We used survey data collected from a large plot (20 ha) of sub-tropical forest in the Dinghushan Nature Reserve, Guangdong Province, southern China, in 2005 to test the comparative performance of nine speciesrichness estimators (number of observed species, three species-individual curve models, five nonparametric estimators). As the true species richness, we used the 210 free-standing shrub and tree species of >1 cm diameter at breast height recorded during the survey. This true species richness was then used to calculate performance measures of bias, accuracy, and precision for each estimator, whereby we distinguished performance for low, medium, and high sampling intensity. Unsurprisingly, all estimators performed better than the number of observed species in terms of bias and accuracy. Surprisingly, however, two curve models (logistic and logarithm) outperformed all other estimators in terms of bias, accuracy, and precision, which is in contrast to most other previous studies, in which nonparametric methods usually outperform curve models. Intriguingly, relative estimator performance changed between low, medium, and high sampling intensity, sometimes dramatically, reinforcing the assertion that the influence of sampling intensity on estimator performance is an important aspect to investigate and to consider when choosing estimators for ecological surveys. Because these results are based on only one dataset, the results should be treated with caution, both because (1) the generality of these results needs to be confirmed with simulated datasets and (2) more work is needed to establish what ''true'' species richness is extrapolated by each of the tested estimators in both the statistical and the practical sense. Nevertheless, the two curve estimators, namely Logistic and Logarithm, should be considered in future studies of comparative performance of species-richness estimators because of their outstanding performance in this study.

Spatial distribution pattern of biological related species present unique opportunities and challenges to explain species coexistence. In this study, we explored the spatial distributions and associations among congeneric species at both the species and genus levels to explain their coexistence through examining the similarities and differences at these two levels. We first used DNA and cluster analysis to confirmed the relative relationship of eight species within a 20 ha subtropical forest in southern China. We compared Diameter at breast height (DBH) classes, aggregation intensities and spatial patterns, associations, and distributions of four closely related species pairs to reveal similarities and differences at the species and genus levels. These comparisons provided insight into the mechanisms of coexistence of these congeners. O-ring statistics were used to measure spatial patterns of species. Ω 0–10, the mean conspecific density within 10 m of a tree, was used as a measure of the intensity of aggregation of a species, and g-function was used to analyze spatial associations. Our results suggested that spatial aggregations were common, but the differences between spatial patterns were reduced at the genus level. Aggregation intensity clearly reduced at the genus level. Negative association frequencies decreased at the genus level, such that independent association was commonplace among all four genera. Relationships between more closely related species appeared to be more competitive at both the species and genus levels. The importance of competition on interactions is most likely influenced by similarity in lifestyle, and the habitat diversity within the species’ distribution areas. Relatives with different lifestyles likely produce different distribution patterns through different interaction process. In order to fully understand the mechanisms generating spatial distributions of coexisting siblings, further research is required to determine the spatial patterns and associations at other classification levels.

Microsatellites are important genetic markers and have been broadly employed in many genetic studies. Currently, polymorphisms in microsatellites are often detected by an automated system of capillary electrophoresis with fluorescent dyes. In this situation, different dye combinations may cause pull-up/bleed-through problems, which introduce noise signals from one dye channel into another, causing genotyping errors. Here, we report the detection of such a problem at two microsatellite loci that used the HEX dye. Using three datasets, we tested for noise effects in four allele-scoring programmes:Genemapper, Genemarker, Gelquest and Fragman. We found that, because some allele sizes were identical or close to the size of one of the internal size standards, all four programmes gave allele size calling errors due to wrongly identifying pull-up signals as the internal size standard. In addition, because allele miscalling in this study was caused by the fluorescent dye that the microsatellites used introducing noise of the same colour as the internal size standard used, the pull-up correction function in Genemapper, Genemarker and Fragman failed to deal with this. Considering that pull-up peak scoring errors can occur with any dye colour, the phenomenon is not limited to the current HEX dye. Using different software and visual scoring of each result will allow accurate sizing of microsatellite alleles.