Incorporating plant functional diversity effects in ecosystem service assessments

Abstract

Global environmental change affects the sustained provision of a wide set of ecosystem services. Although the delivery of ecosystem services is strongly affected by abiotic drivers and direct land use effects, it is also modulated by the functional diversity of biological communities (the value, range, and relative abundance of functional traits in a given ecosystem). The focus of this article is on integrating the different possible mechanisms by which functional diversity affects ecosystem properties that are directly relevant to ecosystem services. We propose a systematic way for progressing in understanding how land cover change affects these ecosystem properties through functional diversity modifications. Models on links between ecosystem properties and the local mean, range, and distribution of plant trait values are numerous, but they have been scattered in the literature, with varying degrees of empirical support and varying functional diversity components analyzed. Here we articulate these different components in a single conceptual and methodological framework that allows testing them in combination. We illustrate our approach with examples from the literature and apply the proposed framework to a grassland system in the central French Alps in which functional diversity, by responding to land use change, alters the provision of ecosystem services important to local stakeholders. We claim that our framework contributes to opening a new area of research at the interface of land change science and fundamental ecology.

Fig. 1.

Diagrammatic representation of the steps proposed to reduce uncertainty in the prediction of EP and ES on the basis of plant FD. In stage I, the models tested at each step (M1–M4) link EP with driving factors of different nature: abiotic factors (AF_i), community-aggregated trait value or CWM of any one functional trait (CWM_i), distribution of values of any one trait present in a community (FDvg_i), and local abundance of any one species present in the community (Ab sp_i). At each step, significant factors are identified and conserved for stage II. In stage II, combined models are built by adding statistically significant factors from steps 1–4 and conserving those that significantly improve the model (following a standard criterion, e.g., the Akaike criterion). The process concludes when further information on FD does not reduce uncertainty in EP prediction any further. Generalized models for each step are as follows: M1, EP = ƒ(AF_i, AF_j, …, AF_n); M2, EP = ƒ(CWM_i, CWM_j, …, CWM_n); M3, EP = ƒ(FDvg_i, Fdvg_j, …, Fdvg_n); M4, EP = ƒ(Ab sp_i, Ab sp_j, …, Ab sp_n); M5, EP = ƒ(AF_i, CWM_j, FDvg_k, …, Sp Ab_n); M6, if CWM_i < T then EP = ƒ1(CWM_i); if CWM_i > T then EP = ƒ2(CWM_i); if CWM_i = T then EP cannot be predicted from CWM_i. T = threshold (see stage II, step 6).