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An introduction to model-based data integration for biodiversity assessments 2022


Model-based data integration is a statistical framework to combine the analysis of data from multiple sources to create a firmer evidence base on which to base decisions. Biodiversity data are usually fragmented in multiple datasets collected using a variety of different methods, which are difficult to combine without loss of information and which differ in their potential bias. Model-based data integration provides a solution to make the most of these multiple sources of data to produce robust metrics of biodiversity change.

Model-based data integration has a number of analytical advantages, including increasing the quantity of data available to be included in analysis, deriving more precise metrics, extending the spatial and temporal extent of inference, and better correcting for biases in the data. By using model-based data integration we can make better inferences at smaller spatial scales and produce trends for scarce species. Data integration also creates a shared evidence base amongst conservation stakeholders, informs more efficient and flexible monitoring and can lead to a more diverse and inclusive recording community.

There continue to be challenges and questions about best practices for model-based data integration, and implementation still requires considerable technical skills and statistical knowledge. However, as the availability of novel data sources grows, model-based data integration will become more widespread.

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Resource type Publication

Topic category Environment

Reference date 2022·05·01

Mancini, F., Boersch-Supan, P.H., Robinson, R.A., Harris, M. and Pocock, M.J.O. 2022. An introduction to model-based data integration for biodiversity assessments. JNCC, Peterborough, ISBN 978-1-86107-639-7.

This work was supported by the Terrestrial Surveillance Development and Analysis partnership of the UK Centre for Ecology & Hydrology (UKCEH), British Trust for Ornithology (BTO) and JNCC, and by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

Responsible organisation
Communications, JNCC publisher

Limitations on public access No limitations

Use constraints Available under the Open Government Licence 3.0

Metadata date 2022·05·06

Metadata point of contact
Communications, JNCC

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