Echolocating bats hunt prey on the wing under conditions of poor lighting by emission of loud calls and subsequent auditory processing of weak returning echoes. To do so, they need adequate echo-to-noise ratios (ENRs) to detect and distinguish target echoes from masking noise. Early obstacle avoidance experiments report high resilience to masking in free-flying bats, but whether this is due to spectral or spatiotemporal release from masking, advanced auditory signal detection or an increase in call amplitude (Lombard effect) remains unresolved. We hypothesized that bats with no spectral, spatial or temporal release from masking noise, defend a certain ENR via a Lombard effect. We trained four bats (Myotis daubentonii) to approach and land on a target that broadcasted broadband noise at four different levels. An array of seven microphones enabled acoustic localization of the bats and source level estimation of their approach calls. Call duration and peak frequency did not change, but average call source levels (SL_RMS, at 0.1 m as dB re. 20 μPa, root-mean-square) increased, from 112 dB in the no-noise treatment, to 118 dB (maximum 129 dB) at the maximum noise level of 94 dB. The magnitude of the Lombard effect was small (0.13 dB SL_RMS/dB of noise), resulting in mean broadband and narrowband ENRs of -11 and 8 dB respectively at the highest noise level. Despite these poor ENRs, the bats still performed echo-guided landings, making us conclude that they are very resilient to masking even when they cannot avoid it spectrally, spatially or temporally.

The dataset consists of echolocation call parameters from Daubenton's bats (Myotis daubentonii). These calls were recorded with an array of seven microphones during controlled behavioural experiments in which the bats were exposed to four different levels of broadband, ultrasonic noise. The task that bats were trained to complete was to fly across an anechoic room while using echolocation and land on a spherical transducer that could emit the noise.

There are 5 different datasets:

1. 'atrack_info.mat' (for Matlab): each row corresponds to one trial which was either without noise ('noise level'=20) or had one of the four different levels of noise. The 'calls' column contains call parameters while the 'loctrack' contains localization parameters for these calls.

2. 'full_dataset.csv': this dataset was generated after selecting specific calls from each trial in the 'atrack_info.mat'. These calls can be found in the 'indfinal1' field of the column 'loctrack' in 'atrack_info.mat'.

3. 'dataset_sl.csv': this dataset was generated from 'full_dataset.csv' after selecting the five calls of each trial with the highest SL_RMS ('sl').

4. 'dataset_enr.csv': this dataset was generated from 'full_dataset.csv' after selecting the five calls of each trial with the highest broadband ENR_BB ('enrb').

5. 'dataset_tc.csv': this dataset has only one row per trial and it contains the time needed for each bat to complete the landing task ('time_flight_s').