• Energy Research

Although several studies have investigated bacterial-algal interactions, the bacterial component has often not been identified, and the ecological role of bacterial-algal associations is still unclear. In the present study two different approaches (molecular and culture) have been used to characterize the bacterial community associated with the invasive alga Caulerpa cylindracea (Sonder) over time, in a coastal area of the Mediterranean basin. C. cylindracea is an invasive macroalga in the Mediterranean Sea, able to colonize several types of substrates. Traditional culture-based and PCR-SSCP methods have been used to analyze the bacterial community. Molecular traces of Gammaproteobacteria belonging to the genera Shewanella and Vibrio have been found by both approaches on the surface of C. cylindracea consistently in time, along with those of an unknown species belonging to the Rhodobacteraceae family. Other taxa belonging to Bacillus, Pseudoalteromonas, Tropicibacter, Photobacterium, Exiguobacterium, Kocuria, Ruegeria, and Marinobacter genera have been discovered by culture-based approach. PCR-SSCP method has shown traces of an unknown species of the Bacteroidetes phylum and the Granulosicoccus genus. Our results suggest that C. cylindracea hosts a bacterial assemblage scarcely variable with time. Further studies are needed to clarify the nature of this alga-bacteria association and the potential role in the spreading of this alga using a holistic view considering the seaweed with associated bacteria as an essentially unique meta-organism.

AbstractClimate change is already transforming the seascapes of our oceans by changing the energy availability and the metabolic rates of the organisms. Among the ecosystem-engineering species that structure the seascape, marine animal forests (MAFs) are the most widespread. These habitats, mainly composed of suspension feeding organisms, provide structural complexity to the sea floor, analogous to terrestrial forests. Because primary and secondary productivity is responding to different impacts, in particular to the rapid ongoing environmental changes driven by climate change, this paper presents some directions about what could happen to different MAFs depending on these fast changes. Climate change could modify the resistance or resilience of MAFs, potentially making them more sensitive to impacts from anthropic activities (i.e. fisheries and coastal management), and vice versa, direct impacts may amplify climate change constraints in MAFs. Such changes will have knock-on effects on the energy budgets of active and passive suspension feeding organisms, as well as on their phenology, larval nutritional condition, and population viability. How the future seascape will be shaped by the new energy fluxes is a crucial question that has to be urgently addressed to mitigate and adapt to the diverse impacts on natural systems.

Les forêts macroalgales sont l'un des écosystèmes marins les plus productifs et les plus précieux, mais elles sont néanmoins fortement exposées à la fragmentation et à la perte. Les informations détaillées à grande échelle sur leur distribution font largement défaut, ce qui entrave les initiatives de conservation. Dans cette étude, un effort systématique pour combiner les données spatiales sur les canopées de Cystoseira C. Agardh (Fucales, Phaeophyta) a été réalisé pour développer un modèle d'aptitude à l'habitat (HSM) à l'échelle méditerranéenne, fournissant des outils essentiels pour améliorer la priorisation des sites pour leur gestion, leur restauration et leur protection. Une base de données géoréférencée sur la présence de 20 espèces de Cystoseira a été produite en recueillant toutes les informations disponibles dans la littérature publiée et grise, les portails de données Web et les données personnelles des co-auteurs. Les données ont été associées à 55 couches de variables prédictives au format matriciel (ASCII) et ont été utilisées afin de développer le HSM au moyen d'une forêt aléatoire, une technique d'apprentissage automatique très efficace. Les connaissances sur la répartition du couvert de Cystoseira étaient disponibles pour environ 14 % du littoral méditerranéen. Les données d'absence n'étaient disponibles que pour les 2% du bassin. Malgré ces lacunes, notre HSM a montré des niveaux de précision élevés dans la reproduction de la distribution de Cystoseira, de sorte que les premières cartes continues de l'habitat dans l'ensemble du bassin ont été produites. Des erreurs de classification se sont principalement produites dans les parties est et sud du bassin, où de grandes lacunes de connaissances ont émergé. Les facteurs les plus pertinents étaient les facteurs géomorphologiques, suivis des variables anthropiques représentatives de la pollution et de l'urbanisation. Notre modèle montre l'importance du partage des données pour combiner un grand nombre de données spatiales et environnementales, permettant d'individualiser les zones à forte probabilité d'occurrence de Cystoseira comme adaptées à sa présence. Cette approche encourage l'utilisation de cet outil de modélisation pour la prédiction de la distribution de Cystoseira et pour soutenir et planifier les initiatives de conservation et de gestion. L'étape suivante consiste à affiner les informations spatiales des données de présence-absence sur les auvents de Cystoseira et des prédicteurs environnementaux afin de traiter les évaluations spécifiques aux espèces. Los bosques de macroalgas son uno de los ecosistemas marinos más productivos y valiosos, pero están fuertemente expuestos a la fragmentación y la pérdida. En gran medida, se carece de información detallada a gran escala sobre su distribución, lo que dificulta las iniciativas de protección. En este estudio, se realizó un esfuerzo sistemático para combinar datos espaciales sobre las copas de Cystoseira C. Agardh (Fucales, Phaeophyta) para desarrollar un Modelo de Idoneidad del Hábitat (HSM) a escala mediterránea, proporcionando herramientas críticas para mejorar la priorización del sitio para su manejo, restauración y protección. Se produjo una base de datos georreferenciada sobre la ocurrencia de 20 especies de Cystoseira que recopila toda la información disponible de la literatura publicada y gris, los portales de datos web y los datos personales de los coautores. Los datos se asociaron a 55 capas variables predictoras en formato raster (ASCII) y se utilizaron para desarrollar el HSM mediante un Random Forest, una técnica de Machine Learning muy eficaz. El conocimiento sobre la distribución de las copas de Cystoseira estaba disponible para aproximadamente el 14% de la costa mediterránea. Los datos de ausencia estaban disponibles solo para el 2% de la cuenca. A pesar de estas lagunas, nuestro HSM mostró altos niveles de precisión en la reproducción de la distribución de Cystoseira, de modo que se produjeron los primeros mapas continuos del hábitat en toda la cuenca. Los errores de clasificación se produjeron principalmente en la parte oriental y meridional de la cuenca, donde surgieron grandes lagunas de conocimiento. Los impulsores más relevantes fueron los geomorfológicos, seguidos de las variables antropogénicas proxies de contaminación y urbanización. Nuestro modelo muestra la importancia de compartir datos para combinar una gran cantidad de datos espaciales y ambientales, lo que permite individualizar áreas con alta probabilidad de aparición de Cystoseira como adecuadas para su presencia. Este enfoque fomenta el uso de esta herramienta de modelado para la predicción de la distribución de Cystoseira y para apoyar y planificar iniciativas de protección y gestión. El paso adelante es refinar la información espacial de los datos de presencia-ausencia sobre las copas de Cystoseira y de los predictores ambientales para abordar las evaluaciones específicas de las especies. Macroalgal forests are one of the most productive and valuable marine ecosystems, but yet strongly exposed to fragmentation and loss. Detailed large-scale information on their distribution is largely lacking, hindering conservation initiatives. In this study, a systematic effort to combine spatial data on Cystoseira C. Agardh canopies (Fucales, Phaeophyta) was carried out to develop a Habitat Suitability Model (HSM) at Mediterranean scale, providing critical tools to improve site prioritization for their management, restoration and protection. A georeferenced database on the occurrence of 20 Cystoseira species was produced collecting all the available information from published and grey literature, web data portals and co-authors personal data. Data were associated to 55 predictor variable layers in the (ASCII) raster format and were used in order to develop the HSM by means of a Random Forest, a very effective Machine Learning technique. Knowledge about the distribution of Cystoseira canopies was available for about the 14% of the Mediterranean coastline. Absence data were available only for the 2% of the basin. Despite these gaps, our HSM showed high accuracy levels in reproducing Cystoseira distribution so that the first continuous maps of the habitat across the entire basin was produced. Misclassification errors mainly occurred in the eastern and southern part of the basin, where large gaps of knowledge emerged. The most relevant drivers were the geomorphological ones, followed by anthropogenic variables proxies of pollution and urbanization. Our model shows the importance of data sharing to combine a large number of spatial and environmental data, allowing to individuate areas with high probability of Cystoseira occurrence as suitable for its presence. This approach encourages the use of this modelling tool for the prediction of Cystoseira distribution and for supporting and planning conservation and management initiatives. The step forward is to refine the spatial information of presence-absence data about Cystoseira canopies and of environmental predictors in order to address species-specific assessments. تعد غابات الطحالب الكلية واحدة من أكثر النظم الإيكولوجية البحرية إنتاجية وقيمة، ولكنها معرضة بشدة للتجزئة والفقدان. هناك نقص كبير في المعلومات التفصيلية على نطاق واسع حول توزيعها، مما يعوق مبادرات الحفظ. في هذه الدراسة، تم بذل جهد منهجي لدمج البيانات المكانية على مظلات Cystoseira C. Agardh (Fucales، Phaeophyta) لتطوير نموذج ملاءمة الموائل (HSM) على نطاق البحر الأبيض المتوسط، مما يوفر أدوات حاسمة لتحسين تحديد أولويات الموقع لإدارتها وترميمها وحمايتها. تم إنتاج قاعدة بيانات مرجعية جغرافية حول حدوث 20 نوعًا من Cystoseira لجمع جميع المعلومات المتاحة من الأدبيات المنشورة والرمادية وبوابات بيانات الويب والبيانات الشخصية للمؤلفين المشاركين. ارتبطت البيانات بـ 55 طبقة متغيرة للتنبؤ بتنسيق (ASCII) Raster واستخدمت من أجل تطوير HSM عن طريق غابة عشوائية، وهي تقنية فعالة للغاية للتعلم الآلي. كانت المعرفة حول توزيع مظلات Cystoseira متاحة لحوالي 14 ٪ من ساحل البحر الأبيض المتوسط. كانت بيانات الغياب متاحة فقط لـ 2 ٪ من الحوض. على الرغم من هذه الفجوات، أظهرت وحدة الأمن البشري لدينا مستويات عالية من الدقة في إعادة إنتاج توزيع Cystoseira بحيث تم إنتاج أول خرائط مستمرة للموائل عبر الحوض بأكمله. حدثت أخطاء في التصنيف بشكل رئيسي في الجزء الشرقي والجنوبي من الحوض، حيث ظهرت فجوات كبيرة في المعرفة. كانت الدوافع الأكثر صلة هي العوامل الجيومورفولوجية، تليها المتغيرات البشرية للتلوث والتحضر. يوضح نموذجنا أهمية مشاركة البيانات للجمع بين عدد كبير من البيانات المكانية والبيئية، مما يسمح بتخصيص المناطق ذات الاحتمال الكبير لحدوث Cystoseira كمناسبة لوجودها. يشجع هذا النهج على استخدام أداة النمذجة هذه للتنبؤ بتوزيع Cystoseira ولدعم وتخطيط مبادرات الحفظ والإدارة. تتمثل الخطوة إلى الأمام في تحسين المعلومات المكانية لبيانات غياب الوجود حول مظلات Cystoseira والمتنبئات البيئية من أجل معالجة التقييمات الخاصة بالأنواع.

The process of site selection and spatial planning has received scarce attention in the scientific literature dealing with marine restoration, suggesting the need to better address how spatial planning tools could guide restoration interventions. In this study, for the first time, the consequences of adopting different restoration targets and criteria on spatial restoration prioritization have been assessed at a regional scale, including the consideration of climate changes. We applied the decision-support tool Marxan, widely used in systematic conservation planning on Mediterranean macroalgal forests. The loss of this habitat has been largely documented, with limited evidences of natural recovery. Spatial priorities were identified under six planning scenarios, considering three main restoration targets to reflect the objectives of the EU Biodiversity Strategy for 2030. Results show that the number of suitable sites for restoration is very limited at basin scale, and targets are only achieved when the recovery of 10% of regressing and extinct macroalgal forests is planned. Increasing targets translates into including unsuitable areas for restoration in Marxan solutions, amplifying the risk of ineffective interventions. Our analysis supports macroalgal forests restoration and provides guiding principles and criteria to strengthen the effectiveness of restoration actions across habitats. The constraints in finding suitable areas for restoration are discussed, and recommendations to guide planning to support future restoration interventions are also included.

Bioconstructions such as coralligenous outcrops and maërl beds are typical Mediterranean underwater seascapes. Fine-scale knowledge on the distribution of these sensitive habitats is crucial for their effective management and conservation. In the present study, a thorough review of existing spatial datasets showing the distribution of coralligenous and maërl habitats across the Mediterranean Sea was undertaken, highlighting current gaps in knowledge. Predictive modelling was then carried out, based on environmental predictors, to produce the first continuous maps of these two habitats across the entire basin. These predicted occurrence maps for coralligenous outcrops and maërl beds provide critical information about where the two habitats are most likely to occur. The collated occurrence data and derived distribution model outputs can help addressing the challenge of developing basin-wide spatial plans and to guide cost-effective future surveys and monitoring efforts towards areas that are presently poorly-sampled.

Jellyfish outbreaks in marine coastal areas represent an emergent problem worldwide, with negative consequences on human activities and ecosystem functioning. However, potential positive effects of jellyfish biomass proliferation may be envisaged as a natural source of bioactive compounds of pharmaceutical interest. We investigated the biochemical composition of mature female gonads and lysozyme antibacterial activity of oocytes in the Mediterranean barrel jellyfish Rhizostoma pulmo. Chemical characterization was performed by means of multinuclear and multidimensional NMR spectroscopy. The ovaries of R. pulmo were mainly composed of water (93.7 ± 1.9% of wet weight), with organic matter (OM) and dry weight made respectively of proteins (761.76 ± 25.11 µg mg−1 and 45.7 ± 1.5%), lipids (192.17 ± 10.56 µg mg−1 and 9.6 ± 0.6%), and carbohydrates (59.66 ± 2.72 µg mg−1 and 3.7 ± 0.3%). The aqueous extract of R. pulmo gonads contained free amino acids, organic acids, and derivatives; the lipid extract was composed of triglycerides (TG), polyunsaturated fatty acids (PUFAs), diunsaturated fatty acids (DUFAs), monounsaturated fatty acids (MUFAs), saturated fatty acids (SFAs), and minor components such as sterols and phospholipids. The R. pulmo oocyte lysate exhibited an antibacterial lysozyme-like activity (mean diameter of lysis of 9.33 ± 0.32 mm corresponding to 1.21 mg/mL of hen egg-white lysozyme). The occurrence of defense molecules is a crucial mechanism to grant healthy development of mature eggs and fertilized embryos (and the reproductive success of the species) by preventing marine bacterial overgrowth. As a corollary, these results call for future investigations for an exploitation of R. pulmo biomasses as a resource of bioactive metabolites of biotechnological importance including pharmaceuticals and nutrition.