A Modified-Whittaker nested vegetation sampling method
- Cite this article as:
- Stohlgren, T.J., Falkner, M.B. & Schell, L.D. Vegetatio (1995) 117: 113. doi:10.1007/BF00045503
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A standardized sampling technique for measuring plant diversity is needed to assist in resource inventories and for monitoring long-term trends in vascular plant species richness. The widely used ‘Whittaker plot’ (Shmida 1984) collects species richness data at multiple spatial scales, using 1 m2, 10 m2, and 100 m2 subplots within a 20 m × 50 m (1000 m2) plot, but it has three distinct design flaws involving the shape and placement of subplots. We modified and tested a comparable sampling design (Modified-Whittaker plot) that minimizes the problems encountered in the original Whittaker design, while maintaining many of its attractive attributes. We overlaid the two sampling methods in forest and prairie vegetation types in Larimer County, Colorado, USA (n=13 sites) and Wind Cave National Park, South Dakota, USA (n=19 sites) and showed that the modified design often returned significantly higher (p<0.05) species richness values in the 1 m2, 10 m2, and 100 m2 subplots. For all plots, except seven ecotone plots, there was a significant difference (p<0.001) between the Whittaker plot and the Modified-Whittaker plot when estimating the total number of species in the 1000 m2 plots based on linear regressions of the subplot data: the Whittaker plot method, on average, underestimated plant species richness by 34%. Species-area relationships, using the Modified-Whittaker design, conformed better to published semilog relationships, explaining, on average, 92% of the variation. Using the original Whittaker design, the semilog species-area relationships were not as strong, explaining only 83% of the variation, on average. The Modified-Whittaker plot design may allow for better estimates of mean species cover, analysis of plant diversity patterns at multiple spatial scales, and trend analysis from monitoring a series of strategically-placed, long-term plots.