Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

Yasmin Hageer*, Manuel Esperón-Rodríguez, John B. Baumgartner, Linda J. Beaumont

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
12 Downloads (Pure)


Background: Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods: This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results: The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions: Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants.

Original languageEnglish
Article numbere3446
Pages (from-to)1-22
Number of pages22
Issue number6
Publication statusPublished - 22 Jun 2017

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Australia
  • climate
  • growth form
  • habitat suitability
  • Maxent
  • predictor choice
  • shrubs
  • species distribution modelling
  • species richness
  • soil

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