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The dataset provides data of geographic occurrences of non-native marine macrophytes in the Mediterranean Sea over 200 years. It includes occurrences in their native biogeographical range, along with sea surface temperature data for both the Mediterranean Sea and the native range. Additionally, it provides measures of spread, such as least coastal distances and the number of invaded grid cells, aiding in the calculation of linear spread rates, accumulated, and new invaded areas by non-native macrophytes in the Mediterranean Sea, as outlined in Wesselmann et al., 2024.

Maritime transport is a vital sector for global trade and the world economy. Particularly for islands, there is also an important social dimension of this sector, since island communities strongly rely on it for a connection with the mainland and the transportation of goods and passengers. Furthermore, islands are exceptionally vulnerable to climate change, as the rising sea level and extreme events are expected to induce severe impacts. Such hazards are anticipated to also affect the operations of the maritime transport sector by affecting either the port infrastructure or ships en route. The present study is an effort to better comprehend and assess the future risk of maritime transport disruption in six European islands and archipelagos, and it aims at supporting regional to local policy and decision-making. We employ state-of-the-art regional climate datasets and the widely used impact chain approach to identify the different components that might drive such risks. Larger islands (e.g., Corsica, Cyprus and Crete) are found to be more resilient to the impacts of climate change on maritime operations. Our findings also highlight the importance of adopting a low-emission pathway, since this will keep the risk of maritime transport disruption similar to present levels or even slightly decreased for some islands because of an enhanced adaptation capacity and advantageous demographic changes.

In this work, we explore the performance of a statistical forecasting system for marine-litter concentration in the Mediterranean Sea. In particular, we assess the potential skills of a system based on the analogues method. The system uses a historical database of marine-litter concentration simulated by a high-resolution realistic model and is trained to identify meteorological situations in the past that are similar to the forecasted ones. Then, the corresponding marine-litter concentrations of the past analogue days are used to construct the marine-litter concentration forecast. Due to the scarcity of observations, the forecasting system has been validated against a synthetic reality (i.e., the outputs from a marine-litter-modeling system). Different approaches have been tested to refine the system, and the results show that using integral definitions for the similarity function, based on the history of the meteorological situation, improves the system performance. We also find that the system accuracy depends on the domain of application being better for larger regions. Also, the method performs well in capturing the spatial patterns but performs worse in capturing the temporal variability, especially the extreme values. Despite the inherent limitations of using a synthetic reality to validate the system, the results are promising, and the approach has potential to become a suitable cost-effective forecasting method for marine-litter concentration.

[EN] A coastal sea level reconstruction based on tide gauge observations is developed and applied to the western basin of the Mediterranean sea. The reconstructions are carried out in four frequency bands and are based on an optimal interpolation method in which the correlation between tide gauge data and all coastal points has been determined from the outputs of a numerical model. The reconstructions for frequencies lower than 1 month use monthly observations from the Permanent Service for Mean Sea Level (PSMSL) database and cover the period from 1884 to 2019. For the reconstruction of higher frequencies, hourly observations from the Global Extreme Sea Level Analysis (GESLA-2) dataset are used and cover from 1980 to 2015. Total sea level is retrieved with high accuracy from the merging of the different frequency bands. Results of a cross-validation test show that independent tide gauge series are highly correlated with the reconstructions. Moreover, they correlate significantly better with the reconstructions than with altimetry data in all frequency bands, and therefore the reconstruction represents a valuable contribution to the attempts of recovering coastal sea level. The obtained reconstructions allow us to characterize the coastal sea level variability, estimate coastal sea level trends along the entire coastline, and examine the correlation between western Mediterranean coastal sea level and the main North Atlantic climate indices. The limitations and applicability of the method to other regions are also discussed.

The dataset provides data on relative growth rate per day (RGR d-1), survival %, and maximum quantum yield (MQY) of six marine macrophyte species (Cymodocea nodosa, Posidonia oceanica, Cystoseira compressa, Caulerpa prolifera, Padina pavonica and Halimeda tuna) collected from southwest Mallorca and cultivated in 10 different seawater temperature treatments.