- Top of page
- Measuring multifunctionality
- Supporting Information
Nearly 20 years of empirical work has clearly shown that losing species can impact a wide variety of ecosystem processes such as primary production and nutrient cycling (Balvanera et al. 2006; Cardinale et al. 2006, 2011), and that these impacts may equal or exceed those of many other human drivers of environmental change (Hooper et al. 2012; Tilman, Reich & Isbell 2012). These experiments primarily focus on biodiversity's effect on single functions. However, accumulated evidence suggests that the impact of diversity is different, and potentially stronger, when multiple functions are considered together (Hector & Bagchi 2007; Gamfeldt, Hillebrand & Jonsson 2008). Here, we consider the growth and development of research on biodiversity and multiple ecosystem function, and how we can best evaluate how diversity simultaneously can affect ecosystem ‘multifunctionality’.
Most experiments to date have measured the impacts of diversity loss on one or a few functions considered in isolation (see summaries by Hooper et al. 2005; Stachowicz, Bruno & Duffy 2007; Cardinale et al. 2011). For such individual ecosystem processes, effects of diversity generally saturate at relatively low levels of species richness (see data summaries by Cardinale et al. 2006, 2011; but see Reich et al. 2012). In practice, society values a suite of ecosystem properties, each of which has the potential to respond to diversity loss (e.g. Millennium Ecosystem Assessment 2005). It would clearly be valuable to quantify how ongoing diversity loss simultaneously influences the suite of functions or services that ecosystems provide and whether the effect of diversity on multiple functions is different from its effect on individual functions. Our understanding of how diversity affects ecosystem functioning may be limited or even biased by the current single function approach if trade-offs or synergies among processes are ignored.
A few empirical studies suggest that diversity may increase the provision of several ecosystem processes simultaneously – the so-called ‘multifunctionality’ of ecosystems – and that effects of diversity on multifunctionality may not saturate at the low levels typical of single functions (e.g. Duffy, Richardson & Canuel 2003; Hector & Bagchi 2007). Thus, the magnitude of diversity's impact may be stronger when multifunctionality is considered. Alternatively, trade-offs among different functions could render diverse systems less capable of providing multiple functions compared with monocultures of particular species (Zavaleta et al. 2010; Gamfeldt et al. 2013). The effect of diversity on multifunctionality could thus be smaller than its effect on any single function. However, we cannot assess the strength of diversity's effect on multifunctionality from extant work because the few experiments that considered how diversity affects multiple functions simultaneously have used multiple analytical frameworks to measure multifunctionality.
While we can define multifunctionality as the simultaneous performance of multiple functions, how this definition is operationalized makes a critical difference to the conclusions drawn from an experiment. Researchers have used four basic approaches to explore the relationship between biodiversity and multifunctionality (Table 1). We briefly present and then discuss them in more detail below. The simplest is the single functions approach, which considers a collection of functions and asks qualitatively whether more functions achieve higher values in the diverse mixture than at lower levels of species richness (Duffy, Richardson & Canuel 2003). Analysis of these univariate responses provides information about the diversity–multifunctionality relationship but does not provide any quantitative measure of multifunctionality. A second, related method (Hector & Bagchi 2007; Isbell et al. 2011), the turnover approach, tests whether different sets of species promote different functions and has the potential to quantify the fraction of species that contribute to one or more functions. Third, the averaging approach (Hooper & Vitousek 1998) aims to collapse multifunctionality into a single metric that estimates the average value of multiple functions achieved in a given assemblage or plot. Fourth, the threshold approach (Gamfeldt, Hillebrand & Jonsson 2008; Zavaleta et al. 2010) tallies the number of functions that quantitatively exceed some pre-defined threshold of ‘functionality’ in a given assemblage or plot. These four approaches have primarily been applied to experimental data, but have also shown utility in analysing observational studies as well (Maestre et al. 2012b).
Table 1. Comparison of four approaches previously used to quantify ecosystem multifunctionality, and the new approach recommended here. The table summarizes what questions are addressed by each approach, what unique information is gained, what the limitations are, and references that have used the approach. For each question in the column “Question addressed”, an answer of ‘no’ would correspond to the null hypothesis, and an answer of ‘yes’ would correspond to a testable alternative hypothesis
|Approach||Question addressed||Unique information||Limitations||References|
|1. Single functions||Do more functions achieve high values in a diverse mixture than for any single species?||Direct information about each individual function|| |
Does not provide a metric relating diversity and multifunctionality
|Duffy et al. (2003)|
|2. Turnover||Do different species promote different functions?||Indicates whether different species drive different processes|| |
Does not consider negative effects
Does not measure multifunctionality directly
|Hector & Bagchi (2007), He et al (2009), Isbell et al (2011)|
|3. Averaging||Does the average level of multiple functions increase with the number of species? ||Indicates average diversity effect on functions|| |
Single functions can have large impact.
Cannot distinguish between (i) two functions at similar level and (ii) one function at high level and other function at low level
|Hooper & Vitousek (1998), Mouillot et al. (2011), Maestre et al. (2012a,b)|
|4. Single threshold||Does the number of functions exceeding a threshold increase with the number of species?||Indicates whether multiple functions have high value || |
Does not indicate extent to which threshold is exceeded or not
|Gamfeldt et al (2008), Zavaleta et al (2010), Peter et al. (2011)|
|5. Multiple thresholds||Does diversity influence the level of performance of multiple functions?|| |
Provides a measure of how diversity simultaneously influences multiple functions
Multiple informative metrics describe different aspects of multifunctionality
|Produces a curve rather than a single number||This paper|
These four approaches provide very different means of evaluating the relationship between diversity and multiple ecosystem functions, and they require different assumptions and interpretations. Each has pros and cons (Table 1). As currently implemented, none provides a single omnibus metric of multifunctionality. Moreover, all approaches share issues that require consideration in estimating multifunctionality. For example, an inherent challenge in estimating multifunctionality is deciding whether a negative or positive value of a function is considered ‘desirable’. This decision is necessary to create a single number as an index of multifunctionality. It is also inherently subjective and requires an explicit explanation of the rationale.
In this study, we provide a critical analysis of the four existing approaches for measuring multifunctionality. We demonstrate the insights provided by modified versions of each, and we compare their strengths and weaknesses. We illustrate each technique using the R package multifunc (http://github.com/jebyrnes/multifunc; installation instructions and code for analyses in this paper are in Data S1) applied to data from the European BIODEPTH experiment (Spehn et al. 2005), a series of simultaneous experiments that manipulated diversity of grassland plants at eight locations across Europe. These analyses concern the relationship of species richness to function, but there is no reason that Shannon diversity, evenness or other measures could not be incorporated provided the researcher is aware of their limitations or converts them into effective species richness (Jost 2006). Ultimately, we conclude that a modified version of the threshold approach provides the most comprehensive and informative approach and recommend its use for future research. Our hope is that this analysis will pave the way for more rigorous and consistent analyses of the influence of biodiversity (or other factors) on ecosystem multifunctionality.
- Top of page
- Measuring multifunctionality
- Supporting Information
Understanding how changing biodiversity influences the broad suite of processes that ecosystems perform is not simple. Here, we compared the most common approaches used to characterize multifunctionality. While we have used experimental data as our example, there is no reason that these techniques could not be applied to observational data. Threshold-based approaches and averaging-based approaches merely provide a method for deriving a new response variable from any measured plot. Overlap approaches, if provided with a data set varying widely enough in composition, should work as well for observational data. Our analysis shows that systematically exploring how diversity affects multiple functions across the full range of possible thresholds provides an informative ‘fingerprint’ of diversity effects on multifunctionality. The multiple threshold approach provides the most complete and unambiguous summary of the relationships between biodiversity and multifunctionality to date. It addresses many of the ambiguities and problems of previous methods.
Our analysis has focused on how to summarize information regarding the effects of species richness on multiple ecosystem processes efficiently and accurately. But understanding multifunctionality mechanistically still requires that such analyses of multifunctionality be complemented with analysis of the effects of species richness on individual functions. Moreover, researchers will need to understand whether functions interact with one another, leading to positive or negative correlations between functions that are not driven solely by diversity or species composition (e.g. carbon storage and detritivore driven nutrient recycling). The approaches presented here are not the only available analytic tools. Although beyond the scope of our discussion, other approaches such as Structural Equation Modelling (Grace et al. 2010) are potentially promising for incorporating trade-offs, feedbacks and other interactions among functions in a more explicit mechanistic manner. As BEF experiments often include a large number of experimental units (e.g. all monocultures and one or more polycultures), many studies meet its high sample size requirements. Furthermore, knowledge of trade-offs and correlations between functions as elucidated by SEM, or even an examination of the correlation matrix of functions, will be crucial to develop a mechanistic understanding of why diversity does or does not affect multifunctionality.
Similarly, extrapolating statistical estimates of individual species effects and interactions to simulate and explore untested species compositions may be useful in more thoroughly investigating effects of diversity on multifunctionality. This latter approach can be particularly promising in the presence of complex nonlinearities and species interactions. However, again, knowledge of trade-offs and interactions between functions may be a key to accurate simulations.
The field of biodiversity and ecosystem multifunctionality is still relatively data poor compared with explorations of biodiversity effects on single ecosystem functions. In no small part, this is due to the complex issues generated by the analysis of multifunctionality, the effort to conduct experiments with many levels of species richness, and the difficulty of measuring more than a handful of functions. These logistical issues are surmountable. What is important, now, is to use a common analytical framework to better enable comparisons among experiments as more information becomes available. With results of our comparative analysis in hand, we hope that use of the tools and techniques outlined here and implemented in the multifunc package for R will assist in amassing a solid body of data, amenable to investigation of overall trends and underlying mechanisms. We look forward to seeing the field advance.