An integrated environmental approach to investigate biomarker fluctuations in the blue mussel Mytilus edulis L. in the Vilaine estuary, France

Estuarine areas represent complex and highly changing environments at the interface between freshwater and marine aquatic ecosystems. Therefore, the aquatic organisms living in estuaries have to face highly variable environmental conditions. The aim of this work was to study the influence of environmental changes from either natural or anthropogenic origins on the physiological responses of Mytilus edulis. Mussels were collected in the Vilaine estuary during early summer because this season represents a critical period of active reproduction in mussels and of increased anthropogenic inputs from agricultural and boating activities into the estuary. The physiological status of the mussel M. edulis was evaluated through measurements of a suite of biomarkers related to: oxidative stress (catalase, malondialdehyde), detoxication (benzopyrene hydroxylase, carboxylesterase), neurotoxicity (acetylcholinesterase), reproductive cycle (vitelline, condition index, maturation stages), immunotoxicity (hemocyte concentration, granulocyte percentage, phagocytosis, reactive oxygen species production, oxidative burst), and general physiological stress (lysosomal stability). A selection of relevant organic contaminant (pesticides, (polycyclic aromatic hydrocarbons, polychlorobiphenyls) was measured as well as environmental parameters (water temperature, salinity, total suspended solids, turbidity, chlorophyll a, pheopigments) and mussel phycotoxin contamination. Two locations differently exposed to the plume of the Vilaine River were compared. Both temporal and inter-site variations of these biomarkers were studied. Our results show that reproduction cycle and environmental parameters such as temperature, organic ontaminants, and algal blooms could strongly influence the biomarker responses. These observations highlight the necessity to conduct integrated environmental approaches in order to better understand the causes of biomarker variations.     

The effects of cage-diving activities on the fine-scale swimming behaviour and space use of white sharks

Wildlife tourism has become increasingly popular and is one of the fastest growing sectors of the tourism industry. A radio-acoustic positioning system was deployed to monitor the fine-scale movements of 21 white sharks (Carcharodon carcharias) and investigate the effects of shark cage-diving activities on their swimming behaviour and space use. This study contributes towards improving our understanding of the complex relationship between wildlife tourism and its effects on sharks, and assesses how tourism targeting sharks affects behaviour at a finer spatial scale than previously investigated. Our study demonstrated that shark cage-diving operators (SCDO) influenced the fine-scale three-dimensional spatial distribution and the rate of movement of white sharks at the Neptune Islands. White sharks stayed more than 30 m away from the SCDO on 21 % of the days detected, but spent a significant amount of time in close proximity to the SCDO on the remaining days. Individual variation was detected, with some sharks behaviourally responding to SCDO more than others. The degree of variation between individual sharks and the different levels of interaction (e.g. presence, proximity to SCDO, and consumption of tethered bait) highlights the complexity of the relationships between SCDO and the effects on sharks. To improve our understanding of these relationships, future monitoring of shark cage-diving operations requires proximity to SCDO to be recorded in addition to the presence within the area. Further work is needed to assess whether the observed behavioural changes would affect individual fitness and ultimately population viability, which are critical information to unambiguously assess the potential impacts of wildlife tourism targeting sharks.     

Leveraging social media and deep learning to detect rare megafauna in video surveys

Deep learning has become a key tool for the automated monitoring of animal populations with video surveys. However, obtaining large numbers of images to train such models is a major challenge for rare and elusive species because field video surveys provide few sightings. We designed a method that takes advantage of videos accumulated on social media for training deep-learning models to detect rare megafauna species in the field. We trained convolutional neural networks (CNNs) with social media images and tested them on images collected from field surveys. We applied our method to aerial video surveys of dugongs (Dugong dugon) in New Caledonia (southwestern Pacific). CNNs trained with 1303 social media images yielded 25% false positives and 38% false negatives when tested on independent field video surveys. Incorporating a small number of images from New Caledonia (equivalent to 12% of social media images) in the training data set resulted in a nearly 50% decrease in false negatives. Our results highlight how and the extent to which images collected on social media can offer a solid basis for training deep-learning models for rare megafauna detection and that the incorporation of a few images from the study site further boosts detection accuracy. Our method provides a new generation of deep-learning models that can be used to rapidly and accurately process field video surveys for the monitoring of rare megafauna.     

The Incidence of Tariff Schedules and Price Information on Inattentive Consumers: a Lab Experiment

We design an induced value laboratory consumption choice experiment where complex tariff schemes trigger nonlinear simplification heuristics that lead individuals to over- or underconsume public goods such as electricity, gas, or drinking water. By studying this “schmeduling” bias, we investigate how an informational nudge could reduce it. Participants choose consumption levels repeatedly under different tariff schemes, where the marginal price per unit either remains constant (constant block rate, i.e., CBR) or increases above a certain threshold (increasing block rate, or IBR). We observe that the vast majority of choices are optimal, but a significant number of them reveal overconsumption. To investigate the impact of the informational nudge on these errors, some of our participants received a marginal price reminder. In that case, the learning effect helps to achieve convergence towards the optimal consumption value. To explain these effects, we use econometric models relying on microeconomic behavioral inattention to price to capture the magnitude of consumers’ inattention, observing, in particular, how the informational nudge is decreasing it.

     

Biotransformation of the flame retardant tetrabromo-bisphenol A by human and rat sub-cellular liver fractions

The comparative in vitro metabolism of the flame retardant tetrabromo-bisphenol A was studied in rat and human using a [(14)C]-radio-labelled molecule. Tetrabromo-bisphenol A is metabolised into the corresponding glucuronide (liver S9 fractions) and several other metabolites produced by cytochrome P450 dependent pathways (liver microsomes and liver S9 fractions). No major qualitative differences were observed between rat and human, regardless of the selected concentration, within the 20-200 microM range. Tetrabromo-bisphenol A undergoes an oxidative cleavage near the central carbon of the molecule, that leads to the production of hydroxylated dibromo-phenol, hydroxylated dibromo-isopropyl-phenol and glutathione conjugated dibromo-isopropyl-phenol. The main metabolites of tetrabromo-bisphenol A are two molecules of lower polarity than the parent compound, characterised as a hexa-brominated compound with three aromatic rings and a hepta-brominated dimer-like compound, respectively. Both structures, as well as the lower molecular weight metabolites resulting from the breakdown of the molecule, suggest the occurrence of chemically reactive intermediates formed following a first step oxidation of tetrabromo-bisphenol A.     

Modelling of paralytic shellfish toxin biotransformations in the course of Crassostrea gigas detoxification kinetics

The aim of this study was to evaluate the importance of biotransformation of paralytic shellfish toxins during the detoxification process in contaminated oysters. Mathematical models based upon the detoxification patterns of digestive gland and other tissues were developed. It was demonstrated that biotransformations do not seem to play an important role in digestive gland or other tissue detoxification kinetics with our data set. Moreover, different toxin transfers from digestive gland toward other tissues were investigated. No significant transfer was highlighted in our data set. These first conclusions were drawn after comparing the results obtained from 13 biotransformations and identifiable transfer scenarios. Finally, to determine a more robust model, all 12 states corresponding to toxic compounds and tissues were aggregated into a single state model. The best adjustment was obtained with a simple one-compartment model based on total flesh toxicity with elimination rate expressed by a function depending on initial concentrations of GTX3 and GTX2 (i.e. the two major toxic compounds found in contaminated oysters).     

Development of an in vitro method to estimate lung bioaccessibility of metals from atmospheric particles

The research presented here was initiated to improve the current knowledge on easily released metals from atmospheric particles. The objectives of this paper were to develop an in vitro method to estimate lung bioaccessibility and to provide quantitative data on metals bioaccessibility. A large set of metals has been investigated (Ba, Cd, Ce, Co, Cu, La, Mn, Mo, Ni, Pb, Rb, Sb and Zn) using two distinct fluids (water and Gamble solution) on four reference materials representing different types of particle sources. Through this study, different parameters such as extraction-time, composition of the leaching solution and solid-to-liquid (S/L) ratios were investigated. The findings obtained for four SRMs suggest that the bioaccessibility is maximized after a 24 h extraction for a range of S/L ratios varying from 1/500 to 1/50,000. We clearly demonstrate that the higher bioaccessibility of metals is obtained with the Gamble solution. Moreover, our results imply that bioaccessibility is speciation and element dependent with percentages varying from 3.3% for Pb to 92.5% for Zn. An estimation of uncertainties of 11% to 30% was obtained for metals bioaccessibility in the four reference materials. In addition, the extraction procedure was validated by performing a mass balance on both soluble and insoluble fractions. This developed method may be used to evaluate the pulmonary bioaccessibility of trace elements present in the atmospheric particles without major artefacts.