“With every type of enforcement, the most urgent task is figuring out where the bad guys are,” Allen said. “You might have the best people and equipment money can buy, but unless you know where to direct your response, you’re essentially powerless. The intel this system provides will help conservancies use their limited resources much more effectively.”
Early Results and Next Steps
It will take two to five years to get real measurements from conservationists, but the feedback for DAS has been promising already. Though no animals have yet been saved as a direct result of DAS, rangers in two separate cases were able to use DAS alerts to intercept poachers who had already made a kill.
Poachers are not the only significant threat to wildlife in Africa. The system has helped rangers in Kenya prevent human-wildlife conflict by spelling out which farms’ cattle are most likely to roam into conservation areas. By working with locals to rein in the livestock, rangers can prevent retaliatory killings by farmers that happen when, say, a lion preys on that wandering cow.
Bailes’s Singita is just one glowing report card of several. “The area managers we’ve been working with feel that this is a game-changer,” Schmitt said. “They know they’ve needed something like this. And they’re pretty no-nonsense—they’ll kick something out quickly if it’s not useful.”
What’s been surprising to the Vulcan team is how DAS has unified conservationists who had previously been focused only on their individual reserves. “All of these groups are now sharing best practices with each other, more now than they ever had been. They’d all been doing the best they can in their regions, but stepping back and exchanging information has proved to have tremendous value in itself,” said Schmitt.
The next hurdle is bringing connectivity to places that still don’t have it, such as the jungles and forests of Congo. Enhancing connectivity where it exists but is low will also be key; it’s what will allow DAS to show alerts in real-time (rather than on a delay). Then, Schmitt said, comes the exciting part: “Once you have more and better data, you get to this place where we have real expertise. Where you can ask, ‘How do you analyze data and call up patterns and proactively identify threats?’” If Big Data is step one, machine learning is the very big step two.
For his part, Allen is happy to let his team run wild. “I’ve spent time with these park rangers, so I’m familiar with how difficult their work is. Providing this kind of tool to help them defend endangered species is incredibly fulfilling.”
This article was provided by Bloomberg News.