Capitalising on the Big Data era: establishing a multi-source monitoring framework for England's natural capital assets and flows

Sustainable use and management of the Earth's natural resources is a key target for nations that have ratified the Convention on Biological Diversity and aim to meet the United Nations 2030 sustainable development goals. It is also increasingly recognised as critical for sustainable business practices. Governments and the private sector therefore need to understand what elements of nature are currently present on the planet; what benefits they provide for people; and how they are being affected by human activity and the current global environmental change crisis. This knowledge is vital to generate and implement effective environmental policies and mitigation strategies, and ultimately support sustainable development. From satellite remote sensing to camera traps and observations crowd-sourced from amateur naturalists, recent technological advances have revolutionised our ability to monitor biodiversity and are now providing biologists with a lot of new information. In addition, developments in open source platforms and automated data processes are allowing vast amount of data to be analysed in relatively short time frames. Theoretical work on the valuation of nature has also progressed rapidly over the past decade, which has led to a broad adoption of the natural capital approach by businesses and policy makers. These developments make it possible to assess and report on natural capital assets and flows on an annual basis to national bodies, providing that the various source of information available can be brought together and synthetized efficiently. So far, however, this idea hasn't been tested at large spatial scales. To fill this gap in knowledge, this project aims to demonstrate how large and diverse biodiversity datasets could be effectively combined to regularly assess the distribution and quality of natural capital assets and flows. The project will focus on England, taking advantage of the unprecedented biodiversity data that is available. While testing a number of hypotheses relating to the factors driving changes in assets and flows and the cost-effectiveness of various approaches, the work will develop a series of data flows and processes that will illustrate how automated processes can be developed to efficiently support evaluation exercises and decision making at national scales.