Novel three-step pseudo-absence selection technique for improved species distribution modelling

• Published in: PloS one 2013-08, Vol.8 (8), p.e71218-e71218
• Main Authors: Senay, Senait D, Worner, Susan P, Ikeda, Takayoshi
• Format: Article
• Language: English
• Subjects: Aedes albopictus; Algorithms; Animals; Aquatic insects; Artificial intelligence; Asian tiger mosquito; Biology; Centroids; Clustering; Computer Science; Corn; Culicidae; Diabrotica virgifera virgifera; Earth Sciences; Ecology; Ecosystem; Environment; Environmental aspects; Environmental requirements; Generalized linear models; Methods; Models, Statistical; Mosquitoes; Neural networks; New Zealand; Population Dynamics; Predictions; Reproducibility of Results; Social and Behavioral Sciences; Spatial Analysis; Spatial data; Spatial distribution; Species; Studies; Vector quantization
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0071218

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).