Predicting shifting sustainability trade-offs in marine finfish aquaculture under climate change.

Defining sustainability goals is a crucial but difficult task because it often involves the quantification of multiple interrelated and sometimes conflicting components. This complexity may be exacerbated by climate change, which will increase environmental vulnerability in aquaculture and potentially compromise the ability to meet the needs of a growing human population. Here, we developed an approach to inform sustainable aquaculture by quantifying spatio-temporal shifts in critical trade-offs between environmental costs and benefits using the time to reach the commercial size as a possible proxy of economic implications of aquaculture under climate change. Our results indicate that optimizing aquaculture practices by minimizing impact (this study considers as impact a benthic carbon deposition ≥ 1 g C m-2  day-1 ) will become increasingly difficult under climate change. Moreover, an increasing temperature will produce a poleward shift in sustainability trade-offs. These findings suggest that future sustainable management strategies and plans will need to account for the effects of climate change across scales. Overall, our results highlight the importance of integrating environmental factors in order to sustainably manage critical natural resources under shifting climatic conditions.

Modelling CO2 budget of mussel farms across the Mediterranean Sea.

The role of bivalve aquaculture as a carbon sink is highly debated, without a general consensus on the components to include in the budget. This study proposes to estimate the terms of the budget using a scope-for-growth-based model. The model was applied at 12 Mediterranean sites, with environmental forcings provided by operational oceanography data spanning over 12 years. Mussels were found to be slight sinks, with a limited variability across sites, if all components of the budget, i.e. accumulation in soft tissue, emissions associated with calcification and respiration, are included. The differences found among stations concerning the calcification and soft tissue contributions to the budget were found to be related to site-specific productivity and water chemistry parameters. This led to the identification of a set of meta-models, which could be used for relating the budget to local conditions, at a screening level, rendering them useful for optimal site selection.

A stratified compartmental model for the transmission of Sparicotyle chrysophrii (Platyhelminthes: Monogenea) in gilthead seabream (Sparus aurata) fish farms†.

The rapid development of intensive fish farming has been associated with the spreading of infectious diseases, pathogens and parasites. One such parasite is Sparicotyle chrysophrii (Platyhelminthes: Monogenea), which commonly infects cultured gilthead seabream (Sparus aurata)-a vital species in Mediterranean aquaculture. The parasite attaches to fish gills and can cause epizootics in sea cages with relevant consequences for fish health and associated economic losses for fish farmers. In this study, a novel stratified compartmental epidemiological model of S. chrysophrii transmission was developed and analysed. The model accounts for the temporal progression of the number of juvenile and adult parasites attached to each fish, as well as the abundance of eggs and oncomiracidia. We applied the model to data collected in a seabream farm, where the fish population and the number of adult parasites attached to fish gills were closely monitored in six different cages for 10 months. The model successfully replicated the temporal dynamics of the distribution of the parasite abundance within fish hosts and simulated the effects of environmental factors, such as water temperature, on the transmission dynamics. The findings highlight the potential of modelling tools for farming management, aiding in the prevention and control of S. chrysophrii infections in Mediterranean aquaculture.

Multidisciplinary long-term survey of Manila clam grown in farming sites subjected to different environmental conditions.

In recent years recurrent bivalve mass mortalities considerably increased around the world, causing the collapse of natural and farmed populations. Venice Lagoon has historically represented one of the major production areas of the Manila clam Ruditapes philippinarum in Europe. However, in the last 20 years a 75 % decrease in the annual production has been experienced. While climate change and anthropogenic interventions may have played a key role in natural and farmed stocks reductions, no studies investigated at multiple levels the environmental stressors affecting farmed Manila clam to date. In this work we carried out a long-term monitoring campaign on Manila clam reared in four farming sites located at different distances from the southern Venice Lagoon inlet, integrating (meta)genomic approaches (i.e. RNA-seq; microbiota characterization), biometric measurements and chemical-physical parameters. Our study allowed to characterize the molecular mechanisms adopted by this species to cope with the different environmental conditions characterizing farming sites and to propose hypotheses to explain mortality events observed in recent years. Among the most important findings, the disruption of clam's immune response, the spread of Vibrio spp., and the up-regulation of molecular pathways involved in xenobiotic metabolism suggested major environmental stressors affecting clams farmed in sites placed close to Chioggia's inlet, where highest mortality was also observed. Overall, our study provides knowledge-based tools for managing Manila clam farming on-growing areas. In addition, the collected data is a snapshot of the time immediately before the commissioning of MoSE, a system of mobile barriers aimed at protecting Venice from high tides, and will represent a baseline for future studies on the effects of MoSE on clams farming and more in general on the ecology of the Venice Lagoon.

Digital twins for land-based aquaculture: A case study for rainbow trout ( Oncorhynchus mykiss).

The virtual, digital counterpart of a physical object, referred as digital twin, derives from the Internet of Things (IoT), and involves real-time acquisition and processing of large data sets. A fully implemented system ultimately enables real-time and remote management, as well as the reproduction of real and forecasted scenarios. Under the emerging framework of Precision Fish Farming, which brings control-engineering principles to fish production, we set up digital twin prototypes for land-based finfish farms. The digital twin is aimed at supporting producers in optimizing feeding practices, oxygen supply and fish population management with respect to 1) fish growth performances; 2) fish welfare, and 3) environmental loads. It relies on integrated mathematical models which are fed with data from in-situ sensors and from external sources, and simulate several dynamic processes, allowing the estimation of key parameters describing the ambient environment and the fishes. A conceptual application targeted at rearing cycles of rainbow trout ( Oncorhynchus mykiss) in an operational in-land aquafarm in Italy is presented. The digital twin takes into account the disparate levels of automation and control that are found within this farm, and considerations are made on preferential directions for future developments. In spite of its potential, and not only in the aquaculture sector, the development of digital twins is still at its early stage. Furthermore, Precision Fish Farming applications in land-based systems as well as targeted at rainbow trout are novel developments.

A digital twin is set up for a rainbow trout ( Oncorhynchus mykiss) aquaculture farm located in northern Italy. The farm is equipped with large-scale basins, and are supplied by freshwater from the neighbouring Sarca river. Generally speaking, digital twins are virtual, digital representations which mirror and are connected to real objects, enabling real-time and remote management, as well as the reproduction of real or forecasted scenarios. This is a pioneer application aimed at supporting producers in optimizing feeding practices, oxygen supply and fish population management with respect to 1) fish growth performances; 2) fish welfare, and 3) environmental loads. The digital twin relies on sensors, Big Data, IoT (Internet of Things) and predictive mathematical models. In spite of its potential, and not only in the aquaculture sector, the development of digital twins is still at its early stage. Furthermore, Precision Fish Farming applications in land-based systems as well as targeted at rainbow trout are novel developments.

Identifying knowledge gaps for successful restorative aquaculture of Ostrea edulis: a bibliometric analysis.

Background: 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 19 th 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.

Global uncertainty and sensitivity analysis of a food-web bioaccumulation model.

A global uncertainty and sensitivity analysis (UA/SA) of a state-of-the-art, food-web bioaccumulation model was carried out. We used an efficient screening analysis technique to identify the subset of the most relevant input factors among the whole set of 227 model parameters. A quantitative UA/SA was then applied to this subset to rank the relevance of the parameters and to partition the variance of the model output among them by means of a nonlinear regression of the outcomes of 1,000 Monte Carlo simulations. The concentrations of four representative persistent organic pollutants (POPs) in two representative species of the coastal marine food web of the Lagoon of Venice (Italy) were taken as model outputs. The screening analysis showed that the ranking was remarkably different in relation to the species and chemical being considered. The subsequent Monte Carlo-based quantitative analysis pointed out that the relationships among some of the parameters and the model outputs were nonlinear. The nonlinear regression showed that the fraction of output variance accounted for by each parameter was strongly dependent on the range of the octanol-water partition coefficient (K(OW)) values being considered. For the less hydrophobic chemicals, the main sources of model uncertainty were the parameters related to the respiratory bioaccumulation, whereas for the more hydrophobic ones, K(OW) and the other parameters related to the dietary uptake explained the largest fractions of the variance of the chemical concentrations in the organisms. The analysis highlighted that efforts are still needed for reducing uncertainty of model parameters to get reliable results from the application of food web bioaccumulation models.

Spatially distributed ecological risk for fish of a coastal food web exposed to dioxins.

The ecological risk posed by 2,3,7,8-polychlorodibenzo-p-dioxins and furans (PCDD/Fs) and dioxin-like polychlorobiphenyl (PCB) congeners to five edible fish species of the aquatic food web of Venice Lagoon, Italy, was estimated by applying a state-of the-art kinetic bioaccumulation model. Site-specific data were used to define a representative food web. The experimental data set for model validation and application included PCB and PCDD/F congener concentrations in sediments, in water, and in five organisms (both invertebrates and fish). The spatial distribution of risk was evaluated by dividing the lagoon into six homogeneous areas, and for each area, sediment, water, and organism dioxins concentrations were calculated. The bioaccumulation model was calibrated for both nonmetabolizing and metabolizing congeners, the metabolic elimination rates of which were estimated. The model validation showed an acceptable bioaccumulation estimation, evaluated using the model bias parameter. The calibrated model was applied to the six areas of the lagoon to estimate the fish predicted exposure concentration as 2,3,7,8-tetrachlorodibenzo-p-dioxin toxicity equivalents from sediment concentration. Internal no-effect concentrations were calculated for each fish species from literature data. Risk was estimated by applying the hazard quotient (HQ) approach, obtaining the ecological risk for each fish species on the basis of 90 and 99% protection levels, in each of the six lagoon areas. The sediment dioxins concentration does not pose a significant risk to the selected fish species at the 90% protection target (HQ<1), whereas risk is significant (HQ>1) at the 99% protection target. Risk results were higher near the Porto Marghera industrial district, Italy, and in lagoon zones characterized by a low water-exchange rate and freshwater basin inputs.

An R package for simulating growth and organic wastage in aquaculture farms in response to environmental conditions and husbandry practices.

A new R software package, RAC, is presented. RAC allows to simulate the rearing cycle of 4 species, finfish and shellfish, highly important in terms of production in the Mediterranean Sea. The package works both at the scale of the individual and of the farmed population. Mathematical models included in RAC were all validated in previous works, and account for growth and metabolism, based on input data characterizing the forcing functions-water temperature, and food quality/quantity. The package provides a demo dataset of forcings for each species, as well as a typical set of husbandry parameters for Mediterranean conditions. The present work illustrates RAC main features, and its current capabilities/limitations. Three test cases are presented as a proof of concept of RAC applicability, and to demonstrate its potential for integrating different open products nowadays provided by remote sensing and operational oceanography.