Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance

Wolmarans, Rene, Robertson, Mark P. and van Rensburg, Berndt J. (2010) Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance. Journal of Biogeography, 37 9: 1797-1810. doi:10.1111/j.1365-2699.2010.02325.x


Author Wolmarans, Rene
Robertson, Mark P.
van Rensburg, Berndt J.
Title Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance
Journal name Journal of Biogeography   Check publisher's open access policy
ISSN 0305-0270
1365-2699
Publication date 2010-09
Sub-type Article (original research)
DOI 10.1111/j.1365-2699.2010.02325.x
Volume 37
Issue 9
Start page 1797
End page 1810
Total pages 14
Place of publication Oxford, United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Formatted abstract
Aim  To investigate the impact of geographical bias on the performance of ecological niche models for invasive plant species.
Location  South Africa and Australia.
Methods  We selected 10 Australian plants invasive in South Africa and nine South African plants invasive in Australia. Geographical bias was simulated in occurrence records obtained from the native range of a species to represent two scenarios. For the first scenario (A, worst-case) a proportion of records were excluded from a specific region of a species’ range and for the second scenario (B, less extreme) only some records were excluded from that specific region of the range. Introduced range predictions were produced with the Maxent modelling algorithm where models were calibrated with datasets from these biased occurrence records and 19 bioclimatic variables. Models were evaluated with independent test data obtained from the introduced range of the species. Geographical bias was quantified as the proportional difference between the occurrence records from a control and a biased dataset, and environmental bias was expressed as either the difference in marginality or tolerance between these datasets. Model performance [assessed using the conventional and modified AUC (area under the curve of receiver-operating characteristic plots) and the maximum true skill statistic] was compared between models calibrated with occurrence records from a biased dataset and a control dataset.
Results  We found considerable variation in the relationship between geographical and environmental bias. Environmental bias, expressed as the difference in marginality, differed significantly across treatments. Model performance did not differ significantly among treatments. Regions predicted as suitable for most of the species were very similar when compared between a biased and control dataset, with only a few exceptions.
Main conclusions  The geographical bias simulated in this study was sufficient to result in significant environmental bias across treatments, but despite this we did not find a significant effect on model performance. Differences in the environmental spaces occupied by the species in their native and invaded ranges may explain why we did not find a significant effect on model performance.
Keyword Australia
Ecological niche modelling
Environmental bias
Geographical bias
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Biological Sciences Publications
 
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