Chapter Nine - Scaling from Traits to Ecosystems: Developing a General Trait Driver Theory via Integrating Trait-Based and Metabolic Scaling Theories
Introduction
Understanding and explaining species richness patterns have had far-reaching influence on the development of ecology. Biodiversity science strives to understand the drivers and consequences of variation in the number of species and how species abundances change across spatial and temporal scales (MacArthur, 1972, Rosenzweig, 1995). These changes in species richness have also been linked with changes in ecosystem functioning. The Biodiversity–Ecosystem Functioning (BEF) hypothesis states that ecosystems with greater biodiversity are more productive and stable (Naeem et al., 1994, Tilman, 2001, Tilman et al., 1997). Attempts to answer these questions have led to debates that polarized the field (Wardle, 2002), and a growing consensus that species numbers alone do not inform us about all important aspects of ecosystem functioning and community responses to environmental change (Chapin et al., 2000, Díaz and Cabido, 2001, Diaz et al., 2007, Stevens et al., 2003).
More recently, trait-based approaches have focused on recasting classical questions from the species richness literature (Hillebrand and Matthiessen, 2009, Lamanna et al., 2014, Lavorel and Garnier, 2002, McGill et al., 2006, Violle et al., 2007). Instead of species richness, there is an attempt to focus on functional traits and diversity in trait values (Díaz and Cabido, 2001, Lavorel and Garnier, 2002, Mason et al., 2005, Petchey and Gaston, 2002, Roscher et al., 2012). In addition, metabolic scaling theory or MST has focused on the central role of body size as a critical driver of ecological, ecosystem, and evolutionary patterns and processes (Enquist et al., 1998, Enquist et al., 2003, Gillooly et al., 2005, Savage et al., 2004). One could also ask about diversity in the number and/or range of trait or body size values, and to some degree, this depends on how traits are defined. As discussed by Dell et al. (2015) and Pawar (2015) in this issue, the premise is that measures of traits, including body size, can better reveal the mechanisms and forces that ultimately structure biological diversity (Grime, 2006, McGill et al., 2006, Stegen et al., 2009) and increase the generality and predictability of ecological models (Díaz et al., 2004, Kattge et al., 2011, Webb et al., 2010). Trait-based approaches have especially received attention for plant life histories and strategies due to a renewed interest in measuring traits across different environments and scales (Craine, 2009). While this has long been part of comparative physiology and ecology (see Arnold, 1983, Grime, 1977), it is now being heralded as its central paradigm (Craine, 2009, Westoby and Wright, 2006). Similarly, trait-based approaches are being used to disentangle the forces that structure larger scale biodiversity gradients (Belmaker and Jetz, 2013, Han et al., 2005, Reich, 2005, Reich and Oleksyn, 2004, Safi et al., 2011, Swenson and Enquist, 2007) and to predict large-scale ecosystem shifts due to climate change (Elser et al., 2010, Frenne et al., 2013).
An important limitation to developing a more predictive trait-based ecology is that its focus and implementation have relied almost entirely on empirical correlations and null models (for example, see discussion in Swenson, 2013). There is a need for theory and quantitative arguments to move beyond pattern searching. Further, trait-based ecology has largely developed independently from MST, where the role of body size—arguably a key trait—is central to scaling up organismal processes. Nonetheless, a key focus of trait-based ecology is to identify the general processes underlying trait-based ecology (Enquist, 2010, Shipley, 2010, Suding et al., 2008b, Webb et al., 2010, Weiher et al., 2011). Such an advance would help guide the explosion of trait-based data collection (Dell et al., 2013, Kattge et al., 2011), develop a more predictive ecology, and organize rapidly developing directions in trait-based ecology (Boulangeat et al., 2012, Funk et al., 2008, Lavorel et al., 2011, McGill et al., 2006, Shipley, 2010, Suding et al., 2008b).
Another limitation is the debate about whether biodiversity, trait diversity, or both are important for ecosystem functioning (Hooper et al., 2004, Loreau et al., 2001). We agree with Cardinale et al. (2007) that this debate is largely a false dichotomy. Increasingly, the evidence shows that both the number of species and types of species in an ecosystem impact biomass production. For example, focusing solely on species number has resulted in sometimes positive, negative, or null relationships between species richness and ecosystem functioning (Grace et al., 2007, Roscher et al., 2012).
Lastly, because trait-based ecology measures properties of individuals that are linked to the environment and because it attempts to make predictions for ecosystem functioning, it must be able to scale from individuals to ecosystems. However, achieving this requires an exciting but extremely challenging synthesis of physiology, population biology, evolutionary biology, community ecology, ecosystem ecology, and global ecology (Reich, 2014, Webb et al., 2010). In this chapter, we suggest combining trait-based approaches with MST to make some progress on this problem.
Here, we present a novel theoretical framework to scale from traits to communities to ecosystems and to link measures of diversity. We argue that trait-based ecology can be made more predictive by synthesizing several key areas of research and to focus on the shape and dynamics of trait distributions. Our approach is to develop more of a predictive theory for how environmental changes, including land use and shifts in abiotic factors across geographic and temporal gradients, influence BEF (Naeem et al., 2009). We show how starting with a few simple but general assumptions allows us to build a foundation by which more detailed and complex aspects of ecology and evolution can be added. We show how our approach can reformulate and generalize the arguments of Chapin et al., 2000, McGill et al., 2006, Violle et al., 2014 by integrating several insights from trait-based ecology (Garnier and Navas, 2012) and MST (Enquist et al., 1998, Gillooly et al., 2001, West et al., 1997). In doing so, we can derive a more synthetic theory that can begin to: (i) assess differing assumptions underlying the assembly of species; (ii) assess the relative importance of hypothesized drivers of trait composition and diversity; and (iii) build a more predictive and dynamical framework for scaling from traits to communities and ecosystems. We call this theory, Trait Driver Theory or TDT, because it links how the dynamics of biotic and abiotic environment then drive the performance of individuals and ecosystems via their traits. Combining MST with trait driver approaches allows TDT to work across scales and also addresses one of MST's key criticisms: it does not incorporate ecological variation—such as trait variation—and cannot be applied to understanding the forces that shape the diversity and dynamics of local communities (Coomes, 2006, Tilman et al., 2004).
Section snippets
Trait Driver Theory
TDT is based on a synthesis of three influential bodies of work. The first are trait-based approaches that are largely encapsulated in Grime's Mass Ratio Hypothesis or MRH (Grime, 1998). The MRH states that ecosystem functioning is determined by the characteristics or traits of the dominant (largest biomass) species. Implicit in the MRH is the idea that traits of the dominant species are a more relevant measure than species richness. The second component is the generalized and quantitative
Predictions of TDT
Next, we emphasize the central predictions of TDT. These predictions are also summarized in Table 1 in terms of how different measures of the trait distribution can provide novel insight and predictions regarding the main drivers of the current composition of the species assemblage as well as the future dynamics of the species assemblage.
Prediction (1): Shifts in the environment will cause shifts in the trait distribution (Fig. 3; Table 1).
Prediction (2): The difference between the optimal
Scaling from individuals to ecosystems using MST
So far, TDT assumes that there is no variation in organismal size. Instead, the total biomass associated with a trait, z, is denoted by C(z). This notation avoids ever needing to account for individual organismal mass, M, or even the number of individuals with mass. However, body size can vary greatly—it is also an important trait that influences variation in organismal metabolism (Peters, 1983), population growth rate (Savage et al., 2004), and abundance (Damuth, 1981, Enquist et al., 1998).
Additional Predictions of TDT
In the second column of Table 1, we summarize additional TDT predictions for scaling up community or assemblage trait distributions to predict several ecosystem-level effects. Specifically, the shape of the trait distribution as measured via the central moments of the distribution.
Prediction (6): Ecosystem net primary productivity, dCTot/dt, will scale with the total biomass but will be influenced differently by the mean and variance of the community trait distribution.
A third insight from MST
Quantifying the shape of trait distributions
In order to assess predictions of TDT, it is necessary to quantify the biomass distribution of traits, C(z), in a species assemblage. This involves enough measurements of the trait values and body masses to obtain accurate estimates of the underlying distributions, as guided by sampling theory and statistics (Baraloto et al., 2010, Paine et al., 2011). The sampling must occur across all individuals within our group and thus incorporates both inter- and intraspecific trait variability (see
Community trait shifts across local gradients
Numerous studies have documented shifts in the traits of communities and assemblages across environmental gradients (Ackerly, 2003, Choler, 2005, Fonseca et al., 2000, Swenson and Enquist, 2007). However, many studies generally calculate a species mean trait as part of a species list, thus ignoring intraspecific variation. In contrast, several recent studies have measured traits within communities to assess community-level trait shifts (Albert et al., 2010, Gaucherand and Lavorel, 2007, Hulshof
Discussion
We have shown that TDT can formalize numerous assumptions and approaches in trait-based ecology. We provide examples of how this can be done for several different biodiversity hypotheses in terms of the dispersion of traits (Table 1, Table 3). We further argue that ecological theories need to move beyond species richness and be recast in terms of organismal performance via functional traits. As a result, TDT offers an alternative framework to the standard taxonomic approach for linking
Acknowledgements
We thank Mark Westoby who provided enthusiasm and input during the writing of initial drafts of this chapter. We also thank the ARC-NZ Vegetation Network, working group 36, led by V.M.S., for their generosity and support in bringing us all together to meet and to initiate this collaboration and chapter. We thank other members of working group 36, especially Christine Lamanna, Tony Dell, Mick McCarthy, and Graham D. Farquhar, who helped us solidify our central arguments. C.V. was supported by a
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