J.G. Ferreira

Good Environmental Status of marine ecosystems: What is it and how do we know when we have attained it?

The European Marine Strategy Framework Directive (MSFD) requires EU Member States (MS) to achieve Good Environmental Status (GEnS) of their seas by 2020. We address the question of what GEnS entails especially with regard to the level at which targets are set (descriptors, criteria, indicators), to scales for assessments (regional, sub-divisions, site-specific), and to difficulties in putting into practice the GEnS concept. We propose a refined and operational definition of GEnS, indicating the data and information needed to all parts of that definition. We indicate the options for determining when GEnS has been met, acknowledge the data and information needs for each option, and recommend a combination of existing quantitative targets and expert judgement. We think that the MSFD implementation needs to be less complex than shown for other similar directives, can be based largely on existing data and can be centred on the activities of the Regional Seas Conventions.

Assessment and comparison of the Marennes-Oléron Bay (France) and Carlingford Lough (Ireland) carrying capacity with ecosystem models

Based on the individual growth, food limitation,population renewal through seeding, and individualmarketable size, a theoretical model of the culturedspecies population dynamics was used to assess thecarrying capacity of an ecosystem. It gave adome-shape curve relating the annual production andthe standing stock under the assumption of individual growth limited by the available food inan ecosystem. It also showed the influence ofmortality rate and marketable size on this curve andwas introduced as a means to explore the globalproperties resulting from the interactions betweenthe ecophysiology of the reared species and theenvironment at the ecosystem level.In a second step, an ecosystem model was built to assess the carryingcapacity of Marennes-Oléron bay, the mostimportant shellfish culture site in France, with astanding stock of Crassostrea gigas around 100000tonnes fresh weight (FW) and an annual production of30000 tonnes FW. The ecosystem model focused on theoyster growth rate and considered the interactionbetween food availability, residence time of thewater, oyster ecophysiology and number ofindividuals. It included a spatial discretization ofthe bay (box design) based on a hydrodynamic model,and the nitrogen or carbon cycling betweenphytoplankton, cultured oysters, and detritus. Fromsimulations of the oyster growth with differentseeding values, a curve relating the total annualproduction and the standing stock was obtained. Thiscurve exhibited a dome shape with a maximumproduction corresponding to an optimum standingstock. The model predicted a maximum annualproduction of 45000 tonnes FW for a standing stockaround 115000 tonnes FW. The prediction confirmedsome results obtained empirically in the case ofMarennes-Oléron bay and the results of thetheoretical model. Results were compared with thoseobtained in Carlingford Lough (Ireland) using asimilar ecosystem model. Carlingford Lough is asmall intertidal bay where the same species iscultured at a reduced scale, with current biomassless than 500 tonnes FW. The model showed that thestanding stock can be increased from 200 tonnes FWto approximately 1500 tonnes FW before any decreaseof the production.

Trophic capacity of Carlingford Lough for oyster culture – analysis by ecological modelling

A one-dimensional ecosystem box model is presented forcarrying capacity assessment. The model includesphysical and biological processes. The physicalprocesses are the transport of nutrients, suspendedmatter and phytoplankton through the system boundariesand between model boxes. The biological processes areprimary production and oyster (Crassostreagigas) population dynamics and physiology. The modelwas implemented using an object-oriented approach. Themodel was employed to estimate the carrying capacityof Carlingford Lough (Ireland) for oyster culture. Inthe Lough, low water temperatures prevent the oystersfrom reproducing. Therefore, recruitment ishuman-dependent. Small oyster spat is seeded everyyear during spring and harvested after the summer ofthe next year. During this period oysters reachcommercially harvestable weight. The results obtainedindicate that the carrying capacity of this system isapproximately 0.45 g oysters (AFDW) m-3,determined more by the availability of particulatematter than by phytoplankton. It is suggested that afive-fold increase in oyster seeding may optimiseharvest yield.

The influence of incubation periods on photosynthesis–irradiance curves

In phytoplankton primary production studies, samples for the determination of photosynthesis versus irradiance relationship (P–I) are usually incubated at several irradiance levels during a fixed time period, commonly 2–4 h. However, it is not clear if the use of this fixed incubation time is appropriate to study the P–I relationship in any given ecosystem. The aim of this work was to study the influence of incubation time on the P–I relationship in natural phytoplankton populations from three different coastal ecosystems: an open coastal area, an estuary, and a coastal lagoon. Physical and chemical variables, phytoplankton biomass, species composition, and P–I curves were analysed. The results showed that, in the coastal area and in the estuary, P–I relationships were time dependent, whereas in the coastal lagoon different incubation periods produced the same P–I curve. An underestimation of daily primary production, ranging from 13% to 42.5%, was calculated when data from standard incubation times (2–4 h) were used in ecosystems where P–I curves present a dynamic time-dependent behaviour. This work suggests that the response of the P–I curves to the incubation time varies with the characteristics of the ecosystem and is related to the light regime to which phytoplankton cells are adapted.

A methodology for parameter estimation in seaweed productivity modelling

This paper presents a combined approach for parameter estimation in models of primary production. The focus is on gross primary production and nutrient assimilation by seaweeds.

The Meaningfulness of Consensus and Context in Diagnosing Evolvable Production Systems

An Evolvable Production System (EPS) is a complex and lively entity composed of intelligent modules that interact, through bio-inspired mechanisms, to ensure high system availability and seamless reconfiguration. The diagnosis of such dynamic systems, characterized by constant change, presents new diagnostic challenges and opportunities that can hardly be tackled by traditional approaches. On the one hand, given the decoupled nature of the system, fault interaction and propagation are harder to detect and contain, as is the development of a global diagnostic model, on the other hand local intelligence and careful characterization of the interactions, between the modules, can be explored to emerge the diagnostic functionalities. The impact of simple consensus mechanisms (majority voting) and fault context analysis (module and its current interactions states) is assessed in a multiagent-oriented application in the assembly domain to understand the validity and contribution of this approach in emerging useful self-diagnostic properties in EPS.

Dynamic modelling of photosynthesis in marine and estuarine ecosystems

The relationship between primary productivity and light intensity is usually modelled as a static representation of photosynthesis, assuming that the parameters describing the response to light are constant. However, these parameters have a dynamic behaviour justifying the development of dynamic models in order to improve the description of photosynthesis in the sea.In this work a mathematical model is used to simulate several situations where the phytoplankton exposure to light is controlled by the temporal variation of light intensity and the vertical advective and diffusive flux. The model includes both a static and a dynamic description of photosynthesis. It uses object‐oriented methods to switch between different types of productivity response to light intensity and to potential photoinhibition effects.The main conclusions emerging from the simulations performed are that the dynamic behaviour of the production–light curves is relevant in the simulation of primary productivity, and that this relevance is more pronounced under high light conditions and/or in the absence of vertical mixing. It is suggested that large scale models, where the time and spatial scales are too large to include the dynamic behaviour of the photosynthetic light response, may be parameterized by smaller scale simulations including the mentioned dynamic behaviour.

Accounting for inventory data and methodological choice uncertainty in a comparative life cycle assessment: the case of integrated multi-trophic aquaculture in an offshore Mediterranean enterprise

PurposeIntegrated multi-trophic aquaculture (IMTA), growing different species in the same space, is a technology that may help manage the environmental impacts of coastal aquaculture. Nutrient discharges to seawater from monoculture aquaculture are conceptually minimized in IMTA, while expanding the farm economic base. In this study, we investigate the environmental trade-offs for a small-to-medium enterprise (SME) considering a shift from monoculture towards IMTA production of marine fish.MethodsA comparative life cycle assessment (LCA), including uncertainty analysis, was implemented for an aquaculture SME in Italy. Quantification and simultaneous propagation of uncertainty of inventory data and uncertainty due to the choice of allocation method were combined with dependent sampling to account for relative uncertainties and statistical testing and interpretation to understand the uncertainty analysis results. Monte Carlo simulations were used as a propagation method. The environmental impacts per kilo of fish produced in monoculture and in IMTA were compared. Twelve impact categories were considered. The comparison is first made excluding uncertainty (deterministic LCA) and then accounting for uncertainties.Results and discussionDeterministic LCA results evidence marginal differences between the impacts of IMTA and monoculture fish production. IMTA performs better on all impacts studied. However, statistical testing and interpretation of the uncertainty analysis results showed that only mean impacts for climate change are significantly different for both productive systems, favoring IMTA. For the case study, technical variables such as scales of production of the species from different trophic levels, their integration (space and time), and the choice of species determine the trade-offs. Also, LCA methodological choices such as that for an allocation method and the treatment of relative uncertainties were determinant in the comparison of environmental trade-offs.ConclusionsThe case study showed that environmental trade-offs between monoculture and IMTA fish production depend on technical variables and methodological choices. The combination of statistical methods to quantify, propagate, and interpret uncertainty was successfully tested. This approach supports more robust environmental trade-off assessments between alternatives in LCAs with uncertainty analysis by adding information on the significance of results. It was difficult to establish whether IMTA does bring benefits given the scales of production in the case study. We recommend that the methodology defined here is applied to fully industrialized IMTA systems or bay-scale environments, to provide more robust conclusions about the environmental benefits of this aquaculture type in Europe.

Diagnosis in Networks of Mechatronic Agents : Validation of a Fault Propagation Model and Performance Assessment

Recent shop floor paradigms and approaches increasingly advocate the use of distributed systems and architectures. Plug-ability, Fault Tolerance, Robustness and Preparedness are characteristics believed to emerge by instantiation of these fundamentally new design approaches. However these features, when effectively present, often come at the cost of a greater system complexity. Enclosed in this complexity increase is a plethora on unforeseen interactions between the entities (modules) that compose the system. The purpose of this paper is, in this context, twofold: to validate a fault propagation model in random networks (that simulate the connectivity of modular shop floor systems) and assess the performance of two diagnostic approaches to expose the impact of relying in local or global information.

A co-Evolving Diagnostic Algorithm for Evolvable Production Systems: A Case of Learning

With the systematic implantation and acceptance of IT in the shop-floor a wide range of Production Paradigms have emerged that exploring these technological novelties promise to revolutionize the w ...