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Observer bias and random variation in vegetation monitoring data
Author(s) -
Milberg Per,
Bergstedt Johan,
Fridman Jonas,
Odell Gunnar,
Westerberg Lars
Publication year - 2008
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.3170/2008-8-18423
Subject(s) - observer (physics) , taiga , vegetation (pathology) , abundance (ecology) , ecology , statistics , variance (accounting) , magnitude (astronomy) , vegetation cover , environmental science , physical geography , mathematics , geography , biology , medicine , physics , accounting , pathology , quantum mechanics , astronomy , grazing , business
Question: Detecting species presence in vegetation and making visual assessment of abundances involve a certain amount of skill, and therefore subjectivity. We evaluated the magnitude of the error in data, and its consequences for evaluating temporal trends. Location: Swedish forest vegetation. Methods: Vegetation data were collected independently by two observers in 342 permanent 100‐m 2 plots in mature boreal forests. Each plot was visited by one observer from a group of 36 and one of two quality assessment observers. The cover class of 29 taxa was recorded, and presence/absence for an additional 50. Results: Overall, one third of each occurrence was missed by one of the two observers, but with large differences among species. There were more missed occurrences at low abundances. Species occurring at low abundance when present tended to be frequently overlooked. Variance component analyses indicated that cover data on 5 of 17 species had a significant observer bias. Observer‐explained variance was < 10% in 15 of 17 species. Conclusion: The substantial number of missed occurrences suggests poor power in detecting changes based on presence/absence data. The magnitude of observer bias in cover estimates was relatively small, compared with random error, and therefore potentially analytically tractable. Data in this monitoring system could be improved by a more structured working model during field work.