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Tree diversity

  • Biodiversity

  • Compostion
  • Structure


There is extensive evidence that tree diversity is a key driver of biodiversity and ecosystem functioning (Baeten et al. 2019, van der Plas et al. 2016, Ampoorter et al. 2020). For example, forests with a higher tree species richness have a higher richness of associated lichens and tree species richness in the regeneration layer is positively related to overall richness of other taxa (Storch et al. 2023).

However, other studies have shown that tree species richness is a less important predicter of biodiversity than structural metrics such as vertical structure, large living trees, presence of tree microhabitats and proportion of gaps (Zeller et al. 2022). This may in part reflect that some natural forest types have a low natural level of tree species richness.

Methodology summary

Tree diversity data can be collected at the same time as assessing Vegetation biomass, Vegetation structure, Seedling regeneration, and Tree age.

The UK National Forest Inventory (NFI) provides a standardised methodology to establish fixed area survey plots and record tree growth categories (Forestry Commission 2020). Young trees (seedlings and saplings), diameter at breast height (DBH) < 4 cm and mature trees, DBH > 4 cm, are recorded.

  • Forest is defined as having >20% canopy cover
  • 0.01 ha plots (5.64m radius) are established, the number of plots is determined by the size of the forest area
  • Within each plot DBH is recorded for all mature trees (DBH > 4 cm)
  • Saplings (height > 50 cm, DBH < 4 cm) are recorded in a 2.52 m radius plot at the centre of each 0.01 ha plot
  • Seedlings (height < 50 cm, DBH < 4 cm) are recorded in a 1.78 m radius plot at the centre of each 0.01 ha plot

The NFI Survey Manual provides the methodology:

  • How to allocate plots – Chapter 12 Plot Assessments
  • Recording mature trees – Chapter 13 Tree Assessment Procedures
  • Recording young trees – Chapter 15 Young Tree Assessments

Deriving metrics – see Species diversity for calculation of Simpson’s diversity index.

Metric threshold or direction of change

Generally, increases in species diversity could be seen as positive; however the species composition underlying change in these metrics should be considered. The desired tree diversity will also depend on the target woodland habitat. Increases in target species (e.g. species characteristic of target habitats) are important, whereas increases due to the presence of invasive and/or non-native species may be undesirable.

Technological innovations

  • Hyperspectral imagery and LiDAR data collected using UAVs and machine learning algorithms have shown promise for identifying tree species. However, these models have been developed and parameterised at specific sites (Zhong et al. 2022, Onishi and Ise 2021).
  • 3D laser scanning has also been combined with deep learning for tree species ID, however there are still challenges to this approach (Seidel et al. 2021, Lines et al. 2022).

  • Forest


  • Community


  • Low


  • Tier 2

Technical expertise

  • Medium

Standardised methodology

  • Yes