Below the surface: ecological dynamics behind the observed spatial distribution of cetaceans and sea turtles in the Mediterranean Sea

“Below the surface” of the Mediterranean Sea lies a complex interplay of ecological and oceanographic processes that shape the distribution of its most emblematic species: cetaceans and sea turtles. Despite their ecological importance and conservation relevance, our understanding of where, when, and why these animals occur remains incomplete. Their high mobility, ecological plasticity, and the difficulty of repeatedly observing offshore habitats have long limited basin-scale ecological assessments. This PhD thesis investigates the ecological dynamics behind the observed spatial distribution of Mediterranean marine megafauna by integrating long-term, standardised ferry-based observations from the Fixed Line Transect Mediterranean Network (FLT Med Net), collected continuously since 2007, and harmonised within the LIFE Conceptu Maris project (LIFE20 NAT/IT/001371). This unique dataset, encompassing more than fifteen years of consistent ferry-based surveys across multiple seasons and regions, provides an unprecedented basis to explore how environmental variability drives spatial and temporal changes in species distribution. At the methodological level, the thesis develops and refines a robust modelling framework for dynamic marine environments through global syntheses and large-scale benchmarking of Species Distribution Models (SDMs) applied to the Mediterranean context. The approach is conceived primarily as a tool to answer ecological questions in a statistically rigorous way, ensuring that interpretations of species–environment relationships are both reliable and transferable. These analyses show how modelling choices influence ecological inference and highlight the importance of independent validation. The resulting workflow establishes a reproducible framework directly applicable to other taxa, datasets, and research programs. At the ecological level, building on the methodological robustness developed, the thesis leverages the exceptional temporal and spatial coverage of the FLT Med Net dataset to investigate seasonal and ontogenetic dynamics of two priority species for Mediterranean conservation, the loggerhead turtle (Caretta caretta) and the bottlenose dolphin (Tursiops truncatus), which exhibit contrasting ecological strategies. The results revealed clear and recurring seasonal patterns and life-stage differences in habitat use, illustrating how species adaptively respond to oceanographic variability and highlighting the critical role of temporal resolution in understanding marine spatial ecology. By linking observed patterns to environmental processes, the work advances a dynamic, process-based perspective on species distribution across the Mediterranean. At the applied level, the validated and methodologically robust ecological results are then integrated with spatial risk assessments of key anthropogenic pressures, namely marine litter and maritime traffic. Grounded in the ecological findings, the thesis refines existing analytical approaches, still rarely implemented, both globally and within the Mediterranean context, and applies them to both loggerhead turtles and bottlenose dolphins, to identify areas of heightened exposure to these threats. These spatially explicit results demonstrate how ecological understanding can directly inform conservation, supporting evidence-based management and marine spatial planning under EU frameworks such as the Habitats Directive and the Marine Strategy Framework Directive, within which both species are recognised as conservation priorities. Overall, the thesis strengthens the ecological basis for understanding the processes that govern where, when, and why cetaceans and sea turtles occur. By integrating an exceptional long-term dataset with rigorous modelling approaches, it advances our capacity to link ecological mechanisms with observed spatial patterns, in a framework where conservation emerges as a natural and informed outcome of ecological knowledge.