DECIDE: Delivering Enhanced Biodiversity Information with Adaptive Citizen Science and Intelligent Digital Engagements, 2020-2023

Dyke, Alison and Pateman, Rachel (2024). DECIDE: Delivering Enhanced Biodiversity Information with Adaptive Citizen Science and Intelligent Digital Engagements, 2020-2023. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-856857

Biodiversity is under increasing pressure, with consequent impacts on the benefits people gain from nature. This means that it is vital to include biodiversity in our decision-making and for this we need high quality, fine-resolution, spatial biodiversity information. With this information we can better value nature, and this can be done formally through a process called 'natural capital' assessment, such as by government agencies or local economic partnerships. We also need this information to develop better plans for protecting nature, undertaking ecological restoration to develop resilient ecological networks, and make good decisions about infrastructure development (to achieve net biodiversity gain, as is the ambition in Defra's 25 Year Environment Plan). Much of our existing biodiversity information comes from volunteer-collected species records (a process often called 'citizen science'). However, in many cases, people record where and when they want - leading to large spatial unevenness in recording, both at a national scale and at a local scale. The people and organisations who need to use biodiversity information don't simply require more records: they require better information. This requires us to construct good biodiversity models generated from the available data, communicate these models well, and preferentially target effort to add records from times and places that optimally improve the model outputs. This project seeks to achieve all of this by addressing three important questions. Firstly, can we enhance existing biodiversity information through near real-time, fine resolution, species distribution models? Secondly, can we make biodiversity information more accessible and useful to end users through data flows and automated data communication? Thirdly, can we encourage adaptive sampling behaviour in recorders, by using intelligent digital engagements, so that they re-deploy a portion of their effort to optimally improve biodiversity models? Our team is expertly placed to address these questions because we are a multidisciplinary team (environmental, computer, social and data scientists), and we will use a service design approach that actively engages data users (from national to local levels) and biodiversity recorders alongside the research team. In this project we will produce fine-resolution distribution models for about 1000 insect species across the UK (in this study focusing on butterflies, moths and grasshoppers) using earth observation sensor data, and a data lab (an online analysis platform) to automatically update outputs as new data are available. It is important to communicate these results and their uncertainty so, in collaboration, with data end users we will develop interactive and automatically-generated visualisations and text to do this effectively. We will also develop ways of assessing when and where new data will be most valuable in improving the model outputs. This, when combined with constraints (such as land access or people's recording preferences) will be communicated to recorders as bespoke recommendations via a web app. This will be developed for recording butterflies and grasshoppers (a sunny day activity), and recording moths (supported by our provision of portable, low cost light traps). We will engage recorders through established recording projects across the UK, including with partners in London (many people, but relatively few biodiversity data) and North and East Yorkshire (fewer people, and a wide variety of land uses). Throughout this project our work flows will be implemented in an data lab, so they will be flexible for use with any species and indeed could be adapted for any environmental data. The outcome of this project will be a process for enhancing biodiversity information that can be incorporated into existing recording projects and data streams, so that the outputs will be accessible and useful, for the benefit of nature and people.

Data description (abstract)

This collection comprises two sets of transcripts of semi structured interviews, one with biological recorders (identified with ‘P’ codes) and the other with end users of biodiversity data collected by biological recorders (Identified with ‘DEU ‘codes). These interviews were recorded in 2021 as a part of the DECIDE project. The biological recorder interviews were collected to understand the motivations and practices of biological recorders with the intention of finding points of leverage where behaviours might be modified to fill gaps in existing data. The end user interviews were designed to understand attitudes to modelled biodiversity data and how this might be used in the work of the interviewees. This information was used to develop use cases for modelled data and to produce data products to fit those use cases. The interviewees were drawn from two areas of the UK, however this information is redacted in order to protect the anonymity of the participants. The DEU interview set contains 33 interviews. The biological recorder interview set contains 32 interviews. Interview topic guides are included alongside the interview transcripts, together with a document showing the participant types in the DEU interviews.
Interviews lasted approximately 0.5-1.5 hours.

Data creators: