The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms
Article first published online: 19 JUN 2013
© 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Ecology and Evolution
Volume 3, Issue 8, pages 2739–2750, August 2013
How to Cite
Ecology and Evolution 2013; 3(8): 2739–2750
- Issue published online: 12 AUG 2013
- Article first published online: 19 JUN 2013
- Manuscript Accepted: 14 MAY 2013
- Manuscript Revised: 10 MAY 2013
- Manuscript Received: 13 DEC 2012
- The Netherlands Commission on Genetic Modification
- Dutch NWO-ERGO program. Grant Number: 836.06.081
- Environmental risk assessment;
- experimental design;
- field trials;
- generalized linear models
To fulfill existing guidelines, applicants that aim to place their genetically modified (GM) insect-resistant crop plants on the market are required to provide data from field experiments that address the potential impacts of the GM plants on nontarget organisms (NTO's). Such data may be based on varied experimental designs. The recent EFSA guidance document for environmental risk assessment (2010) does not provide clear and structured suggestions that address the statistics of field trials on effects on NTO's. This review examines existing practices in GM plant field testing such as the way of randomization, replication, and pseudoreplication. Emphasis is placed on the importance of design features used for the field trials in which effects on NTO's are assessed. The importance of statistical power and the positive and negative aspects of various statistical models are discussed. Equivalence and difference testing are compared, and the importance of checking the distribution of experimental data is stressed to decide on the selection of the proper statistical model. While for continuous data (e.g., pH and temperature) classical statistical approaches – for example, analysis of variance (ANOVA) – are appropriate, for discontinuous data (counts) only generalized linear models (GLM) are shown to be efficient. There is no golden rule as to which statistical test is the most appropriate for any experimental situation. In particular, in experiments in which block designs are used and covariates play a role GLMs should be used. Generic advice is offered that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in this testing. The combination of decision trees and a checklist for field trials, which are provided, will help in the interpretation of the statistical analyses of field trials and to assess whether such analyses were correctly applied.