Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea.

Although the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine-learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central-eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiops truncatus, and Risso's dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D-chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context.

A Digital Twin modelling framework for the assessment of seagrass Nature Based Solutions against storm surges.

In this paper we demonstrate a novel framework for assessing nature-based solutions (NBSs) in coastal zones using a new suite of numerical models that provide a virtual "replica" of the natural environment. We design experiments that use a Digital Twin strategy to establish the wave, sea level and current attenuation due to seagrass NBSs. This Digital Twin modelling framework allows us to answer "what if" scenario questions such as: (i) are indigenous seagrass meadows able to reduce the energy of storm surges, and if so how? (ii) what are the best seagrass types and their landscaping for optimal wave and current attenuation? An important result of the study is to show that the landscaping of seagrasses is an important design choice and that seagrass does not directly attenuate the sea level but the current amplitudes. This framework reveals the link between seagrass NBS and the components of the disruptive potential of storm surges (waves and sea level) and opens up new avenues for future studies.