Digital Conservation: Novel methods and online data to address the biodiversity crisis

The world is facing an unprecedented global biodiversity crisis. An estimated 25% of known plant and animal species are threatened with extinction. The dominant driver is human activity: Our impact on non-human nature has been continuously accelerating over the course of the last century.
One of the most substantial impacts on biodiversity is unsustainable use, including the hunting or trapping of animals for consumption, for medicinal purposes, or for keeping them as pets. A tradition reaching back thousands of years, the trade in wildlife has recently taken on dimensions that render large parts of it unsustainable and a threat to the survival of affected species and to biodiversity as a whole. The demand for rhino horn and elephant ivory for medicinal and aesthetic purposes drives poaching in countries where these species occur. An exotic pet market, in which wild-caught animals are popular, puts pressure on wild populations of, e.g., reptiles, songbirds and small primates. We need to understand better why people use wildlife unsustainably to find solutions that can help address the biodiversity crisis. However, this type of research is cumbersome and expensive.
Meanwhile, digital platforms, such as social media, online news, and e-commerce, have become rich and cost-effective data sources for research. People share their lives online; a part of the shared information concerns their interaction with nature. Conservation culturomics and digital conservation use online data to study this engagement with nature. For instance, access statistics to Wikipedia have been used as a proxy for people’s interest in biodiversity, and geo-located photos posted on Instagram to infer national park visitors’ demographics. Increasingly sophisticated analyses use novel methods from computer vision and natural language processing to make sense of more complex online data, such as text, images, or video.
In this thesis, I explore, develop, and evaluate novel methods and approaches to unlock the previously unused opportunities digital media offer to conservation science. My research resulted in five journal articles that form chapters of this thesis. In the first chapter, I present a comprehensive review of relevant data sources and advanced methods for spatial-temporal, content, and network analyses. The focus of the second chapter is on data privacy issues that arise from handling digital media data in conservation research. Using the European Union’s General Data Privacy Regulations as a benchmark, I develop a framework with practical guidelines to help meet legal and ethical standards when using social media data. The third chapter proposes a workflow using artificial intelligence methods to collect, filter and identify social media data linked to illegal wildlife trade. It can help provide a manageable amount of data, which is cleaned to remove irrelevant content and annotated to identify and quantify image and text content, for further analysis. In the fourth chapter, I detect conservation-related events using the general public’s sentiment on social media and online news. Relative changes in sentiment and post count reliably predict all major events related to rhinoceros conservation in a time series over the study period. For the fifth, final, chapter, I use data on three threatened species from three online sources and employ methods from natural language processing and computer vision to investigate the online songbird trade in Indonesia, its price structure and the spatial characteristics of its supply chain.
The results of my thesis highlight the potential that advanced analysis of digital media can have for conservation science. I introduce new methods and demonstrate the use of diverse data sources. The concepts and the openly-available tools developed in this thesis should guide conservation scientists and practitioners, as well as researchers in other fields, and inspire them to use digital media data in their own work.