BY Mathias Tobler, Ph.D.
Director of Population Sustainability for San Diego Zoo Wildlife Alliance
Data’s Role in Wildlife Conservation
Artificial intelligence (AI) and machine learning have emerged as powerful tools for wildlife research, with the potential to transform the way we study and understand wildlife, providing new insights into animal behavior, habitat use, and population dynamics. The San Diego Zoo Wildlife Alliance (SDZWA) Conservation Technology Lab has been working on training and implementing AI algorithms to support our conservation work around the world.
One of the main applications of AI and machine learning in wildlife research is the analysis of large data sets. Wildlife ecologists collect vast amounts of data on animal behavior, habitat use, and population dynamics using a range of tools, including remote cameras, satellite tracking, and acoustic monitoring devices. Traditionally, analyzing these data sets has been a time-consuming and labor-intensive process, requiring researchers to manually review and categorize thousands of images, track wildlife movements over time, and identify individual animals from acoustic recordings. With the help of AI algorithms, however, this process can be automated, significantly reducing the time and effort required to analyze these data sets.
For example, machine learning algorithms can be used to automatically classify images of wildlife taken by remote cameras, with a high accuracy. A typical 3-month trail camera survey for jaguars in the Peruvian Amazon can produce over 500,000 images and videos that would take researchers several months to classify manually. An AI algorithm trained to identify individual species can process the same data set in less than a day, allowing scientists to focus on answering their research questions or making informed management decisions. When combined with trail cameras that can send images over a cellular network, AI can also be used to create a real-time alert system that can notify rangers to the presence of poachers, or communities to an approaching predator.
AI can not only identify species, but also individual animals. Scientists use fur patterns or other distinctive features to identify wildlife in photographs. However, these methods can be time-consuming and unreliable. With AI, researchers can use algorithms to automatically match patterns across large data sets. This has been particularly useful in studies of species with unique patterns such as giraffe, leopards, and tigers, but new methods based on facial features are being developed for species such as Andean bears and mountain lions, where traditional methods of identification are often impractical.
Behavior analysis is another area where AI can greatly improve our ability to answer novel questions. Scientists can use algorithms to analyze video footage or sensor data to identify patterns in wildlife behavior. For example, we use machine learning in combination with accelerometers to study the foraging behavior and energy use of polar bears, or the social behavior of elephants. This technology can provide insights into the behavior of animals that are difficult to observe directly, helping researchers to better understand their ecological roles and interactions.
AI and machine learning have the potential to revolutionize wildlife research, providing new insights into animal behavior, habitat use, and population dynamics. By automating the analysis of large data sets and developing predictive models, these technologies can help guide conservation efforts and inform management decisions. Population monitoring using AI can help identify areas where conservation efforts are most needed and where interventions will have the greatest impact, and behavioral studies can show how wildlife are affected by human activities or how their energy expenditures change in marginal habitats. As new developments in AI will change our personal lives, we are harnessing its power to increase our conservation impact around the world.
3 Reasons to “Log On”
Technology gives conservation a cyber-boost in numerous ways, including:
1. Wildlife “Selfies”
Machine learning algorithms can help classify trail camera images accurately—and at astonishing speed. This gives scientists precious time to complete other tasks.
2. Crime Watch
AI can be used to create a real-time alert system that notifies rangers of poaching activity in the area.
3. Keeping Track
This technology can give scientists a perspective on wildlife behavior that could be difficult or intrusive to observe directly; helping them better understand their ecological roles and interactions.
(Top illustration by AntonioKHR, IStock, Getty Images)