Improving models of wild boar hunting yield distribution: new insights for predictions at fine spatial resolution
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Abstract
ENETWILD consortium has developed methodologies for modelling wild boar abundance distribution based on hunting yield (HY) data. Although the methodologies reached an acceptable reliability, when models were downscaled to higher spatial resolution the predictions of absolute numbers of hunted animals tended to overprediction. Some important issues such as HY‐surface relationship and the spatial autocorrelation of HY data or the accuracy of downscaled predictions were not fully addressed yet due to the complexity of dealing with huge datasets at a European scale. In this report we (i) explored the use of hunted wild boar densities (numbers of hunted wild boar relative to surface) instead of raw counts (numbers of hunted animals) as response variable, and (ii) introduced intrinsic Conditional Auto‐Regressive models (iCAR) taking into account spatial autocorrelation. Using simulations and actual wild boar data, these new actions were aimed to produce high resolution predictions (2x2 km grid) with higher accuracy. We assessed model fitting in two different regions in Europe with high quality resolution HY data: Aragón autonomous region (North East Spain, belonging to South Bioregion as defined by ENETWILD) and the whole country of Slovenia (East Bioregion). We found that the marked overprediction, as observed in previous reports when models were downscaled, was now controlled by using hunted wild boar densities as response variable. Additionally, higher accuracy in model predictions was reached when iCAR approach was used to control for spatial autocorrelation. This high accuracy was maintained even when high resolution predictions were aggregated and compared to actual wild boar HY. These approaches should be considered in future models and represent an important step forward to model the distribution of wild boar abundance and other wildlife at high resolution over Europe.