Making the most of machine learning to conserve botanical biodiversity
0Approximately 350,000 species of plants are known to science, and around 2 in 5 are thought to be threatened. However, comprehensive assessment and monitoring of the extinction risk of all plant species is an impossible task; currently, just 1 in 6 have global conservation assessments on the International Union for Conservation of Nature (IUCN) Red List. \n\nWhile accurately and reliably automating individual species’ assessments remains an outstanding challenge, we can leverage machine learning techniques to predict which species are likely to be threatened, using their known characteristics (e.g. lifeform, geographic distribution and evolutionary history). By combining a Bayesian statistical approach with the machine learning classifier, we can also obtain robust uncertainty estimates for our species-level extinction risk estimates, making these predictions invaluable for conservation prioritisation and use in future research. \n\nWe can also use unsupervised learning to tease out hidden patterns in global biodiversity: in another recent work, we identified human-induced changes in global floristic bioregions (‘phytoregions’) by using a community network algorithm, Infomap, to analyse supergraphs representing the geographic distributions of all plant species. \n\nIn this AI for Good session, we will describe the challenges and opportunities for using machine learning in plant-focused conservation science and present the results of recent work by the Conservation Assessment and Analysis team at the Royal Botanic Gardens, Kew (United Kingdom).\n\nSpeakers:\nMatilda Brown\nConservation Science Analyst\nRoyal Botanic Gardens\nModerator(s):\n\nMike Gill\nDirector\nNatureServe’s Biodiversity Indicators Program \n\nJoin the Neural Network!\nhttps://aiforgood.itu.int/neural-network/\nThe AI for Good networking community platform powered by AI. \nDesigned to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\n\n Watch the latest #AIforGood videos!\n\n\n Stay updated and join our weekly AI for Good newsletter:\nhttp://eepurl.com/gI2kJ5\n\nCheck out the latest AI for Good news:\nhttps://aiforgood.itu.int/newsroom/\n\nExplore the AI for Good blog:\nhttps://aiforgood.itu.int/ai-for-good-blog/\n\n Connect on our social media:\nWebsite: https://aiforgood.itu.int/\nTwitter: https://twitter.com/AIforGood\nLinkedIn Page: https://www.linkedin.com/company/26511907 \nLinkedIn Group: https://www.linkedin.com/groups/8567748 \nInstagram: https://www.instagram.com/aiforgood \nFacebook: https://www.facebook.com/AIforGood\n\nWhat is AI for Good?\nWe have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.\nMore than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.\nAI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.\n\nDisclaimer:\nThe views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.