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BUGS in the Analysis of Biodiversity Experiments: Species Richness and Composition Are of Similar Importance for Grassland Productivity

By Andy Hector, Thomas Bell, Yann Hautier, Forest Isbell, Marc Kéry, Peter B. Reich, Jasper van Ruijven and Bernhard Schmid

Abstract

The idea that species diversity can influence ecosystem functioning has been controversial and its importance relative to compositional effects hotly debated. Unfortunately, assessing the relative importance of different explanatory variables in complex linear models is not simple. In this paper we assess the relative importance of species richness and species composition in a multilevel model analysis of net aboveground biomass production in grassland biodiversity experiments by estimating variance components for all explanatory variables. We compare the variance components using a recently introduced graphical Bayesian ANOVA. We show that while the use of test statistics and the R2 gives contradictory assessments, the variance components analysis reveals that species richness and composition are of roughly similar importance for primary productivity in grassland biodiversity experiments

Topics: Research Article
Publisher: Public Library of Science
OAI identifier: oai:pubmedcentral.nih.gov:3047546
Provided by: PubMed Central

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Citations

  1. (1967). A fresh look at the basic principles of the design and analysis of experiments.
  2. (1994). A language and program for complex Bayesian modelling.
  3. (2009). A Linear Model Method for Biodiversity–Ecosystem Functioning Experiments.
  4. (1996). An introduction to Bayesian inference for ecological research and environmental decision-making.
  5. (2005). Analysis of variance - Why it is more important than ever.
  6. (2010). Analysis of Variance with Unbalanced Data: An Update for Ecology & Evolution.
  7. (1997). Bayes for beginners? Some reasons to hesitate.
  8. (2004). Bayesian inference in ecology.
  9. (2001). Bayesian Statistics: Estimating Plant Demographic Parameters.
  10. (2000). Biodiversity and Ecosystem Function: an Issue in Ecology.
  11. (1997). Biodiversity and ecosystem function: the debate deepens.
  12. (2009). Biodiversity, Ecosystem Functioning, and Human Wellbeing: An Ecological and Economic Perspective:
  13. (2008). Cascading effects of predator richness.
  14. (1960). Complex analyses of variance: general problems.
  15. (2007). Contrasting effects of diversity on the temporal stability of plant populations.
  16. (2009). Core Team
  17. (2003). Data Analysis and Graphics Using R: An Example-Based Approach. Cambridge:
  18. (2007). Data Analysis Using Multiple Regression and Multilevel/Heirarchical Models. Cambridge:
  19. (1997). Distinguishing between the effects of species diversity and species composition.
  20. (2009). Diversity enhances community recovery, but not resistance, after drought.
  21. (2005). Diversity-productivity relationships: initial effects, long-term patterns, and underlying mechanisms.
  22. (2007). Ecological applications of multilevel analysis of variance.
  23. (2005). Ecosystem effects of the manipulation of plant diversity in European grasslands.
  24. (2005). Effects of biodiversity on ecosystem functioning: a consensus of current knowledge and needs for future research.
  25. (2009). Elevated CO2 reduces losses of plant diversity caused by nitrogen deposition.
  26. (2002). Experimental Design and Data Analysis for Biologists. Cambridge:
  27. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (including supplementary online chapters):
  28. Gelman A (2005) R2WinBUGS: A Package for Running WinBUGS from R.
  29. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution.
  30. (1997). Graphen A
  31. (2006). Hector A
  32. (1997). Hidden treatments in ecological experiments: re-evaluating the ecosystem function of biodiversity.
  33. (2004). How do different measures of functional diversity perform?
  34. (2006). Introduction to Mixed Modelling.
  35. (2010). Introduction to WinBUGS for Ecologists. - A Bayesian approach to regression, ANOVA, mixed models and related analyses.
  36. (1996). Introduction: Ecological applications of Bayesian inference.
  37. (2009). Long-term persistence of a positive plant diversity-productivity relationship in the absence of legumes.
  38. (2009). Longterm effects of plant diversity and composition on soil nematode communities in model grasslands.
  39. (2000). Mixed effects models in S and S-Plus.
  40. (2008). Mixed-effects modeling with crossed random effects for subjects and items.
  41. (2010). On the application of multilevel modeling in environmental and ecological studies.
  42. (2005). Overyielding in experimental grassland communities - irrespective of species pool or spatial scale.
  43. (2001). Plant diversity enhances ecosystem responses to elevated CO2 and nitrogen deposition.
  44. (2003). Positive effects of plant species diversity on productivity in the absence of legumes.
  45. (1971). Recovery of interblock information when block sizes are unequal.
  46. (1996). Should ecologists become Bayesians?
  47. (2004). Species and functional group diversity independently influence biomass accumulation and its response to CO2 and N.
  48. (2001). Statistical significance versus fit: estimating the importance of individual factors in ecological analysis of variance.
  49. (1991). That BLUP is a good thing: The estimation of random effects.
  50. (2009). The analysis of biodiversity exeriments: from pattern toward mechanism.
  51. (1947). The assumptions underlying the analysis of variance.
  52. (1995). The Computer Analysis Of Factorial Experiments: In Memoriam - Frank Yates.
  53. (2002). The design and analysis of biodiversity experiments.
  54. (2009). The effect of growth conditions on the seed size/number trade-off.
  55. (1997). The effects of plant composition and diversity on ecosystem processes.
  56. (2011). The Functional Role of Producer Diversity in Ecosystems.
  57. (1997). The influence of functional diversity and composition on ecosystem processes.
  58. (2010). The Jena Experiment: six years of data from a grassland biodiversity experiment.
  59. (1994). The Statistics Of Linear-Models - Back To Basics.
  60. (2009). Why biodiversity is important to the functioning of real-world ecosystems.
  61. (1986). Why Isn’t Everyone A Bayesian.
  62. Wilsey BJ (In press) Increasing native, but not exotic, biodiversity increases aboveground productivity in both ungrazed and intensely grazed grasslands.
  63. (2000). WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility.