Abstract: Species richness variation along altitudinal gradients has been among the hot topics in ecology. Models that estimate richness based on species–area relationships are dependent on representativeness, sampling scale and the classification of samples. In this paper, we have derived a new method to estimate minimum plot number (MPN), which is the minimum number of plots required to accurately estimate the species richness of a concerned region. Our approach combines the rarefaction approach that has been widely used to estimate regional species richness with the concept of the minimum area needed to survey a plant community. We evaluated our approach in a case study using survey data on the arborous species of natural forests on the southern slope of the Qinling Mountain Range. We divided one hundred and thirty five 20 m×20 m plots into a number of plot subgroups along an altitudinal gradient in three different ways and calculated the richness of each subgroup using the rarefaction approach in order to assess the distributional pattern of species richness along the altitudinal gradient. The preliminary results are as follows: (1) Rarefaction approach can help to calculate the species richness in each subgroup with full consideration in its different plot number, and at the same time in the species composition features in the study area, avoiding fluctuates influenced by the individual plot. Our approach of estimating species richness using rarefaction and considering minimum plot number also has the advantage of being able to recognize non-linear relationships between plot area and species number within a plot. (2) The elevational zone with the highest arborous species richness (≥80 sp.) emerged as 1,400–1,900 m a.s.l., and the peak species density (=9.5 sp./km²) occurred at an altitude of 1,890 m a.s.l. (3) Compared with the other two methods of plot sub-grouping, the moving average method under the criteria of equal-plot number intervals based on MPN can not only get more detailed plot subgroups, but also avoid influence resulting from unequal area of plot subgroups. If aided with species density data from the same region, we believe this is an ideal sub-grouping approach to calculating species richness because the curves it generates provide conclusions that are consistent with previous studies, implying its applicability to other regional species richness studies.