Finessing atlas data for species distribution models

Aidin Niamir*, Andrew K. Skidmore, Albertus G. Toxopeus, Antonio R. Muñoz, Raimundo Real

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli's eagle (Aquila fasciata) at a resolution 100 times finer than available atlas data.

Location Malaga province, Andalusia, southern Spain.

Methods A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1×1km) than the original atlas data (10×10km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic.

Results The models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model.

Main conclusions The proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli's eagle at a local scale from a national-level occurrence data set; however, the usefulness of this approach may be limited to well-known species.

Original languageEnglish
Pages (from-to)1173-1185
Number of pages13
JournalDiversity and Distributions
Volume17
Issue number6
DOIs
Publication statusPublished - 2011
Externally publishedYes

Keywords

  • Aquila fasciata
  • Bayesian expert system
  • Bonelli's eagle
  • downscaling
  • Malaga
  • maximum entropy
  • sampling
  • Spain

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