Combined Color Semantics and Deep Learning for the Automatic Detection of Dolphin Dorsal Fins

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.

Behavioral Pattern of Risso’s Dolphin (Grampus griseus) in the Gulf of Taranto (Northern Ionian Sea, Central-Eastern Mediterranean Sea)

Relatively scant information is available on the Risso’s dolphin in comparison to the other species regularly present in the Mediterranean Sea. Recently, its conservation status has been updated to Endangered by the International Union for Conservation of Nature (IUCN) in this Sea. Therefore, the need to increase information on its biology and ecology is even more urgent. This study reports the first preliminary information on the behavioral traits of the species occurring in the Gulf of Taranto (Northern Ionian Sea). Data on predominant behavioral activity states and on a set of group composition variables (group formation, cruising speed, dive duration and interaction between individuals) were collected from April 2019 to September 2021, applying the focal-group protocol with instantaneous scan sampling. Group size, depth and group composition variables were compared between activity states. Results highlight that both the group size and the several variables considered varied significantly depending on activity state. The group size was significantly smaller during feeding than resting and traveling and a characterization in terms of group formation, cruise speed, dive duration and interaction between animals is provided for the different activity states. Moreover, a list of behavioral events which occurred, as well as their relative frequency of distribution among activity states, is reported. Finally, details on the sympatric occurrences between Risso’s and striped dolphins, as well as the repetitive interaction observed between adult individuals and plastic bags floating on the sea surface, are reported and discussed.

A Novel Approach for the Automatic Estimation of the Ciliated Cell Beating Frequency

The qualitative and quantitative evaluation of nasal epithelial cells is interesting in chronic infectious and inflammatory pathologies of the nose and sinuses. Among the cells of the population of the nasal mucosa, ciliated cells are particularly important. In fact, the observation of these cells is essential to investigate primary ciliary dyskinesia, a rare and severe disease associated with other serious diseases such as respiratory diseases, situs inversus, heart disease, and male infertility. Biopsy or brushing of the ciliary mucosa and assessment of ciliary function through measurements of the Ciliary Beating Frequency (CBF) are usually required to facilitate diagnosis. Therefore, low-cost and easy-to-use technologies devoted to measuring the ciliary beating frequency are desirable. We have considered related works in this field and noticed that up to date an actually usable system is not available to measure and monitor CBF. Moreover, performing this operation manually is practically unfeasible or demanding. For this reason, we designed BeatCilia, a low cost and easy-to-use system, based on image processing techniques, with the aim of automatically measuring CBF. This system performs cell Region of Interest (RoI) detection basing on dense optical flow computation of cell body masking, focusing on the cilia movement and taking advantage of the structural characteristics of the ciliated cell and CBF estimation by applying a fast Fourier transform to extract the frequency with the peak amplitude. The experimental results show that it offers a reliable and fast CBF estimation method and can efficiently run on a consumer-grade smartphone. It can support rhinocytologists during cell observation, significantly reducing their efforts.