The freshwater littoral meiofauna in a South Carolina reservoir receiving thermal effluents*

SUMMARY. The freshwater littoral meiofauna along a temperature gradient in Par Pond (a cooling reservoir receiving thermal effluents from a nuclear reactor) was sampled from September 1975 to October 1976. Monthly samples were taken at three stations; ‘hot-water’ (15–40°C), ‘warm-water’ (13–37°C) and ambient or ‘cold-water’ (8–37°C) sites. Total numbers of individuals at the ambient site ranged from 971 to 3674 per 10 cm² (mean = 2263), approximating the density reported from productive estuarine environments. Nematodes, rotifers, ostracods, cladocerans and mites comprised 80% of overall density. When compared to the ambient site, thermally affected sites demonstrated reduced faunal density. Contrary to other environmental perturbation studies, the Shannon-Weaver diversity index (H') did not reflect alteration of structural complexity within the rotifer taxocene when hot-water, warm-water and cold-water sites were compared. Though there was a significant reduction in number of species at the thermally altered sites, high ‘equitability’ among the reduced species resulted in H’ values comparable to those in natural communities. Distinct winter-spring and summer-autumn assemblages were evident in the ambient community while no seasonal clustering was apparent at the thermally affected sites. It is hypothesized that the maintenance of temperatures above ambient normals masks environmental cues that normally would elicit seasonal changes in the rotifer fauna at those two sites.     

Spatial autocorrelation in biology 2. Some biological implications and four applications of evolutionary and ecological interest

Spatial autocorrelation analysis tests whether the observed value of a variable at one locality is significantly dependent on values of the variable at neighbouring localities. The method was extended by us in an earlier paper to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities, and summarize the patterns of geographic variation exhibited by the response surface of any given variable. Identical variation patterns lead to identical correlograms, but different patterns may or may not yield different correlograms. Similarity in the correlograms of different variation patterns suggests similarity in the generating mechanism of the pattern.
The inferences that can be drawn from correlograms are discussed and illustrated. Examination and analysis of variation patterns of several characters or gene frequencies for one population, or of several populations in different places or at different times, permit some conclusions about the nature of the populational processes generating the observed patterns.
Autocorrelation analysis is applied to four biological situations differing in the nature of the data (interval or nominal), in the type of grid connecting the localities (regular or irregular), and the field of application (evolution or ecology). The examples comprise genotypes of individual mice, blood group frequencies in humans, gene frequency variation in a perennial herb, and the distribution of species of trees. The implications of our findings are discussed.     

Spatial autocorrelation in biology 1. Methodology

Spatial autocorrelation analysis tests whether the observed value of a nominal, ordinal, or interval variable at one locality is independent of values of the variable at neighbouring localities. The computation of autocorrelation coefficients for nominal, ordinal, and for interval data is illustrated, together with appropriate significance tests. The method is extended to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities being considered, and summarize the patterns of geographic variation exhibited by the response surface of any given variable.
Autocorrelation analysis is applied to microgeographic variation of allozyme frequencies in the snail Helix aspersa. Differences in variational patterns in two city blocks are interpreted.
The inferences that can be drawn from correlograms are discussed and illustrated with the aid of some artificially generated patterns. Computational formulae, expected values and standard errors are furnished in two appendices.