Posts Tagged ‘endangered species’

By Ashley Strickland

Dogs and their sensitive noses are known for finding people during search and rescue efforts, sniffing out drugs and even diseases like cancer. But the powerful canine nose can also act like radar for other things that are hidden from our sight.

Now, they’re acting like watchdogs for endangered species and assisting with conservation efforts.
Organizations like Working Dogs for Conservation train dogs to identify the scents of endangered animals and their droppings, which helps scientists track species that may be declining.

Tracking animal scat, or fecal matter, can reveal where endangered species live, how many of them are living in an area and what might be threatening them. And it’s a less stressful way of monitoring species than trapping and releasing them.

Previously, conversation dogs have successfully tracked the San Joaquin kit fox, gray wolves, cougars, bobcats, moose, river otters, American minks, black-footed ferrets and even the North Atlantic right whale, according to a new study published Wednesday in the Journal of Wildlife Management.

In the new study, scientists trained conservation dogs to focus on a new kind of animal: reptiles. They wanted to track the elusive and endangered blunt-nosed leopard lizard in the San Joaquin Valley. The experienced conservation dogs, including one female German shepherd and two male border collies, were trained to detect the scent of the lizard’s scat.

Then, the scientists could retrieve the samples and determine the gender, population genetics, diet, hormones, parasites, habitat use and health of the lizards. Humans have a difficult time identifying such small samples by sight because they are hard to distinguish from the environment. They can also be very similar to other scat.

The blunt-nosed leopard lizard is a fully protected species in California. It’s endangered because its habitat has been destroyed. Surveying the species and their habitat can help scientists to understand if existing conservation efforts are helping.

Over four years, scientists took the dogs out to the desert to detect and collect samples. The dogs would signal their discovery by laying down next to the scat. Then, they would be rewarded by a toy or play session.

Working between one and two hours a day, the dogs went out with survey teams from the end of April to mid May, when the lizards would emerge from brumation, otherwise known as reptile hibernation, according to the study. The dogs were trained not to approach the lizards if they saw them.

Over four years, they collected 327 samples and 82% of them were confirmed as belonging to blunt-nosed leopard lizards.

The researchers believe this method of tracking has potential and now they want to refine the method to see if it will work on a larger scale.

“So many reptilian species have been hit so hard,” said Mark Statham, lead study author and associate researcher with the Mammalian Ecology and Conservation Unit of the UC Davis School of Veterinary Medicine. “A large proportion of them are endangered or threatened. This is a really valuable way for people to be able to survey them.”

https://www.cnn.com/2019/10/30/world/conservation-dogs-endangered-lizard-scn/index.html?utm_source=The+Good+Stuff&utm_campaign=91b09c3d68-EMAIL_CAMPAIGN_2019_10_30_05_15&utm_medium=email&utm_term=0_4cbecb3309-91b09c3d68-103653961


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 http://www.zooniverse.org 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.”

http://www.uwyo.edu/uw/news/2018/06/researchers-use-artificial-intelligence-to-identify,-count,-describe-wild-animals.html

by Merrit Kennedy

The U.S. Fish and Wildlife Service has given endangered status to seven species of yellow-faced bees native to the Hawaiian islands. These are “the first bees in the country to be protected under the Endangered Species Act,” according to the Xerces Society, which advocated for the new designation.

The new rule designating protections for the bees, published Friday in the Federal Register, states that yellow-faced bees are known “for their yellow-to-white facial markings.” They look like small wasps, according to the rule, except for their “plumose [branched] hairs on the body that are longest on the sides of the thorax, which readily distinguish them from wasps.”

The yellow-faced bee is the only bee native to Hawaii, meaning that it was able to reach the Hawaiian Islands on its own, according to a fact sheet provided by the University of Hawaii’s Master Gardner Program. “From that one original colonist they evolved into 63 known endemic species, about 10% of the world’s yellow-faced bees and more than are found in this genus in all of North America.”

But the populations of these seven species are getting smaller and smaller, according to Fish and Wildlife. For example, the Hylaeus anthracinus was once found in dozens of locations around Hawaii but is now in only 15 — while Hylaeus hilaris and Hylaeus kuakea are each found only in one location.

The seven endangered species are impacted by a wide variety of threats, including habitat destruction because of urbanization or nonnative animals, the introduction of nonnative plant species, wildfires, nonnative predators and natural events such as hurricanes, tsunamis and drought.

The protected status “will allow authorities to implement recovery programs, access funding and limit their harm from outside sources,” as Gregory Koob of the Fish and Wildlife Service told The Associated Press. He added that “all federal agencies must consult with the Fish and Wildlife Service when interacting with endangered species.”

The Xerces Society called the new rule “excellent news” but added that “there is much work that needs to be done to ensure that Hawaii’s bees thrive.”

“These bees are often found in small patches of habitat hemmed in by agricultural land or developments,” the group said. “Unfortunately, the [Fish and Wildlife Service] has not designated any ‘critical habitat’ areas of land of particular importance for the endangered bees.”

As we’ve reported, pollinators are under threat around the world. A U.N.-sponsored report released in February found that “about 40 percent of invertebrate pollinator species (such as bees and butterflies) are facing extinction.” This could have major implications for world food supply, because “about 75 percent of the world’s food crops … depend at least partly on pollination.”

Despite the threats, the University of Hawaii says these bees “have managed to persist with amazing tenacity.” While this group of species is now endangered, new species of the genus are discovered regularly — “11 new native species have been found in the past 15 years.”

The rule, which goes into effect at the end of the month, also gives the endangered designation to 39 plant species found on the islands and to three other animals native to Hawaii — the band-rumped storm-petrel, the orangeblack Hawaiian damselfly, and the anchialine pool shrimp.

The anchialine pool shrimp is known for its unusual longevity — according to the Hawaii Nature Journal, the shrimp can live for 10 to 15 years in the wild.

http://www.npr.org/sections/thetwo-way/2016/10/03/496402620/bee-species-added-to-u-s-endangered-species-list-for-1st-time