How Big Data Helps with Wildlife Conservation?

Big data can be used to help with wildlife conservation in a number of ways. Some potential applications of big data analytics in wildlife conservation include:

1. Habitat mapping: Big data analytics can be used to create detailed maps of animal habitats, allowing conservationists to understand the distribution and movements of different species and to identify areas that are important for conservation efforts.
2. Population monitoring: Big data analytics can be used to track the populations of different species over time, allowing conservationists to monitor population trends and to identify any potential threats to their survival.
3. Environmental monitoring: Big data analytics can be used to monitor environmental factors such as weather patterns, air and water quality, and land use changes, which can all impact the health and survival of wildlife.
4. Predictive modeling: Big data analytics can be used to build predictive models that help conservationists to understand how different factors, such as climate change or habitat loss, may impact wildlife populations in the future.
5. Decision-making: Big data analytics can be used to help conservationists make informed decisions about how to allocate resources and prioritize conservation efforts. For example, they can use data on population trends and habitat quality to identify the most threatened species and the areas where conservation efforts will have the greatest impact.

The methods used to monitor and surveil wildlife currently in use are insufficient and ineffective. It is either physically impossible or difficult to mount them. Thanks to recent advancements in artificial intelligence and big data, conservation initiatives are now significantly more thorough and effective. Along with the advancement of less expensive, more effective, and more potent hardware, efforts to bring AI technology to the furthest reaches of the natural world have increased significantly.

The Protection Assistant for Wildlife Security has also used regional information and historical poaching data to predict future poacher behaviour. By combining these tools with the Spatial Monitoring and Reporting Tool (SMART), it may be possible to map wildlife with such accuracy that PAWS will be better able to anticipate additional environmental crimes like illicit fishing and logging.