ALAN DEIDUN
Prof. Alan Deidun, a marine biologist by training, is Malta’s first-ever Ocean Ambassador and, since September 2019, serving as a member of the EU Commission’s Ocean Mission Board between 2019 and 2024. He directs the Malta Training Centre of the International Ocean Institute (IOI) and is a Fellow of the Royal Society of Biology (London). He is also a Full Professor within the Faculty of Science of the University of Malta, where he coordinates the Master of Science in Applied Oceanography course and acts as the Rector’s Delegate on the SEA-EU 2.0 (University of the Sea) alliance, which features a Consortium of nine European Universities. He has served as a Board member of the Environment and Resources Authority (ERA) of Malta between February 2016 and May 2021 and, since 2018, is the Maltese national delegate for CIESM (the International Science Commission for the Mediterranean). He is a national and European ocean literacy and citizen science champion, being twice featured on EuroNews and in leading European newspapers, being bestowed in 2021 with the National Order of Merit recognition by the President of Malta for his efforts. He also serves as Malta’s national delegate on the Mediterranean Science Commission (CIESM). Prof. Alan Deidun has published over 190 peer-reviewed papers and contributions in high-profile academic journals as well as in conference proceedings and his academic work has currently been cited over 3500 times (H index of 32). Since 2005, he has acted as a freelance marine ecology consultant on numerous EIA and AA studies linked with a considerable number of proposed, high-profile coastal and marine projects in the Maltese Islands and in Libya. Prof. Deidun has participated as Principal Investigator for the University of Malta in numerous EU-funded projects within ENI-CBCMED, Interreg, FP7, Horizon 2020, COST and Erasmus+ frameworks, having attracted over 5 million euros to the same University through such endeavours.
Phone: +35679604109
Address: Room 211 B, Maths and Physics Building, University of Malta Tal-Qroqq campus, Msida MSD 2080 MALTA
Phone: +35679604109
Address: Room 211 B, Maths and Physics Building, University of Malta Tal-Qroqq campus, Msida MSD 2080 MALTA
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Papers by ALAN DEIDUN
expands, the demand for high-capacity vessels increases, raising concerns about their impact on the
marine environment. While awaiting access to port facilities, vessels often anchor to save fuel and
prevent drifting, but this practice is a significant cause of mechanical disturbance to the seafloor and
benthic habitats. Identifying and quantifying anchoring pressure is essential for better managing and
mitigating the damage to the seafloor. The Automatic Identification System (AIS) can be utilized to
assess anchoring pressure by transmitting vessel information (e.g., position, type and size) to other
vessels and coastal stations. This research evaluates anchoring pressure in a strategically located
bunkering area around the Maltese Islands using AIS data collected from an antenna at the University
of Malta. An arbitrary index was developed to determine anchoring pressure, and the AIS data was
used to create GIS maps showing the location and size of vessels within the bunkering area, as well
as plots depicting anchoring pressure by vessel type, seasonality, and density. This study serves as
a blueprint for future assessments of anchoring pressures from various maritime activities in other
areas around the Maltese Islands and provides a decision support tool for national policy-making
related to Descriptor 6 (Seafloor Integrity) of the Marine Strategy Framework Directive (MSFD), the
Maritime Spatial Planning Directive (MSPD), and the management plan for Sites of Community
Interest (SCI) and Marine Protected Areas (MPAs).
awareness is often limited to the visible litter that washes up on beaches or floats on the ocean surface. Less attention
is paid to marine litter that is deposited and accumulates on the seafloor, due to operational constraints. The
Mediterranean Bottom Trawl Survey (MEDITS) can provide insights into the state of seabed litter distribution and
composition collaterally with the primary fish shock assessment. This study analyses the MEDITS 2020/2021 marine
litter dataset in terms of a spatial, temporal and depth distribution in the Geographical Subarea (GSA) 15, i.e. off the
coast of Malta. The composition of the litter and its potential major sources were determined. For these analyses, a
two-sided independent t-test was applied using SPSS. Visualization was done by creating maps and bar charts using
QGIS and MATLAB. Results revealed tourism and household items as primary contributors to marine litter, with
plastics comprising the majority. Surprisingly, the onset of the COVID-19 pandemic appears correlated with a
significant reduction in seafloor litter accumulation. Spatial distribution dynamics suggest that subsurface currents
influence the transport of light litter items like plastic, while heavy litter, such as metals, tends to remain localised.
Compared to other Mediterranean regions, the seabed off Malta shows a relatively clean status. This study not only
provides valuable insights into the local marine environment but also underscores the need for novel global strategies
to address the marine litter issue. These findings prompt considerations for future environmental management
practices and highlight potential areas for further research in the broader context of marine environmental
monitoring.
explored impacts from artificial light at night (ALAN), which influences natural light–dark cycles
and affects marine ecosystems. This study investigates the impact of ALAN on coastal infralittoral
assemblages in Malta, where such effects remain unexplored. Using Baited Remote Underwater
Videos (BRUVs), we examined the influence of different light intensities on species assemblages
and behaviour at two sites: a light-polluted harbour and a darker reef area. Our findings reveal
significant differences in fish community composition between light treatments and habitats. Among
the 23,955 individuals recorded across multiple taxa, Boops boops accounted for 80% of observations.
From our results, light intensity had a more substantial impact on community structure than habitat
type, with species-specific responses to light. Predatory species such as Trachurus trachurus displayed
increased activity under high-intensity white light, while Apogon imberbis and Serranus scriba were
more abundant under red light, irrespective of habitat. These results underscore the role of ALAN in
altering marine community dynamics and emphasise the need for sustainable management strategies
to mitigate its impact on the biodiversity of the Mediterranean. This study provides initial empirical
evidence of ALAN’s effects in Maltese waters, contributing to broader efforts to understand and
manage light pollution in marine ecosystems.
adopted as this study’s target species. Through the use of machine-learning models and transfer learning, the proposed solution seeks to enable precise, on-the-spot species recognition. The methodology
involved collecting and organising images as well as training the models with consistent datasets to ensure comparable results. After trying a number of models, ResNet18 was found to be the most accurate and reliable, with YOLO v8 following closely behind. While the performance of YOLO was reasonably good, it exhibited less consistency in its results. These results underline the potential of the developed algorithm to significantly aid marine biology research, including citizen science initiatives, and promote environmental management efforts through accurate fish species identification.
expands, the demand for high-capacity vessels increases, raising concerns about their impact on the
marine environment. While awaiting access to port facilities, vessels often anchor to save fuel and
prevent drifting, but this practice is a significant cause of mechanical disturbance to the seafloor and
benthic habitats. Identifying and quantifying anchoring pressure is essential for better managing and
mitigating the damage to the seafloor. The Automatic Identification System (AIS) can be utilized to
assess anchoring pressure by transmitting vessel information (e.g., position, type and size) to other
vessels and coastal stations. This research evaluates anchoring pressure in a strategically located
bunkering area around the Maltese Islands using AIS data collected from an antenna at the University
of Malta. An arbitrary index was developed to determine anchoring pressure, and the AIS data was
used to create GIS maps showing the location and size of vessels within the bunkering area, as well
as plots depicting anchoring pressure by vessel type, seasonality, and density. This study serves as
a blueprint for future assessments of anchoring pressures from various maritime activities in other
areas around the Maltese Islands and provides a decision support tool for national policy-making
related to Descriptor 6 (Seafloor Integrity) of the Marine Strategy Framework Directive (MSFD), the
Maritime Spatial Planning Directive (MSPD), and the management plan for Sites of Community
Interest (SCI) and Marine Protected Areas (MPAs).
awareness is often limited to the visible litter that washes up on beaches or floats on the ocean surface. Less attention
is paid to marine litter that is deposited and accumulates on the seafloor, due to operational constraints. The
Mediterranean Bottom Trawl Survey (MEDITS) can provide insights into the state of seabed litter distribution and
composition collaterally with the primary fish shock assessment. This study analyses the MEDITS 2020/2021 marine
litter dataset in terms of a spatial, temporal and depth distribution in the Geographical Subarea (GSA) 15, i.e. off the
coast of Malta. The composition of the litter and its potential major sources were determined. For these analyses, a
two-sided independent t-test was applied using SPSS. Visualization was done by creating maps and bar charts using
QGIS and MATLAB. Results revealed tourism and household items as primary contributors to marine litter, with
plastics comprising the majority. Surprisingly, the onset of the COVID-19 pandemic appears correlated with a
significant reduction in seafloor litter accumulation. Spatial distribution dynamics suggest that subsurface currents
influence the transport of light litter items like plastic, while heavy litter, such as metals, tends to remain localised.
Compared to other Mediterranean regions, the seabed off Malta shows a relatively clean status. This study not only
provides valuable insights into the local marine environment but also underscores the need for novel global strategies
to address the marine litter issue. These findings prompt considerations for future environmental management
practices and highlight potential areas for further research in the broader context of marine environmental
monitoring.
explored impacts from artificial light at night (ALAN), which influences natural light–dark cycles
and affects marine ecosystems. This study investigates the impact of ALAN on coastal infralittoral
assemblages in Malta, where such effects remain unexplored. Using Baited Remote Underwater
Videos (BRUVs), we examined the influence of different light intensities on species assemblages
and behaviour at two sites: a light-polluted harbour and a darker reef area. Our findings reveal
significant differences in fish community composition between light treatments and habitats. Among
the 23,955 individuals recorded across multiple taxa, Boops boops accounted for 80% of observations.
From our results, light intensity had a more substantial impact on community structure than habitat
type, with species-specific responses to light. Predatory species such as Trachurus trachurus displayed
increased activity under high-intensity white light, while Apogon imberbis and Serranus scriba were
more abundant under red light, irrespective of habitat. These results underscore the role of ALAN in
altering marine community dynamics and emphasise the need for sustainable management strategies
to mitigate its impact on the biodiversity of the Mediterranean. This study provides initial empirical
evidence of ALAN’s effects in Maltese waters, contributing to broader efforts to understand and
manage light pollution in marine ecosystems.
adopted as this study’s target species. Through the use of machine-learning models and transfer learning, the proposed solution seeks to enable precise, on-the-spot species recognition. The methodology
involved collecting and organising images as well as training the models with consistent datasets to ensure comparable results. After trying a number of models, ResNet18 was found to be the most accurate and reliable, with YOLO v8 following closely behind. While the performance of YOLO was reasonably good, it exhibited less consistency in its results. These results underline the potential of the developed algorithm to significantly aid marine biology research, including citizen science initiatives, and promote environmental management efforts through accurate fish species identification.
In summer of 2015, the highest levels of MP were reported in Pretty Bay at 10.81 items/1000cm3 of wet sand with the lowest being in Għajn Tuffieħa, at 0.72 items/1000 cm3. In general, levels of MP in the dry season were found to be higher than those recorded in the wet season (winter). Higher MP concentration was recorded at 10 m up-shore as opposed to the strandline. Furthermore, surface sands contained a higher concentration of MP when compared with the subsurface sediments, though this was not was not the case at Pretty Bay in the wet season. These results are interpreted in terms of different beach profiles, beach dynamics, sand properties and potential sources of MP.
The local level of occurrence of MP seems to be lower when compared to other European locations studied so far. The fact that in this study, MP below 1mm were not included in the data, as well as the lack of rivers in the Maltese islands, regular beach clean ups and other factors may explain this. Data on the characterisation of MP found are provided. For example, polyethylene and polypropylene were the most common polymers recorded at Għajn Tuffieħa Bay whereas polyethylene and paint fragments were the most common MP recorded at Pretty Bay. This investigation is a contribution to our knowledge of how levels of MP in sandy beaches may be affected by sand properties and dynamics, beach profiles and other factors.