Thesis project concerning the development of predictive regression models for the estimation of cetacean abundance in the Gulf of Taranto using climatic and morphological variables. Thesis project developed during the internship at @ CNR-STIIMA under the supervision of Dr. Rosalia Maglietta and Dr. Giovanni Dimauro.
The thesis carried out aims to identify if and how the geographical formation of the territory, anthropogenic pressure and climate change impact on the distribution and migration of cetaceans. Starting from a set of data collected by the research association Jonian Dolphin Conservation on the sightings of Stenella Striata (Stenella Coeruleoalba), Bottlenose dolphin (Tursiops Truncatus) and Grampo (Grampus Griseus), which took place from 2009 to 2019 in the Gulf of Taranto, built predictive regression models to estimate cetacean abundance. To this end, the data on sightings are accompanied by a set of climatic and geographical variables.
The first phase of the work was focused on the study and development of data preparation techniques, to ensure a good initial quality of the data under study.
Subsequently, a data analysis was conducted, using univariate and multivariate descriptive statistics techniques. In particular, the techniques applied were boxplots, T-tests, correlation coefficients and matrices, trend analysis and PCA.
Subsequently, regression models able to estimate the abundance of cetaceans were studied and implemented. The algorithms used were Random Forest and LSBoost, whose performances were compared with the most modern deep learning techniques. The regression models developed have shown good performance in predicting the abundance of the less populous species, in particular Grampus Griseus and Tursiops Truncatus. In the case of the Stenella Coeruleoalba, in which the number of observable individuals is very variable, the performances can be greatly improved and therefore will be the subject of future studies.
This software is released under the MIT License. See the LICENSE file for details.