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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.
Natural History Museum of Los Angeles County
The Natural History Museum of Los Angeles County (NHM) was the fortunate recipient of a Wildlife Acoustics Scientific Product Grant during the second quarter of 2016. Wildlife Acoustics generously awarded NHM four SM4BAT FS recorders, four SM-UU1 microphones, four external power cords, and Kaleidoscope Pro software. The equipment was incorporated into an unprecedented large scale urban biodiversity survey called the SuperProject.
The SuperProject is a multi-year backyard survey of various taxonomic groups and environmental data along multiple habitat gradients. Homeowners agree to host research equipment (e.g., flying insect traps) which are monitored by scientists, in addition to completing diurnal citizen science surveys for various taxonomic groups. The overall objective behind the incorporation of a bat study into the SuperProject was to allow us to measure the impacts of environmental variables such as foraging availability on bat species activity and species richness in backyards. The results of the study will provide data that can inform property owners and city planners how to provide suitable habitat for bat species in urbanized and urbanizing landscapes.
The equipment was received in June 2016 and deployed later that month. The bat detectors were supplemented by an SM3BAT funded by NHM, providing a total of 5 bat detectors to the project. The current transect is a "Coast to Desert" transect from Santa Monica, CA to Riverside, CA. The objective of phase one was to act as a pilot for future transects due to the fact that the sampling period was ending in only 3 months. Instead of only monitoring 5 sites for the duration of the transect, we chose to nearly double the number of sites sampled instead. Four bat detectors were moved to new sites that represented new backyard (microhabitat) and landscape types (macrohabitat) along the transect. Bat detectors remain deployed in backyards, preventing us from providing a complete summary of results. However data from the first five backyards yielded some interesting results. For instance, more urban sites had half the species richness as suburban sites (Table 1).
Species were detected in all backyards regardless of environmental variables, confirming that some species were more urban adapted than others. Tadarida brasiliensis was by far the most ubiquitous species throughout the transect. An initial analysis confirms at least eight total species were detected but species richness, activity, and composition varied at each site (Table 1). Although it is unclear how the different covariates are influencing bat activity, there seem to be trends emerging. In addition to providing baseline urban bat data for each region, the data will prove that concurrent sampling of more sites and longer term monitoring is necessary to take full advantage of this opportunity. The number of species detected in such a short amount of time will intrigue the public and inspire potential donors to support the expansion of our study. As forage availability (e.g., nocturnal flying insects) are quantified as well as other environmental variables, we hope more trends will emerge.
The Natural History Museum of Los Angeles County intends to analyze the bat data along with insect and GIS land use data to tease out any interesting trends. Once bat data is analyzed, data will be submitted to an online bat echolocation database called the Bat Acoustic Monitoring Portal (BatAMP). BatAMP will incorporate our valuable urban data into a larger network of data that aims to better understand migratory movements of bats and seasonal activity throughout North America. NHM will also use the results to seek more funding opportunities to monitor more sites simultaneously. NHM scientists are still deciding the start date and location of the upcoming transect. Also, scientists are determining how they may change the sampling methods and degree of involvement of the citizen scientists (homeowners). Homeowners have expressed interest in being further integrated into the scientific process. Finally, an abstract was submitted to present the SuperProject Backyard Bat Study at the 2017 Citizen Science Association Conference as a poster or presentation. We look forward to using this opportunity to inspire more urban bat research, bat citizen science, as well as gather information on how to improve our study for the benefit of citizen scientists and bats.
Previous bat research in the Greater Los Angeles area has been limited to public open spaces on the edge of urban and suburban landscapes. Through this limited research, biologist estimate that 18 species have historically called Los Angeles home. However, due to rapid urban development, these estimates may be outdated. Further, private land which comprise most of the landscape in the Greater Los Angeles area is understudied and bat activity in these areas is unknown.
The Natural History Museum of L.A. County initiated a citizen science based backyard survey in 2016 between the cities of Santa Monica, CA and Riverside, CA (Figure 1). The 2016 pilot season occurred between July 2016 and October 2016. Although the number of sites were limited by the short sampling season, the results were promising.
In total, nine sites were sampled and every site detected bats. Twelve total species were detected within the study area. Five of the twelve species were California Species of Special Concern (CSSC). Eight of nine sites detected at least one CSSC. These results suggest that even bat species previously thought to be sensitive to urbanization may be using backyards as a resource.
The next steps are to publish our 2016 results in a scientific journal and move our study to the San Fernando Valley and South Los Angeles region of the Greater L.A. area. We also plan to add our data to the Bat Acoustic Monitoring Portal (BatAMP).
The 2016 pilot season ended successfully with the documentation of 12 species, including 5 California Species of Special Concern. The sampling occurred across 9 urban and suburban backyards spaced out along a coast to desert transect between Santa Monica, CA and Riverside, CA. Our research team looks forward to the 2017 field season because we will begin our surveys in new regions of the L.A. area. The sampling period for 2017 is taking place between May 2017 and May 2018.
During the 2017 sampling year, we will be surveying 18 backyards spaced out throughout the San Fernando Valley (11 sites) and South Los Angeles (7 sites) regions of the Greater Los Angeles Area (Figure 1). South Los Angeles has fewer sites because we plan to make this underserved region our main focus next year and we want to engage with them a year in advance.
We will be rotating 4 bat detectors to new sites every month in an effort to cover all 18 sites multiple times by the conclusion of the sampling year. Citizen scientists are tasked with hosting the bat detector at their home and checking the status of the battery and memory card on a regular basis.
Initial data is still coming in so we are currently processing the first round of 2017 data. The Urban Nature Research Center staff are preparing a publication summarizing the results from our 2016 pilot season. We applied for a grant that would allow us to purchase enough bat detectors to sample all 18 backyards concurrently. We will be notified of our grant application results in Fall 2017.
Living Options Devon is a registered charity working across the southwest of the United Kingdom to ensure that people who are deaf or with disabilities can live the lives they choose. The organization's program, Heritage Ability, is intended to make heritage sites accessible to all visitors, regardless of physical abilities.
Karen Fisher Favret
Spatial Temporal Earth and École Étoile filante
Our project has launched! We inaugurated the Mon Paysage Sonore/My Soundscape project at École Étoile filante on May 20th, 2016 with a sound-experiment workshop involving twenty students from grades 1 and 2. Students each made their own noise-producing device using materials ranging from straws and toilet paper tubes to rubber-bands and popsicle sticks. After students put batteries and data cards into the recorders, each student got to make their own sound for the new SM 4 recorders from Wildlife Acoustics. Next, recorders were placed outside windows of the school, behind metal grids facing the playground in two different directions for our first week of recording.
Using the Songscope software, SongScapes from both recorders were created for the first week of data on a laptop dedicated by the school for the project. We created a prototype news letter for the project, which will be produced in both French and English. Our project team gained two volunteer naturalists. One studies bats for her dissertation research, and she will lead bat walks later in the summer. Our second new volunteer led a group of kids from our first partner school, École Charles LeMoyne, to a spot in the woods where baby owls recently fledged to do a sound experiment… do the bird songs around us change when everyone is silent for one or two minutes before parading past the recorders?
In our first month of recording, we have now recorded at several locations around two schools in Montreal, in two very different neighbourhoods. École Étoile filante is located in a neighbourhood filled with detached duplexes on tree-lined streets, across the street from a large city park. Our partner school is in a denser neighbourhood where freight trains pass by just across the street many times a day. We also recorded on a field-trip in the woods of Parc Angrignon, and in a backyard patch of flowers where bees were actively pollinating. Our dedicated laptop is located in the school library, where students can stop to watch the SongScapes evolving for each location.
Because we are doing this project with elementary school students, we are adapting everything from our deployment strategy to our lesson plans as we go. Students have begun creating their own drawings illustrating Biophony, Geophony and Anthrophony, all documenting the project in their own way. This summer, we will inaugurate our bat-detector and hydrophone during activities open to students and families from both schools. We are also testing various mounting configurations for the recorders, because we need the locations to be safe for students. Since the new SM 4 can run on our rechargeable D cell batteries for over a week, we have eliminated concerns over needing to have external batteries in the school. Now it is possible to mount the recorders outside windows, preferred by educators over letting kids on the roof. By the fall, we aim to have identified our "permanent" listening location for the school. Stay tuned!
Our major logistical goal for starting the first phase of this project was to identify a recording location where students could take responsibility for maintaining the recorders, in a good recording location with both minimal noise from generators and minimal concerns for overhearing conversations. The window locations on the first floor are ideal for convenience and safety of students, but less than ideal for minimizing noise and staff concerns. The library window location currently under consideration requires new hardware to attach the recorder to the masonry of the school, and a plan to prevent batteries from being dropped from the third story. The roof location originally proposed led to an extensive process involving the school board to gain access, and while we currently have one SM 4 on the roof for comparison to the first floor window, no students can be involved in setting up or maintaining the recorders here.
However, the delay in finding the "permanent" recording location for the school has led to a variety of experiments for recorder locations. The recorders have been fairly successful between the first story gym window and the protective metal grid outside, facing the playground. One recorder was tested suspended from wire shelving in the indoor garden just inside an open window for a week at our partner school. It was also taken on a field-trip to the woods, and placed by students about 50 yards away from where they were playing. They performed a miniature sound experiment, maintaining silence for a full minute before going to retrieve the recorder, to see if we can identify changes in the bird-song. The naturalist leading the activity is an experienced birder and educator. She teaches kids to distinguish five different types of bird calls on nature walks, and generally encourages greater understanding of how natural systems work all around us. We now have on recorder on a third story roof of École Étoile filante, where both downtown and the St Lawrence are in view, but it is subject to quite a bit of wind, and not accessible to the students. We tested one new SM 4 was tested alongside an SM 3 and SM 2 from Spatial Temporal Earth. The SM4 runs much longer on one set of batteries compared to the others, and we will compare the data from the three types of recorders for a thunderstorm that passed through during the test.
So far, the major impact we have had is in creating a general awareness at the school about the existence of soundscapes, and the three categories of sound, Biophony, Geophony, and Anthrophony. I am collaborating with a professional translator on the educational and outreach materials in french, a professor at McGill University on the extension and distribution of our citizen science efforts at the school, a student completing her dissertation on bat conservation in Quebec, and a naturalist from the Friends of Mont Royal. CalculQuebec, the Quebec arm of Compute Canada, is currently considering hosting our data, and possibly building an interface for schools to share their data, after a staff member found out about our project on your website and proposed it to her managers.
Students are starting to discover soundscapes with your recorders! We are forming a strong team, including several native French speakers to help translate, people familiar with local wildlife, and educators. Over the summer we will continue recording, and offer a chance for families from both schools involved so far the chance to come and try out the hydrophone and bat detector. We will also work on the curriculum to prepare for the fall. We plan to introduce all six classes at Étoile filante to the project, and begin annotating the data recorded with small groups of students, who will decide on their own projects to pursue.
We had a small issue at the beginning when we put the recorders in the gym windows just a half story above the playground, because although the teachers knew about the project, the daycare workers did not, and some were afraid the recorders were put there to listen to them. I went to talk to the director of the Service de garde, which takes care of the students before school, at lunch, and after school, and explained that we were listening to the birds, thunder, and traffic outside, and then put up a small sign, with drawings defining Biophony, Geophony, and Anthrophony (Biophonie, Géophonie, and Anthrophonie in French), after which everyone understood. Next time, we will prepare a little explanation sign in a sheet protector to post with the recorders and explain the science when we deploy the recorders.
This is a more general problem with using first floor windows in areas where people often talk (or in the case of kids, yell at the recorder for fun). For this reason, I am focussing on finding a way to mount the recorders in the third floor library window, which would allow the kids to change the batteries and data cards, but have less of a chance of recording intelligible speech. It seems unlikely that we can get permission to have kids on the roof on a regular basis, and this would be a problem at every school in the project. An extendable arm from the window seems like a possibility. The kids are very excited about being part of the project, and I think it is really important to find a way that would work for most schools, and limit any needless risk. Exploring the different mounting options is on ongoing process we hope to complete before school begins in the fall.
Our deployment of the "permanent" SM4 and our Soundwalks with the Echometer Touches and "roving" SM4 have begun! Students helped select a tree-mounted site in front of the school to place the "permanent" recorder, and collaborated to pick a kid-friendly height so that even the first-graders can help change batteries and data cards. We are making full use of the cabling option in this deployment location. In spite of being located across from a busy city park, and just a city block away from a major intersection, there is no evidence of tampering so far.
However, students do make it a point drop by and inspect the recorder, and sometimes groups of kids conspire to make special types of noise when they are near the recorder. Everyone is having fun learning to operate both the recorder and the padlock. Groups of students also enjoy conducting distance-from-the-recorder soundtests using disposable pie-tins. As evidence of their creative streak, the first-graders chose to simplify the soundtest, leaving behind the chop-sticks I provided as strikers, and using their heads instead…
We gathered for our first batwalk with the Echometer Touches on July 18, 2016 with a local bat expert, Julie, who is working on her PhD nearby. She introduced us to the basics before we left the school to walk around the neighbourhood recording bats. After the walk, we "loaned out" the two ET's to walk participants for the remainder of the summer, and we are now gathering the data from quite a few locations in Quebec that got sampled by our volunteers over the summer.
We began our series of Wednesday afternoon Soundwalks on September 28th, 2016, starting out by letting the students make noise for the permanent recorder, and moving on to illustrating our Core Concept: Biophony, Geophony, and Anthrophony, before coming up with french translations for: Soundwalk (Le marche des sons), Sound-signals (Signaux sonores), and Sound-markers (Marquers snores) that we hope to incorporate into a french wikipedia page that links to the Soundwalk page in english. Younger students also provided illustrations that led me to add "Imagineaphony" to the lexicon, as they like to illustrate imaginary animals, with made-up sounds. They also surprised me with their lists of sound signals. I was expecting the lists to say "fire truck siren", or "person yelling", but instead got "whoo-whoo" and "hi hi hi" (in french the latter sounds like "hee hee hee"). In fact, they easily illustrated all four of the core values of the school in their approach to the soundwalk: Autonomy: Kids who had already done the soundtest, and enjoyed it, came back and rounded up other kids to show them how to do it.
Collaboration: The kids stood near the playground, collaborating on what to write down, and how to spell out sounds.
Creativity: Kids illustrated the concept of sound markers, or the sounds that are typical of a place and make it what it is, by making rubbings of the tree bark on the favourite climbing tree in front of the school
Openness: Kids readily accepted the ides of others about how to approach the project. They had their own discussion of how to translate "Soundwalk" into french, and decided on "Le marche des sons".
Our major logistical goal was to select our recording location where students could take responsibility for maintaining the recorders while minimizing concerns about overhearing conversations. The students helped select a tree in front of the school, and using both the pad-lock and cabling options led to a successful deployment over the summer. We began rotating both the "roving" SM4 and both ET"s to families to gather data. We now have data!
Students are becoming familiar with Biophony, Geophony, and Anthrophony/ Biophonie, Géophonie, et Anthtophonie, and teaching their parents, too. CalculQuebec, the Quebec arm of Compute Canada, has asked us to outline our needs for hosting the data, and we are preparing to have a meeting to discuss the collaboration further. École Étoile filante is continuing to support the project, allowing time for an introduction at the General Assembly at the beginning of the year, and including us in a project-based afternoon opportunity to consolidate English skills, which allows me to run the project as a bilingual activity instead of the otherwise mandated French-only approach. We aim to have the older students provide a french translation/version of the Wikipedia "Soundwalk" page soon.
The roving recorder also participated in a pilot study for comparing traditional (insect trap-based) and bioacoustical data for comparing biodiversity between sites in the Quebec forest. It was located near a stream and a small cabin, a short distance from the insect trap, and students will get to see both the insect and bioacousticsl data from this site.
Students are now recording permanent soundscapes of the areas around their school, homes, and cottages in Quebec. We gathered data almost continuously last summer, which will provide the basis for our first soundscape project. We plan to post weekly soundscapes in the hallway of the school, one week at a time, with examples of the sounds that students pick to annotate from each period in the hallway as our near-term project objective. We have both a closed-Group Mon Paysage Sonore and an open Page Mon Paysage Sonore My Soundscape on Facebook, and are working on the website as we prepare for the meeting with Calcul Quebec.
This quarter, we took a poster describing our project to the Canada"s Arctic Biodiversity: The next 150 years Symposium at the Canadian Museum of Nature. With our presenters, from grades 1 and 2 at École Étoile filante, we made several connections with researchers who agreed to take our project to schools in the Arctic directly. Kullik Elementary School, our first Arctic Partner school, is in Cambridge Bay, Nunavut. The students at École Étoile filante are now engaged packing up their SM4 recorder for delivery by staff members of Polar Knowledge Canada in April. We were also invited to present in the Soundscapes in the Anthropocene at the upcoming Urban Wildlife Conference in San Diego! We held Soundwalks, did sound experiments, and started learning to navigate our sound recordings in the Kaleidoscope software. Students were excited that we could not only hear the dawn chorus from our Mon Passage Sonore/My Soundscape Tree, but also had a resident cicada. We learned what sirens and power drills look like on a spectrogram. And both the teachers of the second cycle (grades 3-4) picked our project for the final class projects. Students have identified and chosen from over 20 different projects, which they will now complete in groups of three or four students between now and the end of the year.
Our project is now officially bilingual, being taught in French and English at the school depending on when the activities are held (during or after school). We will soon make it trilingual when our first Arctic partner joins us. Students in grades 1 and 2 participate in the project as part of the English Club on Wednesday afternoons, while students in grades 3 and 4 are starting their own projects, ranging from monitoring the sound levels in the hallways of the school to staging an event including the "Guess that Sound" game for the entire school at the end of the year. We have now had our second recorder, destined for the Arctic, in the backyards or on the roof of five different families at the school, allowing us to start looking at how soundscapes vary between the school and the neighbourhoods where we live. Students are now able to download Kaleidoscope Viewer at home, which allows families to become much more involved in the project. People are digging out sounds they recorded on their iPhones on summer vacation, using Zamzar, and kids are presenting them in class. At the Arctic Symposium, we were asked by the Chief Biodiversity Scientist of Parks Canada to send a project description, which was translated into french by a volunteer professional translator parent. It also became apparent that we need a new character to represent Biophony, as there are no frogs in the Arctic. We held a vote for various logo combinations, and the Owl ended up beating the frog, chicken and geese in V formation by a margin.
Our major goals were to find our partner school, define our strategy for getting kids to work on the data, and distill the themes to hit while teaching. With the help of the Arctic Symposium participants, we now have a connection to our first Arctic partner school, and we are in the process of making sure we have everything they need to start the project. Students are working on the data using kaleidoscope, which is well tailored for them to find and save out interesting parts of the spectrograms and sound record. We came up with two basic sets of terms to use. One set characterizes sounds by their source, Biophony- Living organisms, Geophony- Physical forces, Anthrophony- Human Activities. The second set describes how the sounds act in the soundscape: Sound Markers- tell you where you are; Sound Signals- communicate something; Sound Disrupters- interrupt the soundscape of that place and time; and Sound Potentials- wait their turn to make noise (term prompted by squirrels seen but not heard on a soundwalk we held).
Students are becoming familiar with their soundscape, and are becoming aware of the changes over different timescales. They especially enjoyed hearing the start of the dawn chorus from the summer recordings, "catching" cars speeding way too fast, hearing themselves sing on the recordings,and trying to duplicate the spectrogram of sirens we recorded in front of the school. Polar Knowledge Canada is building a new High Arctic Research Station in Cambridge Bay, Nunavut, and one researcher there has just connected us to Kullick Elementary School. We are packing up supplies for our new partners so that they can begin recording along with us. Since transportation to the Arctic is complicated, we are really lucky to have this particular site as our first partner school because Polar Knowledge Canada will hand-deliver the supplies for us from Ottawa, and can likely help the community there if they run into technological glitches. It will also allow us to send additional supplies to our partner school in the coming months. Given it is truly in the High Arctic, we are going to have to supply them with a cabled microphone to allow them to record in the extreme temperatures occurring most of the year there.
Near term, we are trying to anticipate the challenges for a new teacher to launch the Mon Passage program and the recorder successfully at her school in the High Arctic. We will now have a trilingual project to manage, so will be working on getting our classroom materials together. Our next major challenge is handling the data. We have filled our first 8 TB hard drive, and now have a partner school as well. This will shape our upcoming discussions with Calcul Quebec.
In the final quarter of our Phase 1 demonstration year, we delivered the Arctic recorder to Ottawa for transport to our first partner school, Kullick Ilikhakvik in Cam- bridge Bay, Nunavut. Polar Knowledge Canada shipped the recorder for us, and showed interest in continuing to support the project when their office relo- cates there. We presented our now trilingual project at the Soundscapes in the Anthropocene session of the Urban Wildlife Conference in San Diego in early June. In mid-June, Étoile filante students in Grades 3 and 4 presented their final Projects. Students brainstormed 18 different sound projects, ranging from "name that sound" quizzes to comparing noise levels in different parts of the school or around town, and then presented their projects. On the last day of school we removed Étoile filante's recorder from its tree just before it was cut down, and that concluded our first year.
Students in the second cycle (Grades 3-4) worked hard on their final projects, making recordings inside and outside their school using Song Sleuth on school iPads or their parent's iPhones. They presented their work at the end of June, and had an impressive response from their peers as they led quiz shows, invented and recorded new instruments, and documented levels of sound in different venues of the school at different times.
Students at Étoile filante wrote wel- come notes to the kids in the Arctic, either in French which was translated by parents, or during their English class. Our partner school in the Arctic received their SM4 recorder with the notes, and planned a trip to make recordings outside the town but were stymied by bad weather, (snow storms that lasted into early June). They are currently assessing where they can place their recorder near the school with least risk of theft, and planning for the fall term.
At the end of the year, the Parent's Foundation purchased two iPads for our project. We found BigGrips pro- tectors that allow the EchoMeter Touch microphones to be used effec- tively so that kids can safely handle the iPads during nighttime BatWalks as well as SoundWalks.
During the end of the year party for the school, students invented a new game, trying to get SongSleuth to think they were ducks, chickadees, or seagulls. The are constantly finding new ways to use the spectrograms, and thinking in new ways about the sounds around them.
Our major goal is to create and distribute tools for hands-on projects connected to ob- serving the soundscapes students inhabit. We aim to increase scientific literacy by cre- ating citizen scientists who: 1) actively and quantitatively observe their own environ- ment; 2) know how interpret and use their observations; and 3) have a scientific context to integrate additional data into to better understand the changing world around them- selves and their communities.
To reflect the now trilingual components of our partnership between Montreal and Cambridge Bay, we have proposed to move forward as the NIPI Project, (Nature, Investigation Plus Integration). Nipi is the Innuinaqtun word for sound, noise, and voices. The same acronym works for Projet NIPI in french (La nature, l'investigation plus l'integration). École Étoile filante will continue to call their section of the project Mon Passage Sonore, and Ilikhakvik will pick it's own name in the fall.
The two iPads that Étoile filante's parent-foundation bought will be used in a variety of activities documenting the biodiversity and environment around the school during and after the major re-construction of the school's founda- tion and yard. We plan to use Song Sleuth as a major part of this. Since the construction project led to the removal of the Mon paysage sonore tree, we will be relocating the recorder in the fall. We are hoping the new location will be in the school garden that we are starting, and that we can document the changes as the garden matures and we in- vestigate different strategies to increase biodiversity by providing microhabitats in our garden.
The impact of our project in the scientific community now extends from the Arctic research community that helped match us with our Arctic partner school to the Urban Wildlife Community. Researchers from zoos, the National Park Service, and regional and local parks authorities presented work on soundscapes during the International Urban Wildlife conference in San Diego, and response to our project was very positive. We have been contacted by the Sonoma Ecology Institute, which is working with Bernie Krause on outreach to 6th graders. We are par- ticipating in gathering soundscapes during the eclipse on August 21, 2017, including daytime Bat recordings suggested by the Urban Soundscapes session coordinator Han Li, from UNC. We have made contact with a family from Iqaluit Nunavut, that is familiar with the french school there and plan to see if they would be interest in partnering with one of the schools in Montreal that have expressed interest in participating on our project.
Now that we have completed our pilot year, we are working toward fully launching Project NIPI with the High Arctic SM4 recording simultaneously with the Montréal SM4. Calcul Quebec is interested in supporting the project. We are testing various strategies for including bats in our recordings, in- cluding a backyard demo-project setting up an iPad with an Echo Meter Touch next to our back- yard SM3. This real-time stamping project allows us to start looking at how bat activity fits in the soundscape, but requires vigilance to make sure that the iPads do not end up in a cloudburst! We also tested using Stand Up Paddle (SUP) boards with the hydrophone, to develop a protocol for in- dividual students to perform their own hydrophone transects. To insure that we provide connections to STEM goals in the educational community, we are developing a curriculum with hands on experi- ments that demonstrate the science of sound for all the elementary age groups so that new teach- ers coming on board have a path to follow. We aim to expand the project by the end of this year to include two new partners, so that we can reach out to more new citizen scientists.
We would like to acknowledge all the students, parents, teachers, and administrators at Étoile filante and Ilikhakvik who are participating in our new project. This project would not have been possible without the sup- port of the Wildlife Acoustics Scientific Product Grant, and we are supremely grateful for this support. The SM4 recorders, hydrophone, and Echo Meter Touches are providing a new generation of citizen sci- entists with the tools to better understand their world.
Adao Henrique Rosa Domingos
IPBio-Biodiversity Research Institute, Atlantic Forest Research Center/Betary Reserve, Sáo Paulo, Brazil
With the support of a bilingual volunteer we have translated section 3 and 4 of the SM4 Manual so that all our national biologists, volunteers, interns and visiting researches can understand and become familiar with the technology. Henrique Domingos, IPBio biologist, set up a test with the Song Meter SM4 recorder to get some practice with the device and software (figure 1).This test was conducted in May and June. We selected different points in terms of habitat composition and altitudinal gradient.
The first point was on the banks of a pond, at an altitude of 100 meters above sea level, in an area of forest in its initial stage of regeneration. The Song Meter SM4 was programmed to record at night to detect the diversity of amphibians in this period of the year.
The second point was situated in a forest area at an advanced regeneration stage, at an altitude of 150 meters above sea level, and the recordings were made in the morning.
The third point was a well preserved forest area, at an altitude of 356 meters above sea level. At this third point we installed the Song Meter SM4 along with a Bushnell camera trap attempting to record audio and capture images of the Sapajus nigritus.
The data is still under review, but in a short period of time, we were able to detect approximately 30 species of birds in these 3 points, among them the Carpornis melanocephala considered rare or scarce, and is categorized as "vulnerable" according to the List of Threatened Species of the Brazilian Fauna, 2014 (figure 2). Moreover, we detected two mammalian species, namely the Sapajus nigritus and Alouatta guariba, and three species of amphibians Sphaenorhynchus caramaschi, Dendropsophus werneri and Scinax sp.
After a series of tests on the Betary Reserve, we selected certain points we considered as top priority due to our past experience of encountering high levels of activity in these areas, to carry out bioacoustics monitoring. The Betary Reserve contains various stages of forest regeneration, which makes it a place with a high level of diversity. One such point M1 (Hill 1) is 356m altitude and we shall monitor it for a year. The idea is to check the variation of species according to the seasonal period. This location has high potential to record sounds of birds and some mammals. One thing about this point that caught our attention was the presence of black capuchin monkeys (Sapajus nigritus). We managed to record some sounds emitted by these animals and we also captured images of them as we are installing a Bushnell Camera Trap along with the Song Meter SM4. The next step will be to start assembling the recognizers using the scope song application.
Since October, IPBio has been monitoring the SM4 recorder on Hill M1 during peak activity hours in the morning and evening and has since set up another recorder at a second site, Pitfall 5. Where Hill M1 is a site relatively isolated from frequent human contact, Pitfall 5 is a site close to Betary Reserve headquarters with a high potential for capturing bird and amphibian vocalizations.
Every week at the Pitfall 5 site, we record the first 10 minutes of every hour for 24 hours with the aim of tracking species activity levels throughout the day. This data will prove valuable in showing us how activity levels for each species vary during the course of the day and year and will help the reserve monitor any year-to-year changes that may result from changes to the surrounding environment. Further, we shall be able to compare data collected from Hill M1 and Pitfall 5 to begin understanding the differences in biodiversity and activity levels between the sites. Significant differences between the sites may serve as a basis for future study.
Beyond monitoring species activity, IPBio will have generated a collection of sound recordings that will serve both research and educational purposes. IPBio frequently receives student groups and families interested in learning more about Brazil's Atlantic Forest, so having a diverse bank of sounds will be useful for teaching about specific animals which are difficult to see in the forest due to the region's very dense vegetation. One such recording is that of the howler monkey (Alouatta clamitans), a mammal commonly found on the Betary Reserve that can be heard clearly from 3 miles away, but is not easy to observe visually.
To date, IPBio has collected a total of approximately 1000 hours (or about 41 days) of audio from 4 sites on the Betary Reserve. The two sites previously identified as good long-term research locations will continue recording until at least February 2018 so that IPBio can study species activity levels over the course of an entire year. Both sites will continue to use camera traps nearby to complement the auditory data collected by the SM4 recorder with photos and videos of animals in the area.
Our bioacoustics project is an inventory of all species in the site locations that produce sound, and while the team at IPBio has been able to identify many of the species recorded, identifying the diverse range of birds, amphibians, mammals, and insects found on the reserve presents a unique challenge! To aid in the identification of unknown species, we are enlisting the help of experts and researchers at a few of Brazil's universities.
Over the course of 8 months, we have generated and used recognizers within the SongScope software to automatically identify species, enabling us to observe variations in activity throughout the day and from month to month. As an example, above is a graph of a single day at our Pitfall 5 site in March 2017. The variation in number of active species as well as species type throughout the day is evident, with distinct changes around sunrise (06:19) and sunset (18:24). The graph demonstrates clearly that the birds near our site are active during the day, while insects dominate the nighttime soundscape. This trend for insects dominant during the night can be seen in the below soundscape generated for the same time period, which represents an entire day's worth of audio recordings at Pitfall 5. The bright green areas in the middle of the soundscape are sounds generated by insects during the nighttime hours.
When we have finished data collection, IPBio will be able to generate graphs similar to the one above to show not only activity variation throughout the day, but also seasonal variation over the course of an entire year. We will analyse the data at both the species type and individual species level. Finally, the collection of sound bites for each species will continue to be useful as we teach visitors about the wildlife in the Atlantic Forest.
IPBio has been focused on developing a sound bank of all the species of mammals, amphibians and birds in the Atlantic Forest. Since 2016, after receiving the Scientific Product Grant, we began using the SM4 recorder to monitor a site on our reserve for over a year with the aim of understanding the daily and seasonal patterns of these animals. We began using Song Scope to analyze the recording and started developing recognizers for each species with plans of running all of the recognizers after 2 or 3 years of data collection. In May of 2018, IPBio received an additional grant from Wildlife Acoustics with their updated software named Kaleidoscope. The benefits were apparent immediately. Clustering technology was featured in the new software that removed the tedious job of manually annotating every audio, as the new software would automatically cluster similar vocalizations. This left us with a much simpler task of checking through the already automatically organized vocalizations to confirm the software was sorting efficiently. Moreover, the transition from recognizers to classifiers was massively beneficial as while recognizers were manually fed sample recording to produce a recognizer, the new classifier system essentially allowed us to consistently improve it while we went through more data.
In June of 2018, we received an intern from Antioch University who dedicated here time to graphing the vocalization of three species of frogs over a year long period of data collection to understand their seasonal variations. With the new kaleidoscope software she was able to learn how to use the software from scratch and conduct this study in approximately a month. This speed of analysis was unprecedented for IPBio and has made the process of developing recognizer, now known as classifiers, substantially more efficient.
Over the coming years IPBio will continue to develop a sound bank and study the seasonal habits of wildlife on our reserve in the Atlantic Forest and then expand to other biomes in Brazil as our organization grows.
Richmond upon Thames is situated 10 miles southwest of Central London. The River Thames runs for over 10 miles through the borough and the town is the starting point for a scenic nature walk along the Thames. The Duke of Northumberland's River (DNR) runs for approximately four kilometers from where it leaves the River Crane and leads into the Thames. This river channel dates back to the 1500s as a mill stream and water supply for the region.
In Maryland, USA, a group of 1300 amateur scientists teamed up to document species of amphibians and reptiles by using high tech acoustic monitoring devices and mapping technology. They're the seventh graders of Calvert County School System and they're an integral part of the Maryland Amphibian and Reptile Atlas (MARA).
MARA is a five-year project created by the Natural History Society of Maryland and the Maryland Department of Natural Resources. The aim of the project was to document the statewide distribution of Maryland's amphibians and reptiles and to find rare species locations for future conservation efforts. The project relied heavily on the participation of citizen scientists, such as the students of Calvert County, to report sightings and log locations of amphibians and reptiles.
Calvert County Public School System has a valuable environmental education program (CHESPAX) in which the Board of Education staff works closely with local, state and regional partner agencies to provide hands-on environmental education experiences for the students of Calvert County. Tom Harten, CHESPAX teacher in the Calvert Public Schools, decided the MARA project would be a great learning opportunity for his students to actively participate in species monitoring and conservation efforts. His goal was to involve every seventh grade class in Calvert County with the MARA project and embed it within the science curriculum for the entire county for six middle schools.
Since amphibians primarily vocalize at night, Mr. Harten needed a tool that would allow students to record those sounds automatically. Some research led Mr. Harten to Wildlife Acoustics and the Song Meter.
Funding for Song Meters came from county STEM (Science, Technology, Engineering and Math) funding and a grant from the Chesapeake Bay Trust. The primary stakeholders were the participating, 1300, seventh grade students and their teachers. Each school received at least one Unit Resource Kit which included a Song Meter, a handheld recorder, a CD with instructional information, resources to assist with species ID and much more.
Tom Harten explains, "Our goal was to determine amphibian species presence at multiple locations in Calvert County, Maryland as a part of the MARA project." Tom continues, "We conducted fairly extensive teacher training programs as a way to obtain buy-in. The nature of the project really engaged the students--so that contributed greatly to the buy in as well. All of our deployments were done by middle school teachers and their students. Our staff programmed the Song Meters for the classes prior to distribution."
Mr. Harten said, "Some of our best recordings came from schoolyard sediment control ponds where access was easiest for the students. Our staff also deployed the Song Meters in targeted areas that would be difficult for students to access easily. So we had data from different landscapes. The Song Meters were quite essential to the project."
Periodically, students listened and analyzed the vocalizations in their classrooms. The location and identity of the species were then logged into the MARA database. CHESPAX even created an impressive blog full of photos, stories, and updates called their Frog Blog. The teachers could easily share the data in the FrogBlog, thus allowing all of the students and teachers to collaborate on the recordings.
Mr. Harten measured success from the teacher evaluations he received and from anecdotal feedback. The program was extremely popular with students and teachers. Mr. Harten affirms, "Students that participated in the program really know their amphibian calls when they attend a different field experience with our staff. We travel by canoe out onto a creek and the students start rattling off the names of frogs and toads when they hear the vocalizations." Since the MARA project ended in 2015, Mr. Harten and CHESPAX are looking at a possible shift in the program to examine global climate change and its impact on wildlife, including amphibians. This would be an eighth grade program that would begin in the spring of 2018. In the meantime, he and his students have been doing a more localized study with the Calvert County Natural Resources Division, so the project will continue with the data staying in-county for the most part.
Mr. Harten recommends, that before creating a similar project, other school systems should provide as much professional development for the teachers as possible. Since students respond to the real world nature of a project, try and connect any student monitoring program with a local or regional authentic study.
**Special thanks to Mr. Harten who provided the majority of content for this case study.
Environment and Climate Change Canada is the federal government department that is responsible for the conservation and management of migratory birds in Canada. However, the status of most songbirds breeding in northern boreal regions is poorly known given limited geographic coverage of current monitoring programs.
Dr. Jessica Bryantthe
The Zoological Society of London
The team traveled to Bawangling National Nature Reserve on Hainan Island in January, to explain the Wildlife Acoustics technology and overall methodology to the local reserve staff, identify sites for Song Meter SM3 deployment, and collect recordings from the Hainan gibbon population to assist later analyses. The Song Meter SM3 recorders were successfully deployed at the start of this month (March), and are now out in the Bawangling landscape, recording the songs of the Hainan gibbon groups. The Song Meters will remain out in the forest for at least four more months during which time they will record from before dawn every morning for several hours, and be replenished with new batteries and memory cards every month to ensure they continue to record the gibbon's daily songs. In the meantime, the team will use the recordings compiled from January to commence the initial stages of analysis to develop recognizers within Song Scope. This approach will permit later analyses using the several months' worth of SM3 recordings.
The Song Meter SM3 recorders remain deployed in the nature reserve, where they are fastidiously collecting call data from one hour before sunrise, at which point the gibbons being to sing their bond-reinforcing songs, until the early afternoon, when the gibbons are less likely to call as they begin to settle down in their sleeping trees. The recorders are located near to each of the three, well-established Hainan gibbon family groups in the hope that the team might be able to detect between-group song differences at the analysis stage. Dr. Bryant has commenced recogniser development by annotating calls recorded earlier on the year within Song Scope.
Following deployment of our Song Meter SM3 recorders in Bawangling National Nature Reserve (BNNR) for a total of 6 months (since March 2016), all recorders were successfully retrieved from the forest in early September (after recording ceased in late August). This signals the end of this stage of the project. We have around 5,500 hours of raw recording data, although not all of these will necessarily contain gibbon songs! This will depend on whether the gibbon groups ranged close enough to the recorders for their song to be captured, and the extent of programming issues we encountered during redeployment associated with operation of the recorders by our non-English speaking BNNR Management Office collaborators. We have now begun the analysis phase of the project using Song Scope. Along with formal analysis of the recordings to determine the capture rate for gibbons in this type of mountainous environment, and whether we can discern the individual gibbon social groups using this approach, a key step will also be to determine the overall success to date and feasibility of this method as a long term monitoring technique for BNNR Management Office staff, given the language and skill barriers encountered with station deployment and maintenance. Together, data on these different aspects will allow us to thoroughly assess the practicability of this new technique as a potential monitoring tool to strengthen the capacity of BNNR Management Office to detect and monitor Hainan gibbons at Bawangling.
Following the conclusion of our field phase of the project, in the last quarter we progressed to analysis. Using a total of almost nine hours of reference recordings, we constructed individual recognisers within SongScope for a repertoire of Hainan gibbon vocalisations: solo male song, male-female duet, alarm call, and where possible, group-specific recognisers for each of these (note: there are currently four Hainan gibbon social groups, but we have recordings for only three of these due to the sensitivity of the newly formed fourth group to human presence).
Using ten different recogniser configurations, we scanned 12 files of 8 hours duration each known to contain Hainan gibbon calls (so 96 hours of raw recording data in total, and approximately 2% of the total hours of raw recording data). We then compared the performance of these recogniser configurations in terms of the number of true positives, false positives, false negatives and their relative proportions. Using the recogniser configuration that performed the best in these aspects, we then further refined the minimum score and quality settings through further testing and performance comparison to determine the most successful recogniser settings for our data.
We were therefore able to create two general (species-specific but non group-specific) recognisers for the Hainan gibbon (solo male song, male-female duet) that successfully detected the species' calls within our Song Meter SM3 recordings, with a suitable balance of true versus false positives and negatives. Due to a lower amount of reference data for the alarm call vocalisations generally, and all vocalisation types for certain gibbon social groups (those that are unhabituated to human presence), the group-specific recognisers did not prove to be as successful as the general species recognisers, with a higher than desired ratio of false positives (and false negatives) to true positives. As such, further testing and refinement of the group-specific recognisers is needed and will be carried out as soon as we can collect additional individual group reference calls, as and when this is possible.
We now plan to liaise with Bawangling National Nature Reserve staff and Hainan Wildlife Conservation Bureau regarding the use of the developed Hainan gibbon recognisers and SM3 recorders to monitor Hainan gibbons within Bawangling, and also hopefully to survey for any possible remnant Hainan gibbon populations outside BNNR (in other key reserves across Hainan) using this tool. However, it is clear that we will need to carry out additional work to streamline the recorder deployment process, and possibly collaborate with appropriate parties to develop a Chinese language version of the Song Meter (SM3) firmware –as we found that deployment success was constrained by the English-only interface. These steps could certainly help to enhance this passive acoustic detection technique as a potential monitoring tool for Hainan gibbons at Bawangling, and possibly beyond, into the future.
Teton Raptor Center
Spring is in the air for high altitude forest raptors! The owls have been advertising their territories and looking for mates while the diurnal raptors call to their partners from time to time at dawn. For the past six weeks, the Teton Raptor Center crew has been deploying Wildlife Acoustics's SM3 units graciously donated to the project to record all these sounds. Coupled with the recordings, the crews have been skiing and snowshoeing all night in the backcountry of the Greater Yellowstone Ecosystem surveying for owls in the same locations as the recorders. While we sift through all of the recordings with the help of SongScope over the next few months, we'll be starting to piece together how recorders and surveyors compare and begin to understand how the raptors distribute themselves across the southern Greater Yellowstone Ecosystem.
Sifting through over 6 terabytes of data isn't easy! All spring and summer, our dedicated team of trained staff and volunteers have been reviewing sounds gathered this spring around Great Gray Owl territories. Because Great Grays call around 250 dB, automatically detecting the faint, low calls has not been possible using automated processing software. So we're manually sifting through the hours. We have already documented several types of owl calls not previously recorded for this elusive sensitive forest owl. We also have confirmed using Song Meters to be an effective method for simultaneous detecting other forest raptors, such as Northern Goshawks, Cooper's Hawks, Northern Pygmy Owls, and Long-eared Owls. One aspect from our study that has become apparent, the Song Meter SM4 recorders are better at detecting owls than traditional callback survey techniques! We'll be continuing to review recordings over the coming months to describe the vocal behaviors of Great Grays and other owls and to create a monitoring strategy using recorders.
All spring and summer our dedicated team of trained staff and volunteers have been reviewing sounds gathered this spring around Great Gray Owl territories.
Because Great Grays call around 250 dB, automatically detecting the faint, low calls has not been possible using automated processing software. So we're manually sifting through the hours. We have already documented several types of owl calls not previously recorded for this elusive sensitive forest owl.
We also have confirmed using recorders to be an effective method for simultaneous detecting other forest raptors, such as Northern Goshawks, Cooper's Hawks, Northern Pygmy Owls, and Long-eared Owls.
One aspect from our study that has become apparent, the recorders are better at detecting owls than traditional callback survey techniques!
We'll be continuing to review recordings over the coming months to describe the vocal behaviors of Great Grays and other owls and to create a monitoring strategy using recorders.
Forest owls are typically surveyed for and censused using nighttime callback survey methodologies, which require traveling to a territory and broadcasting a conspecific call to elicit a response from territorial breeding owls. However, the effectiveness of surveys is not well documented and our previous data on Great Gray Owls suggests they may not be adequate to assess territory occupancy. In 2016, we simultaneously conducted callback surveys and continuously recorded all sounds within 18 known Great Gray Owl territories in western Wyoming to compare the two protocols to determine occupancy. We also investigate several recording and data analysis systems. Using two surveys in a week, we would have estimated that 40% of territories were unoccupied, whereas recorders detected 100% occupancy rates in weeks currently investigated. We found that manual analysis is the most precise way to determine if and when owls are calling, but automated band limiting energy detectors in conjunction with random forest classifications to determine positive calls may be the most effective methods for determining Great Gray Owl occupancy. We are currently continuing data analysis to further refine results and effectively evaluate methodologies, but the data are clear that nighttime surveys is not an appropriate method for determining occupancy of nesting Great Gray Owls.
Boreal Owl, Northern Pygmy-Owl, and Great Gray Owl are all designated Wyoming Species of Greatest Conservation Need (SGCN) that occur in boreal and montane forest habitats. These habitats are at risk due to increasing frequency and intensity of wildfires, large-scale insect infestations, and disease. In addition to these risk factors, which are exacerbated by ongoing climate changes, timber harvest and forest thinning treatments can reduce and fragment stands of high quality habitat. Boreal Owls (NSS3) are classified as vulnerable due to severe limiting factors. Few scientific studies have been conducted on this species. Great Gray Owl and Northern Pygmy-Owl populations are thought to be stable but limited. These owls are classified as NSSU (unknown status) because populations are difficult to assess given current monitoring methods.
Monitoring of these species and their habitats has typically depends on detection of territorial owls during the early spring courtship period using nighttime callback techniques. Federal land managers are tasked with determining if species of conservation concern occur in project areas and typically use callback surveys to detect Boreal, Northern Pygmy-, and Great Gray Owls when planning habitat treatments. Callback detection methods include playing recorded territorial calls of target owl species at night during the courtship and nesting seasons and documenting responses. Night surveys are often difficult to conduct in Wyoming due to the rugged, remote terrain typically occupied by owls, lack of trails and roads, heavy snow loads, snow melt conditions, and severe temperatures during February, March, and April. Night surveys also create safety hazards for field personnel and are expensive to conduct, which severely limits the number and scope of surveys that can be completed each year. Further, detection rates related to callback techniques remain unknown, making it difficult to assess occupancy and population status accurately.
During our studies of Great Gray Owls in western Wyoming from 2012-2016, we documented 25 nesting territories within the study area using a variety of techniques, including callback surveys, radio-tracking, systematic nest searching, and opportunistic sightings. Our data strongly suggest that not all nesting pairs respond to callback surveys and other owls also respond in areas without nests. Both issues have significant implications for population monitoring and habitat treatment assessments.
Detection rates have not been determined for any of the forest owl species so it is unknown what proportion of the nesting population is being measured and what portion is missed during callback surveys. Recent owl studies suggest that using automated recording devices can be effective in monitoring owls within designated areas (e.g., Grava et al. 2008, Rognan et al. 2008, Goyette et al. 2011). These devices can detect all calling owls present within an area over multiple nights and weeks in contrast to the traditional callback survey method where surveys in a particular area are often restricted to only one or two nights during a season. Furthermore, recorders can be set up and checked during the day and in favorable weather conditions which can reduce risk with field work.
In 2016, we conducted a study to test and evaluate the use of automated recording devices for monitoring Great Gray Owls during the breeding season. We tested two recording systems, simultaneous callback and recording surveys, and different analysis software and methodologies. Our goals were to determine the effectiveness, ease of use, and methodologies of automated recording systems to monitor forest raptors by testing the systems in known Great Gray Owl territories.
We designed our surveys to simultaneously capture data from automated recording systems and traditional callback survey methodologies for Great Gray Owls. Because our goal was to test the effectiveness of automated recorders, we only surveyed in known, previously active territories within our existing study area. In each territory, we conducted two survey periods to correspond with the early and late calling periods for Great Gray Owls in western Wyoming. The early survey period was a three week window from 25 February – 16 March and the second period extended from 16 March – 6 April, 2016. During each period, we deployed three automated recorders in a triangular array, 150m from the 2015 nest site (Figure 1). Because the survey distance of recorders was unknown, we chose a conservative detection distance of 150m to conduct this test.
We tested two different recording units (Figure 2). We used one array of Wildlife Acoustics Song Meter SM-3 recording devices donated by Wildlife Acoustics. We set these recorders to record from sunset to sunrise to ensure the batteries would last one week. The SM- 3s recorded sounds in 16-bit .WAV format and saved separate files each hour. We also custom built our own recording systems after discussions with and advice from other sound ecologists, such as Shan Burson from Grand Teton National Park. To make this system, we used Roland R- 05 hand-held sound recorders placed in waterproof housings with two ultra-low noise floor microphone electrets to record in stereo. We also tested two rechargeable batteries for different lifespans. These systems continuously recorded sound in 128 kb/s .MP3 format during the entire deployment and saved separate files at 64 mb size (ca. 1.1hr). We also tested these systems side- by-side in trials prior to deployment and tested detection distances using our broadcast survey callers.
We deployed three recorders in five territories per week and re-deployed in each territory during the second survey period. This gave us a total sample of 15 territories that were recorded for one week each in the early and late periods. We also deployed arrays at three additional owl territories to continuously record during the entire calling period with no calling period (control territories), for a total of 18 territories sampled in 2016.
While recorders were deployed, we also conducted traditional nocturnal callback surveys. We conducted callback surveys on two nights each week the recorders were deployed in each territory with at least three days between surveys. Call back survey locations were predetermined in a triangular fashion, opposite the recording devices, 200m from the nest (Figure 1).
We began surveys no earlier than one half hour after sunset and typically completed before 02:00 hours. In order to maximize surveys during the study and because of concurrent study objectives for Boreal Owls (Aegolius funereus), we called for both Great Gray Owls and Boreal Owls at all survey locations using a FOXPRO caller (Foxpro NX4). Each calling period consisted of a 2-min listening period, followed by the Boreal Owl territorial call, a 1-min listening period, the Great Gray Owl territorial call, a 1-min listening period, the Great Gray Owl territorial call again, and a final 2-min listening period. When we detected a Great Gray Owl at a territory, we did not continue surveying any of the remaining points at that territory and instead determined it occupied and proceeded to the next territory.
We processed recordings using a variety of techniques. First, we manually analyzed recordings by plotting spectrograms in a free software called Audacity®. We plotted recordings as spectrograms and visually searched for all owl calls during from sunset to sunrise, recording species, gender (when known), call type, frequency of call, time, duration, and number of calls.
Second, we investigated the use of Song Scope® software produced by Wildlife Acoustics and Raven Pro® software produced by Cornell University. The process involved four steps; 1. Creating a band limited energy detector (BLED) in Raven to select individual notes in a pre-set frequency range, 2. Construct training data sets by classifying selections from step one as true Great Gray Owl territorial notes or false positives to train the random forest (RF) classification, 3. Predict true positive territorial notes using a random forest classification in R, and 4. Manually classify predictions as true detections or false positives.
We use BLEDs constructed by Medley (2013) to identify selections – notes in the same frequency Great Gray Owl territorial calls. Medley constructed separate BLEDs for male and female Great Gray Owls in California. In our audio recordings, we found that Medley's male detector selected more true female territorial calls than Medley's female detector selected, so we used his male detector for both sexes.
To identify territorial owl calls in selections provided by the BLEDs, we trained random forest (RF) (Breiman 2001) supervised learning algorithms, in conjunction with Recursive Partitioning (Rpart; Therneau and Atkinson 2015) and Random Forest (Breiman and Cutler 2015) packages. To train the RF classifier, we classified all BLED selections from approximately 30 hours of recordings from 9 sites as Great Gray Owl notes or not Great Gray Owl notes. We classified all Great Gray Owl notes as territorial (t), agitated (a), defensive (d), contact (c). Only territorial calls were abundant enough in our training recordings to train the RF algorithms.
Using a custom R Script (Medley 2013), we used the resulting RF classifiers (territorial, false) to predict GGOW territorial notes from unlabeled BLED selection tables output from Raven Pro®. Using R, we combined notes less than two seconds apart into sequences, decreasing the amount of time necessary for technician classification and enabling us to count territorial sequences, rather than individual notes.
Recorders Function and Performance
In our trial tests of the two recording systems, we found the SM-3 had greater functionality to our custom-made units but increased sensitivity and ease of finding calls with our system. The three SM3 units can be programmed with custom recording schedules, automatically assess sunrise and sunset by day, and have an integrated light sensor. They only record in .WAV format that at 329mb/hr, requiring >5 times as much digital storage as our custom recorders. We were able to custom make 25 recorders, which recorded sounds 24 hrs/day with less digital storage requirements.
We were able to document owls simultaneously at multiple recorders, suggesting the detection radius of the units was >200m. In our initial testing, we could detect calls up to 300m, but were unsure of how the volume on the broadcast caller directly relates to wild owls calling. We have been unable to confirm a Great Gray Owl detection on all three recorders in the array simultaneously and most detections are only recorded on one unit, suggesting that a 200-250m detection radius is appropriate for Great Gray Owls.
We conducted a total of 60 play-back surveys surrounding 15 known Great Gray Owl nest sites while simultaneously recording (two surveys/week in each early and late deployments, Table 1). We detected Great Gray owls during 18 of our surveys and most detections were at the first survey point within a territory (13 of 18 detections). Owls were not detected until the second survey point in three instances, and in two instances, owls were not detected until the final survey point. We only recorded a pair of owls in three territories; all others were single individuals.
Great Gray Owls were detected at 11 of the territories across the entire study period and we did not detect any owls at four of the surveyed territories using this methodology. Typically surveys are conducted only once or twice within territories. When looking at the one-week survey windows (early and late) for each territory (this assumes we only did two surveys within a territory, not four), callback surveys would have estimated that 40% of territories were unoccupied using the early season window. Using the late season window, we did not detect owls in 53% of territories.
Of the four territories in which we did not detect an owl, all had an active nest and successfully fledged young in 2016. Across the four survey nights within each territory, we detected owls in 25% of the territories only night, 25% two nights, and 19% three nights. We did not detect owls in all four survey nights within any territory. Owls were detected in nine territories in the early round of surveys and seven in the late round. At five territories, owls were detected in both rounds of surveying. (Table 1)
Of the observations of Great Gray Owls during play-back surveys within known territories, three detections were visual with no accompanying vocalizations by the owls. Of all vocalizations detected, 16 were territorial calls, three were agitated calls, and one was a defensive hoot.
Manual Sound Analysis
We gathered a total of 32,391 hours of recordings for this project; 28,112 hours from our recorders and 4,278 hours from SM3 units. Of the total, roughly 14,800 hours were recorded from the three control territories at which recorders were placed for the entire courtship period. Excluding daytime recordings (roughly 12 hours/day) and the control nests, we have gathered ca. 10,934 hours of recordings from dusk to dawn at known Great Gray Owl territories. Currently, we have manually analyzed 2,607 hours of those recordings. We have recorded all known call types and several previous undescribed calls or variations of calls from Great Gray Owls. All territories that have been analyzed had positive detections for Great Gray Owls within each week surveyed. We detected >7,899 calls (not notes) across the hours analyzed ranging in frequency from 150-450 Hz. Males and female territorial calls can be relatively easy to distinguish within a territory but difficult to distinguish among territories. As many as three individual Great Gray Owls have been detected within one night at a single territory and determining individuals by frequency and call pattern exhibits great promise.
We also detected Boreal Owls, Northern Pygmy Owls, Saw-whet Owls, Great Horned Owls, Long-eared Owls, and Northern Goshawks during our analysis. For Great Gray Owls, we detected 4,658 territorial calls, 2543 territorial calls as part of a duet, 276 agitated calls, 167 begging calls, 50 defensive calls, 27 begging calls, and 1 chitter call. From the data analyzed to date, the longest stretch in which we did not detect any Great Gray Owls calling was six days.
However, this stretch of inactivity was recorded after March 25th, 2016 and that particular pair initiated incubation on March 25th in 2015 (the nest was not located in 2016 so incubation date was unknown). Most owls reduce calling after the onset of incubation, which may explain the reduced calling after March 25th in 2016. Excluding that territory, the longest stretch of calling inactivity was two days.
Owls were detected within all territories we have reviewed, meaning that we have 100% territory occupancy rates. At each site, we also detected at least two owls at all sites. Using only one recorder per territory to eliminate the potential of sampling a call twice, we found that Great Gray Owls call throughout the night, but have a greater frequency of calling from 11pm – 4am (Figure 3).
Automated Recorder Analysis
We determined that Song Scope ® was not going to be adequate for the analysis of Great Gray Owl calls because it would not allow for user configurations in key analysis components. Songs are individually identified by the user and unknown controls within the software create the algorithm to search for calls. We wanted greater control of variable control and decided to first use Raven Pro® software.
To date, we have analyzed 942 hours of recordings using the Raven detector and RF classification for Great Gray Owl territorial calls on one recorder within each array from eight different territories. Across the 94 sampling nights, the detector found owl calls in 46 nights (49%; Table 3). The automated detector classified a total of 2,456 territorial calls (after notes were aggregated into calls). Similar to our manual detections, the highest frequency of calls was in the 3:00 and 4:00 hours (Figure 4).
Raven only analyzes .WAV files and conversion from .MP3 was one of the most time- consuming steps that requires a vast amount of temporary storage, followed by running the BLED. However, both the conversion process and running BLEDs are passive processing times running on a computer that can be run on large file batches overnight to maximize processing time. Person-time is needed to annotate calls in the BLED selection tables and verifying results of the RF classification. We recorded time needed to annotate and verify calls for 626.77 hours of recordings and it required a total of 7.8 person-hours, or ca. 45 seconds/hour of recording.
Timing of verification depends largely on the number of Great Horned Owl or other false positives within the recordings or sites that need verification.
MANUAL RECORDER ANALYSIS VS. CALLBACK SURVEYS
To date, we have manually analyzed a total of 19 nights in which simultaneous callback surveys were conducted. Of the nights analyzed, the manual analysis of the recordings was vastly superior at detecting owls than callback surveys (Figure 5). We detected calling owls on the recorders in 11 nights where surveyors did not detect an owl (Table 3). Only once was an owl detected on a survey that was not detected by a recorder and that was a visual observation of the owl.
It took a total of 48 person-nights to complete all surveys in 2016 (two teams of two people each, 12 nights). Time spent traveling to survey location and surveying by teams was a total of 92.7 hours, or 185.4 person-hours. We have only been able to manually analyze 93 nights of recordings to-date due to the significant amount of time needed to review each hour of recordings. However, due to concurrent objectives of quantifying and describing details of each call from all owl species (not just Great Gray Owls) in addition to reviewing three recorders per territory, describing time taken to analyze the recordings is not possible. If an experienced observer was reviewing recordings simply for presence/absence of only Great Gray Owls, it takes approximately five minutes to review each hour. Time needed deploying and retrieving recorders is highly variable depending on study area, remoteness of territories, terrain, snow cover, etc. Generally, within our study area, we could retrieve and re-deploy all 15 recorders in different territories with 3-4 people in one day.
MANUAL RECORDER ANALYSIS VS. AUTOMATED DETECTOR IN RAVEN
We were able to directly compare a total of 93 nights in which we analyzed entire nights both manually and with the automated detector in Raven (Table 3). This was comprised of 10 weeks (51 individual nights) from different territories where we conducted callback surveys and 36 nights from two of our control territories. Raven correctly classified nights as owls present or absent in 81% of nights (Figure 6). Raven did not find any calls during nights in which our manual analysis did not find calls, but it did miss owls in 23 nights (19%). In determining territory occupancy, Raven would have classified eight of 11 territories as occupied with one week of recorder deployment.
Raven was consistent with our callback survey results on nine of 15 survey nights, but detected owls on five nights in which surveyors did not (Table 3). Only in one case did surveyors detect an owl that the detector missed, which was a visual detection of a non-calling owl. Time needed to analyze files is more than drastically reduced using BLEDs.
We found that traditional callback surveys drastically underestimated occupancy of Great Gray Owl territories in 2016. Even with four surveys conducted per territory, we did not detect owls in 26% of territories surveyed, whereas recorders detected owls in 100% of territories.
Based on our results to-date, the use of traditional callback surveys may not be appropriate to determine occupancy or territory locations for this Wyoming species of concern.
There are several possible reasons why owls did not respond to callback surveys. First, our results to-date indicate that peak calling periods for Great Grays occurs from 11pm-4am. Our callback surveys were conducted from dusk until ca. 1am, but most often completed by 11pm in 2016. It is possible that males may be foraging outside of the immediate nest areas during that time and are not available to respond to playbacks. Alternatively, owls may respond after the survey period is complete. In at least one territory, we detected owls on the recorders ca. 10min after the surveyors had left the area. Finally, it is possible that owls had become habituated to the survey caller after repeated visits to a nesting territory, considering we have surveyed within many of the territories in previous years.
The use of recorders to detect owls can be extremely affective but requires a vast amount of time if reviewing all sound files and recording details of all owl species, as we have been. It appears that three days of recorder deployment may be adequate to detect if owls are present within a territory during the courtship period, prior to incubation. However, deployment for one week is likely a better protocol to ensure detection of owls if incubation has been initiated.
Deployment of recorders requires fewer people than nighttime callback surveys, can be done during the daytime hours for safer backcountry travel, and provides continuous data collection over multiple days. More territories can also be simultaneously surveyed by recorders than an individual. In areas of known nest sites, deployment of one recorder to determine occupancy is likely adequate. This technique can be used to locate new nesting territories or to determine owl presence in unknown areas by setting up arrays of recorders during the courtship period 500m apart (250m detection radius).
Automated recognizers in programs like Raven® and Kaleidoscope® will be key in reducing time needed to review recordings. Recognizers have to be created for each species and requires hundreds of known calls to train the software. Particularly for Great Gray Owls that have a territorial call 400Hz, creating detectors can be extremely difficult due to normal background noise at low frequencies. The software can have more difficulty determining the decibel difference between a call note and background noise at these low levels. It is likely that detectors for species that call at higher frequencies will perform better than Great Gray Owl detectors.
Currently, the automated detectors would have misclassified two of eight territories as inactive. We have only run detectors for Great Gray Owl territorial calls, which may have lead, in part, to the reduced detections compared to our manual analysis. For example, Raven did not detect any owl calls in territory C (Table 3), when we found 24 territorial calls during the deployment week. However, we also detected 15 agitated, 9 contact, and 2 begging calls at that territory, which the Raven detector did not search for. Further, we only ran detectors on one of the three recorders within a given territory. Presumably, the number of detections using automated software will increase after the creation of detectors for multiple call types and using all recorders within each territory.
Misclassifying or missing calls using detectors would generally be acceptable in instances when the goal was to determine occupancy only if the proportion missed calls still resulted in at least one detection during the deployment period. In our instance, the detector under-performed for one week of deployment using one recorder. The quality of automated detectors relies on the balance of false positives and false negatives. By relaxing the parameters within the detector to find more calls, the detector also will flag more false positives. Utilizing the RF classification helps eliminate some false positives but many flagged detections still are incorrect, requiring verification of all calls flagged as possible detections. More work needs to be conducted to refine the detector for Wyoming Great Gray Owls.
The analysis for the project is not yet complete. We will continue to determine Great Gray Owl presence/absence for all recordings to compare to both callback surveys and machine detections. In a subset of recordings, we will also continue to record details on all owl calls to help assess calling patterns. Using these data, we will investigate the role of weather, moon phase, and cloud cover on calling patterns. We also will investigate the use of multiple recorders in each territory to determine if one recorder is adequate to assess territory occupancy by using our three control nests to help determine this because they had no influence of callback surveys. Similarly, we will look at the effect of callback surveys on call rates by comparing control to surveyed territories.
We will continue the analysis to determine calling patterns in the early and late periods to determine if there is an ideal time to deploy recorders. Further detailed analysis of the sensitivity and ability of Raven to detect of the two recording systems needs to be conducted. We are also continue to work towards discriminating male and female calls and individual call frequencies to document the number of individuals calling each night.
One major aspect of this analysis is creating automated detectors. We have been focused on creating detectors for Great Gray Owl territorial calls using Raven Pro® software. We will soon investigate the use of Kaleidoscope® software for ease of use and accuracy. Kaleidoscope differs from Raven by searching for entire calls, instead of the individual notes within the call as Raven does. This may lead to fewer false positive detections. Because we are manually analyzing all recordings, this affords us a unique opportunity to accurately estimate effectiveness of each software. Key aspects to assess software are how often the detector misses calls, the rate at which it inaccurately identifies a call, time needed to verify results, deployment period needed to accurately assess occupancy, and machine learning ability.
This project could not have been completed without the dedication and enthusiasm of team leader, Katherine Gura, and field technician, Nick Ciaravella. Joe Medley provided the inital Raven detector, random forest programming, and critical advice. Carrie Ann Adams conducted the automated recording process. Superstar volunteers Tim Griffith, Steve Poole, and Bev Boyton manually reviewed recordings and helped deploy recorders. Ellen Yeatman, Emily Smith, Neil White, Daniel Gura, Carrie-Ann Adams, Meghan Warren, and Susan Patla all helped conduct owl surveys. Sarah Ramirez, Arron Couch, Josh Seibel, and Josh Metten and also helped deploy recorders. Susan Patla was integral in the logistics of this project. Wildlife Acoustics provided the SM3 recording units and SongScope software. Field work was funded by Wyoming Game and Fish Department.
Ecoacoustics is an emerging cross discipline combining bioacoustics and ecology as a method for monitoring and managing the environment. The Centro Interdisciplinare di Bioacustica e Ricerche Ambientali (CIBRA) at Università degli Studi di Pavia has been a key player in the development and progression of Ecoacoustics research. In Professor Gianni Pavan's paper, "Bioacoustics and Ecoacoustics Applied to Environmental Monitoring and Management," Ecoacoustics attempts to, "describe the acoustic environment in terms of quality, quietness, richness, complexity and diversity".
In the UK, the monitoring of bats is undertaken on a large scale through the National Bat Monitoring Programme (NBMP) run by the Bat Conservation Trust (www.bats.org.uk). This long-term monitoring programme relies upon trained volunteers to provide robust population trends for 11 of the UK's 17 breeding bat species. Not all bat species, however, are easily monitored through the NBMP.
Florida Panther National Wildlife Refuge and Friends of the Florida Panther Refuge
The SM3BAT and Echo Meter Touch have been tested, and the search has begun for new roosts on the refuge. Leah Miller, a volunteer of the Friends of the Florida Panther Refuge volunteer, has gathered initial data on bat utilization of two refuge areas using an SM3BAT recorder. Known roost emergence counts were performed using the SM3BAT and Echo Meter Touch. Data analysis will begin shortly, following installation of Kaleidoscope Pro software. The software will be uploaded to a Toughbook which will be accessible to both Refuge staff and to volunteers.
Dr. Liz Braun de Torrez continues to use the SM3BAT in a study to assess the affects of prescribed burns on the Florida bonneted bat, Eumops floridanus.
The main cavity hole to the bonneted bat roost discovered on the refuge last July can be seen in a reflection of an EM Touch screen used by Mark Danaher, U.S. Fish & Wildlife Service biologist for the Florida Panther Wildlife Refuge, while he prepares for an emergence count.
Florida Panther Refuge staff and volunteers are learning to use Kaleidoscope in order to survey for Florida bonneted bats as well as to ID other species on the Refuge. The recordings are generated from the SM3BAT and Echo Meter Touch. The Echo Meter Touch is being used to search for new roosts and to collect calls during emergences from the Refuge's known roost. Dr. Braun de Torrez is using the SM3BAT in her study to examine the effects of prescribed burns on the Florida bonneted bat. In this regard, eighteen locations in both pines and prairies on the Refuge have been monitored four times each this year.
Attempts to conduct emergence counts this quarter confirmed that in June, Florida bonneted bats are no longer living in the roost discovered on the Florida Panther National Wildlife Refuge last year. In July, the SM3BAT was deployed at the roost to monitor whether or not bats return to the roost. Using Kaleidoscope software, Refuge staff can see that bonneted bats are foraging in the area, but so far none have returned to inhabit the roost tree.
In other news, Refuge volunteers are learning Kaleidoscope software, and our Echo Meter Touch is being used along with other SM3BAT meters to search for new roosts. Plans are to use an SM3BAT to monitor anurans at the Refuge in 2017.
Additionally, the Florida Panther National Wildlife Refuge and Friends group are planning a bat education night for our members and the public. The purpose of the event will be to educate about the Florida bonneted bat, its habitat on the Refuge, and the efforts -using Wildlife Acoustic equipment- to study the species.
Dr. Desley Whisson
Deakin University, Victoria, Australia
Field work has been completed, and analysis of recordings is about to commence. The effort was undertaken in November and December 2015 when koala breeding activity and frequency of vocalizations were at their peak.
In each of four sites, a Song Meter SM3 programmed to record continuously between 8pm and 2am each night for four weeks was deployed. A standard visual survey for koalas showed that densities ranged from 2.0 to 7.7 koalas/hectare per site.
The SM3 recordings now need to be analyzed to determine if the frequency of vocalizations is related to koala density. The Acoustic Biodiversity Monitoring Laboratory at the Queensland University of Technology (research.ecosounds.org) has generously offered to assist with this part of the project.
A sub-sample of recordings have been reviewed in Songscope to identify (and annotate) male vocalisations and male-female interactions. Recognizers were built using these annotations and tested but still need refining to eliminate many false positives and to detect distant (but still audible) calls. The male-female interactions are proving to be especially difficult to create a recognizer for because of the high variation in the female call during the interactions (Figure 1). The recordings soon will be analysed using a detector that has been used to quantify koala vocalisations at a site in Queensland.
Preliminary results do not suggest that vocalization frequency is related to population density (Figure 2). In reviewed recordings, bellow frequency is relatively high in the site with the lowest population density.
A number of other species have been detected during review of the recordings (Figure 3). Yellow-bellied gliders have been recorded at two sites and southern boobooks at all sites. Bioacoustics may prove useful for detecting these species.
Over the last few months, the team continued its efforts towards developing accurate recognizers for vocalisations of males, and male - female interactions. The best Songscope recognizer was not entirely effective, detecting less than about 30% of any koala vocalisations. Desley and her colleagues are seeking assistance from the bioacoustics group at the Queensland University of Technology to improve performance of the recognizer.
This study found a correlation between the frequency of female but not male vocalisations and a visual estimate of koala population density at four sites during the koala breeding season (Figure 1). Females vocalised only during interactions with males, whereas males vocalised both spontaneously and during interactions. Lack of a relationship between spontaneous male vocalisations and population density may indicate either a density dependent relationship (i.e. more frequent vocalisations where densities are lower) or poor performance of a single visual survey in estimating population density.
Population densities ranged from 2.5 to 6.0 koalas per hectare at each of the four sites used in this study. Songmeters (SM3) programmed to record from 2000h to 0200h per night (period of greatest koala activity) were deployed at each site for one month between October and December, recording for between 27 and 32 days (total of 708h). Recordings from some days (2 – 5 per site; total of 14 days) were discarded due to poor weather that negatively influenced detection of vocalisations. From 624h of recordings, there were 1525 koala vocalisations, including 1251 spontaneous male bellows and 274 interactions (females and males vocalising together). Vocalisations of yellow-bellied gliders (Petaurus australis) and common brush-tailed possums (Trichosurus vulpecula) that are common in the area, were also detected.
Songscope had limited effectiveness for detecting koala vocalisations, with a high rate of false positives as well as low recall (i.e. true call detection). Recordings were therefore manually processed (i.e. spectrograms visually scanned). This was time-consuming but manageable for the recordings obtained in this study, but development of an automated recogniser will be critical to facilitate the processing of recordings from larger studies or in applying bioacoustics to regional monitoring of koala populations.
The primary goal of this study was to determine the relationship between the frequency of koala vocalisations and population density, and thus the potential for using bioacoustics for indexing koala populations. Results suggest that the frequency of female vocalisations (indicating interactions between females and males) are correlated with population density; however, given the low frequency of these vocalisations and the small number of sites sampled, sampling across more sites is necessary to confirm this.
Development of a cost-effective and accurate method of surveying koalas is important for gaining a better understanding of the distribution and conservation status of koalas throughout their range. This study has indicated that bioacoustics may have potential as a non-invasive survey method. However, testing at more sites and developing automated methods of detecting vocalisations in recordings are necessary to confirm this. We developed a relationship with the QUT Ecoacoustics Research Group during the study and hope to continue working with them to develop improved recognisers for southern koalas. The project also has led to a collaborative project with another university to assess the effectiveness of bioacoustics for detecting other cryptic arboreal mammals.
Following the results of this project, we are now conducting a state-wide study using bioacoustics to both monitor koala populations and to examine the seasonality in their breeding behaviour. We have deployed Songmeters (SM4) at 11 sites throughout Victoria, and will operate them for one year.