AI's Role in Wildlife Conservation and Restoration Efforts
The field of conservation biology is changing as fast as the climate. Artificial Intelligence is leading the charge. Using AI in conservation efforts is a net positive in the fight to protect the world’s wildlife.
By Jessica Miles
November 8, 2020

If I asked you to imagine what Artificial Intelligence (AI) looked like, how would you respond?

Maybe you’d think of Arnold Schwarzenegger and the Terminator. Robo Cop is another possibility.

There’s also Netflix’s new documentary, The Social Dilemma to consider.

 Hopefully, at least one person would tell me that the answer to life, the universe, and everything is 42. (Of course, that person would be right!)

The potential for AI in conservation efforts is so much broader. While science fiction and pop culture have been obsessed with the idea of a humanity destroying computers for generations, humanity is currently in the middle of a sixth extinction event that poses an existential threat to our future without needing our refrigerator to become self-aware. Although the sci-fi genre focuses on potential futures, what seemed fantastical forty years ago, may appear commonplace today. Applying AI to conservation biology is not only possible, but has a huge beneficial potential. There are two key reasons why I am optimistic about AI’s ability to aid wildlife conservation efforts.


The Ability to Stop Poaching

Despite wildlife conservationists’ best efforts, poaching remains a huge problem, particularly in sub-Saharan Africa. Poaching poses a direct threat to both wildlife and people. According to the National Geographic, 100,000 African elephants were killed between 2014 and 2017 for their ivory; and nearly 600 rangers were killed by poachers between 2009 and 2016.

Conservationists, researchers, and rangers can’t fight what they can’t see. Poachers have had the upper hand over outgunned and outmanned wildlife rangers for a long time. In the past, proposed solutions have included educational and economic efforts such as providing locals sustainable and fiscally equivalent alternatives. While effective, these solutions take both time and resources. That is time that wildlife populations don’t have. Further still, there is no guarantee that those struggling to survive will be receptive to a new and uncertain lifestyle change.

Without a shakeup of the conservation movement, it will be too late. Desperation breeds innovation, and right now, things are looking pretty grim for wildlife.

We live in an uber-connected world where everything we do, and everywhere we go, are tracked remotely. Phones are able to intelligently recognize individual human faces. Is it so crazy then, to imagine a world where that same technology is used to benefit wildlife?

Wildlife.ai is a charitable trust based in New Zealand, whose goal is to help communities, wildlife managers, and researchers use AI to maximize their conservation efforts. However, this doesn’t mean using AI indiscriminately or without oversight. In fact, Wildlife.ai advocates for the use of AI in wildlife conservation in an ethical and transparent manner.

Cambodia’s tiger population has been eradicated as a result of poaching, and its leopard population is on the verge of extinction. The Protection Assistant for Wildlife Security (PAWS) predictive AI software aids Cambodian rangers. PAWS uses mathematical modeling and game theory to intelligently randomize rangers’ schedules, effectively keeping poachers off balance. In addition, the PAWS software created color coded maps to highlight areas with the highest probability of poaching. From mid-December to the end of January, Cambodian rangers discovered twice as many snares as normal. In Uganda, using PAWS over the course of five months allowed rangers to triple the number of discovered snares.

The opportunities to utilize AI for species conservation are numerous. For example, Wildlife.ai enumerates multiple positive case studies on their website, from using AI and aerial drone mapping to identify poachers on the ground, to control invasive species, and to predict the conservation status of various plants.

Updating Conservation Efforts  

Camera traps are a popular method used by conservation biologist to capture and track wildlife population and movement. Currently, it can take up to a year for a camera trap photo to be properly annotated by volunteers (Deep Mind). This limits the flexibility and dynamism of conservation biologists, prohibiting them from reacting adaptively to changes in populations or population dynamics. Handing the task to AI and machine learning opens the field of conservation biology to a world of possibilities.

By working collaboratively with wildlife conservationists, Deep Mind has created a method for annotating wildlife photos that performs on par with or better than human annotators. Additionally, it shortens the processing time by up to 9 months. Wide adoption of a similar AI methodology has radical implications.

And Deep Mind isn’t the only one working on the issue. Wildbook, is a software program capable of identifying individual animals by their unique coat pattern or other identifying features. Thanks to AI, researchers are able to gather population information over the course of a weekend. Wildbook already has databases for twenty species, but its latest innovation is an intelligent bot that combs through YouTube for whale shark videos uploaded by tourists and divers. The bot is able to identify the unique signatures of individual animals as well as the date and location of their sighting before uploading the information to a larger database. Speed is the name of the game and in May 2017 alone, the bot identified 1,900 whale shark photos. Utilizing smart technology like Wildbook gives researchers access to a wealth of otherwise missing data. Consequently, conservation biologists can make better predictions about species population numbers.

Citizen scientists are also a huge part of effective AI use in conservation. Smartphone cameras can be data collection gamechangers, and platforms like Wildbook can make the process accessible. Both AI and citizen scientists can collect data from places researchers otherwise can’t.


The Bottom Line

Reason and logic underscore the point that there isn’t one cure-all for the crisis we face. Yet for all our handwringing, current efforts are either not enough or will bear fruition too late. It is therefore worth entertaining the notion that perhaps AI is not the means to our damnation (as is so readily assumed). Instead, perhaps, Artificial Intelligence is a key to our salvation.


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