Anyone who has been annoyed by the slightest technology glitches in the workplace should consider what Anthony Pagano is up against in his day job. He is tasked with tracking 700-pound polar bears with high-tech collars and custom apps in the middle of the Arctic Ocean as part of a greater effort to save the animals from the perils of climate change.
In a place where winter temperatures hover around -40 F, where connectivity is almost impossible to get and where his brawny team of workers is rough on the equipment, the gadgets he needs give “rugged” a whole new meaning. “Most of the [hardware] manufacturers I’ve dealt with can’t even fathom the challenges involved,” says Pagano, a wildlife biologist with the U.S. Geological Survey.
But Pagano isn’t complaining. For all the massive hurdles he faces, the opportunities that consumer-driven technology has opened up to him and his peers in the animal conservation world have been remarkable. Software and hardware breakthroughs are enabling wildlife biologists to track and study threatened species in ways that were once considered pie in the sky.
Here are a few examples:
While the volume of new data on the well-being of wildlife is opening up new possibilities for biologists, many observers caution that the digital age is just dawning. The problem of providing adequate devices to capture that data isn’t just that the conditions are “either really wet or really hot and dry,” as Colby Loucks, a conservation biologist at the World Wildlife Fund, puts it. Existing satellite technology, for example, doesn’t have the capacity to collect the data generated every 16 seconds by Pagano’s polar bear collars; he’ll have to retrieve the collars himself at a later date.
Similarly, a laser technology known as LIDAR (light detection and ranging), which many biologists see as their best hope to penetrate clouds and dense forests, requires huge computing capacity. And what about the universal trap of insufficient battery life? Don’t get a conservationist started.
Just as tricky a problem is how to sort through the data deluge. “Ecologists have been swamped with so much high-quality data that they can’t, literally, see the forest for the trees,” says James Sheppard, a spatial ecologist with the San Diego Zoo Global.
Sheppard tapped the immense computing muscle of the San Diego Supercomputer Center to tweak the algorithms that now make it possible for scientists to study and build models of animal habitats in 3-D from their desktops. “It’s taken a while,” says Sheppard, who also worked with math whizzes at the U.S. Geological Survey, “for the computing power to catch up with the sheer quality and quantity of data.”
“Ecologists have been swamped with so much high-quality data that they can’t, literally, see the forest for the trees.”— James Sheppard, spatial ecologist, San Diego Zoo Global
For Thomas Snitch, “a while” took seven years. That’s how long the professor at the University of Maryland Institute for Advanced Computer Studies spent building the mathematical model he thinks will combat the rapidly growing problem of animal poaching. His approach relies on a mix of satellite imagery, predictive analytics, ranger patrols and drones equipped with infrared cameras to catch thieves in Africa who kill elephants for their ivory tusks and rhinoceroses for their horns.
“The mathematics will tell you where there is a 90 percent chance of a poaching incident between 6:30 and 8:00 at night,” says Snitch, who licenses his software for free to wildlife conservation groups.
His model relies on more than just a few data points. Snitch and his team compiled topographical data from satellite imagery to determine where elephants and rhinos are unlikely to roam. “Now, instead of an area the size of New Jersey, we’re looking at an area the size of Atlantic City and 30 miles around it,” says Snitch. Next he layered on years of historical data captured from tagged elephants and rhinos to learn about their movements over time. Then he gathered facts from poaching reports that included the location and the time of the attack. He studied the weather conditions on those days: How fast was the wind blowing? Did it rain? What was the phase of the moon? He calculated distances from a slain animal to the nearest villages.
By looking at the data “you suddenly start to see very distinctive patterns of how these activities occur,” says Snitch. Most poaching incidents, for example, occur within five days of a full moon. Killings after 11 p.m.? He’s never heard of any.
Likewise, Pagano is optimistic that the knowledge gleaned from GPS collars will one day help him forecast with precision how melting ice caps are impacting the roughly 20,000 to 25,000 polar bears that experts believe remain in the Arctic. For now, though, Pagano’s challenge is more fundamental: to figure out if bears traveling longer distances are moving on their own—and if so, whether they’re swimming or walking—or simply hitching a ride on a slab of sea ice.
“Sea ice moves around a lot, so we can have a GPS location in one point and then a GPS location at another point but no way of knowing if the bear moved at all on its own,” says Pagano, who notes that satellites can’t detect polar bears against a white backdrop.
Remember that the next time your Internet connection’s on the fritz.
Illustrations by Always With Honor.