AI for Wildlife Conservation: The Future of Conservation Efforts
In recent years, the use of artificial intelligence (AI) has been making strides in different areas of life. With its promising ability to make complex tasks easier and more efficient, AI has begun to show some remarkable result when it comes to wildlife conservation. In the field of conservation, AI has the potential to revolutionize how we manage and safeguard endangered species. From tracking specific species to developing models that inform conservation decisions, AI has an exciting future in the wildlife conservation space.
Artificial intelligence can be described as a branch of computer science that deals with building computer systems that can perform tasks that normally require human intelligence, such as learning, reasoning, problem-solving, and decision making. With regard to wildlife conservation, AI has the potential to revolutionize the field by increasing efficiency, accuracy, and speed in acquiring and analyzing data. In the following sections, we will explore three main areas where AI is being used for wildlife conservation: monitoring, prediction, and decision-making.
Monitoring
One of the primary applications of AI in wildlife conservation is in monitoring different species. This involves using machine learning models that can be trained to detect and identify certain species or behaviors through images or audio data. AI can be particularly helpful in this area because it can analyze vast amounts of data faster and more accurately than humans can. This makes it easier to track and manage wildlife populations with greater efficiency and effectiveness.
For example, using AI-powered cameras to monitor wildlife, conservationists can monitor the activities and movements of various animals. It also makes it easier to identify new species, monitor migration patterns, and track changes in wildlife population numbers and activity. Machine learning models can help conservationists in tracking endangered species such as tigers, elephants, birds of prey, and many more by identifying their scat, fur, and audio calls.
Prediction
Another application of AI in wildlife conservation is in predicting the patterns that animals follow. For example, AI tools can analyze data to understand the changes in weather conditions, human intervention, or natural disasters that may have affected the natural habitat of different species. This allows conservationists to predict future changes and adapt their conservation plans accordingly.
AI can also help detect many environmental factors that affect wildlife such as water availability, temperature, and food supplies. With this information, conservationists can identify the consequences these environmental factors will have on wildlife populations; this information can then be used to craft conservation policies and strategies that are more effective. AI tools can also help project into the future, anticipating the future configuration of various environments. For example, it is critical to know whether a region a certain species lives in will be cleared for development in the coming years. AI models can analyze data to predict a range of potential scenarios, allowing conservationists to plan for the future and be proactive about any potential threats.
Decision-Making
The final application of AI in wildlife conservation is in support of conservation decision-making. AI tools can help decision-makers to analyze data and build models that help create the best possible policies and strategies that are most beneficial to the wildlife populations. By utilizing AI-driven insights, conservationists can make well-informed decisions about how to deploy resources optimally and in a way that is most effective.
AI has already made an impact on conservation decision-making by making it much easier to calculate and analyze a vast array of data. For example, AI can analyze data about the location of a specific species, the habitats they depend on, the demographics of the population, and additional details that inform the best way to conserve that species in a particular area. With AI insights, conservationists can then develop policies that make sense, both in terms of being effective and practical.
Real-life Example
The use of AI in wildlife conservation is growing in popularity. The nonprofit organization, LookingGlass, is using AI to help combat illegal hunting and poaching in Gabon, Africa. With the help of machine learning models, the organization can detect gunshots from poachers up to half a mile away, thus helping anti-poaching teams respond quickly. The new technology is critical as poachers in Africa have become increasingly high-tech, using night vision goggles and other advanced equipment to evade detection. Thus fighting against poaching is now more challenging, and AI-powered technology can provide valuable assistance.
LookingGlass has installed sensors throughout the protected areas of the park, and these sensors detect the sound of gunshots. When the sensors detect a gunshot, the data is analyzed by an AI algorithm that determines the location of the bullet impact, allowing it to identify areas that require immediate attention. With this timely information, rangers can quickly pinpoint poachers’ location and take immediate action accordingly. As a result, the system has effectively reduced and deterred poaching in the area.
Conclusion
AI is an evolving technology that has the potential to transform how conservationists safeguard wildlife. From monitoring to predicting and decision-making, the applications of AI in conservation have already started making an impact, augmenting the effectiveness of data collection and analysis, allowing conservationists to make well-informed decisions. The future holds vast technological advancements, and AI is at the forefront of that change. By embracing and implementing AI, conservationists can work more efficiently and effectively to save some of our world’s most precious species. AI implementation should be supported and promoted to ensure the planet and our future generations’ sustainability.