## 3 Results  
All parameters measured (Tables 2 and 3), except those from calls from _Plecotus auritus_, agreed with those already published for the same dataset [18]. The duration of _P. auritus_ calls was approximately half that reported previously, and this may account for the higher center and end frequency measurements of this study. Discriminant function analysis classified calls to genus with an overall accuracy of 81% (Table 4). Calls of _Rhinolophus_ were all correctly identified, and more than 90% of the calls from _Pipistrellus_, _Myotis_, and _Barbastella_ were identified correctly. Discriminant function analysis identified correctly 71% of the calls from _Plecotus_, which were mainly confused with _Barbastella_ calls (21%). Calls from _Nyectalus_ were identified with the lowest accuracy (58%) and were confused predominantly with calls from _Eptiesicus_ (37%). Discriminant function analysis correctly classified 73% of the calls to species (Table 5). Calls from both species of _Rhinolophus_ were all classified correctly, and calls from _Barbastella_ were classified with more than 90% accuracy. Calls from _M. mystacinus_ were least accurately classified (42%), and were mainly misclassified as being from other _Myotis_ species (28% of calls were confused as _M. brandtii_, 8% as _M. daubentonii_, and 8% as _M. bechsteinii_). Calls from _N. leisleri_ were also poorly classified (45%) and were misclassified mostly as _E. serotinus_ (33%). Just 56% of calls from _E. serotinus_ were classified correctly, and were mainly misclassified as calls of _N. leisleri_. We assessed the relative importance of each parameter to the DFA classification of calls using Wilks' lambda values (Table 6). Parameters that score a lambda close to zero indicate a greater importance for species differentiation, whereas those that have a lambda close to one are less important. The most useful parameters for discriminanting between different species were the five base parameters, followed by the standardised bandwidth measurement, call type classification, and quarterly amplitude measurements. The spectrogram rate of change estimate was the least important parameter. A Kruskal-Wallis test was applied to each parameter to test if they were significantly different between species. After corrections for multiple comparisons, all parameters had a \(\chi^{2}\) value much greater than the critical value. \begin{table}
\begin{tabular}{l l} Parameter & Values \\ \hline Learning Rate & 0.01, 0.02, 0.05, 0.08, 0.1, 0.2, 0.3, 0.4, 0.5 \\ Momentum & 0.0, 0.1, 0.2, 0.3, 0.4, 0.5 \\ Hidden Layers & 1, 2 \\ Neurons per hidden layer & 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30 \\ \end{tabular}
\end{table} 
Table 1: Ranges of values for neural network configuration. The top performing 50 permutations of the parameters were refined to increase the probability of finding an optimal neural network by retraining with randomly initialised weights 30 times. +++ ==ERROR: No output for this page== \begin{table}
\begin{tabular}{l c c c c c c c c c c} Species & Duration & Fstart & Fend & Fender & Fender & Emax \\ \hline _Barbasstella barbasstellus_ & 3.17\(\pm\)0.77 & 39.19\(\pm\)0.85 & 28.6\(\pm\)0.79 & 35.84\(\pm\)0.72 & 35.63\(\pm\)0.74 \\  & (0.43) & (0.14) & (0.15) & (0.12) & (0.12) \\ _Eptesicus serotinus_ & 7.44\(\pm\)1.25 & 63.26\(\pm\)0.8 & 28.18\(\pm\)0.92 & 35.03\(\pm\)0.88 & 33.88\(\pm\)0.82 \\  & (0.46) & (0.1) & (0.17) & (0.15) & (0.14) \\ _Myotis bechsteinii_ & 1.9\(\pm\)0.28 & 115.25\(\pm\)1.13 & 37.95\(\pm\)0.45 & 74.24\(\pm\)0.67 & 71.52\(\pm\)0.58 \\  & (0.21) & (0.11) & (0.07) & (0.08) & (0.07) \\ _Myotis brandtii_ & 3.99\(\pm\)0.46 & 106.9\(\pm\)0.83 & 33.16\(\pm\)0.93 & 61.53\(\pm\)0.95 & 55.16\(\pm\)0.84 \\  & (0.23) & (0.08) & (0.16) & (0.12) & (0.11) \\ _Myotis daubentonii_ & 2.44\(\pm\)0.52 & 87.53\(\pm\)0.47 & 33.04\(\pm\)0.54 & 57.45\(\pm\)0.45 & 55.57\(\pm\)0.59 \\  & (0.33) & (0.05) & (0.09) & (0.06) & (0.08) \\ \end{tabular}
\end{table} 
Table 3: Base parameters measured from automatically extracted echolocation calls.