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BAT DETECTORS
ОглавлениеThe transformation of ultrasound into a signal audible to human ears requires the use of specialised bat detectors, of which there are three main types. Advances in digital technology have completely revolutionised the means available to study bat sonar today, even compared to three decades ago. Thus, the pioneering work by M. B. Fenton and colleagues in southern Africa (e.g. Fenton et al. 1977, Fenton and Bell 1981) required heavy, expensive analog equipment, particularly to record ultrasonic calls (Kunz 1988). While the best of the modern digital bat detectors, coupled with powerful laptop computers, are still relatively costly, they are much more affordable to researchers. More compact digital ultrasonic detectors have radically lowered the costs of functional bat detectors, which has opened up their affordability to non-specialist enthusiasts to survey bats.
Heterodyne detectors convert bat calls into electronic signals, which are then compared to an internal signal of a particular frequency. The internal frequencies of detectors are ‘tunable’ so that the bat’s actual peak frequency can be estimated within a limited band of frequencies, usually 10 kHz. Two disadvantages of this system are that bat passes can be missed because the full range of possible echolocation frequencies used by bats (e.g. between 10 and 214 kHz in southern Africa) has to be scanned ∼10 kHz at a time, and bandwidth information is lost. Advantages of heterodyne detectors are that they work in real time, are easy to use, and are the least expensive. In fact, in Europe, heterodyne detectors such as the Magenta, Maplin, and Batbox III are very popular among the public and scientists for basic field identification of species and the monitoring of bat activity, because calls of local species are increasingly well known.
Frequency division detectors digitally scale down the entire ultrasonic frequency range of a bat call into the human hearing range. This is done by converting the call into a square wave, called a zero-crossing signal. The square wave is divided by a constant factor, usually 10, meaning that a frequency of 50 kHz will be converted to 5 kHz. In other words, frequency division detectors count the number of cycles of the ultrasonic signal (N), and effectively divide the frequency by N, where N is usually 10. Frequency division detectors are only capable of tracking one frequency (harmonic) at a time. Usually this is the fundamental frequency. Consequently, it is difficult to perform a harmonic analysis from a frequency division signal. Advantages of frequency division detectors are that they are reasonably priced, work in real time and cover a broad bandwidth, in other words, all of the ultrasonic frequencies of co-existing bats are recorded, without missing frequencies of species because of tuning choices. Because some scientists use the ANABAT frequency division bat detector (Titley Electronics, Australia) to identify bat species and monitor bat activity (but see Fenton et al. 2001), echolocation data of southern African bat species that were recorded by the authors with the ANABAT system are presented in Table 3.
TABLE 3.
TABLE 3. Anabat call data, listing the mean (± standard deviation) echolocation parameters of 29 bat species caught in southern africaRecordings were made with anabat bat detectors and analysed with analook software. n = number of bats in sample; fc = characteristic frequency, the frequency at the end or flattest portion of the call; fk = frequency at the ‘knee’ or the point at which the slope of the call abruptly changes from a downward slope to a more level slope; fmin = minimum call frequency; fmax = maximum frequency; dur = total duration of the call; tc = time from the start of the call to fc; tk = time from the start of the call to fk.
| SPECIES | N | FMAX | FMIN | FC | FK | DUR | TC | TK |
|---|---|---|---|---|---|---|---|---|
| HIPPOSIDERIDAE | ||||||||
| Hipposideros caffer | 6 | 143.8±1.1 | 125.5±13.2 | 142.6±1.2 | 142.3±1.5 | 4.5±0.5 | 3.2±0.3 | 0.9±0.5 |
| Hipposideros ruber | 4 | 136.6±2.7 | 106.9±21.2 | 136.3±2.9 | 135.8±2.9 | 4.8±0.5 | 3.9±0.8 | 1.1±1.5 |
| Macronycteris vittatus | 2 | 65.3±1.9 | 57.9±0.4 | 64.5±1.7 | 64.9±2.1 | 9.5±1.3 | 7.6±1.8 | 0.4±0.07 |
| RHINONYCTERIDAE | ||||||||
| Cloeotis percivali | 4 | 103.2±0.7 | 99.4±4.3 | 102.4±0.7 | 103.1±0.8 | 1.9±0.6 | 1.6±0.3 | 0 |
| Triaenops afer | 15 | 77.5±5.5 | 67.4±5.3 | 77.1±5.6 | 77.0±5.5 | 8.8±3.2 | 7.2±2.5 | 0.4±0.6 |
| RHINOLOPHIDAE | ||||||||
| Rhinolophus blasii | 3 | 86.6±0.2 | 77.3±0.9 | 86.0±0.3 | 86.3±0.4 | 20.6±0.1 | 19.3±1.8 | 2.2±0.4 |
| Rhinolophus clivosus | 7 | 90.8±1.2 | 76.4±6.1 | 89.6±2.2 | 89.9±1.78 | 29.7±7.9 | 25.7±7.7 | 2.9±2.3 |
| Rhinolophus darlingi | 4 | 85.6±0.2 | 72.2±3.9 | 85.4±0.3 | 85.0±0.4 | 21.5±6.3 | 21.6±5.6 | 3.0±1.4 |
| Rhinolophus mossambicus | 5 | 35.9±1.2 | 24.4±10.5 | 35.3±0.2 | 34.8±1.1 | 29.9±2.7 | 27.3±3.1 | 2.2±0.6 |
| Rhinolophus deckenii | 3 | 72.0±0.02 | 56.6±5.1 | 71.9±0.1 | 71.9±0.02 | 27.6±5 | 23.4±5 | 2.2±1.2 |
| Rhinolophus simulator | 6 | 84.1±0.3 | 41.7±22.2 | 83.9±0.7 | 83.8±0.6 | 22.5±3.6 | 17.2±5.5 | 1.2±0.5 |
| EMBALLONURIDAE | ||||||||
| Taphozous mauritianus | 2 | 28.0±0.7 | 25.3±0.4 | 25.7±0.5 | 27.9±0.7 | 2.3±0.2 | 2.1±0.2 | 0.1±0 |
| NYCTERIDAE | ||||||||
| Nycteris thebaica | 3 | 75.4±7.5 | 63.1±2.8 | 68.2±5.2 | 71.0±4.6 | 0.8±0.2 | 0.7±0.2 | 0.2±0.1 |
| MOLOSSIDAE | ||||||||
| Chaerephon ansorgei | 3 | 26.3±2.1 | 20.9±0.6 | 21.6±0.5 | 23.7±0.7 | 7.4±4 | 4.7±2.8 | 2.3±0.8 |
| Chaerephon pumilus | 11 | 29.2±4.7 | 24.6±2.8 | 25.1±3 | 27.7±3.4 | 6.2±2.8 | 7.0±4.5 | 1.3±1.3 |
| Mops condylurus | 6 | 37.3±2.9 | 19.1±6.4 | 26.5±1 | 30.1±0.9 | 7.0±1.6 | 4.7±0.6 | 2.0±1 |
| MINIOPTERIDAE | ||||||||
| Miniopterus fraterculus | 2 | 62.3±0.5 | 55.5±0.1 | 55.8±0.03 | 58.4±1 | 2.3±0.1 | 2.5±0.3 | 0.6±0.07 |
| Miniopterus inflatus | 7 | 58.1±6.4 | 47.2±0.9 | 47.4±0.8 | 50.0±1 | 3.2±0.8 | 3.0±0.6 | 1.0±0.3 |
| Miniopterus natalensis | 7 | 61.6±3.6 | 53.1±0.8 | 53.3±0.7 | 55.6±0.5 | 2.3±0.5 | 1.9±0.5 | 0.4±0.2 |
| VESPERTILIONIDAE | ||||||||
| Kerivoula lanosa | 7 | 148.7±16.9 | 81.9±6.6 | 131.3±17.3 | 141.2±15.4 | 0.8±0.2 | 0.3±0.1 | 0.1±0.05 |
| Myotis bocagii | 2 | 56.8±0.7 | 37.7±6.7 | 44.0±4.2 | 49.1±3.4 | 1.4±0.4 | 0.8±0.07 | 0.3±0 |
| Myotis tricolor | 3 | 58.1±5.3 | 38.1±3.8 | 50.7±1.1 | 54.3±1.8 | 1.7±1.3 | 0.5±0.2 | 0.2±0.3 |
| Neoromicia capensis | 2 | 52.7±11.9 | 39.3±0.7 | 41.7±0.1 | 44.9±4.4 | 1.3±0.1 | 0.8±0.4 | 0.2±0 |
| Neoromicia nana | 3 | 86.0±10.4 | 67.9±4.3 | 75.3±7.8 | 81.4±14.6 | 1.3±1.2 | 0.9±0.8 | 0.2±0.2 |
| Neoromicia zuluensis | 4 | 62.8±5.4 | 48.8±3.7 | 50.4±3.7 | 54.8±5.9 | 2.7±1.2 | 2.4±1.5 | 1.0±0.6 |
| Nycticeinops schlieffeni | 8 | 50.6±4.7 | 39.4±3.1 | 41.1±1.9 | 43.8±2.2 | 2.2±0.9 | 2.0±0.9 | 0.8±0.4 |
| Pipistrellus hesperidus | 10 | 65.4±5.7 | 46.9±2 | 50.4±1.9 | 54.8±2.9 | 2.0±0.7 | 2.5±1.4 | 1.0±0.5 |
| Scotophilus dinganii | 11 | 44.2±6.6 | 33.6±2.5 | 34.0±2.8 | 36.6±2.9 | 3.0±1.1 | 2.8±1.1 | 1.2±0.6 |
| Scotophilus viridis | 4 | 57.5±5.6 | 41.1±4.2 | 42.9±4.8 | 46.3±4.6 | 3.7±2.7 | 2.3±1.8 | 0.7±0.4 |
Time expansion detectors digitise bat calls at a high sampling rate and replay them at a lower sampling rate afterwards. Typically, the sampling rate ratios vary from 1:10 to 1:32. This is the equivalent of recording sound on a high-speed tape recorder and then playing it back at a slower speed – time is effectively ‘expanded’ (slowed down) by a set factor (e.g. 10). Real-time full-spectrum detectors record the full frequency range up to a limit determined by the sampling rate used (where maximum recordable frequency is half the recording sampling rate, i.e. if the detector’s sampling rate is, e.g., 256 kHz, it will record sounds up to 128 kHz). The main disadvantage of time expansion and real-time full-spectrum bat detectors is that they are usually expensive. The main advantage of these detectors is that all information of bat calls – including amplitude, frequency and harmonic structure – is preserved, making it ideal for detailed analyses of call characteristics. Echolocation data used to generate the spectrograms in this book were recorded with time-expansion and real-time full-spectrum bat detectors: the Pettersson D980 detector (Pettersson Elektronik AB, Uppsala, Sweden) and the Avisoft Ultrasoundgate 416 and 116 detectors fitted with Ultrasoundgate CM16 microphones (Avisoft Bioacoustics, Berlin, Germany).
Note that some modern bat detectors are capable of recording in multiple modes.
Sampling bat diversity using bat detectors. Bat detectors are increasingly being used to survey bats for scientific research (e.g. Schoeman and Waddington 2011, Taylor et al. 2013a, Schoeman 2016, Mtsetfwa et al. 2018, McCleery et al. 2018), and acoustic monitoring has been recommended as the principal way of surveying bats for environmental impact assessments in South Africa (Sowler and Stoffberg 2012). To use bat detectors effectively, it is imperative to develop ‘local call libraries’ (i.e. recordings of echolocation calls from a particular locality) because these calls may vary geographically (e.g. Stoffberg et al. 2012). Without a call library, the chances of correctly identifying bats by their echolocation calls can be difficult, particularly in species-rich and poorly sampled regions. Examples of call libraries for southern Africa include those by Schoeman (2006), Taylor et al. (2013b) and Monadjem et al. (2017). In addition, bat calls, like all waves, attenuate with distance and this is proportional to the frequency of the call; the further away the bat detector, the greater the chance of missing high-frequency calls. Consequently, bat communities sampled using only bat detectors will probably underrepresent bats with high-frequency calls. A correction factor can improve the probability of acoustic surveys representing the actual bat community (see Monadjem et al. (2017) for a correction factor for southern African bats).
