Posts Tagged ‘zoology’

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


When zoologist Ivan Sazima went for a walk in the park in southeastern Brazil on a warm September day in 2013, he was hoping to find noteworthy animal behavior to study.

But he did not expect to witness lizard necrophilia. Right in front of him, he saw a male reptile trying to court and mate with a dead female of the same species, Salvator merianae, commonly known as the black-and-white tegu.

“I felt a sense of wonder, because I did not observe this behavior in lizards before, only in frogs,” said Sazima, of the Zoology Museum of the University of Campinas in São Paulo.

Necrophilia occurs in other lizard species, but it’s the first documented instance in black-and-white tegus, one of the most common lizards in South America.

Sazima watched the male lizard flick his tongue at the deceased female—a common courtship behavior—and try to mate with her for about five minutes. Then a group of geese showed up, causing the confused suitor to flee.

The scientist returned to the same spot the next afternoon. By that time, the corpse was bloated and had begun to rot and smell.

But even the stench did not discourage another male black-and-white tegu from attempting to have sex with the dead body—this time for nearly an hour.

During this time, the new male embraced the dead female and bit her head, another courtship behavior. He rested on her body from time to time, taking breaks from the exhausting sexual activity, before finally flicking his tongue on the corpse and leaving, according to the study, published in January in the journal Herpetology Notes.

Sazima’s encounter adds to several reported instances of necrophilia in the animal world.

Henrique Caldeira Costa of the Federal University of Minas Gerais, in Belo Horizonte, Brazil, reported necrophilia in male green ameiva lizards in Brazil in 2010. The female had likely been hit by a vehicle on the road, he wrote in the journal Herpetology Notes.

In another incident, Kamelia Algiers, a biologist at Ventura College in California, described a necrophiliac long-nosed leopard lizard in Nevada, in the western United States.

The animal attempted to copulate with a roadkill female, whose “intestines were sticking out, and there were ants crawling all over it,” said Algiers, who described the event in 2005 in Herpetological Review.

What’s more, mating with the dead isn’t restricted to reptiles and amphibians: Ducks, penguins, sea lions, pigeons, and even ground squirrels have also been caught in the grisly act.

Why Mate With the Dead?

So, what exactly draws some male lizards to female corpses? Despite many scientific observations, “necrophilia in lizards is still poorly understood,” said Costa, who wasn’t involved in the new tegu research.

But as for those amorous black-and-white tegus, the Zoology Museum’s Sazima has a theory: The males may have been simply fooled into thinking the female was alive.

For one, the dead female lizard was still warm: Though dead, her body temperature was probably close to that of the ambient air. And her pheromones, likely still detectable on her body after death, may have allured the male admirers.

Federal University’s Costa agrees this is a valid theory, and suspects that the female’s high body temperature and pheromones might have explained the lizard necrophiliac he described in 2010.

Interestingly, necrophilia seems to be beneficial for at least one species: a small frog in Amazonian Brazil called Rhinella proboscidea.

A 2013 study showed that R. proboscidea males can extract eggs from dead sexual partners and fertilize them, a process called “functional necrophilia.”

Thanks to Da Brayn for bringing this to the attention of the It’s Interesting community.