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Do wolves talk? A bioacoustics study in the Greater Yellowstone | Summer Speaker Series 2024

Do wolves talk? A bioacoustics study in the Greater Yellowstone | Summer Speaker Series 2024

This fascinating talk by Jeff Reed, Ph.D. was part of Yellowstone Forever's 2024 Summer Speaker Series. The event took place on 6/20/2024 at YF's Gardiner headquarters. For the full Speaker Series schedule, go to https://www.yellowstone.org/summer-speaker-series/. Description: Join us as we delve into new techniques that help unravel the mysteries of animal communication, with a focus on wolf vocalizations. We'll highlight the Cry Wolf project, a collaboration of Yellowstone's renowned Wolf Project. The session includes insights into bioacoustics (i.e. the study of the sounds made by living things), autonomous recording technologies (that you can use to record your own backyard), and most importantly, an immersive experience of listening to and discussing the possible meanings of actual wolf communication in Yellowstone National Park. Bio: Jeff Reed is a software engineer for Yellowstone National Park’s Cry Wolf bioacoustics project, a long-term study of wolf communication. Jeff was born and raised in the Greater Yellowstone Ecosystem in southwest Montana, where he grew up exploring the Absaroka mountains. With a PhD in computational linguistics and 30 years in the technology industry, Jeff now develops camera and acoustic recorders to assist our understanding of animal behavior (https://www.grizcam.com). An advocate for hands-on, measurable conservation, he works to preserve the Greater Yellowstone through business alliances (https://www.wildlivelihoods.com) and inspirational auditory projects (https://www.thelanguagesoflife.com, https://www.fivetoedwolf.com). Cover Image: Tom Murphy
49 Variations of Wolf Howls

49 Variations of Wolf Howls

This video contains 49 variations of wolf howls, and one human, who were recorded in Yellowstone National Park. As you can see and hear, wolves have a variety of howls, and these are just a representative sampling. Can you find the pups? The human? 907F - the matriarch of the Junction Butte pack? Or the now deceased alpha male of the Rescue Creek pack? The spectral analyzer at the bottom of the video shows the frequency at which the current audio is playing. To the far bottom-left are sounds with a pitch of 200 hertz, and the bottom-right at 1,400 hertz (or 1.4 kilohertz). A common wolf howl is 340 hertz (or Middle F on your piano keyboard) on the low end, but they will frequently howl up towards 500 hertz, and sometimes higher. Larger body sized wolves can likely achieve lower pitches. Pups typically howl at around 1,000 hertz, but within 3-5 months of leaving the den, their voices will lower in to their adult range. The parallel lines you see running horizontally in the each of the square images are harmonics, and your ears focus on the lowest line, or fundamental frequency of the howl; but as you can see on the spectral analyzer, the harmonics show up as multiple bumps, each an octave apart. The lower that line is on the y-axis of each square, the lower the pitch of that wolf. Wolves sometimes combine other sounds, such as growls or barks, with a howl to indicate a threat. When they do this, they often modulate their howl in what shows up as a wavy line in the spectrogram. This is a very basic form of syntax (what is called a two-slot syntax). The second to last vocalization is actually a human, pronouncing the word "woof" and using the "oo" (as in "hoot") vowel to best emulate a wolf. Our vowels have harmonics too. And even though it is easy for you to understand your own language, wolves are also likely able to distinguish the various nuances in timing, emphasis, and pitch for deciphering other wolves. The Cry Wolf bioacoustics project is studying how wolves can identify each other through their howls as well as how different types of howls may mean different things.
Interaction between Mollie's and Junction Butte packs in Yellowstone National Park (January 2024)

Interaction between Mollie's and Junction Butte packs in Yellowstone National Park (January 2024)

On January 4th 2024 at about 8am in Yellowstone National Park, two separate wolf packs called the Junction Butte pack and the Mollie's pack came together in a tense reunion which ended amicably. In addition, a dispersing black wolf (1407M) from the Willow Creek pack is seen attentively trying to figure out the players surrounding him. Howling and other wolf communication can be heard throughout this video. Acoustic recorders also captured the two packs chorus howling towards one another the night before. Wolves have many variations of howls with which to communicate, though little is known as to whether different types have different functions. There is reasonable evidence, however, that larger bodied wolves have lower pitched howls, which can serve as an honest signal to other wolves about their size. In addition, barks (which you'll hear in one segment of this video) definitely have a different function than howls. Barks come in different forms such as woofs, disturbance barks (which are shorter in duration and lower in pitch) and agonistic barks (which are longer in duration and have more harmonics). Agonistic barks are often emitted by the dominant individual and often show dominance displays (e.g. tail held high). Disturbance barks are only emitted in conflict scenarios…for example towards other packs, humans, cougars or bears. There are several types of different howls in this video, many of which we are still trying to decipher as to possible functions. In addition, at 4:28 in to the video you'll hear a "woa" vocalization from several wolves off camera when a group of the wolves first reunited, and towards whom a different black wolf is running. The "woa" call is done in contexts of social bonding where wolves are often muzzling one another. The Cry Wolf project (grizcam.com/crywolf) is studying the frequency at which wolves vocalize to better model their population and occupancy, as well as the different functions of their sounds.
Howl of 1048M of Mollie's Pack

Howl of 1048M of Mollie's Pack

Typically, when a wolf howls, the fundamental frequency (or first harmonic) is the loudest and the one your brain "hears" the most. Less frequently, wolves howl where the second harmonic is louder than the first, and it slightly changes the overall quality of the howl. This video by Taylor Rabe of 1048M of the Mollie's pack in April 2025 is an example. We don't know if the signal carries any meaning, or if it is used in IDing a wolf (we just don't have enough samples), but it is an interesting phenomenon. Note: 1048M weighted 119lbs when collared in 2019. Assuming a similar weight, that might explain the lower than average fundamental frequency (325hz) of this howl. The second harmonic can be louder than the fundamental frequency due to the interplay of vocal tract resonance, vocal fold dynamics, and non-linearities in the vocal system. Resonance in the vocal tract can amplify harmonics that match formant frequencies, particularly the second harmonic. The shape and tension of the vocal folds also influence harmonic content, and a sharper glottal pulse can enhance higher harmonics. The glottal pulse is the waveform produced by the opening and closing of the vocal folds during phonation, generating the fundamental frequency and its harmonics. Non-linear wave propagation further contributes to the amplification of certain harmonics. This phenomenon is used by singers, such as those practicing overtone singing, to manipulate vocal harmonics effectively. In other words, the complex dynamics of the vocal folds and the vocal tract can introduce non-linearities that amplify certain harmonics, such as the second harmonic, making them more prominent than the fundamental frequency.

Yellowstone National Park and Wild Livelihoods Business Coalition, in collaboration with Grizzly Systems and several academic institutions have partnered to deploy low-power, AI-infused monitoring devices that capture acoustic and visual data for behavioral research and for monitoring the presence and distribution of wolves across the Greater Yellowstone Ecosystem. Accurate population and occupancy estimates play a vital role in shaping state and federal management policies. Through the use of various artificial intelligence algorithms, scientists can efficiently analyze large data sets of audio and video/stills to find and then study wolf communication behavior.

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Because wolf vocalizations carry for relatively long distances, AI-infused autonomous recording units (ARUs) that are integrated into camera traps can serve as low-cost tools to enhance existing census efforts. Furthermore, better approaches to livestock-conflict deterrence may be possible with playbacks of scientifically validated wolf or guardian dog vocalizations, triggered by the AI devices. Finally, audio and video educational tools can be created via an end-to-end software platform that responsibly showcases the biodiversity in some regions in order to encourage other regions to follow suite. 

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A new cell-phone-sized device—which can be deployed in vast, remote areas—is using AI to identify and geolocate wildlife...

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A new way to help protect wildlife.

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In order to capture 24-hour audio, Grizzly Systems spearheaded the development of a new recorder with extended battery life and a compact design.

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Using AI and bioacoustics, America's first national park stands at the forefront of global efforts to translate the sonorous communication of wolves.

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50+ Locations

80,000 hours of audio

240 hours of wolves 

1,500 observation hours

182 other species detected

Passive Acoustic Monitoring (PAM) has emerged as a cost-effective and noninvasive technique for wolf surveys, providing detection probabilities exceeding those attained through camera trapping. We are building ARU's with classifiers for real-time detection, as well as ML models for post-processing analysis of the behavioral functions of wolf vocalizations. While bioacoustic monitoring is not a novel concept, the advent of advanced AI algorithms has opened up new possibilities to reduce costs and enhance researcher productivity in telemetry monitoring (for more information see Using machine learning to decode animal communication). The Greater Yellowstone region holds realistic, lower-cost potential for bioacoustic research, due to the long-term knowledge already gained from radio collaring, flight surveys, camera traps, and field surveys. As such, this collaborative research project aims to collect 24x7x365 bioacoustics data at pre-determined locations in the GYE which can be set aside, similar to genetic data, and used later for research of any species that vocalizes below 12khz.

Some Initial Findings

 

  • wolves predominantly vocalize during night time hours

  • wolves increase daytime vocalizations during the winter breeding season

  • wolves rapidly modulate their howls during "stressful" situations (inter-pack conflict)

  • wolves respond to coyote vocalizations, but do not silence the coyotes

  • wolf individuals can be identified by the pitch of their howl

  • female wolves play a significant role in how a pack communicates

Collaboration Partners

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A Little about the Technology

 

Supervised Wolf Bioacoustic Detection

There is extensive precedent for applying ML for supervised bioacoustic detection tasks; examples include a sperm whale click detector, a humpback detector, and a model that detects and classifies birdsong, among many others. Employing similar methods, we can train a convolutional neural network (CNN) either from scratch or using pretrained weights to classify an acoustic window as non-signal or wolf signal depending on the absence or presence of a wolf vocalization in the given acoustic segment.

 

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Gabe, a highschool intern annotating a wolf chorus howl for our machine learning algorithm

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Supervised Wolf Chorus Counting

To our knowledge, there are no attempts at automated acoustic counting of overlapping signals, though there are several approaches that may be promising. We are training models (e.g. LSTM-CRF) to predict the number of overlapping spectral elements at fine timescales using open-source data. Assess the model’s ability to generalize to new datasets. Train a model to predict the number of wolves in a chorus based on human annotation of the number of wolves vocalizing concurrently.

 

Unsupervised Wolf Source Separation

Using previous work in source separation and emphasizing the unsupervised MixIT training algorithm used to separate overlapping birdsong mixtures, we can attempt to separate wolf choruses into predictions for the individuals present in the chorus. Though not functionally limited in the number of sources it can handle, it is unclear how the model will perform as the number of concurrently vocalizing wolves increases.

Unsupervised Meaning Discovery in Wolf Vocalizations

The CETI project has produced machine learning models, with little or no understanding of a species vocal repertoire, can be used to reveal meaningful units in the sounds. The approach in this paper, APPROACHING AN UNKNOWN COMMUNICATION SYSTEM BY LATENT SPACE EXPLORATION AND CAUSAL INFERENCE, with modification for wolf vocalizations, is promising.

Conservation Value

  1. Non-invasive wolf population monitoring: occupancy, abundance, population trends

  2. Assessing wolf pack structure and social dynamics: reproduction, pack identity, individual identity, changes in pack membership, aid in understanding effects of hunting, poaching, or environmental changes on pack dynamics

  3. Tracking wolf movement and territory: monitor habitat use, overturn in territory, index of habitat quality, influence of humans on territory use.

  4. Understanding responses to environmental and human disturbances: target areas of protection or corridors; mitigate human impacts

  5. Monitoring reintroduction and conservation success in other places where wolves are returning

  6. Conservation of cultural and ecosystem roles of wolves 

  7. Supporting law enforcement efforts and human-livestock conflicts: monitoring and responding to potential illegal activity such as poaching and gunshots inside protected lands; developing potential tools to mitigate depredations with livestock. 

  8. Technological advances to aid in species monitoring: developing reliable, long-lasting, cost-effective advanced camera traps with acoustic recorders; develop AI models for processing data

  9. Education and outreach about wolves, animal communication, ecosystem processes, and natural soundscapesUmbrella research: recording soundscapes for wolves yields data to aid with conservation and monitoring of other species (e.g., bird) and natural soundscape.

Related Scientific Research

Donate

Donate Financially to the Project

Yellowstone National Park's Wolf Project Team appreciates your interest in financially supporting the Cry Wolf Bioacoustics project.  All donations go through Yellowstone Forever, the official non-profit of Yellowstone National Park. To ensure that your funds go to the Cry Wolf Project, click on the Donate Now button below. Put "For Dr Dan Stahler and The Cry Wolf Project" in the optional comments field. Thank you!

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