Posts Tagged ‘ecology’

The presence of people in remote areas of the Santa Cruz Mountains turns mountain lions into veritable fraidy-cats and strikes so much fear in bobcats, skunks and opossums that they change their behavior to avoid detection, a new study has found.

Rats and mice, on the other hand, actually forage more in areas where homo sapien voices are heard, probably because they know fewer rodent-eating predators are around, the UC Santa Cruz study concluded.

The paper, published Wednesday in the journal Ecology Letters, describes how humans create a “landscape of fear” among both large and small predators just by being around, a situation that is ripe for exploitation by rodents and, potentially, other pests like ticks.

“We already know that humans are incredibly lethal predators. We kill other predators at much higher rates than any other predator kills predators,” said Chris Wilmers, an associate professor of environmental studies who co-authored the paper with doctoral students. “What we didn’t know was the impact of just our presence in the forest.”

The findings, part of the university-managed Santa Cruz Puma Project, came two years after a previous study by Wilmers showed cougars on trail cameras abandoning deer carcasses and turning tail and running when recorded human voices suddenly started playing near them.

This time Wilmers wanted to find out the wider impact of the human presence, so his research team selected two remote locations closed to the public that some of the more than 40 cougars fitted with GPS and radio telemetry collars are known to frequent.

Twenty-five speakers were spaced evenly in five rows of five in each of the two square-kilometer grids, one inside the Sierra Azul Preserve, just south of Los Gatos, and the other in the San Vicente Redwoods, east of the coastal town of Davenport. There were about 200 meters between each speaker.

Between May 29 and Aug. 31, 2017, the speakers alternated broadcasts of human voices and Pacific tree frog vocalizations for five weeks each, with long silences in between. The researchers then compared the behavior and responses of the various animals.

The seven mountain lions they observed changed their behavior dramatically when the human voices were playing, becoming more cautious and avoiding the area where they perceived there was a human presence. The cougars increased their distance from the nearest speaker by 29% and were detected inside the test areas 30% less often when human voices were being broadcast.

“They both avoided the grid and changed their behavior,” said Justin Suraci, a post doctoral student in Wilmers’ lab and the lead author of the study. “They slowed down their movement speed, which we interpreted as increased caution.”

Bobcats reduced their daytime activity by 31%, skunk activity decreased 40%and opossums foraged 66% less when people were talking. All the medium-sized carnivores were detected less on camera at feed stations when the human voices were within earshot.

“All three of the meso predators were behaviorally suppressed by the presence of humans,” Suraci said. “As it turns out humans are sufficiently scary that it was better to be more cautious and avoid a risky human encounter.”

The opposite happened with mice and rats. Deer mice expanded their range by 45% when people were heard talking in the forest. Both mice and woodrats increased their foraging activities by 17% compared to times when human voices weren’t playing, according to the study.

None of the animals in the experiment changed their behavior or reacted in any noticeable way to the sound of tree frogs.

Wilmers said the sudden boldness of rodents is probably as significant to us as the fear displayed by the mountain lions. It could mean more tick and insect-borne diseases, like Lyme disease, are being spread by rodents and other prey species when predators aren’t around.

Previous studies of cougars in the Santa Cruz Mountains showed that they kill more deer in residential areas, but spend less time feeding when they are near humans. Researchers believe they abandon prey more often around people and then must kill more deer because they are hungry.

Wilmers said human-cougar encounters in the Bay Area mostly occur because mountain lion travel corridors have been blocked by development. One such incident occurred in May 2014 when a large male puma hid behind a small hedge on a busy street in Mountain View for nine hours as pedestrians and bicyclists passed only a few feet away.

The confused cat eventually was tranquilized amid a community furor and released in the hills, but he was later killed trying to cross Interstate 280.


Motion sensor “camera traps” unobtrusively take pictures of animals in their natural environment, oftentimes yielding images not otherwise observable. The artificial intelligence system automatically processes such images, here correctly reporting this as a picture of two impala standing.

A new paper in the Proceedings of the National Academy of Sciences (PNAS) reports how a cutting-edge artificial intelligence technique called deep learning can automatically identify, count and describe animals in their natural habitats.

Photographs that are automatically collected by motion-sensor cameras can then be automatically described by deep neural networks. The result is a system that can automate animal identification for up to 99.3 percent of images while still performing at the same 96.6 percent accuracy rate of crowdsourced teams of human volunteers.

“This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behavior into ‘big data’ sciences. This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems,” says Jeff Clune, the senior author of the paper. He is the Harris Associate Professor at the University of Wyoming and a senior research manager at Uber’s Artificial Intelligence Labs.

The paper was written by Clune; his Ph.D. student Mohammad Sadegh Norouzzadeh; his former Ph.D. student Anh Nguyen (now at Auburn University); Margaret Kosmala (Harvard University); Ali Swanson (University of Oxford); and Meredith Palmer and Craig Packer (both from the University of Minnesota).

Deep neural networks are a form of computational intelligence loosely inspired by how animal brains see and understand the world. They require vast amounts of training data to work well, and the data must be accurately labeled (e.g., each image being correctly tagged with which species of animal is present, how many there are, etc.).

This study obtained the necessary data from Snapshot Serengeti, a citizen science project on the platform. Snapshot Serengeti has deployed a large number of “camera traps” (motion-sensor cameras) in Tanzania that collect millions of images of animals in their natural habitat, such as lions, leopards, cheetahs and elephants. The information in these photographs is only useful once it has been converted into text and numbers. For years, the best method for extracting such information was to ask crowdsourced teams of human volunteers to label each image manually. The study published today harnessed 3.2 million labeled images produced in this manner by more than 50,000 human volunteers over several years.

“When I told Jeff Clune we had 3.2 million labeled images, he stopped in his tracks,” says Packer, who heads the Snapshot Serengeti project. “We wanted to test whether we could use machine learning to automate the work of human volunteers. Our citizen scientists have done phenomenal work, but we needed to speed up the process to handle ever greater amounts of data. The deep learning algorithm is amazing and far surpassed my expectations. This is a game changer for wildlife ecology.”

Swanson, who founded Snapshot Serengeti, adds: “There are hundreds of camera-trap projects in the world, and very few of them are able to recruit large armies of human volunteers to extract their data. That means that much of the knowledge in these important data sets remains untapped. Although projects are increasingly turning to citizen science for image classification, we’re starting to see it take longer and longer to label each batch of images as the demand for volunteers grows. We believe deep learning will be key in alleviating the bottleneck for camera-trap projects: the effort of converting images into usable data.”

“Not only does the artificial intelligence system tell you which of 48 different species of animal is present, but it also tells you how many there are and what they are doing. It will tell you if they are eating, sleeping, if babies are present, etc.,” adds Kosmala, another Snapshot Serengeti leader. “We estimate that the deep learning technology pipeline we describe would save more than eight years of human labeling effort for each additional 3 million images. That is a lot of valuable volunteer time that can be redeployed to help other projects.”

First-author Sadegh Norouzzadeh points out that “Deep learning is still improving rapidly, and we expect that its performance will only get better in the coming years. Here, we wanted to demonstrate the value of the technology to the wildlife ecology community, but we expect that as more people research how to improve deep learning for this application and publish their datasets, the sky’s the limit. It is exciting to think of all the different ways this technology can help with our important scientific and conservation missions.”

The paper that in PNAS is titled, “Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.”,-count,-describe-wild-animals.html