Amalgamating data
 
Much of the analysis explained above presupposes that the data have been obtained using standardised techniques either from a single survey (or series of coordinated surveys), or from different surveys that are similar enough for the data to be combined into a single data set. However, where there are substantial differences in techniques, there will clearly be an effect on the composition of the combined dataset.
 
It is a common experience that datasets reflect the skills and biases of the recorders, even when the techniques are nominally equivalent between surveys. It may require the editing out of dubious identifications from one or more datasets, or the use of higher taxonomic units than those supplied.
 
Transformation of counts or even reduction to SACFOR or presence/absence may be needed to find a common denominator between datasets, especially where it is suspected that counts show a bias. It may be important to remove rarities from the datasets so that the transformed data reflect the species occurrences that can be regarded with confidence.
 
Data might be in the form of habitat classes and these may need to be translated into the latest classification nomenclature. Where there is no clear translation, the habitats may need to be amalgamated using a higher common level in the classification system. This may lead to a loss of information and an alternative is to use a life form or biotope complex that captures the essence of the biota of the habitats.
 

All material variously copyrighted by MESH project partners 2004-2010

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