A Review on Detection of Species Extinction Risk Through Artificial Intelligence

Abstract

Species extinction is a major concern for sustenance of ecosystem and its conservation.Species are exposed to different levels of threats due to over exploitation, habitat loss, climate change, introduction of invasive species, genetic assimilation and pollution. These challenges ultimately lead to complete disappearance of species from the face of the Earth, known as the extinction of species. With the advent of artificial intelligence, interest in stimulated in assessment of risk to species. These risks or threats responsible for species extinction can now be detected by automated analysis of vast and complex ecological datasets. This helps in real time monitoring and tracking of endangered animals. Computer vision, machine learning, and acoustic sensors are employed in detection of species extinction risk. Artificial intelligence process camera trap footages, images and audios for tracking population of species, identification of threats such as poaching and prediction of habitat loss. AI algorithms are able to detect, in real time, hunting activities, forests fires and distress in animals., requiring immediate intervention. Machine learning models help in predicting species extinction risk by analyzing ecosystem interactions and helping in preventing extinction cascades. AI models are able to connect field data with satellite imagery for generating maps indicating likelihood of habitat availability for endangered species. Automated Wildlife Monitoring Platforms such as Conservation AI and Wildlife Insights are employing models such as SpeciesNet for analyzing millions of images and videos from drone and camera-trap footages. This automation can identify species with an accuracy of around 99%. AI tools such as Perch analyze soundscapes for identification of rare species and can monitor health of biodiversity through bird calls.