A Linear Model Method for Biodiversity-Ecosystem Functioning Experiments

Abstract

Experiments that manipulate species richness and measure ecosystem functioning attempt to separate the effects of species richness (the number of species) from those of species identity. We introduce an experimental design that ensures that each species is selected the same number of times at each level of species richness. In combination with a linear model analysis, this approach is able to unambiguously partition the variance due to different species identities and the variance due to nonlinear species richness, a proxy measure for interactions among species. Our design and analysis provide several advantages over methods that are currently used. First, the linear model method has the potential to directly estimate the role of various ecological mechanisms (e.g., competition, facilitation) rather than the consequences of those mechanisms (e.g., the "complementarity effect"). Second, unlike other methods that are currently used, this one is able to estimate the impact of diversity when the contribution of individual species in a mixture is unknown. abstract: Experiments that manipulate species richness and measure ecosystem functioning attempt to separate the effects of species richness (the number of species) from those of species identity. We introduce an experimental design that ensures that each species is selected the same number of times at each level of species richness. In combination with a linear model analysis, this approach is able to unambiguously partition the variance due to different species identities and the variance due to nonlinear species richness, a proxy measure for interactions among species. Our design and analysis provide several advantages over methods that are currently used. First, the linear model method has the potential to directly estimate the role of various ecological mechanisms (e.g., competition, facilitation) rather than the consequences of those mechanisms (e.g., the "complementarity effect"). Second, unlike other methods that are currently used, this one is able to estimate the impact of diversity when the contribution of individual species in a mixture is unknown.

Bee diversity effects on pollination depend on functional complementarity and niche shifts

“…Each bee species was present in cages across the diversity gradient. As is often the case in biodiversity experiments, it was impossible to realize all possible species combinations (Bell et al 2009); in our case this was due to limited availability of some bee species.…”

Microbial diversity determines the invasion of soil by a bacterial pathogen

“…S1) [ANOVA F (4,40) = 69.56; P = 0.0001] over the entire experiment. Natural soil showed the highest mean species richness value and the 10 6 treatment the lowest.…”

“…Bacterial strains were isolated from soil (as described previously), and a set of 40 different strains, distinguished according to colony morphology and BOX-PCR (39), was selected. Briefly, the strains were used to create communities of increasing species richness, following a broken stick model (39,40) to create communities varying in species richness and composition. Bacterial species were randomly ordered, creating one stick, which was then subsequently "broken", giving two assemblages of 20 species, four of 10 species, and eight of 5 species.…”

Biodiversity acts as insurance of productivity of bacterial communities under abiotic perturbations

“…The linear and quadratic effects of diversity (species richness, phylogenetic diversity, mean pairwise distance) and presence of tolerant species were calculated through regression. The effects of species richness (linear and fixed factor), absence/presence of individual species and compositions were analysed with general linear models in R version 2.15.1 (http://www.r-project.org) (Bell et al, 2009). For calculating F-statistic, the effects of variables were tested against specific error terms to avoid the problem of pseudoreplication.…”