Welcome to the
Strategic Nature Network (SNN) 1.0 Dashboard

The Core Zone is made up of places that already hold recognised biodiversity value, including: 

• Ancient woodland 
• National Nature Reserves 
• Local Nature Reserves 
• Sites of Special Scientific Interest 
• Special Areas of Conservation 
• Special Protection Areas 
• Ramsar sites 
• RSPB reserves 
• Wildlife Trust reserves 
• Woodland Trust reserves (where available) 
• Scotland’s OECMs (“Other Effective Area-Based Conservation Measures”) 

This zone is our starting point: the best of what remains. 

Importantly, being in the Core Zone does not mean it is in good condition, many sites still need restoration and investment. But for modelling purposes, it is assumed these places should be protected and improved over time.  

All marine designations were removed so the SNN 1.0 Core Zone remains purely terrestrial, while still including all UK islands. 

Nation 

Core Zone Data Source 

England 

Areas of Particular Importance for Biodiversity (APIB), supplemented with Wildlife Trust and RSPB reserves, without Local Wildlife Sites due to data availability 

Wales 

DEFRA APIB definition re-created for Wales, supplemented with Wildlife Trust and RSPB reserves, without Local Wildlife Sites due to data availability 

Scotland 

DEFRA APIB definition re-created for Scotland, supplemented with Nature 30/Other Effective Area-Based Conservation Measures (OECMs), Wildlife Trust and RSPB reserves, without Local Wildlife Sites due to data availability 

Northern Ireland 

DEFRA APIB definition re-created for Northern Ireland, supplemented with Wildlife Trust and RSPB reserves, without Local Wildlife Sites due to data availability 

A 1km grid across the UK was used to calculate the density of the Core Zone. This approach allows for areas of high density, small core sites (e.g. patches of ancient woodland) to be represented as a large nature reserve would, and for medium sized, isolated sites to be removed as they would not be national priorities for defragmentation. The percentage of each 1km grid cell which is covered by the core zone was calculated. Using this, nodes were classified to be 1km cells with at least 60% of its area is covered by core zone habitat. 

The Restoration Zone represents areas with the highest potential for habitat improvement. These are the parcels that will form ecological corridors between core zones. The restoration zone is built from local datasets where available, supplemented by a proxy layer based on national datasets where no suitable local dataset exists. The Restoration Zone excludes areas already covered by the Core Zone. 

Nation 

Restoration Zone Data Source 

England 

Areas that Could Become of Particular Importance for Biodiversity (ACB) from Local Nature Recovery Strategies*; where not available, proxy layer used 

Wales 

Priority Ecological Networks 

Scotland 

Central Scotland Green Network Opportunity Areas; for other regions in Scotland a proxy layer was used 

Northern Ireland 

Nature Recovery Network 

*see Appendix 2 for regions covered 

The proxy layer was constructed by intersecting habitat-specific buffers around priority habitat parcels (England Priority Habitats Inventory, Scotland Annex I habitats), flood zones and rivers with low-grade agricultural land. Datasets and attribution are listed in Appendix 3.

The terrestrial Restoration Zone is complemented with a Marine Restoration Zone, representing areas with the highest potential to become high-quality marine habitat. Datasets included are listed below, and will be expanded in future versions of the SNN as data becomes available.  

• England:  
• Native Oyster Bed Potential (Environment Agency 2020);  
• Seagrass Potential (Environment Agency 2015) 
• Wales:  
• Native Oyster Opportunities (National Resources Wales); 
• Saltmarsh Mudflats Opportunities Floodplains SMP (National Resources Wales) 
• Equivalent datasets for Scotland and Northern Ireland not available at time of assembly.  

To form the basis of the connectivity analysis, a landscape resistance surface was developed to represent the relative ease or difficulty with which a generalist species can move through different land cover types across the terrestrial landscape. The resistance surface was derived from the UKCEH Land Cover Map 2024, which provides a national land-cover classification at a 1 km spatial resolution. 

Resistance values were assigned to each land cover class based on stakeholder-derived cost scores. These values represent the relative resistance of different land-cover types to the movement of generalist terrestrial species, with lower values assigned to permeable habitats and higher values assigned to land uses and landscape features that restrict movement. 

For example: 

• Woodland = low resistance 
• Arable farmland = medium resistance 
• Urban = high resistance 
• Roads, motorways, railways, rivers = very high resistance 

The land cover resistance surface was supplemented through the incorporation of linear infrastructure datasets, including roads and railways, to better represent barriers to species movement. Linear infrastructure was assigned elevated resistance values to reflect its potential to reduce landscape permeability for species movement and fragment habitat networks. 

To account for existing crossing opportunities, data supplied by National Highways on the location of bridges, underpasses, culverts and other crossing structures were incorporated into the resistance surface. Resistance values associated with road infrastructure were reduced at these locations to reflect their role in facilitating species movement across transport infrastructure.  

Connectivity modelling was undertaken to identify national priority areas for habitat defragmentation and ecological network enhancement. The analysis aimed to identify both the corridors that facilitate species movement between important habitat areas and the locations where habitat restoration would deliver the greatest improvements to connectivity across the wider landscape. To achieve this, two complementary modelling approaches were used: Linkage Mapper to identify least-cost movement corridors and Condatis to identify connectivity bottlenecks. 

Using the resistance values informed by stakeholder input, a least-cost path analysis model was ran to identify the paths of “least resistance” between our nodes of 60% Core Zone habitat. The model finds the routes animals are most likely to use, weaving around barriers and following features like river corridors or green spaces. This creates the least‑cost paths which are effectively the key routes that support wildlife movement between existing habitats across the wider landscape.  

Least Cost Pathways, which form the connective corridors between existing habitats, have been developed using a modelling software Linkage Mapper v3.1.01. The software calculates the least-costly routes for species to move from one Core Zone to another, using the resistance map developed using stakeholder input. Network adjacency was calculated using both Euclidean and cost-weighted distance metrics, with connections established between the four nearest neighbouring Core Zones according to cost-weighted distance. Corridor generation was constrained using a 200 km truncation distance, a 20 km bounding circle, a maximum cost-weighted corridor distance of 5,000 cost units, and a maximum Euclidean distance of 50 km. Routes connecting Core Zones through major urban areas (e.g. London, Birmingham and Glasgow) were excluded from the analysis. Routes that crossed marine environments, including Scottish lochs, were also removed, as this first iteration of the network focuses exclusively on terrestrial species.  

For each Core Zone, four connectivity routes were identified to build a network of movement options across the landscape. These routes were then buffered to a width of 2 km to create ecologically-meaningful corridors at a national scale. The rationale for the 2 km corridor width is based on a review of literature which indicates that wider corridors are generally more effective at maintaining habitat continuity, facilitating species movement and genetic exchange and reducing edge effects. Given the large size of the Core Zones, the variable length of the modelled corridors, and the multi-species objectives of the network, a width of 2 km was considered an appropriate and precautionary representation of functional ecological connectivity. 

As an ever-adapting model, the connectivity network may be adjusted and refined as new information becomes available, including a future phase to incorporate regional ground-truthing measures. Future iterations may also explore the potential to utilise connectivity flow outputs from Circuitscape connectivity modelling software to inform the least cost pathways. This would enable both landscape resistance and species dispersal processes to be considered within the connectivity analysis. 

Complementary to the connectivity network developed using the least cost path analysis,  Condatis software was used to identify national bottlenecks, which are specific locations where habitat restoration would provide the greatest increase in landscape connectivity. Using a habitat flow approach, the model identifies locations that are disproportionately important for maintaining movement through the wider ecological network. Woodland and grassland habitat networks were modelled separately, with habitat flow assessed between Areas of Particular Importance for Biodiversity (APIBs). Woodland and grassland data was derived from UKCEH Land Cover 2024. Following the assumption that species distributions are likely to shift northwards under climate change, habitat flow was modelled from south to north. The analysis used a dispersal distance parameter of 0.5 and a reproductive rate of 1,000 and identified the 500 locations with the highest bottleneck scores as priorities for habitat restoration and creation. 

The Restoration Zone was intersected with the corridors to produce the SNN Restoration Zone, representing areas with the highest potential to become high-quality habitat, forming ecological corridors between core zones. The bottlenecks identified from the Condatis modelling were overlaid with the SNN Restoration Zone to identify the highest priorities for achieving de-fragmentation. If an area of the Recovery Zone falls within a bottleneck, it has been identified twice over for defragmentation importance and therefore allocated the highest priority. 

Together, the Core Zone and Restoration Zone form the Strategic Nature Network 1.0. 

Data Caveats 

UKCEH Land Cover Map data was used to map habitats because it covers the entire UK, is freely available for non-commercial work and has better accuracy than other accessible UK-wide datasets. However, the accuracy of the habitat classification is not 100%, it is limited to 21 categories, and the resolution is 25m so smaller patches of habitat and streams may not be represented. 

Due to the scale and early stages of the mapping, the fine-scale location of the corridors has not been scrutinised at a local level.