Dr. Thilina Surasinghe and Maria Armour
Bridgewater State University Foundation
As the Third Quarter grant recipients in 2017, we have now been able to begin the analysis portion of our first season of ultrasonic and acoustic data. For our collaborative project, this summer we have collected both acoustic and ultrasonic recordings at our study sites in Southeastern Massachusetts. This grant has allowed us to run our six months of calls through Kaleidoscope Pro. Over these last few months, a member of our team has attended a workshop run by Wildlife Acoustics and we all have learned how to apply Kaleidoscope Pro software. Our research team has just begun looking at our first season of recordings with less than 10% of our 2017 recordings analyzed.
Our goal for this project is to assess occupancies of bats and anuran taxa in Massachusetts protected and private areas and to analyze the overall soundscape for these sites. Two of our three field sites are located within Mass Audubon's Moosehill Wildlife Sanctuary (Sharon, MA) and a third, private site, in Bridgewater, MA. We deployed Wildlife Acoustics SM3BAT systems from May 2017 – October 2017 at the Mass Audubon sites. Along with learning the software ourselves, during the fall semester we began training two Undergraduate Biology majors enrolled in research credits. This has been the students' first true involvement in conducting scientific research.
Both of our SM3BAT hardware systems were fitted with ultrasonic and acoustic microphones. Since ending our field season in October, we have only been able to scratch the surface of our recordings; analyzing a couple hundred ultrasonic, bat calls. Non-ultrasonic, soundscape recordings have yet to be examined. In the coming semester, we along with our trained research students and new undergraduate students will begin acoustic analyses and continue cluster analysis on our ultrasonic recordings. Come March, our research team will begin the second field season.
Kaleidoscope Pro will continually be used moving forward with this project on bat and anuran taxa. Now that we have learned how to use this software, this spring we hope to complete analysis on our first season of acoustic recordings.
The bioacoustics research lab at Bridgewater State University has made significant progress in the soundscape analysis portion of this project since the last report. Our undergraduate research students, Joshua Kelleher and Adam Enos, have been busy over the spring semester running manual species ID for bats on Kaleidoscope Pro. Through many hours at the computer, Josh and Adam have been able to put together preliminary results of our first season in two posters that they will present next month at the 2018 New England Natural History Conference (NENHC) in Burlington, VT. This will be the first academic conference presentation for both students and the first "publishing outlet" for the research we proposed. Josh's poster is titled "Differences in Seasonal Occurrence and Activity of Bat Species within Private Conservation Land in Massachusetts" and Adam's is "Bat Occupancy in Two Habitat Types in Private Conservation Lands of Southeastern Massachusetts". It is because of the Wildlife Acoustic grant that our two undergraduate student researchers are able to present on their research this coming April.
As we wrap up analysis on season 2017 and prepare for the regional conference, our second acoustic season is already underway. Due to multiple severe snowstorms we have had in the last two months, we have only now been able to access our acoustic recorder deployment sites. During this final week in March, the students and I re-deployed two SM3BAT systems at Mass Audubon's Moose Hill Sanctuary in Sharon, MA. Microphone position was slightly altered at both sites to minimize unwanted echoes from water surfaces. Our deployment setup has been given an update through the support of the Wildlife Acoustics grant. Each system is now housed in the SM3BAT Armor, which offers increased protection and a piece of mind during times of deployment. We also have installed a Garmin GPS unit. An external battery or solar power option is being investigated to extend our deployment dates.
We look forward to sharing our 2017 results that include both bat and amphibian analysis in the next progress report quarter.
During the third quarter of our Wildlife Acoustic’s Equipment Grant our lab successfully completed 2017 bat analyses using Kaleidoscope Pro and presented our results at a regional conference. Throughout the 2017 active season (May through October) we deployed SM3BAT ultrasonic recording devices at two sites (1 device per site) within the Mass Audubon Moose Hill Wildlife Sanctuary in Sharon, MA. Following Kaleidoscope Pro and manual analysis, our results for this season include 4900+ bat passes being recorded over 31 nightly sessions at the vernal pool site and 2700+ bat passes over 21 nightly sessions for the forest edge/barn site. Highest month per night average at the vernal pool site was EPTFUS during each month except July, when MYOLUC was the highest recorded passes. At the forest edge/barn site MYOLUC was the highest passes during May per night average, followed by EPTFUS being the highest for the duration of the season. We also manually confirmed passes at both sites for: LASBOR, LASCIN, LASNOC, and PERSUB. Although these 2017 results gave evidence of high bat activity at both sites, after running statistical analysis (Wilcoxon-Mann-Whitney Test and Kruskal-Wallis Tests) we found that there is no significant difference in bat community presence and activity levels of species between months and habitat type. Our study taxa are known to utilize a diversity of habitat types. Evidence that activity levels were high in our two habitats may be important to include habitat landscape in conservation efforts, not just a single habitat type.
In April our Undergraduate research students, Adam Enos and Joshua Kelleher, presented two posters at The Northeast Natural History Conference (Burlington, VT) on Southeastern Massachusetts bat community composition and activity during the 2017 season. Each student’s project was a part of a larger research project of Co-PI’s Surasinghe and Armour, which utilize Wildlife Acoustic equipment to study both anuran and bat communities. Their abstracts follow this project report. Both Adam and Josh just received their B.S. in Biology in May from Bridgewater State University and are motivated to secure a position in the field of wildlife ecology due to their positive experiences in undergraduate research.
This summer the lab is very active with Dr. Surasinghe training several undergraduate students in anuran field identification and Ms. Armour conducting active capture and release of bats at our two sites to confirm species presence and to collect biological data. We are also in the process of training two new undergraduate researchers who will join our lab in the fall. Both are being trained on SM3BAT deployment and Kaleidoscope Pro analysis. One will continue the bat research project started by Josh and Adam and the other student will investigate anuran and non-bat recordings from 2017-2018.
A portion of this project’s goals is to conduct community outreach. Along with time in the field, summer months are an opportunity to invite the public to join in and learn from our ongoing scientific research. Conservation of bats is often challenging due to unwarranted misconceptions surrounding them; community activities such as a bat walk help the public gain appreciation for this taxa and support conservation efforts of protecting their habitats. Ms. Armour will continue to run her annual public bat walks at Mass Audubon’s Moose Hill Sanctuary in Sharon, MA in July and Old Westbury Gardens, NY in August using Wildlife Acoustic equipment including the user-friendly Echo Meter Touch.
Bats (Order: Chiroptera) are among the most diverse mammalian lineages in North America, and they occupy a wide variety of habitats. Different types of habitats- open spaces, forest edges, and forest interior- substantially vary in resource distribution and spatial structure (clutter), and therefore foraging strategies as well as echolocation signatures of bats can vary substantially among different habitats. In order to explore this hypothesis, we deployed two, SM3BAT automated Bioacoustics recorders (Wildlife Acoustics, Inc.) in a forest-edge habitat and a cluttered habitat located in Mass Audubon’s Moose Hill Wildlife Sanctuary, in Sharon, MA. Forest edge habitat is a low-shrub dominant open area surrounded by a deciduous forest edge while the cluttered habitat is a mixed hardwood-coniferous forest containing two vernal pools. Analysis of the bioacoustics data through Kaleidoscope Pro software confirmed the presence of six bat species during the 2017 active season. Our preliminary analysis showed relative high nightly passes of Myotis lucifugus (Little Brown Bat) altered from the forest edge habitat in early spring to the closed habitat in mid to late summer. Our preliminary conclusions concerning M. lucifugus are that this could be related to either: changes in foliage density as the season progressed or food availability. Further investigation and data is required. We plan on continuing our research and data collection through the 2018 season.
There are nine Vespertilionid species of bat documented within Massachusetts; five of these have been state-listed as Endangered. The long-term assessment of bat activity and presence may offer valuable population data on the affect environmental and human-driven pressures (wind turbines, human disturbance and diseases including White Nose Syndrome) have on our regional bat populations. This study has investigated bat species composition and occurrence within two habitat types (forest edge and forest interior) in Mass Audubon’s Moose Hill Wildlife Sanctuary in Sharon, MA. Passive ultrasonic recordings were made using the automated bioacoustic recorder SM3BAT (Wildlife Acoustics Inc.) during active season months in 2017. Recordings were then run through Kaleidoscope Pro Analysis Software and manual species identification was conducted. Throughout the active season, Eptesicus fuscus (Big Brown Bat) was consistently present at both deployment sites. The months of May and June have a greater presence per recorded night of two migrating species within the forest interior when compared to mid or late summer months: Lasiurus cinereus (Hoary Bat) and Lasionycteris noctivagans (Silver-haired Bat). Finally, Perimyotis subflavus (Tricolored Bat) echolocation pulses were only recorded in May for the forest edge site, but present in the forest interior during early, mid, and late summer. We plan on correlating these preliminary results with classified foraging and migratory strategies of Massachusetts bat species to help determine a baseline for species occurrence and activity levels. This first season of data will aid in a long-term study of bat populations within this protected area.
During the fourth quarter of this research project, our progress has hit several roadblocks. This has made for a challenging end to the active season for us. Issues we faced included failure of internal batteries, calibration issues, and scheduling. This year, to save card space and extend our deployment periods, we decided to start recording in WAV files when programing our SM3BAT. Early on in the season, limitation of these files was discovered as the SM3BAT system was not able to dynamically change channels (only ultrasonic mic triggered recordings) while recording in WAV. Fortunately, this user error was identified through contacting Wildlife Acoustic’s support team and reading Jeff King’s whitepaper “Acoustic (Bird/Amphibian) and Ultrasonic (Bat) Recording with the SM3BAT”. Dual trigger capabilities were allowed once recordings were in WAC files. A positive outcome of this past season was being able to extend deployment periods by using external batteries connected to a solar panel (figure 1). This extended system was possible due to the creativity, knowledge, and effort of our University’s Analytical Instrumentation Staff, Rob Monteith.
Conservation outreach has been a major goal of this project. Through the collaboration with staff at Mass Audubon, Moose Hill Wildlife Sanctuary (our project’s field site) has created an educational exhibit based on our research with bats. Visitors of this sanctuary are able to learn about the different species of bat in Massachusetts and get informed about the acoustic study being conducted on the grounds. This sanctuary has several bat houses that have been erected throughout the trails and on buildings. I captured thermal images of a Big Brown Bat (Eptesicus fuscus) roost while mist netting this summer (figure 2a and 2b) and the Sanctuary’s wildlife camera captured activity of both a bat (species unknown) and two fox cubs near our open field site (figure 3).
The Acoustic lab here at Bridgewater has recently started training two new undergraduate researchers who will be assisting in the analysis of ultrasonic recording (Catherine Cameron) and acoustic recordings (Ashley Zimmerman) this coming academic year. Both are keen to get started with their respective training on Kaleidoscope Pro. Our lab plans on utilizing the new Kaleidoscope Pro Cloud Account to maximize our efforts even when we are not in our lab. The 2018 recordings are in the early stages of being analyzed.
Nantucket Conservation Foundation
Prior to 2015, the federally threatened Northern long-eared bat was not known to be present on the tiny coastal island of Nantucket. The presence of this species was confirmed when a dead specimen of a lactating female was handed in to the Nantucket Conservation Foundation. Given that this species is doing so poorly due to white nose syndrome elsewhere in the northeast, and due to the fact that so little was known about the habitat requirements of northern long-eareds on Nantucket, it became a high priority for us to find out more about what areas of the island these bats were occupying and how populations were faring here. Since receiving a SM4Bat FS recorder from the Wildlife Acoustics Scientific Product Grant earlier this year, we have been able to survey much of the island in order to document areas of activity of Northern long-eareds. Additionally, the data recorded from our SM4Bat has helped us pin point potential areas to mist net so that we can efficiently capture bats, place transmitters on them and locate maternity colonies. We have detected Northern long-eared bats in nearly every location that we've put out our detector!
Our summer field season is winding down, but we still have bats on the brain. Now that we know that Nantucket is home to many Northern long-eared bats, we must find out if they are hibernating here. We will continue to deploy our detector throughout the winter in order to document any winter time activity for these bats and to help us pin down potential locations of hibernacula.
Over the summer field season, we used our SM4Bat FS recorder to survey much of Nantucket in order to document areas of activity of Northern long-eared bats as very little is known about habitat use by this species on the island. We documented acoustic evidence of Northerns in nearly every location that we put out our detector, however the highest number of calls were recorded in the vicinity of pitch pine stands and fewer in hardwood forests. Beginning in mid-September, we began placing our detector in areas where we had found particularly high levels of acoustic activity in order to identify potential locations to place mist nets for late fall capture. As we experienced an unusually warm fall, we continued to collect calls on most nights of in to late November. Based on our acoustic data, we set mist nets near a pitch pine stand close to a water source, and captured 10 Northern long-eared bats in late October. We placed radio transmitters on them and tracked them to what we assume to be potential hibernation sites that we will be monitoring over the winter. Nantucket lacks mines and caves – traditional hibernacula for Northern long-eared bats – so we have placed a high priority on finding and characterizing alternative hibernacula here. We will keep our detector deployed throughout much of the winter in order to document any activity and to help us locate potential locations of other hibernacula.
All has been fairly quiet on the acoustic front this winter. After a successful late fall 2017 mist-netting and radio-telemetry session, we were able to identify some areas that may contain hibernacula for Northern long-eared bats on Nantucket Island. We detected NLEB on our SM4Bat FS through mid-December and believe they are hibernating here in crawl spaces of houses. We deployed our detector throughout the winter in the vicinity of where we think they are hibernating. We did not record any calls in January or February, and the back to back to back March Nor'easters are not helping either. As soon as we start seeing calls of NLEBs on our detector again, we will begin mist-netting with hopes of catching bats as soon as they come out of hibernation. Another aspect of our project that we know little about is the exposure of Island bats to Pd, the fungus that causes White-nose Syndrome. To date, only one island bat has tested positive for Pd at very low levels. Otherwise, our bats appear healthy and suffering the effects of WNS to a lesser degree than bats elsewhere in the Northeast. Capture rates remain high and several maternity colonies have been identified. Swabbing bats as soon as they emerge from hibernation will give us a better idea of the prevalence of exposure to Pd on Nantucket. Our detector will help us to know as soon as bats start flying this spring!
For a tiny island with more than 45% protected open space and a long history of visiting and resident scientists, natural historians and conservationists, the recent addition of a new mammal species to the list for Nantucket Island was quite a surprise. In the summer of 2015, a dead specimen of the federally threatened Northern long-eared bat was found on a trail in a pitch pine forest on the Island. This discovery kicked off a flurry of activity for us at the Nantucket Conservation Foundation. Populations of this species have declined across the northeast by >90% due to White-nose Syndrome, so it immediately it became a high priority for us to learn about habitat use on island and how populations are faring here. The SM4Bat FS detector and Kaleidoscope Pro software we received from the Wildlife Acoustics Scientific Product Grant has allowed us to begin to survey much of the island in order to document areas of activity of Northern long-eared bats.
In the summer of 2017, we moved our detector weekly to various locations across the island to get a handle on the types of vegetation communities with high activity of Northern long-eared bats. We documented acoustic evidence of Northerns in nearly every location that we put out our detector, however the highest number of calls were recorded in the vicinity of pitch pine stands and fewer in hardwood forests and scrub oak shrublands. Our detector also helped us pin point potential locations to place mist-nets in order to capture bats and affix them with radio transmitters to document locations of maternity colonies.
As a bonus, our detector and the software helped us learn what other bat species were present on the island in the summer. It was always assumed that Nantucket had no resident bat species outside of the spring and fall migration season. We were able to determine that we likely have breeding red bats on Nantucket as well.
In mid-September, in anticipation off fall swarming activity, we began placing our detector in areas where we had found particularly high levels of acoustic activity throughout the summer. We experienced an unusually warm fall and continued to collect calls on most nights through mid- December. Nantucket lacks mines and caves – traditional hibernacula for Northern long-eared bats – so we have placed a high priority on finding and characterizing alternative hibernacula here. We kept our detector deployed throughout much of the winter in order to document any activity lending further evidence that northerns are present throughout the winter and likely hibernating locally.
A further piece of the puzzle that we wished to explore was whether Nantucket bats were exposed to Pd, the fungus that causes White-nose Syndrome. We did not record any calls in January or February, but began to pick up a bit of activity towards the end of March and early April. We began mist-netting soon after they emerged from hibernation and with the help of Sam Hoff, a PhD student from University of Albany, we were able to collect swabs to sample for Pd presence. To date, only one apparently healthy island bat tested positive for Pd at very low levels. Otherwise, our bats appear healthy and we are optimistic about the status of the Northern long-eared bat on Nantucket. We will continue to deploy our bat detector across the island in to the future to keep tabs on their populations here.
I arrived on the island of Rota in mid-August and the new equipment arrived in perfect condition. Some Åga (Mariana Crow) pairs have just begun nesting, and this breeding season is looking like it will be a good one.
This season my study will be comparing the vocalizations of captive-reared young to those of wild-reared young. While I wait for the San Diego Zoo Global team to collect the first eggs and chicks for the captive rear-and-release program (scheduled for early October), I am beginning to collect behavioral observations and recordings of wild Åga with my handheld microphone for later characterizing vocalizations.
I have also begun to test out Kaleidoscope's clustering analysis on recordings made in Åga territories last summer using SM3 and SM4 recorders. I am very pleased with how well Kaleidoscope is performing and is able to find Åga vocalizations even with other background noise in the recordings. It even found vocalizations that I missed when scanning through spectrograms visually!
Very soon I will be recording Åga at their nests with the new external microphones and extra long 50m cables I was awarded, resulting in thousands of hours of audio by the end of the season. Using Kaleidoscope's clustering analysis to find and identify Åga vocalizations recorded at these nests should allow me to analyze a substantially larger dataset compared to what might be possible using other available software. I can't wait till the first wild nest recordings start rolling in!
A lot has happened since my last update! Collections of wild åga eggs and chicks for the captive rear-and-release program (through San Diego Zoo Global) are now complete. The last of thirteen chicks have hatched, and I have been getting some quality recordings of them during their first months. We have found that the åga chicks make the tiniest begging "cheeps" even during their first feedings while only a few hours old!
In the wild, we have been steadily finding åga nests, and I have been getting out the ARUs to some of them. Working with a highly intelligent AND critically endangered species means that extra precautions must be taken when monitoring nests. This is where the external microphones and 50m long cables from my Wildlife Acoustics product grant come in. When we find a nest that I think will be good for recording, I hike in under the cover of darkness and set up the microphone in a tree near the nest, camouflage the cable with leaves and stretch the cable 30-50m away where I attach the ARU to another tree. I also camouflage the ARUs just in case. This is all to keep the åga from noticing the equipment and if they do see it then hopefully they won't associate it with humans. After this is all set up at night I can then check the batteries and SD cards weekly during the day with a much lower risk of disturbing the nesting pair.
The breeding season is now winding down and we're expecting to find only a few more nests between now and May. I am now focusing more on trying to get more observations and recordings of wild fledglings and adults as they move away from the nests to add to my library of vocalizations to characterize. I have already collected almost one and a half terabytes of audio so far this season! Very soon I will be putting Kaleidoscope to the full test as I begin analyzing this mountain of recordings I have accumulated.
The Åga (Corvus kubaryi) is a critically endangered forest crow endemic to Guam and Rota of the Mariana Islands; less than 200 individuals remain (KRONER & HA 2018). A new captive rear- and-release program, in which hatchlings from wild-collected eggs are puppet-reared in captivity, began on Rota in 2016. While the rearing environment allows for social interaction between nestlings and with visiting wild adult conspecifics, missing are interactions with parents and their offspring- directed vocalizations.
Early studies of ‘Alala (C. hawaiiensis) captive-rearing suggest that social interactions at young stages, or lack thereof, has profound effects in the interactions of adults, including the ability to breed and rear their own young in captivity (Harvey et al. 2002). Vocalizations are an integral part of avian social interactions, but infrequently provided during captive-rearing and rarely evaluated for long-term effects. Crows’ appropriate use of vocalizations in their many contexts is likely influenced by social and vocal experience across their long developmental period (Brown & Farabaugh 1997). Lack of exposure to parental vocalizations during early development may lead to reduced repertoires and poor behavioral responses in older juveniles. This study is a first step in evaluating the importance of early experience with adult vocalizations on the post-release social success of captive-reared crows.
The objectives of this project were originally stated as: (1) Characterize and archive calls and contexts of nesting-related calls of wild Åga, (2) Determine if there are stage-specific vocalizations between wild parents and offspring. (3) Document, by comparisons with these normal vocal exchanges and responses, any abnormal vocalizations or use of vocalizations (e.g. contexts and responses) by captive puppet-reared juvenile Åga. In this report I will detail the status and findings, to date, with regard to these objectives.
All recordings for this study were collected from August 2017 through May 2018. Twenty wild Åga nests were recorded with SM3 and SM4 units at a distance of 15m. When possible, an extended microphone (SMM-A2) was used 15m from the nest while the unit was placed up to 50m from the nest. ARUs were initially scheduled to record from solar sunrise to sunset, daily, at a sample rate of 24000Hz. However, I found that Mariana Crow calls cover a very broad frequency range, and calling can begin before solar sunrise. Therefore, in November 2017 I changed the settings to record at 44100Hz and 48000Hz on the SM4 and SM3, respectively, from civil sunrise to sunset. To mitigate for the increased file sizes at higher sample rates, I also reduced the scheduled recordings to every-other day. Nests varied with regard to stage when recordings began based on when the nest was found but continued until several days post- fledging or failure. Nests were recorded for seven to 67 days. In total, 4160 hours of recordings of nests are archived. Recordings of mobile juveniles and adults were taken with a handheld recorder and microphone opportunistically during regular monitoring and behavioral observations.
Recordings of captive-reared chicks began with the first collection in October 2017. An SM3 ARU recorded 15 chicks housed indoors during feeding sessions from hatch through approximately day 22. Recorders were mounted on a wall or shelf about 1 meter from the feeding table and set to record daily during the first 3 hours after sunrise. After day 22, chicks began spending time in outdoor aviaries. Recordings were made of chicks outdoors until approximately day 60, and again for one week in May 2018 (ages 5-8 months), for a total of 390 hours of recordings. However, the proximity of the aviaries with 2017’s cohort of young Åga to that of aviaries with older Åga, means that individuals of interest were not acoustically isolated. Therefore, further analyses will be necessary to correct for this and analyses of captive-reared Åga beyond day 20 will not be included in this report.
To locate Åga calls in recordings of wild nests, I created a classifier with Kaleidoscope (v4.5). This classifier used approximately 15hrs of training data pulled from ARU recordings made during the non-breeding season in 2016. Nine nests were run with this classifier. Each nest was classified separately, and up to 900 calls were positively identified and labeled from each. I then extracted (using the “save wav” function) 25 calls from each, avoiding calls that occurred within the same hour of the same day, when possible.
Analysis of the captive-reared chicks took a subset of four individuals (two males and two females), that had recordings available at the same four time points from 1 day post-hatch through day 20. These ages were day 1-2, day 7-8, day 13-14, and day 19-20. Fifteen begging vocalizations were selected and measured from each individual at each of the four time points using Raven Pro sound software (v1.5). I included 13 measurements in the time and frequency domains that are considered to be robust to human error (Raven Pro 1.5 manual). I used simple ANOVA and linear regressions to analyze the effects of age, sex, and weight on acoustic measurements.
Statistical analyses were performed in R (v3.5.2). For analyses of acoustic measurements, a Bonferroni correction was applied to all p-values (a= 0.003) to determine level of significance.
With the improvement autonomous recorders and increased data storage capabilities, software such as Kaleidoscope is critical to analyzing the vast amount of data one may collect. In this study, I have amassed over 4000 hours of recordings, which would be nearly impossible to manually sample any appreciable amount. However, building the best classifier can be rather time consuming depending on one’s goals. I found that the best classifier for my needs used training data from non-breeding season recordings which appear to include a wider range of vocalizations and are less biased toward alarm and territorial-type calls than at nests in the breeding season. This classifier had, on average, an 85 percent accuracy rate (up to 0.5 distance from cluster center) in correctly identifying Åga vocalizations. However, I found this to be highly variable between sites, ranging from as low as 55% accuracy at some sites, up to 98% at others. Attempts to further fine-tune this classifier by adding breeding season training data and captive chick recordings, led to classifiers that either had high accuracy but low variation in calls, or good variation but very low accuracy. Additionally, at this time, it appears that Kaleidoscope will not work well for searching nest recordings for young chick vocalizations. Given the very different recording environments, captive chick recordings did not aid in building a classifier that could detect wild chick calls. Even with more explicit training, using manually extracted calls of wild chicks, this software is unlikely to discriminate the quiet calls, with very little structure, against the variety of background noises. Lastly, attempts to use Kaleidoscope to separate Åga call types, as a method to characterize their repertoire, have so far been unsuccessfu.l
Using the classifier described above, I ran a subset of 9 nests through Kaleidoscope. For each, I manually identified 150-900 calls within the Åga cluster; the variation was due to total hours of recordings per nest and availability of true Åga calls within the cluster. I found that most Åga calls occur during the first hour of the day at 6am, and generally declined through the day, with a slight increase around 4pm (Fig 1). The types of vocalizations used at different stages of nesting may vary, but these analyses are pending a full characterization of this species repertoire. For example, I noticed in at least one nesting pair where a call type that was not present during the first several days of recording emerged, and dominated, immediately following the “failure” of their nest due to egg collection for the captive-rearing program. This will need to be further investigated to determine if it is truly associated with nest-failure, or merely coincidental.
Captive-reared chicks In an analysis of a subset of four captive-reared Åga, I found a general decrease in frequency (Hz) measurements, such as peak frequency and first-quartile frequency, as age increased. This was not unexpected since body size was also increasing. However, the rate of change differed between males and females at different ages. Using the peak frequency (Hz) measurement as an example, females changed most significantly from day 1 to day 7, where changes from day 7 to day 13 and 13 to 19 were not significant (p= 0.32 and 0.97, respectively) (Fig 2). Conversely, males changed most significantly between days 7 and 13 (p<0.0001), while changes from day 1 to 7 and day 13 to 19 were not significant (p=0.89 and 1.0, respectively). Frequency-based differences between the sexes were strongest at day 7 (p=0.0002), followed by day 1 (p=0.01). Days 13 and 19 showed no sex-based differences in any acoustic measure. It is interesting that these sex-based frequency differences occur at the two youngest ages, then disappear by day 20, while weight differences between sexes are not significant until day 20.
Captive vs. wild-reared chicks
Unfortunately, due to permit requirements, microphones at nests were not able to be as close as I would have preferred in order to get high quality recordings of young, wild Åga chicks. Therefore, direct comparisons of fine-scale acoustic measurements between captive-reared and wild-reared chicks may not be possible due the attenuation of sound in these very different settings. Based on personal observations, however, chick differences in the broad types of calls are unlikely to show at nestling ages (< 35 days old), as I only observed each making about three broad types of calls by the end of that stage. However, it is still possible for early experience to influence adult repertoire, as in other oscine songbirds, while not exhibiting differences at young ages (Boughman & Moss 2003).
Wild Åga juveniles are dependent on their parents for eight months, on average (Morton et al. 1999). During this time, most vocalizations appear to fall into three categories: begging, food receiving “gobbles”, and contact calls (personal obs.). It seems that most wild Åga do not begin using/practicing a larger repertoire until they become more independent (personal obs). Anecdotally, captive chicks actually seemed to make more types of calls by 5-8 months of age, which may represent more “vocal play”, or practicing, as seen in wild sub-adults; possibly a result of their being artificially more “independent”. Wild chicks are often still dependent at this age, so most of their vocalizations still fall into the three categories described above. However, this will need to be quantified and it will be important to follow-up on captive-reared Åga post- release to understand how their adult repertoire develops compared to wild-reared counterparts.
Perhaps the most important next step is to characterize the full Åga repertoire. This will allow for a better understanding of potential age, nest-stage and/or season specific call types and make comparisons between wild and captive reared Åga. My preferred method is to use a method of unsupervised clustering to accomplish this, as many Åga vocalizations seems graded between call types, as in some primate repertoires, as opposed discrete song-types in other songbirds, making human classification quite subjective (Wadewitz et al. 2015).
Next, I will compare the repertoire of the juvenile (>35 days old) captive-reared Åga to the wild juveniles. Because of the nature of the recordings at the aviaries, it will not be possible to analyze the captive juveniles individually, rather I will need to use them as a group. Although, I may be able to separate 2017’s cohort from the other captive Åga by creating an amplitude cut- off, since the microphones were nearest to the 2017 cohort.
Finally, I will integrate what is gained from this project with the active Åga recovery program. When manually scanning recordings, I was able to detect some nestlings as young as 2 days old. Since most young nestling vocalizations only occur during feeding, sub-sampling these recordings could reveal natural feeding rates, better than the normally brief visual observations that happen in the field, which could help improve captive-rearing methods. I will be providing the captive-rearing program with recordings from nests to play to captive-reared chicks and will work with the captive-rearing team to gather new recordings of the next cohort.
Boughman JW, Moss CF. 2003. Social sounds: vocal learning and development of mammal and bird calls. Pages 138–224 in A. M. Simmons, R. . Fay, and A. N. Popper, editors. Acoustic Communication—Springer Handbook of Auditory Research. Springer- Verlag, New York.
Brown ED, Farabaugh SM. 1997. What birds with complex social relationships can tell us about vocal learning: Vocal sharing in avian groups. Pages 98–127 in C. Snowdon and M. Hausberger, editors. Social influences on vocal development. Cambridge University Press, Cambridge, U.K.
Harvey NC, Farabaugh SM, Druker BB. 2002. Effects of early rearing experience on adult behavior and nesting in captive Hawaiian crows (Corvus hawaiiensis). Zoo Biology 21:59–75.
KRONER A, HA RR. 2018. An update of the breeding population status of the critically endangered Mariana Crow Corvus kubaryi on Rota, Northern Mariana Islands 2013–2014. Bird Conservation International 28:416–422.
Morton JM, Plentovich S, Sharp T. 1999. Reproduction and juvenile dispersal of Mariana Crows on Rota 1996-1999.
Wadewitz P, Hammerschmidt K, Battaglia D, Witt A, Wolf F, Fischer J. 2015. Characterizing vocal repertoires - Hard vs. Soft classification approaches. PLoS ONE 10:1–16.
Uxbridge Secondary School, Durham District School Board
The start of the new school year is always busy, throw in two canoe trips within the first five weeks and it is downright chaotic. That's how its been here at Uxbridge Secondary School this semester with the Outer's Club heading to Algonquin Park and the Outdoor Education class venturing further north to Kilarney Provincial Park. Along on these trips are an Echo Meter Touch bat detector and occasionally a SM4BAT Recorder. Students gather at the water's edge at dusk to look for, and hopefully record, foraging bats. Back in the classroom students will process the recordings with Kaleidoscope software.
Over the past summer some bat recordings were collected at our Outdoor Education Center and these recordings will be analyzed by the students in the coming months.
A busy fall semester saw the Outers Club and the Outdoor Education class take part in several overnight out-trips this year. Along on these trips were an Echo Meter Touch and a SM4BAT recorder. While camping in Algonquin Park and while staying at Camp Kandalore, students were able, on a few nights, to survey for bats (unfortunately poor weather conditions on some of these nights prevented bats from flying). We did manage to get some good recordings of Big Brown/Silver Haired bats on those nights.
With the tripping season over students have now had time to formally learn more about Ontario's bats. Through power point presentations and first-hand demonstrations students, including those who are not able to attend over- night trips, have learned how bio-acoustics is helping bat researchers study bats.
Some of our students are using the Kaleidoscope software to analyze recordings made over the summer from various locations within Central Ontario and at our residential Outdoor Education Center. While these students and myself! are getting more familiar with the software we (it has) have managed to identify four of Ontario's eight species so far. Some of our recordings will require further vetting from more knowledgeable bat experts.
With semester two fast approaching another group students will soon have the opportunity to learn more about Ontario's bats and the technologies being used to study them. We are patiently looking forward to the upcoming spring field season as well.
As another school year has come to an end it is nice to know that many of our students at Uxbridge Secondary School now have a better understanding of, and appreciation for, Ontario's bats. In-class presentations on bat biology and the threats facing our native bat populations were complimented with over-night field trips where students were able to make real-time acoustic recordings of bat calls. Camping trips to Algonquin, Kilarney, and Queen Elizabeth Provincial Parks provided the students with opportunities to use Wildlife Acoustic's Echo Meter Touch and App to capture the calls of five of Ontario's eight bat species. Several of our students downloaded the app onto their personal devices and borrowed the EMT's to record bats in their own neighbourhoods. "I never realized we had this type of bat activity in my own backyard" one of my students told me excitedly the morning after having used the EMT at home that night"
These recordings were then brought back into the classroom where students (after having watched the tutorial videos) would run them through the Kaleidoscope software program. (A SM4 BAT was setup at our residential outdoor education center several times throughout the field season and collected a number of recordings that were also included in analysis). The more technically savvy students picked up this skill up quiet quickly. In any case, a real appreciation for the science and technology that is used in bat research was gained by these students.
In all, hundreds of recordings were collected that represent five different bat species (Big Brown/Silver-haired, Eastern Red, Hoary, and Myotis spp.). A collection of these recordings has been sent off to more qualified personnel for further vetting. Our data will be shared with our Provincial Ministry of Natural Resources and Forestry.
Staff at Uxbridge Secondary School hope to continue taking students into the field to experience the excitement of "hearing" bats.
Thank you to Wildlife Acoustics for the grant of the Kaleidoscope Software.
Amy Thurston Toronto and Region Conservation Community Engagement Team
To date, Toronto and Region Conservation's (TRCA) Education and Community Engagement teams have successfully met their public engagement target for 2017. All Community Engagement staff have been trained in the Bats in Your Backyard program and use of the Wildlife Acoustic Echo Meter Touch modules and app. Our Field Centre staff have also been trained and bat detectors have been deployed to Albion Hills, Kortright, Claremont and Lake St. George Field Centres to be used during school programming beginning this fall. Through the Bats in Your Backyard program, our citizen scientists have found three species (Hoary bat, Big brown bat and Silver haired bat) in five locations.
As our program expands and additional locations are surveyed, we anticipate we will find more species. Participants provided positive feedback about the Bats in your Backyard program and were excited to have the opportunity to see bats in the night sky, where otherwise they might fly by silently. When asked during a pre-program survey, 32% of participants thought that the majority of local bats carried rabies. Following the program, 100% of participants understood that only a small percentage of bats carry rabies indicating that the program is having a positive impact on participants' knowledge and attitude about bats. The Echo Meter Touch modules and Echo Meter App have allowed TRCA's teams to provide a visual experience for participants to learn about acoustic bat identification. The data being collected through the use of the Echo Meter Touch devices will be submitted to the TRCAs Terrestrial Inventories and Monitoring Team to use and share the data with other interested parties.
Toronto and Region Conservation's (TRCA) Bats in Your Backyard program engaged 105 participants through five community events from June to August 2017. Each program included a presentation on the ecology of local bats, threats facing bats including white nose syndrome, and actions they could take to protect them. During the project, five bat boxes were also constructed to create habitat. Participants were taken on a guided bat survey which acoustically monitored bats through presence/apparent absence data. They learned about bat survey methodology and undertook the completion of citizen science data recording sheets. Data collected included: date, time, location, suggested auto-id, call frequency range, whether a terminal buzz was heard, weather conditions, temperature, and wind conditions. This data was recorded when an echolocation call was heard over the Echo Meter module. Participants were careful not to make a second observation unless a second bat was visually confirmed or at least 100m passed since the previous observation. In total 23 unique observations were recorded, finding two species of bats including: Big brown bat (Eptesicus fuscus) and Hoary bat (Lasiurus cinereus). As well, several additional observations of either the Big brown bat or Silver haired bat (Lasionycteris noctivagans) were made but the species could not be confirmed due to the similarity of their calls. The data collected was shared with TRCA's Terrestrial Inventories and Monitoring team to assist in their research into local bats, and will be shared with other interested parties.
A pre and post survey of participants using true or false statements was undertaken to evaluate the impact of the program. Results are in the table below and indicate a clear increase in both the knowledge and attitudes of participants as well as a willingness to take action to help the conservation of bats. As one participant remarked in part, "I got to be a citizen scientist tonight, and it was very cool. I was thrilled to actually see and hear several Big Brown Bats who were flying low overhead looking for insects a little after sunset. They were hunting insects that would otherwise have been hunting me out there...so I've been converted as a bat fan. This science stuff is fun!".
2017 also saw the training of four of TRCA's Field Centres on the program and the use of modules in preparation for spring. This, combined with an earlier seasonal start time and expanded marketing through new and continued partnerships, should result in further engagement in 2018.
Florida Atlantic University
Our lab studies animal communication, in particular, the acoustic structure and social function of bird song. One of our ongoing projects at Johnathan Dickinson State Park is to study the structure and function of female song in the Bachman's sparrow. In mid-July, we rotated our SM4 recorder near the nests of several of the mated pairs we were monitoring, trying to capture recordings of the elusive females. Female birds do not sing in the majority or North American songbird species, but Bachman's sparrow females do sing. They don't sing with the showiness or bravado that their mates do, but yet they do produce song-like vocalizations. We want to know why.
Over the past two breeding seasons, we made several observations of females singing in the proximity of their mates, and near the nests they were building. We obtained good quality recordings from one female, which will allow us to develop a protocol for making larger scale acoustic comparisons between the songs of males and females next season. In addition, we placed the recorder on the territories of males that had been subjects in the aggression experiment we were completing. We recorded each male for 24-48 hours, two critical pieces of information: singing patterns from pre-dawn to post-dusk, and singing patterns during undisturbed, unprovoked singing. We are now comparing those singing patterns to the patterns we recorded in response to a simulated territorial intrusion by a singing rival male. In April 2018, we will obtain recordings of 8-10 females to use for acoustic analysis and for playback experiments designed to test when and why females sing.
Female birds do not sing in the majority or North American songbird species. Bachman's sparrow females do not sing with the showiness or bravado that their mates do, but they do produce song-like vocalizations. Below is a spectrogram (a visual representation of sound plotting song pitch over time, much like music is visualized) showing an example of one female's song that we recorded using our SM4 song meter. Also pictured are examples of male broadcast songs (called primary song) and an example of "warbled song," which is quite distinct from primary song. The female songs we have visualized so far bear some resemblance to male warbled song, being a non-stereotyped, seemingly jumbled series of notes. During the next field season, we will use the songs we have recorded to perform playback experiments to measure female behavioral responses to a simulated female intruder. We bought four additional SM4 units, which will allow us to rotate the meters among the territories of many more females to capture song at different stages of the nesting cycle. In addition to our study of female song, we used the SM4 meter to record many hours of male Bachman's sparrows singing at the dawn chorus, and throughout the day. From these recordings we are gaining understanding about how males use their song type repertoires in different behavioral contexts, and the degree to which males share song types. Our preliminary data suggest that neighbors share a large number of song types on average (> 50%) while non-neighbors share fewer song types (< 30%). This pattern has implications for how males use their songs to communicate with neighbors, and how song type sharing may influence where young males choose to defend a territory.
We continue our efforts to gather data on the acoustic structure and social function of female song in Bachman's Sparrow. This is a shy, elusive species. Females are tricky to find, and even more challenging to record, because they sing infrequently. In 2017 we captured a few recordings of female song. So far in 2018, we have observed females singing in the field, but are still working to capture audio recordings.
Our observations suggest that females sing when fertile, in temporal proximity to copulation. Perhaps their songs serve as an invitation to mate? Working on this hunch, we are placing our SM4 recorders on trees within 10 meters or so of nests that are being built, and as eggs are being laid. We have not yet analyzed the many hours of recordings obtained so far (over 620 hours!), but we are hopeful that we havecaptured examples of female song from several different birds.
With these recordings, we will compare the acoustic structure of female song to those of males, and quantify variation in female songs both within and between females. Male Bachman's sparrows sing over 40 types of Primary Song – do females also sing many song types? Does an individual's song vary from day to day, or does she sing a consistent song? How will females respond to songs of other females played on their territories? We look forward to digging into our data to answer these and other questions about this interesting species.
Our lab studies animal communication, in particular, the acoustic structure and social function of bird song. One of our projects is to study the structure and function of female song in the Bachman's sparrow. Female birds do not sing in most North American songbird species, but Bachman's sparrow females produce song-like vocalizations. We want to know why.
Over the past two breeding seasons here in South Florida, we made several observations of females singing in the proximity of their mates, and near the nests they were building. In April 2018 we began placing our SM4 recorders near nests during the building stage, hoping to capture recordings of female song. This has proven to be a challenging task! In for a total of 118 hours of recordings. From April – July 2018, we recorded on a total of 2017, we recorded on three territories 38 territories, placing the recorders near known active nests, for a total of 3,108 hours of recording.
So far we have found three good examples of female song, and these recordings will serve as stimuli for a playback experiment next spring in which we will test the responses of territorial pairs to playbacks of female song at different stages of the nesting cycle. Our primary goal for the next several months is to analyze the many hours of recordings we gathered this past season to find additional examples of female song.
We will tackle this challenge using a custom software program written by one of our undergraduate students. This program can be "trained" to look for vocalizations matching the acoustic qualities of female Bachman's sparrow song. This program will automate and thus greatly speed-up the process of combing through the recordings, and we hope to find at least a dozen examples of female song by March 2019.
In addition to using our SM4 recorders to capture female song, we have been using them to "eavesdrop" on the natural singing interactions of neighboring male sparrows. Bachman's sparrow males have large repertoires of broadcast song types, and neighboring males tend to share quite a few types in common. In the field, we often hear males within ear-shot of each other counter-singing by matching each other's song types. We are using the SM4 recorders to capture the natural dynamics of these social interactions. This season we recorded a total of 10 sets of neighbors by placing an SM4 recorder near the boundary between neighboring territories. This season we recorded approximately 12 hours a day for several days for each pair of neighbors (2 hours before and 4 hours after sunrise, and 4 hours before and 2 hours after sunset). One student in the lab is now pouring over these recordings to document and describe cases of natural song type matching interactions, which has not been done for this species. So far she has found several astonishing examples of song matching, in which males matched song-for-song during bouts of counter-singing. Why do they do this? We will use these SM4 data along with song playback experiments to try and uncover the social significance of song matching behavior.
In addition, a graduate student in the lab will be analyzing the 3,108 hours of territorial recordings from the SM4 recorders to gain two critical pieces of information: singing patterns of individual birds from pre-dawn to post-dusk, and singing patterns during undisturbed, unprovoked singing. We have many hours of recordings of males singing in response to simulated territorial intrusions, in which we use song playback to provoke territorial behavior. However, little is known about how male Bachman's sparrows utilize their large repertoires during bouts of natural advertisement singing, which are most common at dawn and dusk.
Each field season with Bachman's sparrow brings new challenges and exciting new questions to tackle. We are very enthusiastic to be adding to general knowledge about this understudied and enigmatic species, and why it has evolved such a large and varied vocal communication system. In addition, we are beginning new projects with the Northern cardinal in South Florida, in which we will continue our studies of female song, and will ask new questions about how vocal communication differs across urbanization gradients. We are deeply appreciative to Wildlife Acoustics for their Scientific Product Award of an SM4 meter, which has been a game-changer for our research!
Alessandro Catenazzi Southern Illinois University, Carbondale, IL
On 1 April 2017 Wildlife Acoustics we received a Wildlife Acoustics grant (license to use software) in support of our project "Escape from deadly disease: Can environmental refugia save tropical mountain frogs from extinction?". The main goal of this project is to ground-truth predictions of a habitat distribution modeling map produced for the highly virulent, pathogenic fungus Batrachochytrium dendrobatidis, which has been linked to worldwide amphibian declines and extinctions. Our study area, in the eastern slopes of Peruvian Andes, is among the most amphibian species-rich regions on Earth. An additional objective, as part of our field expeditions to areas predicted to have low occurrence of the fungus, is to search for relictual populations of threatened species known to have disappeared from areas where disease prevalence is high.
Field work for this project is supported by grants from the Eppley Foundation, the Chicago Board of Trade Endangered Species Fund, Southern Illinois University Carbondale startup funds to A. Catenazzi, personal funds donated by A. Catenazzi, and volunteering by local biologists Alex Ttito, Isabel Diaz and William Tito, and Dr. Sarah Kupferberg.
From May to August 2017 we conducted field expeditions to six different regions of central Peru: Pampas Galeras (Ayacucho), Kosñipata valley near Manu National Park (Cusco), Marcapata Valley (Cusco), San Gabán Valley (Puno), upper Guacamayo watershed in Bahuaja-Sonene National Park (Puno), and the Santo Domingo Valley (Puno). We surveyed amphibian populations from 500 to 4000 m elevation, capturing over 900 individuals, which were identified, sexed, measured and swabbed for detection of fungal infection. We also surveyed >60 streams (375–4700 m elevation) for presence of fungal disease by filtering 1–3 L of water/stream through environmental DNA (eDNA) analysis. We deployed Sound Meter recorders (SM 1; purchased in 2008 with support from the Rufford Foundation) at the Santo Domingo site to survey for Noblella peruviana, a species described from this location but that has not seen since the late 1800s or early 1900s, and the threatened harlequin toad Atelopus erythropus and A. tricolor, not seen since 2004 and 2007 respectively.
We were able to rediscover Noblella peruviana, more than 100 years since it had last been seen. Moreover, sound recordings allowed us to detect the presence of a second, cryptic species of Noblella, which appears to live in the same habitats along with N. peruviana. We are in the process of describing this new species. We are still processing sound recordings to screen for the presence of advertisement calls of other amphibian species, and specifically of the two species of Atelopus, a process which is made more difficult because the call of A. erythropus has never been recorded. We are also proceeding with analyses of skin swab and eDNA samples, and identifying collected material, and we expect this collection should contain at least 5 new species discovered during our field work.
Third Millennium Alliance
Our team has spent the past couple of months compiling all known population data for the Tandayapa Andean Toad (Rhaebo olallai) from the Manduriacu Reserve and testing the SM4 Song Meter settings with the Configurator tool. With this information we have narrowed our site selection for the initial deployment of the Song Meters. At the start of October we will be running a short pilot deployment to train the local park ranger how to use the Song Meters and how to change the batteries and memory cards. Because few audio recordings exist for the species, we will use this initial deployment to determine if it will be possible to narrow the recording time to peak calling hours. During the initial pilot deployment we will leave the Song Meters in the field for one month recording for 15 minutes each hour from dusk to dawn. In the meantime our team will be working with collaborators from Texas State University to learn more about the Kaleidoscope software and the development of recognizers. Once this first pilot deployment is complete we will review the results and determine if any changes to the methodology are needed before the next deployment.
Since our last update our team has successfully deployed four Song Meters in the Manduriacu Reserve, Ecuador, which protects habitat for the only known population of Rhaebo olallai. The Song Meters we're deployed at four specific locations across a 2.2km area within the currently known range of Rhaebo olallai. Once we obtain sufficient audio material for the species we will be moving the recorders to more distant areas where the species has not yet been recorded using visual encounter surveys. After working with the configurator tool we elected to record 20 minutes on the hour each hour between sundown and sunrise. We have recorded the species calling throughout this time frame, so our initial recordings will be used to fine tune our recording schedule before moving the recorders to the new sites. Our next field visit to download the audio files is planned for late March, at which point we will begin our initial review of the data.
Since our last update our team has dedicated much of their attention to running our network of four Song Meters in the Manduriacu Reserve, Ecuador. We have now have been running the Song Meters for eight months in the Reserve and are preparing to move two of the units to more remote sites on the periphery of the Reserve. We unfortunately haven't yet had a chance to dive into the analysis of the data because our team has been dedicating much of their time to fighting new mining threats in the area. Over the past year the Ecuadorian government opened new mining concessions across much of the region where the Manduriacu Reserve is located, so our team is working hard to protect as much habitat for R. olallai and other threatened species found in the Manduriacu Reserve before its too late. The audio data being recorded with our Song Meters will play an important role in documenting the status of R. olallai in the Reserve as mining activities expand across the region.
Dr. Eric Baitchman, DVM, DACZM
Zoo New England, Franklin Park Zoo, Boston, MA
Zoo New England and Grassroots Wildlife Conservation are working on the Franklin Park Biodiversity Project to assess and preserve the natural biodiversity of Franklin Park in Boston, MA, and engage the local community in education of biodiversity and conservation.
Seasonal surveys have been conducted to record observations of wildlife within the "Wilderness" section of the park, outside of Franklin Park Zoo's gates. Each survey, or bio-blitz, is conducted over nine days during which time staff and public participants catalogue observations of mammals, amphibians, birds, reptiles, invertebrates, plants, and fungi, using iNaturalist. Over 300 species have been identified thus far.
We just completed this year's summer surveys this past week and were finally able to include bats in our observations by using the Echo Meter Touch. We held a well-attended public bat walk where we made our first recordings of local bat species. A dozen participants had a very good experience, and the EMT was key to that success. The device provides such an engaging and educational interface to get people excited about finding bats and "experiencing" exactly what's going on as the often unseen shapes fly by in the night. At least two different bat species were added to our biodiversity survey, including big brown bats and red bats, and possibly a third, the silver haired bat, though we are still learning about determining accuracy of the identifications made with the device.
Franklin Park Zoo will host an additional public walk this summer and we are excited to be joined by a Wildlife Acoustics staff member, who will help both the public participants and ourselves to learn more about how the technology works and how to better interpret the results.
We held a second public bat walk around Scarboro Pond in Franklin Park this September. Members of the public were led by ZNE Education and Conservation staff, as well as Wildlife Acoustics, Inc. employees, Mona Doss and Ali Donargo. We were so lucky to have Mona and Ali with us, as they were able to augment our program significantly, by sharing their expertise with our guests and providing additional Echo Meter Touch units to allow more people to have hands-on experience with detecting bats. Guests learned about bat diversity in our region, how that diversity has changed in recent decades, natural history of our native bats, the enormous ecological services bats provide us, and the conservation challenges facing bats today. The technology of the EMT allows such a personal experience for an entire family to have closer engagement to the bats surrounding them. Everyone from children to adults light up as they get to watch signals from bats being instantly translated to a species identification, complete with accompanying portrait! That personal level of interaction is what our public programs are all about, inspiring citizen scientists to recognize and care for the natural diversity present in their own neighborhoods.
Our staff learned a great deal from Mona and Ali as well, about how the technology works and about interpreting results from our EMT units, giving us a greater degree of confidence in our identifications. On the September walk, we identified big brown bats, red bats, and silver-haired bats.
Dr. David C. Lahti, Queens College, City University of New York
Our project on weaverbird song, which began in Sept 2016 and is set to end in June 2018 (pre-publication) as per our grant application, is proceeding well. However, we have had to move portions of our work around due to two sorts of constraints-- one in the field and one in the lab. In the field, breeding was delayed at first due to odd weather, but we have been recording wonderfully since January. We know have hundreds of good songs, which are waiting for analysis.
The songs were to be at least pre-analyzed by this month, however, the software we use in the lab is specialized for tonal songs, and is performing poorly on weaver song that has not only diphony (two notes being sung at the same time), but also a lot of harmonics and broadband elements (clicks, rattles). We knew weaver song would be complex and difficult to quantify, but it was anyone's guess how we would solve this problem once we got songs in the lab. I have decided to switch software to Sound Analysis Pro (Ofer Tchernichovski), as that was designed for zebra finch songs and so can deal well with these elements. However, to get a student in Ofer's lab learning how to use that software will have to wait until this September. In the meantime recording and data manipulation will continue, although there's very little we can say about the songs themselves until we can analyze them.
The globular nests and complex rambling songs of Ploceus weaverbirds are striking and familiar features of the sub-Saharan African landscape. The village weaver (Ploceus cucullatus), the most abundant member of the genus, breeds in colonies where the cacophony of their simultaneous singing can hardly be overlooked along gallery forests and waterways, around agricultural fields, and within villages and towns. Precisely because of their coloniality and the fact that much of their courtship and breeding interactions occur within the colony, individual village weaver songs are rarely recorded. We consequently have little understanding of how these songs vary between individuals, over geographic distances, or between species. Moreover, weaverbirds often change their behavior in response to human intrusion, decreasing the opportunity for researchers to record their song, and potentially rendering them different than if they had been recorded during ordinary activity. Because of the Wildlife Acoustics grant of a SongMeter SM4, we have been able to record weaverbirds individually, and without having to be physically present so the focal birds are not alarmed but sing and behave normally. We have also been able to record begging calls from young birds, which we would otherwise never have been able to do in a colonial species in a natural context.
Since we received the Wildlife Acoustics SongMeter SM4 in 2016 for this project, our recordist Clive Barlow has made a variety of excellent recordings of the whole range of Gambian village weaver vocalizations, from the calls of nestlings, fledglings, parents, and mates, to the competitive and mate- attractive songs of males of all ages in both breeding and eclipse (nonbreeding) plumage. These recordings are revealing the diversity of vocalization in this species, and will also constitute the first element in a broader comparative study of song across the genus Ploceus. Specifically in the village weaver, the SongMeter SM4 has already provided us unprecedented access to two important vocal features: individual song structure, and the relationship between the begging calls of weavers and the cuckoo chicks that parasitize their nests.
The complex songs of the village weaver are consistently delivered as a series of phrases of multiple types, which in succession give a distinct impression of a rise in intensity. This impression is corroborated by analysis of amplitude (volume), frequency (pitch), bandwith (pitch range), and note rate, all of which tend to increase across comparable elements in the course of a complete song. The phrases are each either extended or repeated indeterminately, and might at any juncture either lose steam and peter out or else build and transition to a predictable next phrase type in the series. Often the full songs can be divided into a chirpy introduction, two or three variations on a warble-trill-buzz theme, and a trilled coda. The following recording illustrates two renditions back-to-back of the song of a typical Gambian adult male, recorded by the SM4 in June 2016. A series of call-like notes build to a complex warble that ends in a long flat buzz; then comes a brief warble, trill, and a second buzz, this one rising; this is followed by a brief warble and trill and a descending whistly buzz; the song ends with an extended rapid trill.
The diederik cuckoo (Chrysococcyx caprius) is a brood parasite of the village weaver, meaning that its adult females lay eggs in weaver nests rather than laying in nests of their own. A successfully parasitized weaver raises none of its own young, but only a cuckoo chick, during that reproductive attempt—the cuckoo mother and (eventually) the chick remove any weaver eggs and nestlings from the nest. One might expect cuckoo chicks either to mimic weaver chicks in their food begging calls, or simply to deliver more engaging calls in some way to compensate for the fact that they are not otherwise similar in appearance to young weavers, especially as they grow older and beg for food outside of the nest. Recordings by the SM4 of both weaver and cuckoo juveniles begging from weaver parents (or foster parents) so far show a similar rate of delivery, but no evidence of structural mimicry. Cuckoo begging calls appear to be more tonal (melodic) than weaver calls at a given age.
The following audio tracks correspond to the recording in Figure 3. Track A is at normal speed. Track B is slowed to one-quarter speed to show the acoustic detail, which in some respects also more faithfully represents the listening experience of a songbird, given how quickly they process audio input and distinguish rapidly delivered notes.
Christopher E. Comer
Stephen F. Austin State University – Arthur Temple College of Forestry and Agriculture, Nacogdoches, TX
The primary use of the 2 Echo Meter Touch units during this period was for outreach and education activities. These activities consisted primarily of the following three events:
The 2 Echo Meter Touch units were used during this period for one event. On the weekend of September 22-24, 2017, we hosted a "bioblitz" event at the Pineywoods Conservation Center in Nacogdoches, TX. The event was hosted by undergraduate students in the Arthur Temple College of Forestry and Agriculture here at Stephen F. Austin State University. It was open to the public and all data were recorded through iNaturalist. One component of the bioblitz was a bat walk using the bat Echo Meter touch units on the night of September 22. Ten individuals participated in the bat walk and they recorded 5 species of bats (Lasiurus borealis, L. cinereus, L. seminolus, Nycticeius humeralis, and Tadarida brasiliensis).
The primary use of the 2 Echo Meter Touch units during this period was for outreach and education activities. During the summer field station experience for forestry undergraduates at SFASU, we used the Echo Meter Touch units as part of the Field Wildlife Techniques class on the evenings of June 4-6, 2018, in conjunction with mist netting activity. This included 56 undergraduate wildlife and forestry students.
San Diego Zoo Global Institute of Conservation Research
July brought us some logistical challenges! Colleague Diego had set up 20 SM4 audio recorders in the Maijuna-Kichwa Regional Conservation Area two months previously. He had been rained on copiously, and enjoyed fast-flowing, deep streams to access the forest. The recorders had spend six weeks listening for gunshots, and animals, recording 24 hours a day with 512GB SD cards and a large car battery each. So with some excitement, I went up the Sucusari River to collect those recorders and their paired camera traps. Unfortunately the water level dropped much earlier than expected, and the river dried to a trickle in some places. River sections that took Diego and the team an hour, now took half a day hauling the canoes and clearing logs with the chainsaw. It was clear that the top half of the stream was not even accessible. With great effort, and a LOT of hiking through a very try 'rainforest', we extracted half the recorders and camera traps. Half the recorders are still in the forest, and we have to wait another couple of months to bring them out. On the plus side, the car batteries were still fresh - the units used far less power than I expected. This means I'll get another months worth of data upriver until the cards fill up.
One of the main applications we have is to listen for gunshots in this attractive reserve, and I completed some important range tests while bringing in these first recorders. Conditions were perfect - low wind and no rain, and even before analysis, it is clear that the under these conditions the range is close to 2km... given that our sample points are typically about 2km apart in our camera trap and acoustic surveys - this means that we have almost complete coverage in perfect conditions. We will have more 'real word' tests when we combine our audio data with spatial hunting data from our GPS tracked hunters who are registering their hunts in a wider range of conditions.
I now have to repeat my expedition in October, to get the units we failed to retrieve before. Then we face the not insignificant task of analyzing about 8 weeks worth of data from 20 SM4+ recorders, all recording 24 hours per day. We plan to use Wildlife Acoustic's 'Kaleidoscope software to analyze maybe 25,000 hours of recordings, first for gunshots, but then for peccaries, woolly monkeys and a range of key species. Beyond that we will have a resource available with which could survey birds, amphibians or insects. But first, I'm looking forward to getting back out to the Sucusari to recover our recorders.
This month we completed the installation of 20 SM4 recorders at 2km intervals in a large array in the Maijuna Regional Conservation Area (MRCA), on the Rio Napo in the Peruvian Amazon. The recorders are in trees to keep them safe from curious people, and hopefully to maximize the range we get from the units. This was backbreaking work for field assistant Diego, who had to climb all the trees! The SM4s are connected to car batteries and we hope to have at least six weeks constant recording from each unit, in which we should be able to find recordings of a wide range on animals. We hope to find these with the help of Wildlife Acoustics Kaleidoscope software, but a camera trap close to each recorder will give us a clue as to where to find sample recordings of noisy animals like peccaries. The main reason for deploying the recorders, however, is to record gunshots in the area. the MRCA is an extractive reserve, so there is legal hunting by residents, managed by the community themselves, and informed by our research. We track much of this hunting with GPS trackers on the guns of hunters, and can pinpoint the location of kills. With this we can calibrate the range of our recorders under varying conditions through several weeks. We will also be able to detect hunting by unknown individuals who may be hunting in the area without permission, directly informing the community management of the area. We hope audio recorders will prove to be a highly efficient way of monitoring hunting that can be expanded through forests across the globe.
Kaleidoscope is was able to find virtually all our test gunshots – even most of those as far as 2km away. This is another step towards the autonomous monitoring of hunting across forested areas. Now we have the small matter of 4TB of recordings to process - to find the real-world gunshots, and especially those of our GPS tracked hunters. This means building 'recognisers' that Kaleidoscope can use to sort through the data more quickly and accurately, ignoring (for now) the hundreds of thousands of bird, insect and mammal noises that we have also recorded in our 2-month 24-hour recordings. Bottom line though - IT WORKS! We have a complete record of the hunting for two months on the Sucusari River basin.
Meanwhile, the recorders have not been idle, we have employed them on an exploratory project to find arguably the world's rarest bird. Confined to a few small patches of unusual white-sand forests, most of the bird's habit has been consumed by the demand for sand in the city of Iquitos. Now confined to The Allpahuayo Mishana National Reserve, we don't know where or how many there are- it could be as few at 20 pairs. We are using the SM4s and Kaleidoscope with the help of the local bird experts to try to find the bird and perhaps determine patch occupancy. So far we have not heard it, but have discovered another rare bird that is new to the Reserve - the barred tinamou- hardly ever seen or heard because it sings at 4am in the morning! The SM4s will soon go back out to listen for gunshots - this time on the Yavari river, but it goes to show how valuable these units are once you have them in the projects armory.
Amy K. Wray
University of Wisconsin, Madison
Our project, which focuses on investigating the effects of bat declines from White-nose Syndrome (WNS) on insect communities in Southern Wisconsin, has entered its third and penultimate field season. We continue to collect insect samples for microscope identification, bat guano for molecular analyses, and passive acoustic recordings to assess levels of bat activity in each area. Using the Kaleidoscope Pro software provided by the Wildlife Acoustics Scientific Product Grant, we have processed our acoustic data from 2015 and 2016 field seasons to assess bat activity levels at each of our 20 study sites. Our preliminary results from Kaleidoscope analyses indicate that 80% of our study sets met our a priori assumptions for having high Pre-WNS bat activity levels, with slight declines detected in 2016. We will use the data generated by Kaleidoscope's analyses, as well as data from future field seasons, to population occupancy models that will then be used to correlate bat activity with changes in insect community composition. As this year marks the first instances where dramatic declines in bat populations have been observed, the data that we are currently collecting for this field season will be essential for comparing differences between pre- and post-WNS years. We will continue to use Kaleidoscope to analyze bat activity levels at our sites in order to investigate correlates between bat diet composition, insect communities, and potential shifts related to disease-related bat declines. In the future, the results from our study will be used to inform management strategies and to promote bat bat conservation in Wisconsin and throughout the Midwestern region.
My primary research goals involve investigating the effects of bat declines from White-nose syndrome (WNS) on insect communities in Southern Wisconsin. For this project, I use a combination of insect community sampling, genetic analysis of bat guano, and acoustic monitoring to assess bat activity levels. At each of my 20 field sites, we use acoustic monitors to record nightly bat activity in zero crossing format. Additionally, for the recent 2017 field season, we also used SM4BAT detectors to record bat activity in full spectrum at sites with varying degrees of agricultural and forest landscape composition. These data, recorded in FS, will be used to better understand seasonal changes in bat foraging patterns and how these relate to landscape composition variables. All acoustic recordings will be assessed using Kaleidoscope PRO software in order to automatically classify and manually check bat identifications. From these analyses, spatial and temporal shifts in bat activity levels will be used to assess changes in bat communities and bat activity levels following declines related to WNS. The results from this study, including aspects involving acoustic monitoring, have been presented at the Midwest Bat Working group annual meetings, and will also be presented this year at the North American Symposium on Bat Research.
My current research on the effects of bat declines from White-nose syndrome (WNS) involves incorporating data from insect communities, bat acoustics, and next-generation sequencing of guano in Southern Wisconsin. Currently, we have processed acoustic data from 2015 and 2016. Based on preliminary results, we have found a significant decline in little brown bat activity across all sites, but did not detect a significant decline in big brown bat activity. These results are consistent with reports from the Wisconsin DNR and the Great Wisconsin Bat Count, which have reported declines in colonies throughout the state based on pre- and post-volancy bat counts. Insect counts from 2015-2017 have been completed, with nearly 2 million insect specimens identified and counted. In the near future of this project, this information will be used to assess whether bat acoustic activity correlates with changes in local insect abundances during the summer. The results from this study were presented this year at the NASBR meeting in Knoxville, Tennessee, and will also be presented next year at the American Society of Mammalogists meeting.
Hummingbird Monitoring Network
With the grant of 2 licenses of Wildlife Acoustics Kaleidoscope Pro 4.1 software with acoustic Cluster Analysis, the Hummingbird Monitoring Network (HMN) could now analyze recordings taken in fields of hummingbird-visited flowers during southbound migration. The science objectives of the study are to determine how weather, plant phenology and abundance of available nectar influence hummingbird migration. The community objectives of the study are to employ and engage high school students in STEM (Science, Technology, Engineering, and Mathematics) activities.
In 2013 and 2014, we recorded daytime activity of hummingbirds in 7 flower patches for 5 weeks during southbound migration in the Chiricahua Mountains of southeastern Arizona. In 2015 and 2016, we worked with Songscope software to build recognizers of hummingbird sounds. This effort had limited success and we were anxious to learn the Kaleidoscope software. During spring semester 2017, Patagonia High School students easily learned how to use the Kaleidoscope software and began identifying clusters with hummingbird chirp notes, vocalizations, and wing trills. By the end of this semester's program, students had iteratively defined clusters and were beginning to refine the classifiers that identify hummingbird chirp notes and vocalizations to species. The refinement of the classifier will continue during fall semester with the goal of having complete classifiers for the three hummingbird species known to have used these flower patches. Upon completion of the hummingbird classifiers, HMN's science collaborators will complete the analyses for the study.
Building classifiers with the Kaleidoscope software was an excellent project for high school students. They became proficient at identifying hummingbird sounds and classifying clusters into different vocalization categories. Our workflow was somewhat unique because it was multi-threaded. Two students, each using a license of Kaleidoscope, built classifiers from different recordings. We, then, wanted to combine the classifiers and re-run the cluster analyzer to continue refining the classifiers. We were unable to figure out how to do this, so we contacted Wildlife Acoustic's technical support team and worked with Chris Warren. He quickly helped identify how to combine the efforts as well as answered additional questions that arose throughout the semester.
We think passive recordings are an excellent field technique; have encouraged others to use it as well as engage high school students to help build the classifiers. We thank Wildlife Acoustics for this grant and particularly thank Chris Warren for his timely and extremely helpful guidance as we learned how to use Kaleidoscope.
No progress has been made with this project since the last report. Building the hummingbird classifiers are part of a STEM program with Patagonia Union High School. At the end of the program last March, students had iteratively defined clusters and were beginning to refine the classifiers to identify hummingbird chip notes and vocalizations to species. Due to lack of funding for the PASEO program (Patagonia After School Employment Opportunities), it was not offered to students this Fall semester. The high school student, Nick Botz, who mastered Kaleidoscope, is an accomplished musician and strong science student and will be working with HMN from late November to January. We expect to complete building the hummingbird classifiers by the end of his employment. In 2018, we will begin integrating the results of Kaleidoscope with the environmental data to identify the weather/climate factors influencing hummingbird migration.
The purpose of HMN’s study is to see how weather patterns affect the migration of hummingbirds in our area. To do that, they need to be able to accurately estimate the number of hummingbirds visiting a flower patch in a set period of time. The data for the study came from seven different sites in the Chiricahua Mountains: Barfood Park, Coal Pit, El Coronado, Long Park, Onion Saddle, Saulsberry, and Turkey Creek. The hummingbirds tagged in these sites were Broad-tailed (BTLH), Black-chinned (BCHU), Rufous (RUHU), and Magnificent (MAHU). Recording took place during August and September of 2013 and 2014 with Wildlife Acoustics’ Song Monitor devices. It was these recordings that would become the key to proceeding with the study.
That is where Wildlife Acoustics’ Kaleidoscope 4.1.0a comes in. This software allows you to build a classifier, a special cluster.kcs file that Kaleidoscope uses to process audio recordings, creating a “cluster.csv” Excel document as its results table. The end result is a machine that can pick out each individual hummingbird sound over the course of weeks for our review. The final step is to combine all the resulting Excel files to create one spreadsheet telling how many hummingbirds visited the flower patches for each day of the study.
Kaleidoscope uses a small set of sample recordings to create a classifier, which can then be used on new recordings to sort vocalizations into “clusters”, or groups of vocalizations with similarities. It is the job of the human user to train the classifier to discriminate between different species. Processing a set of recordings for the first time creates a cluster.csv and cluster.kcs file. The cluster.csv is an Excel document containing all the meta data from the scan. This is what the human user opens and edits with Kaleidoscope. Changes to the cluster.csv create a new cluster.kcs, and this is the file that the software uses to cluster new recordings with its many complex models.
The entire process can be hard to grasp, so Wildlife Acoustics has provided a series of tutorial videos on their website to train new users. They also conducted a free workshop at the US Fish and Wildlife Service office, where representatives answered questions and ran through training simulations, which was very helpful in getting familiar with the software.
The Cornell Lab of Ornithology’s Macaulay Library contains audio samples from every species in the study, so it proved extremely useful in discovering the subtle differences between different vocalizations. The website uses a black-against-white style for its spectrogram plots, which makes it a little more difficult to compare and contrast with Kaleidoscope’s dotted green-against-black style. The audio files from the Macaulay library can be downloaded as MP3’s, then processed through the media.io engine to convert them to WAV files, allowing them to be viewed with Kaleidoscope. This was just for the purpose of reference; the Macaulay downloads were not mixed into the field recordings.
This was advantageous also because Kaleidoscope allows the user to pick a certain range of kHz they want to hear when they play the audio. This meant that the loud, distracting background noise could be filtered out (you can see such noise at the bottom of the above spectrogram) to quite literally get a clearer picture of the vocalizations.
The first step of building any classifier is to select a set of recordings and run them through Kaleidoscope’s simplest action: “scan and cluster recordings to create cluster.kcs and cluster.csv.” In the tutorial videos, the creators recommend using training recordings, but those were unavailable, so the classifier was created from the field recordings themselves. The sample recordings were selected by site and date using data from the tagging study that accompanied the recording study. The sites with the most tagged hummingbirds from each species were selected in the hopes of obtaining enough vocalizations to include every species in the classifier. With that, the first scan commenced. Mention additional recordings of known hummingbird chips, Provide enough detail to convince the reader that you identified all vocalizations and chip notes. This justifies the use of the cluster analyses for relative abundance estimates.
Once the recordings were scanned, they could be examined in the Kaleidoscope viewer. It was then time to determine the correct signal parameters for the classifier. Kaleidoscope’s signal parameters are a range of length (seconds) and frequency (Hz) allowed for scanning. The idea was to adjust them to fit snugly around a single chip note so that noises such as frog trills, which sound nothing like a hummingbird chip, would be left out.
The method for figuring out the signal parameters was simple. First, take the tallest chip note and the widest, measuring the range of x(Seconds) and y(Hz).
The next step in the process is clustering, or going through every detection and entering a name for it in the MANUAL ID column. This can be done two ways, with two different kinds of results. The first is by clicking “Bulk ID” and assigning a species identification to an entire cluster of detections at once. This is far quicker and results in a simple classifier with low accuracy. The second method is actually viewing every single detection and assigning it its own species identification. With a results table containing hundreds of thousands of detections, this is a lot more tedious and time-consuming, but the result is an advanced classifier that has an accuracy of about 89%. The CHIP classifier was made using both of these methods. First, a simple classifier was made by entering either CHIP or NOTCHIP in the “Bulk ID” tab. Then, the .csv file was processed through the “rescan recordings and edited cluster.csv to create new cluster.kcs with pairwise classifiers and cluster.csv” action, separating the hummingbird sounds from everything else. Then, work could commence on the advanced classifier by entering species names into the “MANUAL ID” column.
Out of the original four species present in field study, the MAHU chips were so few and far between that the software actually left them out after the first re-scan. That turned out not to be a problem, since data collected from on-the-scene monitoring shows that BTLH, BCHU, and RUHU have higher populations in our area and migrate much further than MAHU, making them more suitable subjects for the study.
Once every detection had been manually labeled, the file was ready to be rescanned again and become an advanced classifier. After just one rescan, the classifier was still full of false positives. Creating a high-accuracy classifier is a reiterative process, with each rescan weeding out a little more of the false positives. The classifier reaches its maximum accuracy once a rescan consistently creates 10% or fewer false positives. From the first basic classifier scan to the final advanced rescan, it ultimately took 11 rescans to finalize the chip classifier.
With the 2013 and 2014 data extracted, it was time for our first big milestone: getting results from the chip classifier. We collated every cluster.csv into one giant Excel file, which was only possible within the 1,000,000 row limit because we removed the NOTHUM detections. Using the quantity of every CHIP detection and the date/time the recordings were taken, this graph was made.
The chip classifier was only half the battle. To include every sound made by the hummingbirds in the study, a second classifier had to be made that would have different signal parameters in order to capture hummingbird vocalizations, which are snippets of hummingbird song with a clear beginning and end.
That meant starting from the very beginning, using the same recordings that were selected for the chip classifier because of their relative abundance of hummingbirds.
The signal parameters were found for the vocalizations using the same method as for the chips. This time the vocalizations with the largest range of length (seconds) and frequency (Hz) were used to set the signal parameters. Predictably enough, the frequency did not need to be changed, but the length was extended to 4.2 seconds. Changing the inter-syllable gap was very important, since it allowed the vocalization and all its syllables to be grouped together instead of being pulled apart and counted separately the way the chips were. Kaleidoscope’s default for this setting is 0.35 seconds, which ended up being enough to detect entire vocalizations.
In the same way that MAHU chips were so underrepresented in the Chip classifier that the software would not cluster them, BTLH vocalizations did not make it into the vocalization classifier. However, there were a few unexpected Calliope Hummingbird detections that did get clustered and incorporated into the classifier. A surprise, to be sure, but a welcome one...
As was the case with the chip classifier, one scan was not nearly enough. To remove as many false positives as possible and reach a final product, the cluster.csv had to be processed through the “rescan recordings and edited cluster.csv to create new cluster.kcs with pairwise classifiers and cluster.csv” action 6 times.
In 2013 and 2014, passive recordings with 7 Wildlife Acoustics Songmeters (SM3) were made in 7 flower patches for 5 weeks each year during southbound migration in the Chiricahua Mountains of southeastern Arizona. During this time, weekly field surveys were conducted to estimate hummingbird activity and floral abundance so abundance estimates from the recordings could be calibrated. The science objectives of the study are to determine how weather, plant phenology and abundance of available nectar influence hummingbird migration. The community objectives of the study are to employ and engage high school students in STEM (Science, Technology, Engineering, and Mathematics) activities.
In 2015 and 2016, we worked with Songscope software to build recognizers of hummingbird sounds. This effort had limited success and we were anxious to learn Kaleidoscope, the replacement software for Songscope. With the grant of two licenses of Kaleidoscope at the end of 2017, we were prepared to continue extracting hummingbird vocalizations from the recordings.
In 2017, Patagonia High School students started learning Kaleidoscope and began identifying clusters with hummingbird chip notes, vocalizations, and wing trills. By the end of this semester’s program, students had begun to iteratively define clusters and refine the classifiers. At this time, funding was lacking and this project was placed on hold.
In 2018 and 2019, we hired Patagonia High School sophomore Nick Botz to continue building classifiers with Kaleidoscope. Nick is a talented musician and is interested in science. He was the ideal student to continue this project. He became proficient at identifying hummingbird sounds and classifying clusters into different vocalization categories. During the summer of 2018, we attended a day long workshop on Kaleidoscope offered by Wildlife Acoustics through the USFWS office in Tucson. This workshop helped identify ways in which we could still improve the development of the classifiers. In Spring 2019, Nick was confident that he had extracted all the hummingbird vocalizations from the recordings. His final task was to write a report that described how he developed the classifier and that could educate the next person working on the project. His report follows this summary. Upon finishing this project, Nick enrolled in an online Audio Engineering course. He’s using Audacity software to mix & edit tracks, and suddenly looking at waveform plots again!
Now, we are collaborating with scientists in the School of Natural Resource and the Environment at University of Arizona to integrate the weather, field, and vocalization data so we can explore how weather, plant phenology and abundance of available nectar influence hummingbird migration. The resulting science is dependent upon fully extracting all hummingbird vocalizations. Upon reading Nick’s report by a Wildlife Acoustic technician, we would appreciate learning if there was something else that we could have done to improve the extractions.
Dr. Amy Belaire
St. Edward's University, Wild Basin Creative Research Center, Austin, TX
Wild Basin is a 227-acre natural area that provides 3 miles of trails within a 10-minute drive of downtown Austin, Texas. Our biodiversity monitoring project began in early March 2017 to coincide with the avian breeding season in Austin, Texas. Our team this spring included three St. Edward's University student interns, Gabby Macias, Olivia Leos, and Anne-Marie Walker, who were advised and mentored by Dr. Amy Belaire.
The team set up the Wildlife Acoustics SongMeter SM4 units in a transect design within Wild Basin (Fig. 1). The transect began near the preserve boundary, which is adjacent to a major highway, and extended perpendicular to the highway and into the preserve along a riparian corridor (Bee Creek). Three SM4 units were set up approximately 300 meters apart along this transect line; a fourth SM4 unit was deployed to alternating locations during the breeding season to maximize detection of individual golden-cheeked warblers (a federally endangered songbird with breeding habitat in Wild Basin). We set a schedule for each unit to maximize detection of songbirds, with 1 hour of recording each day immediately after sunrise. We also recorded during night hours in attempt to detect frogs and toads in the surrounding riparian habitat. In addition to these recordings, we also used an iPad equipped with an auxiliary microphone to measure ambient anthropogenic noise levels (primarily from the adjacent highway) along the same transect.
Throughout the spring, our team documented and shared our progress with multiple blog posts that described the study design, installing units in the field, conducting regular measurements of anthropogenic noise levels, and running through preliminary analyses. Please see the following online links to review the updates:
Cameron Brown, Save Tootgarook Swamp, Inc., Victoria, Australia
The Tootgarook Swamp is the largest remaining shallow freshwater marsh in the Western Port and Port Phillip Bay region and contains the largest intact stands of tall marsh and sedge wetlands on the Mornington Peninsula. The loss and alteration of these habitats in the region has resulted in a reduction in the occurrence of several freshwater wetland obligates including the Australasian Bittern, Botaurus poiciloptilus.
The Australasian Bittern has been regularly documented within the Tootgarook Swamp since 1891. Recent observations, including breeding calls in spring, lead to the belief that breeding could potentially be occurring in the 650-hectare wetland. The Australian Bittern is listed federally as Endangered under the Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) and identifying and securing habitat for the species is a priority to its conservation.
Given its cryptic appearance and behavior of the species and the logistical difficulties in conducting biodiversity surveys in preferred wintering habitat (often dense tall vegetation) the project sought to compliment traditional survey methods with remote sensing technology including song meters and wildlife cameras.
From 07/2016 to 12/2016 the monitoring project complimented on-ground physical surveys with deployment of:
Australasian Bittern was recorded in a combination of all survey techniques. The new combined survey methods also detected 20 additional species (17 birds, 2 frogs and a bat) to the manual observation survey, with 12 of these species purely recorded by the SM3+ Song Meter. Wildlife cameras and UAV have also been able to record unique behaviours that previously have not been seen before in the swamp, with the animal acting more natural in their environment.
Information gathered from the Song Meter and the Wildlife cameras data indicates that several species of birds were recorded on the cameras that were not picked up by the Song Meter, as well as birds that were not picked up by Song Meter and wildlife cameras that was through manual observation and vise-versa. Overall it is the authors view that the overall strategic approach to combined observation techniques gave an overarching interpretation of the avian species composition in the area.
These findings confirm the application of remote sensing technology is an effective method for detecting fauna in wetland environments.
Passive surveillance is an important facet of capturing images or sounds of wildlife with minimal disturbance by humans as wildlife can sense and/or have an acute awareness of human behaviour. Active surveys can deal completely different results as most wildlife try to avoid humans if possible.
This is something you can witness even just sitting quietly in a car [example 1] at a nature reserve or park, when wildlife may be around but the minute you exit the vehicle the wildlife becomes aware you may be a threat.
This really shows the importance of using different methods and equipment when conducting fauna surveys to detect species. Desktop surveys of fauna species should only be used as a guide to potential expected species encounters in the field, thus allowing time for the appropriate preparatory measures to be undertaken prior to survey start. Recorded data from the song meters has been sent to Birdlife Australia for analysis. The recorded field data was also analysed by the author.
Dr. Lindsey Swierk and Dr. Jennifer Tennessen
Yale University, New Haven, CT
The 2017 suburban amphibian monitoring season has begun! Our Song Meter SM4 recorders have been deployed to examine the effects of land use change and noise pollution on wood frog choruses. Wood frogs sing in choruses in preparation of their spring breeding season, and it's unknown how noise pollution will affect the abilities of these frog populations to persist in suburbanizing landscapes. We are working to answer this question through a multi-year comparison of suburbanized and forested breeding ponds in Connecticut.
At this point, we have selected and begun to monitor the six ponds that we will track for the next several years. In the third week of February, we placed one Song Meter SM4 at each pond prior to the annual migration of wood frogs. The recorders will collect data throughout each day until wood frogs leave the ponds. These recordings will complement other data on the effects of suburbanization on wood frogs, including behavioral, morphological, and physiological measures that we are collecting this year. While placing the recorders, we had the opportunity to communicate our research to interested residents and their children – all were excited to be living so close to where "real" science was taking place!
The six Song Meter SM4 recorders successfully collected two months' of recordings surrounding the wood frog breeding season at suburban and forested ponds in Connecticut. Wood frog breeding was unusual this year: an unexpected late-season blizzard in March divided the season in half by several weeks. We noted some mortality of adults and fertilized eggs in the breeding ponds following the blizzard, although most choruses managed to recover after the weather warmed. This extreme weather event will allow us to not only examine how wood frog choruses are affected by anthropogenic noise (for example, the large amount of traffic noise that that interfered with some choruses), but also how indirect anthropogenic influences, such as extreme weather events that are predicted to increase in frequency in many global climate change scenarios, will affect chorusing behavior of amphibians. We are currently preparing the sound files for analysis with Wildlife Acoustics' Kaleidoscope program.
The analysis of our Song Meter data is underway! We began our comparison of the effects of suburbanization on wood frog breeding activity by quantifying the number of advertisement calls that were performed in the 5 minutes at the start of every other hour throughout the breeding season. (The record high so far is almost 7000 calls per 5 minutes!) Not only will this allow us to compare breeding season durations, start and end times, and a proxy of the number of animals in the chorus at ponds over a suburbanization gradient, but we will also be able to quantify how different aspects of weather (temperature, wind speed, water temperature, humidity, etc.) affect the call rate at ponds in different environments. Interestingly, the 2016 pilot data from our most and least suburbanized ponds and already show some interesting trends. Frog choruses peak during the nighttime hours in the most suburbanized pond but, in our most forested (least suburbanized) pond, this pattern isn't as apparent. If this trend holds true, it could be evidence that frogs are altering their calling behavior in noisier environments. Stay tuned!
We continue to examine data from the first year of our multi-year study on the effects of suburbanization on wood frog choruses. Our 2017 Song Meter data have already taught us quite a bit about wood frog chorusing behavior across the suburban gradient. Suburban wood frog choruses appear to be more robust to unfavorable weather; colder and windier days appear to be less of an impediment to suburban wood frogs than to those in the forest. Adult male population size, as estimated by call-counting proxy, is not directly related to suburbanization but instead to pond size and habitat. That said, our most suburbanized pond (surrounded by 70% suburban development to 200 m) hosted the smallest population despite its close similarity in size to several of the other ponds in the study. The figure shown here depicts the number of call detections in 5 minutes collected every 2 hours over the breeding season in each of six ponds, from "1" (most forested) to "6" (most suburban). We are currently developing zero-inflated time series statistical models of count data to quantify the effects of multiple environmental parameters on calling rates (e.g., water temperature's effect on calling rates, as shown in the figure depicting a single pond's chorus from our pilot study). Such models will enable us to pinpoint how individual parameters differ in their effects on calling rates between suburban and forested populations. We are also in the process of quantifying characteristics of individual calls within choruses and examining how these relate to each pond's degree of suburbanization.
One of the best aspects of Song Meter data collection is the ability to re-use data in unanticipated ways. The wood frog breeding season in 2017 was interrupted by a late-season blizzard (note the division of calling behavior in the six-panel figure), decimating the breeding wood frog adult population of many ponds in our study area. With our Song Meter data, we were able to document the blizzard's effect on calling behavior, which we plan to compare to other (non-blizzard) years in the future. We hope to be able to explore wood frog population sensitivity and recovery to extreme-weather events, and how suburbanization affects these responses. Contrary to our expectations, we observed that suburban wood frog choruses rebounded more quickly after the blizzard, despite the fact there was no difference in pre-blizzard chorus start dates. We are interested in investigating if suburban development may alleviate the effects of severe weather by causing ponds to warm more quickly, post-blizzard.
Dr. Darren S Proppe
Calvin College, Grand Rapids, MI
I am pleased to announce that Chad Apol, an undergraduate biology student at Calvin College, has been hired to conduct full-time research this summer on the impacts of noise on detectability in acoustic recordings. Chad has spent the semester learning the techniques of bioacoustical analysis and is currently becoming proficient in Kaleidoscope. He has just begun a course in field natural history that will prepare him to identify the birds he will be seeing and hearing this summer. We have familiarized ourselves with the SM4 recordings units, and we have purchased a noise-making sleep machine that will be used to introduce noise to our recordings on a very limited spatial scale. We are in the process of finalizing field sites for our experiments. We will use at least 10 abandoned oil pads located in Northern Michigan. They provide both a forested and open ecosystem for our work. We intend to test varying levels of white, pink, and Brownian noise; comparing Kaleidoscope's ability to detect and appropriately cluster bird vocalizations in comparison to quiet controls. Recording will begin at the end of May and continue throughout the summer. No data yet, but we are itching to get started. More soon!
Recorders are in the field! We are up to 16 complete trials, each containing one microphone exposed to noise playback, and a control microphone that is unexposed. Noise playback comes in the form of white, pink, and brownian played at 40, 50, 60, and 70 dBA. We have trained Kaleidoscope to detect five species commonly found in our recordings: blue jay (Cyanocitta cristata), ovenbird (Seiurus aurocapilla), red-eyed vireo (Vireo olivaceus), Eastern wood-pewee (Contopus virens), and black-throated green warbler (Setophaga virens). The first results are coming in, but it's too early to describe any patterns. We've also decided to add a human detection component. Chad will be visually detecting vocalizations in a subset of our recordings to compare human detection rates in varying noise levels to the capabilities of Kaleidoscope. Stay tuned...
Kaleidoscope analysis is well underway, with nearly half of the data from our sites having been compiled. We are already seeing significant trends for a number of parameters. One of the expected, yet interesting, preliminary results is that Kaleidoscope software has been more successful in correctly detecting vocalizations from a control SM4 unit compared to a SM4 unit subjected to noise input (see Figure). We will be completing data analysis soon and are looking forward to reporting additional trends related to noise level and noise type.
Human development can introduce significant amounts of noise pollution into the environment, often greatly exceeding the amplitude of natural ambient noise. Anthropogenic noise has been shown to negatively impact the reproduction of certain bird species (Kight et al 2012), change the vocalizations and behavior of others (Francis et al 2011), and decrease the detectability of biotic vocalizations in birds (Leonard et al. 2015) and humans (Koper et al. 2016). The detectability of biotic vocalizations is an integral aspect of avian population and community research, which often consists of surveys that locate birds through the identification of songs and calls. The use of passive acoustic recorders, such as those produced by Wildlife Acoustics, has increased dramatically in recent years, enhancing our ability to collect large acoustic datasets on avian vocal behavior. However, an increasing number of acoustic studies now occur in urban and suburban areas where anthropogenic noise is prevalent. Although increased noise levels would be expected to mask vocalizations and reduce their detectability, the extent to which this impacts acoustic detection in passive acoustic recorders is relatively unknown. Further, noise varies in frequency and amplitude, and minimal information is available on how these nuances impact detectability. We tested whether increasing the amplitude of three different types of ambient noise impacted the detectability of vocalizations in five bird species.
We placed two SM4 passive acoustic recorders (Wildlife Acoustics, Inc.) in 20 remote hardwood forests in Northern Michigan, USA. Apple earbuds were placed on one microphone of one SM4 unit, broadcasting noise tracks in 5 minute increments. Tracks included a control with no noise, and three different types of noise which vary in their spectral characteristics (brown, pink and white). Each noise type was played at amplitude 40, 50, 60 and 70 dB(A). The opposing microphone on the same unit was used as a within unit control, and a microphone on a second unit that was placed 3m away along the same azimuth was used as a between unit control. The results from the two controls did not differ for any treatment, therefore, only the between unit control was retained. Kaleidoscope Pro detection software was pre-trained using commercially available field recordings of red-eyed vireos, blue jays, black-throated green warblers, ovenbirds, and wood-pewees because these species were common at our sites.
The number of detections made by Kaleidoscope was recorded for each site, track, and species - false detections were visually inspected and removed. Statistics were carried out in program R (V3.3.3). There was a significant overall difference in the mean number of detections between the control and noise treatments at amplitudes greater than 50 dB (Figure 1), with the control microphone detecting significantly more vocalizations than the noise treatment during the same timeframe. A poisson regression model was fitted to determine whether the impacts on detection differed by noise type (Figure 2). While detection decreased with amplitude for all noise types, each was impacted differently, with white noise being least impacted and pink being most impacted. Each species was also impacted differently (Figure 3a & 3b), although none were exempt from the masking effects of noise. The red-eyed vireo is graphed separately because the number of correct detections was substantially higher than the other four species, likely because of its propensity to vocalizing continuously.
Our results reveal that ambient noise levels ≥ 50 dB can significantly impact the detectability of bird vocalizations, while ambient noise levels ≥ 70 dB may eliminate almost all detections. Although not significant, a drop in detection rate is also visible at 40 dB. Our models show that white noise, which spreads acoustic energy across all frequencies equally, impacted vocal detection less than brownian or pink noise, which concentrate more energy in the lower frequencies. This may be due to the uniform background produced by white noise, which enables easier detection of energy bursts, such as is found in bird song. However, anthropogenic noise sources tend to be concentrated in the lower frequencies, more similar to pink or brownian noise. The impacts of noise varied somewhat by species, but none were exempt from the masking effects of noise. Further work is needed to determine whether noise filters, or visual screen counts can improve the results from noise-impacted data collected from passive acoustic recorders. Nonetheless, our results suggest that caution is needed when using passive acoustic recorders in noisy areas, especially if comparisons are to be made with quiet regions.
Kight, C. R., Saha, M. S., & Swaddle, J. P. (2012). Anthropogenic noise is associated with reductions in the productivity of breeding Eastern Bluebirds (Sialia sialis). Ecological Applications, 22(7), 1989-1996.
Koper, N., Leston, L., Baker, T. M., Curry, C., & Rosa, P. (2016). Effects of ambient noise on detectability and localization of avian songs and tones by observers in grasslands. Ecology and evolution, 6(1), 245-255.
Leonard, M. L., Horn, A. G., Oswald, K. N., & McIntyre, E. (2015). Effect of ambient noise on parent-offspring interactions in tree swallows. Animal behaviour, 109, 1-7.
Francis, C. D., Ortega, C. P., & Cruz, A. (2011). Vocal frequency change reflects different responses to anthropogenic noise in two suboscine tyrant flycatchers. Proceedings of the Royal Society B, 278(1714), 2025-2031.