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Novel Three-Step Pseudo-Absence Selection Technique for Improved Species Distribution Modelling

Overview of attention for article published in PLOS ONE, August 2013
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Title
Novel Three-Step Pseudo-Absence Selection Technique for Improved Species Distribution Modelling
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0071218
Pubmed ID
Authors

Senait D. Senay, Susan P. Worner, Takayoshi Ikeda

Abstract

Pseudo-absence selection for spatial distribution models (SDMs) is the subject of ongoing investigation. Numerous techniques continue to be developed, and reports of their effectiveness vary. Because the quality of presence and absence data is key for acceptable accuracy of correlative SDM predictions, determining an appropriate method to characterise pseudo-absences for SDM's is vital. The main methods that are currently used to generate pseudo-absence points are: 1) randomly generated pseudo-absence locations from background data; 2) pseudo-absence locations generated within a delimited geographical distance from recorded presence points; and 3) pseudo-absence locations selected in areas that are environmentally dissimilar from presence points. There is a need for a method that considers both geographical extent and environmental requirements to produce pseudo-absence points that are spatially and ecologically balanced. We use a novel three-step approach that satisfies both spatial and ecological reasons why the target species is likely to find a particular geo-location unsuitable. Step 1 comprises establishing a geographical extent around species presence points from which pseudo-absence points are selected based on analyses of environmental variable importance at different distances. This step gives an ecologically meaningful explanation to the spatial range of background data, as opposed to using an arbitrary radius. Step 2 determines locations that are environmentally dissimilar to the presence points within the distance specified in step one. Step 3 performs K-means clustering to reduce the number of potential pseudo-absences to the desired set by taking the centroids of clusters in the most environmentally dissimilar class identified in step 2. By considering spatial, ecological and environmental aspects, the three-step method identifies appropriate pseudo-absence points for correlative SDMs. We illustrate this method by predicting the New Zealand potential distribution of the Asian tiger mosquito (Aedes albopictus) and the Western corn rootworm (Diabrotica virgifera virgifera).

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Geographical breakdown

Country Count As %
United States 6 1%
Brazil 6 1%
United Kingdom 4 <1%
Germany 2 <1%
Peru 2 <1%
Australia 1 <1%
France 1 <1%
Switzerland 1 <1%
Canada 1 <1%
Other 6 1%
Unknown 405 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 95 22%
Researcher 93 21%
Student > Master 61 14%
Student > Bachelor 35 8%
Student > Doctoral Student 24 6%
Other 72 17%
Unknown 55 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 200 46%
Environmental Science 95 22%
Earth and Planetary Sciences 19 4%
Biochemistry, Genetics and Molecular Biology 9 2%
Unspecified 7 2%
Other 30 7%
Unknown 75 17%