The section on ‘Consolidation’ provided evidence that the loss of species or their change in relative abundance should matter for ecosystem functions as soon as there is a relationship between functional traits of the organisms lost and their effect traits. So the relevant question might not be whether there is a BDEF relationship, but why the diversity effects observed in recent reviews (Balvanera et al. 2006; Cardinale et al. 2006a) are not generally much stronger. A major point of dissent on BDEF research is the structural simplicity of most empirical systems (and most models) used to test this concept. Ecologists experience ecosystems as complex systems with spatial and temporal heterogeneity within and across local habitat patches and characterized by a multitude of biotic and abiotic processes leading to very intricate states with regard to standing stocks, nutrient content and different aspects of stability. Most experiments in the BDEF framework do not reflect this complexity. Mainly one aspect of biodiversity (number of species) was manipulated for one (rarely two) trophic group(s) of organisms to assess the effect on one (rarely two or more) ecosystem process(es) or state variable(s). Most experimental systems and models lacked environmental heterogeneity in space and time, most studies ran too short to assess whether species were able to coexist and the array of functions addressed is a very narrow subset of important ecosystem processes.
However, the addition of environmental complexity to BDEF research only makes sense if it actually changes our understanding of functional consequences of altered biodiversity. If the simple experiments and models already capture the majority of the dynamics of BDEF relationships, additional aspects should – in analogy to Occam’s razor – not be added without necessity. The plea for more realistic studies is not new (Loreau et al. 2001; Hooper et al. 2005) and over the last years, BDEF studies have evolved to include more aspects of this complexity (Gamfeldt & Hillebrand 2008). We will address in this section, whether these additional aspects have changed the general conclusion that diversity affects ecosystem process rates. For each of these points, we will additionally address open questions to highlight some new directions in BDEF research.
In a series of papers, Duffy (2002, 2003) highlighted the need to address consequences of consumer richness loss (in contrast to the predominant focus on plants and algae), as consumers in general were more prone to extinction and had strong effects on ecosystem function (see also Stachowicz et al. 2007). Consequently, trophic structure has been implemented in numerous studies, investigating the consequences of changes in microbivore, herbivore and predator richness or prey richness (see recent reviews in Duffy et al. 2007; Srivastava et al. 2009). At first glance, effect sizes for biodiversity on productivity and resource use efficiency did not significantly differ between trophic levels (Cardinale et al. 2006a). However, we suggest two specific aspects of trophic structure that are missing in many studies, which might limit our ability to predict effects of trophic diversity (or vertical biodiversity according to Duffy et al. 2007) on ecosystem functions:
First, very few studies have analysed both prey and consumer diversity changes simultaneously (Gamfeldt et al. 2005a; Bruno et al. 2008), although verbal arguments (Hillebrand & Shurin 2005) and theoretical insights (Thebault & Loreau 2003, 2005) propose that diversity changes across both levels result in highly interdependent consequences for consumption rates, resource use efficiency and resistance to consumption. In aquatic microcosms, e.g., a more diverse assemblage of consumers was more responsive to altered prey diversity than a single consumer species (Gamfeldt et al. 2005a). In a benthic marine system, both predator and herbivore diversity had unique effects on different ecosystem processes (Douglass et al. 2008).
Second, the details of trophic interactions are not well resolved in most studies. In their model, Thebault & Loreau (2003, 2005) elegantly showed that the consequences of consumer diversity can strongly depend on consumer specialization and the edibility of prey. However, few studies have actually manipulated the degree of specialization in trophic links. In a very insightful study (Finke & Denno 2005), increasing predator diversity increased the strength of trophic cascades only if intraguild predation was absent. If omnivores were involved, more predator species weakened the trophic cascades. The same should be true if interference competition is the main mode of consumer interaction (Amarasekare 2003).
Spatial and temporal heterogeneity
The BDEF experiments analysed by Cardinale et al. (2006a) showed the general trend that mixtures were more effective in biomass production and resource use than the average monoculture, whereas there was no consistent transgressive overyielding compared to the best monoculture. In other words, the most productive single species was on average as productive as the mixture. Such transgressive overyielding was only found in long-term experiments (> 4.5 years for plant experiments, Cardinale et al. 2007), indicating that biodiversity plays a different role on short time scales compared to the long-term. Empirical evidence suggests an initially increasing and then stabilizing complementarity effect (Cardinale et al. 2007; van Ruijven & Berendse 2009). Likewise, marine seaweed diversity had higher impacts on ecosystem functions in long-term compared to short-term experiments, showing that the ‘mainstream’ short-term experiment published on BDEF reflect only a small subset of potential mechanisms detailing how diversity can affect ecosystem processes and properties (Stachowicz et al. 2008).
In the short-term, a single species may be able to outperform a species mixture. The single most productive species can even show higher biomass yield than a corresponding mixture, when in the mixtures resources are channelled into less productive species (Norberg et al. 2001). However, over longer time scales, more traits are needed to allow for higher community flexibility, which enhances the importance of diversity for function (Norberg et al. 2001). Across a temporal gradient, the sign of the diversity – function relationship might change as different species become dominant with different traits (Weis et al. 2007). Otto et al. (2008) provided evidence that additive effects of additional predators in a trophic cascade relied on temporal niche separation. In this case, the phenology of arthropod predators played a substantial role such that increasing temporal niche complementarity (non-overlapping phenology) increased the additive effect of predator richness. Otto et al. (2008) also concluded that aspects of the identity of species (analogous to trait dissimilarity and divergence, Box 1) become more important in variable than in uniform environments.
The same argument holds for spatial heterogeneity. Most BDEF experiments have been conducted in highly uniform environments, although spatially more heterogeneous environments enhance the number of potential mechanisms linking trait diversity to ecosystem function (Stachowicz et al. 2008). Some aspects of heterogeneity have been addressed in recent BDEF experiments, with somewhat mixed results. When manipulating diversity and soil heterogeneity in a grassland experiment, soil heterogeneity increased the complementarity component of the net diversity effect, whereas in uniform environments selection effects prevailed (Wacker et al. 2008). Using structural equation models, Tylianakis et al. (2008) were able to show that the effect of diversity on different ecosystem functions (production, pollination, predation) increased with increasing spatial heterogeneity in resource distribution. However, an algal microcosm study showed that spatial variation in resource conditions did not per se lead to stronger BDEF relationship, leading to the conclusion that heterogeneity has to be coupled to differences in the relative fitness of organisms to enhance BDEF (Weis et al. 2008).
The few studies analysing multifunctionality converge on the conclusion that consequences of diversity loss appear more dramatic if more functions are addressed (Hector & Bagchi 2007; Gamfeldt et al. 2008). However, these results were derived using calculations from monocultures and have not yet been analysed across diversity gradients. Moreover, the concept presented so far only comprised redundancy across function, which is based on the fact that a species sustaining one function in an assemblage might be less able to perform a second function due to ‘functional trade-offs’. Such a functional trade-off involves different adaptations to, e.g., growth and competition, or carbon fixation and habitat structuring. As an example, the efficiency of resource use for one resource often is negatively correlated to the resource use efficiency of another resource (Tilman et al. 1982) such that more species lead to a more complete resource use (Bracken & Stachowicz 2006). Therefore the optimization of multiple functions (or more generally ecosystem multifunctionality) depends on more species than any single function (Gamfeldt et al. 2008).
Multifunctionality comprising different functions might be intensified if different species carry out a function along an environmental gradient in time or space as indicated above. A species may have limited ability to perform a certain function under different environmental conditions. In a spatially heterogeneous habitat or along temporal changes in the environments, we might see compositional turnover, i.e. the decay of similarity with increasing spatial distance (Soininen et al. 2007) or temporal distance (Korhonen et al. in press). In that case different species maintain certain functions under different conditions and the larger the environmental difference, the stronger the need for high trait dissimilarity.
In consequence, functional trade-offs and compositional turnover will lead to functional turnover (FTO), which we define as the rate of increase in the minimum number of species needed to perform a threshold level of each function in a multifunctional framework (Fig. 3). If FTO is based on functional trade-offs, the proportion p contributed by each species i for two functions A and B can be calculated (Fig. 3a). If the traits needed to perform these two functions are positively correlated (limiting case of no trade-off), a high ability to perform A includes a high ability to perform B. Then, the proportional contributions of each species to the functions A and B are positively correlated and there is no FTO (Fig. 3a). Thus, Smin remains constant if the number of functions considered increases (Fig. 3b). If the traits required for the different functions are uncorrelated (r = 0), FTO is estimated to be 0.5 (Fig. 3a), i.e., there is a 50% chance that species driving function A are also able to drive B. In this case, Smin increases gradually for each new function considered, resulting in a monotonically increasing, but decelerating function of Smin with the number of processes considered (Fig. 3b). If the functional trade-offs for function A and B are strong, a negative correlation between piA and piB appears (Fig. 3a). In this case the species needed to perform function A do not overlap with those performing function B, leading to a FTO = 1 and a linear increase of Smin with increasing number of functions (Fig. 3b). (Actually, linearity would require an unrestricted species pool, whereas – if the species pool is finite – the relationship between Smin and number of functions will decelerate and saturate).
Figure 3. Conceptual diagram on functional turnover. (a) Correlation between proportional contributions p of each species i to two different functions, A and B. r = correlation coefficient, FTO = functional turnover. (b) Minimum species richness (Smin) needed to maintain a certain threshold level of multiple functions depending on the number of functions considered. (c) Decay of similarity of species composition with environmental distance, b = slope of the similarity vs. distance relationship. (d) Minimum species richness depending on the environmental distance.
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This graphical display represents limiting cases assuming that all functions are either positively or negatively correlated. In reality, trait correlations may be nonlinear (Litchman et al. 2007) and vary for different pairs of functions as some require similar and other dissimilar adaptations (Vinebrooke et al. 2004; Litchman & Klausmeier 2008). Thus, the average correlation between proportional contributions to different functions may be close to zero. In fact, Gamfeldt et al. (2008) found very weak correlations (−0.2 < r < 0.3) between proportional contributions of species to different functions, suggesting uncorrelated functional traits.
In addition, FTO might also arise from temporal or spatial complementarity of species. If species are adapted to certain conditions, the similarity of species composition will decrease with increasing environmental distance, i.e., slope b < 0 (Fig. 3c). In a spatially or temporally heterogeneous environment, more species are therefore needed to maintain an overall threshold level of function across all environmental conditions (Fig. 3d). Only if single species show very broad environmental tolerances, similarity does not decay over environmental distance (Fig. 3c; slope b = 0) and Smin does not increase with increasing environmental distance (Fig. 3d).
The implementation of FTO into BDEF research might strongly enhance our ability to retrieve more realistic estimates for biodiversity effect sizes. Especially, it remains to be evaluated how the two sources of FTO, functional trade-offs and compositional turnover, interact. FTO from considering multiple environments or multiple functions might be additive or interactive (sub- or super-additive), potentially increasing the role biodiversity plays in ecosystem functioning.
In addition to the spatial heterogeneity within patches, spatial dynamics between patches have been considered in BDEF experiments recently. Metacommunity dynamics have been explicitly used in models (Mouquet et al. 2002; Loreau et al. 2003; Mouquet & Loreau 2003; Cardinale et al. 2004) and experiments (France & Duffy 2006; Matthiessen & Hillebrand 2006; Matthiessen et al. 2007; Venail et al. 2008). The inclusion of spatial dynamics is important for two reasons: on the one hand spatial dynamics allows for natural community assembly and the establishment of diversity gradients (in contrast to the artificial maintenance of gradients by the experimentator). In fact, the effects of species in a community can depend on their temporal arrival (Fukami & Morin 2003). On the other hand, spatial dynamics provide different mechanisms of coexistence, and we will show below that these different mechanisms relate to different expectations for the BDEF relationship (see ‘coexistence’). Another appeal of the metacommunity framework is that the alteration of spatial dynamics directly corresponds to anthropogenic fragmentation and isolation, which are major drivers of global biodiversity decline.
We see mainly two aspects how this inclusion could be more fruitful. First, spatial dynamics have been analysed mainly within trophic groups, although space use probably increases with increasing trophic position if predators are more mobile than their prey. In a terrestrial study, the diversity effect by a mobile ladybeetle predator guild on aphid prey localized in constrained habitat patches was mainly negative due to interference competition, whereas patchiness in prey availability led to aggregation of ladybeetles in habitats with high aphid density and thus to higher predator richness (Cardinale et al. 2006b). Second, the importance of temporal dynamics and synchronicity in metacommunities is poorly acknowledged. Temporal synchronization of within patch dynamics may lead to the regional dominance of species (Hillebrand et al. 2008), which will alter regional coexistence. If local patches are synchronized, the same species will dominate all patches, and only this species will profit from spatial dynamics, leading to low diversity and altered ecosystem functions. Corroborating this expectation, non-synchronizing fluctuations enhanced the stabilizing effect of diversity in experimental plankton communities (Downing et al. 2008).