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Publication Information

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Title: Design issues for evaluating seedling exposure studies.

Author: Peterson, E. Charles Jr.; Mickler, A. Robert.;

Date: 1993

Source: In: Rennolls, K.; Gertner, G., eds. Proceedings of the 1991 IUFRO S4.11 conference: The Optimal Design of Forest Experiments and Forest Surveys. London, UK: University of Greenwich, School of Mathematics, Statistics and Computing. 8 p

Publication Series: Scientific Journal (JRNL)

Description: Tree seedling studies, covering a wide range of experimental conditions in pollutant treatment, species, facilities, and exposure regimes, have become commonplace in forestry research for assessing the actual and potential environmental effects of air pollutants on forest ecosystems. While assuring a wide breadth of scientific information, sufficient consideration has not been given to either the comparability of such population studies or to their appropriate inference. Th e populations of seedlings for which seedling experiments have inference, including the limitations in national or regional generalizations, should be made explicit in the results. Furthermore, the extent to which seedling results are applicable to mature trees and forests condition should not be left in doubt. Finally, the statistical power of any given analysis is almost never discussed, particularly when the outcomes are inconclusive. The approach for control of the exposure regime (i.e., Achieving treatment target levels) is often assumed, rather than assured through documentation.

Keywords: Power, data quality, population inference, pollutants, seedlings

Publication Notes:

  • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

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Peterson, E. Charles, Jr.; Mickler, A. Robert. 1993. Design issues for evaluating seedling exposure studies. In: Rennolls, K.; Gertner, G., eds. Proceedings of the 1991 IUFRO S4.11 conference: The Optimal Design of Forest Experiments and Forest Surveys. London, UK: University of Greenwich, School of Mathematics, Statistics and Computing. 8 p

 


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