Predicting species richness through environmental heterogeneity

Focusing on variability rather than absolute values of environmental variables may improve models of species richness

GBIF-mediated data resources used : 926,837 species occurrences
Erica perspicua var. perspicua

Erica perspicua var. perspicua by Peter Slingsby. Photo licensed under CC BY-NC 4.0.

Plant species richness is influenced by resource availability, environmental stability and heterogeneity, however, most attempts to model global richness focus on absolute values of environmental variables rather than measures of their variability.

In this study, researchers hypothesized that including heterogeneities of environmental variables would improve models of plant species richness. Using GBIF-mediated occurrences of vascular plants in South Africa combined with data on climate, soil, fire frequency and distance from nearest coast, they construct a boosted regression tree (BRT) model of richness able to predict 68 per cent of species richness and 95 per cent of biome richness.

The impact of ‘roughness’, i.e. spatial heterogeneity of environmental variables was overwhelmingly important with the most critical predictor being diurnal temperature range roughness.

Facilitating regional coexistence and powering speciation, environmental heterogeneity, as shown by this study, is an important predictor of species richness to be considered in modelling.

Cramer MD and Verboom GA (2016) Measures of biologically relevant environmental heterogeneity improve prediction of regional plant species richness. Journal of Biogeography. Wiley-Blackwell 44(3): 579–591. Available at:

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  • South Africa
  • {{'resourceSearch.filters.countriesOfCoverage' | translate}}:
  • South Africa
  • {{'resourceSearch.filters.topics' | translate}}:
  • Biodiversity science