Use of forest strata by bats in temperate forests

Bat responses to changes in forest composition and prey abundance depend on landscape matrix and stand structure

Despite the key importance of the landscape matrix for bats, we still not fully understand how the effect of forest composition interacts at combined stand and landscape scales to shape bat communities. In addition, we lack detailed knowledge on the effects of local habitat structure on bat-prey relationships in forested landscapes. We tested the assumptions that (i) forest composition has interacting effects on bats between stand and landscape scales; and (ii) stand structure mediates prey abundance effects on bat activity. Our results indicated that in conifer-dominated landscapes (> 80% of coniferous forests) bat activity was higher in stands with a higher proportion of deciduous trees while bats were less active in stands with a higher proportion of deciduous trees in mixed forest landscapes (~ 50% of deciduous forests). Moth abundance was selected in the best models for six among nine bat species. The positive effect of moth abundance on Barbastella barbastellus was mediated by vegetation clutter, with dense understory cover likely reducing prey accessibility. Altogether, our findings deepen our understanding of the ecological processes affecting bats in forest landscapes and strengthen the need to consider both landscape context and trophic linkage when assessing the effects of stand-scale compositional and structural attributes on bats.

Bidirectional Echolocation in the Bat Barbastella barbastellus: Different Signals of Low Source Level Are Emitted Upward through the Nose and Downward through the Mouth

The Barbastelle bat (Barbastella barbastellus) preys almost exclusively on tympanate moths. While foraging, this species alternates between two different signal types. We investigated whether these signals differ in emission direction or source level (SL) as assumed from earlier single microphone recordings. We used two different settings of a 16-microphone array to determine SL and sonar beam direction at various locations in the field. Both types of search signals had low SLs (81 and 82 dB SPL rms re 1 m) as compared to other aerial-hawking bats. These two signal types were emitted in different directions; type 1 signals were directed downward and type 2 signals upward. The angle between beam directions was approximately 70°. Barbastelle bats are able to emit signals through both the mouth and the nostrils. As mouth and nostrils are roughly perpendicular to each other, we conclude that type 1 signals are emitted through the mouth while type 2 signals and approach signals are emitted through the nose. We hypothesize that the “stealth” echolocation system of B. barbastellus is bifunctional. The more upward directed nose signals may be mainly used for search and localization of prey. Their low SL prevents an early detection by eared moths but comes at the expense of a strongly reduced detection range for the environment below the bat. The more downward directed mouth signals may have evolved to compensate for this disadvantage and may be mainly used for spatial orientation. We suggest that the possibly bifunctional echolocation system of B. barbastellus has been adapted to the selective foraging of eared moths and is an excellent example of a sophisticated sensory arms race between predator and prey.

Operator bias in software-aided bat call identification

Software-aided identification facilitates the handling of large sets of bat call recordings, which is particularly useful in extensive acoustic surveys with several collaborators. Species lists are generated by “objective” automated classification. Subsequent validation consists of removing any species not believed to be present. So far, very little is known about the identification bias introduced by individual validation of operators with varying degrees of experience. Effects on the quality of the resulting data may be considerable, especially for bat species that are difficult to identify acoustically. Using the batcorder system as an example, we compared validation results from 21 volunteer operators with 1–26 years of experience of working on bats. All of them validated identical recordings of bats from eastern Austria. The final outcomes were individual validated lists of plausible species. A questionnaire was used to enquire about individual experience and validation procedures. In the course of species validation, the operators reduced the software's estimate of species richness. The most experienced operators accepted the smallest percentage of species from the software's output and validated conservatively with low interoperator variability. Operators with intermediate experience accepted the largest percentage, with larger variability. Sixty-six percent of the operators, mainly with intermediate and low levels of experience, reintroduced species to their validated lists which had been identified by the automated classification, but were finally excluded from the unvalidated lists. These were, in many cases, rare and infrequently recorded species. The average dissimilarity of the validated species lists dropped with increasing numbers of recordings, tending toward a level of ˜20%. Our results suggest that the operators succeeded in removing false positives and that they detected species that had been wrongly excluded during automated classification. Thus, manual validation of the software's unvalidated output is indispensable for reasonable results. However, although application seems easy, software-aided bat call identification requires an advanced level of operator experience. Identification bias during validation is a major issue, particularly in studies with more than one participant. Measures should be taken to standardize the validation process and harmonize the results of different operators.