## 4 Discussion
All species and genera were classified with higher rates than the neural network trained in the previous study [21]. Importantly, the lowest correct classification rates achieved by the genus- and species-level networks were 95% and 91% respectively. Despite the hierarchical ENN containing classifiers trained for a more specific task, it produced a slightly lower overall classification rate. This contrasted with our expectation that classification rates would be increased by including classifiers in the ENN that were trained to classify calls belonging to a single genus, rather than classifiers trained to classify calls from all species. The initial weights and configuration parameters (learning rate, momentum constant, architecture etc) all predetermine the resulting classification abilities of a neural network [19]. We speculate that even with the extensive amount of network training we carried out, the optimal configuration was not obtained. Given the number of possible configurations of the neural networks, and the amount of time to train each network, we could not test all possible network configurations. The ENNs we trained used 21 neural networks and fortunately always provided a majority vote for a particular target. However, with 21 neural networks, it is possible to obtain a result where an input is classified as three separate species with equal votes. A simple work around for such a case would be to output the result as ambiguous. However, using a prime number of neural networks will always prevent a tie vote. Other studies that have used classification techniques such as neural networks or support vector machines have shown similar promise in other areas of biology. For example, hidden Markov models have been used to classify bird song elements from zebra finches and indigo buntings [31]. The task of bird song classification has similar complexity to bat call classification in that it requires a complex classifier to discriminate small differences between classes. The Markov model was able to classifymore than 90% of syllables from both bird species correctly, and in many cases, correct classification exceeded 95%. While the results from this study show promise for automated classification of bat calls, there are several caveats associated with this system. The calls in this study were all high quality recordings with relatively low amounts of background noise. In practice, field recording often produces many lower quality recordings that would confound the automated parameter extraction of this study and yield poor results. However, experienced researchers are unable to classify such poor quality recordings any better than automated algorithms [9]. This system is also biased towards high intensity calls, which are more likely to be recorded with higher quality than low intensity calls. Relatively quiet calls are also often recorded with underestimated bandwidth [23]. The difference in call intensity can be substantial; _P. auritus_ produces echolocation calls as quiet as 76 dB peSPL at 10 cm [32], whereas _Eptesicus bottae_ can produce calls with an intensity of 133 dB peSPL at 10 cm [33]. This highlights the system's reliance on high quality recordings to accurately identify bat species. Systems have been developed that can better isolate calls from high-noise recordings [15]. However, information lost from calls due to atmospheric attenuation will remain lost to the researcher no matter what the recording equipment or field conditions. The system used in this study has only been applied to calls from the United Kingdom to date, and other species assemblages may present calls that are inseparable by this system due to similarity. Furthermore, the number of species in this study is relatively low and adding more species would significantly increase the complexity of the classification, and therefore impact the classification rates. The adoption of a quantitative approach to the identification of echolocation calls offers consistent and repeatable classification of unknown calls.