• Acacia aneura;
  • Australia;
  • autologistic regression;
  • binomial errors;
  • generalized linear model;
  • habitat suitability model;
  • Poisson errors;
  • simultaneous autoregressive model;
  • spatial autocorrelation;
  • spinifex


Aim  To develop a new modelling approach for spatially autocorrelated non-normal data, and apply it to a case study of the role that fire–vegetation–soil feedbacks play in maintaining boundaries between fire-sensitive and fire-promoted plant communities.

Location  A mulga (Acacia aneura) shrubland–spinifex (Triodia spp.) grassland mosaic, central Australia.

Methods  Autoregressive error models were extended to non-normal data by incorporating neighbourhood values of the response and predictor variables into generalized nonlinear models. These models were used to examine the environmental correlates of three response variables: mulga cover; fire frequency in areas free of mulga; and the presence of mulga banding. Mulga cover and mulga banding were assessed visually by overlaying 4477 × 1 km2 grid cells on both Landsat 7 ETM+ and very high resolution imagery. Fire frequency was estimated from an existing fire history for central Australia, based on remotely sensed fire scars.

Results  The autoregressive error models explained 27%, 47% and 57% of the null deviance of mulga cover, fire frequency and mulga banding, respectively, with 12%, 15% and 24% of the null deviance being explained by environmental variables alone. These models accounted for virtually all residual spatial autocorrelation. While there was a clear negative relationship between mulga cover and fire frequency, there was little evidence that mulga was being restricted to parts of the landscape with inherently low fire frequencies. Mulga was most abundant at very low slope angles and on red earths, both of which are likely to reflect high site productivity, while fire frequency was not clearly affected by slope angle and was also relatively high on red earths.

Main conclusions  The modelling approach we have developed provides a much needed way of analysing spatially autocorrelated non-normal data and can be easily incorporated into an information-theoretic modelling framework. Using this approach, we provide evidence that mulga and spinifex have a highly antagonistic relationship. In more productive parts of the landscape, mulga suppresses spinifex and fire, while in less productive parts of the landscape, fire and spinifex suppress mulga, leading to the remarkable abruptness of mulga–spinifex boundaries that are maintained via fire–vegetation–soil feedbacks.