Behavior of Vegetation Sampling Methods in the Presence of Spatial Autocorrelation

• Published in: Plant ecology Vol. 187; no. 2; pp. 203 - 212
• Main Author: Goslee, Sarah C.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01-12-2006; Springer Nature B.V
• Subjects: Autocorrelation; Estimation methods; Methods; Pixels; Plant ecology; Plants; Random sampling; Rare species; Sampling methods; Species; Species richness; Survey design; Vegetation; Vegetation structure; Vegetation surveys
• ISSN: 1385-0237; 1573-5052
• DOI: 10.1007/s11258-005-3495-x

Spatial autocorrelation in vegetation has been discussed extensively, but little is yet known about how standard plant sampling methods perform when confronted with varying levels of patchiness. Simulated species maps with a range of total abundance and spatial autocorrelation (patchiness) were sampled using four methods: strip transect, randomly located quadrats, the non-nested multiscale modified Whittaker plot and the nested multiscale North Carolina Vegetation Survey (NCVS) plot. Cover and frequency estimates varied widely within and between methods, especially in the presence of high patchiness and for species with moderate abundances. Transect sampling showed the highest variability, returning estimates of 19-94% cover for a species with an actual cover of 50%. Transect and random methods were likely to miss rare species entirely unless large numbers of quadrats were sampled. NCVS plots produced the most accurate cover estimates because they sampled the largest area. Total species richness calculated using semilog species-area curves was overestimated by transect and random sampling. Both multiscale methods, the modified Whittaker and the NCVS plots, overestimated species richness when patchiness was low, and underestimated it when patchiness was high. There was no clear distinction between the nested NCVS or the non-nested modified Whittaker plot for any of the measures assessed. For all sampling methods, cover and especially frequency estimates were highly variable, and depended on both the level of autocorrelation and the sampling method used. The spatial structure of the vegetation must be considered when choosing field sampling protocols or comparing results between studies that used different methods.