## 2 Methods  
We used the recordings from a previously published study [21] as a source of calls to train and test the SVMs and ENNs; methods used for the collection, recording, digitization and selection of calls can be found in that paper. From these recordings we obtained a library of 713 search-phase echolocation calls from 14 species of bat resident in Britain (see Tables 2 and 4 for species and sample sizes, respectively). Only one call was used from each individual bat recorded. We used an automated algorithm implemented in Matlab (v7, Mathworks, Natick, MA) to extract calls from the background noise and measure parameters. Prior to the extraction of a call, a 10 th order Butterworth high-pass filter (cut-off frequency of 15 kHz) was applied to improve the signal-noise ratio. Extraction of a call from a recording began by finding the approximate centre of the echolocation call by applying a 100:1 moving average filter to the absolute value of the signal and finding the position of highest amplitude within the recording. The algorithm then iterated forward through the recording, calculating power spectra containing 256 data points (representing 5 us). The data were then zero-padded to 1024 points and a power spectrum of the same size was calculated. The algorithm iterated through the recording in steps of 6 points, giving 98% overlap between successive power spectra (final resolution 78 Hz). The power spectrum was then smoothed using a 20:1 moving average filter and normalised between 0 and 1. The algorithm would stop iterating through the recording if one of two criteria were met: 1) the frequency with highest energy of the power spectrum was more than 8 kHz different from that of the previous iteration, or 2) the average signal to noise ratio of the normalised power spectrum exceeded a value of 0.01 (Equation 1). Both criteria were indicative of the algorithm reaching the end of a call and moving into the background noise. Once the algorithm found the end of the call, it repeated the search but this time moving from the point with highest energy in the call back towards its beginning. \[T=\frac{\sum\limits_{i=0}^{k-1}\left|S_{i}-S_{i+1}\right|}{k} \tag{1}\]  
Equation 1: Calculation of the hill climbing threshold parameter for the noise estimation call isolation algorithm. The threshold parameter (T) is obtained dynamically for each given signal. (S = absolute value of the power spectrum data points, k = number of samples in the power spectrum). Using deterministic algorithms, twelve parameters were extracted automatically from each call. Five of these parameters: call duration (ms), frequency at the start of the call (kHz), frequency at the end of the call (kHz), frequency at half the duration of the call (kHz) and frequency at the maximum energy of the call (kHz) have been used in previous studies [11, 21, 34], and are referred to here as the 'base' parameters. Once isolated from the background signal, the call duration was measured by dividing the number of samples in the isolated call by the sample rate. Frequency at the start of the extracted call was obtained by taking a 1024-point power spectrum of the first 256 \(\upmu\)s of the call (resolution 3.9 kHz) and noting the frequency with most energy; the signal was zero padded to 1024 samples before the FFT was applied. This procedure was repeated for the centre and final 256 \(\upmu\)s of the call to obtain the frequency at half the duration of the call and the frequency at the end of the call. The frequency with maximum energy was obtained by applying a FFT to the entire isolated call. As calls varied greatly in length, and therefore number of samples, each call was first zero padded to increase its length to the nearest power of two before the FFT was applied. The resulting power spectrum was then reduced to 1024-samples using a moving average filter and the frequency at the maximum energy of the call measured. The sixth call parameter estimated the rate of change (i.e.