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Diggle's tests of spatial randomness based on empirical distributions of interpoint distances can be performed with and without edge-effect correction. We present here numerical results illustrating that tests without the edge-effect correction proposed by Diggle (1979, Biometrics 35, 87-101) have a higher power for small sample sizes than those with correction. Ignoring the correction enables detection of departure from spatial randomness with smaller samples (down to 10 points vs. 30 points for the tests with correction). These results are confirmed by an example with ecological data consisting of maps of two species of trees in a West African savanna. Tree numbers per species per map were often less than 20. For one of the species, for which maps strongly suggest an aggregated pattern, tests without edge-effect correction enabled rejection of the null hypothesis on three plots out of five vs. on only one for the tests with correction.

The work on group testing described here is complementary to experiments reported previously (M AU xv et al., 1983) dealing with the potential of ELISA in testing soybean seed for soybean mosaic virus. The biological part of this paper (part I) shows that group testing gave similar results when the determination of the percentage of transmission was done using, in a comparative way, groups of 30 embryos, groups of 30 axes or groups of 30 seeds. Whatever the number of infected testas in the groups of 30 seeds, it was possible to prevent the viral antigen from testas from altering the results : indeed, after soaking seeds and grinding them briefly in a blendor, it was observed that only the embryos were ground. These results show that the procedure is practical for routine use. Part II gives mathematical elements allowing an estimate of the percentage of transmission with confidence intervals. Charts enable one to read these values directly for n = 30 seeds per group and N = 10, 30, 60 and 200 groups. An aim of this part was also to guide a good choice of n and N and to plan analyses. These mathematical data could potentially be used for group analysis with any other biological material.

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