Abstract

Sample-based estimates of species richness for large heterogeneous regions may be affected by interactions between the sampling design and sub-regional heterogeneity in species richness and diversity. To investigate this issue a Monte Carlo simulation study was conducted with actual forest inventory tree species incidence data from two large regions, four estimators of species richness, three sampling designs, two sampling units (fixed area plots and tree nearest to plot center), and five sample sizes. The regions represent forested areas in central and eastern Canada and three states in the USA. Design effects in estimates of regional species richness were generally weak, of little practical import, and limited to settings with the smallest sample sizes. The study shows that accurate regional estimates of richness can be obtained by pooling sub-regional species incidence data. Sampling with fixed area plots was, with few estimator-dependent exceptions, considerably more efficient than sampling with one tree per sample location. A proposed ratio-based procedure for combining sub-regional estimates of richness broadens the options for regional estimation.

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