Reproducibility of vegetation measurements is critical for large-scale or long-term studies, where numerous observers collect data, but past studies have questioned reproducibility of some techniques. Five methods of evaluating understory composition were appraised for reproducibility among six observers in two forest types in south-central Alaska: ocular estimates in quadrats, overall community species rank and cover estimates, nested rooted frequency, horizontal-vertical profiles, and pin drop (systematic points). One forest type was selected to represent structure of coastal communities, another to represent structure of interior Alaska communities. Three general methods of evaluating reproducibility were considered: standard deviations (precision among observers), components of variance (percentage of total variance attributable to observers), and analysis of variance (significance of observer variance). Observer variances were generally similar among techniques and significant in most cases. No technique stood out as being more reproducible than others. Features of techniques other than reproducibility may be more important when selecting a technique. Management decisions based on vegetation cover data should consider the observer errors involved as well as biological significance.
Abbreviations: CV = Coefficient of variation; HV = Horizontal-vertical; SD = Standard deviation.