Hydrocarbons contamination and hypoxia are two stressors that can coexist in coastal ecosystems. At present, few studies evaluated the combined impact of these stressors on fish physiology and behavior. Here, we tested the effect of the combination of hypoxia and petrogenic hydrocarbons on the anti-predator locomotor performance of fish. Specifically, two groups of European sea bass (Dicentrarchus labrax) were exposed to clean water (Ctrl) or oil-contaminated water (Oil). Subsequently, fish of both groups were placed in normoxic (norx) or hypoxic (hyp) experimental tanks (i.e. four groups of fish were formed: Ctrl norx, Ctrl hyp, Oil norx, Oil hyp). In these tanks, escape response was elicited by a mechano-acoustic stimulus and recorded with a high speed camera. Several variables were analyzed: escape response duration, responsiveness (percentage of fish responding to the stimulation), latency (time taken by the fish to initiate a response), directionality (defined as away or toward the stimulus), distance-time variables (such as speed and acceleration), maneuverability variables (such as turning rate), escape trajectory (angle of flight) and distancing of the fish from the stimulus. Results revealed (i) effects of stressors (Ctrl hyp, Oil norx and Oil hyp) on the directionality; (ii) effects of Oil norx and Oil hyp on maneuverability and (iii) effects of Oil hyp on distancing. These results suggest that individual stressors could alter the escape response of fish and that their combination could strengthen these effects. Such an impact could decrease the probability of prey escape success. By investigating the effects of hydrocarbons (and the interaction with hypoxia) on the anti-predator behavior of fish, this work increases our understanding of the biological impact of oil spill. Additionally, the results of this study are of interest for oil spill impact evaluation and also for developing new ecotoxicological tools of ecological significance.

Pollutant emissions in ship exhaust have been continually increasing. SO₂ is one of the main gaseous pollutants in ship exhaust, resulting from the use of marine heavy fuel oil with high sulfur content. Therefore, it is necessary to detect the fuel sulfur content (FSC) to regulate ship exhaust emissions. Optical remote sensing methods, such as differential optical absorption spectroscopy (DOAS), light detection and ranging (LIDAR), and ultraviolet (UV) camera techniques, are regarded as simple and effective remote monitoring methods. One common technique is to estimate the SO₂ concentration in a ship plume using its local optical characteristics and use this to calculate FSC. One drawback of this technique is that there are always errors in the estimations of the SO₂ concentration despite the continuous improvement of such estimations. Another drawback is that calculating FSC from SO₂ often requires additional measurement methods. Here, a sulfur content prediction model based on a deep convolutional neural network using a UV camera is introduced. First, a ship benchmark test is performed. In the test, a large number of ultraviolet characteristic images of the ship exhaust plume are taken with a UV camera and the corresponding FSC data are collected. Next, a visual geometry group (VGG)-16 convolutional neural network model based on transfer learning is built. The model extracts all the features of the exhaust plume image as input data to the deep neural network and outputs the predicted FSC as a classification label. The results show that the model can predict the FSC value with high accuracy corresponding to the exhaust plume image. This study proves that it is theoretically feasible to apply a convolutional neural network to learn features of ultraviolet ship exhaust plume images for FSC predictions, which can provide guidance for the remote regulation of ship exhaust emissions.

Avoiding foraging under increased predation risk is a common anti-predator behaviour. Using artificial light to amplify predation risk at ecologically valuable sites has been proposed to deter introduced mice (Mus musculus) and ship rats (Rattus rattus) from degrading biodiversity in island ecosystems. However, light may adversely affect native species; in particular, little is known about invertebrate responses to altered lighting regimes. We investigated how endemic orthopterans responded to artificial light at Maungatautari Ecological Island (Waikato, New Zealand). We predicted that based on their nocturnal behaviour, ecology and evolutionary history, tree weta (Hemideina thoracica) and cave weta (Rhaphidophoridae) would reduce their activity under illumination. Experimental stations (n = 15) experienced three evenings under each treatment (order randomised): (a) light (illuminated LED fixture), (b) dark (unilluminated LED fixture) and (c) baseline (no lighting fixture). Weta visitation rates were analysed from images captured on infra-red trail cameras set up at each station. Light significantly reduced the number of observations of cave (71.7% reduction) and tree weta (87.5% reduction). In observations where sex was distinguishable (53% of all visits), male tree weta were observed significantly more often (85% of visits) than females (15% of visits) and while males avoided illuminated sites, no detectable difference was observed across treatments for females. Sex could not be distinguished for cave weta. Our findings have implications for the use of light as a novel pest management strategy, and for the conservation of invertebrate diversity and abundance within natural and urban ecosystems worldwide that may be affected by light pollution.

Light pollution or artificial light at night (ALAN) is an increasing, worldwide challenge that affects many aspects of animal behaviour. Interestingly, the response to ALAN varies widely among individuals within a population and variation in personality (consistent individual differences in behaviour) may be an important factor explaining this variation. Consistent individual differences in exploration behaviour in particular may relate to the response to ALAN, as increasing evidence indicates its relation with how individuals respond to novelty and how they cope with anthropogenic modifications of the environment. Here, we assayed exploration behaviour in a novel environment as a proxy for personality variation in great tits (Parus major). We observed individual sleep behaviour over two consecutive nights, with birds sleeping under natural dark conditions the first night and confronted with ALAN inside the nest box on the second night, representing a modified and novel roosting environment. We examined whether roosting decisions when confronted with a camera (novel object), and subsequently with ALAN, were personality-dependent, as this could potentially create sampling bias. Finally, we assessed whether experimentally challenging individuals with ALAN induced personality-dependent changes in sleep behaviour.Slow and fast explorers were equally likely to roost in a nest box when confronted with either a camera or artificial light inside, indicating the absence of personality-dependent sampling bias or avoidance of exposure to ALAN. Moreover, slow and fast explorers were equally disrupted in their sleep behaviour when challenged with ALAN. Whether other behavioural and physiological effects of ALAN are personality-dependent remains to be determined. Moreover, the sensitivity to disturbance of different behavioural types might depend on the behavioural context and the specific type of challenge in question. In our increasingly urbanized world, determining whether the effects of anthropogenic stressors depend on personality type will be of paramount importance as it may affect population dynamics.

Aerial and underwater imaging is being widely used for monitoring litter objects found at the sea surface, beaches and seafloor. However, litter monitoring requires a considerable amount of human effort, indicating the need for automatic and cost-effective approaches. Here we present an object detection approach that automatically detects seafloor marine litter in a real-world environment using a Region-based Convolution Neural Network. The neural network is trained on an imagery with 11 manually annotated litter categories and then evaluated on an independent part of the dataset, attaining a mean average precision score of 62%. The presence of other background features in the imagery (e.g., algae, seagrass, scattered boulders) resulted to higher number of predicted litter items compare to the observed ones. The results of the study are encouraging and suggest that deep learning has the potential to become a significant tool for automatically recognizing seafloor litter in surveys, accomplishing continuous and precise litter monitoring.

This study was aimed at monitoring beach litter using an unmanned aerial vehicle (UAV) in the coastal city of Fuzhou, China. The data analysis shows that the optical images obtained by digital cameras on the UAV can help to identify and monitor beach litter using remote sensing and GIS technologies. The threshold method can effectively segment the UAV image in the beach area. It is useful for quickly monitoring the distribution of beach litter in the area of interest, and hence it can help to provide effective technical support for the investigation and assessment of coastal beach litter.

Colour change in Acropora tenuis, a representative species of Indo-Pacific hard coral, in response to low concentrations of Irgarol 1051 was examined in the laboratory. Branches of A. tenuis were exposed to 0, 1, and 10μgIrgarol1051/L for 14days, and photographed daily using digital camera. These Irgarol 1051 concentrations were similar to those recorded at a number of sea ports. Red, green and blue (RGB) coral colour values were quantified from the photographs, with black represented by R=G=B=0 and white as R=G=B=255. Exposure to Irgarol 1051 caused RGB values to increase, moving towards the ‘white’ end of the spectrum as Irgarol 1051 concentration increased. These results suggest that the ambient levels of Irgarol 1051 recorded from port environments could be implicated in coral bleaching, if concentrations over nearby reef ecosystems are similar.

This study quantifies the effect of oil layer thickness on entrainment and dispersion of oil into seawater, using a plunging jet with a camera system. In contrast to what is generally assumed, we revealed that for the low viscosity “surrogate MC252 oil” we used, entrainment rate is directly proportional to layer thickness. Furthermore, the volume of stably suspended small oil droplets increases with energy input (plunge height) and is mostly proportional to layer thickness. Oil pre-treated with dispersants (dispersant-oil ratio ranges from 1:50 to 1:300) is greatly entrained in such large amounts of small droplets that quantification was impossible with the camera system. Very low interfacial tension causes entrainment by even minor secondary surface disturbances. Our results indicate that the effect of oil layer thickness should be included in oil entrainment and dispersion modelling.

A field experiment in the waters off the south-eastern coast of Cyprus was carried out to study near-field formation of sediment plumes from dumping. Different loads of sediment were poured into calm and limpid waters one at the time from just above the sea surface. The associated plumes, gravitating towards the seafloor, were filmed simultaneously by four divers situated at different depths in the water column, and facing the plume at different angles. The processes were captured using GoPro-Hero-series cameras. The high-quality underwater footage from near-surface, mid-depth and near-bed positions gives unique insight into the dynamics of the descending plume and near-field dispersion processes, and enables good understanding of flow and sediment transport processes involved from-release-to-deposition of the load in a non-scaled environment. The high resolution images and footages are available through the link provided herein. Observations support the development of a detailed multi-fractional sediment plume model.

Remote-sensing for oil spills is reviewed. The use of visible techniques is ubiquitous, however it gives only the same results as visual monitoring. Oil has no particular spectral features that would allow for identification among the many possible background interferences. Cameras are only useful to provide documentation. In daytime oil absorbs light and remits this as thermal energy at temperatures 3–8K above ambient, this is detectable by infrared (IR) cameras.Laser fluorosensors are useful instruments because of their unique capability to identify oil on backgrounds that include water, soil, weeds, ice and snow. They are the only sensor that can positively discriminate oil on most backgrounds. Radar detects oil on water by the fact that oil will dampen water-surface capillary waves under low to moderate wave/wind conditions. Radar offers the only potential for large area searches, day/night and foul weather remote sensing.