## 4 Discussion
While both studies used the same dataset, this study extracted the parameters from the calls using a different automated method, and this likely accounts for the disparity. Any extraction method is likely to contain biases that may cause parameters measured from different species or genera to overlap, resulting in already problematic genera or species becoming more so. Alternatively, features that may help to separate cryptic (sibling) species may become more apparent in the dataset when using alternate algorithms to extract parameters, and the value of these features may be enhanced by the use of more complex classification algorithms [17, 27]. The correct classification rates of this study exceed that of other studies that have attempted to classify bat echolocation calls. A study that used multivariate analysis to classify calls of bats in southwest England was able to reliably identify the species of bat in 83% of 5519 bat passes [11] while a study on Swiss bats classified a wide range of species with varying degrees of accuracy using DFA and a synergetic pattern recognition algorithm [28]. Classification rates achieved by the Swiss study for species in common with the UK were 65% using DFA and 75% using synergetic pattern recognition (after filtering, with random selection). Previous research [21] using the same library of calls analysed in this study was able to achieve 79% correct classification rate using DFA and 87% using a neural network. Despite the promising results of this study, the confusion between calls of species in the _Myovtis_ genus is well known [21, 28] and remains troublesome. This illustrates the difficulty of the task of separating calls having many dimensions of variability, and the perhaps unavoidable problem that some call recordings lack sufficient information to enable correct classification. Of the top five parameters measured, the most important for the separation of bats to genus in this study was duration, which was the least important parameter in the previous study [21] for classification to both genus and species. Previous Wilks' lambda values [21] were all below 0.4, which suggests that further parameters would be beneficial in separation of the classes. In this study, genus classification was done predominantly by duration, start frequency, end frequency and center frequency. Species classification was achieved mainly with the five base parameters. However, call type and the four quarter parameters also assisted. The rate of change parameter provided little assistance classifying species or genera. Our SVMs and ENNs outperformed hierarchical networks [21] and showed similar identification rates for non-_Myotis_ species to those obtained using speech recognition algorithms [15]; the only other published study to use machine learning techniques to classify calls of bats. The SVM classifier achieved higher classification rates for most species and genera. The minimum classification rate achieved by the SVM at genus and species level was higher when compared with the neural networks [21] and overall, the SVMs performed marginally better for genus classification (~1%) and species classification (2%). Support vector machines will always find the global minimum of the configuration [29] unlike the other classifiers used in this study, since the classification algorithm is absent of local minima [30]. Application of the minimised function onto unseen data may be less effective than other techniques however [29], which is likely to explain why the ENNs performed better. Specifically, the SVM confused around 13% of calls from _E. serotinus_ with calls from both species of _Nyctalus_, and about 25% of calls from _Nyctalus_ species with _E. serotinus_. Up to 14% of calls from species within the _Myotis_ genus were also confused with other _Myotis_ species, or remained unclassified. ENNs trained to classify calls to genus and species performed well with both achieving correct classification rates exceeding 97%.