Conservation

Surveying and Monitoring

Surveys of wildlife populations are a critical first step for informing policy makers, national strategies, and conservation programs. While critically important, existing survey methods are costly and manual. There is both a need and an opportunity to revolutionize wildlife surveys to better inform wildlife policy and management.
Delivering Accurate Counts
We’re working to improve how scientists survey and monitor wildlife to help conservation organizations and governments collaborate and prioritize protection efforts. We help our partners perform accurate wildlife surveys by using new scientific methods, technologies, and machine learning to provide high quality data and analysis that leads to accurate counts.

A Data-Driven Approach to Protecting Wildlife

Before action can be taken to protect endangered species, it’s critical to have accurate population counts, which are often missing, incomplete, or too expensive to procure. We aim to improve counting methods and make high-quality, less expensive data available in a timely manner to drive better-informed policy and scientific decisions.

Great Elephant Census

In 2013 we conducted the Great Elephant Census, which brought together more than 90 scientists, six NGOs, and 18 countries using standardized data collection and validation methods to calculate the number of African savannah elephants remaining. This is crucial, because having accurate and reliable data is invaluable when it comes to enacting timely, protective actions and long-term, conservation-management plans with governments and NGOs. The Great Elephant Census identified a 30% decrease in savannah elephant populations, prompting immediate action from across the globe.

Partners
Save the Elephants, Elephants Without Borders, African Parks, Frankfurt Zoological Society, IUCN Elephant Specialist Group, The Nature Conservancy, Wildlife Conservation Society, TAWIRI: Tanzanian Wildlife Research Institute

Great Elephant Census: Forest Initiative

To build on the success of The Great Elephant Census, which focused on savannah elephants, our Great Elephant Census Forest Initiative is working to produce better knowledge of forest elephants. We're collaborating with our partners to use both traditional and new state-of-the-art scientific methods for counting forest elephants in Gabon. In addition to providing updated information about the status of forest elephants, the new methods are expected to make future surveys more accurate.

Partners
Wildlife Conservation SocietyAgence Nationale des Parcs Nationaux (ANPN)

Global FinPrint

Together with our partners, we’re creating the world’s first definitive, global survey data of shark and ray density and diversity in various coral reef ecosystems. Global FinPrint also provides a critical baseline for conservation and protection efforts in more than 400 coral reef locations across four regions.*

Partners
Florida International University, the Australian Institute of Marine Science, James Cook University, Curtin University, Dalhousie University

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"Naledi: A Baby Elephant's Tale"

“Naledi: A Baby Elephant's Tale” is a Vulcan Productions film that tells the true story of a baby elephant born into a rescue camp in the Botswana wilderness. Naledi’s fight for survival unfolds against the backdrop of the survey work being completed by the Great Elephant Census team as they fight for the survival of all elephants. “Naledi” was awarded Outstanding Nature Documentary at The News and Documentary Emmy Awards. 

Partners
Off the Fence, National Wildlife Federation

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Improving Animal Survey Methods

There is an opportunity to revolutionize wildlife surveys so they are better suited to inform wildlife policy and management. We aim to make wildlife surveys less expensive and improve the quality and accuracy of the results by using technologies such as image recognition and artificial intelligence.

Advancing Methods For Surveying Animals

We focus on advancing methods for animal surveys to help NGOs and officials in protected areas perform surveys more often – and to have greater confidence in the data they generate. We do this by augmenting laborious, human-dependent actions with automated technologies that use machine learning to do things faster, more accurately, and more efficiently. 

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