A systematic review of the statistical scope of restoration ecology of invaded grassland ecosystems
- Published
- Accepted
- Subject Areas
- Conservation Biology, Ecology, Environmental Sciences, Plant Science, Statistics
- Keywords
- grassland, invasion, PRISMA, restoration ecology, statistics, synthesis, systematic review, restoration, plant
- Copyright
- © 2014 Liczner
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. A systematic review of the statistical scope of restoration ecology of invaded grassland ecosystems. PeerJ PrePrints 2:e558v1 https://doi.org/10.7287/peerj.preprints.558v1
Abstract
Restoration ecology is a rapidly growing field of research. The statistical analyses and experimental designs used in this field have likely also expanded. In this review, the statistical scope of the restoration ecology of invaded grasslands will be investigated. A systematic review was conducted on 103 articles to examine the types of statistical tests used and how they changed over time, if assumptions are tested, and how the number of statistical tests and the experimental design influence both the citation rate of articles and the impact factor of journals where these articles are published. ANOVAs have consistently been the dominant test. Statistical test diversity has increased since the year 2000. Most articles did test the assumptions of statistical analyses. The number of tests, and sample size of experiments are both positively correlated with the average citation rate of articles and the impact factor of the journal while the number of factors was negatively correlated. GLMs are recommended as a statistical test to be used more frequently in the future over ANOVAs. There is room for improvement in terms of reporting statistics accurately, including testing assumptions. When possible, sample sizes should be increased to both increase the quality of data, and the citation rate and the journal impact where articles are published. When possible and appropriate, sample sizes and the number of statistical tests should be increased. Adding factors in experimental designs should only be done so without compromising sample size as it has been shown to hinder the citation rate and journal impact.
Author Comment
This is the first draft of a systematic review that may be submitted for review in the future.