CBMA Conferences

INTRODUCING CONSERVATION CULTUROMICS

Ricardo Correia – Helsinki Lab for Interdisciplinary Conservation Science (HELICS)

11 Nov | 14:00 | IB-S ou Online

Abstract

The ongoing loss of biological diversity is primarily the result of unsustainable human behaviour, so the long-term success of biodiversity conservation efforts depends on a thorough understanding of human-nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, we live the Information Age where many aspects of our daily lives, including interactions with nature, are continuously being recorded. The emerging field of conservation culturomics aims to take advantage of digital data and methods to study human-nature interactions, providing new tools for studying conservation-related topics at relevant temporal and spatial scales and improve the sustainability of our interactions with nature. In this presentation, I will introduce conservation culturomics and highlight how this emerging research area can contribute to biodiversity conservation. I will present recent work on developing a conservation culturomics research framework, outline the main characteristics of relevant data and methods, and examples of their application. I also highlight challenges associated with culturomics research, including issues of interdisciplinarity, ethics, data biases and validation, and actions needed to increase the relevance and impact of conservation culturomics research.

BIO

Ricardo Correia is a conservation scientist working at the Helsinki Lab for Interdisciplinary Conservation Science (HELICS), part of the Department of Geosciences and Geography of the University of Helsinki, Finland. His research aims to explore how the digital revolution can contribute towards biodiversity conservation science and practice. He is particularly interested in understanding how new digital data sources (e.g. social media platforms, search engines) and analytical methods (e.g. machine learning, natural language processing) can be used to generate novel insights on the relationship between humans and nature to inform conservation action and policy.