Using ecological theory and data to evaluate and improve industry’s measurement of Biodiversity Net Gain
This PhD project will bring together ecological theory, data modelling, and stakeholder interviews to explore questions surrounding the Biodiversity Net Gain (BNG).
about the project
Growing awareness of the rapid rate of biodiversity loss, and the ultimate impacts on human wellbeing, has resulted in increasing policy commitments that infrastructure development should deliver a Net Gain in biodiversity. Biodiversity Net Gain (BNG) is development that leaves biodiversity in a measurably better state than before by avoiding and minimize impacts, before restoring or offsetting residual biodiversity loss. Major UK companies (including Highways England, the Berkley Group) have made voluntary commitments to BNG. Now the Environment Bill progressing through parliament requires developers in England to deliver a minimum 10% increase in ‘biodiversity units’.
This PhD project will bring together ecological theory, data modelling, and stakeholder interviews to explore:
- The extent to which the DEFRA metric aligns with other measures of biodiversity (e.g. taxonomic diversity, functional diversity and evolutionary distinctiveness)?
- How precisely the metric can detect change?
- How delivery risk and temporal risk can be best incorporated?
- How optimal monitoring theory could evaluate which indicators offer the greatest efficiency?
- Potential win-wins between BNG and other environmental targets such as e.g. flood resilience.
- The relationship between biodiversity units generated by a Net Gain offset and how local stakeholders value offsets.
The project will contribute new understanding on biodiversity measurement as well as actionable information to improve outcomes for nature from infrastructure development.
Applicants should hold a minimum of a UK Honors Degree at 2:1 level or equivalent in Environmental Science, Ecology, Statistics (or a related degree). They should have strong quantitative skills, preferably with knowledge of R software. Applicants with Masters degrees, relevant research experience, or publications will be highly competitive. Interest and willingness to engage with industry is also essential.