Optimum Sample Size to Detect Perturbation Effects: The ...

8 Ortiz
Conclusions
The decrease of the sampling variability (coefficient of variability) is definitively an
excellent and useful strategy to be applied when the magnitude of effect size is small
and, therefore, a large sample size is required. However, this procedure must be applied
once the size of the perturbation is known, not before. Therefore, any sampling program
design should be avoided that could have deleterious consequences for natural systems,
especially when the acceptance of the null hypothesis was incorrect! This situation
could support misguided management plans, as was described extensively by Peterman
(1990a). In conclusion, the magnitude of perturbation (effect size) is the most relevant
information for any hypothesis testing and more efforts must be focused towards its estimation
(Rotenberry & Wiens, 1985).
Acknowledgements
I would like to thank Prof. Dr. M. Wolff, M.Sc. C. Jimenez and Dr. S. Jesse and the anonymous reviewers for
criticising and improving the manuscript.
References
Andrew, N. & B. Mapstone, 1987: Sampling and the description of spatial pattern in marine ecology. Oceanogr.
Mar. Biol. Annu. Rev., 25: 39±90.
Bernstein, B. & J. Zalinski, 1983: An optimum sampling design and power tests for environmental biologists.
J. Environ. Manage., 16: 35±43.
Cohen, J., 1988: Statistical power analysis for the behavioral sciences. 2 nd edition. L. Erlbaum Associates,
Hillsdale, N.Y.; 567 pp.
Dixon, W. & F. Massey, 1969: Introduction to statistical analysis. 3 rd edition. McGraw Hill Book Co., N.Y.;
638 pp.
Gerrodette, T., 1987: A power analysis for detecting trends. Ecology, 68(5): 1364±1372.
Gray, J., 1996: Environmental science and a precautionary approach revisited. Mar. Pollut. Bull., 32(7): 532±
534.
Green, R., 1989: Power analysis and practical strategies for environmental monitoring. Environ. Res., 50:
195±205.
Mapstone, B., 1995: Scalable decision rules for environmental impacts studies: Effect size, Type I and Type II
errors. Ecol. Appl., 5(2): 401±410.
Mouillot, D., J.-M. Culioli, A. Leprete & J.-A. Tomasini, 1999: Dispersion statistics for three fish species
(Symphodus ocellatus, Serranus scriba and Diplodus annularis) in the Lavezzi Islands Marine Reserve
(South Corsica, Mediterranean Sea). P.S.Z.N.: Marine Ecology, 20(1): 19±34.
Peterman, R., 1990a: Statistical power analysis can improve fisheries research and management. Can. J.
Aquat. Sci., 47: 1±15.
Peterman, R., 1990b: The importance of reporting statistical power: the forest decline and acidic deposition
example. Ecology, 71(5): 2024±2027.
Peterman, R. & M. M'Gonigle, 1992: Statistical power analysis and the precautionary principle. Mar. Pollut.
Bull., 24(5): 231±234.
Ribic, Ch. & L. Ganio, 1996: Power analysis for beach surveys of marine debris. Mar. Pollut. Bull., 32(7):
554±557.
Rotenberry, J. & J. Wiens, 1985: Statistical Power Analysis and community-wide patterns. Am. Nat., 125:
164±168.
Schlese, W. & W. Nelson, 1996: A power analysis of methods for assessment of change in seagrass cover.
Aquat. Bot., 53: 227±233.
Sheppard, Ch., 1999: How large should my sample be? Some quick guides to sample size and the power of
test. Mar. Pollut. Bull., 38(6): 439±447.
Sokal, R. & F. Rohlf, 1995: Biometry. 3 rd edition. W.H. Freeman and Co., San Francisco; 878 pp.
Tiku, M., W. Tan & N. Balakrishnan, 1986: Robust Inference. Marcel Dekker, Inc., N.Y.; 321 pp.