Using PCA scores to classify species communities: An example for pelagic seabird distribution
Using Principal Component Analysis (PCA) in order to classify animal communities from transect counts is a widely used method. One problem with this approach is determining an appropriate cut-off point on the Principal Component (PC) axis to separate communities. We have developed a method using the...
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
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| Online Access: | http://www.tandfonline.com/doi/abs/10.1080/02664760120074933 |
| Summary: | Using Principal Component Analysis (PCA) in order to classify animal communities from transect counts is a widely used method. One problem with this approach is determining an appropriate cut-off point on the Principal Component (PC) axis to separate communities. We have developed a method using the distribution of PC scores of individual species along transects from the PIROP (Programme Integrede Recherches sur les Oiseaux Pelagiques) database for seabirds at sea in the Northwest Atlantic in winter 1965- 1992. This method can be applied generally to wildlife species, and also facilitates the evaluation, justification and stratification of PCs and community classifications in a transparent way. A typical application of this method is shown for three Principal Components; spatial implications of the cut-off decision for PCs are also discussed, e.g. for habitat studies. |
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