Understanding population status is often challenged by scant abundance and distribution data for many threatened species both in marine and terrestrial environments. Occurrence records are often scarce and opportunistic, and fieldwork to retrieve additional data is expensive and prone to failure, particularly if the species is highly mobile. Integrating various data sources becomes crucial to developing species distribution models for informed sampling and conservation purposes.  Dolphins, sea turtles and other cetaceans are currently monitored in the Italian Mediterranean. ISPRA, Sapienza and other scientific institutions implemented several rigorous survey designs to collect presence data on these species. However, due to the high cost, these designs explore only a specific portion of the Italian Mediterranean, limiting the generalization of the results. Other marine species, such as the white shark, are rare but persistent inhabitants of the Mediterranean Sea. Information on the species' presence is almost never connected to rigorous surveys. Here, we will explore some examples where occasional sightings ("citizen science") of a species are included in the species distribution model. Two issues are presented and addressed: 1- the data records only the presence (no information on the absence is available) 2- the occasional sightings are not connected to a known observation mechanism, and this, too, is a serious issue!

Earthquakes and tectonic fault slip are among the most hazardous and unpredictable natural phenomena. Fluids play a key role in tectonic faulting and recent research suggests that fluids are central in both human induced seismicity and the mode of fault slip, ranging from episodic tremor and slip to slow earthquakes. However, the lack of accessibility to earthquake faults and the complexity of physical processes has limited our ability to develop holistic models for fault zone behavior. Geophysical observations have the potential for illuminating precursors to failure for the spectrum of tectonic faulting, however we lack key laboratory data to connect these observations with predictive, physics-based models. In the past 5 years we have developed two prototypes state-of-the-art rock deformation apparatuses that allows us to illuminate the details of fault behavior under a range of boundary conditions. On the ground of theoretical consideration we have been able to reproduce the full spectrum of fault slip behavior, from aseismic creep, slow earthquakes to elasto-dynamic rupture. We find systematic variation of elastic wave propagation and acoustic precursors to failure. We train machine learning models to successfully predict lab earthquakes.