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The Delivering Enhanced Biodiversity Information with Adaptive Citizen Science and Intelligent Digital Engagements (DECIDE) project aims to collect new data to improve biodiversity models for decision-making by putting the recorders’ motivations at the heart of the process. Novel ways of communicating and visualizing results to end-users, together with model uncertainties, will also be developed.

Inactive project


Project contact

Alison Dyke /

Butterfly on a plant. Nick Windsor / Pixabay

Butterflies, moths and grasshoppers are the species DECIDE will focus on. Photo: Nick Windsor / Pixabay.

Scientific challenge:

Nature is in long-term decline in the UK, which has detrimental impacts on people and the environment. The UK Government has committed to reverse this trend and Defra’s 25 Year Environment Plan provides a vision for restoration.

To achieve this, high-quality fine-scale, detailed information on biodiversity is required in order to make informed decisions about nature and the benefits it provides us. Through ‘citizen science’, the UK’s community of tens of thousands of volunteer wildlife recorders make irreplaceable, vital contributions to understanding the national state of biodiversity. However, there are gaps in recording, at both national and at local scales.

The people and organizations who need to use biodiversity information don’t simply need more records – they need better and more accessible information. Users of this data, such as planners and policymakers, need biodiversity information for effective decision making, whether for the country, a region or a particular location. Meeting this need requires a move away from basing decisions on species records alone to using comprehensive models showing how species distribution and habitat quality are linked.

Project overview

To improve biodiversity models for decision-making, DECIDE will collect this new data by putting recorders’ motivations at the heart of the process. Focusing initially on butterflies, moths and grasshoppers, this pioneering project aims to map 1000 new species at fine-resolution and to improve these models through the records submitted by volunteer recorders. Recorders will be guided where and when to conduct studies in their region, so that the information they provide can optimally improve the species maps – a process called ‘adaptive sampling’.

Ultimately, this information will contribute to the development of an intuitive system that provides recorders with automated, customized recommendations of when and where to look out for insect species, based on a combination of each person’s particular recording interests and local places where data is most needed.

DECIDE Project Methodology Infographic

DECIDE project methodology infographic. Source: DECIDE.

DECIDE also aims to improve the quality of these biodiversity models by making them more useful for data users. By combining species data with large-scale earth observation data such as land use and weather, it will enable them to make informed decisions based on near real-time outputs at fine scales (about 100m), for the benefit of nature and people.

Co-design is at the core of the DECIDE project. Users of fine-scale species data, from local NGOs to national government, will co-create outputs that are fit for purpose. Meanwhile volunteer naturalists will be working alongside the DECIDE team to create an app that provides real benefits to themselves, and not just to the data users.

Alison Dyke

Research Fellow

SEI York

Rachel Pateman


SEI York

Sarah West

Centre Director

SEI York

Jana Busch

Communications Specialist


SEI York


DECIDE is run by a multidisciplinary team covering ecology, data science, computer science, social science, and data communication, who collaborate with a range of local and regional partners.



Digital Environment Projects

UKRI NERC is supporting a range of funded research projects under the Constructing a Digital Environment programme. The projects, bring together the ‘Digital Environment’ community through multi-disciplinary and interdisciplinary research and innovation to deliver increased benefit from existing and new sensor networks technology and their associated infrastructure. The funded projects are exploring the methodologies and tools for assessing, analyzing, monitoring and forecasting the state of the natural environment at higher spatial and temporal resolutions than previous possible.

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