Sex change and spatial distribution pattern in an intertidal holothurian Polycheira rufescens in the reproductive season

The patterns of sex change and spatial distribution in an intertidal holothurian, Polycheira rufescens (Chiridotidae; Echinodermata), were investigated on a stony beach in Amakusa, western Kyushu. Field caging experiments revealed that some individuals of P. rufescens underwent a sequential sex change from male to female via hermaphrodite stages and back to male again within a single reproductive season. The sex ratio of the population gradually changed from male dominance at first to equal proportions of males and females as the reproductive season progressed. Toward the end of the reproductive season, immature or spent individuals increased in proportion. Stone size appeared to be an important factor affecting the occurrence of P. rufescens individuals on a stony beach. Analysis of spatial distribution by means of Morisita's index of dispersion and nearest neighbor distances indicated that (i) males showed a slightly stronger tendency to aggregate than females, while females had a tendency toward uniform distribution; (ii) females tended to attract males, as shown by relatively short female-to-male distances; and (iii) there was a substantial variation in male-to-female distances, such that some males were positioned close to females while others were not. It may be suggested that spatial distribution of P. rufescens individuals during the reproductive period is partly dictated by the differential needs of individuals of different sexual states. Received: January 25, 1999 / Accepted: June 10, 1999     

On the mathematical basis of the variance-mean power relationship

The mathematical basis of a widely-known variance-mean power relationship of ecological populations was examined. It is shown that the log variance (S ²)—log mean, (m) plot is virtually delimited by two lines logS ²=logn+2 logm and logS ²=logm, thus increasing the chance that a linear regression line can be successfully fitted, without a profoundly behavioural background. This makes difficult the task of interpreting a successful fit of the power law regression and its parameterb in a biologically meaningful manner. In comparison with the power law regression, Iwao'sm ^*-m regression is structurally less constrained, i.e. has a wider spatial region in which data points can scatter. This suggests that a comparison between the two methods in terms of how good a fit is achieved for a particular data set is largely meaningless, since the power law regression may inherently produce a better fit due to its constrained spatial entity. Furthermore, it could be argued that a successful fit in Iwao's method, when found, is less taxed with mathematical arterfacts and perhaps more clearly linked to some biological mechanisms underlying spatial dispersion of populations.     

Dynamics of distribution in animal communities: Theory and analysis

Summary Theoretical and analytical problems of the dynamics of distribution and abundance in animal communities were examined. In many communities, species with low abundance and of limited spatial occurrence (i.e., rare species) typically form a conspicuous peak when a frequency distribution of the number of species is constructed with respect to the proportion of sites occupied within an area of distribution. Models of distribution dynamics, including a new model proposed here, were compared with a range of animal community data using a new procedure to assess single- and bi-modal patterns in frequency distributions of spatial occurrence. Data reveal that single-modality with an excess of rare species occurs more frequently than bimodality. Even when bimodality is detected, the mode representing wide-spread species is in the majority of cases smaller than that for rare species. Thus, a new model in which the rate of local extinctions is assumed to be negatively related to patch occupancy (or population abundance) is in better agreement with observed data than earlier models. Some problems of analysis, in particular model assumptions and testing, are discussed.