Recording ultrasonic echolocation calls of bats using bat-detectors is often used for wide-scale monitoring in studies on bat management and conservation. In Europe, the most important legal instrument for bat conservation is the Habitat Directive (43/92/EEC), which defines various levels of species (and habitat) protection for different bat species and/or genera. Thus for most management needs, the usefulness of bat-monitoring techniques depends on the possibility to determine to species/genus. We compared the discrimination performances of 4 statistical methods applied to identify bat species from their ultrasonic echolocation calls. In 3 different areas of northern Italy, we made recordings of 20 species of bat (60% of those occurring in Italy), 17 from the family Vespertilionidae and 3 from Rhinolophidae. Calls of bats identified to species level from morphological and genetic characters were time-expanded and recorded on release. We measured 7 variables from each call, and we developed classification models through both conventional tests (multiple discriminant analysis and cluster analysis) that were based on a classical statistical approach, and through 2 nonconventional classifiers (classification and regression trees, and neural networks) that relied on generalization and fuzzy reasoning. We compared the performance of the 4 techniques using the percentage of cases classified correctly in 5 classification trials at various taxonomic levels that were characterized by an increasingly difficult identification task: (1) family level (Rhinolophidae vs. Vespertilionidae), (2) species level within genus Rhinolophus, (3) genus level within Vespertilionidae, (4) species level within genus Myotis, and (5) all species. Multiple discriminant function analysis (DFA) correctly classified marginally more cases than artificial neural networks (ANN; 74–100% against 64–100%), especially at the species level (trial 4, species of genus Myotis; trial 5, all species). Both these techniques performed better than cluster analysis or classification and regression trees, the latter reaching only 56 and 41% in Myotis species and all species trials. Artificial neural networks do not yet seem to offer a major advantage over conventional multivariate methods (e.g., DFA) for identifying bat species from their ultrasonic echolocation calls.
IDENTIFYING BATS FROM TIME-EXPANDED RECORDINGS OF SEARCH CALLS: COMPARING CLASSIFICATION METHODS
PREATONI, DAMIANO;NODARI, MOSE';TOSI, GUIDO;WAUTERS, LUCAS ARMAND;MARTINOLI, ADRIANO
2005-01-01
Abstract
Recording ultrasonic echolocation calls of bats using bat-detectors is often used for wide-scale monitoring in studies on bat management and conservation. In Europe, the most important legal instrument for bat conservation is the Habitat Directive (43/92/EEC), which defines various levels of species (and habitat) protection for different bat species and/or genera. Thus for most management needs, the usefulness of bat-monitoring techniques depends on the possibility to determine to species/genus. We compared the discrimination performances of 4 statistical methods applied to identify bat species from their ultrasonic echolocation calls. In 3 different areas of northern Italy, we made recordings of 20 species of bat (60% of those occurring in Italy), 17 from the family Vespertilionidae and 3 from Rhinolophidae. Calls of bats identified to species level from morphological and genetic characters were time-expanded and recorded on release. We measured 7 variables from each call, and we developed classification models through both conventional tests (multiple discriminant analysis and cluster analysis) that were based on a classical statistical approach, and through 2 nonconventional classifiers (classification and regression trees, and neural networks) that relied on generalization and fuzzy reasoning. We compared the performance of the 4 techniques using the percentage of cases classified correctly in 5 classification trials at various taxonomic levels that were characterized by an increasingly difficult identification task: (1) family level (Rhinolophidae vs. Vespertilionidae), (2) species level within genus Rhinolophus, (3) genus level within Vespertilionidae, (4) species level within genus Myotis, and (5) all species. Multiple discriminant function analysis (DFA) correctly classified marginally more cases than artificial neural networks (ANN; 74–100% against 64–100%), especially at the species level (trial 4, species of genus Myotis; trial 5, all species). Both these techniques performed better than cluster analysis or classification and regression trees, the latter reaching only 56 and 41% in Myotis species and all species trials. Artificial neural networks do not yet seem to offer a major advantage over conventional multivariate methods (e.g., DFA) for identifying bat species from their ultrasonic echolocation calls.
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