AN AUTOLOGISTIC MODEL FOR THE SPATIAL-DISTRIBUTION OF WILDLIFE

1. A new method for estimating the geographical distribution of plant and animal species from incomplete field survey data is developed. 2. Wildlife surveys are often conducted by dividing a study region into a regular grid and collecting data on abundance or on presence/absence from some or all of the squares in the grid. Generalized linear models (GLMs) can be used to model the spatial distribution of a species wit hin such a grid by relating the response variable (abundance or presen ce/absence) to spatially referenced covariates. 3. Such models ignore or at best indirectly model dependence on unmeasured covariates, and t he intrinsic spatial autocorrelation arising for example in gregarious populations. 4. We describe a procedure for use with presence/absence data in which spatial autocorrelation is modelled explicitly. We achi eve this by extending a logistic model to include an extra covariate w hich is derived from the responses at neighbouring squares. The extend ed model is known as an autologistic model. 5. To allow fitting of the autologistic model when only a random sample of squares is surveyed, we use the Gibbs sampler to predict presence/absence at unsurveyed squ ares. 6. We compare the autologistic model with the ordinary logistic model using red deer census data. Both models are fitted to a subsampl e of 20% of the data and results are compared with the 'true' abundanc e and spatial distribution indicated by the full census. We conclude t hat the autologistic model is superior for estimating the spatial dist ribution of the deer, whereas the ordinary logistic model yields more precise estimates of the overall number of squares occupied by deer at the time of the survey.