Seasonal rhythm in a Mediterranean coastal fish community as monitored by a cabled observatory

To adequately describe seasonal rhythms in habitat utilization by fish communities, observations are needed that occur at a relatively high frequency and over large temporal windows. For the first time, images collected from a cabled video-observatory (the western Mediterranean expandable SEAfloor OBservatory; OBSEA) were utilized to examine habitat utilization by coastal fishes. Over the course of a year, hourly digital images of the water column and an artificial reef were obtained during daylight hours. For each image, the total number of species, an estimate of the abundance of individuals, and the Shannon Diversity Index were quantified. A total of 22 fish species commonly associated with nearshore western Mediterranean habitats were identified, and significant spatial (water column vs. artificial reef), seasonal (spring, summer, autumn, and winter), and daily (morning, midday, and sunset) differences in habitat utilization occurred. Four species (Diplodus vulgaris, Diplodus cervinus, Diplodus sargus, and Scorpaena porcus) were associated with the artificial reef, while one species (Pagrus pagrus) chiefly occurred in the water column. Chromis chromis and Diplodus annularis occurred at the site more frequently in the winter and autumn, respectively, while 14 other species utilized the site more frequently in either the spring (8 spp) or summer (6 spp). In addition, Dentex dentex and Spicara maena occurred more frequently at sunset, while D. cervinus displayed a crepuscular rhythm (occurring more frequently in the morning and at sunset). Species diversity was highest in the summer and lowest in the winter. If not taken into account when planning in situ sampling, such seasonal and/or diel differences may lead to spurious estimates of population sizes and biodiversity. We suggest that cabled video-observatories offer a non-invasive and reliable technology for faunistic sampling and population assessment in coastal water of the Mediterranean and likely elsewhere.     

Dynamic Games in the Economics and Management of Pollution

The paper provides a survey of the literature which utilizes dynamic state-space games to formulate and analyze intertemporal, many decision-maker problems in the economics and management of pollution.     

A Bayesian network analysis of workplace accidents caused by falls from a height

This article analyses, using Bayesian networks, the circumstances surrounding workplace tasks performed using auxiliary equipment (ladders, scaffolding, etc.) that may result in falls. The information source was a survey of employees working at a height. We were able to determine the usefulness of this approach – innovative in the accident research field – in identifying the causes that have the greatest bearing on accidents involving auxiliary equipment: in these cases, the adoption of incorrect postures during work and a worker’s inadequate knowledge of safety regulations. Likewise, the duration of tasks was also associated with both these variables, and therefore, with the accident rate. Bayesian networks also enable dependency relationships to be established between the different causes of accidents. This information – which is not usually furnished by conventional statistical methods applied in the field of labour risk prevention – allow a causality model to be defined for workplace accidents in a more realistic way. With this statistic tool, the expert is also provided with useful information that can be input to a management model for labour risk prevention.     

Environmental quality assessment combining sediment metal levels, biomarkers and macrobenthic communities: application to the óbidos coastal lagoon (Portugal)

Macroinvertebrate benthic communities are one of the key biological components considered for the assessment of benthic integrity in the context of the Water Framework Directive (WFD). However, under moderate contamination scenarios, the assessment of macrobenthic alterations at community level alone could be insufficient to discriminate the environmental quality of coastal and transitional waters. Keeping this in view, sediment quality of moderately contaminated sites in a coastal lagoon (óbidos lagoon, Portugal) was assessed by the combination of sediment metal levels, Carcinus maenas biomarkers (accumulated metals and oxidative stress responses) and macrobenthic communities. Two sites were selected in confined inner branches (BS and BB) and a third one in the middle lagoon (ML). The site BB presented slightly higher levels of metals in sediment but biological variables calculated for macrobenthic data were not significantly different between sites. The biotic index M-AMBI that is being applied to assess environmental quality of transitional waters in the scope of the WFD pointed either to high (site ML) or good quality status (BS and BB) in the selected sites. However, crabs from BB site presented significantly higher levels of Ni in hepatopancreas than those from ML and macrobenthic community structure was significantly different between BB and ML. Additionally, spatial differences were obtained for oxidative stress parameters suggesting that BB site presented stressors for crabs (higher GST and lower GSH_t at BB site). Factor analysis (PCA) integrating sediment contamination, biomarkers in crabs and macrobenthic data also distinguished BB site as the most environmentally disturbed. On the other hand, at ML site, some macrobenthic variables (equitability and polychaetes’ diversity) were found to be enhanced by current environmental conditions, suggesting the existence of a better sediment quality. Current results pointed to the usefulness of integrating macrobenthic community alterations with responses at organism level (bioaccumulation and biochemical endpoints) in order to increase the accuracy of environmental quality assessment in lagoon systems. Moreover, the application of different statistical methods was also found to be recommendable.     

Carbon footprint of canned mussels from a business-to-consumer approach. A starting point for mussel processors and policy makers

The increasing demand for environmental information on the global warming impact of products requires a solid methodological framework which guarantees comparability and communicability. The publicly available specification PAS 2050 combines approaches to a variety of greenhouse gas specific assessment issues to deliver a globally applicable product Carbon Footprinting (CF) method, which is expected to be widely accepted. Specifically, this paper aims to demonstrate the implementation of a CF scheme for a common canned mussel product according to PAS 2050 guidelines. A final value of 4.35 kg CO₂e per triple pack of round cans of mussels was calculated. Furthermore, this CF study led to identify primary packaging (can production) and mussel shell management as the main activities where efforts should focus for climate change mitigation. Throughout this case study, CF opportunities and drawbacks are discussed. The whole text tries to provide a starting point for both mussel processors and policy makers to benefit from the potential advantages of a responsible use of this increasingly popular tool.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.