Non-linear partitioning of biodiversity effects on ecosystem functioning

To assess the consequences of ongoing biodiversity changes, hundreds of biodiversity experiments have been carried out since the 1990s. However, changes in ecosystem functioning between systems can not only result from differences in the number of species, but also from differences in what species are present (i.e. species identities). Additive partitioning methods are therefore generally used in biodiversity research. These factor out species identify effects by comparing the observed level of ecosystem functioning against that predicted by the null model for the given species composition of the system. These species’ deviations from the null can be partitioned between several terms reflecting the various mechanisms through which biodiversity can affect ecosystem functioning. Current partitioning methods, however, quantify biodiversity effects based on linear relationships between species functional traits and their deviations from the null model. In this paper, we demonstrate that non-linear relationships frequently occur, and derived a non-linear extension of additive partitioning methods to quantify these more complex biodiversity effects on ecosystem functioning.


Scientific Abstract

1. Assessing the consequences of biodiversity changes for ecosystem functioning requires separating the net effect of biodiversity from potential confounding effects such as the identity of the gained or lost species. Additive partitioning methods allow factoring out these species identify effects by comparing species’ functional contributions against the predictions of a null model under which functional contributions are independent of biodiversity.
2. Classic additive partitioning methods quantify biodiversity effects based on a linear relationship between species deviations from the null model and their functional traits. However, based on ecological theory, nonlinear relationships are also possible.
3. Here, we demonstrate how additive-partitioning methods can be extended to describe such nonlinear relationships, and explain how nonlinear biodiversity effects can be interpreted.
4. We apply both linear and nonlinear partitioning methods to the Cedar Creek Biodiversity II experiment. Nonlinear relationships were detected in the majority of plots, and increased with diversity. Nonlinear partitioning thereby identified a convex relationship between species functional traits and their deviations from the null model, driven by strong positive effects of both species with low and high functional trait values trait values on ecosystem functioning.
5. The presented nonlinear extension of additive partitioning methods is therefore essential for revealing more complex biodiversity effects on ecosystem functioning, that are likely to occur in biodiversity experiments.


Full reference (link)

Baert JM, Jaspers S, Janssen CR, De Laender F, Aerts M. 2017. Non-linear partitioning of biodiversity effects on ecosystem functioning. Methods in Ecology and Evolution (Accepted article).

Category: