Modelling Hazards in Fisheries and Aquaculture Activities in the Mediterranean Sea and the Risk of their Transmission and Dispersion. Is it Feasible?
Triantafyllou George*, Triantaphyllidis George and Pollani Annika
Hellenic Centre for Marine Research, Institute of Oceanography, Greece
Submission: January 02, 2019; Published: January 11, 2019
*Correspondence author: Triantafyllou George, Hellenic Centre for Marine Research, Institute of Oceanography, 46.7 km Athens-Sounio Ave, PO Box 712 Anavyssos, Attica, GR-190 13, Greece.
How to cite this article:Triantafyllou G, Triantaphyllidis G, Pollani A. Modelling Hazards in Fisheries and Aquaculture Activities in the Mediterranean Sea and
the Risk of their Transmission and Dispersion. Is it Feasible?. Oceanogr Fish Open Access J. 2019; 8(5): 555748. DOI: 10.19080/OFOAJ.2019.08.555748
Modelling approaches in marine science is a controversial issue as no model is, or can be, a perfect representation of nature. Models can provide useful information for the dynamics of ecosystems and inform us about the likely consequences of human activities in fisheries and aquaculture. Applying a suite of dynamic models can be valuable predictive tools for modelling hazards transmission in fisheries and aquaculture activities in the Mediterranean Sea. These should include
a. A low trophic level ecosystem model and sub-models to describe the ecosystem functioning of the sea for the background physical information and a biogeochemical sub-model which simulates functional groups.
b. A pelagic fish individual-based model (IBM) to describe the bio-accumulation of chemical and biological hazards.
c. An aquaculture integrated model, a mass balance model, to calculate the input of effluents into the environment as a result of the fish farm operations and feeding regimes.
d. A dynamic energy budget (model for cultured bivalve species to predict the bioaccumulation of hazards such as heavy metals or toxins from harmful algal blooms.
These dynamic models can contribute to develop and/or improve systems ensuring process efficacy and validation for hazard control by identifying “hot spot” zones and concentrations of hazard agents above certain limits, improve the effectiveness and efficiency of the controls performed by food safety Competent Authorities along the seafood chain, identify areas of hazard agents accumulation and contribute to the transparency and reliability of food safety in the Mediterranean fisheries and aquaculture production sites.
Keywords: Hazards; Aquaculture; Fisheries; Lower Trophic Level (LTL) Model; Individual Based Model (IBM); Aquaculture Integrated Model; Dynamic Energy Budget (DEB) Model; Micro-Plastics; Nano-Plastics; Dispersion Risk
Abbrevations: CAS: Competent Authorities; FAO: Food and Agriculture Organization of the United Nations; EFSA: European Food Safety Authority; ERSEM: European Regional Seas Ecosystem Model; HABS: Harmful Algal Blooms; IBM: Individual Based Model; LTL: Lower Trophic Level; MPs: Micro-Plastics; NPs: Nano-Plastics; STECF: Scientific, Technical and Economic Committee for Fisheries; POM: Princeton Ocean Model
The term “hazard” (the intrinsic potential to cause harm) is often confused with the term “risk” (the probability of harm occurring at a given exposure) and often the public do not generally differentiate between these two terms . The microbiological definition of hazard in Codex Alimentarius  is “inherent property of an agent or situation having the potential to cause adverse effects when an organism, system, or (sub)population is exposed to that agent” whereas of risk is “the probability of an adverse effect in an organism, system, or (sub)population caused under specified circumstances by exposure to an agent”. Aquaculture and fisheries are important
activities in the Mediterranean Sea entailing significant socioeconomic implications [3,4]. Both activities face some potential hazards including natural, anthropic or both causes. Aquaculture hazards include diseases in both fish and shellfish whereas high stocking densities may lead to chronic stress that has important implications for fish immuno-competence [5-9], but relationships with infection levels are variable . In addition, marine litter and especially micro-plastics (MPs - 0.1 to 5,000 μm) and nano-plastics (NPs - 0.001–0.1 μm) concerns for potential toxicity of the plastic particles towards human health are growing, as they could potentially induce physical damages through particles themselves and biological stress through
MPs/NPs alone or leaching of additives (inorganic and organic)
Is it feasible to model hazards in fisheries and aquaculture
activities in the Mediterranean Sea and the risk of their
transmission and dispersion? The use of modelling approaches
in marine science is a controversial issue as no model is, or
can be, a perfect representation of nature . However, as
predictive modelling can provide a better understanding and
potentially either prevent hazardous events or identify hotspot
areas or describe the spread patterns and concentration of
hazards (pollutants, infectious diseases etc), their transmission
and dispersion needs to be integrated in dynamic models but in
an environmentally realistic manner [13,7]. Therefore, a suite of
dynamic models can be valuable predictive tools for modelling
hazards transmission in fisheries and aquaculture activities in
the Mediterranean Sea.
The low trophic level (LTL) ecosystem model must be
fully dynamic and to consist mainly of two sub-models: a
hydrodynamics sub-model that describe the ecosystem
functioning of the sea area and will provide the background
physical information e.g. a Princeton Ocean Model – POM 
or MICOM  or ROMS etc, to a second biogeochemical submodel
which simulates functional groups. The LTL ecosystem
model shall provide the dynamics of biological functional groups
that consist of population processes (growth and mortality)
and physiological processes (ingestion, respiration, excretion
and egestion). The biotic system is subdivided into three
functional types, producers (phytoplankton), decomposers
(pelagic and benthic bacteria) and consumers (zooplankton and
zoobenthos). These broad functional classifications are usually
subdivided, according to their trophic level (derived according
to size classes or feeding method) to create a food web. The
plankton pool should have the functional groups based on size
and ecological properties like diatoms, nanophytoplankton,
picophytoplankton, and dinoflagellates. Bacteria, heterotrophic
nano flagellates and microzooplankton represent the microbial
loop. All groups in the phytoplankton and the microbial loop
have dynamically varying C:N:P ratios. The chemical dynamics
of nitrogen, phosphorus, silicate and oxygen are coupled with
the biologically driven carbon dynamics.
The health benefits associated with seafood consumption
are coupled with concerns about potential health risks
associated with the presence of hazards (chemical and biological
contaminants), both those occurring naturally and those resulting
from human activities, in seafood. As the bio-accumulated
hazards sooner or later will end up in our plates through the
consumption of seafood, there is a need to understand this
process and what is transferred from one trophic level to the
other. In the Mediterranean, landings continued to increase until
1994, reaching 1,087,000 tonnes, and subsequently declined
irregularly to 787,000 in 2013, with a group of 13 main species
accounting for some 65 percent of landings, with anchovy
(393,500 tonnes) and sardine (186,100 tonnes) being by far the
dominant species . Pelagic species catches are higher than
the ones of demersal and 30 species contribute to 90 percent of
the landings in all Mediterranean subareas.
For pelagic fish, the fish model must be on-line coupled
with the LTL model (described above). Currently, it has been
developed and is a full-life cycle, individual based model (IBM)
that includes two species, the European anchovy (Engraulis
encrasicolus) and the European sardine (Sardina pilchardus)
. Early larvae feed on microzooplankton, late larvae start
consuming mesozooplankton and juveniles/adults interact
only with the mesozooplankton compartment of the LTL model.
The plankton biomass (micro- and mesozooplankton) that is
consumed by the fish is removed in the LTL model, while fish
bio-products from egestion, excretion and specific dynamic
action are directed to the LTL particulate organic matter and
dissolved inorganic nutrient pools.
That exist in the marine environment . These hazards
may be attached to phytoplankton, which subsequently may be
consumed by zooplankton groups. In addition, they might be
attached to zooplankton organisms and these will eventually
be grazed by small pelagic fish. Also, the fish may randomly
attach fibres, microplastics, oil spill particles on their gills and
body. Small-scale lab experiments will be needed to quantify the
above processes in order to describe some parameters and to
customize the models.
In order to calculate the input of effluents into the environment
as a result of the farm operations and feeding regime, a mass
balance model should be applied for the fish farms. Mass balance
models have been developed based on nutrition (feed type) and
conditions for salmonids [18,19], sea bream and sea bass among
others [20-23]. Input of nitrogen and phosphorus supplied
in fish feed can be used to calculate the amount harvested as
fish, excreted in dissolved form (Urea, NH4, PO4) and excreted
in particulate form (uneaten feed, faeces). Tsapakis et al. 
and Lupatsch and Kissil  calculated that the largest portion
of nitrogen supplied is excreted in the dissolved form as Urea
(41%) and ammonium (26%), while phosphates losses account
for 22% of phosphorus supplied. Conversely particulates
released consist mainly of organic phosphorus accounting for
44% of phosphorus supplied whereas particulate nitrogen losses
account for 10% of the nitrogen supplied. It is also estimated
that approximately 5% of feed is settling uneaten, either being consumed by wild fish or contributing to the organic load of the
underlying sediment [24,25].
The aquaculture modelling tool could be based on the
above-described LTL model and can be used for ecosystem
monitoring of the dispersion of parameters, including bacteria
biomass and bacterial production and can be used to model
the spread of diseases from farms. It can also be adopted to
predict the dispersion of parasites. This will allow the creation
of zones that might get infected and therefore might trigger the
use of antibiotics and other chemoprophylaxis treatments. If
such treatments are not properly followed, the fish that will be
harvested and directed to the value/market chain might include
those chemicals and all this is a potential hazard. This mapping
of the zones, can be evolved to a DSS tool that will identify critical
zones of potential hazards and prioritise the controls of the
Competent Authorities to identify contaminants, pathogens etc.
The bivalve mollusc model in combination with the LTL
model can be useful to predict the bioaccumulation of hazards
such as heavy metals, toxins from harmful algal blooms (HABs)
etc. A dynamic energy budget (DEB) model for bivalve molluscs
should investigate the growth and reproduction of cultured
bivalve species raised under different environmental conditions
(varying phytoplankton carbon biomass, particulate organic
carbon and temperature) and can be tuned against field data.
The interested reader may refer to Zaldívar  and Casas &
Bacher  for a full description of the model equations used.
Such model has been described by Hatzonikolakis et al. [6, 27-
Models can bridge the gap of the complex links between
existing environmental variation and hazards presence and risk
of transmission although responses of hazards over and above
natural variation might be challenging. There are tools available
that can be further improved to describe the most important
physical and biochemical processes that, combined together,
determine the dynamics of the ecosystem. Given the complexity
of these processes and their interactions, mathematical models
can be regarded as unique tools to deliver integrated approaches
and better understand the mechanisms of hazards transmission
and risk of dispersion and inform about the likely consequences
of human actions.
The above-described dynamic models can contribute to:
a. Develop and/or improve systems ensuring process
efficacy and validation for hazards control by identifying
“hot spot” zones and concentrations of hazard agents above
b. Improve the effectiveness and efficiency of the controls
performed by the food safety Competent Authorities (CAs)
along the seafood chain by direct and guide their sampling
effort to detect contaminants (Decision Support Tool that
will identify critical zones of potential hazards) and prioritise
the controls of the CAs to identify contaminants, pathogens
c. Identify areas of hazard agent’s accumulation (e.g.
microplastics and Nano plastics, HABs, areas that antibiotics
and chemicals are used etc).
d. Assist activities that will develop detection and
monitoring tools that will allow for the data collection,
integration, validation and analysis.
e. Contribute to the transparency and reliability of food
safety in the Mediterranean production sites.
f. Future research should evaluate trophic transfer of
hazards with their associated risks through the marine
food web for humans. Models that describe the evolution
of hazards are in progress and will be published in the near