Biology 130: Methods in Field Biology
|Field Technique: Accoustic Surveys Bats|
Bats find their way around in the dark by echolocating. They make high frequency sounds and listen for the echoes that bounce back to give them information about the world around them.
The sounds that bats make are usually high above our hearing. Human hearing generally can take in sounds up to about 20kHz. Most bat calls are between 20 and over 100kHz, far above our range. The detector brings the frequencies down to around 0.1 – 10kHz so that we can hear them.
How far away a detector can pick up a bat will depend on how loud the bat is calling and how sensitive the detector is. Some bats like noctules make very loud echolocation calls and can be heard 50-100m away while others like brown long-eared bats may make very quiet calls that can only be heard less than 10 metres away.
In order to get the best out of the detector make sure you hold it towards the bats, keep the batteries fresh and avoid moisture on the microphone.
Bat Detector Bandwidth
Different bats use different frequencies for example noctules echolocate strongly around 20kHz and common pipistrelles around 45kHz. If you are tuned at 25kHz you can pick up noctules but not common pipistrelles.
That is because heterodyne or tuneable detectors like these can only scan a few frequencies above and below where you are tuned on the dial. This is called the detector bandwidth. Bandwidths are usually +/- 5kHz (check your detector instruction leaflet). If for example you are tuned at 25kHz with a +/-5kHz bandwidth, you would pick up any sounds up to 30kHz and down to 20kHz.
Because you cannot listen in on all the frequencies at the same time with a tuneable detector, in order to survey for different species we ask you to change the frequency between the walks and spots and listen only for particular species.
When you are out listening for bats in general with a tuneable detector (not during this survey) you can gradually scan up and down through the frequencies to pick up different bat sounds.
Finding The Peak Frequency
To do this when a bat comes by, you wobble the dial to a slightly higher frequency and then to a slightly lower frequency. In one direction the tone or pitch of the sound will generally become noticeably lower and in the other direction it will become higher.
Continue to turn the dial in the direction of the DEEPENING note. The sound should also start to get louder.
When you get to the deepest note you can read the dial and that should give you the peak frequency the bat is using. It is usually just beyond the loudest frequency.
Many bat species use typical peak frequencies and finding this can help us to identify them.
Peak Frequency Example
As the detector carries on tuning down again the pitch gets higher again as it is moving farther away from the peak.
The box in this example is outputting a pure signal at 38kHz. Bats do not use pure frequencies like this but you can tune into the overall pitch in a similar way to find their peak frequency.
Interpreting Detector Sounds
There are 4 main elements to listen for when using heterodyne detectors to help you to tell the species apart:
The repetition rate refers to how fast each bat call follows the previous one in a series. In general it is related to the size of the bat (big bats tend to be slower) and the habitat (out in the open bats make slower calls than when flying between obstacles among trees, hedgerows etc).
Rhythms relate to how the bat is flying. Bats flying in straight lines have regular rhythms and bats that tend to make lots of twists and turns tend to have erratic rhythms.
The tonal quality or flavour of the call varies between species. Some use deep, rich sounds and others flatter, ‘tinny’ calls. It relates to the duration of the call and long calls sound richer than short ones. The pitch or note helps us to find the peak frequency or where the bat is putting more energy into the call.
Different bat species will show different call characteristics based upon the type of flight they use, their wing and body shape, where they feed and how they feed.
Some may make fairly slow and rich sounding calls in an erratic pattern and others make fast and dry sounding calls in a regular pattern.
The following is excerpted from Assessing and Analyzing Bat Activity with Acoustic Monitoring: Challenges and Interpretations, Amanda Murphy Adams
Our results demonstrate that there is significant variation in detection efficacy among commercially available bat detectors. The differences in the detection abilities of these microphones, particularly in relation to differing frequency sensitivity, illustrate the hazards of comparing data collected by different detecting systems. Our results show that detection of different frequencies varied among detector systems and was affected by the distance and angle of the signal from the detector. Avisoft and Batlogger detected more of the highest frequency signals we tested than the other detectors, but as expected, these signals were detected at much shorter ranges. Detection distance for the 55 kHz synthetic signals (detected by all systems) is particularly relevant because this frequency is in the range of most species of bats that occur in temperate regions. In Hawaii, where only one species of bat occurs (L. cinereus semotus), any of the systems we used would suffice, although each would provide quite a different view of bat activity. In Newfoundland, where two species occur (Myotis lucifugus, M. septentrionalis) any of the systems we tested would suffice for M. lucifugus (echolocation call frequency of most energy ~40 kHz, maximum frequency ~81 kHz), but only some would accurately document activity by M. septentrionalis, which uses calls dominated by higher frequencies (frequency of most energy ~60 kHz, maximum frequency ~126 kHz; (Faure et al. 1993, Ratcliffe and Dawson 2003). In Newfoundland, some systems would be better than others. In other parts of the world, some bat species use echolocation calls dominated by frequencies >85 kHz. For these bat communities, the detection distance of the 85 kHz synthetic signals in our study is important to consider. Monitoring the activity of vespertilionid bats in the subfamilies Kerivoulinae and Murininae would be difficult with any of the systems we tested because these species produce high frequency (80 ‒ 200 kHz), frequencymodulated sweeps.
Variation in detection distance among detectors has important practical implications. For many studies, it is particularly important to understand the volume of airspace being sampled, such as when interpreting the results of pre-construction acoustic surveys conducted at potential wind energy facility sites where high bat mortality is a concern (Kunz et al. 2007). On modern wind turbines, the lower edge of the blade swept area is ~20 m above-ground (Barclay et al. 2007). Our data demonstrate detection ranges of 7 – 16 m, and therefore, none of the ground-based microphone systems we tested can detect bats flying in the area swept by the blades of wind turbines. Even a detector placed on the nacelle of a turbine (in the center of the blade swept area) would sample no more than one-third of the area swept by 50 m long blades (Kunz et al. 2007). When we focus on detection of echolocation calls from free-flying bats, bat detectors fell into one of two performance groups. AnaBat, Batcorder, and Song Meter did not differ significantly in the number of hoary bat echolocation calls detected. These bats produce high intensity echolocation calls with a minimum frequency which is typically ~17 kHz (Obrist 1995). The minimum frequency of hoary bat calls is lower than the lowest frequency of our synthetic calls. Consequently, our free-flying bat results represent a best-case scenario; we used only high intensity, low-frequency calls and our sampling method, counting all calls regardless of quality, presented the most optimistic view of activity. In reality, many species are much less detectable and the quality of many recorded calls is too poor to be identified to species or counted as a bat call. Using automated detection algorithms with recording quality standards will provide more objective call counts when measuring activity. If we had looked at passes from any of the Ontario Myotis species (calls with a minimum frequency range of ~34 - 40 kHz; Thomas et al. 1987), it is likely that the results from our free-flying passes would have mirrored the results from our synthetic call trials.
Among the detectors we tested, AnaBat is unique in that it is the only detector to use zero-crossing analysis which may (Corben and Fellers 2001) or may not (Fenton 2000) provide an adequate picture of bat activity. Our data contributes to this discussion, demonstrating that AnaBat is capable of performing similarly to a full-spectrum detector (Fig. 2.4), but in most cases it detects fewer calls (Fig. 2.3). Therefore, we emphasize the importance of considering the research questions and local bat fauna. While our results from the synthetic-call trials agree that full-spectrum detectors are more sensitive, our free-flying bat trial showed that there are circumstances where the differences are not substantial. Ultimately, the specific hypotheses and objectives of a study will dictate the suitability of various detectors (Limpens and McCracken 2004). No one recording system is ideal for all situations and thus it is the responsibility of the researcher (and the reader) to consider how the performance of the recording system will impact the results and conclusions of the study.
It is important to note that regardless of recording system, all microphones detect only a subset of the calls present in the environment (e.g. in our playback experiment the best system detected only 25% of the calls we played). However, our findings show that some subsets are significantly larger than others. This discrepancy is essential to remember when attempting to compare datasets collected with different detecting systems. Even when comparing multiple detectors of the same model, the microphones must be calibrated to ensure comparable performance (Larson and Hayes 2000). With an increasing number of threats to bat populations (e.g., wind turbines, white-nose syndrome) there may be a drive to develop more rigorous monitoring programs with standardized protocols for bat surveys. Our results highlight the importance of considering the specific detector used, and the variation that may arise from different microphones.
As technology continues to evolve, the number of commercially available detectors will increase. As with the current proliferation in detectors on the market, many brands will persist (e.g., AnaBat, Avisoft) and new brands will emerge (e.g., Batlogger). In such a specialized market there will probably be few dramatic changes in the technology; we would expect to see increases in microphone sensitivity, battery life, and storage capacity, along with continued software upgrades to improve detection algorithms. With a high diversity of detectors, each with a wide range of settings and technical capabilities, it is now necessary to report not only the type of detector used, but also the settings chosen (e.g., Table 2.2) and as many hardware details as possible. The extent that detector-specific settings have on performance and accuracy between detectors of the same brand remains to be seen. Finally, it comes to the issue of comparability of results; different detectors will give different results, which must be taken into account.
Whether the bat-detecting system you are using hears the same signals as the one I am using depends upon the echolocation calls. There are numerous factors that contribute to variation in datasets from acoustic monitoring; our results demonstrate that the detector plays a role in this variation. Ultimately, it is crucial that differences in detector performance be considered when designing studies and comparing results from different detectors, whether among models included in our study, other extant models, or those yet to be invented. No detector is ideal for all research questions and methods, and conversely, not all detectors are appropriate for a given question or methodology.
A limitation of acoustic monitoring is the relative nature of data interpretation. Previously, conclusions have been based on subjective assessments about the relative importance of sites or species-specific activity patterns. I proposed two methods for objectively identifying peaks in bat activity at various scales: percentile thresholds (Chapter 3) and SaTScan (Chapter 4). Using percentile thresholds to assess acoustic data permits an unbiased measure of the importance of a site and is a replicable method of describing within-night activity patterns. The strength of this method is evaluating activity levels at several thresholds based on a larger distribution of activity among sites. SaTScan is a valuable tool for quickly identifying peaks with an objective, replicable, and statistically-sound method that can be applied at various temporal and spatial scales.
Using these two methods in combination permits a thorough investigation of activity levels and patterns at a site, from the magnitude of species-specific activity to comparison of timing of peaks among species or sites.
Variation within sites
Bat activity can vary temporally (e.g, Hayes 1997, Milne et al. 2005), but within-site spatial variation has been too often overlooked (Britzke 2003, Fischer et al. 2009). I found that within site factors are very important for understanding variation in bat activity, being as or more important than differences among sites (Chapter 5). The high degree of variation within sites can affect sampling design, including necessary sampling effort, and requires the use of multiple detectors recording simultaneously within a site.
Detector placement within a site dramatically impacts the depictions of activity, in turn impacting estimates of levels and patterns of activity. An a priori understanding of the survey effort necessary should ensure statistically powerful sampling designs, clearer data interpretation, and more successful management and conservation actions.
Recommendations for future acoustic surveys
To use acoustic monitoring to address ecological questions, it is important to know how sources of variation affect data collection and thus the data itself. While there is no simple formula for what constitutes an ideal survey effort, it is clear that additional effort will result in more precise estimates of activity. Accuracy increases with the number of nights sampled and detectors deployed. It is important to first clearly define the research question and decide on the best study design to test the predictions. If the aim of a study is to determine overall activity levels at a site then a site in Ontario would require sampling for at least four nights with four detectors within a season, but would require an increased sampling effort when evaluating species-specific activity. It is difficult to extrapolate from my results because the degree of habitat heterogeneity differs among sites. I recommend using preliminary studies to determine the number of detectors and nights necessary to obtain an accurate estimate of activity before establishing a long-term monitoring program. I echo the recommendations of other authors (Hayes 1997, Skalak et al. 2012) that monitoring should be done continuously through the night. Ideally, sampling should occur for as long as possible; this is relatively easy with passive methods, but long-term datasets can be inhibiting in terms of analysis.
It is important to use a single brand of detector for a monitoring program and to report detector settings in publications to ensure comparable results among locations and years (Adams et al. 2012/Chapter 2). Detectors should be calibrated to reduce variation among detectors of the same brand and among sampling periods (Larson and Hayes 2000).
Passive detection systems with an automatic trigger are best for developing standardized sampling protocols because they remove biased sampling methods and require little effort for deployment (Stahlschmidt and Brühl 2012). Choice of bat detector will depend on the research question being asked and potentially be influenced by budgetary constraints.
Study location and focal species will determine which detectors are appropriate based on their frequency response. Wildlife Acoustics’ SongMeter SM2BAT has two different models that differ in sampling rate and the lower sampling rate model would not be adequate to record all species present in the Neotropics. Full-spectrum detectors are a better choice for the majority of research questions since they are more sensitive, with greater detection ranges (Adams et al. 2012/Chapter 2), and collect more information than frequency division systems, leading to more accurate species identification (Fenton 2000). If asking questions about echolocation behavior then a more sensitive and calibrated microphone will be important. Research questions about activity levels at a particular site will require decisions on a trade-off between the more expensive detector (i.e., Avisoft, Batlogger) that detects calls in a larger volume of airspace at a given location or a less expensive option (i.e., SongMeter). Also involved in the decision is the importance of simultaneously monitoring multiple locations within a site. Sampling area heterogeneity and access to multiple detectors will impact this decision.
Successful application of acoustic monitoring to detect within-site variation requires the use of multiple detectors simultaneously (Chapter 5). Understanding structural heterogeneity at a site can determine the number of detectors necessary to capture vertical and horizontal variation in bat activity. A reasonable survey effort will depend on the objectives of a particular study. While my recommendations are for surveys sampling patterns and levels of activity, they are relatively in line with surveys for species richness.
Finally, it is necessary to use objective analytic methods for acoustic data because of the already inherent relative nature of the data. Use of programs, such as SaTScan, makes analysis consistent and replicable. It is necessary to measure activity levels relative to a large distribution, which is closer to the ground truth of what is present in nature. The next step is to create a public repository of acoustic datasets to evaluate activity of a species in the context of its entire range, allowing standardization of terms such as “high activity.” Standardization makes it possible to review methods used for environmental assessments and creation of protocols for unified monitoring programs among regions.
Future research directions
It is clear from my results (Chapter 5) and those of Fischer et al. (2009) that activity from a single location does not reflect all locations within a site. My specific findings about within-site variation are unlikely to be directly applicable to other regions because of varying habitat heterogeneity. Vertical spatial partitioning is evident in many habitats (Hecker and Brigham 1999, Kalcounis et al. 1999, Hayes and Gruver 2000), but it has not been established what acoustic sampling effort is necessary to detect these patterns.
Most bat surveys in temperate areas primarily use acoustic methods because of the detectability of the echolocation calls of most insectivorous species, allowing development of standardized protocols based on acoustic monitoring in these regions. However, acoustic monitoring is not a “silver-bullet” for sampling all bat communities; capture methods are required to sample whispering bats with low intensity echolocation calls and those that do not echolocate at all (Griffin 1958, Fenton 2003). Creating standardized sampling protocols for regions with greater species diversity, such as the tropics, will require an understanding of factors influencing capture success (Kalko and Handley 2001) and how recommendations for acoustic sampling effort would differ.
A major limitation to conservation and management efforts is knowledge of where important sites are for bats. Research into the detectability of special sites, such as roosts, hibernacula, swarming sites, and migration stopover sites, would be extremely valuable.
Walking and driving transects are methods used increasingly for standardized bat surveys. The UK’s National Bat Monitoring Program uses volunteers with detectors walking 1 km transects to sample bats and has been successful at detecting population trends over time (Walsh et al. 2001). At least 17 states (Herzog and Britzke 2009) and one province use driving transects to sample bat activity levels post-white-nose syndrome (WNS, Britzke and Herzog). Efforts to collect long-term datasets in a standardized and comparable fashion are laudable, but there is little scientific literature to support the use of this method. Russ et al. (2003) describe the use of a driving transect and discuss its validity, but make no effort to compare the method, and this is likely what most driving surveys protocols are based on. Stahlschmidt and Brühl (2012) have been the only researchers to compare moving transects to stationary detectors, finding that walking transects fail to represent the heterogeneous bat activity patterns and stationary detectors have the greatest potential for standardized surveys. There is an urgent need for research into the feasibility of moving transect surveys.
While much research is focused on how to survey at wind energy developments to determine which sites will be high-risk for bats (Reynolds 2006, Arnett et al. 2011, Korner-Nievergelt et al. 2011), there is still little information available to guide policy and permit critical evaluation of wind energy development proposals and environmental assessment reports. Percentile thresholds (Chapter 3) are the first proposed method for objectively comparing the importance of a site to a species of bat, but we need continued development and research into how environmental recommendations, with the potential to impact survival of numerous bats species, are determined.
The Mexican free-tailed bat (Tadarida brasiliensis), also known as the Brazilian free-tailed bat, is a medium-sized bat that is native to the Americas and is widely regarded as one of the most abundant mammals in North America.
Mexican free-tailed bats use echolocation for navigation and detecting prey. Traveling calls are of a brief but constant frequency. However, they switch modulated frequency calls between 40 and 75 kHz if they detect something. Typically, the frequency range of their echolocation is between 49 and 70 kHz, but can be between 25 and 40 kHz if something crosses their path while in flight.
On 6 November 2014, Aaron Corcoran, a biologist at Wake Forest University, North Carolina, reported online in Science that he and his team had detected Mexican free-tailed bats emitting ultrasonic vocalizations which had the effect of jamming the echolocation calls of a rival bat species hunting moths. The ‘jamming’ call led to an increased chance of the rival missing its prey, which the Mexican free-tailed bat was then able to eat itself. Earlier researchers had discovered some 15 types of social calls made by Mexican free-tailed bats and reported that they could adjust their calls to avoid interfering with others in range of their calls.
The silver-haired bat (Lasionycteris noctivagans) is a species of vesper bat in the family Vespertilionidae and the only member of the genus Lasionycteris.
The pallid bat (Antrozous pallidus) is a species of bat that ranges from western Canada to central Mexico. It is the sole species of its genus and is closely related to Van Gelder's bat (Bauerus dubiaquercus), which is sometimes included in Antrozous. Although it has in the past been placed in its own subfamily (Antrozoinae) or even family (Antrozoidae), it is now considered part of the subfamily Vespertilioninae and the tribe Antrozoini.
The hoary bat (Lasiurus cinereus) is a species of bat in the vesper bat family, Vespertilionidae. It occurs throughout most of North America and much of South America, with disjunct populations in the Galápagos Islands. The Hawaiian hoary bat (ssp. semotus), an endangered subspecies, is endemic to Hawaii.
The big brown bat (Eptesicus fuscus) is native to North America, Central America, the Caribbean, and extreme northern South America.
The long-eared myotis (Myotis evotis) is a species of vesper bat. It can be found in western Canada, the western United States, and Baja California in Mexico.
The California myotis (Myotis californicus) is a species of vesper bat. It is found in British Columbia in Canada, Guatemala, Mexico, and in the western United States, including California.
Townsends big-eared bat
Quick and Cool Facts
On the Channel Islands, big-eared bats are found only on Santa Cruz Island. The species was first observed in 1939 on Santa Cruz Island in a historic 2-story ranch house at Prisoner's Harbor, which hosted a large maternity colony of over 300 individuals. Subsequent studies showed that the population resided in the same area until 1974, when the structure was removed. From 1974 to 1988, no other Townsend's Big-eared Bats were seen on Santa Cruz Island, before Dr. Pat Brown of UCLA, in 1991, was made aware of a colony of Townsend's roosting in the bakery room of the Scorpion adobe building. Presently, a large maternity colony continues to use the building and, occasionally, the rock caves in the Scorpion area. According to the 1994 Department of Fish and Game report, the Scorpion roost is one of only two or three coastal maternity colonies known to exist south of Pt. Conception.
In the summer, the females form a nesting roost. Males are solitary during the maternity periods. The maternity colonies consist of one or more small clusters, which rarely exceed 100 bats. Females are alert and active in the maternity roosts and prefer dark places for their roosts. These colonies form between March and June (depending on climate), with pups born between May and July. Maternity colonies choose sites that have warm, stable temperatures for pup rearing. Female bats usually only have one young a year. The newborns range in weight from 2.1-2.7 grams. There is a strong maternal bond and the young bats squawk when the mother is away. The young bats, however, grow quickly, being able to fly within three weeks. After two months, many of the young bats have left the nursery roosts, with male bats leaving before female. In their first year, male bats are almost certainly incapable of breeding while female bats are able to reproduce at the age of four months.
A study sponsored by the California Department of Fish and Game in the late 1980's documented a population decline of 40-60% in the past 30 years. Only about half of the maternity colonies known to exist in California prior to 1980 were active by 1991, resulting in an estimated 54% decline of adult females. Only three maternity colonies increased in size during the period, and all three are located in National Park areas (Point Reyes National Park, Lava Beds National Monument, and Pinnacles National Monument). Of the 23 roosts that are no longer available to bats, 9 (mostly buildings) have been demolished, 4 (all buildings) have burned, 4 (all buildings) have been renovated in such a way that bats were excluded, and 6 (including buildings, caves, mines, and a water diversion tunnel) have had the entrance closed.
Consequently, for this species to exist, minimization of human disturbance is essential. In additional, it is essential that habitat be preserved.
Human intervention may have helped conserved the population on the west coast of North America, because man-made structures provide a shelter for big-eared bats.1 Channel Islands National Park staff continues with a program of monitoring and surveying this species to ensure its well-being.
In 2008, the ICUN listed this species as Least Concern because of its wide distribution, presumed large population, the occurrence in a number of protected areas and because it wasis unlikely to be declining at nearly the rate required to qualify for listing in a threatened category. However, today the Townsend's Big-eared Bat, is state-listed as an Endangered species in Washington, a Sensitive species in Oregon, and as a Species of Special Concern in Texas, Montana and California, and they are on the Blue List in British Columbia.
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