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Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance
Author(s) -
Wolmarans René,
Robertson Mark P.,
van Rensburg Berndt J.
Publication year - 2010
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/j.1365-2699.2010.02325.x
Subject(s) - range (aeronautics) , ecological niche , statistic , geography , invasive species , ecology , species distribution , niche , statistics , physical geography , environmental science , biology , habitat , mathematics , materials science , composite material
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 M axent 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.