Including community composition in biodiversity–productivity models

Abstract

Summary1. Studies on biodiversity and ecosystem functioning (BEF) have elicited debate over the interpretation of the positive relationship between species richness and plant productivity. Manipulating richness cannot be achieved without affecting composition; it is thus essential to consider the latter in statistical models. 2. We firstly review existing approaches that use species richness as an explanatory variable and propose modifications to improve their performance. We use an original data set to illustrate the analyses. The classical method where composition is coded as a factor with a level for each different species mixture can be improved by defining the levels using clustering. Methods based on ordinations reduce the dimensionality of plant composition and use the new coordinates as fixed effects; they provide a much better fit to our observations. 3. Secondly, we develop a new method where composition is included as a similarity matrix affecting the residual variance-covariance. Similarity in composition between plots is treated in the same way as shared evolutionary history between species in phylogenetic regression. We find that it outperforms the other models. 4. We discuss the different approaches and suggest that our method is particularly suited for observational studies or for manipulative studies where plant diversity is not kept constant by weeding. By treating species composition in an intuitive and sensible way, it offers a valuable and powerful complement to existing models.

Higher fungal diversity is correlated with lower CO2 emissions from dead wood in a natural forest

“…The second is a ‘species-selection’ effect where more diverse fungal communities might be more likely to contain particular species that cause slow decomposition and somehow also govern the overall decomposition rate of the wood piece. To differentiate these two, we used a method devised by Sandau et al 24. to generate a parameter λ for each regression in Fig.…”

Higher fungal diversity is correlated with lower CO2emissions from dead wood in a natural forest: BioRxiv preprint

“…The second is a 'species-selection' effect where more diverse fungal communities might be more likely to contain particular species that cause slow decomposition and somehow also govern the overall decomposition rate of the wood piece. To differentiate these two, we used a method devised by Sandau et al 24 to generate a parameter λ for each regression in Fig. 1 (statistical details in Supporting Information S3).…”

The relative contributions of species richness and species composition to ecosystem functioning

“…Analyses of the role of composition in modulating the diversity–productivity relationships have yielded contrasting results, from studies reporting commensurate importance of diversity and composition (Hector et al ), to those emphasizing the role of composition (Harvey et al , Sandau et al ). Attempts to disentangle diversity effects from compositional effects are complicated because of the very different nature of the two variables: plant species richness is a quantitative variable that is easy to include in statistical models; whereas composition can be described by the presence/absence or abundance of each species in the experimental plots.…”

“…Composition is thus inherently multivariate, and each plot can be visualized as a point in an n‐dimensional space, where n corresponds to the total number of species in the study. The contrasting nature of richness and composition raises analytical problems when incorporated together into a statistical model (Sandau et al ).…”

“…These models are tailored for experimental studies and their application to observational data is difficult or even impossible. Kahmen et al () and Sandau et al () have proposed statistical methods suitable for such situations. The first one uses an ordination method to reduce the multidimensionality of species composition to few axes; the coordinates of the plots on these axes can then be used as explanatory variables in regression models.…”