Kasey Cope and Slaney Stringer
El Dorado High School Natural Resources Program
Receiving this grant has definitely been the highlight, as well as the turning point of our senior project. We had no idea how easy it would be to collect so much data in such a short period of time; it has made every aspect of our population more successful.
The Song Meter SM4 has worked amazingly well; it was extremely easy to program the recorder to record for 30 minutes off and on every day. This fixed one of our main issues, which was not being able to survey bird life at sunrise and sunset, when most of the birds are out. Now the data we have collected from one of our forests, a sugar pine grove, is much more consistent, unbiased, broad, and accurate. We mounted the Song Meter approximately 11 feet above the ground in the middle of the forest after programming it, and left it out for several weeks. After plugging the SD cards into our computer, we were able to see the all of the files with bird audio data. Since then, we have undergone a few struggles using the software. We had difficulty figuring out what all of the numbers in the data sheets meant, how to read the frequency distributions, and how to lower the sound contrast so that it is easier to hear the bird sounds. However, we learned how to do all of these things by talking to Chris from Wildlife Acoustics customer support. He was extremely helpful and now we have no problem listening to the audio files, identifying them with the help of the Song Sleuth mobile app, and sorting similar files into clusters. At the end of this year our data will be very substantial for our campus so that future students that take on this project have a strong foundation to build off of.
One thing that we had the privilege of doing in January is visiting a class of 7th graders to discuss our project and things we have learned. We gave a presentation about the importance of biodiversity, as well as explained the concept of our population study. We then had the kids participate in a scavenger hunt to identify birds at different stations that had pictures, recordings, and feathers. They learned how scientists can identify and record information about wildlife using field guides, data sheets, and audio recorders, like the one we have. It was very rewarding for us to see how much fun they had because of the knowledge we have gained over the past several months.
Finally, we have another event coming up that is an opportunity for us to further share this knowledge. On April 13th, our Natural Resources program hosts Natural Connections Day; this is when many classes from middle schools in our area come to our campus for a day and learn about various topics related to Natural Resources.
Devon Wildlife Trust
Since the grant was awarded we have been busy using our SM4 detectors and the new microphones. They have been out as part of two citizen science winter research surveys – why leave the detectors idle all winter?! – looking at landscape usage by greater horseshoe bats in the colder months. We are just starting to analyze the results and have picked up lots of recordings. Unfortunately we have lost some as the batteries are losing voltage in the really cold weather – but we hope to be able to report on the findings over coming months.
The project team are just gearing up to the start of our main citizen science programme – the Devon Bat Survey! The idea behind this is that anyone can take part by borrowing a bat detector from one of our 20 monitoring centres and surveying a chosen 1km grid square. Bookings opened two weeks ago and over a third of the 991 slots are already booked for this summer! The detectors and all the kit are in the process of being taken out to the monitoring centres, ready for the first sessions to start on March 29th. We are looking forward to getting the first sound files back for analysis and seeing if we can add to our knowledge of where Devon's bats are!
With only five weeks to go until the end of the 2018 Devon Bat Survey, we have received 622 SD cards back from participants, processed 650,000 sound files to look for bats and sent out 528 reports! We have two dedicated volunteers who are helping with the survey, one of which has already verified 1,327 greater horseshoe bat passes. We have uncovered new hotspots of activity in the county and will use these to focus our research work in 2019. We aim to have updated hotspot maps for greater horseshoes on our website by the end of 2018.
Our winter research surveys revealed some interesting behaviour. Greater horseshoes were recorded 1.15km away from their known hibernation roost, and the average temperature on the night with the highest activity (31 passes) was just 30C. This has led to more questions and we hope to be able to extend the survey this winter via a passive surveillance method of selecting twenty sites around the roost and leaving the detectors in situ for three months. Results of this will be available by mid-2019.
We're now two months into the 2018 Devon Bat Survey and 658 people have requested a square to survey – an amazing two-thirds of available booking slots! The project team has been busy supporting the 20 Monitoring Centres (see map with this report), dealing with any queries from surveyors and processing the returned data cards. A volunteer assists in downloading the data and returning the SD cards to the centres to ensure the smooth running of the survey. Tweaks and updates to last year's survey have helped to improve the efficiency and running of the survey.
Feedback from surveyors is very positive overall – they find that the instructions are clear and the equipment is easy to use. We have started to send out reports to those who have taken part – most people are amazed at the variety of bat species using the countryside near their homes – up to 10 or 12 species in some cases. Data from the winter surveys will be collated and analyzed; this all takes time as records needs verifying. Overall, this is a popular project that people all across the county are keen to get involved in.
The Devon Bat Survey’s 2018 season ended on November 2nd, with 728 people having taken part and returned sound recordings. This generated just shy of a million sound files! The team are still in the process of verifying the number of bat passes, but initial analysis shows that there is likely to be in excess of 500,000 confirmed passes. This year we recorded 3,376 greater horseshoe passes at 270 locations – a much higher figure than in 2017. This included two new hotspots that the project was previously unaware of, and these will be investigated further in 2019 with targeted surveys. Results from 2018 can now be found on our website.
All the data for non-horseshoe bat species is currently being manually checked by our project volunteers to confirm the species identification assigned by our automated classifier. Volunteers have also received training on sonogram analysis, starting with AnalookW software and progressing to Kaleidoscope. Once checked the records are sent to the Devon Biodiversity Records Centre where they are available for use to inform conservation priorities in the county. The greater horseshoe records are already contributing to a new Supplementary Planning Document for one area of Devon, providing additional protection to populations at risk from housing developments.
All greater horseshoe records are helping the project to focus its land management advisory work. It allows us to link up areas of known greater horseshoe use, providing the bats with additional routes through the countryside, potentially opening up new feeding grounds to them. It is also opening up investigatory routes to look for new roosts. Some of the records from 2018 will now be cross- referenced to the database of existing roost location records. Where no roosts are currently known in the locality, we will focus recording efforts in 2019 and 2020 to try to establish where these bats are living.
The winter 2017/18 detector survey provoked some interesting questions about landscape usage during what is normally assumed to be a hibernation period for greater horseshoe bats (Braunton report attached). Because of this, a second winter survey has been set up to attempt to answer further questions in 2018/19. Twenty SM4 detectors have recently been placed in static locations within the countryside around a known hibernation roost (including a detector at the roost exit). The detectors will record for at least three months during the main winter period, and all activity will be compared to weather data and habitat type. Any significant findings will help to tailor our land management advice around hibernation roosts and will be disseminated to project partners and the wider public.
Rohit Chakravarty and Dr. Anand Krishnan
Indian Institute of Science Education and Research-Pune
We received our SM4BAT recorders and an Echo Meter Touch 2 Pro in early February, and have been hard at work getting them tested and recording bats in Pune, prior to fieldwork in the Himalayas. First, we tested each of the recorders overnight on the IISER Pune Campus, and recorded a great deal of bat activity as the weather warms up! We have trained a student volunteer, Ram Mohan, in the use of these bat detectors and in basic analytical tools, and he will play a major part in this season's fieldwork. To get our educational and outreach activities started, we also conducted a bat walk on campus using the Echo Meter Touch 2 Pro. During both these activities, we identified and recorded calls of at least five bat species, including the Indian pipistrelle (Pipistrellus coromandra), the greater yellow house-bat (Scotophilus heathii) and the Egyptian free-tailed bat (Tadarida aegyptiaca).
All the other equipment for fieldwork has also been procured, and RC has now left for the Himalayas in Uttarakhand to begin our first field season. This work will last until mid-June at least, and will involve both our student volunteer and two field assistants (both of whom will also be trained in the use of recorders). Fieldwork from now on will focus on identifying suitable recording sites at four different elevations and getting acoustic data. Bat activity is likely to pick up by late March, so hopefully we will gather useful data on distributions and activity patterns soon!
In mid-March we were into the most anticipated leg of our project: field work! We reached Kedarnath Wildlife Sanctuary on 13 March and conducted fieldwork up till 15 May. After nine days of reconnaissance, we had finalised our recording sites. In order to have coverage of various habitat types and species, we placed our recorders in three habitat types: forest, forest edge and open areas. Our sampling locations also covered an elevational gradient from 1400 to 3700 m above sea level. Depending on the area of the sampling location and the habitat heterogeneity at various elevations, we had up to three spatial replicates of each habitat type. A breakdown of our recording sites is given in the following table. We recorded for two consecutive nights at each recording site. Assuming the night length to be 10 hours on an average, we collected roughly 760 hours of acoustic data!
During the course of fieldwork, we trained our field assistants Zareef Khan Lodha and Baseer Baniya Gujjar. Zareef and Baseer are from a small village in Uttarakhand and have been assisting biologists for the last five years. We have trained them in all aspects of bat research, and they are now well-trained not only in setting up bat detectors but also in identifying different genera of bats from recordings!
We are now back at IISER Pune and we have started analysing the recordings. Our intern, Ram Mohan is playing an important role in this step. Preliminary checks suggest that we have recorded about 15 species of bats across the elevational gradient, including the highest elevation record of the European Free-tailed Bat (Tadarida teniotis). We also have possibly the first recordings of social calls of bats from the Himalayas. We cannot wait to unravel more exciting distribution and activity patterns!
Dr. Michael Schöner
University of Costa Rica
The aim of our project, effectiveness of artificial roosts for Neotropical bat species is to efficiently attract especially vulnerable bat species to artificial roosts via sensory cues. Currently, we are running pre-experiments to find the most promising sensory cues before we start opening the artificial roosts. As a model species we chose wrinkled-lipped bats (Trachops cirrhosus). Using Wildlife Acoustic SM4s we recorded individuals in their roosts during the early morning hours when the individuals start coming back to their roosts. As groups of T. cirrhosis usually roost in relatively stable groups, we assume that conspecifics attract each other via calls. We then caught individuals in their roosts and brought them to a flight arena where we had set up two artificial roosts. From one we played back purely social calls (contact calls), from the other a mix of contact and echolocation calls was broadcasted. So far we found that although the contact calls are more attractive to the individuals (as indicated by the number of approaches to the referring artificial roosts), in the end the bats preferred to stay inside the roosts where the mixture of calls had been broadcasted. Moreover, we tested if olfactory cues also might be effective in attracting the individuals. As T. cirrhosis is usually roosting in mixed species groups, e.g. with Micronycteris spp., we tested pure T. cirrhosis feces against a feces mix of different bat species that we had freshly collected inside the roosts. It seems that the T. cirrhosis individuals prefer feces of their own species. We will continue testing further sensory cues until we have a large enough sample size to ensure that we collected the best working ones. After that we will start opening the artificial roosts in the wild to experimentally test which sensory stimulus (acoustic, olfactory or a mixture) is most reliable in attracting wild bats to novel roosts.
With our project, Effectiveness of artificial roosts for Neotropical bat species" we want to attract bats to artificial roosts via sensory cues. For this aim, we use fringe-lipped bats (Trachops cirrhosus) as study species. Currently, we are still running pre-experiments to find the most attractive acoustic and olfactory cues. This has turned out to be more challenging than previously thought. To find optimal cues, we are running pre-experiments in a flight cage where individual T. cirrhosus can select between roosts with different acoustic cues (playbacks of T. cirrhosus social calls vs. a mix of social and echolocation calls). However, we needed to change the acoustic set-up several times and are still searching for the best possible acoustic design. Because finding an attractive sensory lure is so critical to the success of the project, we are investing considerable time and care in these pre-experiments to ensure a well-working study design once we open the artificial roosts. Additionally, we have started to conduct a second type of pre-experiments in old bunkers that are frequently used as roosts by wild-ranging T. cirrhosus. During the last months, we rarely recorded any individuals of T. cirrhosus with the SM4s inside the bunkers but our impression is that the individuals nevertheless preferred those bunkers where we played back a mix of social and echolocation calls of conspecifics compared to control bunkers without any playbacks. We will need more time and more recordings of individuals inside the bunkers to evaluate this statistically.
During the last months we were very successful and could record many videos from the pre-experiments, which aim to evaluate the most promising sensory cues to be tested in the field.
I am currently analysing the videos, which takes a lot of time as one video lasts 8 to 12 hours. In a flight cage we have set up two artificial roosts from where different sensory cues are displayed (e.g., pure Trachops social calls vs. a mix of social and echolocation calls). For the video analysis I evaluate which roost/sensory cue is visited most often and which eventually serves as roost, i.e. where does the bat stay in the end. I will then run the according statistics.
We are about to finish the pre-experiments within the next one or two weeks. My colleagues could catch a lot of Trachops individuals for the pre-experiments and most of them were cooperative. When broadcasting bat calls with a BatLure from out of artificial roosts inside a flight cage, it seems that the Trachops prefer the mixed calls (social + echolocation calls) over the pure social calls. This would be close to what happens in nature, where there are also no pure calls of a certain type (social vs. echolocation call). We will need a few more of these pre-experiments to get significant values.
Situation is different when we are testing olfaction cues in further pre-experiments. Here, the bats randomly choose one of the presented olfactory cues (pure feces from Trachops vs. a mix of feces from Trachops and other bat species that can be found roosting together). It is difficult to say if olfaction does not play a role for roost selection or if the Trachops simply do not care. I hope that the field-experiment will tell us more on this question. For the field experiment, we are as well going to use a feces mix (Trachops + other bat species), as this resembles the most natural situation.
Finally, as we are so close to start with the field experiment, we are testing the set-up within the artificial roosts in our study sites and how to arrange the devices inside the roosts (e.g., we need to avoid that the SM4s constantly record the bat calls emitted by the BatLures. I have attached a short graph on how we are planning the set-up). This way, we will be able to directly start with the field-experiment once the last pre-experiment has been conducted.
I am very confident that we can finally start with the field-experiment mid of November.
Habitat loss, conversion and fragmentation have posed major threats to bat species worldwide. This is especially true for bats in tropical rainforests. Many tropical bat species rely on intact forests not only for foraging but also, critically, for roosting. Several studies from Europe and North America have tested the effectiveness of artificial bat roosts (ABRs) as surrogates for the tree cavities found in older growth forests, critical roosting resources that are often lacking in forests suffering from anthropogenic impact. In contrast, in the tropics ABRs have rarely been offered to bats; knowledge on their effectiveness is lacking. Our project, “Effectiveness of artificial roosts for Neotropical bat species,” aims to investigate natural colonization of ABRs (Fig. 1), and to test whether the addition of bat-specific sensory stimuli enhance the roost colonization process. In addition to daily acoustic and visual monitoring of ABRs, we conducted field experiments with sensitive gleaning phyllostomids. We focus on Neotropical fringe-lipped bats (Trachops cirrhosus) as a model species to investigate which stimuli effectively attract individuals to ABRs. Knowledge gained from these investigations will offer valuable insight for conservation strategies that can be widely applied.
We hypothesized that 1) certain bat species would colonize ABRs more quickly than others and 2) these colonization processes might reflect succession patterns, with certain species depending on others for roost discovery: highly exploratory species colonizing ABRs first, less exploratory species following only once others had become established within a roost. 3) We further hypothesized that species which are less likely to use ABRs are at more risk from habitat loss, because they are more likely to rely on specific characteristics for roost finding, and are less flexible in adapting to changing environmental features. We predicted that by introducing sensory lures we could help close this gap, making new, artificial roosts attractive to sensitive species as well. We specifically predicted that multi-modal sensory lures (acoustic + olfaction cues combined) would be more effective in attracting bats than uni-modal lures (acoustic or olfactory cues alone), which in turn should be more effective than controls with no added sensory cues. Finally, we predicted that ABRs would be more effective when situated in disturbed forests where natural roosts are rare.
The project is located in the secondary tropical forests of Soberanía National Park near Gamboa, Panama (28 ABRs; Fig. 2), and in Osa, southwestern Costa Rica (40 ABRs). We are currently conducting experiments in the Panama study site; we focus on these results for this report. Moderate to more extensive land use characterizes the chosen study areas. For a comparative approach between disturbed and intact forests, the same experiments will be conducted on Barro Colorado Island, which is the site of more pristine, undisturbed forests. Afterwards, we will repeat the experiments in Costa Rica to investigate if the results transfer to other regions.
Over 70 species of bats can be found in the forests around Gamboa, including the fringe-lipped bat, Trachops cirrhosus (Fig. 3), which is known for eavesdropping on the mating calls of its frog and insect prey. It is particularly known for feeding on the túngara frog, Engystomops pustulosus.
To select the sensory stimuli most effective at attracting our focal bat, T. cirrhosus, we tested different acoustic and olfactory cues. From earlier studies we knew that individuals of T. cirrhosus can be attracted with acoustic lures broadcasting the mating songs of túngara frogs. Such playbacks have been used for behavioral experiments in the context of foraging, but did not make sense in the context of roost finding. For roost attraction we instead used the calls of conspecifics. Using mist nets, we captured T. cirrhosus from their natural roosts and foraging sites around Gamboa, and brought them to our lab. After collecting standard measurements (e.g. forearm, weight, reproductive status, etc.) and marking them with PIT-tags for individual recognition, we fed them fish and offered water ad libitum. At dusk we put the bats in a flight cage in which we had set up an artificial roost. As soon as the bats had started to occupy the artificial roost, we recorded their calls (Avisoft Bioacoustics, Germany: USGH with condenser microphone CM16; Wildlife Acoustics, US: SM4BAT FS with SMM-U1 external ultrasonic microphones). We then released the bats into their natural habitat again.
To test which types of calls attracted bats to the roost, we conducted a series of behavioral choice experiments with new individuals (Fig. 4). We set up two artificial curtain roosts inside the flight cage and placed one ultrasound loudspeaker (Avisoft Bioacoustics, Germany: UltraSoundGate Player 416 H with Ultrasonic Dynamic Speaker Vifa; Apodemus, Netherlands: BatLure) behind each curtain. Each loudspeaker played back a different type of bat call. The test stimuli included social calls, echolocation calls, and a mixture of echolocation and social calls.
Similarly, we tested bat responses to different olfactory stimuli. We first collected feces from T. cirrhosus individuals in the lab and from a natural roost exclusively used by T. cirrhosus. These pure T. cirrhosus feces were tested against a mix of feces from species that commonly roost together (e.g., T. cirrhosus, Micronycteris spp. and Saccopteryx spp.), freshly collected from natural roosts. We placed each of the two olfactory stimuli in one of the two arms of a Y-maze and placed a T. cirrhosus individual in the starting arm (Fig. 5). Beginning at approximately midnight each bat had 7 hours to decide if it stays in the starting arm, in the arm that contained pure Trachops feces, or in the arm with the mixed species feces.
In all experiments the different stimuli were randomly allocated to one of the two sides. All experiments were filmed with camcorders and infrared lights to see for which stimulus the bats finally decided as indicated by their final choice in the early morning hours.
ABRs are located in sets of four in the same area (plots of 100 m²) to minimize the effects of environmental variation on colonization rates. Within a set we randomly assigned each ABR to one of four treatments to determine what sensory stimuli best attract bats. ABRs were fitted with (1) an acoustic lure (Apodemus, Netherlands: BatLure) broadcasting the bat calls found to be most attractive in our lab experiments, (2) an olfactory lure, using the fecal cues found to be most attractive in our lab experiments, (3) a multi-modal lure (both acoustic + olfactory lures), and (4) no sensory lures, as a control. ABRs are monitored daily by visual inspection and acoustically using ultrasonic recorders (Wildlife Acoustics, US: SM4BAT FS with SMM-U1 external ultrasonic microphones) to assess the colonization process. We opened the first set of ABRs November 2018. We plan to continue opening the remaining ABRs in groups of four as described above, and monitor each set for a minimum of 8 weeks.
We recorded echolocation (Fig. 7) and social calls (Fig. 8) from T. cirrhosus individuals. By presenting these calls to new individuals (n = 10), we were able to select attracting (and not repelling) social calls. When testing these attracting social calls against echolocation calls, we found that the bats are generally more attracted by the social calls than by echolocation calls as indicated by the number of approaches to one of the two artificial roosts inside the flight cage. However, after initial approach bats were reluctant to stay inside artificial roosts. We then tested the same social calls against a combination of echolocation and social calls. Still, the bats approached more often to roosts where the social calls were presented. However, when it came to roosting, the bats preferred to stay in the roost with the combination of echolocation and social calls. While these results are preliminary and we are continuing to run tests with more individuals, this is a strong hint that the bats prefer the most natural situation where individuals inside a roost display a mixture of echolocation and social calls and not only one call type.
Regarding the olfaction stimuli, the results from the preliminary experiments are less clear as the tested individuals (n = 6) randomly selected pure T. cirrhosus feces or the feces mix. Because a mixture of feces from different species most closely resembles natural conditions in many of the roosts, we decided to use the feces mix as olfactory stimulus in the field-experiments.
We currently only have one set of opened ABRs, so it is too early to draw conclusions from the field experiments. We do see some interesting preliminary findings, however. As predicted, acoustic monitoring with SM4s shows the highest bat activity at the ABR where acoustic and olfactory stimuli are presented together, followed by the ABR with acoustic stimuli alone, which is in turn followed by the ABR with olfactory stimuli alone. Lowest bat activity was measured at the control ABR which had no sensory lures.
We were excited to see that not only did bats inspect the ABRs as evidenced by the acoustic recordings, they also very quickly moved in. We found our first bat residents 5 days after opening the roosts. During our daily visual checks of the roosts, we have found a total 12 Micronycteris (gleaning insectivorous bat), 2 Glossophaga (nectarivorous bat), and 1 Carollia (frugivorous bat) roosting in the ABRs since roost opening. These species are known to be exploratory and rapid roost colonizers, so it confirms our predictions that they would find our roosts first. We hope that with more time, and with these pioneer species residing in the ABRs and thus also acting as attractant lures, the more sensitive bat species such as T. cirrhosus will also find these new roosting resources. Opening the other ABRs and monitoring colonization of these roosts by different species will enable us to better implement ABRs in tropical forests, thereby protecting those vulnerable bat species which are affected most by roost destruction. By doing so, we will be able to make concrete recommendations for applied conservation projects, helping bats recover from anthropogenic impacts.
For all their help in the field and/or ideas and suggestions we are deeply grateful to Detlev Kelm, Nikolai Meyer, Ram Mohan, Michelle Nowak and Adriana Tapia. We cordially thank Wildlife Acoustics for providing us with four SM4BAT FS and associated acoustic equipment, especially Alexandra Donargo for her help throughout this project.
Dr. Thilina Surasinghe and Maria Armour
Bridgewater State University Foundation
As the Third Quarter grant recipients in 2017, we have now been able to begin the analysis portion of our first season of ultrasonic and acoustic data. For our collaborative project, this summer we have collected both acoustic and ultrasonic recordings at our study sites in Southeastern Massachusetts. This grant has allowed us to run our six months of calls through Kaleidoscope Pro. Over these last few months, a member of our team has attended a workshop run by Wildlife Acoustics and we all have learned how to apply Kaleidoscope Pro software. Our research team has just begun looking at our first season of recordings with less than 10% of our 2017 recordings analyzed.
Our goal for this project is to assess occupancies of bats and anuran taxa in Massachusetts protected and private areas and to analyze the overall soundscape for these sites. Two of our three field sites are located within Mass Audubon's Moosehill Wildlife Sanctuary (Sharon, MA) and a third, private site, in Bridgewater, MA. We deployed Wildlife Acoustics SM3BAT systems from May 2017 – October 2017 at the Mass Audubon sites. Along with learning the software ourselves, during the fall semester we began training two Undergraduate Biology majors enrolled in research credits. This has been the students' first true involvement in conducting scientific research.
Both of our SM3BAT hardware systems were fitted with ultrasonic and acoustic microphones. Since ending our field season in October, we have only been able to scratch the surface of our recordings; analyzing a couple hundred ultrasonic, bat calls. Non-ultrasonic, soundscape recordings have yet to be examined. In the coming semester, we along with our trained research students and new undergraduate students will begin acoustic analyses and continue cluster analysis on our ultrasonic recordings. Come March, our research team will begin the second field season.
Kaleidoscope Pro will continually be used moving forward with this project on bat and anuran taxa. Now that we have learned how to use this software, this spring we hope to complete analysis on our first season of acoustic recordings.
The bioacoustics research lab at Bridgewater State University has made significant progress in the soundscape analysis portion of this project since the last report. Our undergraduate research students, Joshua Kelleher and Adam Enos, have been busy over the spring semester running manual species ID for bats on Kaleidoscope Pro. Through many hours at the computer, Josh and Adam have been able to put together preliminary results of our first season in two posters that they will present next month at the 2018 New England Natural History Conference (NENHC) in Burlington, VT. This will be the first academic conference presentation for both students and the first "publishing outlet" for the research we proposed. Josh's poster is titled "Differences in Seasonal Occurrence and Activity of Bat Species within Private Conservation Land in Massachusetts" and Adam's is "Bat Occupancy in Two Habitat Types in Private Conservation Lands of Southeastern Massachusetts". It is because of the Wildlife Acoustic grant that our two undergraduate student researchers are able to present on their research this coming April.
As we wrap up analysis on season 2017 and prepare for the regional conference, our second acoustic season is already underway. Due to multiple severe snowstorms we have had in the last two months, we have only now been able to access our acoustic recorder deployment sites. During this final week in March, the students and I re-deployed two SM3BAT systems at Mass Audubon's Moose Hill Sanctuary in Sharon, MA. Microphone position was slightly altered at both sites to minimize unwanted echoes from water surfaces. Our deployment setup has been given an update through the support of the Wildlife Acoustics grant. Each system is now housed in the SM3BAT Armor, which offers increased protection and a piece of mind during times of deployment. We also have installed a Garmin GPS unit. An external battery or solar power option is being investigated to extend our deployment dates.
We look forward to sharing our 2017 results that include both bat and amphibian analysis in the next progress report quarter.
During the third quarter of our Wildlife Acoustic’s Equipment Grant our lab successfully completed 2017 bat analyses using Kaleidoscope Pro and presented our results at a regional conference. Throughout the 2017 active season (May through October) we deployed SM3BAT ultrasonic recording devices at two sites (1 device per site) within the Mass Audubon Moose Hill Wildlife Sanctuary in Sharon, MA. Following Kaleidoscope Pro and manual analysis, our results for this season include 4900+ bat passes being recorded over 31 nightly sessions at the vernal pool site and 2700+ bat passes over 21 nightly sessions for the forest edge/barn site. Highest month per night average at the vernal pool site was EPTFUS during each month except July, when MYOLUC was the highest recorded passes. At the forest edge/barn site MYOLUC was the highest passes during May per night average, followed by EPTFUS being the highest for the duration of the season. We also manually confirmed passes at both sites for: LASBOR, LASCIN, LASNOC, and PERSUB. Although these 2017 results gave evidence of high bat activity at both sites, after running statistical analysis (Wilcoxon-Mann-Whitney Test and Kruskal-Wallis Tests) we found that there is no significant difference in bat community presence and activity levels of species between months and habitat type. Our study taxa are known to utilize a diversity of habitat types. Evidence that activity levels were high in our two habitats may be important to include habitat landscape in conservation efforts, not just a single habitat type.
In April our Undergraduate research students, Adam Enos and Joshua Kelleher, presented two posters at The Northeast Natural History Conference (Burlington, VT) on Southeastern Massachusetts bat community composition and activity during the 2017 season. Each student’s project was a part of a larger research project of Co-PI’s Surasinghe and Armour, which utilize Wildlife Acoustic equipment to study both anuran and bat communities. Their abstracts follow this project report. Both Adam and Josh just received their B.S. in Biology in May from Bridgewater State University and are motivated to secure a position in the field of wildlife ecology due to their positive experiences in undergraduate research.
This summer the lab is very active with Dr. Surasinghe training several undergraduate students in anuran field identification and Ms. Armour conducting active capture and release of bats at our two sites to confirm species presence and to collect biological data. We are also in the process of training two new undergraduate researchers who will join our lab in the fall. Both are being trained on SM3BAT deployment and Kaleidoscope Pro analysis. One will continue the bat research project started by Josh and Adam and the other student will investigate anuran and non-bat recordings from 2017-2018.
A portion of this project’s goals is to conduct community outreach. Along with time in the field, summer months are an opportunity to invite the public to join in and learn from our ongoing scientific research. Conservation of bats is often challenging due to unwarranted misconceptions surrounding them; community activities such as a bat walk help the public gain appreciation for this taxa and support conservation efforts of protecting their habitats. Ms. Armour will continue to run her annual public bat walks at Mass Audubon’s Moose Hill Sanctuary in Sharon, MA in July and Old Westbury Gardens, NY in August using Wildlife Acoustic equipment including the user-friendly Echo Meter Touch.
Bats (Order: Chiroptera) are among the most diverse mammalian lineages in North America, and they occupy a wide variety of habitats. Different types of habitats- open spaces, forest edges, and forest interior- substantially vary in resource distribution and spatial structure (clutter), and therefore foraging strategies as well as echolocation signatures of bats can vary substantially among different habitats. In order to explore this hypothesis, we deployed two, SM3BAT automated Bioacoustics recorders (Wildlife Acoustics, Inc.) in a forest-edge habitat and a cluttered habitat located in Mass Audubon’s Moose Hill Wildlife Sanctuary, in Sharon, MA. Forest edge habitat is a low-shrub dominant open area surrounded by a deciduous forest edge while the cluttered habitat is a mixed hardwood-coniferous forest containing two vernal pools. Analysis of the bioacoustics data through Kaleidoscope Pro software confirmed the presence of six bat species during the 2017 active season. Our preliminary analysis showed relative high nightly passes of Myotis lucifugus (Little Brown Bat) altered from the forest edge habitat in early spring to the closed habitat in mid to late summer. Our preliminary conclusions concerning M. lucifugus are that this could be related to either: changes in foliage density as the season progressed or food availability. Further investigation and data is required. We plan on continuing our research and data collection through the 2018 season.
There are nine Vespertilionid species of bat documented within Massachusetts; five of these have been state-listed as Endangered. The long-term assessment of bat activity and presence may offer valuable population data on the affect environmental and human-driven pressures (wind turbines, human disturbance and diseases including White Nose Syndrome) have on our regional bat populations. This study has investigated bat species composition and occurrence within two habitat types (forest edge and forest interior) in Mass Audubon’s Moose Hill Wildlife Sanctuary in Sharon, MA. Passive ultrasonic recordings were made using the automated bioacoustic recorder SM3BAT (Wildlife Acoustics Inc.) during active season months in 2017. Recordings were then run through Kaleidoscope Pro Analysis Software and manual species identification was conducted. Throughout the active season, Eptesicus fuscus (Big Brown Bat) was consistently present at both deployment sites. The months of May and June have a greater presence per recorded night of two migrating species within the forest interior when compared to mid or late summer months: Lasiurus cinereus (Hoary Bat) and Lasionycteris noctivagans (Silver-haired Bat). Finally, Perimyotis subflavus (Tricolored Bat) echolocation pulses were only recorded in May for the forest edge site, but present in the forest interior during early, mid, and late summer. We plan on correlating these preliminary results with classified foraging and migratory strategies of Massachusetts bat species to help determine a baseline for species occurrence and activity levels. This first season of data will aid in a long-term study of bat populations within this protected area.
During the fourth quarter of this research project, our progress has hit several roadblocks. This has made for a challenging end to the active season for us. Issues we faced included failure of internal batteries, calibration issues, and scheduling. This year, to save card space and extend our deployment periods, we decided to start recording in WAV files when programing our SM3BAT. Early on in the season, limitation of these files was discovered as the SM3BAT system was not able to dynamically change channels (only ultrasonic mic triggered recordings) while recording in WAV. Fortunately, this user error was identified through contacting Wildlife Acoustic’s support team and reading Jeff King’s whitepaper “Acoustic (Bird/Amphibian) and Ultrasonic (Bat) Recording with the SM3BAT”. Dual trigger capabilities were allowed once recordings were in WAC files. A positive outcome of this past season was being able to extend deployment periods by using external batteries connected to a solar panel (figure 1). This extended system was possible due to the creativity, knowledge, and effort of our University’s Analytical Instrumentation Staff, Rob Monteith.
Conservation outreach has been a major goal of this project. Through the collaboration with staff at Mass Audubon, Moose Hill Wildlife Sanctuary (our project’s field site) has created an educational exhibit based on our research with bats. Visitors of this sanctuary are able to learn about the different species of bat in Massachusetts and get informed about the acoustic study being conducted on the grounds. This sanctuary has several bat houses that have been erected throughout the trails and on buildings. I captured thermal images of a Big Brown Bat (Eptesicus fuscus) roost while mist netting this summer (figure 2a and 2b) and the Sanctuary’s wildlife camera captured activity of both a bat (species unknown) and two fox cubs near our open field site (figure 3).
The Acoustic lab here at Bridgewater has recently started training two new undergraduate researchers who will be assisting in the analysis of ultrasonic recording (Catherine Cameron) and acoustic recordings (Ashley Zimmerman) this coming academic year. Both are keen to get started with their respective training on Kaleidoscope Pro. Our lab plans on utilizing the new Kaleidoscope Pro Cloud Account to maximize our efforts even when we are not in our lab. The 2018 recordings are in the early stages of being analyzed.
Nantucket Conservation Foundation
Prior to 2015, the federally threatened Northern long-eared bat was not known to be present on the tiny coastal island of Nantucket. The presence of this species was confirmed when a dead specimen of a lactating female was handed in to the Nantucket Conservation Foundation. Given that this species is doing so poorly due to white nose syndrome elsewhere in the northeast, and due to the fact that so little was known about the habitat requirements of northern long-eareds on Nantucket, it became a high priority for us to find out more about what areas of the island these bats were occupying and how populations were faring here. Since receiving a SM4Bat FS recorder from the Wildlife Acoustics Scientific Product Grant earlier this year, we have been able to survey much of the island in order to document areas of activity of Northern long-eareds. Additionally, the data recorded from our SM4Bat has helped us pin point potential areas to mist net so that we can efficiently capture bats, place transmitters on them and locate maternity colonies. We have detected Northern long-eared bats in nearly every location that we've put out our detector!
Our summer field season is winding down, but we still have bats on the brain. Now that we know that Nantucket is home to many Northern long-eared bats, we must find out if they are hibernating here. We will continue to deploy our detector throughout the winter in order to document any winter time activity for these bats and to help us pin down potential locations of hibernacula.
Over the summer field season, we used our SM4Bat FS recorder to survey much of Nantucket in order to document areas of activity of Northern long-eared bats as very little is known about habitat use by this species on the island. We documented acoustic evidence of Northerns in nearly every location that we put out our detector, however the highest number of calls were recorded in the vicinity of pitch pine stands and fewer in hardwood forests. Beginning in mid-September, we began placing our detector in areas where we had found particularly high levels of acoustic activity in order to identify potential locations to place mist nets for late fall capture. As we experienced an unusually warm fall, we continued to collect calls on most nights of in to late November. Based on our acoustic data, we set mist nets near a pitch pine stand close to a water source, and captured 10 Northern long-eared bats in late October. We placed radio transmitters on them and tracked them to what we assume to be potential hibernation sites that we will be monitoring over the winter. Nantucket lacks mines and caves – traditional hibernacula for Northern long-eared bats – so we have placed a high priority on finding and characterizing alternative hibernacula here. We will keep our detector deployed throughout much of the winter in order to document any activity and to help us locate potential locations of other hibernacula.
All has been fairly quiet on the acoustic front this winter. After a successful late fall 2017 mist-netting and radio-telemetry session, we were able to identify some areas that may contain hibernacula for Northern long-eared bats on Nantucket Island. We detected NLEB on our SM4Bat FS through mid-December and believe they are hibernating here in crawl spaces of houses. We deployed our detector throughout the winter in the vicinity of where we think they are hibernating. We did not record any calls in January or February, and the back to back to back March Nor'easters are not helping either. As soon as we start seeing calls of NLEBs on our detector again, we will begin mist-netting with hopes of catching bats as soon as they come out of hibernation. Another aspect of our project that we know little about is the exposure of Island bats to Pd, the fungus that causes White-nose Syndrome. To date, only one island bat has tested positive for Pd at very low levels. Otherwise, our bats appear healthy and suffering the effects of WNS to a lesser degree than bats elsewhere in the Northeast. Capture rates remain high and several maternity colonies have been identified. Swabbing bats as soon as they emerge from hibernation will give us a better idea of the prevalence of exposure to Pd on Nantucket. Our detector will help us to know as soon as bats start flying this spring!
For a tiny island with more than 45% protected open space and a long history of visiting and resident scientists, natural historians and conservationists, the recent addition of a new mammal species to the list for Nantucket Island was quite a surprise. In the summer of 2015, a dead specimen of the federally threatened Northern long-eared bat was found on a trail in a pitch pine forest on the Island. This discovery kicked off a flurry of activity for us at the Nantucket Conservation Foundation. Populations of this species have declined across the northeast by >90% due to White-nose Syndrome, so it immediately it became a high priority for us to learn about habitat use on island and how populations are faring here. The SM4Bat FS detector and Kaleidoscope Pro software we received from the Wildlife Acoustics Scientific Product Grant has allowed us to begin to survey much of the island in order to document areas of activity of Northern long-eared bats.
In the summer of 2017, we moved our detector weekly to various locations across the island to get a handle on the types of vegetation communities with high activity of Northern long-eared bats. We documented acoustic evidence of Northerns in nearly every location that we put out our detector, however the highest number of calls were recorded in the vicinity of pitch pine stands and fewer in hardwood forests and scrub oak shrublands. Our detector also helped us pin point potential locations to place mist-nets in order to capture bats and affix them with radio transmitters to document locations of maternity colonies.
As a bonus, our detector and the software helped us learn what other bat species were present on the island in the summer. It was always assumed that Nantucket had no resident bat species outside of the spring and fall migration season. We were able to determine that we likely have breeding red bats on Nantucket as well.
In mid-September, in anticipation off fall swarming activity, we began placing our detector in areas where we had found particularly high levels of acoustic activity throughout the summer. We experienced an unusually warm fall and continued to collect calls on most nights through mid- December. Nantucket lacks mines and caves – traditional hibernacula for Northern long-eared bats – so we have placed a high priority on finding and characterizing alternative hibernacula here. We kept our detector deployed throughout much of the winter in order to document any activity lending further evidence that northerns are present throughout the winter and likely hibernating locally.
A further piece of the puzzle that we wished to explore was whether Nantucket bats were exposed to Pd, the fungus that causes White-nose Syndrome. We did not record any calls in January or February, but began to pick up a bit of activity towards the end of March and early April. We began mist-netting soon after they emerged from hibernation and with the help of Sam Hoff, a PhD student from University of Albany, we were able to collect swabs to sample for Pd presence. To date, only one apparently healthy island bat tested positive for Pd at very low levels. Otherwise, our bats appear healthy and we are optimistic about the status of the Northern long-eared bat on Nantucket. We will continue to deploy our bat detector across the island in to the future to keep tabs on their populations here.
I arrived on the island of Rota in mid-August and the new equipment arrived in perfect condition. Some Åga (Mariana Crow) pairs have just begun nesting, and this breeding season is looking like it will be a good one.
This season my study will be comparing the vocalizations of captive-reared young to those of wild-reared young. While I wait for the San Diego Zoo Global team to collect the first eggs and chicks for the captive rear-and-release program (scheduled for early October), I am beginning to collect behavioral observations and recordings of wild Åga with my handheld microphone for later characterizing vocalizations.
I have also begun to test out Kaleidoscope's clustering analysis on recordings made in Åga territories last summer using SM3 and SM4 recorders. I am very pleased with how well Kaleidoscope is performing and is able to find Åga vocalizations even with other background noise in the recordings. It even found vocalizations that I missed when scanning through spectrograms visually!
Very soon I will be recording Åga at their nests with the new external microphones and extra long 50m cables I was awarded, resulting in thousands of hours of audio by the end of the season. Using Kaleidoscope's clustering analysis to find and identify Åga vocalizations recorded at these nests should allow me to analyze a substantially larger dataset compared to what might be possible using other available software. I can't wait till the first wild nest recordings start rolling in!
A lot has happened since my last update! Collections of wild åga eggs and chicks for the captive rear-and-release program (through San Diego Zoo Global) are now complete. The last of thirteen chicks have hatched, and I have been getting some quality recordings of them during their first months. We have found that the åga chicks make the tiniest begging "cheeps" even during their first feedings while only a few hours old!
In the wild, we have been steadily finding åga nests, and I have been getting out the ARUs to some of them. Working with a highly intelligent AND critically endangered species means that extra precautions must be taken when monitoring nests. This is where the external microphones and 50m long cables from my Wildlife Acoustics product grant come in. When we find a nest that I think will be good for recording, I hike in under the cover of darkness and set up the microphone in a tree near the nest, camouflage the cable with leaves and stretch the cable 30-50m away where I attach the ARU to another tree. I also camouflage the ARUs just in case. This is all to keep the åga from noticing the equipment and if they do see it then hopefully they won't associate it with humans. After this is all set up at night I can then check the batteries and SD cards weekly during the day with a much lower risk of disturbing the nesting pair.
The breeding season is now winding down and we're expecting to find only a few more nests between now and May. I am now focusing more on trying to get more observations and recordings of wild fledglings and adults as they move away from the nests to add to my library of vocalizations to characterize. I have already collected almost one and a half terabytes of audio so far this season! Very soon I will be putting Kaleidoscope to the full test as I begin analyzing this mountain of recordings I have accumulated.
The Åga (Corvus kubaryi) is a critically endangered forest crow endemic to Guam and Rota of the Mariana Islands; less than 200 individuals remain (KRONER & HA 2018). A new captive rear- and-release program, in which hatchlings from wild-collected eggs are puppet-reared in captivity, began on Rota in 2016. While the rearing environment allows for social interaction between nestlings and with visiting wild adult conspecifics, missing are interactions with parents and their offspring- directed vocalizations.
Early studies of ‘Alala (C. hawaiiensis) captive-rearing suggest that social interactions at young stages, or lack thereof, has profound effects in the interactions of adults, including the ability to breed and rear their own young in captivity (Harvey et al. 2002). Vocalizations are an integral part of avian social interactions, but infrequently provided during captive-rearing and rarely evaluated for long-term effects. Crows’ appropriate use of vocalizations in their many contexts is likely influenced by social and vocal experience across their long developmental period (Brown & Farabaugh 1997). Lack of exposure to parental vocalizations during early development may lead to reduced repertoires and poor behavioral responses in older juveniles. This study is a first step in evaluating the importance of early experience with adult vocalizations on the post-release social success of captive-reared crows.
The objectives of this project were originally stated as: (1) Characterize and archive calls and contexts of nesting-related calls of wild Åga, (2) Determine if there are stage-specific vocalizations between wild parents and offspring. (3) Document, by comparisons with these normal vocal exchanges and responses, any abnormal vocalizations or use of vocalizations (e.g. contexts and responses) by captive puppet-reared juvenile Åga. In this report I will detail the status and findings, to date, with regard to these objectives.
All recordings for this study were collected from August 2017 through May 2018. Twenty wild Åga nests were recorded with SM3 and SM4 units at a distance of 15m. When possible, an extended microphone (SMM-A2) was used 15m from the nest while the unit was placed up to 50m from the nest. ARUs were initially scheduled to record from solar sunrise to sunset, daily, at a sample rate of 24000Hz. However, I found that Mariana Crow calls cover a very broad frequency range, and calling can begin before solar sunrise. Therefore, in November 2017 I changed the settings to record at 44100Hz and 48000Hz on the SM4 and SM3, respectively, from civil sunrise to sunset. To mitigate for the increased file sizes at higher sample rates, I also reduced the scheduled recordings to every-other day. Nests varied with regard to stage when recordings began based on when the nest was found but continued until several days post- fledging or failure. Nests were recorded for seven to 67 days. In total, 4160 hours of recordings of nests are archived. Recordings of mobile juveniles and adults were taken with a handheld recorder and microphone opportunistically during regular monitoring and behavioral observations.
Recordings of captive-reared chicks began with the first collection in October 2017. An SM3 ARU recorded 15 chicks housed indoors during feeding sessions from hatch through approximately day 22. Recorders were mounted on a wall or shelf about 1 meter from the feeding table and set to record daily during the first 3 hours after sunrise. After day 22, chicks began spending time in outdoor aviaries. Recordings were made of chicks outdoors until approximately day 60, and again for one week in May 2018 (ages 5-8 months), for a total of 390 hours of recordings. However, the proximity of the aviaries with 2017’s cohort of young Åga to that of aviaries with older Åga, means that individuals of interest were not acoustically isolated. Therefore, further analyses will be necessary to correct for this and analyses of captive-reared Åga beyond day 20 will not be included in this report.
To locate Åga calls in recordings of wild nests, I created a classifier with Kaleidoscope (v4.5). This classifier used approximately 15hrs of training data pulled from ARU recordings made during the non-breeding season in 2016. Nine nests were run with this classifier. Each nest was classified separately, and up to 900 calls were positively identified and labeled from each. I then extracted (using the “save wav” function) 25 calls from each, avoiding calls that occurred within the same hour of the same day, when possible.
Analysis of the captive-reared chicks took a subset of four individuals (two males and two females), that had recordings available at the same four time points from 1 day post-hatch through day 20. These ages were day 1-2, day 7-8, day 13-14, and day 19-20. Fifteen begging vocalizations were selected and measured from each individual at each of the four time points using Raven Pro sound software (v1.5). I included 13 measurements in the time and frequency domains that are considered to be robust to human error (Raven Pro 1.5 manual). I used simple ANOVA and linear regressions to analyze the effects of age, sex, and weight on acoustic measurements.
Statistical analyses were performed in R (v3.5.2). For analyses of acoustic measurements, a Bonferroni correction was applied to all p-values (a= 0.003) to determine level of significance.
With the improvement autonomous recorders and increased data storage capabilities, software such as Kaleidoscope is critical to analyzing the vast amount of data one may collect. In this study, I have amassed over 4000 hours of recordings, which would be nearly impossible to manually sample any appreciable amount. However, building the best classifier can be rather time consuming depending on one’s goals. I found that the best classifier for my needs used training data from non-breeding season recordings which appear to include a wider range of vocalizations and are less biased toward alarm and territorial-type calls than at nests in the breeding season. This classifier had, on average, an 85 percent accuracy rate (up to 0.5 distance from cluster center) in correctly identifying Åga vocalizations. However, I found this to be highly variable between sites, ranging from as low as 55% accuracy at some sites, up to 98% at others. Attempts to further fine-tune this classifier by adding breeding season training data and captive chick recordings, led to classifiers that either had high accuracy but low variation in calls, or good variation but very low accuracy. Additionally, at this time, it appears that Kaleidoscope will not work well for searching nest recordings for young chick vocalizations. Given the very different recording environments, captive chick recordings did not aid in building a classifier that could detect wild chick calls. Even with more explicit training, using manually extracted calls of wild chicks, this software is unlikely to discriminate the quiet calls, with very little structure, against the variety of background noises. Lastly, attempts to use Kaleidoscope to separate Åga call types, as a method to characterize their repertoire, have so far been unsuccessfu.l
Using the classifier described above, I ran a subset of 9 nests through Kaleidoscope. For each, I manually identified 150-900 calls within the Åga cluster; the variation was due to total hours of recordings per nest and availability of true Åga calls within the cluster. I found that most Åga calls occur during the first hour of the day at 6am, and generally declined through the day, with a slight increase around 4pm (Fig 1). The types of vocalizations used at different stages of nesting may vary, but these analyses are pending a full characterization of this species repertoire. For example, I noticed in at least one nesting pair where a call type that was not present during the first several days of recording emerged, and dominated, immediately following the “failure” of their nest due to egg collection for the captive-rearing program. This will need to be further investigated to determine if it is truly associated with nest-failure, or merely coincidental.
Captive-reared chicks In an analysis of a subset of four captive-reared Åga, I found a general decrease in frequency (Hz) measurements, such as peak frequency and first-quartile frequency, as age increased. This was not unexpected since body size was also increasing. However, the rate of change differed between males and females at different ages. Using the peak frequency (Hz) measurement as an example, females changed most significantly from day 1 to day 7, where changes from day 7 to day 13 and 13 to 19 were not significant (p= 0.32 and 0.97, respectively) (Fig 2). Conversely, males changed most significantly between days 7 and 13 (p<0.0001), while changes from day 1 to 7 and day 13 to 19 were not significant (p=0.89 and 1.0, respectively). Frequency-based differences between the sexes were strongest at day 7 (p=0.0002), followed by day 1 (p=0.01). Days 13 and 19 showed no sex-based differences in any acoustic measure. It is interesting that these sex-based frequency differences occur at the two youngest ages, then disappear by day 20, while weight differences between sexes are not significant until day 20.
Captive vs. wild-reared chicks
Unfortunately, due to permit requirements, microphones at nests were not able to be as close as I would have preferred in order to get high quality recordings of young, wild Åga chicks. Therefore, direct comparisons of fine-scale acoustic measurements between captive-reared and wild-reared chicks may not be possible due the attenuation of sound in these very different settings. Based on personal observations, however, chick differences in the broad types of calls are unlikely to show at nestling ages (< 35 days old), as I only observed each making about three broad types of calls by the end of that stage. However, it is still possible for early experience to influence adult repertoire, as in other oscine songbirds, while not exhibiting differences at young ages (Boughman & Moss 2003).
Wild Åga juveniles are dependent on their parents for eight months, on average (Morton et al. 1999). During this time, most vocalizations appear to fall into three categories: begging, food receiving “gobbles”, and contact calls (personal obs.). It seems that most wild Åga do not begin using/practicing a larger repertoire until they become more independent (personal obs). Anecdotally, captive chicks actually seemed to make more types of calls by 5-8 months of age, which may represent more “vocal play”, or practicing, as seen in wild sub-adults; possibly a result of their being artificially more “independent”. Wild chicks are often still dependent at this age, so most of their vocalizations still fall into the three categories described above. However, this will need to be quantified and it will be important to follow-up on captive-reared Åga post- release to understand how their adult repertoire develops compared to wild-reared counterparts.
Perhaps the most important next step is to characterize the full Åga repertoire. This will allow for a better understanding of potential age, nest-stage and/or season specific call types and make comparisons between wild and captive reared Åga. My preferred method is to use a method of unsupervised clustering to accomplish this, as many Åga vocalizations seems graded between call types, as in some primate repertoires, as opposed discrete song-types in other songbirds, making human classification quite subjective (Wadewitz et al. 2015).
Next, I will compare the repertoire of the juvenile (>35 days old) captive-reared Åga to the wild juveniles. Because of the nature of the recordings at the aviaries, it will not be possible to analyze the captive juveniles individually, rather I will need to use them as a group. Although, I may be able to separate 2017’s cohort from the other captive Åga by creating an amplitude cut- off, since the microphones were nearest to the 2017 cohort.
Finally, I will integrate what is gained from this project with the active Åga recovery program. When manually scanning recordings, I was able to detect some nestlings as young as 2 days old. Since most young nestling vocalizations only occur during feeding, sub-sampling these recordings could reveal natural feeding rates, better than the normally brief visual observations that happen in the field, which could help improve captive-rearing methods. I will be providing the captive-rearing program with recordings from nests to play to captive-reared chicks and will work with the captive-rearing team to gather new recordings of the next cohort.
Boughman JW, Moss CF. 2003. Social sounds: vocal learning and development of mammal and bird calls. Pages 138–224 in A. M. Simmons, R. . Fay, and A. N. Popper, editors. Acoustic Communication—Springer Handbook of Auditory Research. Springer- Verlag, New York.
Brown ED, Farabaugh SM. 1997. What birds with complex social relationships can tell us about vocal learning: Vocal sharing in avian groups. Pages 98–127 in C. Snowdon and M. Hausberger, editors. Social influences on vocal development. Cambridge University Press, Cambridge, U.K.
Harvey NC, Farabaugh SM, Druker BB. 2002. Effects of early rearing experience on adult behavior and nesting in captive Hawaiian crows (Corvus hawaiiensis). Zoo Biology 21:59–75.
KRONER A, HA RR. 2018. An update of the breeding population status of the critically endangered Mariana Crow Corvus kubaryi on Rota, Northern Mariana Islands 2013–2014. Bird Conservation International 28:416–422.
Morton JM, Plentovich S, Sharp T. 1999. Reproduction and juvenile dispersal of Mariana Crows on Rota 1996-1999.
Wadewitz P, Hammerschmidt K, Battaglia D, Witt A, Wolf F, Fischer J. 2015. Characterizing vocal repertoires - Hard vs. Soft classification approaches. PLoS ONE 10:1–16.
Uxbridge Secondary School, Durham District School Board
The start of the new school year is always busy, throw in two canoe trips within the first five weeks and it is downright chaotic. That's how its been here at Uxbridge Secondary School this semester with the Outer's Club heading to Algonquin Park and the Outdoor Education class venturing further north to Kilarney Provincial Park. Along on these trips are an Echo Meter Touch bat detector and occasionally a SM4BAT Recorder. Students gather at the water's edge at dusk to look for, and hopefully record, foraging bats. Back in the classroom students will process the recordings with Kaleidoscope software.
Over the past summer some bat recordings were collected at our Outdoor Education Center and these recordings will be analyzed by the students in the coming months.
A busy fall semester saw the Outers Club and the Outdoor Education class take part in several overnight out-trips this year. Along on these trips were an Echo Meter Touch and a SM4BAT recorder. While camping in Algonquin Park and while staying at Camp Kandalore, students were able, on a few nights, to survey for bats (unfortunately poor weather conditions on some of these nights prevented bats from flying). We did manage to get some good recordings of Big Brown/Silver Haired bats on those nights.
With the tripping season over students have now had time to formally learn more about Ontario's bats. Through power point presentations and first-hand demonstrations students, including those who are not able to attend over- night trips, have learned how bio-acoustics is helping bat researchers study bats.
Some of our students are using the Kaleidoscope software to analyze recordings made over the summer from various locations within Central Ontario and at our residential Outdoor Education Center. While these students and myself! are getting more familiar with the software we (it has) have managed to identify four of Ontario's eight species so far. Some of our recordings will require further vetting from more knowledgeable bat experts.
With semester two fast approaching another group students will soon have the opportunity to learn more about Ontario's bats and the technologies being used to study them. We are patiently looking forward to the upcoming spring field season as well.
As another school year has come to an end it is nice to know that many of our students at Uxbridge Secondary School now have a better understanding of, and appreciation for, Ontario's bats. In-class presentations on bat biology and the threats facing our native bat populations were complimented with over-night field trips where students were able to make real-time acoustic recordings of bat calls. Camping trips to Algonquin, Kilarney, and Queen Elizabeth Provincial Parks provided the students with opportunities to use Wildlife Acoustic's Echo Meter Touch and App to capture the calls of five of Ontario's eight bat species. Several of our students downloaded the app onto their personal devices and borrowed the EMT's to record bats in their own neighbourhoods. "I never realized we had this type of bat activity in my own backyard" one of my students told me excitedly the morning after having used the EMT at home that night"
These recordings were then brought back into the classroom where students (after having watched the tutorial videos) would run them through the Kaleidoscope software program. (A SM4 BAT was setup at our residential outdoor education center several times throughout the field season and collected a number of recordings that were also included in analysis). The more technically savvy students picked up this skill up quiet quickly. In any case, a real appreciation for the science and technology that is used in bat research was gained by these students.
In all, hundreds of recordings were collected that represent five different bat species (Big Brown/Silver-haired, Eastern Red, Hoary, and Myotis spp.). A collection of these recordings has been sent off to more qualified personnel for further vetting. Our data will be shared with our Provincial Ministry of Natural Resources and Forestry.
Staff at Uxbridge Secondary School hope to continue taking students into the field to experience the excitement of "hearing" bats.
Thank you to Wildlife Acoustics for the grant of the Kaleidoscope Software.
Amy Thurston Toronto and Region Conservation Community Engagement Team
To date, Toronto and Region Conservation's (TRCA) Education and Community Engagement teams have successfully met their public engagement target for 2017. All Community Engagement staff have been trained in the Bats in Your Backyard program and use of the Wildlife Acoustic Echo Meter Touch modules and app. Our Field Centre staff have also been trained and bat detectors have been deployed to Albion Hills, Kortright, Claremont and Lake St. George Field Centres to be used during school programming beginning this fall. Through the Bats in Your Backyard program, our citizen scientists have found three species (Hoary bat, Big brown bat and Silver haired bat) in five locations.
As our program expands and additional locations are surveyed, we anticipate we will find more species. Participants provided positive feedback about the Bats in your Backyard program and were excited to have the opportunity to see bats in the night sky, where otherwise they might fly by silently. When asked during a pre-program survey, 32% of participants thought that the majority of local bats carried rabies. Following the program, 100% of participants understood that only a small percentage of bats carry rabies indicating that the program is having a positive impact on participants' knowledge and attitude about bats. The Echo Meter Touch modules and Echo Meter App have allowed TRCA's teams to provide a visual experience for participants to learn about acoustic bat identification. The data being collected through the use of the Echo Meter Touch devices will be submitted to the TRCAs Terrestrial Inventories and Monitoring Team to use and share the data with other interested parties.
Toronto and Region Conservation's (TRCA) Bats in Your Backyard program engaged 105 participants through five community events from June to August 2017. Each program included a presentation on the ecology of local bats, threats facing bats including white nose syndrome, and actions they could take to protect them. During the project, five bat boxes were also constructed to create habitat. Participants were taken on a guided bat survey which acoustically monitored bats through presence/apparent absence data. They learned about bat survey methodology and undertook the completion of citizen science data recording sheets. Data collected included: date, time, location, suggested auto-id, call frequency range, whether a terminal buzz was heard, weather conditions, temperature, and wind conditions. This data was recorded when an echolocation call was heard over the Echo Meter module. Participants were careful not to make a second observation unless a second bat was visually confirmed or at least 100m passed since the previous observation. In total 23 unique observations were recorded, finding two species of bats including: Big brown bat (Eptesicus fuscus) and Hoary bat (Lasiurus cinereus). As well, several additional observations of either the Big brown bat or Silver haired bat (Lasionycteris noctivagans) were made but the species could not be confirmed due to the similarity of their calls. The data collected was shared with TRCA's Terrestrial Inventories and Monitoring team to assist in their research into local bats, and will be shared with other interested parties.
A pre and post survey of participants using true or false statements was undertaken to evaluate the impact of the program. Results are in the table below and indicate a clear increase in both the knowledge and attitudes of participants as well as a willingness to take action to help the conservation of bats. As one participant remarked in part, "I got to be a citizen scientist tonight, and it was very cool. I was thrilled to actually see and hear several Big Brown Bats who were flying low overhead looking for insects a little after sunset. They were hunting insects that would otherwise have been hunting me out there...so I've been converted as a bat fan. This science stuff is fun!".
2017 also saw the training of four of TRCA's Field Centres on the program and the use of modules in preparation for spring. This, combined with an earlier seasonal start time and expanded marketing through new and continued partnerships, should result in further engagement in 2018.
Florida Atlantic University
Our lab studies animal communication, in particular, the acoustic structure and social function of bird song. One of our ongoing projects at Johnathan Dickinson State Park is to study the structure and function of female song in the Bachman's sparrow. In mid-July, we rotated our SM4 recorder near the nests of several of the mated pairs we were monitoring, trying to capture recordings of the elusive females. Female birds do not sing in the majority or North American songbird species, but Bachman's sparrow females do sing. They don't sing with the showiness or bravado that their mates do, but yet they do produce song-like vocalizations. We want to know why.
Over the past two breeding seasons, we made several observations of females singing in the proximity of their mates, and near the nests they were building. We obtained good quality recordings from one female, which will allow us to develop a protocol for making larger scale acoustic comparisons between the songs of males and females next season. In addition, we placed the recorder on the territories of males that had been subjects in the aggression experiment we were completing. We recorded each male for 24-48 hours, two critical pieces of information: singing patterns from pre-dawn to post-dusk, and singing patterns during undisturbed, unprovoked singing. We are now comparing those singing patterns to the patterns we recorded in response to a simulated territorial intrusion by a singing rival male. In April 2018, we will obtain recordings of 8-10 females to use for acoustic analysis and for playback experiments designed to test when and why females sing.
Female birds do not sing in the majority or North American songbird species. Bachman's sparrow females do not sing with the showiness or bravado that their mates do, but they do produce song-like vocalizations. Below is a spectrogram (a visual representation of sound plotting song pitch over time, much like music is visualized) showing an example of one female's song that we recorded using our SM4 song meter. Also pictured are examples of male broadcast songs (called primary song) and an example of "warbled song," which is quite distinct from primary song. The female songs we have visualized so far bear some resemblance to male warbled song, being a non-stereotyped, seemingly jumbled series of notes. During the next field season, we will use the songs we have recorded to perform playback experiments to measure female behavioral responses to a simulated female intruder. We bought four additional SM4 units, which will allow us to rotate the meters among the territories of many more females to capture song at different stages of the nesting cycle. In addition to our study of female song, we used the SM4 meter to record many hours of male Bachman's sparrows singing at the dawn chorus, and throughout the day. From these recordings we are gaining understanding about how males use their song type repertoires in different behavioral contexts, and the degree to which males share song types. Our preliminary data suggest that neighbors share a large number of song types on average (> 50%) while non-neighbors share fewer song types (< 30%). This pattern has implications for how males use their songs to communicate with neighbors, and how song type sharing may influence where young males choose to defend a territory.
We continue our efforts to gather data on the acoustic structure and social function of female song in Bachman's Sparrow. This is a shy, elusive species. Females are tricky to find, and even more challenging to record, because they sing infrequently. In 2017 we captured a few recordings of female song. So far in 2018, we have observed females singing in the field, but are still working to capture audio recordings.
Our observations suggest that females sing when fertile, in temporal proximity to copulation. Perhaps their songs serve as an invitation to mate? Working on this hunch, we are placing our SM4 recorders on trees within 10 meters or so of nests that are being built, and as eggs are being laid. We have not yet analyzed the many hours of recordings obtained so far (over 620 hours!), but we are hopeful that we havecaptured examples of female song from several different birds.
With these recordings, we will compare the acoustic structure of female song to those of males, and quantify variation in female songs both within and between females. Male Bachman's sparrows sing over 40 types of Primary Song – do females also sing many song types? Does an individual's song vary from day to day, or does she sing a consistent song? How will females respond to songs of other females played on their territories? We look forward to digging into our data to answer these and other questions about this interesting species.
Our lab studies animal communication, in particular, the acoustic structure and social function of bird song. One of our projects is to study the structure and function of female song in the Bachman's sparrow. Female birds do not sing in most North American songbird species, but Bachman's sparrow females produce song-like vocalizations. We want to know why.
Over the past two breeding seasons here in South Florida, we made several observations of females singing in the proximity of their mates, and near the nests they were building. In April 2018 we began placing our SM4 recorders near nests during the building stage, hoping to capture recordings of female song. This has proven to be a challenging task! In for a total of 118 hours of recordings. From April – July 2018, we recorded on a total of 2017, we recorded on three territories 38 territories, placing the recorders near known active nests, for a total of 3,108 hours of recording.
So far we have found three good examples of female song, and these recordings will serve as stimuli for a playback experiment next spring in which we will test the responses of territorial pairs to playbacks of female song at different stages of the nesting cycle. Our primary goal for the next several months is to analyze the many hours of recordings we gathered this past season to find additional examples of female song.
We will tackle this challenge using a custom software program written by one of our undergraduate students. This program can be "trained" to look for vocalizations matching the acoustic qualities of female Bachman's sparrow song. This program will automate and thus greatly speed-up the process of combing through the recordings, and we hope to find at least a dozen examples of female song by March 2019.
In addition to using our SM4 recorders to capture female song, we have been using them to "eavesdrop" on the natural singing interactions of neighboring male sparrows. Bachman's sparrow males have large repertoires of broadcast song types, and neighboring males tend to share quite a few types in common. In the field, we often hear males within ear-shot of each other counter-singing by matching each other's song types. We are using the SM4 recorders to capture the natural dynamics of these social interactions. This season we recorded a total of 10 sets of neighbors by placing an SM4 recorder near the boundary between neighboring territories. This season we recorded approximately 12 hours a day for several days for each pair of neighbors (2 hours before and 4 hours after sunrise, and 4 hours before and 2 hours after sunset). One student in the lab is now pouring over these recordings to document and describe cases of natural song type matching interactions, which has not been done for this species. So far she has found several astonishing examples of song matching, in which males matched song-for-song during bouts of counter-singing. Why do they do this? We will use these SM4 data along with song playback experiments to try and uncover the social significance of song matching behavior.
In addition, a graduate student in the lab will be analyzing the 3,108 hours of territorial recordings from the SM4 recorders to gain two critical pieces of information: singing patterns of individual birds from pre-dawn to post-dusk, and singing patterns during undisturbed, unprovoked singing. We have many hours of recordings of males singing in response to simulated territorial intrusions, in which we use song playback to provoke territorial behavior. However, little is known about how male Bachman's sparrows utilize their large repertoires during bouts of natural advertisement singing, which are most common at dawn and dusk.
Each field season with Bachman's sparrow brings new challenges and exciting new questions to tackle. We are very enthusiastic to be adding to general knowledge about this understudied and enigmatic species, and why it has evolved such a large and varied vocal communication system. In addition, we are beginning new projects with the Northern cardinal in South Florida, in which we will continue our studies of female song, and will ask new questions about how vocal communication differs across urbanization gradients. We are deeply appreciative to Wildlife Acoustics for their Scientific Product Award of an SM4 meter, which has been a game-changer for our research!
Alessandro Catenazzi Southern Illinois University, Carbondale, IL
On 1 April 2017 Wildlife Acoustics we received a Wildlife Acoustics grant (license to use software) in support of our project "Escape from deadly disease: Can environmental refugia save tropical mountain frogs from extinction?". The main goal of this project is to ground-truth predictions of a habitat distribution modeling map produced for the highly virulent, pathogenic fungus Batrachochytrium dendrobatidis, which has been linked to worldwide amphibian declines and extinctions. Our study area, in the eastern slopes of Peruvian Andes, is among the most amphibian species-rich regions on Earth. An additional objective, as part of our field expeditions to areas predicted to have low occurrence of the fungus, is to search for relictual populations of threatened species known to have disappeared from areas where disease prevalence is high.
Field work for this project is supported by grants from the Eppley Foundation, the Chicago Board of Trade Endangered Species Fund, Southern Illinois University Carbondale startup funds to A. Catenazzi, personal funds donated by A. Catenazzi, and volunteering by local biologists Alex Ttito, Isabel Diaz and William Tito, and Dr. Sarah Kupferberg.
From May to August 2017 we conducted field expeditions to six different regions of central Peru: Pampas Galeras (Ayacucho), Kosñipata valley near Manu National Park (Cusco), Marcapata Valley (Cusco), San Gabán Valley (Puno), upper Guacamayo watershed in Bahuaja-Sonene National Park (Puno), and the Santo Domingo Valley (Puno). We surveyed amphibian populations from 500 to 4000 m elevation, capturing over 900 individuals, which were identified, sexed, measured and swabbed for detection of fungal infection. We also surveyed >60 streams (375–4700 m elevation) for presence of fungal disease by filtering 1–3 L of water/stream through environmental DNA (eDNA) analysis. We deployed Sound Meter recorders (SM 1; purchased in 2008 with support from the Rufford Foundation) at the Santo Domingo site to survey for Noblella peruviana, a species described from this location but that has not seen since the late 1800s or early 1900s, and the threatened harlequin toad Atelopus erythropus and A. tricolor, not seen since 2004 and 2007 respectively.
We were able to rediscover Noblella peruviana, more than 100 years since it had last been seen. Moreover, sound recordings allowed us to detect the presence of a second, cryptic species of Noblella, which appears to live in the same habitats along with N. peruviana. We are in the process of describing this new species. We are still processing sound recordings to screen for the presence of advertisement calls of other amphibian species, and specifically of the two species of Atelopus, a process which is made more difficult because the call of A. erythropus has never been recorded. We are also proceeding with analyses of skin swab and eDNA samples, and identifying collected material, and we expect this collection should contain at least 5 new species discovered during our field work.
Third Millennium Alliance
Our team has spent the past couple of months compiling all known population data for the Tandayapa Andean Toad (Rhaebo olallai) from the Manduriacu Reserve and testing the SM4 Song Meter settings with the Configurator tool. With this information we have narrowed our site selection for the initial deployment of the Song Meters. At the start of October we will be running a short pilot deployment to train the local park ranger how to use the Song Meters and how to change the batteries and memory cards. Because few audio recordings exist for the species, we will use this initial deployment to determine if it will be possible to narrow the recording time to peak calling hours. During the initial pilot deployment we will leave the Song Meters in the field for one month recording for 15 minutes each hour from dusk to dawn. In the meantime our team will be working with collaborators from Texas State University to learn more about the Kaleidoscope software and the development of recognizers. Once this first pilot deployment is complete we will review the results and determine if any changes to the methodology are needed before the next deployment.
Since our last update our team has successfully deployed four Song Meters in the Manduriacu Reserve, Ecuador, which protects habitat for the only known population of Rhaebo olallai. The Song Meters we're deployed at four specific locations across a 2.2km area within the currently known range of Rhaebo olallai. Once we obtain sufficient audio material for the species we will be moving the recorders to more distant areas where the species has not yet been recorded using visual encounter surveys. After working with the configurator tool we elected to record 20 minutes on the hour each hour between sundown and sunrise. We have recorded the species calling throughout this time frame, so our initial recordings will be used to fine tune our recording schedule before moving the recorders to the new sites. Our next field visit to download the audio files is planned for late March, at which point we will begin our initial review of the data.
Since our last update our team has dedicated much of their attention to running our network of four Song Meters in the Manduriacu Reserve, Ecuador. We have now have been running the Song Meters for eight months in the Reserve and are preparing to move two of the units to more remote sites on the periphery of the Reserve. We unfortunately haven't yet had a chance to dive into the analysis of the data because our team has been dedicating much of their time to fighting new mining threats in the area. Over the past year the Ecuadorian government opened new mining concessions across much of the region where the Manduriacu Reserve is located, so our team is working hard to protect as much habitat for R. olallai and other threatened species found in the Manduriacu Reserve before its too late. The audio data being recorded with our Song Meters will play an important role in documenting the status of R. olallai in the Reserve as mining activities expand across the region.
Dr. Eric Baitchman, DVM, DACZM
Zoo New England, Franklin Park Zoo, Boston, MA
Zoo New England and Grassroots Wildlife Conservation are working on the Franklin Park Biodiversity Project to assess and preserve the natural biodiversity of Franklin Park in Boston, MA, and engage the local community in education of biodiversity and conservation.
Seasonal surveys have been conducted to record observations of wildlife within the "Wilderness" section of the park, outside of Franklin Park Zoo's gates. Each survey, or bio-blitz, is conducted over nine days during which time staff and public participants catalogue observations of mammals, amphibians, birds, reptiles, invertebrates, plants, and fungi, using iNaturalist. Over 300 species have been identified thus far.
We just completed this year's summer surveys this past week and were finally able to include bats in our observations by using the Echo Meter Touch. We held a well-attended public bat walk where we made our first recordings of local bat species. A dozen participants had a very good experience, and the EMT was key to that success. The device provides such an engaging and educational interface to get people excited about finding bats and "experiencing" exactly what's going on as the often unseen shapes fly by in the night. At least two different bat species were added to our biodiversity survey, including big brown bats and red bats, and possibly a third, the silver haired bat, though we are still learning about determining accuracy of the identifications made with the device.
Franklin Park Zoo will host an additional public walk this summer and we are excited to be joined by a Wildlife Acoustics staff member, who will help both the public participants and ourselves to learn more about how the technology works and how to better interpret the results.
We held a second public bat walk around Scarboro Pond in Franklin Park this September. Members of the public were led by ZNE Education and Conservation staff, as well as Wildlife Acoustics, Inc. employees, Mona Doss and Ali Donargo. We were so lucky to have Mona and Ali with us, as they were able to augment our program significantly, by sharing their expertise with our guests and providing additional Echo Meter Touch units to allow more people to have hands-on experience with detecting bats. Guests learned about bat diversity in our region, how that diversity has changed in recent decades, natural history of our native bats, the enormous ecological services bats provide us, and the conservation challenges facing bats today. The technology of the EMT allows such a personal experience for an entire family to have closer engagement to the bats surrounding them. Everyone from children to adults light up as they get to watch signals from bats being instantly translated to a species identification, complete with accompanying portrait! That personal level of interaction is what our public programs are all about, inspiring citizen scientists to recognize and care for the natural diversity present in their own neighborhoods.
Our staff learned a great deal from Mona and Ali as well, about how the technology works and about interpreting results from our EMT units, giving us a greater degree of confidence in our identifications. On the September walk, we identified big brown bats, red bats, and silver-haired bats.
Dr. David C. Lahti, Queens College, City University of New York
Our project on weaverbird song, which began in Sept 2016 and is set to end in June 2018 (pre-publication) as per our grant application, is proceeding well. However, we have had to move portions of our work around due to two sorts of constraints-- one in the field and one in the lab. In the field, breeding was delayed at first due to odd weather, but we have been recording wonderfully since January. We know have hundreds of good songs, which are waiting for analysis.
The songs were to be at least pre-analyzed by this month, however, the software we use in the lab is specialized for tonal songs, and is performing poorly on weaver song that has not only diphony (two notes being sung at the same time), but also a lot of harmonics and broadband elements (clicks, rattles). We knew weaver song would be complex and difficult to quantify, but it was anyone's guess how we would solve this problem once we got songs in the lab. I have decided to switch software to Sound Analysis Pro (Ofer Tchernichovski), as that was designed for zebra finch songs and so can deal well with these elements. However, to get a student in Ofer's lab learning how to use that software will have to wait until this September. In the meantime recording and data manipulation will continue, although there's very little we can say about the songs themselves until we can analyze them.
The globular nests and complex rambling songs of Ploceus weaverbirds are striking and familiar features of the sub-Saharan African landscape. The village weaver (Ploceus cucullatus), the most abundant member of the genus, breeds in colonies where the cacophony of their simultaneous singing can hardly be overlooked along gallery forests and waterways, around agricultural fields, and within villages and towns. Precisely because of their coloniality and the fact that much of their courtship and breeding interactions occur within the colony, individual village weaver songs are rarely recorded. We consequently have little understanding of how these songs vary between individuals, over geographic distances, or between species. Moreover, weaverbirds often change their behavior in response to human intrusion, decreasing the opportunity for researchers to record their song, and potentially rendering them different than if they had been recorded during ordinary activity. Because of the Wildlife Acoustics grant of a SongMeter SM4, we have been able to record weaverbirds individually, and without having to be physically present so the focal birds are not alarmed but sing and behave normally. We have also been able to record begging calls from young birds, which we would otherwise never have been able to do in a colonial species in a natural context.
Since we received the Wildlife Acoustics SongMeter SM4 in 2016 for this project, our recordist Clive Barlow has made a variety of excellent recordings of the whole range of Gambian village weaver vocalizations, from the calls of nestlings, fledglings, parents, and mates, to the competitive and mate- attractive songs of males of all ages in both breeding and eclipse (nonbreeding) plumage. These recordings are revealing the diversity of vocalization in this species, and will also constitute the first element in a broader comparative study of song across the genus Ploceus. Specifically in the village weaver, the SongMeter SM4 has already provided us unprecedented access to two important vocal features: individual song structure, and the relationship between the begging calls of weavers and the cuckoo chicks that parasitize their nests.
The complex songs of the village weaver are consistently delivered as a series of phrases of multiple types, which in succession give a distinct impression of a rise in intensity. This impression is corroborated by analysis of amplitude (volume), frequency (pitch), bandwith (pitch range), and note rate, all of which tend to increase across comparable elements in the course of a complete song. The phrases are each either extended or repeated indeterminately, and might at any juncture either lose steam and peter out or else build and transition to a predictable next phrase type in the series. Often the full songs can be divided into a chirpy introduction, two or three variations on a warble-trill-buzz theme, and a trilled coda. The following recording illustrates two renditions back-to-back of the song of a typical Gambian adult male, recorded by the SM4 in June 2016. A series of call-like notes build to a complex warble that ends in a long flat buzz; then comes a brief warble, trill, and a second buzz, this one rising; this is followed by a brief warble and trill and a descending whistly buzz; the song ends with an extended rapid trill.
The diederik cuckoo (Chrysococcyx caprius) is a brood parasite of the village weaver, meaning that its adult females lay eggs in weaver nests rather than laying in nests of their own. A successfully parasitized weaver raises none of its own young, but only a cuckoo chick, during that reproductive attempt—the cuckoo mother and (eventually) the chick remove any weaver eggs and nestlings from the nest. One might expect cuckoo chicks either to mimic weaver chicks in their food begging calls, or simply to deliver more engaging calls in some way to compensate for the fact that they are not otherwise similar in appearance to young weavers, especially as they grow older and beg for food outside of the nest. Recordings by the SM4 of both weaver and cuckoo juveniles begging from weaver parents (or foster parents) so far show a similar rate of delivery, but no evidence of structural mimicry. Cuckoo begging calls appear to be more tonal (melodic) than weaver calls at a given age.
The following audio tracks correspond to the recording in Figure 3. Track A is at normal speed. Track B is slowed to one-quarter speed to show the acoustic detail, which in some respects also more faithfully represents the listening experience of a songbird, given how quickly they process audio input and distinguish rapidly delivered notes.
Christopher E. Comer
Stephen F. Austin State University – Arthur Temple College of Forestry and Agriculture, Nacogdoches, TX
The primary use of the 2 Echo Meter Touch units during this period was for outreach and education activities. These activities consisted primarily of the following three events:
The 2 Echo Meter Touch units were used during this period for one event. On the weekend of September 22-24, 2017, we hosted a "bioblitz" event at the Pineywoods Conservation Center in Nacogdoches, TX. The event was hosted by undergraduate students in the Arthur Temple College of Forestry and Agriculture here at Stephen F. Austin State University. It was open to the public and all data were recorded through iNaturalist. One component of the bioblitz was a bat walk using the bat Echo Meter touch units on the night of September 22. Ten individuals participated in the bat walk and they recorded 5 species of bats (Lasiurus borealis, L. cinereus, L. seminolus, Nycticeius humeralis, and Tadarida brasiliensis).
The primary use of the 2 Echo Meter Touch units during this period was for outreach and education activities. During the summer field station experience for forestry undergraduates at SFASU, we used the Echo Meter Touch units as part of the Field Wildlife Techniques class on the evenings of June 4-6, 2018, in conjunction with mist netting activity. This included 56 undergraduate wildlife and forestry students.
San Diego Zoo Global Institute of Conservation Research
July brought us some logistical challenges! Colleague Diego had set up 20 SM4 audio recorders in the Maijuna-Kichwa Regional Conservation Area two months previously. He had been rained on copiously, and enjoyed fast-flowing, deep streams to access the forest. The recorders had spend six weeks listening for gunshots, and animals, recording 24 hours a day with 512GB SD cards and a large car battery each. So with some excitement, I went up the Sucusari River to collect those recorders and their paired camera traps. Unfortunately the water level dropped much earlier than expected, and the river dried to a trickle in some places. River sections that took Diego and the team an hour, now took half a day hauling the canoes and clearing logs with the chainsaw. It was clear that the top half of the stream was not even accessible. With great effort, and a LOT of hiking through a very try 'rainforest', we extracted half the recorders and camera traps. Half the recorders are still in the forest, and we have to wait another couple of months to bring them out. On the plus side, the car batteries were still fresh - the units used far less power than I expected. This means I'll get another months worth of data upriver until the cards fill up.
One of the main applications we have is to listen for gunshots in this attractive reserve, and I completed some important range tests while bringing in these first recorders. Conditions were perfect - low wind and no rain, and even before analysis, it is clear that the under these conditions the range is close to 2km... given that our sample points are typically about 2km apart in our camera trap and acoustic surveys - this means that we have almost complete coverage in perfect conditions. We will have more 'real word' tests when we combine our audio data with spatial hunting data from our GPS tracked hunters who are registering their hunts in a wider range of conditions.
I now have to repeat my expedition in October, to get the units we failed to retrieve before. Then we face the not insignificant task of analyzing about 8 weeks worth of data from 20 SM4+ recorders, all recording 24 hours per day. We plan to use Wildlife Acoustic's 'Kaleidoscope software to analyze maybe 25,000 hours of recordings, first for gunshots, but then for peccaries, woolly monkeys and a range of key species. Beyond that we will have a resource available with which could survey birds, amphibians or insects. But first, I'm looking forward to getting back out to the Sucusari to recover our recorders.
This month we completed the installation of 20 SM4 recorders at 2km intervals in a large array in the Maijuna Regional Conservation Area (MRCA), on the Rio Napo in the Peruvian Amazon. The recorders are in trees to keep them safe from curious people, and hopefully to maximize the range we get from the units. This was backbreaking work for field assistant Diego, who had to climb all the trees! The SM4s are connected to car batteries and we hope to have at least six weeks constant recording from each unit, in which we should be able to find recordings of a wide range on animals. We hope to find these with the help of Wildlife Acoustics Kaleidoscope software, but a camera trap close to each recorder will give us a clue as to where to find sample recordings of noisy animals like peccaries. The main reason for deploying the recorders, however, is to record gunshots in the area. the MRCA is an extractive reserve, so there is legal hunting by residents, managed by the community themselves, and informed by our research. We track much of this hunting with GPS trackers on the guns of hunters, and can pinpoint the location of kills. With this we can calibrate the range of our recorders under varying conditions through several weeks. We will also be able to detect hunting by unknown individuals who may be hunting in the area without permission, directly informing the community management of the area. We hope audio recorders will prove to be a highly efficient way of monitoring hunting that can be expanded through forests across the globe.
Kaleidoscope is was able to find virtually all our test gunshots – even most of those as far as 2km away. This is another step towards the autonomous monitoring of hunting across forested areas. Now we have the small matter of 4TB of recordings to process - to find the real-world gunshots, and especially those of our GPS tracked hunters. This means building 'recognisers' that Kaleidoscope can use to sort through the data more quickly and accurately, ignoring (for now) the hundreds of thousands of bird, insect and mammal noises that we have also recorded in our 2-month 24-hour recordings. Bottom line though - IT WORKS! We have a complete record of the hunting for two months on the Sucusari River basin.
Meanwhile, the recorders have not been idle, we have employed them on an exploratory project to find arguably the world's rarest bird. Confined to a few small patches of unusual white-sand forests, most of the bird's habit has been consumed by the demand for sand in the city of Iquitos. Now confined to The Allpahuayo Mishana National Reserve, we don't know where or how many there are- it could be as few at 20 pairs. We are using the SM4s and Kaleidoscope with the help of the local bird experts to try to find the bird and perhaps determine patch occupancy. So far we have not heard it, but have discovered another rare bird that is new to the Reserve - the barred tinamou- hardly ever seen or heard because it sings at 4am in the morning! The SM4s will soon go back out to listen for gunshots - this time on the Yavari river, but it goes to show how valuable these units are once you have them in the projects armory.
Amy K. Wray
University of Wisconsin, Madison
Our project, which focuses on investigating the effects of bat declines from White-nose Syndrome (WNS) on insect communities in Southern Wisconsin, has entered its third and penultimate field season. We continue to collect insect samples for microscope identification, bat guano for molecular analyses, and passive acoustic recordings to assess levels of bat activity in each area. Using the Kaleidoscope Pro software provided by the Wildlife Acoustics Scientific Product Grant, we have processed our acoustic data from 2015 and 2016 field seasons to assess bat activity levels at each of our 20 study sites. Our preliminary results from Kaleidoscope analyses indicate that 80% of our study sets met our a priori assumptions for having high Pre-WNS bat activity levels, with slight declines detected in 2016. We will use the data generated by Kaleidoscope's analyses, as well as data from future field seasons, to population occupancy models that will then be used to correlate bat activity with changes in insect community composition. As this year marks the first instances where dramatic declines in bat populations have been observed, the data that we are currently collecting for this field season will be essential for comparing differences between pre- and post-WNS years. We will continue to use Kaleidoscope to analyze bat activity levels at our sites in order to investigate correlates between bat diet composition, insect communities, and potential shifts related to disease-related bat declines. In the future, the results from our study will be used to inform management strategies and to promote bat bat conservation in Wisconsin and throughout the Midwestern region.
My primary research goals involve investigating the effects of bat declines from White-nose syndrome (WNS) on insect communities in Southern Wisconsin. For this project, I use a combination of insect community sampling, genetic analysis of bat guano, and acoustic monitoring to assess bat activity levels. At each of my 20 field sites, we use acoustic monitors to record nightly bat activity in zero crossing format. Additionally, for the recent 2017 field season, we also used SM4BAT detectors to record bat activity in full spectrum at sites with varying degrees of agricultural and forest landscape composition. These data, recorded in FS, will be used to better understand seasonal changes in bat foraging patterns and how these relate to landscape composition variables. All acoustic recordings will be assessed using Kaleidoscope PRO software in order to automatically classify and manually check bat identifications. From these analyses, spatial and temporal shifts in bat activity levels will be used to assess changes in bat communities and bat activity levels following declines related to WNS. The results from this study, including aspects involving acoustic monitoring, have been presented at the Midwest Bat Working group annual meetings, and will also be presented this year at the North American Symposium on Bat Research.
My current research on the effects of bat declines from White-nose syndrome (WNS) involves incorporating data from insect communities, bat acoustics, and next-generation sequencing of guano in Southern Wisconsin. Currently, we have processed acoustic data from 2015 and 2016. Based on preliminary results, we have found a significant decline in little brown bat activity across all sites, but did not detect a significant decline in big brown bat activity. These results are consistent with reports from the Wisconsin DNR and the Great Wisconsin Bat Count, which have reported declines in colonies throughout the state based on pre- and post-volancy bat counts. Insect counts from 2015-2017 have been completed, with nearly 2 million insect specimens identified and counted. In the near future of this project, this information will be used to assess whether bat acoustic activity correlates with changes in local insect abundances during the summer. The results from this study were presented this year at the NASBR meeting in Knoxville, Tennessee, and will also be presented next year at the American Society of Mammalogists meeting.
Hummingbird Monitoring Network
With the grant of 2 licenses of Wildlife Acoustics Kaleidoscope Pro 4.1 software with acoustic Cluster Analysis, the Hummingbird Monitoring Network (HMN) could now analyze recordings taken in fields of hummingbird-visited flowers during southbound migration. The science objectives of the study are to determine how weather, plant phenology and abundance of available nectar influence hummingbird migration. The community objectives of the study are to employ and engage high school students in STEM (Science, Technology, Engineering, and Mathematics) activities.
In 2013 and 2014, we recorded daytime activity of hummingbirds in 7 flower patches for 5 weeks during southbound migration in the Chiricahua Mountains of southeastern Arizona. In 2015 and 2016, we worked with Songscope software to build recognizers of hummingbird sounds. This effort had limited success and we were anxious to learn the Kaleidoscope software. During spring semester 2017, Patagonia High School students easily learned how to use the Kaleidoscope software and began identifying clusters with hummingbird chirp notes, vocalizations, and wing trills. By the end of this semester's program, students had iteratively defined clusters and were beginning to refine the classifiers that identify hummingbird chirp notes and vocalizations to species. The refinement of the classifier will continue during fall semester with the goal of having complete classifiers for the three hummingbird species known to have used these flower patches. Upon completion of the hummingbird classifiers, HMN's science collaborators will complete the analyses for the study.
Building classifiers with the Kaleidoscope software was an excellent project for high school students. They became proficient at identifying hummingbird sounds and classifying clusters into different vocalization categories. Our workflow was somewhat unique because it was multi-threaded. Two students, each using a license of Kaleidoscope, built classifiers from different recordings. We, then, wanted to combine the classifiers and re-run the cluster analyzer to continue refining the classifiers. We were unable to figure out how to do this, so we contacted Wildlife Acoustic's technical support team and worked with Chris Warren. He quickly helped identify how to combine the efforts as well as answered additional questions that arose throughout the semester.
We think passive recordings are an excellent field technique; have encouraged others to use it as well as engage high school students to help build the classifiers. We thank Wildlife Acoustics for this grant and particularly thank Chris Warren for his timely and extremely helpful guidance as we learned how to use Kaleidoscope.
No progress has been made with this project since the last report. Building the hummingbird classifiers are part of a STEM program with Patagonia Union High School. At the end of the program last March, students had iteratively defined clusters and were beginning to refine the classifiers to identify hummingbird chip notes and vocalizations to species. Due to lack of funding for the PASEO program (Patagonia After School Employment Opportunities), it was not offered to students this Fall semester. The high school student, Nick Botz, who mastered Kaleidoscope, is an accomplished musician and strong science student and will be working with HMN from late November to January. We expect to complete building the hummingbird classifiers by the end of his employment. In 2018, we will begin integrating the results of Kaleidoscope with the environmental data to identify the weather/climate factors influencing hummingbird migration.
The purpose of HMN’s study is to see how weather patterns affect the migration of hummingbirds in our area. To do that, they need to be able to accurately estimate the number of hummingbirds visiting a flower patch in a set period of time. The data for the study came from seven different sites in the Chiricahua Mountains: Barfood Park, Coal Pit, El Coronado, Long Park, Onion Saddle, Saulsberry, and Turkey Creek. The hummingbirds tagged in these sites were Broad-tailed (BTLH), Black-chinned (BCHU), Rufous (RUHU), and Magnificent (MAHU). Recording took place during August and September of 2013 and 2014 with Wildlife Acoustics’ Song Monitor devices. It was these recordings that would become the key to proceeding with the study.
That is where Wildlife Acoustics’ Kaleidoscope 4.1.0a comes in. This software allows you to build a classifier, a special cluster.kcs file that Kaleidoscope uses to process audio recordings, creating a “cluster.csv” Excel document as its results table. The end result is a machine that can pick out each individual hummingbird sound over the course of weeks for our review. The final step is to combine all the resulting Excel files to create one spreadsheet telling how many hummingbirds visited the flower patches for each day of the study.
Kaleidoscope uses a small set of sample recordings to create a classifier, which can then be used on new recordings to sort vocalizations into “clusters”, or groups of vocalizations with similarities. It is the job of the human user to train the classifier to discriminate between different species. Processing a set of recordings for the first time creates a cluster.csv and cluster.kcs file. The cluster.csv is an Excel document containing all the meta data from the scan. This is what the human user opens and edits with Kaleidoscope. Changes to the cluster.csv create a new cluster.kcs, and this is the file that the software uses to cluster new recordings with its many complex models.
The entire process can be hard to grasp, so Wildlife Acoustics has provided a series of tutorial videos on their website to train new users. They also conducted a free workshop at the US Fish and Wildlife Service office, where representatives answered questions and ran through training simulations, which was very helpful in getting familiar with the software.
The Cornell Lab of Ornithology’s Macaulay Library contains audio samples from every species in the study, so it proved extremely useful in discovering the subtle differences between different vocalizations. The website uses a black-against-white style for its spectrogram plots, which makes it a little more difficult to compare and contrast with Kaleidoscope’s dotted green-against-black style. The audio files from the Macaulay library can be downloaded as MP3’s, then processed through the media.io engine to convert them to WAV files, allowing them to be viewed with Kaleidoscope. This was just for the purpose of reference; the Macaulay downloads were not mixed into the field recordings.
This was advantageous also because Kaleidoscope allows the user to pick a certain range of kHz they want to hear when they play the audio. This meant that the loud, distracting background noise could be filtered out (you can see such noise at the bottom of the above spectrogram) to quite literally get a clearer picture of the vocalizations.
The first step of building any classifier is to select a set of recordings and run them through Kaleidoscope’s simplest action: “scan and cluster recordings to create cluster.kcs and cluster.csv.” In the tutorial videos, the creators recommend using training recordings, but those were unavailable, so the classifier was created from the field recordings themselves. The sample recordings were selected by site and date using data from the tagging study that accompanied the recording study. The sites with the most tagged hummingbirds from each species were selected in the hopes of obtaining enough vocalizations to include every species in the classifier. With that, the first scan commenced. Mention additional recordings of known hummingbird chips, Provide enough detail to convince the reader that you identified all vocalizations and chip notes. This justifies the use of the cluster analyses for relative abundance estimates.
Once the recordings were scanned, they could be examined in the Kaleidoscope viewer. It was then time to determine the correct signal parameters for the classifier. Kaleidoscope’s signal parameters are a range of length (seconds) and frequency (Hz) allowed for scanning. The idea was to adjust them to fit snugly around a single chip note so that noises such as frog trills, which sound nothing like a hummingbird chip, would be left out.
The method for figuring out the signal parameters was simple. First, take the tallest chip note and the widest, measuring the range of x(Seconds) and y(Hz).
The next step in the process is clustering, or going through every detection and entering a name for it in the MANUAL ID column. This can be done two ways, with two different kinds of results. The first is by clicking “Bulk ID” and assigning a species identification to an entire cluster of detections at once. This is far quicker and results in a simple classifier with low accuracy. The second method is actually viewing every single detection and assigning it its own species identification. With a results table containing hundreds of thousands of detections, this is a lot more tedious and time-consuming, but the result is an advanced classifier that has an accuracy of about 89%. The CHIP classifier was made using both of these methods. First, a simple classifier was made by entering either CHIP or NOTCHIP in the “Bulk ID” tab. Then, the .csv file was processed through the “rescan recordings and edited cluster.csv to create new cluster.kcs with pairwise classifiers and cluster.csv” action, separating the hummingbird sounds from everything else. Then, work could commence on the advanced classifier by entering species names into the “MANUAL ID” column.
Out of the original four species present in field study, the MAHU chips were so few and far between that the software actually left them out after the first re-scan. That turned out not to be a problem, since data collected from on-the-scene monitoring shows that BTLH, BCHU, and RUHU have higher populations in our area and migrate much further than MAHU, making them more suitable subjects for the study.
Once every detection had been manually labeled, the file was ready to be rescanned again and become an advanced classifier. After just one rescan, the classifier was still full of false positives. Creating a high-accuracy classifier is a reiterative process, with each rescan weeding out a little more of the false positives. The classifier reaches its maximum accuracy once a rescan consistently creates 10% or fewer false positives. From the first basic classifier scan to the final advanced rescan, it ultimately took 11 rescans to finalize the chip classifier.
With the 2013 and 2014 data extracted, it was time for our first big milestone: getting results from the chip classifier. We collated every cluster.csv into one giant Excel file, which was only possible within the 1,000,000 row limit because we removed the NOTHUM detections. Using the quantity of every CHIP detection and the date/time the recordings were taken, this graph was made.
The chip classifier was only half the battle. To include every sound made by the hummingbirds in the study, a second classifier had to be made that would have different signal parameters in order to capture hummingbird vocalizations, which are snippets of hummingbird song with a clear beginning and end.
That meant starting from the very beginning, using the same recordings that were selected for the chip classifier because of their relative abundance of hummingbirds.
The signal parameters were found for the vocalizations using the same method as for the chips. This time the vocalizations with the largest range of length (seconds) and frequency (Hz) were used to set the signal parameters. Predictably enough, the frequency did not need to be changed, but the length was extended to 4.2 seconds. Changing the inter-syllable gap was very important, since it allowed the vocalization and all its syllables to be grouped together instead of being pulled apart and counted separately the way the chips were. Kaleidoscope’s default for this setting is 0.35 seconds, which ended up being enough to detect entire vocalizations.
In the same way that MAHU chips were so underrepresented in the Chip classifier that the software would not cluster them, BTLH vocalizations did not make it into the vocalization classifier. However, there were a few unexpected Calliope Hummingbird detections that did get clustered and incorporated into the classifier. A surprise, to be sure, but a welcome one...
As was the case with the chip classifier, one scan was not nearly enough. To remove as many false positives as possible and reach a final product, the cluster.csv had to be processed through the “rescan recordings and edited cluster.csv to create new cluster.kcs with pairwise classifiers and cluster.csv” action 6 times.
In 2013 and 2014, passive recordings with 7 Wildlife Acoustics Songmeters (SM3) were made in 7 flower patches for 5 weeks each year during southbound migration in the Chiricahua Mountains of southeastern Arizona. During this time, weekly field surveys were conducted to estimate hummingbird activity and floral abundance so abundance estimates from the recordings could be calibrated. The science objectives of the study are to determine how weather, plant phenology and abundance of available nectar influence hummingbird migration. The community objectives of the study are to employ and engage high school students in STEM (Science, Technology, Engineering, and Mathematics) activities.
In 2015 and 2016, we worked with Songscope software to build recognizers of hummingbird sounds. This effort had limited success and we were anxious to learn Kaleidoscope, the replacement software for Songscope. With the grant of two licenses of Kaleidoscope at the end of 2017, we were prepared to continue extracting hummingbird vocalizations from the recordings.
In 2017, Patagonia High School students started learning Kaleidoscope and began identifying clusters with hummingbird chip notes, vocalizations, and wing trills. By the end of this semester’s program, students had begun to iteratively define clusters and refine the classifiers. At this time, funding was lacking and this project was placed on hold.
In 2018 and 2019, we hired Patagonia High School sophomore Nick Botz to continue building classifiers with Kaleidoscope. Nick is a talented musician and is interested in science. He was the ideal student to continue this project. He became proficient at identifying hummingbird sounds and classifying clusters into different vocalization categories. During the summer of 2018, we attended a day long workshop on Kaleidoscope offered by Wildlife Acoustics through the USFWS office in Tucson. This workshop helped identify ways in which we could still improve the development of the classifiers. In Spring 2019, Nick was confident that he had extracted all the hummingbird vocalizations from the recordings. His final task was to write a report that described how he developed the classifier and that could educate the next person working on the project. His report follows this summary. Upon finishing this project, Nick enrolled in an online Audio Engineering course. He’s using Audacity software to mix & edit tracks, and suddenly looking at waveform plots again!
Now, we are collaborating with scientists in the School of Natural Resource and the Environment at University of Arizona to integrate the weather, field, and vocalization data so we can explore how weather, plant phenology and abundance of available nectar influence hummingbird migration. The resulting science is dependent upon fully extracting all hummingbird vocalizations. Upon reading Nick’s report by a Wildlife Acoustic technician, we would appreciate learning if there was something else that we could have done to improve the extractions.