Artificial Intelligence and Crowdsourced Social Media Data for Biodiversity Monitoring and Conservation

Environmental resilience is intrinsically tied to the conservation and promotion of biodiversity at multiple scales, spanning from local ecosystems to the global biosphere. Biodiversity assumes a pivotal role in the capacity of ecosystems to endure and recuperate from diverse perturbations. Human-induced stressors are causing unprecedented losses to biodiversity. Preventing and reversing the global biodiversity crisis necessitates targeted conservation endeavors, yet monitoring efforts are expensive, and conservation resources are limited. This lack of information on biodiversity statuses and trends may obscure population declines and potential extinctions. As a result, there is a pressing need for cost-effective and scalable solutions to monitor biodiversity. Here, we carried out a systematic literature review focusing on the use of artificial intelligence (AI) methods to assess social media data for biodiversity and conservation, identifying 32 articles. Our review focused on capturing which AI approaches were used, and where relevant how studies used multiple AI methodologies for a multimodal approach. Our results highlight significant recent developments in computer vision, natural language programming, and spatial analysis, and discuss their exciting applications to big data from social media for biodiversity monitoring, which hitherto have been underexplored. Social media uniquely allows for multimodal analysis offering a rich understanding of conservation issues by combining multiple data types, such as audio, video, and text. Compared to previous ecological research harnessing AI and social media, a multimodal approach offers additional insight relevant to biodiversity monitoring, including tracking the changes in timing and distribution patterns of biodiversity events and identifying areas affected by invasive species. By harnessing the capabilities of computer vision, natural language processing, and spatial–temporal analysis, we can unlock valuable insights from social media posts and guide conservation strategies for enhancing environmental resilience in an efficient and scalable manner.