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Marine Ecology Progress Series

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MEPS 597:13-22 (2018)  -  DOI: https://doi.org/10.3354/meps12604

Braun-Blanquet data in ANOVA designs: comparisons with percent cover and transformations using simulated data

Bradley T. Furman1,*, Erin H. Leone2, Susan S. Bell3, Michael J. Durako4, Margaret O. Hall1

1Florida Fish Wildlife Conservation Commission, Florida Fish and Wildlife Research Institute, 100 Eighth Avenue, Southeast, St. Petersburg, FL 33701, USA
2Florida Fish Wildlife Conservation Commission, Florida Fish and Wildlife Research Institute, Center for Biostatistics and Modeling, 1105 SW Williston Road, Gainesville, FL 32601, USA
3University of South Florida, Department of Integrative Biology, 4202 East Fowler Avenue, Tampa, FL 33620, USA
4University of North Carolina at Wilmington, Center for Marine Research, 7205 Wrightsville Avenue, Wilmington, NC 28403, USA
*Corresponding author:

ABSTRACT: The Braun-Blanquet (BB) cover-abundance scale is used to visually estimate community composition and species dominance. An 8-division variant was developed for benthic systems in the 1990s; the capacity to speed collection of seagrass coverage data led to its adoption by several large-scale monitoring programs in the USA. However, debate regarding how best to treat ordinal BB data in statistical analysis has stymied progress in the comparison of status and trends. Methods specific to ordinal data exist; however, they have generally been ignored in favor of transformation to percent cover or the use of BB categories as continuous data in parametric statistics and multivariate ordination. To quantify behavior of BB data in 1-way ANOVA, we conducted a series of data simulations using percent cover, BB scores and 3 metric-scale transformations as competing dependent variables in iterated 2-group contrasts. Simulations followed the design of the Fisheries Habitat Assessment Program (FHAP) and covered full ranges of within- and between-group variation. We empirically estimated Type I error and proportional deviance in effect size as measures of performance. Finally, we compared 6 yr of FHAP data to the simulations to identify scenarios likely to be encountered by seagrass ecologists. BB scores performed well as a proxy for continuous data and log-linear transformation allowed more precise effect size estimation. Our results highlight the need for high levels of replication in benthic sampling and provide empirical evidence for the statistical reliability of BB data in parametric analysis.


KEY WORDS: Braun-Blanquet · ANOVA · Categorical · Data simulation · Statistical power · Data transformation · Benthic cover · Seagrass · Benthic monitoring · Percent cover


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Cite this article as: Furman BT, Leone EH, Bell SS, Durako MJ, Hall MO (2018) Braun-Blanquet data in ANOVA designs: comparisons with percent cover and transformations using simulated data. Mar Ecol Prog Ser 597:13-22. https://doi.org/10.3354/meps12604

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