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Identifying individual wild Eastern grey wolves (Canis lupus lycaon) using fundamental frequency and amplitude of howls. Root-Gutteridge H, Bencsik M, Chebli M, Gentle LK, Terrell-Nield C, Bourit A, Yarnell RW. Bioacoustics. 2014 Jan
The use of amplitudes to identify individuals has historically been ignored by bioacoustic researchers due to problems of attenuation. However, recent studies have shown that amplitudes encode identity in a variety of mammal species. Previously, individuality has been demonstrated in both fundamental frequency (F0) and amplitude changes of captive Eastern wolf (Canis lupus lycaon) howls with 100% accuracy where attenuation of amplitude due to distance was controlled in a captive environment. In this study, we aim to determine whether both fundamental frequency and amplitude data collected from vocalizations of wild wolves recorded over unknown distances, in variable conditions and with different recording equipment, can still encode identity. We used a bespoke code, developed in Matlab, to extract simple scalar variables from 67 high-quality solo howls from 10 wild individuals and 112 chorus howls from another 109 individuals, including lower quality howls with wind or water noise. Principal component analysis (PCA) was carried out on the fundamental frequency and normalized amplitude of harmonic 1, yielding histogram-derived PCA values on which discriminant function analysis was applied. An accuracy of 100% was achieved when assigning solo howls to individuals, and for the chorus howls a best accuracy of 97.4% was achieved. We suggest that individual recognition using our new extraction and analysis methods involving fundamental frequency and amplitudes together can identify wild wolves with high accuracy, and that this method should be applied to surveys of individuals in capture–mark–recapture and presence–absence studies of canid species.
Photo A gray wolf peers from a bush in Idaho’s Sawtooth Mountains.
Photograph by Jim and Jamie Dutcher, National Geographic
Wolves Identified by Unique Howls, May Help Rare Species
New method lets scientists ID howling wolves with total accuracy.
ByJennifer S. Holland
If any gray wolves are howling their discontent with a recent proposal to remove what remains of their U.S. federal protection, scientists can now identify the outspoken.
A new, more sophisticated method for analyzing sound recordings of wild wolf howls can, with absolute accuracy, tell individual wolves apart-and may even help save the old dog, according to a new paper in the journal Bioacoustics.
Study leader Holly Root-Gutteridge and colleagues at Nottingham Trent University in the U.K., working with recordings of wild wolves mostly from Algonquin Provincial Park (map) in Ontario, Canada, also found the technique can distinguish a single animal from a chorus of howlers with 97.4 percent accuracy. The team had previously used the method with captive wolves, but this is the first time it’s worked with wild wolf songs and all the ambient sounds that go with them.
Specifically, the team’s more thorough howl analysis looks at pitch—also considered by previous howl-analyzing tools—but also at amplitude, or the acoustic energy, of recorded howls.
“This is like trying to describe the human voice by saying ‘Sandra has a high voice, and Jane has a high voice,'” said Root-Gutteridge, “then refining it by saying ‘Sandra has a soft-spoken voice, but Jane has a loud voice.’ The highness still matters, but if you add the detail about vocal intensity, you’re less likely to confuse Sandra and Jane.”
What’s more, the technology is able to scrutinize howl recordings and throw out extra, unneeded noises like wind and water that might otherwise confuse the data.
Tracking Wolves a Challenge
These majestic canids—which once roamed most of the northern Rockies of the United States and Canada and the forests along the Great Lakes—nearly went extinct in the early 1960s, when they were considered vermin and all but eradicated by hunters. After the shooting stopped, only about 300 gray wolves remained, skulking through the deep woods of upper Michigan and Minnesota.
With protection under the Endangered Species Act, gray wolves have come back from the brink—one of the biggest success stories in U.S. conservation history.
Though nowhere near the historical estimate of more than 400,000 gray wolves in the United States, now as many as 5,000 live in Michigan, Wisconsin, and Minnesota, with another 7,000 in Alaska. Smaller numbers of reintroduced wolves live in Montana, Idaho, and Wyoming.
But monitoring their populations, which remains a vital part of management, has always been an inexact and labor-intensive science.
Methods include tracking the animals based on pawprints and other marks in the snow, which works quite well-when it snows. GPS collaring lets you know where an individual is, but not with whom it spends its time.
Plus, collars are expensive and collaring requires capturing wolves first-a huge and stressful undertaking for all involved, said Root-Gutteridge.
Finally, you can play howl recordings to wolves and listen to their replies-which can carry six miles (ten kilometers)-but you can’t identify individuals and don’t know when one animal is repeating itself or when a new howler has joined in.
DNA analysis of scat has its place, but it is costly and requires finding the wolves first.
Wolves Out of the Woods?
Now that the new technique has been shown to succeed with wild animals, the team sees it as a tool to help conserve wolves in their natural habitats. (See more wolf pictures.)
For instance, tracking howls accurately could make future wolf counts and monitoring of individuals much more precise. If plans go forward to fully drop the gray wolf from the U.S. Endangered Species list and let states do as they please regarding hunting, better monitoring could over time help determine if it was too soon to strip away those last protective rules, as many conservationists argue.
The technology could also be put to use with other canids like African wild dogs and Ethiopian wolves, both of which are endangered in their habitats, said Root-Gutteridge.
“If it howls, the code can extract it and we can identify it.”