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Prediction of marine species distribution from presence–absence acoustic data: comparing the fitting efficiency and the predictive capacity of conventional and novel distribution models

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Abstract

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 (ROC), Kappa statistics, correlation and confusion matrices are used. Boosted Regression Trees (BRT) and Associative Neural Networks (ASNN), which are both within the machine learning category, outperformed the other modelling approaches tested.

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  1. Department of Biology, University of Crete, Vassilika Vouton, P.O. Box 2208, 71 409, Heraklion, Crete, Greece

    A. Palialexis, I. Karakassis & K. Lika

  2. Department of Marine Sciences, University of the Aegean, University Hill, 81 100, Mytilini, Lesvos, Greece

    S. Georgakarakos

  3. Marine GIS Lab, Hellenic Centre for Marine Research, P.O. Box 2214, 71 003, Heraklion, Crete, Greece

    A. Palialexis & V. D. Valavanis

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  1. A. Palialexis
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Guest editors: Graham J. Pierce, Vasilis D. Valavanis, Begoña M. Santos & Julio M. Portela / Marine Ecosystems and Sustainability

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Palialexis, A., Georgakarakos, S., Karakassis, I. et al. Prediction of marine species distribution from presence–absence acoustic data: comparing the fitting efficiency and the predictive capacity of conventional and novel distribution models. Hydrobiologia 670, 241–266 (2011). https://doi.org/10.1007/s10750-011-0673-9

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