Oil droplet fouling on lesser sandeel (Ammodytes marinus) eggshells does not enhance the crude oil induced developmental toxicity.

The oil industry's expansion and increased operational activity at older installations, along with their demolition, contribute to rising cumulative pollution and a heightened risk of accidental oil spills. The lesser sandeel (Ammodytes marinus) is a keystone prey species in the North Sea and coastal systems. Their eggs adhere to the seabed substrate making them particularly vulnerable to oil exposure during embryonic development. We evaluated the sensitivity of sandeel embryos to crude oil in a laboratory by exposing them to dispersed oil at concentrations of 0, 15, 50, and 150 µg/L oil between 2 and 16 days post-fertilization. We assessed water and tissue concentrations of THC and tPAH, cyp1a expression, lipid distribution in the eyes, head and trunk, and morphological and functional deformities. Oil droplets accumulated on the eggshell in all oil treatment groups, to which the embryo responded by a dose-dependent rise in cyp1a expression. The oil exposure led to only minor sublethal deformities in the upper jaw and otic vesicle. The findings suggest that lesser sandeel embryos are resilient to crude oil exposure. The lowest observed effect level documented in this study was 36 µg THC/L and 3 µg tPAH/L. The inclusion of these species-specific data in risk assessment models will enhance the precision of risk evaluations for the North Atlantic ecosystems.

Early ontogeny of the lesser sandeel (Ammodytes marinus).

BACKGROUND: Lesser sandeel (Ammodytes marinus) is widely distributed in North Sea ecosystems. Sandeel acts as a critical trophic link between zooplankton and top predators (fish, mammals, sea birds). Because they live buried in the sand, sandeel may be directly affected by the rapid expansion of anthropogenic activities linked to their habitat on the sea bottom (e.g., hydrocarbon extraction, offshore renewable energy, and subsea mining). It is, therefore, important to understand the impact of cumulative environmental and anthropogenic stressors on this species. A detailed description of the ontogenetic timeline and developmental staging for this species is lacking limiting the possibilities for comparative developmental studies assessing, e.g., the impact of various environmental stressors. RESULTS: A detailed description of the morphological development of lesser sandeel and their developmental trajectory, obtained through visual observations and microscopic techniques, is presented. Methods for gamete stripping and intensive culture of the early life stages are also provided. CONCLUSION: This work provides a basis for future research to understand the effect of cumulative environmental and anthropogenic stressors on development in the early life stages of lesser sandeel.

Magnetic fields generated by the DC cables of offshore wind farms have no effect on spatial distribution or swimming behavior of lesser sandeel larvae (Ammodytes marinus).

In the North Sea, the number and size of offshore wind (OW) turbines, together with the associated network of High Voltage Direct Current (HVDC) subsea cables, will increase rapidly over the coming years. HVDC cables produce magnetic fields (MFs) that might have an impact on marine animals that encounter them. One of the fish species that is at risk of exposure to MF associated with OW is the lesser sandeel (Ammodytes marinus), a keystone species of the North Sea basin. Lesser sandeel could be exposed to MF as larvae, when they drift in proximity of OW turbines. Whether MFs impact the behavior of lesser sandeel larvae, with possible downstream effects on their dispersal and survival, is unknown. We tested the behavior of 56 lesser sandeel larvae, using a setup designed to simulate the scenario of larvae drifting past a DC cable. We exposed the larvae to a MF intensity gradient (150-50 µT) that is within the range of MFs produced by HVDC subsea cables. Exposure to the MF gradient did not affect the spatial distribution of lesser sandeel larvae in a raceway tank 50 cm long, 7 cm wide and 3.5 cm deep. Nor did the MF alter their swimming speed, acceleration or distance moved. These results show that static MF from DC cables would not impact behavior of lesser sandeel larvae during the larval period of their life although it does not exclude the possibility that later life stages could be affected.

Correction: Lopez-Vazquez et al. Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories. Sensors 2020, 20, 726.

The authors wish to correct the following error in the original paper [...].

Measurements of underwater noise radiated by commercial ships at a cabled ocean observatory.

Measurements of underwater noise radiated under ship normal operations are presented. The acoustic data, from the cabled ocean observatory, are analyzed under each identified ship passage, which was obtained by the Automatic Identification System. Under each passage, sound pressure level is calculated to observe local noise variations due to shipping noise. This paper emphasizes the study of noise variations at the observatory, presents the noise measurements under identified ship passages in the last several years, and provides references for predictive models of underwater noise pollution from commercial ship traffic. From the passages of one ship to the passages of 26 ships, the measurements reveal similar variation patterns when the ships traveled at similar courses, but different patterns when they traveled at different courses. When evaluating the noise variations due to ship traffics, it is important to consider the shipping noise propagation as well as ship movement.

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories.

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

A new model for simulating growth in fish.

A real dynamic population model calculates change in population sizes independent of time. The Beverton & Holt (B&H) model commonly used in fish assessment includes the von Bertalanffy growth function which has age or accumulated time as an independent variable. As a result the B&H model has to assume constant fish growth. However, growth in fish is highly variable depending on food availability and environmental conditions. We propose a new growth model where the length increment of fish living under constant conditions and unlimited food supply, decreases linearly with increasing fish length until it reaches zero at a maximal fish length. The model is independent of time and includes a term which accounts for the environmental variation. In the present study, the model was validated in zebrafish held at constant conditions. There was a good fit of the model to data on observed growth in Norwegian spring spawning herring, capelin from the Barents Sea, North Sea herring and in farmed coastal cod. Growth data from Walleye Pollock from the Eastern Bering Sea and blue whiting from the Norwegian Sea also fitted reasonably well to the model, whereas data from cod from the North Sea showed a good fit to the model only above a length of 70 cm. Cod from the Barents Sea did not grow according to the model. The last results can be explained by environmental factors and variable food availability in the time under study. The model implicates that the efficiency of energy conversion from food decreases as the individual animal approaches its maximal length and is postulated to represent a natural law of fish growth.