**4. Discussion**

**3. Results**

12 Bats

*noctivagans*).

the EM3 bat detector.

from the EM3 detector.

The concern that the more sensitive bat detector (miniMIC) would be more adversely impacted by road noise or airflow was fully realized. The detector was sensitive to wind resistance at speeds over 10 km/h, recording tens of thousands of audio files, all obscured with background noise. This made analysis of these data impossible. Therefore, comparisons of continuous and point count sampling results could only be performed with data obtained

Average passes per minute recorded along the 26 transects was not significantly different between continuous sampling versus point count sampling (0.076 vs. 0.067 passes/min, respectively, P = 0.097, **Table 1**). Comparisons of the proportion of bat passes identified to species and total number of species documented using the two approaches revealed largely similar results as well. Of all the passes recorded for the entire sample during continuous sampling, 20% were able to be identified to species, yielding an overall rate of 0.015 passes per minute identified to species (**Table 1**). At point count sites, 24.5% of passes were able to be identified to species, yielding a rate of 0.016 passes per minute identified to species (**Table 1**). Both approaches also documented the same four species: big brown bat (*Eptesicus fuscus*), red bat (*Lasiurus borealis*), evening bat (*Nycticeius humeralis*), and silver-haired bat (*Lasionycteris* 

Since data obtained with the more sensitive miniMIC detector during continuous sampling could not be analyzed, comparisons of the two detectors were restricted to point count sampling. Here, considerable differences were uncovered. The average number of bat passes recorded at each site were significantly higher using the miniMIC detector compared to the less sensitive EM3 detector (mean = 13.17 vs. 0.812 bat passes per site, respectively, N = 260, P < 0.001, **Table 2**). This translates to an average of 1.098 passes per minute for the miniMIC versus 0.067 for the EM3 (**Table 2**). Magnified over 52 hours of recording at the 260 sites, this resulted in a considerably higher number of total bat passes recorded with the miniMIC (3550) compared to the EM3 (211) (**Table 2**). Furthermore, due to the superior resolution of the audio files obtained with the miniMIC, a considerably higher proportion of bat passes were able to be identified to species (64.1% vs. 24.5%, **Table 2**). The combination of a higher number of calls recorded with a higher proportion identified to species meant that the miniMIC

Mean (SD) passes/minute 0.076 (0.073) 0.067 (0.128) Percent passes identified to species 20.0% 24.5% Passes/minute identified to species 0.015 0.016 Total number of species identified 4 4

Passes per minute were not statistically different between the two approaches (Wilcoxon test, N = 26, P = 0.097).

**Table 1.** Comparison of bat detection rates between continuous versus point count sampling along 26 transects using

**Continuous sampling Point count sampling**

Bats face a growing array of threats. Many of these threats have complex and overlapping geographic distributions. Given the uncertainty of how these threats interact and impact bats across the landscape, it is becoming increasingly important to monitor populations across large geographic areas. Driving transects offer one the most cost effective and least laborintensive tools for doing this. However, driving transects can be implemented in different ways and it is important to determine which approach is superior in terms of the amount and quality of data obtained.

When comparing results from a single detector capable of yielding analyzable audio files from both continuous and point count sampling, these two methods appear comparable. Specifically, mean number of passes per minute, percent of passes identified to species, passes per minute identified to species, and number of species identified were similar between the two approaches (**Table 1**). They also documented the same four species. If this holds with other detectors that are similarly unaffected by airflow or driving noises, we conclude that either driving transect technique can be a viable option. With such detectors, the needs of the particular project should dictate which option is selected. For example, if one seeks to test hypotheses about habitat use or other factors, the ability to use a variety of standard statistical techniques such as ANOVA (or nonparametric equivalents) for data from discrete sampling points may indicate the point count method is preferable. If, on the other hand, one simply seeks to document the bat fauna of an area, particularly in places it may not be safe to stop and record for extended periods, continuous sampling might be preferable.

The above conclusions are based on the use of a detector capable of operating while driving at speeds above 10 km/h without significant airflow or driving noise interference. We recommend testing any detectors intended for continuous sampling on driving transects to ensure they yield audio files of adequate quality for extracting bat passes and identifying them to species. Data obtained from the miniMIC suggests not all bat detectors may be capable of this. It remains unclear whether other high-sensitivity detectors are similarly affected, or whether accessory devices such as wind screens can mitigate these issues. Future work should test a variety of high sensitivity bat detectors with different types of wind screens to determine if it is possible to use these devices for continuous sampling. If not, our data suggest overall detector sensitivity is vastly more important than driving transect sampling design.

adapted to interior habitat conditions and avoid or are otherwise negatively impacted by edge conditions [44]. While this is often not a significant problem with insectivorous bats, since many species prefer edges like forest edges [30, 45–47], if there is reason to believe research questions about focal species in the study area might be adversely impacted by sampling at habitat edges, driving transects may not be appropriate. For the region sampled in the present study, driving transects have proven comparable in documenting the bat fauna to unmanned stationary bat

Comparison of Driving Transect Methods for Acoustic Monitoring of Bats

http://dx.doi.org/10.5772/intechopen.75834

15

Like many mammals, bats across the globe face a variety of threats that imperil their very existence. In North America, many of these threats are both increasing and span large geographic areas. The growing and expansive nature of these threats requires the urgent development and deployment of sampling techniques capable of effectively and efficiently documenting changes in the status of bat populations across large areas. Driving transects have been proposed and implemented as a tool for doing precisely that. Unfortunately, previous studies failed to examine the implications of using different sampling methodologies or detectors on

In this study we showed that, with a lower sensitivity detector that is unaffected by wind and driving noise, sampling continuously while driving yields similar results to sampling at discrete sampling points. However, detector sensitivity proved to be much more important than sampling technique in terms of the amount and quality of data obtained. That is, the higher sensitivity detector documented substantially higher numbers of bat passes and species than the lower sensitivity detector. The downside to the former is that data obtained while driving could not be analyzed due to significant interference from driving noise and airflow over the microphone at speeds above 10 km/h. Based on our findings, for most studies using driving transects to study bat populations, we suggest detector sensitivity should take priority over sampling design. If future studies are unable to resolve the problems of using high sensitivity detectors while continuously sampling along driving transects, this would necessitate using point count sampling instead. We recommend selecting the detector capable of obtaining the greatest amount and quality of call sequence recordings under a given research design, then conducting preliminary trials with continuous and point count sampling. If airflow or driving noises significantly diminish the data available with continuous sampling, as in the current study, point count sampling would be the more appropriate sampling regime to use for most

We thank Dr. P. Anderson for help with statistics and programming and Dr. K. Vulinec for helpful editorial comments and input on the project. We thank the Eastern Shore Regional GIS Cooperative for their help in gathering and creating satellite imagery of the peninsula. Lastly, thanks to all the undergraduate students who helped with various aspects of this project,

detectors placed in both interior and edge conditions of different habitats [38].

**5. Conclusions**

the results obtained.

applications.

**Acknowledgements**

Overall, the more sensitive miniMIC recorded nearly 17 times more bat passes than the EM3 (**Table 2**). Factoring in that nearly 3 times as many of the miniMIC passes could be identified to species, this yielded nearly 44 times more calls identified to species and nearly twice as many bat species identified (**Table 2**). These differences are substantial and have profound implications for the types of conclusions that can be drawn from comparably designed studies. The failure of the less sensitive detector to record numerous bat passes at each site lowers the power of a study. It means any differences that may exist in activity among species or habitats may fail to be detected or may not be identified as significantly different due to the small amount of resulting data. Perhaps even more importantly, the fact that nearly half the species present were effectively missed by the less sensitive detector could alter conclusions about species presence, distribution, habitat associations, and many other ecological questions. The findings from the lower sensitivity detector are particularly troubling for research related to species conservation, as the very species typically of greatest concern (rare and threatened species) are the ones most likely to be missed. All three of the additional species recorded with the miniMIC are uncommon or rare in the sampled area [38]. This is especially true of the genus *Myotis*. Most *Myotis* species in eastern North America have been devastated by White Nose Syndrome, with concerns that at least one species is in danger of becoming regionally extinct in the coming decades [42]. Failing to detect these species in areas where they persist could adversely impact conservation efforts. For example, the presumed absence of such species in a given area may fail to trigger recovery measures normally implemented by governmental and nongovernmental organizations when rare or threatened species are detected. It could also lead to the diversion of much needed conservation resources away from areas where the species persist because they are presumed absent. Given these concerns, if future research confirms that higher sensitivity detectors are not viable options for continuous sampling, the greater amount and quality of data obtained from such detectors strongly suggests priority should be given to using these types of detectors at point count sites rather than using lower sensitivity detectors for continuous sampling.

It is important to note that even with a high sensitivity detector operated at point count sites, driving transects have limitations. Some areas or habitats may lack adequate road access. Depending on how limited road access is, this may put analysis of certain habitats off limits, or cause them to be significantly underrepresented in the sample. In such cases, the use of other techniques such as walking transects, mist nets, or unmanned stationary bat detectors may be indicated. Roads are also, by definition, human-altered environments. Their presence and usage can have a variety of impacts on adjacent environments [43]. Even if much of the surrounding habitat is largely intact, the presence of roads effectively creates a habitat edge. Some species are adapted to interior habitat conditions and avoid or are otherwise negatively impacted by edge conditions [44]. While this is often not a significant problem with insectivorous bats, since many species prefer edges like forest edges [30, 45–47], if there is reason to believe research questions about focal species in the study area might be adversely impacted by sampling at habitat edges, driving transects may not be appropriate. For the region sampled in the present study, driving transects have proven comparable in documenting the bat fauna to unmanned stationary bat detectors placed in both interior and edge conditions of different habitats [38].
