, Volume 670, Issue 1, pp 165–188

Fish distribution predictions from different points of view: comparing associative neural networks, geostatistics and regression models


    • Department of BiologyUniversity of Crete
    • Marine GIS Lab, Hellenic Centre for Marine Research
  • S. Georgakarakos
    • Department of Marine SciencesUniversity of the Aegean
  • I. Karakassis
    • Department of BiologyUniversity of Crete
  • K. Lika
    • Department of BiologyUniversity of Crete
  • V. D. Valavanis
    • Marine GIS Lab, Hellenic Centre for Marine Research

DOI: 10.1007/s10750-011-0676-6

Cite this article as:
Palialexis, A., Georgakarakos, S., Karakassis, I. et al. Hydrobiologia (2011) 670: 165. doi:10.1007/s10750-011-0676-6


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.


Species distribution predictions Habitat modelling Models comparison Geostatistics

Copyright information

© Springer Science+Business Media B.V. 2011