Abundance of floor-dwelling frogs was studied in the humid subtropical lowland forest of Iriomote Island, the Ryukyu Archipelago, southern Japan. Field survey was conducted monthly for 1 y, and the seasonal pattern of abundance was examined for each species. We observed 251 frogs belonging to four species of three families in a total sampling of 600 m2. Of these species, Microhyla ornata (66.5% of total frogs) and Fejervarya limnocharis (30.3%) were dominant in number. Total abundance of the frogs was strongly seasonal and peaked in the hottest month, August, while the number of frog species was one to three throughout the year. The seasonal pattern of abundance showed considerable variation among species, most likely due to their differences in reproductive season, and in physiological characteristics under the influence of varying temperature and rainfall. These patterns are more similar to those of temperate species than those of tropical species. The estimated density of all frogs ranged from 0.08 to 1.60 individuals m−1, and their average annual density and biomass were 4180 individuals ha−1 and 11460 g ha−1, respectively. These values, among the highest so far reported for floor-dwelling anurans, may be a feature of amphibian fauna in the humid subtropical forests on a small island.

A version of the Rao–Blackwell theorem is shown to apply to most, but not all, stereological sampling designs. Estimators based on random test grids typically have larger variance than quadrat estimators; random s-dimensional samples are worse than random r-dimensional samples for s < r. Furthermore, the standard stereological ratio estimators of different dimensions are canonically related to each other by the Rao–Blackwell process. However, there are realistic cases where sampling with a lower-dimensional probe increases efficiency. For example, estimators based on (conditionally) non-randomised test point grids may have smaller variance than quadrat estimators. Relative efficiency is related to issues in geostatistics and the theory of wide-sense stationary random fields. A uniform minimum variance unbiased estimator typically does not exist in our context.