Discussion
Automatic analysis of bat echo location calls using Bat Classify yieldsMyotis species’ activity patterns consistent with those gained from trapping. Furthermore, this information is available constantly throughout every night of the swarming season, whereas trapping is inevitably sporadic due to the disturbance caused to the bats and can only be undertaken infrequently during the swarming season. Both methods have their advantages and disadvantages. In the case of trapping, radio-tracking data for male bats suggests that they can learn to avoid traps within the swarming season (personal observations). This “trap-shyness” would affect the numbers captured on subsequent nights (see also Kunz & Brock 1975) and impairs population estimation from data on captures and recaptures within the same year. Additionally, some species, demographic groups, or individuals, may be better at avoiding traps in the first place, leading to potential bias in population estimates.
Whilst acoustic monitoring is most unlikely to affect a bat’s activity, different species have different intensities of call. This leads to a microphone detecting some species at greater distances than others, with consequential bias in comparing abundance between species by this method. Additionally, different species are likely to have different probabilities of identification from analysis of their echo location calls. Absolute activity levels of one species determined by this method cannot therefore be compared directly to those of another species. Absolute abundance is therefore unlikely to be measured effectively by either method, but both have their different advantages in determining changes in relative abundance within taxa, for example across the swarming season and across the night.
Capture gives the opportunity to identify bats in the hand, to note their sex, identify sex ratio biases (Table 1) and in the case of males, to record their reproductive status. In the case of females, it is usually possible to observe whether they have given previously birth, and sometimes whether the individual is a juvenile. In contrast, acoustic monitoring is of no use in determining sex or breeding status of the population. It is, however, useful in determining seasonal (nightly, Figure 4) and overnight (hourly, Figure 5) activity patterns, which have been shown in the present study to vary substantially throughout the swarming period. Video monitoring combined with acoustic detection adds an extra dimension to this information, by revealing thatMyotis bats increasingly accumulate within the cave during the first part of the night, following an initial exodus at dusk (Figure 3).
Manually checking several thousand files of echo location data each night through multiple seasons would prove to be a challenge, hence automatic classification of the calls is the only practical way forward, even though neither approach is likely to be 100% accurate (Russo & Voigt 2016, Rydell et al. 2017). Nevertheless, the aggregated automatically identified acoustic records correspond well with the capture data (Figures 2 and 4).
The notable bimodal peak in swarming activity of Myotis nattereri , shown for the first time in the present study (Figure 4), is unlikely to have been detected by sporadic trapping (cf. Figure 2). A possible explanation for this bimodality is that swarming has multiple functions; for example mating could account for the first peak, and pre-hibernation activity could account for the second peak -but this interpretation clearly needs further investigation. There is a suggestion of similar activity patterns for the other Myotisspecies, although the lower numbers recorded prevented a firm conclusion in these cases.
This study has demonstrated that the use of acoustic monitoring adds significantly to data obtained by catching bats at swarming periods, and for some research questions may provide sufficient or additional information without disturbing the animals. The automated acoustic identification software has its limitations in consistently identifying individual species’ echo location calls, but, over the swarming season, patterns of activity shown by acoustic monitoring are remarkably consistent with those derived from trapping, but allow a far more detailed analysis of temporal (seasonal and overnight) variation. If the efficiency of the algorithms used to identify bats can be improved, this technique for quantifying seasonal variation in bat activity, including swarming activity, will become even more effective. Overall, neither trapping nor acoustic identification alone provide a fully comprehensive and accurate method of studying bat swarming behaviour, but the use of both methods, in parallel with additional approaches such as infra-red video monitoring of cave entrances, represents a powerful combined approach, providing a deeper understanding of bat behaviour than can be gained using either method individually.