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

Reside, April E., Watson, Ian, VanDerWal, Jeremy and Kutt, Alex S. (2011) Incorporating low-resolution historic species location data decreases performance of distribution models. Ecological Modelling, 222 18: 3444-3448. doi:10.1016/j.ecolmodel.2011.06.015


Author Reside, April E.
Watson, Ian
VanDerWal, Jeremy
Kutt, Alex S.
Title Incorporating low-resolution historic species location data decreases performance of distribution models
Journal name Ecological Modelling   Check publisher's open access policy
ISSN 0304-3800
1872-7026
Publication date 2011-09-24
Year available 2011
Sub-type Letter to editor, brief commentary or brief communication
DOI 10.1016/j.ecolmodel.2011.06.015
Open Access Status Not yet assessed
Volume 222
Issue 18
Start page 3444
End page 3448
Total pages 5
Place of publication Amsterdam, 1043 NX Netherlands
Publisher Elsevier BV
Language eng
Formatted 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.
Keyword AUC
Australian tropical savannas
Birds
Environmental covariates
Maxent
Species distribution models
Q-Index Code CX
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Letter to editor, brief commentary or brief communication
Collection: School of Biological Sciences Publications
 
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