From Data Deficient to Big Data in Shark Conservation

Abstract

Citizen science is increasingly harnessed worldwide to gather data otherwise requiring a prohibitive investment of funding and time. Meanwhile, the revolution in digital communication offers opportunities from crowdsourcing, big data approaches and social network mining to quickly and cost-effectively fill major gaps in knowledge necessary to protect endangered populations. Sharks are among the most endangered and data-poor vertebrates in the ocean. Mainly due to overfishing, many shark populations are declining worldwide, while most species lack basic abundance, distribution and life-history data. Hence, filling knowledge gaps across taxa, ecosystems, and regions is urgently needed to increase our understanding of their ecology, develop effective conservation actions and reverse their loss. Here, we introduce a novel citizen science and crowdsourcing approach for conservation through sharkPulse, a new platform automating data ingestion and organisation to build the largest database of shark occurrence records to date. Designed to complement and extend similar biodiversity monitoring tools relying heavily on user submissions, sharkPulse aims to source large streams of online shark images and transform them into occurrence records, filling knowledge gaps in shark ecology and biology. This platform offers a blueprint to leverage AI and big data approaches, social network data mining and participatory science to efficiently and continuously source visual media materials and transform the monitoring of data-limited marine and terrestrial animal populations.