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Landscape diversity

  • Biodiversity

  • Structure


Habitat heterogeneity at the landscape scale (compositional and configurational heterogeneity) has a positive relationship with many taxa (Maskell et al. 2019, Honnay 2003). Spatial variation in habitats can maintain distinct communities by increasing beta-diversity within landscapes (Deane et al. 2020, Veech and Crist 2007) (see Species diversity for explanation of beta diversity).

Different species groups respond to landscape diversity metrics at different scales. For example, landscape diversity at the c.250 m scale best explains diversity pattern in butterflies and birds (Krauss et al. 2003, Morelli et al. 2013). Different arthropod groups respond to landscape diversity metrics at different scales, with many groups showing dispersal 100s of metres from their focal habitat (Dangerfield 2003). However, some beetle groups respond to habitat heterogeneity at 25 m scales (Dangerfield 2003). Within habitat heterogeneity is captured in the metric Vegetation structure.

Methodology summary

Habitats can be mapped using UK Habitats Classification (UKHab)

  • Record habitat type and area for patches 400 sqm or larger (Maskell et al. 2019)
  • Calculate diversity metrics based on the UKHab Level 3 classifications (e.g. acid grassland, calcareous grassland, neutral grassland, dwarf shrub heath, coniferous woodland)
  • Simpson’s diversity index is recommended for its more intuitive interpretation, it reflects the probability of habitat patches being in different habitat classes (higher value = higher diversity)
  • The FRAGSTATS software package can be used to calculate Simpson’s diversity index
  • More information on selecting and interpreting landscape diversity metrics can be found at FRAGSTATS

There are many other metrics (Haines-Young and Chopping 1996, Nagendra and Gadgil 1999, Magurran 2004) under the umbrella of landscape diversity that can be calculated e.g.:

  • Shannon’s diversity index = proportion of landscape occupied by a habitat type (has disadvantages, rare habitats are disproportionately represented)
  • Habitat proportion = % cover of specific habitat type within a focal area
  • Evenness measures (e.g. Simpson’s evenness, Shannon’s evenness)
  • Patch sizes (see Patch size distribution)
  • Measures of Connectivity and fragmentation (more complex analyses that integrate spatial habitat information with species dispersal data)

Metric threshold or direction of change

An increase in Simpson’s diversity index indicates an increase in landscape diversity.

The desired landscape diversity will depend on the project aims – heterogeneous mosaics vs aims to restore, create or manage a specific habitat type.

Landscape diversity can be measured alongside Habitat area and Patch size distribution to capture the need for large patches of core habitat alongside diversity.

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


  • Low


  • Tier 1

Technical expertise

  • High

Standardised methodology

  • Partial