Beyond Habitats: Leveraging Data for Comprehensive Biodiversity Net Gain (BNG) Assessments
Biodiversity Net Gain (BNG) metrics aim to ensure that development projects leave the environment in a measurably better state. While these metrics provide a strong foundation through their focus on habitats, they often fail to address critical aspects of biodiversity, such as species-specific needs and ecological connectivity. These gaps highlight the importance of integrating diverse data sources into habitat assessments to deliver sustainable and ecologically meaningful outcomes.
This article explores how leveraging a range of data sources can create a more comprehensive and actionable framework for BNG.
The Limitations of Habitat-Only Metrics
Current tools like Natural England’s Biodiversity Metric 4.0 rely heavily on habitats as proxies for biodiversity, offering a baseline for environmental improvements (Panks et al., 2022). However, this approach often overlooks:
Species-Specific Needs: Biodiversity varies based on traits such as dispersal capacities, ecological roles, and life-history strategies, which are not captured by habitat-only metrics (Massol et al., 2017).
Ecological Connectivity: Essential for species movement and gene flow, connectivity is often underrepresented, leading to isolated habitats that are less resilient to change (IUCN, 2020; Kor et al., 2022).
To address these shortcomings, assessments must integrate additional data layers, such as species occurrence records and connectivity indices, to better align with ecological integrity and long-term conservation goals (Wurtzebach & Schultz, 2016; Covington et al., 1997).
Key Data Layers for Comprehensive BNG Assessments
Integrating diverse data sources is critical for creating a holistic framework. The following data layers provide essential insights:
1. Satellite Imagery for Development Monitoring
Satellite data, such as Sentinel-2 and Landsat, enable real-time tracking of land-use changes, ensuring accurate and up-to-date habitat maps. This data helps identify unauthorised developments and habitat loss often overlooked in static assessments (ESA, 2023).
2. Digital Terrain Models (DTM) and Slope Data
High-resolution DTMs provide information on topography, which influences water flow, vegetation patterns, and species movement. These insights are crucial for modelling environmental impacts and designing effective interventions (Ordnance Survey, 2023).
3. Local Planning Authority (LPA) Data
LPAs provide insights into zoning regulations, local conservation priorities, and approved developments. Open-access data portals like data.gov.uk allow planners to incorporate these insights into BNG planning (DEFRA, 2023).
4. Habitat Networks
Ecological connectivity is vital for species survival. Tools like the IUCN’s Ecological Connectivity Index highlight potential corridors to enhance biodiversity (IUCN, 2023). These insights are especially relevant in urban areas where natural connections are limited.
5. Protected and Designated Areas
Mapping protected areas, such as Sites of Special Scientific Interest (SSSIs), Areas of Outstanding Natural Beauty (AONBs), and Special Protection Areas (SPAs), ensures critical ecosystems are prioritised. These datasets are available via platforms like MAGIC (Natural England, 2023).
6. Waterways and Wetlands
Hydrological datasets from the Environment Agency, including floodplain and wetland maps, offer crucial insights into aquatic ecosystems. These habitats, often underrepresented in metrics, support diverse and critical species populations (Environment Agency, 2023).
7. Species Observation Data
Species records from platforms like the National Biodiversity Network (NBN) Atlas and Global Biodiversity Information Facility (GBIF) provide granular data for integrating species-specific considerations into planning (GBIF, 2023; NBN, 2023).
8. Ancient Woodlands and Reserves
Ancient woodlands and nature reserves are irreplaceable ecological assets. Data from organisations like Wildlife Trusts support their inclusion in assessments to protect biodiversity-rich areas (Wildlife Trusts, 2023).
9. OpenStreetMap (OSM) Data
Crowdsourced data from OSM can supplement official sources with up-to-date information on infrastructure, green spaces, and other human-modified features (OpenStreetMap, 2023).
Why Holistic Data Integration Matters
Incorporating these diverse data sources allows planners to:
Better Reflect Reality: Avoid errors caused by outdated habitat maps.
Support Species: Address the unique needs of local wildlife, from amphibians to pollinators.
Enhance Connectivity: Identify and improve ecological networks rather than focusing solely on isolated habitats.
Plan Strategically: Gain insights into long-term landscape changes and pressures.
Furthermore, it allows for more accurate and actionable BNG assessments. For example:
Habitat Condition + Satellite Imagery: Pairing habitat surveys with satellite indices like NDVI helps monitor vegetation health, prioritising areas for restoration (Pettorelli et al., 2014).
Waterways + Species Data: Linking hydrology data with species records identifies key aquatic habitats and areas vulnerable to water pollution or hydrological change (Donati et al., 2022).
Connectivity + Land Use Data: Combining connectivity indices with land-use maps pinpoints bottlenecks in ecological networks, guiding restoration efforts in urban areas (Belote et al, 2022).
Topographic Data + Habitat Networks: Reveals barriers to species movement, enabling targeted interventions (Moilanen et al., 2009).
Protected Areas + Ownership Data: Mapping protected zones alongside ownership data identifies opportunities to expand buffers or create ecological corridors (Hansen et al., 2011).
Conclusion
By moving beyond a habitat-centric perspective, planners can align development projects with broader conservation goals. A comprehensive approach that integrates diverse data sources ensures that BNG assessments deliver meaningful outcomes for habitats, species, and ecosystems alike. This strategy balances ecological preservation with urban and industrial growth, fostering biodiversity that thrives in the face of evolving environmental challenges.
References
Baker, J., Hoskin, R., & Butterworth, T. (2021). Navigating small site metrics for effective biodiversity net gain compliance. Journal of Environmental Management, 287, 112268. https://doi.org/10.1016/j.jenvman.2021.112268
Belote, R. T., Barnett, K., Zeller, K., Brennan, A., & Gage, J. (2022). Examining local and regional ecological connectivity throughout North America. Landscape Ecology, 37(12), 2977-2990. https://doi.org/10.1007/s10980-022-01530-9
Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J., & Milner-Gulland, E. J. (2013). Biodiversity offsets in theory and practice. Oryx, 47(3), 369–380. https://doi.org/10.1017/S003060531200172X
Covington, W. W., Niering, W. A., Starkey, E., & Walker, J. (1997). Ecosystem restoration and management: Scientific principles and concepts. USDA Forest Service. Retrieved from https://www.srs.fs.usda.gov/pubs/misc/misc_covington.pdf
DEFRA. (2023). Data services platform. Retrieved from https://data.gov.uk/
Environment Agency. (2023). Flood Map for Planning. Retrieved from https://flood-map-for-planning.service.gov.uk/
Donati, L., et al. (2022). Integrated frameworks for biodiversity planning: Enhancing connectivity for amphibian species. Ecological Applications, 32(2), e02489. https://doi.org/10.1002/eap.2489
ESA. (2023). Copernicus Sentinel Data. Retrieved from https://www.copernicus.eu/en
GBIF. (2023). Global Biodiversity Information Facility. Retrieved from https://www.gbif.org/
Hansen, A. J., Davis, C. R., Piekielek, N., Gross, J., Theobald, D. M., Goetz, S., Melton, F., & DeFries, R. (2011). Delineating the ecosystems containing protected areas for monitoring and management. BioScience, 61(5), 363-373. https://doi.org/10.1525/bio.2011.61.5.5
IUCN (2020). Review protocol for biodiversity net gain. IUCN. Retrieved from https://portals.iucn.org/library/sites/library/files/documents/2017-033.pdf
IUCN. (2023). Ecological Connectivity Index. Retrieved from https://iucn.org/
Kor, L., O’Hickey, B., Hanson, M., & Coroi, M. (2022). Assessing habitat connectivity in environmental impact assessment: a case-study in the UK context. Impact Assessment and Project Appraisal, 40(6), 495–506. https://doi.org/10.1080/14615517.2022.2128557
Massol, F., Gravel, D., Mouquet, N., Cadotte, M. W., Fukami, T., & Leibold, M. A. (2017). Linking community and ecosystem dynamics through spatial ecology and dispersal. Ecology Letters, 20(4), 385-401. https://doi.org/10.1111/ele.12739
Moilanen, A., Wilson, K. A., & Possingham, H. P. (2009). Spatial Conservation Prioritization: Quantitative Methods and Computational Tools. Oxford University Press.
Natural England. (2023). Biodiversity Metric 4.0 Guidance. Retrieved from http://GOV.UK
NBN. (2023). National Biodiversity Network Atlas. Retrieved from https://nbnatlas.org/
OpenStreetMap. (2023). OpenStreetMap Data. Retrieved from https://www.openstreetmap.org/
Ordnance Survey. (2023). Terrain 5 Dataset. Retrieved from https://www.ordnancesurvey.co.uk/business-government/products/terrain-5
Panks, S., White, N., Newsome, A., Nash, M., Potter, J., Heydon, M., Mayhew, E., Alvarez, M., Russell, T., Cashon, C., Goddard, F., Scott, S. J., Heaver, M., Scott, S. H., Treweek, J., Butcher, B., & Stone, D. (2022). Biodiversity metric 3.1: Auditing and accounting for biodiversity – User guide. Natural England.
Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. (2014). Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology, 51(4), 839-848. https://doi.org/10.1111/1365-2664.12261
Wildlife Trusts. (2023). Nature Reserves and Ancient Woodlands. Retrieved from https://www.wildlifetrusts.org/nature-reserves
Wurtzebach, Z., & Schultz, C. (2016). Measuring ecological integrity: History, practical applications, and research opportunities. BioScience, 66(6), 446-457. https://doi.org/10.1093/biosci/biw037