1,721,109 research outputs found
Identifying knowledge gaps for successful restorative aquaculture of Ostrea edulis: a bibliometric analysis
No full textBackground: Active restoration is necessary to enhance the recovery of Ostrea edulis reefs, which contribute to many ecosystem services. Restoration can be integrated within aquaculture practices, bringing positive environmental changes while maximising space utilisation. The restoration project MAREA (MAtchmaking Restoration Ecology and Aquaculture) aims to bring back O. edulis in the North-West Adriatic addressing the feasibility of its cultivation. Both successful restoration and sustainable aquaculture require a thorough understanding of the ecological needs, as the requirements of both activities need to be harmonized. Therefore, one of the preliminary activities before embarking on the pilot was the completion of a thorough literature review to identify research directions and gaps required for ‘restorative aquaculture’, aiming to gather the most up to date O. edulis knowledge on a global and local scale.
Methods: Internet (Web of Science, Scopus, Google scholar) and physical resources (libraries) were searched for all available global and local knowledge on O. edulis. Bibliometrix was used to identify the main research topics using keywords, titles, and abstracts analyses. Studies were then manually screened and summarised to extract knowledge specific to restoration and aquaculture.
Results: While restoration studies are recent, evidence for the loss of this species and potential causes (and solutions) have been discussed since the end of the 19th century. While diseases were a leading cause for reef loss, substratum limitation appears to be one of the leading limiting factors for both restoration and aquaculture of O. edulis, and was already mentioned in the early texts that were found.
Conclusions: The review highlighted that restoration success and aquaculture feasibility depend upon the crucial stage of settlement. The project ‘MAREA’ will therefore increase its focus on this stage, both in terms of timing, location, and materials for settlement plates placement
Influence of Intra-Seasonal Variability of Metabolic Rates on the Output of a Steady-State Food Web ModelModels of the Ecological Hierarchy - From Molecules to the Ecosphere
No full textMonitoring and modeling for investigating driver/pressure-state/impact relationships in coastal ecosystems: Examples from the Lagoon of Venice
No full textIn this paper, we show how the integration of monitoring data and mathematical model can generate valuable information by using a few examples taken from a well studied but complex ecosystem, namely the Lagoon of Venice. We will focus on three key issues, which are of concern also for many other coastal ecosystems, namely: (1) Nitrogen and Phosphorus annual budgets; (2) estimation of Net Ecosystem Metabolism and early warnings for anoxic events; (3) assessment of ecosystem status. The results highlight the importance of framing monitoring activities within the "DPSIR" conceptual model, thus going far beyond the monitoring of major biogeochemical variables and including: (1) the estimation of the fluxes of the main constituents at the boundaries; (2) the use of appropriate mathematical models. These tools can provide quantitative links among Pressures and State/Impacts, thus enabling decision makers and stakeholders to evaluate the effects of alternative management scenarios
A comparison between the uncertainties in model parameters and in forcing functions: its application to a 3D water-quality model
No full textFunctional clustering by smoothing quantile regression
No full textThis work presents a functional clustering procedure applied to environmental time series of a physical parameter (the chlorophyll type A concentration) in the coastal area of the Adriatic Sea. The data for the classification analysis is formed by glob-colours data during the period 2002–2012 (monthly values, 11 calendar years) provided by the ACRI server (http://hermes.acri.fr/) using satellite data source combining information of MERIS, Seaways and MODIS optical sensors. The choice of a basis implies the type of features of the series that are to be enhanced or hidden in the representation. Our proposal combines time series interpolation with smoothing quantile splines and the agglomerative clustering algorithm, such as partitioning around medoids technique. Our final purpose is to obtain a classification of the coastal areas in to homogeneous zones in order to select areas at high impact of chlorophyll type A concentrations. The analysis was performed by R software. This approach permits to take into account the quantile of interest and to calculate a more robust clustering procedure respect to other classical methods
Forecasting Dissolved Oxygen Level in Land-Based Fish Farms using a Context-Aware Recurrent Neural Network
No full textPredicting Dissolved Oxygen (DO) levels in precision fish farming is crucial as it directly impacts the well-being and growth of fishes. In this paper, we propose a sensing method that is suitable to be used in edge-computing and which makes use of deep learning to estimate dissolved oxygen in fish farms based on a context-aware recurrent neural network trained by the relationship between the inlet dissolved oxygen, the estimated biomass, the period and time of measurement, and the food given to the fish. The proposed technique has been applied to a real-world dataset coming from a trout fish farm located in Trentino, a region in Northern Italy
ESTIMATION OF RAINBOW TROUT (ONCORHYNCHUS MYKISS) RESPIRATION RATE WITHIN A COMMERCIAL RACEWAY USING A DATA ASSIMILATION APPROACH
No full textInnovations and decrease in costs of sensors are making it possible to apply to aquaculture the concept of “Precision
Livestock Farming”, introduced in the agrifood sector in early 2000. The implementation of the “Precision Fish Farming”
(PFF) framework (Fore et. al., 2018) is likely to revolutionize the aquaculture industry, leading to a new generation of
softwares and decision support tools, based on dynamic data driven models. In a previous work (Royer et al., 2021), we
proposed a PFF based model of oxygen mass-balance for rainbow trout within a commercial raceway, showing that it is
possible to dynamically estimate hourly fish respiration rate in commercial farming condition and, on this basis, to improve
current control systems. In this study, the estimation method was improved by introducing a data assimilation procedure
(Kalman Filter) that allows one to correct the respiration rate as data acquisition goes by and, on this basis, to obtain more accurate short-term predictions of DO concentration
Spatial clustering of curves with an application of satellite data
Full text linkWater quality indicators are important to identify risks to the environment, society and human health. The European Community Water Framework Directive establishes guidelines for the classification of all water bodies across Europe and chemical and biological indicators were used to this scope. In particular, the Chlorophyll type A index (Chl-a) is a shared indicator of trophic status and monitoring activities may be useful to explain its spatial distribution and to discover local dangerous behaviours (for example the anoxic events). Differently by the classical approach based on an “average” values over a period, we propose a functional clustering model that takes into account temporal and spatial dependence of Chl-a concentrations in the Adriatic Sea for defining appropriate clusters of sites. We use satellite monthly data, during the period 2002–2012, and we model the spatial dependence among the sites by means of a Markov random field model. Compared to similar attempts in literature by Jiang and Serban (2012) our formulation includes spatial covariates. This inclusion allows for more flexibility to obtain more homogeneous and representative clusters of sites in the Adriatic Sea. The estimation of the model and the identification of the number of clusters are carried out using a pseudolikelihood function. A small simulation study complements the real data analysis
The Extended Kalman Filter as a tool for the assimilation of high frequency water quality data
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