caa a22 4500 445893117 CHVBK 20180317145623.0 cr unu---uuuuu 170323e20110701xx s 000 0 eng 10.1007/s10750-011-0676-6 doi (NATIONALLICENCE)springer-10.1007/s10750-011-0676-6 Fish distribution predictions from different points of view: comparing associative neural networks, geostatistics and regression models [Elektronische Daten] [A. Palialexis, S. Georgakarakos, I. Karakassis, K. Lika, V. Valavanis] 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 (ANNs) 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. Springer Science+Business Media B.V., 2011 Species distribution predictions nationallicence Habitat modelling nationallicence Models comparison nationallicence Geostatistics nationallicence Palialexis A. Department of Biology, University of Crete, Vassilika Vouton, P.O. Box 2208, 71 409, Heraklion, Crete, Greece aut Georgakarakos S. Department of Marine Sciences, University of the Aegean, University Hill, 81 100, Mytilini, Lesvos, Greece aut Karakassis I. Department of Biology, University of Crete, Vassilika Vouton, P.O. Box 2208, 71 409, Heraklion, Crete, Greece aut Lika K. Department of Biology, University of Crete, Vassilika Vouton, P.O. Box 2208, 71 409, Heraklion, Crete, Greece aut Valavanis V. Marine GIS Lab, Hellenic Centre for Marine Research, Thalassocosmos, 71 003, Heraklion, Crete, Greece aut Hydrobiologia Springer Netherlands 670/1(2011-07-01), 165-188 0018-8158 670:1<165 2011 670 10750 https://doi.org/10.1007/s10750-011-0676-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10750-011-0676-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Palialexis A. Department of Biology, University of Crete, Vassilika Vouton, P.O. Box 2208, 71 409, Heraklion, Crete, Greece aut NATIONALLICENCE 700 1- Georgakarakos S. Department of Marine Sciences, University of the Aegean, University Hill, 81 100, Mytilini, Lesvos, Greece aut NATIONALLICENCE 700 1- Karakassis I. Department of Biology, University of Crete, Vassilika Vouton, P.O. Box 2208, 71 409, Heraklion, Crete, Greece aut NATIONALLICENCE 700 1- Lika K. Department of Biology, University of Crete, Vassilika Vouton, P.O. Box 2208, 71 409, Heraklion, Crete, Greece aut NATIONALLICENCE 700 1- Valavanis V. Marine GIS Lab, Hellenic Centre for Marine Research, Thalassocosmos, 71 003, Heraklion, Crete, Greece aut NATIONALLICENCE 773 0- Hydrobiologia Springer Netherlands 670/1(2011-07-01), 165-188 0018-8158 670:1<165 2011 670 10750 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer