The value of biodiversity experiments

ARTICLE IN PRESS Basic and Applied Ecology 5 (2004) 535—542

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The value of biodiversity experiments Bernhard Schmid, Andy Hector Institute of Environmental Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland Received 14 June 2004; accepted 2 July 2004

KEYWORDS Biodiversity experiments; Ecosystem functioning; Extinction scenarios; Generality; Observational studies; Realism

Summary Recent biodiversity experiments have investigated the relationship between diversity and ecosystem functioning by synthesizing plant communities from pools of species that have been experimentally manipulated to vary numbers and types of species present while holding abiotic factors constant. Biodiversity experiments therefore focus on a previously under-explored aspect of global change: the feedback from diversity to environment. Consequences of random manipulation of species communities may not correspond well to those of specific extinction sequences observed in the past in response to extinction drivers that cause highly non-random loss. However, random manipulation provides a good starting point given that existing communities could undergo many alternative orders of species loss in the future in response to a variety of different potential extinction drivers. Further, the effects of some extinction drivers are currently poorly understood and therefore difficult to predict (e.g. climate change) and it may be premature to dismiss the predictions of random scenarios as irrelevant to all real examples of species loss. The first generations of biodiversity experiments have provided valuable, and sometimes unexpected, discoveries about the general nature of the relationship between diversity and ecosystem functioning. These discoveries could not have been made using observational studies. We propose that different examples of extinction loss in the real or a potential future world form a continuum from situations where the results of the first-generation biodiversity experiments will be highly relevant to less relevant. At the one extreme are examples where the effects of biodiversity on ecosystem functioning will be overwhelmed by direct effects of the extinction driver on processes (e.g. chronic eutrophication). At the other extreme are situations where ecosystem processes are not strongly affected by direct effects of the extinction driver and where the effects of species loss on functioning may be more important (e.g. habitat fragmentation). Given the unprecedented uncertainty about the future of biodiversity and the functioning of ecosystems, a general approach with randomly varying species pools was the right place to start in order to provide a general foundation. The new challenge is to test for effects of biodiversity on functioning in real-world examples of species loss. r 2004 Elsevier GmbH. All rights reserved.

Corresponding author. Tel.: +1-635-5205: fax: +1-635-5711.

E-mail addresses: [email protected] (B. Schmid), [email protected] (A. Hector). 1439-1791/$ - see front matter r 2004 Elsevier GmbH. All rights reserved. doi:10.1016/j.baae.2004.07.001

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Zusammenfassung Biodiversita ¨tsversuche zeichnen sich dadurch aus, dass natu ¨rliche Artenpools experimentell reduziert werden und anschlieXend der Zusammenhang zwischen der ¨ kosystemfunktionen unter konstanten abiotischen UmweltbedingunArtenzahl und O gen untersucht wird. Dadurch unterscheiden sich Biodiversita ¨tsexperimente grundsa ¨tzlich von anderen Versuchen, die die Biodiversita ¨t als Zielvariable behandeln und stattdessen die abiotische Umwelt manipulieren. Die Auswahl der Arten fu ¨r die reduzierten Artenpools in Biodiversita ¨tsexperimenten erfolgte bisher meist zufa ¨llig, wa ¨hrend natu ¨rliche Aussterbefaktoren wie Eutrophierung nicht alle Arten gleichermassen gefa ¨hrden. Fu ¨r verschiedene Aussterbefaktoren ist aber so wenig bekannt, dass ein zufa ¨lliges Aussterbeszenario die beste gegenwa ¨rtig verfu ¨gbare Option ist. Dies trifft insbesondere fu ¨r mo ¨gliche zuku ¨nftige Aussterbeprozesse zu, die durch globale Umweltvera ¨nderungen (Klima, biologische Invasionen) oder Habitatsfragmentierung ausgelo ¨st werden ko ¨nnten. Die erste Generation von Biodiversita ¨tsexperimenten mit zufa ¨lligen Aussterbeszenarien hat wertvolle, teilweise unerwartete, ¨ kosystemfunktionen aufgedeckt. generelle Zusammenha ¨nge zwischen Artenzahl und O Diese Zusammenha ¨nge lieXen sich durch vergleichende Studien nicht erkennen. In Zukunft sollten Biodiversita ¨tsexperimente dennoch vermehrt Aussterbeszenarien simulieren, die in der realen Umwelt mit gro ¨Xter Wahrscheinlichkeit auftreten. r 2004 Elsevier GmbH. All rights reserved.

Introduction Despite the large number of biodiversity experiments reporting positive effects of plant species richness on ecosystem functioning (reviewed in Schla ¨pfer & Schmid, 1999; Schmid, Joshi, & Schla ¨pfer, 2002b; Hooper et al., 2004) there is still some debate about the relevance of these results to real examples of species loss (Lepsˇ, 2004). The initial debate—which focused on the mechanisms generating the results—has been resolved by showing that more diverse communities generally have increased values of productivity and related ecosystem properties due to the inclusion of particular species with key traits or due to the presence of combinations of species with complementary niche differences (sampling/selection effect and complementarity effects—Loreau et al., 2001; Hooper et al., 2004). Progress has been made through the rejection of two hypotheses: that the results of biodiversity experiments can be entirely explained as sampling effects (Loreau & Hector, 2001; Hector, Bazeley–White, Loreau, Otway, & Schmid, 2002) or due to the fertilizing effects of legumes (van Ruijven & Berendse, 2003). Lepsˇ (2004) raises a further criticism: that biodiversity experiments manipulate species richness in ways that do not mimic extinction sequences in the real world. Therefore, he argues, the experimental results cannot be used to predict potential consequences of species loss on ecosystem functioning in the real world. We agree that extinction in the real world will often be non-random, particularly in situations like the example given of nutrient enrichment of

grasslands, and that future biodiversity experiments should consider realistic extinction scenarios. However, we believe that random extinction scenarios have enabled us to take the first steps in biodiversity and ecosystem functioning research and that they have provided some valuable, and sometimes unexpected, discoveries about the general nature of the relationship. Furthermore, we believe that some interpretations of the intentions of biodiversity experiments and assertions about potential alternative approaches need clarification. We take productivity as an example ecosystem process.

What is the manipulated explanatory variable in biodiversity experiments? Lepsˇ (2004) points out that the species composition and richness of a plant community depends on three factors: environmental harshness, competitive exclusion, and species pool limitation (alternative schemes are possible but beyond the scope of this article). He implies that these three factors will shape the relationship between species richness and productivity in nature. It is therefore argued that biodiversity experiments with random species compositions will yield unrealistic results because, ‘‘in nature, species composition matches the environment, in biodiversity experiments, it does not’’ (Lepsˇ, 2004). However, given our far from perfect ability to predict species loss, how can we know that

ARTICLE IN PRESS The value of biodiversity experiments combinations that appear unrealistic at present will not occur in a future world? Furthermore, the total species pool in biodiversity experiments is usually carefully selected to include species that naturally occur in the experimental environments and further restrictions may be imposed to exclude unrealistic experimental combinations of only subordinate species (see e.g. Diemer, Joshi, Ko ¨rner, Schmid, & Spehn, 1997). Thus, biodiversity experiments take account of limitations by existing abiotic conditions. These experiments deliberately hold abiotic conditions constant and manipulate the species pools so that they vary in richness and composition. Of course, it is also possible to include the manipulation of abiotic conditions as additional treatment factor in more complex experiments (e.g. Reich et al., 2001; He, Bazzaz, & Schmid, 2002). A further possibility is two-stage experiments, in which experimental extinctions are applied in the first step and the resulting pools are then used for the biodiversity comparison (Schmid et al., 2002b). In a recent experiment we applied experimental extinction using a highintensity nutrient treatment (Schla ¨pfer, Pfisterer, & Schmid, 2004). Interestingly, the result of experimental extinction driven by nutrient addition had almost as severe negative effects on productivity as random extinction sequences because species persistence during the extinction treatment was not well correlated with performance in the post-extinction environment. This emphasizes the point that the question is not only how well depauperate communities perform under changed environmental conditions (e.g. an impoverished community of high-N adapted species under fertilized conditions) but also how these communities perform in the future if the abiotic conditions return to prior levels (e.g. an impoverished community of high-N adapted species under unfertilized conditions at a later time).

What do biodiversity experiments tell us about the real world? The surprising result of biodiversity experiments is that randomly manipulating species pools generally produces a positive relationship between productivity and increasing numbers of plant species. We believe that this result, despite being widely repeatable, remains controversial in some areas because it appears to conflict with results from observational studies (Lepsˇ, 2004). However, we believe that there is no conflict once the intention of the experiments is correctly understood. First,

537 by excluding confounding effects, the experiments aim to predict what would happen if only the species pool was manipulated. Observational studies do the opposite in that they usually have a constant species pool but variable abiotic environments and biotic interactions. Second, species richness in natural communities—as suggested by Lepsˇ (2004)—is the result of the available species pool, environmental harshness, competitive exclusion, etc. and thus the processes leading to this result are confounded and can only be studied experimentally by independently manipulating these factors. Each biodiversity experiment can therefore be considered as a manipulative experiment focusing on the effect of the species pool as the factor of interest. One way to test how the other factors shape the relationship between biodiversity and productivity is to examine changes in ecosystem functioning once the experimental manipulation of species richness is discontinued at the end of an experiment. We have done this at the Swiss site of the European BIODEPTH project (Pfisterer, Joshi, Schmid, & Fischer, 2004). Within 2 years mean species richness converged to 12 species per 4-m2 plot and mean yearly productivity to 530 g/m2, which was slightly below the average diversity–productivity line of the previous four experimental years (Fig. 1a). The remarkable points about this convergence are that: (i) allowing invasions (re-entry of species into the pool from which they were simulated as going [locally] extinct) leads to declines in both diversity and productivity in communities that were previously kept above these site-equilibrium values by weeding; (ii) convergence is towards the predicted average line obtained during the four experimental years (there was some decline in site fertility over the experimental period because the nutrients removed with biomass harvests were not replaced); and (iii), a large part of the residual variation between the realized species richness and productivity of the resulting communities may be due to variation in abiotic conditions between plots (Lepsˇ’ 2004) first factor). The ubiquity of the proposed hump-shaped curve is still far from clear (Grace, 1999; Mittelbach et al., 2001) but it may be possible to reconcile experimental results with this regional pattern since a hump-shaped line can be drawn around the post-weeding data points. This hypothetical resolution (Schmid, 2002) predicts that once weeding ceases in biodiversity experiments, diversity and productivity should converge to natural levels at each site and when many such points are compared they should form the proposed regional hump-shaped curve. Following this hypothesis, extrapolating the above result from the

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Swiss BIODEPTH experiment to all eight BIODEPTH field sites predicts different points of convergence, depending on the average site conditions following the cessation of the experimental manipulations of pool richness (Fig. 1b). At least for Germany (M. Scherer-Lorenzen, pers. comm.) the data confirm our anticipation. Note that after convergence the relationship between diversity and productivity that emerges in biodiversity experiments is hidden by abiotic factors and we cannot derive conclusions about the relationship between pool richness and biodiversity–ecosystem functioning relationships any more. Obviously, discontinuing the manipulations of pool richness is only an option in experiments and not in the real situation experiments tried to mimic. In the real world, species that have gone globally extinct would first have to re-evolve before the community could be brought back to its original state.

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Are there better alternatives than biodiversity experiments? Lepsˇ (2004, citing Aarssen, 2001) claims that ‘‘it is more important which species are lost than how many of them’’ and therefore suggests that experiments simulating the loss of particular species would be a better alternative than deleting species randomly from a pool. We disagree for the following reason. The distinction drawn between species numbers and species identity (species composition would perhaps be a better term since it also includes species interactions) misses the fact that identity effects are to some degree intrinsically confounded with number effects, because each loss of a particular species is always also a loss in species numbers. For example, most biodiversity experiments that report legume effects do not distinguish their effect from the number effect of Figure 1. (a) Diversity–productivity relationship at the Swiss site of the BIODEPTH project (Pfisterer et al., 2004) during the experimental phase 1996–1999, indicated by the average regression line using log sown species number as explanatory variable, and in June 2001, after convergence of sown species numbers to a mean realized species number of 12 per 2  2 m plot and mean yearly (extrapolated from June harvest, see Pfisterer et al., 2004) productivity of 530 g/m2 (indicated by the large point); small points indicate yearly productivity of individual plots in June 2001. Weeding stopped after September harvest in 1999. For further explanation see text. (b) Hypothetical resolution of biodiversity experiments with regional hump-shaped relationships. The extrapolation of the results from the Swiss site to all BIODEPTH sites following the hypothesis in Schmid (2002) predicts that experimental reductions of species pools lead to reduced productivity along the regression lines (data from Hector et al. (1999) but with separate lines fitted for each site) because environmental conditions are more or less constant within sites. Note that in Switzerland experimental increases in species pool richness above natural levels led to increased productivity along the same regression line because species that would reduce diversity and productivity were kept out by weeding (see Pfisterer et al., 2004). Note further that the experimental diversity–productivity relationships do not decrease with increasing site fertility, a result consistently found in biodiversity experiments carried out at different soil fertility levels (e.g. Reich et al., 2001; He et al., 2002; Fridley, 2002, 2003) and somewhat counterintuitive because fertilization usually reduces species richness in plant communities. This is indicated in the figure by the dashed curve indicating the predicted diversity–productivity convergence points reached when experimental manipulations of species pools are discontinued. Alternatively, the BIODEPTH sites may not lie on a hump-shaped curve or converge as predicted.

ARTICLE IN PRESS The value of biodiversity experiments having one functional group, i.e. non-legumes, versus having two, i.e. non-legumes+legumes. In biodiversity experiments it is convenient to first look at overall number effects and then at identity effects within the number effects, and to test the number effects against the identity effects (Schmid et al., 2002a). In this way, significant diversity effects indicate ‘‘pure number’’ effects that occur over and above any identity effects. Further, Lepsˇ (2004) suggests that it would be easy to predict future community compositions from realistic extinction-scenario simulations, and then derive further predictions about ecosystem functioning, because (in this example) Ellenberg light and nutrient ‘‘values are considered reasonably reliable’’ for species of the Central European flora (Ellenberg et al., 1992). However, these values themselves have been derived from observational studies and apply only as long as the species occur in their typical communities, i.e. again as long as species pools are not changed. Indeed, some of the best examples of the dependence of species performance on the particular abiotic and biotic environmental conditions are provided by Ellenberg himself (Ellenberg, 1953; Mueller-Dombois & Ellenberg, 1974). Thus whereas Lepsˇ (2004) argues that, ‘‘species traits are usually known and the predictions based on them will be more successful than predictions based on change in species number’’, we argue that species traits for many systems are still not well known and making predictions based on them is in any case not straightforward.

Realism and generality In his seminal book, ‘‘Evolution in changing environments’’ Levins (1968) pointed out the difficulty of simultaneously maximizing generality, realism, and precision. Whereas Lepsˇ (2004) feels that biodiversity experiments lack realism we maintain that their focus on general patterns has advantages for both fundamental and pragmatic reasons. First, in many cases we have limited knowledge about extinction in the real world and the exact contributions of individual species to ecosystem functioning—therefore we need a general foundation before we proceed to specific cases. Second, species are not totally unique in all their characteristics, which, if they were, would not allow statistical generalizations across communities with different species compositions but constant species richness. Rather there are many similarities between species, for example recognized by func-

539 tional groups, which make them to some degree substitutable, in particular within functional groups. Third, the fundamental relationship between pool richness and the relationship between biodiversity and ecosystem functioning shows up even if pool richness is reduced randomly, i.e. independently of species identities and traits. In this way, the first generation of experiments have therefore allowed us to identify that there is a general relationship between diversity and functioning—something that was not expected by many ecologists (and which some are still uncomfortable with). Introducing dependence between functional traits and extinction (e.g. Gonzalez & Chaneton, 2002) is an interesting next step that will allow the comparison of potential differences between results obtained with random extinction scenarios and observations made in the real world, as Leps (2004) suggests.

The value of biodiversity experiments The value of biodiversity experiments lies in this demonstration of what would happen to the relationship between biodiversity and ecosystem functioning if random extinction scenarios did in fact reduce species pools. Other ecological experiments treat species pools as constant and species richness in communities as the response variable, as suggested by Lepsˇ (2004). But, where is the feedback from biodiversity to ecosystem functioning in these experiments, and how can it be identified?—It cannot. Similarly, global change models predict vegetation shifts as a result of changed environmental conditions. These experiments and models obviously cannot make any predictions about what would happen if, for example, the global pool of plant species were reduced by 50%, either randomly or by deliberately removing particular species such as for example all trees. Given the uncertainty in predictions, general approaches are necessary for understanding the range of potential future realities. Unexpected conditions that do not exist at present may nevertheless occur in the future (e.g. elevated CO2 levels). Experiments are necessary since without manipulation we are never able to understand why things are they way they are. What the biodiversity experiments carried out so far tell us is that manipulating species pools while keeping environmental conditions constant has significant effects on ecosystem functioning and that these correspond most often to a positive linear relationship between the logarithm of

ARTICLE IN PRESS 540 species richness and ecosystem functioning (Schla ¨pfer & Schmid, 1999; Schmid et al., 2002b; Hooper et al., 2004). In fact, some of our results suggest that maintaining artificially large species pools by weeding out the species not included in the random selection can lead to higher than observed average productivity in plant communities (Pfisterer et al., 2004 and Fig. 1), and this may be what can be observed in small family gardens with high labor input, e.g. fruit gardens in south-east Asia (E. Linsenmair, pers. comm.). Adding species to recommended agricultural mixtures which were thought to be the subset of necessary species has also proved effective in increasing yields (Bullock, Pywell, Burke, & Walker, 2001). Further, biodiversity experiments carried out under contrasting environmental conditions tell us that positive diversity–productivity relationships in plant communities are decreased rather than increased under harsh environmental conditions (see e.g. Reich et al., 2001; He et al., 2002; Fridley, 2002, 2003; Pfisterer & Schmid, 2002; Dimitrakopoulos & Schmid, 2004). It would be an interesting exercise to implement the results from biodiversity experiments into global climate models. How would maps for temperature, precipitation, productivity etc. look if the global pool of plant species were reduced randomly to 25%? Currently we do not know. Returning from these hypothetical considerations to the more realistic ones, we conclude by arguing that the random extinction scenarios used in biodiversity experiments are a good place to start given the many alternative future sequences of species loss that could be generated by different extinction drivers (e.g. habitat fragmentation vs. climate change). Species extinctions due to habitat destruction, habitat fragmentation, and declining population sizes are hard to predict (Fischer & Sto ¨cklin, 1997) and probably have a large stochastic element, as suggested by Lepsˇ (2004). Extinctions caused by changed biotic interactions (such as invading species or introduced diseases) are probably similarly difficult to predict and often rather stochastic. But even extinction caused by environmental harshness may only be correlated to ecosystem functioning as long as extinction and post-extinction environment are the same, which may rarely be the case in the long term (Schla ¨pfer et al., 2004). Thus, with the knowledge currently available, random extinction scenarios may be the best starting point and in some cases may approximate observed ones. Nevertheless, the biodiversity experiments carried out so far even incorporate one type of non-randomness in their extinction scenarios (Schmid et al., 2002a): they

B. Schmid, A. Hector sometimes manipulated both functional group richness and species richness (e.g. Hector et al., 1999) and in other cases manipulated species richness while maintaining functional group richness at constant level (within functional groups or ‘‘redundancy richness’’, e.g. the European CLUE project, Lepsˇ et al., 2001). Not surprisingly, reducing functional group richness tends to have the stronger effects and reducing redundancy richness tends to have the weaker effects on ecosystem functioning variables such as productivity. We finish by proposing a categorization, which may help reduce some of the apparent conflict between the results of observational and experimental biodiversity research. We suggest that a continuum of situations exist in which the results of biodiversity experiments will be more or less relevant to what is observed in the real world. At the one extreme are examples like the one given by Lepsˇ (2004)—changes in species loss and productivity due to eutrophication. In this example, changes in species richness may affect ecosystem functioning but we cannot see any effect because of overwhelming direct effects of the extinction driver on processes. That is, it is not possible to see if losses in species richness affect productivity because productivity is directly increased by the additional nutrients. At the other extreme are situations like habitat fragmentation where ecosystem processes in the remaining habitat are not strongly affected by direct effects of the extinction driver but where functioning may well be impacted by the species loss that results from the fragmentation. There will be a continuum of intermediate situations, as well as many possible outcomes of global change that we cannot currently predict. Given the unprecedented uncertainty about the future of biodiversity and the functioning of ecosystems we feel that the general approach was the right place to start.

Acknowledgements We thank Jan Lepsˇ and Andrea Pfisterer for comments on the manuscript. B.S. was supported by grants from the Swiss (Nr. 31–65224.01) and the German National Science Foundations (Nr. FOR 456—WE 2618/6-1 to W.W. Weisser).

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