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Connectivity and fragmentation

  • Biodiversity

  • Structure


Connectivity and Fragmentation are complementary metrics assessing the distribution of habitat within a landscape. There has been extensive debate on the role of spatial habitat configuration vs habitat loss on overall biodiversity (Fahrig et al. 2019, Fletcher et al. 2018). Habitat quality, loss, patch area, and connectivity have complex and interrelated effects on biodiversity (Wilson et al. 2016, Hanski 2011).

Fragmentation and connectivity determine species movements and therefore influence the total availability of habitat to a species (Hanski 2011). Isolation of populations by fragmentation contributes to inbreeding and the accumulation of negative mutations in populations, reducing future population viability (Hanski 2011). Under a future changing climate, species’ abilities to track their climatic envelopes will depend on habitat connectivity/fragmentation (Rudnick et al. 2012).

Connectivity and fragmentation can be described by structural metrics of spatial habitat configuration, however the most informative metrics integrate biological data on the focal species (Kindlmann and Burel 2008, Calabrese and Fagan 2004).

Methodology summary

Data collected during assessment of Landscape diversity can be used to derive structural connectivity and fragmentation metrics. The FRAGSTATS software package can be used to calculate metrics such as:

  • Mean nearest neighbour distance – the distance from a focal patch to its nearest neighbouring patch
  • Edge density – the ratio of habitat edge to area
  • Contagion – degree of aggregation at the landscape scale, inversely related to edge density, i.e. contagion is high if a single habitat type occupies a large percentage of the landscape
  • Proximity index – size and proximity of all patches with edges within certain radius of focal patch
  • Perimeter-area fractal dimension – relationship between patch area and patch perimeter, can be calculated at the habitat type or landscape level
  • Patch density – number of patches per unit area, allowing comparisons between different sized landscapes
  • Clumpiness index – frequency with which different pairs of patch types appear side-by-side, scaled by proportion of focal habitat class within an area

More information on selecting and interpreting metrics can be found at FRAGSTATS.

Functional metrics of connectivity and fragmentation that integrate biological information of the focal species with structural metrics require more bespoke, specialist modelling approaches.

Metric threshold or direction of change

The ideal level of connectivity will be context and species dependent, influenced by the habitat matrix and species’ dispersal ability/behaviour.

Technological innovations

  • In Switzerland, high resolution (1m) aerial imagery (land cover, Landsat, LiDAR) was used to classify habitats with high accuracy, although rarer and finer-scale habitats were less accurate, highlighting the need for ground-truthing (Price et al. 2023).
  • Machine-learning approaches were used to classify Natura 2000 Habitat Types in Germany (Sittaro et al. 2022).
  • Other examples of classifying habitat types using Earth Observation data in Lausch et al. 2016.

  • Agricultural
  • Forest
  • Grassland
  • Heathland
  • Other
  • Peatland
  • Saltmarsh
  • Wetland


  • Landscape


  • Medium


  • Future

Technical expertise

  • High

Standardised methodology

  • No