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• W2118086197 abstract "Soil microorganisms mediate below- and aboveground processes, but it is difficult to monitor such organisms because of the inherent cryptic nature of the soil. Traditional `blind' sampling methods yield high sample variance. Coupled with low sample size, this results in low statistical power and thus high type II error rates. Consequently, when null hypotheses are rejected they are difficult to interpret further (either biologically insignificant or biologically significant but statistically insignificant). To help alleviate this problem and remove the `blindness' from belowground sampling we suggest researchers perform geostatistical analyses to describe the spatial distribution of the organisms/processes coupled with power analyses to assess required sample sizes. To illustrate this we intensively sampled the soil of a 3 m ×10 m plot from a southern Californian chaparral ecosystem and spatially-described a series of biological and chemical parameters. We then sampled again and stratified the data in relation to plant location and evaluated the probability of detecting a 30% increase in abundance for each variable. Overall, we found that soil organisms do not all function at similar scales, and preliminary spatial analyses help determine which organisms are suitable for study under the scales of interest. Furthermore, the results predict that required sample sizes and type II error rates will be significantly reduced for many belowground variables parameters when using a stratified sampling design. An understanding of how this spatial structure changes over time is also required to properly design stratifications and avoids bias. Thus, a priori spatial- and power-analyses can be useful tools in constructing sampling-strategies for belowground field studies." @default.
• W2118086197 created "2016-06-24" @default.
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• W2118086197 date "1999-07-01" @default.
• W2118086197 modified "2023-10-01" @default.
• W2118086197 title "Designing belowground field experiments with the help of semi-variance and power analyses" @default.
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• W2118086197 doi "https://doi.org/10.1016/s0929-1393(99)00014-1" @default.
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