Technological advances are improving the collection, processing and analysis of ecological data. One of these technologies that has been adopted in recent studies by ecologists is computer vision (CV). CV is a rapidly developing area of machine learning that aims to infer image content at the same level humans can by extracting information from pixels (LeCun et al., 2015; Weinstein, 2018). CV in ecology has gained much attention as it can quickly and accurately process image from remote video imagery while allowing scientists to monitor both individuals and populations at unprecedented spatial and temporal scales. Automated analysis of imagery through CV has also become more accurate and streamlined with the implementation of deep learning (a subset of machine learning) models that have improved the capacity to processes raw images compared to traditional machine learning methods (LeCun et al., 2015; Villon et al., 2016). As the use of camera systems for monitoring fish abundances is common practice in conservation ecology (Gilby et al., 2017; Whitmarsh et al., 2017; Langlois et al., 2020), deep learning allows for the automated processing of big data from video or images, a step which usually creates a bottleneck when these data must be analyzed manually.
Tools and Data
High-resolution ocean biophysical models are now routinely being conducted at basin and global-scale, opening opportunities to deepen our understanding of the mechanistic coupling of physical and biological processes at the mesoscale. Prior to using these models to test scientific questions, we need to assess their skill. While progress has been made in validating the mean field, little work has been done to evaluate skill of the simulated mesoscale variability. Here we use geostatistical 2-D variograms to quantify the magnitude and spatial scale of chlorophyll a patchiness in a 1/10th-degree eddy-resolving coupled Community Earth System Model simulation. We compare results from satellite remote sensing and ship underway observations in the North Atlantic Ocean, where there is a large seasonal phytoplankton bloom. The coefficients of variation, i.e., the arithmetic standard deviation divided by the mean, from the two observational data sets are approximately invariant across a large range of mean chlorophyll a values from oligotrophic and winter to subpolar bloom conditions. This relationship between the chlorophyll a mesoscale variability and the mean field appears to reflect an emergent property of marine biophysics, and the high-resolution simulation does poorly in capturing this skill metric, with the model underestimating observed variability under low chlorophyll a conditions such as in the subtropics.
A long-standing problem in maritime operations and ocean development projects has been the prediction of instantaneous wave energy. Wave measurements collected using an array of freely drifting arrays of Surface Wave Instrument Float with Tracking (SWIFT) buoys are used to test methods for phase-resolved wave prediction in a wide range of observed sea states. Using a linear inverse model in directionally-rich, broadbanded wave fields can improve instantaneous heave predictions by an average of 63% relative to statistical forecasts based on wave spectra. Numerical simulations of a Gaussian sea, seeded with synthetic buoys, were used to supplement observations and characterize the spatiotemporal extent of reconstruction accuracy. Observations and numerical results agree well with theoretical deterministic prediction zones proposed in previous studies and suggest that the phase-resolved forecast horizon is between 1–3 average wave periods for a maximum measurement interval of 10 wave periods for ocean wave fields observed during the experiment. Prediction accuracy is dependent on the geometry and duration of the measurements and is discussed in the context of the nonlinearity and bandwidth of incident wave fields.
The ocean delivers many ecosystem services to human society in providing food, livelihoods, and recreation and is crucial for regulating the global climate. Coastal cities, which have become the backbone of national economies, are highly dependent on the ecosystem services supported by the ocean. As a global coastal megacity, Shanghai has benefited enormously from its relationship with the ocean but its burgeoning population and rampant economic development in recent decades have applied great pressures on the associated coastal ecosystems and have reduced the ocean's capacity to provide ecosystem services and, meanwhile, have led to the demand for greater investment in ocean ecosystem restoration. To support the goal of long-term sustainability and facilitate appropriate management decisions, it is essential to assess the current health status of the coastal ecosystems of Shanghai and evaluate potential future risks. Here we apply the Ocean Health Index (OHI) framework, with indicators and reference points adjusted based on the unique coastal environment in Shanghai. The results reveal that the city obtained an overall OHI of 59 (out of 100) for the period 2012 to 2016. Individual indicators for Clean Waters (22) and Fisheries (39) exhibit particularly low values, indicating that the coastal waters around Shanghai are heavily polluted and that marine fishing is unsustainable. The city’s highest OHI scores are in the sectors of Coastal Livelihoods and Economies (93), and Tourism and Recreation (93), indicating that Shanghai’s coastal ecosystems contribute significantly to people’s livelihoods and regional economies, while marine recreational areas and related leisure activities add considerably to the quality of life in the region. This study demonstrates the value of the OHI in assessing ocean health at the city scale and reveals its potential for application in other coastal localities. In so doing, the findings provide a valuable benchmark against which to measure progress towards the sustainable development of Shanghai's oceans.
Arctic sea ice is shifting from a year-round to a seasonal sea ice cover. This substantial transformation, via a reduction in Arctic sea ice extent and a thinning of its thickness, influences the amount of light entering the upper ocean. This in turn impacts under-ice algal growth and associated ecosystem dynamics. Field campaigns have provided valuable insights as to how snow and ice properties impact light penetration at fixed locations in the Arctic, but to understand the spatial variability in the under-ice light field there is a need to scale up to the pan-Arctic level. Combining information from satellites with state-of-the-art parameterizations is one means to achieve this. This study combines satellite and modeled data products to map under-ice light on a monthly time-scale from 2011 through 2018. Key limitations pertain to the availability of satellite-derived sea ice thickness, which for radar altimetry, is only available during the sea ice growth season. We clearly show that year-to-year variability in snow depth, along with the fraction of thin ice, plays a key role in how much light enters the Arctic Ocean. This is particularly significant in April, which in some regions, coincides with the beginning of the under-ice algal bloom, whereas we find that ice thickness is the main driver of under-ice light availability at the end of the melt season in October. The extension to the melt season due to a warmer Arctic means that snow accumulation has reduced, which is leading to positive trends in light transmission through snow. This, combined with a thinner ice cover, should lead to increased under-ice PAR also in the summer months.
Given the considerable range of applications within the European Union Copernicus system, sustained satellite altimetry missions are required to address operational, science and societal needs. This article describes the Copernicus Sentinel-6 mission that is designed to provide precision sea level, sea surface height, significant wave height, inland water heights and other products tailored to operational services in the ocean, climate, atmospheric and land Copernicus Services. Sentinel-6 provides enhanced continuity to the very stable time series of mean sea level measurements and ocean sea state started in 1992 by the TOPEX/Poseidon mission and follow-on Jason-1, Jason-2 and Jason-3 satellite missions. The mission is implemented through a unique international partnership with contributions from NASA, NOAA, ESA, EUMETSAT, and the European Union (EU). It includes two satellites that will fly sequentially (separated in time by 5 years). The first satellite, named Sentinel-6 Michael Freilich, launched from Vandenburg Air Force Base, USA on 21st November 2020. The satellite and payload elements are explained including required performance and their operation. The main payload is the Poseidon-4 dual frequency (C/Ku-band) nadir-pointing radar altimeter that uses an innovative interleaved mode. This enables radar data processing on two parallel chains the first provides synthetic aperture radar (SAR) processing in Ku-band to improve the received altimeter echoes through better along-track sampling and reduced measurement noise; the second provides a Low Resolution Mode that is fully backward-compatible with the historical reference altimetry measurements, allowing a complete inter-calibration between the state-of-the-art data and the historical record. A three-channel Advanced Microwave Radiometer for Climate (AMRC) provides measurements of atmospheric water vapour to mitigate degradation of the radar altimeter measurements. The main data products are explained and preliminary in-orbit Poseidon-4 altimeter data performance data are presented that demonstrate the altimeter to be performing within expectations.
The paper aims to elucidate the physico-chemical characteristics of the shell of mangrove horseshoe crabs (Carcinoscorpius rotundicauda) and determine the compilation matrix for the first time. The shell composition matrix of C. rotundicauda has never been studied in detail before, especially the shape of the foam, the chemical composition, the functional groups and the mechanical-physical and thermal properties of the shell. Based on this study, the shell structure of the mangrove horseshoe crab has the potential to be used as the base structure for developing bio-foam insulator material in the future. Therefore, the shell of mangrove horseshoe crabs has a unique natural structure in the form of foam. Its robust and elastic structure has the potential for further development for new marine biomaterials. The formation and composition of horseshoe crab shells foam are also believed to be multifunctional in mobility, used for defense mechanisms and thermal stability. The horseshoe crab samples were collected from Pacitan coastal waters, East Java, Indonesia. The research was conducted using physico-chemical and mechanical-physical analysis. The scanning electron microscopy was used in order to clarify the physico-chemical characteristics. The measurements of the mechanical-physical characteristics included density, unit cell size, and water absorption. The tensile strength and compressive strength were analyzed based on the American Society for Testing Material. Thermal resistance was measured by thermal gravimetric analysis. The results showed that the horseshoe crab shells have a unique structure, where chitin, protein and some minerals are the main chemical elements. The combination and major constituents of the horseshoe crab shell material provide strong and plastic mechanical properties with a maximum tensile strength of 60.46 kPa and maximum compressive strength of 110.55 kPa, water absorption of 0.01195 ± 0.001% and a density value of 0.1545 ± 0.011 g/cm3 as well as the capability to withstand thermal loads with peak decomposition values of 267.4–823.2°C and thermal stability of 60.59%. Using natural marine biomaterials in the future will be beneficial because it leaves no harmful residues and therefore has environmental advantages and at the same time, it is also more cost-effective.
Recently, new steps have been taken for the development of operational applications in coastal areas which require very high resolutions both in modeling and remote sensing products. In this context, this work describes a complete monitoring of an oil spill: we discuss the performance of high resolution hydrodynamic models in the area of Gran Canaria and their ability for describing the evolution of a real-time event of a diesel fuel spill, well-documented by port authorities and tracked with very high resolution remote sensing products. Complementary information supplied by different sources enhances the description of the event and supports their validation.
The Southeast Pacific comprises two Large Marine Ecosystems, the Pacific Central-American Coastal and the Humboldt Current System; and is one of the less well known in the tropical subregions in terms of biodiversity. To address this, we compared DNA barcoding repositories with the marine biodiversity species for the Southeast Pacific. We obtained a checklist of marine species in the Southeast Pacific (i.e. Colombia, Ecuador, Chile, and Peru) from the Ocean Biodiversity Information System (OBIS) database and compared it with species available at the Barcoding of Life Data System (BOLD) repository. Of the 5504 species records retrieved from OBIS, 42% of them had at least one registered specimen in BOLD (including specimens around the world); however, only 4.5% of records corresponded to publicly available DNA barcodes including specimens collected from a Southeast Pacific country. The low representation of barcoded species does not vary much across the different taxonomic groups or within countries, but we observed an asymmetric distribution of DNA barcoding records for taxonomic groups along the coast, being more abundant for the Humboldt Current System than the Pacific Central-American Coastal. We observed high-level of barcode records with Barcode Index Number (BIN) incongruences, particularly for fishes (Actinopterygii = 30.27% and Elasmobranchii = 24.71%), reflecting taxonomic uncertainties for fishes, whereas for Invertebrates and Mammalia more than 85% of records were classified as data deficient or inadequate procedure for DNA barcoding. DNA barcoding is a powerful tool to study biodiversity, with a great potential to increase the knowledge of the Southeast Pacific marine biodiversity. Our results highlight the critical need for increasing taxonomic sampling effort, the number of trained taxonomic specialists, laboratory facilities, scientific collections, and genetic reference libraries.
Knowledge about fish behavior is crucial to be able to influence the capture process and catch species composition. The rapid expansion of the use of underwater cameras has facilitated unprecedented opportunities for studying the behavior of species interacting with fishing gears in their natural environment. This technological advance would greatly benefit from the parallel development of dedicated methodologies accounting for right-censored observations and variable observation periods between individuals related to instrumental, environmental and behavioral events. In this paper we proposed a methodological framework, based on a parametric Weibull mixture model, to describe the process of escapement attempts through time, test effects of covariates and estimate the probability that a fish will attempt to escape. We additionally proposed to better examine the escapement process at the individual level with regard to the temporal dynamics of escapement over time. Our approach was used to analyze gadoids swimming and escapement behaviors collected using a video set up in front of a selective device known to improve selectivity on gadoids in the extension of a bottom trawl. Comparison of the fit of models indicates that i) the instantaneous rate of escape attempts is constant over time and that the escapement process can be modelled using an exponential law; ii) the mean time before attempting to escape increases with the increasing number of attempts; iii) more than 80% of the gadoids attempted to escape through the selective device; and iv) the estimated probability of success was around 15%. Effects of covariates on the probability of success were investigated using binomial regression but none of them were significant. The data set collected is insufficient to make general statements, and further observations are required to properly investigate the effect of intrinsic and extrinsic factors governing gadoids behavior in trawls. This methodology could be used to better characterize the underlying behavioral process of fish in other parts of a bottom trawl or in relation to other fishing gears.