Human well-being largely depends on benefits derived from ecological systems known as ecosystem services. However, anthropogenic drivers of change are having widespread effects on ecosystems, potentially compromising their ability to continue to provide these benefits (Millennium Ecosystem Assessment 2005, Cardinale et al. 2012). Understanding risks to ecosystem services presents a formidable intellectual challenge to ecologists, conservation scientists, and evolutionary biologists. Here, we integrate recent ecological research on functional traits of organisms with phylogenetic comparative approaches and show how the two approaches together provide new ways to refine assessments of the vulnerability of ecosystem properties and services in the face of environmental change.
From the perspective of functional ecology, functional traits (Box 1) are known to underpin both species’ contributions to ecosystem properties and services and their tolerance to environmental stressors and disturbances (Suding et al. 2008). Crucially, however, species’ contributions and vulnerabilities will often depend not on individual functional traits but on combinations of traits. In parallel, a phylogenetic perspective has been widely used in functional trait studies at levels ranging from individuals to communities (Webb et al. 2002; Cavender-Bares et al. 2009, 2012; Pausas and Verdu 2010; Pillar and Duarte 2010; Srivastava et al. 2012). The potential usefulness of a phylogenetic perspective for studying ecosystem properties has also been recognized for some time (Edwards et al. 2007; Cavender-Bares et al. 2009; Matthews et al. 2011; Gravel et al. 2012), but work to date has focused mostly on individual traits, and connections between phylogenies and ecosystem services have only recently been proposed (Faith et al. 2010; Srivastava et al. 2012). Here, we introduce evolutionary concepts to the risk assessment of ecosystem properties by applying phylogenetic tools to integrative measures of species’ effects on ecosystem processes and their responses to environmental drivers.
Box 1. Key definitions in the functional trait approach
Functional traits are morphological, biochemical, physiological, structural, phenological, or behavioral characteristics that are expressed in phenotypes of individual organisms and are considered relevant to the response of such organisms to the environment and/or their effects on ecosystem properties (Violle et al. 2007). This crucial position of functional traits at the crossroads between responses to the environment and ecosystem properties explains the increasing attention given to them by both evolutionary biologists and functional ecologists. This duality is reflected in the literature by distinguishing between effect and response traits (Díaz and Cabido 2001; Lavorel and Garnier 2002; Naeem and Wright 2003; Suding et al. 2008).
Effect traits of a species underlie its impacts on ecosystem properties and the services or disservices that human societies derive from them (Aerts and Chapin 2000; Grime 2001; Lavorel and Garnier 2002), whether or not such traits represent an adaptive advantage to the individual itself. Examples of effect traits include water retention capacity in bryophytes (regulating ecosystem hydrology), leaf nitrogen content in vascular plants (accelerating nutrient cycling rate), and burrowing behavior (altering soil structure) or gut digestive features (influencing nutrient turnover) in animals (more examples, with references, are given in Table S1A).
Response traits influence the abilities of species to colonize or thrive in a habitat and to persist in the face of environmental changes. Plant examples include seed size (related to recruitment capacity under different disturbance regimes), bark thickness (conferring fire tolerance), and leaf size (leading to different heat balances). Animal examples are bill shape and size (allowing the capture of food items of different kind and size) in birds, desiccation tolerance in soil arthropods, and tongue length (giving access to nectar contained in flowers of different size and shape) in pollinators (see Table S1B for more examples and references). The same trait may in some cases act as both response and effect traits (Suding et al. 2008): for instance, leaf nitrogen content in plants and body size in animals both underlie multiple responses to the environment and effects on ecosystem properties (Table S1A, B).
Traits can be the joint expression of underlying biophysical and biochemical properties and processes of an organism; whether a trait is a combination of such properties or itself one of such properties is a question of judgment and objective of the study. For example, leaf toughness can be seen as a trait that depends on anatomical characteristics such as venation architecture and density and chemical characteristics such as lignin concentration, or lignin concentration itself can be considered a trait. In this article, we take a broad view of traits without specification of whether or not they can be deconstructed into simpler characteristics. The relevance of functional traits in species’ response to the environment or species’ effect on ecosystems is usually established empirically by observation or manipulation of the ecosystem under study or by extrapolation from other studies. Examples of characteristics or suites of properties commonly regarded as effect or response traits can be found in Table S1.
Organismal traits, ecosystem function, and vulnerability to environmental change
The erosion of the world's biota by environmental change carries a dual risk. One risk is that the continuing delivery of ecosystem services may be compromised as the ecosystem functions and processes regulated by biotic communities are altered (Cardinale et al. 2006; Worm et al. 2006; Estes et al. 2011). The other is the risk of local or even global loss of species whose tolerance is surpassed (Butchart et al. 2010; Secretariat of the Convention on Biological Diversity 2010; Larigauderie et al. 2012), and the associated reduction in evolutionary capital — the ability of evolutionary processes to deliver future benefits to society (Faith et al. 2010). These risks emphasize different aspects of living organisms: the continuity of species’ contribution to ecosystem properties and services, and their capacity to survive and thrive in the face of changing selective pressures. Interactions between these two aspects lead to the identification of two concepts central to our framework for predicting vulnerability of ecosystem properties and benefits to environmental change: Specific Effect Functions and Specific Response Functions. Here, we use the word “function” in its original sense, that is, to “execute or perform”. “Specific function” refers to the “execution” of an effect of a species on a given ecosystem property and also to its “performance” in the face of given environmental drivers. The terms Specific Effect and Response Functions are used to distinguish these two aspects, in keeping with the already widespread concepts of functional effect traits and functional response traits (Lavorel and Garnier 2002; Naeem and Wright 2003; Suding et al. 2008).
A Specific Effect Function (SEF) is the per-unit capacity of a species to influence an ecosystem property or service. The relevant units will depend on the organism and the ecosystem property or service in question: for example, number of seeds dispersed per individual in birds, volume of water transpired per unit leaf area per unit time in trees, and amount of a heavy metal absorbed per unit length of hyphae in mycorrhizas. SEF is equivalent to E in Suding et al.'s (2008) response–effect model for plants, on which our framework builds. In other words, an SEF is the difference made to a particular process at the ecosystem level by a standard “amount” of a species. An SEF depends on one or more functional traits of the species; such traits are termed functional effect traits (Box 2, Fig. 1). An example of an SEF is the capacity of some rodents and ungulates to redistribute nutrients over the landscape and thereby affect resource availability for other organisms; this SEF – which could be expressed as, for example, amount of nitrogen and phosphorus redistributed per individual vole or rhinoceros per month – is underpinned by a set of effect traits including body size, mobility-related characteristics such as limb anatomy, and behavioral traits such as defecation patterns, territorial displays, or solitary versus colonial habit. Although the traits that underlie an SEF are often linked to survival and reproduction and therefore under natural selection, the SEF itself does not need to be. Rather, SEFs are often a side effect of selection on traits that affect fitness. In the example above, the nutrient redistribution capacity (the SEF) may not itself directly link to rodent or ungulate fitness, although most or all the underlying traits presumably do. The same effect trait will often influence more than one SEF; for example, leaf nitrogen content influences plant relative growth rate, palatability to herbivores and litter decomposability (Aerts and Chapin 2000) (see Table S1A for further examples of SEFs and the effect traits that underpin them, with references). Note that although we mostly deal with ecosystem services, that is, species effects on ecosystem properties that are beneficial to people, the concept of SEFs can be equally applied to ecosystem disservices, that is, ecosystem properties that are detrimental.
A Specific Response Function (SRF) is the ability of a species to maintain or enhance its quantity in response to a specified change in the abiotic or biotic environment, or to invade this environment afresh. SRF is measured as the proportional change in the biomass of the species – for which changes in ground cover or population size will often be good proxies – per unit of change in an environmental driver. SRF is strongly rooted in the concept of Rj set out by Suding et al. (2008), but goes further by explicitly distinguishing between species’ responsiveness (SRF) and species’ realized response to environmental drivers, the latter depending both on SRF and the specific environmental context. SRF is also analogous to, but broader than, the concept of elasticity or proportional sensitivity sensu Caswell et al. (1984) and Caswell (2000); for instance, SRFs can be applied readily to multispecies assemblages in ecosystems. An SRF typically depends on some combination of underlying functional response traits (Box 1, Fig. 1). Typical SRFs are the tolerances of species to different abiotic stresses (e.g., frost, drought, pollution, low nutrient availability), biotic pressures (e.g., competitors, pathogens, herbivores, predators, parasites), or particular disturbance regimes (e.g., fire); high values of an SRF indicate that the species is able to tolerate the regime well, whereas low values indicate that it is susceptible. Examples of SRFs illustrate the concept: drought tolerance in vascular plants (SRF) may depend on several response traits including xylem vessel diameter, leaf phenology, chemical and physiological traits, root depth, and water storage adaptations; the capacity of angiosperms to persist in the face of repeated aboveground disturbance in mesic and cold climates (SRF) depends on the capacity of their seeds to get buried quickly and form a persistent soil bank, which in turn is well correlated with the response traits seed size and shape; and the ability of vertebrate species to withstand harvesting by humans (SRF) depends on traits such as elusiveness, poor palatability, or rapid reproductive rates. See Table S1B for references and more examples. Comparative data on realized responses may be our most direct information about species’ SRF values, but can only be safely compared at face value if all species have experienced the same intensity of drivers. Otherwise, responses must be modeled as a function of driver intensities, with the residuals reflecting SRF values.
Importantly, there is no reason to expect one-to-one mapping between SEFs and effect trait values, or between SRFs and response trait values. This is because a given SEF may be achievable through many different combinations of effect traits and their values; the same is true of SRFs and response trait values (Fig. 1; see Wainwright 2007 for a detailed exposition in the context of morphometric traits). For instance, leaf litter decomposability and palatability to herbivores are SEFs that depend on combinations of leaf toughness and concentrations of nutrients, lignin, polyphenols, specific toxins, and leaf cuticle properties. Physically tough leaves (e.g., in Proteaceae) and juicy, nutritious leaves with powerful toxins (e.g., tropane alkaloids in Solanaceae) will both be unpalatable to a broad range of herbivores, affecting the ecosystem's carrying capacity for animals. Likewise, the capacity to persist in the face of drought and fire is an SRF that can be produced using different strategies: some plants have leaves that are structurally or physiologically resistant to drought; others instead have dormant buds that resprout after the leaves have been lost, using resources stored in deep belowground organs; while a third strategy is to regenerate from seed. Populations with a low SRF under environmental change will experience directional selection for higher SRF values, which could arise through multiple combinations of changes in functional trait values.
The same traits may underpin both SRFs and SEFs (Lavorel and Garnier 2002; Suding et al. 2008; Lavorel and Grigulis 2012; Luck et al. 2012); also compare traits listed in sections A and B of Table S1). For instance, a large body size of birds and mammals influences both their capacity to disperse seeds and to be good providers of meat protein (SEFs), but will be correlated with their susceptibility to decline in the face of hunting (SRF) (see case study below). Similarly, a large body size of litter-eating invertebrates such as woodlice relates both to how efficiently they fragment litter, thereby accelerating its decomposition (SEF), and to their desiccation tolerance (SRF). Furthermore, what constitutes a relevant SEF is context dependent. For example, the capacity of predators to catch particular prey types is an SEF, if the interest is in maintaining primary productivity in the presence of herbivores, but the same capacity can be seen as an SRF if the focus is on protecting an endangered predator in the face of decreasing prey numbers.
The extent to which a species contributes to ecosystem properties and services at the scale of local plots or patches contained in a landscape will often (though not always) depend strongly on the species’ local abundance (see Box 4). However, the contribution can only be made if the species is present. In other words, focal community- and ecosystem-level properties (linked to SEFs) can be strongly limited by species availability in and recruitment from the species pool (Symstad and Tilman 2001; Naeem and Wright 2003; Zobel et al. 2006) – and these are directly governed by SRFs, rather than SEFs. Our focus in this article is on this broader scale: how environmental changes interact with species’ SRFs to shape the suite of species and thence the SEF values that persist and so can contribute to ecosystem properties and services.
In the next section we show that both the correlation of SEF with SRF across species and the extent to which each of them tends to be similar among closely related species are important in assessing the risk that environmental change will impact the relevant species pool and thus the potential to deliver ecosystem properties and benefits to people. We then explore – using case studies from different systems – how these functions, traits, and phylogenetic signals relate to each other and to the vulnerability of ecosystem properties and benefits. The relevant species pool is defined by the scale of the ecosystem property or service of interest. In this article we focus at the local (i.e., landscape-level) species pool because most of the ecosystem properties and benefits we discuss occur at the level of local plots or patches in a landscape mosaic. The relevant pool would be different, however, if the ecosystem property or benefit of interest operates at a broader scale (e.g., the heat exchange with the atmosphere that underpins regional weather patterns, or the provision of corridors needed for large mammal migration).
Linking traits, functions, and phylogeny
Functional traits are aspects of organismal phenotypes and have evolved along the branches of phylogeny. Because evolutionarily closely related species tend to be ecologically similar and respond similarly to selection, functional trait values are likely to show phylogenetic signal – that is, they will tend to be more similar in closely related species than in distant relatives (Harvey and Pagel 1991; Freckleton et al. 2002; Ackerly 2009). Note that this argument does not mean that close relatives have to be similar – adaptive radiations show that change can be rapid when lineages adapt to new niches (Gavrilets and Losos 2009; Yoder et al. 2010) – just that they will tend to be similar. However, because SEFs and SRFs reflect combinations of multiple functional traits, they can display a very different tempo and mode of change from those seen in the traits themselves. In the simplest case, a function may be the same in all species within part of a clade (strong phylogenetic signal and a low inferred rate of change), whereas the underlying traits that confer that function might take different values in different species and show weak phylogenetic signal and apparently rapid change. For example, the use of birds as the predominant seed disperser might characterize an entire family of plants, despite multiple independent evolutionary shifts within that family in traits that make this mode of dispersal possible, such as the color of the fruit used to attract birds. Conversely, a function may show a much weaker phylogenetic signal and faster evolution than its constituent traits. As an illustration, seed dispersal by birds might have arisen independently in many different plant lineages (weak phylogenetic signal and a rapid overall rate of change) depending on the evolution of different underlying characters which, themselves, may evolve slowly and show strong phylogenetic signal. For example, all the species in one lineage might present their seeds in drupaceous fleshy fruits, whereas the species in another lineage might attract birds by having colorful seeds.
Combining SRFs and SEFs integrates the evolution of functional traits, ecosystem properties and services, and their vulnerability to environmental change drivers into a single framework (Fig. 2). Whereas ecosystem properties and services are underpinned by the distribution of the SEFs, it is the SRFs that are the targets of selection and sorting by environmental drivers. The future potential species complement of any ecosystem is therefore directly related to the SRFs of the species within the source pool rather than their SEFs; the SEFs and the resulting (dis)continuity of ecosystem benefits to people are side effects of this process of environmental filtering and selection. Drivers of change will tend to eliminate those species in the species pool whose SRFs leave them susceptible, while promoting the invasion and expansion of species with high SRFs under the new environmental conditions. However, the properties of, and the services from, the resulting assemblage of species will depend not on the SRF but mostly on the SEF. These different causes and consequences of changes to SEFs and SRFs make patterns of association between them of particular importance for predicting the ecosystem-level consequences of change.
Risk assessment for ecosystem properties and services
The correlation between SEFs and SRFs and their phylogenetic distributions will influence the potential of the ecosystem to deliver function under different environmental changes. Any ecosystem property or benefit is at risk from a driver of change if the species whose SEFs underpin the property have SRF values that make the species susceptible to that driver. We argue that a system is likely to be more vulnerable to disruption whenever an important range of SEF values is provided solely by members of a single clade (set of species that are each other's closest relatives) in the local species pool, rather than by members of multiple, independently evolved clades. This argument assumes that distantly related species providing a given range of SEF values are more likely than close relatives to differ in their values of SRFs and the traits that underpin them. Thus, even if one lineage conferring the function is lost, others may remain, which would safeguard the potential of the species pool to provide the function. If we knew the susceptibility of each species in the pool directly, we might not need the phylogenetic information; however, in the absence of such complete information, the knowledge that the functionality evolved separately in distantly related lineages can help us gauge the vulnerability of the system. The phylogeny also provides a basis for generalizing to unstudied or poorly known members of the relevant clades: if all the well-studied members of a clade are known to be susceptible, then unstudied members may be inferred to be similarly susceptible (see, e.g., Willis et al. 2008). In the absence of phylogenetic knowledge there would be little basis for such generalizations.
The extent to which an SEF and SRF are correlated and show phylogenetic signal can help to provide a risk assessment for ecosystem-level functionality. Box 2 depicts four extreme situations, from minimum concern when SEF and SRF are neither correlated nor show a phylogenetic signal to maximum concern when they are negatively correlated (i.e., the most important species are the least tolerant) and phylogenetically patterned (lacking independent backup). The frequency of the four plausible outcomes in real-world ecological systems is an open question that needs empirical evidence. Many functional traits show some degree of phylogenetic signal (Freckleton et al. 2002; Ackerly 2009), which seems to suggest that Figure 3D could be more prevalent than Figure 3A. However, other traits show only weak phylogenetic signal (reviewed by Srivastava et al. 2012) and, as argued above, SEFs and SRFs may anyway show different phylogenetic patterns from their underlying traits, so the question remains open. Comparative analyses of how species respond to particular drivers show a range of outcomes. Global mammalian IUCN Red List data show strong phylogenetic signal for extinction risk from overexploitation (mediated by life-history traits that themselves show very strong phylogenetic pattern), but only moderate signal from habitat loss (Fritz and Purvis 2010). Sensitivity to disturbance repeatedly shows strong phylogenetic pattern in lake zooplankton communities (Helmus et al. 2010). However, Thuiller et al. (2011) report that projected responses to climate change in European mammals, birds, and plants show only weak phylogenetic signal. Furthermore, there are at least two reasons why SEFs and SRFs may not generally be as strongly patterned as individual functional traits. The first is that any ecosystem contains only a small, usually nonrandom, sample of the global set of species: functional trait values may be strongly patterned in the global phylogeny, but random or nearly so within the phylogeny of species at smaller spatial scales. Second, the mapping between functional trait and function need not be simple: as discussed previously, the phylogenetic signal of SEFs and SRFs can differ from that of the underpinning traits.
Box 2. Phylogeny of Function and Vulnerability to Environmental Change Drivers
When considering an ecosystem's potential to produce a given property or service when faced with a given driver of change, the outcome depends on the correlation and phylogenetic patterning of SEF and SRF, as shown in Figure 3. There are four extremes in terms of phylogenetic patterning, as listed below Figure 3:
- Neither SEF nor SRF shows phylogenetic signal. The environmental change driver will lead to the loss of a phylogenetically random set of species, in which case the overall amount of evolutionary history in the local species pool will largely be maintained under environmental change (Nee and May 1997). If the SEF and SRF are strongly negatively correlated, such that species contributing most to the process are also the most sensitive, then the function is at risk. If SEF and SRF are uncorrelated, the capacity to provide the function will be resistant, as the distribution of SEF values after the driver has operated (SEF′ in Fig. 3A) will be a random sample of the initial SEF distribution, with a similar mean and range.
- The SEF shows strong phylogenetic signal, but the SRF does not (Fig. 3B). In such cases, the capacity to provide the function is resistant. The fact that SEFs and SRFs differ greatly in their phylogenetic patterning precludes a strong correlation between them, so the distribution of SEF values remaining after the driver has operated will be a random sample of the initial spectrum, with a similar mean and range. Additionally, no major clades are removed by the driver, so the overall amount of evolutionary history will be largely maintained unless species loss is extremely high (Nee and May 1997).
- The SEF shows no phylogenetic signal, but the SRF shows strong signal (Fig. 3C). Again, the mismatch between phylogenetic signal strength means that the capacity to provide the function is retained. However, the signal in the SRF means that whole clades are likely to be lost with a corresponding reduction in overall evolutionary history in the local species pool, arguably conferring an increased vulnerability to other drivers of change.
- SEF and SRF both show strong phylogenetic signal (Fig. 3D). The overall evolutionary history in the local species pool will be strongly reduced, as in Figure 3C. The impact on the SEF distribution depends on the correlation between SEF and SRF across the species in the source pool from which the community is drawn. A strong correlation leads to a reduction in the range of SEF values and to a possible loss of function (depending on whether the part of the SEF range that is removed is important for function).
Correlations between SEF and SRF can be assessed either phylogenetically or nonphylogenetically. In terms of the outcome for a particular system or set of systems, the nonphylogenetic correlation is the more important: it does not matter whether the SEF and SRF have changed together through phylogeny, just whether species with high SEF values have SRF values that confer high susceptibility. However, studies aiming to make more general predictions will sometimes benefit from a phylogenetic analysis, particularly when focusing on evolutionary questions. We thus report both phylogenetic and nonphylogenetic correlations between SEFs and SRFs in our case studies.
Applying the framework: case studies
We applied the framework to five case studies involving plants and animals in different environmental contexts, for which species data were available on (i) either an SEF relevant to an ecosystem property or service or the species’ realized effect on it, which incorporates species abundance and which is sometimes easier to measure than SEF (see Box 4); (ii) functional traits thought likely to cause it; (iii) an SRF (or, for three of the studies, a proxy for an SRF) relevant to a well-identified environmental change; and (iv) a phylogeny for the relevant species pool. In each case study, we used a phylogenetic comparative method (phylogenetic GLS; Freckleton et al. 2002) to model how SEF or realized effect values depend on species trait values; such a method is appropriate because we wish to infer which traits underpin the SEF, so need to control for the effects of phylogenetically patterned confounding variables (Harvey and Pagel 1991). We also estimated the strength of the phylogenetic signal in the SEF or species realized effect, effect traits, and SRF, using Pagel's λ statistic (which ranges from 0 for phylogenetic randomness to 1 for strong signal; Pagel 1999; Freckleton et al. 2002) to quantify signal strength for continuous variables, and Fritz and Purvis's (2010) D (here, expressed as 1−D to put it on the same scale because D is a measure of phylogenetic dispersion rather than concentration) for binary variables. Finally, we assess the correlation across species between SEF and SRF, both phylogenetically and nonphylogenetically (as discussed above). The results from the case studies are in Table 1. Figures 4 and 5 depict two of the five examples, for which species’ SEF values were available. The left-hand side of each figure shows the phylogeny of the species in the local species pool; labeled clades are referred to in the text. Species-specific values for the effect traits and SEF are proportional to the sizes of the filled squares. Species’ SRF values are on a continuous scale, but, to aid presentation, we have split species into two equal-sized groups; the species more able to persist in the face of a particular driver are shown with large symbols, those less able to persist with small. The last column shows the SEF values of those species more able to persist. The distribution of SEF values among species is shown in the upper histogram (“SEF before filter”); the lower histogram (“SEF after filter”) shows the distribution of SEF values among the species better able to persist. Comparing these histograms shows the change in distribution of SEF values expected in response to the driver. Marked changes in the SEF distribution in the postfiltering set of species are interpreted as likely to cause changes in the ecosystem property or benefit in question. For simplicity, we did not consider in these five examples the influence that any new additions to the local species pool might have on the distribution of SEFs; we come back to this general topic in Box 4.
|System and data references||SEF||Phylogenetic signal in SEF (λ)||SRF||Phylogenetic signal in SRF||Phylogenetic correlation of SEF and SRF, r2||Nonphylogenetic correlation of SEF and SRF, r2||Risk of loss of specified function under this driver|
|45 species in the Sheffield flora, Northern England (Figure 4) (Thompson et al. 1993; Cornelissen 1996)||Decomposability; high values needed for rapid nutrient cycling and soil fertility||Moderate (0.32)||Persistence in the seed bank; high values needed to survive under frequent aboveground disturbance associated with agricultural intensification||None (1−D = 0.13)||<0.001||<0.001||Very low|
|24 species in the Central Argentina flora (Funes et al. 1999; Pérez-Harguindeguy et al. 2000)||Decomposability; high values needed for rapid nutrient cycling and soil fertility||Strong (0.70)||Persistence in the seed bank; as above||Moderate (1−D = 0.25)||<0.001||0.027||Low|
|345 North-Central and West African mammal species (Fa et al. 2005; Schipper et al. 2008; Jones et al. 2009)||Bushmeat harvest rate; high values support provision of key ecosystem benefit (high-protein food) to local people||Weak (0.22)||Harvesting tolerance (indicated by intrinsic rate of population growth, rmax, calculated from life-history data); high growth rates associated with greater tolerance||Strong (λ = 1.00)||<0.001||0.018||Low|
|33 Mediterranean bird species (Figure 5) (Jordano et al. 2007 and P. Jordano and J. Bascompte's unpubl. data)||Long-distance seed dispersal; high values support natural patch recolonization by woody plants of socioeconomic importance||Strong (1.00)||Hunting tolerance (indicated by small body size); small body size associated with high tolerance||Strong (λ = 1.00)||0.42||0.57||High|
|33 Mediterranean bird species (Jordano et al. 2007 and P. Jordano and J. Bascompte's unpubl. data)||Overall seed dispersal; high values needed for successful natural regeneration of woody plants of socioeconomic importance||Moderate (0.39)||Hunting tolerance (indicated by small body size); as above||Strong (λ = 1.00)||0.001||0.02||Very low|
From our examples it appears that phylogenetic signal in SEFs, realized effects, and SRFs may be common, as it is in the functional traits themselves. Signal was often strong in SRFs. This could be a general tendency – the physiological tolerances that determine the limits of where species persist may be more slowly evolving traits (Wiens and Donoghue 2004; Crisp et al. 2009), as are many life-history traits that underpin populations’ ability to compensate for extra mortality by increasing recruitment (Blomberg et al. 2003) – but we caution that the SRF measures showing strong phylogenetic signal in Table 1 are only proxies: more direct measures of tolerance might show much weaker phylogenetic signal. However, some published analyses show strongly patterned responses to particular drivers (Lessard et al. 2009; Fritz and Purvis 2010; Helmus et al. 2010; Guenard et al. 2011). If strong phylogenetic patterning in SRFs does turn out to be common, it would imply that much phylogenetic diversity is potentially at risk within many systems, with drivers of change tending to remove whole clades from local species pools. Such a scenario would reduce the phylogenetic redundancy of SEF provision, even if the range of SEFs is not strongly affected; this lowering of phylogenetic redundancy could reduce a system's resilience to future drivers of change (Cavender-Bares et al. 2009; Faith et al. 2010; Matthews et al. 2011). Even if SRFs are often strongly phylogenetically patterned, it is possible that strong correlations with SEFs are the exception rather than the rule, except when the same traits underpin both response and effect traits (Suding et al. 2008): if SEFs and SRFs are generated by different suites of traits that have been evolving separately, phylogenetic correlations between them are likely to be weak, although nonphylogenetic correlations could still often be strong because of the similarity of close relatives (Felsenstein 1985). If this speculative hypothesis receives support from further studies, it would suggest that ecosystem functionality may prove to be more resistant to some drivers – those unlinked to effect traits – than had been expected.
More empirical studies – especially using direct information on SRFs – are needed to see where most systems fall in the scenario matrix of Figure 3. The considerations above prompt a series of tractable research questions, listed in Box 3.
Box 3. Combining Functional Ecology and Evolution to Assess Ecosystem Function Vulnerability: Six New Research Questions
- What phylogenetic patterns are most common in SEFs and SRFs? How often do they tend to show strong phylogenetic signal? What are their short-term rates of evolution? Are SRFs more phylogenetically conserved than SEFs? Do traits and effect functions with clear adaptive value (such as relative growth rate) show different patterns from those without or with less clear adaptive value (such as decomposability)?
- How do SEFs and SRFs relate to underlying species effect and response traits? Can any generalities be drawn about groups of traits that tend to be particularly conserved or labile?
- How commonly do SEFs correlate strongly and negatively with SRFs? Such correlations automatically ring alarm bells that the range of SEFs present in the species pool could be reduced by the corresponding driver.
- Do different drivers of change systematically differ in the degree of phylogenetic signal in their SRFs? Drivers associated with strongly patterned SRFs are most worrying, from the perspective of ecosystem function and evolutionary capital, because of their potential to remove entire clades from the system.
- Are particular combinations of SEF and SRF values repeatedly associated with subclades having unusual variability or diversification history? Do small-range endemics, taxa at risk of extinction, or other critical elements of the biota consistently occupy particular places along SEF or SRF distributions?
- How do particular combinations of SEF and SRF map onto complex networks of ecological interactions? Are high SEF values consistently located at the core of these networks? Do they consistently correlate negatively with SRFs, such that their loss may entail the collapse of the whole network? Or are core species characterized by broad tolerances (high SRFs)? How are SEF and SRF distributions divided up among specific modules (sets of more strongly interacting species) within the networks?
- Can we apply the phylogeny of SRFs and SEFs to better predict which species from the local or regional species pool are likely candidates to become invasive newcomers detrimental or beneficial to the properties of a given ecosystem subject to environmental change (see Box 4)?