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Biomass (measure of abundance)

  • Biodiversity

  • Compostion
  • Function
  • Structure


Changes in abundance have greater functional consequences for an ecosystem than changes in simple metrics such as species richness (Hillebrand et al. 2018, Buckland et al. 2005). Collecting abundance data as biomass provides additional functional information, structural information, and accounts for size differences between organisms (Llopis-Belenguer et al. 2018, O’Connor et al. 2017). Trends in mean abundance can detect early signals of species decline and are less sensitive to demographic stochasticity (Santini et al. 2017, van Strien et al. 2012).

It is easier to measure abundance accurately at smaller scales (community, population), than at landscape scales (Chiarucci et al. 2011). Abundance data should be collected in a spatially explicit way (e.g. fixed area plots, defined density of sampling points per unit area) (Chase and Knight 2013).

Methodology summary

See Vegetation biomass, Invertebrate biomass and Mammal biomass for methods.

Biomass of species collected across multiple trophic levels can contribute to calculation of Energy flow rates.

Metric threshold or direction of change

The desired direction of change will depend on project objectives and the ecosystem type.

In woody systems, particularly projects aiming to sequester carbon, an increase in vegetation biomass is often desirable. In grassland systems, increasing vegetation biomass over time may indicate nutrient enrichment and can be correlated with a loss of plant diversity.

Increasing invertebrate biomass, particularly of functionally important species is generally likely to be desirable, but in agricultural systems an increase in pest species biomass is undesirable.

Changes in mammal biomass will depend on the objectives of the project, for example in woody systems it is important that numbers of herbivorous mammals don’t prevent tree and shrub regeneration.

Technological innovations

  • LiDAR sensors on airplanes or UAVs and spaceborne LiDAR can be used to generate 3D canopy height models. Metrics such as aboveground vegetation biomass can be derived from these (Jucker et al. 2023).
  • Global equations linking tree height and crown size to aboveground biomass show promise for converting remote-sensed data to biomass (Jucker et al. 2017).
  • Terrestrial laser scanning addresses uncertainties in estimating aboveground biomass using allometric equations or Earth Observation methods (Demol et al. 2022, Calders et al. 2022).
  • Automated image-recognition can recognise invertebrates and the area of the specimen in the image used for biomass estimation (Ärje et al. 2020).
  • Metabarcoding of bulk invertebrate samples is less reliable for determining abundance of individuals (Bista et al. 2018).
  • Earth Observation sensors generate high resolution imagery, which can be used to identify large mammal species. (Lausch et al. 2016).
  • eDNA could provide a reliable alternative to camera trapping for identifying all species present within a project (Leempoel et al. 2020).
  • Advances in machine learning are increasing the efficiency of classifying and processing camera trap imagery (Tabak et al. 2018).

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


  • High


  • Future

Technical expertise

  • High

Standardised methodology

  • Partial