E-Locus - Institutional Repository of the University of Crete

Home Collections Type of Work Doctoral theses

Doctoral theses

Search command : Author="Στεφανίδης" And Author="Κωνσταντίνος"

Current Record: 2179 of 2443

[Add to Basket]

Identifier

000372468

Title

Μοντέλα κατανομής ιχθυομάζας πελαγικών ειδών : αξιολόγηση των διαδεδομένων τεχνικών, εισαγωγή νέων, έλεγχος της αποτελεσματικότητας τους σε αλιευτικά δεδομένα, διερεύνηση σχέσεων ειδών-περιβάλλοντος

Alternative Title

Smalll pelagic species distribution models: evaluating of well established modelling techniques, introducing new innovative approaches, assessing their efficiency to fisheries data, and identifying species-environment relationships

Author

Παλιαλέξης, Ανδρέας

Thesis advisor

Καρακάσης, Ιωάννης

Abstract

Accurate prediction of species distributions based on sampling and environmental data is essential for further scientific analysis, such as stock assessment, detection of abundance fluctuation due to climate change or overexploitation, and to underpin management and legislation processes. The evolution of computer science and statistics has allowed the development of sophisticated and well-established modelling techniques as well as a variety of promising innovative approaches for modelling species distribution. The appropriate selection of modelling approach is crucial to the quality of predictions about species distribution. In this study, modelling techniques based on different approaches are compared and evaluated in relation to their predictive performance, utilizing fish density acoustic data. Generalized additive models and mixed models amongst the regression models, associative neural networks and artificial neural networks ensemble amongst the artificial neural networks and ordinary Kriging amongst the geostatistical techniques are applied and evaluated. A verification dataset is used for estimating the predictive performance of these models. A combination of outputs from the different models is applied for prediction optimization to exploit the ability of each model to explain certain aspects of variation in species acoustic density. Neural networks and especially ANNs appear to provide more accurate results in fitting the training dataset while generalized additive models appear more flexible in predicting the verification dataset. The efficiency of each technique in relation to certain sampling and output strategies is also discussed. The accurate representation of species distribution derived from sampled data is essential for management purposes and to underpin population modelling. Additionally, the prediction of species distribution for an expanded area, beyond the sampling area can reduce sampling costs. Here, several well-established and recently developed habitat modelling techniques are investigated in order to identify the most suitable approach to use with presence–absence acoustic data. The fitting efficiency of the modelling techniques are initially tested on the training dataset while their predictive capacity is evaluated using a verification set. For the comparison among models, Receiver Operating Characteristics, Kappa statistics, correlation and confusion matrices are used. Boosted Regression Trees and Associative Neural Networks, which are both within the machine learning category, outperformed the other modelling approaches tested.

Language

Greek

Subject

Habitat maps

Habitat modelling

Models comparison

Spatial autocorrelation

Species distribution predictions

Μέθοδοι σύγκρισης μοντέλων

Μικρά πελαγικά είδη

Μοντελοποίηση ενδιαιτημάτων

Χάρτες ενδιαιτημάτων