Folia Geobotanica

, Volume 43, Issue 3, pp 289–304

Sampling-Design Effects on Properties of Species-Area Relationships – A Case Study from Estonian Dry Grassland Communities


    • Plant Systematics, Biocentre Klein FlottbekUniversity of Hamburg
  • Steffen Boch
    • Institute of Plant SciencesUniversity of Bern

DOI: 10.1007/s12224-008-9018-5

Cite this article as:
Dengler, J. & Boch, S. Folia Geobot (2008) 43: 289. doi:10.1007/s12224-008-9018-5


Despite widespread use of species-area relationships (SARs), dispute remains over the most representative SAR model. Using data of small-scale SARs of Estonian dry grassland communities, we address three questions: (1) Which model describes these SARs best when known artifacts are excluded? (2) How do deviating sampling procedures (marginal instead of central position of the smaller plots in relation to the largest plot; single values instead of average values; randomly located subplots instead of nested subplots) influence the properties of the SARs? (3) Are those effects likely to bias the selection of the best model? Our general dataset consisted of 16 series of nested-plots (1 cm2–100 m2, any-part system), each of which comprised five series of subplots located in the four corners and the centre of the 100-m2 plot. Data for the three pairs of compared sampling designs were generated from this dataset by subsampling. Five function types (power, quadratic power, logarithmic, Michaelis-Menten, Lomolino) were fitted with non-linear regression. In some of the communities, we found extremely high species densities (including bryophytes and lichens), namely up to eight species in 1 cm2 and up to 140 species in 100 m2, which appear to be the highest documented values on these scales. For SARs constructed from nested-plot average-value data, the regular power function generally was the best model, closely followed by the quadratic power function, while the logarithmic and Michaelis-Menten functions performed poorly throughout. However, the relative fit of the latter two models increased significantly relative to the respective best model when the single-value or random-sampling method was applied, however, the power function normally remained far superior. These results confirm the hypothesis that both single-value and random-sampling approaches cause artifacts by increasing stochasticity in the data, which can lead to the selection of inappropriate models.


ArtifactBiodiversityEstoniaLogarithmic functionNested-plot designPower functionStatistical analysis

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© Institute of Botany, Academy of Sciences of the Czech Republic 2008