Artificial Intelligence in Biodiversity Conservation: Bridging Traditional and Contemporary Method

Gaps persist between existing knowledge and what is needed to tackle the biodiversity crisis. While preserving biodiversity is essential for ecological balance, traditional conservation methods face limitations, particularly in scalability and access to current data. This underscores the need for more robust forecasting tools to better understand ecological dynamics and inform effective strategies. With advancements in technology, artificial intelligence (AI) offers significant potential to bridge these gaps, though the areas of greatest impact remain uncertain. Recent advances in artificial intelligence (AI) hold transformative potential for modern conservation. Increasingly, AI tools such as machine learning and data analytics are being applied to species identification, habitat monitoring, and threat assessment with high precision. While most applications to date focus on tracking wildlife and modelling species distributions, AI is rapidly expanding into areas like phylogenetic analysis. AI processes images with greater consistency and efficiency than manual methods. It supports ecologists in monitoring air quality, assessing ecosystem changes, and tracking species distributions. Beyond enabling large-scale data collection and analysis, AI is advancing predictive modeling, offering transformative potential for ecology and environmental sciences—much like statistics reshaped these fields in the twentieth century. Its greatest untapped potential lies in addressing complex ecological questions that require integrating diverse data types images, video, text, audio, and DNA. Such advances could significantly expand biodiversity knowledge, from genes to ecosystems, and may prove critical for achieving the 2030 Global Biodiversity Framework targets.