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Keywords:

  • Australia's Wet Tropics;
  • Boosted Regression Trees;
  • climate downscaling;
  • spatial climate layers;
  • spatial weather layers;
  • species distribution model

Abstract

Aim

Correlative species distribution models (SDMs) combined with spatial layers of climate and species' localities represent a frequently utilized and rapid method for generating spatial estimates of species distributions. However, an SDM is only as accurate as the inputs upon which it is based. Current best-practice climate layers commonly utilized in SDM (e.g. ANUCLIM) are frequently inaccurate and biased spatially. Here, we statistically downscale 30 years of existing spatial weather estimates against empirical weather data and spatial layers of topography and vegetation to produce highly accurate spatial layers of weather. We proceed to demonstrate the effect of inaccurately quantified spatial data on SDM outcomes.

Location

The Australian Wet Tropics.

Methods

We use Boosted Regression Trees (BRTs) to generate 30 years of spatial estimates of daily maximum and minimum temperature for the study region and aggregate the resultant weather layers into ‘accuCLIM’ climate summaries, comparable with those generated by current best-practice climate layers. We proceed to generate for seven species of rainforest skink comparable SDMs within species; one model based on ANUCLIM climate estimates and another based on accuCLIM climate estimates.

Results

Boosted Regression Trees weather layers are more accurate with respect to empirically measured temperature, particularly for maximum temperature, when compared to current best-practice weather layers. ANUCLIM climate layers are least accurate in heavily forested upland regions, frequently over-predicting empirical mean maximum temperature by as much as 7°. Distributions of the focal species as predicted by accuCLIM were more fragmented and contained less core distributional area.

Conclusion

Combined these results reveal a source of bias in climate-based SDMs and indicate a solution in the form of statistical downscaling. This technique will allow researchers to produce fine-grained, ground-truthed spatial estimates of weather based on existing estimates, which can be aggregated in novel ways, and applied to correlative or process-based modelling techniques.