ABSTRACT

Concerns over the consequences of species loss for ecosystem functions and the services they underpin have led to a proliferation of experiments in which functional group identity is manipulated. The norm in these experiments has been to assemble communities from arbitrary groups. However, there is evidence to suggest that such groups are unable to adequately predict ecosystem function because the mean trait values per group are not sufficiently different. More meaningful functional groupings can be developed by classifying organisms by their functional effects traits, thus making the link between the biota and the functions of interest easier to identify. Here I describe a process for establishing functional groups based upon the identification of traits that affect the function of interest by using divisive hierarchical cluster analysis. This approach can be applied to a wide range of ecosystems to assess the impact of functional diversity upon ecosystem processes, particularly when functions to be measured are relevant predetermined. I suggest two rigorous procedures for validating the groups, and present an example of a large-scale field experiment where this approach has been implemented. This method is applicable to a wide range of communities and trophic levels. I intend that it should be used to contribute to a more standardised approach to grouping species, which in turn will aid predictive power of ecosystem models.

2.2 INTRODUCTION

Concerns over the implications of global biodiversity decline for ecosystem functioning and associated services has led to a proliferation of biodiversity and ecosystem function (BDEF) experiments (Hooper et al. 2005; Cardinale et al. 2006; Hillebrand & Matthiessen 2009). These have generally found that the diversity of species and functional groups positively influences function but our mechanistic understanding of this relationship is incomplete, with the species traits that influence function often remaining unidentified.

It is impractical to establish experiments containing all possible combinations of species from a given species pool, and there are advantages to simplifying diversity by reducing it to functional groupings based upon morphological, physiological and/or phenological traits to study ecosystem functions (Dìaz & Cabido 1997; Lavorel & Garnier 2002; Petchey 2004). This is more comprehensive and defendable than choosing taxonomic species combinations, which some experiments have done in the past, because the aim is to create groups where trait variation is lower within groups than between (Tilman et al. 1996; Fridley 2003). The assumption is that if the traits chosen are closely linked to the functions to be studied, the groups will exert discrete effects on their surroundings. This approach also allows researchers to evaluate influential trait combinations, which can then be compared across experiments and possibly lead to generalisation across systems.

At present there is no standardised measure of functional diversity in a given assemblage, and there have been many calls for a universal protocol for both trait measurement and the classification of functional groups (Lavorel et al. 1997; Tilman 1999; Lavorel & Garnier, 2002; Cornelissen et al. 2003; Naeem & Wright 2003; Harrington et al. 2010). Many field experiments, which have commonly been performed in grasslands, have used the traditional grass/forb/legume (GFL) classification, or slightly more mechanistic groupings using rooting depth and the seasonality of annuals, to investigate links with decomposition and nitrogen cycling (Hooper 1998; Wright et al. 2006). These classifications lack a sound mechanistic basis and make no concession to the high likelihood of substantial within-group variation in other functionally important traits, or the possibility of strong trait overlap between groups. A multi-site analysis by Wright et al. (2006) re-sorted species from GFL groups into random combinations from a number of experiments and related the new groupings to function data. Their conclusion was that that the GFL classification has no

greater explanatory power than randomly allocated groups. There is a vast amount of literature that describes potential methods of grouping, but a good starting point for linking effect to response is to use a hierarchical technique based upon measuring plant functional traits, and choosing to use traits that have the best support in the literature for correlation with function (Lavorel et al. 1997).

Here I describe a systematic process for the formulation of a-priori functional effects groupings for use in BDEF experiments, illustrated with a worked example. It builds upon the work of Roscher et al. (2004) and the use of functional diversity measures (e.g. Petchey & Gaston 2002) to produce a scheme that allows for both rigorous hypothesis testing and continuous trait based approaches to explain function (e.g. Dìaz et al. 2007b). In this method the functions of interest and the species pool are defined, appropriate species traits are selected for measurement and a dendrogram of species is constructed, segregated by cluster analysis. The species groups returned by the dendrogram should show similar within-group relationships to the chosen functions, thus allowing for clear and testable predictions for the effect that they have on ecosystem function. A forerunner to our approach is that employed in the Jena experiment, a long-term field study that used customised functional groups to predict the role of functional diversity in a grassland with respect to a wide range of ecosystem functions, properties and services (Roscher, 2004). Their clustering method for various reasons, (including the double weighting of the nitrogen fixation trait), produced groupings that closely align with the GFL classification. Such approaches have also often used morphological traits, such as plant height and leaf size, but lack a clear hypothesised link to most ecosystem functions.

My approach to grouping species has been refined from clustering strategies that originally sought to describe plant life history (Raunkiaer 1934), habitat preferences (Ellenberg et al. 1991) and life history strategies (Grime 1988). I offer this process as a successor to these studies, aiming to group species into the most functionally homogeneous clusters possible, disregarding taxonomic, life history and morphological associations. In doing so I acknowledge that this approach is more suitable for explaining biogeochemical processes than functions based upon co-evolved species interactions (e.g. pollination) or interactions with ecosystem physiognomy (e.g. those involving habitat selection). I illustrate our approach with an example of a field experiment investigating the modification of plant functional diversity on the nitrogen and water cycles of a grassland ecosystem affected by climate change. My example focuses on plant traits and terrestrial ecosystems, but the

process should be applicable to a wide range of systems. I also discuss the pitfalls and advances that such an approach offers, with consideration given to potential problems, such as a lack of empirical evidence for trait-function linkages.