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Incorporating low-resolution historic species location data decreases performance of distribution models

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  • Reside, April E.
  • Watson, Ian
  • VanDerWal, Jeremy
  • Kutt, Alex S.

Abstract

Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R2=0.163–0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause.

Suggested Citation

  • Reside, April E. & Watson, Ian & VanDerWal, Jeremy & Kutt, Alex S., 2011. "Incorporating low-resolution historic species location data decreases performance of distribution models," Ecological Modelling, Elsevier, vol. 222(18), pages 3444-3448.
  • Handle: RePEc:eee:ecomod:v:222:y:2011:i:18:p:3444-3448
    DOI: 10.1016/j.ecolmodel.2011.06.015
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    References listed on IDEAS

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    1. VanDerWal, Jeremy & Shoo, Luke P. & Graham, Catherine & Williams, Stephen E., 2009. "Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?," Ecological Modelling, Elsevier, vol. 220(4), pages 589-594.
    2. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
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    2. Matawa, Farai & Murwira, Amon & Schmidt, Karin S., 2012. "Explaining elephant (Loxodonta africana) and buffalo (Syncerus caffer) spatial distribution in the Zambezi Valley using maximum entropy modelling," Ecological Modelling, Elsevier, vol. 242(C), pages 189-197.

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