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Dominance-diversity curves

  • Biodiversity

  • Compostion


Dominance-diversity curves summarise the abundance and evenness of species within a community, visually representing simple diversity metrics and showing patterns of competition and niche differentiation (Wilson 1991, Whittaker 1965). Changes in dominant and rare species can have important consequences for ecosystem functioning and are not captured by metrics such as species richness (Hillebrand et al. 2018).

The x-axis of the curve displays the abundance rank, with the most abundant species ranked 1, the second most abundant 2 etc. The y-axis displays the relative abundance of the species. Species richness is summarised as the number of species ranked on the x-axis. Species evenness is summarised in the slope of the curve – the steeper the gradient the lower evenness.

Methodology summary

Species diversity monitoring generates abundance and diversity data that can be used to calculate dominance-diversity curves (see Species diversity, Relative abundance for methodologies).

The goeveg package in R contains the racurve function, for fitting Whittaker plots for community data.

Metric threshold or direction of change

Targets on changes in relationship between species evenness and abundance of specific species will depend on the project aims (e.g. dominance relationships between target species for different habitats).

Technological innovations

  • Earth Observation sensors generate high resolution imagery, which can be used to identify larger species. (Lausch et al. 2016).
  • Advances in machine learning are increasing the efficiency of classifying and processing camera trap imagery (Tabak et al. 2018).
  • Sequencing methods have shown some promise in detecting invertebrate biomass, with mitogenomic sequencing achieving higher accuracy than metabarcoding. However detection is still poor for low abundance species (Bista et al. 2018).

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


  • Community


  • Medium


  • Tier 1

Technical expertise

  • High

Standardised methodology

  • Partial