welfare in sea-cage aquaculture at an industrial scale, characterizing for the first time the diel swimming and distribution patterns of gilthead seabream (Sparus aurata) at fine-scale. Ten fish were implanted with acoustic tags equipped with pressure and acceleration sensors, and monitored in a commercial-size sea-cage for a period of 1 month. Overall, fish exhibited clear differences in day vs. night patterns both on swimming activity and vertical distribution throughout the experiment. Space use increased at night after the implementation of structural environmental enrichment in the sea-cage. Acoustic telemetry may represent an advancement to monitor fish farming procedures and conditions, helping to promote fish welfare and product quality.
In marine research, image data sets from the same area but collected at different times allow seafloor fauna communities to be monitored over time. However, ongoing technological developments have led to the use of different imaging systems and deployment strategies. Thus, instances of the same class exhibit slightly shifted visual features in images taken at slightly different locations or with different gear. These shifts are referred to as concept drift in the domains computational image analysis and machine learning as this phenomenon poses particular challenges for these fields. In this paper, we analyse four different data sets from an area in the Peru Basin and show how changes in imaging parameters affect the classification of 12 megafauna morphotypes with a 34-layer ResNet. Images were captured using the ocean floor observation system, a traditional sled-based system, or an autonomous underwater vehicle, which is used as an imaging platform capable of surveying larger regions. ResNet applied on separate individual data sets, i.e., without concept drift, showed that changing object distance was less important than the amount of training data. The results for the image data acquired with the ocean floor observation system showed higher performance values than data collected with the autonomous underwater vehicle. The results from this concept drift studies indicate that collecting image data from many dives with slightly different gear may result in training data well-suited for learning taxonomic classification tasks and that data volume can compensate for light concept drift.
Structure-from-Motion (SfM) photogrammetry can be used with digital underwater photographs to generate high-resolution bathymetry and orthomosaics with millimeter-to-centimeter scale resolution at relatively low cost. Although these products are useful for assessing species diversity and health, they have additional utility for quantifying benthic community structure, such as coral growth and fine-scale elevation change over time, if accurate length scales and georeferencing are included. This georeferencing is commonly provided with “ground control,” such as pre-installed seafloor benchmarks or identifiable “static” features, which can be difficult and time consuming to install, survey, and maintain. To address these challenges, we developed the SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5), a towed surface vehicle with an onboard survey-grade Global Navigation Satellite System (GNSS) and five rigidly mounted downward-looking cameras with overlapping views of the seafloor. The cameras are tightly synchronized with both the GNSS and each other to collect quintet photo sets and record the precise location of every collection event. The system was field tested in July 2019 in the U.S. Florida Keys, in water depths ranging from 3 to 9 m over a variety of bottom types. Surveying accuracy was assessed using pre-installed stations with known coordinates, machined scale bars, and two independent surveys of a site to evaluate repeatability. Under a range of sea conditions, ambient lighting, and water clarity, we were able to map living and senile coral reef habitats and sand waves at mm-scale resolution. Data were processed using best practice SfM techniques without ground control and local measurement errors of horizontal and vertical scales were consistently sub-millimeter, equivalent to 0.013% RMSE relative to water depth. Survey-to-survey repeatability RMSE was on the order of 3 cm without georeferencing but could be improved to several millimeters with the incorporation of one or more non-surveyed marker points. We demonstrate that the SQUID-5 platform can map complex coral reef and other seafloor habitats and measure mm-to-cm scale changes in the morphology and location of seafloor features over time without pre-existing ground control.
With large-scale human interventions and climate change unfolding as they are now, coastal changes at decadal timescales are not limited to incremental modifications of systems that are fixed in their general geometry, but often show significant changes in layout that may be catastrophic for populations living in previously safe areas. This poses severe challenges that are difficult to meet for existing models. A new free-form coastline model, ShorelineS, is presented that is able to describe large coastal transformations based on relatively simple principles of alongshore transport gradient driven changes as a result of coastline curvature, including under highly obliquely incident waves, and consideration of splitting and merging of coastlines, and longshore transport disturbance by hard structures. An arbitrary number of coast sections is supported, which can be open or closed and can interact with each other through relatively straightforward merging and splitting mechanisms. Rocky parts or structures may block wave energy and/or longshore sediment transport. These features allow for a rich behavior including shoreline undulations and formation of spits, migrating islands, merging of coastal shapes, salients and tombolos. The main formulations of the (open-source) model, which is freely available at www.shorelines.nl, are presented. Test cases show the capabilities of the flexible, vector-based model approach, while field validation cases for a large-scale sand nourishment (the Sand Engine; 21 million m3) and an accreting groin scheme at Al-Gamil (Egypt) show the model’s capability of computing realistic rates of coastline change as well as a good representation of the shoreline shape for real situations.