Content uploaded by Volker Runkel
Author content
All content in this area was uploaded by Volker Runkel on Dec 10, 2018
Content may be subject to copyright.
A preview of the PDF is not available
Akustische Erfassung, Bestimmung und Bewertung von Fledermausaktivität
Abstract and Figures
Keine andere Methode der Fledermauserfassung erfreut sich heute einer so großen Beliebtheit und Verbreitung im Rahmen von Umweltgutachten wie die akustische Erfassung. Der große Bedarf an Daten für die Bewertung des Ausbaus erneuerbarer Energien - und hier vor allem der Windenergie - sowie die Vielzahl an hochentwickelten Geräten auf dem Markt sind sicherlich ein Grund hierfür. Ein anderer ist, dass sich diese Methode langsam aber sicher etabliert und eine Vielzahl von Untersuchungen ermöglicht. Und nicht zu Letzt haben eine Vielzahl an modernen Geräten diese Methode dorthin gebracht, wo sie sich heute befindet.
Wer sich mit der akustischen Erfassung beschäftigt wird schnell erkennen, dass es bei aller Euphorie doch auch recht viele offene Fragen zu den Möglichkeiten und Grenzen solcher Systeme gibt. Klare Definitionen zum Umgang mit den Daten fehlen meist. So gibt es zum Beispiel keine eindeutig beschriebenen Aktivitätsindizes.
Dieses Buch hat als Ziel einen Überblick der möglichen Anwendungen der akustischen Fledermauserfassung zu liefern. Ausführliche technische Vergleiche werden jedoch, abgesehen von wenigen Ausnahmen, nicht vorgenommen. Vielmehr werden die zahlreichen typischen Fragen zur Anwendung aufgegriffen. Wichtige technische Begriffe und physikalische Grundlagen zur Arbeit mit Ultraschall werden im letzten Kapitel kurz erläutert.
Figures - uploaded by Volker Runkel
Author content
All figure content in this area was uploaded by Volker Runkel
Content may be subject to copyright.
... For automated identification of bat calls it is important to prevent the recording of reflecting echoes. Echoes of bat calls are similar to actual bat calls, but the characteristics of echoes are different and, as such, would lead to false identifications (ruNKel & gerdiNg 2016). Therefore, we made recordings more than three meters from reflecting surfaces such as the ground surface, trees or water by installing the Batcorder on a rod or tower. ...
... Finally, echoes of bat calls can be problematic for an automated identification of bats (ruNKel & gerdiNg 2016). In nine examples in the current study, BatIdent identified a Nyctalus sp. as a Pipistrellus sp. ...
Commercially available automated bat identification software packages are widely used in environmental studies to identify bat species from recordings of bat echolocation calls. Caution is, however, needed if the results are used without further verification, as the programs do not guarantee that the results are correct, and wrong species identifications often happen. Taking automated species identifications for granted might hence lead to erroneous conclusions in environmental studies. The goal of our study was to objectively assess the performance of four commercially available and commonly used automated identification software programs by processing an identical reference dataset with all four programs. The reference dataset consisted of nine species selected based on their preference for open habitats in Western Europe or because they occur as vagrants at sea and therefore are vulnerable to the development of onshore and offshore wind farms. Offshore areas are being increasingly examined, as recent studies have identified possible conflicts of offshore wind farms and certain bat species. In our test, we included two automated identification programs that have not yet been tested in other studies, and a reference dataset from a different geographical region (Western-Europe) with a different species composition compared to other studies. Our data hence add to the knowledge base needed for an appropriate assessment of the reliability of analytical software. In general, BatIdent (77% correct species identifications) and Kaleidoscope (71%) seem to be relatively reliable while the performance of BatExplorer (31%) is relatively poor. SonoChiro correctly identified 65% of the sequences to species level. While the tested programs may be considered valuable tools to detect bat calls from the recordings, a trained bat expert needs to cross-check the automated species identifications to avoid erroneous conclusions. Our test hence affirms the conclusions of previous studies in Northern Europe and the USA.
... Die Einstellungen der Auswertungsprogramme können ebenso einen starken Einfluss auf die Auswertung und Messung der Spektren und Sonagramme haben. Einen guten Überblick über diese Tücken geben Runkel & Gerding (2016). ...
We review how time-expanded recordings and direct sampling of sound can be used to identify some bat species from their vocalizations, and how such methods may be applied to field surveys of bat activity. Some species emit distinctive social calls that permit identification, and social calls may be individually distinctive within species. Analysis of echolocation calls can identify cryptic species that are very similar in appearance, but differ in echolocation calls. Echolocation calls vary according to ecology and morphology both across and within species. Although some species emit distinctive echolocation calls, great care must be used in the acoustic identification of other species, especially in cluttered habitats. Species identification from echolocation calls is best approached by quantitative analysis, such as discriminant function analysis (DFA) based on several measurements taken from calls. DFA gives an objective measure of confidence in species identification, and can be applied to surveys of habitat use providing that certain limitations are recognised. Discrimination of species from their echolocation calls may be improved by quantifying the shape of calls from their frequency-time course, and by using artificial neural networks. Feeding rates can be estimated in bat detector surveys, though not all terminal buzzes emitted prior to prey capture are associated with successful capture of prey. Time expansion and direct sampling methods retain more detail of individual echolocation calls than do other methods. Not only can these methods be used to a certain extent for species identification and habitat use studies, they remain the only suitable methods for describing call design in bats from a detailed bioacoustics perspective.
Thanks to recent advances in technology, even the most inexperienced enthusiasts can now provide reliable bat-monitoring data, enhancing community involvement and engagement.
A survey of bat communities was conducted in Missouri during the summer of 1998 using both mist nets and Anabat. Mist nets provide valuable information about population demographics and species diversity within bat communities. However, they can yield biased samples of bat community activity and may cause undue stress to captured bats. The use of ultrasonic bat detectors like Anabat can ameliorate some of these problems. Bat detectors are relatively easy to set up, require no direct contact with bats and can sample a wider variety of habitats. To test the relative merits of these two methods, we sampled a variety of habitats including ponds, streams, and fly ways using Anabat and mist nets in a paired design. This allowed for the simultaneous sampling of bat community activity necessary for direct comparison. Echolocation calls recorded by the Anabat were identified using a discriminate function analysis model based upon a library of known call sequences. Overall, values for species richness were significantly higher for Anabat II than for mist nets. Species richness was consistently higher for Anabat for all habitats and seven individual species and one species group was detected more frequently with Anabat than with mist nets.
To evaluate the efficacy of the Anabat II ultrasonic detector and analysis system for use as a tool for conducting inventories, we compared results of acoustic versus capture techniques in the southwestern United States. We sampled 57 locations using standard methods (mist nets and double-frame harp traps) and simultaneously with an ultrasonic detector (Anabat II). Assuming total number of species obtained by both methods equaled a complete inventory, captures accounted for 63.5% and acoustic sampling 86.9% of the combined species present. Acoustic sampling was capable of sampling bats that routinely flew outside the sampling capabilities of nets and traps. We found no statistical difference between capture and acoustic sampling with respect to species that use low-intensity echolocation. Acoustic sampling of bat communities is a powerful tool but should be used with various capture techniques to perform the most accurate inventory.
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.
Bat detectors increasingly are used in studies of the ecology and behavior of bats. A number of assumptions are implicit to these studies, although these assumptions rarely are stated explicitly and sometimes are not recognized by researchers. The strength of inference resulting from echolocation-monitoring studies is, in part, a function of the extent to which underlying assumptions are met. Recognition of underlying assumptions is thus an important facet of the design and interpretation of echolocation-monitoring studies. In this paper, I outline and discuss six key assumptions underlying most echolocation-monitoring studies. Accounting for sources of temporal, spatial, and sampling variation is key for designing robust studies and for meeting the assumptions underlying echolocation-monitoring studies.
Monitoring and identifying echolocating bats in the field has been used extensively for many years, and O'Farrell et al. (1999) present an updated “qualitative” method for identifying species from their echolocation calls. However, they make the assumption that, like bird songs, echolocation calls of bats are species-specific. There is no reason to expect that natural selection favored incorporation of this information. Although identification of some species of bats by their calls is possible, variation at several levels makes other species indistinguishable. Monitoring echolocation in the field and identifying species can be useful in determining presence or absence of species and factors influencing activity of bats. However, limitations and degree of reliability of the technique must be recognized. Only by more explicitly defining how the method is used and quantifying characteristics that define calls of recognizable species will the method be scientifically sound and repeatable. I suggest a number of guidelines for use of the method in field studies.
I used Anabat II bat detectors to monitor echolocation calls of bats over two streams in the Oregon Coast Range for a total of 195 detector-nights. Activity of bats was positively correlated with biomass of insects and minimum nightly temperature, and was negatively correlated with length of night: activity levels at the two streams were positively correlated. Activity of bats was not significantly correlated with either hours of moonlight or with phase of moon. Level of activity within a night generally peaked shortly after sunset with a second, smaller peak in activity shortly before sunrise, but patterns varied substantially among nights. Total nightly activity at a site also varied substantially among nights, sometimes varying several-fold on consecutive nights. To assess the implications of temporal variation in activity of bats on sampling, I randomly sampled subsets of the data using from 2- to 12-night sample periods and calculated mean levels of activity for each subset. For subsets with seven or more nights, >60% of the subsets had means that were within 20% of the mean of the entire dataset. Less than 50% of the subsets had means within 10% of the mean of the entire dataset for any number of nights subsampled. When comparing activity between sites. use of blocked or paired designs improved sampling efficiency by 20%. Failure to account for temporal variation in activity of bats when designing research projects and monitoring programs could result in biased estimates of activity of bats.
Bat populations may be vulnerable to the barrier impacts of roads, including habitat restriction and traffic mortality. Under‐road passageways may reduce these impacts, but little is known about the ecological factors influencing their use by bats. The study area provided a natural experimental design, in that adjacent under‐motorway passageways had contrasting dimensions (two long, narrow drainage pipes within Rhinolophus hipposideros, Myotis nattereri and Plecotus auritus flew through the narrow drainage pipes. These species are adapted for flight and foraging in cluttered airspace, in terms of wing morphology and echolocation signal design. Edge‐space species (Pipistrellus pipistrellus, Pipistrellus pygmaeus) were highly active in the area but never flew through the narrow pipes. All species, except the open‐adapted Nyctalus leisleri, flew through the large underpass. Simultaneous recordings made above and below this underpass (16 nights) also indicated that species' tendency to cross over, rather than under, the structure was inversely related to the degree of clutter‐adaptation. If motorways are built through bat habitat, trade‐offs between optimal mitigation of impacts on protected bats and cost/engineering practicality are inevitable. Large underpasses are advisable where possible as they accommodate a wider range of species, and bats are less likely to fly over them. However, their construction is costly and is dependent on landscape topology. If the target species for mitigation are clutter‐adapted bats, our findings indicate that incorporation of a greater number of suitably located small tunnels into new roads may facilitate safe passage more effectively than fewer large underpasses.