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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, MT headquarters just outside the park's north entrance. 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. The Yellowstone Wolf Project is reliant on the annual generosity of donors to make this important and groundbreaking work possible. To learn more about and support the Yellowstone Wolf Project, go to https://www.yellowstone.org/wolf-project. Thank you! 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
Mollie's and Junction Butte wolf packs meetup in Yellowstone National Park

Mollie's and Junction Butte wolf packs meetup in Yellowstone National Park

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 (www.thecrywolfproject.com) is studying the frequency at which wolves vocalize to better model their population and occupancy, as well as the different functions of their sounds.
907F Howl

907F Howl

This is the howl of a wolf, but not just any wolf—this is 907F, one of the most enduring souls to ever roam Yellowstone. Born in 2013 to the Junction Butte pack’s second litter, she was eleven years old at the time of this recording—a lifetime for most wolves. What makes her tale more extraordinary is that she’s lived much of it with just one good eye. Yet, despite that, she’s risen time and again, leading her pack as the alpha female on multiple occasions, especially during the years when their numbers swelled to an impressive 35 wolves, and they became masters of hunting bison. In 2024, after the pack lost its leading female, 907F took up the mantle once more, delivering her tenth litter—three pups this time. After everything she’s been through, you can’t blame her for keeping the numbers manageable. When she made the howls you're about to hear, she was separated from the rest of her pack, who had chased elk by a hidden audio recorder just an hour prior. An hour later and alone by herself standing next to the recorder, she let out a sequence of howls that went on for over 30 minutes. Close your eyes and picture the scene. The snow fell, soft yet persistent, matching her steady breath. She paced back and forth over the rocks beneath the fir tree which held the recorder ten feet off the rocky floor. She paused, just feet away, her howl alive in a fog of snowflakes, each breath a murmur that seemed to echo from some ancient, forgotten place. Dr Jeffrey T Reed
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.

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.

 

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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Our moonshot is to decode wolf communication as fully as humanly possible. But in the process, we’re building something with the potential to transform global conservation, all rooted in the incredible research happening right here in Yellowstone. Through our technology and research, we aim to:

  • Enhance wildlife population monitoring with greater accuracy;

  • Reduce conflicts between wildlife and livestock through better understanding and prediction of behavior;

  • Lower government wildlife management costs using advanced Artificial Intelligence;

  • Compensate private landowners for their ecosystem services;

  • Inspire future generations to appreciate both the economic and intrinsic value of wildlife;

  • And perhaps, along the way, help you understand your own pet just a little bit better.

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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

  • one of the better ways to deter wolves from livestock is with large-pack chorus howl

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

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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 The Cry Wolf Bioacoustics Project" in the optional comments field. Thank you!

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